ISIMIP3b simulation round simulation protocol - Diarrhoeal diseases

Introduction
Scenarios & Experiments
- Scenario definitions
- Experiments
Input data
Output data
- Output variables
Reporting model results
References

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:

A common set of climate and other forcing data which will be distributed via a central database;
A common modelling protocol to ensure consistency across sectors and scales in terms of data, format and scenario set-up;
A central archive where the output data will be collected and made available to the scientific community.

ISIMIP3b

GCM-based simulations assuming fixed 2015 direct human influences for the future

The ISIMIP3b part of the third simulation round is dedicated to a quantification of climate-related risks at different levels of climate change and socio-economic conditions. The group 1 simulations refer to the pre-industrial and historical period of the CMIP6-based climate simulations. Group 2 covers all future projections assuming fixed 2015 levels of socio-economic forcing and different future projections of climate (SSP126, SSP37 and SSP585). Group3 simulations account for future changes in socio-economic drivers and are intended to be started in summer 2021.

You can find the ISIMIP3a protocol, which is is dedicated to impact model evaluation and improvement and detection and attribution of observed impacts, here.

Simulation protocol

In this protocol we describe the scenarios & experiments in ISIMIP3b simulation round, the different input datasets, the output variables, and how to report model results specifically for Diarrhoeal diseases. 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
picontrol	Pre-industrial climate as simulated by the GCMs.
historical	Historical climate as simulated by the GCMs.
ssp126	SSP1-RCP2.6 climate as simulated by the GCMs.
ssp370	SSP3-RCP7 climate as simulated by the GCMs.
ssp585	SSP5-RCP8.5 climate as simulated by the GCMs.

Table 2: Socio-economic scenario specifiers (`soc-scenario`).
Scenario specifier	Description
1850soc	Fixed year-1850 direct human influences (e.g. land use, nitrogen deposition and fertilizer input, fishing effort). Please label your simulations `1850soc` if they do not at all account for historical changes in direct human forcing, but they do represent constant year-1850 levels of direct human forcing for at least some direct human forcings.
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 1850co2 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	Pre-industrial 1601-1849	Historical 1850-2014	Future 2015-2100
pre-industrial control histsoc 1st priority	CF: no climate change, pre-industrial CO₂ fixed at 1850 levels	picontrol	picontrol	picontrol
pre-industrial control histsoc 1st priority	DHF: varying management before 2015, then fixed at 2015 levels thereafter	1850soc	histsoc	2015soc
pre-industrial control 2015soc 1st priority	CF: no climate change, pre-industrial CO₂ fixed at 1850 levels	does not have to be simulated as the following year already provide a large sample of years with stable climate and constant (2015soc) / no (nat) DHF for period	picontrol	picontrol
pre-industrial control 2015soc 1st priority	DHF: fixed at 2015 levels for all periods		2015soc	2015soc
RCP2.6 histsoc 1st priority	CF: Simulated historical climate and CO₂ in historical period, then SSP1-RCP2.6 climate & CO₂	"histsoc" version of the pre-industrial period of the pre-industrial control experiment	historical	ssp126
RCP2.6 histsoc 1st priority	DHF: varying management before 2015, then fixed at 2015 levels thereafter		histsoc	2015soc
RCP2.6 2015soc 1st priority	CF: Simulated historical climate and CO₂ in historical period, then SSP1-RCP2.6 climate & CO₂	"2015soc" version of the pre-industrial period of the pre-industrial control experiment	historical	ssp126
RCP2.6 2015soc 1st priority	DHF: fixed at 2015 levels for all periods		2015soc	2015soc
RCP7 histsoc 1st priority	CF: SSP3-RCP7 climate & CO₂	"histsoc" version of pre-industrial of pre-industrial control experiment runs	"histsoc" version of the historical period of the RCP2.6 experiment	ssp370
RCP7 histsoc 1st priority	DHF: varying management before 2015, then fixed at 2015 levels thereafter			2015soc
RCP7 2015soc 1st priority	CF: SSP3-RCP7 climate & CO₂	"2015soc" version of pre-industrial of pre-industrial control experiment runs	"2015soc" version of the historical period of the RCP2.6 experiment	ssp370
RCP7 2015soc 1st priority	DHF: fixed at 2015 levels for all periods			2015soc
RCP8.5 histsoc 1st priority	CF: SSP5-RCP8.5 climate & CO₂	"histsoc" version of pre-industrial of pre-industrial control experiment runs	"histsoc" version of the historical period of the RCP2.6 experiment	ssp585
RCP8.5 histsoc 1st priority	DHF: varying management before 2015, then fixed at 2015 levels thereafter			2015soc
RCP8.5 2015soc 1st priority	CF: SSP5-RCP8.5 climate & CO₂	"2015soc" version of pre-industrial of pre-industrial control experiment runs	"2015soc" version of the historical period of the RCP2.6 experiment	ssp585
RCP8.5 2015soc 1st priority	DHF: fixed at 2015 levels for all periods			2015soc

Note regarding models requiring spin-up

For models requiring spin-up, please use the pre-industrial control data and CO₂ concentration and DHF fixed at 1850 levels for the spin up as long as needed. Please note that the "pre-industrial control run" from 1601-1849 is part of the regular experiments that should be reported and hence the spin-up has to be finished before that.

Input data

The base directory for input data at DKRZ is:

/work/bb0820/ISIMIP/ISIMIP3b/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

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

climate/atmosphere/bias-adjusted/global/daily/<climate-scenario>/<climate-forcing>/<climate-forcing>_<ensemble-member>_<bias-adjustment>_<climate-scenario>_<climate-variable>_global_daily_<start-year>_<end-year>.nc

Table 5: Climate and climate-related forcing data (`climate-forcing`).
Title	Specifier	Institution	Original resolution	Ensemble member	Priority
GFDL-ESM4	gfdl-esm4	National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA	288x180	r1i1p1f1	1
UKESM1-0-LL	ukesm1-0-ll	Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK	192x144	r1i1p1f2	2
MPI-ESM1-2-HR	mpi-esm1-2-hr	Max Planck Institute for Meteorology, Hamburg 20146, Germany	384x192	r1i1p1f1	3
IPSL-CM6A-LR	ipsl-cm6a-lr	Institut Pierre Simon Laplace, Paris 75252, France	144x143	r1i1p1f1	4
MRI-ESM2-0	mri-esm2-0	Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan	320x160	r1i1p1f1	5

Note on climate forcing priority

The priority for the different climate forcing datasets is from top to bottom. If you cannot use all climate forcing datasets, please concentrate on those at the top of the table.

Table 6: Climate forcing variables for ISIMIP3b simulations (`climate-variable`).
Variable	Variable specifier	Unit	Resolution	Models
Atmospheric variables mandatory
Near-Surface Relative Humidity	hurs	%	0.5° grid daily	GFDL-ESM4 UKESM1-0-LL MPI-ESM1-2-HR IPSL-CM6A-LR MRI-ESM2-0
Near-Surface Specific Humidity	huss	kg kg-1	0.5° grid daily	GFDL-ESM4 UKESM1-0-LL MPI-ESM1-2-HR IPSL-CM6A-LR MRI-ESM2-0
Precipitation	pr	kg m-2 s-1	0.5° grid daily	GFDL-ESM4 UKESM1-0-LL MPI-ESM1-2-HR IPSL-CM6A-LR MRI-ESM2-0
Snowfall Flux	prsn	kg m-2 s-1	0.5° grid daily	GFDL-ESM4 UKESM1-0-LL MPI-ESM1-2-HR IPSL-CM6A-LR MRI-ESM2-0
Surface Air Pressure	ps	Pa	0.5° grid daily	GFDL-ESM4 UKESM1-0-LL MPI-ESM1-2-HR IPSL-CM6A-LR MRI-ESM2-0
Surface Downwelling Longwave Radiation	rlds	W m-2	0.5° grid daily	GFDL-ESM4 UKESM1-0-LL MPI-ESM1-2-HR IPSL-CM6A-LR MRI-ESM2-0
Surface Downwelling Shortwave Radiation	rsds	W m-2	0.5° grid daily	GFDL-ESM4 UKESM1-0-LL MPI-ESM1-2-HR IPSL-CM6A-LR MRI-ESM2-0
Near-Surface Wind Speed	sfcwind	m s-1	0.5° grid daily	GFDL-ESM4 UKESM1-0-LL MPI-ESM1-2-HR IPSL-CM6A-LR MRI-ESM2-0
Near-Surface Air Temperature	tas	K	0.5° grid daily	GFDL-ESM4 UKESM1-0-LL MPI-ESM1-2-HR IPSL-CM6A-LR MRI-ESM2-0
Daily Maximum Near-Surface Air Temperature	tasmax	K	0.5° grid daily	GFDL-ESM4 UKESM1-0-LL MPI-ESM1-2-HR IPSL-CM6A-LR MRI-ESM2-0
Daily Minimum Near-Surface Air Temperature	tasmin	K	0.5° grid daily	GFDL-ESM4 UKESM1-0-LL MPI-ESM1-2-HR IPSL-CM6A-LR MRI-ESM2-0

Other climate datasets

Table 7: Other climate datesets for ISIMIP3b simulation round.
Variable	Variable specifier	Unit	Resolution	Datasets
Atmospheric composition mandatory
Atmospheric CO2 concentration	`climate/atmosphere_composition/co2/<climate-scenario>/co2_<climate-scenario>_annual_<start_year>_<end_year>.txt`
Atmospheric CO2 concentration	co2	ppm	global annual	Meinshausen, Raper, & Wigley (2011) for 1850-2005 and 2016-2100 and Dlugokencky & Tans (2019) from 2006-2015

Socioeconomic forcing

Table 8: Socioeconomic datasets for ISIMIP3b 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_2014.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) flag to indicate that the year of dam construction has been artificially set to 1850 if not existing (FLAG_ART=1, otherwise 0) 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) main purpose(s) of dam (PURPOSE) source of information (SOURCE) other notes (COMMENTS)	1850-2014	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 1901-2014	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.
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 1901-2014	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)	1850-2014	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	1850-2014	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.
Population density 5' grid	`socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_5arcmin_annual_<start-year>_<end-year>.nc`
	total number of people per square kilometer (popc) rural number of people per square kilometer (rurc) urban number of people per square kilometer (urbc)	1850-1900 1901-2014	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 density 0.5° grid	`socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_30arcmin_annual_<start-year>_<end-year>.nc`
	total number of people per square kilometer (popc) rural number of people per square kilometer (rurc) urban number of people per square kilometer (urbc)	1850-2014	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 density national	`socioeconomic/pop/<soc_scenario>/population-density_<soc_scenario>_national_annual_<start-year>_<end-year>.csv`
	total number of people per square kilometer	1850-2014	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)	1850-2014	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 ISIMIP3b 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 (Lange, 2019a; Cucchi et al., 2020). 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)
River routing
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

ISIMIP output variables are usually reported with the dimensions (time,lat,lon). For variables with a number of levels (e.g. layers or depth), an alternative set of dimensions is given in the comment column in the table below. More information about level dimensions can be found here and here on the ISIMIP webpage.

Please note that unless otherwise defined, the variables in ISIMIP should be reported relative to the grid cell land area.

Output variables

No output variables have been defined for Diarrhoeal diseases, yet.

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:

Report one variable per file
In filenames, use lowercase letters only
Use underscore (_) to separate identifiers
Variable names consist of a single word without hyphens or underscores
Use hyphens (-) to separate strings within an identifier, e.g. in a model name
If no specific sens-scenario is given in the experiments table, use default.
NetCDF file extension is .nc

Please name the files in the Diarrhoeal diseases sector according to the following pattern:

<model>_<climate-forcing>_<bias-adjustment>_<climate-scenario>_<soc-scenario>_<sens-scenario>_<variable>_<region>_<time-step>_<start-year>_<end-year>.nc

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

lpjml_gfdl-esm4_w5e5_picontrol_histsoc_default_qtot_global_annual_2001_2010.nc
lpjml_ukesm1-0-ll_w5e5_ssp585_2015soc_2015co2_yield-mai-noirr_global_annual_2006_2010.nc

The following regular expression can be used to validate and parse the file name for the diarrhoeal diseases sector:

(?P<model>[a-z0-9-+.]+)_(?P<climate_forcing>[a-z0-9-]+)_(?P<bias_adjustment>[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<region>(global))_(?P<time_step>[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.

References

Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D. and Worley, S. J.: The twentieth century reanalysis project, Quarterly Journal of the Royal Meteorological Society, 137(654), 1–28, doi:10.1002/qj.776, 2011.

Cucchi, M., Weedon, G. P., Amici, A., Bellouin, N., Lange, S., Müller Schmied, H., Hersbach, H. and Buontempo, C.: WFDE5: Bias-adjusted ERA5 reanalysis data for impact studies, Earth System Science Data, 12(3), 2097–2120, doi:10.5194/essd-12-2097-2020, 2020.

Dirmeyer, P. A., Gao, X., Zhao, M., Guo, Z., Oki, T. and Hanasaki, N.: GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface, Bulletin of the American Meteorological Society, 87(10), 1381–1398, doi:10.1175/BAMS-87-10-1381, 2006.

Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, Natl. Ocean. Atmos. Adm. Earth Syst. Res. Lab. [online] Available from: https://www.esrl.noaa.gov/gmd/ccgg/trends/, 2019.

Geiger, T.: Continuous national gross domestic product (GDP) time series for 195 countries: Past observations (1850–2005) harmonized with future projections according to the Shared Socio-economic Pathways (2006–2100), Earth System Science Data, 10(2), 847–856, doi:10.5194/essd-10-847-2018, 2018.

Hurtt, G. C., Chini, L., Sahajpal, R., Frolking, S., Bodirsky, B. L., Calvin, K., Doelman, J. C., Fisk, J., Fujimori, S., Goldewijk, K. K., Hasegawa, T., Havlik, P., Heinimann, A., Humpenöder, F., Jungclaus, J., Kaplan, J., Kennedy, J., Kristzin, T., Lawrence, D., Lawrence, P., Ma, L., Mertz, O., Pongratz, J., Popp, A., Poulter, B., Riahi, K., Shevliakova, E., Stehfest, E., Thornton, P., Tubiello, F. N., Vuuren, D. P. van and Zhang, X.: Harmonization of Global Land-Use Change and Management for the Period 850–2100 (LUH2) for CMIP6, Geoscientific Model Development Discussions, 1–65, doi:https://doi.org/10.5194/gmd-2019-360, 2020.

Klein Goldewijk, K., Beusen, A., Doelman, J. and Stehfest, E.: Anthropogenic land use estimates for the Holocene – HYDE 3.2, Earth System Science Data, 9(2), 927–953, doi:10.5194/essd-9-927-2017, 2017.

Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geoscientific Model Development, 12(7), 3055–3070, doi:10.5194/gmd-12-3055-2019, 2019a.

Lange, S.: WFDE5 over land merged with ERA5 over the ocean (W5E5), v1.0, GFZ Data Services, doi:10.5880/pik.2019.023, 2019b.

Lange, S.: ISIMIP3BASD, v2.4.1, doi:10.5281/zenodo.3898426, 2020.

Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N. and Wisser, D.: Global Reservoir and Dam Database, Version 1 (GRanDv1): Dams, Revision 01. Palisades, NY, NASA Socioeconomic Data and Applications Center (SEDAC), doi:10.7927/H4N877QK, 2011a.

Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N. and Wisser, D.: High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management, Frontiers in Ecology and the Environment, 9(9), 494–502, doi:10.1890/100125, 2011b.

Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M. L. T., Lamarque, J.-F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M. and Vuuren, D. P. P. van: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Climatic Change, 109(1), 213, doi:10.1007/s10584-011-0156-z, 2011.

Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., Berg, M. van den, Velders, G. J. M., Vollmer, M. K. and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geoscientific Model Development, 13(8), 3571–3605, doi:10.5194/gmd-13-3571-2020, 2020.

Messager, M. L., Lehner, B., Grill, G., Nedeva, I. and Schmitt, O.: Estimating the volume and age of water stored in global lakes using a geo-statistical approach, Nature Communications, 7(1), 13603, doi:10.1038/ncomms13603, 2016.

Murakami, D. and Yamagata, Y.: Estimation of Gridded Population and GDP Scenarios with Spatially Explicit Statistical Downscaling, Sustainability, 11(7), 2106, doi:10.3390/su11072106, 2019.

Portmann, F. T., Siebert, S. and Döll, P.: MIRCA2000—global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling, Global Biogeochemical Cycles, 24(1), doi:10.1029/2008GB003435, 2010.

Reyer, C., Silveyra Gonzalez, R., Dolos, K., Hartig, F., Hauf, Y., Noack, M., Lasch-Born, P., Rötzer, T., Pretzsch, H., Meesenburg, H., Fleck, S., Wagner, M., Bolte, A., Sanders, T., Kolari, P., Mäkelä, A., Vesala, T., Mammarella, I., Pumpanen, J., Matteucci, G., Collalti, A., D’Andrea, E., Foltýnová, L., Krejza, J., Ibrom, A., Pilegaard, K., Loustau, D., Bonnefond, J.-M., Berbigier, P., Picart, D., Lafont, S., Dietze, M., Cameron, D., Vieno, M., Tian, H., Palacios-Orueta, A., Cicuendez, V., Recuero, L., Wiese, K., Büchner, M., Lange, S., Volkholz, J., Kim, H., Weedon, G., Sheffield, J., Vega del Valle, I., Suckow, F., Horemans, J., Martel, S., Bohn, F., Steinkamp, J., Chikalanov, A. and Frieler, K.: The PROFOUND database for evaluating vegetation models and simulating climate impacts on forests. v. 0.1.12., GFZ Data Services, doi:10.5880/PIK.2019.008, 2019.

Reyer, C. P. O., Silveyra Gonzalez, R., Dolos, K., Hartig, F., Hauf, Y., Noack, M., Lasch-Born, P., Rötzer, T., Pretzsch, H., Meesenburg, H., Fleck, S., Wagner, M., Bolte, A., Sanders, T. G. M., Kolari, P., Mäkelä, A., Vesala, T., Mammarella, I., Pumpanen, J., Collalti, A., Trotta, C., Matteucci, G., D’Andrea, E., Foltýnová, L., Krejza, J., Ibrom, A., Pilegaard, K., Loustau, D., Bonnefond, J.-M., Berbigier, P., Picart, D., Lafont, S., Dietze, M., Cameron, D., Vieno, M., Tian, H., Palacios-Orueta, A., Cicuendez, V., Recuero, L., Wiese, K., Büchner, M., Lange, S., Volkholz, J., Kim, H., Horemans, J. A., Bohn, F., Steinkamp, J., Chikalanov, A., Weedon, G. P., Sheffield, J., Babst, F., Vega del Valle, I., Suckow, F., Martel, S., Mahnken, M., Gutsch, M. and Frieler, K.: The PROFOUND database for evaluating vegetation models and simulating climate impacts on european forests, Earth System Science Data, 12(2), 1295–1320, doi:10.5194/essd-12-1295-2020, 2020.

Tian, H., Yang, J., Lu, C., Xu, R., Canadell, J. G., Jackson, R. B., Arneth, A., Chang, J., Chen, G., Ciais, P., Gerber, S., Ito, A., Huang, Y., Joos, F., Lienert, S., Messina, P., Olin, S., Pan, S., Peng, C., Saikawa, E., Thompson, R. L., Vuichard, N., Winiwarter, W., Zaehle, S., Zhang, B., Zhang, K. and Zhu, Q.: The Global N2O Model Intercomparison Project, Bulletin of the American Meteorological Society, 99(6), 1231–1251, doi:10.1175/BAMS-D-17-0212.1, 2018.