Modelling the impact of climate extremes: an overview of the MICE project

Abstract

This paper provides an overview of the aims, objectives, research activities undertaken, and a selection of results generated in the European Commission-funded project entitled "Modelling the Impact of Climate Extremes" (MICE) -a pan-European end-to-end assessment, from climate model to impact model, of the potential impacts of climate change on a range of economic sectors important to the region. MICE focussed on changes in temperature, precipitation and wind extremes. The research programme had three main themes -the evaluation of climate model performance, an assessment of the potential future changes in the occurrence of extremes, and an examination of the impacts of changes in extremes on six activity sectors using a blend of quantitative modelling and expert judgement techniques. MICE culminated in a large stakeholder-orientated workshop, the aim of which was not only to disseminate project results but also to develop new stakeholder networks, whose expertise can be drawn on in future projects such as ENSEMBLES. MICE is part of a cluster of three projects, all related to European climate change and its impacts. The other projects in the cluster are PRUDENCE (Prediction of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects) and STARDEX (Statistical and Regional Dynamical Downscaling of Extremes for European Regions).

Trend detection in river flow indices in Poland

“…For instance, flood risk reduction is based on the concept of design flood, with the probability of exceedance in any one year being, depending on the area, 0.01, 0.05, 0.1, etc. River flow corresponding to a given probability of exceedance is the parameter of importance for people responsible for flood preparedness, both from the viewpoint of general natural disaster risk reduction and climate change adaptation, as flood risk tends to increase in the warming climate, with more room for water vapour is the warmer atmosphere (Hanson et al 2007).…”

The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data

“…Satellite and, for precipitation, radar data have complete coverage, but can have significant spatiotemporal biases and only cover a relatively short period of time (Gerstner and Heinemann 2008;New et al 2001;Reynolds 1988). Reanalysis data, which are derived from numerical weather prediction hindcasts with assimilated observations, have the advantage that they are gridded and readily available (Hanson et al 2007), but the data are only comparable to observations from stations after 1979 in the case of temperature, because during late 1978 the observing system used for reanalysis data improved strongly, with, among others, better satellite temperature and humidity soundings (Simmons et al 2004). For precipitation, reanalyses exhibit large errors and systematic biases.…”

“…For precipitation, reanalyses exhibit large errors and systematic biases. Indeed, Hanson et al (2007) find that reanalysis data underestimate precipitation and temperature extremes significantly. Nonetheless, reanalysis data have been used for regional climate model (RCM) evaluation (e.g.…”

Design and implementation of weather fore casting system based on cloud computing and data mining techniques

“…This procedure is repeated again and again using the output from one cycle as the input for the next cycle. Climate change is considered as significant change in Statistical distributions of weather patterns with respective weather conditions [8]. Here data mining techniques are used for finding relevant information from huge quantity of data.…”