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Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

This dataset contains the land area fractions and population fractions exposed ('le' for land exposed and 'pe' for population exposed) to the following six extreme climate impact events: crop failures (lec/pec), drought (led/ped), heatwaves (leh/peh), river floods (ler/per), tropical cyclones (let/pet) and wildfire (lew/pew). It is the data behind Lange et al., 2020. The data are provided on a global 0.5° grid and in annual time steps. It was derived from multi-model climate impacts simulations generated within the second round (ISIMIP2b, https://www.isimip.org/protocol/2b, Frieler et al., 2017) of the Intersectoral Impact Model Intercomparison Project (ISIMIP, https://www.isimip.org). The simulations cover the pre-industrial and historical periods, as well as future projections until 2100 for the low emission Representative Concentration Pathway RCP2.6 and the high emission one RCP6.0. This is reflected in the naming convention of the files which gives the following information: publication_impact model_climate forcing data set_bias adjustment method_climate scenario_socio-economic scenario_sensitivity scenario_variable_region_time step. The data are available as ‘derived output’ via the ISIMIP data portal (data.isimip.org). It is based on the ISIMIP2b biomes model simulations (Reyer et al., 2019), the ISIMIP2b hydrological simulations (Gosling et al., 2020), the ISIMIP2b crop model simulations (Arneth et al., 2020), and ISIMIP2b tropical cyclone track data that are available for research purposes from K. E. (emanuel@mit.edu) on request.

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).

Flood processing methods used, i.a., in the publication S.N. Willner, A. Levermann, F. Zhao, K. Frieler, Adaptation required to preserve future high-end river flood risk at present levels. Sci. Adv. 4, eaao1914 (2018).

Dataset accompanying the publication S.N. Willner, A. Levermann, F. Zhao, K. Frieler, Adaptation required to preserve future high-end river flood risk at present levels. Sci. Adv. 4, eaao1914 (2018). The dataset includes the increase in flood protection that is required to keep the observed high-end flood risk of the past constant in the next 25 years as well as the affected population in both periods used (1971-2004 and 2035-2044).