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Impact of weather and climate variability on terrestrial fluxes: integration of EO data into models Jean-Christophe Calvet, Gianpaolo Balsamo, Alina Barbu,

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Presentation on theme: "Impact of weather and climate variability on terrestrial fluxes: integration of EO data into models Jean-Christophe Calvet, Gianpaolo Balsamo, Alina Barbu,"— Presentation transcript:

1 Impact of weather and climate variability on terrestrial fluxes: integration of EO data into models Jean-Christophe Calvet, Gianpaolo Balsamo, Alina Barbu, Alessandro Cescatti, Sébastien Lafont, Fabienne Maignan, Dario Papale, Camille Szczypta, Balazs Szintai, and Bart van den Hurk

2 Context WWOSC, Montreal, 18 August Heritage: geoland2 FP7 project ( ), macc2 ( ) Copernicus Atmosphere service (http://copernicus-atmosphere.eu/)http://copernicus-atmosphere.eu/ Global atmospheric CO 2 forecasting (ECMWF). Copernicus Global Land service (http://land.copernicus.eu/global/)http://land.copernicus.eu/global/ Near-real-time production of satellite-derived LAI, FAPAR, surface albedo (SA), land surface temperature (LST), and surface soil moisture (SSM) products at a global scale, together with other vegetation indices, burnt areas, water bodies. Started 1 January ImagineS FP7 project ( ) - From biophysical variables to agricultural indicators. - Performs the research needed in support to Copernicus GLS. - Use of European space-borne instruments (SPOT-VGT, PROBA-V, METOP-ASCAT, … future Copernicus Sentinel missions) and of geostationary satellites for LST

3 Data assimilation WWOSC, Montreal, 18 August Numerical models contain errors that increase with time due to model imperfections and uncertainties in initial and boundary conditions. Data assimilation minimizes these errors by correcting the model stats using new observations. Paul R. Houser, Figure from

4 Data assimilation WWOSC, Montreal, 18 August Numerical models contain errors that increase with time due to model imperfections and uncertainties in initial and boundary conditions. Data assimilation minimizes these errors by correcting the model stats using new observations. Paul R. Houser, Figure from

5 Data assimilation WWOSC, Montreal, 18 August Numerical models contain errors that increase with time due to model imperfections and uncertainties in initial and boundary conditions. Data assimilation minimizes these errors by correcting the model stats using new observations. Paul R. Houser, Figure from

6 LDAS objectives WWOSC, Montreal, 18 August From reanalyses to large scale land monitoring: integration of satellite products into a land surface model Use of a Land Data Assimilation System (LDAS) able to perform the sequential assimilation of LAI and SSM satellite-derived observations. Assess several products jointly and evaluate their consistency The LDAS is able to integrate/simulate most of the Copernicus GLS products and is used to: - perform the cross-cutting quality control of LAI, SSM, FAPAR, surface albedo, LST - detect possible product anomalies (comparing recent months to past multi- annual analyses) - produce agricultural indicators (above-ground biomass, GPP, …) and drought indices

7 LDAS-France WWOSC, Montreal, 18 August Within the SURFEX modeling platform of Meteo-France - Interoperable with operational real-time applications: weather forecast, hydrology, atmospheric CO 2 inversions - Shared by many meteorological services in Europe and North Africa - Used in CNRM-ARPEGE climate model (IPCC simulations) - Version 8 will be open-source (end 2014) ISBA-A-gs land surface model - Photosynthesis-driven phenology (no growing degree-days) - All the atmospheric variables impact phenology - Simulates the impact of long-term changes of atmospheric CO 2 - Interannual variability of LAImax is modeled - LAI is flexible and can be analyzed at a given time - FAPAR and SA are modeled - SSM is modeled

8 (LAI, soil moisture, + FAPAR, surface albedo, surface temperature) Extended Kalman filter H observation operator K Kalman gain LDAS-France WWOSC, Montreal, 18 August Joint assimilation of LAI and surface soil moisture (8km x 8km) Barbu et al. 2014, HESS

9 LDAS-France WWOSC, Montreal, 18 August Able to cope with uncertainties in the model and in the meteorological inputs Example: no-precipitation test (model error  3) Parrens et al. 2014, HESS

10 FORECAST ANALYSIS LDAS-France WWOSC, Montreal, 18 August Accounts for sub-grid heterogeneity (8km x 8km) Barbu et al. 2014, HESS

11 LDAS-France WWOSC, Montreal, 18 August Accounts for sub-grid heterogeneity Grid cell near Toulouse (8km x 8km) Barbu et al. 2014, HESS

12 LDAS-France WWOSC, Montreal, 18 August LAI analysis (mean value for France) Barbu et al. 2014, HESS

13 LDAS-France WWOSC, Montreal, 18 August Soil moisture change rate in 2011 (extreme spring drought) Barbu et al. 2014, HESS

14 LDAS-France WWOSC, Montreal, 18 August daily GPP change rate in 2011 (extreme spring drought) Barbu et al. 2014, HESS

15 Difficulties WWOSC, Montreal, 18 August Assimilation of vegetation products: LAI vs. FAPAR - FAPAR has very little sensitivity to LAI changes for LAI > 2 - FAPAR is a radiative product (has a diurnal cycle, depends on atmospheric variables) - New LAI products are designed to limit the saturation effect (ex. GEOV1, Baret et al. 2013) - However, FAPAR is highly informative at wintertime Assimilation of surface soil moisture - ASCAT product has seasonal and interannual issues (vegetation impact on the C-band signal should be better described) - Decoupling in dry conditions - Couple with assimilation of surface temperature (more sensitive to soil water content in dry conditions) ?

16 LDAS-France WWOSC, Montreal, 18 August Assimilation of FAPAR Jacobians (sensitivity of FAPAR to LAI perturbations) Grid cell near Toulouse (8km x 8km) LDAS-France

17 WWOSC, Montreal, 18 August Date of lowest ASCAT SSM value in 2009 DoY MODEL ASCAT CORRECTED ASCAT Barbu et al. 2014, HESS

18 LDAS-France WWOSC, Montreal, 18 August Increments

19 LDAS-France WWOSC, Montreal, 18 August Increments Too high SSM observations ?

20 LDAS-France WWOSC, Montreal, 18 August Assimilation of SSM in a multilayer soil hydrology model Jacobian profiles (sensitivity of SSM to perturbations of deep layers) Parrens et al. 2014, HESS

21 Model calibration/validation WWOSC, Montreal, 18 August Surface soil moisture (ESA-CCI microwave-derived product) Correlations ( day-to-day variability) Szczypta et al. 2014, GMD

22 Model calibration/validation WWOSC, Montreal, 18 August Leaf Area Index (GEOV1 Copernicus Global Land product) Correlations ( daily interannual variability) Szczypta et al. 2014, GMD

23 Model calibration/validation WWOSC, Montreal, 18 August Agricultural production ( interannual variability) winter/spring cereal grain yield grassland dry matter yield Canal et al. 2014, HESSD Red:p > 1% Yellow:p < 1% Dark : p < 0.1%

24 Model calibration/validation WWOSC, Montreal, 18 August Agricultural production ( interannual variability) Canal et al. 2014, HESSD

25 Global atmospheric CO 2 forecasting WWOSC, Montreal, 18 August ECMWF IFS 16 km, free-running CO 2 Good skill for real-time synoptic variability Excellent background state for - assimilating GOSAT and OCO-2 data - assessing/improving surface flux models SURFACE FLUXES  Anthropogenic emissions (EDGARv4.2) trend (Janssens-Maenhout et al. 2012)  Ocean climatology (Takahashi 2009)  Near-real-time GFAS biomass burning (MACC, Kaiser et al. 2011)  Real-time Net Ecosystem Exchange: CTESSEL (Boussetta et al., 2013), based on ISBA-A-gs Agusti-Panareda et al. 2014, ACPD

26 Global atmospheric CO 2 forecasting WWOSC, Montreal, 18 August Importance of having online CTESSEL NEE in the ECMWF IFS CO 2 forecast Synoptic variability is better represented with online CTESSEL Agusti-Panareda et al. 2014, ACPD OBS (NOAA/ESRL) CTESSEL online NEE CTESSEL monthly mean NEE

27 Conclusions WWOSC, Montreal, 18 August Demonstrated synergies between land and atmosphere Copernicus services Sequential assimilation vs. parameter optimization - Joint sequential assimilation of vegetation variables and soil moisture permits: - synergies between satellite observations in the visible and microwave spectra - controlling the model trajectory (needed during extreme events) - MaxAWC governs the vegetation interannual variability and can be retrieved LDAS-France is operational - Cross-cutting validation reports are generated every 6 months for the Copernicus Global Land service - Possible application for land reanalyses and drought monitoring - Very good representation of grassland dry matter yield provided MaxAWC is known

28 Prospects WWOSC, Montreal, 18 August Ongoing activities Comparisons with WOFOST MARS simulations (wheat) Test the assimilation of FAPAR and other variables Multi-layer soil hydrology Use decadal LAI time series to better constrain MaxAWC From EKF to EnKF Link to hydrology (in situ river discharge observations used for validation) Medium term objectives Go global (LDAS-Monde) Build a multi-decadal global land reanalysis integrating the existing vegetation and soil moisture satellite-derived CDRs Intercomparison of global land reanalyses (eartH2Observe project)


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