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Qingtao Song, Dudley B. Chelton, Steven K. Esbensen, Nicolai Thum, and Larry W. O’Neill

downwind SST gradient, and 3) wind curl is linearly related to the crosswind SST gradient. Depending on the resolution of the SST fields used as the bottom boundary condition, most research mesoscale models and operational numerical weather prediction (NWP) models successfully reproduce a positive correlation between surface wind speed anomalies and SST anomalies ( Small et al. 2003 ; Song et al. 2004 ; Chelton et al. 2004 ; Small et al. 2005 ; Chelton 2005 ; Chelton and Wentz 2005 ; Haack et al

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Gerrit Hansen, Maximilian Auffhammer, and Andrew R. Solow

1. Introduction Climate change is predicted to increase the frequency of extreme weather events like intense hurricanes ( Webster et al. 2005 ) and heat waves ( Meehl and Tebaldi 2004 ). It is natural, therefore, to ask when an event such as the European heat wave in 2003 or Hurricane Sandy in 2012 occurs if it can be attributed to climate change. This attribution question has gained some prominence with efforts to assess liability for weather-related damages due to climate change ( Allen 2003

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G. M. Martin, S. F. Milton, C. A. Senior, M. E. Brooks, S. Ineson, T. Reichler, and J. Kim

processes and the errors in modeling them. A possible way forward is to use short-range (1–5 day) forecasts from a numerical weather prediction (NWP) framework as a means of evaluating parameterizations in climate models ( Phillips et al. 2004 ). This has a number of advantages. First, the NWP forecasts are run from initial states generated with state-of-the-art variational data assimilation (e.g., Lorenc et al. 2000 ; Rawlins et al. 2007 ). This means that the errors in the large-scale synoptic flow

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Xianglei Huang, Xiuhong Chen, Mark Flanner, Ping Yang, Daniel Feldman, and Chaincy Kuo

the CESM to answer this question. The remaining sections are arranged as follows. Section 2 introduces the CESM, the primary modeling tool used in our study, and describes modifications to the CESM that facilitate spectrally varying surface emissivity. Section 3 presents the impact of incorporating surface spectral emissivity in the CESM on its simulated mean climate state. The possible sea ice emissivity feedback in response to the doubling of CO 2 is discussed in section 4 . Conclusions

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Benjamin Pohl and Nicolas Fauchereau

between 30% and 60% of that simulated by the 20CR. The regimes perform well in the midlatitudes (especially over Australia and the southwest Pacific basin) but are less satisfactory near and over Antarctica (15%). Thus, depending on the regions, 40%–85% of the local climate changes can be explained by the intrinsic variability within each regime. That is to say, about half of the decadal variability can be explained by modifications in the weather regime occurrences, with the remaining part indicating

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Estela A. Collini, Ernesto H. Berbery, Vicente R. Barros, and Matthew E. Pyle

, but the effect of surface conditions is also recognized. Betts and Viterbo (2005) , employing the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), found that the total cloud and precipitation fields in the southwestern Amazon seem closely linked to the vertical velocity, implying that dynamic forcings are more relevant than the surface effects. Nevertheless, these authors also found a strong coupling between the top-layer soil moisture and relative humidity

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Larry W. O’Neill, Dudley B. Chelton, Steven K. Esbensen, and Frank J. Wentz

Escarpment ( Lutjeharms and van Ballegooyen 1984 ). As the winds blow across these meanders, the sharp SST front modifies the marine atmospheric boundary layer (MABL), resulting in perturbations in surface winds and cloud cover. Annually averaged SST gradients across the ARC exceed 4°C (100 km) −1 in some regions, which are among the strongest in the World Ocean. All-weather satellite SST measurements with a resolution of about 58 km have recently become available for the first time over the ARC from

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Kit K. Szeto

1970–99. Both the NCEP–NCAR reanalysis data and climate indices used in the discussion of results are provided by the National Oceanic and Atmospheric Administration–Cooperative Institute for Research in Environmental Sciences (NOAA–CIRES) Climate Diagnostics Center (available online at http://www.cdc.noaa.gov/ ). Although there are newer global reanalysis datasets [e.g., the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40; Uppala et al. 2005 )], it has been

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Karin L. Gleason, Jay H. Lawrimore, David H. Levinson, Thomas R. Karl, and David J. Karoly

found in observed surface temperature and precipitation across the contiguous United States. It was not designed to identify causes and origins of variability and change in climate and weather extremes. The CEI values provide information on the percentage of the contiguous United States that experienced extreme conditions during any given year or period. Individual indicators that comprise the overall index provide details regarding the spatial characteristics of the various parameters over time

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I. Zurbenko, P. S. Porter, R. Gui, S. T. Rao, J. Y. Ku, and R. E. Eskridge

called the Comprehensive Aerological Reference Data Set tocreate an upper-air database consisting of radiosondes, pibals, surface reports, and station histories for theNorthern and Southern Hemispheres. Unfortunately, these data contain systematic errors caused by changes ininstruments, data acquisition procedures, etc. It is essential that systematic errors be identified and/or removedbefore these data can be used confidently in the context of greenhouse-gas-induced climate modification. The

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