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Uncertainties in observed records of atmospheric temperature aloft remain poorly quantified. This has resulted in considerable controversy regarding signals of climate change over recent decades from temperature records of radiosondes and satellites. This work revisits the problems associated with the removal of inhomogeneities from the historical radiosonde temperature records, and provides a method for quantifying uncertainty in an adjusted radiosonde climate record due to the subjective choices made during the data homogenization. This paper presents an automated homogenization method designed to replicate the decisions made by manual judgment in the generation of an earlier radiosonde dataset [i.e., the Hadley Centre radiosonde temperature dataset (HadAT)]. A number of validation experiments have been conducted to test the system performance and impact on linear trends. Using climate model data to simulate biased radiosonde data, the authors show that limitations in the homogenization method are sufficiently large to explain much of the tropical trend discrepancy between HadAT and estimates from satellite platforms and climate models. This situation arises from the combination of systematic (unknown magnitude) and random uncertainties (of order 0.05 K decade-1) in the radiosonde data. Previous assessment of trends and uncertainty in HadAT is likely to have underestimated the systematic bias in tropical mean temperature trends. This objective assessment of radiosonde homogenization supports the conclusions of the synthesis report of the U.S. Climate Change Science Program (CCSP), and associated research, regarding potential bias in tropospheric temperature records from radiosondes. [ABSTRACT FROM AUTHOR]

Changes in extreme temperatures, heat waves, and heavy rainfall events have adverse effects on human health, air quality, and water resources. With aerosol-only (AER) and greenhouse gas-only (GHG) simulations from 1860 to 2005 in the GFDL CM3 chemistry-climate model, aerosol-induced versus greenhouse gas-induced changes in temperature (summer) and precipitation (all seasons) extremes over the United States are investigated. Small changes in these extremes in the all forcing simulations reflect cancellations between the effects of increasing anthropogenic aerosols and greenhouse gases. In AER, extreme high temperatures and the number of days with temperatures above the 90th percentile decline over most of the United States. The strongest response occurs in the western United States (−2.0°C and −14 days, regionally averaged) and the weakest response occurs in the southeastern United States (−0.6°C and −4.8 days). An opposite-signed response pattern occurs in GHG (+2.3°C and +11.5 days over the western United States and +1.6°C and +7.2 days over the southeastern United States). The similar spatial response patterns in AER versus GHG suggest a preferred regional mode of response that is largely independent of the type of forcing. Extreme precipitation over the eastern United States decreases in AER, particularly in winter, and increases over the eastern and central United States in GHG, particularly in spring. Over the twenty-first century under the representative concentration pathway 8.5 (RCP8.5) emissions scenario, the patterns of extreme temperature and precipitation associated with greenhouse gas forcing dominate. [ABSTRACT FROM AUTHOR]

An examination of five temperature and five precipitation extreme indicators reveals an increase in both temperature and precipitation extremes over the 1926–2000 period in the northeast United States, with most of this increase occurring over the past four decades. Empirical orthogonal function (EOF) analysis of winter frost days (FD) and warm nights (TN90) and also winter consecutive dry days (CDD) and very wet days (R95T) over the 1950–2000 period reveals that some of the variability associated with changes in these extremes may be explained by variations in the Arctic Oscillation (AO), El Niño–Southern Oscillation (ENSO), and Pacific–North American (PNA) pattern. The most prominent feature of these results was the high correlation between the leading EOF of frost days and warm nights and the AO. Winter composites of temperature and precipitation extreme indicators were examined for different phases of the AO and ENSO during the 1926–2000 period. Overall, the AO is a better predictor of winter warm nights, while the ENSO is a better predictor of consecutive dry days in the northeast United States. [ABSTRACT FROM AUTHOR]