implementing trickleback changes and updating to data release 1.1

DATA_RELEASE_NOTES.md

@@ -0,0 +1,56 @@
+# Release notes for the *IAMC 1.5°C Scenario Explorer and Data hosted by IIASA*
+
+This document keeps track of the data changes of the scenario ensemble
+compiled in the *IAMC 1.5°C Scenario Explorer and Data hosted by IIASA*.
+
+
+## Release 1.1
+
+This release includes additional timeseries data to increase reproducibility
+of the figures and tables in the IPCC SR15, and it corrects a number of
+data issues identified since Release 1.0. None of the changes have
+any impact on the assessment in the IPCC SR15.
+
+### Detailed list of changes
+
+ - Reporting error for primary energy from fossils (aggregate) submitted by
+   the 'MESSAGE-GLOBIOM 1.0' scenarios as part of the 'EMF33' project.
+   See [#1](https://github.com/iiasa/ipcc_sr15_scenario_analysis/issues/1)
+   for details.
+
+ - Typo in a variable name and incorrect values in two timeseries of 
+   the reference data from 'IEA Statistics' included in the release.
+   See [#2](https://github.com/iiasa/ipcc_sr15_scenario_analysis/issues/2)
+   for details.
+
+ - Missing subsectoral CO2 emissions for scenarios from the SSP, CD-LINKS
+   and LED projects.
+   See [#3](https://github.com/iiasa/ipcc_sr15_scenario_analysis/issues/3)
+   and [#4](https://github.com/iiasa/ipcc_sr15_scenario_analysis/issues/4)
+   for details.
+
+ - Missing timeseries of harmonized emissions as used for climate impact
+   assessment by the MAGICC and FAIR models.
+   See [#5](https://github.com/iiasa/ipcc_sr15_scenario_analysis/issues/5)
+   for details.
+
+ - Error in index year for the variable 'Price|Agriculture|*' in scenarios
+   submitted by the 'AIM' model.
+   See [#7](https://github.com/iiasa/ipcc_sr15_scenario_analysis/issues/7)
+   for details.
+
+ - Missing value for cumulative BECCS in the metadata for
+   the 'LowEnergyDemand' scenario submitted by the 'MESSAGEix-GLOBIOM' model.
+   See [#9](https://github.com/iiasa/ipcc_sr15_scenario_analysis/issues/9)
+   for details.
+
+ - Incorrect regional data (other than 'World') for scenario submitted
+   by the 'POLES' model as part ofthe 'EMF33' project.
+   See [#12](https://github.com/iiasa/ipcc_sr15_scenario_analysis/issues/12)
+   for details.
+
+
+## Release 1.0
+
+Scenario ensemble release for the soft launch of the IPCC SR15
+following the approval plenary in Incheon, Republic of Korea.

NOTICE

@@ -2,7 +2,7 @@
 
 ## License
 
-Copyright 2018 IIASA
+Copyright 2018-2019 IIASA
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.

README.md

@@ -2,7 +2,7 @@
 
 ## License
 
-Copyright 2018 IIASA
+Copyright 2018-2019 IIASA
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
@@ -46,7 +46,7 @@ publication of the final, copy-edited version of the SR1.5
 The scenario ensemble used for this assessment is available for download
 at [data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer).
 
-The scenario data is licensed under a derivative of the Creative Commons CC-BY 4.0 License.
+The scenario data is released under a custom license.
 If appropriate reference is made to the data source, it is permitted to use
 the data for scientific research and science communication.
 However, redistribution of substantial portions of the data is restricted.

assessment/spm_sr15_figure_3a_global_emissions_pathways.ipynb

@@ -16,7 +16,12 @@
     "This notebook extracts the emissions pathways for Figure 3a in the Summary for Policymakers\n",
     "of the IPCC's _\"Special Report on Global Warming of 1.5°C\"_.\n",
     "\n",
-    "The scenario data used in this analysis can be accessed and downloaded at [https://data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer)."
+    "The scenario data used in this analysis can be accessed and downloaded at [https://data.ene.iiasa.ac.at/iamc-1.5c-explorer](https://data.ene.iiasa.ac.at/iamc-1.5c-explorer).\n",
+    "\n",
+    "### *Disclaimer*\n",
+    "\n",
+    "*The figures shown in this notebook are NOT the same figures as used in Figure 3a of the SPM.\n",
+    "They are simplified figures included here only for reference.*"
    ]
   },
   {
@@ -63,7 +68,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.xlsx')"
+    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.1.xlsx')"
    ]
   },
   {
@@ -84,8 +89,12 @@
     "with open(\"sr15_specs.yaml\", 'r') as stream:\n",
     "    specs = yaml.load(stream)\n",
     "\n",
+    "rc = pyam.run_control()\n",
+    "for item in specs.pop('run_control').items():\n",
+    "    rc.update({item[0]: item[1]})\n",
     "cats = specs.pop('cats')\n",
     "cats_15 = specs.pop('cats_15')\n",
+    "cats_15_no_lo = specs.pop('cats_15_no_lo')\n",
     "marker= specs.pop('marker')"
    ]
   },
@@ -116,6 +125,16 @@
     "df = sr1p5.filter(category=cats_15, kyoto_ghg_2010='in range')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.set_meta(meta='1.5C limited overshoot', name='supercategory', index=df.filter(category=cats_15_no_lo))\n",
+    "rc.update({'color': {'supercategory': {'1.5C limited overshoot': 'xkcd:bluish'}}})"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -145,7 +164,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Plot different emissions pathways by category"
+    "## Plot different emissions pathways by category\n",
+    "\n",
+    "### Net carbon dioxide emissions for all pathways limiting global warming to 1.5°C by the end of the century"
    ]
   },
   {
@@ -154,7 +175,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "writer = pd.ExcelWriter('output/spm_sr15_figure3a_data_table.xlsx')"
+    "co2 = (\n",
+    "    df.filter(variable='Emissions|CO2')\n",
+    "    .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
+    ")\n",
+    "data.append(('Net carbon dioxide', co2))"
    ]
   },
   {
@@ -163,14 +188,51 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "co2 = (\n",
-    "    df.filter(variable='Emissions|CO2')\n",
-    "    .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
-    "    .timeseries()\n",
-    ")\n",
+    "_co2 = co2.filter(category=cats_15, year=range(2010, 2101, 5))\n",
     "\n",
-    "pyam.utils.write_sheet(writer, 'Net CO2',\n",
-    "                       pyam.filter_by_meta(co2, **filter_args), index=True)"
+    "fig, ax = plt.subplots()\n",
+    "_co2.filter(year=[2010]).line_plot(ax=ax, color='category', linewidth=2)\n",
+    "_co2.line_plot(ax=ax, color='category', linewidth=0.1, fill_between=True, final_ranges=True)\n",
+    "_co2.filter(marker=marker).line_plot(ax=ax, color='category')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Emissions of methane, black carbon and nitrous oxide for 1.5°C pathways with limited overshoot\n",
+    "\n",
+    "The figures below are shown as reduction relative to 2010."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_relative(data, baseyear=2010):\n",
+    "    _data = data.timeseries()\n",
+    "    _data_rel = pd.DataFrame()\n",
+    "    for y in range(2010, 2101, 5):\n",
+    "        _data_rel[y] = _data[y] / _data[2010]\n",
+    "\n",
+    "    _data_rel.reset_index(inplace=True)\n",
+    "    _data_rel['unit'] = 'relative to {}'.format(baseyear)\n",
+    "    _df = pyam.IamDataFrame(_data_rel)\n",
+    "    _df.set_meta(meta='1.5C limited overshoot', name='supercategory')\n",
+    "    _df.filter(supercategory='1.5C limited overshoot', year=range(2010, 2101, 5))\\\n",
+    "        .line_plot(color='supercategory', linewidth=0.1, fill_between=True, legend=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ch4 = df.filter(variable='Emissions|CH4')\n",
+    "data.append(('Methane', ch4))"
    ]
   },
   {
@@ -179,9 +241,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "ch4 = df.filter(variable='Emissions|CH4').timeseries()\n",
-    "pyam.utils.write_sheet(writer, 'CH4',\n",
-    "                       pyam.filter_by_meta(ch4, **filter_args), index=True)"
+    "plot_relative(ch4)"
    ]
   },
   {
@@ -191,8 +251,7 @@
    "outputs": [],
    "source": [
     "bc = df.filter(variable='Emissions|BC')\n",
-    "pyam.utils.write_sheet(writer, 'Black carbon',\n",
-    "                       pyam.filter_by_meta(bc.timeseries(), **filter_args), index=True)"
+    "data.append(('Black carbon', bc))"
    ]
   },
   {
@@ -201,9 +260,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "n2o = df.filter(variable='Emissions|N2O').timeseries()\n",
-    "pyam.utils.write_sheet(writer, 'N2O',\n",
-    "                       pyam.filter_by_meta(n2o, **filter_args), index=True)"
+    "plot_relative(bc)"
    ]
   },
   {
@@ -212,9 +269,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fgases = df.filter(variable='Emissions|F-Gases').timeseries()\n",
-    "pyam.utils.write_sheet(writer, 'F-Gases',\n",
-    "                       pyam.filter_by_meta(fgases, **filter_args), index=True)"
+    "n2o = df.filter(variable='Emissions|N2O')\n",
+    "n2o.convert_unit({'kt N2O/yr': ('Mt N2O/yr', 0.001)}, inplace=True)\n",
+    "data.append(('Nitrous oxide', n2o))"
    ]
   },
   {
@@ -223,9 +280,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "so2 = df.filter(variable='Emissions|Sulfur').timeseries()\n",
-    "pyam.utils.write_sheet(writer, 'Sulfur',\n",
-    "                       pyam.filter_by_meta(so2, **filter_args), index=True)"
+    "plot_relative(n2o)"
    ]
   },
   {
@@ -241,6 +296,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "writer = pd.ExcelWriter('output/spm_sr15_figure3a_data_table.xlsx')\n",
+    "for (name, _df) in data:\n",
+    "    pyam.utils.write_sheet(writer, name,\n",
+    "                           pyam.filter_by_meta(_df.timeseries(), **filter_args), index=True)\n",
     "writer.save()"
    ]
   },

assessment/spm_sr15_figure_3b_illustrative_pathways.ipynb

@@ -62,7 +62,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.xlsx')"
+    "sr1p5 = pyam.IamDataFrame(data='../data/iamc15_scenario_data_world_r1.1.xlsx')"
    ]
   },
   {
@@ -160,7 +160,7 @@
    "outputs": [],
    "source": [
     "co2 = (\n",
-    "    df.filter(variable='Emissions|CO2', year=years)\n",
+    "    df.filter(kyoto_ghg_2010='in range', variable='Emissions|CO2', year=years)\n",
     "    .convert_unit({'Mt CO2/yr': ('Gt CO2/yr', 0.001)})\n",
     "    .timeseries()\n",
     ")"
@@ -185,7 +185,7 @@
    "outputs": [],
    "source": [
     "kyoto_ghg = (\n",
-    "    df.filter(variable='Emissions|Kyoto Gases (SAR-GWP100)', year=years)\n",
+    "    df.filter(kyoto_ghg_2010='in range', variable='Emissions|Kyoto Gases (SAR-GWP100)', year=years)\n",
     "    .convert_unit({'Mt CO2-equiv/yr': ('Gt CO2-equiv/yr', 0.001)})\n",
     "    .timeseries()\n",
     ")\n",
@@ -356,7 +356,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Land cover for energy crops"
+    "### Land cover for energy crops\n",
+    "\n",
+    "Convert unit to SI unit (million square kilometers)."
    ]
   },
   {
@@ -365,7 +367,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "energy_crops = df.filter(variable='Land Cover|Cropland|Energy Crops', year=2050).timeseries()"
+    "energy_crops = (\n",
+    "    df.filter(variable='Land Cover|Cropland|Energy Crops', year=2050)\n",
+    "    .convert_unit({'million ha': ('million km2', 0.01)})\n",
+    "    .timeseries()\n",
+    ")"
    ]
   },
   {
@@ -374,7 +380,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "stats.add(energy_crops[2050], header='Land are for energy crops (MHa)')"
+    "stats.add(energy_crops[2050], header='Land are for energy crops (million km2)')"
    ]
   },
   {
@@ -400,7 +406,7 @@
    "outputs": [],
    "source": [
     "for n in species:\n",
-    "    data = df.filter(variable='Emissions|{}|AFOLU'.format(n), year=years).timeseries()\n",
+    "    data = df.filter(kyoto_ghg_2010='in range', variable='Emissions|{}|AFOLU'.format(n), year=years).timeseries()\n",
     "\n",
     "    for y in compare_years:\n",
     "        stats.add((data[y] / data[base_year] - 1) * 100,\n",