Climate change is a potential threat to achieving food security, particularly in the most food insecure regions. However, interpreting climate change projections to better understand the potential impacts of a changing climate on food security outcomes is challenging. This paper addresses this challenge through presenting a framework that enables rapid country-level assessment of vulnerability to food insecurity under a range of climate change and adaptation investment scenarios. The results show that vulnerability to food insecurity is projected to increase under all emissions scenarios, and the geographic distribution of vulnerability is similar to that of the present-day; parts of sub-Saharan Africa and South Asia are most severely affected. High levels of adaptation act to off-set these increases; however, only the scenario with the highest level of mitigation combined with high levels of adaptation shows improvements in vulnerability compared to the present-day. The results highlight the dual requirement for mitigation and adaptation to avoid the worst impacts of climate change and to make gains in tackling food insecurity. The approach is an update to the existing Hunger and Climate Vulnerability Index methodology to enable future projections, and the framework presented allows rapid updates to the results as and when new information becomes available, such as updated country-level yield data or climate model output. This approach provides a framework for assessing policy-relevant human food security outcomes for use in long-term climate change and food security planning; the results have been made available on an interactive website for policymakers (www.metoffice.gov.uk/food-insecurity-index).
For those involved in planning for regional and local scale changes in future climate, there is a requirement for climate information to be available in a context more usually associated with meteorological timescales. Here we combine a tool used in numerical weather prediction, the 30 weather patterns produced by the Met Office, which are already applied operationally to numerical weather prediction models, to assess changes in the UK Climate Projections (UKCP) Global ensemble. Through assessing projected changes in the frequency of the weather patterns at the end of the 21st Century, we determine that future changes in large-scale circulation tend towards an increase in winter of weather patterns associated with cyclonic and westerly wind conditions at the expense of more anticyclonic, settled/blocked weather patterns. In summer, the results indicate a shift towards an increase in dry settled weather types with a corresponding reduction in the wet and windy weather types. Climatologically this suggests a shift towards warmer, wetter winters and warmer, drier summers; which is consistent with the headline findings from the UK Climate Projections 2018. This paper represents the first evaluation of weather patterns analysis within UKCP Global. It provides a detailed assessment of the changes in these weather patterns through the 21st Century and how uncertainty in emissions, structural and perturbed parameters affects these results. We show that the use of these weather patterns in tandem with the UKCP projections is useful for future work investigating changes in a range of weather-related climate features such as extreme precipitation.
Daily maximum and minimum summer temperatures have increased throughout the majority of Europe over the past few decades, along with the frequency and intensity of heat waves. It is essential to learn whether this rise is expected to continue in the future for adaptation purposes. A study of predictability of European temperature indices with the Met Office Hadley Centre Decadal Prediction System (DePreSys) has revealed significant skill in predictions of 5-and 10-yr average indices of the summer mean and maximum 5-day average temperatures based on daily maximum and minimum temperatures for a large area of Europe, particularly in the Mediterranean. In contrast, the decadal forecasts of winter mean/minimum 5-day average temperature indices show poorer skill than the summer indices. Significant skill is shown for the United Kingdom in some cases but less than for the European/Mediterranean regions.Comparison of two parallel ensembles, one initialized with observations and one without initialization, has shown that the skill largely originates from external forcing. However, there were a few cases with hints of additional skill in forecasts of decadal mean indices due to the initialization.Model realizations of extreme indices can have large biases compared to observations that are different from those of the mean climate indices. Several methods were tested for correcting biases, as well as for testing the significance and quantifying uncertainty of the results to rule out cases of spurious skill. Bias correction of each index individually is required as biases vary across different extremes.
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