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An update of IPCC climate reference regions for subcontinental analysis of climate model data: definition and aggregated datasets

Abstract: Several sets of reference regions have been used in the literature for the regional synthesis of observed and modelled climate and climate change information. A popular example is the series of reference regions used in the Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Adaptation (SREX). The SREX regions were slightly modified for the Fifth Assessment Report of the IPCC and used for reporting subcontinental observed and projected changes over a reduced number (33) of climatologically consistent regions encompassing a representative number of grid boxes. These regions are intended to allow analysis of atmospheric data over broad land or ocean regions and have been used as the basis for several popular spatially aggregated datasets, such as the Seasonal Mean Temperature and Precipitation in IPCC Regions for CMIP5 dataset. We present an updated version of the reference regions for the analysis of new observed and simulated datasets (including CMIP6) which offer an opportunity for refinement due to the higher atmospheric model resolution. As a result, the number of land and ocean regions is increased to 46 and 15, respectively, better representing consistent regional climate features. The paper describes the rationale for the definition of the new regions and analyses their homogeneity. The regions are defined as polygons and are provided as coordinates and a shapefile together with companion R and Python notebooks to illustrate their use in practical problems (e.g. calculating regional averages).We also describe the generation of a new dataset with monthly temperature and precipitation, spatially aggregated in the new regions, currently for CMIP5 and CMIP6, to be extended to other datasets in the future (including observations). The use of these reference regions, dataset and code is illustrated through a worked example using scatter plots to offer guidance on the likely range of future climate change at the scale of the reference regions. The regions, datasets and code (R and Python notebooks) are freely available at the ATLAS GitHub repository: https://github.com/SantanderMetGroup/ATLAS (last access: 24 August 2020), https://doi.org/10.5281/zenodo.3998463 (Iturbide et al., 2020).

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Autoría: Iturbide M., Gutiérrez J.M., Alves L.M., Bedia J., Cerezo-Mota R., Cimadevilla E., Cofiño A.S., Luca A.D., Faria S.H., Gorodetskaya I.V., Hauser M., Herrera S., Hennessy K., Hewitt H.T., Jones R.G., Krakovska S., Manzanas R., Martínez-Castro D., Narisma G.T., Nurhati I.S., Pinto I., Seneviratne S.I., Hurk B.v.d., Vera C.S.,

 Fuente: Earth Syst. Sci Data, 12, 2959-2970,2020

Editorial: Copernicus

 Fecha de publicación: 18/11/2020

Nº de páginas: 12

Tipo de publicación: Artículo de Revista

 DOI: 10.5194/essd-12-2959-2020

ISSN: 1866-3508,1866-3516

Proyecto español: MdM-2017-0714; MdM2017-0765; PID2019-111481RB-100

Url de la publicación: https://doi.org/10.5194/essd-12-2959-2020

Autores/as

ALVES, LINCOLN M.

CEREZO MOTA, RUTH

FARIA, SERGIO HENRIQUE

GORODETSKAYA, IRINA V.

HAUSER, MATHIAS

HENNESSY, KEVIN

HEWITT, HELENE T.

JONES, RICHARD G.

KRAKOVSKA, SVITLANA

MARTÍNEZ CASTRO, DANIEL

NARISMA, GEMMA T.

NURHATI, INTAN S.