Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11765/10757
Development of an empirical model for seasonal forecasting over the Mediterranean
Title: Development of an empirical model for seasonal forecasting over the Mediterranean
Authors: Rodríguez Guisado, EstebanSerrano de la Torre, Antonio Ángel ORCID RESEARCHERID Sánchez García, Eroteida ORCID RESEARCHERID Domínguez Alonso, MartaRodríguez Camino, Ernesto
Keywords: Seasonal forecasting; Empirical model; Global climate indices; Surface temperature; MEDSCOPE project
Issue Date: 2019
Publisher: Copernicus Publications
Citation: Advances in Science and Research. 2019, 16, p. 191–199
Publisher version: https://dx.doi.org/10.5194/asr-16-191-2019
Abstract: In the frame of MEDSCOPE project, which mainly aims at improving predictability on seasonal timescales over the Mediterranean area, a seasonal forecast empirical model making use of new predictors based on a collection of targeted sensitivity experiments is being developed. Here, a first version of the model is presented. This version is based on multiple linear regression, using global climate indices (mainly global teleconnection patterns and indices based on sea surface temperatures, as well as sea-ice and snow cover) as predictors. The model is implemented in a way that allows easy modifications to include new information from other predictors that will come as result of the ongoing sensitivity experiments within the project. Given the big extension of the region under study, its high complexity (both in terms of orography and landsea distribution) and its location, different sub regions are affected by different drivers at different times. The empirical model makes use of different sets of predictors for every season and every sub region. Starting from a collection of 25 global climate indices, a few predictors are selected for every season and every sub region, checking linear correlation between predictands (temperature and precipitation) and global indices up to one year in advance and using moving averages from two to six months. Special attention has also been payed to the selection of predictors in order to guaranty smooth transitions between neighbor sub regions and consecutive seasons. The model runs a three-month forecast every month with a one-month lead time.
Description: Número monográfico dedicado al "18th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2018"
Sponsorship : This research has been supported by MEDSCOPE project, cofunded by the European Comission as part of ERA4CS, an ERANET initiated by JPI Climate (grant agreement 690462.5).
URI: http://hdl.handle.net/20.500.11765/10757
ISSN: 1992-0628
1992-0636
Appears in Collections:Artículos científicos 2019


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