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WIREs Clim Change
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On the use and misuse of climate change projections in international development

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Climate resilience is increasingly prioritized by international development agencies and national governments. However, current approaches to informing communities of future climate risk are problematic. The predominant focus on end‐of‐century projections neglects more pressing development concerns, which relate to the management of shorter‐term risks and climate variability, and constitutes a substantial opportunity cost for the limited financial and human resources available to tackle development challenges. When a long‐term view genuinely is relevant to decision‐making, much of the information available is not fit for purpose. Climate model projections are able to capture many aspects of the climate system and so can be relied upon to guide mitigation plans and broad adaptation strategies, but the use of these models to guide local, practical adaptation actions is unwarranted. Climate models are unable to represent future conditions at the degree of spatial, temporal, and probabilistic precision with which projections are often provided, which gives a false impression of confidence to users of climate change information. In this article, we outline these issues, review their history, and provide a set of practical steps for both the development and climate scientist communities to consider. Solutions to mobilize the best available science include a focus on decision‐relevant timescales, an increased role for model evaluation and expert judgment and the integration of climate variability into climate change services. This article is categorized under: Climate and Development > Knowledge and Action in Development
Approximate time horizons of decision‐making in the agricultural sector. The time horizons relate to the period of time between when a decision is taken and when the implications of that decision have expired. For example, the top entry describes a variety of field operations, which are planned around expected weather and other logistical factors over the coming days to 1–2 weeks. Crop cultivars are selected before planting according to expected yields for the coming 2–3 months. Overall, the majority of practical decisions are taken with a view to managing the coming days up to a few years ahead, with fewer issues requiring a longer‐term view. Policy direction setting, education and training, and research and development decisions are usually made at governmental level in order to prepare for the coming 10–30 years. Very few decisions in agriculture have a time horizon longer than about 30 years. Note the approximate logarithmic time scale on the horizontal axis
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Evaluation of soil moisture in five widely‐used climate models from the CMIP5 ensemble compared with ERA Interim/Land reanalysis over the period 1979–2005. (a) Annual mean soil moisture bias over Bangladesh (left axis: Raw bias in m3 m−2; right axis: Bias normalized by observed annual mean); (b) seasonal cycles of soil moisture in ERA Interim/Land and in each of the five climate models (CCSM4, CSIRO‐Mk3.6.0, GFDL‐ESM2G, HadGEM2‐CC and IPSL‐CM5A‐LR), normalized by the annual mean for comparison on a common scale. ERA Interim/Land reanalysis provides a continuous dataset of soil moisture over a sufficiently long historical period to evaluate the climate models; other products do not span the full study period. Soil moisture is computed using near‐surface meteorological fields from the ERA Interim analysis and the HTESSEL land‐surface model
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(a) Multimodel mean projected change in March–May rainfall (mm/month) over East Africa between 1970–2000 and 2035–2050 (b) Difference in March–May rainfall (mm/month) between 1977–1998 and 1999–2010. The difference between (a) and (b) demonstrates that multidecadal projections may not be representative of decadal changes, either because the projections are inaccurate (climate model error) or because of natural decadal variability around the long‐term trend. (c) Timescales of variability for March–May rainfall over the red box shown in (a) and (b): Average March–May precipitation anomalies are shown in black, the long‐term trend in red, fitted decadal cycles in green and the residual interannual fluctuations in dashed blue (see Greene, Goddard, and Cousin () for methodology). The legend indicates the percentage of total variance in March–May precipitation explained by the trend (5%), decadal (17%) and interannual variability (77%), illustrating the prominence of year to year fluctuations compared with longer timescales of variability. Observations in (a) and (c) were taken from the Global Precipitation Climatology Center (GPCC) version 7. For (b) the multimodel mean was calculated across 39 models in the CMIP5 ensemble. (a) and (b) were reproduced from Lyon and Vigaud ()
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