Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate

Dosio, Alessandro; Paruolo, Paolo

doi:10.1029/2011jd015934

Cited by 200 publications

(192 citation statements)

References 34 publications

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“…Dosio and Paruolo 2011). This approach suffers from the same limitations as the aforementioned statistical bias correction of GCMs and has the additional complication that GCM biases may irretrievably change-or even destroy-the high-impact weather signal of interest (Ehret et al 2012;Done et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Bias corrections of global models for regional climate simulations of high-impact weather

et al. 2013

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All global circulation models (GCMs) suffer from some form of bias, which when used as boundary conditions for regional climate models may impact the simulations, perhaps severely. Here we present a bias correction method that corrects the mean error in the GCM, but retains the sixhourly weather, longer-period climate-variability and climate change from the GCM. We utilize six different bias correction experiments; each correcting different bias components. The impact of the full bias correction and the individual components are examined in relation to tropical cyclones, precipitation and temperature. We show that correcting of all boundary data provides the greatest improvement.

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Section: Introductionmentioning

confidence: 99%

Bias corrections of global models for regional climate simulations of high-impact weather

et al. 2013

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“…RCMs however suffer from the "garbage in, garbage out" syndrome (e.g., Wilby and Fowler 2010;Rummukainen 2010) in the sense that their simulation inherits the biases of the imposed BC. Hence in practice, the RCM-simulated data is often subjected to ESD to remove biases before it is used for climate impact studies (Christensen et al 2008;Dosio and Paruolo 2011).…”

Section: Introductionmentioning

confidence: 99%

3-Step dynamical downscaling with empirical correction of sea-surface conditions: application to a CORDEX Africa simulation

et al. 2016

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WAM precipitation, mainly due to the correction of SSCs. In fact, our results show that proper sea surface temperature (SST) in the Gulf of Guinea is a necessary condition for an adequate simulation of WAM precipitation, especially over the equatorial region of West Africa. It was found that the climate-change projections under RCP4.5 scenario obtained with the 3-step approach are substantially different from those obtained with usual downscaling approach in which the RCM is directly driven by the CGCM output; in the WAM region most of the differences in the projected climate changes came mainly from the empirical correction of SST.

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“…However, there is no reason the present methodology should not be similarly applied to these new products. The bias-correction step realized by Dosio and Paruolo (2011) will need to be repeated, bearing in mind caveats regarding the spatial scale (Maraun 2013b), since the European part of CORDEX data is at a finer spatial resolution (11 km) than ENSEMBLES.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the bias-corrections, the future weather dataset proposed by Dosio and Paruolo (2011) is still inadequate to properly run process-based crop simulation models to assess climate change impacts on crop growth and yield. The main issue is the lack of consistency of weather variables resulting from the fact that the bias-correction is done only on a subset of the necessary variables: surface air temperature and rainfall.…”

Section: Complementing the Weather Dataset With Biophysical Variablesmentioning

confidence: 99%

“…The original GCMs simulations are publicly available from the European ENSEMBLES project (Van der Linden and Mitchell 2009; http://ensemblesrt3.dmi.dk/). This data was bias-corrected (Dosio and Paruolo 2011;Dosio et al 2012) by applying an extension of the technique proposed by earlier studies (Piani et al 2009;Piani et al 2010) in which both precipitation and temperature values are corrected independently with respect to the E-OBS dataset (Haylock et al 2008). From this bias-corrected dataset, simulations of the A1B scenario by different coupled GCM-RCM models were selected (from now onwards, when the notation GCM is used in this paper, it refers to the bias-corrected dataset).…”

Section: Future Weather Datamentioning

confidence: 99%

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A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

Duveiller

Donatelli

Fumagalli

et al. 2015

Theor Appl Climatol

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Coupled atmosphere-ocean general circulation models (GCMs) simulate different realizations of possible future climates at global scale under contrasting scenarios of land-use and greenhouse gas emissions. Such data require several additional processing steps before it can be used to drive impact models. Spatial downscaling, typically by regional climate models (RCM), and bias-correction are two such steps that have already been addressed for Europe. Yet, the errors in resulting daily meteorological variables may be too large for specific model applications. Crop simulation models are particularly sensitive to these inconsistencies and thus require further processing of GCM-RCM outputs. Moreover, crop models are often run in a stochastic manner by using various plausible weather time series (often generated using stochastic weather generators) to represent climate time scale for a period of interest (e.g. 2000 ± 15 years), while GCM simulations typically provide a single time series for a given emission scenario. To inform agricultural policy-making, data on near-and medium-term decadal time scale is mostly requested, e.g. 2020 or 2030. Taking a sample of multiple years from these unique time series to represent time horizons in the near future is particularly problematic because selecting overlapping years may lead to spurious trends, creating artefacts in the results of the impact model simulations. This paper presents a database of consolidated and coherent future daily weather data for Europe that addresses these problems. Input data consist of daily temperature and precipitation from three dynamically downscaled and bias-corrected regional climate simulations of the IPCC A1B emission scenario created within the ENSEMBLES project. Solar radiation is estimated from temperature based on an auto-calibration procedure. Wind speed and relative air humidity are collected from historical series. From these variables, reference evapotranspiration and vapour pressure deficit are estimated ensuring consistency within daily records. The weather generator ClimGen is then used to create 30 synthetic years of all variables to characterize the time horizons of 2000, 2020 and 2030, which can readily be used for crop modelling studies.

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Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate

Cited by 200 publications

References 34 publications

Bias corrections of global models for regional climate simulations of high-impact weather

Bias corrections of global models for regional climate simulations of high-impact weather

3-Step dynamical downscaling with empirical correction of sea-surface conditions: application to a CORDEX Africa simulation

A dataset of future daily weather data for crop modelling over Europe derived from climate change scenarios

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