Data assimilation in land surface schemes Mathew Williams University of Edinburgh.

Slides:

Advertisements

Similar presentations

Land Surface Evaporation 1. Key research issues 2. What we learnt from OASIS 3. Land surface evaporation using remote sensing 4. Data requirements Helen.

Advertisements

Greenhouse gases Observing SATellite Observing SATellite 29 March 2011 Hyper Spectral Workshop The ACOS/GOSAT Experience David Crisp JPL/CALTECH.

GHG Verification & the Carbon Cycle 28 September 2010 JH Butler, NOAA CAS Management Group Meeting Page 1 Global Monitoring, Carbon Cycle Science, and.

GHG Verification & the Carbon Cycle Hyperspectral Workshop JH Butler, NOAA 31 March 2011 Page 1 Greenhouse gases – What we do well and what we need to.

Analysis of the terrestrial carbon cycle through data assimilation and remote sensing Mathew Williams, University of Edinburgh Collaborators L Spadavecchia,

Monitoring Effects of Interannual Variation in Climate and Fire Regime on Regional Net Ecosystem Production with Remote Sensing and Modeling D.P. Turner.

CarbonFusion meeting, 4 or 5 June 2008 Building from the bottom-up and learning as we go: data requirements for upscaling ecosystem function Paul Stoy.

MODIS Science Team Meeting - 18 – 20 May Routine Mapping of Land-surface Carbon, Water and Energy Fluxes at Field to Regional Scales by Fusing Multi-scale.

Using Simulated OCO Measurements for Assessing Terrestrial Carbon Pools in the Southern United States PI: Nick Younan Roger King, Surya Durbha, Fengxiang.

The Orbiting Carbon Observatory Mission: Effects of Polarization on Retrievals Vijay Natraj Advisor: Yuk Yung Collaborators: Robert Spurr (RT Solutions,

The Averaging Kernel of CO2 Column Measurements by the Orbiting Carbon Observatory (OCO), Its Use in Inverse Modeling, and Comparisons to AIRS, SCIAMACHY,

Princeton University Global Evaluation of a MODIS based Evapotranspiration Product Eric Wood Hongbo Su Matthew McCabe.

Data Assimilation in the NCEO (National Centre for Earth Observation) Peter Jan van Leeuwen Data-Assimilation Research Centre DARC University of Reading.

Compatibility of surface and aircraft station networks for inferring carbon fluxes TransCom Meeting, 2005 Nir Krakauer California Institute of Technology.

4. Testing the LAI model To accurately fit a model to a large data set, as in the case of the global-scale space-borne LAI data, there is a need for an.

Improving and extending CMS land surface carbon flux products including estimates of uncertainties in fluxes and biomass. Jim Collatz, Randy Kawa, Lesley.

Forrest G. Hall 1 Thomas Hilker 1 Compton J. Tucker 1 Nicholas C. Coops 2 T. Andrew Black 2 Caroline J. Nichol 3 Piers J. Sellers 1 1 NASA Goddard Space.

1 D. Crisp for the ACOS/OCO-2 Team Jet Propulsion Laboratory, California Institute of Technology OCO-2 Science Lead 7 June 2011 Copyright 2011 California.

Observational Needs for Testing Global Model Parameterizations Andrew Gettelman, NCAR Thanks to: J. Kiehl, W. Collins, P. Rasch.

Preparatory work on the use of remote sensing techniques for the detection and monitoring of GHG emissions from the Scottish land use sector P.S. Monks.

ICDC7, Boulder, September 2005 CH 4 TOTAL COLUMNS FROM SCIAMACHY – COMPARISON WITH ATMOSPHERIC MODELS P. Bergamaschi 1, C. Frankenberg 2, J.F. Meirink.

Page 1© Crown copyright WP4 Development of a System for Carbon Cycle Data Assimilation Richard Betts.

1 Remote Sensing and Image Processing: 9 Dr. Hassan J. Eghbali.

The role of the Chequamegon Ecosystem-Atmosphere Study in the U.S. Carbon Cycle Science Plan Ken Davis The Pennsylvania State University The 13 th ChEAS.

The Merton Report an AIMES/IGBP-ESA partnership As Earth System science advances and matures, it must be supported by robust and integrated observation.

The Orbiting Carbon Observatory (OCO) Mission Watching Earth Breathe…Mapping Carbon Dioxide from Space Science Writers’ Workshop American Geophysical Union.

Page 1 1 of 14, Vijay, Ge152 The Orbiting Carbon Observatory(OCO) Mission The Orbiting Carbon Observatory (OCO) Mission

Indianapolis flux (INFLUX) in-situ network: quantification of urban atmospheric boundary layer greenhouse gas dry mole fraction enhancements 18 th WMO/IAEA.

Translation to the New TCO Panel Beverly Law Prof. Global Change Forest Science Science Chair, AmeriFlux Network Oregon State University.

Validating the GHG production chain at multiple levels Julia Marshall (MPI-BGC), Richard Engelen (ECMWF), Cyril Crevoisier (LMD), Peter Bergamaschi (JRC),

Page 1 1 of 20, Crisp, OCO May 2006 The Orbiting Carbon Observatory: Sampling Approach and Anticipated Data Products David Crisp, OCO PI

Integration of biosphere and atmosphere observations Yingping Wang 1, Gabriel Abramowitz 1, Rachel Law 1, Bernard Pak 1, Cathy Trudinger 1, Ian Enting.

1 Remote Sensing and Image Processing: 9 Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd Floor, Chandler House Tel: (x24290)

Development of an EnKF to estimate CO 2 fluxes from realistic distributions of X CO2 Liang Feng, Paul Palmer

Flux observation: Integrating fluxes derived from ground station and satellite remote sensing 王鹤松 Hesong Wang Institute of atmospheric physics, Chinese.

Improving carbon cycle models with radar retrievals of forest biomass data Mathew Williams, Tim Hill and Casey Ryan School of GeoSciences, University of.

Site-Level Model-Data Comparison A Proposed NACP Interim Synthesis Project Ken Davis, Peter Thornton, Kevin Schaefer, Dan Riciutto Coordinators.

Carbon dioxide from TES Susan Kulawik F. W. Irion Dylan Jones Ray Nassar Kevin Bowman Thanks to Chip Miller, Mark Shephard, Vivienne Payne S. Kulawik –

Using data assimilation to improve estimates of C cycling Mathew Williams School of GeoScience, University of Edinburgh.

FastOpt Quantitative Design of Observational Networks M. Scholze, R. Giering, T. Kaminski, E. Koffi P. Rayner, and M. Voßbeck Future GHG observation WS,

WP3 WP6 USE CASE DATA MODEL FUSION USING PHENOLOGICAL DATA TO INFORM PRODUCTIVITY MODEL Andy Fox, David Moore, Jesus Marco de Lucas, Jeff Taylor, and many.

Using data assimilation to improve understanding and forecasts of the terrestrial carbon cycle Mathew Williams School of GeoSciences, University of Edinburgh.

Arctic Biosphere-Atmosphere Coupling across multiple Scales (ABACUS) Why is this SO important for understanding global change?

Dept of Mathematics University of Surrey VAR and modelling the carbon cycle Sylvain Delahaies Ian Roulstone Dept of Mathematics University of Surrey NCEO.

Biases in land surface models Yingping Wang CSIRO Marine and Atmospheric Research.

Goal: to understand carbon dynamics in montane forest regions by developing new methods for estimating carbon exchange at local to regional scales. Activities:

Recursive Calibration of Ecosystem Models Using Sequential Data Assimilation Mingshi Chen¹, Shuguang Liu¹, Larry L. Tieszen², and David Y. Hollinger 3.

Edinburgh, June 2008Markus Reichstein Critical issues when using flux data for reducing Land Surfcace Model uncertainties – towards full uncertainty accounting?

ICDC7, Boulder September 2005 Estimation of atmospheric CO 2 from AIRS infrared satellite radiances in the ECMWF data assimilation system Richard.

CFusion and NCEO. NCEO Components Ciais et al IGOS-P Integrated Global Carbon Observing Strategy Global Carbon Data Assimilation System.

Data assimilation as a tool for C cycle studies Collaborators: P Stoy, J Evans, C Lloyd, A Prieto Blanco, M Disney, L Street, A Fox (Sheffield) M Van Wijk.

Data assimilation as a tool for biogeochemical studies Mathew Williams University of Edinburgh.

Success and Failure of Implementing Data-driven Upscaling Using Flux Networks and Remote Sensing Jingfeng Xiao Complex Systems Research Center, University.

The Orbiting Carbon Observatory (OCO) Mission: Retrieval Characterisation and Error Analysis H. Bösch 1, B. Connor 2, B. Sen 1, G. C. Toon 1 1 Jet Propulsion.

Using Modelling to Address Problems Scientific Enquiry in Biology and the Environmental Sciences Modelling Session 2.

Geogg124: Data assimilation P. Lewis. What is Data Assimilation? Optimal merging of models and data Models Expression of current understanding about process.

Data assimilation in C cycle science Strand 2 Team.

Improving understanding and forecasts of the terrestrial carbon cycle Mathew Williams School of GeoSciences, University of Edinburgh With input from: BE.

Results from the Reflex experiment Mathew Williams, Andrew Fox and the Reflex team.

Forest Research, 20 February 2009 Understanding the carbon cycle of forest ecosystems: a model-data fusion approach Mathew Williams School of GeoSciences,

Integrating ecological data and models to enable understanding and forecasting Mathew Williams, University of Edinburgh.

NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS SURFACE PRESSURE MEASUREMENTS FROM THE ORBITING CARBON OBSERVATORY-2.

Global Characterization of X CO2 as Observed by the OCO (Orbiting Carbon Observatory) Instrument H. Boesch 1, B. Connor 2, B. Sen 1,3, G. C. Toon 1, C.

Carbon Cycle Data Assimilation with a Variational Approach (“4-D Var”) David Baker CGD/TSS with Scott Doney, Dave Schimel, Britt Stephens, and Roger Dargaville.

The Lodore Falls Hotel, Borrowdale

CO2 sources and sinks in China as seen from the global atmosphere

Data Assimilation and Carbon Cycle Working Groups

Surface Pressure Measurements from the NASA Orbiting Carbon Observatory-2 (OCO-2) Presented to CGMS-43 Working Group II, agenda item WGII/10 David Crisp.

An Integrated View of North American Biosphere Carbon Flux Inter-annual Variability: from satellite CO2 to phenology Junjie Liu1, Kevin Bowman1, Dave.

Carbon Model-Data Fusion

Presentation transcript:

Data assimilation in land surface schemes Mathew Williams University of Edinburgh

Global forecast capability Key processes Challenges for the JULES team Initial conditions Parameters Errors EO & sensor networks Continual testing & development JULES

The philosophy of data assimilation Observations and models contain useful information about the target system But ALL observations and models are subject to error - and may be subject to bias Models and observations can be combined to optimise information

Improving estimates of land surface process MODELS OBSERVATIONS FUSION ANALYSIS MODELS + Capable of interpolation & forecasts ─ Subjective & inaccurate? OBSERVATIONS + Clear confidence limits ─ Incomplete, patchy ─ Net fluxes ANALYSIS + Complete + Clear confidence limits + Capable of forecasts + Data consistency

Approaches to data assimilation Sequential (predictor-corrector)

The Kalman Filter MODEL AtAt F t+1 F´ t+1 OPERATOR A t+1 D t+1 Assimilation Initial stateForecast Observations Predictions Analysis P Ensemble Kalman Filter Drivers

F t+1 AtAt AtAt The Ensemble Kalman Filter MODEL AtAt F t+1 OPERATOR F´ t+1 A t+1 D t+1 Assimilation Initial stateForecast Observations Predictions Analysis P Drivers

Observations – Ponderosa Pine, OR (Bev Law) Flux tower (2000-2) Sap flow Soil/stem/leaf respiration LAI, stem, root biomass Litter fall measurements

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

Time (days since 1 Jan 2000) Williams et al (2005)

Time (days since 1 Jan 2000) Williams et al (2005)  = observation — = mean analysis | = SD of the analysis

Time (days since 1 Jan 2000) Williams et al (2005)

Time (days since 1 Jan 2000) Williams et al (2005) = observation — = mean analysis | = SD of the analysis

Data brings confidence Williams et al (2005)  = observation — = mean analysis | = SD of the analysis

Approaches to data assimilation Sequential (predictor-corrector) Inversion techniques – Monte Carlo – Adjoint

Monte Carlo Inversion MODEL F F´F´ OPERATOR Forecasts Observations Predictions P Drivers Initial J D Cost function

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

Scaling

A multiscale approach

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

Linking fluxes to atmospheric [CO 2 ] Figure by Paul Parrish, data from J Moncrieff & J Grace

The Orbiting Carbon Observatory (OCO) Approach: Collect spatially resolved, high resolution spectroscopic observations of CO 2 and O 2 absorption in reflected sunlight Use these data to resolve spatial and temporal variations in the column averaged CO2 dry air mole fraction, X CO2 over the sunlit hemisphere Employ independent calibration and validation approaches to produce X CO2 estimates with random errors and biases no larger than ppm ( %) on regional scales at monthly intervals OCO will acquire the space-based data needed to identify CO 2 sources and sinks and quantify their variability over the seasonal cycle Source: David Crisp, JPL

Spanning measurement scales OCO will make precise global measurements of X CO2 over the range of scales needed to monitor CO 2 fluxes on regional to continental scales. Spatial Scale (km) CO 2 Error (ppm) OCO Flask Site Aqua AIRS Aircraft 0 Flux Tower Globalview Network NOAA TOVS ENVISAT SCIAMACHY Tall tower Source: David Crisp, JPL

A strategy for JULES? LOCAL: DA for local parameter PDFs, process testing, C-water interactions, full state descriptions. FluxNet, IPSL, NCAR, ACCESS. REGIONAL: upscaling, coupling to/inverting atmospheric data/models. CarboEurope, ABACUS. GLOBAL: Global assimilation with optical, CO 2, water, temperature remote sensing, flasks. NCEO & CCDAS.

THANKS!

Tim Hill, PhD thesis Monte Carlo Inversion