Search

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]

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]