1 / 24

PI: Nick Younan Roger King, Surya Durbha, Fengxiang Han

Using Simulated OCO Measurements for Assessing Terrestrial Carbon Pools in the Southern United States. PI: Nick Younan Roger King, Surya Durbha, Fengxiang Han Zhiling Long, Narendra Rongali, Haiqing Zhu. Orbiting Carbon Observatory (OCO). Introduction.

pegeen
Download Presentation

PI: Nick Younan Roger King, Surya Durbha, Fengxiang Han

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Using Simulated OCO Measurements for Assessing Terrestrial Carbon Pools in the Southern United States PI: Nick Younan Roger King, Surya Durbha, Fengxiang Han Zhiling Long, Narendra Rongali, Haiqing Zhu

  2. Orbiting Carbon Observatory (OCO)

  3. Introduction • Estimated global total net flux of carbon from changes in land use increased from 503 Tg C (1012 g) in 1850 to 2376 Tg C in 1991 and then declined to 2081 Tg C in 2000. • The global net flux during the period 1850-2000 was 156 Pg C (1015 g), about 63% of which was from the tropics. • The US estimated flux is a net source to the atmosphere of 7 Pg C for the period 1850-2000, but a net sink of 1.2 Pg C for the 1980s and 1.1 Pg C for the 1990s. • Hence, better estimates at regional level are required to understand and reduce the uncertainties in the sink/source estimations Source:http://www.netl.doe.gov/technologies/carbon_seq/overview/images/carbon-flux-diagram.gif Data Source: Houghton, R.A, 1999. The annual net flux of carbon to the atmosphere from the changes in land use 1850-1990. Tellus 51B:298-313

  4. Currently funded DOE project for leverage • What are the current annual rates of terrestrial carbon sequestration in each state of the region? • What's the overall contribution of terrestrial carbon sequestration in each state of the region to mitigating its total greenhouse gas emission? • What's the current baseline for possible carbon trading in the region? • What's the potential of further enhancing terrestrial carbon sequestration in the region? • What are the overall economic impacts of current and potential terrestrial carbon sequestration on the region?

  5. County-level Surface Soil organic C Density(0-30 cm, kg C/m2)

  6. Total Soil Organic C Density(kg C/m2)

  7. County-level MS Forest C density (kg C/m2)

  8. Comparison of Soil C and Forest C Storage in regions of MS Total terrestrial carbon storage and pools in the Study Area

  9. Focus Areas of the Project (Plan B) • The RPC experiment seeks to address the following questions: • What information about carbon exchange can be obtained from OCO high-precision column measurements of CO2? • How can we integrate top-down OCO measurements with ground based measurements, atmospheric and terrestrial ecosystem models to quantify carbon exchange over different ecosystems? • What are the current annual rates of terrestrial carbon sequestration in each state of the Southeast and South-central U.S.? • What is the current baseline in the region for possible carbon trading? • What is the potential for enhancing terrestrial carbon sequestration?

  10. NASA-CASA Model • NASA-CASA (Carnegie Ames Stanford Approach) model is designed to estimate monthly patterns in carbon fixation, plant biomass, nutrient allocation, litter fall, soil nutrient mineralization, and CO2 exchange, including carbon emissions from soils world-wide. • Assimilates satellite NDVI data from the MODIS sensor into the NASA-CASA model to estimate • Spatial variability in monthly net primary production (NPP), • biomass accumulation, • and litter fall inputs to soil carbon pools

  11. CASA Model-Inputs/Outputs • Data • Inputs: • NDVI ( MODIS) , Soil (SSURGO), Precipitation (PRISM), Air Temperature (PRISM), Land Mask, Solar Radiation (NARR), Vegetation type. • Outputs: • Carbon pools, LAI, NPP, NEP, AET,APAR , FAPR, LEAFFR, NBP, NPP moist, NPP temp, PET, resp, rootfr, soilc, stemfr. • Other: • Soil, Land cover, Parameters. Soil Types (SSURGO) Precipitation (PRISM)

  12. CASA output fits/reflects well with the combination of Soil C and forest C in county-level of MS Soil Microbial Respiration source of Carbon Total Soil Carbon

  13. Leaf Area Index (LAI)-2002 May Jun July

  14. Net Primary Productivity (NPP)-2002 May Jun July Monthly NPP was estimated in CASA as : NPP=f(NDVI)x PAR x LUE x g(T) x h(W)

  15. Net Ecosystem Productivity (NEP)-2002 May Jun July

  16. Fossil Fuels • OCO, Networks • Winds, cloud mass fluxes, model Parameters • Assimilation of aircraft measurements, satellite data (precipitable water, surface winds) Meteorology (e.g. GOES data analysis) • Forward Transport Model Transport Model [CO2] OBS • Vegetation Indices • Biome type • Soil properties • Weather Reanalysis 1 year spinup (2002) Land Surface Model (CASA) • Terrestrial CO2 surface flux Inversion • 1 year spinup • Monthly RPC Experimental Design (Modified)

  17. Surface Fluxes CASA Model Transport Model Perturbation With Errors Simulated OCO Observations Estimated Fluxes Perturbation With Errors Ensemble Based Inversion Evaluation Simulated Priors Design of Simulation Experiments • Simulated OCO data not available from NASA yet. • Currently use data generated on our own.

  18. Observations Initial Estimates Background Estimates Forecast Update Updated Estimates Kalman Filter Two main approaches can be followed to handle observations (Mathieu et al, 2008): A Filter, whereby the analysis is only influenced by observations made in the past, which is the case for real-time applications and forecasting. A smoother, where the analysis is influenced by all observation available over a given period “T” ( assimilation window) • Bayesian data assimilation is conceptually simple but computationally prohibitive for application on large problems. • Kalman filter is a simplified approximation to the Bayesian estimation, which assumes: • Normality of error statistics, and • Linearity of error growth.

  19. Ensemble-based Update Observations Mean Initial Ensemble Background Ensemble Error Covariance Forecast Model Errors Addition Statistical Analysis Update Kalman Gain Reduced Kalman Gain Updated Ensemble Ensemble Based Assimilation • Ensemble based approaches combine the Kalman filter concept with Monte-Carlo techniques. • More accurate than the Kalman filter because there are no assumptions about the normality and linearity of errors. • Investigated two methods for the update process: deterministic (EnSRF) and stochastic (EnKF).

  20. Ground Truth Fluxes Observations source 100 75 sink 50 Assimilation Results Assimilation Errors 25 0 -25 -50 Example Assimilation Results (I) • The synthetic ground truth fluxes simulate one source area and one sink area. • The ensemble based technique was able to assimilate the observations to generate flux estimates with small errors.

  21. Error Statistics Obtained from a 10-Step Assimilation Experiment Standard Deviation Mean Time Steps Time Steps Example Assimilation Results (II) • Errors are consistent throughout all time steps. • Results are similar in this case for both the deterministic (EnSRF) and the stochastic (EnKF) methods. • Working on Implementing the covariance localization technique for the update process. • Estimates for background error covariance may be inaccurate when small ensembles are utilized. This technique helps to improve the accuracy for such estimation based on small ensembles.

  22. Tasks Completed/Ongoing • Input data sets for the CASA model conditioned ( written several scripts, ArcMap models) for the southern United States • CASA model simulations for the entire Southern United states in progress. • Sensitivity studies of CASA model outputs with NASA-CQUEST is being performed. • In situ soil carbon studies completed for Southern United States • Explored several transport models for suitability for carbon fluxes transport. Currently working on WRF-CHEM for this purpose. • Assimilation Code-based on Ensemble Kalman filter(both stochastic and deterministic update methods) developed in Matlab. • Participated in 2008 Carbon Cycle and Ecosystems Joint Science Workshop to be held April 28 - May 2, 2008

  23. Publications • Younan, N. H. , Durbha, S. S., King, R. L., Han, F. X, Long, Z., Rongali, N., Zhu, H., (2009) . "Data Assimilation for Assessing Terrestrial Carbon Pools in the Southern United States”. 33rd International Symposium on Remote Sensing of Environment (ISRSE), Italy. • Younan, N. H., King, R. L., Durbha, S. S., Han, F. X, Long,  Z., Chen, J. (2007). “Using Simulated OCO Measurements for Assessing Terrestrial Carbon Pools in the Southern United States”. American Geophysical Union ( AGU) , Fall Meeting . • Durbha, S. S., Younan, N., King, R., Han, F. X., Long, Z. (2008). A Rapid Prototyping Capability Experiment to Assess Terrestrial Carbon Pools in Southern United States. 2008 NASA Carbon Cycle and Ecosystems Joint Science Workshop, Maryland, USA. • Nutrient fertilizer requirements for sustainable biomass supply to meet U.S. bioenergy goal (In revision). • County-level distribution of soil and forest carbon storage in Mississippi ( under preparation) • Validation of NASA-CASA model for terrestrial carbon pools in Mississippi. • ( under preparation)

  24. Questions? Source :http://earthobservatory.nasa.gov/Features/CarbonCycle/Images/carbon_cycle_diagram.jpg

More Related