Japan Geoscience Union Meeting 2019

Presentation information

[E] Oral

M (Multidisciplinary and Interdisciplinary) » M-GI General Geosciences, Information Geosciences & Simulations

[M-GI30] Data assimilation: A fundamental approach in geosciences

Wed. May 29, 2019 1:45 PM - 3:15 PM 104 (1F)

convener:Shin ya Nakano(The Institute of Statistical Mathematics), Yosuke Fujii(Meteorological Research Institute, Japan Meteorological Agency), SHINICHI MIYAZAKI(Graduate School of Science, Kyoto University), Takemasa Miyoshi(RIKEN), Chairperson:Takemasa Miyoshi(RIKEN Advanced Institute for Computational Science), Shin'ya Nakano(情報・システム研究機構 統計数理研究所)

2:15 PM - 2:30 PM

[MGI30-03] Data assimilation experiments with MODIS LAI observations and the dynamic global vegetation model SEIB-DGVM over Siberia

★Invited Papers

*Hazuki Arakida1, Shunji Kotsuki1, Shigenori Otsuka1, Yohei Sawada1,2, Takemasa Miyoshi1 (1.RIKEN Center for Computational Science, 2.Meteorological Research Institute)

Keywords:data assimilation, vegetation model, satellite data

In the previous study, Arakida et al. (2017) developed a data assimilation system with the SEIB-DGVM (Spatially Explicit Individual-Based Dynamic Global Vegetation Model, Sato et al. 2007) and assimilated satellite-observed Leaf Area Index (LAI) successfully at a flux measurement site in Siberia. In this study, we extend the experiment to a large domain in Siberia and estimate the vegetation functions and structures, and model parameters related to the phenology of the deciduous needle-leaved tree and the undergrowth. At the annual meeting in 2017, we presented the results of total LAI, tree LAI, above ground biomass, carbon flux, and the model parameters. This year, we present further comparisons with the previous studies: carbon flux (FLUXCOM: Tramontana et al., 2016, Jung et al., 2017) estimated by a machine learning method using in-situ flux measurements and various explanatory data, tree LAI (Delbart et al., 2005, Kobayashi et al., 2010) estimated by a radiative transfer model and satellite observations, and above ground biomass (Liu et al., 2015) estimated by satellite observations. The results showed high correlations between this study and the previous studies except for above ground biomass. This is probably because above ground biomass of Liu et al. [2015] is estimated from microwave-based observations, different from optical-based observations in this study, Kobayashi et al. [2010], and FLUXCOM. The results suggest that the type of the satellite sensor affect the estimation results. Most of the estimated variables by DA were highly correlated with the observed LAI and the previous studies, indicating that the DA system generally performed well over Siberia.

Arakida, H., T. Miyoshi, T. Ise, S. I. Shima, and S. Kotsuki (2017), Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model, Nonlinear Proc. Geoph., 24, 553-567, doi:10.5194/npg-24-553-2017.

Delbart, N., L. Kergoat, T. L. Toan, J. Lhermitte, and G. Picard (2005), Determination of phenological dates in boreal regions using normalized difference water index, Remote Sens. Environ., 97, 26-38, doi:10.1016/j.rse.2005.03.011.

Jung, M., M. Reichstein, C. R. Schwalm, C. Huntingford, S. Sitch, A. Ahlström, A. Arneth, G. Camps-Valls, P. Ciais, P. Friedlingstein, F. Gans, K. Ichii, A. K. Jain, E. Kato, D. Papale, B. Poulter, B. Raduly, C. Rödenbeck, G. Tramontana, N. Viovy, Y. P. Wang, U. Weber, S. Zaehle and N. Zeng (2017), Compensatory water effects link yearly global land CO2 sink changes to temperature, Nature, 541, 516-520, doi:10.1038/nature20780.

Kobayashi, H., N. Delbart, R. Suzuki, and K. Kushida (2010), A satellite-based method for monitoring seasonality in the overstory leaf area index of Siberian larch forest, J. Geophys. Res., 115, G01002, doi: 10.1029/2009JG000939.

Liu, Y. Y., A. I. J. M. van Dijk, R. A. M. de Jeu, J. G. Canadell, M. F. McCabe, J. P. Evans, and G. Wang (2015), Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change, 5, 470-474, doi:10.1038/nclimate2581.

Sato, H., A. Itoh, and T. Kohyama (2007), SEIB–DGVM: A new Dynamic Global Vegetation Model using a spatially explicit individual-based approach, Ecol. Model., 200, 279–307, doi: 10.1016/j.ecolmodel.2006.09.006.

Tramontana, G., M. Jung, C. R. Schwalm, K. Ichii, G. Camps-Valls, B. Ráduly, M. Reichstein, M. A. Arain, A. Cescatti, G. Kiely, L. Merbold, P. Serrano-Ortiz, S. Sickert, S. Wolf, and D. Papale (2016), Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291-4313, doi:10.5194/bg-13-4291-2016.