Professor Myles Allen CBE FRS

Abstract:

Understanding, quantifying and attributing the impacts of extreme weather and climate events in the terrestrial biosphere is crucial for societal adaptation in a changing climate. However, climate model simulations generated for this purpose typically exhibit biases in their output that hinders any straightforward assessment of impacts. To overcome this issue, various bias correction strategies are routinely used to alleviate climate model deficiencies most of which have been criticized for physical inconsistency and the non-preservation of the multivariate correlation structure. In this study, we introduce a novel, resampling-based bias correction scheme that fully preserves the physical consistency and multivariate correlation structure of the model output. This procedure strongly improves the representation of climatic extremes and variability in a large regional climate model ensemble (HadRM3P, climateprediction.net/weatherathome), which is illustrated for summer extremes in temperature and rainfall over Central Europe. Moreover, we simulate biosphere–atmosphere fluxes of carbon and water using a terrestrial ecosystem model (LPJmL) driven by the bias corrected climate forcing. The resampling-based bias correction yields strongly improved statistical distributions of carbon and water fluxes, including the extremes. Our results thus highlight the importance to carefully consider statistical moments beyond the mean for climate impact simulations. In conclusion, the present study introduces an approach to alleviate climate model biases in a physically consistent way and demonstrates that this yields strongly improved simulations of climate extremes and associated impacts in the terrestrial biosphere. A wider uptake of our methodology by the climate and impact modelling community therefore seems desirable for accurately quantifying past, current and future extremes.

Abstract:

© 2015 The Author(s) Extreme weather events are a significant cause of loss of life and livelihoods, particularly in vulnerable countries and communities in Africa. Such events or their probability of occurring may be, or are, changing due to climate change with consequent changes in the associated risks. To adapt to, or to address loss and damage from, this changing risk we need to understand the effects of climate change on extreme weather events and their impacts. The emerging science of probabilistic event attribution can provide scientific evidence about the contribution of anthropogenic climate change to changes in risk of extreme events. This research has the potential to be useful for climate change adaptation, but there is a need to explore its application in vulnerable developing countries, particularly those in Africa, since the majority of existing event attribution studies have focused on mid-latitude events. Here we explain the methods of, and implications of, different approaches to attributing extreme weather events in an African context. The analysis demonstrates that different ways of framing attribution questions can lead to very different assessments of change in risk. Crucially, defining the most appropriate attribution question to ask is not a science decision but one that needs to be made in dialogue with those stakeholders who will use the answers. This is true of all attribution studies but may be particularly relevant in a tropical context, suggesting that collaboration between scientists and policy-makers is a priority for Africa.

Abstract:

In recent years several methodologies have been developed to combine and interpret ensembles of climate models with the aim of quantifying uncertainties in climate projections. Constrained climate model forecasts have been generated by combining various choices of metrics used to weight individual ensemble members, with diverse approaches to sampling the ensemble. The forecasts obtained are often significantly different, even when based on the same model output. Therefore, a climate model forecast classification system can serve two roles: to provide a way for forecast producers to self-classify their forecasts; and to provide information on the methodological assumptions underlying the forecast generation and its uncertainty when forecasts are used for impacts studies. In this review we propose a possible classification system based on choices of metrics and sampling strategies. We illustrate the impact of some of the possible choices in the uncertainty quantification of large scale projections of temperature and precipitation changes, and briefly discuss possible connections between climate forecast uncertainty quantification and decision making approaches in the climate change context.