Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850

Brohan, Philip; Kennedy, John J.; Harris, Ian; Tett, Simon F. B.; Jones, Phil

doi:10.1029/2005jd006548

Cited by 1,779 publications

(1,651 citation statements)

References 31 publications

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“…The 2-m air temperatures are compared to the monthly average land station data from the Climate Research Unit at the University of East Anglia (Brohan et al 2006). Figure 1 shows the station locations, the seasonalmean bias, and the anomaly correlations for each of the reanalysis models for which the monthly mean of each station and each model at the station location is first removed.…”

Section: Comparison Of Reanalysis Products To Independent Observationsmentioning

confidence: 99%

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

et al. 2014

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Atmospheric reanalyses depend on a mix of observations and model forecasts. In data-sparse regions such as the Arctic, the reanalysis solution is more dependent on the model structure, assumptions, and data assimilation methods than in data-rich regions. Applications such as the forcing of ice-ocean models are sensitive to the errors in reanalyses. Seven reanalysis datasets for the Arctic region are compared over the 30-yr period 1981-2010: National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research Reanalysis 1 (NCEP-R1) and NCEP-U.S. Department of Energy Reanalysis 2 (NCEP-R2), Climate Forecast System Reanalysis (CFSR), Twentieth-Century Reanalysis (20CR), Modern-Era Retrospective Analysis for Research and Applications (MERRA), ECMWF Interim Re-Analysis (ERA-Interim), and Japanese 25-year Reanalysis Project (JRA-25). Emphasis is placed on variables not observed directly including surface fluxes and precipitation and their trends. The monthly averaged surface temperatures, radiative fluxes, precipitation, and wind speed are compared to observed values to assess how well the reanalysis data solutions capture the seasonal cycles. Three models stand out as being more consistent with independent observations: CFSR, MERRA, and ERA-Interim. A coupled ice-ocean model is forced with four of the datasets to determine how estimates of the ice thickness compare to observed values for each forcing and how the total ice volume differs among the simulations. Significant differences in the correlation of the simulated ice thickness with submarine measurements were found, with the MERRA products giving the best correlation (R 5 0.82). The trend in the total ice volume in September is greatest with MERRA (24.1 3 10 3 km 3 decade 21 ) and least with CFSR (22.7 3 10 3 km 3 decade 21 ).

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Section: Comparison Of Reanalysis Products To Independent Observationsmentioning

confidence: 99%

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

et al. 2014

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“…In 2009, on examining the available data sets and the description of the algorithm 35 , J.G.-C. identified a number of errors (the software he used to check the meteorological database is available upon request). One set of errors was procedural, and involved incorrect computation of historical average temperatures in a number of records in New Zealand and Australia.…”

Section: Boxmentioning

confidence: 99%

The case for open computer programs

Ince

Hatton

Graham-Cumming³

2012

Nature

449

356

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Scientific communication relies on evidence that cannot be entirely included in publications, but the rise of computational science has added a new layer of inaccessibility. Although it is now accepted that data should be made available on request, the current regulations regarding the availability of software are inconsistent. We argue that, with some exceptions, anything less than the release of source programs is intolerable for results that depend on computation. The vagaries of hardware, software and natural language will always ensure that exact reproducibility remains uncertain, but withholding code increases the chances that efforts to reproduce results will fail.T he rise of computational science has led to unprecedented opportunities for scientific advance. Ever more powerful computers enable theories to be investigated that were thought almost intractable a decade ago, robust hardware technologies allow data collection in the most inhospitable environments, more data are collected, and an increasingly rich set of software tools are now available with which to analyse computer-generated data.However, there is the difficulty of reproducibility, by which we mean the reproduction of a scientific paper's central finding, rather than exact replication of each specific numerical result down to several decimal places. We examine the problem of reproducibility (for an early attempt at solving it, see ref. 1) in the context of openly available computer programs, or code. Our view is that we have reached the point that, with some exceptions, anything less than release of actual source code is an indefensible approach for any scientific results that depend on computation, because not releasing such code raises needless, and needlessly confusing, roadblocks to reproducibility.At present, debate rages on the need to release computer programs associated with scientific experiments 2-4 , with policies still ranging from mandatory total release to the release only of natural language descriptions, that is, written descriptions of computer program algorithms. Some journals have already changed their policies on computer program openness; Science, for example, now includes code in the list of items that should be supplied by an author 5 . Other journals promoting code availability include Geoscientific Model Development, which is devoted, at least in part, to model description and code publication, and Biostatistics, which has appointed an editor to assess the reproducibility of the software and data associated with an article 6 .In contrast, less stringent policies are exemplified by statements such as 7 ''Nature does not require authors to make code available, but we do expect a description detailed enough to allow others to write their own code to do similar analysis.'' Although Nature's broader policy states that ''...authors are required to make materials, data and associated protocols promptly available to readers...'', and editors and referees are fully empowered to demand and evaluate any specific code, we belie...

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“…For comparison the radiosonde datasets have been vertically averaged with weightings to create equivalent MSU retrieved bulk temperatures for T4 (stratosphere), T2 (troposphere), and T2LT (lower troposphere). For reference we also show the trend from the Hadley Centre Climatic Research Unit, version 3 (HadCRUT3), surface records (Brohan et al 2006), and theoretical expectation from an ensemble of climate models (Santer et al 2005). Figure 9 shows the spread of trend estimates from this ensemble of homogenizations for the tropical satellite era.…”

Section: Spanning the Range And Comparison To Other Datasetsmentioning

confidence: 99%

Assessing Bias and Uncertainty in the HadAT-Adjusted Radiosonde Climate Record

et al. 2008

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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 Ϫ1 ) 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.

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Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850

Cited by 1,779 publications

References 31 publications

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic*

The case for open computer programs

Assessing Bias and Uncertainty in the HadAT-Adjusted Radiosonde Climate Record

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