A multiyear estimate of methane fluxes in Alaska from CARVE atmospheric observations

Abstract

Methane (CH4) fluxes from Alaska and other arctic regions may be sensitive to thawing permafrost and future climate change, but estimates of both current and future fluxes from the region are uncertain. This study estimates CH4 fluxes across Alaska for 2012–2014 using aircraft observations from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) and a geostatistical inverse model (GIM). We find that a simple flux model based on a daily soil temperature map and a static map of wetland extent reproduces the atmospheric CH4 observations at the state-wide, multi-year scale more effectively than global-scale, state-of-the-art process-based models. This result points to a simple and effective way of representing CH4 flux patterns across Alaska. It further suggests that contemporary process-based models can improve their representation of key processes that control fluxes at regional scales, and that more complex processes included in these models cannot be evaluated given the information content of available atmospheric CH4 observations. In addition, we find that CH4 emissions from the North Slope of Alaska account for 24% of the total statewide flux of 1.74 ± 0.44 Tg CH4 (for May–Oct.). Contemporary global-scale process models only attribute an average of 3% of the total flux to this region. This mismatch occurs for two reasons: process models likely underestimate wetland area in regions without visible surface water, and these models prematurely shut down CH4 fluxes at soil temperatures near 0°C. As a consequence, wetlands covered by vegetation and wetlands with persistently cold soils could be larger contributors to natural CH4 fluxes than in process estimates. Lastly, we find that the seasonality of CH4 fluxes varied during 2012–2014, but that total emissions did not differ significantly among years, despite substantial differences in soil temperature and precipitation; year-to-year variability in these environmental conditions did not affect obvious changes in total CH4 fluxes from the state.

Estimating regional-scale methane flux and budgets using CARVE aircraft measurements over Alaska

“…Our mean estimates are within the uncertainties of these other studies, especially when we account for the ∼ 20 % greater area in our study domain. It is promising that our relatively simple method for calculating budgets using selected profiles from the CARVE aircraft observations arrives at similar estimates to values derived from the much more complex geostatistical inverse model used by Miller et al (2016), particularly as their study was constrained by all the aircraft observations as well as hourly-averaged observations from the CRV tower.…”

“…Using multiple linear regression models, we investigate the relationship between land surface properties and observed atmospheric CH 4 . Our method finds similar results to the complex geostatistical inversion model employed by Miller et al (2016) and could provide a simple diagnostic tool for regional methane cycle analysis.…”

“…Previous estimates of emissions from Alaska during the growing season include the study by Chang et al (2014), who used a similar method to estimate the May-September 2012 emissions to be 2.1 ± 0.5 Tg, as well as the geostatistical inversion of the CARVE observations by Miller et al (2016), who estimated May-October CH 4 emissions of 1.80 ± 0.45, 1.65 ± 0.43, and 1.77 ± 0.45 Tg CH 4 for 2012, 2013, and 2014, respectively. Our mean estimates are within the uncertainties of these other studies, especially when we account for the ∼ 20 % greater area in our study domain.…”

“…This work uses similar methods to those used by Chang et al (2014), but extends the analysis to include the growing seasons of 2013 and 2014 and explores how their inter-annual and intra-annual variability can be explained by hydrological and environmental controls at a regional scale. A recent study by Miller et al (2016) explores similar questions using a more complex geostatistical inversion model constrained by a much larger data set. Using multiple linear regression models, we investigate the relationship between land surface properties and observed atmospheric CH 4 .…”

Diagnostic methods for atmospheric inversions of long-lived greenhouse gases

“…The inverse problem then amounts to using those gradients to recover information about the flux patterns. From a scientific perspective, an additional goal is often to also gain insight into the enviro-climatic factors driving these patterns (e.g., Gourdji et al, 2012;Fang and Michalak, 2015;Miller et al, 2014Miller et al, , 2016b. Although the principle is simple, the atmospheric inverse problem is ill-conditioned because the diffusive nature of atmospheric transport means that relatively small variations or errors in observed or modeled atmospheric concentrations can correspond to relatively large differences or errors in the inferred flux quantities and patterns.…”