This paper describes the formation of, and initial results for, a new FLUXNET coordination network for ecosystem-scale methane (CH4) measurements at 60 sites globally, organized by the Global Carbon Project in partnership with other initiatives and regional flux tower networks. The objectives of the effort are presented along with an overview of the coverage of eddy covariance (EC) CH4 flux measurements globally, initial results comparing CH4 fluxes across the sites, and future research directions and needs. Annual estimates of net CH4 fluxes across sites ranged from −0.2 ± 0.02 g C m–2 yr–1 for an upland forest site to 114.9 ± 13.4 g C m–2 yr–1 for an estuarine freshwater marsh, with fluxes exceeding 40 g C m–2 yr–1 at multiple sites. Average annual soil and air temperatures were found to be the strongest predictor of annual CH4 flux across wetland sites globally. Water table position was positively correlated with annual CH4 emissions, although only for wetland sites that were not consistently inundated throughout the year. The ratio of annual CH4 fluxes to ecosystem respiration increased significantly with mean site temperature. Uncertainties in annual CH4 estimates due to gap-filling and random errors were on average ±1.6 g C m–2 yr–1 at 95% confidence, with the relative error decreasing exponentially with increasing flux magnitude across sites. Through the analysis and synthesis of a growing EC CH4 flux database, the controls on ecosystem CH4 fluxes can be better understood, used to inform and validate Earth system models, and reconcile differences between land surface model- and atmospheric-based estimates of CH4 emissions.

Wetland methane (CH4) emissions ($${F}_{{{CH}}_{4}}$$ F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.

Summer fallowthe practice of keeping a field out of production during the growing seasonis a common practice in dryland wheat (Triticum aestivum L.) cropping systems to conserve soil water resources. Fallow also depletes soil carbon stocks and thereby soil quality. The area of fallow has decreased by tens of millions of hectares since the 1970s in the northern North American Great Plains as producers have recognized that avoiding fallow usually confers both an economic and soil conservation benefit. Observed summertime cooling across parts of this region has coincided with fallow reduction, suggesting that the role of fallow in atmospheric processes needs to be ascertained. We measured carbon dioxide, latent heat, and sensible heat flux across a winter wheatspring wheatfallow sequence in Montana, USA to determine the effects of dryland crop management on ecosystem carbon resources and energy partitioning at the surfaceatmosphere interface. Winter wheat and spring wheat fields were carbon sinks (F c =-203 ± 52 g

Tropical biogenic sources are a likely cause of the recent increase in global atmospheric methane concentration. To improve our understanding of tropical methane sources, we used the eddy covariance technique to measure CH 4 flux (F CH4 ) between a tropical peat forest ecosystem and the atmosphere in Malaysian Borneo over a 2-month period during the wet season. Mean daily F CH4 during the measurement period, on the order of 0.024 g C-CH 4 ·m À2 ·day À1 , was similar to eddy covariance F CH4 measurements from tropical rice agroecosystems and boreal fen ecosystems. A linear modeling analysis demonstrated that air temperature (T air ) was critical for modeling F CH4 before the water table breached the surface and that water table alone explained some 20% of observed F CH4 variability once standing water emerged. Future research should measure F CH4 on an annual basis from multiple tropical ecosystems to better constrain tropical biogenic methane sources.Plain Language Summary Methane (CH 4 ) is the third most potent greenhouse gas, and its reduction is seen as an effective method for meeting global temperature targets, but the global growth rate of atmospheric CH 4 concentration has risen to 10.3 ± 2.1 ppb/year from 2014 to 2016 after a period of relative stagnation from 2000 to 2006. Recent research has pointed to tropical biogenic sources as a likely cause. However, no studies to our knowledge have measured whole-ecosystem CH 4 flux (F CH4 ) from a tropical peat forested wetland to date despite the importance of tropical wetlands to global CH 4 budget. To improve our understanding of tropical methane sources, we measured F CH4 between a tropical peat forest ecosystem in Malaysian Borneo and the atmosphere over a 2-month period during the dry to wet season transition. Mean daily F CH4 during the measurement period, on the order of 0.024 g C-CH 4 ·m À2 ·day À1 , are similar to eddy covariance measurements from tropical rice agroecosystems and boreal fen ecosystems. A linear modeling analysis demonstrated the important role of air temperature (T air ) during unsaturated conditions and water table during saturated conditions and further emphasizes the critical role of simulating temperature and water table accurately for accurate modeled ecosystem scale F CH4 estimates.

The new discoveries reveal that the carbon pool of tropical peatland is four-to fivefold higher (Gumbricht et al., 2017) than the previous estimates (Maltby & Immirzi, 1993). While covering for only some 15% of the total area of tropical peatlands, Southeast Asian peatlands are a critical carbon reservoir with their extensive and thick deposits of peat. There remain considerable uncertainties about their present role in global carbon cycle. Tropical forests are now thought to represent a global net

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<p>During the spring of 2020, many countries around the world imposed lockdown measures involving economic activity and movement restrictions to contain the outbreak of the novel coronavirus disease (COVID-19), thereby leading to changes in air pollutant concentrations (Venter et al., 2020). The unprecedented reductions in primary pollutant emissions created a unique opportunity to assess the response of photosynthetic activity of terrestrial ecosystems to atmospheric changes in air quality. Our hypothesis was that a concentration decrease in particulate matter (PM) and the resulting change in light scattering may have affected photosynthesis via changes in direct and diffuse radiation, while a reduction of ozone precursor emissions may have negatively impacted the formation of ozone and reduced its phytotoxic effects. Thus, we analysed turbulent fluxes from eddy covariance measurements and meteorological data collected at the Integrated Carbon Observation System (ICOS) ecosystem stations, and also air pollution data from a continental-scale chemistry transport model (LOTOS-EUROS). Using observations from 44 sites in Europe spanning eleven countries and nine vegetation types, we calculated a 4-month (March-April-May-June, hereafter ‘spring’) anomaly of gross primary productivity (GPP) as the cumulative difference of GPP between 2020 and the reference period from 2015 to 2019. For 34 out of 44 sites, we found that the means between 2020 and the reference GPP were different at the 5% significance level. We further classify these sites into four groups according to modelling and simulation analyses and related data.</p><ul><li>Group 1 included 16 sites where the GPP anomaly was predominantly driven by changes in meteorology. A 7-31% GPP reduction of eight sites in this group was attributed to several different factors such as reduced incoming shortwave radiation (SW_IN), increased vapour pressure deficit (VPD), late growing season and legacy effects. The remaining eight sites experienced an increase in GPP (5-20%) which coincided with increased SW_IN and reduced diffuse fraction (<em>K<sub>d</sub></em>).</li> <li>Group 2 consisted of five sites where the GPP anomaly was primarily linked to drought-related effects as indicated by an exceptional increase in the Bowen ratio (δß > 29%), declines in soil water content (SWC) and precipitation.</li> <li>Group 3 was represented by five sites where the GPP anomaly was presumably affected by both meteorology and pollutants. All sites in this group experienced an increase in GPP of 14-47% that coincided with enhanced SW_IN (2-13%), reduced atmospheric concentrations of NO<sub>2</sub> (28-47%), NO (33-57%), O<sub>3 </sub>(2-3%), SO<sub>2 </sub>(5-7%), PM10 (4-14%), PM2.5 (9-17%) and increased NH<sub>3</sub> (1-5%).</li> <li>There were eight grassland and savannah sites in Group 4 where the ecosystem management interacted with meteorology to mainly increase GPP by 10-41%.</li> </ul><p>We first conclude that meteorology and pollutant concentrations during the spring were different between 2020 and 2015-2019 period. Second, our analyses showed that the GPP anomaly in the spring of 2020 was explained by the balance between positive and negative impacts of biophysical drivers. GPP increased when the combined effects of enhanced SW_IN, increased air temperature and reduced pollutant concentrations overtook the negative impact of changes in VPD, SWC and <em>K<sub>d</sub></em>.</p><p><strong>Acknowledgements.</strong> We would like to thank ICOS site investigators for sharing eddy covariance data.</p>