Homogenization of the Global Radiosonde Temperature Dataset through Combined Comparison with Reanalysis Background Series and Neighboring Stations

Haimberger, Leopold; Tavolato, Christina; Sperka, Stefan

doi:10.1175/jcli-d-11-00668.1

Cited by 150 publications

(177 citation statements)

References 54 publications

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“…Thus, for a given level of wave activity, mean winter temperatures have been getting colder over the past three decades by −0.52 ± 0.49 K decade −1 . Again, this is comparable to the near-global mean cooling of about −0.5 K decade −1 at this level from ERA-Interim (Table 3) and radiosonde observations (Randel et al, 2009 (IPCC, 2000(IPCC, , 2007. Sea surface temperatures (SST) and sea ice concentrations (SIC) are simulated through previous offline model calculations.…”

Section: Past Arctic Temperature Changessupporting

confidence: 64%

“…ECMWF ERA-Interim (Dee et al, 2011) and MERRA (Rienecker et al, 2011) reanalyses, together with RICH adjusted radiosonde data (Haimberger et al, 2012(Haimberger et al, ) from 1980(Haimberger et al, -2011 used to determine temperature trends. ERA-Interim data have been obtained from the ECMWF data server at a horizontal resolution of 1.5 • × 1.5 • .…”

Section: Past Arctic Temperature Changesmentioning

confidence: 99%

“…SSTs and SICs were taken from a previous coupled simulation of the ECHAM5 climate model with the ocean model MPI-OM from Max-Planck-Institute in Hamburg. The A1B scenario (IPCC, 2000(IPCC, , 2007 was applied in the EMAC simulation. Volcanic eruptions, solar variability and QBO were not included, in accordance with REF-B2 specifications.…”

Section: Past Arctic Temperature Changesmentioning

confidence: 99%

“…We focus on temperatures in the lower stratosphere at 50 hPa, as this is the region most critical for Arctic ozone depletion. Unless otherwise noted, we consider averages over 60-90 • N. We use reanalyses from the European Centre for Medium Range Weather Forecasts (ECMWF) ERA-Interim project (Dee et al, 2011) and NASA's Modern-Era Retrospective Analysis for Research and Applications (MERRA) (Rienecker et al, 2011) together with the Radiosonde Innovation Composite Homogenization (RICH) adjusted radiosonde data set (Haimberger et al, 2012) for the past 32 yr to determine temperature trends (Sect. 2).…”

Section: Introductionmentioning

confidence: 99%

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Radiative and dynamical contributions to past and future Arctic stratospheric temperature trends

et al. 2014

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Abstract. Arctic stratospheric ozone depletion is closely linked to the occurrence of low stratospheric temperatures. There are indications that cold winters in the Arctic stratosphere have been getting colder, raising the question if and to what extent a cooling of the Arctic stratosphere may continue into the future. We use meteorological reanalyses from the European Centre for Medium Range Weather Forecasts (ECMWF) ERA-Interim and NASA's ModernEra Retrospective-Analysis for Research and Applications (MERRA) for the past 32 yr together with calculations of the chemistry-climate model (CCM) ECHAM/MESSy Atmospheric Chemistry (EMAC) and models from the ChemistryClimate Model Validation (CCMVal) project to infer radiative and dynamical contributions to long-term Arctic stratospheric temperature changes. For the past three decades the reanalyses show a warming trend in winter and cooling trend in spring and summer, which agree well with trends from the Radiosonde Innovation Composite Homogenization (RICH) adjusted radiosonde data set. Changes in winter and spring are caused by a corresponding change of planetary wave activity with increases in winter and decreases in spring. During winter the increase of planetary wave activity is counteracted by a residual radiatively induced cooling. Stratospheric radiatively induced cooling is detected throughout all seasons, being highly significant in spring and summer. This means that for a given dynamical situation, according to ERA-Interim the annual mean temperature of the Arctic lower stratosphere has been cooling by −0.41 ± 0.11 K decade −1 at 50 hPa over the past 32 yr. Calculations with state-of-the-art models from CCMVal and the EMAC model qualitatively reproduce the radiatively induced cooling for the past decades, but underestimate the amount of radiatively induced cooling deduced from reanalyses. There are indications that this discrepancy could be partly related to a possible underestimation of past Arctic ozone trends in the models. The models project a continued cooling of the Arctic stratosphere over the coming decades that is for the annual mean about 40 % less than the modeled cooling for the past, due to the reduction of ozone depleting substances and the resulting ozone recovery. This projected cooling in turn could offset between 15 and 40 % of the Arctic ozone recovery.

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Section: Past Arctic Temperature Changessupporting

confidence: 64%

Section: Past Arctic Temperature Changesmentioning

confidence: 99%

Section: Past Arctic Temperature Changesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Radiative and dynamical contributions to past and future Arctic stratospheric temperature trends

et al. 2014

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“…However, most long-time climate records do not adequately represent climate change characteristics because of changes in instrumentation, observation methods, station relocation, and other non-climate factors; these changes result in a distortion in climate change monitoring (Aguilar et al 2003;Jiang et al 2010). Therefore, many studies have investigated climate data homogenization globally, as early as the beginning of the 1980s (Quayle Robert et al 1991;Chenoweth 1992;Peterson Thomas 2003;Wijngaard Janet et al 2003;Della-Marta Paul and Wanner 2006;Haimberger et al 2012). These studies found that it is more accurate and easier to analyze meteorological elements based on time series where significant breakpoints have been removed (Winkler Julie et al 1981;Alexandersson 1986).…”

Section: Introductionmentioning

confidence: 99%

Homogenization of Tianjin monthly near-surface wind speed using RHtestsV4 for 1951–2014

Luo

Liang

2017

Theor Appl Climatol

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Historical Chinese surface meteorological records provided by the special fund for basic meteorological data from the National Meteorological Information Center (NMIC) were processed to produce accurate wind speed data. Monthly 2-min near-surface wind speeds from 13 observation stations in Tianjin covering 1951-2014 were homogenized using RHtestV4 combined with their metadata. Results indicate that 10 stations had significant breakpoints-77% of the Tianjin stations-suggesting that inhomogeneity was common in the Tianjin wind speed series. Instrument change accounted for most changes, based on the metadata, including changes in type and height, especially for the instrument type. Average positive quantile matching (QM) adjustments were more than negative adjustments at 10 stations; positive biases with a probability density of 0.2 or more were mainly concentrates in the range 0.2 m s −1 to 1.2 m s −1 , while the corresponding negative biases were mainly in the range −0.1 to −1.2 m s −1 . Here, changes in variances and trends in the monthly mean surface wind speed series at 10 stations before and after adjustment were compared. Climate characteristics of wind speed in Tianjin were more reasonably reflected by the adjusted data; inhomogeneity in wind speed series was largely corrected. Moreover, error analysis reveals that there was a high consistency between the two datasets here and that from the NMIC, with the latter as the reference. The adjusted monthly near-surface wind speed series shows a certain reliability for the period 1951-2014 in Tianjin.

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Observation errors in early historical upper‐air observations

2013

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[1] Upper-air observations are a fundamental data source for global atmospheric data products, but uncertainties, particularly in the early years, are not well known. Most of the early observations, which have now been digitized, are prone to a large variety of undocumented uncertainties (errors) that need to be quantified, e.g., for their assimilation in reanalysis projects. We apply a novel approach to estimate errors in upper-air temperature, geopotential height, and wind observations from the Comprehensive Historical Upper-Air Network for the time period from 1923 to 1966. We distinguish between random errors, biases, and a term that quantifies the representativity of the observations. The method is based on a comparison of neighboring observations and is hence independent of metadata, making it applicable to a wide scope of observational data sets. The estimated mean random errors for all observations within the study period are 1.5 K for air temperature, 1.3 hPa for pressure, 3.0 ms -1 for wind speed, and 21.4 ı for wind direction. The estimates are compared to results of previous studies and analyzed with respect to their spatial and temporal variability.

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Homogenization of the Global Radiosonde Temperature Dataset through Combined Comparison with Reanalysis Background Series and Neighboring Stations

Cited by 150 publications

References 54 publications

Radiative and dynamical contributions to past and future Arctic stratospheric temperature trends

Radiative and dynamical contributions to past and future Arctic stratospheric temperature trends

Homogenization of Tianjin monthly near-surface wind speed using RHtestsV4 for 1951–2014

Observation errors in early historical upper‐air observations

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