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Imke Durre and John M. Wallace

Abstract

In light of numerous studies documenting a decline in the diurnal temperature range (DTR) over much of the globe, some authors have in recent years examined the annual march of the DTR in an effort to understand better the factors that influence the DTR’s seasonal variations. These papers show that, over the southern two-thirds of the eastern United States, the DTR’s climatology features peaks in spring and autumn and minima in winter and mid-to-late summer. However, the factors responsible for these characteristics remain uncertain. In this study, the DTR climatology of the eastern United States is analyzed in detail using daily surface and 850-mb data, with emphasis on possible relationships to seasonal changes in vegetation. It is shown that the warm season dip in the DTR deepens and widens from north to south across the study area, in accordance with a lengthening of the growing season. Furthermore, the dip is particularly prominent in the annual march of the DTR on mostly sunny days, indicating that seasonal variations in cloudiness are not responsible for this feature. The climatologies of daily maximum and daily minimum temperatures are found to be very different from each other: the former flattens out after the springtime peak in the DTR whereas the latter exhibits a pronounced mid-to-late summer maximum. These findings suggest that, by inhibiting daytime surface heating, evapotranspiration from vegetation contributes significantly to the dip in the DTR during the warm season in the eastern United States.

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Imke Durre and John M. Wallace

Abstract

This study examines the contributions of sunshine duration, snow cover extent, and the atmospheric circulation to variations of the cold-season diurnal temperature range (DTR) in eight regions of the contiguous United States. The goal of the research is to facilitate the interpretation of long-term changes in the DTR in light of the possible anthropogenic role in these trends. For the cold seasons (Nov–Mar) between 1958/59 and 1994/95, daily surface observations at more than 200 stations from the First Summary of the Day (FSOD) dataset as well as selected daily fields from the NCEP–NCAR 40-Year Reanalysis Project are analyzed using compositing, correlation, and regression techniques. For each region, a sea level pressure anomaly pattern is identified that is linearly related to daily variations in the DTR. It is found that the presence of positive sea level pressure anomalies over a region, clear skies, and the absence of snow on the ground all favor high values of the regionally averaged DTR. The strength of these associations varies geographically because of the effects of nonlinear relationships, the frequency of snow cover, and the complexity of local dynamics.

The cold-season trends of several variables for the period 1965/66–1994/95 are also analyzed. During the 30-yr period of record, the central and southern United States experienced a decrease in the DTR, while the northeast, Pacific coast, and portions of the interior west experienced an increase. Variations in the DTR-related sea level pressure patterns and sunshine duration explain significant fractions of the DTR increase in the coastal Northwest and the DTR decrease in the south-central states. The DTR trends over the rest of the country are largely unrelated to linear trends in sunshine duration, snow cover, or the sea level pressure field. The spatial pattern of DTR trends is reproduced when homogeneity-adjusted data from the Global Historical Climatology Network are used in lieu of FSOD data. Hence, it appears that the geographical pattern of trends is not a result of inhomogeneities in the FSOD data. The findings presented here suggest that many of the observed cold-season trends in the DTR are not induced by linearly related changes in the atmospheric circulation and, therefore, are attributable either to internal nonlinear relationships in the climate system or to anthropogenic factors such as urbanization and increasing concentrations of greenhouse gases and tropospheric aerosols.

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Imke Durre, John M. Wallace, and Dennis P. Lettenmaier

Abstract

The paper presents an analysis of the dependence of summertime daily maximum temperature on antecedent soil moisture using daily surface observations from a selection of stations in the contiguous United States and daily time series of soil moisture computed with a simple local water balance model. The computed soil moisture time series are offered as an alternative to Palmer’s soil moisture anomaly (Z) index, the Palmer Drought Severity Index (PDSI), and other such time series. In contrast to other water balance models that have been designed for the computation of soil moisture time series, the model herein is driven by daily rather than monthly data, uses the Priestley–Taylor method in lieu of Thornthwaite’s method to calculate potential evapotranspiration, allows for runoff during dry periods as well as when soil moisture is not at field capacity, includes a crude scheme for taking into account the effects of snowmelt on the water balance, and permits geographical variations in soil water capacity. The Priestley–Taylor method is considered to yield more realistic estimates of evapotranspiration than Thornthwaite’s method since it accounts for net radiation and represents a special case of the widely used Penman–Monteith method. Total runoff is parameterized according to the Variable Infiltration Capacity model. Based on a comparison with soil moisture measurements at Peoria, Illinois, the model appears to simulate the variability of soil moisture anomalies (W′) reasonably well.

Analysis of the relationship between W′ and daily maximum temperatures (T max) shows that in the central and eastern United States during the summer, the entire frequency distribution of standardized T max is shifted toward higher values following a “low-W′” day (i.e., a day on which W′ falls into the bottom quartile of its frequency distribution). The shift is most pronounced at the high end of the temperature distribution, indicating that as the soil gets drier, hot days tend to get hotter to a greater degree than cool days get warmer. Over the southeastern United States, where local evapotranspiration contributes a significant portion of the moisture available for precipitation, the temperature signal is particularly prominent and persists for up to several weeks after the soil moisture anomaly is observed. The relationship between temperature and daily precipitation is found to be much weaker and less persistent than the T maxW′ association. Thus, the frequency of record and near-record high temperatures is shown to be sensitive to soil moisture conditions, particularly on timescales shorter than one month.

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Imke Durre, Thomas C. Peterson, and Russell S. Vose

Abstract

The effect of the Luers–Eskridge adjustments on the homogeneity of archived radiosonde temperature observations is evaluated. Using unadjusted and adjusted radiosonde data from the Comprehensive Aerological Reference Dataset (CARDS) as well as microwave sounding unit (MSU) version-d monthly temperature anomalies, the discontinuities in differences between radiosonde and MSU temperature anomalies across times of documented changes in radiosonde are computed for the lower to midtroposphere, mid- to upper troposphere, and lower stratosphere. For this purpose, a discontinuity is defined as a statistically significant difference between means of radiosonde–MSU differences for the 30-month periods immediately prior to and following a documented change in radiosonde type. The magnitude and number of discontinuities based on unadjusted and adjusted radiosonde data are then compared. Since the Luers–Eskridge adjustments have been designed to remove radiation and lag errors from radiosonde temperature measurements, the homogeneity of the data should improve whenever these types of errors dominate.

It is found that even though stratospheric radiosonde temperatures appear to be somewhat more homogeneous after the Luers–Eskridge adjustments have been applied, transition-related discontinuities in the troposphere are frequently amplified by the adjustments. Significant discontinuities remain in the adjusted data in all three atmospheric layers. Based on the findings of this study, it appears that the Luers–Eskridge adjustments do not render upper-air temperature records sufficiently homogeneous for climate change analyses. Given that the method was designed to adjust only for radiation and lag errors in radiosonde temperature measurements, its relative ineffectiveness at producing homogeneous time series is likely to be caused by 1) an inaccurate calculation of the radiation or lag errors and/or 2) the presence of other errors in the data that contribute significantly to observed discontinuities in the time series.

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Imke Durre, Russell S. Vose, and David B. Wuertz

Abstract

This paper provides a general description of the Integrated Global Radiosonde Archive (IGRA), a new radiosonde dataset from the National Climatic Data Center (NCDC). IGRA consists of radiosonde and pilot balloon observations at more than 1500 globally distributed stations with varying periods of record, many of which extend from the 1960s to present. Observations include pressure, temperature, geopotential height, dewpoint depression, wind direction, and wind speed at standard, surface, tropopause, and significant levels.

IGRA contains quality-assured data from 11 different sources. Rigorous procedures are employed to ensure proper station identification, eliminate duplicate levels within soundings, and select one sounding for every station, date, and time. The quality assurance algorithms check for format problems, physically implausible values, internal inconsistencies among variables, runs of values across soundings and levels, climatological outliers, and temporal and vertical inconsistencies in temperature. The performance of the various checks was evaluated by careful inspection of selected soundings and time series.

In its final form, IGRA is the largest and most comprehensive dataset of quality-assured radiosonde observations freely available. Its temporal and spatial coverage is most complete over the United States, western Europe, Russia, and Australia. The vertical resolution and extent of soundings improve significantly over time, with nearly three-quarters of all soundings reaching up to at least 100 hPa by 2003. IGRA data are updated on a daily basis and are available online from NCDC as both individual soundings and monthly means.

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Melissa Free, James K. Angell, Imke Durre, John Lanzante, Thomas C. Peterson, and Dian J. Seidel

Abstract

The utility of a “first difference” method for producing temporally homogeneous large-scale mean time series is assessed. Starting with monthly averages, the method involves dropping data around the time of suspected discontinuities and then calculating differences in temperature from one year to the next, resulting in a time series of year-to-year differences for each month at each station. These first difference time series are then combined to form large-scale means, and mean temperature time series are constructed from the first difference series. When applied to radiosonde temperature data, the method introduces random errors that decrease with the number of station time series used to create the large-scale time series and increase with the number of temporal gaps in the station time series. Root-mean-square errors for annual means of datasets produced with this method using over 500 stations are estimated at no more than 0.03 K, with errors in trends less than 0.02 K decade−1 for 1960–97 at 500 mb. For a 50-station dataset, errors in trends in annual global means introduced by the first differencing procedure may be as large as 0.06 K decade−1 (for six breaks per series), which is greater than the standard error of the trend. Although the first difference method offers significant resource and labor advantages over methods that attempt to adjust the data, it introduces an error in large-scale mean time series that may be unacceptable in some cases.

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