Statistically downscaled climate dataset for East Africa

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Beitragende

  • Solomon H. Gebrechorkos - , Professur für Meteorologie, United Nations University - Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES) (Autor:in)
  • Stephan Hülsmann - , United Nations University - Institute for Integrated Management of Material Fluxes and of Resources (UNU-FLORES) (Autor:in)
  • Christian Bernhofer - , Professur für Meteorologie (Autor:in)

Abstract

For many regions of the world, current climate change projections are only available at coarser spatial resolution from Global Climate Models (GCMs) that cannot directly be used in impact assessment and adaptation studies at regional and local scale. Impact assessment studies require high-resolution climate data to drive impact assessment models. To overcome this data challenge, we produced a station based climate projection (precipitation and maximum and minimum temperature) for Ethiopia, Kenya, and Tanzania using observed daily data from 211 stations obtained from the National Meteorological Agency of Ethiopia and international databases. Moreover, 26 large-scale climate variables derived from the National Centers for Environmental Prediction reanalysis data (1961–2005) and second generation Canadian Earth System Model (CanESM2, 1961–2100) are used. Statistical Down-Scaling Model (SDSM) is used to produce the required high-resolution climate projection by developing a statistical relationship between the large- and local-scale climate variables. The predictors are analysed more than 16458 times and we provided 20 ensembles for the current (1961–2005) and future (2006–2100, under RCP2.6, RCP4.5, and RCP8.5) climate.

Details

OriginalspracheEnglisch
Aufsatznummer31
FachzeitschriftScientific data
Jahrgang6
Ausgabenummer1
PublikationsstatusVeröffentlicht - 1 Dez. 2019
Peer-Review-StatusJa

Externe IDs

PubMed 30988412