Meteorologically consistent bias correction of climate time series for agricultural models
- 443 Downloads
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.
KeywordsBias Correction Global Radiation Quantile Mapping Diffuse Radiation Simulated Time Series
The project was supported by the Ministry for Science and Culture of Lower Saxony within the network KLIFF—climate impact and adaptation research in Lower Saxony. We thank Dr. Daniela Jacob and Dr. Christopher Moseley (Max Planck Institute for Meteorology, Hamburg, Germany) for their support.
- Coles SG (2001) An introduction to statistical modelling of extreme values. Springer, Berlin, p 208Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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