Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 630

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Towards process-informed bias correction of climate change simulations

Open access
Maraun, Douglas, Shepherd, Theodore G., Widmann, Martin, Zappa, Giuseppe, Walton, Daniel, Gutiérrez, José M., Hagemann, Stefan, Richter, Ingo, Soares, Pedro M. M., Hall, Alex, Mearns, Linda O.·2017·Nature Climate Change
DOI 10.1038/nclimate3418
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/nclimate3418

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 6 references

Research works printed but not linked: 82

  1. Hewitson, B. C., Daron, J., Crane, R. G., Zermoglio, M. F. & Jack, C. Interrogating empirical-statistical downscaling. Climatic Change 122, 539–554 (2014). · DOI · Paper page
  2. Flato, G. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 741–866 (IPCC, Cambridge Univ. Press, 2013).
  3. Kotlarski, S. et al. Regional climate modelling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geosci. Model. Dev. Discuss. 7, 217–293 (2014). · DOI
  4. Maurer, E. P., Brekke, L., Pruitt, T. & Duffy, P.B. Fine-resolution climate projections enhance regional climate change impact studies. Eos 88, 504 (2007). · DOI · Paper page
  5. Li, H., Sheffield, J. & Wood, E. F. Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. J. Geophys. Res. 115, D10101 (2010). · DOI · Paper page
  6. Dosio, A., Paruolo, P. & Rojas, R. Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: analysis of the climate change signal. J. Geophys. Res. 117, D17110 (2012). · DOI
  7. Hagemann, S. et al. Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models. J. Hydrometeorol. 12, 556–578 (2011). · DOI · Paper page
  8. Stoner, A. M. K., Hayhoe, K., Yang, X. & Wuebbles, D. J. An asynchronous regional regression model for statistical downscaling of daily climate variables. Int. J. Climatol. 33, 2473–2494 (2013). · DOI · Paper page
  9. Girvetz, E. H. et al. Making Climate Data Relevant to Decision Making: The Important Details of Spatial and Temporal Downscaling (The World Bank, 2013).
  10. Maurer, E. P. et al. An enhanced archive facilitating climate impact and adaptation analysis. Bull. Am. Meteorol. Soc. 95, 1011–1019 (2014). · DOI
  11. Gangopadhyay, S., Pruitt, T., Brekke, L. & Raff, D. Hydrologic projections for the Western United States. Eos 92, 441–442 (2011). · doi:10.1029/2011eo480001
  12. Hagemann, S. et al. Climate change impact on available water resources obtained using multiple global climate and hydrology models. Earth Syst. Dynam. 4, 129–144 (2013). · DOI · Paper page
  13. Warszawski, L. et al. The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP): Project framework. Proc. Natl Acad. Sci. USA 111, 3228–3232 (2014). · DOI · Paper page
  14. Cayan, D. et al. In Assessment of Climate Change in the Southwest United States: A Report Prepared for the National Climate Assessment 101–125 (Island Press, 2013). · DOI
  15. Maraun, D. et al. Precipitation downscaling under climate change. Recent developments to bridge the gap between dynamical models and the end user. Rev. Geophys. 48, RG3003 (2010). · DOI · Paper page
  16. Teutschbein, C. & Seibert, J. Bias correction of regional climate model simulations for hydrological climate-change impact studies: review and evaluation of different methods. J. Hydrol. 456, 12–29 (2012). · DOI · Paper page
  17. Piani, C., Haerter, J. O. & Coppola, E. Statistical bias correction for daily precipitation in regional climate models over Europe. Theor. Appl. Climatol. 99, 187–192 (2010). · DOI · Paper page
  18. Vannitsem, S. Bias correction and post-processing under climate change. Nonlin. Proc. Geophys. 18, 911–924 (2011). · DOI
  19. Eden, J., Widmann, M., Grawe, D. & Rast, S. Skill, correction, and downscaling of GCM-simulated precipitation. J. Clim. 25, 3970–3984 (2012). · DOI · Paper page
  20. Maraun, D. Bias correction, quantile mapping and downscaling: revisiting the inflation issue. J. Clim. 26, 2137–2143 (2013). · DOI · Paper page
  21. Maraun, D. & Widmann, M. The representation of location by a regional climate model in complex terrain. Hydrol. Earth Syst. Sci. 19, 3449–3456 (2015). · DOI
  22. Addor, N., Rohrer, M., Furrer, R. & Seibert, J. Propagation of biases in climate models from the synoptic to the regional scale: implications for bias adjustment. J. Geophys. Res. 121, 2075–2089 (2016). · DOI
  23. Stocker, T. F., Dahe, Q., Plattner, G.-K. & Tignor, M. IPCC Workshop on Regional Climate Projections and their Use in Impacts and Risk Analysis Studies (2015); https://www.ipcc.ch/pdf/supporting-material/RPW_WorkshopReport.pdf
  24. Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K. & Liebert, J. Should we apply bias correction to global and regional climate model data? Hydrol. Earth Syst. Sci. 16, 3391–3404 (2012). · DOI · Paper page
  25. Haerter, J. O., Hagemann, S., Moseley, C. & Piani, C. Climate model bias correction and the role of timescales. Hydrol. Earth Syst. Sci. 15, 1065–1079 (2011). · DOI · Paper page
  26. Johnson, F. & Sharma, A. A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations. Wat. Resour. Res. 48, W01504 (2012).
  27. Michelangeli, P.-A., Vrac, M. & Loukos, H. Probabilistic downscaling approaches: application to wind cumulative distribution functions. Geophys. Res. Lett. 36, L11708 (2009). · DOI
  28. Piani, C. & Haerter, J. O. Two dimensional bias correction of temperature and precipitation copulas in climate models. Geophys. Res. Lett. 39, L20401 (2012). · DOI · Paper page
  29. Vrac, M. & Friederichs, P. Multivariate-intervariable, spatial, and temporal-bias correction. J. Clim. 28, 218–237 (2015). · DOI · Paper page
  30. Levy, A. A. L. et al. Can correcting feature location in simulated mean climate improve agreement on projected changes? Geophys. Res. Lett. 40, 354–358 (2013). · DOI
  31. Barsugli, J. J. et al. The practitioner’s dilemma: How to assess the credibility of downscaled climate projections. Eos 94, 424–425 (2013). · DOI
  32. Wang, C., Zhang, L., Lee, S.-K., Wu, L. & Mechoso, C. R. A global perspective on CMIP5 climate model biases. Nat. Clim. Change 4, 201–205 (2014). · DOI · Paper page
  33. Maraun, D. Nonstationarities of regional climate model biases in European seasonal mean temperature and precipitation sums. Geophys. Res. Lett. 39, L06706 (2012). · DOI · Paper page
  34. Teutschbein, C. & Seibert, J. Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions? Hydrol. Earth Syst. Sci. 17, 5061–5077 (2013). · DOI · Paper page
  35. Johnson, F. & Sharma, A. Accounting for interannual variability: a comparison of options for water resources climate change impact assessments. Wat. Resour. Res. 47, W04508 (2011). · DOI
  36. Berg, P., Feldmann, H. & Panitz, H.-J. Bias correction of high resolution regional climate model data. J. Hydrol. 448–449, 80–92 (2012). · DOI · Paper page
  37. Chen, C., Haerter, J. O., Hagemann, S. & Piani, C. On the contribution of statistical bias correction to the uncertainty in the projected hydrological cycle. Geophys. Res. Lett. 38, L20403 (2011).
  38. Dosio, A. & Paruolo, P. Bias correction of the ENSEMBLES high resolution climate change projections for use by impact models: evaluation on the present climate. J. Geophys. Res. 116, D16106 (2011). · DOI · Paper page
  39. Themeßl, M. J., Gobiet, A. & Leuprecht, A. Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. Int. J. Climatol. 31, 1530–1544 (2011). · DOI · Paper page
  40. Rajczak, J., Kotlarski, S. & Schär, C. Does quantile mapping of simulated precipitation correct for biases in transition probabilities and spell lengths? J. Clim. 29, 1605–1615 (2016). · DOI
  41. Masato, G., Hoskins, B. J. & Woollings, T. Winter and summer Northern Hemisphere blocking in CMIP5 models. J. Clim. 26, 7044–7059 (2013). · DOI · Paper page
  42. Woollings, T. Dynamical influences on European climate: an uncertain future. Phil. Trans. R. Soc. A 368, 3733–3756 (2010). · DOI · Paper page
  43. Zappa, G., Shaffrey, L. C. & Hodges, K. I. The ability of CMIP5 models to simulate North Atlantic extratropical cyclones. J. Clim. 26, 5379–5396 (2013). · DOI · Paper page
  44. Davini, P. et al. Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model. Geosci. Model Dev. 10, 1383–1402 (2017). · DOI
  45. Pithan, F., Shepherd, T. G., Zappa, G. & Sandu, I. Climate model biases in jet streams, blocking and storm tracks resulting from missing orographic drag. Geophys. Res. Lett. 43, 7231–7240 (2016). · DOI
  46. van Niekerk, A., Scinocca, J. F. & Shepherd, T. G. The modulation of stationary waves, and their response to climate change, by parameterized orographic drag. J. Atmos. Sci. 74, 2557–2574 (2017). · DOI
  47. Ashfaq, M., Skinner, C. B. & Diffenbaugh, N. S. Influence of SST biases on future climate change projections. Clim. Dynam. 36, 1303–1319 (2011). · DOI
  48. Scaife, A. A. et al. Improved Atlantic winter blocking in a climate model. Geophys. Res. Lett. 38, L23703 (2011). · DOI · Paper page
  49. Keeley, S. P. E., Sutton, R. T. & Shaffrey, L. C. The impact of North Atlantic sea surface temperature errors on the simulation of North Atlantic European region climate. Q. J. R. Meteorol. Soc. 138, 1774–1783 (2012). · doi:10.1002/qj.1912
  50. Shepherd, T. G. Atmospheric circulation as a source of uncertainty in climate change projections. Nat. Geosci. 7, 703–708 (2014). · DOI · Paper page
  51. Hall, A. Projecting regional change. Science 346, 1461–1462 (2014). · DOI · Paper page
  52. Colette, A., Vautard, R. & Vrac, M. Regional climate downscaling with prior statistical correction of the global climate forcing. Geophys. Res. Lett. 39, L13707 (2012). · DOI
  53. Maurer, E. P. & 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 (2014). · DOI · Paper page
  54. Gobiet, A., Suklitsch, M. & 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 (2015). · DOI
  55. Pierce, D. W., Cayan, D. R., Maurer, E. P., Abatzoglou, J. T. & Hegewisch, K. C. Improved bias correction techniques for hydrological simulations of climate change. J. Hydrometeorol. 16, 2421–2442 (2015). · DOI · Paper page
  56. Switanek, M. B. et al. Scaled distribution mapping: a bias correction method that preserves raw climate model projected changes. Hydrol. Earth Syst. Sci. 21, 2649–2666 (2017). · DOI · Paper page
  57. Bellprat, O., Kotlarski, S., Lüthi, D. & Schär, C. Physical constraints for temperature biases in climate models. Geophys. Res. Lett. 40, 4042–4047 (2013). · DOI
  58. Bellenger, H., Guilyardi, E., Leloup, J., Lengaigne, M. & Vialard, J. ENSO representation in climate models: from CMIP3 to CMIP5. Clim. Dynam. 42, 1999–2018 (2014). · DOI · Paper page
  59. Chen, L., Li, T. & Yu, Y. Causes of strengthening and weakening of ENSO amplitude under global warming in four CMIP5 models. J. Clim. 28, 3250–3274 (2015). · DOI
  60. Collins, M. et al. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1029–1136 (IPCC, Cambridge Univ. Press, 2013).
  61. Annamalai, H., Hafner, J., Sooraj, K. P. & Pillai, P. Global warming shifts the monsoon circulation, drying South Asia. J. Clim. 26, 2701–2718 (2013). · DOI · Paper page
  62. Roxy, M. K. et al. Drying of indian subcontinent by rapid indian ocean warming and a weakening land-sea thermal gradient. Nat. Commun. 6, 7423 (2015). · DOI · Paper page
  63. Willison, J., Robinson, W. A. & Lackmann, G. M. North atlantic storm-track sensitivity to warming increases with model resolution. J. Clim. 28, 4513–4524 (2015). · DOI
  64. Hall, A., Qu, X. & Neelin, J. D. Improving predictions of summer climate change in the united states. Geophys. Res. Lett. 35, L01702 (2008). · DOI
  65. Christensen, J. H. & Boberg, F. Temperature dependent climate projection deficiencies in CMIP5 models. Geophys. Res. Lett. 39, L24705 (2012). · DOI
  66. Meredith, E. P., Maraun, D., Semenov, V. A. & Park, W. Evidence for added value of convection permitting models for studying changes in extreme precipitation. J. Geophys. Res. 120, 12500–12513 (2015). · doi:10.1002/2014jc010462
  67. Collins, M. et al. Quantifying future climate change. Nat. Clim. Change 2, 403–409 (2012). · DOI · Paper page
  68. Simpson, I. R., Seager, R., Ting, M. & Shaw, T. A. Causes of change in northern hemisphere winter meridional winds and regional hydroclimate. Nat. Clim. Change 6, 65–70 (2016). · DOI
  69. Qu, X. & Hall, A. On the persistent spread in snow-albedo feedback. Clim. Dynam. 4, 69–81 (2014). · DOI · Paper page
  70. Dawson, A., Palmer, T. N. & Corti, S. Simulating regime structures in weather and climate prediction models. Geophys. Res. Lett. 39, L21805 (2012).
  71. Liu, P. et al. An MJO simulated by the NICAM at 14- and 7-km resolutions. Mon. Weath. Rev. 137, 3254–3268 (2009). · DOI
  72. Prein, A. F. et al. A review on regional convection-permitting climate modeling: demonstrations, prospects, and challenges. Rev. Geophys. 53, 323–361 (2015). · DOI · Paper page
  73. Volosciuk, C., Maraun, D., Vrac, M. & Widmann, M. A combined statistical bias correction and stochastic downscaling method for precipitation. Hydrol. Earth Syst. Sci. 21, 1693–1719 (2017). · DOI
  74. Walton, D. B., Sun, F., Hall, A. & Capps, S. A hybrid dynamical–statistical downscaling technique. part i: Development and validation of the technique. J. Clim. 28, 4597–4617 (2015). · DOI
  75. Maraun, D. et al. VALUE: a framework to validate downscaling approaches for climate change studies. Earth’s Future 3, 1–14 (2015). · DOI · Paper page
  76. Prudhomme, C., Wilby, R. L., Crooks, S., Kay, A. L. & Reynard, N. S. Scenario-neutral approach to climate change impact studies: application to flood risk. J. Hydrol. 390, 198–209 (2010). · DOI · Paper page
  77. Schär, C., Frei, C., Lüthi, D. & Davies, H. C. Surrogate climate-change scenarios for regional climate models. Geophys. Res. Lett. 23, 669–672 (1996). · DOI · Paper page
  78. Hazeleger, W. et al. Tales of future weather. Nat. Clim. Change 5, 107–113 (2015). · DOI · Paper page
  79. Klein Tank, A. M. G. et al. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. Int. J. Climatol. 22, 1441–1453 (2002). · DOI · Paper page
  80. Gutiérrez, J. M., Cano, R., Cofiño, A. S. & Sordo, C. Analysis and downscaling multi-model seasonal forecasts in Peru using self-organizing maps. Tellus A 57, 435–447 (2005). · doi:10.1111/j.1600-0870.2005.00128.x
  81. Maurer, E. P., Wood, A. W., Adam, J. C., Lettenmaier, D. P. & Nijssen, B. A long-term hydrologically-based data set of land surface fluxes and states for the conterminous United States. J. Clim. 15, 3237–3251 (2002). · DOI · Paper page
  82. van Vuuren, D. P. et al. The Representative Concentration Pathways: an overview. Climatic Change 109, 5–31 (2011). · DOI · Paper page

Cited by

See all 630

Metadata

Authors
Maraun, D., Shepherd, T. G., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J. M., Hagemann, S., Richter, I., Soares, P. M. M., Hall, A., & Mearns, L. O.
Venue
Nature Climate Change
Publisher
Springer Science and Business Media LLC
Published
2017-11
Volume
7
Issue
11
Pages
764-773
ISSN
  • 1758-678X (ISSN-L)
  • 1758-6798
DOI
10.1038/nclimate3418
OpenAlex
W2766597251
Cited by
630
References
6
FWCI
26.71
Type
journal article
Language
en
Open access
bronze
Licence
www.springer.com/tdm (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Towards process-informed bias correction of climate change simulations. https://app.cochat.ai/papers/10.1038/nclimate3418
The paper
Maraun, D., Shepherd, T. G., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J. M., Hagemann, S., Richter, I., Soares, P. M. M., Hall, A., & Mearns, L. O. (2017). Towards process-informed bias correction of climate change simulations. Nature Climate Change. https://doi.org/10.1038/nclimate3418

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.