A new paper published in Environmental Research Letters finds that current climate models are not able to predict regional, seasonal temperature and precipitation changes and have huge "mean errors between 1 and 18 °C." Therefore, according to the authors, the models are especially unable to predict the impacts of regional temperature and precipitation changes. According to the authors, "no single [climate model] 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." The majority of the IPCC AR4 report discusses the alleged regional impacts of climate change based on these same models, but according to the authors, the models won't be ready to predict the impacts of climate change for another 5 to 50 years, stating, "we estimate that at least 5–30 years of [computer modeling research] 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."
Julian Ramirez-Villegas et al
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Abstract: 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.
Take away lesson: Spend billion$ more on climate research to improve on the abject failure of climate models