Browsing by Subject "Downscaling"

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Downscaling of ECMWF SEAS5 seasonal forecasts over the Horn of Africa using the WRF model
(2023) Mori, Paolo; Wulfmeyer, Volker
Several studies have shown the potential for downscaling seasonal forecasts on a convection-permitting (CP) scale using limited-area models (LAMs). In most cases, such experiments initial and boundary conditions are derived from atmospheric and surface analyses, which use measurements to constrain the model evolution. For operational use, the boundary conditions are derived from global seasonal forecasts, which only evolve depending on numerical models. This difference will affect the downscaling process and potentially the results’ skill. In this work, the SEAS5 seasonal forecasts are downscaled to address this gap in our understanding. Specifically, the research questions are: What advantages of a CP simulation are present when dynamically downscaling ensemble seasonal forecasts with a LAM? How do boundary conditions and physics parametrization perturbations affect a LAM ensemble in terms of spread and reliability? What perturbations produce more ensemble spread for temperature and precipitation? The study area chosen is the Horn of Africa. The effects of climate change have become much more apparent in East Africa in the last decade: the rainy season has repeatedly failed, which has led to extreme droughts. Therefore, any improvements in this regions seasonal forecasts can help to develop adaptation strategies further. In addition, areas with complex topography benefit the most from increased spatial resolution, and the global models skill is higher in the tropics and subtropics than in middle latitudes. Thus, it is likely that downscaling can extract helpful information in this region. Four global ECWMF SEAS5 ensemble members were dynamically downscaled for summer 2018 over the Horn of Africa using the Weather Research and Forecasting (WRF) model to investigate the potential of a seasonal forecast on convection-permitting resolution (3 km). A total of 16 WRF ensemble members with varied initial and boundary conditions and different physical schemes were used to evaluate the impact of the downscaling. The analysis assessed the effects of perturbations on surface temperature and rainfall in terms of bias, spatial distribution, probability of extreme events, rain belt movements, and ensemble spread. The main findings of this work are the following: the WRF simulations reproduced the spatial distribution of the 2m temperature and precipitation patterns. The bias present in SEAS5 was transferred to the limited-area model, and the signal is even intensified in some areas. For example, while the four SEAS5 members deviated only by +0.2°C on average compared to the ECMWF analyses, the WRF ensemble bias was +1.1°C. The WRF ensemble simulated an average of 264 mm of rain, compared to 248 mm for SEAS5 and 236 mm for the GPM-IMERG satellite product. The convection-permitting resolution reproduced the precipitation probability density function slightly better than the global model and simulated extreme precipitation events missing in SEAS5. However, it overestimated their frequency compared to observations. In addition, WRF can reproduce the daily precipitation cycle well: the peak times coincide with measurements, showing an accurate representation of convection initiation in the area and the potential of dynamical downscaling at convection-permitting resolution. The boundary conditions limited the movement of the rain belt associated with the inter-tropical convergence zone in the downscaling. For example, the north extension of the tropical rain belt decreased in both models by 2 degrees of latitude compared to GPM-IMERG, and the global model timing strongly influenced the movements of the rain belt in WRF. The SEAS5 has shown moderate skill in precipitation forecasts in Ethiopia. Still, a better understanding of the yearly variability of the rain belt position is necessary, as it is a crucial factor in high-resolution downscaling in the region. The downscaling increased the ensemble spread for precipitation by an average of 60%, partially correcting the SEAS5 under-dispersion. In the Ethiopian highlands, perturbed boundary conditions are primarily responsible for the WRF ensemble spread. Their effect is often 50% greater than the variability resulting from the various physics parameterizations. The results show that boundary-conditions perturbations are necessary to generate adequate ensemble dispersion in a limited-area model with complex topography. The analysis partially confirmed the potential to improve seasonal forecasts through downscaling, especially concerning convective precipitation timing and heavy rainfall events. Some advantages of downscaling atmospheric analysis are lost due to the inaccuracies in the forcing derived from SEAS5 and model bias. It also highlights the necessity of further research on physics schemes or combinations suitable to convection-permitting resolutions.
Reducing uncertainty in prediction of climate change impacts on crop production in Ethiopia
(2024) Rettie, Fasil Mequanint; Streck, Thilo
Ethiopia, with an economy heavily reliant on agriculture, is among the countries most vulnerable to climate change. It faces recurrent climate extreme events that result in devastating impacts and acute food shortages for millions of people. Studies that focus on their influence on agriculture, especially crop productivity, are of particular importance. However, only a few studies have been conducted in Ethiopia, and existing studies are spatially limited and show considerable spatial invariance in predicted impacts, as well as discrepancies in the sign and direction of impacts. Therefore, a robust, regionally focused, and multi-model assessment of climate change impacts is urgently needed. To guide policymaking and adaptation strategies, it is essential to quantify the impacts of climate change and distinguish the different sources of uncertainty. Against this backdrop, this study consisted of several key components. Using a multi-crop model ensemble, we began with a local climate change impact assessment on maize and wheat growth and yield across three sites in Ethiopia . We quantified the contributions of different sources of uncertainty in crop yield prediction. Our results projected a of 36 to 40% reduction in wheat grain yield by 2050, while the impact on maize was modest. A significant part of the uncertainty in the projected impact was attributed to differences in the crop growth models. Importantly, our study identified crop growth model-associated uncertainty as larger than the rest of the model components. Second, we produced a high-resolution daily projections database for rainfall and temperature to serve the requirement for impact modeling at regional and local levels using a statistical downscaling technique based on state-of-the-art GCMs under a range of emission scenarios called Shared Socioeconomic Pathways (SSPs). The evaluated results suggest that the downscaling strategy significantly reduced the biases between the GCM outputs and the observation data and minimized the errors in the projections. Third, we explored the magnitude and spatial patterns of trends in observed and projected changes in climate extremes indices based on downscaled high-resolution daily climate data to serve as a baseline for future national or regional-level impact assessment. Our results show largely significant and spatially consistent trends in temperature-derived extreme indices, while precipitation-related extreme indices are heterogeneous in terms of spatial distribution, magnitude, and statistical significance coverage. The projected changes in temperature-related indices are dominated by the uncertainties in the GCMs, followed by uncertainties in the SSPs. Unlike the temperature-related indices, the uncertainty from internal climate variability constitutes a considerable proportion of the total uncertainty in the projected trends. Fourth, we examined the regional-scale impact of climate change on maize and wheat yields by crop modeling, in which we calibrated and validated three process-based crop models to guide the design of national-level adaptation strategies in Ethiopia. Our analysis showed that under a high-emissions scenario, the national-level median wheat yield is expected to decrease by 4%, while maize yield is expected to increase by 2.5% by the end of the century. The CO2 fertilization effect on the crop simulations would offset the projected negative impact. Crop model spread followed by GCMs was identified as the largest contributor to overall uncertainty to the estimated yield changes. In summary, our study quantifies the impact of climate change and demonstrates the importance of a multi-model ensemble approach. We highlight the significant impacts of climate change on wheat yield in Ethiopia and the importance of crop model improvements to reduce overall uncertainty in the projected impact.