Clustering XCO2 temporal change to assess CO2 exchanging strength of biosphere-atmosphere with GOSAT observations
Abstract
The temporal change of atmospheric carbon dioxide (CO2) concentration, greatly related to the local activities of CO2 uptake and emission, including biospheric exchange and anthropogenic emission, is one of important information for regions identification of carbon source and sink. Satellite observations of CO2 has been used for detecting the change of CO2 concentration for a long time. In this study, we used the grid data of column-averaged CO2 dry air mole fraction (XCO2) with the spatial resolution of 1 degree and the temporal resolution of 3 days from 1 June 2009 to 31 May 2014 over the land area of 30° - 60° N to implement a clustering of temporal changing characteristics for the Greenhouse Gases Observing Satellite (GOSAT) XCO2 retrievals. Grid data is derived using the gap filling method of spatio-temporal geostatistics. The clustering method is one adjusted K-mean for the gap existed time-series data. As a result, types and number of clusters are specified based on the temporal characteristic of XCO2 by using the optimal clustering parameters. The biospheric absorption and surface emission of atmospheric CO2 is discussed through the analysis of the different yearly increase and seasonal amplitude of XCO2 each cluster combined with correlation analysis with vegetation index from the Moderate-resolution Imaging Spectroradiometer (MODIS) and fossil fuel CO2 emission data from Open-source Data Inventory for Anthropogenic CO2 (Odiac). Regions of strong or weak biosphere-atmosphere exchange, or significant disturbance from anthropogenic activities can be identified. In conclusion, gap filled XCO2 from satellite observations can help us to take an analysis of atmospheric CO2, results of the coupled biosphere-atmosphere, by their spatio-temporal characteristics as well as the relationship with the other remote sensing parameters e.g. MODIS related with biospheric photosynthetic or respiration activities.
- Publication:
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EGU General Assembly Conference Abstracts
- Pub Date:
- April 2017
- Bibcode:
- 2017EGUGA..19.4408H