JpGU-AGU Joint Meeting 2020

セッション情報

[E] 口頭発表

セッション記号 A (大気水圏科学) » A-CG 大気海洋・環境科学複合領域・一般

[A-CG47] Global Carbon Cycle Observation and Analysis

コンビーナ:市井 和仁(千葉大学)、Prabir Patra(Research Institute for Global Change, JAMSTEC)、Forrest M. Hoffman(Oak Ridge National Laboratory)、Makoto Saito(National Institute of Environmental Studies)

The Paris Agreement under the United Nations Framework Convention on Climate Change (UNFCCC) is a landmark agreement, which aims at reduction of greenhouse gases (GHGs) emission to keep the global warming below 2 degC. The national commitments and progress should be carefully monitored and verified by international bodies using different but complementary methodologies.

In recent years, many observations and techniques to monitor GHGs budget have expanded. The improvements include observational platforms for monitoring atmospheric GHGs, national or regional emission inventories, top-down models (e.g. atmospheric inverse models), and bottom-up models (e.g. process-based models). However, due to uncertainties in modeling and sparse observation networks, large uncertainties exist in GHG sources/sinks estimations at global and regional scales. These uncertainties lead to large variations in future projections of GHG budgets and climate changes.

The purpose of the session is to discuss state-of-the-art techniques for estimation of GHG (e.g. CO2, CH4, N2O) budgets at global and regional scales. The topic includes natural and anthropogenic processes, various methodologies (e.g. in-situ observation, aircraft monitoring, remote sensing, modeling), and various targets (e.g. atmosphere, terrestrial, and ocean), various spatial and temporal coverage (e.g. regional to global scales and past-present-future). Improved estimates of emissions from land use change, forest fires, and other anthropogenic sources (urban developments and thermal power station etc.) are also of interest. We also welcome discussions for designs and plans for future studies targeting urban and rural scale emission estimations using sophisticated modeling tools and inventories.