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This paper describes an improved edition of the climate division dataset for the conterminous United States (i.e., version 2). The first improvement is to the input data, which now include additional station networks, quality assurance reviews, and temperature bias adjustments. The second improvement is to the suite of climatic elements, which now includes both maximum and minimum temperatures. The third improvement is to the computational approach, which now employs climatologically aided interpolation to address topographic and network variability. Version 2 exhibits substantial differences from version 1 over the period 1895-2012. For example, divisional averages in version 2 tend to be cooler and wetter, particularly in mountainous areas of the western United States. Division-level trends in temperature and precipitation display greater spatial consistency in version 2. National-scale temperature trends in version 2 are comparable to those in the U.S. Historical Climatology Network whereas version 1 exhibits less warming as a result of historical changes in observing practices. Divisional errors in version 2 are likely less than 0.5°C for temperature and 20 mm for precipitation at the start of the record, falling rapidly thereafter. Overall, these results indicate that version 2 can supersede version 1 in both operational climate monitoring and applied climatic research. [ABSTRACT FROM AUTHOR]

In this paper, wintertime precipitation from a variety of observational datasets, regional climate models (RCMs), and general circulation models (GCMs) is averaged over the state of California and compared. Several averaging methodologies are considered and all are found to give similar values when the model grid spacing is less than 3°. This suggests that California is a reasonable size for regional intercomparisons using modern GCMs. Results show that reanalysis-forced RCMs tend to significantly overpredict California precipitation. This appears to be due mainly to the overprediction of extreme events; RCM precipitation frequency is generally underpredicted. Overprediction is also reflected in wintertime precipitation variability, which tends to be too high for RCMs on both daily and interannual scales. Wintertime precipitation in most (but not all) GCMs is underestimated. This is in contrast to previous studies based on global blended gauge-satellite observations, which are shown here to underestimate precipitation relative to higher-resolution gauge-only datasets. Several GCMs provide reasonable daily precipitation distributions, a trait that does not seem to be tied to model resolution. The GCM daily and interannual variabilities are generally underpredicted. [ABSTRACT FROM AUTHOR]

The winter climate of the British Isles is characterized by considerable interannual variability, which, because of the general climate sensitivity of a number of health outcomes, places at times considerable pressure on the provision of health services. Seasonal climate forecasts potentially could improve management within the health sector and assist in hedging against the vagaries of climatic variability. For this reason, an exploratory analysis of the potential utility of seasonal climate forecasting for the health sector in the United Kingdom is presented here. Study results revealed that the general level of winter mortality at the monthly to seasonal time scale possesses a strong association with simple descriptors of winter climate such as maximum temperature and the number of days below a given temperature threshold. Because such climate indices can be derived from the output of coupled seasonal climate prediction models, predictions of general levels of mortality may be possible using simple transfer functions that describe winter climate and health associations. Despite the potential one-month-ahead and one-season-ahead predictability of winter mortality levels, the predictability of the key climate indices by coupled climate models is shown to be somewhat limited, which compromises the ability to predict general levels of winter mortality for all months except February. [ABSTRACT FROM AUTHOR]