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Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20g g€¯S to 20g g€¯N) the spring onset of elevated CH4 emissions starts 3g€¯d earlier, and the CH4 emission season lasts 4g€¯d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at 10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.

Large datasets of greenhouse gas and energy surface-atmosphere fluxes measured with the eddy-covariance technique (e.g., FLUXNET2015, AmeriFlux BASE) are widely used to benchmark models and remote-sensing products. This study addresses one of the major challenges facing model-data integration: To what spatial extent do flux measurements taken at individual eddy-covariance sites reflect model- or satellite-based grid cells? We evaluate flux footprints—the temporally dynamic source areas that contribute to measured fluxes—and the representativeness of these footprints for target areas (e.g., within 250–3000 m radii around flux towers) that are often used in flux-data synthesis and modeling studies. We examine the land-cover composition and vegetation characteristics, represented here by the Enhanced Vegetation Index (EVI), in the flux footprints and target areas across 214 AmeriFlux sites, and evaluate potential biases as a consequence of the footprint-to-target-area mismatch. Monthly 80% footprint climatologies vary across sites and through time ranging four orders of magnitude from 103 to 107 m2 due to the measurement heights, underlying vegetation- and ground-surface characteristics, wind directions, and turbulent state of the atmosphere. Few eddy-covariance sites are located in a truly homogeneous landscape. Thus, the common model-data integration approaches that use a fixed-extent target area across sites introduce biases on the order of 4%–20% for EVI and 6%–20% for the dominant land cover percentage. These biases are site-specific functions of measurement heights, target area extents, and land-surface characteristics. We advocate that flux datasets need to be used with footprint awareness, especially in research and applications that benchmark against models and data products with explicit spatial information. We propose a simple representativeness index based on our evaluations that can be used as a guide to identify site-periods suitable for specific applications and to provide general guidance for data use.

Croplands are highly productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO2 seasonal uptake over agricultural regions.

Earth system processes exhibit complex patterns across time, as do the models that seek to replicate these processes. Model output may or may not be significantly related to observations at different times and on different frequencies. Conventional model diagnostics provide an aggregate view of model-data agreement, but usually do not identify the time and frequency patterns of model misfit, leaving unclear the steps required to improve model response to environmental drivers that vary on characteristic frequencies. Wavelet coherence can quantify the times and frequencies at which models and measurements are significantly different. We applied wavelet coherence to interpret the predictions of twenty ecosystem models from the North American Carbon Program (NACP) Site-Level Interim Synthesis when confronted with eddy covariance-measured net ecosystem exchange (NEE) from ten ecosystems with multiple years of available data. Models were grouped into classes with similar approaches for incorporating phenology, the calculation of NEE, and the inclusion of foliar nitrogen (N). Models with prescribed, rather than prognostic, phenology often fit NEE observations better on annual to interannual time scales in grassland, wetland and agricultural ecosystems. Models that calculate NEE as net primary productivity (NPP) minus heterotrophic respiration (HR) rather than gross ecosystem productivity (GPP) minus ecosystem respiration (ER) fit better on annual time scales in grassland and wetland ecosystems, but models that calculate NEE as GPP-ER were superior on monthly to seasonal time scales in two coniferous forests. Models that incorporated foliar nitrogen (N) data were successful at capturing NEE variability on interannual (multiple year) time scales at Howland Forest, Maine. Combined with previous findings, our results suggest that the mechanisms driving daily and annual NEE variability tend to be correctly simulated, but the magnitude of these fluxes is often erroneous, suggesting that model parameterization must be improved. Few NACP models correctly predicted fluxes on seasonal and interannual time scales where spectral energy in NEE observations tends to be low, but where phenological events, multi-year oscillations in climatological drivers, and ecosystem succession are known to be important for determining ecosystem function. Mechanistic improvements to models must be made to replicate observed NEE variability on seasonal and interannual time scales. [ABSTRACT FROM AUTHOR]

The availability of nitrogen represents a key constraint on carbon cycling in terrestrial ecosystems, and it is largely in this capacity that the role of N in the Earth's climate system has been considered. Despite this, few studies have included continuous variation in plant N status as a driver of broad-scale carbon cycle analyses. This is partly because of uncertainties in how leaf-level physiological relationships scale to whole ecosystems and because methods for regional to continental detection of plant N concentrations have yet to be developed. Here, we show that ecosystem CO[sub2] uptake capacity in temperate and boreal forests scales directly with whole-canopy N concentrations, mirroring a leaf-level trend that has been observed for woody plants worldwide. We further show that both CO[sub2] uptake capacity and canopy N concentration are strongly and positively correlated with shortwave surface albedo. These results suggest that N plays an additional, and overlooked, role in the climate system via its influence on vegetation reflectivity and shortwave surface energy exchange. We also demonstrate that much of the spatial variation in canopy N can be detected by using broad-band satellite sensors, offering a means through which these findings can be applied toward improved application of coupled carbon cycle-climate models. [ABSTRACT FROM AUTHOR]

Nocturnal eddy-covariance carbon dioxide fluxes have uncertainties arising from non-stationary atmospheric processes. Low-level jets (LLJ) are one of the prominent nocturnal boundary-layer phenomena observed over non-mountainous terrain, and are capable of generating shear and turbulence close to the ground. The influence of intermittent LLJ activity on nocturnal carbon dioxide exchange measurements is investigated using wind profile observations and eddy-covariance flux measurements over a tall forest canopy. Results suggest that the buildup and venting of CO2 are closely associated with LLJ activity during the night. Of significance in quantifying nocturnal fluxes, this paper demonstrates how low-level jet activity introduces sporadic coupling between the canopy and the atmosphere. [ABSTRACT FROM AUTHOR]

Organismic and Evolutionary Biology