Unit on Climate Change,Research and Development Initiative, Chuo University

GOSAT Validation in Mongolia

PROJECT OVERVIEW

Accuracy Evaluation of Greenhouse Gas Emissions Estimates Using the GOSAT Series Targeting Mongolia

Introduction

The Ministry of the Environment, in pursuit of continuous contributions to the advancement of climate change science and international climate change policies, has developed, launched, operated, and provided data free of charge for the Greenhouse gases Observing Satellite “IBUKI” (GOSAT) and its successor “IBUKI-2” (GOSAT-2). This initiative is in accordance with the Basic Space Plan and is a collaborative effort with the National Institute for Environmental Studies and the Japan Aerospace Exploration Agency (JAXA).

Eleven years after its launch in 2009, GOSAT continues its operation, and since February 2019, following its launch at the end of October 2018, GOSAT-2 has been in regular operation. After evaluating the performance of its observation sensors and the effectiveness of its observation data, the provision of Level 1 products (spectral data) began in August, and the provision of Level 2 products (concentration data) to researchers is being carried out sequentially.

Like GOSAT, GOSAT-2 monitors global concentrations of carbon dioxide and methane. It also aims to improve the estimation of anthropogenic emissions through the world’s first simultaneous observation of carbon dioxide and carbon monoxide from space, aiming to identify sources of carbon dioxide emitted by human activities and improve the accuracy of emission estimates.

International Trends

Under the Paris Agreement, countries are obligated to report their anthropogenic greenhouse gas (GHG) emissions. To enhance the transparency of these reports, Japan promotes the comparison and verification of emission reports by each country using data observed by the GOSAT series.

Accurate estimation of emissions from cities, one of the main sources of carbon dioxide, requires a precise understanding and correction of influences such as atmospheric transport and vegetation fluxes.

Project Objectives

Continuing from the previous fiscal year, this project aims to validate and underpin the effectiveness and reliability of Japan’s GOSAT series by comparing carbon dioxide emissions recorded in the greenhouse gas inventory for Mongolia, focusing on Ulaanbaatar and its vast grassland areas, with emissions estimates derived from satellite observation data including the GOSAT series.

Evaluation of the Accuracy of GHG Emission and Absorption Estimates in Ulaanbaatar City and Its Surroundings

Based on Ulaanbaatar city and its surroundings in 2018, the spatial distribution of CO2 concentrations estimated with WRF-Chem based on the CO2 inventory and atmospheric transport model was compared with the spatial distribution of CO2 concentrations from GOSAT products estimated using satellite observation data and atmospheric transport models. An inverse analysis was conducted by constructing a Green’s function to optimize the difference, using GOSAT observation results and WRF-Chem simulation results.

As a result, a model was constructed that allows estimating the posterior CO2 emissions with an error range of about +0.3% to +4.3% from the 2018 GHG emission inventory (GDP estimated value).

This error range indicates the errors that arise when setting the observational error for XCO2 by GOSAT at 2 ppmv and the emission error at 200, 400, 800 tons/h as a priori (pre-experimental) errors.

 

Main Modules Related to Methane in WRF-Chem V4 and Evaluation of GHG Emission and Absorption Estimates Accuracy Across Mongolia

 

 

Evaluation of the Accuracy of GHG Emission and Absorption Estimates in Mongolia

For the entire territory of Mongolia in 2018, the spatial distribution of GHG concentrations estimated with WRF-Chem based on the GHG inventory and atmospheric transport model was compared with the spatial distribution of GHG concentrations from GOSAT products estimated using satellite observation data and atmospheric transport models. The “Evaluation of the Accuracy of GHG Emission and Absorption Estimates in Ulaanbaatar City and Its Surroundings” used the post-emission amounts optimized from this comparison as input values for the WRF-Chem and VPRM models to calculate CO2 emission and absorption in forests, which are included in the Mongolian National GHG Inventory.

Comparing the results from the Mongolian National GHG Emission Inventory and this model revealed differences, with the Mongolian National GHG Absorption Inventory showing -24,452 GgCO2 (for 2014) and this model showing -176,930 GgCO2 (for 2018). However, since there are no significant discrepancies between past studies, local grassland flux observations, and model results, further refinement in collaboration with the Mongolian side is warranted.

Spatial distribution differences between GOSAT’s XCO2 (ppm) and XCO2 simulation results from the WRF-Chem model using post-emission amounts constructed in this project showed cases where GOSAT’s XCO2 appeared lower. This suggests the possibility of missing sources underestimated in the TM3 data, the global CO2 atmospheric transport simulation results from the Max Planck Institute used as boundary conditions this time, especially near borders or outside the country. Future enhancements could be expected by constructing models that can identify emissions from sources outside the computational domain, such as using the Lagrangian particle model.

Collection and Maintenance of Ground Observation Data for GHG Emission Estimation Accuracy Evaluation

We collected CO2 flux observation data and measured GHG concentrations as necessary.

At the observation sites near Ulaanbaatar, Nalaikh acted as a sink, Ovorzaisan was nearly net-zero, and notably, Hustai was a source of CO2 emissions throughout the year. Differences in precipitation, vegetation, and soil moisture among sites might contribute to the variations in CO2 emission and absorption. This CO2 flux observation data was used for comparison in the “Evaluation of GHG Emission and Absorption Estimates Accuracy Across Mongolia.”

In Ulaanbaatar, data acquisition, calibration, and management for CO2 flux observation, as well as flask sampling at ground level and on the TV tower, were conducted in collaboration with the Institute of Meteorology and Hydrology Research (IRIMHE) and the Institute of Geography and Geoecology of the Mongolian Academy of Sciences (IGGMAS). Simultaneously, atmospheric pollutants, including PM2.5, were collected and organized.

Analysis of atmospheric pollutants showed that the PM2.5 concentration significantly improved due to the Mongolian government’s air pollution improvement policy, which included banning coal combustion in ger areas and transitioning to briquettes for fuel. Continuous ground observation results of CO2 did not show a significant reduction effect from fuel conversion, but ground-level CO2 concentrations estimated using GOSAT data showed a decreasing trend.

  • Nalaikh, HustaiにおけるCO2フラックス測定システムの設置場所

Support for Information Disclosure

After sharing the outcomes with the Ministry of the Environment officials, two meetings were held:

A) A progress briefing for Mongolian government officials led by the climate change envoy Batjargal on February 1, 2021, confirmed these results. It was agreed to reference this work in creating the 2018 Mongolian National GHG Emission Inventory submitted to the UNFCCC and to collaborate towards refining CO2 absorption amounts.

B) An online meeting with Youssef Nassef, director of the Intergovernmental Support and Collective Progress (ISCP) Division overseeing the Global Stocktake at the UNFCCC on February 12, 2021, received positive evaluations that the GHG inventory estimation method developed in this project is a very effective approach since it is developed in collaboration with Mongolia to establish a sustainable method for Mongolia’s own use, and if demonstrated in several other countries, it is expected to be recognized as one of the global standards. There was an intention to introduce specific countries, such as Kazakhstan, for horizontal deployment in the Global Stocktake verification process and the UNFCCC’s Global Stocktake expert group.

Participation in The 16th International Workshop on Greenhouse Gas Measurements from Space (IWGGMS-16) online collected trend information on inverse analysis methods using satellite data. Various methodologies for inverse analysis, including Lagrangian particle models and concentration difference methods, were reported, highlighting the current state of trial among diverse methodologies without an established routine method or conditions.

Main Results

  1. By conducting inverse analysis using Green’s functions with WRF-Chem and VPRM models based on GOSAT-series observation results, a model was constructed that can estimate the posterior GHG emission inventory with an error range of about +0.3% to +4.3% from the Mongolian National GHG Emission Inventory (GDP estimated value).
  2. Comparing CO2 emission and absorption amounts in forests, which are part of the Mongolian National GHG Inventory, showed differences, with the national GHG emission inventory recording -24,452 GgCO2 in 2014, and our model showing -177,975 GgCO2 in 2018. Given the lack of significant discrepancy between our model results and previous studies or grassland flux observations, further refinement in collaboration with Mongolia is required.
  3. The Mongolian government’s air pollution improvement policies, such as banning coal burning in the Ger district and transitioning to fuel briquettes, significantly improved PM2.5 concentrations. Continuous ground observation results of CO2 did not show a significant reduction effect from the fuel transition, but the ground CO2 concentrations estimated using GOSAT data showed a decreasing trend.
  4. A GOSAT progress briefing for the Mongolian government was held on February 1, 2021, to confirm the above results. It was agreed to reference this work in the creation of the 2018 Mongolian National GHG Emission Inventory for submission to the UNFCCC and to collaborate towards refining CO2 absorption amounts.