14:30 〜 14:45
[ACG22-14] 経験的広域化手法による大気―陸域間の熱・物質循環の広域推定手法の現状と応用
キーワード：陸域生態系、広域化、物質循環
The terrestrial biosphere plays important roles in regional and global energy and carbon cycles through biogeochemical and biophysical processes, in turn affecting the trajectory of climate change. Despite the importance of this issue, model intercomparison efforts have revealed large and persistent uncertainties in CO2 fluxes among terrestrial biosphere models. Reducing uncertainties in terrestrial carbon cycle simulation is a challenging task because of insufficient observed CO2 fluxes, which serve as references for refining terrestrial biosphere models at regional and global scales. Recently, the network of eddy-covariance observation has increased, and more data have become available to public. These datasets allow data-driven modeling (empirical upscaling) of terrestrial CO2 and H2O fluxes, and their application has shown significant progresses. Since data-driven models rely on the statistical relationship between observed fluxes and explanatory variables, the estimated flux is independent from terrestrial ecosystem models. Therefore, the results provide a new data constraint to terrestrial carbon and energy cycle communities.
In this presentation, we introduce an overview and applications of data-driven modelling to terrestrial biogeochemical studies. We used regional and global networks of eddy-covariance observations (e.g. AsiaFlux and FLUXNET) and remote sensing as the forcing of data-driven model, and conducted various applications using them. First, we will show the methodology and algorithms of data-driven model. Second, we will show the applications of the resulting data: i.e., spatio-temporal variability in terrestrial CO2 flux (Saigusa et al. 2010; Ueyama et al. 2013) and energy balance (Ueyama et al. 2014). Third, we will present evaluation of data-driven models with an assimilation of atmospheric CO2 from GOSAT Level 4A product (top-down approach) (e.g. Kondo et al. 2015). Fourth, we will demonstrate that regional/global CO2 and H2O fluxes upscaled by data-driven models can be used to refine terrestrial ecosystem models (e.g. Ichii et al. 2009).
Reference
Ichii et al. (2009) Agr. For. Met., 149, 1907-1918.
Kondo et al. (2015) JGR Biogeosciences.120, 1226–1245, doi:10.1002/2014JG002866.
Saigusa et al. (2010) Biogeosciences, 7, 641-655.
Ueyama et al. (2013) JGR Biogeosciences, 118, 1266–1281, doi:10.1002/jgrg.20095.
Ueyama et al. (2014) JGR Biogeosciences, 119, 1947–1969, doi:10.1002/2014JG002717.
Acknowledgement
This study was supported by the Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan, the JAXA Global Change Observation Mission (GCOM) project (grant No. 115), and the JSPS KAKENHI (grant No. 25281003).
In this presentation, we introduce an overview and applications of data-driven modelling to terrestrial biogeochemical studies. We used regional and global networks of eddy-covariance observations (e.g. AsiaFlux and FLUXNET) and remote sensing as the forcing of data-driven model, and conducted various applications using them. First, we will show the methodology and algorithms of data-driven model. Second, we will show the applications of the resulting data: i.e., spatio-temporal variability in terrestrial CO2 flux (Saigusa et al. 2010; Ueyama et al. 2013) and energy balance (Ueyama et al. 2014). Third, we will present evaluation of data-driven models with an assimilation of atmospheric CO2 from GOSAT Level 4A product (top-down approach) (e.g. Kondo et al. 2015). Fourth, we will demonstrate that regional/global CO2 and H2O fluxes upscaled by data-driven models can be used to refine terrestrial ecosystem models (e.g. Ichii et al. 2009).
Reference
Ichii et al. (2009) Agr. For. Met., 149, 1907-1918.
Kondo et al. (2015) JGR Biogeosciences.120, 1226–1245, doi:10.1002/2014JG002866.
Saigusa et al. (2010) Biogeosciences, 7, 641-655.
Ueyama et al. (2013) JGR Biogeosciences, 118, 1266–1281, doi:10.1002/jgrg.20095.
Ueyama et al. (2014) JGR Biogeosciences, 119, 1947–1969, doi:10.1002/2014JG002717.
Acknowledgement
This study was supported by the Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan, the JAXA Global Change Observation Mission (GCOM) project (grant No. 115), and the JSPS KAKENHI (grant No. 25281003).