diff --git a/driver/pace/driver/driver.py b/driver/pace/driver/driver.py
index 0ef3263b..56f7e53c 100644
--- a/driver/pace/driver/driver.py
+++ b/driver/pace/driver/driver.py
@@ -24,7 +24,11 @@
 
 # TODO: move update_atmos_state into pace.driver
 from pace.stencils import update_atmos_state
-from pace.util.communicator import CubedSphereCommunicator
+from pace.util.communicator import (
+    Communicator,
+    CubedSphereCommunicator,
+    TileCommunicator,
+)
 from pace.util.logging import pace_log
 
 from . import diagnostics
@@ -90,6 +94,7 @@ class DriverConfig:
     nz: int
     layout: Tuple[int, int]
     dt_atmos: float
+    grid_type: Optional[int] = 0
     grid_config: GridInitializerSelector = dataclasses.field(
         default_factory=lambda: GridInitializerSelector(
             type="generated", config=GeneratedGridConfig()
@@ -158,7 +163,7 @@ def apply_tendencies(self) -> bool:
 
     def get_grid(
         self,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         quantity_factory: Optional[pace.util.QuantityFactory] = None,
     ) -> Tuple[
         pace.util.grid.DampingCoefficients,
@@ -187,7 +192,7 @@ def get_grid(
 
     def get_driver_state(
         self,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         damping_coefficients: pace.util.grid.DampingCoefficients,
         driver_grid_data: pace.util.grid.DriverGridData,
         grid_data: pace.util.grid.GridData,
@@ -213,7 +218,7 @@ def get_driver_state(
             if stencil_factory is None:
                 grid_indexing = (
                     pace.dsl.stencil.GridIndexing.from_sizer_and_communicator(
-                        sizer=sizer, cube=communicator
+                        sizer=sizer, comm=communicator
                     )
                 )
                 stencil_factory = pace.dsl.StencilFactory(
@@ -275,7 +280,7 @@ def from_dict(cls, kwargs: Dict[str, Any]) -> "DriverConfig":
             )
             grid_type = kwargs["grid_config"].config.grid_type
             # Copy grid_type to the DycoreConfig if it's not the default value
-            if grid_type != 0: 
+            if grid_type != 0:
                 kwargs["dycore_config"].grid_type = grid_type
 
         if (
@@ -404,11 +409,19 @@ def __init__(
                 if self.config.performance_config.collect_communication
                 else None
             )
-            communicator = CubedSphereCommunicator.from_layout(
-                comm=self.comm,
-                layout=self.config.layout,
-                timer=comm_timer,
-            )
+            communicator: Communicator
+            if self.config.grid_type <= 3:
+                communicator = CubedSphereCommunicator.from_layout(
+                    comm=self.comm,
+                    layout=self.config.layout,
+                    timer=comm_timer,
+                )
+            else:
+                communicator = TileCommunicator.from_layout(
+                    comm=self.comm,
+                    layout=self.config.layout,
+                    timer=comm_timer,
+                )
             self._update_driver_config_with_communicator(communicator)
 
             if self.config.stencil_config.compilation_config.run_mode == RunMode.Build:
@@ -544,7 +557,7 @@ def exit_instead_of_build(self):
         pace_log.info("initialization of the object done")
 
     def _update_driver_config_with_communicator(
-        self, communicator: CubedSphereCommunicator
+        self, communicator: Communicator
     ) -> None:
         dace_config = DaceConfig(
             communicator=communicator,
@@ -707,7 +720,7 @@ def log_subtile_location(partitioner: pace.util.TilePartitioner, rank: int):
 
 def _setup_factories(
     config: DriverConfig,
-    communicator: pace.util.CubedSphereCommunicator,
+    communicator: pace.util.Communicator,
     stencil_compare_comm,
 ) -> Tuple[pace.util.QuantityFactory, pace.dsl.StencilFactory]:
     """
@@ -735,7 +748,7 @@ def _setup_factories(
     )
 
     grid_indexing = pace.dsl.stencil.GridIndexing.from_sizer_and_communicator(
-        sizer=sizer, cube=communicator
+        sizer=sizer, comm=communicator
     )
     quantity_factory = pace.util.QuantityFactory.from_backend(
         sizer, backend=config.stencil_config.compilation_config.backend
diff --git a/driver/pace/driver/grid.py b/driver/pace/driver/grid.py
index c184d566..9d13158b 100644
--- a/driver/pace/driver/grid.py
+++ b/driver/pace/driver/grid.py
@@ -10,7 +10,7 @@
 import pace.stencils
 import pace.util.grid
 from pace.stencils.testing import TranslateGrid
-from pace.util import CubedSphereCommunicator, QuantityFactory
+from pace.util import Communicator, QuantityFactory
 from pace.util.grid import (
     DampingCoefficients,
     DriverGridData,
@@ -35,7 +35,7 @@ class GridInitializer(abc.ABC):
     def get_grid(
         self,
         quantity_factory: pace.util.QuantityFactory,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
     ) -> Tuple[DampingCoefficients, DriverGridData, GridData]:
         ...
 
@@ -62,7 +62,7 @@ def register(cls, type_name):
     def get_grid(
         self,
         quantity_factory: QuantityFactory,
-        communicator: CubedSphereCommunicator,
+        communicator: Communicator,
     ) -> Tuple[DampingCoefficients, DriverGridData, GridData]:
         return self.config.get_grid(
             quantity_factory=quantity_factory, communicator=communicator
@@ -103,7 +103,7 @@ class GeneratedGridConfig(GridInitializer):
     def get_grid(
         self,
         quantity_factory: QuantityFactory,
-        communicator: CubedSphereCommunicator,
+        communicator: Communicator,
     ) -> Tuple[DampingCoefficients, DriverGridData, GridData]:
 
         metric_terms = MetricTerms(
@@ -158,7 +158,7 @@ def _f90_namelist(self) -> f90nml.Namelist:
     def _namelist(self) -> Namelist:
         return Namelist.from_f90nml(self._f90_namelist)
 
-    def _serializer(self, communicator: pace.util.CubedSphereCommunicator):
+    def _serializer(self, communicator: pace.util.Communicator):
         import serialbox
 
         serializer = serialbox.Serializer(
@@ -170,7 +170,7 @@ def _serializer(self, communicator: pace.util.CubedSphereCommunicator):
 
     def _get_serialized_grid(
         self,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         backend: str,
     ) -> pace.stencils.testing.grid.Grid:  # type: ignore
         ser = self._serializer(communicator)
@@ -182,7 +182,7 @@ def _get_serialized_grid(
     def get_grid(
         self,
         quantity_factory: QuantityFactory,
-        communicator: CubedSphereCommunicator,
+        communicator: Communicator,
     ) -> Tuple[DampingCoefficients, DriverGridData, GridData]:
 
         backend = quantity_factory.empty(
diff --git a/driver/pace/driver/initialization.py b/driver/pace/driver/initialization.py
index 2b6471a8..f707d013 100644
--- a/driver/pace/driver/initialization.py
+++ b/driver/pace/driver/initialization.py
@@ -37,7 +37,7 @@ def start_time(self) -> datetime:
     def get_driver_state(
         self,
         quantity_factory: pace.util.QuantityFactory,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         damping_coefficients: pace.util.grid.DampingCoefficients,
         driver_grid_data: pace.util.grid.DriverGridData,
         grid_data: pace.util.grid.GridData,
@@ -74,7 +74,7 @@ def start_time(self) -> datetime:
     def get_driver_state(
         self,
         quantity_factory: pace.util.QuantityFactory,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         damping_coefficients: pace.util.grid.DampingCoefficients,
         driver_grid_data: pace.util.grid.DriverGridData,
         grid_data: pace.util.grid.GridData,
@@ -105,11 +105,12 @@ class BaroclinicInit(Initializer):
     def get_driver_state(
         self,
         quantity_factory: pace.util.QuantityFactory,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         damping_coefficients: pace.util.grid.DampingCoefficients,
         driver_grid_data: pace.util.grid.DriverGridData,
         grid_data: pace.util.grid.GridData,
     ) -> DriverState:
+        assert isinstance(communicator, pace.util.CubedSphereCommunicator)
         dycore_state = baroclinic_init.init_baroclinic_state(
             grid_data=grid_data,
             quantity_factory=quantity_factory,
@@ -149,12 +150,12 @@ class TropicalCycloneConfig(Initializer):
     def get_driver_state(
         self,
         quantity_factory: pace.util.QuantityFactory,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         damping_coefficients: pace.util.grid.DampingCoefficients,
         driver_grid_data: pace.util.grid.DriverGridData,
         grid_data: pace.util.grid.GridData,
     ) -> DriverState:
-
+        assert isinstance(communicator, pace.util.CubedSphereCommunicator)
         dycore_state = tc_init.init_tc_state(
             grid_data=grid_data,
             quantity_factory=quantity_factory,
@@ -198,7 +199,7 @@ class RestartInit(Initializer):
     def get_driver_state(
         self,
         quantity_factory: pace.util.QuantityFactory,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         damping_coefficients: pace.util.grid.DampingCoefficients,
         driver_grid_data: pace.util.grid.DriverGridData,
         grid_data: pace.util.grid.GridData,
@@ -247,7 +248,7 @@ def start_time(self) -> datetime:
     def get_driver_state(
         self,
         quantity_factory: pace.util.QuantityFactory,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         damping_coefficients: pace.util.grid.DampingCoefficients,
         driver_grid_data: pace.util.grid.DriverGridData,
         grid_data: pace.util.grid.GridData,
@@ -296,7 +297,7 @@ def _namelist(self) -> Namelist:
 
     def _get_serialized_grid(
         self,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         backend: str,
     ) -> pace.stencils.testing.grid.Grid:  # type: ignore
         ser = self._serializer(communicator)
@@ -305,7 +306,7 @@ def _get_serialized_grid(
         ).python_grid()
         return grid
 
-    def _serializer(self, communicator: pace.util.CubedSphereCommunicator):
+    def _serializer(self, communicator: pace.util.Communicator):
         import serialbox
 
         serializer = serialbox.Serializer(
@@ -318,7 +319,7 @@ def _serializer(self, communicator: pace.util.CubedSphereCommunicator):
     def get_driver_state(
         self,
         quantity_factory: pace.util.QuantityFactory,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         damping_coefficients: pace.util.grid.DampingCoefficients,
         driver_grid_data: pace.util.grid.DriverGridData,
         grid_data: pace.util.grid.GridData,
@@ -345,7 +346,7 @@ def get_driver_state(
 
     def _initialize_dycore_state(
         self,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         backend: str,
     ) -> fv3core.DycoreState:
 
@@ -396,7 +397,7 @@ class PredefinedStateInit(Initializer):
     def get_driver_state(
         self,
         quantity_factory: pace.util.QuantityFactory,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         damping_coefficients: pace.util.grid.DampingCoefficients,
         driver_grid_data: pace.util.grid.DriverGridData,
         grid_data: pace.util.grid.GridData,
diff --git a/driver/pace/driver/state.py b/driver/pace/driver/state.py
index cccdcba7..54241b1d 100644
--- a/driver/pace/driver/state.py
+++ b/driver/pace/driver/state.py
@@ -77,7 +77,7 @@ def load_state_from_restart(
         grid_data: pace.util.grid.GridData,
     ) -> "DriverState":
         comm = driver_config.comm_config.get_comm()
-        communicator = pace.util.CubedSphereCommunicator.from_layout(
+        communicator = pace.util.Communicator.from_layout(
             comm=comm, layout=driver_config.layout
         )
         sizer = pace.util.SubtileGridSizer.from_tile_params(
@@ -172,7 +172,7 @@ def _restart_driver_state(
     path: str,
     rank: int,
     quantity_factory: pace.util.QuantityFactory,
-    communicator: pace.util.CubedSphereCommunicator,
+    communicator: pace.util.Communicator,
     damping_coefficients: pace.util.grid.DampingCoefficients,
     driver_grid_data: pace.util.grid.DriverGridData,
     grid_data: pace.util.grid.GridData,
diff --git a/dsl/pace/dsl/dace/build.py b/dsl/pace/dsl/dace/build.py
index 7d8f3db2..f2939d80 100644
--- a/dsl/pace/dsl/dace/build.py
+++ b/dsl/pace/dsl/dace/build.py
@@ -31,7 +31,7 @@ def unblock_waiting_tiles(comm, sdfg_path: str) -> None:
             comm.send(sdfg_path, dest=tile * tilesize + comm.Get_rank())
 
 
-def get_target_rank(rank: int, partitioner: pace.util.CubedSpherePartitioner):
+def get_target_rank(rank: int, partitioner: pace.util.Partitioner):
     """From my rank & the current partitioner we determine which
     rank we should read from.
     For all layout >= 3,3 this presumes build has been done on a
diff --git a/dsl/pace/dsl/dace/dace_config.py b/dsl/pace/dsl/dace/dace_config.py
index e6f3e7df..37910de3 100644
--- a/dsl/pace/dsl/dace/dace_config.py
+++ b/dsl/pace/dsl/dace/dace_config.py
@@ -7,7 +7,7 @@
 
 from pace.dsl.gt4py_utils import is_gpu_backend
 from pace.util._optional_imports import cupy as cp
-from pace.util.communicator import CubedSphereCommunicator
+from pace.util.communicator import Communicator
 
 
 # This can be turned on to revert compilation for orchestration
@@ -57,7 +57,7 @@ def __call__(self):
 class DaceConfig:
     def __init__(
         self,
-        communicator: Optional[CubedSphereCommunicator],
+        communicator: Optional[Communicator],
         backend: str,
         tile_nx: int = 0,
         tile_nz: int = 0,
diff --git a/dsl/pace/dsl/dace/wrapped_halo_exchange.py b/dsl/pace/dsl/dace/wrapped_halo_exchange.py
index ad88fb11..7d7eed44 100644
--- a/dsl/pace/dsl/dace/wrapped_halo_exchange.py
+++ b/dsl/pace/dsl/dace/wrapped_halo_exchange.py
@@ -2,7 +2,7 @@
 from typing import List, Optional
 
 from pace.dsl.dace.orchestration import dace_inhibitor
-from pace.util.communicator import CubedSphereCommunicator
+from pace.util.communicator import Communicator
 from pace.util.halo_updater import HaloUpdater
 
 
@@ -21,7 +21,7 @@ def __init__(
         state,
         qty_x_names: List[str],
         qty_y_names: List[str] = None,
-        comm: Optional[CubedSphereCommunicator] = None,
+        comm: Optional[Communicator] = None,
     ) -> None:
         self._updater = updater
         self._state = state
diff --git a/dsl/pace/dsl/stencil.py b/dsl/pace/dsl/stencil.py
index 26454ef8..29a66e15 100644
--- a/dsl/pace/dsl/stencil.py
+++ b/dsl/pace/dsl/stencil.py
@@ -595,7 +595,7 @@ def domain(self, domain):
 
     @classmethod
     def from_sizer_and_communicator(
-        cls, sizer: pace.util.GridSizer, cube: pace.util.CubedSphereCommunicator
+        cls, sizer: pace.util.GridSizer, comm: pace.util.Communicator
     ) -> "GridIndexing":
         # TODO: if this class is refactored to split off the *_edge booleans,
         # this init routine can be refactored to require only a GridSizer
@@ -603,10 +603,10 @@ def from_sizer_and_communicator(
             Tuple[int, int, int],
             sizer.get_extent([pace.util.X_DIM, pace.util.Y_DIM, pace.util.Z_DIM]),
         )
-        south_edge = cube.tile.partitioner.on_tile_bottom(cube.rank)
-        north_edge = cube.tile.partitioner.on_tile_top(cube.rank)
-        west_edge = cube.tile.partitioner.on_tile_left(cube.rank)
-        east_edge = cube.tile.partitioner.on_tile_right(cube.rank)
+        south_edge = comm.tile.partitioner.on_tile_bottom(comm.rank)
+        north_edge = comm.tile.partitioner.on_tile_top(comm.rank)
+        west_edge = comm.tile.partitioner.on_tile_left(comm.rank)
+        east_edge = comm.tile.partitioner.on_tile_right(comm.rank)
         return cls(
             domain=domain,
             n_halo=sizer.n_halo,
diff --git a/dsl/pace/dsl/stencil_config.py b/dsl/pace/dsl/stencil_config.py
index 4e555bdb..79eff931 100644
--- a/dsl/pace/dsl/stencil_config.py
+++ b/dsl/pace/dsl/stencil_config.py
@@ -7,9 +7,9 @@
 
 from pace.dsl.dace.dace_config import DaceConfig, DaCeOrchestration
 from pace.dsl.gt4py_utils import is_gpu_backend
-from pace.util.communicator import CubedSphereCommunicator
+from pace.util.communicator import Communicator
 from pace.util.decomposition import determine_rank_is_compiling, set_distributed_caches
-from pace.util.partitioner import CubedSpherePartitioner
+from pace.util.partitioner import Partitioner
 
 
 class RunMode(enum.Enum):
@@ -35,7 +35,7 @@ def __init__(
         device_sync: bool = False,
         run_mode: RunMode = RunMode.BuildAndRun,
         use_minimal_caching: bool = False,
-        communicator: Optional[CubedSphereCommunicator] = None,
+        communicator: Optional[Communicator] = None,
     ) -> None:
         if (not ("gpu" in backend or "cuda" in backend)) and device_sync is True:
             raise RuntimeError("Device sync is true on a CPU based backend")
@@ -57,11 +57,11 @@ def __init__(
         if communicator:
             set_distributed_caches(self)
 
-    def check_communicator(self, communicator: CubedSphereCommunicator) -> None:
+    def check_communicator(self, communicator: Communicator) -> None:
         """Checks that the communicator has a square layout
 
         Args:
-            communicator (CubedSphereCommunicator): communicator to use
+            communicator (Communicator): communicator to use
 
         Raises:
             RuntimeError: If non-square layout is given
@@ -72,7 +72,7 @@ def check_communicator(self, communicator: CubedSphereCommunicator) -> None:
             )
 
     def determine_compiling_equivalent(
-        self, rank: int, partitioner: CubedSpherePartitioner
+        self, rank: int, partitioner: Partitioner
     ) -> int:
         """From my rank & the current partitioner we determine which
         rank we should read from"""
@@ -117,12 +117,12 @@ def determine_compiling_equivalent(
         raise RuntimeError("Illegal partition specified")
 
     def get_decomposition_info_from_comm(
-        self, communicator: Optional[CubedSphereCommunicator]
+        self, communicator: Optional[Communicator]
     ) -> Tuple[int, int, int, bool]:
         if communicator:
             self.check_communicator(communicator)
             rank = communicator.rank
-            size = communicator.partitioner.total_ranks
+            size = communicator.size
             if self.use_minimal_caching:
                 equivalent_compiling_rank = self.determine_compiling_equivalent(
                     rank, communicator.partitioner
diff --git a/examples/notebooks/stencil_definition.ipynb b/examples/notebooks/stencil_definition.ipynb
index 251dcd77..b1c67e3f 100644
--- a/examples/notebooks/stencil_definition.ipynb
+++ b/examples/notebooks/stencil_definition.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "559427b6-c6c4-4e98-9ab4-f321f8c4999d",
    "metadata": {
@@ -13,6 +14,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "777c9dae-cb6f-49a8-963a-a85031d169af",
    "metadata": {},
@@ -37,6 +39,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3acf8ab8-73b5-45f3-9605-6f5929ebdbb3",
    "metadata": {},
@@ -76,6 +79,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "e3ff42c6-2fcb-4cbf-af4e-a41d5bbb0c95",
    "metadata": {
@@ -139,6 +143,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "7d5797e0-b80a-4b27-be30-5d138deda105",
    "metadata": {},
@@ -177,6 +182,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "cb397fe7-9ae0-46c0-8056-d35cde629a4b",
    "metadata": {},
@@ -238,7 +244,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -265,6 +271,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6562a18e",
    "metadata": {},
@@ -328,6 +335,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "b5546849",
    "metadata": {},
@@ -566,6 +574,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "9965c31a",
    "metadata": {},
@@ -636,6 +645,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "6eea64a5",
    "metadata": {},
@@ -685,7 +695,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1000x400 with 4 Axes>"
       ]
@@ -710,7 +720,7 @@
     "from units_config import units\n",
     "\n",
     "fvf_prep = FiniteVolumeFluxPrep(\n",
-    "    stencil_factory, grid_data\n",
+    "    stencil_factory, grid_data, 0\n",
     ")\n",
     "\n",
     "crx = domain_configuration[\"quantity_factory\"].zeros(\n",
@@ -767,6 +777,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "aadc97e9",
    "metadata": {},
@@ -797,6 +808,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "8cbf898b",
    "metadata": {},
@@ -867,6 +879,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "de2681d7",
    "metadata": {},
@@ -1705,7 +1718,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x400 with 6 Axes>"
       ]
@@ -1747,6 +1760,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "d41c4b7b",
    "metadata": {},
@@ -2621,7 +2635,7 @@
     },
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x400 with 6 Axes>"
       ]
@@ -2663,6 +2677,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "79388634",
    "metadata": {},
@@ -2761,7 +2776,7 @@
     },
     {
      "data": {
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88IGRZO64445yz01p5m3dutVERkaaxx9/3Ofxb775xtSrV6/c+pP5m3OVPZ+Vzdk3Z84cExER4fP3gzFlcwh++umn3nVV/T11KlWNLBw7dqyJjIys8LFmzZqZYcOGVbnv2NjYctl4slCdvySzevVq77pt27aZ6Oho87vf/S6g/gDcDdmivn37qlOnTuXWnzh3zn//+1/l5+erT58++uqrr7zrFy1aJI/Ho/HjxysiwvclKL0D07Jly5SXl6frrrtOe/fu9S6RkZFKS0vThx9+WO7YJ35zVBmPx6NFixZp8ODB5b4ZOfH4ixcvVs+ePXXBBRd4H2vcuLFGjx6trVu36vvvv/fZbuTIkT5zKZWOVPvpp58kHf82ZsuWLbrrrrvKfSNVesz9+/frgw8+0LXXXqsDBw54z3nfvn3KyMjQpk2b9Msvv/hsO3r0aJ+7VvXp00clJSXatm3bKZ+LUqNGjVJkZKTPuhNfx6NHj2rfvn1q37694uLifF7LQCxevFhJSUm67rrrvOvq16+vO+64QwcPHtTKlSt92g8dOtRn7ouTn9PKuN1u7+9VSUmJ9u3bp8aNG+vss8+23PdTee6557Rs2bJTLn/605+82xw+fNjb35NFR0d7HwfqCnKFXAlUbc+V+fPnKzY2VpdeeqnP72RqaqoaN25c7neyTZs2ysjIsHy8igSbRXFxcfruu++0adOmgI8d6PknJyf7jEiKiYnR8OHDtXbtWuXk5ATdH8CpSkpKtHTpUg0ZMkQtW7b0ru/YsWPI31Os5M7JmWElr8eMGePzc58+fXze299++225XC5NmDCh3LalmffPf/5THo9H1157rc9xk5KSdOaZZ1Z43BNVR85Jx98rO3bsqA4dOvj06+KLL5akcv2q7O+pYBw+fLjSeYP9zYovvvhCO3fuDPjYgZ5/r169lJqa6v25ZcuWuuKKK7R06VKVlJQE3R/UHVyGbFGbNm0qXP/uu+/qscce07p161RUVORdf+IHj82bNysiIqLKN7HSP/JK3wROFhMT4/NzvXr1dMYZZ5yy33v27FFBQYF3CHJltm3bprS0tHLrSy+F2rZtm88+TgxeSd4PI//9738lHT9nSVUe98cff5QxRo888ogeeeSRCtvs3r1bLVq08Pu4/qjotTx8+LCysrI0a9Ys/fLLLzLGeB/Lz8/3e98n2rZtm84888xyH+RPfE5PZPXcPB6PXnjhBb3yyivasmWLNxQk6fTTT7fU91M5MZD8VfoHxYn/TkpVNWk94FTkCrkSqNqeK5s2bVJ+fr4SEhIqfLz05gGlKvs3Eoxgs+jRRx/VFVdcobPOOkudO3fWgAEDdOONN6pr166nPHag59++fXuff/eSdNZZZ0mStm7dqqSkpKD6AzjVnj17dPjwYZ155pnlHjv77LO1ePHikB3LSu6c/N4WaF5HR0f7TGsiHX9/P/G9ffPmzUpOTlZ8fHylfd+0aZOMMRU+T5KqnK5Cqp6cK+3X+vXry51jKbuyori4uMLH/MmKp59+WiNGjFBKSopSU1N12WWXafjw4Wrbtu0pjx3o+Vf0+p111lk6dOiQ9uzZo6SkpKD6g7qDYqFFFb0hfPzxx7r88st14YUX6pVXXlHz5s1Vv359zZo1S3Pnzg1o/x6PR9Lx+SqSkpLKPV56t75SJ37rXxNOHkFR6sSQOJXSc7733nsr/Zavffv2IT9uRa/l7bffrlmzZumuu+5Sr169FBsbK5fLpWHDhnn7Wd2sntsTTzyhRx55RL///e81efJkxcfHKyIiQnfddVe19X3//v2VBuiJGjRooNjYWElS8+bNJUm7du1SSkqKT7tdu3apZ8+eoe8oEMbIFV/kSuiFW654PB4lJCTojTfeqPDxkz8YVceXSMFm0YUXXqjNmzfrnXfe0X/+8x/95S9/0fPPP68ZM2bolltuqXLbQM/fH8H0B0DwrOTOye9tgeZ1Ze/tgfJ4PHK5XPq///u/CvdZOodeZaor5zwej7p06aIpU6ZU+PjJ793VlRUlJSXavXu3zxc8xcXF2rdvn5KTk6vc/tprr1WfPn20cOFC/ec//9Ezzzyjp556Sv/85z81cODAKrcN9Pz9EUx/UHdQLKzEyd/c+uPtt99WdHS0li5d6nM5y6xZs3zatWvXTh6PR99//726d+9e4b7atWsnSUpISFB6enrAfalMs2bNFBMTo2+//bbKdq1atdLGjRvLrd+wYYP38UCUns+3335b6fmUfpNRv379kJ6zlddywYIFGjFihJ577jnvuiNHjigvL8/yvlu1aqWvv/5aHo/H5wO41ee0MgsWLNBFF12k119/3Wd9Xl6e96YKUuDPS1Xtr7zyynKXu1VkxIgRmj17tiR5f/dXr17t82Fs586d+vnnnzV69OiA+geEO3KFXHF6rlSmsnNq166d3n//fZ1//vk1Npo8FFkUHx+vkSNHauTIkTp48KAuvPBCTZw40VucC9X5l45YOnF/P/zwgyT5TOB/qv4AdU2zZs3UoEGDCi/PryiXghGK3KmOvG7Xrp2WLl2q/fv3Vzq6sF27djLGqE2bNt5Ry4HwN+cqU9V75f/7f/9Pl1xyiaX8DYUTs+Kyyy7zrl+9erU8Hk+lf3udqHnz5rrtttt02223affu3Tr33HP1+OOPe4tzoTr/in7Pf/jhBzVs2NDnS6hT9QdgzsJKNGrUSJL8fnOTjn+r43K5fC7P2bp1a7k7FA0ZMkQRERF69NFHy33LUvrtfkZGhmJiYvTEE09UeJfZPXv2+N2vE0VERGjIkCH697//rdWrV5d7vPT4l112mVatWqXs7GzvY4WFhXr11VfVunXrgOeBOPfcc9WmTRtNnTq13HNaesyEhAT169dPf/7zn7Vr165y+7B6zo0aNQrodZSOv5Ynj7R46aWXfF7b0n1L/v2eXHbZZcrJyfHeUVM6frfRl156SY0bN1bfvn0D6mNlKur7/Pnzy82PEujveFXtrcxZeM4556hDhw569dVXfZ7X6dOny+Vy6eqrr/arX0BtQa6QK07PlcpUdk7XXnutSkpKNHny5HLbHDt2LODn2Ipgs2jfvn0+Pzdu3Fjt27f3uaw5VOe/c+dOLVy40PtzQUGB/va3v6l79+7e0Uf+9AeoayIjI5WRkaFFixZp+/bt3vXr16/X0qVLQ3qsUOROdeT1VVddJWOMJk2aVO6x0vf3K6+8UpGRkZo0aVK593xjTLn3l5P5m3OVKb0j8smuvfZa/fLLL3rttdfKPXb48GHvHaSr08UXX6z4+HhNnz7dZ/306dPVsGFDDRo0qNJtS0pKyp1XQkKCkpOTy2VFKM4/OzvbZ47IHTt26J133lH//v0VGRnpd38ARhZWonQOtoceekjDhg1T/fr1NXjwYO8ffBUZNGiQpkyZogEDBuj666/X7t27NW3aNLVv315ff/21t1379u310EMPafLkyerTp4+uvPJKud1uffnll0pOTlZWVpZiYmI0ffp03XjjjTr33HM1bNgwNWvWTNu3b9d7772n888/Xy+//LKlc3viiSf0n//8R3379vXefn3Xrl2aP3++PvnkE8XFxen+++/XP/7xDw0cOFB33HGH4uPj9de//lVbtmzR22+/HfClaREREZo+fboGDx6s7t27a+TIkWrevLk2bNig7777zhvU06ZN0wUXXKAuXbpo1KhRatu2rXJzc5Wdna2ff/5Z/+///b+Azzc1NVXTp0/XY489pvbt2yshIaHSOUBK/fa3v9WcOXMUGxurTp06KTs7W++//365uZm6d++uyMhIPfXUU8rPz5fb7dbFF19c4fxDo0eP1p///GfddNNNWrNmjVq3bq0FCxbo008/1dSpU9WkSZOAz62yvj/66KMaOXKkevfurW+++UZvvPFGuTko2rVrp7i4OM2YMUNNmjRRo0aNlJaWVuk8H1X9m7AyZ6EkPfPMM7r88svVv39/DRs2TN9++61efvll3XLLLd45twCnIFfIFafnSmVKf/fvuOMOZWRkKDIyUsOGDVPfvn31hz/8QVlZWVq3bp369++v+vXra9OmTZo/f75eeOEFy18c5efn66WXXpIkffrpp5Kkl19+WXFxcYqLi1NmZqa3bTBZ1KlTJ/Xr10+pqamKj4/X6tWrtWDBAp/9h+r8zzrrLN1888368ssvlZiYqJkzZyo3N9dnpLE//QHqokmTJmnJkiXq06ePbrvtNu8XK+ecc45PnoZCsLlTHXl90UUX6cYbb9SLL76oTZs2acCAAfJ4PPr444910UUXKTMzU+3atdNjjz2mBx54QFu3btWQIUPUpEkTbdmyRQsXLtTo0aN17733VnoMf3OuMqmpqZo3b57GjRun8847T40bN9bgwYN144036q233tKYMWP04Ycf6vzzz1dJSYk2bNigt956S0uXLq3wBmv+2LZtm+bMmSNJ3i88H3vsMUnHR+ffeOONko5f2jx58mSNHTtW11xzjTIyMvTxxx/r73//ux5//PEq54I8cOCAzjjjDF199dXq1q2bGjdurPfff19ffvmlzyjMUJ1/586dlZGRoTvuuENut1uvvPKKJHkLxf72B6j43uAwxhgzefJk06JFCxMREWEkmS1bthhjjt+SvLJbjb/++uvmzDPPNG6323To0MHMmjXLTJgwocLbsM+cOdP85je/MW6325x22mmmb9++ZtmyZT5tPvzwQ5ORkWFiY2NNdHS0adeunbnpppt8boc+YsQI06hRo4DObdu2bWb48OGmWbNmxu12m7Zt25qxY8eaoqIib5vNmzebq6++2sTFxZno6GjTs2dP8+6775brnyQzf/58n/VbtmwxksysWbN81n/yySfm0ksvNU2aNDGNGjUyXbt2NS+99JJPm82bN5vhw4ebpKQkU79+fdOiRQvz29/+1ixYsMDbZtasWUaS+fLLLyvsz4cffuhdl5OTYwYNGmSaNGliJJm+fftWuQ9jjPnvf/9rRo4caZo2bWoaN25sMjIyzIYNG0yrVq3MiBEjfNq+9tprpm3btiYyMtLn2H379vUeq1Rubq53v1FRUaZLly7lnqPS5+6ZZ54p1y9JZsKECeXWn+jIkSPmnnvuMc2bNzcNGjQw559/vsnOzq6wP++8847p1KmTqVevXoWv18kq+zcRjIULF5ru3bsbt9ttzjjjDPPwww+b4uLioPcLhCNyhVxxeq5U9Lt87Ngxc/vtt5tmzZoZl8tV7nf31VdfNampqaZBgwamSZMmpkuXLuZPf/qT2blzp7dNq1atzKBBg6rsZ0XnXNHSqlWrcu2tZtFjjz1mevbsaeLi4kyDBg1Mhw4dzOOPP+6zbSjPf+nSpaZr167e94OT/5340x+grlq5cqVJTU01UVFRpm3btmbGjBkV5unJ78uVvYdWllfGBJc7J+7fal5XdF7Hjh0zzzzzjOnQoYOJiooyzZo1MwMHDjRr1qzxaff222+bCy64wDRq1Mg0atTIdOjQwYwdO9Zs3Lixwn6W8jfnKns+Dx48aK6//noTFxdX7r26uLjYPPXUU+acc87x/o2TmppqJk2aZPLz873tqvp7qiKlr2FFy8n5Zszx9+uzzz7bREVFmXbt2pnnn3/eeDyeKo9RVFRk/vjHP5pu3bp5/1bp1q2beeWVV6rt/P/+9797/3b8zW9+4/M3jL/9AVzGBDBjNwAAAABbtW7dWp07d9a7775b010BAIQpl8ulsWPHWr5SBDgRcxYCAAAAAAAAkESxEAAAAAAAAMCvKBYCAAAAAAAAkESxEAAc56OPPtLgwYOVnJwsl8ulRYsWnXKbFStW6Nxzz5Xb7Vb79u01e/bsau8nAMA/W7durdPzFZJrAHBqxhjmK0TIUCwEAIcpLCxUt27dNG3aNL/ab9myRYMGDdJFF12kdevW6a677tItt9yipUuXVnNPAQA4NXINAAB7cTdkAHAwl8ulhQsXasiQIZW2ue+++/Tee+/p22+/9a4bNmyY8vLytGTJEht6CQCAf8g1AACqX72a7sDJPB6Pdu7cqSZNmsjlctV0dwDUYcYYHThwQMnJyYqICG4g9pEjR1RcXBxUX05+T3S73XK73UH1S5Kys7OVnp7usy4jI0N33XVX0PsGuQYgfIRLrlVnpknkWnUj1wCEg1BmmhRcrkVFRSk6OjroPoSTsCsW7ty5UykpKTXdDQDw2rFjh8444wzL2x85ckRtWjVWzu4Sy/to3LixDh486LNuwoQJmjhxouV9lsrJyVFiYqLPusTERBUUFOjw4cNq0KBB0Meoy8g1AOGmpnOtOjNNIteqG7kGIJwEm2nSr7nWoIFyLG6flJSkLVu2OKpgGHbFwiZNmkiSLtBlqqf6NdwbAHXZMR3VJ1rsfV+yqri4WDm7S7RlTSvFNAn8W6+CAx61Sd2mHTt2KCYmxrs+VCMwUL3INQDhIhxyjUyr/Up/f3Zs3+7zGgKAnQoKCpTSsmXQmSb9mmuSdrhcCvRdrUBSSk6OiouLKRZWp9Kh7PVUX/VcfKgCUIN+ndE1VJfYxDSJsFQs9G4fE1Mtf5QnJSUpNzfXZ11ubq5iYmIYfREC5BqAsBFGuVZdmSaRa9Wt9PenOl9DAPBXKKdDiJEUE+j+HHobkLArFgKAU5UYj0osZEmJ8YS+Myfo1auXFi9e7LNu2bJl6tWrV7UeFwBQu1nJterONIlcAwBYFBEhWSkWllifbipcBT8LJADALx4Zy0sgDh48qHXr1mndunWSpC1btmjdunXavn27JOmBBx7Q8OHDve3HjBmjn376SX/605+0YcMGvfLKK3rrrbd09913h+zcAQDOY0emSeQaAMAmERHWFgdiZCEA2MQjj6yMpwh0q9WrV+uiiy7y/jxu3DhJ0ogRIzR79mzt2rXL+wFLktq0aaP33ntPd999t1544QWdccYZ+stf/qKMjAwLvQUA1BVWcs1KEpJrAABbWB1Z6EAUCwHAJiXGqMRCmAS6Tb9+/WSq2Gb27NkVbrN27dpAuwYAqMOs5JqVHCTXAAC2oFjoRbEQAGxi9fIrK9sAAFDdrOQamQYACFsUC72ceXE1AAAAAAAAgIAxshAAbOKRUQkjCwEADmEl18g0AEDYYmShF8VCALAJlyEDAJyEy5ABAI5CsdCLYiEA2MSuG5wAAGAHu25wAgCALSgWelEsBACbeH5drGwHAEC4sZJrZBoAIGy5XMcLhoHwODPZuMEJAAAAAAAAAEmMLAQA25RYvMGJlW0AAKhuVnKNTAMAhK2IiMBHFjoUxUIAsEmJOb5Y2Q4AgHBjJdfINABA2KJY6EWxEABswpyFAAAnYc5CAICjUCz0olgIADbxyKUSBXh3rV+3AwAg3FjJNTINABC2KBZ6USwEAJt4zPHFynYAAIQbK7lGpgEAwhbFQq+AnoWsrCydd955atKkiRISEjRkyBBt3LjRp02/fv3kcrl8ljFjxoS00wAAhAK5BgBwCjINABAqARULV65cqbFjx+rzzz/XsmXLdPToUfXv31+FhYU+7UaNGqVdu3Z5l6effjqknQaA2qjk18u1rCyoHuQaAFhHpoUXMg0AglQ6sjDQxYECugx5yZIlPj/Pnj1bCQkJWrNmjS688ELv+oYNGyopKSk0PQQAh7D6IYkPVtWHXAMA66zkGplWfcg0AAiSg4t/gQrqWcjPz5ckxcfH+6x/44031LRpU3Xu3FkPPPCADh06VOk+ioqKVFBQ4LMAgBN5jMvyAnuQawDgPzItvIUi0yRyDUAdwshCL8s3OPF4PLrrrrt0/vnnq3Pnzt71119/vVq1aqXk5GR9/fXXuu+++7Rx40b985//rHA/WVlZmjRpktVuAECtwcjC8EauAUBgGFkYvkKVaRK5BqAOcbkCL/4ZZ965y2WMtTO79dZb9X//93/65JNPdMYZZ1Ta7oMPPtAll1yiH3/8Ue3atSv3eFFRkYqKirw/FxQUKCUlRf10heq56lvpGgCExDFzVCv0jvLz8xUTE2N5PwUFBYqNjdUH36aocZPAv3k6eMCjizvvCLofqBq5BsDpwiHXyDR7hCrTpMpzLT8vj9cQQI0pKChQbFxcSPKkNNfyzzxTMZGRgW1bUqLYTZscl2uWRhZmZmbq3Xff1UcffVRl+EhSWlqaJFUaQG63W26320o3AAAICXINAOAUocw0iVwDgLoooGKhMUa33367Fi5cqBUrVqhNmzan3GbdunWSpObNm1vqIAA4hbE4V5NhfqdqQ64BgHVWco1Mqz5kGgAEycochA69DDmgZ2Hs2LH6+9//rrlz56pJkybKyclRTk6ODh8+LEnavHmzJk+erDVr1mjr1q3617/+peHDh+vCCy9U165dq+UEAKC2KJ3bycqC6kGuAYB1ZFp4IdMAIEg23uBk2rRpat26taKjo5WWlqZVq1b5td2bb74pl8ulIUOGWDquvwIaWTh9+nRJUr9+/XzWz5o1SzfddJOioqL0/vvva+rUqSosLFRKSoquuuoqPfzwwyHrMADUViUmQiUm8DApceaXVWGBXAMA66zkGplWfcg0AAiSTSML582bp3HjxmnGjBlKS0vT1KlTlZGRoY0bNyohIaHS7bZu3ap7771Xffr0CfiYgQr4MuSqpKSkaOXKlUF1CACcyiOXPIEN6P51Oz5ZVRdyDQCss5JrZFr1IdMAIEg2FQunTJmiUaNGaeTIkZKkGTNm6L333tPMmTN1//33V7hNSUmJbrjhBk2aNEkff/yx8vLyAj5uIKyNlwQABIzLkAEATkKmAQAcJYjLkAsKCnyWE+8if6Li4mKtWbNG6enpJxw2Qunp6crOzq60a48++qgSEhJ08803h/acK0GxEAAAAAAAALAoJSVFsbGx3iUrK6vCdnv37lVJSYkSExN91icmJionJ6fCbT755BO9/vrreu2110Le78oEdBkyAMA663MWcskWACD8WJuzkEwDAISpIC5D3rFjh2JiYryr3W53SLp04MAB3XjjjXrttdfUtGnTkOzTHxQLAcAmx+d2CvzyKyvbAABQ3azkGpkGAAhbQRQLY2JifIqFlWnatKkiIyOVm5vrsz43N1dJSUnl2m/evFlbt27V4MGDves8Ho8kqV69etq4caPatWsXWJ/9QLEQAGziUYRKuMEJAMAhrOQamQYACFsuV+DFwl8Ld/6KiopSamqqli9friFDhvy6C4+WL1+uzMzMcu07dOigb775xmfdww8/rAMHDuiFF15QSkpKYP31E8VCALAJlyEDAJyEy5ABhJoJYPSxiy8fqkWdfg2sjCwMtL2kcePGacSIEerRo4d69uypqVOnqrCw0Ht35OHDh6tFixbKyspSdHS0Onfu7LN9XFycJJVbH0oUCwHAJh5FyMPIQgCAQ1jJNTINABC2bCoWDh06VHv27NH48eOVk5Oj7t27a8mSJd6bnmzfvl0RFvYbShQLAQAAAAAAAJtkZmZWeNmxJK1YsaLKbWfPnh36Dp2EYiEA2KTEuFRiAp/Y3co2AABUNyu5RqYBAMKWTSMLawOKhQBgkxKLNzgp4ZItAEAYspJrZBoAIGxRLPSiWAgANvGYCHks3ODEw2TwAIAwZCXXyDQAQNiiWOhFsRAAbMLIQgCAkzCyEADgKBQLvSgWAoBNPLI2V5Mn9F0BACBoVnKNTAMAhC2KhV7OPCsAAAAAAAAAAWNkIQDYxKMIeSx8R2NlGwAAqpuVXCPTAABhi5GFXhQLAcAmJSZCJRZucGJlGwAAqpuVXCPTAABhy+UKvPjnCnyaqdqAYiEA2MQjlzyyMmehMwMIAFC7Wck1Mg2oewz/7lFbMLLQi2IhANiEkYUAACdhZCEAwFEoFnpRLAQAm5QoQiUW5mqysg0AANXNSq6RaQCAsEWx0MuZZwUAAAAAAAAgYIwsBACbeIxLHmNhzkIL2wAAUN2s5BqZBgAIW4ws9KJYCAA28Vi8DNnDIHAAQBiykmtkGgAgbFEs9KJYCAA28ZgIeSxM7G5lGwAAqpuVXCPTAABhi2KhF8VCALBJiVwqUeCXX1nZBgCA6mYl18g0AEDYoljoRbEQAGzCyEIAgJMwshAA4CgUC72ceVYAAAAAAAAAAsbIQgCwSYmsXX5VEvquAAAQNCu5RqYBAMKWyxX4SEGXM6fXYGQhANik9HItK0ugpk2bptatWys6OlppaWlatWpVle2nTp2qs88+Ww0aNFBKSoruvvtuHTlyxOqpAgDqALsyTSLXAMAuLhm/F8cpvQw50MWBGFkIADYpMREqsfAhKdBt5s2bp3HjxmnGjBlKS0vT1KlTlZGRoY0bNyohIaFc+7lz5+r+++/XzJkz1bt3b/3www+66aab5HK5NGXKlID7CwCoG6zkmpUcJNcAALZgzkIvZ54VAIQhI5c8FhYT4CVeU6ZM0ahRozRy5Eh16tRJM2bMUMOGDTVz5swK23/22Wc6//zzdf3116t169bq37+/rrvuulOO2gAA1G1Wci3QTJPINQCATRhZ6OXMswKAMFQ6AsPK4q/i4mKtWbNG6enp3nURERFKT09XdnZ2hdv07t1ba9as8X6I+umnn7R48WJddtllwZ0wAMDRqjvTJHINAGAjioVeXIYMALVEQUGBz89ut1tut9tn3d69e1VSUqLExESf9YmJidqwYUOF+73++uu1d+9eXXDBBTLG6NixYxozZowefPDB0J4AAAC/8ifTJHINAICa4MwSKACEIY9xWV4kKSUlRbGxsd4lKysrJP1asWKFnnjiCb3yyiv66quv9M9//lPvvfeeJk+eHJL9AwCcKRwzTSLXAAAWMbLQi5GFAGCTEkWoxMJ3NKXb7NixQzExMd71FY3AaNq0qSIjI5Wbm+uzPjc3V0lJSRXu/5FHHtGNN96oW265RZLUpUsXFRYWavTo0XrooYcU4dAABAAEx0quBZJpErkGALARNzjxcuZZAUAYCnZkYUxMjM9S0QerqKgopaamavny5WXH9Xi0fPly9erVq8J+HTp0qNwHp8jISEmSMSZUpw8AcJjqzjSJXAMA2IiRhV6MLAQAm3gUIY+F72gC3WbcuHEaMWKEevTooZ49e2rq1KkqLCzUyJEjJUnDhw9XixYtvJd8DR48WFOmTNFvfvMbpaWl6ccff9QjjzyiwYMHez9cAQBwMiu5ZiUHyTUAgC0YWehFsRAAbFJiXCr5dURFoNsFYujQodqzZ4/Gjx+vnJwcde/eXUuWLPFODr99+3afERcPP/ywXC6XHn74Yf3yyy9q1qyZBg8erMcffzzgvgIA6g4ruWYlB8k1AIAtXK7Ai3+uwHOtNqBYCAAOlJmZqczMzAofW7Fihc/P9erV04QJEzRhwgQbegYAQODINQAA7EOxEABscuJcTYFuBwBAuLGSa2QaACBscRmyF8VCALCJMRHymMDDxFjYBgCA6mYl18g0AFVxiZsQoQZRLPSiWAgANimRSyWyMGehhW0AAKhuVnKNTAMAhC2KhV4UCwHAJh5j7fIrD1+wAgDCkJVcI9MAAGGLYqEXxUIAsInH4mXIVrYBAKC6Wck1Mg0AELYoFno586wAAAAAAAAABIyRhQBgE49c8liYq8nKNgAAVDcruUamAQDCFiMLvSgWAoBNSoxLJRbmLLSyDQAA1c1KrpFpAICwRbHQK6CzysrK0nnnnacmTZooISFBQ4YM0caNG33aHDlyRGPHjtXpp5+uxo0b66qrrlJubm5IOw0AtVHp3E5WFlQPcg0ArCPTwguZBgBBKi0WBro4UEBntXLlSo0dO1aff/65li1bpqNHj6p///4qLCz0trn77rv173//W/Pnz9fKlSu1c+dOXXnllSHvOADUNh655DEWFi7ZqjbkGgBYZynXyLRqQ6YBQJBcrsALhS5n5lpAlyEvWbLE5+fZs2crISFBa9as0YUXXqj8/Hy9/vrrmjt3ri6++GJJ0qxZs9SxY0d9/vnn+p//+Z/Q9RwAahljcc5CwwerakOuAYB1VnKNTKs+ZBoABInLkL2COqv8/HxJUnx8vCRpzZo1Onr0qNLT071tOnTooJYtWyo7O7vCfRQVFamgoMBnAQCgJpBrAACnCEWmSeQaANRFlouFHo9Hd911l84//3x17txZkpSTk6OoqCjFxcX5tE1MTFROTk6F+8nKylJsbKx3SUlJsdolAAhrli5B/nVB9SPXACAwZFr4ClWmSeQagDqEOQu9LJ/V2LFj9e233+rNN98MqgMPPPCA8vPzvcuOHTuC2h8AhCtucBLeyDUACAyZFr5ClWkSuQagDqFY6BXQnIWlMjMz9e677+qjjz7SGWec4V2flJSk4uJi5eXl+XxjlZubq6SkpAr35Xa75Xa7rXQDAGoVqyMqGIVR/cg1AAiclVwj06pfKDNNItcA1CHMWegV0FkZY5SZmamFCxfqgw8+UJs2bXweT01NVf369bV8+XLvuo0bN2r79u3q1atXaHoMALWU59eJ4K0sqB7kGgBYR6aFFzINAILEyEKvgEYWjh07VnPnztU777yjJk2aeOe2iI2NVYMGDRQbG6ubb75Z48aNU3x8vGJiYnT77berV69e3F0LQJ3HyMLwQ64BgHWMLAwvZBoABImRhV4BFQunT58uSerXr5/P+lmzZummm26SJD3//POKiIjQVVddpaKiImVkZOiVV14JSWcBAAglcg0A4BRkGgAgVAIqFhpjTtkmOjpa06ZN07Rp0yx3CgCciJGF4YdcAwDrGFkYXsg0AAgSIwu9LN3gBAAQOIqFAAAnoVgIAHAUioVezjwrAAhDpR+qrCwAAIQbMg0A4CguV+A3N3FZy7Vp06apdevWio6OVlpamlatWlVp29dee019+vTRaaedptNOO03p6elVtg8FioUAYBMja3eOPPVFRQAA2M9KrpFpAICwZdPdkOfNm6dx48ZpwoQJ+uqrr9StWzdlZGRo9+7dFbZfsWKFrrvuOn344YfKzs5WSkqK+vfvr19++SXYM64UxUIAAAAAAADABlOmTNGoUaM0cuRIderUSTNmzFDDhg01c+bMCtu/8cYbuu2229S9e3d16NBBf/nLX+TxeLR8+fJq6yPFQgCwCZchAwCchEwDADiKDSMLi4uLtWbNGqWnp59w2Ailp6crOzvbr30cOnRIR48eVXx8fEDHDgQ3OAEAm3CDEwCAk3CDEwCAowRxg5OCggKf1W63W263u1zzvXv3qqSkRImJiT7rExMTtWHDBr8Oed999yk5Odmn4BhqjCwEAJswshAA4CRkGgDAUYIYWZiSkqLY2FjvkpWVVS1dfPLJJ/Xmm29q4cKFio6OrpZjSIwsBADbMLIQAOAkjCwEADhKECMLd+zYoZiYGO/qikYVSlLTpk0VGRmp3Nxcn/W5ublKSkqq8lDPPvusnnzySb3//vvq2rVrYP0MECMLAcAmxrgsLwAAhBsyDQDgKEGMLIyJifFZKisWRkVFKTU11efmJKU3K+nVq1elXXv66ac1efJkLVmyRD169AjteVeAkYUAAAAAAACADcaNG6cRI0aoR48e6tmzp6ZOnarCwkKNHDlSkjR8+HC1aNHCeynzU089pfHjx2vu3Llq3bq1cnJyJEmNGzdW48aNq6WPFAsBwCYeueSRhcuQLWwDAEB1s5JrZBoAIGwFcRlyIIYOHao9e/Zo/PjxysnJUffu3bVkyRLvTU+2b9+uiBP2O336dBUXF+vqq6/22c+ECRM0ceLEgI/vD4qFAGAT5iwEADgJcxYCABzFpmKhJGVmZiozM7PCx1asWOHz89atWy0dIxgUCwHAJlbnamJ+JwBAOLKSa2QaACBsuVyBF/9czsw1ioUAYBNGFgIAnISRhQAAR7FxZGG4o1gIADZhZCEAwEkYWQgAcBSKhV7OPCsAAAAAAAAAAWNkIQDYxFi8DJlRGACAcGQl18g0AAhvJoC71rtkqrEnNYCRhV4UCwHAJkaSsZCnDotgAIBDWMk1Mg0AELYoFnpRLAQAm3jkkiuAb+pO3A4AgHBjJdfINABA2KJY6EWxEABswg1OAABOwg1OAACOQrHQi2IhANjEY1xyWfiQZGWeQwAAqpuVXCPTAABhi2KhlzPPCgAAAAAAAEDAGFkIADYxxuINTpgNHgAQhqzkGpkGAAhbjCz0olgIADZhzkIAgJMwZyEAwFEoFnpRLAQAm1AsBAA4CcVCAICjuFyBF/9czsw1ioUAYBNucAIAcBJucAIAcBRGFnpRLAQAmzBnIQDASZizEADgKBQLvSgWos6JaNDAv4YBDCc2RUX+ty0p8bstAABVcdWr73fbiAbRfrcNJKvIQABAqBw95v9nsPoRAWTKkSP+tYuK8n+f9ZxZTnHJ/291jPx/vQLZL2qeM3+7ASAMHR+BYWXOwmroDAAAQbKSa2QaACBsMbLQi2IhANiEG5wAAJyEG5wAAByFYqEXxUIAsIn5dbGyHQAA4cZKrpFpAICwRbHQi2IhANiEkYUAACdhZCEAwFEoFno586wAIByZIJYATZs2Ta1bt1Z0dLTS0tK0atWqKtvn5eVp7Nixat68udxut8466ywtXrw48AMDAOoOmzJNItcAADYoLRYGujgQIwsBwGHmzZuncePGacaMGUpLS9PUqVOVkZGhjRs3KiEhoVz74uJiXXrppUpISNCCBQvUokULbdu2TXFxcfZ3HgCAk5BrAADYi2IhANjF4mXICnCbKVOmaNSoURo5cqQkacaMGXrvvfc0c+ZM3X///eXaz5w5U/v379dnn32m+vXrS5Jat24deD8BAHWLlVyzkIPkGgDAFlyG7OXMswKAMGSM9UWSCgoKfJaioqJyxyguLtaaNWuUnp7uXRcREaH09HRlZ2dX2K9//etf6tWrl8aOHavExER17txZTzzxhEpKSqrleQAAOEN1Z5pErgEAbORyBX4JssuZc/FSLAQAm5ROBG9lkaSUlBTFxsZ6l6ysrHLH2Lt3r0pKSpSYmOizPjExUTk5ORX266efftKCBQtUUlKixYsX65FHHtFzzz2nxx57LPRPAgDAMao70yRyDQBgI+Ys9OIyZACwi3FZuvyqdJsdO3YoJibGu9rtdoekWx6PRwkJCXr11VcVGRmp1NRU/fLLL3rmmWc0YcKEkBwDAOBAVnKtmjNNItcAABZxGbIXxUIAsMmJl18Fup0kxcTE+HywqkjTpk0VGRmp3Nxcn/W5ublKSkqqcJvmzZurfv36ioyM9K7r2LGjcnJyVFxcrKioqMA7DQBwPCu5FkimSeQaAMBGFAu9KBYibEUE8ofcOe39bnqg3an/MJUkT33/D994xxG/29b7dqvfbUvy8vzvBCApKipKqampWr58uYYMGSLp+AiL5cuXKzMzs8Jtzj//fM2dO1cej0cRv4bdDz/8oObNm/OBCqgB9RKa+d32SNdWfrc92ML/YKtX5H8FKGZjgd9tXRu2+NXOc6Ti+esqZDz+t0WtQ64B4enAQf9HFf/8s//7bdjQ/7atmvr/GUwbNvjXLpDCT3v/P4MGdGK1qPjkkoWREKgVas9vIQDUdiaIJQDjxo3Ta6+9pr/+9a9av369br31VhUWFnrvIjl8+HA98MAD3va33nqr9u/frzvvvFM//PCD3nvvPT3xxBMaO3ZscOcLAHA2GzJNItcAADZhzkIvRhYCgE1OnNg90O0CMXToUO3Zs0fjx49XTk6OunfvriVLlngnh9++fbt3pIV0fJL5pUuX6u6771bXrl3VokUL3XnnnbrvvvsC7isAoO6wkmtWcpBcAwDYgsuQvSgWAoCdbBqpn5mZWenlWStWrCi3rlevXvr888+ruVcAAMch1wAATkGx0ItiIQDYxK6RhQAA2MGukYUAANiCYqEXxUIAsIvFuZqYNxgAEJas5BqZBgAIVxQLvZx5VgAAAAAAAAACFnCx8KOPPtLgwYOVnJwsl8ulRYsW+Tx+0003yeVy+SwDBgwIVX8BoBZzBbGgOpBpABAMMi3ckGsAEASXK/A7IbucmWsBFwsLCwvVrVs3TZs2rdI2AwYM0K5du7zLP/7xj6A6CQCOYIJYUC3INAAIApkWdsg1AAhCoIVCK5ct1xIBz1k4cOBADRw4sMo2brdbSUlJljsFAI7EnIVhh0wDgCAwZ2HYIdcAIAjMWehVLWe1YsUKJSQk6Oyzz9att96qffv2Vdq2qKhIBQUFPgsAOJJxWV9QYwLJNIlcA1CHkGm1ErkGAJVgZKFXyO+GPGDAAF155ZVq06aNNm/erAcffFADBw5Udna2IiMjy7XPysrSpEmTQt0NOICrQzu/2267LM7vtvV7/tevdvUiPX7vc/u60/1u29LVxu+2Ednf+t3WHDvqd1vUDGOOL1a2Q80INNMkcg2Vi2jY0K92hT39z4ntV/ufVfFN9/vdNq8w2u+2Bz+P87vtGfub+tdwT9XFixN5Dh/xu62M/88XTs1KrpFpNYtcw6kcPeZfQf/77/3f54YN/rft3dv/tvIzVyVJO3f61y4nx/99Nm7sf9uEhOrZr0MLVTWGkYVeIS8WDhs2zPv/Xbp0UdeuXdWuXTutWLFCl1xySbn2DzzwgMaNG+f9uaCgQCkpKaHuFgAAAQs00yRyDQAQvsg1AIA/qr0E2rZtWzVt2lQ//vhjhY+73W7FxMT4LADgSNzgpNY7VaZJ5BqAOoRMq/XINQA4AZche4V8ZOHJfv75Z+3bt0/Nmzev7kMBQHizOlcT8zuFDTINAE5gJdfItLBCrgHACbgM2SvgYuHBgwd9vnnasmWL1q1bp/j4eMXHx2vSpEm66qqrlJSUpM2bN+tPf/qT2rdvr4yMjJB2HABqG5c5vljZDtWDTAMA66zkGplWvcg1AAgCxUKvgIuFq1ev1kUXXeT9uXT+ihEjRmj69On6+uuv9de//lV5eXlKTk5W//79NXnyZLnd7tD1GgBqI6uXX/HBqtqQaQAQBCu5RqZVK3INAIJAsdAr4GJhv379ZKq4jdnSpUuD6hAAOBaXIYcdMg0AgsBlyGGHXAOAILhcgRf/XM7MNWeWQAEAAAAAAAAErNpvcAIA+BWXIQMAnITLkAEATsJlyF4UCwHALhQLAQBOQrEQAOAkFAu9KBYCgF0oFgIAnIRiIQDASSgWelEsBAC7cIMTAICTcIMTAICTUCz0olgIW7nq1fe77cF2MX63jeiR73fb17rO8atdfVeJ3/v8g+t//W5bsLGp323jv27od9uSfP+fA9QMlzm+WNkOQO0XEX+aX+32n+3/n2d/6LHM77bpjb/3u+1Xh1v53faJgkF+ty35NM6vdhH5B/zep6uoyO+2xv9ohx+s5BqZBoS3Q4f8a7d1q//7DKRt587+tzXy/8sH1/79/jX8+Wf/O7B7t/9tY/z/bKuG/n8G9LdQFdBzVZeHgFMs9HLmWQEAAAAAAABhaNq0aWrdurWio6OVlpamVatWVdl+/vz56tChg6Kjo9WlSxctXry4WvtHsRAA7GKCWAAACDdkGgDASUpHFga6BGjevHkaN26cJkyYoK+++krdunVTRkaGdlcyWvWzzz7Tddddp5tvvllr167VkCFDNGTIEH377bfBnnGlKBYCAAAAAACgbrOpWDhlyhSNGjVKI0eOVKdOnTRjxgw1bNhQM2fOrLD9Cy+8oAEDBuiPf/yjOnbsqMmTJ+vcc8/Vyy+/HOwZV4piIQDYxKWy+Z0CWmq64wAAVMBSrtV0pwEAqEwQxcKCggKfpaiSOZWLi4u1Zs0apaenn3DYCKWnpys7O7vCbbKzs33aS1JGRkal7UOBYiEA2KX0rpFWFgAAwg2ZBgBwECOXpUWSUlJSFBsb612ysrIqPMbevXtVUlKixMREn/WJiYnKycmpcJucnJyA2ocCd0MGALtYnauJ+Z0AAOHISq6RaQCAMOXxHF8C3UaSduzYoZgT7nrtdrtD2DP7USwEAAAAAAAALIqJifEpFlamadOmioyMVG5urs/63NxcJSUlVbhNUlJSQO1DgcuQAcAu3A0ZAOAkZBoAwEFKRxYGugQiKipKqampWr58+QnH9Wj58uXq1atXhdv06tXLp70kLVu2rNL2ocDIQgCwSenk7la2AwAg3FjJNTINABCugrkMORDjxo3TiBEj1KNHD/Xs2VNTp05VYWGhRo4cKUkaPny4WrRo4Z338M4771Tfvn313HPPadCgQXrzzTe1evVqvfrqq4Ef3E8UCwHALsxZCABwEuYsBAA4iF3FwqFDh2rPnj0aP368cnJy1L17dy1ZssR7E5Pt27crIqLsQuDevXtr7ty5evjhh/Xggw/qzDPP1KJFi9S5c+fAD+4nioUAYBeKhQAAJ6FYCABwELuKhZKUmZmpzMzMCh9bsWJFuXXXXHONrrnmGmsHs4BiIQDYhMuQAQBOwmXIAAAnsbNYGO64wQkAAAAAAAAASYwsBAD7GNfxxcp2AACEGyu5RqYBAMIUIwvLUCwEALswZyEAwEmYsxAA4CAUC8tQLAQAmzBnIQDASZizEADgJMYEXvwzDs01ioUAYBdGFgIAnISRhQAAB2FkYRlucAIAAAAAAABAEiMLAcA+Fi9DZhQGACAsWck1Mg0AEKYYWViGYiEA2IXLkAEATsJlyAAAB6FYWIZiIQDYhWIhAMBJKBYCAByEYmEZioWwlTl21O+2jTcX+N123+rT/G47KuJGv9rVi/T/X/3Bdaf73bbl1sN+t/UUHvK7LcIfd0MG6jbP/v/61S5+Ywu/9/nn1X39bju/6bl+tz1QGO1325jv6/vdNjI3x692nuJiv/dpPLxJ1hTuhgw4T8OG/rVr3dr/fR454n/bmBj/27oC+fYhPt6/dgHkjxIS/G8b7X+uKiL0t5YI6LmqwygWlqFYCAAAAAAAgDqNYmEZ7oYMAAAAAAAAQBIjCwHAPsxZCABwEuYsBAA4CCMLy1AsBACbMGchAMBJmLMQAOAkFAvLUCwEADvxIQkA4CTkGgDAIYwJvPhnHJqDFAsBwC5chgwAcBIuQwYAOAgjC8tQLAQAm3AZMgDASbgMGQDgJBQLy3A3ZAAAAAAAAACSGFkIAPbhMmQAgJNwGTIAwEEYWViGkYUAYJPSy7WsLIGaNm2aWrdurejoaKWlpWnVqlV+bffmm2/K5XJpyJAhgR8UAFCn2JVpErkGAKh+pcXCQBcnolgIAHYxQSwBmDdvnsaNG6cJEyboq6++Urdu3ZSRkaHdu3dXud3WrVt17733qk+fPoEdEABQN9mQaRK5BgCwB8XCMlyGjLBlNmz2u23LyPZ+tz2wMdavdp76fu9SLXcc8rttvW+3+t225NhR/zuB8GfTZchTpkzRqFGjNHLkSEnSjBkz9N5772nmzJm6//77K9ympKREN9xwgyZNmqSPP/5YeXl5FjoKoCqeQ/5lRaNVW/zeZ5sjrfxue7BFvN9tmxb5/8YTszHP77ae3Xv9a3ekyO99yjj0r/TawKbLkMk1wD716/n3j7RTJ5ff+4yJ8f/4UVH+t5WfuSpJSk72r90ZZ/i/z6Qk/9s2bOh/2wjGdNUULkMuw28hANgk2MuQCwoKfJaiovIfpouLi7VmzRqlp6d710VERCg9PV3Z2dmV9u3RRx9VQkKCbr755pCfNwDAmao70yRyDQBgH0YWlqFYCAC1REpKimJjY71LVlZWuTZ79+5VSUmJEhMTfdYnJiYqJyenwv1+8sknev311/Xaa69VS78BADiZP5kmkWsAANQELkMGALsEeRnyjh07FHPCdRxutzvoLh04cEA33nijXnvtNTVt2jTo/QEA6pAgLkOujkyTyDUAgHVchlyGYiEA2CXIYmFMTIzPB6uKNG3aVJGRkcrNzfVZn5ubq6QK5lXZvHmztm7dqsGDB3vXeX5NvHr16mnjxo1q166dhU4DABwviGKhP5kmkWsAAPsYE3jxz1j5fFcLcBkyANgk2DkL/REVFaXU1FQtX77cu87j8Wj58uXq1atXufYdOnTQN998o3Xr1nmXyy+/XBdddJHWrVunlJSUUJw6AMCBqjvTJHINAGAf5iwsw8hCALCLTXdDHjdunEaMGKEePXqoZ8+emjp1qgoLC713kRw+fLhatGihrKwsRUdHq3Pnzj7bx8XFSVK59QAA+LDpbsjkGgDADlyGXCbgkYUfffSRBg8erOTkZLlcLi1atMjncWOMxo8fr+bNm6tBgwZKT0/Xpk2bQtVfAKi17BhZKElDhw7Vs88+q/Hjx6t79+5at26dlixZ4p0cfvv27dq1a1c1nGHtQ6YBgHV2ZJpErgWCXAMA6xhZWCbgYmFhYaG6deumadOmVfj4008/rRdffFEzZszQF198oUaNGikjI0NHjhwJurMAAP9kZmZq27ZtKioq0hdffKG0tDTvYytWrNDs2bMr3Xb27NnlPlw4FZkGALUDueYfcg0AEAoBX4Y8cOBADRw4sMLHjDGaOnWqHn74YV1xxRWSpL/97W9KTEzUokWLNGzYsOB6CwC1mU2XIcN/ZBoABMGmy5DhP3INAKzjMuQyIb3ByZYtW5STk6P09HTvutjYWKWlpSk7O7vCbYqKilRQUOCzAIAjmSAW2M5KpknkGoA6hEyrVcg1AKgalyGXCekNTnJyciTJO39IqcTERO9jJ8vKytKkSZNC2Q04hKe42P/Ga7/3u2mTDQ38a+hy+b1PU1Tkd9uSkhK/28JZXL8uVraD/axkmkSuIXjHdu/xu239FXl+tz29QbTfbU0AWRVIBgayX4Q/K7lGptUccg2h1KSx/5X/Dh38/5d/7FgAnYjwP9fUoYN/7aKi/N9nPe4XawJ4V3fVgm+LGFlYJqQjC6144IEHlJ+f71127NhR010CgOrByMI6gVwDUGeQaXUCuQagrmBkYZmQlsKTkpIkSbm5uWrevLl3fW5urrp3717hNm63W263O5TdAICwZPUukFa2QfCsZJpErgGoO6zkGplWc8g1AKgaIwvLhHRkYZs2bZSUlKTly5d71xUUFOiLL75Qr169QnkoAACqFZkGAHAScg0A4K+ARxYePHhQP/74o/fnLVu2aN26dYqPj1fLli1111136bHHHtOZZ56pNm3a6JFHHlFycrKGDBkSyn4DQO3D3ZDDDpkGAEHgbshhh1wDAOuMCXykoHForgVcLFy9erUuuugi78/jxo2TJI0YMUKzZ8/Wn/70JxUWFmr06NHKy8vTBRdcoCVLlig6OoDJRwHAqRwaJrUVmQYAQSLXwgq5BgDWcRlymYCLhf369ZOponTqcrn06KOP6tFHHw2qYwDgNMxZGH7INACwjjkLww+5BgDWUSwsw72+AcAuXIYMAHASLkMGADgIxcIyFAsBwCaMLAQAOAkjCwEATkKxsExI74YMAAAAAAAAoPZiZCEA2IXLkAEATsJlyAAAB2FkYRmKhQBgEy5DBgA4CZchAwCchGJhGYqFqHM8hw/XdBdQVzGyEECImWNH/W5bcsD/toBfGFkIIMTq1wvkTSKAWdUaNgy4L3WVkcvvti6HvalTLCxDsRAA7EKxEADgJBQLAQAOQrGwDMVCALAJlyEDAJyEy5ABAE5CsbAMd0MGAAAAAAAAIImRhQBgHy5DBgA4CZchAwAcxJjARwoah+YaIwsBwCYuYywvAACEGzINAOAkpZchB7pUl/379+uGG25QTEyM4uLidPPNN+vgwYNVtr/99tt19tlnq0GDBmrZsqXuuOMO5efnB3xsRhYCgF0YWQgAcBJGFgIAHCTc5iy84YYbtGvXLi1btkxHjx7VyJEjNXr0aM2dO7fC9jt37tTOnTv17LPPqlOnTtq2bZvGjBmjnTt3asGCBQEdm2IhANiEG5wAAJyEG5wAAJwknIqF69ev15IlS/Tll1+qR48ekqSXXnpJl112mZ599lklJyeX26Zz5856++23vT+3a9dOjz/+uP73f/9Xx44dU716/pcAuQwZAOxiglgAAAg3ZBoAwEHC6TLk7OxsxcXFeQuFkpSenq6IiAh98cUXfu8nPz9fMTExARUKJUYWAgAAAAAAAJYVFBT4/Ox2u+V2uy3vLycnRwkJCT7r6tWrp/j4eOXk5Pi1j71792ry5MkaPXp0wMdnZCEA2KT0ci0rCwAA4YZMAwA4STAjC1NSUhQbG+tdsrKyKjzG/fffL5fLVeWyYcOGoM+loKBAgwYNUqdOnTRx4sSAt2dkIQDYhRucAACchBucAIDjuOrwG3Uwcxbu2LFDMTEx3vWVjSq85557dNNNN1W5z7Zt2yopKUm7d+/2WX/s2DHt379fSUlJVW5/4MABDRgwQE2aNNHChQtVv379U5/ISSgWAoBNuMEJAMBJuMEJAMBJgikWxsTE+BQLK9OsWTM1a9bslO169eqlvLw8rVmzRqmpqZKkDz74QB6PR2lpaZVuV1BQoIyMDLndbv3rX/9SdHS0fydyEi5DBgC7cIMTAICTkGkAAAcJpxucdOzYUQMGDNCoUaO0atUqffrpp8rMzNSwYcO8d0L+5Zdf1KFDB61atUrS8UJh//79VVhYqNdff10FBQXKyclRTk6OSkpKAjo+IwsBwEaMqAAAOAm5BgBwCmMCL/6ZaszBN954Q5mZmbrkkksUERGhq666Si+++KL38aNHj2rjxo06dOiQJOmrr77y3im5ffv2PvvasmWLWrdu7fexKRYCAAAAAAAAYSQ+Pl5z586t9PHWrVvLnFCt7Nevn8/PwaBYCAB2McbaV0/V+XUVAABWWck1Mg0AEKaCmbPQaSgWAoBNuMEJAMBJuMEJAMBJKBaWoVgIAHaxOrE7H6wAAOHISq6RaQCAMEWxsAzFQgCwictzfLGyHQAA4cZKrpFpAIBwRbGwDMVCALALIwsBAE7CyEIAgINQLCwTUdMdAAAAAAAAABAeGFkIADbhBicAACfhBicAACdhZGEZioUAYBdjji9WtgMAINxYyTUyDQAQpigWlqFYCAA2YWQhAMBJGFkIAHASioVlKBYCgF24wQkAwEm4wQkAwEGMCbz459QB8xQLAcAmjCwEADgJIwsBAE7CyMIy3A0ZAAAAAAAAgCRGFgKAfbjBCQDASbjBCQDAQRhZWIZiIQDYhMuQAQBOwmXIAAAnoVhYhmIhANiFG5wAAJyEG5wAAByEYmEZioUAYBNGFgIAnISRhQAAJ6FYWIZiIQDYxWOOL1a2AwAg3FjJNTINABCmKBaW4W7IAAAAAAAAACQxshAA7MOchQAAJ2HOQgCAgzCysAzFQgCwiUsW5ywMeU8AAAielVwj0wAA4YpiYRkuQwYAuxhjfQnQtGnT1Lp1a0VHRystLU2rVq2qtO1rr72mPn366LTTTtNpp52m9PT0KtsDACDJtkyTyDUAQPUzpqxg6O9iMdbCHsVCALBJ6V0jrSyBmDdvnsaNG6cJEyboq6++Urdu3ZSRkaHdu3dX2H7FihW67rrr9OGHHyo7O1spKSnq37+/fvnllxCcNQDAqezINIlcAwDYI9BCoZWRiLUFxUIAcJgpU6Zo1KhRGjlypDp16qQZM2aoYcOGmjlzZoXt33jjDd12223q3r27OnTooL/85S/yeDxavny5zT0HAKA8cg0AAHtRLAQAu5ggFj8VFxdrzZo1Sk9P966LiIhQenq6srOz/drHoUOHdPToUcXHx/t/YABA3VPNmSaRawAA+zCysAw3OAEAm7iMkcvCpBal2xQUFPisd7vdcrvdPuv27t2rkpISJSYm+qxPTEzUhg0b/Drefffdp+TkZJ8PZgAAnMxKrgWSaRK5BgCwDzc4KRPykYUTJ06Uy+XyWTp06BDqwwBA7eMJYpGUkpKi2NhY75KVlRXyLj755JN68803tXDhQkVHR4d8/7URuQYAlQjzTJPItZORaQBQOUYWlqmWkYXnnHOO3n///bKD1GMAIwAEO7Jwx44diomJ8a6vaARG06ZNFRkZqdzcXJ/1ubm5SkpKqvI4zz77rJ588km9//776tq1a8D9dDJyDQDKC2ZkoT+ZJpFr1YFMA4CKMbKwTLUkQ7169U4Z3gBQ51iYq8m7naSYmBifD1YViYqKUmpqqpYvX64hQ4ZIkndS98zMzEq3e/rpp/X4449r6dKl6tGjh4VOOhu5BgAVsJJrAWSaRK5VBzINACpGsbBMtdzgZNOmTUpOTlbbtm11ww03aPv27ZW2LSoqUkFBgc8CALBu3Lhxeu211/TXv/5V69ev16233qrCwkKNHDlSkjR8+HA98MAD3vZPPfWUHnnkEc2cOVOtW7dWTk6OcnJydPDgwZo6hbBDrgFAzSHXQiuQTJPINQCoi0JeLExLS9Ps2bO1ZMkSTZ8+XVu2bFGfPn104MCBCttnZWX5zFeSkpIS6i4BQHgwxvoSgKFDh+rZZ5/V+PHj1b17d61bt05LlizxTg6/fft27dq1y9t++vTpKi4u1tVXX63mzZt7l2effTakp19bkWsAUAkbMk0i10Ip0EyTyDXYy8jl9wKEGnMWlnEZYyGxA5CXl6dWrVppypQpuvnmm8s9XlRUpKKiIu/PBQUFSklJUT9doXqu+tXZNQCo0jFzVCv0jvLz8/26VKoyBQUFio2NVd/ej6hevcAnVz927IhWfjY56H4gNMg1ALVVOOQamRZeTpVpUuW5lp+Xx2sIv1RXYc9laX4fOEVBQYFi4+JCkieluXbttfmKigpsX8XFBXrrrVjH5Vq1z2YbFxens846Sz/++GOFj7vd7konNAYAR7E4osLSNqg25BoA/MpKrpFpYeVUmSaRawDqDmMCHyno1FirljkLT3Tw4EFt3rxZzZs3r+5DAUBYc3msLwgf5BoAHEem1X5kGgCU4TLkMiEvFt57771auXKltm7dqs8++0y/+93vFBkZqeuuuy7UhwKA2sWmOQsRWuQaAFSCTKt1yDQAqBzFwjIhvwz5559/1nXXXad9+/apWbNmuuCCC/T555+rWbNmoT4UAADVjlwDADgFmQYA8EfIi4VvvvlmqHcJAM5gfl2sbIcaQ64BQCWs5BqZVqPINAConJWRgowsBAAExWWMXBYuv7KyDQAA1c1KrpFpAIBwRbGwDMVCALALd0MGADgJd0MGADgIxcIyFAsBwC5GkpUw4XMVACAcWck1Mg0AEKYoFpahWAgANuEyZACAk3AZMgDASSgWlomo6Q4AAAAAAAAACA+MLAQAuxhZnLMw5D0BACB4VnKNTAOAsGbk8ruty2Fv6owsLEOxEADswg1OAABOwg1OAAAOYkzgxT+nxhrFQgCwi0cK4Is63+0AAAg3VnKNTAMAhClGFpahWAgANuEGJwAAJ+EGJwAAJ6FYWIZiIQDYhcuQAQBOwmXIAAAHoVhYhrshAwAAAAAAAJDEyEIAsA8jCwEATsLIQgCAgzCysAzFQgCwC8VCAICTUCwEADgIxcIyFAsBwC7cDRkA4CTcDRkA4CAUC8tQLAQAm3A3ZACAk3A3ZACAk1AsLMMNTgDALqWXa1lZAAAIN2QaAMBBSouFgS7VZf/+/brhhhsUExOjuLg43XzzzTp48KBf2xpjNHDgQLlcLi1atCjgY1MsBAAAAAAA1cIl4/cCoMwNN9yg7777TsuWLdO7776rjz76SKNHj/Zr26lTp8rlsjIH1nFchgwAdvEYyWXhjyAPfzgBAMKQlVwj0wAAYcqYwEcKVteA+fXr12vJkiX68ssv1aNHD0nSSy+9pMsuu0zPPvuskpOTK9123bp1eu6557R69Wo1b97c0vEZWQgAduEyZACAk5BpAAAHCeYy5IKCAp+lqKgoqL5kZ2crLi7OWyiUpPT0dEVEROiLL76odLtDhw7p+uuv17Rp05SUlGT5+BQLAcA2Vj9U8cEKABCOyDQAgHMEUyxMSUlRbGysd8nKygqqLzk5OUpISPBZV69ePcXHxysnJ6fS7e6++2717t1bV1xxRVDH5zJkALCL1REVjMIAAIQjK7lGpgEAwpTHIwU6zV9psXDHjh2KiYnxrne73RW2v//++/XUU09Vuc/169cH1olf/etf/9IHH3ygtWvXWtr+RBQLAcAuHosjKpjfCQAQjqzkGpkGAAhTwRQLY2JifIqFlbnnnnt00003Vdmmbdu2SkpK0u7du33WHzt2TPv376/08uIPPvhAmzdvVlxcnM/6q666Sn369NGKFStO2b9SFAsBAAAAAACAatasWTM1a9bslO169eqlvLw8rVmzRqmpqZKOFwM9Ho/S0tIq3Ob+++/XLbfc4rOuS5cuev755zV48OCA+kmxEADsYjzHFyvbAQAQbqzkGpkGAAhTwYwsDLWOHTtqwIABGjVqlGbMmKGjR48qMzNTw4YN894J+ZdfftEll1yiv/3tb+rZs6eSkpIqHHXYsmVLtWnTJqDjUywEALswZyEAwEmYsxAA4CDhVCyUpDfeeEOZmZm65JJLFBERoauuukovvvii9/GjR49q48aNOnToUMiPTbEQAOzCnIUAACdhzkIAgIOEW7EwPj5ec+fOrfTx1q1by5ziS7hTPV4ZioUAYBdGFgIAnISRhQAABwm3YmFNolgIAHYxslgsDHlPAAAInpVcI9MAVMHFm0SNq8uvgTGBF/+c+h1YRE13AAAAAAAAAEB4YGQhANiFy5ABAE7CZcgAAAexckkxlyEDAILj8UgigQAADmEl18g0AECYolhYhmIhANiFkYUAACdhZCEAwEEoFpahWAgAdqFYCABwEoqFAAAHoVhYhmIhANjFY2TpNpAePlgBAMKQlVwj0wAAYYpiYRnuhgwAAAAAAABAEiMLAcA2xnhkTOBfPVnZBgCA6mYl18g0AEC4YmRhGYqFAGAXY6xdfsX8TgCAcGQl18g0AECYolhYhmIhANjFWJyzkA9WAIBwZCXXyDQAQJiiWFiGYiEA2MXjkVwW0oRLtgAA4chKrpFpAIAwZUzgxT+nfgdGsRAA7MLIQgCAkzCyEADgIB6P5HIFto1TY427IQMAAAAAAACQxMhCALCN8XhkLFyGzJ0jAQDhyEqukWkAgHDFyMIyFAsBwC5chgwAcBIuQwYAOAjFwjIUCwHALh4juSgWAgAcwkqukWkAgDBFsbAMxUIAsIsxkqzcDdmhCQQAqN2s5BqZBgAIUxQLy1AsBACbGI+RsTCy0Dg1gQAAtZqVXCPTAADhimJhmWq7G/K0adPUunVrRUdHKy0tTatWraquQwEAThLoe/D8+fPVoUMHRUdHq0uXLlq8eLFNPa0dyDQAqFnkWmiRawCAqlRLsXDevHkaN26cJkyYoK+++krdunVTRkaGdu/eXR2HA4DawXisLwEI9D34s88+03XXXaebb75Za9eu1ZAhQzRkyBB9++23oTjrWo9MA4BK2JBpErkWauQaAFTM47G2OJHLVMO1AGlpaTrvvPP08ssvS5I8Ho9SUlJ0++236/77769y24KCAsXGxqqfrlA9V/1Qdw0A/HbMHNUKvaP8/HzFxMRY3o/3fc31O0vva8fMUa0wC/3uR6DvwUOHDlVhYaHeffdd77r/+Z//Uffu3TVjxoyA++s0wWSaRK4BCB/hkGuBZppEroVaqHItPy8vqN8jAAhGQUGBYuPigs40775iY+Vy5cvlCmxfxhTImNiQ9COchHzOwuLiYq1Zs0YPPPCAd11ERITS09OVnZ1drn1RUZGKioq8P+fn50uSjumo5NBrvwHUDsd0VFLo5lc6Zoosjago7UdBQYHPerfbLbfb7bMu0PdgScrOzta4ceN81mVkZGjRokUB99VprDyf5BqAcBUOuRZIpknkWqiFMtdOfg0BwE6l70GhHP92vPAXcE9CdvxwEvJi4d69e1VSUqLExESf9YmJidqwYUO59llZWZo0aVK59Z+IeUUAhIcDBw4oNjbW8vZRUVFKSkrSJznW39caN26slJQUn3UTJkzQxIkTfdYF+h4sSTk5ORW2z8nJsdxfp7DyfJJrAMJdTeeav5kmkWuhFspcS2nZslr6CACBCDbTpLJcy8lJOXXjCiQlJSkqKiqoPoSbGr8b8gMPPODzzV9eXp5atWql7du3B/2Ch5OCggKlpKRox44djhqaynnVLpxXYIwxOnDggJKTk4PaT3R0tLZs2aLi4uKg+uI66dZcFY3AQM0j12o3zqt24bwCEy65RqbVLuRa7cZ51S6cl/9ClWlS8LkWFRWl6OjooPsRTkJeLGzatKkiIyOVm5vrsz43N1dJSUnl2ld2yUFsbKyj/nGUiomJ4bxqEc6rdqmO8wrVH8HR0dG2BEig78HS8W/CAmlfl1h5Psk1Z+C8ahfOy3/kWt1Grp0a7ye1C+dVu4T6vEL5hYVduVZbhPxuyFFRUUpNTdXy5cu96zwej5YvX65evXqF+nAAgBNYeQ/u1auXT3tJWrZsGe/ZItMAoKaRa6FFrgEA/FEtlyGPGzdOI0aMUI8ePdSzZ09NnTpVhYWFGjlyZHUcDgBwglO9Bw8fPlwtWrRQVlaWJOnOO+9U37599dxzz2nQoEF68803tXr1ar366qs1eRphg0wDgJpFroUWuQYAOJVqKRYOHTpUe/bs0fjx45WTk6Pu3btryZIl5SbSrYjb7daECRMcN28J51W7cF61i1PPy6pTvQdv375dERFlA8t79+6tuXPn6uGHH9aDDz6oM888U4sWLVLnzp1r6hTCSjCZJjn395Pzql04r9rFqedlFbkWWuRaxTiv2oXzql2cel5O5jKhvM80AAAAAAAAgFor5HMWAgAAAAAAAKidKBYCAAAAAAAAkESxEAAAAAAAAMCvKBYCAAAAAAAAkBSGxcJp06apdevWio6OVlpamlatWlXTXQrKxIkT5XK5fJYOHTrUdLcC9tFHH2nw4MFKTk6Wy+XSokWLfB43xmj8+PFq3ry5GjRooPT0dG3atKlmOhuAU53XTTfdVO71GzBgQM10NgBZWVk677zz1KRJEyUkJGjIkCHauHGjT5sjR45o7NixOv3009W4cWNdddVVys3NraEe+8ef8+rXr1+512zMmDE11GPUdU7LNIlcC3dOzDUyjUxD+HBarpFp4c2JmSaRa+Ra7RBWxcJ58+Zp3LhxmjBhgr766it169ZNGRkZ2r17d013LSjnnHOOdu3a5V0++eSTmu5SwAoLC9WtWzdNmzatwseffvppvfjii5oxY4a++OILNWrUSBkZGTpy5IjNPQ3Mqc5LkgYMGODz+v3jH/+wsYfWrFy5UmPHjtXnn3+uZcuW6ejRo+rfv78KCwu9be6++279+9//1vz587Vy5Urt3LlTV155ZQ32+tT8OS9JGjVqlM9r9vTTT9dQj1GXOTXTJHItnDkx18g0Mg3hwam5RqaFLydmmkSukWu1hAkjPXv2NGPHjvX+XFJSYpKTk01WVlYN9io4EyZMMN26davpboSUJLNw4ULvzx6PxyQlJZlnnnnGuy4vL8+43W7zj3/8owZ6aM3J52WMMSNGjDBXXHFFjfQnlHbv3m0kmZUrVxpjjr8+9evXN/Pnz/e2Wb9+vZFksrOza6qbATv5vIwxpm/fvubOO++suU4Bv3JiphlDrpFrNY9MA2qGE3ONTCPTwgG5hnAUNiMLi4uLtWbNGqWnp3vXRUREKD09XdnZ2TXYs+Bt2rRJycnJatu2rW644QZt3769prsUUlu2bFFOTo7PaxcbG6u0tLRa/9pJ0ooVK5SQkKCzzz5bt956q/bt21fTXQpYfn6+JCk+Pl6StGbNGh09etTnNevQoYNatmxZq16zk8+r1BtvvKGmTZuqc+fOeuCBB3To0KGa6B7qMCdnmkSu1Xa1PdfINDIN9nNyrpFptVttzzSJXCPXwlO9mu5Aqb1796qkpESJiYk+6xMTE7Vhw4Ya6lXw0tLSNHv2bJ199tnatWuXJk2apD59+ujbb79VkyZNarp7IZGTkyNJFb52pY/VVgMGDNCVV16pNm3aaPPmzXrwwQc1cOBAZWdnKzIysqa75xePx6O77rpL559/vjp37izp+GsWFRWluLg4n7a16TWr6Lwk6frrr1erVq2UnJysr7/+Wvfdd582btyof/7znzXYW9Q1Ts00iVyrLe+RlantuUamkWmoGU7NNTKtdrxHVqa2Z5pErpFr4StsioV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       "text/plain": [
        "<Figure size 1600x400 with 6 Axes>"
       ]
diff --git a/fv3core/pace/fv3core/_config.py b/fv3core/pace/fv3core/_config.py
index 51fb609f..e2f5c1f5 100644
--- a/fv3core/pace/fv3core/_config.py
+++ b/fv3core/pace/fv3core/_config.py
@@ -284,6 +284,9 @@ def __post_init__(self):
             dycore_config = self.from_f90nml(f90_nml)
             for var in dycore_config.__dict__.keys():
                 setattr(self, var, dycore_config.__dict__[var])
+        # Single tile cartesian grids
+        if self.grid_type > 3:
+            self.nf_omega = 0
 
     @classmethod
     def from_f90nml(self, f90_namelist: f90nml.Namelist) -> "DynamicalCoreConfig":
diff --git a/fv3core/pace/fv3core/initialization/dycore_state.py b/fv3core/pace/fv3core/initialization/dycore_state.py
index 9e4e4f1f..4901c799 100644
--- a/fv3core/pace/fv3core/initialization/dycore_state.py
+++ b/fv3core/pace/fv3core/initialization/dycore_state.py
@@ -365,7 +365,7 @@ def from_fortran_restart(
         cls,
         *,
         quantity_factory: pace.util.QuantityFactory,
-        communicator: pace.util.CubedSphereCommunicator,
+        communicator: pace.util.Communicator,
         path: str,
     ):
         state_dict: Mapping[str, pace.util.Quantity] = pace.util.open_restart(
diff --git a/fv3core/pace/fv3core/initialization/geos_wrapper.py b/fv3core/pace/fv3core/initialization/geos_wrapper.py
index 4fc34052..e08cbf9e 100644
--- a/fv3core/pace/fv3core/initialization/geos_wrapper.py
+++ b/fv3core/pace/fv3core/initialization/geos_wrapper.py
@@ -96,7 +96,7 @@ def __init__(
         )
 
         self._grid_indexing = pace.dsl.stencil.GridIndexing.from_sizer_and_communicator(
-            sizer=sizer, cube=self.communicator
+            sizer=sizer, comm=self.communicator
         )
         stencil_factory = pace.dsl.StencilFactory(
             config=stencil_config, grid_indexing=self._grid_indexing
diff --git a/fv3core/pace/fv3core/stencils/a2b_ord4.py b/fv3core/pace/fv3core/stencils/a2b_ord4.py
index 65ecfd51..ffbb807a 100644
--- a/fv3core/pace/fv3core/stencils/a2b_ord4.py
+++ b/fv3core/pace/fv3core/stencils/a2b_ord4.py
@@ -1,5 +1,6 @@
 import gt4py.cartesian.gtscript as gtscript
 from gt4py.cartesian.gtscript import (
+    __INLINED,
     PARALLEL,
     asin,
     computation,
@@ -506,6 +507,27 @@ def a2b_interpolation(
         qout = 0.5 * (qxx + qyy)
 
 
+@gtscript.function
+def doubly_periodic_a2b_ord4(qin):
+
+    qx = b1 * (qin[-1, 0, 0] + qin) + b2 * (qin[-2, 0, 0] + qin[1, 0, 0])
+    qy = b1 * (qin[0, -1, 0] + qin) + b2 * (qin[0, -2, 0] + qin[0, 1, 0])
+    qout = 0.5 * (
+        a1 * (qx[0, -1, 0] + qx + qy[-1, 0, 0] + qy)
+        + a2 * (qx[0, -2, 0] + qx[0, 1, 0] + qy[-2, 0, 0] + qy[1, 0, 0])
+    )
+    return qout
+
+
+def doubly_periodic_a2b_ord4_stencil(qout: FloatField, qin: FloatField):
+    from __externals__ import replace
+
+    with computation(PARALLEL), interval(...):
+        qout = doubly_periodic_a2b_ord4(qin)
+        if __INLINED(replace):
+            qin = qout
+
+
 class AGrid2BGridFourthOrder:
     """
     Fortran name is a2b_ord4, test module is A2B_Ord4
@@ -528,131 +550,144 @@ def __init__(
             replace: boolean, update qin to the B grid as well
         """
         orchestrate(obj=self, config=stencil_factory.config.dace_config)
-        assert grid_type < 3
+        assert grid_type in [0, 4]
         self._idx: GridIndexing = stencil_factory.grid_indexing
         self._stencil_config = stencil_factory.config
-        self._dxa = grid_data.dxa
-        self._dya = grid_data.dya
-
-        self._lon_agrid = grid_data.lon_agrid
-        self._lat_agrid = grid_data.lat_agrid
-        self._lon = grid_data.lon
-        self._lat = grid_data.lat
-        # TODO: maybe compute locally edge_* variables
-        # This is the only place the model uses them
-        self._edge_w = grid_data.edge_w
-        self._edge_e = grid_data.edge_e
-        self._edge_s = grid_data.edge_s
-        self._edge_n = grid_data.edge_n
-
-        self.replace = replace
-
-        self._tmp_qx = quantity_factory.zeros(
-            dims=[X_INTERFACE_DIM, Y_DIM, z_dim],
-            units="unknown",
-            dtype=Float,
-        )
-        self._tmp_qy = quantity_factory.zeros(
-            dims=[X_DIM, Y_INTERFACE_DIM, z_dim],
-            units="unknown",
-            dtype=Float,
-        )
-        # TODO: the dimensions of tmp_qout_edges may not be correct, verify
-        # with Lucas and either update the code or remove this comment
-        self._tmp_qout_edges = quantity_factory.zeros(
-            dims=[X_DIM, Y_DIM, z_dim],
-            units="unknown",
-            dtype=Float,
-        )
 
-        _, (z_domain,) = self._idx.get_origin_domain([z_dim])
-        corner_domain = (1, 1, z_domain)
+        if grid_type < 3:
+            self._dxa = grid_data.dxa
+            self._dya = grid_data.dya
+
+            self._lon_agrid = grid_data.lon_agrid
+            self._lat_agrid = grid_data.lat_agrid
+            self._lon = grid_data.lon
+            self._lat = grid_data.lat
+            # TODO: maybe compute locally edge_* variables
+            # This is the only place the model uses them
+            self._edge_w = grid_data.edge_w
+            self._edge_e = grid_data.edge_e
+            self._edge_s = grid_data.edge_s
+            self._edge_n = grid_data.edge_n
+
+            self.replace = replace
+            self.grid_type = grid_type
+
+            self._tmp_qx = quantity_factory.zeros(
+                dims=[X_INTERFACE_DIM, Y_DIM, z_dim],
+                units="unknown",
+                dtype=Float,
+            )
+            self._tmp_qy = quantity_factory.zeros(
+                dims=[X_DIM, Y_INTERFACE_DIM, z_dim],
+                units="unknown",
+                dtype=Float,
+            )
+            # TODO: the dimensions of tmp_qout_edges may not be correct, verify
+            # with Lucas and either update the code or remove this comment
+            self._tmp_qout_edges = quantity_factory.zeros(
+                dims=[X_DIM, Y_DIM, z_dim],
+                units="unknown",
+                dtype=Float,
+            )
 
-        self._sw_corner_stencil = stencil_factory.from_origin_domain(
-            _sw_corner,
-            origin=self._idx.origin_compute(),
-            domain=corner_domain,
-        )
-        self._nw_corner_stencil = stencil_factory.from_origin_domain(
-            _nw_corner,
-            origin=(self._idx.iec + 1, self._idx.jsc, self._idx.origin[2]),
-            domain=corner_domain,
-        )
-        self._ne_corner_stencil = stencil_factory.from_origin_domain(
-            _ne_corner,
-            origin=(self._idx.iec + 1, self._idx.jec + 1, self._idx.origin[2]),
-            domain=corner_domain,
-        )
-        self._se_corner_stencil = stencil_factory.from_origin_domain(
-            _se_corner,
-            origin=(self._idx.isc, self._idx.jec + 1, self._idx.origin[2]),
-            domain=corner_domain,
-        )
-        js2 = self._idx.jsc + 1 if self._idx.south_edge else self._idx.jsc
-        je1 = self._idx.jec if self._idx.north_edge else self._idx.jec + 1
-        dj2 = je1 - js2 + 1
-
-        # edge_w is singleton in the I-dimension to work around gt4py not yet
-        # supporting J-fields. As a result, the origin has to be zero for
-        # edge_w, anything higher is outside its index range
-        self._qout_x_edge_west = stencil_factory.from_origin_domain(
-            qout_x_edge,
-            origin={
-                "_all_": (self._idx.isc, js2, self._idx.origin[2]),
-                "edge_w": (0, js2),
-            },
-            domain=(1, dj2, z_domain),
-        )
-        self._qout_x_edge_east = stencil_factory.from_origin_domain(
-            qout_x_edge,
-            origin={
-                "_all_": (self._idx.iec + 1, js2, self._idx.origin[2]),
-                "edge_w": (0, js2),
-            },
-            domain=(1, dj2, z_domain),
-        )
+            _, (z_domain,) = self._idx.get_origin_domain([z_dim])
+            corner_domain = (1, 1, z_domain)
 
-        is2 = self._idx.isc + 1 if self._idx.west_edge else self._idx.isc
-        ie1 = self._idx.iec if self._idx.east_edge else self._idx.iec + 1
-        di2 = ie1 - is2 + 1
-        self._qout_y_edge_south = stencil_factory.from_origin_domain(
-            qout_y_edge,
-            origin=(is2, self._idx.jsc, self._idx.origin[2]),
-            domain=(di2, 1, z_domain),
-        )
-        self._qout_y_edge_north = stencil_factory.from_origin_domain(
-            qout_y_edge,
-            origin=(is2, self._idx.jec + 1, self._idx.origin[2]),
-            domain=(di2, 1, z_domain),
-        )
+            self._sw_corner_stencil = stencil_factory.from_origin_domain(
+                _sw_corner,
+                origin=self._idx.origin_compute(),
+                domain=corner_domain,
+            )
+            self._nw_corner_stencil = stencil_factory.from_origin_domain(
+                _nw_corner,
+                origin=(self._idx.iec + 1, self._idx.jsc, self._idx.origin[2]),
+                domain=corner_domain,
+            )
+            self._ne_corner_stencil = stencil_factory.from_origin_domain(
+                _ne_corner,
+                origin=(self._idx.iec + 1, self._idx.jec + 1, self._idx.origin[2]),
+                domain=corner_domain,
+            )
+            self._se_corner_stencil = stencil_factory.from_origin_domain(
+                _se_corner,
+                origin=(self._idx.isc, self._idx.jec + 1, self._idx.origin[2]),
+                domain=corner_domain,
+            )
+            js2 = self._idx.jsc + 1 if self._idx.south_edge else self._idx.jsc
+            je1 = self._idx.jec if self._idx.north_edge else self._idx.jec + 1
+            dj2 = je1 - js2 + 1
+
+            # edge_w is singleton in the I-dimension to work around gt4py not yet
+            # supporting J-fields. As a result, the origin has to be zero for
+            # edge_w, anything higher is outside its index range
+            self._qout_x_edge_west = stencil_factory.from_origin_domain(
+                qout_x_edge,
+                origin={
+                    "_all_": (self._idx.isc, js2, self._idx.origin[2]),
+                    "edge_w": (0, js2),
+                },
+                domain=(1, dj2, z_domain),
+            )
+            self._qout_x_edge_east = stencil_factory.from_origin_domain(
+                qout_x_edge,
+                origin={
+                    "_all_": (self._idx.iec + 1, js2, self._idx.origin[2]),
+                    "edge_w": (0, js2),
+                },
+                domain=(1, dj2, z_domain),
+            )
 
-        self._ppm_volume_mean_x_stencil = stencil_factory.from_dims_halo(
-            ppm_volume_mean_x,
-            compute_dims=[X_INTERFACE_DIM, Y_DIM, z_dim],
-            compute_halos=(0, 2),
-        )
+            is2 = self._idx.isc + 1 if self._idx.west_edge else self._idx.isc
+            ie1 = self._idx.iec if self._idx.east_edge else self._idx.iec + 1
+            di2 = ie1 - is2 + 1
+            self._qout_y_edge_south = stencil_factory.from_origin_domain(
+                qout_y_edge,
+                origin=(is2, self._idx.jsc, self._idx.origin[2]),
+                domain=(di2, 1, z_domain),
+            )
+            self._qout_y_edge_north = stencil_factory.from_origin_domain(
+                qout_y_edge,
+                origin=(is2, self._idx.jec + 1, self._idx.origin[2]),
+                domain=(di2, 1, z_domain),
+            )
 
-        self._ppm_volume_mean_y_stencil = stencil_factory.from_dims_halo(
-            ppm_volume_mean_y,
-            compute_dims=[X_DIM, Y_INTERFACE_DIM, z_dim],
-            compute_halos=(2, 0),
-        )
+            self._ppm_volume_mean_x_stencil = stencil_factory.from_dims_halo(
+                ppm_volume_mean_x,
+                compute_dims=[X_INTERFACE_DIM, Y_DIM, z_dim],
+                compute_halos=(0, 2),
+            )
 
-        origin, domain = self._idx.get_origin_domain(
-            dims=(X_INTERFACE_DIM, Y_INTERFACE_DIM, z_dim),
-        )
-        origin, domain = self._exclude_tile_edges(origin, domain)
+            self._ppm_volume_mean_y_stencil = stencil_factory.from_dims_halo(
+                ppm_volume_mean_y,
+                compute_dims=[X_DIM, Y_INTERFACE_DIM, z_dim],
+                compute_halos=(2, 0),
+            )
 
-        ax_offsets = self._idx.axis_offsets(
-            origin,
-            domain,
-        )
-        self._a2b_interpolation_stencil = stencil_factory.from_origin_domain(
-            a2b_interpolation, externals=ax_offsets, origin=origin, domain=domain
-        )
-        self._copy_stencil = stencil_factory.from_dims_halo(
-            copy_defn, compute_dims=[X_INTERFACE_DIM, Y_INTERFACE_DIM, z_dim]
-        )
+            origin, domain = self._idx.get_origin_domain(
+                dims=(X_INTERFACE_DIM, Y_INTERFACE_DIM, z_dim),
+            )
+            origin, domain = self._exclude_tile_edges(origin, domain)
+
+            ax_offsets = self._idx.axis_offsets(
+                origin,
+                domain,
+            )
+            self._a2b_interpolation_stencil = stencil_factory.from_origin_domain(
+                a2b_interpolation, externals=ax_offsets, origin=origin, domain=domain
+            )
+            self._copy_stencil = stencil_factory.from_dims_halo(
+                copy_defn, compute_dims=[X_INTERFACE_DIM, Y_INTERFACE_DIM, z_dim]
+            )
+
+        else:  # grid type >= 3:
+            self._doubly_periodic_a2b_ord4 = stencil_factory.from_origin_domain(
+                doubly_periodic_a2b_ord4_stencil,
+                externals={
+                    "replace": replace,
+                },
+                origin=self._idx.origin_compute(),
+                domain=self._idx.domain_compute(),
+            )
 
     def _exclude_tile_edges(self, origin, domain, dims=("x", "y")):
         """
@@ -687,81 +722,85 @@ def __call__(self, qin: FloatField, qout: FloatField):
             qout (out): Output on B-grid
         """
 
-        self._sw_corner_stencil(
-            qin,
-            qout,
-            self._tmp_qout_edges,
-            self._lon_agrid,
-            self._lat_agrid,
-            self._lon,
-            self._lat,
-        )
+        if self.grid_type < 3:
 
-        self._nw_corner_stencil(
-            qin,
-            qout,
-            self._tmp_qout_edges,
-            self._lon_agrid,
-            self._lat_agrid,
-            self._lon,
-            self._lat,
-        )
-        self._ne_corner_stencil(
-            qin,
-            qout,
-            self._tmp_qout_edges,
-            self._lon_agrid,
-            self._lat_agrid,
-            self._lon,
-            self._lat,
-        )
-        self._se_corner_stencil(
-            qin,
-            qout,
-            self._tmp_qout_edges,
-            self._lon_agrid,
-            self._lat_agrid,
-            self._lon,
-            self._lat,
-        )
+            self._sw_corner_stencil(
+                qin,
+                qout,
+                self._tmp_qout_edges,
+                self._lon_agrid,
+                self._lat_agrid,
+                self._lon,
+                self._lat,
+            )
 
-        if self._idx.west_edge:
-            self._qout_x_edge_west(
-                qin, self._dxa, self._edge_w, qout, self._tmp_qout_edges
+            self._nw_corner_stencil(
+                qin,
+                qout,
+                self._tmp_qout_edges,
+                self._lon_agrid,
+                self._lat_agrid,
+                self._lon,
+                self._lat,
             )
-        if self._idx.east_edge:
-            self._qout_x_edge_east(
-                qin, self._dxa, self._edge_e, qout, self._tmp_qout_edges
+            self._ne_corner_stencil(
+                qin,
+                qout,
+                self._tmp_qout_edges,
+                self._lon_agrid,
+                self._lat_agrid,
+                self._lon,
+                self._lat,
+            )
+            self._se_corner_stencil(
+                qin,
+                qout,
+                self._tmp_qout_edges,
+                self._lon_agrid,
+                self._lat_agrid,
+                self._lon,
+                self._lat,
             )
 
-        if self._idx.south_edge:
-            self._qout_y_edge_south(
-                qin, self._dya, self._edge_s, qout, self._tmp_qout_edges
+            if self._idx.west_edge:
+                self._qout_x_edge_west(
+                    qin, self._dxa, self._edge_w, qout, self._tmp_qout_edges
+                )
+            if self._idx.east_edge:
+                self._qout_x_edge_east(
+                    qin, self._dxa, self._edge_e, qout, self._tmp_qout_edges
+                )
+
+            if self._idx.south_edge:
+                self._qout_y_edge_south(
+                    qin, self._dya, self._edge_s, qout, self._tmp_qout_edges
+                )
+            if self._idx.north_edge:
+                self._qout_y_edge_north(
+                    qin, self._dya, self._edge_n, qout, self._tmp_qout_edges
+                )
+
+            self._ppm_volume_mean_x_stencil(
+                qin,
+                self._tmp_qx,
+                self._dxa,
             )
-        if self._idx.north_edge:
-            self._qout_y_edge_north(
-                qin, self._dya, self._edge_n, qout, self._tmp_qout_edges
+            self._ppm_volume_mean_y_stencil(
+                qin,
+                self._tmp_qy,
+                self._dya,
             )
 
-        self._ppm_volume_mean_x_stencil(
-            qin,
-            self._tmp_qx,
-            self._dxa,
-        )
-        self._ppm_volume_mean_y_stencil(
-            qin,
-            self._tmp_qy,
-            self._dya,
-        )
-
-        self._a2b_interpolation_stencil(
-            self._tmp_qout_edges,
-            qout,
-            self._tmp_qx,
-            self._tmp_qy,
-        )
-        if self.replace:
-            self._copy_stencil(
+            self._a2b_interpolation_stencil(
+                self._tmp_qout_edges,
                 qout,
-                qin,
+                self._tmp_qx,
+                self._tmp_qy,
             )
+            if self.replace:
+                self._copy_stencil(
+                    qout,
+                    qin,
+                )
+        else:  # grid type >= 3:
+            self._doubly_periodic_a2b_ord4(qout, qin)
diff --git a/fv3core/pace/fv3core/stencils/c_sw.py b/fv3core/pace/fv3core/stencils/c_sw.py
index ebb226c6..46b6030a 100644
--- a/fv3core/pace/fv3core/stencils/c_sw.py
+++ b/fv3core/pace/fv3core/stencils/c_sw.py
@@ -1,6 +1,6 @@
-from gt4py.cartesian.gtscript import (
+from gt4py.cartesian.gtscript import (  # noqa
+    __INLINED,
     PARALLEL,
-    compile_assert,
     computation,
     horizontal,
     interval,
@@ -71,89 +71,109 @@ def divergence_corner(
         rarea_c (in): inverse cell areas on c-grid
         divg_d (out): divergence on d-grid (cell corners)
     """
-    from __externals__ import i_end, i_start, j_end, j_start
+    # TODO: move grid metric terms to externals to import them at compile time
+
+    from __externals__ import grid_type, i_end, i_start, j_end, j_start
 
     with computation(PARALLEL), interval(...):
-        uf = (
-            (u - 0.25 * (va[0, -1, 0] + va) * (cos_sg4[0, -1] + cos_sg2))
-            * dyc
-            * 0.5
-            * (sin_sg4[0, -1] + sin_sg2)
-        )
-        """c-grid (?) contravariant component of the wind in the x-direction"""
-        # TODO: refactor this into a call to contravariant()
-
-        vf = (
-            (v - 0.25 * (ua[-1, 0, 0] + ua) * (cos_sg3[-1, 0] + cos_sg1))
-            * dxc
-            * 0.5
-            * (sin_sg3[-1, 0] + sin_sg1)
-        )
+        if __INLINED(grid_type == 4):
+            # with horizontal(region[i_start - 1: i_end + 2, j_start - 1: j_end + 2]):
+            # extend computation into the halo?
+            uf = u * dyc
+            vf = v * dxc
+            divg_d = rarea_c * (vf[0, -1, 0] - vf + uf[-1, 0, 0] - uf)
 
-        divg_d = (vf[0, -1, 0] - vf + uf[-1, 0, 0] - uf) * rarea_c
-
-        # The original code is:
-        # ---------
-        # with horizontal(region[:, j_start], region[:, j_end + 1]):
-        #     uf = u * dyc * 0.5 * (sin_sg4[0, -1] + sin_sg2)
-        # with horizontal(region[i_start, :], region[i_end + 1, :]):
-        #     vf = v * dxc * 0.5 * (sin_sg3[-1, 0] + sin_sg1)
-        # with horizontal(region[i_start, j_start], region[i_end + 1, j_start]):
-        #     divg_d = (-vf + uf[-1, 0, 0] - uf) * rarea_c
-        # with horizontal(region[i_end + 1, j_end + 1], region[i_start, j_end + 1]):
-        #     divg_d = (vf[0, -1, 0] + uf[-1, 0, 0] - uf) * rarea_c
-        # ---------
-        #
-        # Code with regions restrictions:
-        # ---------
-        # variables ending with 1 are the shifted versions
-        # in the future we could use gtscript functions when they support shifts
-
-        with horizontal(region[i_start, :], region[i_end + 1, :]):
-            vf0 = v * dxc * 0.5 * (sin_sg3[-1, 0] + sin_sg1)
-            vf1 = v[0, -1, 0] * dxc[0, -1] * 0.5 * (sin_sg3[-1, -1] + sin_sg1[0, -1])
-            uf1 = (
-                (
-                    u[-1, 0, 0]
-                    - 0.25
-                    * (va[-1, -1, 0] + va[-1, 0, 0])
-                    * (cos_sg4[-1, -1] + cos_sg2[-1, 0])
-                )
-                * dyc[-1, 0]
+        else:
+            uf = (
+                (u - 0.25 * (va[0, -1, 0] + va) * (cos_sg4[0, -1] + cos_sg2))
+                * dyc
                 * 0.5
-                * (sin_sg4[-1, -1] + sin_sg2[-1, 0])
+                * (sin_sg4[0, -1] + sin_sg2)
             )
-            divg_d = (vf1 - vf0 + uf1 - uf) * rarea_c
-
-        with horizontal(region[:, j_start], region[:, j_end + 1]):
-            uf0 = u * dyc * 0.5 * (sin_sg4[0, -1] + sin_sg2)
-            uf1 = u[-1, 0, 0] * dyc[-1, 0] * 0.5 * (sin_sg4[-1, -1] + sin_sg2[-1, 0])
-            vf1 = (
-                (
-                    v[0, -1, 0]
-                    - 0.25
-                    * (ua[-1, -1, 0] + ua[0, -1, 0])
-                    * (cos_sg3[-1, -1] + cos_sg1[0, -1])
-                )
-                * dxc[0, -1]
+            """c-grid (?) contravariant component of the wind in the x-direction"""
+            # TODO: refactor this into a call to contravariant()
+
+            vf = (
+                (v - 0.25 * (ua[-1, 0, 0] + ua) * (cos_sg3[-1, 0] + cos_sg1))
+                * dxc
                 * 0.5
-                * (sin_sg3[-1, -1] + sin_sg1[0, -1])
+                * (sin_sg3[-1, 0] + sin_sg1)
             )
-            divg_d = (vf1 - vf + uf1 - uf0) * rarea_c
 
-        with horizontal(region[i_start, j_start], region[i_end + 1, j_start]):
-            uf1 = u[-1, 0, 0] * dyc[-1, 0] * 0.5 * (sin_sg4[-1, -1] + sin_sg2[-1, 0])
-            vf0 = v * dxc * 0.5 * (sin_sg3[-1, 0] + sin_sg1)
-            uf0 = u * dyc * 0.5 * (sin_sg4[0, -1] + sin_sg2)
-            divg_d = (-vf0 + uf1 - uf0) * rarea_c
+            divg_d = (vf[0, -1, 0] - vf + uf[-1, 0, 0] - uf) * rarea_c
+
+            # The original code is:
+            # ---------
+            # with horizontal(region[:, j_start], region[:, j_end + 1]):
+            #     uf = u * dyc * 0.5 * (sin_sg4[0, -1] + sin_sg2)
+            # with horizontal(region[i_start, :], region[i_end + 1, :]):
+            #     vf = v * dxc * 0.5 * (sin_sg3[-1, 0] + sin_sg1)
+            # with horizontal(region[i_start, j_start], region[i_end + 1, j_start]):
+            #     divg_d = (-vf + uf[-1, 0, 0] - uf) * rarea_c
+            # with horizontal(region[i_end + 1, j_end + 1], region[i_start, j_end + 1]):
+            #     divg_d = (vf[0, -1, 0] + uf[-1, 0, 0] - uf) * rarea_c
+            # ---------
+            #
+            # Code with regions restrictions:
+            # ---------
+            # variables ending with 1 are the shifted versions
+            # in the future we could use gtscript functions when they support shifts
+
+            with horizontal(region[i_start, :], region[i_end + 1, :]):
+                vf0 = v * dxc * 0.5 * (sin_sg3[-1, 0] + sin_sg1)
+                vf1 = (
+                    v[0, -1, 0] * dxc[0, -1] * 0.5 * (sin_sg3[-1, -1] + sin_sg1[0, -1])
+                )
+                uf1 = (
+                    (
+                        u[-1, 0, 0]
+                        - 0.25
+                        * (va[-1, -1, 0] + va[-1, 0, 0])
+                        * (cos_sg4[-1, -1] + cos_sg2[-1, 0])
+                    )
+                    * dyc[-1, 0]
+                    * 0.5
+                    * (sin_sg4[-1, -1] + sin_sg2[-1, 0])
+                )
+                divg_d = (vf1 - vf0 + uf1 - uf) * rarea_c
+
+            with horizontal(region[:, j_start], region[:, j_end + 1]):
+                uf0 = u * dyc * 0.5 * (sin_sg4[0, -1] + sin_sg2)
+                uf1 = (
+                    u[-1, 0, 0] * dyc[-1, 0] * 0.5 * (sin_sg4[-1, -1] + sin_sg2[-1, 0])
+                )
+                vf1 = (
+                    (
+                        v[0, -1, 0]
+                        - 0.25
+                        * (ua[-1, -1, 0] + ua[0, -1, 0])
+                        * (cos_sg3[-1, -1] + cos_sg1[0, -1])
+                    )
+                    * dxc[0, -1]
+                    * 0.5
+                    * (sin_sg3[-1, -1] + sin_sg1[0, -1])
+                )
+                divg_d = (vf1 - vf + uf1 - uf0) * rarea_c
+
+            with horizontal(region[i_start, j_start], region[i_end + 1, j_start]):
+                uf1 = (
+                    u[-1, 0, 0] * dyc[-1, 0] * 0.5 * (sin_sg4[-1, -1] + sin_sg2[-1, 0])
+                )
+                vf0 = v * dxc * 0.5 * (sin_sg3[-1, 0] + sin_sg1)
+                uf0 = u * dyc * 0.5 * (sin_sg4[0, -1] + sin_sg2)
+                divg_d = (-vf0 + uf1 - uf0) * rarea_c
 
-        with horizontal(region[i_end + 1, j_end + 1], region[i_start, j_end + 1]):
-            vf1 = v[0, -1, 0] * dxc[0, -1] * 0.5 * (sin_sg3[-1, -1] + sin_sg1[0, -1])
-            uf1 = u[-1, 0, 0] * dyc[-1, 0] * 0.5 * (sin_sg4[-1, -1] + sin_sg2[-1, 0])
-            uf0 = u * dyc * 0.5 * (sin_sg4[0, -1] + sin_sg2)
-            divg_d = (vf1 + uf1 - uf0) * rarea_c
+            with horizontal(region[i_end + 1, j_end + 1], region[i_start, j_end + 1]):
+                vf1 = (
+                    v[0, -1, 0] * dxc[0, -1] * 0.5 * (sin_sg3[-1, -1] + sin_sg1[0, -1])
+                )
+                uf1 = (
+                    u[-1, 0, 0] * dyc[-1, 0] * 0.5 * (sin_sg4[-1, -1] + sin_sg2[-1, 0])
+                )
+                uf0 = u * dyc * 0.5 * (sin_sg4[0, -1] + sin_sg2)
+                divg_d = (vf1 + uf1 - uf0) * rarea_c
 
-        # ---------
+            # ---------
 
 
 def geoadjust_ut(
@@ -330,8 +350,7 @@ def transportdelp_update_vorticity_and_kineticenergy(
     from __externals__ import grid_type, i_end, i_start, j_end, j_start
 
     with computation(PARALLEL), interval(...):
-        compile_assert(grid_type < 3)
-        # additional assumption (not grid.nested)
+        # assume (not grid.nested)
         # corresponds to x fluxes function, but for y-direction
         fy1 = delp[0, -1, 0] if vtc > 0.0 else delp
         fy = pt[0, -1, 0] if vtc > 0.0 else pt
@@ -346,20 +365,20 @@ def transportdelp_update_vorticity_and_kineticenergy(
 
     with computation(PARALLEL), interval(...):
         # update vorticity and kinetic energy
-        compile_assert(grid_type < 3)
 
         ke = uc if ua > 0.0 else uc[1, 0, 0]
         vort = vc if va > 0.0 else vc[0, 1, 0]
 
-        with horizontal(region[:, j_start - 1], region[:, j_end]):
-            vort = vort * sin_sg4 + u[0, 1, 0] * cos_sg4 if va <= 0.0 else vort
-        with horizontal(region[:, j_start], region[:, j_end + 1]):
-            vort = vort * sin_sg2 + u * cos_sg2 if va > 0.0 else vort
+        if __INLINED(grid_type < 3):
+            with horizontal(region[:, j_start - 1], region[:, j_end]):
+                vort = vort * sin_sg4 + u[0, 1, 0] * cos_sg4 if va <= 0.0 else vort
+            with horizontal(region[:, j_start], region[:, j_end + 1]):
+                vort = vort * sin_sg2 + u * cos_sg2 if va > 0.0 else vort
 
-        with horizontal(region[i_end, :], region[i_start - 1, :]):
-            ke = ke * sin_sg3 + v[1, 0, 0] * cos_sg3 if ua <= 0.0 else ke
-        with horizontal(region[i_end + 1, :], region[i_start, :]):
-            ke = ke * sin_sg1 + v * cos_sg1 if ua > 0.0 else ke
+            with horizontal(region[i_end, :], region[i_start - 1, :]):
+                ke = ke * sin_sg3 + v[1, 0, 0] * cos_sg3 if ua <= 0.0 else ke
+            with horizontal(region[i_end + 1, :], region[i_start, :]):
+                ke = ke * sin_sg1 + v * cos_sg1 if ua > 0.0 else ke
 
         ke = 0.5 * dt2 * (ua * ke + va * vort)
 
@@ -431,12 +450,12 @@ def update_x_velocity(
     from __externals__ import grid_type, i_end, i_start
 
     with computation(PARALLEL), interval(...):
-        compile_assert(grid_type < 3)
-        # additional assumption: not __INLINED(spec.grid.nested)
+        # assume: not __INLINED(spec.grid.nested)
 
         tmp_flux = dt2 * (velocity - velocity_c * cosa) / sina
-        with horizontal(region[i_start, :], region[i_end + 1, :]):
-            tmp_flux = dt2 * velocity
+        if __INLINED(grid_type < 3):
+            with horizontal(region[i_start, :], region[i_end + 1, :]):
+                tmp_flux = dt2 * velocity
 
         flux = vorticity[0, 0, 0] if tmp_flux > 0.0 else vorticity[0, 1, 0]
         velocity_c = velocity_c + tmp_flux * flux + rdxc * (ke[-1, 0, 0] - ke)
@@ -465,13 +484,13 @@ def update_y_velocity(
     from __externals__ import grid_type, j_end, j_start
 
     with computation(PARALLEL), interval(...):
-        compile_assert(grid_type < 3)
-        # additional assumption: not __INLINED(spec.grid.nested)
+        # assume: not __INLINED(spec.grid.nested)
 
         # first-order upwind voriticity flux
         tmp_flux = dt2 * (velocity - velocity_c * cosa) / sina
-        with horizontal(region[:, j_start], region[:, j_end + 1]):
-            tmp_flux = dt2 * velocity
+        if __INLINED(grid_type < 3):
+            with horizontal(region[:, j_start], region[:, j_end + 1]):
+                tmp_flux = dt2 * velocity
 
         flux = vorticity[0, 0, 0] if tmp_flux > 0.0 else vorticity[1, 0, 0]
         # forward-stepped y velocity
@@ -498,6 +517,8 @@ def __init__(
         self.grid_data = grid_data
         self._dord4 = True
         self._fC = self.grid_data.fC
+        self._grid_data = grid_data
+        self._grid_type = grid_type
         # TODO: double-check the dimensions on these, they may be incorrect
         # as they are only documentation and not used by the code
         self.delpc = quantity_factory.zeros(
@@ -550,6 +571,7 @@ def make_quantity() -> pace.util.Quantity:
             self._divergence_corner = stencil_factory.from_dims_halo(
                 func=divergence_corner,
                 compute_dims=[X_INTERFACE_DIM, Y_INTERFACE_DIM, Z_DIM],
+                externals={"grid_type": grid_type},
             )
         else:
             self._divergence_corner = None
@@ -566,12 +588,13 @@ def make_quantity() -> pace.util.Quantity:
             compute_halos=(1, 1),
         )
 
-        self._fill_corners_x_delp_pt_w_stencil = stencil_factory.from_dims_halo(
-            fill_corners_delp_pt_w,
-            externals={"fill_corners_func": corners.fill_corners_2cells_x},
-            compute_dims=[X_DIM, Y_DIM, Z_DIM],
-            compute_halos=(3, 3),
-        )
+        if grid_type < 3:
+            self._fill_corners_x_delp_pt_w_stencil = stencil_factory.from_dims_halo(
+                fill_corners_delp_pt_w,
+                externals={"fill_corners_func": corners.fill_corners_2cells_x},
+                compute_dims=[X_DIM, Y_DIM, Z_DIM],
+                compute_halos=(3, 3),
+            )
 
         self._compute_nonhydro_fluxes_x_stencil = stencil_factory.from_dims_halo(
             compute_nonhydrostatic_fluxes_x,
@@ -579,12 +602,13 @@ def make_quantity() -> pace.util.Quantity:
             compute_halos=(1, 1),
         )
 
-        self._fill_corners_y_delp_pt_w_stencil = stencil_factory.from_dims_halo(
-            fill_corners_delp_pt_w,
-            externals={"fill_corners_func": corners.fill_corners_2cells_y},
-            compute_dims=[X_DIM, Y_DIM, Z_DIM],
-            compute_halos=(3, 3),
-        )
+        if grid_type < 3:
+            self._fill_corners_y_delp_pt_w_stencil = stencil_factory.from_dims_halo(
+                fill_corners_delp_pt_w,
+                externals={"fill_corners_func": corners.fill_corners_2cells_y},
+                compute_dims=[X_DIM, Y_DIM, Z_DIM],
+                compute_halos=(3, 3),
+            )
 
         self._transportdelp_updatevorticity_and_ke = stencil_factory.from_dims_halo(
             func=transportdelp_update_vorticity_and_kineticenergy,
@@ -703,13 +727,15 @@ def __call__(
         )
 
         # TODO(eddied): We pass the same fields 2x to avoid GTC validation errors
-        self._fill_corners_x_delp_pt_w_stencil(delp, pt, w, delp, pt, w)
+        if self._grid_type < 3:
+            self._fill_corners_x_delp_pt_w_stencil(delp, pt, w, delp, pt, w)
         # TODO: why is there only a "x" version of this? Is the "y" verison folded
         # into the next routine?
         self._compute_nonhydro_fluxes_x_stencil(
             delp, pt, ut, w, self._tmp_fx, self._tmp_fx1, self._tmp_fx2
         )
-        self._fill_corners_y_delp_pt_w_stencil(delp, pt, w, delp, pt, w)
+        if self._grid_type < 3:
+            self._fill_corners_y_delp_pt_w_stencil(delp, pt, w, delp, pt, w)
         self._transportdelp_updatevorticity_and_ke(
             delp,
             pt,
diff --git a/fv3core/pace/fv3core/stencils/d2a2c_vect.py b/fv3core/pace/fv3core/stencils/d2a2c_vect.py
index e42d6972..1b3e4d33 100644
--- a/fv3core/pace/fv3core/stencils/d2a2c_vect.py
+++ b/fv3core/pace/fv3core/stencils/d2a2c_vect.py
@@ -391,6 +391,9 @@ def __init__(
         grid_type: int,
         dord4: bool,
     ):
+        if grid_type not in [0, 4]:
+            raise NotImplementedError(f"unimplemented grid_type {grid_type}")
+
         orchestrate(obj=self, config=stencil_factory.config.dace_config)
 
         grid_indexing = stencil_factory.grid_indexing
@@ -406,9 +409,8 @@ def __init__(
         self._sin_sg2 = grid_data.sin_sg2
         self._sin_sg3 = grid_data.sin_sg3
         self._sin_sg4 = grid_data.sin_sg4
+        self._grid_type = grid_type
 
-        if grid_type >= 3:
-            raise NotImplementedError("unimplemented grid_type >= 3")
         self._big_number = 1e30  # 1e8 if 32 bit
         nx = grid_indexing.iec + 1  # grid.npx + 2
         ny = grid_indexing.jec + 1  # grid.npy + 2
@@ -416,9 +418,38 @@ def __init__(
         j1 = grid_indexing.jsc - 1
         id_ = 1 if dord4 else 0
         pad = 2 + 2 * id_
-        npt = 4 if not nested else 0
-        if npt > grid_indexing.domain[0] - 1 or npt > grid_indexing.domain[1] - 1:
-            npt = 0
+        if (grid_type < 3) and (not nested):
+            npt = 4
+            if npt > grid_indexing.domain[0] - 1 or npt > grid_indexing.domain[1] - 1:
+                npt = 0
+            ifirst = (
+                grid_indexing.isc + 2
+                if grid_indexing.west_edge
+                else grid_indexing.isc - 1
+            )
+            ilast = (
+                grid_indexing.iec - 1
+                if grid_indexing.east_edge
+                else grid_indexing.iec + 2
+            )
+
+            jfirst = (
+                grid_indexing.jsc + 2
+                if grid_indexing.south_edge
+                else grid_indexing.jsc - 1
+            )
+            jlast = (
+                grid_indexing.jec - 1
+                if grid_indexing.north_edge
+                else grid_indexing.jec + 2
+            )
+        else:
+            npt = -2
+            ifirst = grid_indexing.isc - 1
+            ilast = grid_indexing.iec + 2
+            jfirst = grid_indexing.jsc - 1
+            jlast = grid_indexing.jec + 2
+
         self._utmp = quantity_factory.zeros(
             [X_DIM, Y_DIM, Z_DIM],
             units="m/s",
@@ -430,30 +461,29 @@ def __init__(
             dtype=Float,
         )
 
-        js1 = npt + OFFSET if grid_indexing.south_edge else grid_indexing.jsc - 1
-        je1 = ny - npt if grid_indexing.north_edge else grid_indexing.jec + 1
-        is1 = npt + OFFSET if grid_indexing.west_edge else grid_indexing.isd
-        ie1 = nx - npt if grid_indexing.east_edge else grid_indexing.ied
+        if (grid_type < 3) and (not nested):
+            js1 = npt + OFFSET if grid_indexing.south_edge else grid_indexing.jsc - 1
+            je1 = ny - npt if grid_indexing.north_edge else grid_indexing.jec + 1
+            is1 = npt + OFFSET if grid_indexing.west_edge else grid_indexing.isd
+            ie1 = nx - npt if grid_indexing.east_edge else grid_indexing.ied
 
-        is2 = npt + OFFSET if grid_indexing.west_edge else grid_indexing.isc - 1
-        ie2 = nx - npt if grid_indexing.east_edge else grid_indexing.iec + 1
-        js2 = npt + OFFSET if grid_indexing.south_edge else grid_indexing.jsd
-        je2 = ny - npt if grid_indexing.north_edge else grid_indexing.jed
+            is2 = npt + OFFSET if grid_indexing.west_edge else grid_indexing.isc - 1
+            ie2 = nx - npt if grid_indexing.east_edge else grid_indexing.iec + 1
+            js2 = npt + OFFSET if grid_indexing.south_edge else grid_indexing.jsd
+            je2 = ny - npt if grid_indexing.north_edge else grid_indexing.jed
 
-        ifirst = (
-            grid_indexing.isc + 2 if grid_indexing.west_edge else grid_indexing.isc - 1
-        )
-        ilast = (
-            grid_indexing.iec - 1 if grid_indexing.east_edge else grid_indexing.iec + 2
-        )
-        idiff = ilast - ifirst + 1
+        else:
+            js1 = grid_indexing.jsc - 1
+            je1 = grid_indexing.jec + 1
+            is1 = grid_indexing.isd
+            ie1 = grid_indexing.ied
 
-        jfirst = (
-            grid_indexing.jsc + 2 if grid_indexing.south_edge else grid_indexing.jsc - 1
-        )
-        jlast = (
-            grid_indexing.jec - 1 if grid_indexing.north_edge else grid_indexing.jec + 2
-        )
+            is2 = grid_indexing.isc - 1
+            ie2 = grid_indexing.iec + 1
+            js2 = grid_indexing.jsd
+            je2 = grid_indexing.jed
+
+        idiff = ilast - ifirst + 1
         jdiff = jlast - jfirst + 1
 
         self._set_tmps = stencil_factory.from_dims_halo(
@@ -482,12 +512,13 @@ def __init__(
         else:
             d2a2c_avg_offset = 3
 
-        self._avg_box = stencil_factory.from_dims_halo(
-            func=avg_box,
-            externals={"D2A2C_AVG_OFFSET": d2a2c_avg_offset},
-            compute_dims=[X_DIM, Y_DIM, Z_DIM],
-            compute_halos=(3, 3),
-        )
+        if self._grid_type < 3:
+            self._avg_box = stencil_factory.from_dims_halo(
+                func=avg_box,
+                externals={"D2A2C_AVG_OFFSET": d2a2c_avg_offset},
+                compute_dims=[X_DIM, Y_DIM, Z_DIM],
+                compute_halos=(3, 3),
+            )
 
         self._contravariant_components = stencil_factory.from_origin_domain(
             func=contravariant_components,
@@ -510,17 +541,18 @@ def __init__(
             domain=(idiff, grid_indexing.domain[1] + 2, grid_indexing.domain[2]),
         )
 
-        self._east_west_edges = stencil_factory.from_origin_domain(
-            func=east_west_edges,
-            externals={
-                "i_end": ax_offsets_edges["i_end"],
-                "i_start": ax_offsets_edges["i_start"],
-                "local_je": ax_offsets_edges["local_je"],
-                "local_js": ax_offsets_edges["local_js"],
-            },
-            origin=origin_edges,
-            domain=domain_edges,
-        )
+        if grid_type < 3:
+            self._east_west_edges = stencil_factory.from_origin_domain(
+                func=east_west_edges,
+                externals={
+                    "i_end": ax_offsets_edges["i_end"],
+                    "i_start": ax_offsets_edges["i_start"],
+                    "local_je": ax_offsets_edges["local_je"],
+                    "local_js": ax_offsets_edges["local_js"],
+                },
+                origin=origin_edges,
+                domain=domain_edges,
+            )
 
         # Ydir:
         self._fill_corners_y = stencil_factory.from_origin_domain(
@@ -532,19 +564,20 @@ def __init__(
             domain=domain_edges,
         )
 
-        self._north_south_edges = stencil_factory.from_origin_domain(
-            func=north_south_edges,
-            externals={
-                "j_end": ax_offsets_edges["j_end"],
-                "j_start": ax_offsets_edges["j_start"],
-                "local_ie": ax_offsets_edges["local_ie"],
-                "local_is": ax_offsets_edges["local_is"],
-                "local_je": ax_offsets_edges["local_je"],
-                "local_js": ax_offsets_edges["local_js"],
-            },
-            origin=origin_edges,
-            domain=domain_edges,
-        )
+        if grid_type < 3:
+            self._north_south_edges = stencil_factory.from_origin_domain(
+                func=north_south_edges,
+                externals={
+                    "j_end": ax_offsets_edges["j_end"],
+                    "j_start": ax_offsets_edges["j_start"],
+                    "local_ie": ax_offsets_edges["local_ie"],
+                    "local_is": ax_offsets_edges["local_is"],
+                    "local_je": ax_offsets_edges["local_je"],
+                    "local_js": ax_offsets_edges["local_js"],
+                },
+                origin=origin_edges,
+                domain=domain_edges,
+            )
 
         self._vt_main = stencil_factory.from_origin_domain(
             func=vt_main,
@@ -583,12 +616,13 @@ def __call__(self, uc, vc, u, v, ua, va, utc, vtc):
         )
 
         # tmp edges
-        self._avg_box(
-            u,
-            v,
-            self._utmp,
-            self._vtmp,
-        )
+        if self._grid_type < 3:
+            self._avg_box(
+                u,
+                v,
+                self._utmp,
+                self._vtmp,
+            )
 
         # contra-variant components at cell center
         self._contravariant_components(
@@ -617,19 +651,20 @@ def __call__(self, uc, vc, u, v, ua, va, utc, vtc):
             utc,
         )
 
-        self._east_west_edges(
-            u,
-            ua,
-            uc,
-            utc,
-            self._utmp,
-            v,
-            self._sin_sg1,
-            self._sin_sg3,
-            self._cosa_u,
-            self._rsin_u,
-            self._dxa,
-        )
+        if self._grid_type < 3:
+            self._east_west_edges(
+                u,
+                ua,
+                uc,
+                utc,
+                self._utmp,
+                v,
+                self._sin_sg1,
+                self._sin_sg3,
+                self._cosa_u,
+                self._rsin_u,
+                self._dxa,
+            )
 
         # Ydir:
         self._fill_corners_y(
@@ -639,19 +674,20 @@ def __call__(self, uc, vc, u, v, ua, va, utc, vtc):
             va,
         )
 
-        self._north_south_edges(
-            v,
-            va,
-            vc,
-            vtc,
-            self._vtmp,
-            u,
-            self._sin_sg2,
-            self._sin_sg4,
-            self._cosa_v,
-            self._rsin_v,
-            self._dya,
-        )
+        if self._grid_type < 3:
+            self._north_south_edges(
+                v,
+                va,
+                vc,
+                vtc,
+                self._vtmp,
+                u,
+                self._sin_sg2,
+                self._sin_sg4,
+                self._cosa_v,
+                self._rsin_v,
+                self._dya,
+            )
 
         self._vt_main(
             self._vtmp,
diff --git a/fv3core/pace/fv3core/stencils/d_sw.py b/fv3core/pace/fv3core/stencils/d_sw.py
index cc602f4e..e08af776 100644
--- a/fv3core/pace/fv3core/stencils/d_sw.py
+++ b/fv3core/pace/fv3core/stencils/d_sw.py
@@ -239,10 +239,16 @@ def compute_kinetic_energy(
             as defined in FV3 documentation by equation 6.3, multiplied by dt
         dt: timestep
     """
+    from __externals__ import grid_type
+
     with computation(PARALLEL), interval(...):
-        ub_contra, vb_contra = interpolate_uc_vc_to_cell_corners(
-            uc, vc, cosa, rsina, uc_contra, vc_contra
-        )
+        if __INLINED(grid_type < 3):
+            ub_contra, vb_contra = interpolate_uc_vc_to_cell_corners(
+                uc, vc, cosa, rsina, uc_contra, vc_contra
+            )
+        else:
+            ub_contra = 0.5 * (uc[0, -1, 0] + uc)
+            vb_contra = 0.5 * (vc[-1, 0, 0] + vc)
         advected_v = advect_v_along_y(v, vb_contra, rdy=rdy, dy=dy, dya=dya, dt=dt)
         advected_u = advect_u_along_x(u, ub_contra, rdx=rdx, dx=dx, dxa=dxa, dt=dt)
         # makes sure the kinetic energy part of the governing equation is computed
@@ -757,7 +763,7 @@ def __init__(
         self._do_stochastic_ke_backscatter = config.do_skeb
 
         self.grid_indexing = stencil_factory.grid_indexing
-        assert config.grid_type < 3, "ubke and vbke only implemented for grid_type < 3"
+        self._grid_type = config.grid_type
         assert not config.inline_q, "inline_q not yet implemented"
         assert (
             config.d_ext <= 0
@@ -855,6 +861,7 @@ def make_quantity():
         self.fv_prep = FiniteVolumeFluxPrep(
             stencil_factory=stencil_factory,
             grid_data=grid_data,
+            grid_type=self._grid_type,
         )
         self.divergence_damping = DivergenceDamping(
             stencil_factory,
@@ -887,6 +894,7 @@ def make_quantity():
                 "mord": config.hord_mt,
                 "xt_minmax": False,
                 "yt_minmax": False,
+                "grid_type": config.grid_type,
             },
         )
         self._apply_fluxes = stencil_factory.from_dims_halo(
@@ -932,7 +940,7 @@ def make_quantity():
             )
         )
 
-        if (self._d_con > 1.e-5) or (self._do_stochastic_ke_backscatter):
+        if (self._d_con > 1.0e-5) or (self._do_stochastic_ke_backscatter):
             self._accumulate_heat_source_and_dissipation_estimate_stencil = (
                 stencil_factory.from_dims_halo(
                     func=accumulate_heat_source_and_dissipation_estimate,
@@ -1254,11 +1262,11 @@ def __call__(
             self._column_namelist["d_con"],
         )
 
-        if (self._d_con > 1.e-5) or (self._do_stochastic_ke_backscatter):
+        if (self._d_con > 1.0e-5) or (self._do_stochastic_ke_backscatter):
             self._accumulate_heat_source_and_dissipation_estimate_stencil(
                 self._tmp_heat_s, heat_source, self._tmp_diss_e, diss_est
             )
-        
+
         self._update_u_and_v_stencil(
             self._tmp_ut,
             self._tmp_vt,
diff --git a/fv3core/pace/fv3core/stencils/divergence_damping.py b/fv3core/pace/fv3core/stencils/divergence_damping.py
index 7aac03e7..a3e5a32b 100644
--- a/fv3core/pace/fv3core/stencils/divergence_damping.py
+++ b/fv3core/pace/fv3core/stencils/divergence_damping.py
@@ -6,6 +6,7 @@
     horizontal,
     interval,
     region,
+    sqrt,
 )
 
 import pace.fv3core.stencils.basic_operations as basic
@@ -14,7 +15,10 @@
 from pace.dsl.dace.orchestration import dace_inhibitor, orchestrate
 from pace.dsl.stencil import StencilFactory, get_stencils_with_varied_bounds
 from pace.dsl.typing import Float, FloatField, FloatFieldIJ, FloatFieldK
-from pace.fv3core.stencils.a2b_ord4 import AGrid2BGridFourthOrder
+from pace.fv3core.stencils.a2b_ord4 import (
+    AGrid2BGridFourthOrder,
+    doubly_periodic_a2b_ord4,
+)
 from pace.fv3core.stencils.d2a2c_vect import contravariant
 from pace.util import X_DIM, X_INTERFACE_DIM, Y_DIM, Y_INTERFACE_DIM, Z_DIM
 from pace.util.grid import DampingCoefficients, GridData
@@ -251,6 +255,50 @@ def smagorinsky_diffusion_approx(delpc: FloatField, vort: FloatField, absdt: Flo
         vort = absdt * (delpc ** 2.0 + vort ** 2.0) ** 0.5
 
 
+def smag_corner(
+    u: FloatField,
+    v: FloatField,
+    dx: FloatFieldIJ,
+    dxc: FloatFieldIJ,
+    dy: FloatFieldIJ,
+    dyc: FloatFieldIJ,
+    rarea: FloatFieldIJ,
+    rarea_c: FloatFieldIJ,
+    smag_c: FloatField,
+    dt: Float,
+):
+    """
+    Smagorinsky diffusion for a doubly-periodic domain
+    Args:
+        u (in): d-grid u wind
+        v (in): d-grid v wind
+        dx (in): Distance between grid corners along the x-direction
+        dxc (in): Distance between grid centers along the x-direction
+        dy (in): Distance between grid corners along the y-direction
+        dyc (in): Distance between grid centers along the y-direction
+        rarea (in): 1/cell area
+        rarea_c (in): 1/ c-grid cell area
+        smag_c (out): tension shear strain on cell corners
+        dt (in): timestep
+    """
+
+    with computation(PARALLEL), interval(...):
+        # compute tension strain at corners:
+        shear = 0.0
+
+        ut = u * dyc
+        vt = v * dxc
+        smag_c_t = rarea_c * (vt[0, -1, 0] - vt - ut[-1, 0, 0] + ut)
+
+        # compute shear strain:
+        vt2 = u * dx
+        ut2 = v * dy
+        wk = rarea * (vt2 - vt2[0, 1, 0] + ut2 - ut2[1, 0, 0])
+
+        shear = doubly_periodic_a2b_ord4(wk)
+        smag_c = dt * sqrt(shear ** 2 + smag_c_t ** 2)
+
+
 class DivergenceDamping:
     """
     A large section in Fortran's d_sw that applies divergence damping
@@ -277,7 +325,7 @@ def __init__(
         )
         self.grid_indexing = stencil_factory.grid_indexing
         assert not nested, "nested not implemented"
-        assert grid_type < 3, "Not implemented, grid_type>=3, specifically smag_corner"
+        # assert grid_type < 3, "Not implemented, grid_type>=3"
         # TODO: make dddmp a compile-time external, instead of runtime scalar
         self._dddmp = dddmp
         # TODO: make da_min_c a compile-time external, instead of runtime scalar
@@ -287,6 +335,7 @@ def __init__(
         self._grid_type = grid_type
         self._nord_column = nord_col
         self._d2_bg_column = d2_bg
+        self._rarea = grid_data.rarea
         self._rarea_c = grid_data.rarea_c
         self._sin_sg1 = grid_data.sin_sg1
         self._sin_sg2 = grid_data.sin_sg2
@@ -296,6 +345,8 @@ def __init__(
         self._cosa_v = grid_data.cosa_v
         self._sina_u = grid_data.sina_u
         self._sina_v = grid_data.sina_v
+        self._dx = grid_data.dx
+        self._dy = grid_data.dy
         self._dxc = grid_data.dxc
         self._dyc = grid_data.dyc
         # TODO: maybe compute locally divg_* grid variables
@@ -433,21 +484,31 @@ def __init__(
             compute_halos=(self.grid_indexing.n_halo, self.grid_indexing.n_halo),
         )
 
-        self.a2b_ord4 = AGrid2BGridFourthOrder(
-            stencil_factory=high_k_stencil_factory,
-            quantity_factory=quantity_factory,
-            grid_data=grid_data,
-            grid_type=self._grid_type,
-            replace=False,
-        )
+        if self._grid_type < 3:
+            self.a2b_ord4 = AGrid2BGridFourthOrder(
+                stencil_factory=high_k_stencil_factory,
+                quantity_factory=quantity_factory,
+                grid_data=grid_data,
+                grid_type=self._grid_type,
+                replace=False,
+            )
 
-        self._smagorinksy_diffusion_approx_stencil = (
-            high_k_stencil_factory.from_dims_halo(
-                func=smagorinsky_diffusion_approx,
+            self._smagorinksy_diffusion_approx_stencil = (
+                high_k_stencil_factory.from_dims_halo(
+                    func=smagorinsky_diffusion_approx,
+                    compute_dims=[X_INTERFACE_DIM, Y_INTERFACE_DIM, Z_DIM],
+                    compute_halos=(0, 0),
+                )
+            )
+        else:
+            self._smag_corner = high_k_stencil_factory.from_dims_halo(
+                func=smag_corner,
+                externals={
+                    "replace": False,
+                },
                 compute_dims=[X_INTERFACE_DIM, Y_INTERFACE_DIM, Z_DIM],
                 compute_halos=(0, 0),
             )
-        )
 
         self._damping_nord_highorder_stencil = high_k_stencil_factory.from_dims_halo(
             func=damping_nord_highorder_stencil,
@@ -614,12 +675,26 @@ def __call__(
             # take the cell centered relative vorticity and regrid it to cell corners
             # for smagorinsky diffusion
             #
-            self.a2b_ord4(rel_vort_agrid, damped_rel_vort_bgrid)
-            self._smagorinksy_diffusion_approx_stencil(
-                delpc,
-                damped_rel_vort_bgrid,
-                abs(dt),
-            )
+            if self._grid_type < 3:
+                self.a2b_ord4(rel_vort_agrid, damped_rel_vort_bgrid)
+                self._smagorinksy_diffusion_approx_stencil(
+                    delpc,
+                    damped_rel_vort_bgrid,
+                    abs(dt),
+                )
+            else:
+                self._smag_corner(
+                    u,
+                    v,
+                    self._dx,
+                    self._dxc,
+                    self._dy,
+                    self._dyc,
+                    self._rarea,
+                    self._rarea_c,
+                    damped_rel_vort_bgrid,
+                    abs(dt),
+                )
 
         da_min: Float = self._get_da_min()
         if self._stretched_grid:
diff --git a/fv3core/pace/fv3core/stencils/dyn_core.py b/fv3core/pace/fv3core/stencils/dyn_core.py
index 7a75790b..d8e3b8d3 100644
--- a/fv3core/pace/fv3core/stencils/dyn_core.py
+++ b/fv3core/pace/fv3core/stencils/dyn_core.py
@@ -243,7 +243,7 @@ class _HaloUpdaters(object):
 
         def __init__(
             self,
-            comm: pace.util.CubedSphereCommunicator,
+            comm: pace.util.Communicator,
             grid_indexing: GridIndexing,
             quantity_factory: pace.util.QuantityFactory,
             state: DycoreState,
@@ -364,7 +364,7 @@ def __init__(
 
     def __init__(
         self,
-        comm: pace.util.CubedSphereCommunicator,
+        comm: pace.util.Communicator,
         stencil_factory: StencilFactory,
         quantity_factory: pace.util.QuantityFactory,
         grid_data: GridData,
@@ -380,14 +380,14 @@ def __init__(
     ):
         """
         Args:
-            comm: object for cubed sphere inter-process communication
+            comm: object for tile or cubed-sphere inter-process communication
             stencil_factory: creates stencils
             quantity_factory: creates quantities
             grid_data: metric terms defining the grid
             damping_coefficients: damping configuration
-            grid_type: ???
-            nested: ???
-            stretched_grid: ???
+            grid_type: grid geometry used
+            nested: if the grid contains a nested, high-res region
+            stretched_grid: if the grid is stretched so tile faces cover different areas
             config: configuration settings
             pfull: atmospheric Eulerian grid reference pressure (Pa)
             phis: surface geopotential height
@@ -560,6 +560,7 @@ def __init__(
                 quantity_factory=quantity_factory,
                 area=grid_data.area,
                 dp_ref=grid_data.dp_ref,
+                grid_type=config.grid_type,
             )
         )
 
diff --git a/fv3core/pace/fv3core/stencils/fv_dynamics.py b/fv3core/pace/fv3core/stencils/fv_dynamics.py
index 3299e501..0ffc7572 100644
--- a/fv3core/pace/fv3core/stencils/fv_dynamics.py
+++ b/fv3core/pace/fv3core/stencils/fv_dynamics.py
@@ -90,7 +90,7 @@ class DynamicalCore:
 
     def __init__(
         self,
-        comm: pace.util.CubedSphereCommunicator,
+        comm: pace.util.Communicator,
         grid_data: GridData,
         stencil_factory: StencilFactory,
         quantity_factory: pace.util.QuantityFactory,
@@ -103,7 +103,7 @@ def __init__(
     ):
         """
         Args:
-            comm: object for cubed sphere inter-process communication
+            comm: object for cubed sphere or tile inter-process communication
             grid_data: metric terms defining the model grid
             stencil_factory: creates stencils
             damping_coefficients: damping configuration/constants
@@ -276,7 +276,13 @@ def __init__(
             self.config.nf_omega,
         )
         self._cubed_to_latlon = CubedToLatLon(
-            state, stencil_factory, quantity_factory, grid_data, config.c2l_ord, comm
+            state,
+            stencil_factory,
+            quantity_factory,
+            grid_data,
+            self.config.grid_type,
+            config.c2l_ord,
+            comm,
         )
         self._cappa = self.acoustic_dynamics.cappa
 
diff --git a/fv3core/pace/fv3core/stencils/fxadv.py b/fv3core/pace/fv3core/stencils/fxadv.py
index 8fc410fa..1527c898 100644
--- a/fv3core/pace/fv3core/stencils/fxadv.py
+++ b/fv3core/pace/fv3core/stencils/fxadv.py
@@ -1,4 +1,11 @@
-from gt4py.cartesian.gtscript import PARALLEL, computation, horizontal, interval, region
+from gt4py.cartesian.gtscript import (
+    __INLINED,
+    PARALLEL,
+    computation,
+    horizontal,
+    interval,
+    region,
+)
 
 from pace.dsl.dace import orchestrate
 from pace.dsl.stencil import StencilFactory
@@ -28,24 +35,36 @@ def main_uc_vc_contra(
         uc_contra (out): contravariant c-grid x-wind
         vc_contra (out): contravariant c-grid y-wind
     """
-    from __externals__ import j_end, j_start, local_ie, local_is, local_je, local_js
+    from __externals__ import (
+        grid_type,
+        j_end,
+        j_start,
+        local_ie,
+        local_is,
+        local_je,
+        local_js,
+    )
 
     with computation(PARALLEL), interval(...):
-        utmp = uc_contra
-        with horizontal(region[local_is - 1 : local_ie + 3, :]):
-            # for C-grid, v must be regridded to lie at the same point as u
-            v = 0.25 * (vc[-1, 0, 0] + vc + vc[-1, 1, 0] + vc[0, 1, 0])
-            uc_contra = contravariant(uc, v, cosa_u, rsin_u)
-        # TODO: investigate whether this region operation is necessary
-        with horizontal(
-            region[:, j_start - 1 : j_start + 1], region[:, j_end : j_end + 2]
-        ):
-            uc_contra = utmp
-
-        with horizontal(region[:, local_js - 1 : local_je + 3]):
-            # for C-grid, u must be regridded to lie at same point as v
-            u = 0.25 * (uc[0, -1, 0] + uc[1, -1, 0] + uc + uc[1, 0, 0])
-            vc_contra = contravariant(vc, u, cosa_v, rsin_v)
+        if __INLINED(grid_type < 3):
+            utmp = uc_contra
+            with horizontal(region[local_is - 1 : local_ie + 3, :]):
+                # for C-grid, v must be regridded to lie at the same point as u
+                v = 0.25 * (vc[-1, 0, 0] + vc + vc[-1, 1, 0] + vc[0, 1, 0])
+                uc_contra = contravariant(uc, v, cosa_u, rsin_u)
+            # TODO: investigate whether this region operation is necessary
+            with horizontal(
+                region[:, j_start - 1 : j_start + 1], region[:, j_end : j_end + 2]
+            ):
+                uc_contra = utmp
+
+            with horizontal(region[:, local_js - 1 : local_je + 3]):
+                # for C-grid, u must be regridded to lie at same point as v
+                u = 0.25 * (uc[0, -1, 0] + uc[1, -1, 0] + uc + uc[1, 0, 0])
+                vc_contra = contravariant(vc, u, cosa_v, rsin_v)
+        else:
+            uc_contra = uc
+            vc_contra = vc
 
 
 def uc_contra_y_edge(
@@ -496,12 +515,14 @@ def __init__(
         self,
         stencil_factory: StencilFactory,
         grid_data: GridData,
+        grid_type: int,
     ):
         orchestrate(
             obj=self,
             config=stencil_factory.config.dace_config,
         )
         grid_indexing = stencil_factory.grid_indexing
+        self._grid_type = grid_type
         self._tile_interior = not (
             grid_indexing.west_edge
             or grid_indexing.east_edge
@@ -533,26 +554,30 @@ def __init__(
             "domain": domain_corners,
         }
         self._main_uc_vc_contra_stencil = stencil_factory.from_origin_domain(
-            main_uc_vc_contra, **kwargs
-        )
-        self._uc_contra_y_edge_stencil = stencil_factory.from_origin_domain(
-            uc_contra_y_edge, **kwargs
-        )
-        self._vc_contra_y_edge_stencil = stencil_factory.from_origin_domain(
-            vc_contra_y_edge, **kwargs
-        )
-        self._vc_contra_x_edge_stencil = stencil_factory.from_origin_domain(
-            vc_contra_x_edge, **kwargs
-        )
-        self._uc_contra_x_edge_stencil = stencil_factory.from_origin_domain(
-            uc_contra_x_edge, **kwargs
-        )
-        self._uc_contra_corners_stencil = stencil_factory.from_origin_domain(
-            uc_contra_corners, **kwargs_corners
-        )
-        self._vc_contra_corners_stencil = stencil_factory.from_origin_domain(
-            vc_contra_corners, **kwargs_corners
+            main_uc_vc_contra,
+            externals={"grid_type": grid_type, **ax_offsets},
+            origin=origin,
+            domain=domain,
         )
+        if self._grid_type < 3:
+            self._uc_contra_y_edge_stencil = stencil_factory.from_origin_domain(
+                uc_contra_y_edge, **kwargs
+            )
+            self._vc_contra_y_edge_stencil = stencil_factory.from_origin_domain(
+                vc_contra_y_edge, **kwargs
+            )
+            self._vc_contra_x_edge_stencil = stencil_factory.from_origin_domain(
+                vc_contra_x_edge, **kwargs
+            )
+            self._uc_contra_x_edge_stencil = stencil_factory.from_origin_domain(
+                uc_contra_x_edge, **kwargs
+            )
+            self._uc_contra_corners_stencil = stencil_factory.from_origin_domain(
+                uc_contra_corners, **kwargs_corners
+            )
+            self._vc_contra_corners_stencil = stencil_factory.from_origin_domain(
+                vc_contra_corners, **kwargs_corners
+            )
         self._fxadv_fluxes_stencil = stencil_factory.from_origin_domain(
             fxadv_fluxes_stencil, **kwargs
         )
@@ -607,41 +632,46 @@ def __call__(
             uc_contra,
             vc_contra,
         )
-        if not self._tile_interior:
-            self._uc_contra_y_edge_stencil(uc, self._sin_sg1, self._sin_sg3, uc_contra)
-            self._vc_contra_y_edge_stencil(
-                vc,
-                self._cosa_v,
-                uc_contra,
-                vc_contra,
-            )
-            self._vc_contra_x_edge_stencil(vc, self._sin_sg2, self._sin_sg4, vc_contra)
-            self._uc_contra_x_edge_stencil(
-                uc,
-                self._cosa_u,
-                vc_contra,
-                uc_contra,
-            )
-            # NOTE: this is aliasing memory
-            self._uc_contra_corners_stencil(
-                self._cosa_u,
-                self._cosa_v,
-                uc,
-                vc,
-                uc_contra,
-                uc_contra,
-                vc_contra,
-            )
-            # NOTE: this is aliasing memory
-            self._vc_contra_corners_stencil(
-                self._cosa_u,
-                self._cosa_v,
-                uc,
-                vc,
-                uc_contra,
-                vc_contra,
-                vc_contra,
-            )
+        if self._grid_type < 3:
+            if not self._tile_interior:
+                self._uc_contra_y_edge_stencil(
+                    uc, self._sin_sg1, self._sin_sg3, uc_contra
+                )
+                self._vc_contra_y_edge_stencil(
+                    vc,
+                    self._cosa_v,
+                    uc_contra,
+                    vc_contra,
+                )
+                self._vc_contra_x_edge_stencil(
+                    vc, self._sin_sg2, self._sin_sg4, vc_contra
+                )
+                self._uc_contra_x_edge_stencil(
+                    uc,
+                    self._cosa_u,
+                    vc_contra,
+                    uc_contra,
+                )
+                # NOTE: this is aliasing memory
+                self._uc_contra_corners_stencil(
+                    self._cosa_u,
+                    self._cosa_v,
+                    uc,
+                    vc,
+                    uc_contra,
+                    uc_contra,
+                    vc_contra,
+                )
+                # NOTE: this is aliasing memory
+                self._vc_contra_corners_stencil(
+                    self._cosa_u,
+                    self._cosa_v,
+                    uc,
+                    vc,
+                    uc_contra,
+                    vc_contra,
+                    vc_contra,
+                )
         self._fxadv_fluxes_stencil(
             self._sin_sg1,
             self._sin_sg2,
diff --git a/fv3core/pace/fv3core/stencils/tracer_2d_1l.py b/fv3core/pace/fv3core/stencils/tracer_2d_1l.py
index 475be6c5..02bc2dd6 100644
--- a/fv3core/pace/fv3core/stencils/tracer_2d_1l.py
+++ b/fv3core/pace/fv3core/stencils/tracer_2d_1l.py
@@ -181,7 +181,7 @@ def __init__(
         quantity_factory: pace.util.QuantityFactory,
         transport: FiniteVolumeTransport,
         grid_data,
-        comm: pace.util.CubedSphereCommunicator,
+        comm: pace.util.Communicator,
         tracers: Dict[str, pace.util.Quantity],
     ):
         orchestrate(
diff --git a/fv3core/pace/fv3core/stencils/updatedzc.py b/fv3core/pace/fv3core/stencils/updatedzc.py
index 70e004e6..bb0703ba 100644
--- a/fv3core/pace/fv3core/stencils/updatedzc.py
+++ b/fv3core/pace/fv3core/stencils/updatedzc.py
@@ -124,9 +124,11 @@ def __init__(
         quantity_factory: pace.util.QuantityFactory,
         area: pace.util.Quantity,
         dp_ref: pace.util.Quantity,
+        grid_type,
     ):
         grid_indexing = stencil_factory.grid_indexing
         self._area = area
+        self.grid_type = grid_type
         # TODO: this is needed because GridData.dp_ref does not have access
         # to a QuantityFactory, we should add a way to perform operations on
         # Quantity and persist the QuantityFactory choices
@@ -158,18 +160,21 @@ def __init__(
         )
 
         ax_offsets = grid_indexing.axis_offsets(full_origin, full_domain)
-        self._fill_corners_x_stencil = stencil_factory.from_origin_domain(
-            corners.fill_corners_2cells_x_stencil,
-            externals=ax_offsets,
-            origin=full_origin,
-            domain=full_domain,
-        )
-        self._fill_corners_y_stencil = stencil_factory.from_origin_domain(
-            corners.fill_corners_2cells_y_stencil,
-            externals=ax_offsets,
-            origin=full_origin,
-            domain=full_domain,
-        )
+
+        if self.grid_type < 3:
+            self._fill_corners_x_stencil = stencil_factory.from_origin_domain(
+                corners.fill_corners_2cells_x_stencil,
+                externals=ax_offsets,
+                origin=full_origin,
+                domain=full_domain,
+            )
+            self._fill_corners_y_stencil = stencil_factory.from_origin_domain(
+                corners.fill_corners_2cells_y_stencil,
+                externals=ax_offsets,
+                origin=full_origin,
+                domain=full_domain,
+            )
+
         self._update_dz_c = stencil_factory.from_origin_domain(
             update_dz_c,
             origin=grid_indexing.origin_compute(add=(-1, -1, 0)),
@@ -202,8 +207,9 @@ def __call__(
         self._double_copy_stencil(gz, self._gz_x, self._gz_y)
 
         # TODO(eddied): We pass the same fields 2x to avoid GTC validation errors
-        self._fill_corners_x_stencil(self._gz_x, self._gz_x)
-        self._fill_corners_y_stencil(self._gz_y, self._gz_y)
+        if self.grid_type < 3:
+            self._fill_corners_x_stencil(self._gz_x, self._gz_x)
+            self._fill_corners_y_stencil(self._gz_y, self._gz_y)
 
         self._update_dz_c(
             self._dp_ref,
diff --git a/fv3core/pace/fv3core/stencils/xppm.py b/fv3core/pace/fv3core/stencils/xppm.py
index 675d022f..239e2d7f 100644
--- a/fv3core/pace/fv3core/stencils/xppm.py
+++ b/fv3core/pace/fv3core/stencils/xppm.py
@@ -156,7 +156,7 @@ def compute_al(q: FloatField, dxa: FloatFieldIJ):
     Returns:
         q interpolated to x-interfaces
     """
-    from __externals__ import i_end, i_start, iord
+    from __externals__ import grid_type, i_end, i_start, iord
 
     compile_assert(iord < 8)
 
@@ -166,17 +166,21 @@ def compute_al(q: FloatField, dxa: FloatFieldIJ):
         compile_assert(False)
         al = max(al, 0.0)
 
-    with horizontal(region[i_start - 1, :], region[i_end, :]):
-        al = ppm.c1 * q[-2, 0, 0] + ppm.c2 * q[-1, 0, 0] + ppm.c3 * q
-    with horizontal(region[i_start, :], region[i_end + 1, :]):
-        al = 0.5 * (
-            ((2.0 * dxa[-1, 0] + dxa[-2, 0]) * q[-1, 0, 0] - dxa[-1, 0] * q[-2, 0, 0])
-            / (dxa[-2, 0] + dxa[-1, 0])
-            + ((2.0 * dxa[0, 0] + dxa[1, 0]) * q[0, 0, 0] - dxa[0, 0] * q[1, 0, 0])
-            / (dxa[0, 0] + dxa[1, 0])
-        )
-    with horizontal(region[i_start + 1, :], region[i_end + 2, :]):
-        al = ppm.c3 * q[-1, 0, 0] + ppm.c2 * q[0, 0, 0] + ppm.c1 * q[1, 0, 0]
+    if __INLINED(grid_type < 3):
+        with horizontal(region[i_start - 1, :], region[i_end, :]):
+            al = ppm.c1 * q[-2, 0, 0] + ppm.c2 * q[-1, 0, 0] + ppm.c3 * q
+        with horizontal(region[i_start, :], region[i_end + 1, :]):
+            al = 0.5 * (
+                (
+                    (2.0 * dxa[-1, 0] + dxa[-2, 0]) * q[-1, 0, 0]
+                    - dxa[-1, 0] * q[-2, 0, 0]
+                )
+                / (dxa[-2, 0] + dxa[-1, 0])
+                + ((2.0 * dxa[0, 0] + dxa[1, 0]) * q[0, 0, 0] - dxa[0, 0] * q[1, 0, 0])
+                / (dxa[0, 0] + dxa[1, 0])
+            )
+        with horizontal(region[i_start + 1, :], region[i_end + 2, :]):
+            al = ppm.c3 * q[-1, 0, 0] + ppm.c2 * q[0, 0, 0] + ppm.c1 * q[1, 0, 0]
 
     return al
 
@@ -248,7 +252,7 @@ def bl_br_edges(bl, br, q, dxa, al, dm):
 
 @gtscript.function
 def compute_blbr_ord8plus(q: FloatField, dxa: FloatFieldIJ):
-    from __externals__ import i_end, i_start, iord
+    from __externals__ import grid_type, i_end, i_start, iord
 
     dm = dm_iord8plus(q)
     al = al_iord8plus(q, dm)
@@ -256,12 +260,14 @@ def compute_blbr_ord8plus(q: FloatField, dxa: FloatFieldIJ):
     compile_assert(iord == 8)
 
     bl, br = blbr_iord8(q, al, dm)
-    bl, br = bl_br_edges(bl, br, q, dxa, al, dm)
 
-    with horizontal(
-        region[i_start - 1 : i_start + 2, :], region[i_end - 1 : i_end + 2, :]
-    ):
-        bl, br = ppm.pert_ppm_standard_constraint_fcn(q, bl, br)
+    if __INLINED(grid_type < 3):
+        bl, br = bl_br_edges(bl, br, q, dxa, al, dm)
+
+        with horizontal(
+            region[i_start - 1 : i_start + 2, :], region[i_end - 1 : i_end + 2, :]
+        ):
+            bl, br = ppm.pert_ppm_standard_constraint_fcn(q, bl, br)
 
     return bl, br
 
@@ -304,7 +310,7 @@ def __init__(
         # Arguments come from:
         # namelist.grid_type
         # grid.dxa
-        assert grid_type < 3
+        assert (grid_type < 3) or (grid_type == 4)
         self._dxa = dxa
         ax_offsets = stencil_factory.grid_indexing.axis_offsets(origin, domain)
         self._compute_flux_stencil = stencil_factory.from_origin_domain(
@@ -315,6 +321,7 @@ def __init__(
                 "xt_minmax": True,
                 "i_start": ax_offsets["i_start"],
                 "i_end": ax_offsets["i_end"],
+                "grid_type": grid_type,
             },
             origin=origin,
             domain=domain,
diff --git a/fv3core/pace/fv3core/stencils/xtp_u.py b/fv3core/pace/fv3core/stencils/xtp_u.py
index 5568376f..1b511e00 100644
--- a/fv3core/pace/fv3core/stencils/xtp_u.py
+++ b/fv3core/pace/fv3core/stencils/xtp_u.py
@@ -17,7 +17,7 @@ def get_bl_br(u, dx, dxa):
         bl: ???
         br: ???
     """
-    from __externals__ import i_end, i_start, iord, j_end, j_start
+    from __externals__ import grid_type, i_end, i_start, iord, j_end, j_start
 
     if __INLINED(iord < 8):
         u_on_cell_corners = xppm.compute_al(u, dx)
@@ -32,20 +32,24 @@ def get_bl_br(u, dx, dxa):
         compile_assert(iord == 8)
 
         bl, br = xppm.blbr_iord8(u, u_on_cell_corners, dm)
-        bl, br = xppm.bl_br_edges(bl, br, u, dxa, u_on_cell_corners, dm)
-
-        with horizontal(region[i_start + 1, :], region[i_end - 1, :]):
-            bl, br = ppm.pert_ppm_standard_constraint_fcn(u, bl, br)
-
-    # Zero corners
-    with horizontal(
-        region[i_start - 1 : i_start + 1, j_start],
-        region[i_start - 1 : i_start + 1, j_end + 1],
-        region[i_end : i_end + 2, j_start],
-        region[i_end : i_end + 2, j_end + 1],
-    ):
-        bl = 0.0
-        br = 0.0
+
+        if __INLINED(grid_type < 3):
+            bl, br = xppm.bl_br_edges(bl, br, u, dxa, u_on_cell_corners, dm)
+
+            with horizontal(region[i_start + 1, :], region[i_end - 1, :]):
+                bl, br = ppm.pert_ppm_standard_constraint_fcn(u, bl, br)
+
+    if __INLINED(grid_type < 3):
+        # Zero corners
+        with horizontal(
+            region[i_start - 1 : i_start + 1, j_start],
+            region[i_start - 1 : i_start + 1, j_end + 1],
+            region[i_end : i_end + 2, j_start],
+            region[i_end : i_end + 2, j_end + 1],
+        ):
+            bl = 0.0
+            br = 0.0
+
     return bl, br
 
 
diff --git a/fv3core/pace/fv3core/stencils/yppm.py b/fv3core/pace/fv3core/stencils/yppm.py
index b2ed1f2d..69389e2b 100644
--- a/fv3core/pace/fv3core/stencils/yppm.py
+++ b/fv3core/pace/fv3core/stencils/yppm.py
@@ -156,7 +156,7 @@ def compute_al(q: FloatField, dya: FloatFieldIJ):
     Returns:
         q interpolated to y-interfaces
     """
-    from __externals__ import j_end, j_start, jord
+    from __externals__ import grid_type, j_end, j_start, jord
 
     compile_assert(jord < 8)
 
@@ -166,17 +166,21 @@ def compute_al(q: FloatField, dya: FloatFieldIJ):
         compile_assert(False)
         al = max(al, 0.0)
 
-    with horizontal(region[:, j_start - 1], region[:, j_end]):
-        al = ppm.c1 * q[0, -2, 0] + ppm.c2 * q[0, -1, 0] + ppm.c3 * q
-    with horizontal(region[:, j_start], region[:, j_end + 1]):
-        al = 0.5 * (
-            ((2.0 * dya[0, -1] + dya[0, -2]) * q[0, -1, 0] - dya[0, -1] * q[0, -2, 0])
-            / (dya[0, -2] + dya[0, -1])
-            + ((2.0 * dya[0, 0] + dya[0, 1]) * q[0, 0, 0] - dya[0, 0] * q[0, 1, 0])
-            / (dya[0, 0] + dya[0, 1])
-        )
-    with horizontal(region[:, j_start + 1], region[:, j_end + 2]):
-        al = ppm.c3 * q[0, -1, 0] + ppm.c2 * q[0, 0, 0] + ppm.c1 * q[0, 1, 0]
+    if __INLINED(grid_type < 3):
+        with horizontal(region[:, j_start - 1], region[:, j_end]):
+            al = ppm.c1 * q[0, -2, 0] + ppm.c2 * q[0, -1, 0] + ppm.c3 * q
+        with horizontal(region[:, j_start], region[:, j_end + 1]):
+            al = 0.5 * (
+                (
+                    (2.0 * dya[0, -1] + dya[0, -2]) * q[0, -1, 0]
+                    - dya[0, -1] * q[0, -2, 0]
+                )
+                / (dya[0, -2] + dya[0, -1])
+                + ((2.0 * dya[0, 0] + dya[0, 1]) * q[0, 0, 0] - dya[0, 0] * q[0, 1, 0])
+                / (dya[0, 0] + dya[0, 1])
+            )
+        with horizontal(region[:, j_start + 1], region[:, j_end + 2]):
+            al = ppm.c3 * q[0, -1, 0] + ppm.c2 * q[0, 0, 0] + ppm.c1 * q[0, 1, 0]
 
     return al
 
@@ -248,7 +252,7 @@ def bl_br_edges(bl, br, q, dya, al, dm):
 
 @gtscript.function
 def compute_blbr_ord8plus(q: FloatField, dya: FloatFieldIJ):
-    from __externals__ import j_end, j_start, jord
+    from __externals__ import grid_type, j_end, j_start, jord
 
     dm = dm_jord8plus(q)
     al = al_jord8plus(q, dm)
@@ -256,12 +260,14 @@ def compute_blbr_ord8plus(q: FloatField, dya: FloatFieldIJ):
     compile_assert(jord == 8)
 
     bl, br = blbr_jord8(q, al, dm)
-    bl, br = bl_br_edges(bl, br, q, dya, al, dm)
 
-    with horizontal(
-        region[:, j_start - 1 : j_start + 2], region[:, j_end - 1 : j_end + 2]
-    ):
-        bl, br = ppm.pert_ppm_standard_constraint_fcn(q, bl, br)
+    if __INLINED(grid_type < 3):
+        bl, br = bl_br_edges(bl, br, q, dya, al, dm)
+
+        with horizontal(
+            region[:, j_start - 1 : j_start + 2], region[:, j_end - 1 : j_end + 2]
+        ):
+            bl, br = ppm.pert_ppm_standard_constraint_fcn(q, bl, br)
 
     return bl, br
 
@@ -304,7 +310,7 @@ def __init__(
         # Arguments come from:
         # namelist.grid_type
         # grid.dya
-        assert grid_type < 3
+        assert (grid_type < 3) or (grid_type == 4)
         self._dya = dya
         ax_offsets = stencil_factory.grid_indexing.axis_offsets(origin, domain)
         self._compute_flux_stencil = stencil_factory.from_origin_domain(
@@ -315,6 +321,7 @@ def __init__(
                 "yt_minmax": True,
                 "j_start": ax_offsets["j_start"],
                 "j_end": ax_offsets["j_end"],
+                "grid_type": grid_type,
             },
             origin=origin,
             domain=domain,
diff --git a/fv3core/pace/fv3core/stencils/ytp_v.py b/fv3core/pace/fv3core/stencils/ytp_v.py
index 7d2acad4..8b4cb7d3 100644
--- a/fv3core/pace/fv3core/stencils/ytp_v.py
+++ b/fv3core/pace/fv3core/stencils/ytp_v.py
@@ -17,7 +17,7 @@ def get_bl_br(v, dy, dya):
         bl: ???
         br: ???
     """
-    from __externals__ import i_end, i_start, j_end, j_start, jord
+    from __externals__ import grid_type, i_end, i_start, j_end, j_start, jord
 
     if __INLINED(jord < 8):
         v_on_cell_corners = yppm.compute_al(v, dy)
@@ -32,20 +32,23 @@ def get_bl_br(v, dy, dya):
         compile_assert(jord == 8)
 
         bl, br = yppm.blbr_jord8(v, v_on_cell_corners, dm)
-        bl, br = yppm.bl_br_edges(bl, br, v, dya, v_on_cell_corners, dm)
-
-        with horizontal(region[:, j_start + 1], region[:, j_end - 1]):
-            bl, br = ppm.pert_ppm_standard_constraint_fcn(v, bl, br)
-
-    # Zero corners
-    with horizontal(
-        region[i_start, j_start - 1 : j_start + 1],
-        region[i_end + 1, j_start - 1 : j_start + 1],
-        region[i_start, j_end : j_end + 2],
-        region[i_end + 1, j_end : j_end + 2],
-    ):
-        bl = 0.0
-        br = 0.0
+        if __INLINED(grid_type < 3):
+            bl, br = yppm.bl_br_edges(bl, br, v, dya, v_on_cell_corners, dm)
+
+            with horizontal(region[:, j_start + 1], region[:, j_end - 1]):
+                bl, br = ppm.pert_ppm_standard_constraint_fcn(v, bl, br)
+
+    if __INLINED(grid_type < 3):
+        # Zero corners
+        with horizontal(
+            region[i_start, j_start - 1 : j_start + 1],
+            region[i_end + 1, j_start - 1 : j_start + 1],
+            region[i_start, j_end : j_end + 2],
+            region[i_end + 1, j_end : j_end + 2],
+        ):
+            bl = 0.0
+            br = 0.0
+
     return bl, br
 
 
diff --git a/fv3core/tests/conftest.py b/fv3core/tests/conftest.py
index 23b9c366..f7e506a6 100644
--- a/fv3core/tests/conftest.py
+++ b/fv3core/tests/conftest.py
@@ -17,6 +17,7 @@ def pytest_addoption(parser):
     parser.addoption("--data_path", action="store", default="./")
     parser.addoption("--threshold_overrides_file", action="store", default=None)
     parser.addoption("--compute_grid", action="store_true")
+    parser.addoption("--dperiodic", action="store_true")
 
 
 def pytest_configure(config):
diff --git a/fv3core/tests/mpi/test_doubly_periodic.py b/fv3core/tests/mpi/test_doubly_periodic.py
index b129a913..5a4e6aa6 100644
--- a/fv3core/tests/mpi/test_doubly_periodic.py
+++ b/fv3core/tests/mpi/test_doubly_periodic.py
@@ -87,7 +87,7 @@ def setup_dycore() -> Tuple[pace.fv3core.DynamicalCore, List[Any]]:
         tile_rank=communicator.rank,
     )
     grid_indexing = pace.dsl.stencil.GridIndexing.from_sizer_and_communicator(
-        sizer=sizer, cube=communicator
+        sizer=sizer, comm=communicator
     )
     quantity_factory = pace.util.QuantityFactory.from_backend(
         sizer=sizer, backend=backend
diff --git a/fv3core/tests/savepoint/translate/translate_a2b_ord4.py b/fv3core/tests/savepoint/translate/translate_a2b_ord4.py
index 1d9290b3..be786a04 100644
--- a/fv3core/tests/savepoint/translate/translate_a2b_ord4.py
+++ b/fv3core/tests/savepoint/translate/translate_a2b_ord4.py
@@ -16,7 +16,15 @@ def __init__(self, stencil_factory: StencilFactory) -> None:
             dace_compiletime_args=["divdamp"],
         )
 
-    def __call__(self, divdamp, wk, vort, delpc, dt):
+    def __call__(
+        self,
+        divdamp,
+        wk,
+        vort,
+        delpc,
+        dt,
+        grid_type,
+    ):
         # this function is kept because it has a translate test, if its
         # structure is changed significantly from __call__ of DivergenceDamping
         # consider deleting this method and the translate test, or altering the
@@ -26,12 +34,15 @@ def __call__(self, divdamp, wk, vort, delpc, dt):
             divdamp._set_value(vort, 0.0)
         else:
             # TODO: what is wk/vort here?
-            divdamp.a2b_ord4(wk, vort)
-            divdamp._smagorinksy_diffusion_approx_stencil(
-                delpc,
-                vort,
-                abs(dt),
-            )
+            if grid_type < 3:
+                divdamp.a2b_ord4(wk, vort)
+                divdamp._smagorinksy_diffusion_approx_stencil(
+                    delpc,
+                    vort,
+                    abs(dt),
+                )
+            else:
+                pass
 
 
 class TranslateA2B_Ord4(TranslateDycoreFortranData2Py):
@@ -42,6 +53,7 @@ def __init__(
         stencil_factory: pace.dsl.StencilFactory,
     ):
         super().__init__(grid, namelist, stencil_factory)
+        assert namelist.grid_type < 3
         self.in_vars["data_vars"] = {"wk": {}, "vort": {}, "delpc": {}, "nord_col": {}}
         self.in_vars["parameters"] = ["dt"]
         self.out_vars: Dict[str, Any] = {"wk": {}, "vort": {}}
diff --git a/fv3core/tests/savepoint/translate/translate_cubedtolatlon.py b/fv3core/tests/savepoint/translate/translate_cubedtolatlon.py
index 0cf420ca..526a61e3 100644
--- a/fv3core/tests/savepoint/translate/translate_cubedtolatlon.py
+++ b/fv3core/tests/savepoint/translate/translate_cubedtolatlon.py
@@ -31,6 +31,7 @@ def __init__(
             "v": self.grid.x3d_domain_dict(),
         }
         self.stencil_factory = stencil_factory
+        self.grid_type = namelist.grid_type
 
     def compute_parallel(self, inputs, communicator):
         self._base.make_storage_data_input_vars(inputs)
@@ -53,6 +54,7 @@ def compute_parallel(self, inputs, communicator):
             grid_data=self.grid.grid_data,
             order=self.namelist.c2l_ord,
             comm=communicator,
+            grid_type=self.grid_type,
         )
         self._cubed_to_latlon(**inputs)
         return self._base.slice_output(inputs)
diff --git a/fv3core/tests/savepoint/translate/translate_fxadv.py b/fv3core/tests/savepoint/translate/translate_fxadv.py
index 2338e546..3dec8293 100644
--- a/fv3core/tests/savepoint/translate/translate_fxadv.py
+++ b/fv3core/tests/savepoint/translate/translate_fxadv.py
@@ -23,6 +23,7 @@ def __init__(
         self.compute_func = FiniteVolumeFluxPrep(  # type: ignore
             self.stencil_factory,
             self.grid.grid_data,
+            namelist.grid_type,
         )
         self.in_vars["data_vars"] = {
             "uc": {},
diff --git a/fv3core/tests/savepoint/translate/translate_updatedzc.py b/fv3core/tests/savepoint/translate/translate_updatedzc.py
index ea9541ff..ab0d11fa 100644
--- a/fv3core/tests/savepoint/translate/translate_updatedzc.py
+++ b/fv3core/tests/savepoint/translate/translate_updatedzc.py
@@ -21,6 +21,7 @@ def __init__(
             quantity_factory=self.grid.quantity_factory,
             area=grid.grid_data.area,
             dp_ref=grid.grid_data.dp_ref,
+            grid_type=namelist.grid_type,
         )
 
         def compute(**kwargs):
diff --git a/fv3core/tests/savepoint/translate/translate_xtp_u.py b/fv3core/tests/savepoint/translate/translate_xtp_u.py
index e19682c7..39832a3d 100644
--- a/fv3core/tests/savepoint/translate/translate_xtp_u.py
+++ b/fv3core/tests/savepoint/translate/translate_xtp_u.py
@@ -34,7 +34,7 @@ def __init__(
             raise NotImplementedError(
                 "Currently xtp_v is only supported for hord_mt == 5,6,7,8"
             )
-        assert grid_type < 3
+        assert (grid_type < 3) or (grid_type == 4)
         grid_indexing = stencil_factory.grid_indexing
 
         origin = grid_indexing.origin_compute()
@@ -49,6 +49,7 @@ def __init__(
                 "iord": iord,
                 "mord": iord,
                 "xt_minmax": False,
+                "grid_type": grid_type,
                 **ax_offsets,
             },
             origin=origin,
diff --git a/fv3core/tests/savepoint/translate/translate_ytp_v.py b/fv3core/tests/savepoint/translate/translate_ytp_v.py
index 63e779df..bf0afd16 100644
--- a/fv3core/tests/savepoint/translate/translate_ytp_v.py
+++ b/fv3core/tests/savepoint/translate/translate_ytp_v.py
@@ -34,7 +34,7 @@ def __init__(
             raise NotImplementedError(
                 "Currently ytp_v is only supported for hord_mt == 5,6,7,8"
             )
-        assert grid_type < 3
+        assert (grid_type < 3) or (grid_type == 4)
         grid_indexing = stencil_factory.grid_indexing
 
         origin = grid_indexing.origin_compute()
@@ -50,6 +50,7 @@ def __init__(
                 "jord": jord,
                 "mord": jord,
                 "yt_minmax": False,
+                "grid_type": grid_type,
                 **ax_offsets,
             },
             origin=origin,
diff --git a/physics/tests/conftest.py b/physics/tests/conftest.py
index 23b9c366..f7e506a6 100644
--- a/physics/tests/conftest.py
+++ b/physics/tests/conftest.py
@@ -17,6 +17,7 @@ def pytest_addoption(parser):
     parser.addoption("--data_path", action="store", default="./")
     parser.addoption("--threshold_overrides_file", action="store", default=None)
     parser.addoption("--compute_grid", action="store_true")
+    parser.addoption("--dperiodic", action="store_true")
 
 
 def pytest_configure(config):
diff --git a/stencils/pace/stencils/c2l_ord.py b/stencils/pace/stencils/c2l_ord.py
index 7c16eb4f..23a68774 100644
--- a/stencils/pace/stencils/c2l_ord.py
+++ b/stencils/pace/stencils/c2l_ord.py
@@ -1,4 +1,11 @@
-from gt4py.cartesian.gtscript import PARALLEL, computation, horizontal, interval, region
+from gt4py.cartesian.gtscript import (
+    __INLINED,
+    PARALLEL,
+    computation,
+    horizontal,
+    interval,
+    region,
+)
 
 import pace.dsl.gt4py_utils as utils
 import pace.util
@@ -10,10 +17,45 @@
 from pace.util.grid import GridData
 
 
+A1 = 0.5625
+A2 = -0.0625
 C1 = 1.125
 C2 = -0.125
 
 
+def mock_exchange(
+    quantity,
+    domain_2d,
+):
+    isc = domain_2d[0][0]
+    iec = domain_2d[0][1]
+    isd = domain_2d[1][0]
+    ied = domain_2d[1][1]
+    jsc = domain_2d[2][0]
+    jec = domain_2d[2][1]
+    jsd = domain_2d[3][0]
+    jed = domain_2d[3][1]
+    nhalo = isc - isd
+    
+    quantity[isd:isc, :, :] = quantity[iec - nhalo + 1 : iec + 1, :, :]
+    quantity[iec + 1 : ied + 1, :, :] = quantity[isc : isc + nhalo, :, :]
+    quantity[:, jsd:jsc, :] = quantity[:, jec - nhalo + 1 : jec + 1, :]
+    quantity[:, jec + 1 : jed + 1, :] = quantity[:, jsc : jsc + nhalo, :]
+
+    quantity[isd:isc, jsd:jsc, :] = quantity[
+        iec - nhalo + 1 : iec + 1, jec - nhalo + 1 : jec + 1, :
+    ]
+    quantity[isd:isc, jec + 1 : jed + 1, :] = quantity[
+        iec - nhalo + 1 : iec + 1, jsc : jsc + nhalo, :
+    ]
+    quantity[iec + 1 : ied + 1, jsd:jsc, :] = quantity[
+        isc : isc + nhalo, jec - nhalo + 1 : jec + 1, :
+    ]
+    quantity[iec + 1 : ied + 1, jec + 1 : jed + 1, :] = quantity[
+        isc : isc + nhalo, jsc : jsc + nhalo, :
+    ]
+
+
 @utils.mark_untested("This namelist option is not tested")
 def c2l_ord2(
     u: FloatField,
@@ -40,15 +82,21 @@ def c2l_ord2(
         ua (out):
         va (out):
     """
+    from __externals__ import grid_type
+
     with computation(PARALLEL), interval(...):
-        wu = u * dx
-        wv = v * dy
-        # Co-variant vorticity-conserving interpolation
-        u1 = 2.0 * (wu + wu[0, 1, 0]) / (dx + dx[0, 1])
-        v1 = 2.0 * (wv + wv[1, 0, 0]) / (dy + dy[1, 0])
-        # Cubed (cell center co-variant winds) to lat-lon
-        ua = a11 * u1 + a12 * v1
-        va = a21 * u1 + a22 * v1
+        if __INLINED(grid_type < 4):
+            wu = u * dx
+            wv = v * dy
+            # Co-variant vorticity-conserving interpolation
+            u1 = 2.0 * (wu + wu[0, 1, 0]) / (dx + dx[0, 1])
+            v1 = 2.0 * (wv + wv[1, 0, 0]) / (dy + dy[1, 0])
+            # Cubed (cell center co-variant winds) to lat-lon
+            ua = a11 * u1 + a12 * v1
+            va = a21 * u1 + a22 * v1
+        else:
+            ua = 0.5 * (u + u[0, 1, 0])
+            va = 0.5 * (v + v[1, 0, 0])
 
 
 def ord4_transform(
@@ -77,24 +125,28 @@ def ord4_transform(
         va (out):
     """
     with computation(PARALLEL), interval(...):
-        from __externals__ import i_end, i_start, j_end, j_start
+        from __externals__ import grid_type, i_end, i_start, j_end, j_start
 
-        utmp = C2 * (u[0, -1, 0] + u[0, 2, 0]) + C1 * (u + u[0, 1, 0])
-        vtmp = C2 * (v[-1, 0, 0] + v[2, 0, 0]) + C1 * (v + v[1, 0, 0])
+        if __INLINED(grid_type < 4):
+            utmp = C2 * (u[0, -1, 0] + u[0, 2, 0]) + C1 * (u + u[0, 1, 0])
+            vtmp = C2 * (v[-1, 0, 0] + v[2, 0, 0]) + C1 * (v + v[1, 0, 0])
 
-        # south/north edge
-        with horizontal(region[:, j_start], region[:, j_end]):
-            vtmp = 2.0 * ((v * dy) + (v[1, 0, 0] * dy[1, 0])) / (dy + dy[1, 0])
-            utmp = 2.0 * (u * dx + u[0, 1, 0] * dx[0, 1]) / (dx + dx[0, 1])
+            # south/north edge
+            with horizontal(region[:, j_start], region[:, j_end]):
+                vtmp = 2.0 * ((v * dy) + (v[1, 0, 0] * dy[1, 0])) / (dy + dy[1, 0])
+                utmp = 2.0 * (u * dx + u[0, 1, 0] * dx[0, 1]) / (dx + dx[0, 1])
 
-        # west/east edge
-        with horizontal(region[i_start, :], region[i_end, :]):
-            utmp = 2.0 * ((u * dx) + (u[0, 1, 0] * dx[0, 1])) / (dx + dx[0, 1])
-            vtmp = 2.0 * ((v * dy) + (v[1, 0, 0] * dy[1, 0])) / (dy + dy[1, 0])
+            # west/east edge
+            with horizontal(region[i_start, :], region[i_end, :]):
+                utmp = 2.0 * ((u * dx) + (u[0, 1, 0] * dx[0, 1])) / (dx + dx[0, 1])
+                vtmp = 2.0 * ((v * dy) + (v[1, 0, 0] * dy[1, 0])) / (dy + dy[1, 0])
 
-        # Transform local a-grid winds into latitude-longitude coordinates
-        ua = a11 * utmp + a12 * vtmp
-        va = a21 * utmp + a22 * vtmp
+            # Transform local a-grid winds into latitude-longitude coordinates
+            ua = a11 * utmp + a12 * vtmp
+            va = a21 * utmp + a22 * vtmp
+        else:
+            ua = A2 * (u[0, -1, 0] + u[0, 2, 0]) + A1 * (u + u[0, 1, 0])
+            va = A2 * (v[-1, 0, 0] + v[2, 0, 0]) + A1 * (v + v[1, 0, 0])
 
 
 class CubedToLatLon:
@@ -108,8 +160,9 @@ def __init__(
         stencil_factory: StencilFactory,
         quantity_factory: pace.util.QuantityFactory,
         grid_data: GridData,
+        grid_type: int,
         order: int,
-        comm: pace.util.CubedSphereCommunicator,
+        comm: pace.util.Communicator,
     ):
         """
         Initializes stencils to use either 2nd or 4th order of interpolation
@@ -120,9 +173,23 @@ def __init__(
             order: Order of interpolation, must be 2 or 4
         """
         grid_indexing = stencil_factory.grid_indexing
+        isc = grid_indexing.isc
+        jsc = grid_indexing.jsc
+        iec = grid_indexing.iec
+        jec = grid_indexing.jec
+        isd = grid_indexing.isd
+        jsd = grid_indexing.jsd
+        ied = grid_indexing.ied
+        jed = grid_indexing.jed
+        self._domain = [[isc, iec], [isd, ied], [jsc, jec], [jsd, jed]]
+
         self._n_halo = grid_indexing.n_halo
         self._dx = grid_data.dx
         self._dy = grid_data.dy
+        if comm.size == 1:
+            self.one_rank = True
+        else:
+            self.one_rank = False
 
         # TODO: maybe compute locally a* variables
         # They depend on z* and sin_sg5, which
@@ -141,30 +208,34 @@ def __init__(
             halos = (0, 0)
             func = ord4_transform
         self._compute_cubed_to_latlon = stencil_factory.from_dims_halo(
-            func=func, compute_dims=[X_DIM, Y_DIM, Z_DIM], compute_halos=halos
+            func=func,
+            externals={"grid_type": grid_type},
+            compute_dims=[X_DIM, Y_DIM, Z_DIM],
+            compute_halos=halos,
         )
 
         origin = grid_indexing.origin_compute()
         shape = grid_indexing.max_shape
-        full_size_xyiz_halo_spec = quantity_factory.get_quantity_halo_spec(
-            dims=[X_DIM, Y_INTERFACE_DIM, Z_DIM],
-            n_halo=grid_indexing.n_halo,
-            dtype=Float,
-        )
-        full_size_xiyz_halo_spec = quantity_factory.get_quantity_halo_spec(
-            dims=[X_INTERFACE_DIM, Y_DIM, Z_DIM],
-            n_halo=grid_indexing.n_halo,
-            dtype=Float,
-        )
-        self.u__v = WrappedHaloUpdater(
-            comm.get_vector_halo_updater(
-                [full_size_xyiz_halo_spec], [full_size_xiyz_halo_spec]
-            ),
-            state,
-            ["u"],
-            ["v"],
-            comm=comm,
-        )
+        if not self.one_rank:
+            full_size_xyiz_halo_spec = quantity_factory.get_quantity_halo_spec(
+                dims=[X_DIM, Y_INTERFACE_DIM, Z_DIM],
+                n_halo=grid_indexing.n_halo,
+                dtype=Float,
+            )
+            full_size_xiyz_halo_spec = quantity_factory.get_quantity_halo_spec(
+                dims=[X_INTERFACE_DIM, Y_DIM, Z_DIM],
+                n_halo=grid_indexing.n_halo,
+                dtype=Float,
+            )
+            self.u__v = WrappedHaloUpdater(
+                comm.get_vector_halo_updater(
+                    [full_size_xyiz_halo_spec], [full_size_xiyz_halo_spec]
+                ),
+                state,
+                ["u"],
+                ["v"],
+                comm=comm,
+            )
 
     def __call__(
         self,
@@ -180,10 +251,14 @@ def __call__(
             v: y-wind on D-grid (in)
             ua: x-wind on A-grid (out)
             va: y-wind on A-grid (out)
-            comm: Cubed-sphere communicator
+            comm: Cubed-sphere or Tile communicator
         """
         if self._do_ord4:
-            self.u__v.update()
+            if self.one_rank:
+                mock_exchange(u[:, :-1, :], self._domain)
+                mock_exchange(v[:-1, :, :], self._domain)
+            else:
+                self.u__v.update()
         self._compute_cubed_to_latlon(
             u,
             v,
diff --git a/stencils/pace/stencils/fv_update_phys.py b/stencils/pace/stencils/fv_update_phys.py
index 751d985a..d57ca4d9 100644
--- a/stencils/pace/stencils/fv_update_phys.py
+++ b/stencils/pace/stencils/fv_update_phys.py
@@ -87,12 +87,13 @@ def __init__(
         quantity_factory: pace.util.QuantityFactory,
         grid_data: GridData,
         namelist,
-        comm: pace.util.CubedSphereCommunicator,
+        comm: pace.util.Communicator,
         grid_info: DriverGridData,
         state: fv3core.DycoreState,
         u_dt: pace.util.Quantity,
         v_dt: pace.util.Quantity,
     ):
+        self._grid_type = namelist.grid_type
         orchestrate(
             obj=self,
             config=stencil_factory.config.dace_config,
@@ -125,6 +126,7 @@ def __init__(
             grid_data=grid_data,
             order=namelist.c2l_ord,
             comm=comm,
+            grid_type=self._grid_type,
         )
         origin = grid_indexing.origin_compute()
         shape = grid_indexing.max_shape
diff --git a/stencils/pace/stencils/testing/conftest.py b/stencils/pace/stencils/testing/conftest.py
index ce9946fc..256db95e 100644
--- a/stencils/pace/stencils/testing/conftest.py
+++ b/stencils/pace/stencils/testing/conftest.py
@@ -12,7 +12,7 @@
 from pace.dsl.dace.dace_config import DaceConfig
 from pace.stencils.testing import ParallelTranslate, TranslateGrid
 from pace.stencils.testing.savepoint import SavepointCase, dataset_to_dict
-from pace.util.communicator import CubedSphereCommunicator
+from pace.util.communicator import CubedSphereCommunicator, TileCommunicator
 from pace.util.mpi import MPI
 
 
@@ -103,8 +103,12 @@ def get_parallel_savepoint_names(metafunc, data_path):
 
 def get_ranks(metafunc, layout):
     only_rank = metafunc.config.getoption("which_rank")
+    dperiodic = metafunc.config.getoption("dperiodic")
     if only_rank is None:
-        total_ranks = 6 * layout[0] * layout[1]
+        if dperiodic:
+            total_ranks = layout[0] * layout[1]
+        else:
+            total_ranks = 6 * layout[0] * layout[1]
         return range(total_ranks)
     else:
         return [int(only_rank)]
@@ -133,6 +137,7 @@ def sequential_savepoint_cases(metafunc, data_path, namelist_filename, *, backen
     stencil_config = get_config(backend, None)
     ranks = get_ranks(metafunc, namelist.layout)
     compute_grid = metafunc.config.getoption("compute_grid")
+    dperiodic = metafunc.config.getoption("dperiodic")
     return _savepoint_cases(
         savepoint_names,
         ranks,
@@ -141,6 +146,7 @@ def sequential_savepoint_cases(metafunc, data_path, namelist_filename, *, backen
         backend,
         data_path,
         compute_grid,
+        dperiodic,
     )
 
 
@@ -152,6 +158,7 @@ def _savepoint_cases(
     backend,
     data_path,
     compute_grid: bool,
+    dperiodic: bool,
 ):
     return_list = []
     ds_grid: xr.Dataset = xr.open_dataset(os.path.join(data_path, "Grid-Info.nc")).isel(
@@ -165,7 +172,7 @@ def _savepoint_cases(
             backend=backend,
         ).python_grid()
         if compute_grid:
-            compute_grid_data(grid, namelist, backend, namelist.layout)
+            compute_grid_data(grid, namelist, backend, namelist.layout, dperiodic)
         stencil_factory = pace.dsl.stencil.StencilFactory(
             config=stencil_config,
             grid_indexing=grid.grid_indexing,
@@ -191,12 +198,12 @@ def _savepoint_cases(
     return return_list
 
 
-def compute_grid_data(grid, namelist, backend, layout):
+def compute_grid_data(grid, namelist, backend, layout, dperiodic):
     grid.make_grid_data(
         npx=namelist.npx,
         npy=namelist.npy,
         npz=namelist.npz,
-        communicator=get_communicator(MPI.COMM_WORLD, layout),
+        communicator=get_communicator(MPI.COMM_WORLD, layout, dperiodic),
         backend=backend,
     )
 
@@ -205,7 +212,8 @@ def parallel_savepoint_cases(
     metafunc, data_path, namelist_filename, mpi_rank, *, backend: str, comm
 ):
     namelist = get_namelist(namelist_filename)
-    communicator = get_communicator(comm, namelist.layout)
+    dperiodic = metafunc.config.getoption("dperiodic")
+    communicator = get_communicator(comm, namelist.layout, dperiodic)
     stencil_config = get_config(backend, communicator)
     savepoint_names = get_parallel_savepoint_names(metafunc, data_path)
     compute_grid = metafunc.config.getoption("compute_grid")
@@ -217,6 +225,7 @@ def parallel_savepoint_cases(
         backend,
         data_path,
         compute_grid,
+        dperiodic,
     )
 
 
@@ -261,9 +270,13 @@ def generate_parallel_stencil_tests(metafunc, *, backend: str):
     )
 
 
-def get_communicator(comm, layout):
-    partitioner = pace.util.CubedSpherePartitioner(pace.util.TilePartitioner(layout))
-    communicator = pace.util.CubedSphereCommunicator(comm, partitioner)
+def get_communicator(comm, layout, dperiodic):
+    if (MPI.COMM_WORLD.Get_size() > 1) and (not dperiodic):
+        partitioner = pace.util.CubedSpherePartitioner(pace.util.TilePartitioner(layout))
+        communicator = pace.util.CubedSphereCommunicator(comm, partitioner)
+    else:
+        partitioner = pace.util.TilePartitioner(layout)
+        communicator = pace.util.TileCommunicator(comm, partitioner)
     return communicator
 
 
@@ -280,3 +293,7 @@ def failure_stride(pytestconfig):
 @pytest.fixture()
 def compute_grid(pytestconfig):
     return pytestconfig.getoption("compute_grid")
+
+@pytest.fixture()
+def dperiodic(pytestconfig):
+    return pytestconfig.getoption("dperiodic")
diff --git a/stencils/pace/stencils/testing/test_translate.py b/stencils/pace/stencils/testing/test_translate.py
index 14e8cef8..b2884c2d 100644
--- a/stencils/pace/stencils/testing/test_translate.py
+++ b/stencils/pace/stencils/testing/test_translate.py
@@ -327,6 +327,12 @@ def get_communicator(comm, layout):
     return communicator
 
 
+def get_tile_communicator(comm, layout):
+    partitioner = pace.util.TilePartitioner(layout)
+    communicator =  pace.util.TileCommunicator(comm, partitioner)
+    return communicator
+
+
 @pytest.mark.parallel
 @pytest.mark.skipif(
     MPI is None or MPI.COMM_WORLD.Get_size() == 1,
@@ -343,11 +349,18 @@ def test_parallel_savepoint(
     compute_grid,
     xy_indices=True,
 ):
-    layout = (
-        int((MPI.COMM_WORLD.Get_size() // 6) ** 0.5),
-        int((MPI.COMM_WORLD.Get_size() // 6) ** 0.5),
-    )
-    communicator = get_communicator(MPI.COMM_WORLD, layout)
+    if MPI.COMM_WORLD.Get_size()%6 != 0:
+        layout = (
+            int(MPI.COMM_WORLD.Get_size() ** 0.5),
+            int(MPI.COMM_WORLD.Get_size() ** 0.5),
+        )
+        communicator = get_tile_communicator(MPI.COMM_WORLD, layout)
+    else:
+        layout = (
+            int((MPI.COMM_WORLD.Get_size() // 6) ** 0.5),
+            int((MPI.COMM_WORLD.Get_size() // 6) ** 0.5),
+        )
+        communicator = get_communicator(MPI.COMM_WORLD, layout)
     caplog.set_level(logging.DEBUG, logger="fv3core")
     if case.testobj is None:
         pytest.xfail(
diff --git a/stencils/pace/stencils/update_atmos_state.py b/stencils/pace/stencils/update_atmos_state.py
index cb97fabb..789e40ea 100644
--- a/stencils/pace/stencils/update_atmos_state.py
+++ b/stencils/pace/stencils/update_atmos_state.py
@@ -242,7 +242,7 @@ def __init__(
         stencil_factory: StencilFactory,
         grid_data: GridData,
         namelist,
-        comm: pace.util.CubedSphereCommunicator,
+        comm: pace.util.Communicator,
         grid_info: DriverGridData,
         state: fv3core.DycoreState,
         quantity_factory: pace.util.QuantityFactory,
diff --git a/stencils/pace/stencils/update_dwind_phys.py b/stencils/pace/stencils/update_dwind_phys.py
index 6be604a4..7fde5369 100644
--- a/stencils/pace/stencils/update_dwind_phys.py
+++ b/stencils/pace/stencils/update_dwind_phys.py
@@ -149,6 +149,19 @@ def update_vwind_stencil(
         v = v + dt5 * (ve_1 * ew2_1 + ve_2 * ew2_2 + ve_3 * ew2_3)
 
 
+def doubly_periodic_wind_update(
+    u: FloatField,
+    v: FloatField,
+    u_dt: FloatField,
+    v_dt: FloatField,
+):
+    from __externals__ import dt5
+
+    with computation(PARALLEL), interval(...):
+        u = u + dt5 * (u_dt[0, -1, 0] + u_dt)
+        v = v + dt5 * (v_dt[-1, 0, 0] + v_dt)
+
+
 class AGrid2DGridPhysics:
     """
     Fortran name is update_dwinds_phys
@@ -174,6 +187,7 @@ def __init__(
         self._jm2 = int((npy - 1) / 2) + 2
         self._subtile_index = partitioner.subtile_index(rank)
         layout = self.namelist.layout
+        self._grid_type = namelist.grid_type
 
         self._subtile_width_x = int((npx - 1) / layout[0])
         self._subtile_width_y = int((npy - 1) / layout[1])
@@ -190,233 +204,262 @@ def __init__(
         def make_quantity():
             return quantity_factory.zeros(dims=[X_DIM, Y_DIM, Z_DIM], units="unknown")
 
-        self._ue_1 = make_quantity()
-        self._ue_2 = make_quantity()
-        self._ue_3 = make_quantity()
-        self._ut_1 = make_quantity()
-        self._ut_2 = make_quantity()
-        self._ut_3 = make_quantity()
-        self._ve_1 = make_quantity()
-        self._ve_2 = make_quantity()
-        self._ve_3 = make_quantity()
-        self._vt_1 = make_quantity()
-        self._vt_2 = make_quantity()
-        self._vt_3 = make_quantity()
-
-        self._update_dwind_prep_stencil = stencil_factory.from_origin_domain(
-            update_dwind_prep_stencil,
-            origin=(grid_indexing.n_halo - 1, grid_indexing.n_halo - 1, 0),
-            domain=(nic + 2, njc + 2, npz),
-        )
-
-        self._set_winds_to_zero_stencil = stencil_factory.from_origin_domain(
-            set_winds_zero,
-            origin=(grid_indexing.n_halo - 1, grid_indexing.n_halo - 1, 0),
-            domain=(nic + 2, njc + 2, npz),
-        )
+        if self._grid_type <= 3:
+            self._ue_1 = make_quantity()
+            self._ue_2 = make_quantity()
+            self._ue_3 = make_quantity()
+            self._ut_1 = make_quantity()
+            self._ut_2 = make_quantity()
+            self._ut_3 = make_quantity()
+            self._ve_1 = make_quantity()
+            self._ve_2 = make_quantity()
+            self._ve_3 = make_quantity()
+            self._vt_1 = make_quantity()
+            self._vt_2 = make_quantity()
+            self._vt_3 = make_quantity()
+
+            self._update_dwind_prep_stencil = stencil_factory.from_origin_domain(
+                update_dwind_prep_stencil,
+                origin=(grid_indexing.n_halo - 1, grid_indexing.n_halo - 1, 0),
+                domain=(nic + 2, njc + 2, npz),
+            )
 
-        self.global_is, self.global_js = self.local_to_global_indices(
-            grid_indexing.isc, grid_indexing.jsc
-        )
-        self.global_ie, self.global_je = self.local_to_global_indices(
-            grid_indexing.iec, grid_indexing.jec
-        )
+            self._set_winds_to_zero_stencil = stencil_factory.from_origin_domain(
+                set_winds_zero,
+                origin=(grid_indexing.n_halo - 1, grid_indexing.n_halo - 1, 0),
+                domain=(nic + 2, njc + 2, npz),
+            )
 
-        if self.west_edge:
-            je_lower = self.global_to_local_y(min(self._jm2, self.global_je))
-            origin_lower = (grid_indexing.n_halo, grid_indexing.n_halo, 0)
-            self._domain_lower_west = (
-                1,
-                je_lower - grid_indexing.jsc + 1,
-                npz,
+            self.global_is, self.global_js = self.local_to_global_indices(
+                grid_indexing.isc, grid_indexing.jsc
             )
-            if self.global_js <= self._jm2:
-                if self._domain_lower_west[1] > 0:
-                    self._update_dwind_y_edge_south_stencil1 = (
-                        stencil_factory.from_origin_domain(
-                            update_dwind_y_edge_south_stencil,
-                            origin=origin_lower,
-                            domain=self._domain_lower_west,
-                        )
-                    )
-            if self.global_je > self._jm2:
-                js_upper = self.global_to_local_y(max(self._jm2 + 1, self.global_js))
-                origin_upper = (grid_indexing.n_halo, js_upper, 0)
-                self._domain_upper_west = (
+            self.global_ie, self.global_je = self.local_to_global_indices(
+                grid_indexing.iec, grid_indexing.jec
+            )
+
+            if self.west_edge:
+                je_lower = self.global_to_local_y(min(self._jm2, self.global_je))
+                origin_lower = (grid_indexing.n_halo, grid_indexing.n_halo, 0)
+                self._domain_lower_west = (
                     1,
-                    grid_indexing.jec - js_upper + 1,
+                    je_lower - grid_indexing.jsc + 1,
                     npz,
                 )
-                if self._domain_upper_west[1] > 0:
-                    self._update_dwind_y_edge_north_stencil1 = (
-                        stencil_factory.from_origin_domain(
-                            update_dwind_y_edge_north_stencil,
-                            origin=origin_upper,
-                            domain=self._domain_upper_west,
+                if self.global_js <= self._jm2:
+                    if self._domain_lower_west[1] > 0:
+                        self._update_dwind_y_edge_south_stencil1 = (
+                            stencil_factory.from_origin_domain(
+                                update_dwind_y_edge_south_stencil,
+                                origin=origin_lower,
+                                domain=self._domain_lower_west,
+                            )
                         )
+                if self.global_je > self._jm2:
+                    js_upper = self.global_to_local_y(
+                        max(self._jm2 + 1, self.global_js)
                     )
-                    self._copy3_stencil1 = stencil_factory.from_origin_domain(
-                        copy3_stencil,
-                        origin=origin_upper,
-                        domain=self._domain_upper_west,
+                    origin_upper = (grid_indexing.n_halo, js_upper, 0)
+                    self._domain_upper_west = (
+                        1,
+                        grid_indexing.jec - js_upper + 1,
+                        npz,
                     )
-            if self.global_js <= self._jm2 and self._domain_lower_west[1] > 0:
-                self._copy3_stencil2 = stencil_factory.from_origin_domain(
-                    copy3_stencil, origin=origin_lower, domain=self._domain_lower_west
-                )
-        if self.east_edge:
-            i_origin = shape[0] - grid_indexing.n_halo - 1
-            je_lower = self.global_to_local_y(min(self._jm2, self.global_je))
-            origin_lower = (i_origin, grid_indexing.n_halo, 0)
-            self._domain_lower_east = (
-                1,
-                je_lower - grid_indexing.jsc + 1,
-                npz,
-            )
-            if self.global_js <= self._jm2:
-                if self._domain_lower_east[1] > 0:
-                    self._update_dwind_y_edge_south_stencil2 = (
-                        stencil_factory.from_origin_domain(
-                            update_dwind_y_edge_south_stencil,
-                            origin=origin_lower,
-                            domain=self._domain_lower_east,
+                    if self._domain_upper_west[1] > 0:
+                        self._update_dwind_y_edge_north_stencil1 = (
+                            stencil_factory.from_origin_domain(
+                                update_dwind_y_edge_north_stencil,
+                                origin=origin_upper,
+                                domain=self._domain_upper_west,
+                            )
                         )
+                        self._copy3_stencil1 = stencil_factory.from_origin_domain(
+                            copy3_stencil,
+                            origin=origin_upper,
+                            domain=self._domain_upper_west,
+                        )
+                if self.global_js <= self._jm2 and self._domain_lower_west[1] > 0:
+                    self._copy3_stencil2 = stencil_factory.from_origin_domain(
+                        copy3_stencil,
+                        origin=origin_lower,
+                        domain=self._domain_lower_west,
                     )
-
-            if self.global_je > self._jm2:
-                js_upper = self.global_to_local_y(max(self._jm2 + 1, self.global_js))
-                origin_upper = (i_origin, js_upper, 0)
-                self._domain_upper_east = (
+            if self.east_edge:
+                i_origin = shape[0] - grid_indexing.n_halo - 1
+                je_lower = self.global_to_local_y(min(self._jm2, self.global_je))
+                origin_lower = (i_origin, grid_indexing.n_halo, 0)
+                self._domain_lower_east = (
                     1,
-                    grid_indexing.jec - js_upper + 1,
+                    je_lower - grid_indexing.jsc + 1,
                     npz,
                 )
-                if self._domain_upper_east[1] > 0:
-                    self._update_dwind_y_edge_north_stencil2 = (
-                        stencil_factory.from_origin_domain(
-                            update_dwind_y_edge_north_stencil,
+                if self.global_js <= self._jm2:
+                    if self._domain_lower_east[1] > 0:
+                        self._update_dwind_y_edge_south_stencil2 = (
+                            stencil_factory.from_origin_domain(
+                                update_dwind_y_edge_south_stencil,
+                                origin=origin_lower,
+                                domain=self._domain_lower_east,
+                            )
+                        )
+
+                if self.global_je > self._jm2:
+                    js_upper = self.global_to_local_y(
+                        max(self._jm2 + 1, self.global_js)
+                    )
+                    origin_upper = (i_origin, js_upper, 0)
+                    self._domain_upper_east = (
+                        1,
+                        grid_indexing.jec - js_upper + 1,
+                        npz,
+                    )
+                    if self._domain_upper_east[1] > 0:
+                        self._update_dwind_y_edge_north_stencil2 = (
+                            stencil_factory.from_origin_domain(
+                                update_dwind_y_edge_north_stencil,
+                                origin=origin_upper,
+                                domain=self._domain_upper_east,
+                            )
+                        )
+                        self._copy3_stencil3 = stencil_factory.from_origin_domain(
+                            copy3_stencil,
                             origin=origin_upper,
                             domain=self._domain_upper_east,
                         )
-                    )
-                    self._copy3_stencil3 = stencil_factory.from_origin_domain(
+                if self.global_js <= self._jm2 and self._domain_lower_east[1] > 0:
+                    self._copy3_stencil4 = stencil_factory.from_origin_domain(
                         copy3_stencil,
-                        origin=origin_upper,
-                        domain=self._domain_upper_east,
+                        origin=origin_lower,
+                        domain=self._domain_lower_east,
                     )
-            if self.global_js <= self._jm2 and self._domain_lower_east[1] > 0:
-                self._copy3_stencil4 = stencil_factory.from_origin_domain(
-                    copy3_stencil, origin=origin_lower, domain=self._domain_lower_east
+            if self.south_edge:
+                ie_lower = self.global_to_local_x(min(self._im2, self.global_ie))
+                origin_lower = (grid_indexing.n_halo, grid_indexing.n_halo, 0)
+                self._domain_lower_south = (
+                    ie_lower - grid_indexing.isc + 1,
+                    1,
+                    npz,
                 )
-        if self.south_edge:
-            ie_lower = self.global_to_local_x(min(self._im2, self.global_ie))
-            origin_lower = (grid_indexing.n_halo, grid_indexing.n_halo, 0)
-            self._domain_lower_south = (
-                ie_lower - grid_indexing.isc + 1,
-                1,
-                npz,
-            )
-            if self.global_is <= self._im2:
-                if self._domain_lower_south[0] > 0:
-                    self._update_dwind_x_edge_west_stencil1 = (
+                if self.global_is <= self._im2:
+                    if self._domain_lower_south[0] > 0:
+                        self._update_dwind_x_edge_west_stencil1 = (
+                            stencil_factory.from_origin_domain(
+                                update_dwind_x_edge_west_stencil,
+                                origin=origin_lower,
+                                domain=self._domain_lower_south,
+                            )
+                        )
+                if self.global_ie > self._im2:
+                    is_upper = self.global_to_local_x(
+                        max(self._im2 + 1, self.global_is)
+                    )
+                    origin_upper = (is_upper, grid_indexing.n_halo, 0)
+                    self._domain_upper_south = (
+                        grid_indexing.iec - is_upper + 1,
+                        1,
+                        npz,
+                    )
+                    self._update_dwind_x_edge_east_stencil1 = (
                         stencil_factory.from_origin_domain(
-                            update_dwind_x_edge_west_stencil,
-                            origin=origin_lower,
-                            domain=self._domain_lower_south,
+                            update_dwind_x_edge_east_stencil,
+                            origin=origin_upper,
+                            domain=self._domain_upper_south,
                         )
                     )
-            if self.global_ie > self._im2:
-                is_upper = self.global_to_local_x(max(self._im2 + 1, self.global_is))
-                origin_upper = (is_upper, grid_indexing.n_halo, 0)
-                self._domain_upper_south = (
-                    grid_indexing.iec - is_upper + 1,
-                    1,
-                    npz,
-                )
-                self._update_dwind_x_edge_east_stencil1 = (
-                    stencil_factory.from_origin_domain(
-                        update_dwind_x_edge_east_stencil,
+                    self._copy3_stencil5 = stencil_factory.from_origin_domain(
+                        copy3_stencil,
                         origin=origin_upper,
                         domain=self._domain_upper_south,
                     )
-                )
-                self._copy3_stencil5 = stencil_factory.from_origin_domain(
-                    copy3_stencil, origin=origin_upper, domain=self._domain_upper_south
-                )
-            if self.global_is <= self._im2 and self._domain_lower_south[0] > 0:
-                self._copy3_stencil6 = stencil_factory.from_origin_domain(
-                    copy3_stencil, origin=origin_lower, domain=self._domain_lower_south
-                )
-        if self.north_edge:
-            j_origin = shape[1] - grid_indexing.n_halo - 1
-            ie_lower = self.global_to_local_x(min(self._im2, self.global_ie))
-            origin_lower = (grid_indexing.n_halo, j_origin, 0)
-            self._domain_lower_north = (
-                ie_lower - grid_indexing.isc + 1,
-                1,
-                npz,
-            )
-            if self.global_is < self._im2:
-                if self._domain_lower_north[0] > 0:
-                    self._update_dwind_x_edge_west_stencil2 = (
-                        stencil_factory.from_origin_domain(
-                            update_dwind_x_edge_west_stencil,
-                            origin=origin_lower,
-                            domain=self._domain_lower_north,
-                        )
+                if self.global_is <= self._im2 and self._domain_lower_south[0] > 0:
+                    self._copy3_stencil6 = stencil_factory.from_origin_domain(
+                        copy3_stencil,
+                        origin=origin_lower,
+                        domain=self._domain_lower_south,
                     )
-            if self.global_ie >= self._im2:
-                is_upper = self.global_to_local_x(max(self._im2 + 1, self.global_is))
-                origin_upper = (is_upper, j_origin, 0)
-                self._domain_upper_north = (
-                    grid_indexing.iec - is_upper + 1,
+            if self.north_edge:
+                j_origin = shape[1] - grid_indexing.n_halo - 1
+                ie_lower = self.global_to_local_x(min(self._im2, self.global_ie))
+                origin_lower = (grid_indexing.n_halo, j_origin, 0)
+                self._domain_lower_north = (
+                    ie_lower - grid_indexing.isc + 1,
                     1,
                     npz,
                 )
-                if self._domain_upper_north[0] > 0:
-                    self._update_dwind_x_edge_east_stencil2 = (
-                        stencil_factory.from_origin_domain(
-                            update_dwind_x_edge_east_stencil,
+                if self.global_is < self._im2:
+                    if self._domain_lower_north[0] > 0:
+                        self._update_dwind_x_edge_west_stencil2 = (
+                            stencil_factory.from_origin_domain(
+                                update_dwind_x_edge_west_stencil,
+                                origin=origin_lower,
+                                domain=self._domain_lower_north,
+                            )
+                        )
+                if self.global_ie >= self._im2:
+                    is_upper = self.global_to_local_x(
+                        max(self._im2 + 1, self.global_is)
+                    )
+                    origin_upper = (is_upper, j_origin, 0)
+                    self._domain_upper_north = (
+                        grid_indexing.iec - is_upper + 1,
+                        1,
+                        npz,
+                    )
+                    if self._domain_upper_north[0] > 0:
+                        self._update_dwind_x_edge_east_stencil2 = (
+                            stencil_factory.from_origin_domain(
+                                update_dwind_x_edge_east_stencil,
+                                origin=origin_upper,
+                                domain=self._domain_upper_north,
+                            )
+                        )
+                        self._copy3_stencil7 = stencil_factory.from_origin_domain(
+                            copy3_stencil,
                             origin=origin_upper,
                             domain=self._domain_upper_north,
                         )
-                    )
-                    self._copy3_stencil7 = stencil_factory.from_origin_domain(
+                if self.global_is < self._im2 and self._domain_lower_north[0] > 0:
+                    self._copy3_stencil8 = stencil_factory.from_origin_domain(
                         copy3_stencil,
-                        origin=origin_upper,
-                        domain=self._domain_upper_north,
+                        origin=origin_lower,
+                        domain=self._domain_lower_north,
                     )
-            if self.global_is < self._im2 and self._domain_lower_north[0] > 0:
-                self._copy3_stencil8 = stencil_factory.from_origin_domain(
-                    copy3_stencil, origin=origin_lower, domain=self._domain_lower_north
-                )
-        self._update_uwind_stencil = stencil_factory.from_origin_domain(
-            update_uwind_stencil,
-            origin=(grid_indexing.n_halo, grid_indexing.n_halo, 0),
-            domain=(nic, njc + 1, npz),
-        )
-        self._update_vwind_stencil = stencil_factory.from_origin_domain(
-            update_vwind_stencil,
-            origin=(grid_indexing.n_halo, grid_indexing.n_halo, 0),
-            domain=(nic + 1, njc, npz),
-        )
-        # [TODO] The following is waiting on grid code vlat and vlon
-        self._vlon1 = grid_info.vlon1
-        self._vlon2 = grid_info.vlon2
-        self._vlon3 = grid_info.vlon3
-        self._vlat1 = grid_info.vlat1
-        self._vlat2 = grid_info.vlat2
-        self._vlat3 = grid_info.vlat3
-        self._edge_vect_w = grid_info.edge_vect_w
-        self._edge_vect_e = grid_info.edge_vect_e
-        self._edge_vect_s = grid_info.edge_vect_s
-        self._edge_vect_n = grid_info.edge_vect_n
-        self._es1_1 = grid_info.es1_1
-        self._es1_2 = grid_info.es1_2
-        self._es1_3 = grid_info.es1_3
-        self._ew2_1 = grid_info.ew2_1
-        self._ew2_2 = grid_info.ew2_2
-        self._ew2_3 = grid_info.ew2_3
+            self._update_uwind_stencil = stencil_factory.from_origin_domain(
+                update_uwind_stencil,
+                origin=(grid_indexing.n_halo, grid_indexing.n_halo, 0),
+                domain=(nic, njc + 1, npz),
+            )
+            self._update_vwind_stencil = stencil_factory.from_origin_domain(
+                update_vwind_stencil,
+                origin=(grid_indexing.n_halo, grid_indexing.n_halo, 0),
+                domain=(nic + 1, njc, npz),
+            )
+            # [TODO] The following is waiting on grid code vlat and vlon
+            self._vlon1 = grid_info.vlon1
+            self._vlon2 = grid_info.vlon2
+            self._vlon3 = grid_info.vlon3
+            self._vlat1 = grid_info.vlat1
+            self._vlat2 = grid_info.vlat2
+            self._vlat3 = grid_info.vlat3
+            self._edge_vect_w = grid_info.edge_vect_w
+            self._edge_vect_e = grid_info.edge_vect_e
+            self._edge_vect_s = grid_info.edge_vect_s
+            self._edge_vect_n = grid_info.edge_vect_n
+            self._es1_1 = grid_info.es1_1
+            self._es1_2 = grid_info.es1_2
+            self._es1_3 = grid_info.es1_3
+            self._ew2_1 = grid_info.ew2_1
+            self._ew2_2 = grid_info.ew2_2
+            self._ew2_3 = grid_info.ew2_3
+
+        else:  # grid_type > 3:
+            self._doubly_periodic_wind_update = stencil_factory.from_origin_domain(
+                doubly_periodic_wind_update,
+                externals={
+                    "dt5": self._dt5,
+                },
+                origin=grid_indexing.origin_compute(),
+                domain=grid_indexing.domain_compute(),
+            )
 
     def global_to_local_1d(self, global_value, subtile_index, subtile_length):
         return global_value - subtile_index * subtile_length
@@ -454,87 +497,97 @@ def __call__(
         Transforms the wind tendencies from A grid to D grid for the final update
         """
 
-        self._update_dwind_prep_stencil(
-            u_dt,
-            v_dt,
-            self._vlon1,
-            self._vlon2,
-            self._vlon3,
-            self._vlat1,
-            self._vlat2,
-            self._vlat3,
-            self._ue_1,
-            self._ue_2,
-            self._ue_3,
-            self._ve_1,
-            self._ve_2,
-            self._ve_3,
-        )
-        self._set_winds_to_zero_stencil(u_dt, v_dt)
-        if self.west_edge:
-            if self.global_js <= self._jm2:
-                if self._domain_lower_west[1] > 0:
-                    self._update_dwind_y_edge_south_stencil1(
-                        self._ve_1,
-                        self._ve_2,
-                        self._ve_3,
-                        self._vt_1,
-                        self._vt_2,
-                        self._vt_3,
-                        self._edge_vect_w,
-                    )
-            if self.global_je > self._jm2:
-                if self._domain_upper_west[1] > 0:
-                    self._update_dwind_y_edge_north_stencil1(
-                        self._ve_1,
-                        self._ve_2,
-                        self._ve_3,
-                        self._vt_1,
-                        self._vt_2,
-                        self._vt_3,
-                        self._edge_vect_w,
-                    )
-                    self._copy3_stencil1(
-                        self._vt_1,
-                        self._vt_2,
-                        self._vt_3,
-                        self._ve_1,
-                        self._ve_2,
-                        self._ve_3,
-                    )
-            if self.global_js <= self._jm2 and self._domain_lower_west[1] > 0:
-                self._copy3_stencil2(
-                    self._vt_1,
-                    self._vt_2,
-                    self._vt_3,
-                    self._ve_1,
-                    self._ve_2,
-                    self._ve_3,
-                )
-        if self.east_edge:
-            if self.global_js <= self._jm2:
-                if self._domain_lower_east[1] > 0:
-                    self._update_dwind_y_edge_south_stencil2(
-                        self._ve_1,
-                        self._ve_2,
-                        self._ve_3,
+        if self._grid_type <= 3:
+            self._update_dwind_prep_stencil(
+                u_dt,
+                v_dt,
+                self._vlon1,
+                self._vlon2,
+                self._vlon3,
+                self._vlat1,
+                self._vlat2,
+                self._vlat3,
+                self._ue_1,
+                self._ue_2,
+                self._ue_3,
+                self._ve_1,
+                self._ve_2,
+                self._ve_3,
+            )
+            self._set_winds_to_zero_stencil(u_dt, v_dt)
+            if self.west_edge:
+                if self.global_js <= self._jm2:
+                    if self._domain_lower_west[1] > 0:
+                        self._update_dwind_y_edge_south_stencil1(
+                            self._ve_1,
+                            self._ve_2,
+                            self._ve_3,
+                            self._vt_1,
+                            self._vt_2,
+                            self._vt_3,
+                            self._edge_vect_w,
+                        )
+                if self.global_je > self._jm2:
+                    if self._domain_upper_west[1] > 0:
+                        self._update_dwind_y_edge_north_stencil1(
+                            self._ve_1,
+                            self._ve_2,
+                            self._ve_3,
+                            self._vt_1,
+                            self._vt_2,
+                            self._vt_3,
+                            self._edge_vect_w,
+                        )
+                        self._copy3_stencil1(
+                            self._vt_1,
+                            self._vt_2,
+                            self._vt_3,
+                            self._ve_1,
+                            self._ve_2,
+                            self._ve_3,
+                        )
+                if self.global_js <= self._jm2 and self._domain_lower_west[1] > 0:
+                    self._copy3_stencil2(
                         self._vt_1,
                         self._vt_2,
                         self._vt_3,
-                        self._edge_vect_e,
-                    )
-            if self.global_je > self._jm2:
-                if self._domain_upper_east[1] > 0:
-                    self._update_dwind_y_edge_north_stencil2(
                         self._ve_1,
                         self._ve_2,
                         self._ve_3,
-                        self._vt_1,
-                        self._vt_2,
-                        self._vt_3,
-                        self._edge_vect_e,
                     )
-                    self._copy3_stencil3(
+            if self.east_edge:
+                if self.global_js <= self._jm2:
+                    if self._domain_lower_east[1] > 0:
+                        self._update_dwind_y_edge_south_stencil2(
+                            self._ve_1,
+                            self._ve_2,
+                            self._ve_3,
+                            self._vt_1,
+                            self._vt_2,
+                            self._vt_3,
+                            self._edge_vect_e,
+                        )
+                if self.global_je > self._jm2:
+                    if self._domain_upper_east[1] > 0:
+                        self._update_dwind_y_edge_north_stencil2(
+                            self._ve_1,
+                            self._ve_2,
+                            self._ve_3,
+                            self._vt_1,
+                            self._vt_2,
+                            self._vt_3,
+                            self._edge_vect_e,
+                        )
+                        self._copy3_stencil3(
+                            self._vt_1,
+                            self._vt_2,
+                            self._vt_3,
+                            self._ve_1,
+                            self._ve_2,
+                            self._ve_3,
+                        )
+                if self.global_js <= self._jm2 and self._domain_lower_east[1] > 0:
+                    self._copy3_stencil4(
                         self._vt_1,
                         self._vt_2,
                         self._vt_3,
@@ -542,79 +595,79 @@ def __call__(
                         self._ve_2,
                         self._ve_3,
                     )
-            if self.global_js <= self._jm2 and self._domain_lower_east[1] > 0:
-                self._copy3_stencil4(
-                    self._vt_1,
-                    self._vt_2,
-                    self._vt_3,
-                    self._ve_1,
-                    self._ve_2,
-                    self._ve_3,
-                )
-        if self.south_edge:
-            if self.global_is <= self._im2:
-                if self._domain_lower_south[0] > 0:
-                    self._update_dwind_x_edge_west_stencil1(
-                        self._ue_1,
-                        self._ue_2,
-                        self._ue_3,
-                        self._ut_1,
-                        self._ut_2,
-                        self._ut_3,
-                        self._edge_vect_s,
-                    )
-            if self.global_ie > self._im2:
-                if self._domain_upper_south:
-                    self._update_dwind_x_edge_east_stencil1(
-                        self._ue_1,
-                        self._ue_2,
-                        self._ue_3,
-                        self._ut_1,
-                        self._ut_2,
-                        self._ut_3,
-                        self._edge_vect_s,
-                    )
-                    self._copy3_stencil5(
-                        self._ut_1,
-                        self._ut_2,
-                        self._ut_3,
-                        self._ue_1,
-                        self._ue_2,
-                        self._ue_3,
-                    )
-            if self.global_is <= self._im2 and self._domain_lower_south[0] > 0:
-                self._copy3_stencil6(
-                    self._ut_1,
-                    self._ut_2,
-                    self._ut_3,
-                    self._ue_1,
-                    self._ue_2,
-                    self._ue_3,
-                )
-        if self.north_edge:
-            if self.global_is < self._im2:
-                if self._domain_lower_north[0] > 0:
-                    self._update_dwind_x_edge_west_stencil2(
-                        self._ue_1,
-                        self._ue_2,
-                        self._ue_3,
+            if self.south_edge:
+                if self.global_is <= self._im2:
+                    if self._domain_lower_south[0] > 0:
+                        self._update_dwind_x_edge_west_stencil1(
+                            self._ue_1,
+                            self._ue_2,
+                            self._ue_3,
+                            self._ut_1,
+                            self._ut_2,
+                            self._ut_3,
+                            self._edge_vect_s,
+                        )
+                if self.global_ie > self._im2:
+                    if self._domain_upper_south:
+                        self._update_dwind_x_edge_east_stencil1(
+                            self._ue_1,
+                            self._ue_2,
+                            self._ue_3,
+                            self._ut_1,
+                            self._ut_2,
+                            self._ut_3,
+                            self._edge_vect_s,
+                        )
+                        self._copy3_stencil5(
+                            self._ut_1,
+                            self._ut_2,
+                            self._ut_3,
+                            self._ue_1,
+                            self._ue_2,
+                            self._ue_3,
+                        )
+                if self.global_is <= self._im2 and self._domain_lower_south[0] > 0:
+                    self._copy3_stencil6(
                         self._ut_1,
                         self._ut_2,
                         self._ut_3,
-                        self._edge_vect_n,
-                    )
-            if self.global_ie >= self._im2:
-                if self._domain_upper_north[0] > 0:
-                    self._update_dwind_x_edge_east_stencil2(
                         self._ue_1,
                         self._ue_2,
                         self._ue_3,
-                        self._ut_1,
-                        self._ut_2,
-                        self._ut_3,
-                        self._edge_vect_n,
                     )
-                    self._copy3_stencil7(
+            if self.north_edge:
+                if self.global_is < self._im2:
+                    if self._domain_lower_north[0] > 0:
+                        self._update_dwind_x_edge_west_stencil2(
+                            self._ue_1,
+                            self._ue_2,
+                            self._ue_3,
+                            self._ut_1,
+                            self._ut_2,
+                            self._ut_3,
+                            self._edge_vect_n,
+                        )
+                if self.global_ie >= self._im2:
+                    if self._domain_upper_north[0] > 0:
+                        self._update_dwind_x_edge_east_stencil2(
+                            self._ue_1,
+                            self._ue_2,
+                            self._ue_3,
+                            self._ut_1,
+                            self._ut_2,
+                            self._ut_3,
+                            self._edge_vect_n,
+                        )
+                        self._copy3_stencil7(
+                            self._ut_1,
+                            self._ut_2,
+                            self._ut_3,
+                            self._ue_1,
+                            self._ue_2,
+                            self._ue_3,
+                        )
+                if self.global_is < self._im2 and self._domain_lower_north[0] > 0:
+                    self._copy3_stencil8(
                         self._ut_1,
                         self._ut_2,
                         self._ut_3,
@@ -622,32 +675,26 @@ def __call__(
                         self._ue_2,
                         self._ue_3,
                     )
-            if self.global_is < self._im2 and self._domain_lower_north[0] > 0:
-                self._copy3_stencil8(
-                    self._ut_1,
-                    self._ut_2,
-                    self._ut_3,
-                    self._ue_1,
-                    self._ue_2,
-                    self._ue_3,
-                )
-        self._update_uwind_stencil(
-            u,
-            self._es1_1,
-            self._es1_2,
-            self._es1_3,
-            self._ue_1,
-            self._ue_2,
-            self._ue_3,
-            self._dt5,
-        )
-        self._update_vwind_stencil(
-            v,
-            self._ew2_1,
-            self._ew2_2,
-            self._ew2_3,
-            self._ve_1,
-            self._ve_2,
-            self._ve_3,
-            self._dt5,
-        )
+            self._update_uwind_stencil(
+                u,
+                self._es1_1,
+                self._es1_2,
+                self._es1_3,
+                self._ue_1,
+                self._ue_2,
+                self._ue_3,
+                self._dt5,
+            )
+            self._update_vwind_stencil(
+                v,
+                self._ew2_1,
+                self._ew2_2,
+                self._ew2_3,
+                self._ve_1,
+                self._ve_2,
+                self._ve_3,
+                self._dt5,
+            )
+
+        else:  # grid type > 3:
+            self._doubly_periodic_wind_update(u, v, u_dt, v_dt)
diff --git a/tests/savepoint/conftest.py b/tests/savepoint/conftest.py
index 65ef4696..825ddda5 100644
--- a/tests/savepoint/conftest.py
+++ b/tests/savepoint/conftest.py
@@ -31,6 +31,11 @@ def calibrate_thresholds(pytestconfig):
     calibrate_thresholds = pytestconfig.getoption("calibrate_thresholds")
     return calibrate_thresholds
 
+@pytest.fixture()
+def dperiodic(pytestconfig):
+    dperiodic = pytestconfig.getoption("dperiodic")
+    return dperiodic
+
 
 def pytest_addoption(parser):
     parser.addoption(
@@ -51,3 +56,9 @@ def pytest_addoption(parser):
         default=False,
         help="re-calibrate error thresholds for comparison to reference",
     )
+    parser.addoption(
+        "--dperiodic",
+        action="store_true",
+        default=False,
+        help="configure tests for doubly-periodic domain",
+    )
diff --git a/tests/savepoint/test_checkpoints.py b/tests/savepoint/test_checkpoints.py
index dacc6b5c..4d1c8db6 100644
--- a/tests/savepoint/test_checkpoints.py
+++ b/tests/savepoint/test_checkpoints.py
@@ -81,7 +81,7 @@ def test_fv_dynamics(
                 extra_dim_lengths={},
                 layout=namelist.layout,
             ),
-            cube=communicator,
+            comm=communicator,
         ),
     )
     grid = get_grid(
diff --git a/util/pace/util/__init__.py b/util/pace/util/__init__.py
index 4911f2cf..ac68338d 100644
--- a/util/pace/util/__init__.py
+++ b/util/pace/util/__init__.py
@@ -62,6 +62,7 @@
 from .null_comm import NullComm
 from .partitioner import (
     CubedSpherePartitioner,
+    Partitioner,
     TilePartitioner,
     get_tile_index,
     get_tile_number,
diff --git a/util/pace/util/_legacy_restart.py b/util/pace/util/_legacy_restart.py
index d841f591..e43b7f8d 100644
--- a/util/pace/util/_legacy_restart.py
+++ b/util/pace/util/_legacy_restart.py
@@ -5,7 +5,7 @@
 from . import _xarray as xr
 from . import constants, filesystem, io
 from ._properties import RESTART_PROPERTIES, RestartProperties
-from .communicator import CubedSphereCommunicator
+from .communicator import Communicator
 from .partitioner import get_tile_index
 from .quantity import Quantity
 
@@ -19,7 +19,7 @@
 
 def open_restart(
     dirname: str,
-    communicator: CubedSphereCommunicator,
+    communicator: Communicator,
     label: str = "",
     only_names: Iterable[str] = None,
     to_state: dict = None,
@@ -29,7 +29,7 @@ def open_restart(
 
     Args:
         dirname: location of restart files, can be local or remote
-        communicator: object for communication over the cubed sphere
+        communicator: object for communication over the cubed sphere or tile
         label: prepended string on the restart files to load
         only_names (optional): list of standard names to load
         to_state (optional): if given, assign loaded data into pre-allocated quantities
diff --git a/util/pace/util/communicator.py b/util/pace/util/communicator.py
index 0611fa98..abcd9697 100644
--- a/util/pace/util/communicator.py
+++ b/util/pace/util/communicator.py
@@ -76,11 +76,27 @@ def __init__(
     def tile(self) -> "TileCommunicator":
         pass
 
+    @classmethod
+    @abc.abstractmethod
+    def from_layout(
+        cls,
+        comm,
+        layout: Tuple[int, int],
+        force_cpu: bool = False,
+        timer: Optional[Timer] = None,
+    ):
+        pass
+
     @property
     def rank(self) -> int:
         """rank of the current process within this communicator"""
         return self.comm.Get_rank()
 
+    @property
+    def size(self) -> int:
+        """Total number of ranks in this communicator"""
+        return self.comm.Get_size()
+
     def _maybe_force_cpu(self, module: NumpyModule) -> NumpyModule:
         """
         Get a numpy-like module depending on configuration and
@@ -595,6 +611,17 @@ def __init__(
         )
         self.partitioner: TilePartitioner = partitioner
 
+    @classmethod
+    def from_layout(
+        cls,
+        comm,
+        layout: Tuple[int, int],
+        force_cpu: bool = False,
+        timer: Optional[Timer] = None,
+    ) -> "TileCommunicator":
+        partitioner = TilePartitioner(layout=layout)
+        return cls(comm=comm, partitioner=partitioner, force_cpu=force_cpu, timer=timer)
+
     @property
     def tile(self):
         return self
diff --git a/util/pace/util/grid/generation.py b/util/pace/util/grid/generation.py
index b78a7059..e3b8e749 100644
--- a/util/pace/util/grid/generation.py
+++ b/util/pace/util/grid/generation.py
@@ -220,7 +220,7 @@ def __init__(
         self,
         *,
         quantity_factory: util.QuantityFactory,
-        communicator: util.CubedSphereCommunicator,
+        communicator: util.Communicator,
         grid_type: int = 0,
         dx_const: float = 1000.0,
         dy_const: float = 1000.0,
diff --git a/util/pace/util/partitioner.py b/util/pace/util/partitioner.py
index 4ba46325..0e59ddfa 100644
--- a/util/pace/util/partitioner.py
+++ b/util/pace/util/partitioner.py
@@ -54,6 +54,11 @@ def get_tile_number(tile_rank: int, total_ranks: int) -> int:
 
 
 class Partitioner(abc.ABC):
+    @abc.abstractmethod
+    def __init__(self):
+        self.tile = None
+        self.layout = None
+
     @abc.abstractmethod
     def boundary(self, boundary_type: int, rank: int) -> Optional[bd.SimpleBoundary]:
         ...
@@ -119,7 +124,8 @@ def subtile_extent(
         """
         pass
 
-    @abc.abstractproperty
+    @property
+    @abc.abstractmethod
     def total_ranks(self) -> int:
         pass
 
@@ -133,6 +139,7 @@ def __init__(
         """Create an object for fv3gfs tile decomposition."""
         self.layout = layout
         self.edge_interior_ratio = edge_interior_ratio
+        self.tile = self
 
     def tile_index(self, rank: int):
         return 0