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INSAM homepage until September 2013

Last modified April 14, 2014 07:52

INSAM homepage until September 2013

“The impacts of climate change on agriculture are highly uncertain. There is some consensus on the sign of change, however, with negative effects expected on tropical annual cereals and grain legumes and positive impacts predicted for root crops. In assessing such impacts and any required adaptation options to abate them, future projections of climate and agricultural systems play an important role. Nonetheless, future outlooks of agricultural production and food security are contingent on the skill of Global Circulation Models (GCMs) in reproducing seasonal rainfall and temperatures. Thus, accurate climate change projections are important for developing appropriate and effective adaptation strategies and better targeting global emissions reduction goals. In improving projections, enhancing our understanding of important modes of variability, the role of the different forcings in the climate system, as well as the responses of plants to environmental factors are key steps to reducing the uncertainties that can potentially constrain adaptation. The Coupled Model Intercomparison Project (CMIP) has significantly contributed to these needs, as it has coordinated nearly 20 years of climate model improvement.”

This way starts a paper “Implications of regional improvement in global climate models for agricultural impact research” by Julian Ramirez-Villegas, Andrew J Challinor, Philip K Thornton and Andy Jarvis in the “open access” Environmental Research Letters 8 (2013) 024018 (12pp) doi:10.1088/1748-9326/8/2/024018. It continues to state that if impact studies that use CMIP5 are to be designed and interpreted judiciously, a critical and obvious step from the impact community is to assess the skill of impact-relevant variables in CMIP5 model simulations of historical climate. This would foster agricultural researchers’ engagement in the climate model discussion and can help agricultural researchers in deciding how to use CMIP5 model projections into impact models. Furthermore, a better understanding of CMIP5 will facilitate researchers to revisit and, where necessary, make adjustments to national communications to the United Nations, national adaptation plans, scientific priority setting and research experiments aimed at informing climate change impacts and adaptation.

Several questions arise from the results presented. The most overarching one, probably, is whether climate change simulations are useful for impact research. In other words, what kind of information can we usefully extract from climate models? The usefulness of climate model simulations within the context of agricultural impact research is tied to the effect of model bias on simulations of crop productivity. Literature on impacts suggests the range of information extracted from climate models is highly varied, ranging from the sole use of mean changes to the full coupling of crop-climate models. This variation is mostly due to known and expected climate model errors. The seasonal and regional differences in model error reported may also seriously hinder assessments of food systems under future scenarios, as they may imply different degrees of predictability in future impacts for crops sown at different times in the same location or for different locations sowing the same crop.

Therefore, identifying the correct pieces and amounts of information within a GCM simulation that can be used robustly into impact models is important for improving impact estimates. In that sense, a better understanding of the causes of limited model skill as well as of the key drivers of crop yields is needed. Recent research has validly focused on the ways to improve global and regional climate model simulations so as to make them useful for impact research and this focus is suggested to be maintained. Identifying relevant GCM output, however, also depends on when model improvements will meet the (rather high) input standards of the agricultural research community (i.e. high accuracy at high spatio-temporal resolution). Assuming improvements have a linear trend in time, it is estimated in this study that at least 5–30 years of CMIP work are required to improve regional temperature simulations, while 30–50 years may be required for sufficiently accurate regional precipitation simulations, though these figures vary on a regional basis. Ideally, the ultimate goal should be a complete coupling of crop and climate models, as this would allow an appropriate treatment of the feedbacks in the earth system. Nonetheless, in about 30 years, global mean temperature would have already reached dangerous levels, hence stressing the need to use climate model information in offline (i.e. not coupled), but robust and informed ways.

Such informed ways should include: (1) an enhanced understanding of the impact of climate uncertainties on impact estimates, (2) improved quantification of agricultural model uncertainty, (3) a more systematic focus on the assessment of sensitivity of impact models to climate model errors, (4) a better quantification of downscaling and bias-correction uncertainty, (5) a better reporting of results by reporting impacts using raw versus downscaled and/or bias-corrected climate data. Meanwhile, constant improvement in skill of predictions at higher spatio-temporal scales through more investment in climate modeling is warranted in order to meet the largely unfulfilled needs of the impact research communities.

A last crucial question is related to the implications of model improvements (i.e. the differences in skill between CMIP3 and CMIP5) for impact research. This is important because as GCM ensembles increase their complexity, new research questions may arise, and also because as simulations improve in skill, impact estimates may change. It is likely that, provided enough time, CMIP5 will be widely adopted by the impact research community. (……). Further research is needed, however, to understand the effects of the differences between the two ensembles on impact estimates. If effective and appropriate agricultural adaptation is to happen in the next 2–4 decades, uncertainties and lack of skill in simulated regional climates need to be communicated and understood by agricultural researchers and policy makers. One of the main barriers to adaptation lies within the skill with which climate models reproduce climate conditions. (……). Further research is warranted on the diagnosis of errors in remaining impact-relevant variables (e.g. dry-spell frequency, incoming shortwave radiation, evapotranspiration, soil moisture), as well as on the effects of the differences between the two ensembles in impact estimates, as this would strengthen the conclusions reached in the present study.

I thought that the above are issues about which a lot of false ideas do exist that may be redressed by the better knowledge and understanding contributed and transferred this way.

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