Clouds strongly modulate regional radiation balance and their evolution is profoundly influenced by circulations. This study uses 2001–16 satellite and reanalysis data together with regional model simulations to investigate the spring shortwave cloud radiative effect (SWCRE) and the associated circulations over so

Surface water floods (SWFs) that lead to household losses are mainly localized phenomena. Research on describing the associated precipitation characteristics has previously been based on case studies and on the derivation of local rainfall thresholds, but no approaches have yet been presented on the national scale. Here, we propose a new way to overcome this scaling problem.

Changes in precipitation totals and extremes are among the most relevant consequences of climate change, but in particular regional changes remain uncertain. While aggregating over larger regions reduces the noise in time series and typically shows increases in the intensity of precipitation extremes, it has been argued that this may not be the case in water-limited regions.

Climate extremes, such as droughts or heat waves, can lead to harvest failures and threaten the livelihoods of agricultural producers and the food security of communities worldwide. Improving our understanding of their impacts on crop yields is crucial to enhance the resilience of the global food system.

In order to overcome limitations of climate projections from Global Climate Models (GCMs), such as coarse spatial resolution and biases, in this study, the Statistical Down-Scaling Model (SDSM) is used to downscale daily precipitation and maximum and minimum temperature (T-max and T-min) required by impact assessment models.

Using 21st century climate model projections we show that for many crop-producing regions, average precipitation will change by more than the long-term natural variability, even under a low-emission scenario. However, emissions compatible with the Paris Agreement can significantly reduce cropped land affected.

The purpose of this study is to propose the Bayesian Network (BN) model to estimate flood peak from Atmospheric Ensemble Forecasts (AEFs). The Weather Research and Forecasting model was used to simulate historic storms using five cumulus parameterization schemes.