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4.3. Code Languages
Is Required: FALSE Type: STRING Cardinality: 0.N
Code language(s). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.software_properties.code_languages')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
4.4. Components Structure
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe how model realms are structured into independent software components (coupled via a coupler) and internal software components. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.software_properties.components_structure')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
4.5. Coupler
Is Required: FALSE Type: ENUM Cardinality: 0.1
Overarching coupling framework for model. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.software_properties.coupler')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "OASIS"
# "OASIS3-MCT"
# "ESMF"
# "NUOPC"
# "Bespoke"
# "Unknown"
# "None"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
5. Key Properties --> Coupling
**
5.1. Overview
Is Required: TRUE Type: STRING Cardinality: 1.1
Overview of coupling in the model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.coupling.overview')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
5.2. Atmosphere Double Flux
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Is the atmosphere passing a double flux to the ocean and sea ice (as opposed to a single one)? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.coupling.atmosphere_double_flux')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
5.3. Atmosphere Fluxes Calculation Grid
Is Required: FALSE Type: ENUM Cardinality: 0.1
Where are the air-sea fluxes calculated | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.coupling.atmosphere_fluxes_calculation_grid')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Atmosphere grid"
# "Ocean grid"
# "Specific coupler grid"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
5.4. Atmosphere Relative Winds
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Are relative or absolute winds used to compute the flux? I.e. do ocean surface currents enter the wind stress calculation? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.coupling.atmosphere_relative_winds')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6. Key Properties --> Tuning Applied
Tuning methodology for model
6.1. Description
Is Required: TRUE Type: STRING Cardinality: 1.1
General overview description of tuning: explain and motivate the main targets and metrics/diagnostics retained. Document the relative weight given to climate performance metrics/diagnostics versus process oriented metrics/diagnostics, and on the possible conflicts with parameterization level tuning. In particular describe any struggle with a parameter value that required pushing it to its limits to solve a particular model deficiency. | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.description')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6.2. Global Mean Metrics Used
Is Required: FALSE Type: STRING Cardinality: 0.N
List set of metrics/diagnostics of the global mean state used in tuning model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.global_mean_metrics_used')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6.3. Regional Metrics Used
Is Required: FALSE Type: STRING Cardinality: 0.N
List of regional metrics/diagnostics of mean state (e.g THC, AABW, regional means etc) used in tuning model/component | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.regional_metrics_used')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6.4. Trend Metrics Used
Is Required: FALSE Type: STRING Cardinality: 0.N
List observed trend metrics/diagnostics used in tuning model/component (such as 20th century) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.trend_metrics_used')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6.5. Energy Balance
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe how energy balance was obtained in the full system: in the various components independently or at the components coupling stage? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.energy_balance')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
6.6. Fresh Water Balance
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe how fresh_water balance was obtained in the full system: in the various components independently or at the components coupling stage? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.tuning_applied.fresh_water_balance')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
7. Key Properties --> Conservation --> Heat
Global heat convervation properties of the model
7.1. Global
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe if/how heat is conserved globally | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.global')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
7.2. Atmos Ocean Interface
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how heat is conserved at the atmosphere/ocean coupling interface | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.atmos_ocean_interface')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
7.3. Atmos Land Interface
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe if/how heat is conserved at the atmosphere/land coupling interface | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.atmos_land_interface')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
7.4. Atmos Sea-ice Interface
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how heat is conserved at the atmosphere/sea-ice coupling interface | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.atmos_sea-ice_interface')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
7.5. Ocean Seaice Interface
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how heat is conserved at the ocean/sea-ice coupling interface | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.ocean_seaice_interface')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
7.6. Land Ocean Interface
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how heat is conserved at the land/ocean coupling interface | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.heat.land_ocean_interface')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8. Key Properties --> Conservation --> Fresh Water
Global fresh water convervation properties of the model
8.1. Global
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe if/how fresh_water is conserved globally | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.global')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.2. Atmos Ocean Interface
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how fresh_water is conserved at the atmosphere/ocean coupling interface | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.atmos_ocean_interface')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.3. Atmos Land Interface
Is Required: TRUE Type: STRING Cardinality: 1.1
Describe if/how fresh water is conserved at the atmosphere/land coupling interface | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.atmos_land_interface')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.4. Atmos Sea-ice Interface
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how fresh water is conserved at the atmosphere/sea-ice coupling interface | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.atmos_sea-ice_interface')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.5. Ocean Seaice Interface
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how fresh water is conserved at the ocean/sea-ice coupling interface | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.ocean_seaice_interface')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.6. Runoff
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe how runoff is distributed and conserved | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.runoff')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.7. Iceberg Calving
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how iceberg calving is modeled and conserved | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.iceberg_calving')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.8. Endoreic Basins
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how endoreic basins (no ocean access) are treated | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.endoreic_basins')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
8.9. Snow Accumulation
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe how snow accumulation over land and over sea-ice is treated | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.fresh_water.snow_accumulation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
9. Key Properties --> Conservation --> Salt
Global salt convervation properties of the model
9.1. Ocean Seaice Interface
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how salt is conserved at the ocean/sea-ice coupling interface | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.salt.ocean_seaice_interface')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
10. Key Properties --> Conservation --> Momentum
Global momentum convervation properties of the model
10.1. Details
Is Required: FALSE Type: STRING Cardinality: 0.1
Describe if/how momentum is conserved in the model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.key_properties.conservation.momentum.details')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
11. Radiative Forcings
Radiative forcings of the model for historical and scenario (aka Table 12.1 IPCC AR5)
11.1. Overview
Is Required: TRUE Type: STRING Cardinality: 1.1
Overview of radiative forcings (GHG and aerosols) implementation in model | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.overview')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12. Radiative Forcings --> Greenhouse Gases --> CO2
Carbon dioxide forcing
12.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CO2.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
12.2. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CO2.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13. Radiative Forcings --> Greenhouse Gases --> CH4
Methane forcing
13.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CH4.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
13.2. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CH4.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14. Radiative Forcings --> Greenhouse Gases --> N2O
Nitrous oxide forcing
14.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.N2O.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
14.2. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.N2O.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15. Radiative Forcings --> Greenhouse Gases --> Tropospheric O3
Troposheric ozone forcing
15.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.tropospheric_O3.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
15.2. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.tropospheric_O3.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
16. Radiative Forcings --> Greenhouse Gases --> Stratospheric O3
Stratospheric ozone forcing
16.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.stratospheric_O3.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
16.2. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.stratospheric_O3.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
17. Radiative Forcings --> Greenhouse Gases --> CFC
Ozone-depleting and non-ozone-depleting fluorinated gases forcing
17.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CFC.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
17.2. Equivalence Concentration
Is Required: TRUE Type: ENUM Cardinality: 1.1
Details of any equivalence concentrations used | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CFC.equivalence_concentration')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "Option 1"
# "Option 2"
# "Option 3"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
17.3. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.greenhouse_gases.CFC.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
18. Radiative Forcings --> Aerosols --> SO4
SO4 aerosol forcing
18.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.SO4.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
18.2. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.SO4.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
19. Radiative Forcings --> Aerosols --> Black Carbon
Black carbon aerosol forcing
19.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.black_carbon.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
19.2. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.black_carbon.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
20. Radiative Forcings --> Aerosols --> Organic Carbon
Organic carbon aerosol forcing
20.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.organic_carbon.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
20.2. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.organic_carbon.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
21. Radiative Forcings --> Aerosols --> Nitrate
Nitrate forcing
21.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.nitrate.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
21.2. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.nitrate.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22. Radiative Forcings --> Aerosols --> Cloud Albedo Effect
Cloud albedo effect forcing (RFaci)
22.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_albedo_effect.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22.2. Aerosol Effect On Ice Clouds
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Radiative effects of aerosols on ice clouds are represented? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_albedo_effect.aerosol_effect_on_ice_clouds')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
22.3. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_albedo_effect.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
23. Radiative Forcings --> Aerosols --> Cloud Lifetime Effect
Cloud lifetime effect forcing (ERFaci)
23.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
23.2. Aerosol Effect On Ice Clouds
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Radiative effects of aerosols on ice clouds are represented? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.aerosol_effect_on_ice_clouds')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
23.3. RFaci From Sulfate Only
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Radiative forcing from aerosol cloud interactions from sulfate aerosol only? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.RFaci_from_sulfate_only')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
23.4. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.cloud_lifetime_effect.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
24. Radiative Forcings --> Aerosols --> Dust
Dust forcing
24.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.dust.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
24.2. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.dust.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
25. Radiative Forcings --> Aerosols --> Tropospheric Volcanic
Tropospheric volcanic forcing
25.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
25.2. Historical Explosive Volcanic Aerosol Implementation
Is Required: TRUE Type: ENUM Cardinality: 1.1
How explosive volcanic aerosol is implemented in historical simulations | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.historical_explosive_volcanic_aerosol_implementation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Type A"
# "Type B"
# "Type C"
# "Type D"
# "Type E"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
25.3. Future Explosive Volcanic Aerosol Implementation
Is Required: TRUE Type: ENUM Cardinality: 1.1
How explosive volcanic aerosol is implemented in future simulations | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.future_explosive_volcanic_aerosol_implementation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Type A"
# "Type B"
# "Type C"
# "Type D"
# "Type E"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
25.4. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.tropospheric_volcanic.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
26. Radiative Forcings --> Aerosols --> Stratospheric Volcanic
Stratospheric volcanic forcing
26.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
26.2. Historical Explosive Volcanic Aerosol Implementation
Is Required: TRUE Type: ENUM Cardinality: 1.1
How explosive volcanic aerosol is implemented in historical simulations | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.historical_explosive_volcanic_aerosol_implementation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Type A"
# "Type B"
# "Type C"
# "Type D"
# "Type E"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
26.3. Future Explosive Volcanic Aerosol Implementation
Is Required: TRUE Type: ENUM Cardinality: 1.1
How explosive volcanic aerosol is implemented in future simulations | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.future_explosive_volcanic_aerosol_implementation')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "Type A"
# "Type B"
# "Type C"
# "Type D"
# "Type E"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
26.4. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.stratospheric_volcanic.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
27. Radiative Forcings --> Aerosols --> Sea Salt
Sea salt forcing
27.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.sea_salt.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
27.2. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.aerosols.sea_salt.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
28. Radiative Forcings --> Other --> Land Use
Land use forcing
28.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How this forcing agent is provided (e.g. via concentrations, emission precursors, prognostically derived, etc.) | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.other.land_use.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "M"
# "Y"
# "E"
# "ES"
# "C"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
28.2. Crop Change Only
Is Required: TRUE Type: BOOLEAN Cardinality: 1.1
Land use change represented via crop change only? | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.other.land_use.crop_change_only')
# PROPERTY VALUE:
# Set as follows: DOC.set_value(value)
# Valid Choices:
# True
# False
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
28.3. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.other.land_use.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
29. Radiative Forcings --> Other --> Solar
Solar forcing
29.1. Provision
Is Required: TRUE Type: ENUM Cardinality: 1.N
How solar forcing is provided | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.other.solar.provision')
# PROPERTY VALUE(S):
# Set as follows: DOC.set_value("value")
# Valid Choices:
# "N/A"
# "irradiance"
# "proton"
# "electron"
# "cosmic ray"
# "Other: [Please specify]"
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
29.2. Additional Information
Is Required: FALSE Type: STRING Cardinality: 0.1
Additional information relating to the provision and implementation of this forcing agent (e.g. citations, use of non-standard datasets, explaining how multiple provisions are used, etc.). | # PROPERTY ID - DO NOT EDIT !
DOC.set_id('cmip6.toplevel.radiative_forcings.other.solar.additional_information')
# PROPERTY VALUE:
# Set as follows: DOC.set_value("value")
# TODO - please enter value(s)
| notebooks/hammoz-consortium/cmip6/models/sandbox-1/toplevel.ipynb | ES-DOC/esdoc-jupyterhub | gpl-3.0 |
This is a Boundary Value Problem. It's an ordinary differential equation where the boundary conditions are given at different points - here at $x=0$ and $x=1$.
Boundary value problems can be problematic: even when properly set up (same number of boundary conditions as equations, reasonable domain) they need not have any solutions, or they can have a unique solution, or they can have multiple - even infinitely many - solutions! Adding numerics just adds difficulty. However, it's still perfectly feasible to find solutions, when they exist.
Shooting
We can use a lot of the technology and methods we've seen already to solve boundary value problems. This relies on one key feature: if we have a solution to the initial value problem with the same differential equation, with boundary conditions at the start that match the BVP, and a solution that matches the BVP at the end, then it is a solution of the BVP.
To phrase that for the problem above: if we have a value $J$ for $j_p$ at $x=0$ then we know (from the boundary condition at $x=0$) that $j_n=J$. We then solve the initial value problem
\begin{equation}
\frac{\text{d}}{\text{d}x} \begin{pmatrix} j_p \ j_n \end{pmatrix} = \begin{pmatrix} \Theta \left( n_i^2 - n p \right) + G \ -\Theta \left( n_i^2 - n p \right) - G \end{pmatrix}, \qquad \begin{pmatrix} j_p \ j_n \end{pmatrix}(0) = \begin{pmatrix} J \ J \end{pmatrix}.
\end{equation}
This gives us both $j_p$ and $j_n$ as functions of $x$. Our solutions clearly depend on the initial value $J$. If our value of $J$ is such that $j_n(1) = 0$ then we match the boundary condition at $x=1$. We've then built a solution that solves the differential equation, and matches all the boundary conditions: it is a solution of the BVP.
We can solve the initial value problem using any of the techniques used earlier: here we'll use odeint. The solution will be $j_n(x;J)$ and $j_p(x;J)$, showing how the solution depends on the initial data. We can then evaluate this solution at $x=1$: we want
\begin{equation}
F(J) = j_n(1;J) = 0.
\end{equation}
This is a nonlinear root-finding problem, where evaluating the function whose root we are trying to find involves solving an initial value problem.
Let's implement this, assuming $\Theta = 0.9, G = 1, n_i = 0.6$. The critical value of $J$ is between $0$ and $5$. | Theta = 0.9
G = 1
ni = 0.6
from scipy.integrate import odeint
from scipy.optimize import brentq
def f_ivp(y, x):
jp, jn = y
dydx = numpy.zeros_like(y)
dydx[0] = -Theta*(ni**2-n(x)*p(x)) + G
dydx[1] = -dydx[0]
return dydx
def F_root(J):
y0 = [J, J]
y = odeint(f_ivp, y0, [0, 1])
jn1 = y[-1,1]
return jn1
J_critical = brentq(F_root, 0, 5)
solution = odeint(f_ivp, [J_critical, J_critical], x)
pyplot.figure(figsize=(10, 6))
pyplot.plot(x, solution[:,0], label=r"$j_p$")
pyplot.plot(x, solution[:,1], label=r"$j_n$")
pyplot.legend()
pyplot.xlabel(r"$x$")
pyplot.show() | solutions/04-Boundary-Value-Problems.ipynb | IanHawke/Southampton-PV-NumericalMethods-2016 | mit |
Load ground motion records
Please indicate the path to the folder containing the ground motion records to be used in the analysis through the parameter gmrs_folder.
Note: Each accelerogram needs to be in a separate CSV file as described in the RMTK manual.
The parameters minT and maxT are used to define the period bounds when plotting the spectra for the provided ground motion fields. | gmrs_folder = '../../../../../../rmtk_data/accelerograms'
minT, maxT = 0.01, 2.00
gmrs = utils.read_gmrs(gmrs_folder)
#utils.plot_response_spectra(gmrs, minT, maxT) | rmtk/vulnerability/derivation_fragility/equivalent_linearization/miranda_2000_firm_soils/miranda_2000_firm_soils.ipynb | mabevillar/rmtk | agpl-3.0 |
Obtain the damage probability matrix
The parameter damping_ratio needs to be defined in the cell below in order to calculate the damage probability matrix. | damping_ratio = 0.05
PDM, Sds = miranda_2000_firm_soils.calculate_fragility(capacity_curves, gmrs, damage_model, damping_ratio) | rmtk/vulnerability/derivation_fragility/equivalent_linearization/miranda_2000_firm_soils/miranda_2000_firm_soils.ipynb | mabevillar/rmtk | agpl-3.0 |
Fit lognormal CDF fragility curves
The following parameters need to be defined in the cell below in order to fit lognormal CDF fragility curves to the damage probability matrix obtained above:
1. IMT: This parameter specifies the intensity measure type to be used. Currently supported options are "PGA", "Sd" and "Sa".
2. period: This parameter defines the time period of the fundamental mode of vibration of the structure.
3. regression_method: This parameter defines the regression method to be used for estimating the parameters of the fragility functions. The valid options are "least squares" and "max likelihood". | IMT = "Sa"
period = 2.0
regression_method = "least squares"
fragility_model = utils.calculate_mean_fragility(gmrs, PDM, period, damping_ratio,
IMT, damage_model, regression_method) | rmtk/vulnerability/derivation_fragility/equivalent_linearization/miranda_2000_firm_soils/miranda_2000_firm_soils.ipynb | mabevillar/rmtk | agpl-3.0 |
Save fragility functions
The derived parametric fragility functions can be saved to a file in either CSV format or in the NRML format that is used by all OpenQuake input models. The following parameters need to be defined in the cell below in order to save the lognormal CDF fragility curves obtained above:
1. taxonomy: This parameter specifies a taxonomy string for the the fragility functions.
2. minIML and maxIML: These parameters define the bounds of applicability of the functions.
3. output_type: This parameter specifies the file format to be used for saving the functions. Currently, the formats supported are "csv" and "nrml". | taxonomy = "RC"
output_type = "csv"
output_path = "../../../../../../rmtk_data/output/"
utils.save_mean_fragility(taxonomy, fragility_model, minIML, maxIML, output_type, output_path) | rmtk/vulnerability/derivation_fragility/equivalent_linearization/miranda_2000_firm_soils/miranda_2000_firm_soils.ipynb | mabevillar/rmtk | agpl-3.0 |
Loading the data
As in previous examples, we'll use MNIST, because it's a small and easy-to-use dataset that comes bundled with Tensorflow. | from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True) | 03B_Generative_Adversarial_Network.ipynb | kevinjliang/Duke-Tsinghua-MLSS-2017 | apache-2.0 |
Utility functions
Let's define some utility functions that will help us quickly construct layers for use in our model. There are two things worth noting here:
Instead of tf.Variable, we use tf.get_variable.
The reason for this is a bit subtle, and you may want to skip this and come back to it once you've seen the rest of the code. Here's the basic explanation. Later on in this notebook, we will call fully_connected_layer from a couple different places. Sometimes, we will want new variables to be added to the graph, because we are creating an entirely new layer of our network. Other times, however, we will want to use the same weights as an already-existing layer, but acting on different inputs.
For example, the Discriminator network will appear twice in our computational graph; in one case, the input neurons will be connected to the "real data" placeholder (which we will feed MNIST images), and in the other, they will be connected to the output of the Generator. Although these networks form two separate parts of our computational graph, we want them to share the same weights: conceptually, there is one Discriminator function that gets applied twice, not two different functions altogether. Since tf.Variable always creates a new variable when called, it would not be appropriate for use here.
Variable scoping solves this problem. Whenever we are adding nodes to a graph, we are operating within a scope. Scopes can be named, and you can create a new scope using tf.variable_scope('name') (more on this later). When a scope is open, it can optionally be in reuse mode. The result of calling tf.get_variable depends on whether you are in reuse mode or not. If not (this is the default), tf.get_variable will create a new variable, or cause an error if a variable by the same name already exists in the current scope. If you are in reuse mode, the behavior is the opposite: tf.get_variable will look up and return an existing variable (with the specified name) within your scope, or throw an error if it doesn't exist. By carefully controlling our scopes later on, we can create exactly the graph we want, with variables shared across the graph where appropriate.
The variables_from_scope function lists all variables created within a given scope. This will be useful later, when we want to update all "discriminator" variables, but no "generator" variables, or vice versa. | def shape(tensor):
"""
Get the shape of a tensor. This is a compile-time operation,
meaning that it runs when building the graph, not running it.
This means that it cannot know the shape of any placeholders
or variables with shape determined by feed_dict.
"""
return tuple([d.value for d in tensor.get_shape()])
def fully_connected_layer(in_tensor, out_units, activation_function=tf.nn.relu):
"""
Add a fully connected layer to the default graph, taking as input `in_tensor`, and
creating a hidden layer of `out_units` neurons. This should be called within a unique variable
scope. Creates variables W and b, and computes activation_function(in * W + b).
"""
_, num_features = shape(in_tensor)
W = tf.get_variable("weights", [num_features, out_units], initializer=tf.truncated_normal_initializer(stddev=0.1))
b = tf.get_variable("biases", [out_units], initializer=tf.constant_initializer(0.1))
return activation_function(tf.matmul(in_tensor, W) + b)
def variables_from_scope(scope_name):
"""
Returns a list of all variables in a given scope. This is useful when
you'd like to back-propagate only to weights in one part of the network
(in our case, the generator or the discriminator).
"""
return tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=scope_name) | 03B_Generative_Adversarial_Network.ipynb | kevinjliang/Duke-Tsinghua-MLSS-2017 | apache-2.0 |
We'll also provide a simple function for displaying a few 28-pixel images. This will help us understand the progress of our GAN as it trains; we'll use it to visualize the generated 'fake digit' images. | def visualize_row(images, img_width=28, cmap='gray'):
"""
Takes in a tensor of images of given width, and displays them in a column
in a plot, using `cmap` to map from numbers to colors.
"""
im = np.reshape(images, [-1, img_width])
plt.figure()
plt.imshow(im, cmap=cmap)
plt.show() | 03B_Generative_Adversarial_Network.ipynb | kevinjliang/Duke-Tsinghua-MLSS-2017 | apache-2.0 |
Generator
A GAN is made up of two smaller networks: a generator and a discriminator. The generator is responsible for sampling images from a distribution that we hope will get closer and closer, as we train, to the real data distribution.
Neural networks are deterministic, so in order to sample a new image from the generator, we first create some random noise z (in our case, z will be a 100-dimensional uniform random variable) and then feed that noise to the network. You can think of z as being a latent, low-dimensional representation of some image G(z), though in a vanilla GAN, it is usually difficult to interpret z's components in a meaningful way.
Our generator is a dead-simple multi-layer perceptron (feed-forward network), with 128 hidden units. | def generator(z):
"""
Given random noise `z`, use a simple MLP with 128 hidden units to generate a
sample image (784 values between 0 and 1, enforced with the sigmoid function).
"""
with tf.variable_scope("fc1"):
fc1 = fully_connected_layer(z, 128)
with tf.variable_scope("fc2"):
return fully_connected_layer(fc1, 784, activation_function=tf.sigmoid) | 03B_Generative_Adversarial_Network.ipynb | kevinjliang/Duke-Tsinghua-MLSS-2017 | apache-2.0 |
Discriminator
Although it isn't necesssary, it makes some sense for our discriminator to mirror the generator's architecture, as we do here. The discriminator takes in an image (perhaps a real one from the MNIST dataset, perhaps a fake one from our generator), and attempts to classify it as real (1) or fake (0). Our architecture is again a simple MLP, taking 784 pixels down to 128 hidden units, and finally down to a probability. | def discriminator(x):
"""
This discriminator network takes in a tensor with shape [batch, 784], and classifies
each example image as real or fake. The network it uses is quite simple: a fully connected
layer with ReLU activation takes us down to 128 dimensions, then we collapse that to 1 number
in [0, 1] using a fully-connected layer with sigmoid activation. The result can be interpreted
as a probability, the discriminator's strength-of-belief that a sample is from the
real data distribution.
"""
with tf.variable_scope("fc1"):
fc1 = fully_connected_layer(x, 128)
with tf.variable_scope("fc2"):
return fully_connected_layer(fc1, 1, activation_function=tf.sigmoid) | 03B_Generative_Adversarial_Network.ipynb | kevinjliang/Duke-Tsinghua-MLSS-2017 | apache-2.0 |
GAN
Given a generator and discriminator, we can now set up the GAN's computational graph.
We use Tensorflow's variable scope feature for two purposes.
First, it helps separate the variables used by the generator and by the discriminator; this is important, because when training, we want to alternate between updating each set of variables according to a different objective.
Second, scoping helps us reuse the same set of discriminator weights both for the operations we perform on real images and for those performed on fake images. To achieve this, after calling discriminator for the first time (and creating these weight variables), we tell our current scope to reuse_variables(), meaning that on our next call to discriminator, existing variables will be reused rather than creating new ones. | def gan(batch_size, z_dim):
"""
Given some details about the training procedure (batch size, dimension of z),
this function sets up the rest of the computational graph for the GAN.
It returns a dictionary containing six ops/tensors: `train_d` and `train_g`, the
optimization steps for the discriminator and generator, `real_data` and `noise`,
two placeholders that should be fed in during training, `d_loss`, the discriminator loss
(useful for estimating progress toward convergence), and `fake_data`, which can be
evaluated (with noise in the feed_dict) to sample from the generator's distribution.
"""
z = tf.placeholder(tf.float32, [batch_size, z_dim], name='z')
x = tf.placeholder(tf.float32, [batch_size, 784], name='x')
with tf.variable_scope('generator'):
fake_x = generator(z)
with tf.variable_scope('discriminator') as scope:
d_on_real = discriminator(x)
scope.reuse_variables()
d_on_fake = discriminator(fake_x)
g_loss = -tf.reduce_mean(tf.log(d_on_fake))
d_loss = -tf.reduce_mean(tf.log(d_on_real) + tf.log(1. - d_on_fake))
optimize_d = tf.train.AdamOptimizer().minimize(d_loss, var_list=variables_from_scope("discriminator"))
optimize_g = tf.train.AdamOptimizer().minimize(g_loss, var_list=variables_from_scope("generator"))
return {'train_d': optimize_d,
'train_g': optimize_g,
'd_loss': d_loss,
'fake_data': fake_x,
'real_data': x,
'noise': z} | 03B_Generative_Adversarial_Network.ipynb | kevinjliang/Duke-Tsinghua-MLSS-2017 | apache-2.0 |
Training a GAN
Our training procedure is a bit more involved than in past demos. Here are the main differences:
1. Each iteration, we first train the generator, then (separately) the discriminator.
2. Each iteration, we need to feed in a batch of images, just as in previous notebooks. But we also need a batch of noise samples. For this, we use Numpy's np.random.uniform function.
3. Every 1000 iterations, we log some data to the console and visualize a few samples from our generator. | def train_gan(iterations, batch_size=50, z_dim=100):
"""
Construct and train the GAN.
"""
model = gan(batch_size=batch_size, z_dim=z_dim)
def make_noise():
return np.random.uniform(-1.0, 1.0, [batch_size, z_dim])
def next_feed_dict():
return {model['real_data']: mnist.train.next_batch(batch_size)[0],
model['noise']: make_noise()}
initialize_all = tf.global_variables_initializer()
with tf.Session(config=config) as sess:
sess.run(initialize_all)
start_time = time.time()
for t in range(iterations):
sess.run(model['train_g'], feed_dict=next_feed_dict())
_, d_loss = sess.run([model['train_d'], model['d_loss']], feed_dict=next_feed_dict())
if t % 1000 == 0 or t+1 == iterations:
fake_data = sess.run(model['fake_data'], feed_dict={model['noise']: make_noise()})
print('Iter [%8d] Time [%5.4f] d_loss [%.4f]' % (t, time.time() - start_time, d_loss))
visualize_row(fake_data[:5]) | 03B_Generative_Adversarial_Network.ipynb | kevinjliang/Duke-Tsinghua-MLSS-2017 | apache-2.0 |
Moment of truth
It's time to run our GAN! Watch as it learns to draw recognizable digits in about three minutes. | train_gan(25000) | 03B_Generative_Adversarial_Network.ipynb | kevinjliang/Duke-Tsinghua-MLSS-2017 | apache-2.0 |
Before running any GRASS raster modules, you need to set the computational region using g.region. In this example, we set the computational extent and resolution to the raster layer elevation. | gscript.run_command('g.region', raster='elevation') | GSOC/notebooks/Projects/GRASS/python-grass-addons/01_scripting_library.ipynb | OSGeo-live/CesiumWidget | apache-2.0 |
The run_command() function is the most commonly used one. Here, we apply the focal operation average (r.neighbors) to smooth the elevation raster layer. Note that the syntax is similar to bash syntax, just the flags are specified in a parameter. | gscript.run_command('r.neighbors', input='elevation', output='elev_smoothed', method='average', flags='c') | GSOC/notebooks/Projects/GRASS/python-grass-addons/01_scripting_library.ipynb | OSGeo-live/CesiumWidget | apache-2.0 |
Specifics of interactive interpreters
When using the GUI, we can look at the map right away. In scripts, we rarely render a map. In IPython Notebook we are able to show a map as a result. For this we use function view() from a custom Python module render supplied with these notebooks. (This function might be part of GRASS GIS Python API for IPython in the future.) | from render import view
view(rasters=['elev_smoothed']) | GSOC/notebooks/Projects/GRASS/python-grass-addons/01_scripting_library.ipynb | OSGeo-live/CesiumWidget | apache-2.0 |
To simplify the re-running of examples, we set the environmental variable GRASS_OVERWRITE,
which allows direct overwriting of results from previous runs, bypassing the overwrite checks. | import os
os.environ['GRASS_OVERWRITE'] = '1' | GSOC/notebooks/Projects/GRASS/python-grass-addons/01_scripting_library.ipynb | OSGeo-live/CesiumWidget | apache-2.0 |
When an unrecoverable error occurs (due to incorrect parameter use or other issues), the GRASS GIS functions usually print the error message and end the program execution (by calling the exit() function). However, when working in an interactive environment such as IPython, the behavior can be changed using set_raise_on_error() function. The following call will cause GRASS GIS Python functions to raise an exception instead of calling exit(). | gscript.set_raise_on_error(True) | GSOC/notebooks/Projects/GRASS/python-grass-addons/01_scripting_library.ipynb | OSGeo-live/CesiumWidget | apache-2.0 |
Calling GRASS GIS modules with textual input or output
Textual output from modules can be captured using the read_command() function. | print(gscript.read_command('g.region', flags='p'))
print(gscript.read_command('r.univar', map='elev_smoothed', flags='g')) | GSOC/notebooks/Projects/GRASS/python-grass-addons/01_scripting_library.ipynb | OSGeo-live/CesiumWidget | apache-2.0 |
Certain modules can produce output in key-value format which is enabled by the g flag. The parse_command() function automatically parses this output and returns a dictionary. In this example, we call g.proj to display the projection parameters of the actual location. | gscript.parse_command('g.proj', flags='g') | GSOC/notebooks/Projects/GRASS/python-grass-addons/01_scripting_library.ipynb | OSGeo-live/CesiumWidget | apache-2.0 |
For comparison, below is the same example, but using the read_command() function. | print(gscript.read_command('g.proj', flags='g')) | GSOC/notebooks/Projects/GRASS/python-grass-addons/01_scripting_library.ipynb | OSGeo-live/CesiumWidget | apache-2.0 |
Certain modules require the text input be in a file or provided as standard input. Using the write_command() function we can conveniently pass the string to the module. Here, we are creating a new vector with one point with v.in.ascii.
Note that stdin parameter is not used as a module parameter, but its content is passed as standard input to the subprocess. | gscript.write_command('v.in.ascii', input='-', stdin='%s|%s' % (635818.8, 221342.4), output='view_point') | GSOC/notebooks/Projects/GRASS/python-grass-addons/01_scripting_library.ipynb | OSGeo-live/CesiumWidget | apache-2.0 |
Convenient wrapper functions
Some modules have wrapper functions to simplify frequent tasks.
We can obtain the information about the vector layer which we just created with the v.info wrapper. | gscript.vector_info('view_point') | GSOC/notebooks/Projects/GRASS/python-grass-addons/01_scripting_library.ipynb | OSGeo-live/CesiumWidget | apache-2.0 |
It is also possible to retrieve the raster layer history (r.support) and layer information (r.info) or to query (r.what) raster layer pixel values. | gscript.raster_what('elevation', [[635818.8, 221342.4], [635710, 221330]]) | GSOC/notebooks/Projects/GRASS/python-grass-addons/01_scripting_library.ipynb | OSGeo-live/CesiumWidget | apache-2.0 |
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