Articles | Volume 19, issue 10
https://doi.org/10.5194/hess-19-4055-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-19-4055-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
06 Oct 2015
Research article |
| 06 Oct 2015
The effect of empirical-statistical correction of intensity-dependent model errors on the temperature climate change signal
Wegener Center for Climate and Global Change (WegCenter), University of Graz, Graz, Austria
Central Institute for Meteorology and Geodynamics (ZAMG), Department of forecasting models, Vienna, Austria
M. Suklitsch
Central Institute for Meteorology and Geodynamics (ZAMG), Department of forecasting models, Vienna, Austria
G. Heinrich
Wegener Center for Climate and Global Change (WegCenter), University of Graz, Graz, Austria
now at: Department of Geography and Regional Science, University of Graz, Graz, Austria
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- The distinct problems of physical inconsistency and of multivariate bias involved in the statistical adjustment of climate simulations M. Alavoine & P. Grenier 10.1002/joc.7878
- Does Applying Subsampling in Quantile Mapping Affect the Climate Change Signal? P. Reiter & M. Casper 10.3390/hydrology11090143
- Regression-based distribution mapping for bias correction of climate model outputs using linear quantile regression C. Passow & R. Donner 10.1007/s00477-019-01750-7
- Can We Constrain Uncertainty in Hydrologic Cycle Projections? A. Prein & A. Pendergrass 10.1029/2018GL081529
- Two Types of Physical Inconsistency to Avoid with Univariate Quantile Mapping: A Case Study over North America Concerning Relative Humidity and Its Parent Variables P. Grenier 10.1175/JAMC-D-17-0177.1
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- Future projections of wind and solar energy resources over China from regional climate models based on bias correction T. Li et al. 10.1088/2515-7620/acddbb
- Multivariate Bias‐Correction of High‐Resolution Regional Climate Change Simulations for West Africa: Performance and Climate Change Implications D. Dieng et al. 10.1029/2021JD034836
- Estimating daily meteorological data and downscaling climate models over landscapes M. De Cáceres et al. 10.1016/j.envsoft.2018.08.003
- Simulation of flood hazard and risk in the Danube basin with the Future Danube Model F. Hattermann et al. 10.1016/j.cliser.2018.07.001
- Assessing distribution‐based climate model bias correction methods over an alpine domain: added value and limitations M. Ivanov & S. Kotlarski 10.1002/joc.4870
- Relative performance of empirical and physical models in assessing the seasonal and annual glacier surface mass balance of Saint-Sorlin Glacier (French Alps) M. Réveillet et al. 10.5194/tc-12-1367-2018
- Towards process-informed bias correction of climate change simulations D. Maraun et al. 10.1038/nclimate3418
- Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios F. Lehner et al. 10.5194/ascmo-9-29-2023
- The Taiwan Climate Change Projection Information and Adaptation Knowledge Platform: A Decade of Climate Research L. Lin et al. 10.3390/w14030358
- Assessing the impact of bias correction approaches on climate extremes and the climate change signal H. Zhang et al. 10.1002/met.2204
- Leveraging global climate models to assess multi-year hydrologic drought M. Vieira & T. Stadnyk 10.1038/s41612-023-00496-y
- Bias adjustment for threshold-based climate indicators P. Hoffmann et al. 10.5194/asr-15-107-2018
- Characterizing and avoiding physical inconsistency generated by the application of univariate quantile mapping on daily minimum and maximum temperatures over Hudson Bay M. Agbazo & P. Grenier 10.1002/joc.6432
- Climate Model Biases and Modification of the Climate Change Signal by Intensity-Dependent Bias Correction M. Ivanov et al. 10.1175/JCLI-D-17-0765.1
- Multi-component ensembles of future meteorological and natural snow conditions for 1500 m altitude in the Chartreuse mountain range, Northern French Alps D. Verfaillie et al. 10.5194/tc-12-1249-2018
- Future surface temperature changes for the Iberian Peninsula according to EURO-CORDEX climate projections D. Carvalho et al. 10.1007/s00382-020-05472-3
- Climate Change Impacts on the Upper Indus Hydrology: Sources, Shifts and Extremes A. Lutz et al. 10.1371/journal.pone.0165630
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- Uni- and multivariate bias adjustment methods in Nordic catchments: Complexity and performance in a changing climate F. Tootoonchi et al. 10.1016/j.scitotenv.2022.158615
- Robust bias-correction of precipitation extremes using a novel hybrid empirical quantile-mapping method M. Holthuijzen et al. 10.1007/s00704-022-04035-2
- The issue of properly ordering climate indices calculation and bias correction before identifying spatial analogs for agricultural applications P. Grenier et al. 10.1016/j.cliser.2019.100122
- Urban multi-model climate projections of intense heat in Switzerland A. Burgstall et al. 10.1016/j.cliser.2021.100228
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- Evaluation of some distributional downscaling methods as applied to daily maximum temperature with emphasis on extremes J. Lanzante et al. 10.1002/joc.6288
- Time Scale Decomposition of Climate and Correction of Variability Using Synthetic Samples of Stable Distributions M. Gomez‐Garcia et al. 10.1029/2018WR023053
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- Bias in dynamically downscaled rainfall characteristics for hydroclimatic projections N. Potter et al. 10.5194/hess-24-2963-2020
3 citations as recorded by crossref.
- Impact of bias correction and downscaling through quantile mapping on simulated climate change signal: a case study over Central Italy L. Sangelantoni et al. 10.1007/s00704-018-2406-8
- Projections of climate change indices of temperature and precipitation from an ensemble of bias‐adjusted high‐resolution EURO‐CORDEX regional climate models A. Dosio 10.1002/2015JD024411
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Short summary
The effect of empirical-statistical bias correction methods, like quantile mapping (QM), on the simulated climate change signals (CCS) is currently strongly discussed and is often regarded as deficiency of bias correction methods. We demonstrate that, quite the contrary, QM can lead to an improved CCS and also has the potential to serve as an empirical constraint on model uncertainty in climate projections.
The effect of empirical-statistical bias correction methods, like quantile mapping (QM), on the...
