Update 19

ACCESS-NRI · Jan 16, 2025 · d05a3e6 · d05a3e6
1 parent 1c8f01d
commit d05a3e6
Showing 1 changed file with 67 additions and 31 deletions.
diff --git a/19-JJA_ts_teleconnection.ipynb b/19-JJA_ts_teleconnection.ipynb
@@ -6,14 +6,47 @@
    "id": "8e444b8f-e137-4087-8abd-48ef10a4adfb",
    "metadata": {},
    "source": [
-    "# JJA_ts_teleconnection: \n",
+    "# JJA_ts_teleconnection: Structure of Boreal Summer Surface Temperature Anomalies\n",
     "\n",
-    "### Recommended observational datasets\t\t\t\t\n",
-    "variable ts:\tERA5, \t20CRv3, \tNCEP2, \tERA-Interim\n",
-    "#### Reference observations:\n",
-    "ERA-Interim 1979-2018 (main)\n",
+    "The \"JJA_ts_teleconnection\" notebook evaluates the relationship between ENSO (El Niño-Southern Oscillation) and boreal summer surface temperature anomalies, focusing on the region between 60°S and 60°N. It analyses the spatial and temporal structure of surface temperature patterns during the JJA period, providing insights into teleconnections and their potential impacts on global climate variability. \n",
+    " \n",
+    "## Recommended Observational Datasets \n",
     "\n",
-    "TS: 20CRv2 1871-2012, NCEP2 1979-2018\n"
+    "### Surface Temperature\n",
+    "\n",
+    "- **ERA5 (1950-present)**: ERA5 is the fifth generation of global reanalysis data produced by ECMWF, providing high resolution hourly estimates of atmospheric, oceanic, and land-surface variables.\n",
+    "- **20CRv3 (1871-2019)**: The 20th Century Reanalysis provides global atmospheric data based on surface pressure observations and ensemble assimilation techniques, reconstructing weather patterns over the past 140 years.\n",
+    "- **NCEP2 (1979-2018)**: The second version of the NCEP reanalysis provides a range of atmospheric variables, including surface and upper-air data, and corrects issues in the original dataset, making it suitable for long-term climate and weather assessments.\n",
+    "- **ERA-Interim (1979-2018)**: Reanalysis data produced by the European Centre for Medium-Range Weather Forecasts (ECMWF), offering high-resolution atmospheric and surface variables using a global assimilation model."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6c32eb68-48ae-4d1d-848f-c19205616a55",
+   "metadata": {},
+   "source": [
+    "## Dask Cluster  \n",
+    "Start a cluster with multiple cores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "081ec469-454e-4fd8-a215-0e70db805baa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from dask.distributed import Client, progress\n",
+    "client = Client()\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d9a08914-8c91-4a86-b8cf-63daad8df37a",
+   "metadata": {},
+   "source": [
+    "## Datasets"
    ]
   },
   {
@@ -36,7 +69,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_ts_datasets = { \n",
+    "model_ts_datasets = {\n",
     "\"ACCESS-ESM1-5\": \n",
     "    Dataset(\n",
     "    short_name='ts',\n",
@@ -49,37 +82,40 @@
     "    grid=\"gn\"\n",
     ")}\n",
     "\n",
-    "#use ts and tos?\n",
-    "model_tos = {\"ACCESS-ESM1-5\": \n",
-    "    model_ts_datasets[\"ACCESS-ESM1-5\"].copy(short_name='tos',mip=\"Omon\")}\n",
-    "\n",
-    "model_ts_datasets[\"ACCESS-ESM1-5\"].add_supplementary(short_name='areacella', mip='fx')\n",
-    "\n",
-    "obs_tos_datasets = {\n",
-    "\"NCEP-DOE-R2\": #nan data\n",
+    "obs_ts_datasets = {\n",
+    "\"HadISST\": \n",
     "    Dataset(\n",
-    "    short_name='tos',\n",
-    "    dataset='NCEP-DOE-R2',\n",
+    "    short_name='ts',\n",
+    "    dataset='HadISST',\n",
     "    mip=\"Amon\",\n",
-    "    project='OBS6',\n",
+    "    project='OBS',\n",
     "    type='reanaly',\n",
     "    tier=2),\n",
-    "# \"HadISST\": \n",
+    "# \"NOAA-CIRES-20CR-V2\": \n",
+    "#     Dataset(\n",
+    "#     short_name='ts',\n",
+    "#     dataset='NOAA-CIRES-20CR-V2',\n",
+    "#     mip=\"Amon\",\n",
+    "#     project='OBS6',\n",
+    "#     type='reanaly',\n",
+    "#     timerange=\"187101/201212\",\n",
+    "#     tier=2),\n",
+    "# \"NCEP-DOE-R2\": #nan data\n",
     "#     Dataset(\n",
     "#     short_name='tos',\n",
-    "#     dataset='HadISST',\n",
-    "#     mip=\"Omon\",\n",
-    "#     project='OBS',\n",
+    "#     dataset='NCEP-DOE-R2',\n",
+    "#     mip=\"Amon\",\n",
+    "#     project='OBS6',\n",
     "#     type='reanaly',\n",
     "#     tier=2),\n",
-    "\"ERA-Interim\":\n",
-    "    Dataset(\n",
-    "    short_name='ts',\n",
-    "    dataset='ERA-Interim',\n",
-    "    mip=\"Amon\",\n",
-    "    project='OBS6',\n",
-    "    type='reanaly',\n",
-    "    tier=3)\n",
+    "# \"ERA-Interim\":\n",
+    "#     Dataset(\n",
+    "#     short_name='ts',\n",
+    "#     dataset='ERA-Interim',\n",
+    "#     mip=\"Amon\",\n",
+    "#     project='OBS6',\n",
+    "#     type='reanaly',\n",
+    "#     tier=3)\n",
     "}"
    ]
   },
@@ -453,7 +489,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.9"
+   "version": "3.11.11"
   }
  },
  "nbformat": 4,