ISIMIP3b simulation round simulation protocol - Fisheries and Marine Ecosystems (regional)

Introduction
Scenarios & Experiments
- Scenario definitions
- Experiments
Input data
Output data
- Output variables
- Ocean regions
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 Fisheries and Marine Ecosystems (regional). 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 (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
pre-industrial control nat 2nd 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 nat 2nd priority	DHF: No direct human influences		nat	nat
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
RCP2.6 nat 2nd priority	CF: Simulated historical climate and CO₂ in historical period, then SSP1-RCP2.6 climate & CO₂	"nat" version of the pre-industrial period of the pre-industrial control experiment	historical	ssp126
RCP2.6 nat 2nd priority	DHF: No direct human influences		nat	nat
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
RCP7 nat 2nd priority	CF: SSP3-RCP7 climate & CO₂	"nat" version of pre-industrial of pre-industrial control experiment runs	"nat" version of the historical period of the RCP2.6 experiment	ssp370
RCP7 nat 2nd priority	DHF: No direct human influences			nat
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
RCP8.5 nat 2nd priority	CF: SSP5-RCP8.5 climate & CO₂	"nat" version of pre-industrial of pre-industrial control experiment runs	"nat" version of the historical period of the RCP2.6 experiment	ssp585
RCP8.5 nat 2nd priority	DHF: No direct human influences			nat

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/ocean/uncorrected/global/monthly/<climate-scenario>/<climate-forcing>/<climate-forcing>_<ensemble-member>_<climate-scenario>_<climate-variable>_onedeg_global_daily_<start-year>_<end-year>.nc  # 1° grid

climate/ocean/uncorrected/global/monthly/<climate-scenario>/<climate-forcing>/<climate-forcing>_<ensemble-member>_<climate-scenario>_<climate-variable>_halfdeg_global_daily_<start-year>_<end-year>.nc  # 0.5° grid

Variable availability is mainly based on the data published in ESGF and may vary between the CMIP experiments.

Some variables are available as extracted versions from vertically resolved data. Their variable names have been suffixed with -bot (ocean bottom), -surf (surface values) or -vint (vertically integrated), respectively.

Table 5: Climate and climate-related forcing data (`climate-forcing`).
Title	Specifier	Institution	Native resolution	Ensemble member	Priority
GFDL-ESM4	gfdl-esm4	National Oceanic and Atmospheric Administration, Geophysical Fluid Dynamics Laboratory, Princeton, NJ 08540, USA	720x576	r1i1p1f1	1
UKESM1-0-LL	ukesm1-0-ll	Met Office Hadley Centre, Fitzroy Road, Exeter, Devon, EX1 3PB, UK	360x330	r1i1p1f2	2
MPI-ESM1-2-HR	mpi-esm1-2-hr	Max Planck Institute for Meteorology, Hamburg 20146, Germany	802x404	r1i1p1f1	3
IPSL-CM6A-LR	ipsl-cm6a-lr	Institut Pierre Simon Laplace, Paris 75252, France	362x332	r1i1p1f1	4

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
Ocean variables mandatory
Mass Concentration of Total Phytoplankton Expressed as Chlorophyll	chl	kg m-3	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Sea Floor Depth	deptho	m	1° grid fixed	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Downward Flux of Particulate Organic Carbon	expc-bot	mol m-2 s-1	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Particulate Organic Carbon Content	intpoc	kg m-2	1° grid monthly	GFDL-ESM4 MPI-ESM1-2-HR UKESM1-0-LL
Primary Organic Carbon Production by All Types of Phytoplankton	intpp	mol m-2 s-1	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Net Primary Organic Carbon Production by Diatoms	intppdiat	mol m-2 s-1	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR UKESM1-0-LL
Net Primary Mole Productivity of Carbon by Diazotrophs	intppdiaz	mol m-2 s-1	1° grid monthly	CESM2 GFDL-ESM4 MPI-ESM1-2-HR
Maximum Ocean Mixed Layer Thickness Defined by Sigma T	mlotstmax	m	1° grid monthly	CESM2 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Dissolved Oxygen Concentration	o2, o2-bot, o2-surf	mol m-3	1° grid monthly	GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
pH	ph, ph-bot, ph-surf	1	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Phytoplankton Carbon Concentration	phyc, phyc-vint	mol m-3	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Mole Concentration of Diatoms expressed as Carbon in sea water	phydiat, phydiat-vint	mol m-3	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR UKESM1-0-LL
Mole Concentration of Diazotrophs Expressed as Carbon in Sea Water	phydiaz, phydiaz-vint	mol m-3	1° grid monthly	CESM2 GFDL-ESM4 MPI-ESM1-2-HR
Primary Carbon Production by Total Phytoplankton	pp	mol m-3 s-1	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR
Sea Water Salinity	so, so-bot, so-surf	0.001	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Net Downward Shortwave Radiation at Sea Water Surface	rsntds	W m-2	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR
Sea Ice Area Fraction	siconc	%	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Sea Water Potential Temperature	thetao	°C	1° grid 2° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Ocean Model Cell Thickness	thkcello	m	1° grid monthly	GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Sea Water Potential Temperature at Sea Floor	tob	°C	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Sea Surface Temperature	tos	°C	1° grid 2° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Sea Water X Velocity	uo	m s-1	1° grid monthly	CESM2 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Sea Water Y Velocity	vo	m s-1	1° grid monthly	CESM2 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Sea Water Z Velocity	wo	m s-1	1° grid 2° grid monthly	CESM2 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL
Mole Concentration of Mesozooplankton expressed as Carbon in sea water	zmeso, zmeso-vint	mol m-3	1° grid monthly	GFDL-ESM4 IPSL-CM6A-LR UKESM1-0-LL
Mole Concentration of Microzooplankton expressed as Carbon in sea water	zmicro, zmicro-vint	mol m-3	1° grid monthly	GFDL-ESM4 IPSL-CM6A-LR UKESM1-0-LL
Zooplankton Carbon Concentration	zooc, zooc-vint	mol m-3	1° grid monthly	CESM2 GFDL-ESM4 IPSL-CM6A-LR MPI-ESM1-2-HR UKESM1-0-LL

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
Fishing mandatory
Fishing effort	`socioeconomic/fishing/effort_histsoc_1950_2014.csv`
	The generic name of the FAO area in which the Effort/Catch is occurring (FAO) The EEZ / high seas name in which the Effort/Catch is occurring (EEZ) The generic name of the Large Marine ecosystem in which the Effort/Catch is occurring (LME) Functional Group of the target species (FGroup) Nominal (i.e. not including the technological creep) fishing effort corresponding to the reported catch (kW.days) (NomEffReported) Nominal (i.e. not including the technological creep) fishing effort corresponding to the illegal catch and discards (kW.days) (NomEffIUU) Year (end of the year) when the Effort/Catch is occurring (Year) Water surface area (km2) (AreaSqKm2) Number of 0.5 degree cell making up the area (NbrCells) Fishing country, in ISO3 notation. Ex supranational entities (USSR, Yugoslavia) are disaggregated to their constituent countries. Serbian Fishing Effort included with Montenegro (Fcountry) Fishing sector (artisanal/industrial), defined by the law of the country (varies by country) (Sector) A number code to the LME, as per NOAA notation (LMEnbr) A number code to the FAO area (FAOnbr)	1950-2014	Marine ecosystems or exclusive economic zones annual	Data comprise the nominal effort of industrial and artisanal fleets aggregated into 6 functional groups. Source: Rousseau et al., 2019, PNAS 116 (25) 12238-12243.
Fish catch	`socioeconomic/fishing/catch_histsoc_1950_2014.csv`
	The generic name of the FAO area in which the Effort/Catch is occurring (FAO) The EEZ / high seas name in which the Effort/Catch is occurring (EEZ) The generic name of the Large Marine ecosystem in which the Effort/Catch is occurring (LME) Functional Group of the target species (FGroup) Nominal (i.e. not including the technological creep) fishing effort corresponding to the reported catch (kW.days) (NomEffReported) Nominal (i.e. not including the technological creep) fishing effort corresponding to the illegal catch and discards (kW.days) (NomEffIUU) Year (end of the year) when the Effort/Catch is occurring (Year) Water surface area (km2) (AreaSqKm2) Number of 0.5 degree cell making up the area (NbrCells) Fishing country, in ISO3 notation. Ex supranational entities (USSR, Yugoslavia) are disaggregated to their constituent countries. Serbian Fishing Effort included with Montenegro (Fcountry) Fishing sector (artisanal/industrial), defined by the law of the country (varies by country) (Sector) A number code to the LME, as per NOAA notation (LMEnbr) A number code to the FAO area (FAOnbr)	1950-2014	Marine ecosystems or exclusive economic zones annual	Data comprise the nominal effort of industrial and artisanal fleets aggregated into 6 functional groups. Reference for data source: Watson and Tidd, 2018, Marine Policy, 93: 171-177.
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). For 1950-2014, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. 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). For 1950-2014, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. 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 (Klein Goldewijk et al., 2017). For 1950-2014, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. 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). For 1950-2014, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. 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). For 1950-2014, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. 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 (Klein Goldewijk et al., 2017). For 1950-2014, data are based on the 2008 Revision of the United Nations World Population Prospects, which transits from estimates to projections in 2010. 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 historical data provided by Geiger, 2018 (https://www.earth-syst-sci-data.net/10/847/2018/essd-10-847-2018.html). They are derived mainly from Penn World Tables (PWT) for recent decades, and from the Maddison Project database (2018 version, https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/maddison-project-database-2018) for earlier periods where no PWT data is available. Gaps were filled using World Bank data (WDI). No interpolation to SSPs is performed for this historical dataset.

Static geographic 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 (Cucchi et al., 2020; Lange et al., 2021). 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. For further information see also https://www.isimip.org/gettingstarted/input-data-bias-correction/details/41/.
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.

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

Table 10: Output variables for Fisheries and Marine Ecosystems (regional) (`variable`).
Variable long name	Variable specifier	Unit	Resolution	Comments
Mandatory output from global and regional models (provide as many as possible). All biomasses are in wet weight, not g C.
Total Consumer Biomass Density	tcb	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. All consumers (trophic level >1, vertebrates and invertebrates)
Total Consumer Biomass Density in log10 Weight Bins	tcblog10	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. Level dimensions: (time, bins, lat, lon). If the model is size-structured, please provide biomass in equal log 10 g weight bins (1-10g, 10-100g, 100g-1kg, 1-10kg, 10-100kg, >100kg)
Total Pelagic Biomass Density	tpb	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. All pelagic consumers (trophic level >1, vertebrates and invertebrates)
Total Demersal Biomass Density	tdb	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. All demersal consumers (trophic level >1, vertebrates and invertebrates)
Total Catch (all commercial functional groups / size classes)	tc	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. Catch at sea (commercial landings plus discards, fish and invertebrates)
Total Catch in log10 Weight Bins	tclog10	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. Level dimensions: (time, bins, lat, lon). If the model is size-structured, please provide biomass in equal log 10 g weight bins (1-10g, 10-100g, 100g-1kg, 1-10kg, 10-100kg, >100kg)
Total Pelagic Catch	tpc	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. Catch at sea of all pelagic consumers (trophic level >1, vertebrates and invertebrates)
Total Demersal Catch	tdc	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. Catch at sea of all demersal consumers (trophic level >1, vertebrates and invertebrates)
Optional output from global and regional models. All biomasses are in wet weight, not g C.
Biomass Density of Small Pelagics <30cm	bp30cm	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. If a pelagic species and L infinity is <30 cm, include in this variable
Biomass Density of Medium Pelagics >=30cm and <90cm	bp30to90cm	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. If a pelagic species and L infinity is >=30 cm and <90cm, include in this variable
Biomass Density of Large Pelagics >=90cm	bp90cm	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. If a pelagic species and L infinity is >=90cm, include in this variable
Biomass Density of Small Demersals <30cm	bd30cm	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. If a demersal species and L infinity is <30 cm, include in this variable
Biomass Density of Medium Demersals >=30cm and <90cm	bd30to90cm	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. If a demersal species and L infinity is >=30 cm and <90cm, include in this variable
Biomass Density of Large Demersals >=90cm	bd90cm	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. If a demersal species and L infinity is >=90cm, include in this variable
Catch Density of Small Pelagics <30cm	cp30cm	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. Catch at sea of pelagic species with L infinity <30 cm
Catch Density of Medium Pelagics >=30cm and <90cm	cp30to90cm	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. Catch at sea of pelagic species with L infinity >=30 cm and <90 cm
Catch Density of Large Pelagics >=90cm	cp90cm	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. Catch at sea of pelagic species with L infinity >=90 cm
Catch Density of Small Demersals <30cm	cd30cm	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. Catch at sea of demersal species with L infinity <30 cm
Catch Density of Medium Demersals >=30cm and <90cm	cd30to90cm	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. Catch at sea of demersal species with L infinity >=30 cm and <90 cm
Catch Density of Large Demersals >=90cm	cd90cm	g m-2	0.5° grid monthly	Other sectors: marine-fishery_global. Catch at sea of demersal species with L infinity >=90 cm

Ocean regions

Table 11: Ocean regions (`ocean-region`).
Ocean region	Specifier	Coordinates (west, south, east, north)
North Sea	north-sea	-4.5, 50.5, 9.5, 62.5
Baltic Sea	baltic-sea	15.5, 55.5, 23.5, 64.5
North-west Meditteranean	nw-med-sea	-1.5, 36.5, 6.5, 43.5
Adriatic Sea	adriatic-sea	11.5, 39.5, 20.5, 45.5
Mediterranean Sea	med-glob	-6.5, 29.5, 35.5, 45.5
South-East Australia	se-australia	120.5, -47.5, 170.5, -23.5
Eastern Bass Strait	east-bass-strait	145.5, -41.5, 151.5, -37.5
Cook Strait	cook-strait	174.5, -46.5, 179.5, -40.5
North Humboldt Sea	humboldt-n	-93.5, -20.5, -69.5, 6.5
Hawaii	hawaii	0.0, 0.0, 0.0, 0.0
Benguela Current	benguela	0.0, 0.0, 0.0, 0.0
Eastern Bering Sea	east-bering-sea	0.0, 0.0, 0.0, 0.0

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.
Data model is NETCDF4_CLASSIC with minimum compression level of 5.
NetCDF file extension is .nc.
The relative time axis' reference year for ISIMIP3b is 1601.
for daily data use standard, proleptic_gregorian, 366_day, 365_day or 360_day calendar depending on the temporal resolution of your model and the 360_day calendar for non-daily data.
For fixed variables (e.g. cellarea, contfrac) omit the NetCDF-internal times dimension and the period identifiers in the file name.

Please name the files in the Fisheries and Marine Ecosystems (regional) sector according to the following pattern:

<model>_<climate-forcing>_<bias-adjustment>_<climate-scenario>_<soc-scenario>_<sens-scenario>_<variable>_<ocean-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 fisheries and marine ecosystems (regional) 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<ocean_region>[a-z0-9-]+)_(?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., Menz, C., Gleixner, S., Cucchi, M., Weedon, G.P., Amici, A., Bellouin, N., Müller Schmied, H., Hersbach, H., Buontempo, C. and Cagnazzo, C.: WFDE5 over land merged with ERA5 over the ocean (W5E5 v2.0), ISIMIP Repository, doi:10.48364/ISIMIP.342217, 2021.

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.

ISIMIP3b simulation round simulation protocol - Fisheries and Marine Ecosystems (regional)

Introduction

General concept

ISIMIP3b

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

Simulation protocol

Model versioning

Scenarios & Experiments

Scenario definitions

General note regarding sensitivity experiments

Experiments

pre-industrial control

pre-industrial control

pre-industrial control

RCP2.6

RCP2.6

RCP2.6

RCP7

RCP7

RCP7

RCP8.5

RCP8.5

RCP8.5

Note regarding models requiring spin-up

Input data

Climate forcing

Note on climate forcing priority

Other climate datasets

Socioeconomic forcing

Static geographic information

Output data

Output variables

Ocean regions

Reporting model results

References