• Beniston, M., 2004: The 2003 heat wave in Europe: A shape of things to come? An analysis based on Swiss climatological data and model simulations. Geophys. Res. Lett., 31, L02022, doi:10.1029/2003GL018857.

    • Search Google Scholar
    • Export Citation
  • Bisselink, B., and A. J. Dolman, 2008: Precipitation recycling: Moisture sources over Europe using ERA-40 data. J. Hydrometeor., 9, 10731083.

    • Search Google Scholar
    • Export Citation
  • Black, E., M. Blackburn, G. Harrison, B. Hoskins, and J. Methven, 2004: Factors contributing to the 2003 European heat wave. Weather, 59, 217223.

    • Search Google Scholar
    • Export Citation
  • Cassou, C., L. Terray, and A. S. Phillips, 2005: Tropical Atlantic influence on European heat waves. J. Climate, 18, 28052811.

  • Czaja, A., and C. Frankignoul, 1999: Influence of the North Atlantic SST anomalies on the atmospheric circulation. Geophys. Res. Lett., 26, 29692972.

    • Search Google Scholar
    • Export Citation
  • Czaja, A., and C. Frankignoul, 2002: Observed impact of Atlantic SST anomalies on the North Atlantic Oscillation. J. Climate, 15, 606623.

    • Search Google Scholar
    • Export Citation
  • Davies, R., 1982: Documentation of the solar radiation parameterization in the GLAS climate model. NASA Tech. Memo. 83961, 57 pp.

  • DeWitt, D. G., 1996: The effect of the cumulus convection scheme on the climate of the COLA general circulation model. COLA Tech. Rep. 27, 43 pp.

    • Search Google Scholar
    • Export Citation
  • DeWitt, D. G., and E. K. Schneider, 1997: The earth radiation budget as simulated by the COLA GCM. COLA Rep. 35, 39 pp. [Available from COLA, 4041 Powder Mill Rd., Suite 302, Calverton, MD 20705.]

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., and F. J. Zeng, 1999: Precipitation infiltration in the simplified SiB land surface scheme. J. Meteor. Soc. Japan, 78, 291303.

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., Z. Guo, and X. Gao, 2004: Comparison, validation, and transferability of eight multiyear global soil wetness products. J. Hydrometeor., 5, 10111033.

    • Search Google Scholar
    • Export Citation
  • Fan, Y., and H. van den Dool, 2004: Climate Prediction Center global monthly soil moisture data set at 0.5° resolution for 1948 to present. J. Geophys. Res., 109, D10102, doi:10.1029/2003JD004345.

    • Search Google Scholar
    • Export Citation
  • Fennessy, M. J., and J. Shukla, 1999: Impact of initial soil wetness on seasonal atmospheric prediction. J. Climate, 12, 31673180.

  • Fennessy, M. J., and Coauthors, 1994: The simulated Indian monsoon: A GCM sensitivity study. J. Climate, 7, 3343.

  • Ferranti, L., and P. Viterbo, 2006: The European Summer of 2003: Sensitivity to soil water initial conditions. J. Climate, 19, 36593680.

    • Search Google Scholar
    • Export Citation
  • Feudale, L., and J. Shukla, 2007: Role of Mediterranean SST in enhancing the European heat wave of summer 2003. Geophys. Res. Lett., 34, L03811, doi:10.1029/2006GL027991.

    • Search Google Scholar
    • Export Citation
  • Fischer, E., S. I. Seneviratne, P. L. Vidale, D. Lüthi, and C. Schär, 2007: Soil moisture–atmosphere interactions during the 2003 European summer heat wave. J. Climate, 20, 50815099.

    • Search Google Scholar
    • Export Citation
  • Folland, C. K., T. N. Palmer, and D. E. Parker, 1986: Sahel rainfall and worldwide sea temperatures, 1901–85. Nature, 320, 602607.

  • Giannini, A., R. Saravanan, and P. Chang, 2003: Oceanic forcing of Sahel rainfall on interannual to inter-decadal time scales. Science, 302, 10271030.

    • Search Google Scholar
    • Export Citation
  • Harshvardhan, , R. Davies, D. A. Randall, and T. G. Corsetti, 1987: A fast radiation parameterization for atmospheric circulation models. J. Geophys. Res., 92 (D1), 10091016.

    • Search Google Scholar
    • Export Citation
  • Hoerling, M. P., and A. Kumar, 2002: Atmospheric response patterns associated with tropical forcing. J. Climate, 15, 21842203.

  • Hoerling, M. P., and A. Kumar, 2003: The perfect ocean for drought. Science, 299, 691694.

  • Hoerling, M. P., J. W. Hurrell, and T. Xu, 2001: Tropical origins for recent North Atlantic climate change. Science, 292, 9092.

  • International Federation of Red Cross and Red Crescent Societies, 2004: World Disasters Report 2004. Kumarian Press, 231 pp.

  • Kiehl, J. T., J. J. Hack, G. Bonan, B. A. Boville, D. L. Williamson, and P. J. Rasch, 1998: The National Center for Atmospheric Research Community Climate Model: CCM3. J. Climate, 11, 11311149.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and P. C. D. Milly, 1997: The interplay between transpiration and runoff formulations in land surface schemes used with atmospheric models. J. Climate, 10, 15781591.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., Z. Guo, R. Yang, P. A. Dirmeyer, K. Mitchell, and M. J. Puma, 2009: On the nature of soil moisture in land surface models. J. Climate, 22, 43224335.

    • Search Google Scholar
    • Export Citation
  • Luterbacher, J., D. Dietrich, E. Xoplaki, M. Grosjean, and H. Wanner, 2004: European seasonal and annual temperature variability, trends, and extremes since 1500. Science, 303, 14991503.

    • Search Google Scholar
    • Export Citation
  • Meehl, G., and C. Tebaldi, 2004: More intense, more frequent and longer lasting heat waves in the 21st century. Science, 305, 994997.

    • Search Google Scholar
    • Export Citation
  • Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid processes. Rev. Geophys. Space Phys., 20, 851875.

    • Search Google Scholar
    • Export Citation
  • Moorthi, S., and M. J. Suarez, 1992: Relaxed Arakawa–Schubert: A parameterization of moist convection for general circulation models. Mon. Wea. Rev., 120, 9781002.

    • Search Google Scholar
    • Export Citation
  • Nakamura, M., T. Enomoto, and S. Yamane, 2005: A simulation study of 2003 heat wave in Europe. J. Earth Simul., 2, 5569.

  • Pace, G., D. Meloni, and A. di Sarra, 2005: A forest fire aerosol over the Mediterranean basin during summer 2003. J. Geophys. Res., 110, D21202, doi:10.1029/2005JD005986.

    • Search Google Scholar
    • Export Citation
  • Pal, J., F. Giorgi, and X. Bi 2004: Consistency of recent European summer precipitation trends and extremes with future regional climate projections. Geophys. Res. Lett., 31, L13202, doi:10.1029/2004GL019836.

    • Search Google Scholar
    • Export Citation
  • Reinhold, B. B., and R. T. Pierrehumbert, 1982: Dynamics of weather regimes: Quasi-stationary waves and blocking. Mon. Wea. Rev., 110, 11051145.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., and T. M. Smith, 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7, 929948.

    • Search Google Scholar
    • Export Citation
  • Robertson, A. W., C. R. Mechoso, and Y. J. Kim, 2000: The influence of Atlantic sea surface temperature anomalies on the North Atlantic Oscillation. J. Climate, 13, 122138.

    • Search Google Scholar
    • Export Citation
  • Rodwell, M. J., D. P. Rowell, and C. K. Folland, 1999: Oceanic forcing of the wintertime North Atlantic Oscillation and European climate. Nature, 398, 320323.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., J. E. Janowiak, and M. F. Halpert, 1985: The analysis and display of real time surface climate data. Mon. Wea. Rev., 113, 11011107.

    • Search Google Scholar
    • Export Citation
  • Sato, N., P. J. Sellers, D. A. Randall, E. K. Schneider, J. Shukla, J. L. Kinter III, Y.-T. Hou, and E. Albertazzi, 1989: Effects of implementing the Simple Biosphere Model in a general circulation model. J. Atmos. Sci., 46, 27572782.

    • Search Google Scholar
    • Export Citation
  • Schär, C., P. L. Vidale, D. Lüthi, C. Frei, C. Häberli, M. A. Liniger, and C. Appenzeller, 2004: The role of increasing temperature variability in European summer heat waves. Nature, 427, 333336.

    • Search Google Scholar
    • Export Citation
  • Sellers, P. J. 1990: Specifying surface boundary conditions for drought studies. Proc. Workshop on 1988 U.S. Drought, College Park, MD, Dept. of Meteorology, University of Maryland at College Park, 170–172. [Available from Dept. of Meteorology, University of Maryland at College Park, College Park, MD 20742.]

    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., D. Lüthi, M. Litschi, and C. Schär, 2006: Land–atmosphere coupling and climate change in Europe. Nature, 427, 333336.

    • Search Google Scholar
    • Export Citation
  • Teuling, A. J., and S. I. Seneviratne, 2008: Contrasting spectral changes limit albedo impact on land-atmosphere coupling during the 2003 European heat wave. Geophys. Res. Lett., 35, L03401, doi:10.1029/2007GL032778.

    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1984: The effect of penetrative cumulus convection on the large-scale flow in a general circulation model. Beitr. Phys. Atmos., 57, 216239.

    • Search Google Scholar
    • Export Citation
  • Trigo, R. M., R. Garcia-Herrera, J. Diaz, I. F. Trigo, and M. A. Valente, 2005: How exceptional was the early August 2003 heat wave in France? Geophys. Res. Lett., 32, L10701, doi:10.1029/2005GL022410.

    • Search Google Scholar
    • Export Citation
  • World Health Organization, 2004: Heat waves: Risks and responses. Health and Global Environmental Change Series, No. 2, World Health Organization, Regional Office for Europe Rep., 123 pp. [Available from Scherfigsvej 8, DK-2100 Copenhagen Ø, Denmark.]

    • Search Google Scholar
    • Export Citation
  • Xie, P., and P. Arkin, 1996: Analysis of global monthly precipitation using guage observations, satellite estimates, and numerical model predictions. J. Climate, 9, 840858.

    • Search Google Scholar
    • Export Citation
  • Xue, Y.-K., P. J. Sellers, J. L. Kinter, and J. Shukla, 1991: A simplified biosphere model for global climate studies. J. Climate, 4, 345364.

    • Search Google Scholar
    • Export Citation
  • Xue, Y.-K., M. J. Fennessy, and P. J. Sellers, 1996: Impact of vegetation properties on U.S. summer weather prediction. J. Geophys. Res., 101, 74197430.

    • Search Google Scholar
    • Export Citation
  • View in gallery

    Near-surface temperature climatology (°C) for 1982–2001 for (a) NCEP CAMS FMAM, (b) NCEP CAMS JJAS, (c) AGCM CNTRL FMAM, (d) AGCM CNTRL JJAS, and (e) area-average monthly climatology time series for NCEP CAMS (solid) and AGCM CNTRL (dashed), land only.

  • View in gallery

    Precipitation climatology (mm day−1) for 1982–2001 for (a) NCEP CMAP FMAM, (b) NCEP CMAP JJAS, (c) AGCM CNTRL FMAM, (d) AGCM CNTRL JJAS, and (e) area-average monthly climatology time series for NCEP CMAP (solid) and AGCM CNTRL (dashed), land only.

  • View in gallery

    NCEP OISST V2 SST 2003 anomaly (°C) from 1982–2001 climatology for (a) February–March, (b) April–May, (c) June–July, (d) August–September, and (e) area-averaged daily anomaly time series (ocean only).

  • View in gallery

    Near-surface temperature 2003 anomaly from 1982–2001 climatology for (a) NCEP CAMS FMAM, (b) NCEP CAMS JJAS, (c) AGCM CNTRL FMAM, (d) AGCM CNTRL JJAS, and (e) area-average monthly anomaly time series for NCEP CAMS (solid) and AGCM CNTRL (dashed), land only. AGCM anomalies in (c),(d) only displayed if significant at the 10% level using a Student’s t test. Units are standard deviations in (a)–(d) and °C in (e).

  • View in gallery

    Precipitation 2003 anomaly from 1982–2001 climatology for (a) NCEP CMAP FMAM, (b) NCEP CMAP JJAS, (c) AGCM CNTRL FMAM, (d) AGCM CNTRL JJAS, and (e) area-average monthly anomaly time series for NCEP CMAP (solid) and AGCM CNTRL (dashed), land only. AGCM anomalies in (c),(d) only displayed if significant at the 20% level using a Student’s t test. Units are standard deviations in (a)–(d) and mm day−1 in (e).

  • View in gallery

    Difference between 2003 AGCM ensembles and 1982–2001 climatology of AGCM CNTRL ensemble for JJAS for near-surface temperature (°C) for (a) AGCM S25N ensemble and (b) AGCM N25N ensemble.

  • View in gallery

    Soil wetness 2003 anomaly from 1982–2001 climatology for (a) NCEP FV FMAM, (b) NCEP FV JJAS, (c) AGCM CNTRL FMAM, (d) AGCM CNTRL JJAS, and (e) area-average monthly anomaly time series for NCEP FV (solid) and AGCM CNTRL (dashed), land only. AGCM anomalies in (c),(d) only displayed if significant at the 20% level using a Student’s t test. Units are standard deviations in (a)–(d) and percentage of saturation in (e).

  • View in gallery

    Area-averaged soil wetness anomalies (standard deviations) for NCEP FV (solid) and OBISW (dashed) for AGCM (a) surface layer, (b) root zone layer, and (c) drainage layer.

  • View in gallery

    Difference between (a),(e) June, (b),(f) July, (c),(g) August, and (d),(h) September near-surface temperature (°C) for 2003 AGCM OBISW ensemble and (a)–(d) 2003 AGCM CNTRL ensemble, and (e)–(h) observed NCEP CAMS near-surface temperature anomalies from 1982–2001 climatology.

  • View in gallery

    Difference between (a),(e) June, (b),(f) July, (c),(g) August, and (d),(h) September precipitation (mm day−1) for 2003 AGCM OBISW ensemble and (a)–(d) 2003 AGCM CNTRL ensemble, and (e)–(h) observed NCEP CMAP precipitation anomalies from 1982–2001 climatology.

  • View in gallery

    Difference between 2003 AGCM ensembles and 1982–2001 climatology of AGCM CNTRL ensemble for (a),(e) June, (b),(f) July, (c),(g) August, and (d),(h) September for near-surface temperature (°C) for (a)–(d) AGCM CNTRL ensemble and (e)–(h) AGCM OBISW ensemble.

All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 426 231 3
PDF Downloads 270 156 3

Climatic Feedbacks during the 2003 European Heat Wave

View More View Less
  • 1 Institute of Global Environment and Society, Center for Ocean–Land–Atmosphere Studies, Calverton, Maryland
Full access

Abstract

During the summer of 2003, a record heat wave over Europe occurred, to which the deaths of over 20 000 people in 18 countries were attributed, 10 000 of those in France alone. Temperatures across Europe were above normal for most of the summer, but reached their peak during the first two weeks of August, when most of the deaths occurred.

Ensemble simulations done with a recent version of the Center for Ocean–Land–Atmosphere Studies (COLA) atmospheric general circulation model (AGCM) have been analyzed over the European region. The simulations were forced by weekly mean observed sea surface temperature (SST). Relative to the 1982–2001 period, the COLA AGCM simulated anomalous warmth over the European region during June–August 2003, in response to the observed SST; however, the simulated magnitude was smaller than that observed. A series of simulations in which the observed SST was used only south of 25°N or only north of 25°N suggest that it was the influence of the local SST rather than remote SST that was an important influence on the heat wave.

By early June, the soil over much of the European region was anomalously dry, consistent with the below-normal precipitation observed over the region in the preceding months. The model was restarted in early June with observation-based soil wetness anomalies imposed on the soil wetness from the control simulations. Although the model soil wetness was then allowed to evolve as usual, the simulations with the imposed initial soil wetness anomaly enhanced the simulated surface temperature anomaly during June–August by 1°–2°C. This weaker-than-observed temperature anomaly may be related to the persistence of imposed soil wetness anomalies, which was weaker than that observed. The experiments suggest that both the warm local SST and the dry local soil were important in intensifying the 2003 European heat wave.

Corresponding author address: M. J. Fennessy, Center for Ocean–Land–Atmosphere Studies, Institute of Global Environment and Society, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705. E-mail: fen@cola.iges.org

Abstract

During the summer of 2003, a record heat wave over Europe occurred, to which the deaths of over 20 000 people in 18 countries were attributed, 10 000 of those in France alone. Temperatures across Europe were above normal for most of the summer, but reached their peak during the first two weeks of August, when most of the deaths occurred.

Ensemble simulations done with a recent version of the Center for Ocean–Land–Atmosphere Studies (COLA) atmospheric general circulation model (AGCM) have been analyzed over the European region. The simulations were forced by weekly mean observed sea surface temperature (SST). Relative to the 1982–2001 period, the COLA AGCM simulated anomalous warmth over the European region during June–August 2003, in response to the observed SST; however, the simulated magnitude was smaller than that observed. A series of simulations in which the observed SST was used only south of 25°N or only north of 25°N suggest that it was the influence of the local SST rather than remote SST that was an important influence on the heat wave.

By early June, the soil over much of the European region was anomalously dry, consistent with the below-normal precipitation observed over the region in the preceding months. The model was restarted in early June with observation-based soil wetness anomalies imposed on the soil wetness from the control simulations. Although the model soil wetness was then allowed to evolve as usual, the simulations with the imposed initial soil wetness anomaly enhanced the simulated surface temperature anomaly during June–August by 1°–2°C. This weaker-than-observed temperature anomaly may be related to the persistence of imposed soil wetness anomalies, which was weaker than that observed. The experiments suggest that both the warm local SST and the dry local soil were important in intensifying the 2003 European heat wave.

Corresponding author address: M. J. Fennessy, Center for Ocean–Land–Atmosphere Studies, Institute of Global Environment and Society, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705. E-mail: fen@cola.iges.org

1. Introduction

During the summer of 2003, a record heat wave occurred in Europe. Between 22 000 and 35 000 deaths in 18 countries, with nearly 15 000 deaths in France alone as well as an estimated $13 billion (U.S. dollars) in costs, were attributed to the heat wave (World Health Organization 2004; International Federation of Red Cross and Red Crescent Societies 2004). The extreme weather during that summer was also responsible for widespread wildfires and dramatic reductions in bird and fish populations in the region. The Danube River reached its lowest level in more than a century, and the Mediterranean Sea attained a surface temperature (32°C) off the coast of Spain that was the highest ever observed. The temperature across Europe was well above normal for most of the summer, reaching a peak in the first two weeks of August, when most of the human deaths were recorded. The entire summer in Europe was the warmest recorded in the instrumental record, and there is some evidence that it was the warmest in the past 500 yr (Luterbacher et al. 2004). Trigo et al. (2005) showed that, during the core of the heat wave in the first two weeks of August 2003, the spatial structure of the extreme temperatures, both at the surface and in the lower troposphere, were consistent with the distribution of mortality rates.

The trend toward a warmer, drier, and more extreme European climate has been underway for several decades. Schär et al. (2004) showed that the occurrence of an extreme heat wave in 2003 cannot be explained simply by a shift in the mean temperature toward warmer seasonal averages but is consistent with a distribution whose mean and dispersion are both displaced toward higher values. The trend is also consistent with various climate change scenario model simulations, which show that the climate in Europe in response to enhanced greenhouse gas concentrations is warmer and drier than the current climate (Beniston 2004; Pal et al. 2004; Meehl and Tebaldi 2004). Seneviratne et al. (2006) found that the increased summer temperature variability found over central and eastern Europe in association with increased greenhouse gas concentrations was primarily due to soil moisture–temperature and soil moisture–precipitation feedbacks. Bisselink and Dolman (2008) used a dynamic recycling model to show that precipitation recycling is large (relatively high evaporation and relatively low moisture transport) in dry summers over central Europe.

A variety of possible causes of the extreme warmth in Europe in summer 2003 have been examined. The principal synoptic weather explanation was the persistence of an anticyclonic circulation in the midtroposphere that was associated with blocking of the large-scale atmospheric flow (Black et al. 2004). The absence of rainfall prior to and during the summer season contributed to excessively dry soil over most of the continent. The anticyclone or blocking over northern Europe during this period has been ascribed to various causes. A tropical influence has been suggested by various authors in which the northward displacement of the intertropical convergence zone (ITCZ) over the tropical Atlantic Ocean and western African Sahel region is related to both seasonal mean anticyclonic circulation and the regime-like behavior of the large-scale circulation that is frequently invoked to explain blocking (Reinhold and Pierrehumbert 1982). The northward shift of the ITCZ is in turn related to patterns of tropical sea surface temperature (SST) anomalies (Giannini et al. 2003; Cassou et al. 2005). Several studies have linked tropical SST and North Atlantic climate variability at seasonal and longer time scales (e.g., Rodwell et al. 1999; Czaja and Frankignoul 1999, 2002; Robertson et al. 2000; Hoerling et al. 2001). The climate of Europe has also been shown to be influenced by the SST pattern in the entire Atlantic (Folland et al. 1986) and the global SST pattern, particularly in the Pacific and Indian Oceans (e.g., Hoerling and Kumar 2002, 2003). In the case of the 2003 heat wave, there is an indication that the above-normal aerosol loading from forest fires, which were more prevalent in the extreme warmth and dryness, had a positive feedback effect stabilizing the lower troposphere and thereby inhibiting rainfall (Pace et al. 2005). The possibility of an albedo impact during the 2003 heat wave has also been examined. Teuling and Seneviratne (2008) showed that the albedo impact on the land–atmosphere coupling during the 2003 European heat wave was limited because of contrasting effects between the visible and near-infrared bands.

Simulations with global atmospheric general circulation models (AGCMs) have been performed to examine some of the suggested causes of the 2003 European heat wave. Nakamura et al. (2005) were able to reproduce features of the 2003 event with the AGCM for the Earth Simulator (AFES) run at high global resolution (T639) using observed global daily SST as a lower-boundary condition. They also found that the heat wave was reproduced in runs made with observed SST only in selected regions and climatological SST elsewhere. In particular, they found that the simulations made with observed SST only in the Mediterranean Sea and only in the eastern North Atlantic included heat waves over Europe, while simulations made with observed SST in the western North Atlantic or in both the eastern North Atlantic and the Mediterranean did not. They attributed the success and failure of the various simulations to differences in wave activity flux into the European sector, although they were not able to unambiguously associate the various successful SST patterns with a particular wave activity flux pattern. Feudale and Shukla (2007) also used an AGCM to simulate the 2003 event and found that the observed global SST pattern for the first half of 2003 set up the conditions necessary for the persistent anticyclonic circulation. They also found that the Mediterranean SST anomaly by itself was sufficient to explain more than half of the response over Europe.

Because there were such strong and persistent dry anomalies from February through June 2003 over the region, the possibility that the resulting dry soil conditions helped force the summer heat wave arises. Ferranti and Viterbo (2006) performed idealized soil moisture initial condition sensitivity experiments (from very dry to very wet) with the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric model that demonstrated the nonlinear nature of the soil moisture impact. They found that the response of the model to large initial dry soil moisture anomalies greatly exceeded the impact of using real-time-analyzed SST versus climatological SST. Fischer et al. (2007) imposed a series of idealized spring soil moisture perturbations in a regional climate model. They found that dry soil moisture resulted in decreased latent heat fluxes and increased sensible heat fluxes over the region and may have been responsible for as much as 40% of the observed heat anomalies in some regions during the 2003 European heat wave.

Because the soil moisture anomalies utilized in the previous studies were idealized and their impact was greatly dependent on their magnitude, it is hard to contrast the relative impact of dry soil versus SST during the 2003 European heat wave. We attempt to do so here. Simulations with the Center for Ocean–Land–Atmosphere Studies (COLA) AGCM were performed to determine the relative importance of observed SST anomalies and proxy-observed soil wetness anomalies on the summer 2003 European heat wave.

The model used is outlined in section 2. The experiments are described in section 3. The results are discussed in section 4, and the conclusions are given in section 5.

2. Model description

The AGCM used in this study is version 2.2.6 of the COLA global spectral model at T63 horizontal resolution and 18 vertical levels. The model uses the dynamical core of the National Center for Atmospheric Research Climate Community Model version 3 (CCM3) described in Kiehl et al. (1998). The dependent variables of the model are spectrally treated, save the moisture variable, which is advected using the semi-Lagrangian technique. The parameterization of deep convection follows the relaxed Arakawa–Schubert scheme (Moorthi and Suarez 1992) as modified by DeWitt (1996). The parameterization of shallow convection follows Tiedtke (1984). The subgrid-scale exchange of heat, momentum, and moisture is accomplished via a turbulent closure scheme, level 2.0 (Mellor and Yamada 1982). The diagnostic cloud fraction and optical properties are similar to CCM3 (Kiehl et al. 1998) and are described in DeWitt and Schneider (1997). The terrestrial and shortwave radiation follows Harshvardhan et al. (1987) and Davies (1982), respectively. A mean surface orography (Fennessy et al. 1994) is used to represent surface elevation. The atmospheric model is coupled to the Simplified Simple Biosphere model (SSiB) documented in Xue et al. (1991, 1996) and Dirmeyer and Zeng (1999).

3. Experimental design

The experiments conducted with the COLA AGCM 2.2.6 are summarized in Table 1. All experiments save the one labeled CLSST were forced with observed weekly global SST [National Centers for Environmental Prediction (NCEP) optimum interpolation sea surface temperature version 2 (OISST V2); Reynolds and Smith (1994)], which included large positive anomalies in the European region from April 2003 through September 2003. The CLSST experiment was conducted with 1982–2001 monthly climatological SST obtained from NCEP OISST V2. The S25N experiment was conducted with observed SST south of 25°N and climatological SST north of 25°N. The N25N experiment was conducted with observed SST north of 25°N and climatological SST south of 25°N.

Table 1.

Model experiments (Exp Name), atmospheric initial conditions (I.C.), SST, soil wetness initial condition (Initial SW), and integration length (Length).

Table 1.

In all experiments the SSiB soil wetness fields (surface, root, and drainage zones) are predicted after initialization. To obtain more robust results, all experiments are 10-member ensembles and the ensemble means will be analyzed. The initial soil wetness and initial atmospheric states for the control run (CNTRL) were obtained from 10 COLA AGCM runs initialized in late November 1948. The initial atmospheric states for the other three experiments were obtained from the CNTRL integrations, so all ensembles are fully drifted to the model’s inherent climatology. The initial soil wetness for CLSST was obtained from CNTRL. The initial soil wetness for CLISW and OBISW is discussed in section 4.

4. Results

All of the model results shown are 10-member ensemble means.

a. Model climatology

It is important to examine the veracity of the model climatology in the European region before examining the model response to SST and soil wetness anomalies during 2003. For this purpose the 1982–2001 20-yr mean model near-surface temperature and precipitation climatologies from the CNTRL experiment are compared to corresponding observations.

Figure 1 shows the near-surface temperature climatology for February–May (FMAM) and June–September (JJAS) for NCEP Climate Anomaly Monitoring System (CAMS; Ropelewski et al. 1985, Figs. 1a,b) and CNTRL (Figs. 1c,d), respectively. The monthly mean climatologies for area averages over the land points in European region (10°W–30°E, 35°–60°N) for NCEP CAMS (solid) and CNTRL (dashed) are shown in Fig. 1e. The model captures the general climatological near-surface temperature pattern and its seasonal progression from spring through summer. The model area averages are within about 1°C too warm compared to observations.

Fig. 1.
Fig. 1.

Near-surface temperature climatology (°C) for 1982–2001 for (a) NCEP CAMS FMAM, (b) NCEP CAMS JJAS, (c) AGCM CNTRL FMAM, (d) AGCM CNTRL JJAS, and (e) area-average monthly climatology time series for NCEP CAMS (solid) and AGCM CNTRL (dashed), land only.

Citation: Journal of Climate 24, 23; 10.1175/2011JCLI3523.1

Figure 2 shows the precipitation climatology for FMAM and JJAS for NCEP Climate Prediction Center Merged Analysis of Precipitation (CMAP; Xie and Arkin 1996, Figs. 2a,b) and CNTRL (Figs. 2c,d), respectively. The monthly mean climatologies for area averages over the land points in European region (10°W–30°E, 35°–60°N) for NCEP CMAP (solid) and CNTRL (dashed) are shown in Fig. 2e. The model generally simulates the minima and maxima in the observed climatological pattern but simulates more precipitation than observed over most of the region. The model area-averaged precipitation is about 0.5 mm day−1 more than that observed from February through July.

Fig. 2.
Fig. 2.

Precipitation climatology (mm day−1) for 1982–2001 for (a) NCEP CMAP FMAM, (b) NCEP CMAP JJAS, (c) AGCM CNTRL FMAM, (d) AGCM CNTRL JJAS, and (e) area-average monthly climatology time series for NCEP CMAP (solid) and AGCM CNTRL (dashed), land only.

Citation: Journal of Climate 24, 23; 10.1175/2011JCLI3523.1

b. 2003 anomalies

The February–March, April–May, June–July, and August–September 2003 SST anomalies shown in Figs. 3a–d, respectively, are formed by subtracting the 2003 OISST V2 from the 1982–2001 OISST V2 20-yr climatology. The 10°W–30°E, 35°–60°N ocean-only area-average SST anomaly is shown in Fig. 3e for February–October 2003. There are little to no positive SST anomalies present during February–March 2003 in the area (Fig. 3a). During April–May 2003 positive SST anomalies appear east of the British Isles and in the Mediterranean Sea. The area-average value turns positive in May 2003. Very strong positive SST anomalies are present throughout the months of June–September across most of the region but especially east of the British Isles and in the Mediterranean Sea. The area-average value remains above 1°C from June through September.

Fig. 3.
Fig. 3.

NCEP OISST V2 SST 2003 anomaly (°C) from 1982–2001 climatology for (a) February–March, (b) April–May, (c) June–July, (d) August–September, and (e) area-averaged daily anomaly time series (ocean only).

Citation: Journal of Climate 24, 23; 10.1175/2011JCLI3523.1

Simulated 2003 monthly anomalies are formed by subtracting the 20-yr, (1982–2001) 10-member mean CNTRL climatology from the 2003 10-member ensemble mean for each experiment. To make the observed anomalies and the model anomalies directly comparable, both are expressed in terms of their own seasonal standard deviations in Figs. 4a–d. The CNTRL FMAM and JJAS 2003 simulated anomalies for near-surface temperature (Figs. 4c,d) are compared with the corresponding NCEP CAMS observed anomalies (Ropelewski et al. 1985, Figs. 4a,b). The CNTRL-simulated near-surface temperature anomalies are only shown in Figs. 4c and 4d if significantly different from zero at the 10% level on a Student’s t test. The 10°W–30°E, 35°–60°N land-only area-average monthly mean near-surface temperature anomalies (°C) for CAMS (solid) and CNTRL (dashed) are shown in Fig. 4e for February–September 2003. The CNTRL anomalies have a similar time evolution but much weaker magnitude than the very strong anomalies observed. The observed area-average anomaly is above 1.5°C during May–August, while the simulated anomaly is between 0.5° and 1.0°C. It should be noted that the warm anomalies observed over land (Fig. 4e) and the warm SST (Fig. 3e) developed simultaneously, thus cause and effect is not obvious between them.

Fig. 4.
Fig. 4.

Near-surface temperature 2003 anomaly from 1982–2001 climatology for (a) NCEP CAMS FMAM, (b) NCEP CAMS JJAS, (c) AGCM CNTRL FMAM, (d) AGCM CNTRL JJAS, and (e) area-average monthly anomaly time series for NCEP CAMS (solid) and AGCM CNTRL (dashed), land only. AGCM anomalies in (c),(d) only displayed if significant at the 10% level using a Student’s t test. Units are standard deviations in (a)–(d) and °C in (e).

Citation: Journal of Climate 24, 23; 10.1175/2011JCLI3523.1

Figure 5 is similar to Fig. 4, but it compares the CNTRL-simulated precipitation anomalies to NCEP CMAP-observed anomalies. The CNTRL-simulated precipitation anomalies are only shown in Figs. 5c and 5d if significantly different from zero at the 20% level on a Student’s t test. The CNTRL anomalies have a weaker magnitude and smaller areal extent than the very strong anomalies observed. The persistent dry anomalies observed from February through September are weakly simulated only in central Europe, with incorrect positive anomalies simulated to the north and south. Thus, the areal average of the simulated anomalies over the entire region is near zero (Fig. 5e). The simulated negative anomalies over central Europe are persistent from June through September (not shown).

Fig. 5.
Fig. 5.

Precipitation 2003 anomaly from 1982–2001 climatology for (a) NCEP CMAP FMAM, (b) NCEP CMAP JJAS, (c) AGCM CNTRL FMAM, (d) AGCM CNTRL JJAS, and (e) area-average monthly anomaly time series for NCEP CMAP (solid) and AGCM CNTRL (dashed), land only. AGCM anomalies in (c),(d) only displayed if significant at the 20% level using a Student’s t test. Units are standard deviations in (a)–(d) and mm day−1 in (e).

Citation: Journal of Climate 24, 23; 10.1175/2011JCLI3523.1

c. Impact of 2003 SST

The same diagnostics were examined for the CLSST ensemble, which was done with climatological rather than observed 2003 SST. The CLSST February–September 2003 simulated surface temperature anomalies are small and insignificant and show no sign of a persistent warm anomaly throughout the period (not shown). This indicates that the observed SST played a strong role in forcing and/or reinforcing the 2003 European heat wave. Of course, the strong local SST anomalies may have been forced or reinforced by the heat wave itself.

To investigate whether local or remote SST forcing was more important, two additional experiments were done with observed SST south of 25°N and climatology elsewhere (experiment S25N) and with observed SST north of 25°N and climatology elsewhere (experiment N25N), both initialized in June 2003. The JJAS mean near-surface temperature anomalies (formed by subtracting the 20-yr model climatology) in these two experiments are shown in Fig. 6. The S25N anomalies (Fig. 6a) are near zero, while the N25N anomalies (Fig. 6b) are similar to those from the global SST (Fig. 4d), suggesting that it was the local rather than remote SST forcing that was important in forcing the 2003 summer heat wave. The S25N anomalies, which include the entire tropics, did not force the 2003 European heat wave in these experiments.

Fig. 6.
Fig. 6.

Difference between 2003 AGCM ensembles and 1982–2001 climatology of AGCM CNTRL ensemble for JJAS for near-surface temperature (°C) for (a) AGCM S25N ensemble and (b) AGCM N25N ensemble.

Citation: Journal of Climate 24, 23; 10.1175/2011JCLI3523.1

d. Impact of 2003 soil wetness

Because there were such strong and persistent dry anomalies observed from February through June 2003, the possibility that the resulting dry soil conditions helped force the summer heat wave arises. To examine this possibility the soil wetness anomalies that developed in the CNTRL experiment are compared to proxy-observed soil wetness anomalies derived from the observationally based soil moisture dataset of Fan and van den Dool (2004, hereafter FV). The details of how the FV soil moisture was transformed into soil wetness compatible with that used in the AGCM are given in the appendix. Figure 7 is similar to Fig. 4, but it compares the CNTRL-simulated soil wetness anomalies to observed anomalies derived from FV. The CNTRL-simulated soil wetness anomalies are only shown in Figs. 7c and 7d if significant at the 20% level using a Student’s t test. The 10°W–30°E, 35°–60°N land-only area-average monthly mean soil wetness anomalies (percentage of saturation) from FV (solid) and CNTRL (dashed) are shown in Fig. 7e for February–September 2003. Very strong negative anomalies develop in the proxy-observed soil wetness by June 2003 and persist and intensify through September 2003. In the CNTRL simulation only very weak negative anomalies develop, consistent with the weak negative precipitation anomalies simulated.

Fig. 7.
Fig. 7.

Soil wetness 2003 anomaly from 1982–2001 climatology for (a) NCEP FV FMAM, (b) NCEP FV JJAS, (c) AGCM CNTRL FMAM, (d) AGCM CNTRL JJAS, and (e) area-average monthly anomaly time series for NCEP FV (solid) and AGCM CNTRL (dashed), land only. AGCM anomalies in (c),(d) only displayed if significant at the 20% level using a Student’s t test. Units are standard deviations in (a)–(d) and percentage of saturation in (e).

Citation: Journal of Climate 24, 23; 10.1175/2011JCLI3523.1

To determine if the weak soil wetness anomalies that evolved during the course of the CNTRL integrations had an impact on the other simulated variables in the CNTRL integrations, the integrations were restarted on 1 June 2003 with 1 June 1982–2001 model climatological soil wetness in experiment CLISW. The simulated anomalies of near-surface temperature and precipitation in the CLSST experiment were near zero and insignificant for June–September 2003 (not shown).

To determine if more realistic “observed” soil wetness anomalies could have had an impact on the heat wave, experiment OBISW was done in which the CNTRL integrations were restarted on 1 June 2003 with soil wetness derived from the observationally based soil wetness of FV.

The soil wetness is prognostic after initialization. Thus, the initial soil wetness anomaly may or may not persist as the integration continues. Figure 8 shows the ensemble mean, monthly mean soil wetness difference between the OBISW integrations and the control integrations for surface zone (Fig. 8a), root zone (Fig. 8b), and drainage zone (Fig. 8c) for June–September (dashed lines). The observed FV anomalies are shown as a solid line. Since there is only one layer in FV, this solid line is the same in all three frames. All values shown are areal averaged over the heart of the heat wave region: 0°–25°E, 43°–54°N. The units are 1981–2000 JJAS standard deviations for the corresponding soil wetness field. The observed soil wetness anomaly deepens from June through September. The three-model soil wetness difference fields weaken from June through September. This is because the model simulated a much weaker dry precipitation anomaly than observed throughout this period. The soil wetness anomaly persistence is weakest for the shallow surface layer and greatest for the relatively deep drainage zone.

Fig. 8.
Fig. 8.

Area-averaged soil wetness anomalies (standard deviations) for NCEP FV (solid) and OBISW (dashed) for AGCM (a) surface layer, (b) root zone layer, and (c) drainage layer.

Citation: Journal of Climate 24, 23; 10.1175/2011JCLI3523.1

To further examine the importance of the persistence of the soil wetness anomalies, these same fields have been analyzed for each member of the model ensembles. There was considerable variability in how persistent the soil wetness anomalies were among the members. The members with more persistent dry soil wetness anomalies had stronger warm temperature anomalies and low evaporation and precipitation anomalies. This can be summarized by examining the correlation coefficients between the June–August (JJA) areal-averaged (0°–25°E, 43°–54°N) soil wetness anomalies and anomalies of evaporation, near-surface temperature, and precipitation among the ensemble members, as shown in Table 2. The impact of the initial soil wetness anomalies in the OBISW experiment are very evident in the near-surface temperature and precipitation OBISW–CNTRL differences for JJAS 2003 shown in Figs. 9a–d and 10a–d, respectively. For comparison, the corresponding observed NCEP CAMS near-surface temperature anomalies are shown in Figs. 9e–h and NCEP CMAP precipitation anomalies in Figs. 10e–h. The soil wetness–forced anomalies reach 1°–1.5°C during June–August; which is much weaker than the 4°–5°C anomalies observed. It is interesting that both the observed and soil wetness–forced near-surface temperature anomalies are greatly reduced during September. The soil wetness anomalies also force dry precipitation anomalies during all four months that are smaller in magnitude and extent than those observed. Nonetheless the results suggest that the dry soil conditions that existed by early summer of 2003 may well have contributed to the ensuing heat wave. The weaker-than-observed magnitude of the simulated temperature and precipitation anomalies is likely related to the weaker-than-observed persistence of the simulated soil wetness anomalies.

Table 2.

JJA correlation coefficients between soil wetness (SW) anomalies and evaporation, near-surface temperature, and precipitation anomalies.

Table 2.
Fig. 9.
Fig. 9.

Difference between (a),(e) June, (b),(f) July, (c),(g) August, and (d),(h) September near-surface temperature (°C) for 2003 AGCM OBISW ensemble and (a)–(d) 2003 AGCM CNTRL ensemble, and (e)–(h) observed NCEP CAMS near-surface temperature anomalies from 1982–2001 climatology.

Citation: Journal of Climate 24, 23; 10.1175/2011JCLI3523.1

Fig. 10.
Fig. 10.

Difference between (a),(e) June, (b),(f) July, (c),(g) August, and (d),(h) September precipitation (mm day−1) for 2003 AGCM OBISW ensemble and (a)–(d) 2003 AGCM CNTRL ensemble, and (e)–(h) observed NCEP CMAP precipitation anomalies from 1982–2001 climatology.

Citation: Journal of Climate 24, 23; 10.1175/2011JCLI3523.1

Figure 11 shows the month-by-month simulated near-surface temperature anomalies for CNTRL (Figs. 11 a–d) and OBISW (Figs. 11e–h), where the anomalies are formed by subtracting the model 20-yr climatology from CNTRL. Compared with the observed near-surface temperature anomalies in Figs. 9e–h, it is clear that stronger and more realistic simulated anomalies are formed by including the impacts of both SST and soil wetness (Figs. 11e–h) rather than through just SST alone (Figs. 11a–d) or just soil wetness alone (Figs. 9a–d). This suggests that both were important in forcing the 2003 heat wave.

Fig. 11.
Fig. 11.

Difference between 2003 AGCM ensembles and 1982–2001 climatology of AGCM CNTRL ensemble for (a),(e) June, (b),(f) July, (c),(g) August, and (d),(h) September for near-surface temperature (°C) for (a)–(d) AGCM CNTRL ensemble and (e)–(h) AGCM OBISW ensemble.

Citation: Journal of Climate 24, 23; 10.1175/2011JCLI3523.1

5. Discussion and conclusions

Ensemble simulations done with version 2.2.6 of the Center for Ocean–Land–Atmosphere Studies (COLA) atmospheric general circulation model (AGCM) have been analyzed over the European region. The simulations were forced by weekly mean observed sea surface temperature (SST) obtained from the U.S. National Centers for Environmental Prediction (NCEP) and were initialized in late 1981. The 1982–2001 20-yr mean near-surface temperature and precipitation climatologies and seasonal cycles over the European region appear reasonable.

Relative to the 1982–2001 period, the COLA AGCM-simulated anomalous warmth over the European region during June–August 2003, in response to the observed SST; however, the simulated magnitude was smaller than observed. Additional simulations done with climatological SST verified that the simulated warmth was indeed related to the warm SST. Further experiments were done to isolate whether remote SST forcing south of 25°N (S25N) may have contributed as opposed to more local forcing north of 25°N (N25N). The S25N experiment did not force warm temperatures over the European region, while the N25N experiment yielded warm temperatures similar to those simulated by the global SST. Thus it appears that local rather than remote SST forcing was important in helping maintain the heat wave. However, it should be noted that possible forcing from the tropical SST in the S25N experiment could have been impacted by atmospheric circulation features altered by the climatological SST north of 25°N, which in turn could affect propagation and triggering/amplification mechanisms.

Analysis of the near-real-time-calculated global soil wetness provided by NCEP reveals that by early June the soil over much of the European region was anomalously dry, which is consistent with the below-normal precipitation observed over the region in the preceding months. The soil wetness anomalies obtained from NCEP were adapted for use in the COLA AGCM and the model was restarted in early June with the resultant anomalies imposed on the soil wetness from the control simulations. Although the model soil wetness was then allowed to evolve as usual, the simulations with the imposed initial soil wetness anomaly enhanced the simulated surface temperature anomaly during June–August by 1°–2°C. This anomaly is similar in magnitude to that forced by the observed SST. It is likely that the simulated temperature anomaly would be larger if the imposed soil wetness anomalies were more persistent, as observed. This would make the total simulated anomaly more realistic and more consistent with the results of Ferranti and Viterbo (2006), who found a greater impact of soil wetness than SST in more idealized experiments. The lack of persistence of the imposed soil wetness anomalies may be indicative of a weaker-than-realistic soil wetness–precipitation feedback in the model; however, a complete analysis of this feedback is beyond the scope of this paper.

Considered together, the experiments described here suggest that both the warm local SST and the dry local soil were important in forcing the summer 2003 European heat wave.

Acknowledgments

The authors thank Professor J. Shukla for his encouragement and many useful suggestions during the course of this study. We thank Larry Marx for his help preparing the SST boundary condition datasets. We would also like to thank Paul Dirmeyer and two anonymous reviewers for their constructive suggestions on the manuscript. This work was supported by NSF Grant ATM-0332910, NASA Grant NNG04GG46G, and NOAA Grant NA04OAR4310034.

APPENDIX

Transforming “Observed” Soil Moisture to Model Soil Wetness

The problem of utilizing soil moisture information from one model or observing system in another model has long been recognized (Sato et al. 1989; Sellers 1990; Koster and Milly 1997; Fennessy and Shukla 1999; Dirmeyer et al. 2004; Koster et al. 2009). The basic problem is that the physical meaning of a soil moisture or soil wetness anomaly in one model or observing system is different from the physical meaning of that same anomaly in another model.

Here, we address this problem with a relatively simple method that transforms the soil moisture anomaly for 2003 from the calculated proxy-observed dataset of FV to one more suitable for use in the COLA AGCM. This method adjusts the magnitude of the anomaly so it is the same size relative to the variability in the destination model as it was relative to the variability of the source model. This is accomplished by expressing the original anomaly in units of the long-term monthly standard deviation of the source model (FV) and then applying the same number of monthly standard deviations of the destination model, where the destination model (COLA AGCM V2.2.6) standard deviation is calculated in the same way as was the source model standard deviation. This is the same method as used by Dirmeyer et al. (2004).

The long-term mean used here is that available from the AGCM CNTRL experiment, namely 1982–2001. To get an anomaly suitable for 1 June, the May and June monthly mean values were averaged together. Because the AGCM has three different soil layers (surface, root, and drainage) and the variability of the model soil wetness is quite different among these layers, the calculation was done separately for each layer, resulting in initial soil wetness anomalies that have the same pattern but different magnitude among the three layers. After initialization, the soil wetness is each layer is predicted.

REFERENCES

  • Beniston, M., 2004: The 2003 heat wave in Europe: A shape of things to come? An analysis based on Swiss climatological data and model simulations. Geophys. Res. Lett., 31, L02022, doi:10.1029/2003GL018857.

    • Search Google Scholar
    • Export Citation
  • Bisselink, B., and A. J. Dolman, 2008: Precipitation recycling: Moisture sources over Europe using ERA-40 data. J. Hydrometeor., 9, 10731083.

    • Search Google Scholar
    • Export Citation
  • Black, E., M. Blackburn, G. Harrison, B. Hoskins, and J. Methven, 2004: Factors contributing to the 2003 European heat wave. Weather, 59, 217223.

    • Search Google Scholar
    • Export Citation
  • Cassou, C., L. Terray, and A. S. Phillips, 2005: Tropical Atlantic influence on European heat waves. J. Climate, 18, 28052811.

  • Czaja, A., and C. Frankignoul, 1999: Influence of the North Atlantic SST anomalies on the atmospheric circulation. Geophys. Res. Lett., 26, 29692972.

    • Search Google Scholar
    • Export Citation
  • Czaja, A., and C. Frankignoul, 2002: Observed impact of Atlantic SST anomalies on the North Atlantic Oscillation. J. Climate, 15, 606623.

    • Search Google Scholar
    • Export Citation
  • Davies, R., 1982: Documentation of the solar radiation parameterization in the GLAS climate model. NASA Tech. Memo. 83961, 57 pp.

  • DeWitt, D. G., 1996: The effect of the cumulus convection scheme on the climate of the COLA general circulation model. COLA Tech. Rep. 27, 43 pp.

    • Search Google Scholar
    • Export Citation
  • DeWitt, D. G., and E. K. Schneider, 1997: The earth radiation budget as simulated by the COLA GCM. COLA Rep. 35, 39 pp. [Available from COLA, 4041 Powder Mill Rd., Suite 302, Calverton, MD 20705.]

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., and F. J. Zeng, 1999: Precipitation infiltration in the simplified SiB land surface scheme. J. Meteor. Soc. Japan, 78, 291303.

    • Search Google Scholar
    • Export Citation
  • Dirmeyer, P. A., Z. Guo, and X. Gao, 2004: Comparison, validation, and transferability of eight multiyear global soil wetness products. J. Hydrometeor., 5, 10111033.

    • Search Google Scholar
    • Export Citation
  • Fan, Y., and H. van den Dool, 2004: Climate Prediction Center global monthly soil moisture data set at 0.5° resolution for 1948 to present. J. Geophys. Res., 109, D10102, doi:10.1029/2003JD004345.

    • Search Google Scholar
    • Export Citation
  • Fennessy, M. J., and J. Shukla, 1999: Impact of initial soil wetness on seasonal atmospheric prediction. J. Climate, 12, 31673180.

  • Fennessy, M. J., and Coauthors, 1994: The simulated Indian monsoon: A GCM sensitivity study. J. Climate, 7, 3343.

  • Ferranti, L., and P. Viterbo, 2006: The European Summer of 2003: Sensitivity to soil water initial conditions. J. Climate, 19, 36593680.

    • Search Google Scholar
    • Export Citation
  • Feudale, L., and J. Shukla, 2007: Role of Mediterranean SST in enhancing the European heat wave of summer 2003. Geophys. Res. Lett., 34, L03811, doi:10.1029/2006GL027991.

    • Search Google Scholar
    • Export Citation
  • Fischer, E., S. I. Seneviratne, P. L. Vidale, D. Lüthi, and C. Schär, 2007: Soil moisture–atmosphere interactions during the 2003 European summer heat wave. J. Climate, 20, 50815099.

    • Search Google Scholar
    • Export Citation
  • Folland, C. K., T. N. Palmer, and D. E. Parker, 1986: Sahel rainfall and worldwide sea temperatures, 1901–85. Nature, 320, 602607.

  • Giannini, A., R. Saravanan, and P. Chang, 2003: Oceanic forcing of Sahel rainfall on interannual to inter-decadal time scales. Science, 302, 10271030.

    • Search Google Scholar
    • Export Citation
  • Harshvardhan, , R. Davies, D. A. Randall, and T. G. Corsetti, 1987: A fast radiation parameterization for atmospheric circulation models. J. Geophys. Res., 92 (D1), 10091016.

    • Search Google Scholar
    • Export Citation
  • Hoerling, M. P., and A. Kumar, 2002: Atmospheric response patterns associated with tropical forcing. J. Climate, 15, 21842203.

  • Hoerling, M. P., and A. Kumar, 2003: The perfect ocean for drought. Science, 299, 691694.

  • Hoerling, M. P., J. W. Hurrell, and T. Xu, 2001: Tropical origins for recent North Atlantic climate change. Science, 292, 9092.

  • International Federation of Red Cross and Red Crescent Societies, 2004: World Disasters Report 2004. Kumarian Press, 231 pp.

  • Kiehl, J. T., J. J. Hack, G. Bonan, B. A. Boville, D. L. Williamson, and P. J. Rasch, 1998: The National Center for Atmospheric Research Community Climate Model: CCM3. J. Climate, 11, 11311149.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., and P. C. D. Milly, 1997: The interplay between transpiration and runoff formulations in land surface schemes used with atmospheric models. J. Climate, 10, 15781591.

    • Search Google Scholar
    • Export Citation
  • Koster, R. D., Z. Guo, R. Yang, P. A. Dirmeyer, K. Mitchell, and M. J. Puma, 2009: On the nature of soil moisture in land surface models. J. Climate, 22, 43224335.

    • Search Google Scholar
    • Export Citation
  • Luterbacher, J., D. Dietrich, E. Xoplaki, M. Grosjean, and H. Wanner, 2004: European seasonal and annual temperature variability, trends, and extremes since 1500. Science, 303, 14991503.

    • Search Google Scholar
    • Export Citation
  • Meehl, G., and C. Tebaldi, 2004: More intense, more frequent and longer lasting heat waves in the 21st century. Science, 305, 994997.

    • Search Google Scholar
    • Export Citation
  • Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid processes. Rev. Geophys. Space Phys., 20, 851875.

    • Search Google Scholar
    • Export Citation
  • Moorthi, S., and M. J. Suarez, 1992: Relaxed Arakawa–Schubert: A parameterization of moist convection for general circulation models. Mon. Wea. Rev., 120, 9781002.

    • Search Google Scholar
    • Export Citation
  • Nakamura, M., T. Enomoto, and S. Yamane, 2005: A simulation study of 2003 heat wave in Europe. J. Earth Simul., 2, 5569.

  • Pace, G., D. Meloni, and A. di Sarra, 2005: A forest fire aerosol over the Mediterranean basin during summer 2003. J. Geophys. Res., 110, D21202, doi:10.1029/2005JD005986.

    • Search Google Scholar
    • Export Citation
  • Pal, J., F. Giorgi, and X. Bi 2004: Consistency of recent European summer precipitation trends and extremes with future regional climate projections. Geophys. Res. Lett., 31, L13202, doi:10.1029/2004GL019836.

    • Search Google Scholar
    • Export Citation
  • Reinhold, B. B., and R. T. Pierrehumbert, 1982: Dynamics of weather regimes: Quasi-stationary waves and blocking. Mon. Wea. Rev., 110, 11051145.

    • Search Google Scholar
    • Export Citation
  • Reynolds, R. W., and T. M. Smith, 1994: Improved global sea surface temperature analyses using optimum interpolation. J. Climate, 7, 929948.

    • Search Google Scholar
    • Export Citation
  • Robertson, A. W., C. R. Mechoso, and Y. J. Kim, 2000: The influence of Atlantic sea surface temperature anomalies on the North Atlantic Oscillation. J. Climate, 13, 122138.

    • Search Google Scholar
    • Export Citation
  • Rodwell, M. J., D. P. Rowell, and C. K. Folland, 1999: Oceanic forcing of the wintertime North Atlantic Oscillation and European climate. Nature, 398, 320323.

    • Search Google Scholar
    • Export Citation
  • Ropelewski, C. F., J. E. Janowiak, and M. F. Halpert, 1985: The analysis and display of real time surface climate data. Mon. Wea. Rev., 113, 11011107.

    • Search Google Scholar
    • Export Citation
  • Sato, N., P. J. Sellers, D. A. Randall, E. K. Schneider, J. Shukla, J. L. Kinter III, Y.-T. Hou, and E. Albertazzi, 1989: Effects of implementing the Simple Biosphere Model in a general circulation model. J. Atmos. Sci., 46, 27572782.

    • Search Google Scholar
    • Export Citation
  • Schär, C., P. L. Vidale, D. Lüthi, C. Frei, C. Häberli, M. A. Liniger, and C. Appenzeller, 2004: The role of increasing temperature variability in European summer heat waves. Nature, 427, 333336.

    • Search Google Scholar
    • Export Citation
  • Sellers, P. J. 1990: Specifying surface boundary conditions for drought studies. Proc. Workshop on 1988 U.S. Drought, College Park, MD, Dept. of Meteorology, University of Maryland at College Park, 170–172. [Available from Dept. of Meteorology, University of Maryland at College Park, College Park, MD 20742.]

    • Search Google Scholar
    • Export Citation
  • Seneviratne, S. I., D. Lüthi, M. Litschi, and C. Schär, 2006: Land–atmosphere coupling and climate change in Europe. Nature, 427, 333336.

    • Search Google Scholar
    • Export Citation
  • Teuling, A. J., and S. I. Seneviratne, 2008: Contrasting spectral changes limit albedo impact on land-atmosphere coupling during the 2003 European heat wave. Geophys. Res. Lett., 35, L03401, doi:10.1029/2007GL032778.

    • Search Google Scholar
    • Export Citation
  • Tiedtke, M., 1984: The effect of penetrative cumulus convection on the large-scale flow in a general circulation model. Beitr. Phys. Atmos., 57, 216239.

    • Search Google Scholar
    • Export Citation
  • Trigo, R. M., R. Garcia-Herrera, J. Diaz, I. F. Trigo, and M. A. Valente, 2005: How exceptional was the early August 2003 heat wave in France? Geophys. Res. Lett., 32, L10701, doi:10.1029/2005GL022410.

    • Search Google Scholar
    • Export Citation
  • World Health Organization, 2004: Heat waves: Risks and responses. Health and Global Environmental Change Series, No. 2, World Health Organization, Regional Office for Europe Rep., 123 pp. [Available from Scherfigsvej 8, DK-2100 Copenhagen Ø, Denmark.]

    • Search Google Scholar
    • Export Citation
  • Xie, P., and P. Arkin, 1996: Analysis of global monthly precipitation using guage observations, satellite estimates, and numerical model predictions. J. Climate, 9, 840858.

    • Search Google Scholar
    • Export Citation
  • Xue, Y.-K., P. J. Sellers, J. L. Kinter, and J. Shukla, 1991: A simplified biosphere model for global climate studies. J. Climate, 4, 345364.

    • Search Google Scholar
    • Export Citation
  • Xue, Y.-K., M. J. Fennessy, and P. J. Sellers, 1996: Impact of vegetation properties on U.S. summer weather prediction. J. Geophys. Res., 101, 74197430.

    • Search Google Scholar
    • Export Citation
Save