![]() Here we use the Climate model Weighting by Independence and Performance method 35, 36, 37, 38, 39 to weight models by both independence and performance (see “Methods” for details). the models’ ability to simulate historical climate 34. the fact that some GCMs originate from similar development branches or share components and (b) model performance, i.e. To achieve robust multi-model ensemble statistics it is important to account for (a) model independence, i.e. We analyse the simulations of daily precipitation of 25 CMIP6 GCMs for both the historical late twentieth century period (1971–2000, referred to as “historical”) and the future late twenty-first century period (2071–2100, referred to as “future”) forced by four different scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5) 33. Here, we investigate the spatiotemporal patterns of a range of common to rare extremes using a large ensemble of precipitation estimates from the GCMs included in CMIP6 14. The scientific debate regarding the effect of global warming on rainfall extremes has not yet fully addressed this difference in the expected change for the common and rare extremes, and if that differs for different climatic regions across the world. The latter studies point to a possible larger relative increase of the rare extremes. There are fewer studies on the effect of global warming on such rare extremes 30, 31, 32, or on the differences in future changes between “common” to “rare” extremes 15, 27. ![]() #Triangle with circle outside series#28, 29) also exist to increase the time series length. ![]() 26, 27) or spatial pooling-based approaches (e.g. ![]() In hydrology these are typically estimated based on extreme value theory (using a historical time series of the same location), but model-based (e.g. The second type of extremes are the “rare” ones with multi-year or multi-decade return time periods, which are important for infrastructure design 10. These indices are well-studied on global and regional domains, and many regions expect a substantial increase in such common extremes 9, 19, 20, 22, 23, 25. the 90th, 95th, 99th, or 99.9th percentile 20, 21, 22, or indices like R20mm (the number of days per year in which precipitation depth exceeds 20 mm) as defined by “the expert team on climate change detection and indices” 8, 23, 24. Examples of such indices include annual maxima 5, 19, a percentile-based threshold, e.g. Climate indices focusing on “common” extremes typically have probabilistic return times of a year or less. ![]() Studies investigating the simulation of rainfall extremes in global climate models typically focus on one of two types of extremes: (1) common and (2) rare. Yet, when interested in absolute magnitudes or specific locations, a careful selection of models based on observations or advanced bias correction approaches are necessary 16, 17, 18, but these are less relevant when studying relative changes over time. Recent research demonstrates that GCMs included in the Coupled Model Intercomparison Project Phase 6 (CMIP6) 14 have decent skill in modelling extreme rainfall in comparison to observations 15. #Triangle with circle outside drivers#A large limitation is that GCMs do not resolve convective processes, which are important drivers of extreme precipitation 12, 13. Global climate models (GCMs) are the only available tools to study future daily rainfall extremes on the global domain, but come with limitations. An increase in rainfall extremes is already observed in many regions in the world 2, 3, 4, 5, 6, and research shows that extremes will increase in the future depending on the emission scenario 7, 8, 9, 10, 11. ~~.Global warming will result in an intensification of the water cycle 1. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |