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Climate change and regional scenarios

climateChange.png Short description: Trend analysis in observed climate, climate change scenarios, uncertainty estimation and regional projection using statistical and dynamical techniques

Global climate change is one of the major issues of our time due to the implications it has on the planet's future sustainable development. Numerous observations prove that the planet has suffered an overall global warming during the last century, as well as an increase of extreme events such as droughts, heat waves, floods, etc. During the recent decades, the scientific community has dedicated a huge effort to try to explain the possible reasons for the observed change and to forecast future trends. This has been achieved through the development of General Circulation Models (GCMs) which simulate the dynamics and the physics of the climate's components, basically the atmosphere and the ocean, as a function of the different forcings of the system. Model simulations have proved the anthropogenic origin of the observed global warming, basically due to the increase of greenhouse gas emissions.

GCM simulations are nowadays the only available tool to try to estimate future climate trends in a quantitative way. For this purpose, different possible emission scenarios have been developed on the basis of social and economic factors, acting as a forcing of the system.

However, the coarse spatial resolution of these models (200-500 km) isn't enough for reasonable impact studies and, therefore, in the latest years different research groups have started to apply statistical and dynamical donwscaling techniques in order to increase the resolution of the climate models' outputs. Therefore, in the generation of regional climate projections, a great number of uncertainties arise concerning the emission scenarios, conversion to gas concentrations, model errors and regionalisation techniques. Trying to quantify these uncertainties is a key issue in climate change.

In order to provide policy makers and others interested in climate change with an objective source of information about the causes of climate change, its potential environmental and socio-economic consequences and the adaptation and mitigation options to respond to it, the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP) set up the Intergovernmental Panel on Climate Change (IPCC) in 1988. The IPCC has published four assessment reports up to the date (1990, 1996, 2001 and 2007) which constitute the most complete and updated source of information for climate change issues.

Moreover, the development of regional climate change projections has been one of the key objectives of recent European Union Projects such as PRUDENCE, STARDEX, and ENSEMBLES. Spain has also developed a National Programme of Climate Change Adaptation which, amongst others, includes the periodic generation of regional climate change scenarios in order to assess possible local impacts on hydrological resources, biodiversity and coastal areas.

Key Reading:

  • IPCC Fourth Assessment Report (2007), in particular chapters of Climate Models, Global Climate Projections and Regional Climate Projections.
  • G. A. Meehla, C. Coveyb, T. Delworthc, M. Latifd, B. McAvaneye, J. F. B. Mitchellf, R. J. Stoufferc, and K. E. Taylorb. THE WCRP CMIP3 Multimodel Dataset: A New Era in Climate Change Research, Bulletin of the American Meteorological Society, 8, 1383-1394. abstract

A divulgative paper in Spanish can be downloaded here.

View also the presentations of the course Proyección Regional de Escenarios de Cambio Climático. Técnicas y Proyectos en Curso.

Activities of the Santander Meteorology Group:

The work developed in this research topic is basically supported by EU project ENSEMBLES and Cantabrian Government funding (see Regional Climate Change Scenarios in Cantabria project). The group's work is focused on the following issues:

  • Trend analysis in observed climate.
  • Regionalisation of Climate Change Scenarios.
  • Development of a web portal for climate data access and statistical downscaling.
  • Assessment of uncertainties in climate change regionalisations due to the statistical downscaling method applied.
  • Stationarity issues of statistical downscaling methods in climate change (see Frias et al, 2006).

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