[en] Vegetation models for climate adaptation and mitigation strategies require spatially high-resolution climate input data in which the error with respect to observations has been previously corrected. To quantify the impact of bias correction, we examine the effects of quantile-mapping bias correction on the climate change signal (CCS) of climate, extremes, and biological variables from the convective regional climate model COSMO-CLM and two dynamic vegetation models (LPJ-GUESS and CARAIB). COSMO-CLM was driven and analyzed at 3 km horizontal resolution over Central Europe (CE) and the Iberian Peninsula (IP) for the transient period 1980–2070 under the RCP8.5 scenario. Bias-corrected and uncorrected climate simulations served as input to run the dynamic vegetation models over Wallonia. Main result of the impact of bias correction on the climate is a reduction of seasonal absolute precipitation by up to −55% with respect to uncorrected simulations. Yet, seasonal climate changes of precipitation and also temperature are marginally affected by bias correction. Main result of the impact of bias correction on changes in extremes is a robust spatial mean CCS of climate extreme indices over both domains. Yet, local biases can both over- and underestimate changes in these indices and be as large as the raw CCS. Changes in extremely wet days are locally enhanced by bias correction by more than 100%. Droughts in southern IP are exacerbated by bias correction, which increases changes in consecutive dry days by up to 14 days/year. Changes in growing season length in CE are affected by quantile mapping due to local biases ranging from 24 days/year in western CE to −24 days/year in eastern CE. The increase of tropical nights and summer days in both domains is largely affected by bias correction at the grid scale because of local biases ranging within ±14 days/year. Bias correction of this study strongly reduces the precipitation amount which has a strong impact on the results of the vegetation models with a reduced vegetation biomass and increases in net primary productivity. Nevertheless, there are large differences in the results of the two applied vegetation models.
Disciplines :
Earth sciences & physical geography
Author, co-author :
Ugolotti, Alessandro; Kassel Institute for Sustainability, University of Kassel, Kassel, Germany
Anders, Tim; Senckenberg Biodiversity and Climate Research Centre, SBiK-F, Frankfurt, Germany
Hickler, Thomas; Senckenberg Biodiversity and Climate Research Centre, SBiK-F, Frankfurt, Germany ; Department of Physical Geography at Goethe University, Frankfurt, Germany
François, Louis ; Université de Liège - ULiège > Département d'astrophysique, géophysique et océanographie (AGO) > Modélisation du climat et des cycles biogéochimiques
Tölle, Merja H.; Kassel Institute for Sustainability, University of Kassel, Kassel, Germany
Language :
English
Title :
Impact of bias correction on climate change signals over central Europe and the Iberian Peninsula
Multisectoral analysis of climate and land use change impacts on pollinators, plant diversity and crops yields (MAPPY) Projets multilatéraux de recherche (PINT-MULTI)
Funders :
F.R.S.-FNRS - Fonds de la Recherche Scientifique
Funding number :
R.8003.19
Funding text :
This work has been conducted within the MAPPY project (Multisectoral analysis of climate and land use change impacts on pollinators, plant diversity and crop yields) which is part of AXIS, an ERA-NET initiated by JPI Climate, and funded by FFG (Austria), F.R.S.-FNRS and BELSPO (Belgium), DLR/BMBF (Germany), NWO (Netherlands), and AEI (Spain) with co-funding by the European Union (Grant number 776608). This research was funded by the German Research Foundation (DFG) grant number 401857120. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project ID bb118. ERA5 data reformatted by the CLM community provided via the DKRZ data pool were used.
Alexander L. Zhang X. Peterson T. Caesar J. Ba G. Tank A. et al. (2006). Global observed changes in daily climate extremes of temperature and precipitation. J. Geophys. Res. 111, D05109. 10.1029/2005JD006290
Arakawa A. Vivian L., R. (1977). “Computational design of the basic dynamical processes of the ucla general circulation model,” in General circulation models of the atmosphere. Methods in computational physics: Advances in research and applications. Editor Chang J. (Elsevier), 17, 173–265. 10.1016/B978-0-12-460817-7.50009-4
Baldauf M. Seifert A. Förstner J. Majewski D. Raschendorfer M. Reinhardt T. (2011). Operational convective-scale numerical weather prediction with the cosmo model: Description and sensitivities. Mon. Weather Rev. 139, 3887–3905. 10.1175/MWR-D-10-05013.1
Ban N. Caillaud C. Coppola E. Pichelli E. Sobolowski S. Adinolfi M. et al. (2021). The first multi-model ensemble of regional climate simulations at kilometer-scale resolution, part i: Evaluation of precipitation. Clim. Dyn. 57, 275–302. 10.1007/s00382-021-05708-w
Beck H. Zimmermann N. McVicar T. Vergopolan N. Berg A. Wood E. (2018). Present and future köppen-geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214. 10.1038/sdata.2018.214
Breugem A. Wesseling J. Oostindie K. Ritsema C. (2020). Meteorological aspects of heavy precipitation in relation to floods – An overview. Earth-Science Rev. 204, 103171. 10.1016/j.earscirev.2020.103171
Buser C. Künsch H. Lüthi D. Wild M. Schär C. (2009). Bayesian multi-model projection of climate: Bias assumptions and interannual variability. Clim. Dyn. 33, 849–868. 10.1007/s00382-009-0588-6
Casanueva A. Herrera S. Iturbide M. Lange S. Jury M. Dosio A. et al. (2020). Testing bias adjustment methods for regional climate change applications under observational uncertainty and resolution mismatch. Atmos. Sci. Lett. 21, e978. 10.1002/asl.978
Champeaux J.-L. Masson V. Chauvin F. (2006). Ecoclimap: A global database of land surface parameters at 1 km resolution. Meteorol. Appl. 12, 29–32. 10.1017/S1350482705001519
Chen C. Haerter J. O. Hagemann S. Piani C. (2011). On the contribution of statistical bias correction to the uncertainty in the projected hydrological cycle. Geophys. Res. Lett. 38. 10.1029/2011gl049318
Christensen J. H. Carter T. R. Rummukainen M. Amanatidis G. (2007a). Evaluating the performance and utility of regional climate models: The prudence project. [Dataset]
Christensen O. B. Drews M. Christensen J. H. Dethloff K. Ketelsen K. Hebestadt I. et al. (2007b). The hirham regional climate model. version 5 (beta).
Christensen J. H. Boberg F. Christensen O. B. Lucas-Picher P. (2008). On the need for bias correction of regional climate change projections of temperature and precipitation. Geophys. Res. Lett. 35, L20709. 10.1029/2008GL035694
Coppola E. Sobolowski S. Pichelli E. Raffaele F. Ahrens B. Anders I. et al. (2020). A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the mediterranean. Clim. Dyn. 55, 3–34. 10.1007/s00382-018-4521-8
Déqué M. Somot S. Sanchez-Gomez E. Goodess C. Jacob D. Lenderink G. et al. (2012). The spread amongst ensembles regional scenarios: Regional climate models, driving general circulation models and interannual variability. Clim. Dyn. 38, 951–964. 10.1007/s00382-011-1053-x
Dirmeyer P. A. Gao X. Zhao M. Guo Z. Oki T. Hanasaki N. (2006). Gswp-2: Multimodel analysis and implications for our perception of the land surface. Bull. Am. Meteorological Soc. 87, 1381–1398. 10.1175/bams-87-10-1381
Doms G. Baldauf M. (2021). A description of the non-hydrostatic regional cosmo-model, part i: Dynamics and numerics. Deutscher Wetterdienst. 10.5676/DWD_pub/nwv/cosmo-doc_6.00_I
Doms G. Förstner J. Heise E. Herzog H.-J. Mironov D. Raschendorfer M. et al. (2021). A description of the non-hydrostatic regional cosmo-model, part ii: Physical parameterizations. Deutscher Wetterdienst. 10.5676/DWD/pub/nwv/cosmo-doc_6.00_II
Dosio A. (2016). Projections of climate change indices of temperature and precipitation from an ensemble of bias-adjusted high-resolution euro-cordex regional climate models. J. Geophys. Res. Atmos. 121, 5488–5511. 10.1002/2015jd024411
Dury M. Hambuckers A. Warnant P. Henrot A. Favre E. Ouberdous M. et al. (2011). Responses of European forest ecosystems to 21 (st) century climate: Assessing changes in interannual variability and fire intensity. iForest Biogeosci. For. 4, 82–99. 10.3832/ifor0572-004
Dury M. Mertens L. Fayolle A. Verbeeck H. Hambuckers A. François L. (2018). Refining species traits in a dynamic vegetation model to project the impacts of climate change on tropical trees in central Africa. Forests 9, 722. 10.3390/f9110722
Ehret U. Zehe E. Wulfmeyer V. Warrach-Sagi K. Liebert J. (2012). “Hess opinions ”should we apply bias correction to global and regional climate model data? Hydrology Earth Syst. Sci. 16, 3391–3404. 10.5194/hess-16-3391-2012
Fernández-González S. del Río S. Castro A. Penas A. Fernández-Raga M. Calvo A. I. et al. (2012). Connection between nao, weather types and precipitation in león, Spain (1948–2008). Int. J. Climatol. 32, 2181–2196. 10.1002/joc.2431
Fischer G. Nachtergaele F. Prieler S. Van Velthuizen H. Verelst L. Wiberg D. (2008). Global agro-ecological zones assessment for agriculture (gaez 2008), 10. Laxenburg, Austria, Rome, Italy: IIASA, FAO.
Frich P. Alexander V. Della-Marta P. Gleason B. Haylock M. Klein Tank A. et al. (2002). Observed coherent changes in climatic extremes during the second half of the twentieth century. Clim. Res. 19, 193–212. 10.3354/cr019193
Giorgetta M. Jungclaus J. Reick C. Legutke S. Brovkin V. Crueger T. et al. (2012a). Cmip5 simulations of the max planck institute for meteorology (mpi-m) based on the mpi-esm-lr model: The historical experiment, served by esgf. [Dataset]. 10.1594/WDCC/CMIP5.MXELhi
Giorgetta M. Jungclaus J. Reick C. Legutke S. Brovkin V. Crueger T. et al. (2012b). Cmip5 simulations of the max planck institute for meteorology (mpi-m) based on the mpi-esm-lr model: The rcp85 experiment, served by esgf. [Dataset]. 10.1594/WDCC/CMIP5.MXELr8
Giorgetta M. A. Jungclaus J. Reick C. H. Legutke S. Bader J. Böttinger M. et al. (2013). Climate and carbon cycle changes from 1850 to 2100 in mpi-esm simulations for the coupled model intercomparison project phase 5. J. Adv. Model. Earth Syst. 5, 572–597. 10.1002/jame.20038
Giorgi F. Lionello P. (2007). Climate change projections for the mediterranean region. Glob. Planet. Change 63, 90–104. 10.1016/j.gloplacha.2007.09.005
Giorgi F. (2019). Thirty years of regional climate modeling: Where are we and where are we going next? J. Geophys. Res. Atmos. 124. 10.1029/2018JD030094
Haerter J. Hagemann S. Moseley C. Piani C. (2011). Climate model bias correction and the role of timescales. Hydrology Earth Syst. Sci. 15, 1065–1079. 10.5194/hess-15-1065-2011
Hagemann S. Chen C. Haerter J. Heinke J. Gerten D. Piani C. (2011). Impact of a statistical bias correction on the projected hydrological changes obtained from three gcms and two hydrology models. J. Hydrometeorol. 12, 556–578. 10.1175/2011JHM1336.1
Hagemann S. Chen C. Clark D. B. Folwell S. Gosling S. N. Haddeland I. et al. (2013). Climate change impact on available water resources obtained using multiple global climate and hydrology models. Earth Syst. Dyn. 4, 129–144. 10.5194/esd-4-129-2013
Hersbach H. de Rosnay P. Bell B. Schepers D. Simmons A. Soci C. et al. (2018). Operational global reanalysis: Progress, future directions and synergies with nwp. 10.21957/tkic6g3wm
Hickler T. Vohland K. Feehan J. Miller P. A. Smith B. Costa L. et al. (2012). Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model. Glob. Ecol. Biogeogr. 21, 50–63. 10.1111/j.1466-8238.2010.00613.x
Hickler T. Rammig A. Werner C. (2015). Modelling co2 impacts on forest productivity. Curr. For. Rep. 1, 69–80. 10.1007/s40725-015-0014-8
Ho C. Stephenson D. Collins M. Ferro C. Brown S. (2012). Calibration strategies: A source of additional uncertainty in climate change projections. Bull. Am. Meteorol. Soc. 93, 21–26. 10.1175/2011BAMS3110.1
Hübener H. Bülow K. Fooken C. Früh B. Hoffmann P. Höpp S. et al. (2017). ReKliEs-de ERGEBNISBERICHT. 10.2312/WDCC/ReKliEsDe_Ergebnisbericht
IPCC (2022). Annex I: Glossary. Cambridge University Press, 541–562. 10.1017/9781009157940.008
Ivits E. Horion S. Erhard M. Fensholt R. (2016). Assessing European ecosystem stability to drought in the vegetation growing season. Glob. Ecol. Biogeogr. 25, 1131–1143. 10.1111/geb.12472
Jacob D. Bärring L. Christensen O. B. Christensen J. H. de Castro M. Déqué M. et al. (2007). An inter-comparison of regional climate models for Europe: Model performance in present-day climate. Clim. Change 81, 31–52. 10.1007/s10584-006-9213-4
Jacob D. Petersen J. Eggert B. Alias A. Christensen O. B. Bouwer L. M. et al. (2014). Euro-cordex: New high-resolution climate change projections for European impact research. Reg. Environ. Change 14, 563–578. 10.1007/s10113-013-0499-2
Karl T. R. Nicholls N. Ghazi A. (1999). Clivar/gcos/wmo workshop on indices and indicators for climate extremes workshop summary. Clim. Change 42, 3–7. 10.1023/A:1005491526870
Kiktev D. Sexton D. Alexander L. Folland C. (2003). Comparison of modeled and observed trends in indices of daily climate extremes. J. Clim. 16, 3560–3571. 10.1175/1520-0442(2003)016⟨3560:COMAOT⟩2.0.CO;2
Lamichhane J. R. (2021). Rising risks of late-spring frosts in a changing climate. Nat. Clim. Change 11, 554–555. 10.1038/s41558-021-01090-x
Lange S. Volkholz J. Geiger T. Zhao F. Vega I. Veldkamp T. et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth’s Future 11, e2020EF001616. 10.1029/2020EF001616
Lange S. (2019). Trend-preserving bias adjustment and statistical downscaling with isimip3basd (v1.0). Geosci. Model Dev. 12, 3055–3070. 10.5194/gmd-12-3055-2019
Lange S. (2021a). Isimip3b bias adjustment fact sheet.
Lange S. (2021b). Isimip3basd. [Dataset]. 10.5281/zenodo.4686991
Li D. Ju W. Lu D. Zhou Y. Wang H. (2015). Impact of estimated solar radiation on gross primary productivity simulation in subtropical plantation in southeast China. Sol. Energy 120, 175–186. 10.1016/j.solener.2015.07.033
Liu Q. Piao S. Janssens I. Fu Y. Peng S. Lian X. et al. (2018). Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 426. 10.1038/s41467-017-02690-y
Maraun D. Widmann M. (2018). Statistical downscaling and bias correction for climate research. Cambridge University Press.
Maraun D. (2013). Bias correction, quantile mapping, and downscaling: Revisiting the inflation issue. J. Clim. 26, 2137–2143. 10.1175/JCLI-D-12-00821.1
Maraun D. (2016). Bias correcting climate change simulations - a critical review. Curr. Clim. Change Rep. 2, 211–220. 10.1007/s40641-016-0050-x
Masson V. Champeaux J.-L. Chauvin F. Meriguet C. Lacaze R. (2003). Ecoclimap, a global database of land surface parameters at 1km resolution in meteorological and climate models.
Maurer E. P. Pierce D. W. (2014). Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean. Hydrology Earth Syst. Sci. 18, 915–925. 10.5194/hess-18-915-2014
Mauritsen T. Bader J. Becker T. Behrens J. Bittner M. Brokopf R. et al. (2019). Developments in the mpi-m Earth system model version 1.2 (mpi-esm 1.2) and its response to increasing co 2. J. Adv. Model. Earth Syst. 11, 998–1038. 10.1029/2018MS001400
Meinshausen M. Smith S. Calvin K. Daniel J. Kainuma M. Lamarque J.-F. et al. (2011). The rcp greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213–241. 10.1007/s10584-011-0156-z
Min S.-K. Zhang X. Zwiers F. Hegerl G. (2011). Human contribution to more-intense precipitation extremes. Nature 470, 378–381. 10.1038/nature09763
Morak S. Hegerl G. Kenyon J. (2011). Detectable regional changes in the number of warm nights. Geophys. Res. Lett. 38. 10.1029/2011GL048531
Moss R. Edmonds J. Hibbard K. Manning M. Rose S. Vuuren D. et al. (2010). The next generation of scenarios for climate change research and assessment. Nature 463, 747–756. 10.1038/nature08823
Mueller B. Seneviratne S. I. (2012). Hot days induced by precipitation deficits at the global scale. Proc. Natl. Acad. Sci. 109, 12398–12403. 10.1073/pnas.1204330109
Nathan R. Mcmahon T. Peel M. Horne A. (2019). Assessing the degree of hydrologic stress due to climate change. Clim. Change 156, 87–104. 10.1007/s10584-019-02497-4
Papadimitriou L. V. Koutroulis A. G. Grillakis M. G. Tsanis I. K. (2016). High-end climate change impact on European runoff and low flows exploring the effects of forcing biases. Hydrology Earth Syst. Sci. 20, 1785–1808. 10.5194/hess-20-1785-2016
Pereira S. Marta-Almeida M. Carvalho A. c. Rocha A. (2019). Extreme precipitation events under climate change in the iberian peninsula. Int. J. Climatol. 40, 1255–1278. 10.1002/joc.6269
Peterson T. Folland C. Gruza G. Hogg W. Mokssit A. Plummer N. et al. (1998). Report on the activities of the working group on climate change detection and related rapporteurs. WMO, Rep. Geneve, Switzerland. WCDMP-47, WMO-TD 1071.
Piani C. Weedon G. Best M. Gomes S. Viterbo P. Hagemann S. et al. (2010). Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J. Hydrol. 395, 199–215. 10.1016/j.jhydrol.2010.10.024
Pichelli E. Coppola E. Sobolowski S. Ban N. Giorgi F. Stocchi P. et al. (2021). The first multi-model ensemble of regional climate simulations at kilometer-scale resolution part 2: Historical and future simulations of precipitation. Clim. Dyn. 56, 3581–3602. 10.1007/s00382-021-05657-4
Prein A. Langhans W. Fosser G. Ferrone A. Ban N. Goergen K. et al. (2015). A review on regional convection‐permitting climate modeling: Demonstrations, prospects, and challenges. Rev. Geophys. 53, 323–361. 10.1002/2014RG000475
Putra I. D. Rosid M. Sopaheluwakan A. Sianturi Y. (2020). The cmip5 projection of extreme climate indices in Indonesia using simple quantile mapping method. AIP Conf. Proc. 2223, 050008. 10.1063/5.0000849
Radinović D. Ćurić M. (2011). Criteria for heat and cold wave duration indexes. Theor. Appl. Climatol. 107, 505–510. 10.1007/s00704-011-0495-8
Rajczak J. Schär C. (2017). Projections of future precipitation extremes over Europe: A multimodel assessment of climate simulations: Projections of precipitation extremes. J. Geophys. Res. Atmos. 122, 10,773–10,800. 10.1002/2017JD027176
Raschendorfer M. (2001). The new turbulence parametrization of lm. Deutscher Wetterdienst, 89–97.
Razafimaharo C. Krähenmann S. Höpp S. Rauthe M. Deutschländer T. (2020). New high-resolution gridded dataset of daily mean, minimum, and maximum temperature and relative humidity for central Europe (hyras). Theor. Appl. Climatol. 142, 1531–1553. 10.1007/s00704-020-03388-w
Riahi K. Rao S. Krey V. Cho C. Chirkov V. Fischer G. et al. (2011). Rcp8.5 – A scenario of comparatively high greenhouse gas emissions. Clim. Change 109, 33–57. 10.1007/s10584-011-0149-y
Ritter B. Geleyn J. (1992). A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon. Weather Rev. 120, 303–325. 10.1175/1520-0493(1992)120⟨0303:ACRSFN⟩2.0.CO;2
Rockel B. Will A. Hense A. (2008). The regional climate model cosmo-clm (cclm). Meteorol. Z. 17, 347–348. 10.1127/0941-2948/2008/0309
Ruti P. Somot S. Giorgi F. Dubois C. Flaounas E. Obermann-Hellhund A. et al. (2016). Med-cordex initiative for mediterranean climate studies. Bull. Am. Meteorol. Soc. 97, 1187–1208. 10.1175/BAMS-D-14-00176.1
Santos J. A. Belo-Pereira M. Fraga H. Pinto J. G. (2016). Understanding climate change projections for precipitation over Western Europe with a weather typing approach. J. Geophys. Res. Atmos. 121, 1170–1189. 10.1002/2015JD024399
Schrodin R. Heise E. (2001). The multi-layer version of the dwd soil model terra-lm. Deutscher Wetterdienst. Technical Report No. 2. 10.5676/DWD_pub/nwv/cosmo-tr_2
Schuldt B. Buras A. Arend M. Vitasse Y. Beierkuhnlein C. Damm A. et al. (2020). A first assessment of the impact of the extreme 2018 summer drought on central European forests. Basic Appl. Ecol. 45, 86–103. 10.1016/j.baae.2020.04.003
Schulz J.-P. Vogel G. (2020). Improving the processes in the land surface scheme TERRA: bare soil evaporation and skin temperature. Atmospheres 11 (5), 513. 10.3390/atmos11050513
Senf C. Pflugmacher D. Zhiqiang Y. Sebald J. Knorn J. Neumann M. et al. (2018). Canopy mortality has doubled in Europe’s temperate forests over the last three decades. Nat. Commun. 9, 4978–8. 10.1038/s41467-018-07539-6
Sillmann J. Roeckner E. (2007). Indices for extreme events in projections of anthropogenic climate change. Clim. Change 86, 83–104. 10.1007/s10584-007-9308-6
Sillmann J. Kharin V.-V. Zhang X.-B. (2013a). Climate extremes indices in the cmip5 multi-model ensemble: Part 1. J. Geophys. Res. Atmos. 118, 1–18. 10.1002/jgrd.50203
Sillmann J. Kharin V. V. Zwiers F. Zhang X. Bronaugh D. (2013b). Climate extremes indices in the cmip5 multimodel ensemble: Part 2. Future climate projections. J. Geophys. Res. 118, 2473–2493. 10.1002/jgrd.50188
Smiatek G. Rockel B. Schättler U. (2008). Time invariant data preprocessor for the climate version of the cosmo model (cosmo-clm). Meteorol. Z. 17, 395–405. 10.1127/0941-2948/2008/0302
Smith B. Wårlind D. Arneth A. Hickler T. Leadley P. Siltberg J. et al. (2014). Implications of incorporating n cycling and n limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11, 2027–2054. 10.5194/bg-11-2027-2014
Smith B. (2001). Lpj-guess-an ecosystem modelling framework, 12. Sölvegatan: Department of Physical Geography and Ecosystems Analysis, INES. 22362.
Somot S. Sevault F. Déqué M. Crépon M. (2008). 21st century climate change scenario for the mediterranean using a coupled atmosphere–ocean regional climate model. Glob. Planet. Change 63, 112–126. 10.1016/j.gloplacha.2007.10.003
Steppeler J. Doms G. Schättler U. Bitzer H. Gassmann A. Damrath U. et al. (2003). Meso-gamma scale forecasts using the nonhydrostatic model lm. Meteorology Atmos. Phys. 82, 75–96. 10.1007/s00703-001-0592-9
Storch H. v. Zwiers F. W. (1999). Statistical analysis in climate research. Cambridge University Press. 10.1017/CBO9780511612336
Switanek M. Troch P. Castro C. Leuprecht A. Chang H.-I. Mukherjee R. et al. (2017). Scaled distribution mapping: A bias correction method that preserves raw climate model projected changes. Hydrology Earth Syst. Sci. 21, 2649–2666. 10.5194/hess-21-2649-2017
Tebaldi C. Hayhoe K. Arblaster J. Meehl G. (2007). Going to the extremes: An intercomparison of model-simulated historical and future changes in extreme events. Clim. Change 82, 233–234. 10.1007/s10584-007-9247-2
Teutschbein C. Seibert J. (2010). Regional climate models for hydrological impact studies at the catchment scale: A review of recent modeling strategies. Geogr. Compass 4, 834–860. 10.1111/j.1749-8198.2010.00357.x
Tian H. Yang J. Lu C. Xu R. Canadell J. G. Jackson R. B. et al. (2018). The global n2o model intercomparison project. Bull. Am. Meteorological Soc. 99, 1231–1251. 10.1175/bams-d-17-0212.1
Tiedke M. (1989). A comprehensive mass flux scheme for cumulus parameterization. Mon. Weather Rev. 117, 1779–1800.
Tölle M. H. Moseley C. Panferov O. Busch G. Knohl A. (2013). Water supply patterns over Germany under climate change conditions. Biogeosciences 10, 2959–2972. 10.5194/bg-10-2959-2013
Tölle M. Schefczyk L. Gutjahr O. (2018). Scale dependency of regional climate modeling of current and future climate extremes in Germany. Theor. Appl. Climatol. 134, 829–848. 10.1007/s00704-017-2303-6
Van de Velde J. Demuzere M. De Baets B. Verhoest N. E. C. (2022). Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: A case study on data from uccle, Belgium. Hydrology Earth Syst. Sci. 26, 2319–2344. 10.5194/hess-26-2319-2022
van der Linden P. Mitchell J. F. B. (2009). Ensembles: Climate change and its impacts: Summary of research and results from the ensembles project. Met Office Hadley Centre. FitzRoy Road Exeter EX1 3PB UK, 160.
Viceto C. Pereira S. Rocha A. (2019). Climate change projections of extreme temperatures for the iberian peninsula. Atmosphere 10, 229. 10.3390/atmos10050229
Vuuren D. Edmonds J. Kainuma M. Riahi K. Thomson A. Hibbard K. et al. (2011). The representative concentration pathways: An overview. Clim. Change 109, 5–31. 10.1007/s10584-011-0148-z
Warnant P. François L. Strivay D. Gérard J.-C. (1994). Caraib: A global model of terrestrial biological productivity. Glob. Biogeochem. cycles 8, 255–270. 10.1029/94gb00850
Wei L. Xin X. Li Q. Wu Y. Tang H. Li Y. et al. (2022). Simulation and projection of climate extremes in China by multiple coupled model intercomparison project phase 6 models. Int. J. Climatol. 43, 219–239. 10.1002/joc.7751
Weigel A. P. Liniger M. A. Appenzeller C. (2008). Can multi-model combination really enhance the prediction skill of probabilistic ensemble forecasts? Q. J. R. Meteorological Soc. 134, 241–260. 10.1002/qj.210
Wicker L. Skamarock W. (2002). Time-splitting methods for elastic models using forward time schemes. Mon. Weather Rev. 130, 2088–2097. 10.1175/1520-0493(2002)130⟨2088:TSMFEM⟩2.0.CO;2
Zhang H. Tölle M. H. (2021). Evaluation of agriculture-related climate indices in hindcast cosmo-clm simulations over central Europe. Environ. Sci. Proc. 4. 10.3390/ecas2020-08464
