ACG37-05

Room:201A

Time:May 21 15:15-15:30

Global model inter-comparison with GOSAT L4A and support vector machine based estimates of biospheric variables
Masayuki Kondo1∗ , Kazuhito Ichii11Faculty of Symbiotic Systems Science, Fukushima University

Estimation of carbon exchange in terrestrial ecosystem associates with difficulties due to complex entanglement of physical,
chemical, and biological processes: thus, the net ecosystem productivity (NEP) estimated from simulation often differs amongprocess-based terrestrial ecosystem models. In addition to complexity of the system, validation can only be conducted in a point
scale since reliable observation is only available from ground observations. With a lack of reliable spatial data, extension of model
simulation to the global scale results in significant uncertainty in the future carbon balance and climate change. Greenhouse gases
Observing SATellite (GOSAT), launched by the Japanese space agency (JAXA) in January, 2009, is the 1st operational satellite
promised to deliver the net land-atmosphere carbon budget to the terrestrial biosphere research community. Using this information, the model reproducibility of carbon budget is expected to improve: hence, gives a better estimation of the future climatechange.
Because of the direct association with climate change, improving estimation of global NEP is essential; yet, global grossprimary productivity (GPP) and ecosystem respiration (RE) need to improve as well for further sophistication of ecosystemmodeling. In the system of carbon cycle, GPP and RE are the true physiological quantities representing photosynthesis and respiration, and NEP is a byproduct of them. Since a major purpose of process-based ecosystem models is to clarify the mechanismof carbon cycle, it is important to invest efforts to refine GPP and RE as well.
Currently, the most reliable estimate of global GPP is provided by observation-based empirical upscaling with machine learning models [Jung et al. 2011]. Machine learning regression is based on a network of eddy covariance flux tower observation, inconjunction with global satellite remote sensing and meteorological data sets. Because of the high correlation with GPP, availability of long-term global observations of vegetation indexes (e.i. EVI, NDVI, and NDWI) from operational satellites makes
performance of machine learning model finer in prediction of GPP. Because of limited availability of carbon pool data, however,
it is difficult to induce equivalent performance in RE with machine learning models [Jung et al. 2011]. Instead of a direct estimation, combination of global GPP estimated by machine learning regression and NEP from GOSAT L4A would produce a morereliable budget of global RE.
This initial analysis is to compare a set of observation-based global carbon flux products, NEP from GOSAT L4A, GPP fromsupport vector machine regression, and RE from a combination of them, with three types of TEMs and an inversion model:
Biome-BGC (prognostic model), CASA (diagnostic model), LPJ (dynamic vegetation model), and Carbon Tracker (inversionmodel). Comparison was conducted with the standardized format based on GOSAT L4A: 42 sub-continental tiles and monthly
temporal coverage from June 2009 to May 2010. Through the comparison, we discuss similarities and dissimilarities in (1) seasonal variations, (2) global and annual averages, (3) variability with climate (air temperature, precipitation, and solar radiation).
Reference
Jung, M., et al. (2011), Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived fromeddy covariance, satellite, and meteorological observations, Journal of Geophysical Research, 116.
Acknowledgement
The study is financially supported by the Environment Research and Technology Development Fund (RFa-1201) of the Ministry of the Environment of Japan, and Global Change Observation Mission ? Climate (GCOM-C) of Japan Aerospace Exploration Agency.
Keywords: GOSAT, machine learning regression, terrestrial ecosystem model, carbon cycle

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