ISIMIP3 simulation protocol

Last updated: 11 July 2020

Commit hash: 35fa314901d22e6ae6b6cf6b16d3e5c4315b9177

ISIMIP3a simulation round simulation protocol - Lakes (local)

Introduction

General concept

The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios.

ISIMIP is intended to be structured in successive rounds connected to the different phases of the climate model intercomparison CMIP (ISIMIP Mission & Implementation document).

The main components of the ISIMIP framework are:

ISIMIP3a

Historical model evaluation and attribution runs

The ISIMIP3a part of the third round framework is dedicated to i) impact model evaluation and improvement and ii) detection and attribution of observed impacts according to the framework of IPCC AR5 Working Group II Chapter 18. To this end all simulations are forced by observed climate and socio-economic information and a de-trended version of the observed climate allowing for the generation of a “no climate change” baseline (counterfactual).

You can find the ISIMIP3b protocol, which is dedicated to a quantification of climate-related risks at different levels of climate change and socio-economic conditions, here.

Simulation protocol

In this protocol we describe the scenarios & experiments in ISIMIP3a simulation round, the different input datasets, the output variables, and how to report model results specifically for Lakes (local). An overview of all sectors can be found at protocol.isimip.org.

Throughout the protocol we use specifiers that denote a particular scenario, experiment, variable or other parameter. We use these specifiers in the tables below, in the filenames of the input data sets, and ask you to use the same specifiers in your output files. More on reporting data can be found at the end of this document.

Model versioning

To ensure consistency between ISIMIP3a and ISIMIP3b as well as the different experiments within a simulation round, we require that modelling groups use the same version of an impact model for the experiments in ISIMIP3a and ISIMIP3b. If you cannot fulfill this, please indicate that by using a suffix for your model name (e.g. simple version numbering: MODEL-v1, MODEL-v2 or following semantic versioning: MODEL-2.0.0, see also reporting model results).

This versioning does not only apply to changes in the computational logic of the model, but also to input parameters, calibration or setup. If model versions are not reported, we will name them according to the simulation round (e.g. MODEL-isimip3a). We require the strict versioning to ensure that differences between model results are fully attributable to the changes in model forcings.

Scenarios & Experiments

Scenario definitions

Table 1: Climate scenario specifiers (climate-scenario).
Scenario specifier Description
obsclim Observed climate and CO₂ forcing used for model evaluation and the detection and attribution task.
spinclim Detrended version of the observed climate forcing used to spin-up the simulations based on a stable 1900 climate (see explanation below for details regarding the design of the spin-up)
counterclim Detrended version of the observed climate forcing used for the "no climate change" baseline simulations in the context of the detection and attributions task.
Table 2: Socio-economic scenario specifiers (soc-scenario).
Scenario specifier Description
histsoc

Varying direct human influences in the historical period (1850-2014) (e.g. observed changes in historical land use, nitrogen deposition and fertilizer input, fishing effort).

Please label your model run histsoc even if it only partly accounts for varying direct human forcings while another part of the the direct human forcing is considered constant or is ignored.
2015soc

Fixed year-2015 direct human influences (e.g. land use, nitrogen deposition and fertilizer input, fishing effort).

Please label your simulations 2015soc if they do not at all account for historical changes in direct human forcing, but they do represent constant year-2015 levels of direct human forcing for at least some direct human forcings.
nat

No direct human influences (naturalized run).

Please only label your model run nat if it does not at all account for any direct human forcings, including e.g. human land use.
Table 3: Sensitivity scenario specifiers (sens-scenario).
Scenario specifier Description
default For all experiments other than the sensitivity experiments.

General note regarding sensitivity experiments

The sensitivity experiments are meant to be "artificial" deviations from the default settings. So for example if your model does not at all account for changes in CO₂ concentrations (no option to switch it on or off) the run should be labeled as “default” in the sensitivity specifier of the file name even if the run would be identical to the 1901co2 sensitivity setting.

The particular sensitivity scenario for an experiment is given in the experiments table below. For most experiments no sensitivity scenario is given, so the default label applies.

Experiments

Table 4: Experiment set-up: Each experiment is specified by the climate forcing (CF) and the Direct Human Forcing (DHF).
Experiment Short description

Transition from Spin-up to experiment

1850-1900, only if spin-up is needed

Historical

1901-2018

model evaluation

histsoc

1st priority

 CF: No climate change before 1901, observed forcing afterwards; constant 1850 levels of CO₂ before 1850 and based on observations afterwards

spinclim

obsclim

DHF: Fixed 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards

histsoc

histsoc

model evaluation

2015soc

1st priority

 CF: No climate change before 1901, observed forcing afterwards; constant 1850 levels of CO₂ before 1850 and based on observations afterwards

spinclim

obsclim

DHF: Fixed 2015 levels of direct human forcing for the entire time period

2015soc

2015soc

model evaluation

nat

2nd priority

 CF: No climate change before 1901, observed forcing afterwards; 1850 levels of CO₂ before 1850 and based on observations afterwards

spinclim

obsclim

DHF: No direct human influences

nat

nat

counterfactual climate

histsoc

1st priority

 CF: de-trended observational climate forcing (counterfactual "no climate change" situation) + fixed CO₂ concentration at 1901 level "histsoc" version of the transition period of the model evaluation run

counterclim

DHF: 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards

histsoc

counterfactual climate

2015soc

1st priority

 CF: de-trended observational climate forcing (counterfactual "no climate change" situation) "2015soc" version of the transition period of the model evaluation run

counterclim

DHF: fixed 2015 levels of direct human influences for the entire time period

2015soc

counterfactual climate

nat

2nd priority

 CF: de-trended observational climate forcing (counterfactual "no climate change" situation) + fixed CO₂ concentration at 1901 level "nat" version of the transition period of the model evaluation run

counterclim

DHF: No direct human influences

nat

CO₂ sensitivity

histsoc

2nd priority

 CF: no climate change before 1901, observed forcing afterwards + fixed CO₂ concentration at 1901 level "histsoc" version of the transition period of the model evaluation run

obsclim

Sensitivity scenario: 1901co2

DHF: 1850 levels of direct human forcing before 1850, varying direct human influences according to observations afterwards

histsoc

CO₂ sensitivity

2015soc

2nd priority

 CF: no climate change before 1901, observed forcing afterwards + fixed CO₂ concentration at 1901 level "2015soc" version of the transition period of the model evaluation run

obsclim

Sensitivity scenario: 1901co2

DHF: fixed 2015 levels of direct human influences for the entire time period

2015soc

Note regarding models requiring spin-up

For models requiring spin-up, we provide 100 years of spinclim data which is identical with the first 100 years of the counterclim data (files climate/atmosphere/spinclim/<dataset>/<dataset>_spinclim_<variable>_global_daily_<start-year>_<start-year>.nc). If more than 100 years of spin-up are needed, these data can be repeated as often as needed. Use historical CO2 concentration and varying DHF, for the transition period from spin-up to the start of the experiment (1850-1900). When using a longer spin-up period that (nominally) extends back further than 1850, please keep CO2 concentration and DHF constant at 1850 level until reaching the year corresponding to 1850.

Input data

The base directory for input data at DKRZ is:

/work/bb0820/ISIMIP/ISIMIP3a/InputData/

Further information on accessing ISIMIP data can be found at ISIMIP - getting started.

Some of the datasets are tagged as mandatory. This does not mean that the data must be used in all cases, but if your models uses input data of this kind, we require to use the specified dataset. If an alterntive data set is used instead, we cannot consider it an ISIMIP simulation. If the mandatory label is not given, you may use alternative data (please document this clearly).

Climate forcing

Table 5: Climate and climate-related forcing data (climate-forcing).
Title Specifier Time period Reanalysis Bias adjustment target Comments
GSWP3-W5E5 gswp3-w5e5 1901-2016 ERA5 GPCC, CRU Combination of W5E5 for 1979-2016 with GSWP3 homogenized to W5E5 for 1901-1978. The homogenization reduces discontinuities at the 1978/1979 transition and was done using the ISIMIP3BASD v2.4.1 bias adjustment method.
GSWP3 gswp3 1901-2010 20CR GPCC, GPCP, CPC-Unified, CRU, SRB Dynamically downscaled and bias-adjusted 20th Century Reanalysis (20CR) from the Global Soil Wetness Project Phase 3 (GSWP3).
Table 6: Climate forcing variables for ISIMIP3a simulations (climate-variable).
Variable Variable specifier Unit Resolution Datasets
Atmospheric variables mandatory
Near-Surface Relative Humidity hurs %
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Near-Surface Specific Humidity huss kg kg-1
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Precipitation pr kg m-2 s-1
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Surface Air Pressure ps Pa
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Surface Downwelling Longwave Radiation rlds W m-2
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Surface Downwelling Shortwave Radiation rsds W m-2
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Near-Surface Wind Speed sfcwind m s-1
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Near-Surface Air Temperature tas K
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Daily Maximum Near-Surface Air Temperature tasmax K
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)
Daily Minimum Near-Surface Air Temperature tasmin K
  • 0.5° grid
  • daily
  • GSWP3 (obsclim and counterclim, 1901-2010)
  • GSWP3-W5E5 (obsclim and counterclim, 1901-2016)

The climate forcing input files can be found using the following pattern:

climate/atmosphere/<climate-scenario>/<climate-forcing>/<climate-forcing>_<climate-scenario>_<climate-variable>_global_daily_<start-year>_<end-year>.nc

Greenhouse gas forcing

Table 7: Greenhouse gas forcing for ISIMIP3a simulation round.
Variable Variable specifier Unit Resolution Datasets
Atmospheric composition mandatory

Atmospheric CO2 concentration

 composition_atmosphere/co2/co2_historical_annual_1850_2018.txt
co2 ppm
  • global
  • annual

Meinshausen et al. (2011) for 1850-2005 and Dlugokencky & Tans (2019) from 2006-2018.

Socioeconomic forcing

Table 8: Socioeconomic datasets for ISIMIP3a simulation round.
Dataset Included variables (specifier) Covered time period Resolution Reference/Source and Comments
Land use mandatory

Landuse totals

socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-totals_annual_<start_year>_<end_year>.nc
  • share of the total cropland (cropland_total)
  • all of the rainfed cropland (cropland_rainfed)
  • all of the irrigated cropland (cropland_irrigated)
  • share of managed pastures or rangeland (pastures)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual

Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al, in review, see also https://luh.umd.edu).

Downscaling to 5 crops

socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-5crops_annual_<start_year>_<end_year>.nc
  • share of rainfed/irrigated C4 annual crops (c4ann_rainfed, c4ann_irrigated)
  • share of rainfed/irrigated C3 perennial crops (c3per_rainfed, c3per_irrigated)
  • share of rainfed/irrigated C3 N-fixing crops (c3nfx_rainfed, c3nfx_irrigated)
  • share of rainfed/irrigated C4 annual crops (c4ann_rainfed, c4ann_irrigated)
  • share of rainfed/irrigated C4 perennial crops (c4per_rainfed, c4per_irrigated)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual

Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al., in review, see also https://luh.umd.edu).

Downscaling to 15 crops

socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-15crops_annual_<start_year>_<end_year>.nc
  • share of rainfed/irrigated maize (maize_rainfed, maize_irrigated)
  • share of rainfed/irrigated rice (rice_rainfed, rice_irrigated)
  • share of rainfed/irrigated oil crops (groundnut) (oil_crops_groundnut_rainfed, oil_crops_groundnut_irrigated)
  • share of rainfed/irrigated oil crops (rapeseed) (oil_crops_rapeseed_rainfed, oil_crops_rapeseed_irrigated)
  • share of rainfed/irrigated oil crops (soybean) (oil_crops_soybean_rainfed, oil_crops_soybean_irrigated)
  • share of rainfed/irrigated oil crops (sunflower) (oil_crops_sunflower_rainfed, oil_crops_sunflower_irrigated)
  • share of rainfed/irrigated pulses (pulses_rainfed, pulses_irrigated)
  • share of rainfed/irrigated temperate cereals (temperate_cereals_rainfed, temperate_cereals_irrigated)
  • share of rainfed/irrigated temperate roots (temperate_roots_rainfed, temperate_roots_irrigated)
  • share of rainfed/irrigated tropical cereals (tropical_cereals_rainfed, tropical_cereals_irrigated)
  • share of rainfed/irrigated tropical roots (tropical_roots_rainfed, tropical_roots_irrigated)
  • share of rainfed/irrigated C3 annual crops not covered by the above (others_c3ann_rainfed, others_c3ann_irrigated)
  • share of rainfed/irrigated C3 N-fixing crops not covered by the above (others_c3nfx_rainfed, others_c3nfx_irrigated)
  • share of rainfed/irrigated C3 perennial crops (c3per_rainfed, c3per_irrigated)
  • share of rainfed/irrigated C4 perennial crops (c4per_rainfed, c4per_irrigated)
  • share of pastures, both managed and rangeland (pastures)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual

The C4 perennial crops are not further downscaled from the "5 crops" data set and currently only include sugarcane. Similarly, the C3 perennial crops are not downscaled either. The data is derived from the "5 crops" LUH2 data, and the crops have been downscaled to 15 crops according to the ratios given by the Monfreda data set (Monfreda, Ramankutty, & Foley, 2008).

Managed pastures and rangeland

socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-pastures_annual_<start_year>_<end_year>.nc
  • share of managed pastures (managed_pastures)
  • share of rangeland (rangeland)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual

Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al, in review., see also https://luh.umd.edu).

Urban areas

socioeconomic/landuse/<soc_scenario>/<soc_scenario>_landuse-urbanareas_annual_<start_year>_<end_year>.nc
  • share of urban areas (urbanareas)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual

Based on the HYDE 3.2 data set (Klein Goldewijk, 2016), but harmonized by Hurtt et al. (LUH2 v2h data set, see Hurtt, Chini, Sahajpal, Frolking, & et al., in review, see also https://luh.umd.edu).
N-fertilizer mandatory

Nitrogen deposited by fertilizers on croplands

socioeconomic/n-fertilizer/<soc_scenario>/<soc_scenario>_n-fertilizer-5crops_annual_<start_year>_<end_year>.nc
  • deposition of N through fertilizer on cropland with C3 annual crops (fertl_c3ann)
  • C3 perennial crops (fertl_c3per)
  • C3 N-fixing crops (fertl_c3nfx)
  • C4 annual crops (fertl_c4ann)
  • C4 perennial crops (fertl_c4per)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual (growing season)

Based on the LUH2 v2h data set (see Hurtt, Chini, Sahajpal, Frolking, & et al., in in review, see also https://luh.umd.edu).
N-deposition

Reduced nitrogen deposition

socioeconomic/n-deposition/<soc_scenario>/ndep-nhx_<soc_scenario>_monthly_<start_year>_<end_year>.nc
  • NHx deposition
  • 1850-1900
  • 1901-2016
  • 0.5° grid
  • monthly

Simulated by NCAR Chemistry-Climate Model Initiative (CCMI) during 1850-2014. Nitrogen deposition data was interpolated to 0.5° by 0.5° by the nearest grid. Data in 2015 and 2016 is assumed to be same as that in 2014 (Tian et al. 2018)

Oxidized nitrogen deposition

socioeconomic/n-deposition/<soc_scenario>/ndep-noy_<soc_scenario>_monthly_<start_year>_<end_year>.nc
  • NOy deposition
  • 1850-1900
  • 1901-2016
  • 0.5° grid
  • annual

Simulated by NCAR Chemistry-Climate Model Initiative (CCMI) during 1850-2014. Nitrogen deposition data was interpolated to 0.5° by 0.5° by the nearest grid. Data in 2015 and 2016 is assumed to be same as that in 2014 (Tian et al. 2018)
Reservoirs & dams

Reservoirs & dams

socioeconomic/reservoir_dams/reservoirs-dams_1850_2025.xls
  • Unique ID representing a dam and its associated reservoir corresponding to GRanD/KSU IDs (ID)
  • name (DAM_NAME)
  • original location (LON_ORIG, LAT_ORIG)
  • location adjusted to the DDM30 routing network (LON_DDM30, LAT_DDM30)
  • upstream area in DDM30 (CATCH_SKM_DDM30)
  • upstream area in GRanD (CATCH_SKM_GRanD)
  • maximum storage capacity of reservoir (CAP_MCM)
  • year of construction/commissioning (YEAR)
  • alternative year may indicate a multi-year construction or secondary dam (ALT_YEAR)
  • flag of correction if relocation was applied (FLAG_CORR)
  • river name which the dam impounds (RIVER)
  • height of dam (D_Hght_m)
  • maximum inundation area of reservoir (R_Area_km2)
  • length of reservoir (R_Lgth_km)
  • main purpose(s) of dam (PURPOSE)
  • source of information (SOURCE)
  • other notes (COMMENTS)
  • 1850-2025
  • 0.5° grid and original coordinates (degree)
  • annual

Lehner et al. (2011a, https://doi.org/10.7927/H4N877QK), Lehner et al. (2011b, https://dx.doi.org/10.1890/100125), and Jida Wang et al. (KSU/Kansas State University, personal communication). Because the data from KSU is yet unpublished, modeling teams using it are asked to offer co-authorship to the team at KSU on any resulting publications. Please contact info@isimip.org in case of questions.
Water abstraction

Water abstraction for domestic and industrial purposes

socioeconomic/water_abstraction/[domw|indw][w|c]_<soc_scenario>_annual_<start-year>_<end-year>.nc
  • domestic and industrial water withdrawal and consumption (domww, domwc, indww, indwc)
  • 1850-1900
  • 1901-2018
  • 0.5° grid
  • annual

For modelling groups that do not have their own representation, we provide files containing the multi-model mean of domestic and industrial water withdrawal and consumption generated by WaterGAP, PCR-GLOBWB, and H08. This data is based ISIMIP2a varsoc simulations for 1901-2005, and on RCP6.0 simulations from the Water Futures and Solutions project (Wada et al., 2016, http://www.geosci-model-dev.net/9/175/2016/) for after 2005. Years before 1901 have been filled with the value for year 1901.
Lakes

socioeconomic/lakes/pctlake_<soc_scenario>_<start_year>_<end_year>.nc
  • percentage of lakes in grid cell (pct_lake)
  • 0.5° grid

HydroLAKES polygons dataset v1.0 June 2019 and GRanD v1.3, rasterized using the polygon_to_cellareafraction tool (https://github.com/VUB-HYDR/polygon_to_cellareafraction). Reference: Messager et al. (2016, https://dx.doi.org/10.1038/ncomms13603, Lehner et al. (2011b, https://dx.doi.org/10.1890/100125).
Population mandatory

Population 5' grid

socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_5arcmin_annual_<start-year>_<end-year>.nc
  • total number of people (popc)
  • rural number of people (rurc)
  • urban number of people (urbc)
  • 1850-1900
  • 1901-2020
  • 5' grid
  • annual

HYDE v3.2.1 (Klein Goldewijk et al., 2017). Decadal data prior to year 2000 have been linearly interpolated in time.

Population 0.5° grid

socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_30arcmin_annual_<start-year>_<end-year>.nc
  • total number of people (popc)
  • rural number of people (rurc)
  • urban number of people (urbc)
  • 1850-1900
  • 1901-2020
  • 0.5° grid
  • annual

HYDE v3.2.1 (Klein Goldewijk et al., 2017). Decadal data prior to year 2000 have been linearly interpolated in time. Aggregated to 0.5° spatial resolution

Population national

socioeconomic/pop/<soc_scenario>/population_<soc_scenario>_national_annual_<start-year>_<end-year>.csv
  • total number of people per country
  • 1850-1900
  • 1901-2020
  • national
  • annual

HYDE v3.2.1 (based on WPP 2017 revision, following the methodology of Klein Goldewijk et al., 2017). Decadal data prior to year 1950 have been linearly interpolated in time.
GDP mandatory

GDP

socioeconomic/gdp/<soc_scenario>/<soc_scenario>_gdp_annual_<start-year>_<end-year>.nc
  • GDP PPP 2005 USD (gdp)
  • 1850-1900
  • 1901-2016
  • country-level
  • annual

Historic country-level GDP data are an extension of the data provided by Geiger, 2018 (https://www.earth-syst-sci-data.net/10/847/2018/essd-10-847-2018.html), and are derived mainly from the Maddison Project database. Gridded GDP data will be provided by c. 07/2021.

Geographic data and information

Table 9: Geographic data and information for ISIMIP3a simulation round.
Dataset Included variables (specifier) Resolution Reference/Source and Comments
Land/Sea masks

landseamask

geo_conditions/landseamask/landseamask.nc
  • land-sea mask (mask)
 0.5° grid

This is the land-sea mask of the W5E5 dataset. Over all grid cells marked as land by this mask, all climate data that are based on W5E5 (GSWP3-W5E5 as well as climate data bias-adjusted using W5E5) are guaranteed to represent climate conditions over land.

landseamask_no-ant

geo_conditions/landseamask/landseamask_no-ant.nc
  • land-sea mask (mask)
 0.5° grid

Same as landseamask but without Antarctica.

landseamask_water-global

geo_conditions/landseamask/landseamask_water-global.nc
  • land-sea mask (mask)
 0.5° grid

This is the generic land-sea mask from ISIMIP2b that is to be used for global water simulations in ISIMIP3. It marks more grid cells as land than landseamask. Over those additional land cells, the climate data that are based on W5E5 (GSWP3-W5E5 as well as climate data bias-adjusted using W5E5) are not guaranteed to represent climate conditions over land. Instead they may represent climate conditions over sea or a mix of conditions over land and sea.
Soil

gswp3_hwsd

geo_conditions/soil/gswp3_hwsd.nc
  • soiltexture
 0.5° grid

One fixed pattern to be used for all simulation periods. Upscaled Soil texture map (30 arc sec. to 0.5°x0.5° grid) based on Harmonized World Soil Database v1.1 (HWSD) using the GSWP3 upscaling method A (http://hydro.iis.u-tokyo.ac.jp/~sujan/research/gswp3/soil-texture-map.html)
Lakes

lakemask

geo_conditions/lakes/lakemask.nc
  • total lake surface area in grid cell (tot_area)
  • average surface area of lakes in grid cell (avg_area)
 0.5° grid

lakedepth

geo_conditions/lakes/lakedepth.nc
  • lake depth (lakedepth)
 0.5° grid

For these variables, new forcing files will be provided soon: histsoc, accounting for reservoir expansion; and 2015soc.
Other

basins

geo_conditions/river_routing/ddm30_basins_cru_neva.[nc|asc]
  • basin number (basinnumber)
 0.5° grid

DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files.

flowdir

geo_conditions/river_routing/ddm30_flowdir_cru_neva.[nc|asc]
  • flow direction (flowdirection)
 0.5° grid

DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files.

slopes

geo_conditions/river_routing/ddm30_slopes_cru_neva.[nc|asc]
  • slope (slope)
 0.5° grid

DDM30 (Döll & Lehner, 2002). Documentation (pdf) is provided alongside data files.

Output data

Output variables

Table 10: Output variables for Lakes (local) (variable).
Variable Variable specifier Unit Dimensions Resolution Comments
Hydrothermal Variables
Thermal stratification strat 1
  • Representative lake associated with grid cell
  • daily

1 if lake grid cell is thermally stratified, 0 if lake grid cell is not thermally stratified
Depth of Thermocline thermodepth m
  • Representative lake associated with grid cell
  • daily

Depth corresponding the maximum water density gradient
Water temperature watertemp K
  • Representative lake associated with grid cell
  • daily

Depth resolution: Full Profile. Simulated water temperature. Layer averages and full profiles.
Surface temperature surftemp K
  • Representative lake associated with grid cell
  • daily, monthly

Average of the upper layer in case not simulated directly.
Bottom temperature bottemp K
  • Representative lake associated with grid cell
  • daily, monthly

Average of the lowest layer in case not simulated directly.
Lake ice cover ice 1
  • Representative lake associated with grid cell
  • daily

1 if ice cover is present in lake grid cell, 0 if no ice cover is present in lake grid cell
Lake layer ice mass fraction lakeicefrac 1
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Mean epi. Fraction of mass of a given layer taken up by ice.
Ice thickness icethick m
  • Representative lake associated with grid cell
  • daily, monthly
Snow thickness snowthick m
  • Representative lake associated with grid cell
  • daily, monthly
Temperature at the ice upper surface icetemp K
  • Representative lake associated with grid cell
  • daily
Temperature at the snow upper surface snowtemp K
  • Representative lake associated with grid cell
  • daily
Sensible heat flux at the lake-atmosphere interface sensheatf W m-2
  • Representative lake associated with grid cell
  • daily, monthly

At the surface of snow, ice or water depending on the layer in contact with the atmosphere. Positive if upwards.
Latent heat flux at the lake-atmosphere interface latentheatf W m-2
  • Representative lake associated with grid cell
  • daily, monthly

At the surface of snow, ice or water depending on the layer in contact with the atmosphere. Positive if upwards.
Momentum flux at the lake-atmosphere interface momf kg m-1 s-2
  • Representative lake associated with grid cell
  • daily, monthly

At the surface of snow, ice or water depending on the layer in contact with the atmosphere. Positive if upwards.
Upward shortwave radiation flux at the lake-atmosphere interface swup W m-2
  • Representative lake associated with grid cell
  • daily, monthly

At the surface of snow, ice or water depending on the layer in contact with the atmosphere. Positive if upwards. Not to be confused with net shortwave radiation.
Upward longwave radiation flux at the lake-atmosphere interface lwup W m-2
  • Representative lake associated with grid cell
  • daily, monthly

At the surface of snow, ice or water depending on the layer in contact with the atmosphere. Positive if upwards. Not to be confused with net longwave radiation.
Downward heat flux at the lake-atmosphere interface lakeheatf W m-2
  • Representative lake associated with grid cell

At the surface of snow, ice or water depending on the layer in contact with the atmosphere. Positive if upwards. the residual term of the surface energy balance, i.e. the net amount of energy that enters the lake on daily time scale: lakeheatf = swdown - swup + lwdown - lwup - sensheatf - latenheatf (terms defined positive when directed upwards)
Turbulent diffusivity of heat turbdiffheat m2 s-1
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo. Only if computed by the model.
Light extinction coefficient extcoeff m-1
  • Representative lake associated with grid cell
  • constant

Only to be reported for global models, local models should use extcoeff as input.
Sediment upward heat flux at the lake-sediment interface sedheatf W m-2
  • Representative lake associated with grid cell
  • daily, monthly

Positive if upwards. Only if computed by the model.
Water Quality Variables
Chlorophyll Concentration chl g-3 m⁻³
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo. Total water chlorophyll concentration – indicator of phytoplankton.
Phytoplankton Functional group biomass phytobio mole m⁻³ as carbon
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo. Different models will have different numbers of functional groups so that the reporting of these will vary by model.
Phytoplankton Functional group biomass zoobio mole m⁻³ as carbon
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo. Total simulated Zooplankton biomass.
Total Phosphorus tp mole m⁻³
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo.
Particulate Phosphorus pp mole m⁻³
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo.
Total Dissolved Phosphorus tpd mole m⁻³
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo. Some models may also output data for soluble reactive phosphorus (SRP).
Total Nitrogen tn mole m⁻³
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo.
Particulate Nitrogen pn mole m⁻³
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo.
Total Dissolved Nitrogen tdn mole m⁻³
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo. Some models may also output data for Nitrate (N02) nitrite (NO3) and ammonium (NH4).
Dissolved Oxygen do mole m⁻³
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo.
Dissolved Organic Carbon doc mole m⁻³
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo. Not always available.
Dissolved Silica si mole m⁻³
  • Representative lake associated with grid cell
  • daily, monthly

Depth resolution: Either full profile, or mean epi and mean hypo. Not always available.
Other
Surface albedo of lake lakealbedo 1
  • Representative lake associated with grid cell
  • daily, monthly

Albedo of the lake surface interacting with the atmosphere (water, ice or snow).

Lake sites

Table 11: Lake site specifications for local lake models (lake-site).
Lake name Specifier Reservoir or lake Country Coordinates (Lat, Lon)
Alqueva Reservoir alqueva reservoir Portugal 38.2, -7.49
Lake Annecy annecy lake France 45.87, 6.17
Lake Annie annie lake USA 27.21, -81.35
Lake Argyle argyle reservoir Australia -16.31, 128.68
Lake Biel biel lake Switzerland 47.08, 7.16
Big Muskellunge Lake big-muskellunge lake USA 46.02, -89.61
Black Oak Lake black-oak lake USA 46.16, -89.32
Lake Bourget bourget lake France 45.76, 5.86
Lake Burley Griffin burley-griffin reservoir Australia -35.3, 149.07
Crystal Lake crystal-lake lake USA 46.0, -89.61
Crystal Bog crystal-bog lake USA 46.01, -89.61
Delavan Lake delavan lake USA 42.61, -88.6
Dickie Lake dickie lake Canada 45.15, -79.09
Eagle Lake eagle lake Canada 44.68, -76.7
Ekoln basin of Mälaren ekoln lake Sweden 59.75, 17.62
Lake Erken erken lake Sweden 59.84, 18.63
Esthwaite Water esthwaite-water lake United Kingdom 54.37, -2.99
Falling Creek Reservoir falling-creek reservoir USA 37.31, -79.84
Lake Feeagh feeagh lake Ireland 53.9, -9.5
Fish Lake fish lake USA 43.29, -89.65
Lake Geneva geneva lake France/Switzerland 46.45, 6.59
Great Pond great lake USA 44.53, -69.89
Green Lake green lake USA 43.81, -89.0
Harp Lake harp lake Canada 45.38, -79.13
Kilpisjärvi kilpisjarvi lake Finland 69.03, 20.77
Lake Kinneret kinneret lake Israel 32.49, 35.35
Lake Kivu kivu lake Rwanda/DR Congo -1.73, 29.24
Klicava Reservoir klicava reservoir Czechia 50.07, 13.93
Lake Kuivajarvi kuivajarvi lake Finland 60.47, 23.51
Lake Langtjern langtjern lake Norway 60.37, 9.73
Laramie Lake laramie lake USA 40.62, -105.84
Lower Lake Zurich lower-zurich lake Switzerland 47.28, 8.58
Lake Mendota mendota lake USA 43.1, -89.41
Lake Monona monona lake USA 43.06, -89.36
Mozhaysk reservoir mozhaysk reservoir Russia 55.59, 35.82
Mt Bold mt-bold reservoir Australia -35.12, 138.71
Lake Müggelsee mueggelsee lake Germany 52.43, 13.65
Lake Neuchâtel neuchatel lake Switzerland 46.54, 6.52
Ngoring ngoring lake China 34.9, 97.7
Lake Nohipalo Mustjärv nohipalo-mustjaerv lake Estonia 57.93, 27.34
Lake Nohipalo Valgejärv nohipalo-valgejaerv lake Estonia 57.94, 27.35
Okauchee Lake okauchee lake USA 43.13, -88.43
Lake Pääjärvi paajarvi lake Finland 61.07, 25.13
Rappbode Reservoir rappbode reservoir Germany 51.74, 10.89
Rimov Reservoir rimov reservoir Czechia 48.85, 14.49
Lake Rotorua rotorua lake New Zealand -38.08, 176.28
Lake Sammamish sammamish lake USA 47.59, -122.1
Sau Reservoir sau reservoir Spain 41.97, 2.4
Sparkling Lake sparkling lake USA 46.01, -89.7
Lake Stechlin stechlin lake Germany 53.17, 13.03
Lake Sunapee sunapee lake USA 43.23, -72.5
Lake Tahoe tahoe reservoir USA 39.09, -120.03
Lake Tarawera tarawera lake New Zealand -38.21, 176.43
Lake Taupo taupo lake New Zealand -38.8, 175.89
Toolik Lake toolik lake USA 68.63, -149.6
Trout Lake trout-lake lake USA 46.03, -89.67
Trout Bog trout-bog lake USA 46.04, -89.69
Two Sisters Lake two-sisters lake USA 45.77, -89.53
Lake Vendyurskoe vendyurskoe lake Russia 62.1, 33.1
lake Võrtsjärv vortsjaerv lake Estonia 58.31, 26.01
Lake Waahi waahi lake New Zealand 37.33, 175.07
Lake Washington washington lake USA 47.64, -122.27
Windermere windermere lake United Kingdom 54.31, -2.95
Lake Wingra wingra lake USA 43.05, -89.43
Zlutice Reservoir zlutice reservoir Czechia 50.09, 13.11

A document with additional information is maintained by the sector coordinators and provided at https://docs.google.com/spreadsheets/d/1UY_KSR02o7LtmNoOs6jOgOxdcFEKrf7MmhR2BYDlm-Q/edit#gid=555498854.

Reporting model results

The specification on how to submit the data, as well as further information and instructions are given on the ISIMIP website at:

https://www.isimip.org/protocol/preparing-simulation-files

It is important that you comply precisely with the formatting specified there, in order to facilitate the analysis of your simulation results in the ISIMIP framework. Incorrect formatting can seriously delay the analysis. The ISIMIP Team will be glad to assist with the preparation of these files if necessary.

File names consist of a series of identifier, separated by underscores. Things to note:

Please name the files in the Lakes (local) sector according to the following pattern:

<model>_<climate-forcing>_<climate-scenario>_<soc-scenario>_<sens-scenario>_<variable>_<lake-site>_<timestep>_<start-year>_<end-year>.nc

and replace the identifiers with the specifiers given in the tables of this document. Examples would be:

lpjml_gswp3_obsclim_histsoc_default_qtot_global_annual_1901_1910.nc
lpjml_gwsp3_counterclim_2015soc_1901co2_yield-mai-noirr_global_annual_2006_2010.nc

The following regular expression can be used to validate and parse the file name for the lakes (local) sector:

(?P<model>[a-z0-9-+.]+)_(?P<climate_forcing>[a-z0-9-]+)_(?P<climate_scenario>[a-z0-9-]+)_(?P<soc_scenario>[a-z0-9-]+)_(?P<sens_scenario>[a-z0-9-]+)_(?P<variable>[a-z0-9]+)_(?P<lake_site>[a-z0-9-]+)_(?P<timestep>[a-z0-9-]+)_(?P<start_year>\d{4})_(?P<end_year>\d{4}).nc

For questions or clarifications, please contact info@isimip.org or the data managers directly (isimip-data@pik‐potsdam.de) before submitting files.

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