Articles | Volume 15, issue 15
https://doi.org/10.5194/gmd-15-6165-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6165-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
MIdASv0.2.1 – MultI-scale bias AdjuStment
Hydrology Research Unit, Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Thomas Bosshard
Hydrology Research Unit, Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Hydrology Research Unit, Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
Klaus Zimmermann
Hydrology Research Unit, Swedish Meteorological and Hydrological Institute, Folkborgsvägen 17, 601 76 Norrköping, Sweden
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
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Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
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We have developed a tool to visualize rainfall observations, based on a combination of meteorological stations and weather radars, over Sweden in near real-time. By accumulating the rainfall in time (1–12 h) and space (hydrological basins), the tool is designed mainly for hydrological applications, e.g. to support flood forecasters and to facilitate post-event analyses. Despite evident uncertainties, different users have confirmed an added value of the tool in case studies.
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European extreme precipitation is expected to change in the future; this is based on climate model projections. But, since climate models have errors, projections are uncertain. We study this uncertainty in the projections by comparing results from an ensemble of 19 climate models. Results can be used to give improved estimates of future extreme precipitation for Europe.
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When bias adjusting climate model data using quantile mapping, one needs to prescribe what to do at the tails of the distribution, where a larger data range is likely encountered outside of the calibration period. The end result is highly dependent on the method used. We show that, to avoid discontinuities in the time series, one needs to exclude data in the calibration range to also activate the extrapolation functionality in that time period.
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for routine evaluation of Earth system models. Originally, ESMValTool was designed to process reformatted output provided by large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP). Here, we describe a new extension of ESMValTool that allows for reading and processing native climate model output, i.e., data that have not been reformatted before.
Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillaume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
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Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.
Erika Médus, Emma D. Thomassen, Danijel Belušić, Petter Lind, Peter Berg, Jens H. Christensen, Ole B. Christensen, Andreas Dobler, Erik Kjellström, Jonas Olsson, and Wei Yang
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Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021, https://doi.org/10.5194/gmd-14-3159-2021, 2021
Short summary
Short summary
This work presents new diagnostics for the Earth System Model Evaluation Tool (ESMValTool) v2.0 on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The ESMValTool v2.0 diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) with a focus on the ESMs participating in the Coupled Model Intercomparison Project (CMIP).
Jonas Olsson, Peter Berg, and Remco van de Beek
Adv. Sci. Res., 18, 59–64, https://doi.org/10.5194/asr-18-59-2021, https://doi.org/10.5194/asr-18-59-2021, 2021
Short summary
Short summary
We have developed a tool to visualize rainfall observations, based on a combination of meteorological stations and weather radars, over Sweden in near real-time. By accumulating the rainfall in time (1–12 h) and space (hydrological basins), the tool is designed mainly for hydrological applications, e.g. to support flood forecasters and to facilitate post-event analyses. Despite evident uncertainties, different users have confirmed an added value of the tool in case studies.
Peter Berg, Fredrik Almén, and Denica Bozhinova
Earth Syst. Sci. Data, 13, 1531–1545, https://doi.org/10.5194/essd-13-1531-2021, https://doi.org/10.5194/essd-13-1531-2021, 2021
Short summary
Short summary
HydroGFD3.0 (Hydrological Global Forcing Data) is a data set of daily precipitation and temperature intended for use in hydrological modelling. The method uses different observational data sources to correct the variables from a model estimation of precipitation and temperature. An openly available data set covers the years 1979–2019, and times after this are available by request.
Torben Schmith, Peter Thejll, Peter Berg, Fredrik Boberg, Ole Bøssing Christensen, Bo Christiansen, Jens Hesselbjerg Christensen, Marianne Sloth Madsen, and Christian Steger
Hydrol. Earth Syst. Sci., 25, 273–290, https://doi.org/10.5194/hess-25-273-2021, https://doi.org/10.5194/hess-25-273-2021, 2021
Short summary
Short summary
European extreme precipitation is expected to change in the future; this is based on climate model projections. But, since climate models have errors, projections are uncertain. We study this uncertainty in the projections by comparing results from an ensemble of 19 climate models. Results can be used to give improved estimates of future extreme precipitation for Europe.
Cited articles
Berg, P., Feldmann, H., and Panitz, H.-J.: Bias correction of high resolution
regional climate model data, J. Hydrol., 448–449, 80–92,
https://doi.org/10.1016/j.jhydrol.2012.04.026, 2012. a
Berg, P., Bosshard, T., and Yang, W.: Model Consistent Pseudo-Observations of
Precipitation and Their Use for Bias Correcting Regional Climate Models,
Climate, 3, 118–132, https://doi.org/10.3390/cli3010118, 2015. a, b
Berg, P., Almén, F., and Bozhinova, D.: HydroGFD3.0 (Hydrological Global Forcing Data): a 25 km global precipitation and temperature data set updated in near-real time, Earth Syst. Sci. Data, 13, 1531–1545, https://doi.org/10.5194/essd-13-1531-2021, 2021a. a
Berg P., Bosshard, T., Yang, W., and Zimmermann, K.:
MIdAS version 0.1: framtagande och utvärdering av ett nytt verktyg för biasjustering,
SMHI, 63, KLIMATOLOGI, ISSN 1654-2258, https://www.diva-portal.org/smash/get/diva2:1578567/FULLTEXT01.pdf (last access: 1 August 2022), 2021b. a
Berg, P., Bosshard, T., Yang, W., and Zimmermann, K.: MIdAS (MultI-scale bias AdjuStment),
Zenodo [code], https://doi.org/10.5281/zenodo.6624233, 2022a. a
Berg, P., Bosshard, T., and Yang, W.: MIdAS: Bias adjustment inter-comparison and evaluation scripts, Zenodo [data set], https://doi.org/10.5281/zenodo.6043222, 2022b. a
Berg, P., Bosshard, T., Yang, W., and Zimmermann, K.: MIdAS git repository, SMHI [code], https://git.smhi.se/midas/midas, last access: 1 August 2022, 2022c. a
Boberg, F. and Christensen, J. H.: Overestimation of Mediterranean summer
temperature projections due to model deficiencies, Nat. Clim. Change,
2, 433–436, https://doi.org/10.1038/nclimate1454, 2012. a
Buser, C. M., Künsch, H. R., Lüthi, D., Wild, M., and Schär, C.:
Bayesian multi-model projection of climate: bias assumptions and interannual
variability, Clim. Dynam., 33, 849–868, https://doi.org/10.1007/s00382-009-0588-6, 2009. a
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias Correction of GCM
Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in
Quantiles and Extremes?, J. Climate, 28, 6938–6959,
https://doi.org/10.1175/jcli-d-14-00754.1, 2015. a, b
Casanueva, A., and Bedia, J., and Herrera, S., and Fernández, J.,
and Gutiérrez, J. M.: Direct and component-wise bias correction of
multi-variate climate indices: the percentile adjustment function diagnostic tool,
Climatic Change, 147, 411–425, https://doi.org/10.1007/s10584-018-2167-5, 2018. a
Dask Development Team: Dask: Library for dynamic task scheduling, https://dask.org (last access: 1 August 2022), 2016. a
Dierckx, P.: An algorithm for smoothing, differentiation and integration of
experimental data using spline functions, J. Comput. Appl. Math., 1, 165–184, https://doi.org/10.1016/0771-050x(75)90034-0, 1975. a
Dierckx, P: An improved algorithm for curve fitting with spline functions, TW Reports, Department of Computer Science, K.U. Leuven, Belgium, 1981. a
Dierckx, P.: A Fast Algorithm for Smoothing Data on a Rectangular Grid while
Using Spline Functions, SIAM J. Numer. Anal., 19, 1286–1304,
https://doi.org/10.1137/0719093, 1982. a
Dierckx, P.: Curve and surface fitting with splines, first edn., in: Monographs on numerical analysis, Oxford University Press, ISSN 0540-6919,
1995. a
Fiddes, J., Aalstad, K., and Lehning, M.: TopoCLIM: rapid topography-based downscaling of regional climate model output in complex terrain v1.1, Geosci. Model Dev., 15, 1753–1768, https://doi.org/10.5194/gmd-15-1753-2022, 2022. a
François, B., Vrac, M., Cannon, A. J., Robin, Y., and Allard, D.: Multivariate bias corrections of climate simulations: which benefits for which losses?, Earth Syst. Dynam., 11, 537–562, https://doi.org/10.5194/esd-11-537-2020, 2020. a, b
Gleick, P. H.: Methods for evaluating the regional hydrologic impacts of global
climatic changes, J. Hydrol., 88, 97–116,
https://doi.org/10.1016/0022-1694(86)90199-x, 1986. a
Gudmundsson, L.: qmap: Statistical transformations for post-processing climate
model output, R package version 1.0-4, CRAN [code], https://cran.r-project.org/package=qmap (last access: 4 November 2020), 2016. a
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
Haerter, J. O., Eggert, B., Moseley, C., Piani, C., and Berg, P.: Statistical
precipitation bias correction of gridded model data using point
measurements, Geophys. Res. Lett., 42, 1919–1929,
https://doi.org/10.1002/2015GL063188, 2015. a
Hassler, B. and Lauer, A.: Comparison of Reanalysis and Observational
Precipitation Datasets Including ERA5 and WFDE5, Atmosphere, 12, 1462,
https://doi.org/10.3390/atmos12111462, 2021. a
Ivanov, M. A., Luterbacher, J., and Kotlarski, S.: Climate Model Biases
and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction,
J. Climate, 31, 6591–6610, https://doi.org/10.1175/jcli-d-17-0765.1, 2018. a
Johnson, F. and Sharma, A.: A nesting model for bias correction of variability
at multiple time scales in general circulation model precipitation simulations,
Water Resour. Res., 48, W01504, https://doi.org/10.1029/2011wr010464, 2012. a
Knutti, R., Masson, D., and Gettelman, A.: Climate model genealogy: Generation
CMIP5 and how we got there, Geophys. Res. Lett., 40, 1194–1199,
https://doi.org/10.1002/grl.50256, 2013. a, b
Lange, S.: Trend-preserving bias adjustment and statistical downscaling with ISIMIP3BASD (v1.0), Geosci. Model Dev., 12, 3055–3070, https://doi.org/10.5194/gmd-12-3055-2019, 2019. a
Maraun, D.: Nonstationarities of regional climate model biases in European
seasonal mean temperature and precipitation sums, Geophys. Res. Lett.,
39, L06706, https://doi.org/10.1029/2012gl051210, 2012. a
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,
Curr. Clim. Change Rep., 2, 211–220, https://doi.org/10.1007/s40641-016-0050-x,
2016. a, b
Maraun, D. and Widmann, M.: Cross-validation of bias-corrected climate simulations is misleading, Hydrol. Earth Syst. Sci., 22, 4867–4873, https://doi.org/10.5194/hess-22-4867-2018, 2018. a
Maraun, D., Shepherd, T. G., Widmann, M., Zappa, G., Walton, D.,
Gutiérrez, J. M., Hagemann, S., Richter, I., Soares, P. M. M., Hall,
A., and Mearns, L. O.: Towards process-informed bias correction of climate
change simulations, Nat. Clim. Change, 7, 764–773,
https://doi.org/10.1038/nclimate3418, 2017. a, b
Mehrotra, R. and Sharma, A.: Correcting for systematic biases in multiple raw
GCM variables across a range of timescales, J. Hydrol., 520,
214–223, https://doi.org/10.1016/j.jhydrol.2014.11.037, 2015. a, b
Met Office: Iris v1.2, 2010–2013, Exeter, Devon, http://scitools.org.uk/ (last access: 1 August 2022), 2021. a
Michelangeli, P.-A., Vrac, M., and Loukos, H.: Probabilistic downscaling
approaches: Application to wind cumulative distribution functions,
Geophys. Res. Lett., 36, L11708, https://doi.org/10.1029/2009gl038401, 2009. 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
Pechlivanidis, I., Olsson, J., Bosshard, T., Sharma, D., and Sharma, K.:
Multi-Basin Modelling of Future Hydrological Fluxes in the Indian
Subcontinent, Water, 8, 177, https://doi.org/10.3390/w8050177, 2016. a
Photiadou, C., Arheimer, B., Bosshard, T., Capell, R., Elenius, M., Gallo, I.,
Gyllensvärd, F., Klehmet, K., Little, L., Ribeiro, I., Santos, L., and
Sjökvist, E.: Designing a Climate Service for Planning Climate Actions in
Vulnerable Countries, Atmosphere, 12, 121, https://doi.org/10.3390/atmos12010121, 2021. a
Piani, C. and Haerter, J. O.: Two dimensional bias correction of temperature
and precipitation copulas in climate models, Geophys. Res. Lett.,
39, L20401, https://doi.org/10.1029/2012gl053839, 2012. a, b
Piani, C., Weedon, G., Best, M., Gomes, S., Viterbo, P., Hagemann, S., and
Haerter, J.: Statistical bias correction of global simulated daily
precipitation and temperature for the application of hydrological models,
J. Hydrol., 395, 199–215, https://doi.org/10.1016/j.jhydrol.2010.10.024, 2010. a
Räisänen, J. and Räty, O.: Projections of daily mean temperature
variability in the future: cross-validation tests with ENSEMBLES regional
climate simulations, Clim. Dynam., 41, 1553–1568,
https://doi.org/10.1007/s00382-012-1515-9, 2012. a
Räty, O., Räisänen, J., and Ylhäisi, J. S.: Evaluation of delta
change and bias correction methods for future daily precipitation: intermodel
cross-validation using ENSEMBLES simulations, Clim. Dynam., 42,
2287–2303, https://doi.org/10.1007/s00382-014-2130-8, 2014. a
Schmith, T., Thejll, P., Berg, P., Boberg, F., Christensen, O. B., Christiansen, B., Christensen, J. H., Madsen, M. S., and Steger, C.: Identifying robust bias adjustment methods for European extreme precipitation in a multi-model pseudo-reality setting, Hydrol. Earth Syst. Sci., 25, 273–290, https://doi.org/10.5194/hess-25-273-2021, 2021. 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
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, b
Themeßl, M. J., and Gobiet, A., and Heinrich, G.:
Empirical-statistical downscaling and error correction of regional
climate models and its impact on the climate change signal,
Climatic Change, 112, 449–468, https://doi.org/10.1007/s10584-011-0224-4, 2011. a
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T.,
Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J.,
van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N.,
Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat,
İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J.,
Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M.,
Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., Vijaykumar, A., Bardelli,
A. P., Rothberg, A., Hilboll, A., Kloeckner, A., Scopatz, A., Lee, A., Rokem,
A., Woods, C. N., Fulton, C., Masson, C., Häggström, C., Fitzgerald,
C., Nicholson, D. A., Hagen, D. R., Pasechnik, D. V., Olivetti, E., Martin,
E., Wieser, E., Silva, F., Lenders, F., Wilhelm, F., Young, G., Price, G. A.,
Ingold, G.-L., Allen, G. E., Lee, G. R., Audren, H., Probst, I., Dietrich,
J. P., Silterra, J., Webber, J. T., Slavič, J., Nothman, J., Buchner,
J., Kulick, J., Schönberger, J. L., de Miranda Cardoso, J. V., Reimer,
J., Harrington, J., Rodríguez, J. L. C., Nunez-Iglesias, J.,
Kuczynski, J., Tritz, K., Thoma, M., Newville, M., Kümmerer, M.,
Bolingbroke, M., Tartre, M., Pak, M., Smith, N. J., Nowaczyk, N., Shebanov,
N., Pavlyk, O., Brodtkorb, P. A., Lee, P., McGibbon, R. T., Feldbauer, R.,
Lewis, S., Tygier, S., Sievert, S., Vigna, S., Peterson, S., More, S.,
Pudlik, T., Oshima, T., Pingel, T. J., Robitaille, T. P., Spura, T., Jones,
T. R., Cera, T., Leslie, T., Zito, T., Krauss, T., Upadhyay, U., Halchenko,
Y. O., and Vázquez-Baeza, Y.: SciPy 1.0: fundamental algorithms for
scientific computing in Python, Nat. Methods, 17, 261–272,
https://doi.org/10.1038/s41592-019-0686-2, 2020.
a
Vrac, M. and Friederichs, P.: Multivariate–Intervariable, Spatial,
and Temporal–Bias Correction, J. Climate, 28, 218–237,
https://doi.org/10.1175/jcli-d-14-00059.1, 2014. a
Vrac, M. and Michelangeli, P.-A.: CDFt: Downscaling and Bias Correction via
Non-Parametric CDF-Transform, R package version 1.0.1, CRAN [code], https://CRAN.R-project.org/package=CDFt (last access: 4 November 2020), 2009. a
Vrac, M., Noël, T., and Vautard, R.: Bias correction of precipitation
through Singularity Stochastic Removal: Because occurrences matter,
J. Geophys. Res.-Atmos., 121, 5237–5258,
https://doi.org/10.1002/2015jd024511, 2016. a
Wood, A. W., Maurer, E., Kumar, A., and Lettenmaier, D.: Long-range
experimental hydrological forecasting for the eastern United States,
J. Geophys. Res., 107, 4429, https://doi.org/10.1029/2001JD000659,
2002. a
Yang, W., Andréasson, J., Graham, L. P., 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
When performing impact analyses with climate models, one is often confronted with the issue that the models have significant bias. Commonly, the modelled climatological temperature deviates from the observed climate by a few degrees or it rains excessively in the model. MIdAS employs a novel statistical model to translate the model climatology toward that observed using novel methodologies and modern tools. The coding platform allows opportunities to develop methods for high-resolution models.
When performing impact analyses with climate models, one is often confronted with the issue that...