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HydrOlogical cYcle Understanding vIa Process-bAsed GlObal Detection, Attribution and prediction (Horyuji PAGODA)

Funder: UK Research and InnovationProject code: NE/I006672/1
Funded under: NERC Funder Contribution: 807,791 GBP
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HydrOlogical cYcle Understanding vIa Process-bAsed GlObal Detection, Attribution and prediction (Horyuji PAGODA)

Description

PAGODA will focus on the global dimensions of changes in the water cycle in the atmosphere, land, and oceans. The overarching aim is to increase confidence in projections of the changing water cycle on global-to-regional scales through a process-based detection, attribution and prediction. The scientific scope prioritises themes 2,1,3,4 in the AO, adopting a focus on climate processes to extend our understanding of the causes of water source/sink uncertainty at the regional scale, which is where GCMs show huge variations concerning projected changes in precipitation, evaporation, and other water related variables. This model uncertainty is closely linked to shifts in large-scale circulation patterns and surface feedback processes, which differ between models. Furthermore, even where models agree with each other (for example, the suggested trend towards wetter winters and drier summers in Europe, connected to storm tracks and land surface processes), consistency with the real world cannot be taken for granted. The importance of quantitative comparisons between models and observations cannot be overstated: there is opportunity and urgent need for research to understand the processes that are driving changes in the water cycle, on spatial scales that range from global to microscopic, and to establish whether apparent discrepancies are attributable to observational uncertainties, to errors in the specification of forcings, or to model limitations. PAGODA will achieve its scientific objectives by confronting models with observations and reconciling observations, which possess inherent uncertainty and heterogeneity, with robust chains of physical mechanisms - employing model analysis and experiments in an integral way. Detection and attribution is applied throughout, in an iterative fashion, to merge the understanding from observations and models consistently, in order to isolate processes and identify causality. PAGODA is designed to focus specifically on the processes that govern global-to-regional scale changes in the water cycle, particularly on decadal timescales (the timescale of anthropogenic climate change). It addresses processes in the atmosphere, land and oceans, and brings together experts in climate observations, climate models, and detection and attribution. It seeks to exploit important new opportunities for research progress, including new observational data sets (e.g. ocean salinity reanalysis, TRMM and SSMIS satellite products, long precipitation records), new models (HadGEM3 & new capabilities for high resolution simulations), and the new CMIP5 model inter-comparison and to develop new methodologies for process-based detection, attribution and prediction.

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