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WFDE5 over land merged with ERA5 over the ocean (W5E5)

The W5E5 dataset was compiled to support the bias adjustment of climate input data for the impact assessments carried out in phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b).Version 1.0 of the W5E5 dataset covers the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2016. Data sources of W5E5 are version 1.0 of WATCH Forcing Data methodology applied to ERA5 data (WFDE5; Weedon et al., 2014; Cucchi et al., 2020), ERA5 reanalysis data (Hersbach et al., 2019), and precipitation data from version 2.3 of the Global Precipitation Climatology Project (GPCP; Adler et al., 2003).Variables (with short names and units in brackets) included in the W5E5 dataset are Near Surface Relative Humidity (hurs, %), Near Surface Specific Humidity (huss, kg kg-1), Precipitation (pr, kg m-2 s-1), Snowfall Flux (prsn, kg m-2 s-1), Surface Air Pressure (ps, Pa), Sea Level Pressure (psl, Pa), Surface Downwelling Longwave Radiation (rlds, W m-2), Surface Downwelling Shortwave Radiation (rsds, W m-2), Near Surface Wind Speed (sfcWind, m s-1), Near-Surface Air Temperature (tas, K), Daily Maximum Near Surface Air Temperature (tasmax, K), Daily Minimum Near Surface Air Temperature (tasmin, K), Surface Altitude (orog, m), and WFDE5-ERA5 Mask (mask, 1).W5E5 is a merged dataset. It combines WFDE5 data over land with ERA5 data over the ocean. The mask used for the merge is included in the dataset. The mask is equal to 1 over land and equal to 0 over the ocean.Over land, orog is the surface altitude used for elevation corrections in WFDE5. For all other variables already included in WFDE5 (huss, prsn, ps, rlds, rsds, sfcWind, tas), W5E5 data over land are equal to the daily mean values of the corresponding hourly WFDE5 data. W5E5 hurs over land is the daily mean of hourly hurs computed from hourly WFDE5 huss, ps, and tas using the equations of Buck (1981) as described in Weedon et al. (2010). W5E5 pr over land is the daily mean of the sum of hourly WFDE5 rainfall and snowfall. Note that W5E5 pr and prsn over land are based on WFDE5 rainfall and snowfall bias-adjusted using GPCC monthly precipitation totals. W5E5 psl over land is the daily mean of hourly psl computed from hourly WFDE5 orog, ps, and tas according to psl = ps * exp((g * orog) / (r * tas)), where g is gravity, and r is the specific gas constant of dry air. Lastly, W5E5 tasmax and tasmin over land are the daily maximum and minimum, respectively, of hourly WFDE5 tas.Over the ocean, W5E5 data are based on temporally (from hourly to daily resolution) and spatially (from 0.25° to 0.5° horizontal resolution) aggregated ERA5 data. The spatial aggregation using first-order conservative remapping was always done after the temporal aggregation. For tasmax and tasmin, hourly tas values were aggregated to daily maximum and minimum values, respectively. For all other variables, hourly values were aggregated to daily mean values. Variables unavailable in ERA5 (huss, hurs, sfcWind, orog) were first derived from available variables at hourly temporal and 0.25° horizontal resolution and then aggregated like all other variables. huss and hurs were derived from Near Surface Dewpoint Temperature, ps, and tas using the equations of Buck (1981) as described in Buck (2010). sfcWind was derived from Eastward Near-Surface Wind (uas) and Northward Near-Surface Wind (vas) according to sfcWind = sqrt(uas * uas + vas * vas). orog is equal to surface geopotential divided by gravity. Lastly, pr and prsn were bias-adjusted such that monthly W5E5 precipitation totals match GPCP version 2.3 values over the ocean. Monthly rescaling factors used for this purpose were computed following the scale-selective rescaling procedure described by Balsamo et al. (2010).

EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI)

VERSION HISTORY:- On June 26, 2018 all files were republished due to the incorporation of additional observational data covering years 2014 to 2016. Prior to that date, the dataset only covered years 1979 to 2013. Data for all years prior to 2014 are identical in this and the original version of the dataset.DATA DESCRIPTION:The EWEMBI dataset was compiled to support the bias correction of climate input data for the impact assessments carried out in phase 2b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b; Frieler et al., 2017), which will contribute to the 2018 IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways.The EWEMBI data cover the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2013. Data sources of EWEMBI are ERA-Interim reanalysis data (ERAI; Dee et al., 2011), WATCH forcing data methodology applied to ERA-Interim reanalysis data (WFDEI; Weedon et al., 2014), eartH2Observe forcing data (E2OBS; Calton et al., 2016) and NASA/GEWEX Surface Radiation Budget data (SRB; Stackhouse Jr. et al., 2011). The SRB data were used to bias-correct E2OBS shortwave and longwave radiation (Lange, 2018).Variables included in the EWEMBI dataset are Near Surface Relative Humidity, Near Surface Specific Humidity, Precipitation, Snowfall Flux, Surface Air Pressure, Surface Downwelling Longwave Radiation, Surface Downwelling Shortwave Radiation, Near Surface Wind Speed, Near-Surface Air Temperature, Daily Maximum Near Surface Air Temperature, Daily Minimum Near Surface Air Temperature, Eastward Near-Surface Wind and Northward Near-Surface Wind. For data sources, units and short names of all variables see Frieler et al. (2017, Table 1).

EartH2Observe, WFDEI and ERA-Interim data Merged and Bias-corrected for ISIMIP (EWEMBI)

The EWEMBI dataset was compiled to support the bias correction of climate input data for the impact assessments carried out in phase 2b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b; Frieler et al., 2017), which will contribute to the 2018 IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways.The EWEMBI data cover the entire globe at 0.5° horizontal and daily temporal resolution from 1979 to 2013. Data sources of EWEMBI are ERA-Interim reanalysis data (ERAI; Dee et al., 2011), WATCH forcing data methodology applied to ERA-Interim reanalysis data (WFDEI; Weedon et al., 2014), eartH2Observe forcing data (E2OBS; Calton et al., 2016) and NASA/GEWEX Surface Radiation Budget data (SRB; Stackhouse Jr. et al., 2011). The SRB data were used to bias-correct E2OBS shortwave and longwave radiation (Lange, 2018).Variables included in the EWEMBI dataset are Near Surface Relative Humidity, Near Surface Specific Humidity, Precipitation, Snowfall Flux, Surface Air Pressure, Surface Downwelling Longwave Radiation, Surface Downwelling Shortwave Radiation, Near Surface Wind Speed, Near-Surface Air Temperature, Daily Maximum Near Surface Air Temperature, Daily Minimum Near Surface Air Temperature, Eastward Near-Surface Wind and Northward Near-Surface Wind. For data sources, units and short names of all variables see Frieler et al. (2017, Table 1).

Bias corrected GCM input data for ISIMIP Fast Track

The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) is a community-driven modelling effort bringing together impact models across sectors and scales to create consistent and comprehensive projections of the impacts of different levels of global warming. This entry holds the input data of the ISIMIP Fast Track Initiative consisting of bias corrected daily data for from the following five CMIP5 Global Climate Models (GCMs): GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM and NorESM1-M. Bias corrections has been processed by Sabrina Hempel at PIK and is described in "A trend-preserving bias correction -- the ISIMIP approach" by Hempel et al. (2013) The input data section of the ESGF project referenced in this entry holds the initial version of the bias-corrected GCM input data and was used to force impact models in the ISIMIP Fast Track phase. It should only be used for the ISIMIP2 catch-up experiments for sectors that were already part of the Fast Track phase. For all other purposes, i.e. future runs for new ISIMIP 2 sectors and modeling exercises with no relation to ISIMIP, the corrected and extended version published under the ISIMIP 2 ESGF project should be used. It overcomes several limitations in adjusting the daily variability (denoted as ISIe in Hempel et al., 2013). Data access links are provided to the PIK node of the Earth System Grid Federation (ESGF, https://esg.pik-potsdam.de/). There is currently no directly linked data available, please take a look at the input data of the ISIMIP Fast Track Initiative via https://esg.pik-potsdam.de/search/isimip-ft/. For technical support please have a look at the ESGF FAQ (http://esgf.github.io/esgf-swt/index.html) and the tutorials (https://www.earthsystemcog.org/projects/cog/tutorials_web).

ISIMIP2b Simulation Data from Agricultural Sector

The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for advanced estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate impacts across sectors. ISIMIP2b is the second simulation round of the second phase of ISIMIP. ISIMIP2b considers impacts on different sectors at the global and regional scales: water, fisheries and marine ecosystems, energy supply and demand, forests, biomes, agriculture, agro-economic modeling, terrestrial biodiversity, permafrost, coastal infrastructure, health and lakes. ISIMIP2b simulations focus on separating the impacts and quantifying the pure climate change effects of historical warming (1861-2005) compared to pre-industrial reference levels (1661-1860); and on quantifying the future (2006-2099) and extended future (2006-2299) impact projections accounting for low (RCP2.6), mid-high (RCP6.0) and high (RCP8.5) greenhouse gas emissions, assuming either constant (year 2005) or dynamic population, land and water use and -management, economic development, bioenergy demand, and other societal factors. The scientific rationale for the scenario design is documented in Frieler et al. (2017). The ISIMIP2b bias-corrected observational climate input data (Lange, 2018; Frieler et al., 2017) consists of an updated version of the observational dataset EWEMBI at daily temporal and 0.5° spatial resolution, which better represents the CMIP5 GCM ensemble in terms of both spatial model resolution and equilibrium climate sensitivity. The bias correction methods (Lange, 2018; Frieler et al., 2017; Lange, 2016) were applied to CMIP5 output of GDFL-ESM2M, HadGEM2-ES, IPSL-CM5A-LP and MIROC5. Access to the input data for the impact models, and further information on bias correction methods, is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/isimip2b-bias-correction). This entry refers to the ISIMIP2b simulation data from three agricultural models: GEPIC, LPJmL and PEPIC. ---------------------------------------------------------------------------- The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/ (ISIMIP Changelog) and https://www.isimip.org/outputdata/dois-isimip-data-sets/ (ISIMIP DOI publications). ----------------------------------------------------------------------------

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