On the need of bias adjustment for more plausible climate change projections of extreme heat
| Autor: | Sixto Herrera, José M. Gutiérrez, Maialen Iturbide, Josipa Milovac, Joaquín Bedia, Ana Casanueva |
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| Přispěvatelé: | Universidad de Cantabria, European Research Council, European Commission, Ministerio de Ciencia, Innovación y Universidades (España), Agencia Estatal de Investigación (España), Gobierno de Cantabria |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Climate impacts
Atmospheric Science 010504 meteorology & atmospheric sciences climate indices 0207 environmental engineering Climate change 02 engineering and technology 01 natural sciences bias correction Extreme heat 13. Climate action Climatology Meteorology. Climatology Bias correction Environmental science CMIP5 Climate indices QC851-999 020701 environmental engineering climate impacts 0105 earth and related environmental sciences |
| Zdroj: | Atmospheric Science Letters, Vol 23, Iss 2, Pp n/a-n/a (2022) Atmospheric Science Letters 2022, 23 (2), e1072 UCrea Repositorio Abierto de la Universidad de Cantabria Universidad de Cantabria (UC) Digital.CSIC. Repositorio Institucional del CSIC instname |
| Popis: | The assessment of climate change impacts in regions with complex orography and land-sea interfaces poses a challenge related to shortcomings of global climate models. Furthermore, climate indices based on absolute thresholds are especially sensitive to systematic model biases. Here we assess the effect of bias adjustment (BA) on the projected changes in temperature extremes focusing on the number of annual days with maximum temperature above 35°C. To this aim, we use three BA methods of increasing complexity (from simple scaling to empirical quantile mapping) and present a global analysis of raw and BA CMIP5 projections under different global warming levels. The main conclusions are (1) BA amplifies the magnitude of the climate change signal (in some regions by a factor 2 or more) achieving a more plausible representation of future heat threshold-based indices; (2) simple BA methods provide similar results to more complex ones, thus supporting the use of simple and parsimonious BA methods in these studies. Joaquín Bedia, Ana Casanueva and Sixto Herrera acknowledge funding from the Project INDECIS, part of European Research Area for Climate Services Consortium (ERA4CS) with co-funding by the European Union Grant 690462. José Manuel Gutiérrez and Josipa Milovac acknowledge the support of the Spanish Government through the Agencia Estatal de Investigación (project PID2019-111481RB-I00 and “Unidad de excelencia María de Maeztu” MdM-2017-0765). Maialen Iturbide acknowledges support from Universidad de Cantabria and Consejería de Universidades, Igualdad, Cultura y Deporte del Gobierno de Cantabria via the “instrumentación y ciencia de datos para sondear la naturaleza del universo” project. |
| Databáze: | OpenAIRE |
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