Articles | Volume 9, issue 1
https://doi.org/10.5194/ascmo-9-29-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-9-29-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
24 Apr 2023
Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios
Institute of Meteorology and Climatology, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences (BOKU), Gregor Mendel Straße 33, 1180 Vienna, Austria
Imran Nadeem
Institute of Meteorology and Climatology, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences (BOKU), Gregor Mendel Straße 33, 1180 Vienna, Austria
Herbert Formayer
Institute of Meteorology and Climatology, Department of Water, Atmosphere and Environment, University of Natural Resources and Life Sciences (BOKU), Gregor Mendel Straße 33, 1180 Vienna, Austria
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Fabian Lehner, Imran Nadeem, and Herbert Formayer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-498, https://doi.org/10.5194/hess-2021-498, 2021
Manuscript not accepted for further review
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Climate model output usually has systematic errors which can be reduced with statistical methods. We review existing bias adjustment methods for climate data and discuss their shortcomings. We define three demands for the method and combine two existing methods for our own approach. We then test it in comparison to two other methods using real and artificially created daily temperature and precipitation data for Austria. The results show that our method is able to meet all three demands.
Fabian Lehner, Imran Nadeem, and Herbert Formayer
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We present an improved statistical bias correction method for climate models and put a focus on precipitation. Our method corrects model data so that it matches the observations in the historical period in a climatological sense better than other state-of-the-art methods while at the same time keeping long term trends in the future period unmodified. The corrected data can serve as a more accurate input for climate impact studies e.g. in hydrology or forestry.
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Manuscript not accepted for further review
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Climate model output usually has systematic errors which can be reduced with statistical methods. We review existing bias adjustment methods for climate data and discuss their shortcomings. We define three demands for the method and combine two existing methods for our own approach. We then test it in comparison to two other methods using real and artificially created daily temperature and precipitation data for Austria. The results show that our method is able to meet all three demands.
Fabian Lehner, Imran Nadeem, and Herbert Formayer
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-515, https://doi.org/10.5194/hess-2020-515, 2020
Manuscript not accepted for further review
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We present an improved statistical bias correction method for climate models and put a focus on precipitation. Our method corrects model data so that it matches the observations in the historical period in a climatological sense better than other state-of-the-art methods while at the same time keeping long term trends in the future period unmodified. The corrected data can serve as a more accurate input for climate impact studies e.g. in hydrology or forestry.
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At catchment scale, it is challenging to provide the required input parameters for physically based slope stability models. In the present study, the parameterization of such a model is optimized against observed shallow landslides during two triggering rainfall events. With the resulting set of parameters the model reproduces the location and the triggering timing of most observed landslides. Based on that, potential effects of increasing precipitation intensity on slope stability are assessed.
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Adv. Stat. Clim. Meteorol. Oceanogr., 8, 187–203, https://doi.org/10.5194/ascmo-8-187-2022, https://doi.org/10.5194/ascmo-8-187-2022, 2022
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Most climate time series contain annual and diurnal cycles. However, an objective criterion for deciding whether two time series have statistically equivalent annual and diurnal cycles is lacking, particularly if the residual variability is serially correlated. Such a criterion would be helpful in deciding whether a new version of a climate model better simulates such cycles. This paper derives an objective rule for such decisions based on a rigorous statistical framework.
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Sophie C. Lewis, Sarah E. Perkins-Kirkpatrick, and Andrew D. King
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Alex G. Libardoni, Chris E. Forest, Andrei P. Sokolov, and Erwan Monier
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Ralf Lindau and Victor Karel Christiaan Venema
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Erik Fraza, James B. Elsner, and Thomas H. Jagger
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Giang T. Tran, Kevin I. C. Oliver, András Sóbester, David J. J. Toal, Philip B. Holden, Robert Marsh, Peter Challenor, and Neil R. Edwards
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In this work, we combine the information from a complex and a simple atmospheric model to efficiently build a statistical representation (an emulator) of the complex model and to study the relationship between them. Thanks to the improved efficiency, this process is now feasible for complex models, which are slow and costly to run. The constructed emulator provide approximations of the model output, allowing various analyses to be made without the need to run the complex model again.
Cited articles
Bao, Y. and Wen, X.: Projection of China’s near- and long-term climate in a
new high-resolution daily downscaled dataset NEX-GDDP, J.
Meteorol. Res., 31, 236–249, https://doi.org/10.1007/s13351-017-6106-6, 2017. a
Boberg, F. and Christensen, J.: Overestimation of Mediterranean summer
temperature projections due to model deficiencies, Nat. Clim. Change, 2,
433–436, https://doi.org/10.1038/nclimate1454, 2012. a
Boé, J., Terray, L., Habets, F., and Martin, E.: Statistical and dynamical
downscaling of the Seine basin climate for hydro-meteorological studies,
Int. J. Climatol., 27, 1643–1655, https://doi.org/10.1002/joc.1602,
cited By 264, 2007. a
Bürger, G., Schulla, J., and Werner, A.: Estimates of future flow, including
extremes, of the Columbia River headwaters, Water Resour. Res., 47, W10520,
https://doi.org/10.1029/2010WR009716, 2011. a, b
Bürger, G., Sobie, S., Cannon, A., Werner, A., and Murdock, T.: Downscaling
extremes: An intercomparison of multiple methods for future climate, J. Climate, 26, 3429–3449, https://doi.org/10.1175/JCLI-D-12-00249.1, 2013. a
Cannon, A.: Multivariate quantile mapping bias correction: an N-dimensional
probability density function transform for climate model simulations of
multiple variables, Clim. Dynam., 50, 31–49,
https://doi.org/10.1007/s00382-017-3580-6, 2018. a, b, c, d
Casanueva, A., Herrera, S., Iturbide, M., Lange, S., Jury, M., Dosio, A.,
Maraun, D., and Gutiérrez, J.: Testing bias adjustment methods for regional
climate change applications under observational uncertainty and resolution
mismatch, Atmos. Sci. Lett., 21, e978, https://doi.org/10.1002/asl.978, 2020. a, b, c
Charles, S. P., Chiew, F. H. S., Potter, N. J., Zheng, H., Fu, G., and Zhang, L.: Impact of downscaled rainfall biases on projected runoff changes, Hydrol. Earth Syst. Sci., 24, 2981–2997, https://doi.org/10.5194/hess-24-2981-2020, 2020. a, b, c, d
Déqué, M.: Frequency of precipitation and temperature extremes over France in
an anthropogenic scenario: Model results and statistical correction according
to observed values, Global Planet. Change, 57, 16–26,
https://doi.org/10.1016/j.gloplacha.2006.11.030, 2007. a, b
Di Luca, A., de Elía, R., Bador, M., and Argüeso, D.: Contribution of mean
climate to hot temperature extremes for present and future climates, Weather
and Climate Extremes, 28, https://doi.org/10.1016/j.wace.2020.100255,
2020a. a
Di Luca, A., Pitman, A., and de Elía, R.: Decomposing Temperature Extremes
Errors in CMIP5 and CMIP6 Models, Geophys. Res. Lett., 47, e2020GL088031,
https://doi.org/10.1029/2020GL088031, 2020b. a
Doblas-Reyes, F. J., Sörensson, A. A., Almazroui, M., Dosio, A., Gutowski,
W. J., Haarsma, R., Hamdi, R., Hewitson, B., Kwon, W.-T., Lamptey, B. L.,
Maraun, D., Stephenson, T. S., Takayabu, I., Terray, L., Turner, A., and Zuo,
Z.: Chapter 10: Linking Global to Regional Climate Change, in: Climate Change
2021: The Physical Science Basis. Contribution of Working Group I to the
Sixth Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan,
C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M.,
Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K., Waterfield, T.,
Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, 2021. a
Gobiet, A., Suklitsch, M., and Heinrich, G.: The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal, Hydrol. Earth Syst. Sci., 19, 4055–4066, https://doi.org/10.5194/hess-19-4055-2015, 2015. a
Grillakis, M., Koutroulis, A., and Tsanis, I.: Multisegment statistical bias
correction of daily GCM precipitation output, J. Geophys. Res.-Atmos., 118, 3150–3162, https://doi.org/10.1002/jgrd.50323, 2013. a, b
Grillakis, M. G., Koutroulis, A. G., Daliakopoulos, I. N., and Tsanis, I. K.: A method to preserve trends in quantile mapping bias correction of climate modeled temperature, Earth Syst. Dynam., 8, 889–900, https://doi.org/10.5194/esd-8-889-2017, 2017. a, b
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012. a, b, c
Gutiérrez, J., Maraun, D., Widmann, M., Huth, R., Hertig, E., Benestad, R.,
Roessler, O., Wibig, J., Wilcke, R., Kotlarski, S., San Martín, D., Herrera,
S., Bedia, J., Casanueva, A., Manzanas, R., Iturbide, M., Vrac, M.,
Dubrovsky, M., Ribalaygua, J., Pórtoles, J., Räty, O., Räisänen, J.,
Hingray, B., Raynaud, D., Casado, M., Ramos, P., Zerenner, T., Turco, M.,
Bosshard, T., Štěpánek, P., Bartholy, J., Pongracz, R., Keller, D.,
Fischer, A., Cardoso, R., Soares, P., Czernecki, B., and Pagé, C.: An
intercomparison of a large ensemble of statistical downscaling methods over
Europe: Results from the VALUE perfect predictor cross-validation experiment,
Int. J. Climatol., 39, 3750–3785, https://doi.org/10.1002/joc.5462,
2019. a, b
Hagemann, S., Chen, C., Haerter, J., Heinke, J., Gerten, D., and Piani, C.:
Impact of a statistical bias correction on the projected hydrological changes
obtained from three GCMs and two hydrology models, J. Hydrometeorol., 12, 556–578, https://doi.org/10.1175/2011JHM1336.1, 2011. a
Hatchett, B., Koračin, D., Mejía, J., and Boyle, D.: Assimilating urban heat
island effects into climate projections, J. Arid Environ., 128,
59–64, https://doi.org/10.1016/j.jaridenv.2016.01.007, 2016. a
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013. a, b, c
Hiebl, J. and Frei, C.: Daily temperature grids for Austria since
1961 – concept, creation and applicability, Theor. Appl.
Climatol., 124, 161–178, https://doi.org/10.1007/s00704-015-1411-4, 2016. a
Hiebl, J. and Frei, C.: Daily precipitation grids for Austria since
1961 – development and evaluation of a spatial dataset for hydroclimatic
monitoring and modelling, Theor. Appl. Climatol., 132, 327–345,
https://doi.org/10.1007/s00704-017-2093-x, 2018. a
Hofstätter, M., Jacobeit, J., Homann, M., Lexer, A., Chimani, B., Philipp,
A., Beck, C., and Ganekind, M.: Wetrax: Auswirkungen des Klimawandels auf
großflächige Starkniederschläge in Mitteleuropa, Analyse der
Veränderungen von Zugbahnen und Großwetterlagen, Abschlussbericht
WEather Patterns, CycloneTRAcks and related precipitation EXtremes,
Geographica Augustana, ISSN 1862-8680, 2015. a
Horton, R., De Mel, M., Peters, D., Lesk, C., Bartlett, R., Helsingen, H.,
Bader, D., Capizzi, P., Martin, S., and Rosenzweig, C.: Assessing Climate
Risk in Myanmar: Technical Report, New York, NY, USA: Center for Climate
Systems Research at Columbia University, WWF-US and WWF-Myanmar, 2017. a
Jandl, R., Ledermann, T., Kindermann, G., Freudenschuss, A., Gschwantner, T.,
and Weiss, P.: Strategies for climate-smart forest management in Austria,
Forests, 9, 592, https://doi.org/10.3390/f9100592, 2018. a
Li, H., Sheffield, J., and Wood, E.: Bias correction of monthly precipitation
and temperature fields from Intergovernmental Panel on Climate Change AR4
models using equidistant quantile matching, J. Geophys. Res.-Atmos., 115, D10101, https://doi.org/10.1029/2009JD012882, 2010. a, b, c, d
Maraun, D.: Bias correction, quantile mapping, and downscaling: Revisiting the
inflation issue, J. Climate, 26, 2137–2143,
https://doi.org/10.1175/JCLI-D-12-00821.1, 2013. a
Maraun, D.: Bias Correcting Climate Change Simulations – a Critical Review,
Current Climate Change Reports, 2, 211–220, https://doi.org/10.1007/s40641-016-0050-x,
2016. a, b, c, d
Maraun, D., Shepherd, T., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J.,
Hagemann, S., Richter, I., Soares, P., Hall, A., and Mearns, L.: Towards
process-informed bias correction of climate change simulations, Nat.
Clim. Change, 7, 764–773, https://doi.org/10.1038/nclimate3418, 2017. a
Maraun, D., Huth, R., Gutiérrez, J., Martín, D., Dubrovsky, M., Fischer, A.,
Hertig, E., Soares, P., Bartholy, J., Pongrácz, R., Widmann, M., Casado, M.,
Ramos, P., and Bedia, J.: The VALUE perfect predictor experiment: Evaluation
of temporal variability, Int. J. Climatol., 39,
3786–3818, https://doi.org/10.1002/joc.5222, 2019. a, b, c, d
Maraun, D., Truhetz, H., and Schaffer, A.: Regional Climate Model Biases, Their
Dependence on Synoptic Circulation Biases and the Potential for Bias
Adjustment: A Process-Oriented Evaluation of the Austrian Regional Climate
Projections, J. Geophys. Res.-Atmos., 126, e2020JD032824,
https://doi.org/10.1029/2020JD032824, 2021. a, b
Maurer, E. P. and Pierce, D. W.: Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean, Hydrol. Earth Syst. Sci., 18, 915–925, https://doi.org/10.5194/hess-18-915-2014, 2014. a
Mearns, L., Bukovsky, M., Leung, R., Qian, Y., Arritt, R., Gutowowski, W.,
Takle, E., Biner, S., Caya, D., Correia Jr., J., Jones, R., Sloloan, L., and
Snyder, M.: Reply to “Comments on `the North American regional climate change
assessment program: overview of phase I results”', B. Am.
Meteorol. Soc., 94, 1077–1078, https://doi.org/10.1175/BAMS-D-13-00013.1,
2013. a
Mehrotra, R. and Sharma, A.: A multivariate quantile-matching bias correction
approach with auto- and cross-dependence across multiple time scales:
implications for downscaling, J. Climate, 29, 3519–3539,
https://doi.org/10.1175/JCLI-D-15-0356.1, 2016. a, b, c
Mehrotra, R. and Sharma, A.: A Resampling Approach for Correcting Systematic
Spatiotemporal Biases for Multiple Variables in a Changing Climate, Water Resour. Res., 55, 754–770, https://doi.org/10.1029/2018WR023270, 2019. a, b
Mehrotra, R., Johnson, F., and Sharma, A.: A software toolkit for correcting
systematic biases in climate model simulations, Environ. Model.
Softw., 104, 130–152, https://doi.org/10.1016/j.envsoft.2018.02.010, 2018. a, b, c
Navarro-Racines, C., Tarapues, J., Thornton, P., Jarvis, A., and
Ramirez-Villegas, J.: High-resolution and bias-corrected CMIP5 projections
for climate change impact assessments, Sci. Data, 7, 7,
https://doi.org/10.1038/s41597-019-0343-8, 2020. a
Nguyen, H., Mehrotra, R., and Sharma, A.: Correcting for systematic biases in
GCM simulations in the frequency domain, J. Hydrol., 538, 117–126,
https://doi.org/10.1016/j.jhydrol.2016.04.018, 2016. a, b
Nguyen, H., Mehrotra, R., and Sharma, A.: Can the variability in precipitation
simulations across GCMs be reduced through sensible bias correction?, Clim.
Dynam., 49, 3257–3275, https://doi.org/10.1007/s00382-016-3510-z, 2017. a, b
Pastén-Zapata, E., Jones, J., Moggridge, H., and Widmann, M.: Evaluation of
the performance of Euro-CORDEX Regional Climate Models for assessing
hydrological climate change impacts in Great Britain: A comparison of
different spatial resolutions and quantile mapping bias correction methods,
J. Hydrol., 584, 124653, https://doi.org/10.1016/j.jhydrol.2020.124653, 2020. a, b
Piani, C., Haerter, J., and Coppola, E.: Statistical bias correction for daily
precipitation in regional climate models over Europe, Theor. Appl.
Climatol., 99, 187–192, https://doi.org/10.1007/s00704-009-0134-9, 2010. a, b, c, d
Potter, N. J., Chiew, F. H. S., Charles, S. P., Fu, G., Zheng, H., and Zhang, L.: Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections, Hydrol. Earth Syst. Sci., 24, 2963–2979, https://doi.org/10.5194/hess-24-2963-2020, 2020. a, b, c
Seaby, L., Refsgaard, J., Sonnenborg, T., Stisen, S., Christensen, J., and
Jensen, K.: Assessment of robustness and significance of climate change
signals for an ensemble of distribution-based scaled climate projections,
J. Hydrol., 486, 479–493, https://doi.org/10.1016/j.jhydrol.2013.02.015,
2013. a
Seidl, R., Albrich, K., Erb, K., Formayer, H., Leidinger, D., Leitinger, G.,
Tappeiner, U., Tasser, E., and Rammer, W.: What drives the future supply of
regulating ecosystem services in a mountain forest landscape?, Forest Ecol. Manage., 445, 37–47, https://doi.org/10.1016/j.foreco.2019.03.047, 2019. a
Sillmann, J., Kharin, V., Zhang, X., Zwiers, F., and Bronaugh, D.: Climate
extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation
in the present climate, J. Geophys. Res.-Atmos., 118,
1716–1733, https://doi.org/10.1002/jgrd.50203, 2013. a
Smith, A., Freer, J., Bates, P., and Sampson, C.: Comparing ensemble
projections of flooding against flood estimation by continuous simulation,
J. Hydrol., 511, 205–219, https://doi.org/10.1016/j.jhydrol.2014.01.045,
2014. a
Stauffer, R., Mayr, G., Messner, J., Umlauf, N., and Zeileis, A.:
Spatio-temporal precipitation climatology over complex terrain using a
censored additive regression model, Int. J. Climatol., 37,
3264–3275, https://doi.org/10.1002/joc.4913, 2017. a
Switanek, M. B., Troch, P. A., Castro, C. L., Leuprecht, A., Chang, H.-I., Mukherjee, R., and Demaria, E. M. C.: Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes, Hydrol. Earth Syst. Sci., 21, 2649–2666, https://doi.org/10.5194/hess-21-2649-2017, 2017. a, b, c, d, e, f, g, h, i, j, k
Teng, J., Potter, N. J., Chiew, F. H. S., Zhang, L., Wang, B., Vaze, J., and Evans, J. P.: How does bias correction of regional climate model precipitation affect modelled runoff?, Hydrol. Earth Syst. Sci., 19, 711–728, https://doi.org/10.5194/hess-19-711-2015, 2015. a, b
Teutschbein, C. and Seibert, J.: Bias correction of regional climate model
simulations for hydrological climate-change impact studies: Review and
evaluation of different methods, J. Hydrol., 456-457, 12–29,
https://doi.org/10.1016/j.jhydrol.2012.05.052, 2012. a
Themeßl, M., Gobiet, A., and Heinrich, G.: Empirical-statistical downscaling
and error correction of regional climate models and its impact on the climate
change signal, Clim. Change, 112, 449–468,
https://doi.org/10.1007/s10584-011-0224-4, 2012. a, b
Thom, H. C.: A note on the gamma distribution, Mon. Weather Rev., 86,
117–122, 1958. a
Thrasher, B., Maurer, E. P., McKellar, C., and Duffy, P. B.: Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping, Hydrol. Earth Syst. Sci., 16, 3309–3314, https://doi.org/10.5194/hess-16-3309-2012, 2012. a
Unterberger, C., Brunner, L., Nabernegg, S., Steininger, K., Steiner, A.,
Stabentheiner, E., Monschein, S., and Truhetz, H.: Spring frost risk for
regional apple production under a warmer climate, PLoS ONE, 13, 1–18,
https://doi.org/10.1371/journal.pone.0200201, 2018. a
Vlček, O. and Huth, R.: Is daily precipitation Gamma-distributed?. Adverse
effects of an incorrect use of the Kolmogorov-Smirnov test, Atmos.
Res., 93, 759–766, https://doi.org/10.1016/j.atmosres.2009.03.005, 2009.
a, b
Volosciuk, C., Maraun, D., Vrac, M., and Widmann, M.: A combined statistical bias correction and stochastic downscaling method for precipitation, Hydrol. Earth Syst. Sci., 21, 1693–1719, https://doi.org/10.5194/hess-21-1693-2017, 2017. a, b
Vrac, M., Noël, T., and Vautard, R.: Bias correction of precipitation through
singularity stochastic removal: Because occurrences matter, J.
Geophys. Res., 121, 5237–5258, https://doi.org/10.1002/2015JD024511, 2016. a, b
Widmann, M., Bretherton, C., and Salathé Jr., E.: Statistical precipitation
downscaling over the northwestern united states using numerically simulated
precipitation as a predictor, J. Climate, 16, 799–816,
https://doi.org/10.1175/1520-0442(2003)016<0799:SPDOTN>2.0.CO;2, 2003. a
Widmann, M., Bedia, J., Gutiérrez, J., Bosshard, T., Hertig, E., Maraun, D.,
Casado, M., Ramos, P., Cardoso, R., Soares, P., Ribalaygua, J., Pagé, C.,
Fischer, A., Herrera, S., and Huth, R.: Validation of spatial variability in
downscaling results from the VALUE perfect predictor experiment,
Int. J. Climatol., 39, 3819–3845, https://doi.org/10.1002/joc.6024,
2019. a, b, c, d
Wiens, D. P., Cheng, J., and Beaulieu, N. C.: A class of method of moments
estimators for the two-parameter gamma family, Pak. J.
Stat., 19, 129–141, 2003. a
Yang, W., Andréasson, J., Graham, L., Olsson, J., Rosberg, J., and Wetterhall,
F.: Distribution-based scaling to improve usability of regional climate model
projections for hydrological climate change impacts studies, Hydrol.
Res., 41, 211–229, https://doi.org/10.2166/nh.2010.004, 2010. a
Short summary
Climate model output has systematic errors which can be reduced with statistical methods. We review existing bias-adjustment methods for climate data and discuss their skills and issues. We define three demands for the method and then evaluate them using real and artificially created daily temperature and precipitation data for Austria to show how biases can also be introduced with bias-adjustment methods themselves.
Climate model output has systematic errors which can be reduced with statistical methods. We...
