Data assimilation in C cycle science Strand 2 Team.

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Presentation transcript:

Data assimilation in C cycle science Strand 2 Team

Rationale  Problem: Current C models show significant disagreements  Complication: Mismatch between GCM grid cells (~270 x 270 Km) and flux footprints (~1 x 1 km)  Solution: A model-data framework

Process models -upscaling EO data: -landcover -phenology Flux towers -processes -parameters Geostats -Spatial drivers -Uncertainty Tall tower /aircraft -Check on upscaling -Inversions

Progress  Uncertainty in parameter extrapolation from flux towers (REFLEX)  Improved modelling of deciduous systems using EO  Assimilating reflectance data into C models  Generating spatial errors for drivers  Using tall towers to constrain C models

REgional Flux Estimation eXperiment (REFLEX)  To compare the strengths and weaknesses of various MDF/DA techniques  To quantify errors and biases introduced when extrapolating fluxes

REgional Flux Estimation eXperiment (REFLEX) FluxNet data MODIS MDF Full analysis Model parameters DALEC model Training Runs Deciduous forest sites Coniferous forest sites Assimilation Output

REgional Flux Estimation eXperiment (REFLEX) FluxNet data MODIS MDF Full analysis Model parameters DALEC model Testing site forecasts with limited EO data MDF MODIS Analysis FluxNet data testing Assimilation

GPP C root C wood C foliage C litter C SOM/CWD RaRa AfAf ArAr AwAw LfLf LrLr LwLw RhRh D C labile A tolab A fromlab DALEC

A phenology module allows data assimilation at deciduous sites and an improved capacity to assimilate EO data

Assimilating EO data  Last year we showed a capability for assimilating LAI products  Now we can assimilate reflectance data  And we can cope with snow contamination

Observation operator - GORT Leaf reflectance - PROSPECT Empirical soil reflectance function

Observation operator - GORT Shaded crown Illuminated crown Illuminated soil Shaded soil

Modelled vs. observed reflectance Band 1Band 2

Canopy foliage results No assimilation Assimilating MODIS (bands 1 and 2)

GPP results No assimilation Assimilating MODIS (bands 1 and 2)

NEP results No assimilation Assimilating MODIS (bands 1 and 2)

Uncertainty in spatial variation in key controls on the C cycle, such as meteorology, can be quantified using geostatistical techniques

Planetary Boundary Layer model Canopy Flux model Albedo, LE, Sens. heat, Ts, Evaporation, Transpiration NEP Wind speed, Air temp., PAR, Precipitation, VPD, CO 2 conc. Carbon dynamics model Monte Carlo methods provide a powerful means to invert both flux tower and aircraft data to provide estimates of critical C model parameters

Modelled and measured boundary layer profiles of potential temperature, mixing ratio and CO2 concentration, BOREAS

Retrieving parameters using tower data

Plans  Manage, process and publish the outputs of REFLEX  Demonstrate regional coupled biosphere- atmosphere model (DALEC-NAME) for tall tower and aircraft inversion