Extreme precipitation events pose a significant threat to public safety, natural and managed resources, and the functioning of society. Changes in such high-impact, low-probability events have profound implications for decision-making, preparation and costs of mitigation and adaptation efforts. Understanding how extreme precipitation events will change in the future and enabling consistent and robust projections is therefore important for the public and policymakers as we prepare for consequences of climate change.

Projection of extreme precipitation events, however, particularly at the local scale, presents a critical challenge: the climate model-based simulations of precipitation that we currently rely on for such projections—general circulation models (GCMs)—are not very realistic, mainly due to the models’ coarse spatial resolution. This coarse resolution precludes adequate representation of highly influential, small-scale features such as moisture convection and topography. Regional circulation models (RCMs) provide much higher resolution and better representation of such features, and are thus often perceived as an optimum approach to producing more accurate heavy precipitation statistics than GCMs. However, they are much more computationally intensive, time-consuming and expensive to run.

In a previous paper, the researchers developed an algorithm that detects the occurrence of heavy precipitation events based on climate models’ well-resolved, large-scale atmospheric circulation conditions associated with those events—rather than relying on these models’ simulated precipitation. The algorithm’s results corresponded with observations with much greater precision than the model-simulated precipitation.

In this paper, the researchers show that using output from RCMs rather than GCMs for the new algorithm does not improve the precision of simulated extreme precipitation frequency. The algorithm thus presents a robust and economic way to assess extreme precipitation frequency across a broad range of GCMs and multiple climate change scenarios with minimal computational requirements.   

[Executive Summary: 150 kB] [Appendix C: 1 MB] [Appendix D: 650 kB] [Data Tables: 500 kB]

The MIT Emissions Prediction and Policy Analysis model is applied to an assessment of a set of cap-and-trade proposals being considered by the U.S. Congress in spring 2007. The bills specify emissions reductions to be achieved through 2050 for the standard six-gas basket of greenhouse gases. They fall into two groups: one specifies emissions reductions of 50% to 80% below 1990 levels by 2050; the other establishes a tightening target for emissions intensity and stipulates a time path for a "safety valve" limit on the emission price that approximately stabilizes U.S. emissions at the 2008 level. A set of three synthetic emissions paths are defined that span the range of stringency of these proposals, and these "core" cases are analyzed for their consequences in terms of emissions prices, effects on energy markets, welfare cost, the potential revenue generation if allowances are auctioned and the gains if permit revenue were used to reduce capital or labor taxes.

Initial period prices for the first group of proposals, in carbon dioxide equivalents, are estimated between $30 and $50 per ton CO2-e depending on where each falls in the 50% to 80% range, with these prices rising by a factor of four by 2050. Welfare costs are less than 0.5% at the start, rising in the most stringent case to near 2% in 2050. If allowances were auctioned these proposals could produce revenue between $100 billion and $500 billion per year depending on the case. Emissions prices for the second group, which result from the specified safety-valve path, rise from $7 to $40 over the study period, with welfare effects rising from near zero to approximately a 0.5% loss in 2050. Revenue in these proposals depends on how many allowances are freely distributed.

To analyze these proposals assumptions must be made about mitigation effort abroad, and simulations are provided to illuminate terms-of-trade effects that influence the emissions prices and welfare effects, and even the environmental effectiveness, of U.S. actions. Sensitivity tests also are provided of several of the design features imposed in the "core" scenarios including the role of banking, the specification of less than complete coverage of economic sectors, and the development of international permit trading. Also, the effects of alternative assumptions about nuclear power development are explored. Of particular importance in these simulations is the role of biofuels, and analysis is provided of the implications of these proposals for land use and agriculture.

Finally, the U.S. proposals, and the assumptions about effort elsewhere, are extended to 2100 to allow exploration of the potential role of these bills in the longer-term challenge of reducing climate change risk. Simulations using the MIT Integrated System Model show that the 50% to 80% targets are consistent with global goals of atmospheric stabilization at 450 to 550 ppmv CO2 but only if other nations, including the developing countries, follow.

Appendix D (added February 2008) [PDF: 416 kB]
Since this report was completed there has been an effort in the Senate to draft legislation that would unify support behind common legislation. One result is the Climate Security Act (S. 2191) sponsored by Senators Lieberman and Warner. In this appendix we provide an analysis of the Act's provisions as they relate to key features governing the cap-and-trade system, comparing results with the analysis in the body of the report. The analysis does not consider other features of the bill, such as the effects of how auction revenue is used, which could affect the overall cost estimates. Some of these other features are discussed, but not quantitatively analyzed, in Section D4. Also, as noted in the body of the report many uncertainties exist in projecting policy costs of an emissions constraint, including the rate of economic and emissions growth, the evolution of conditions abroad, the potential cost and availability of new technology, and different ways of interpreting the provisions of the legislation. The body of the report investigates the effects of varying some of these conditions, but we do not attempt in this short appendix to re-investigate the sensitivity of the results to key assumptions. Thus, the results presented here are based on one representation of the future conditions in a particular model.