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Hervé Douville Météo-France/CNRM herve.douville@meteo.fr Acknowledgements: B. Decharme, R. Alkama

Hervé Douville Météo-France/CNRM herve.douville@meteo.fr Acknowledgements: B. Decharme, R. Alkama and Y. Peings. Land surface contribution to climate variability and predictability. WCRP Seasonal Prediction Workshop, Exeter, 1-3 December 2010. Outlines. Background and motivations

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Hervé Douville Météo-France/CNRM herve.douville@meteo.fr Acknowledgements: B. Decharme, R. Alkama

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  1. Hervé Douville Météo-France/CNRM herve.douville@meteo.fr Acknowledgements: B. Decharme, R. Alkama and Y. Peings Land surface contributionto climate variability and predictability WCRP Seasonal Prediction Workshop, Exeter, 1-3 December 2010

  2. Outlines • Background and motivations • Land surface data and statistical studies • Global land surface products • Data intercomparison and model evaluation • Statistical evidence of predictable land surface impacts • Numerical sensitivity experiments • Pioneering studies • Numerical evidence of local land surface impacts • Numerical evidence of remote land surface impacts • Conclusions, prospects and issues

  3. The stratospheric component The anthropogenic radiative component The land surface component A « slave » component ? Seasonal prediction:A question of remote control ? The forecast The AOGCM « Need to improve the representation of climate system interactions and their potential to improve forecast quality. » (WCRP position paper, Barcelona 2007)

  4. ? GLACE: Global Land-Atmosphere Coupling Experiment (a GEWEX & CLIVAR initiative) GLACE-1 multi-model land-atmosphere coupling strength based on the reproductibility of 5-day precipitation(Koster et al. 2006). Not sufficient to evaluate the impact of land state initialization on seasonal forecast skill => GLACE-2

  5. Relevance of land-atmosphere coupling for climate predictability: At least 3 conditions • Land surface anomalies must have sizeable (i.e. potential predictability) and realistic (i.e. effective predictability) impacts on atmospheric variability • Land surface anomalies must be predictable at the selected timescale (using dynamical and/or statistical tools) • Real-time global land surface analyses must be available for initializing the relevant land surface variables (soil moisture, snow mass, …) • NB: focus on monthly to seasonal timescale only.

  6. (lack of) Land surface dataAnd statistical studies • Global (satellite) land surface observations • Snow: visible (since 1967), passive micro. (SMMR since 1978, …) • Soil moisture: passive & active micro. (AMSR since 2002, ASCAT, …) • Total water storage variations: gravimetry (GRACE since 2002) • Off-line land surface model simulations • GSWP-2 (1986-1995): 13 models driven with ISLSCP2 forcing data • GLDAS (1979-present): 4 models driven with bias-corrected reanalyses or NOAA/GDAS real-time analyses (since 2000) • VIC (Sheffield and Wood 2008) or ISBA (Alkama et al. 2010) driven with 1950-2006 Princeton Univ. (Sheffield et al. 2006) • On-line LDAS systems • Soil moisture analysis based on the assimilation of screen-level temperature and humidity (e.g. Météo-France, ECMWF, Met Office, …) • Assimilation of NESDIS snow extent (e.g. ECMWF since 2004) • Assimilation of ASCAT soil moisture (e.g. Met Office since July 2010)

  7. ISLSCP-2 (1986-1995), Princeton Univ. (1950-2006), … 3-hourly atmospheric forcing Fixed or monthly physiography Soil moisture & snow mass climatology Runoff Evaporation AGCM RRM Satellite Data Discharge In Situ Observ. T2m et P Off-line land surface simulations LSM

  8. Land surface data intercomparisonEx: Central Europe vs 1989-1995 climatology • ISBA driven by Princeton University atm. forcings (1950-2006) • ERA-Interim (1989-2010) • ERA40 (1958-2001) • GSWP multi-model driven by ISLSCP2 atm. forcings (1986-1995)

  9. Monthly water storage variation (kg/m²/day) anomalies and mean annual cycle ISBA = soil moisture + snow + river Monthly river discharge (kg/m²/day) anomalies and mean annual cycle 7 Land surface model evaluationISBA-TRIP vs GRACE and GRDC data Alkama et al., J. Hydromet., 2010

  10. Statistical evidenceof land surface contribution to predictability • North American summer temperature (e.g. Alfaro et al. 2006) and precipitation (e.g. Quiring and Kluver 2009) • Sahelian summer monsoon precipitation (e.g. Philippon and Fontaine 2002, Douville et al. 2007) • Indian summer monsoon precipitation (e.g. Blanford 1884, Fasullo 2004, Peings and Douville 2009) • Winter North Atlantic Oscillation (e.g. Cohen and Entekhabi 1999, Hardiman et al. 2008, Cohen et al. 2010)

  11. Northern Great Plains heavy & light AM snowfall composites (1929-1999) with interquartile range. Quiring and Kluver 2009 T2m (°C) Cum. P (mm) Statistical evidence:North America T2m & P Maps of JJA Tmax prediction skill (cross-validation over 1950-2001) using May Pacific SST and/or PDSI (soil moisture proxy) predictors. Alfaro et al. 2006

  12. Statistical evidence:West African summer monsoon P • Hypothesis: 2nd rainy season over the Guinean Coast affects subsequent summer monsoon rainfall over the Sahel through a soil moisture memory effect (Landsea et al. 1993, Philippon and Fontaine 2002) • But: Stochastic artefact mediated through tropical SST and partly due to multi-decadal variability ? (Douville et al. 2007)

  13. Statistical evidence:Indian summer monsoon P • Hypothesis: Winter and spring Eurasian snow cover affects subsequent summer monsoon rainfall over India (Blanford 1884, Fasullo 2004) • But: Such a statistical relationship is neither robust nor stationary in the instrumental record and is not captured by CMIP3 historical simulations (Peings and Douville 2009)

  14. Statistical evidence:Wintertime N.H. extratropical variability SnowCast Observations JFM 2010 forecasted vs observed temperature anomalies (Cohen et al. 2010) A negative AO/NAO winter preceded by above normal Siberian snow cover • Hypothesis: Fall (i.e. October) snow cover over Siberia affects subsequent winter NAO (Cohen and Entekhabi 1999) • But: Not found in CMIP3 models (Hardiman et al. 2008)though the observed relationship is robust and was verified in winter 2009-2010 (Cohen et al. 2010)

  15. Further evidence based onnumerical sensitivity experiments • Pionneering studies: Land vs SST impact on precipitation variability (e.g. Koster et al. 2000), dynamical vs non-dynamical feedback (e.g. Douville et al. 2001) • GLACE-2 and related studies (e.g. Douville 2009, Koster et al. 2010, Peings et al. 2010) • Remote impacts of Eurasian snow cover (e.g. Fletcher et al. 2009, Peings et al. in preparation)

  16. Variance of annual precipitation Control experiment ALO A: Atmosphere only L: Interactive Land Hydrology O: Observed instead of climatol. monthly mean SST 2ALO / 2AO Impact of Land vs SST variability on annual mean precipitation (Koster et al. 2000)

  17. Anom. E P-E P Sahel dry dry dry dry dry dry wet wet wet wet wet wet South Asia Dynamical (P-E) versus non-dynamical (E) soil moisture feedbacks (Douville et al. 2001)

  18. 75°N 75°N 75°N 55°S 55°S 55°S SST vs land surface impacts on monthly T2m predictability over land (Douville 2009) Zonal mean annual cycle of: Stdev Pot. Pred. (ANOVA) Skill (ACC) Control No nudging Obs. SST Nudging Obs. SST Nudging Clim. SST

  19. GLACE-2 coordinated experiments“Consensus” skill due to land initialization temperature precipitation 16-30 days 31-45 days 46-60 days • 2-months hindcasts initialized on 1st & 15th June, July and August => 6 hindcasts x 10 years (1986-1995) x 10 members = 600 runs. • 13 models (“weaker” models are averaged in with “stronger” ones). • Conditional skill show stronger increase. 19 Koster et al., GRL, 2010

  20. Impact of snow boundary / initial conditions on springtime (MAM) T2m (Peings et al. 2010) 3 ensemble experiments: Control (CTL) Interactive snow cover SBC – CTL Impact of snow relaxation SIC – CTL Impact of snow initialization Total Stdev Pot. Predictability Skill

  21. a) DSWnet (d1-d15) b) DMSLP (d24-d50) 2 pairs of 100-member ensemble experiments: High minus Low fall snow cover over Siberia A snow-NAO relationship through a stratospheric pathway Remote impact of Siberian snow coveron DJF NAO (Fletcher et al. 2009)

  22. MSLP (hPa) Zonal mean Z (m) 2 pairs of 50-member ensemble experiments: DSS - CTL Deep Snow over Siberia DSS* - CTL* Improved polar vortex climatology through equatorial stratospheric nudging Remote impact of Siberian snow coveron DJF NAO (Peings et al. 2011)

  23. CONCLUSIONS • Growing statistical and numerical evidence of both local and remote impacts of land surface initial conditions on climate predictability (though some of these studies are questionnable); • Such impacts are highly model-dependent, variable across regions and seasons, and sensitive to the magnitude of the land surface anomalies; • Long-range predictability of the land surface hydrology seems limited (mainly by the low predictability of precipitation) but needs further evaluation (i.e. new observations and data assimilation systems); • Land surface impacts do not amount to simple changes in the surface energy budget, but also involve large-scale dynamical and cloud feedbacks; • Land surface contribution to climate predictabilityshould not be neglected given the weak SST impact on extratropical predictability.

  24. PROSPECTS(OPEN FOR DISCUSSION) • Observations: SMOS (L-band, 2010) & SMAP (Soil Moisture Active and Passive, 2015) for upper soil moisture, improved use of passive microwave data for snow (until ESA’s CoReH20 mission), GRACE for total water storage variations, SWOT (Surface Water and Ocean Topography) , … • Land Surface Models & Data Assimilation Systems: increased vertical discretization, simulation of water bodies including floodplains, improved representation of snow under canopy (e.g. SnowMIP), multi-spectral surface albedo and related data assimilation(MSG, MODIS), off-line model inter-comparison without (GSWP-3?) and with (PILDAS?) data assimilation, global & multi-decadal (at least since 1989) surface reanalysis, … • Sensitivity experiments: SCM studies, follow-on of GLACE-2 looking at soil moisture but also snow water equivalent and possibly surface albedo, GLACE-type versus state-of-the-art (rather than random) initialization, coupled vs AMIP-type experiments, process-oriented case studies, statistical adaptation of dynamical forecasts using land surface variables, …

  25. What about vegetation ? Difference in statistical significance of temporal ACCs between two sets of hindcasts of JJA T2m using observed vs climatological vegetation (red / bluemeans increased / decreased significance) (Gao et al. 2008) • Statistical benchmarks ACC and RMSS differences between sCast and DEMETER hindcasts of DJF surface temperature (72/73 to 92/93) (red / bluemeans sCast has greater / lowerskill) (Cohen and Fletcher 2007) (CONTROVERSIAL) ISSUES

  26. Seamless is not questionless… Bekele ESM Bolt NWP (CONTROVERSIAL) ISSUES • Towards decadal predictions ? Verification of the first genuine dynamical decadal prediction by Keenlyside et al. 2008 for global mean temperature (from http://www.realclimate.org) A land surface contribution would be welcome but is unlikely…

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