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Hervé Douville Météo-France/CNRM Acknowledgements: B. Decharme, R. Alkama and Y. Peings WCRP Seasonal Prediction Workshop, Exeter,

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Presentation on theme: "Hervé Douville Météo-France/CNRM Acknowledgements: B. Decharme, R. Alkama and Y. Peings WCRP Seasonal Prediction Workshop, Exeter,"— Presentation transcript:

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

2 2 Outlines Background and motivations 1.Land surface data and statistical studies Global land surface products Data intercomparison and model evaluation Statistical evidence of predictable land surface impacts 2.Numerical sensitivity experiments Pioneering studies Numerical evidence of local land surface impacts Numerical evidence of remote land surface impacts Conclusions, prospects and issues

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

4 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 5 Relevance of land-atmosphere coupling for climate predictability: At least 3 conditions 1.Land surface anomalies must have sizeable (i.e. potential predictability) and realistic (i.e. effective predictability) impacts on atmospheric variability 2.Land surface anomalies must be predictable at the selected timescale (using dynamical and/or statistical tools) 3.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 6 (lack of) Land surface data And statistical studies Global (satellite) land surface observations oSnow: visible (since 1967), passive micro. (SMMR since 1978, …) oSoil moisture: passive & active micro. (AMSR since 2002, ASCAT, …) oTotal water storage variations: gravimetry (GRACE since 2002) Off-line land surface model simulations oGSWP-2 (1986-1995): 13 models driven with ISLSCP2 forcing data oGLDAS (1979-present): 4 models driven with bias-corrected reanalyses or NOAA/GDAS real-time analyses (since 2000) oVIC (Sheffield and Wood 2008) or ISBA (Alkama et al. 2010) driven with 1950-2006 Princeton Univ. (Sheffield et al. 2006) On-line LDAS systems oSoil moisture analysis based on the assimilation of screen-level temperature and humidity (e.g. Météo-France, ECMWF, Met Office, …) oAssimilation of NESDIS snow extent (e.g. ECMWF since 2004) oAssimilation of ASCAT soil moisture (e.g. Met Office since July 2010)

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

8 8 Land surface data intercomparison Ex: Central Europe 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) vs 1989-1995 climatology

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

10 10 Statistical evidence of 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 11 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 Northern Great Plains heavy & light AM snowfall composites (1929-1999) with interquartile range. Quiring and Kluver 2009 T2m (°C) Cum. P (mm)

12 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 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 14 Statistical evidence: Wintertime N.H. extratropical variability 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) JFM 2010 forecasted vs observed temperature anomalies (Cohen et al. 2010) A negative AO/NAO winter preceded by above normal Siberian snow cover SnowCast Observations

15 15 Further evidence based on numerical 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 16 2 ALO / 2 AO Control experiment ALO A : Atmosphere only L : Interactive Land Hydrology O : Observed instead of climatol. monthly mean SST Variance of annual precipitation Impact of Land vs SST variability on annual mean precipitation (Koster et al. 2000)

17 17 Sahel drywet drywetdrywet South Asia drywetdrywetdrywet PEAnom.P-E Dynamical (P-E) versus non-dynamical (E) soil moisture feedbacks (Douville et al. 2001)

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

19 19 16-30 days 31-45 days 46-60 days temperatureprecipitation GLACE-2 coordinated experiments Consensus skill due to land initialization 2-months hindcasts initialized on 1 st & 15 th 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. Koster et al., GRL, 2010

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

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

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

23 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 predictability should not be neglected given the weak SST impact on extratropical predictability.

24 24 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 ESAs 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, … PROSPECTS (OPEN FOR DISCUSSION)

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

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

27 End

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