Browsing by Person "Weber, Tobias K. D."

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Comprehensive assessment of climate extremes in high-resolution CMIP6 projections for Ethiopia
(2023) Rettie, Fasil M.; Gayler, Sebastian; Weber, Tobias K. D.; Tesfaye, Kindie; Streck, Thilo
Climate extremes have more far-reaching and devastating effects than the mean climate shift, particularly on the most vulnerable societies. Ethiopia, with its low economic adaptive capacity, has been experiencing recurrent climate extremes for an extended period, leading to devastating impacts and acute food shortages affecting millions of people. In face of ongoing climate change, the frequency and intensity of climate extreme events are expected to increase further in the foreseeable future. This study provides an overview of projected changes in climate extremes indices based on downscaled high-resolution (i.e., 10 × 10 km2) daily climate data derived from global climate models (GCMs). The magnitude and spatial patterns of trends in the projected climate extreme indices were explored under a range of emission scenarios called Shared Socioeconomic Pathways (SSPs). The performance of the GCMs to reproduce the observed climate extreme trends in the base period (1983–2012) was evaluated, the changes in the climate projections (2020–2100) were assessed and the associated uncertainties were quantified. Overall, results show largely significant and spatially consistent trends in the projected temperature-derived extreme indices with acceptable model performance in the base period. The projected changes are dominated by the uncertainties in the GCMs at the beginning of the projection period while by the end of the century proportional uncertainties arise both from the GCMs and SSPs. The results for precipitation-related extreme indices are heterogeneous in terms of spatial distribution, magnitude, and statistical significance coverage. Unlike the temperature-related indices, the uncertainty from internal climate variability constitutes a considerable proportion of the total uncertainty in the projected trends. Our work provides a comprehensive insight into the projected changes in climate extremes at relatively high spatial resolution and the related sources of projection uncertainties.
Diagnosing similarities in probabilistic multi-model ensembles: An application to soil–plant-growth-modeling
(2022) Schäfer Rodrigues Silva, Aline; Weber, Tobias K. D.; Gayler, Sebastian; Guthke, Anneli; Höge, Marvin; Nowak, Wolfgang; Streck, Thilo
There has been an increasing interest in using multi-model ensembles over the past decade. While it has been shown that ensembles often outperform individual models, there is still a lack of methods that guide the choice of the ensemble members. Previous studies found that model similarity is crucial for this choice. Therefore, we introduce a method that quantifies similarities between models based on so-called energy statistics. This method can also be used to assess the goodness-of-fit to noisy or deterministic measurements. To guide the interpretation of the results, we combine different visualization techniques, which reveal different insights and thereby support the model development. We demonstrate the proposed workflow on a case study of soil–plant-growth modeling, comparing three models from the Expert-N library. Results show that model similarity and goodness-of-fit vary depending on the quantity of interest. This confirms previous studies that found that “there is no single best model” and hence, combining several models into an ensemble can yield more robust results.
High-resolution CMIP6 climate projections for Ethiopia using the gridded statistical downscaling method
(2023) Rettie, Fasil M.; Gayler, Sebastian; Weber, Tobias K. D.; Tesfaye, Kindie; Streck, Thilo
High-resolution climate model projections for a range of emission scenarios are needed for designing regional and local adaptation strategies and planning in the context of climate change. To this end, the future climate simulations of global circulation models (GCMs) are the main sources of critical information. However, these simulations are not only coarse in resolution but also associated with biases and high uncertainty. To make the simulations useful for impact modeling at regional and local level, we utilized the bias correction constructed analogues with quantile mapping reordering (BCCAQ) statistical downscaling technique to produce a 10 km spatial resolution climate change projections database based on 16 CMIP6 GCMs under three emission scenarios (SSP2-4.5, SSP3-7.0, and SSP5-8.5). The downscaling strategy was evaluated using a perfect sibling approach and detailed results are presented by taking two contrasting (the worst and best performing models) GCMs as a showcase. The evaluation results demonstrate that the downscaling approach substantially reduced model biases and generated higher resolution daily data compared to the original GCM outputs.
Proposal and extensive test of a calibration protocol for crop phenology models
(2023) Wallach, Daniel; Palosuo, Taru; Thorburn, Peter; Mielenz, Henrike; Buis, Samuel; Hochman, Zvi; Gourdain, Emmanuelle; Andrianasolo, Fety; Dumont, Benjamin; Ferrise, Roberto; Gaiser, Thomas; Garcia, Cecile; Gayler, Sebastian; Harrison, Matthew; Hiremath, Santosh; Horan, Heidi; Hoogenboom, Gerrit; Jansson, Per-Erik; Jing, Qi; Justes, Eric; Kersebaum, Kurt-Christian; Launay, Marie; Lewan, Elisabet; Liu, Ke; Mequanint, Fasil; Moriondo, Marco; Nendel, Claas; Padovan, Gloria; Qian, Budong; Schütze, Niels; Seserman, Diana-Maria; Shelia, Vakhtang; Souissi, Amir; Specka, Xenia; Srivastava, Amit Kumar; Trombi, Giacomo; Weber, Tobias K. D.; Weihermüller, Lutz; Wöhling, Thomas; Seidel, Sabine J.
A major effect of environment on crops is through crop phenology, and therefore, the capacity to predict phenology for new environments is important. Mechanistic crop models are a major tool for such predictions, but calibration of crop phenology models is difficult and there is no consensus on the best approach. We propose an original, detailed approach for calibration of such models, which we refer to as a calibration protocol. The protocol covers all the steps in the calibration workflow, namely choice of default parameter values, choice of objective function, choice of parameters to estimate from the data, calculation of optimal parameter values, and diagnostics. The major innovation is in the choice of which parameters to estimate from the data, which combines expert knowledge and data-based model selection. First, almost additive parameters are identified and estimated. This should make bias (average difference between observed and simulated values) nearly zero. These are “obligatory” parameters, that will definitely be estimated. Then candidate parameters are identified, which are parameters likely to explain the remaining discrepancies between simulated and observed values. A candidate is only added to the list of parameters to estimate if it leads to a reduction in BIC (Bayesian Information Criterion), which is a model selection criterion. A second original aspect of the protocol is the specification of documentation for each stage of the protocol. The protocol was applied by 19 modeling teams to three data sets for wheat phenology. All teams first calibrated their model using their “usual” calibration approach, so it was possible to compare usual and protocol calibration. Evaluation of prediction error was based on data from sites and years not represented in the training data. Compared to usual calibration, calibration following the new protocol reduced the variability between modeling teams by 22% and reduced prediction error by 11%.