Estimation of Carbon Fluxes for the South Asian Region using Maximum Likelihood Ensemble Filter(MLEF) Some Basic Results K. M. P. Perera Department of Statistics University of Sri Jayewardenepura Sri Lanka 5th International Conference on “Earth Science & Climate Change” July 25-27, 2016 Bangkok, Thailand

Overview Background Methods used to assimilate carbon data Objectives Pseudodata experiment Results Ongoing and Future works References Acknowledgements

Background Climate change is a critical environmental issue closely linked with the increase of greenhouse gases in the atmosphere. CO2 plays the main role in greenhouse effect.

Figure 1: Atmospheric CO2 at Mauna Loa Observatory (http://en.wikipedia.org/wiki/Mauna_Loa_Observatory)

During the past two decades, greenhouse gas emissions from Asian countries have been increased rapidly mainly due to industrialization population growth It is more important to estimate the CO2 fluxes with high precision for the Asian region. CONTRAIL (Comprehensive Observation Network for Trace gases by Airliner) aircraft measurements are now available – covers the South Asian region. Data Assimilation method is going to be used to estimate CO2 fluxes.

Methods used to assimilate Carbon Data Batch mode inversion Variational data assimilation Ensemble method New ensemble method - MLEF

Objective To estimate carbon fluxes more precisely using MLEF for the globe with newly available South Asian measurements. Modified the MLEF algorithm to assimilate CONTRAIL (Comprehensive Observation Network for Trace gases by Airliner) aircraft data other than the existing flasks and continuous measurements. Carried out a pesudodata experiment on the global domain to test the performance of MLEF on assimilating CONTRAIL and exiting data.

Method: MLEF (Zupanski M., 2005) we optimize (minimize) cost function using MLEF Assimilation scheme Where y = vector of observations H = observation operator (PCTM) β = vector of unknowns (state vector) βb = prior (background) estimate R = observation error covariance matrix Pf = prior (forecast) error covariance matrix

Log of the posterior distribution MLEF (contd..) In statistical point of view, we optimize (maximize) posteriori distribution = - log P(β) Log of the prior distribution = - log P(y│β) Log of the likelihood = - log P(β│y) Log of the posterior distribution Optimal solution : Mode of the posterior distribution

CO2 fluxes on the Earth’s surface CO2 concentrations in the atmosphere Transport Model Transport model CO2 fluxes on the Earth’s surface CO2 concentrations in the atmosphere Observation operator : PCTM (Parametrerized Chemistry Transport Model) (Kawa et al., 2004)

Estimate the gridded surface carbon fluxes Ensemble based data assimilation system: MLEF (The inversion procedure developed) + PCTM (Parameterized Cemistry Transport Model) (Lokupitiya et al., 2008) Estimate the gridded surface carbon fluxes CO2 observations (Flasks,in-situ continuous data and CONTRAIL aircraft data) Background information

Observation Network Figure 2: Flask and continuous stations Open circles - continuous measurement sites Crosses - flask-sampling locations (NOAA-ESRL network)

Figure 3: CME flight tracks for year 2006

Pesudodata Experiment

Fluxes are in 2.50 (lon) × 20(lat) Ocean fluxes (monthly) Takahashi monthly values interpolated to model time Fossil fuel emissions interpolated to the year SiB3 model land fluxes (hourly) Mathematical representation of the Variations of the surface fluxes where NEE = Net Ecosystem Exchange RESP = Ecosystem respiration GPP = Gross Primary Production Ocean = Air-sea gas exchange of CO2 FF = Emissions due to fossil fuel combustion x, y = Spatial coordinates and t = time ’s = Multiplicative biases of the grid-scale component fluxes Solve for biases in 100 (lon) × 60(lat) resolution

First assimilation cycle (“cold start”) – Artificially generated CO2 observations (pseudo observations) were sampled at observation locations by running the PCTM forward for a year after three year spin-up (2000-2002). (ii) Simulated CO2 at the observation sites using data assimilation with a 4 week moving window First assimilation cycle (“cold start”) – first guess (unbiased case was assumed) Priors: β = 0 (unbiased case), σNEE = 0.2, σOcean = 0.1 perturbed background vectors (ensemble members) Compute the hourly CO2 fluxes and run through the transport model for 4- weeks to get simulated CO2 at the observation sites

(iv) End of the cycle, 3-D CO2 field was saved (used to start (III) The optimized biases were obtained by minimizing the distance between the simulated and observed CO2 concentrations using the method of MLEF (iv) End of the cycle, 3-D CO2 field was saved (used to start the next cycle) by running the transport model with optimized biases (v) This process was followed for the remaining cycles In each cycle, minimize the cost function by maximum likelihood method. Minimization is done in a reduced space (preconditioned space).

Assimilation Scheme . . . 1st Cycle 13th Cycle 4 Weeks 2nd Cycle . . . 13th Cycle 4 Weeks 2nd Cycle 4 Weeks 3rd Cycle 4 Weeks 30 Ensemble members Results are given for 13 cycles

Results Figure 4: Map of the monthly means of true land fluxes for year 2006 KgC/m2/Sec (Units are in 10-8 )

% uncertainty reduction Figure 5. Mean annual percentage uncertainty reduction for recovered biases using continuous and flask measurements

% uncertainty reduction Figure 6. Mean annual percentage uncertainty reduction for recovered biases using continuous, flask and CONTRAIL measurements

Ongoing and future work:   (I) Test the model performance for the pseudodata experiment by increasing the ensemble size. (II) Run the experiment with real data.

References Baker, I. T., Denning, A. S., Hanan, N., Prihodko, L., Vidale, P. –L., Davis, K., and Bakwin, P. 2003. Simulated and observed fluxes of sensible and latent heat and CO2 at the WLEF-TV Tower using SiB2.5.Global Change Biol. 9, 1262-1277.   CONTRAIL, 2013. Comprehensive Observation Network for Trace Gases by Airliner. (Available online at: http://www.cger.nies.go.jp/contrail/fstatis2012) [Accessed: 10th December 2013]. Jiang, F., Wang, H. W., Chen, J. M., Zhou, L. X., Ju, W. M., Ding, A. J., Liu, L. X., and Peters, W. 2013. Nested atmospheric inversion for the terrestrial carbon sources and sinks in China. Biogeosciences, 10, 5311–5324.   Kawa, S. R., Erickson III, D. J., Pawson, S., and Zhu, Z. 2004. Global CO2 transport simulations using meteorological data from the NASA data assimilation system. J. Geophys. Res. 109(D18), doi: 10.1029/2004JD004554.

Lokupitiya, R. S. , Zupanski, D. , Denning, A. S. , Kawa, S. R Lokupitiya, R. S., Zupanski, D., Denning, A. S., Kawa, S. R., Gurney, K. R., and Zupanski, M. 2008. Estimation of global CO2 fluxes at regional scale using the maximum likelihood ensemble filter. J. Geophys. Res. 113, D20110, doi:10.1029/2007JD009679. Niwa, Y., Machida, T., Sawa, Y., Matsueda, H., Schuck, T. J., Brenninkmeijer, C. A. M., Imasu, R., and Satoh, M. 2012. Imposing strong constraints on tropical terrestrial CO2 fluxes using passenger aircraft based measurements. J. Geophys. Res. 117, D11303, doi:10.1029/2012JD017474. Patra, P. K., Niwa, Y., Schuck, T. J., Brenninkmeijer, C. A. M., Machida, T., Matsueda, H., and Sawa, Y. 2011. Carbon balance of South Asia constrained by passenger aircraft CO2 measurements. Atmos. Chem. Phys, 11, 4163-4175.   Patra, P. K., Canadell, J. G., Houghton, R. A., Piao, S. L., Oh, N. –H., Ciais, P., Manjunath, K. R., Chhabra, A., Wang, T., Bhattacharya, T., Bousquet, P., Hartman, J., Ito, A., Mayorga, E., Niwa, Y., Raymond, P. A., Sarma, V. V. S. S., and Lasco, R. 2013. The carbon budget of South Asia.Biogeosciences, 10, 513-527.

Peters, W. , Miller, J. B. , Whitaker, J. , Denning, A. S. , Hirsch, A Peters, W., Miller, J. B., Whitaker, J., Denning, A. S., Hirsch, A., Krol, M. C., Zupanski, D., Bruhwiler, L., and Tans, P. P. 2005. An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations. J. Geophys. Res. 110(D24), doi:10.1029/2005JD006157. Zupanski, D., Denning, A. S., Uliasz, M., Zupanski, M., Schuh, A. E., Rayner, P. J., and Peters, W. 2007. Carbon flux bias estimation employing Maximum Likelihood Ensemble Filter (MLEF). J. Geophys. Res. 112, D17107, doi:10.1029/2006JD008371.   Zupanski, M. 2005. Maximum likelihood ensemble filter: Theoretical aspects. Mon. Wea. Rev.133, 1710-1726.  

Acknowledgements: This research is supported by the grants from National Research Council, Sri Lanka I would also like to thank Dr. Prabir Patra for providing CONTRAIL aircraft information.

My supervisor (P.I.): Co-investigators: Dr. R.S. lokupitiya (P.I.), University of Sri Jayawardenepura, Sri Lanka Co-investigators: Dr. E. Y. K. Lokupitiya, University of colombo, Sri Lanka Prof. R. G. N. Meegama, University of Sri Jayawardenepura, Sri Lanka Dr. Prabir Kumar Patra, Research Institute for Global Change, Japan Prof. A. Scott Denning, Colorado State University, USA

Thank You