The Republic of Yemen faces a critical gap in climate studies. This is partially attributed to a sparse observational network, inconsistent data records, institutional challenges, and protracted conflicts. All this exacerbates the country's vulnerability to climate change and hinders the development of a National Climate Change Plan or Strategy. To address these challenges, this study relies on state‐of‐the‐art climate dataset...

High-resolution hydrodynamic data are essential for coastal and estuarine management. However, traditional downscaling methods based on numerical modeling remain computationally expensive, limiting their applicability for long-term hindcasts and operational forecasting systems. This study evaluates the use of machine learning for the reconstruction of sea surface height and surface currents in a semi-enclosed estuary, using Sa...

Internal variability, multiple emission scenarios, and different model responses to anthropogenic forcing are ultimately behind a wide range of uncertainties that arise in climate change projections. Model weighting approaches are generally used to reduce the uncertainty related to the choice of the climate model. This study compares three multi-model combination approaches: a simple arithmetic mean and two recently developed...

In a recent paper, Baño-Medina et al. (Configuration and Intercomparison of deep learning neural models for statistical downscaling. preprint, 2019) assessed the suitability of deep convolutional neural networks (CNNs) for downscaling of temperature and precipitation over Europe using large-scale ‘perfect’ reanalysis predictors. They compared the results provided by CNNs with those obtained from a set of standard methods which...

Seasonal forecasts of variables like near-surface temperature or precipitation are becoming increasingly important for a wide range of stakeholders. Due to the many possibilities of recalibrating, combining, and verifying ensemble forecasts, there are ambiguities of which methods are most suitable. To address this we compare approaches how to process and verify multi-model seasonal forecasts based on a scientific assessment pe...