the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 21 Sep 2026
Methane Inversion inter-Comparison (MICA): A Multi-model Estimation of Regional and National Emissions in Asia
Abstract. Accurate quantification of methane (CH4) emissions from countries/regions is essential for effective emission reduction policy, but large uncertainties persist due to sparse observations and divergent bottom-up inventories. This study establishes a top-down Asian regional methane emission dataset based on the Methane Inversion inter-Comparison for Asia (MICA), using an ensemble of seven inversion systems (five global, two regional) assimilating surface in-situ measurements and GOSAT satellite retrievals for 2010–2021 under a common protocol. Validation against independent surface, aircraft, and balloon data demonstrates substantial improvements in model performance after inversion, with significant reductions in model-observation Root Mean Square Error (RMSE). Posterior estimates indicate an overall reduction of prior emissions by ~15% for the study area, with the largest decline in East Asia (~25.8%). Though a systematic downward adjustment by the inversion ensemble is observed for most top emitting-countries, the median differences between posterior and prior emissions vary across countries, from -0.9% (Mongolia) to -16.2% (China). Sectoral results suggest increasing emissions from waste management across developing Asian countries and a fourfold increase in Indonesian coal-mine emissions. Comparisons with National Greenhouse Gas Inventories (NGHGIs) reveal systematic underreporting by several nations — notably India, Pakistan, and Bangladesh. We find that inter-model spread is driven largely by horizontal resolution (Coefficient of Variation 10–20%), and that joint assimilation of satellite and surface observations is an effective option for constraining emissions in regions with sparse monitoring or complex land–sea geometries. This dataset provides a foundation for regional and national methane budget assessment in Asia and inverse model development, with robust consistency across multiple inversion systems and independent observations. The dataset generated in this study is openly available via Zenodo (https://doi.org/10.5281/zenodo.22699291, Wang et al., 2026).
- Preprint
(1506 KB) - Metadata XML
-
Supplement
(3195 KB) - BibTeX
- EndNote
Status: open (extended)
-
RC1: 'Comment on essd-2026-420', Anonymous Referee #1, 20 Oct 2026
reply
-
AC1: 'Reply on RC1', Fenjuan Wang, 27 Oct 2026
reply
We thank the referee for the careful and constructive review and for the positive assessment of the dataset. The suggestions have helped us improve the manuscript. Our point-by-point responses are given below.
-
AC1: 'Reply on RC1', Fenjuan Wang, 27 Oct 2026
reply
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 464 | 147 | 40 | 651 | 89 | 73 | 64 |
- HTML: 464
- PDF: 147
- XML: 40
- Total: 651
- Supplement: 89
- BibTeX: 73
- EndNote: 64
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Recommendation: Minor revision
General comments
Wang et al. present the MICA dataset, which brings together global and regional methane inversions over Asia for 2010–2021. The coordinated intercomparison, additional Asian surface observations, and comparison of surface- and GOSAT-constrained inversions make this a useful contribution to assessing regional and national methane emissions. The manuscript is well suited to ESSD, and I recommend publication after minor revision. My main comments concern the documentation of experiment configurations and ensemble statistics, the derivation of sectoral products, and the interpretation of their dependence on prior assumptions. Clarifying these points and resolving the inconsistencies noted below would improve the transparency and usability of the dataset.
Specific comments
Lines 45–47: The documented SURF and GOS experiments compare alternative observing systems. Please revise the abstract so that joint surface–satellite assimilation is presented as a potential approach rather than a demonstrated result of MICA, unless a joint-assimilation experiment is documented.
Lines 48–49: Please qualify “robust consistency” by specifying the regions, scales, and validation results that support this statement.
Lines 79–80: Please clarify whether “divergences at finer scales” refers to differences among top-down estimates or between top-down and bottom-up estimates, and provide a concrete example.
Lines 136–139 and 298: The regional lists contain 23 entries, whereas line 298 states that the domain includes 28 countries. Please provide a complete list consistent with the country masks used in the analysis. In particular, check the inclusion of Baker Island and the omission of Sri Lanka, which is discussed in Section 4.3.
Section 2.4, particularly lines 186–187: Please briefly summarize the treatment of methane losses, including OH fields and their temporal variability, and the initial and boundary/background concentrations, with references for details. Clarify the MIROC4-ACTM posterior loss correction and whether any loss terms are optimized. Briefly acknowledge the implications of these assumptions for emission and trend estimates.
Line 193, Table 3, and Sections 3.4 and 4.3: Please clarify how sectoral emissions are derived across the inversion systems. List the six source categories optimized by NTFVAR and explain how they map onto the broader categories in Table 3. For each system, distinguish separately optimized sectors from those allocated using prior fractions or shared scaling factors when only total fluxes or broad source groups are optimized. In Section 4.3, specify whether the reported sectoral differences and trends refer to priors, posteriors, or both. Briefly explain what the existing NTFVAR/NTFVAR2 comparison shows about the dependence of posterior sectoral trends on prior assumptions, and qualify the conclusions accordingly.
Line 198, Table 2 and Section 4.2: Please explain the NTLB resolutions reported as 27 km, 0.27°, and 0.5°. Indicate whether these refer to different transport, inversion, or output grids, and correct any inconsistent values or labels.
Line 209, Tables 2–4 and Section 2.5: Please add the actual simulation periods for each configuration and experiment, preferably to Table 4, and consolidate the model-specific departures from the common protocol already mentioned in the text, including prior and observation products. Consistently distinguish the six inversion systems from the seven submitted configurations, identifying NTFVAR2 as an alternative configuration of NTFVAR.
Section 3.2: Please qualify the attribution to lower satellite sensitivity to local surface fluxes and briefly acknowledge other possible influences, such as observational sampling.
Figures 5 and 6: Please specify the experiment(s) used. Clarify how NTFVAR2 is counted and whether membership changes across years or sectors, given the shorter GEOS-Chem record and the uneven availability of sectoral products in Table 4. For Figure 5, state whether anthropogenic and total emissions use the same members and whether each boxplot summarizes three-year means for individual members or pooled annual estimates. For Figure 6, specify whether the sectoral values are ensemble means or medians. Please explain how sources not shown and soil uptake are accounted for when relating the displayed sectoral values to the total fluxes reported elsewhere.
Please reconcile the apparent contradiction for Southeast Asia: lines 287–289 and Figure 4 indicate lower GOS emissions than SURF, whereas lines 540–541 state the reverse.
Technical comments
Tables S1–S2: Please correct the latitude/longitude unit labels and check the ULD and DCD coordinates in Table S2. The ULD coordinate values appear interchanged, and the DCD entry appears inconsistent with Figure 1. Clarify whether these discrepancies are confined to the tables or also affect model sampling.
Tables S1–S2: Please clarify whether the PDI entries represent distinct aircraft and surface datasets or whether the observation type in Table S1 requires correction. If the same observation records are available through both ObsPack and the additional datasets, briefly explain how duplicate records were handled.
Figure S3: Please check whether “SURF” in the caption should read “GOS”, and specify the averaging period for each panel.
Please check the language and cross-references throughout, including “isd” at line 177, “expectations” at line 354, the duplicated “to” at line 545, and the section references for masks and aggregation.