This manuscript assessed the methane emissions from Northern Europe, focusing on Nordic wetland emissions, using data assimilation approaches. The authors used atmospheric transport model and dynamic global vegetation model, each of which were constrained by atmospheric concentration and terrestrial flux data. They found that methane emissions from Northern Europe was overestimated in existing emission inventory and wetland model. Also, they found remaining large uncertainties in the background data and numerical models.

General comments

This study addressed the regional budget of methane, an important greenhouse gas, using contemporary data assimilation approaches. Although several results were unrealistic (e.g., negative wetland emissions and winter emissions) due to biases in data and models, this study gave a progress in the research field. As a result of this study, much lower Nordic wetland emissions were estimated. This was a bit surprising for me, because I thought that the wetlands in this area have been most intensively investigated by biogeochemical studies.

            The manuscript is well prepared, but I offer reorganization for the Results section. It contains several methodological statements and discussions (see my minor comments). Finally, my recommendation is ‘minor revision’ before being accepted for publication.

Minor comments

Line 1: Abstract gives methodological overview but lacks statement about the outcomes (what did you found) of this study.

Line 117: LUMIA (Monteil and Scholze, 2021)

Line 126: 33°N (not S?)

Line 145: What do you mean for “quantitatively equivalent results”?

Line 227–231: This part should be moved to Method

Table 2: At several stations (hpb, hun, mhd, pal), two records are shown. What is the difference (e.g., in-situ or flask)?

Line 249–254: This part is not about result but about methodology.

Line 290: May (not may).

Line 352–357: This part may be moved to Discussion.

Line 359: Perhaps, a gridded wetland methane flux data generated from in-situ observations by using machine learning algorithms (e.g., UpCH4, McNicol et al., 2023) can be used for the comparison with gridded model-estimated flux.

Line 413: (LUMIA) is (not s) ?

Reference

McNicol, G., et al.: Upscaling wetland methane emissions from the FLUXNET-CH4 eddy covariance network (UpCH4 v1.0): Model development, network assessment, and budget comparison, AGU Advances, 4, e2023AV00956, 10.1029/2023AV000956, 2023.