Theoretical and Applied Climatology

, Volume 110, Issue 1–2, pp 129–141 | Cite as

Meteorologically consistent bias correction of climate time series for agricultural models

Original Paper

Abstract

Conventional bias correction of simulated climate time series for impact models is done separately for climate variables and hence leads to inconsistencies between them. However, agricultural models mostly use several variables, and meteorological consistency is essential. The present work points out meteorological inconsistency due to quantile mapping and describes a new method of consistent bias correction by an optimization approach. Time series of hourly precipitation and global radiation from the regional model REMO5.7 (Run UBA C20/A1B_1) were corrected with site observations from the German Meteorological Service. The results urge to check conventionally corrected series for consistency before using them for multidimensional models. Here, quantile mapping resulted in underestimation of diffuse radiation at hours with precipitation. This deficit was minimized by the developed procedure.

References

  1. Bao L, Gneiting T, Grimit EP, Guttorp P, Raftery AE (2010) Bias correction and Bayesian model averaging for ensemble forecasts of surface wind direction. Mon Weather Rev 138(5):1811–1821CrossRefGoogle Scholar
  2. Berg P, Haerter JO, Thejll P, Piani C, Hagemann S, Christensen JH (2009) Seasonal characteristics of the relationship between daily precipitation intensity and surface temperature. J Geophys Res D Atmos 114(18):D18102CrossRefGoogle Scholar
  3. Boberg F, Berg P, Thejll P, Gutowski WJ, Christensen JH (2009) Improved confidence in climate change projections of precipitation evaluated using daily statistics from the prudence ensemble. Clim Dyn 32(7–8):1097–1106CrossRefGoogle Scholar
  4. Buser CM, Künsch HR, Weber A (2010) Biases and uncertainty in climate projections. Scand J Stat 37(2):179–199CrossRefGoogle Scholar
  5. Caldwell P (2010) California wintertime precipitation bias in regional and global climate models. J Appl Meteorol 49(10):2147–2158CrossRefGoogle Scholar
  6. Christensen JH, Christensen OB (2007) A summary of the prudence model projections of changes in european climate by the end of this century. Clim Change 81(SUPPL. 1):7–30CrossRefGoogle Scholar
  7. Coles SG (2001) An introduction to statistical modelling of extreme values. Springer, Berlin, p 208Google Scholar
  8. Djalalova I, Wilczak J, McKeen S, Grell G, Peckham S, Pagowski M, DelleMonache L, McQueen J, Tang Y, Lee P, McHenry J, Gong W, Bouchet V, Mathur R (2010) Ensemble and bias-correction techniques for air quality model forecasts of surface O3 and PM2.5 during the TEXAQS-II experiment of 2006. Atmos Environ 44(4):455–467CrossRefGoogle Scholar
  9. Durman CF, Gregory JM, Hassell DC, Jones RG, Murphy JM (2001) A comparison of extreme european daily precipitation simulated by a global and a regional model for present and future climates. Q J R Meteorol Soc 127(573):1005–1015CrossRefGoogle Scholar
  10. Durre I, Menne MJ, Gleason BE, Houston TG, Vose RS (2010) Comprehensive automated quality assurance of daily surface observations. J Appl Meteorol 49(8):1615–1633CrossRefGoogle Scholar
  11. Feldmann H, Früh B, Schädler G, Panitz H, Keuler K, Jacob D, Lorenz P (2008) Evaluation of the precipitation for south-western Germany from high resolution simulations with regional climate models. Meteorol Z 17(4):455–465CrossRefGoogle Scholar
  12. Feng S, Hu Q, Qian W (2004) Quality control of daily meteorological data in china, 1951–2000: a new dataset. Int J Climatol 24(7):853–870CrossRefGoogle Scholar
  13. Gilliam RC, Hogrefe C, Rao ST (2006) New methods for evaluating meteorological models used in air quality applications. Atmos Environ 40(26):5073–5086CrossRefGoogle Scholar
  14. Giorgi F, Coppola E (2010) Does the model regional bias affect the projected regional climate change? An analysis of global model projections: a letter. Clim Change 100(3):787–795CrossRefGoogle Scholar
  15. 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 doi:101175/2011JHM13361
  16. Hohenegger C, Brockhaus C, Schär C (2007) Towards climate simulations at cloud-resolving scales. Meteorol Z 17(4): 383–394CrossRefGoogle Scholar
  17. Ines AVM, Hansen JW (2006) Bias correction of daily GCM rainfall for crop simulation studies. Agric For Meteorol 138 (1-4):44–53CrossRefGoogle Scholar
  18. Jacob D (2005) REMO A1B scenario run, UBA project, 0.088 degree resolution, run no. 006211, 1 h data. http://cera-www.dkrz.de/WDCC/ui/Compact.jsp?acronym=REMO_UBA_A1B_1_R006211_1H
  19. Jacob D, Bärring L, Christensen OB, Christensen J, De Castro M, Déqué M, Giorgi F, Hagemann S, Hirschi M, Jones R, Kjellström E, Lenderink G, Rockel B, Sánchez E, Schär C, Seneviratne S, Somot S, Van Ulden A, Van Den Hurk B (2007) An inter-comparison of regional climate models for europe: model performance in present-day climate. Clim Change 81(SUPPL. 1):31–52CrossRefGoogle Scholar
  20. Jacob D, Göttel H, Kotlarski S, Lorenz P, Sieck K (2008) Klimaauswirkungen und Anpassung in Deutschland - Phase 1: Erstellung regionaler Klimaszenarien für Deutschland. Umweltbundesamt, Dessau-Roßlau, 154Google Scholar
  21. Jaeger EB, Anders I, Lüthi D, Rockel B, Schär C, Seneviratne SI (2008) Analysis of ERA 40-driven CLM simulations for Europe. Meteorol Z 17(4):349–367CrossRefGoogle Scholar
  22. Jiménez PA, González-Rouco JF, Navarro J, Montávez JP, García-Bustamante E (2010) Quality assurance of surface wind observations from automated weather stations. J Atmos Oceanic Technol 27(7):1101–1122CrossRefGoogle Scholar
  23. Jones HG, Tardieu F (1998) Modelling water relations of horticultural crops: a review. Sci Hort 74(1–2):21–46CrossRefGoogle Scholar
  24. Kotlarski S, Paul F, Jacob D (2010) Forcing a distributed glacier mass balance model with the regional climate model REMO. Part I: climate model evaluation. J Climate 23(6):1589– 1606CrossRefGoogle Scholar
  25. Krug H, Kahlen K (2008) Modelling production subsystems at high abstraction level—a review. IV. Development–photoperiodism–reproduction and yield (focussed on vegetable crops). Eur J Hortic Sci 73(5):189–195Google Scholar
  26. Krug H, Romey A, Rath T (2007) Decision support for climate dependent greenhouse production planning and climate control by modelling. I. Modelling climate. Eur J Hortic Sci 72(3):97–103Google Scholar
  27. Lenderink G, Van Meijgaard E (2008) Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat Geosci 1(8):511–514CrossRefGoogle Scholar
  28. Lenderink G, Van Meijgaard E (2010) Linking increases in hourly precipitation extremes to atmospheric temperature and moisture changes. Environ Res Lett 5(2)Google Scholar
  29. Li H, Sheffield J, Wood EF (2010) Bias correction of monthly precipitation and temperature fields from intergovernmental panel on climate change AR4 models using equidistant quantile matching. J Geophys Res D Atmos 115(10):D10101CrossRefGoogle Scholar
  30. van der Linden P, Mitchell J (2009) ENSEMBLES: climate change and its impacts at seasonal, decadal and centennial timescales. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3 PB, UKGoogle Scholar
  31. Lussana C, Uboldi F, Salvati MR (2010) A spatial consistency test for surface observations from mesoscale meteorological networks. Q J R Meteorolog Soc 136(649):1075–1088CrossRefGoogle Scholar
  32. Maraun D, Wetterhall F, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themel M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48(3):RG3003CrossRefGoogle Scholar
  33. Meissner C, Schädler G, Panitz H, Feldmann H, Kottmeier C (2009) High-resolution sensitivity studies with the regional climate model COSMO-CLM. Meteorol Z 18(5): 543–557CrossRefGoogle Scholar
  34. Piani C, Haerter J, Coppola E (2010a) Statistical bias correction for daily precipitation in regional climate models over Europe. Theor Appl Climatol 99:187–192CrossRefGoogle Scholar
  35. Piani C, Weedon GP, Best M, Gomes SM, Viterbo P, Hagemann S, Haerter JO (2010b) Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J Hydrol 395(3–4): 199–215CrossRefGoogle Scholar
  36. Porter JR, Semenov MA (2005) Crop responses to climatic variation. Phil Trans R Soc B 360(1463):2021–2035CrossRefGoogle Scholar
  37. Racca P, Kleinhenz B, Jörg E (2007) SIMPEROTA 1/3—a decision support system for blue mould disease of tobacco. EPPO Bull 37(2):368–373CrossRefGoogle Scholar
  38. Reek T, Doty SR, Owen TW (1992) A deterministic approach to the validation of historical daily temperature and precipitation data from the cooperative network. Bull Am Meteorol Soc 73(6):753–762CrossRefGoogle Scholar
  39. Rivington M, Miller D, Matthews KB, Russell G, Bellocchi G, Buchan K (2008) Evaluating regional climate model estimates against site-specific observed data in the UK. Clim Change 88(2):157–185CrossRefGoogle Scholar
  40. Spitters CJT, Toussaint HAJM, Goudriaan J (1986) Separating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis part I. Components of incoming radiation. Agric Forest Meteorol 38(1–3):217–229CrossRefGoogle Scholar
  41. Tapiador FJ (2010) A joint estimate of the precipitation climate signal in Europe using eight regional models and five observational datasets. J Climate 23(7):1719–1738CrossRefGoogle Scholar
  42. Terink W, W L Hurkmans RT, J F Torfs PJ, Uijlenhoet R (2009) Bias correction of temperature and precipitation data for regional climate model application to the Rhine basin. Hydrol Earth Syst Sci Discuss 6(4):5377–5413CrossRefGoogle Scholar
  43. Thomson LJ, Macfadyen S, Hoffmann AA (2010) Predicting the effects of climate change on natural enemies of agricultural pests. Biol Control 52(3):296–306CrossRefGoogle Scholar
  44. Werner PC, Gerstengarbe FW (1997) Proposal for the development of climate scenarios. Climate Res 8(3):171–182CrossRefGoogle Scholar
  45. Wu P, Lin P, Juang HH (2009) Local mean bias correction in a regional model downscaling: a case study of the South China Sea summer monsoon of 1998. Mon Weather Rev 137(9):2869–2892CrossRefGoogle Scholar
  46. Yang W, Andréasson J, Graham LP, Olsson J, Rosberg J, Wetterhall F (2010) Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impacts studies. Hydrol Res 41(3–4):211–229CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Institute for Biological Production SystemsLeibniz Universität HannoverHannoverGermany

Personalised recommendations