User-oriented global predictions of the GPCC drought index for the next decade

Multi-year droughts strongly impact food production and water management. Thus, predictions for the next decade are required for decision makers. This study analyzes the decadal prediction skill of the Global Precipitation Climatology Centre Drought Index (GPCC‑DI) and its components, namely the Standardized Precipitation Index (SPI‑DWD) adapted by the German Meteorological Service (Deutscher Wetterdienst, DWD) and the Standardized Precipitation Evapotranspiration Index (SPEI) within the German global decadal prediction system. The decadal predictions are recalibrated. The prediction skills of the two prediction types ensemble mean predictions and probabilistic predictions are evaluated against those of the commonly applied reference predictions observed climatology and uninitialized simulations. The evaluation of 4‑year mean droughts for the lead-year period 1–4 at 5° spatial resolution shows high prediction skills for the SPEI in the tropics, especially northern Africa, and several heterogeneously distributed hot spots for the SPI‑DWD. The advantage of GPCC‑DI is its global coverage, but it hardly enhances the SPI‑DWD and SPEI skills. The recalibration clearly enhances ensemble mean prediction skills in slightly improving correlations and in strongly reducing standard deviations as well as large conditional biases in decadal predictions. For probabilistic predictions, impacts of conditional biases and recalibration are less prominent. To meet user requirements decadal drought predictions with higher temporal and spatial resolutions are analyzed. 1‑year mean droughts for lead year 1 mostly show smaller prediction skills than 4‑year means because of larger small-scale noise, but some regions reveal improved skills due to regional processes predictable at the 1‑year time scale, e.g. over the western United States. Drought predictions at 2° resolution show similar spatial skill patterns with enhanced fine-scale structures mostly without losing prediction skill. A user-oriented evaluation of the decadal GPCC‑DI prediction for the severe North African drought of 2008–2011 reproduces most observed drought index tendencies in both prediction types, but intensities are often underestimated. Finally, the decadal GPCC‑DI prediction for 2018–2021 presents a drought over North Africa and Arabia and wetting over the Northern Hemisphere in both prediction types. For 2018, predicted patterns are similar but with smoothed intensities. In summary, decadal drought prediction skill depends on the indices, time periods, and areas considered. However, the analyzed drought indices can provide skillful high-resolution information for several future time periods and regions meeting user needs for decadal drought predictions

GrSMBMIP: Intercomparison of the modelled 1980-2012 surface mass balance over the Greenland Ice Sheet

Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will accelerate in the future. However, large uncertainties remain, partly due to different approaches for modelling the GrIS SMB, which have to weigh physical complexity or low computing time, different spatial and temporal resolutions, different forcing fields, and different ice sheet topographies and extents, which collectively make an inter-comparison difficult. Our GrIS SMB model intercomparison project (GrSMBMIP) aims to refine these uncertainties by intercomparing 13 models of four types which were forced with the same ERA-Interim reanalysis forcing fields, except for two global models. We interpolate all modelled SMB fields onto a common ice sheet mask at 1 km horizontal resolution for the period 1980-2012 and score the outputs against (1) SMB estimates from a combination of gravimetric remote sensing data from GRACE and measured ice discharge; (2) ice cores, snow pits and in situ SMB observations; and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting model deficiencies in an accurate representation of the GrIS ablation zone extent and processes related to surface melt and runoff. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of the same order as RCMs compared with observations and therefore remain useful tools for long-term simulations or coupling with ice sheet models. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present-day SMB relative to observations, suggesting that biases are not systematic among models and that this ensemble estimate can be used as a reference for current climate when carrying out future model developments. However, a higher density of in situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 m w.e. yr-1 due to large discrepancies in modelled snowfall accumulation

GrSMBMIP: intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice Sheet

Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will accelerate in the future. However, large uncertainties remain, partly due to different approaches for modelling the GrIS SMB, which have to weigh physical complexity or low computing time, different spatial and temporal resolutions, different forcing fields, and different ice sheet topographies and extents, which collectively make an inter-comparison difficult. Our GrIS SMB model intercomparison project (GrSMBMIP) aims to refine these uncertainties by intercomparing 13 models of four types which were forced with the same ERA-Interim reanalysis forcing fields, except for two global models. We interpolate all modelled SMB fields onto a common ice sheet mask at 1 km horizontal resolution for the period 1980–2012 and score the outputs against (1) SMB estimates from a combination of gravimetric remote sensing data from GRACE and measured ice discharge; (2) ice cores, snow pits and in situ SMB observations; and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting model deficiencies in an accurate representation of the GrIS ablation zone extent and processes related to surface melt and runoff. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of the same order as RCMs compared with observations and therefore remain useful tools for long-term simulations or coupling with ice sheet models. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present-day SMB relative to observations, suggesting that biases are not systematic among models and that this ensemble estimate can be used as a reference for current climate when carrying out future model developments. However, a higher density of in situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 m w.e. yr−1 due to large discrepancies in modelled snowfall accumulation.Project H2020 PROTECT https://protect-slr.eu

Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO2

A new release of the Max Planck Institute for Meteorology Earth System Model version 1.2 (MPI-ESM1.2) is presented. The development focused on correcting errors in and improving the physical processes representation, as well as improving the computational performance, versatility, and overall user friendliness. In addition to new radiation and aerosol parameterizations of the atmosphere, several relatively large, but partly compensating, coding errors in the model's cloud, convection, and turbulence parameterizations were corrected. The representation of land processes was refined by introducing a multilayer soil hydrology scheme, extending the land biogeochemistry to include the nitrogen cycle, replacing the soil and litter decomposition model and improving the representation of wildfires. The ocean biogeochemistry now represents cyanobacteria prognostically in order to capture the response of nitrogen fixation to changing climate conditions and further includes improved detritus settling and numerous other refinements. As something new, in addition to limiting drift and minimizing certain biases, the instrumental record warming was explicitly taken into account during the tuning process. To this end, a very high climate sensitivity of around 7 K caused by low-level clouds in the tropics as found in an intermediate model version was addressed, as it was not deemed possible to match observed warming otherwise. As a result, the model has a climate sensitivity to a doubling of CO2 over preindustrial conditions of 2.77 K, maintaining the previously identified highly nonlinear global mean response to increasing CO2 forcing, which nonetheless can be represented by a simple two-layer model