Abstract
Land surfaces and soil conditions are key sources of climate predictability at the seasonal time scale. In order to estimate how the initialization of the land surface affects the predictability at seasonal time scale, we run two sets of seasonal hindcasts with the general circulation model EC-Earth2.3. The initialization of those hindcasts is done either with climatological or realistic land initialization in May using the ERA-Land re-analysis. Results show significant improvements in the initialized run occurring up to the last forecast month. The prediction of near-surface summer temperatures and precipitation at the global scale and over Europe are improved, as well as the warm extremes prediction. As an illustration, we show that the 2010 Russian heat wave is only predicted when soil moisture is initialized. No significant improvement is found for the retrospective prediction of the 2003 European heat wave, suggesting this event to be mainly large-scale driven. Thus, we confirm that late-spring soil moisture conditions can be decisive in triggering high-impact events in the following summer in Europe. Accordingly, accurate land-surface initial conditions are essential for seasonal predictions.
Similar content being viewed by others
References
Balmaseda M, Anderson D (2009) Impact of initialization strategies and observations on seasonal forecast skill. Geophys Res Lett 36:L01701. doi:10.1029/2008GL035561
Balmaseda M, Anderson D, Vidard A (2007) Impact of Argo on analyses of the global ocean. Geophys Res Lett 34:L16605. doi:10.1029/2007GL030452
Balmaseda M, Vidard A, Anderson DLT (2008) The ECMWF ocean analysis system: ORA-S3. Mon Weather Rev 136:3018–3034. doi:10.1175/2008MWR2433.1
Balmaseda M, Alves O, Arribas A et al (2009) Ocean initialization for seasonal forecasts. Oceanography 22:154–159
Balmaseda M, Mogensen K, Weaver AT (2013) Evaluation of the ECMWF ocean reanalysis system ORAS4. Q J R Meteorol Soc 139:1132–1161. doi:10.1002/qj.2063
Balsamo G, Beljaars A, Scipal K et al (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 10:623–643. doi:10.1175/2008JHM1068.1
Balsamo G et al (2015) ERA-Interim/Land: a global land surface reanalysis data set. Hydrol Earth Syst Sci 19:389–407. doi:10.5194/hess-19-389-2015
Behera S, Ratnam JV, Masumoto Y, Yamagata T (2012) Origin of extreme summers in Europe: the Indo-Pacific connection. Clim Dyn 41:663–676. doi:10.1007/s00382-012-1524-8
Bellprat O, Kotlarski S, Lüthi D, Schär C (2013) Physical constraints for temperature biases in climate models. Geophys Res Lett 40:4042–4047. doi:10.1002/grl.50737
Cassou C, Terray L, Phillips A (2005) Tropical Atlantic influence on European heat waves. J Clim 18:2805–2811
Challinor AJ, Slingo JM, Wheeler TR, Doblas-Reyes FJ (2005) Probabilistic simulations of crop yield over western India using the DEMETER seasonal hindcast ensembles. Tellus A 57:498–512. doi:10.1111/j.1600-0870.2005.00126.x
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 81:7–30
Cohen J, Screen JA, Furtado JC et al (2014) Recent Arctic amplification and extreme mid-latitude weather. Nat Geosci 7:627–637. doi:10.1038/ngeo2234
Dee DP, Berrisford P, Poli P, Fuentes M (2009) ERA-Interim for climate monitoring. ECMWF Newslett 119:5–6
Dee DP, Uppala SM, Simmons AJ et al (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J R Meteorol Soc 137:553–597. doi:10.1002/qj.828
Dirmeyer P (2005) The land surface contribution to the potential predictability of boreal summer season climate. J Hydrometeorol 6:618–632. doi:10.1175/JHM444.1
Doblas-Reyes FJ, Hagedorn R, Palmer TN, Morcrette J-J (2006) Impact of increasing greenhouse gas concentrations in seasonal ensemble forecasts. Geophys Res Lett 33:L07708. doi:10.1029/2005GL025061
Doblas-Reyes FJ, García-Serrano J, Lienert F et al (2013) Seasonal climate predictability and forecasting: status and prospects. Wiley Interdiscip Rev Clim Change 4:245–268. doi:10.1002/wcc.217
Dole R, Hoerling M, Perlwitz J et al (2011) Was there a basis for anticipating the 2010 Russian heat wave? Geophys Res Lett 38:1–5. doi:10.1029/2010GL046582
Douville H (2010) Relative contribution of soil moisture and snow mass to seasonal climate predictability: a pilot study. Clim Dyn 34:797–818. doi:10.1007/s00382-008-0508-1
Eade R, Hamilton E, Smith DM et al (2012) Forecasting the number of extreme daily events out to a decade ahead. J Geophys Res Atmos 117:D21110. doi:10.1029/2012JD018015
Ferranti L, Viterbo P (2006) The European summer of 2003: sensitivity to soil water initial conditions. J Clim 19:3659–3680. doi:10.1175/JCLI3810.1
Feudale L, Shukla J (2011a) Influence of sea surface temperature on the European heat wave of 2003 summer. Part I: an observational study. Clim Dyn 36:1691–1703. doi:10.1007/s00382-010-0788-0
Feudale L, Shukla J (2011b) Influence of sea surface temperature on the European heat wave of 2003 summer. Part II: a modeling study. Clim Dyn 36:1705–1715. doi:10.1007/s00382-010-0789-z
Fichefet T, Maqueda MA (1997) Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics. J Geophys Res 102:12609. doi:10.1029/97JC00480
Fischer EM (2014) Climate science: autopsy of two mega-heatwaves. Nat Geosci 7:332–333. doi:10.1038/ngeo2148
Fischer EM, Seneviratne SI, Lüthi D, Schär C (2007a) Contribution of land–atmosphere coupling to recent European summer heat waves. Geophys Res Lett 34:L06707. doi:10.1029/2006GL029068
Fischer EM, Seneviratne SI, Vidale PL et al (2007b) Soil moisture–atmosphere interactions during the 2003 European summer heat wave. J Clim 20:5081–5099. doi:10.1175/JCLI4288.1
Fosser G, Khodayar S, Berg P (2014) Benefit of convection permitting climate model simulations in the representation of convective precipitation. Clim Dyn 44:45–60. doi:10.1007/s00382-014-2242-1
Frenkel Y, Majda AJ, Khouider B (2012) Using the stochastic multicloud model to improve tropical convective parameterization: a paradigm example. J Atmos Sci 69:1080–1105. doi:10.1175/JAS-D-11-0148.1
García-Herrera R, Díaz J, Trigo RM et al (2010) A review of the European summer heat wave of 2003. Crit Rev Environ Sci Technol 40:267–306. doi:10.1080/10643380802238137
García-Morales M, Dubus L (2007) Forecasting precipitation for hydroelectric power management: how to exploit GCM’s seasonal ensemble forecasts. Int J Climatol 1705:1691–1705. doi:10.1002/joc
Guemas V, Doblas-Reyes FJ, Mogensen K et al (2014) Ensemble of sea ice initial conditions for interannual climate predictions. Clim Dyn. doi:10.1007/s00382-014-2095-7
Hamilton E, Eade R, Graham RJ et al (2012) Forecasting the number of extreme daily events on seasonal timescales. J Geophys Res 117:D03114. doi:10.1029/2011JD016541
Hazeleger W, Wang X, Severijns C et al (2011) EC-Earth 8.2: description and validation of a new seamless earth system prediction model. Clim Dyn 39:2611–2629. doi:10.1007/s00382-011-1228-5
Hirschi M, Seneviratne SI, Alexandrov V et al (2011) Observational evidence for soil-moisture impact on hot extremes in southeastern Europe. Nat Geosci 4:17–21. doi:10.1038/ngeo1032
Hourdin F, Grandpeix J-Y, Rio C et al (2013) LMDZ5B: the atmospheric component of the IPSL climate model with revisited parameterizations for clouds and convection. Clim Dyn 40:2193–2222. doi:10.1007/s00382-012-1343-y
Jaeger EB, Seneviratne SI (2010) Impact of soil moisture–atmosphere coupling on European climate extremes and trends in a regional climate model. Clim Dyn 36:1919–1939. doi:10.1007/s00382-010-0780-8
Kim H-M, Webster PJ, Curry JA (2012) Seasonal prediction skill of ECMWF system 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter. Clim Dyn 39:2957–2973. doi:10.1007/s00382-012-1364-6
Koster RD, Suarez MJ, Liu P et al (2004) Realistic initialization of land surface states: impacts on subseasonal forecast skill. J Hydrometeorol 5:1049–1063
Koster RD, Mahanama SPP, Yamada TJ et al (2010) Contribution of land surface initialization to subseasonal forecast skill: first results from a multi-model experiment. Geophys Res Lett 37:L02402. doi:10.1029/2009GL041677
Koster RD, Mahanama SPP, Yamada TJ et al (2011) The second phase of the global land-atmosphere coupling experiment: soil moisture contributions to subseasonal forecast skill. J Hydrometeorol 12:805–822. doi:10.1175/2011JHM1365.1
Kutiel H, Benaroch Y (2002) North Sea-Caspian Pattern (NCP)—an upper level atmospheric teleconnection affecting the Eastern Mediterranean: identification and definition. Theor Appl Climatol 28:17–28. doi:10.1007/s704-002-8205-x
Landman WA, Beraki A (2012) Multi-model forecast skill for mid-summer rainfall over southern Africa. Int J Climatol 32:303–314. doi:10.1002/joc.2273
Lee S-S, Lee J-Y, Ha K-J et al (2011) Deficiencies and possibilities for long-lead coupled climate prediction of the Western North Pacific–East Asian summer monsoon. Clim Dyn 36:1173–1188. doi:10.1007/s00382-010-0832-0
MacLachlan C, Arribas A, Peterson KA et al (2014) Global Seasonal Forecast System version 5 (GloSea5): a high resolution seasonal forecast system. Q J R Meteorol Soc. doi:10.1002/qj.2396
Madec G (2008) NEMO ocean engine. Note du Pole de modelisation, Institut Pierre-Simon Laplace (IPSL) No 27. ISSN: 1288-1619
Mason S, Mimmack G (1992) The use of bootstrap confidence intervals for the correlation coefficient in climatology. Theor Appl Climatol 45:229–233
Materia S, Borrelli A, Bellucci A et al (2014) Impact of atmosphere and land surface initial conditions on seasonal forecasts of global surface temperature. J Clim 27:9253–9271. doi:10.1175/JCLI-D-14-00163.1
Miralles DG, Teuling AJ, Van Heerwaarden CC (2014) Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat Geosci 7:345–349. doi:10.1038/ngeo2141
Orsolini YJ, Kvamstø NG (2009) Role of Eurasian snow cover in wintertime circulation: decadal simulations forced with satellite observations. J Geophys Res 114:D19108. doi:10.1029/2009JD012253
Orth Rene, Seneviratne SI (2015) Introduction of a simple-model-based land surface dataset for Europe. Environ Res Lett 10:044012. doi:10.1088/1748-9326/10/4/044012
Palmer TN, Shutts GJ, Hagedorn R et al (2005a) Representing model uncertainty in weather and climate prediction. Annu Rev Earth Planet Sci 33:163–193. doi:10.1146/annurev.earth.33.092203.122552
Palmer TN, Doblas-Reyes FJ, Hagedorn R, Weisheimer A (2005b) Probabilistic prediction of climate using multi-model ensembles: from basics to applications. Philos Trans R Soc Lond B Biol Sci 360:1991–1998. doi:10.1098/rstb.2005.1750
Pepler AS, Diaz L, Prodhomme C, Doblas-Reyes FJ, Kumar A (2015) The ability of a multi-model seasonal forecasting ensemble to forecast the seasonal distribution of daily extremes. Weather Clim Extrem 9:68–77
Phelps M, Kumar A, O’Brien J (2004) Potential predictability in the NCEP CPC dynamical seasonal forecast system. J Clim 17:3775–3785
Quesada B, Vautard R, Yiou P et al (2012) Asymmetric European summer heat predictability from wet and dry southern winters and springs. Nat Clim Change 2:736–741. doi:10.1038/nclimate1536
Reinhold B, Pierrehumbert R (1982) Dynamics of weather regimes: quasi-stationary waves and blocking. Mon Weather Rev 110:1105–1145
Rodwell M, Rowell D, Folland C (1999) Oceanic forcing of the wintertime North Atlantic Oscillation and European climate. Nature 398:25–28
Rogers J (1997) North Atlantic storm track variability and its association to the North Atlantic Oscillation and climate variability of northern Europe. J Clim 10:1635–1647
Saha S, Moorthi S, Pan H-L et al (2010) The NCEP climate forecast system reanalysis. Bull Am Meteorol Soc 91:1015–1057. doi:10.1175/2010BAMS3001.1
Scaife AA, Copsey D, Gordon C et al (2011) Improved Atlantic winter blocking in a climate model. Geophys Res Lett 38:L23703. doi:10.1029/2011GL049573
Schär C, Lüthi D, Beyerle U, Heise E (1999) The soil-precipitation feedback: a process study with a regional climate model. J Clim 12:722–741
Seneviratne SI, Lüthi D, Litschi M, Schär C (2006) Land–atmosphere coupling and climate change in Europe. Nature 443:205–209. doi:10.1038/nature05095
Seneviratne SI, Corti T, Davin EL et al (2010) Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci Rev 99:125–161. doi:10.1016/j.earscirev.2010.02.004
Seneviratne SI, Lehner I, Gurtz J et al (2012) Swiss prealpine Rietholzbach research catchment and lysimeter: 32 year time series and 2003 drought event. Water Resour Res 48:1–20. doi:10.1029/2011WR011749
Seneviratne S, Wilhelm M, Stanelle T et al (2013) Impact of soil moisture–climate feedbacks on CMIP5 projections: first results from the GLACE-CMIP5 experiment. Geophys Res Lett 40:5212–5217. doi:10.1002/grl.50956
Shaman J, Tziperman E (2011) An atmospheric teleconnection linking ENSO and southwestern European precipitation. J Clim 24:124–139. doi:10.1175/2010JCLI3590.1
Shukla J, Kinter JL III (2006) Predictability of seasonal climate variations: a pedagogical review. In: Palmer T, Hagedorn R (eds) Predictability of weather and climate. Cambridge University Press, London, pp 306–341
Steiger JH (1980) Tests for comparing elements of a correlation matrix. Psychol Bull. doi:10.1037/0033-2909.87.2.245
Thomson MC, Doblas-reyes FJ, Mason SJ et al (2006) Malaria early warnings based on seasonal climate forecasts from multi-model ensembles. Nature 439:576–579. doi:10.1038/nature04503
Valcke S (2006) OASIS3 user guide (prism_2-5). PRISM support initiative report 3, p 64
van den Hurk BJJM et al (2000) Offline validation of the ERA40 surface scheme. European Centre for Medium-Range Weather Forecasts Technical Memorandum 295. http://nwmstest.ecmwf.int/publications/library/do/references/show?id=83838
van den Hurk BJJM, Doblas-Reyes F, Balsamo G et al (2010) Soil moisture effects on seasonal temperature and precipitation forecast scores in Europe. Clim Dyn 38:349–362. doi:10.1007/s00382-010-0956-2
van Oldenborgh GJ, Doblas Reyes FJ, Drijfhout SS, Hawkins E (2013) Reliability of regional climate model trends. Environ Res Lett 8:014055. doi:10.1088/1748-9326/8/1/014055
von Storch H, Zwiers FW (2001) Statistical analysis in climate research. Cambridge University Press, Cambridge
Wang A, Bohn TJ, Mahanama SP et al (2009) Multimodel ensemble reconstruction of drought over the continental United States. J Clim 22:2694–2712. doi:10.1175/2008JCLI2586.1
Wang G, Dolman AJ, Alessandri A (2011) A summer climate regime over Europe modulated by the North Atlantic Oscillation. Hydrol Earth Syst Sci 15:57–64. doi:10.5194/hess-15-57-2011
Weisheimer A, Doblas-Reyes FJ, Jung T, Palmer TN (2011) On the predictability of the extreme summer 2003 over Europe. Geophys Res Lett. doi:10.1029/2010GL046455
Whan K, Zscheischler J, Orth R et al (2015) Impact of soil moisture on extreme maximum temperatures in Europe. Weather Clim Extrem. doi:10.1016/j.wace.2015.05.001
Acknowledgments
The research leading to these results has received funding from the EU Seventh Framework Programme FP7 (2007–2013) under grant agreements 308378 (SPECS), 282378 (DENFREE) and 607085 (EUCLEIA), and from the Spanish Ministerio de Economía y Competitividad (MINECO) under the project CGL2013-41055-R. We acknowledge the s2dverification R-based package (http://cran.r-project.org/web/packages/s2dverification/index.html). We also thank ECMWF for providing the ERA-Land initial conditions and computing resources through the SPICCF Special Project.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Prodhomme, C., Doblas-Reyes, F., Bellprat, O. et al. Impact of land-surface initialization on sub-seasonal to seasonal forecasts over Europe. Clim Dyn 47, 919–935 (2016). https://doi.org/10.1007/s00382-015-2879-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00382-015-2879-4