Bias-corrected and spatially disaggregated seasonal forecasts: A long-term reference forecast product for the water sector in semi-arid regions

Abstract

Seasonal forecasts have the potential to substantially improve water management particularly in water-scarce regions. However, global seasonal forecasts are usually not directly applicable as they are provided at coarse spatial resolutions of at best 36 km and suffer from model biases and drifts. In this study, we therefore apply a bias-correction and spatial-disaggregation (BCSD) approach to seasonal precipitation, temperature and radiation forecasts of the latest long-range seasonal forecasting system SEAS5 of the European Centre for Medium-Range Weather Forecasts (ECMWF). As reference we use data from the ERA5-Land offline land surface rerun of the latest ECMWF reanalysis ERA5. Thereby, we correct for model biases and drifts and improve the spatial resolution from 36 km to 0.1. This is performed for example over four predominately semi-arid study domains across the world, which include the river basins of the Karun (Iran), the Sao&tild; Francisco River (Brazil), the Tekeze-Atbara river and Blue Nile (Sudan, Ethiopia and Eritrea), and the Catamayo-Chira river (Ecuador and Peru). Compared against ERA5-Land, the bias-corrected and spatially disaggregated forecasts have a higher spatial resolution and show reduced biases and better agreement of spatial patterns than the raw forecasts as well as remarkably reduced lead-dependent drift effects. But our analysis also shows that computing monthly averages from daily bias-corrected forecasts particularly during periods with strong temporal climate gradients or heteroscedasticity can lead to remaining biases especially in the lowest- A nd highest-lead forecasts. Our SEAS5 BCSD forecasts cover the whole (re-)forecast period from 1981 to 2019 and include bias-corrected and spatially disaggregated daily and monthly ensemble forecasts for precipitation, average, minimum, and maximum temperature as well as for shortwave radiation from the issue date to the next 215 d and 6 months, respectively. This sums up to more than 100 000 forecasted days for each of the 25 (until the year 2016) and 51 (from the year 2017) ensemble members and each of the five analyzed variables. The full repository is made freely available to the public via the World Data Centre for Climate at http://doi.org/10.26050/WDCC/SaWaM_D01_SEAS5_BCSD (Domain D01, Karun Basin (Iran), Lorenz et al., 2020b), http://doi.org/10.26050/WDCC/SaWaM_D02_SEAS5_BCSD (Domain D02: Sao&tild; Francisco Basin (Brazil), Lorenz et al., 2020c), http://doi.org/10.26050/WDCC/SaWaM_D03_SEAS5_BCSD (Domain D03: Basins of the Tekeze-Atbara and Blue Nile (Ethiopia, Eritrea, Sudan), Lorenz et al., 2020d), and http://doi.org/10.26050/WDCC/SaWaM_D04_SEAS5_BCSD (Domain D04: Catamayo-Chira Basin (Ecuador, Peru), Lorenz et al., 2020a). It is currently the first publicly available daily high-resolution seasonal forecast product that covers multiple regions and variables for such a long period. It hence provides a unique test bed for evaluating the performance of seasonal forecasts over semi-arid regions and as driving data for hydrological, ecosystem or climate impact models. Therefore, our forecasts provide a crucial contribution for the disaster preparedness and, finally, climate proofing of the regional water management in climatically sensitive regions.