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Reliable and highly resolved information about onshore wind energy potential (WEP) is essential for expanding renewable energy to eventually achieve carbon neutrality. In this pilot study, simulated 60 m wind speeds (ws60m) from a km‐scale, convection‐permitting 3.3 km‐resolution ICON‐LAM simulation and often‐used 31 km‐resolution ERA5 reanalysis are evaluated at 18 weather masts. The estimated ICON‐LAM and ERA5 WEPs are then compared using an innovative approach with 1.8 million eligible wind turbine placements over southern Africa. Results show ERA5 underestimates ws60m with a Mean Error (ME) of −1.8 m s−1 (−27%). In contrast, ICON‐LAM shows a ME of −0.1 m s−1 (−1.8%), resulting in a much higher average WEP by 48% compared to ERA5. A combined Global Wind Atlas‐ERA5 product reduces the ws60m underestimation of ERA5 to −0.3 m s−1 (−4.7%), but shows a similar average WEP compared to ERA5 resulting from the WEP spatial heterogeneity. Plain Language Summary: Onshore wind energy is expected to play a major role in the global energy transition. However, reliable and highly resolved information on the onshore wind energy potential (WEP) crucial for expansion planning is missing over southern Africa. This study evaluated high resolution 3.3 km ICON‐LAM atmospheric simulations and 31 km ERA5 reanalysis against 60 m wind speed (ws60m) observations and compared the corresponding derived WEPs. The results show that ERA5 underestimates ws60m by 27%, resulting in a 48% lower WEP assessment than ICON‐LAM, whose ws60m simulation results show a very small bias. Underestimation of wind energy yields may hinder further expansion of wind energy, as less economic performance is expected, which underlines the importance of highly resolved weather data. Key Points: Simulated ERA5 and km‐scale ICON‐LAM wind speeds are evaluated and corresponding southern Africa wind energy potentials are calculatedERA5 underestimates 60 m wind speed, whereas ICON‐LAM produces lower biases in the wind speed simulationsHigher wind energy potentials are revealed from wind speeds simulated by ICON‐LAM compared to ERA5, which is often used for such assessments [ABSTRACT FROM AUTHOR]

While the impacts of climate change on global food security have been studied extensively, the capability of emerging tools that couple land surface processes and crop growth in reproducing inter‐annual yield variability at regional scale remains to be tested rigorously. In this study, we analyzed the effects of weather variations between years (1999–2019) on regional crop productivity for two agriculturally managed regions with contrasting climate and cropping conditions: the German state of North Rhine‐Westphalia (DE‐NRW) and the Australian state of Victoria (AUS‐VIC), using the latest version of the Community Land Model (CLM5) and the WFDE5 (WATCH Forcing Data methodology applied to ECMWF reanalysis version 5) reanalysis. Overall, the simulation results were able to reproduce the total annual crop yields of certain crops, while also capturing the differences in total yield magnitudes between the domains. However, the simulations showed limitations in correctly capturing inter‐annual differences of crop yield compared to official yield records, which resulted in relatively low correlation coefficients between 0.07 and 0.39 in AUS‐VIC and between 0.11 and 0.42 in DE‐NRW. The mean absolute deviation of simulated winter wheat yields was up to 4.6 times lower compared to state‐wide records from 1999 to 2019. Our results suggest the following limitations of CLM5: (a) limitations in simulating yield responses from plant hydraulic stress; (b) errors in simulating soil moisture contents compared to satellite‐derived data; and (c) errors in the representation of cropland in general, for example, crop parameterizations and human influences. Plain Language Summary: This study evaluates how year‐to‐year weather variations impact crop yield predictions for two regions, North Rhine‐Westphalia in Germany and Victoria in Australia changes. We use the community land model (CLM5) land surface model in combination with reanalysis weather data to investigate the model performance with respect to the representation of crop phenology, plant water stress, and soil moisture. Our results showcase the model's ability to predict total annual crop yield magnitudes for both regions, while also capturing the differences between the respective simulation domains. However, year‐to‐year changes in crop yield were lower in simulation results compared to official records, which indicated a lack of model sensitivity toward drought stress and general limitations in the representation of agricultural land. This research systematically assesses CLM5 model performance over arable land and provides useful insights into limitations of CLM5 that can help guide future empirical and technical model improvements. Key Points: Land surface models (LSMs) with integrated crop models can be used to quantify the impact of climate change on agro‐ecosystemsThe potential value of LSMs for agricultural purposes depends on their ability to adequately simulate inter‐annual variability of yieldThe representation of plant hydraulics and the soil moisture regime play key role in accurately simulating agro‐ecosystems [ABSTRACT FROM AUTHOR]

Renewable energy is recognized in Africa as a means for climate change mitigation, but also to provide access to electricity in sub‐Saharan Africa, where three‐quarters of the global population without electricity resides. Reliable and highly resolved renewable energy potential (REP) information is indispensable to support power plants expansion. Existing atmospheric data sets over Africa that are used for REP estimates are often characterized by data gaps, or coarse resolution. With the aim to overcome these challenges, the ICOsahedral Nonhydrostatic (ICON) Numerical Weather Prediction (ICON‐NWP) model in its Limited Area Mode (ICON‐LAM) is implemented and run over southern Africa in a hindcast dynamical downscaling setup at a convection‐permitting 3.3 km horizontal resolution. The simulation time span covers contrasting solar and wind weather years from 2017 to 2019. To assess the suitability of the novel simulations for REP estimates, the simulated hourly 10 m wind speed (sfcWind) and hourly surface solar irradiance (rsds) are extensively evaluated against a large compilation of in situ observations, satellite, and composite data products. ICON‐LAM reproduces the spatial patterns, temporal evolution, the variability, and absolute values of sfcWind sufficiently well, albeit with a slight overestimation and a mean bias (mean error (ME)) of 1.12 m s−1 over land. Likewise the simulated rsds with an ME of 50 W m−2 well resembles the observations. This new ICON simulation data product will be the basis for ensuing REP estimates that will be compared with existing lower resolution data sets. Plain Language Summary: There are still approximately 580 million people in Africa without reliable electricity supply. Renewable energy is broadly accepted as an important solution for Africa to fill the power supply gap and to mitigate climate change. Renewable energy potential (REP) information is thereby imperative for the expansion of renewable energy power planning. With conventional REP estimates, challenges are often linked to the meteorological input data, due to either the relatively coarse spatial resolution, data gaps in space and time, or data quality in general. In this study, we implemented and ran the atmospheric model ICOsahedral Nonhydrostatic (ICON) from the German Weather Service and partners at 3 km high‐resolution. The renewable energy variables wind speed and solar irradiance from these simulations are evaluated against an extensive in situ observations data set, as well as satellite, and other composite data products. In a comparison with more than 200 stations from three different in situ observation networks, it can be shown that ICON can reproduce REP‐related variables with a level of sophistication that the data is likely to offer added value over conventional inputs to REP assessments. The study is an example on how numerical models can fill in gaps in data‐scarce regions to produce useable information. Key Points: A new convection‐permitting regional ICOsahedral Nonhydrostatic (ICON) model setup over southern Africa is presented and evaluatedThe spatially and temporally highly resolved ICON wind and solar fields are inputs for improved renewable energy potential estimatesICON outputs agree well with hourly in situ observations and satellite data; wind speeds are slightly overestimated [ABSTRACT FROM AUTHOR]

Land surface models (LSMs) are an important tool for advancing our knowledge of the Earth system. LSMs are constantly improved to represent the various terrestrial processes in more detail. High-quality data, freely available from various observation networks, are being used to improve the prediction of terrestrial states and fluxes of water and energy. To optimize LSMs with observations, data assimilation methods and tools have been developed in the past decades. We apply the coupled Community Land Model version 5 (CLM5) and Parallel Data Assimilation Framework (PDAF) system (CLM5-PDAF) for 13 forest field sites throughout Europe covering different climate zones. The goal of this study is to assimilate in situ soil moisture measurements into CLM5 to improve the modeled evapotranspiration fluxes. The modeled fluxes will be evaluated using the predicted evapotranspiration fluxes with eddy covariance (EC) systems. Most of the sites use point-scale measurements from sensors placed in the ground; however, for three of the forest sites we use soil water content data from cosmic-ray neutron sensors, which have a measurement scale closer to the typical land surface model grid scale and EC footprint. Our results show that while data assimilation reduced the root-mean-square error for soil water content on average by 56 % to 64 %, the root-mean-square error for the evapotranspiration estimation is increased by 4 %. This finding indicates that only improving the soil water content (SWC) estimation of state-of-the-art LSMs such as CLM5 is not sufficient to improve evapotranspiration estimates for forest sites. To improve evapotranspiration estimates, it is also necessary to consider the representation of leaf area index (LAI) in magnitude and timing, as well as uncertainties in water uptake by roots and vegetation parameters. [ABSTRACT FROM AUTHOR]

An accurate assessment of the physical states of the Earth system is an essential component of many scientific, societal, and economical considerations. These assessments are becoming an increasingly challenging computational task since we aim to resolve models with high resolutions in space and time, to consider complex coupled partial differential equations, and to estimate uncertainties, which often requires many realizations. Machine learning methods are becoming a very popular method for the construction of surrogate models to address these computational issues. However, they also face major challenges in producing explainable, scalable, interpretable, and robust models. In this paper, we evaluate the perspectives of geoscience applications of physics-based machine learning, which combines physics-based and data-driven methods to overcome the limitations of each approach taken alone. Through three designated examples (from the fields of geothermal energy, geodynamics, and hydrology), we show that the non-intrusive reduced-basis method as a physics-based machine learning approach is able to produce highly precise surrogate models that are explainable, scalable, interpretable, and robust. [ABSTRACT FROM AUTHOR]