tion of uncertainty for specific applications (e.g., model development) and alignment of development priorities with addressing those uncertainties stands to improve the communication of climate change to political decision makers and to organize model development priorities. There is new appreciation for involving decision makers directly in both discussions of uncertainty about climate change, and of their decision-making needs for quantification of uncertainties. For example, in work with the integrated Regional Earth System Model (iRESM), regional decision makers and other stakeholders from the pilot region have been engaged in the modeling process to guide, among other things, uncertainty characterization relevant to their decision making (Rice et al., 2012).

The development of the shared socioeconomic pathways that will be related to the representative concentration pathways used for the IPCC Fifth Assessment Report may provide a new opportunity for quantifying uncertainties in possible future socioeconomic conditions. There may also be means of reducing uncertainty regarding future concentrations of greenhouse gases by better characterization of surface processes (including land-use change) contributing to the concentrations of greenhouse gases and aerosols.

Finding 6.6: Resource managers and decision makers have diverse and evolving methods for handling climate change uncertainty.

THE WAY FORWARD

Knowledge about future climate has increased rapidly over the past two decades, and a number of facts about future climate are robust, such as that global temperature will increase, that greater increases in temperature will occur over land than ocean, that sea level will rise, and that substantial changes in the hydrologic cycle will occur. Nonetheless, important uncertainties remain, particularly regarding climate sensitivity, GHG emissions, and regional details about climate change. As new components of the Earth system are included into models, they may in fact (especially over the short term) increase the spread of certain predictions between models, as uncertainty previously not encompassed within the modeling framework is internalized (e.g., removing flux adjustment from coupled models). Some uncertainties are unlikely to be reduced over the next decade or so (for example, uncertainty in future emissions, a very important component of long-term climate change). But uncertainty due to model inadequacy or incompleteness should be reduced in the next 15-20 years. In addition, adding new components to the model helps reduce uncertainty about their response to a perturbed climate. For instance, adding a well-tested sea-ice representation to a climate



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