Transcript Slide 1
EO data assimilation in land process models Mat Disney and Shaun Quegan No one trusts a model except the man who wrote it; everyone trusts an observation except the man who made it (Harlow Shapley) Concept for Global Carbon Data Assimilation System NB carbon and water are inextricably linked, so this is a more generalised vegetation – soil – water- atmosphere scheme Geo-referenced Geo-referenced emissions emissionsinventories inventories Climate and weather fields Ocean time series Biogeochemical pCO2 Surface observation pCO2 nutrients Water column inventories Atmospheric Atmospheric measurements measurements Remote Remote sensing sensing of of atmospheric atmospheric CO CO22 Atmospheric Atmospheric Transport Transport Model Model Ocean Ocean Carbon Carbon Model Model Coastal Coastal studies studies Optimised Optimised fluxes fluxes Terrestrial Terrestrial Carbon Carbon Model Model rivers Lateral fluxes Data assimilation link Optimised Optimised model model parameters parameters Eddy-covariance flux towers Biomass soil carbon inventories Ecological studies Ocean remote sensing Ocean colour Altimetry Winds SST SSS Remote sensing of vegetation properties Growth cycle Fires Biomass Radiation Land cover/use Ciais et al. 2003 IGOS-P Integrated Global Carbon Observing Strategy Terrestrial Component Remote Remote sensing sensing of of atmospheric CO atmospheric CO22 Climate and weather fields Atmospheric Atmospheric Transport Transport Model Model Optimised Optimised fluxes fluxes Terrestrial Terrestrial Carbon Carbon Model Model rivers Which model(s) should go here? Lateral fluxes Optimised Optimised model model parameters parameters Eddy-covariance flux towers Biomass soil carbon inventories Ecological studies + Water components: SWE soil moisture Remote sensing of vegetation properties Growth cycle Fires Biomass Radiation Land cover/use Land process models Land models need to deal with transfers of - energy - matter - momentum between the land surface and the atmosphere. Three classes of land (coupled carbon-water) models: Models driven by radiation (light use efficiency models) Dynamic Vegetation Models: climate driven Simple box models Some models emphasise hydrology (not discussed here) Light Use Efficiency models Incoming PAR CO2 Absorption fAPAR LUE Photosynthesis Respiration GPP NPP Efficiency coefficient: LUE 365 NPP LUE . fAPAR . PAR day 1 The LUE may depend on biome, soil moisture, temperature, nutrients, age, Measured by satellites Modeled or measured by satellites Notes on LUE models Models built by ecologists tend to focus on leaves as the functional element (e.g. Leaf Area Index). Models built by remote sensors tend to focus on radiation. LUE models are driven by EO data, rather than geared to assimilating data. Properties of DVMs DVMs originally designed to examine long-term trends under climate change so… Data-independent, except for varying climate data and static soil texture data Comprehensive description of biophysics All processes internalised, parameterised Complex, non-linear, non-differentiable, (discontinuities, thresholds) Expensive to run The Structure of a Dynamic Vegetation Model Parameters Climate Sn Soil texture DVM Processes Sn+1 Testing How EO data can affect DVM calculations Phenology Snow water Burnt area fAPAR Parameters Processes Climate Land cover Forest age Sn Soils DVM Sn+1 Observable Possible feedback Testing: Radiance fAPAR Calibration– boreal budburst Offline setting of global parameters can be thought of as a form of DA, but is better described as model calibration. In the following e.g, we use new EO observations that are unaffected by snow-melt to parameterise the spring warming boreal phenology model. The SDGVM budburst algorithm When min(0, T – T0) > Threshold, budburst occurs. days The sum is the red area. Optimise over the 2 parameters, Threshold and T0 (minimum effective temperature). T0 Start of budburst The Date of budburst derived from minimum NDWI (VGT sensor, 2000) N. Delbart, CESBIO Day of year Testing SDGVM with EO data SDGVM can predict satellite ‘observations’ since it contains a canopy model and the concept of radiation interception Model “skill” 1999 SDGVM fAPAR AVHRR NDVI Bad Skill Good Are derived parameters the problem? Is the problem the SDGVM or the derived parameter from the EO signal? The next slide shows the fAPAR derived from Seawifs (JRC) and from MODIS for a site in the UK. The large bias between the two is a general feature of these two datasets. Biases in derived parameters Assimilating products Assumptions Observations Assumptions Observations Data Assimilation Scheme (KF, EnKF, 4DVAR, etc) MODEL Assumptions For example: soil moisture from SMOS, surface temperature, LAI from MODIS Low-level vs derived products similar products give substantially different values; assumptions used to derive products usually inconsistent with biospheric models; Product uncertainties are poorly known Can we use low-level products (Reflectance? BOA radiance? TOA radiance?) Assimilating reflectance Data Assimilation Scheme (KF, EnKF, 4DVAR, etc) Observations Observations Observation Operator Assumptions e.g. reflectance, backscatter, etc… MODEL Assumptions Assumptions in the observation operator are made to be consistent with those in the model Observation operators This approach needs observation operators: translate ecosystem model state vector into observable properties e.g. reflectance data assimilated into DALEC; predicting radar coherence in ERS Tandem data from the SPA model; relating snowpack properties to SSM/I radiometer data; recognising burnt area and severity of burn. Which is the right model? Complex DVM-type models never designed for DA So, pursuing another approach with a simplified box model designed from the start for DA – DALEC The Structure of a Data Assimilation Model (DALEC) EO data (e.g. LAI, VI, reflectance) Blue lines indicate integration of EO data with DALEC Ensemble Kalman Filter Observation model Ra Ppt Rh ET WS1 Cf Cl GPP Cr Cw WS2 Cs Q WSn Stocks and fluxes of carbon (left) and water (right) Observation operator: simple RT model + snow Canopy foliage results No assimilation Assimilating MODIS (bands 1 and 2) Canopy foliage results Assimilating MODIS exc. snow Assimilating MODIS inc. snow Quaife, Williams, Disney et al. RSE in press EO land cover and carbon All EO land cover the same? DGVMs use land cover indirectly – How do we translate land cover classes to PFTs? Quaife, Quegan, Disney et al., submitted EO land cover and carbon Quaife, Quegan, Disney et al., submitted How do we find best model-data framework? Use ‘God’ models to test assumptions of simpler models – DVMs + DALEC-type models Model-data fusion inter-comparison e.g. REFLEX: Regional Flux Estimation Experiment – www.carbonfusion.org – Compare strengths/weaknesses of various modeldata fusion techniques – Quantify errors/biases introduced when extrapolating fluxes in both space and time using a model constrained by model-data fusion methods. Key issues for DA in land models 1 Models – Simple enough for effective DA but complex enough to capture biophysics – Suitable interface with observation operators – preferably differentiable Key issues for DA in land models 2 Data – Same meaning of observed parameters as used in models – Proper characterisation of uncertainty i.e. PDFs – Use OOs to make best use of all available data e.g. optical, LiDAR, RADAR, thermal …. We are still searching for the best model-data structure. Key issues for DA in land models 3 DA through observation operators not only answer, for various practical reasons. Also pursue general concepts of how EO data can reduce the uncertainty in land models – Calibration, testing etc. Thank you Severity of disagreement – AVHRR/SDGVM 1998 r > 0.497 OR r.m.s.e < 0.2 r < 0.497 AND r.m.s.e > 0.2 r < 0.497 AND r.m.s.e > 0.3 Severity of disagreement – example Mid Europe Severity of disagreement – example SW China Lesson 1. The DVM as currently formulated only supports a simple observation operator. This allows meaningful estimates of time series of observables; absolute values of the observables are of dubious value. 2. These time series permit the model to be interrogated with satellite data, and model failures to be identified. Detecting incorrect land cover Crop class incorrectly set Crop class correctly set 0.9 0.0 Pearson’s product moment Temporal correlation Lesson Forward operators may prove a powerful tool in land cover mapping Impact on Carbon Calculations Calibrated model is unbiased, unlike methods based on NDVI 1 day advance: NPP increases by 10.1 gCm-2yr-1 15 days advance: 38% bias in annual NPP Observations calibrate Carbon Calculation Phenology model Picard et al.,GCB, 2005 Dynamic Vegetation Model Comparison Model-EO: RMSE Model needs to be region specific, here include chilling requirement ? NDVI predicted by SDGVM 1998 0 1999 1 0 1 A Dynamic Vegetation Model (SDGVM) ATMOSPHERIC CO2 Photosynthesis Fire GPP GROWTH BIOPHYSICS Mortality NPP Thinning Litter Soil LEACHED NBP Biomass Disturbance Assimilating reflectance Observations Data Assimilation Scheme (KF, EnKF, 4DVAR, etc) MODEL The real world Assumptions But how do we use a nonlinear observation operator? Comparing model and measured fAPAR August 99 May 99 Seawifs SDGVM Model and predicted fAPAR Average over the whole of Europe for 1999 and 2000 Note: if SDGVM were driven by the Seawifs values, most model forests would die Experiments State and parameter estimation. DE1 and EV1 sites, 3 years driving data, all available obs As 1. but using synthetic data (DE2 and EV2) Within site forecasting. Another year of driving data for DE1 and EV1, but no observations As 3. but using synthetic data (DE2 and EV2) Between site extrapolation. DE3 and EV3 sites, 4 years driving data, MODIS LAI only Integrated flux predictions Flux (gC.m-2) NEP GPP Assimilated data Total Standard Deviation Assimilation exc. snow 373.0 151.3 Assimilation inc. snow 404.8 129.6 Williams et al. (2005) 406.0 27.8 Assimilation exc. snow 2620.3 96.8 Assimilation inc. snow 2525.6 42.7 Williams et al. (2005) 2170.3 18.1 REFLEX data sets “Paired” sites to test extrapolation/estimation – Brasschaat (DE2) and Vielsalm (EV2) (MF) – Hainich (DE3) and Hesse (DE1) (DBF) – Loobos (EV1) and Tharandt (EV3) (ENF) Meteorological drivers, fluxes, MODIS LAI and stocks – Attempting to estimate “uncertainty” in fluxes and MODIS LAI REgional Flux Estimation eXperiment (REFLEX) FluxNet data MODIS Training Runs Assimilation MDF DALEC model Output Full analysis Model parameters Deciduous forest sites Coniferous forest sites REgional Flux Estimation eXperiment (REFLEX) FluxNet data MODIS MDF Testing site forecasts with limited EO data DALEC model FluxNet data testing Full analysis Model parameters MDF Assimilation MODIS Analysis