Slide 1 Assimilation of near real time GEOV Albedo and Leaf Area Index within the ECMWF modelling system Souhail Boussetta, Gianpaolo Balsamo, Emanuel Dutra, Anton Beljaars Slide 1 BG2.5 EGU, Vienna 02 May S. Boussetta with the support and partnership of the EU FP7 project

Slide 2 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 2 Why vegetation state is important?  Vegetation was shown to be of critical importance under the NWP framework:  Evapotranspiration  Boundary layer developement  Cloud and precipitation...  Vegetation directly affect the global carbon cycle  Earth System Models are evolving to better represent vegetation dynamic  Satellite observations with channels informative on the vegetation state are becoming more and more available and with higher accuracy & frequency   Assimilation of vegetation state products would allow:  Better represent land biogenic fluxes in interaction with the atmosphere  Monitor present and past vegetation state and its dynamics  Understand and adjust process development within models  Ultimately to improve weather and earth systems prediction.

Slide 3 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 3 The data: The Copernicus GEOV1 LAI/albedo product is based on observations from the VEGETATION sensor on board of SPOT satellite.  Global coverage with 1km / 10 day space/time resolution Produced in the framework of the Copernicus Initial Operation & IMAGINES and Freely available. The model: The ECMWF LSM (CTESSEL) coupled within the Integrated Forecasting system IFS The analysis system: The analysis procedure is an optimal combination of the satellite observations and derived climatology, depending on their associated errors σo and σc.  Suitable for NWP framework and consistent with actual method used for slow evolving variables. clim Analysed trajectory obs var t where How vegetation state is take into account?

Slide 4 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 4 Observation Quality control + sf (albedo) GEOV1 LAI/Alb 9-point Spatial smoothing GEOV1 Observation +  o Analysis GEOV1 Analysis Climatology (a-priori) Quality control + sf (albedo) GEOV1 LAI/Alb Averaging => Gapped Climatology Temporal Gap filling Extraction of 10- day look-up table on land use type GEOV1 Climatology +  c 9-point Spatial smoothing Spatial Gap filling Vegetation state initialization

Slide 5 Europe East China 2003 droughts BG2.5 EGU, Vienna 02 May S. Boussetta Slide 5  NRT analysed LAI seems able to detect/monitor anomalous year  The analysed LAI and albedo signal appear to be covariant mainly during wet year. Horn of Africa 2010 drought Australia drought recover also visible in albedo LAI anomaly Albedo anomaly Russian 2010 Heat wave In which cases vegetation matters most?

Slide 6 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 6 The surface-only simulation setup: To seek the impact of the NRT analysed data four experiments are performed Period: 1999 to 2012 Coverage: Global Resolution: 40km 4 different experiments:  Control: LAI+albedo climatology are used  NRT_ALB_LAI: LAI nrt data + albedo nrt  NRT_LAI: LAI nrt data + albedo climatology  NRT_ALB: LAI climatology + albedo nrt Results evaluated on surface fluxes:  Latent heat flux  Sensible Heat flux  Carbon dioxide flux (Net Ecosystem Exchange of COt) Which processes are affected by vegetation state?

Slide 7 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 7 Clim NRT_ALB - ClimNRT_LAI - Clim NRT_ALB_LAI - Clim Latent heat flux sensitivity

Slide 8 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 8 Clim NRT_ALB - ClimNRT_LAI - Clim NRT_ALB_LAI - Clim Sensible heat flux sensitivity

Slide 9 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 9 Clim NRT_ALB - ClimNRT_LAI - Clim NRT_ALB_LAI - Clim Carbon dioxide flux sensitivity

Slide 10 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 10 The atmospheric coupled simulation setup: To seek the impact of the NRT analysed data four coupled experiments are performed Period: 2010 Coverage: Global Resolution: 40km 4 different experiments:  Control: LAI+albedo climatology are used  NRT_LAI: LAI nrt data + albedo climatology  NRT_ALB: LAI climatology + albedo nrt  NRT_ALB_LAI: LAI nrt data + albedo nrt Results evaluated on weather forecasts for next day :  2m temperature (36-hour forecast range)  2m relative humidity (36-hour forecast range) Is weather affected by vegetation state?

Slide 11 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 11 Horn of Africa drought & Australia drought recover LAI anomaly Albedo anomaly  Check the T 2m and RH on short term forecast fc+36 valid 12 UTC, Nov Sensitivity = (exp –ctl), if >0 => warming/adding moisture, if cooling/removing moisture And Impact = |ctl – analysis| - |exp – analysis|, if >0 => relative error reduction from the analysis (positive impact ) if relative error increase from the analysis (negative impact) Is weather affected by vegetation state (II)? The focus is on:

Slide 12 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 12 Coupled run: NRT_LAI+ALB Vs Clim Sensitivity Impact Weather forecasts sensitivity and impact ΔRH2m Red  Warming Blue  Drying Blue  Better forecast ΔT2mΔErr T2m ΔErr RH2m

Slide 13 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 13 Conclusions & Outlook  The proposed analysis procedure leads to smooth temporal evolution of LAI/Albedo, which is more appropriate for initialization of environmental prediction models.  The analysed NRT LAI is able to detect/monitor anomalous year.  The analysed NRT albedo signal seems covariant with the NRT LAI mainly during wet year (compensation effect may occur between vegetation and bare-ground albedo).  GEOV1 NRT LAI/albedo showed potential for heat waves and drought monitoring but given the impact on energy and carbon fluxes these products are ECVs also for coupled experiments.  Introducing NRT LAI and Albedo in coupled runs is physically justified and has an overall neutral to positive impact on forecasted weather parameters, with the LAI signal being dominant and in some cases enhanced with the Albedo anomaly signal.  In future work, enhanced connections between albedo, LAI (and roughness) in Earth System Models will most likely increase the sensitivity to vegetation dynamics.

Slide 14 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 14 Thank you for your attention Contact:

Slide 15 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 15 Horn of Africa drought & Australia drought recover LAI anomaly Albedo anomaly

Slide 16 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 16 T 2m Sensitivity Impact Rh 2m Yellow  WarmingBlue  Positive impact Blue  drying Coupled run: NRT_LAI Vs Clim

Slide 17 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 17 Coupled run: NRT_ALB Vs Clim T 2m Sensitivity Impact Rh 2m

Slide 18 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 18 LAI anomaly Albedo anomaly 2010 Russian Heat wave

Slide 19 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 19 Latent Heat flux Clim NRT_ALB - ClimNRT_LAI - Clim NRT_ALB_LAI - Clim

Slide 20 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 20 Sensible Heat flux Clim NRT_ALB - ClimNRT_LAI - Clim NRT_ALB_LAI - Clim

Slide 21 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 21 Net Ecosystem Exchange Clim NRT_ALB - ClimNRT_LAI - Clim NRT_ALB_LAI - Clim

Slide 22 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 22 Scores of GEOV1 LAI NRT against GEOV1 LAI climatology for JJA: a) 2m temperature sensitivity [K], b) 2m temperature impact, c) 2m relative humidity sensitivity [%], d) 2m relative humidity impact.  An overall neutral to positive impact. Assimilation of GEOV1 NRT LAI and its potential value (coupled runs) T 2m RH Blue  cooling Blue  drying Blue  Positive impact

Slide 23 BG2.5 EGU, Vienna 02 May S. Boussetta Slide Europe and East China drought LAI anomaly Albedo anomaly

Slide 24 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 24 Latent Heat flux Clim NRT_ALB - ClimNRT_LAI - Clim NRT_ALB_LAI - Clim

Slide 25 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 25 Sensible Heat flux Clim NRT_ALB - ClimNRT_LAI - Clim NRT_ALB_LAI - Clim

Slide 26 BG2.5 EGU, Vienna 02 May S. Boussetta Slide 26 Net Ecosystem Exchange Clim NRT_ALB - ClimNRT_LAI - Clim NRT_ALB_LAI - Clim