Dealing with trend uncertainty in empirical estimates of European rainfall climate for insurance risk management

Meteorological Applications

Stephen Jewson¹  Francesco Comola²  Tanja Dallafior² 

[1] Independent Researcher London UK;Risk Management Solutions Ltd London UK;

关键词: AIC;    BIC;    catastrophe model;    climate change;    cross‐validation;    insurance;   

DOI  :  10.1002/met.2008

来源: DOAJ

【 摘 要 】

Abstract The insurance industry uses mathematical models to estimate the risks due to future natural catastrophes. For climate‐related risks, historical climate data are a key ingredient used in making the models. Historical data for temperature and sea level often show clear and readily quantified climate change driven trends, and these trends would typically be accounted for when building risk models by adjusting earlier values to render the earlier data relevant to the future climate. For other climate variables, such as rainfall in many parts of the world, the questions of whether there are climate change driven trends in the historical data, and how to quantify them if there are, are less simple to answer. We investigate these questions in the context of European rainfall with a specific focus on how to deal with the uncertainty around trend estimates. We compare 10 empirical methodologies that one might use to model and predict trends, including traditional statistical testing and alternatives to statistical testing based on standard methods from model selection and model averaging. We emphasize prediction and risk assessment, rather than detection of trends, as our goal. Viewed in terms of this goal, the methods we consider each have qualitative and quantitative advantages and disadvantages. Understanding these advantages and disadvantages can help risk modellers make a choice as to which method to use, and based on the results we present, we believe that in many common situations model averaging methods, as opposed to statistical testing or model selection, are the most appropriate.

【 授权许可】

Unknown