Presentation is loading. Please wait.

Presentation is loading. Please wait.

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

Similar presentations


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 2014 2 Heritage: geoland2 FP7 project (2008-2012), macc2 (2011-2014) 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 2013. ImagineS FP7 project (2013-2016) - 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 2014 3 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 http://www.hzg.de/institute/coastal_research/cosynahttp://www.hzg.de/institute/coastal_research/cosyna

4 Data assimilation WWOSC, Montreal, 18 August 2014 4 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 http://www.hzg.de/institute/coastal_research/cosynahttp://www.hzg.de/institute/coastal_research/cosyna

5 Data assimilation WWOSC, Montreal, 18 August 2014 5 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 http://www.hzg.de/institute/coastal_research/cosynahttp://www.hzg.de/institute/coastal_research/cosyna

6 LDAS objectives WWOSC, Montreal, 18 August 2014 6 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 2014 7 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 2014 8 Joint assimilation of LAI and surface soil moisture (8km x 8km) Barbu et al. 2014, HESS

9 LDAS-France WWOSC, Montreal, 18 August 2014 9 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 2014 10 Accounts for sub-grid heterogeneity (8km x 8km) Barbu et al. 2014, HESS

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

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

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

14 LDAS-France WWOSC, Montreal, 18 August 2014 14 10-daily GPP change rate in 2011 (extreme spring drought) Barbu et al. 2014, HESS

15 Difficulties WWOSC, Montreal, 18 August 2014 15 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 2014 16 Assimilation of FAPAR Jacobians (sensitivity of FAPAR to LAI perturbations) Grid cell near Toulouse (8km x 8km) LDAS-France

17 WWOSC, Montreal, 18 August 2014 17 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 2014 18 Increments

19 LDAS-France WWOSC, Montreal, 18 August 2014 19 Increments Too high SSM observations ?

20 LDAS-France WWOSC, Montreal, 18 August 2014 20 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 2014 21 Surface soil moisture (ESA-CCI microwave-derived product) Correlations (1991-2008 day-to-day variability) Szczypta et al. 2014, GMD

22 Model calibration/validation WWOSC, Montreal, 18 August 2014 22 Leaf Area Index (GEOV1 Copernicus Global Land product) Correlations (1991-2008 10-daily interannual variability) Szczypta et al. 2014, GMD

23 Model calibration/validation WWOSC, Montreal, 18 August 2014 23 Agricultural production (1994-2010 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 2014 24 Agricultural production (1994-2010 interannual variability) Canal et al. 2014, HESSD

25 Global atmospheric CO 2 forecasting WWOSC, Montreal, 18 August 2014 25 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) 2008 + 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 2014 26 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 2014 27 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 2014 28 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)


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

Similar presentations


Ads by Google