Details

Autor(en) / Beteiligte

• Ramirez-Villegas, Julian
• Challinor, Andrew J
• Thornton, Philip K
• Jarvis, Andy

Titel

Implications of regional improvement in global climate models for agricultural impact research

Ist Teil von

• Environmental research letters, 2013-06, Vol.8 (2), p.24018-12

Ort / Verlag

Bristol: IOP Publishing

Erscheinungsjahr

2013

Link zum Volltext

Quelle

Free E-Journal (出版社公開部分のみ）

Beschreibungen/Notizen

• Global climate models (GCMs) have become increasingly important for climate change science and provide the basis for most impact studies. Since impact models are highly sensitive to input climate data, GCM skill is crucial for getting better short-, medium- and long-term outlooks for agricultural production and food security. The Coupled Model Intercomparison Project (CMIP) phase 5 ensemble is likely to underpin the majority of climate impact assessments over the next few years. We assess 24 CMIP3 and 26 CMIP5 simulations of present climate against climate observations for five tropical regions, as well as regional improvements in model skill and, through literature review, the sensitivities of impact estimates to model error. Climatological means of seasonal mean temperatures depict mean errors between 1 and 18 ° C (2-130% with respect to mean), whereas seasonal precipitation and wet-day frequency depict larger errors, often offsetting observed means and variability beyond 100%. Simulated interannual climate variability in GCMs warrants particular attention, given that no single GCM matches observations in more than 30% of the areas for monthly precipitation and wet-day frequency, 50% for diurnal range and 70% for mean temperatures. We report improvements in mean climate skill of 5-15% for climatological mean temperatures, 3-5% for diurnal range and 1-2% in precipitation. At these improvement rates, we estimate that at least 5-30 years of CMIP work is required to improve regional temperature simulations and at least 30-50 years for precipitation simulations, for these to be directly input into impact models. We conclude with some recommendations for the use of CMIP5 in agricultural impact studies.