Scheda di dettaglio – i prodotti della ricerca
| Dato | Valore |
|---|---|
| Title | BASIN-SCALE EVALUATION OF RCM BIAS USING RAINFALL OBSERVATION NETWORKS |
| Abstract | The local downscaling of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on most ecological processes related to the land/water cycle, such as vegetation dynamics, soil-bacteria activity, and ecological response of water-bodies. In this study, we present a methodology to analyze the predictive performance of a Regional Climate Model (RCM) with regard to daily rainfall fields. A comparison between statistical properties of rainfall observations and model control simulations was performed through a robust and meaningful representation of the precipitation process. Our objectives were, first, to evaluate RCM bias data at basin-scale against daily rainfall records coming from a rain gauge network, and then to propose a simple framework to investigate possible alterations of the daily rainfall occurrence and intensity under climate change by way of a stochastic model suitable to investigate both ordinary regimes and extreme climate events. |
| Source | Fresenius environmental bulletin 19 (10A), pp. 2367–2378 |
| Keywords | Climate change impactlocal precipitation scenariosstochastic downscaling |
| Journal | Fresenius environmental bulletin |
| Editor | Parlar Scientific Publications, Freising, Germania |
| Year | 2010 |
| Type | Articolo in rivista |
| Authors | Bruno, Emanuela; Portoghese, Ivan; Vurro, Michele |
| Text | 420854 2010 ISI Web of Science WOS 000285178700011 Climate change impact local precipitation scenarios stochastic downscaling BASIN SCALE EVALUATION OF RCM BIAS USING RAINFALL OBSERVATION NETWORKS Bruno, Emanuela; Portoghese, Ivan; Vurro, Michele IRSA CNR; Polytech Bari The local downscaling of rainfall predictions provided by climate models is crucial for the assessment of climate change impacts on most ecological processes related to the land/water cycle, such as vegetation dynamics, soil bacteria activity, and ecological response of water bodies. In this study, we present a methodology to analyze the predictive performance of a Regional Climate Model RCM with regard to daily rainfall fields. A comparison between statistical properties of rainfall observations and model control simulations was performed through a robust and meaningful representation of the precipitation process. Our objectives were, first, to evaluate RCM bias data at basin scale against daily rainfall records coming from a rain gauge network, and then to propose a simple framework to investigate possible alterations of the daily rainfall occurrence and intensity under climate change by way of a stochastic model suitable to investigate both ordinary regimes and extreme climate events. The RCM adopted for the the study region produced a general underestimation of mean storm intensity for all seasons in the control run. From the bias analysis at daily scale, the RCM has shown a good capability to simulate the occurrence of wet periods being able to reproduce the winter storm systems, but a poor capability to simulate the need to operate a correction of the climate model output to obtain more realistic input data to be used in impact studies at the local scale. A further result of the adopted bias correction was the significant reduction of the effects of climate change on daily rainfall statistics corresponding to rainfall features much closer to the historical data than in the raw RCM output data. 19 Published version Articolo in rivista Parlar Scientific Publications 1018 4619 Fresenius environmental bulletin Fresenius environmental bulletin Fresenius environ. bull. Fresenius environmental bulletin. FEB Freising. Print Fresenius environmental bulletin Print ivan.portoghese PORTOGHESE IVAN |