Our understanding of how rapid Arctic warming and permafrost thaw affect global climate dynamics is restricted by limited spatiotemporal data coverage due to logistical challenges and the complex landscape of Arctic regions. It is therefore crucial to make best use of the available observations, including the integrated data analysis across disciplines and observational platforms. To alleviate the data compilation process for syntheses, cross-scale analyses, Earth system models, and remote sensing applications, we introduce the ARctic greenhouse Gas Observation metadata version 1 (ARGO), a new meta-dataset comprised of greenhouse gas observations from various observational platforms across the Arctic and boreal biomes within the polar region of the Northern Hemisphere. ARGO provides a centralised repository for metadata on carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) measurements and is linked to an interactive online tool (https://www.bgc-jena.mpg.de/argo/, last access: 6 May 2025). This tool offers prompt metadata visualisation for the research community. Here, we present the structure and features of ARGO, underscoring its role as a valuable resource for advancing Arctic climate research and guiding synthesis efforts in the face of rapid environmental change in northern regions. The ARGO meta-dataset is openly available for download at Zenodo (https://doi.org/10.5281/zenodo.13870389; Vogt et al., 2024 a ).

Arctic methane (CH₄) budgets are uncertain because field measurements often capture only fragments of the wet-to-dry gradient that control tundra CH₄ fluxes. Wet hotspots are over-represented, while dry, net-sink sites are under-sampled. We paired over 13 000 chamber flux measurements during peak growing season in July (2019–2024) from Trail Valley Creek in the western Canadian Arctic with co-registered remotely sensed predictor variables to test how spatial resolution (1 m vs. 10 m) and choice of machine-learning algorithm shape upscaled CH₄ flux maps over our 3.1 km² study domain. Four algorithms for CH₄ flux scaling (Random Forest (RF), Gradient Boosting Machine (GBM), Generalised Additive Model (GAM), and Support Vector Regression (SVR)) were tuned using the same stack of multispectral indices, terrain derivatives and a six-class landscape classification. Tree-based models such as RF and GBM offered the best balance of 10-fold cross-validated R² (≤0.75) and errors, so RF and GBM were used in a subsequent step for upscaling to the study area. With 1 m resolution, GBM captured the full range of microtopographic extremes and predicted a mean July flux of 99 mg CH₄ m⁻² per month. In contrast, RF, which smoothed local extremes, yielded an average flux of 519 mg CH₄ m⁻² per month. The disagreement between flux estimates using GBM and RF correlated mainly with the Normalized Difference Water Index (NDWI), a moisture proxy, and was most pronounced in waterlogged, low-lying areas. Aggregating predictors to 10 m averaged the sharp metre-scale flux highs in hollows and lows on ridges, narrowing the GBM-RF difference to ∼75 mg CH₄ m⁻² per month while broadening the overall flux distribution with more intermediate values. At 1 m, microtopography was the main driver. At 10 m, moisture proxies explained about half of the variance. Our results demonstrate that: (i) metre predictors are indispensable for capturing the wet-dry microtopography and its CH₄ signals, (ii) upscaling algorithm selection strongly controls prediction spread and uncertainty once that microrelief is resolved, and (iii) coarser grids smooth local microtopographic details, resulting in flattened CH₄ flux peaks and wider distribution. At 10 m, however, flux estimates became more consistent between models and better represented broad moisture-driven patterns, suggesting improved generalisability despite some loss of detail. This is supported by findings for remote sensing derived seasonal subsidence which reflects moisture gradients. All factors combined lead to potentially large differences in scaled CH₄ flux budgets, calling for a careful selection of scaling approaches, spatial predictor layers (e.g., vegetation, moisture, topography), and grid resolution. Future work should couple ultra-high-resolution imagery with temporally dynamic indices to reduce upscaling bias along Arctic wetness gradients.

Our understanding of how rapid Arctic warming and permafrost thaw affect global climate dynamics is restricted by limited spatio-temporal data coverage due to logistical challenges and the complex landscape of Arctic regions. It is therefore crucial to make best use of the available observations, including the integrated data analysis across disciplines and observational platforms. To alleviate the data compilation process for syntheses, cross-scale analyses, earth system models, and remote sensing applications, we introduce ARGO, a new meta-dataset comprised of greenhouse gas observations from various observational platforms across the Arctic and boreal biomes within the polar region of the northern hemisphere. ARGO provides a centralised repository for metadata on carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O) measurements linked with an interactive online tool (https://www.bgc-jena.mpg.de/argo/). This tool offers prompt metadata visualisation for the research community. Here, we present the structure and features of ARGO, underscoring its role as a valuable resource for advancing Arctic climate research and guiding synthesis efforts in the face of rapid environmental change in northern regions. The ARGO meta-dataset is openly available for download at Zenodo (https://doi.org/10.5281/zenodo.13870390) (Vogt et al., 2024).

The microtopography of Arctic tundra and the associated soil moisture gradient influence the net ecosystem-atmosphere exchange of methane (CH4) and carbon dioxide (CO2). We aimed for quantifing fine-scale variability in a permafrost ecosystem. We measured growing-season carbon fluxes with closed chambers at Trail Valley Creek, Canada from 2022 to 2024. A total of six landforms were sampled, spanning a wetness gradient from dry (upland tundra, gully) over intermediate (polygons, degraded wetland centers) to wet (transitional zones, trenches) microsites. All landforms were net sources of CH4; only trenches had high emissions, while the other landforms had emissions close to zero. Drier elements (upland tundra, polygons) were net CO2 sinks, while wetter depressions (gullies, degraded centers, transitional zones) were net sources; trenches were a wet exception that still acted as a sink. All fluxes were strongly influenced by temperature, peaking during the hot summer of 2023. CH4 flux variability was dominated by belowground variables (subsurface soil moisture and temperature). For CO2 fluxes, aboveground (air temperature, AT, and photosynthetically active radiation, PAR) and belowground drivers contributed equally. For CH4, setting up statistical models separately for each landform reduced errors but lowered R2, so this approach is preferable when the goal is to minimize absolute error. For GPP and ER, including landform information improved theestimation of fluxes. For NEE, a single model fit to all landforms was sufficient. These results show that field studies focusing on small-scale variability in carbon fluxes should prioritize detailed soil-layer measurements, AT, and PAR, while vegetation metrics are optional.

Arctic methane (CH₄) budgets are uncertain because field measurements often capture only fragments of the wet-to-dry gradient that control tundra CH₄ fluxes. Wet hotspots are over-represented, while dry, net-sink sites are under-sampled. We paired over 13,000 chamber flux measurements during peak growing season in July (2019–2024) from Trail Valley Creek in the western Canadian Arctic with co-registered remotely sensed predictor variables to test how spatial resolution (1 m vs. 10 m) and choice of machine-learning algorithm shape upscaled CH₄flux maps over our 3.1 km² study domain. Four algorithms for CH₄ flux scaling (Random Forest (RF), Gradient Boosting Machine (GBM), Generalised Additive Model (GAM), and Support Vector Regression (SVR)) were tuned using the same stack of multispectral indices, terrain derivatives and a six-class landscape classification. Tree-based models such as RF and GBM offered the best balance of 10-fold cross-validated R² (≤0.75) and errors, so RF and GBM were used in a subsequent step for upscaling to the study area. With 1 m resolution, GBM captured the full range of microtopographic extremes and predicted a mean July flux of 99 mg CH₄ m^-2 month^-1. In contrast, RF, which smoothed local extremes, yielded an average flux of 519 mg CH₄ m^-2 month^-1. The disagreement between flux estimates using GBM and RF correlated mainly with the Normalized Difference Water Index (NDWI), a moisture proxy, and was most pronounced in waterlogged, low-lying areas. Aggregating predictors to 10 m averaged the sharp metre-scale flux highs in hollows and lows on ridges, narrowing the GBM-RF difference to ~75 mg CH₄ m^-2 month^-1while broadening the overall flux distribution with more intermediate values. At 1 m, microtopography is the main driver. At 10 m, moisture proxies explained about half of the variance. Our results demonstrate that: (i) sub‑metre predictors are indispensable for capturing the wet-dry microtopography and its CH₄ signals, (ii) upscaling algorithm selection strongly controls prediction spread and uncertainty once that microrelief is resolved, and (iii) coarser grids smooth local microtopographic details, resulting in flattened CH₄ flux peaks and wider distribution. All factors combined lead to potentially large differences in scaled CH₄ flux budgets, calling for a careful selection of scaling approaches, spatial predictor layers (e.g., vegetation, moisture, topography), and grid resolution. Future work should couple ultra-high-resolution imagery with temporally dynamic indices to reduce upscaling bias along Arctic wetness gradients.

Arctic methane (CH₄) budgets are uncertain because field measurements often capture only fragments of the wet-to-dry gradient that control tundra CH₄ fluxes. Wet hotspots are over-represented, while dry, net-sink sites are under-sampled. We paired over 13,000 chamber flux measurements during peak growing season in July (2019–2024) from Trail Valley Creek in the western Canadian Arctic with co-registered remotely sensed predictor variables to test how spatial resolution (1 m vs. 10 m) and choice of machine-learning algorithm shape upscaled CH₄flux maps over our 3.1 km² study domain. Four algorithms for CH₄ flux scaling (Random Forest (RF), Gradient Boosting Machine (GBM), Generalised Additive Model (GAM), and Support Vector Regression (SVR)) were tuned using the same stack of multispectral indices, terrain derivatives and a six-class landscape classification. Tree-based models such as RF and GBM offered the best balance of 10-fold cross-validated R² (≤0.75) and errors, so RF and GBM were used in a subsequent step for upscaling to the study area. With 1 m resolution, GBM captured the full range of microtopographic extremes and predicted a mean July flux of 99 mg CH₄ m^-2 month^-1. In contrast, RF, which smoothed local extremes, yielded an average flux of 519 mg CH₄ m^-2 month^-1. The disagreement between flux estimates using GBM and RF correlated mainly with the Normalized Difference Water Index (NDWI), a moisture proxy, and was most pronounced in waterlogged, low-lying areas. Aggregating predictors to 10 m averaged the sharp metre-scale flux highs in hollows and lows on ridges, narrowing the GBM-RF difference to ~75 mg CH₄ m^-2 month^-1while broadening the overall flux distribution with more intermediate values. At 1 m, microtopography is the main driver. At 10 m, moisture proxies explained about half of the variance. Our results demonstrate that: (i) sub‑metre predictors are indispensable for capturing the wet-dry microtopography and its CH₄ signals, (ii) upscaling algorithm selection strongly controls prediction spread and uncertainty once that microrelief is resolved, and (iii) coarser grids smooth local microtopographic details, resulting in flattened CH₄ flux peaks and wider distribution. All factors combined lead to potentially large differences in scaled CH₄ flux budgets, calling for a careful selection of scaling approaches, spatial predictor layers (e.g., vegetation, moisture, topography), and grid resolution. Future work should couple ultra-high-resolution imagery with temporally dynamic indices to reduce upscaling bias along Arctic wetness gradients.