Global warming is expected to alter the frequency, intensity, and risk of extreme precipitation events. However, global climate models in general do not correctly reproduce the frequency and intensity distribution of precipitation, especially at the regional scale. We present an analogue method to detect the occurrence of extreme precipitation events without relying on modeled precipitation. Our approach is based on the use of composites to identify the distinct large-scale atmospheric conditions associated with widespread outbreaks of extreme precipitation events across local scales. The development of composite maps, exemplified in the South-Central United States and the Western United States, is achieved through the joint analysis of 27-yr (1979–2005) CPC gridded station data and NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA). Various circulation features and moisture plumes associated with extreme precipitation events are examined. This analogue method is evaluated against the MERRA reanalysis with a success rate of around 80% in detecting extreme events within one or two days. When applied to the climate model simulations of the 20th century from Coupled Model Intercomparison Project Phase 5 (CMIP5), we find the analogues from the CMIP5 models produces more consistent (and less uncertain) total number of extreme events compared against observations as opposed to using their corresponding simulated precipitation over the three regions examined. The analogues also perform better to characterize the interannual range of extreme days with the smaller RMSE across all the models for all the descriptive statistics (minimum, lower and higher quartile, median, and maximum). These results suggest the capability of CMIP5 models to simulate the realistic large-scale atmospheric conditions associated with widespread local-scale extreme events, with a credible frequency. Collectively speaking, the presented analyses clearly highlight the comparative and enhanced nature of these results to studies that consider only modeled precipitation output to assess extreme-event frequency.

An international emissions trading system is a featured instrument in the Kyoto Protocol to the Framework Convention on Climate Change, designed to reduce emissions of greenhouse gases among major industrial countries. The US was the leading proponent of emissions trading in the negotiations leading up to the Protocol, with the European Union initially reluctant to embrace the idea. However the US withdrawal from the Protocol has greatly changed the nature of the agreement. One result is that the EU has moved rapidly ahead, establishing in 2003 the Emission Trading Scheme (ETS) for the period of 2005-2007. This Scheme was intended as a test designed to help its member states transition to a system that would lead to compliance with their Kyoto Protocol commitments, which cover the period 2008-2012. The ETS covers CO2 emissions from big industrial entities in the electricity, heat, and energy-intensive sectors. It is a system that itself is evolving as allocations, rules, and registries were still being finalized in some member states late into 2005, even though the system started in January of that year. We analyze the ETS using the MIT Emissions Prediction and Policy Analysis (EPPA) model. We find that a competitive carbon market clears at a carbon price of about 0.6 to 0.9 €/tCO2 (~2 to 3 €/tC) for the 2005-2007 period in a base run of our model in line with many observers' expectations who saw the cuts required under the system as very mild, but in sharp contrast to the actual history of trading prices, which have settled in the range of 20 to 25 €/tCO2 (~70 to 90 €/tC) by the middle of 2005. In various comparison exercises the EPPA model's estimates of carbon prices have been similar to that of other models, and so the contrast between projection and reality in the ETS raises questions regarding the potential real cost of emissions reductions vis-á-vis expectations previously formed based on results from the modeling community. While it is beyond the scope of this paper to reach firm conclusions on reasons for this difference, what happens over the next few years will have important implications for greenhouse gas emissions trading and so further analysis of the emerging European trading system will be crucial.

Computable general equilibrium (CGE) models seeking to evaluate the impacts of electricity policy face difficulties incorporating detail on the variable nature of renewable energy resources. To improve the accuracy of modeling renewable energy and climate policies, detailed scientific and engineering data are used to inform the parameterization of wind electricity in a new regional CGE model of China. Wind power density (WPD) in China is constructed using boundary layer flux data from the Modern Era Retrospective-analysis for Research and Applications (MERRA) dataset with a 0.5° latitude by 0.67° longitude spatial resolution. Wind resource data are used to generate production cost functions for wind at the provincial level for both onshore and offshore, incorporating technological parameters and geographical constraints. By using these updated wind production cost data to parameterize the wind electricity option in a CGE model, an illustrative policy analysis of the current feed-in tariff (FIT) for onshore wind electricity is performed. Assuming a generous penetration rate, no grid integration cost and no interprovincial interconnection, we find that the economic potential of wind exceeds China’s 2020 wind target by a large margin. Our analysis shows how wind electricity resource can be differentiated based on location and quality in a CGE model and then applied to analyze climate and energy policies.

This study analyzes the trend of CO2 emissions from energy (especially fossil-fuel) consumption in Korea to better understand the relationship between economic growth and CO2 emissions in rapidly growing Asian economies. The study spans the period 1961-94, during which Korea experienced dramatic changes in energy consumption stemming from rapid economic development. Korea is a particularly interesting example, as it typifies the export-led industrialization believed likely to be repeated elsewhere in East Asia. The study explores the relationship between national output and total CO2 emissions by analyzing CO2 intensity (defined as the ratio of CO2 emissions to national output) using the Divisia decomposition analytical method, a useful tool for quantifying factors contributing to changes in a variable of interest.