Joint bias correction of temperature and precipitation in climate model simulations

Journal of Geophysical Research

Volumn 119, Issue 23, 2014, Pages 13,153-13,162

  Li, Chao ^a   Sinha, Eva ^a,b   Horton, Daniel E ^b   Diffenbaugh, Noah S ^b   Michalak, Anna M ^a  

^a GEOPHYSICAL LABORATORY   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE MODELING; CORRECTION; CORRELATION; GENERAL CIRCULATION MODEL; MAPPING; MULTIVARIATE ANALYSIS; PRECIPITATION (CLIMATOLOGY); SIMULATION; STATISTICAL ANALYSIS;

EID: 84919952787     PISSN: 01480227     EISSN: 21562202     Source Type: Journal    
DOI: 10.1002/2014JD022514     Document Type: Article

References (40)

1. Ashfaq, M., L. C. Bowling, K. A. Cherkauer, J. S. Pal, and N. S. Diffenbaugh (2010), Influence of climate model biases and daily-scale temperature and precipitation events on hydrological impacts assessment: A case study of the United States, J. Geophys. Res., 115, D14116, doi:10.1029/2009JD012965.
2. Bárdossy, A., and G. Pegram (2011), Downscaling precipitation using regional climate models and circulation patterns toward hydrology, Water Resour. Res., 47, W04505, doi:10.1029/2010WR009689.
3. Bennett, J., M. R. Grose, S. P. Corney, C. J. White, G. K. Holz, J. J. Katzfey, D. A. Post, and N. L. Bindoff (2013), Performance of an empirical bias-correction of a high-resolution climate dataset, Int. J. Climatol., 34, 2189–2204, doi:10.1002/joc.3830.
4. Berg, P., H. Feldmann, and H. J. Pantiz (2012), Bias correction of high resolution regional climate model data, J. Hydrol., 448-449, 80–92.
5. Buishand, T. A., and T. Brandsma (2001), Multisite simulation of daily precipitation and temperature in the Rhine Basin by nearest-neighbor resampling, Water Resour. Res., 37(11), 2761–2776, doi:10.1002/2001WR000291.
6. Chen, J., F. P. Brissette, D. Chaumont, and M. Braun (2013), Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America, Water Resour. Res., 49, 4187–4205, doi:10.1002/wrcr.20331.
7. Christensen, J. H., F. Boberg, O. B. Christensen, and P. Lucas-Picher (2008), On the need for bias correction of regional climate change projections of temperature and precipitation, Geophys. Res. Lett., 35, L20709, doi:10.1029/2008GL035694.
8. Diffenbaugh, N. S., T. W. Hertel, M. Scherer, and M. Verma (2012), Response of corn markets to climate volatility under alternative energy futures, Nat. Clim. Change, 2, 514–518, doi:10.1038/nclimate1491.
9. Dosio, A., and P. Paruolo (2011), Bias correction of the EMSEMBLES high-resolution climate change projections for use by impact models: Evaluation on the present climate, J. Geophys. Res., 116, D16106, doi:10.1029/2011JD015934.
10. Ehret, U., E. Zehe, V. Wulfmeyer, K. Warrach-Sagi, and J. Liebert (2012), Should we apply bias correction on global and regional climate model data?, Hydrol. Earth Syst. Sci., 16, 3391–3404.
11. Field, C. B., et al. (Eds.) (2012), Managing the Risk of Extreme Events and Disasters to Advance Climate Change Adaptation, Cambridge Univ. Press, New York.
12. Fisher, E., K. W. Oleson, and D. M. Lawrence (2012), Contrasting urban and rural heat stress responses to climate change, Geophys. Res. Lett., 39, L03705, doi:10.1029/2011GL05076.
13. Fisher, E. M., and R. Knutti (2012), Robust projections of combined humidity and temperature extremes, Nat. Clim. Change, 3, 126–130.
14. Grillakis, M. G., A. G. Koutroulis, and I. K. Tsanis (2013), Multisegment statistical bias correction of daily GCM precipitation output, J. Geophys. Res. Atmos., 118, 3150–3162, doi:10.1002/jgrd.50323.
15. Haerter, J. O., S. Hagemann, C. Moseley, and C. Piani (2011), Climate model bias correction and the role of time scales, Hydrol. Earth Syst. Sci., 15, 1065–1079.
16. Hagemann, S., C. Chen, J. O. Haerter, J. Heinke, D. Gerten, and C. Piani (2011), Impact of a statistical bias correction on the projected hydrological changes obtained from three GCMs and two hydrology models, J. Hydrometeorol., 12(6), 556–578.
17. Harding, R., et al. (2011), WATCH: Current knowledge of the terrestrial global water cycle, J. Hydrometeorol., 12(6), 1149–1156.
18. Harris, I., P. D. Jones, T. J. Osborn, and D. H. Lister (2013), Updated high-resolution grids of monthly climatic observations – The CRU TS3.10 Dataset, Int. J. Climatol., 34, 623–642, doi.10.1002/joc.3711.
19. Hempel, S., K. Frieler, L. Warszawski, J. Schewe, and F. Piontek (2013), A trend-preserving bias correction – The ISI-MIP approach, Earth Syst. Dyn., 4, 49–92.
20. Horton, D. E., C. B. Shinner, D. Singh, and N. S. Diffenbaugh (2014), Occurrence and persistence of future atmospheric stagnation events, Nat. Clim. Change, 4(8), 698–703, doi:10.1038/nclimate2272.
21. Immerzeel, W. W., L. Peterson, S. Ragettli, and F. Pellicciotti (2014), The importance of observed gradients of air temperature and precipitation for modeling runoff from a glacierized watershed in the Nepalese Himalayas, Water Resour. Res., 50, 2212–2226, doi:10.1002/2013WR014506.
22. Ines, A. V. M., and J. W. Hansen (2006), Bias correction of daily GCM rainfall for crop simulation studies, Agric. For Meteorol., 138, 44–53.
23. Johnson, F., and A. Sharma (2012), A nesting model for bias correction of variability at multiple time scales in general circulation model precipitation simulations, Water Resour. Res., 48, W01504, doi:10.1029/2011WR010464.
24. Li, C., V. P. Singh, and A. K. Mishra (2013), A bivariate mixed distribution with a heavy-tailed component and its application for single-site daily rainfall simulation, Water Resour. Res., 49, 767–789, doi:10.1002/wrcr20063.
25. Li, H., J. Sheffield, and E. F. Wood (2010), 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, doi:10.1029/2009JD012882.
26. Lobell, D. B., and C. B. Field (2007), Global scale climate-crop yeild relationships and the impacts of recent warming, Environ. Res. Lett. 2, doi:10.1088/1748-9326/2/1/014002.
27. Maurer, E. P., H. G. Hidalgo, T. Das, M. D. Dettinger, and D. R. Cayan (2010), The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California, Hydrol. Earth Syst. Sci., 14, 1125–1138.
28. Mehran, A., A. AghaKouchak, and T. J. Phillips (2014), Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations, J. Geophys. Res. Atmos., 119, 1695–1707, doi:10.1002/2013JD021152.
29. Mueller, B., and S. I. Seneviratne (2014), Systematic land climate and evapotranspiration biases in CMIP5 simulations, Geophys. Res. Lett., 40, 128–134, doi:10.1002/2013GL058055.
30. Piani, C., and J. O. Haerter (2012), Two dimensional bias correction of temperature and precipitation copulas in climate models, Geophys. Res. Lett., 39, L20401, doi:10.1029/2012GL053839.
31. Piani, C., et al. (2010a), Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models, J. Hydrol., 395, 199–215.
32. Piani, C., J. O. Haerter, and E. Coppola (2010b), Statistical bias correction for daily precipitation in regional climate models over Europe, Theor. Appl. Climatol., 99, 187–192.
33. Scherer, M., and N. S. Diffenbaugh (2014), Transient twenty-first century changes in daily-scale temperature extremes in the United States, Clim. Dyn., 42, 1383–1404, doi:10.1007/s00382-013-1829-2.
34. Sherwood, S., and M. Huber (2010), An adaptability limit to climate change due to heat stress, Proc. Natl. Acad. Sci. U.S.A., 107(21), 9552–9555, doi:10.1073/pnas.0913352107.
35. Taylor, K. E., R. K. Stouffer, and G. A. Meehl (2012), An overview of CMIP5 and the experiment design, Bull. Am. Meteorol. Soc., 93(4), 485–498.
36. Tebaldi, C., and D. B. Lobell (2008), Toward probabilistic projections of climate change impacts on global crop yields, Geophys. Res. Lett., 35, L08705, doi:10.1029/2008GL033423.
37. Teutschbein, C., and J. Seibert (2012), 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.
38. Thrasher, B., E. P. Maurer, C. McKellar, and P. V. Duffy (2012), Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping, Hydrol. Earth Syst. Sci., 16, 3309–3314.
39. Warszawski, L., et al. (2014), The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) Project Framework, Proc. Natl. Acad. Sci. U.S.A., 111, 3228–3232, doi:10.1073/pnas.1312330110.
40. White, R. H., and R. Tourmi (2013), The limitations of bias correcting regional climate model inputs, Geophys. Res. Lett., 40, 2907–2912, doi:10.1029/grl.50612.