Lying at the center of Mediterranean basin, one of the most sensitive area to anthropogenic climate change, Italy is expected to be particularly susceptible to global climate change. Unique geographical position and heterogeneous climatic features make difficult defining a comprehensive climate scenario. In such context, establishing regional to local climate information is of strategical importance for all-level society. Doctoral research is based on this conceptual and methodological transition. Two climate scenarios, one at regional and one at local scale are defined. The regional climate scenario considers an area roughly covering Italy, and a local scenario focuses over Marche region stations. Climate scenarios rely on two different-resolution climate model ensemble simulations. Numerical simulations were post-processed according to the quantile mapping (QM) bias correction technique. Original and bias-corrected climate simulations were employed to define 21st century climate change signal (CCS) over principal climate variables. Both scenarios agreed on identifying a severe increase of temperature in all the seasons, especially in summer. Precipitation are projected strongly decrease in summer and increase in winter only over north-Italy. Concerning Marche region stations, newest generation of climate models agree on the severe temperature increase and precipitation reduction in summer but an equivalent increase of autumn-winter precipitation was found. Albeit adopting different configurations, QM coherently affected original CCS in both experiments. Research offers elements to scientific debate on the effect of a common post-processing practice on the CCS. Should we reconsider climate scenarios only relying on original climate model projections? Moreover, following climate services principles, outputs of this research provide comprehensive climate information directly usable by professionals involved in climate risk and impacts research.
Situata al centro del Mediterraneo, uno degli “hot-spot” del cambiamento climatico, l’Italia è considerata una delle aree più suscettibili al cambiamento climatico globale. L’unicità della posizione geografica e l’elevata eterogeneità climatica rendono difficoltoso elaborare uno scenario climatico univoco per i differenti pattern climatici. In questo contesto, disporre di informazioni climatiche da scala regionale a locale diventa di strategica importanza. La ricerca si basa su questa essenziale transizione, definendo due scenari climatici per il 21° secolo a differente scala spaziale. Uno scenario a scala regionale, riferito alla penisola italiana e uno a scala locale che considera stazioni della regione Marche. Gli scenari climatici si basano su simulazioni numeriche di modelli climatici a differente risoluzione. Le simulazioni sono state post-processate con una tecnica di correzione statistica quantile mapping (QM). Entrambi gli scenari indicano un forte incremento delle temperature in tutte le stagioni specialmente in estate. Le piogge sono attese diminuire in estate e moderatamente aumentare nel nord-Italia d’inverno. Per le Marche, l’ultima generazione di modelli climatici è concorde nell’indicare un considerevole incremento delle temperature e diminuzione delle precipitazione durante l’estate. Le piogge autunnali e invernali sono però attese in incremento. Seppur applicato con diverse configurazioni, l’effetto del QM sul segnale climatico è del tutto simile nei due esperimenti. La ricerca vuole apportare nuovi elementi al dibattito scientifico relativo all’effetto di questa tecnica, sul segnale climatico. Ci si interroga sull’eventuale revisione degli attuali scenari climatici basati su simulazioni non soggette a correzione e quindi influenzate da intrinseco errore. La ricerca fornisce infine dei data set di simulazioni climatiche validate e statisticamente corrette direttamente utilizzabili per la riduzione del rischio climatico e dei suoi impatti.
From regional to local climate scenario: toward an integrated strategy for climate impacts reduction / Sangelantoni, Lorenzo. - (2016 Mar 01).
From regional to local climate scenario: toward an integrated strategy for climate impacts reduction
Sangelantoni, Lorenzo
2016-03-01
Abstract
Lying at the center of Mediterranean basin, one of the most sensitive area to anthropogenic climate change, Italy is expected to be particularly susceptible to global climate change. Unique geographical position and heterogeneous climatic features make difficult defining a comprehensive climate scenario. In such context, establishing regional to local climate information is of strategical importance for all-level society. Doctoral research is based on this conceptual and methodological transition. Two climate scenarios, one at regional and one at local scale are defined. The regional climate scenario considers an area roughly covering Italy, and a local scenario focuses over Marche region stations. Climate scenarios rely on two different-resolution climate model ensemble simulations. Numerical simulations were post-processed according to the quantile mapping (QM) bias correction technique. Original and bias-corrected climate simulations were employed to define 21st century climate change signal (CCS) over principal climate variables. Both scenarios agreed on identifying a severe increase of temperature in all the seasons, especially in summer. Precipitation are projected strongly decrease in summer and increase in winter only over north-Italy. Concerning Marche region stations, newest generation of climate models agree on the severe temperature increase and precipitation reduction in summer but an equivalent increase of autumn-winter precipitation was found. Albeit adopting different configurations, QM coherently affected original CCS in both experiments. Research offers elements to scientific debate on the effect of a common post-processing practice on the CCS. Should we reconsider climate scenarios only relying on original climate model projections? Moreover, following climate services principles, outputs of this research provide comprehensive climate information directly usable by professionals involved in climate risk and impacts research.File | Dimensione | Formato | |
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