Posted by: bluesyemre | November 7, 2012

MIT researchers develop tool to assess regional risks of climate change, potential impacts on local infrastructure and planning

Aerial damage from Hurricane Sandy

  • The growing need for risk-based assessments of impacts and adaptation to climate change calls for increased capability in climate projections: the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Herein, we present a technique that extends the latitudinal projections of the 2-D atmospheric model of the MIT Integrated Global System Model (IGSM) by applying longitudinally resolved patterns from observations, and from climate-model projections archived from exercises carried out for the 4th Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The method maps the IGSM zonal means across longitude using a set of transformation coefficients, and we demonstrate this approach in application to near-surface air temperature and precipitation, for which high-quality observational datasets and model simulations of climate change are available. The current climatology of the transformation coefficients is observationally based. To estimate how these coefficients may alter with climate, we characterize the climate models’ spatial responses, relative to their zonal mean, from transient increases in trace-gas concentrations and then normalize these responses against their corresponding transient global temperature responses. This procedure allows for the construction of meta-ensembles of regional climate outcomes, combining the ensembles of the MIT IGSM—which produce global and latitudinal climate projections, with uncertainty, under different global climate policy scenarios—with regionally resolved patterns from the archived IPCC climate-model projections. This hybridization of the climate-model longitudinal projections with the global and latitudinal patterns projected by the IGSM can, in principle, be applied to any given state or flux variable that has the sufficient observational and model-based information.

Quantifying the Likelihood of Regional Climate Change: A Hybridized Approach:



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