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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 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/imag es/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 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? County-level Surface Soil organic C Density (0-30 cm, kg C/m2) 7.0 C Density, kg C/m2 6.0 5.0 4.0 3.0 2.0 1.0 0.0 State SW SE Central Delta North Total Soil Organic C Density (kg C/m2) C Density, kg C/m2 25 20 15 10 5 0 State SW SE Central Delta North County-level MS Forest C density (kg C/m2) C Density, kg C/m2 10 8 5 3 0 State SW SE Central Delta North Comparison of Soil C and Forest C Storage in regions of MS Total Forest C: 392 Tg C Total Soil C: 809 Tg C SW SE Centr al Delta Housing/Furniture C: 661 Tg C, 3.0% Crop C: 85 Tg C, Pasture C: 27.8 Tg C, 0.13% Forest C: Soil Organic Total terrestrial carbon storage and pools in the Study Area Total Terrestrial C Storage: 21762 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 Southcentral U.S.? What is the current baseline in the region for possible carbon trading? What is the potential for enhancing terrestrial carbon sequestration? 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 CASA Model-Inputs/Outputs Data Inputs: Outputs: NDVI ( MODIS) , Soil (SSURGO), Precipitation (PRISM), Air Temperature (PRISM), Land Mask, Solar Radiation (NARR),Vegetation type. 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) CASA output fits/reflects well with the combination of Soil C and forest C in county-level of MS Total Soil Carbon Soil Microbial Respiration source of Carbon Leaf Area Index (LAI)-2002 May Jun July Net Primary Productivity (NPP)-2002 May Jun Monthly NPP was estimated in CASA as : NPP=f(NDVI)x PAR x LUE x g(T) x h(W) July Net Ecosystem Productivity (NEP)-2002 May Jun July RPC Experimental Design (Modified) • Fossil Fuels • Assimilation of aircraft measurements, satellite data (precipitable water, surface winds) Meteorology (e.g. GOES data analysis) • Winds, cloud mass fluxes, model Parameters • Forward Transport Model Transport Model • • • • Vegetation Indices Biome type Soil properties Weather Reanalysis Land Surface Model (CASA) • 1 year spinup • Monthly • OCO, Networks [CO2] OBS 1 year spinup (2002) • Terrestrial CO2 surface flux Inversion Design of Simulation Experiments Surface Fluxes CASA Model Transport Model Perturbation With Errors Simulated OCO Observations Perturbation With Errors Simulated Priors Ensemble Based Inversion Estimated Fluxes Simulated OCO data not available from NASA yet. Currently use data generated on our own. Evaluation Kalman Filter Observations Initial Estimates Forecast Background Estimates Update Updated Estimates 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. Two main approaches can be followed to handle observations (Mathieu et al, 2008): 1.A Filter, whereby the analysis is only influenced by observations made in the past, which is the case for real-time applications and forecasting. 2. A smoother, where the analysis is influenced by all observation available over a given period “T” ( assimilation window) Ensemble Based Assimilation Ensemble-based Update Initial Ensemble Forecast Model Errors Addition Background Ensemble Observations Mean Error Covariance Statistical Analysis Update Kalman Gain Reduced Kalman Gain Updated Ensemble 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). Example Assimilation Results (I) Ground Truth Fluxes Observations source 100 75 sink Assimilation Results 50 Assimilation Errors 25 0 -25 -50 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. Example Assimilation Results (II) Error Statistics Obtained from a 10-Step Assimilation Experiment Standard Deviation Mean Time Steps Time Steps 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. 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 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) Questions? Source :http://earthobservatory.nasa.gov/Features/CarbonCycle/Images/carbon_cycle_diagram.jpg