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Impact of vegetation variability on potential predictability and skill of EC-Earth simulations

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Abstract

Climate models often use a simplified and static representation of vegetation characteristics to determine fluxes of energy, momentum and water vapour between surface and lower atmosphere. In order to analyse the impact of short term variability in vegetation phenology, we use remotely-sensed leaf area index and albedo products to examine the role of vegetation in the coupled land–atmosphere system. Perfect model experiments are carried out to determine the impact of realistic temporal variability of vegetation on potential predictability of evaporation and temperature, as well as model skill of EC-Earth simulations. The length of the simulation period is hereby limited by the availability of satellite products to 2000–2010. While a realistic representation of vegetation positively influences the simulation of evaporation and its potential predictability, a positive impact on 2 m temperature is of smaller magnitude, regionally confined and more pronounced in climatically extreme years.

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References

  • Arora VK, Boer GJ (2006) The temporal variability of soil moisture and surface hydrological quantities in a climate model. J Clim 19(22):5875–5888. doi:10.1175/jcli3926.1

    Article  Google Scholar 

  • Balsamo G, Beljaars A, Scipal K, Viterbo P, van den Hurk B, Hirschi M, Betts AK (2009) A revised hydrology for the ECMWF model: verification from field site to terrestrial water storage and impact in the integrated forecast system. J Hydrometeorol 10(3):623–643. doi:10.1175/2008JHM1068.1

    Article  Google Scholar 

  • Beljaars ACM, Viterbo P, Miller MJ, Betts AK (1996) The anomalous rainfall over the United States during July 1993: sensitivity to land surface parameterization and soil moisture anomalies. Mon Weather Rev 124(3):362–382

    Article  Google Scholar 

  • Boer GJ (2004) Long time-scale potential predictability in an ensemble of coupled climate models. Clim Dyn 23(1):29–44. doi:10.1007/s00382-004-0419-8

    Article  Google Scholar 

  • Boer GJ, Lambert SJ (2008) Multi-model decadal potential predictability of precipitation and temperature. Geophys Res Lett 35(5):L05706. doi:10.1029/2008gl033234

    Article  Google Scholar 

  • Bounoua L, Collatz GJ, Los SO, Sellers PJ, Dazlich DA, Tucker CJ, Randall DA (2000) Sensitivity of climate to changes in NDVI. J Clim 13:2277–2292. doi:10.1175/1520-0442(2000)013<2277:SOCTCI>2.0.CO;2

    Article  Google Scholar 

  • Boussetta S, Balsamo G, Beljaars A, Kral T, Jarlan L (2011) Impact of a satellite-derived leaf area index monthly climatology in a global numerical weather prediction model. Int J Remote Sensing (accepted)

  • Brovkin V (2002) Climate-vegetation interaction. J Phys IV France 12(10):57–72

    Article  Google Scholar 

  • Buermann W, Wang Y, Dong J, Zhou L, Zeng X, Dickinson RE, Potter CS, Myneni RB (2002) Analysis of a multiyear global vegetation leaf area index data set. J Geophys Res 107(D22):4646. doi:10.1029/2001jd000975

    Article  Google Scholar 

  • Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Hólm EV, Isaksen L, Kållberg P, Köhler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thépaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. QJR Meteorol Soc 137:553–597. doi:10.1002/qj.828

    Article  Google Scholar 

  • Dirmeyer PA (2005) The land surface contribution to the potential predictability of boreal summer season climate. J Hydrometeorol 6(5):618–632. doi:10.1175/jhm444.1

    Article  Google Scholar 

  • Dirmeyer PA, Zhao M (2004) Flux replacement as a method to diagnose coupled land-atmosphere model feedback. J Hydrometeorol 5(6):1034–1048. doi:10.1175/jhm-384.1

    Article  Google Scholar 

  • Dirmeyer PA, Schlosser CA, Brubaker KL (2009) Precipitation, recycling, and land memory: an integrated analysis. J Hydrometeor 10:278–288. doi:10.1175/2008JHM1016.1

    Article  Google Scholar 

  • Douville H (2003) Assessing the influence of soil moisture on seasonal climate variability with AGCMs. J Hydrometeorol 4(6):1044–1066. doi:10.1175/1525-7541(2003)004<1044:atiosm>2.0.co;2

    Article  Google Scholar 

  • Dutra E, Balsamo G, Viterbo P, Miranda PMA, Beljaars A, Schär C, Elder K (2010) An improved snow scheme for the ECMWF land surface model: description and offline validation. J Hydrometeorol 11(4):899–916. doi:10.1175/2010JHM1249.1

    Article  Google Scholar 

  • Ferranti L, Viterbo P (2006) The European summer of 2003: sensitivity to soil water initial conditions. J Clim 19(15):3659–3680. doi:10.1175/jcli3810.1

    Article  Google Scholar 

  • Gao X, Dirmeyer PA, Guo Z, Zhao M (2008) Sensitivity of land surface simulations to the treatment of vegetation properties and the implications for seasonal climate prediction. J Hydrometeorol 9(3):348–366. doi:10.1175/2007jhm931.1

    Article  Google Scholar 

  • Garrigues S, Lacaze R, Baret F, Morisette JT, Weiss M, Nickeson JE, Fernandes R, Plummer S, Shabanov NV, Myneni RB, Knyazikhin Y, Yang W (2008) Validation and intercomparison of global Leaf Area Index products derived from remote sensing data. J Geophys Res 113(G2):G02028. doi:10.1029/2007jg000635

  • George SE, Sutton RT (2006) Predictability and skill of boreal winter forecasts made with the ECMWF Seasonal Forecasting System II. QJR Meteorol Soc 132:2031–2053. doi:10.1256/qj.04.180

    Article  Google Scholar 

  • Guillevic P, Koster RD, Suarez MJ, Bounoua L, Collatz GJ, Los SO, Mahanama SPP (2002) Influence of the interannual variability of vegetation on the surface energy balance-a global sensitivity study. J Hydrometeor 3:617–629. doi:10.1175/1525-7541(2002)003<0617:IOTIVO>2.0.CO;2

    Article  Google Scholar 

  • Hazeleger W, Severijns C, Semmler T, Ştefănescu S, Yang S, Wang X, Wyser K, Dutra E, Baldasano JM, Bintanja R, Bougeault P, Caballero R, Ekman AML, Christensen JH, van den Hurk B, Jimenez P, Jones C, Kållberg P, Koenigk T, McGrath R, Miranda P, Van Noije T, Palmer T, Parodi JA, Schmith T, Selten F, Storelvmo T, Sterl A, Tapamo H, Vancoppenolle M, Viterbo P, Willén U (2010) EC-Earth: a seamless earth-system prediction approach in action. Bull Am Meteorol Soc 91(10):1357–1363. doi:10.1175/2010bams2877.1

    Article  Google Scholar 

  • Hazeleger W, Wang X, Severijns C, Ştefănescu S, Bintanja R, Sterl A, Wyser K, Semmler T, Yang S, van den Hurk B, van Noije T, van der Linden E, van der Wiel K (2011) EC-Earth V2: description and validation of a new seamless Earth system prediction model. Submitted to Climate Dynamics

  • Jarlan L, Balsamo G, Lafont S, Beljaars A, Calvet JC, Mougin E (2008) Analysis of leaf area index in the ECMWF land surface model and impact on latent heat and carbon fluxes: application to West Africa. J Geophys Res 113(D24):D24117. doi:10.1029/2007jd009370

  • Jones P, Harris I (2008) CRU Time Series (TS) high resolution gridded datasets. CRU University of East Anglia Climate Research Unit, NCAS British Atmospheric Data Centre. http://badc.nerc.ac.uk/view/badc.nerc.ac.uk__ATOM__dataent_1256223773328276

  • Jönsson P, Eklundh L (2004) TIMESAT—a program for analysing time-series of satellite sensor data. Comput Geosci 30:833–845

    Article  Google Scholar 

  • Koster RD, Suarez MJ, Heiser M (2000) Variance and predictability of precipitation at seasonal-to-interannual timescales. J Hydrometeorol 1(1):26–46. doi:10.1175/1525-7541(2000)001<0026:vapopa>2.0.co;2

    Article  Google Scholar 

  • Koster RD, Dirmeyer PA, Hahmann AN, Ijpelaar R, Tyahla L, Cox P, Suarez MJ (2002) Comparing the degree of land-atmosphere interaction in four atmospheric general circulation models. J Hydrometeorol 3(3):363–375. doi:10.1175/1525-7541(2002)003<0363:ctdola>2.0.co;2

    Article  Google Scholar 

  • Koster RD, Mahanama SPP, Yamada TJ, Balsamo G, Berg AA, Boisserie M, Dirmeyer PA, Doblas-Reyes FJ, Drewitt G, Gordon CT, Guo Z, Jeong JH, Lee WS, Li Z, Luo L, Malyshev S, Merryfield WJ, Seneviratne SI, Stanelle T, van den Hurk BJJM, Vitart F, Wood EF (2011) The second phase of the global land-atmosphere coupling experiment: soil moisture contributions to subseasonal forecast skill. J Hydrometeorol 12(5):805–822. doi:10.1175/2011jhm1365.1

    Article  Google Scholar 

  • Leutbecher M (2007) On the representation of initial uncertainties with multiple sets of singular vectors optimized for different criteria. Quart J R Meteorolog Soc 133:2045–2056. doi:10.1002/qj.174

    Article  Google Scholar 

  • Lorenz EN (1982) Atmospheric predictability experiments with a large numerical model. Tellus 34(6):505–513. doi:10.1111/j.2153-3490.1982.tb01839.x

    Article  Google Scholar 

  • Los SO, Pollack NH, Parris MT, Collatz GJ, Tucker CJ, Sellers PJ, Malmström CM, DeFries RS, Bounoua L, Dazlich DA (2000) A global 9-yr biophysical land surface dataset from NOAA AVHRR data. J Hydrometeorol 1:183–199. doi:10.1175/1525-7541(2000)001<0183:agybls>2.0.co;2

    Article  Google Scholar 

  • Los SO, Collatz GJ, Bounoua L, Sellers PJ, Tucker CJ (2001) Global interannual variations in sea surface temperature and land surface vegetation, air temperature, and precipitation. J Clim 14(7):1535–1549. doi:10.1175/1520-0442(2001)014<1535:giviss>2.0.co;2

    Article  Google Scholar 

  • Maignan F, Bréon F-M, Chevallier F, Viovy N, Ciais P, Garrec C, Trules J, Mancip M (2011) Evaluation of a dynamic global vegetation model using time series of satellite vegetation indices. Geosci Model Dev Discuss 4:907–941. doi:10.5194/gmdd-4-907-2011

    Article  Google Scholar 

  • Molen MK, Hurk BJJM, Hazeleger W (2011) A dampened land use change climate response towards the tropics. Clim Dyn 37(9–10):2035–2043. doi:10.1007/s00382-011-1018-0

    Article  Google Scholar 

  • Moody EG, King MD, Schaaf CB, Platnick S (2008) MODIS-derived spatially complete surface albedo products: spatial and temporal pixel distribution and zonal averages. J Appl Meteorol Climatol 47(11):2879–2894. doi:10.1175/2008jamc1795.1

    Article  Google Scholar 

  • Myneni RB, Yang W, Nemani RR, Huete AR, Dickinson RE, Knyazikhin Y, Didan K, Fu R, Negrón Juárez RI, Saatchi SS, Hashimoto H, Ichii K, Shabanov NV, Tan B, Ratana P, Privette JL, Morisette JT, Vermote EF, Roy DP, Wolfe RE, Friedl MA, Running SW, Votava P, El-Saleous N, Devadiga S, Su Y, Salomonson VV (2007) Large seasonal swings in leaf area of Amazon rainforests. Proc Natl Acad Sci 104(12):4820–4823. doi:10.1073/pnas.0611338104

    Article  Google Scholar 

  • Pohlmann H, Botzet M, Latif M, Roesch A, Wild M, Tschuck P (2004) Estimating the decadal predictability of a coupled AOGCM. J Clim 17(22):4463–4472. doi:10.1175/3209.1

    Article  Google Scholar 

  • Poulter B, Frank DC, Hodson EL, Zimmermann NE (2007) Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO2 airborne fraction. Biogeosciences 8(8). doi:10.5194/bg-8-2027-2011

  • Reale O, Dirmeyer P (2002) Modeling the effect of land surface evaporation variability on precipitation variability. Part I: general response. J Hydrometeorol 3(4):433–450. doi:10.1175/1525-7541(2002)003<0433:mteols>2.0.co;2

    Article  Google Scholar 

  • Rowell DP (1998) Assessing potential seasonal predictability with an ensemble of Multidecadal GCM simulations. J Clim 11(2):109–120. doi:10.1175/1520-0442(1998)011<0109:APSPWA>2.0.CO;2

    Article  Google Scholar 

  • Sellers PJ, Tucker CJ, Collatz GJ, Los SO, Justice CO, Dazlich DA, Randall DA (1996) A revised land surface parameterization (SiB2) for atmospheric GCMS. Part II: the generation of global fields of terrestrial biophysical parameters from satellite data. J Clim 9(4):706–737. doi:10.1175/1520-0442(1996)009<0706:arlspf>2.0.co;2

    Article  Google Scholar 

  • Simmons AJ, Jones PD, da Costa Bechtold V, Beljaars ACM, Kållberg PW, Saarinen S, Uppala SM, Viterbo P, Wedi N (2004) Comparison of trends and low-frequency variability in CRU, ERA-40, and NCEP/NCAR analyses of surface air temperature. J Geophys Res 109(D24):D24115. doi:10.1029/2004jd005306

    Article  Google Scholar 

  • Storch Hv, Zwiers FW (2002) Statistical analysis in climate research. Cambridge University Press, Cambridge

    Google Scholar 

  • Teuling AJ, Seneviratne SI (2008) Contrasting spectral changes limit albedo impact on land-atmosphere coupling during the 2003 European heat wave. Geophys Res Lett 35(3):L03401. doi:10.1029/2007gl032778

    Article  Google Scholar 

  • van den Hurk BJJM, P.Viterbo, Beljaars ACM, Betts AK (2000) Offline validation of the ERA40 surface scheme. ECMWF Tech Memo ECMWF

  • van den Hurk BJJM, Viterbo P, Los SO (2003) Impact of leaf area index seasonality on the annual land surface evaporation in a global circulation model. J Geophys Res 108(D6):4191. doi:10.1029/2002jd002846

    Google Scholar 

  • van den Hurk B, Doblas-Reyes F, Balsamo G, Koster R, Seneviratne S, Camargo H (2010) Soil moisture effects on seasonal temperature and precipitation forecast scores in Europe. Clim Dyn:1–14. doi:10.1007/s00382-010-0956-2

  • Venzke S, Allen MR, Sutton RT, Rowell DP (1999) The atmospheric response over the North Atlantic to decadal changes in sea surface temperature. J Clim 12(8):2562–2584. doi:10.1175/1520-0442(1999)012<2562:tarotn>2.0.co;2

    Article  Google Scholar 

  • Xiao J, Zhuang Q, Liang E, Shao X, McGuire AD, Moody A, Kicklighter DW, Melillo JM (2009) Twentieth-century droughts and their impacts on terrestrial carbon cycling in china. Earth Interact 13:1–31. doi:10.1175/2009EI275.1

    Article  Google Scholar 

  • Yang W, Shabanov NV, Huang D, Wang W, Dickinson RE, Nemani RR, Knyazikhin Y, Myneni RB (2006) Analysis of leaf area index products from combination of MODIS Terra and Aqua data. Remote Sens Environ 104(3):297–312

    Article  Google Scholar 

  • Zwiers FW, Kharin VV (1998) Intercomparison of interannual variability and potential predictability: an AMIP diagnostic subproject. Clim Dyn 14(7):517–528. doi:10.1007/s003820050238

    Article  Google Scholar 

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Acknowledgments

This work was funded by the European Commission’s 7th Framework Programme, under Grant Agreement number 226520, COMBINE project. The authors thank the reviewers for their useful comments.

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Authors and Affiliations

  1. Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands

    Martina Weiss, Bart van den Hurk, Reindert Haarsma & Wilco Hazeleger

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  1. Martina Weiss
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  2. Bart van den Hurk
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  4. Wilco Hazeleger
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Correspondence to Martina Weiss.

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This paper is a contribution to the special issue on EC-Earth, a global climate and earth system model based on the seasonal forecast system of the European Centre for Medium-Range Weather Forecasts, and developed by the international EC-Earth consortium. This special issue is coordinated by Wilco Hazeleger (chair of the EC-Earth consortium) and Richard Bintanja

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Weiss, M., van den Hurk, B., Haarsma, R. et al. Impact of vegetation variability on potential predictability and skill of EC-Earth simulations. Clim Dyn 39, 2733–2746 (2012). https://doi.org/10.1007/s00382-012-1572-0

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  • DOI: https://doi.org/10.1007/s00382-012-1572-0

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