Seasonality in the Surface Energy Balance of Tundra in the Lower Mackenzie River Basin

This study details seasonal characteristics in the annual surface energy balance of upland and lowland tundra during the 1998–99 water year (Y2). It contrasts the results with the 1997–98 water year (Y1) and relates the findings to the climatic normals for the lower Mackenzie River basin region. Both years were much warmer than the long-term average, with Y1 being both warmer and wetter than Y2. Six seasons are defined as early winter, midwinter, late winter, spring, summer, and fall. The most rapid changes in the surface energy balance occur in spring, fall, and late winter. Of these, spring is the most dynamic, and there is distinct asymmetry between rates of change in spring and those in fall. Rates of change of potential insolation (extraterrestrial solar radiation) in late winter, spring, and fall are within 10% of one another, being highest in late winter and smallest in spring. Rates of change in air temperature and ground temperature are twice as large in spring as in fall and late winter, when they are about the same. Rates of change in components of the energy balance in spring are twice and 4 times as large as in fall and late winter, respectively. The timing of snowpack ripening and snowmelt is the major agent determining the magnitude of asymmetry between fall and spring. This timing is a result of interaction between the solar cycle, air temperature, and snowpack longevity. Based on evidence from this study, potential surface responses to a 18C increase in air temperature are small to moderate in most seasons, but are large in spring when increases range from 7% to 10% of average surface energy fluxes

The Annual Carbon Budget for Fen and Forest in a Wetland at Arctic Treeline

Three separate research efforts conducted in the same wetland-peatland system in the northern Hudson Bay Lowland near the town of Churchill, Manitoba, allow a comparison of two carbon budget estimates, one derived from long-term growth rates of organic soil and the other based on shorter-term flux measurements. For a tundra fen and an open subarctic forest, calculations of organic soil accumulation or loss over the last half-century indicate that while the fen on average has lost small amounts of carbon from the ecosystem, the adjacent forest has gained larger amounts of atmospheric carbon dioxide. These longer-term data are supported by shorter-term flux measurements and estimates, which also show carbon loss by the fen and carbon uptake by the forest. The shorter-term data indicate that the fen's carbon loss is largely attributable to exceptionally dry years, especially if they are warm. The forest may gain carbon at an increased rate as it matures and during warm growing seasons. Also, the changes in relief of the dynamic hummock-hollow landscape in the fen may inhibit photosynthesis.Trois travaux de recherche distincts portant sur le même système de marécages/tourbières situés dans la partie septentrionale des basses-terres de la baie d'Hudson, près de la ville de Churchill au Manitoba, permettent de comparer deux estimations du budget de carbone, l'une tirée des taux de croissance à long terme du sol organique et l'autre fondée sur des mesures du flux à plus court terme. Pour une tourbière basse de toundra et une forêt claire subarctique, les calculs de l'accumulation ou de la perte de sol organique au cours des cinquante dernières années révèlent que, si la tourbière basse a perdu en moyenne de petites quantités du carbone présent dans l'écosystème, la forêt adjacente a acquis des quantités plus grandes de bioxyde de carbone atmosphérique. Ces données établies sur une période relativement longue sont étayées par des mesures et estimations du flux à plus court terme, qui révèlent également une perte de carbone par la tourbière basse et une absorption de carbone par la forêt. Les données à plus court terme montrent que la perte de carbone par la tourbière basse est due en grande partie à des années de sécheresse exceptionnelle, surtout s'il fait chaud. Il se peut que la forêt acquière du carbone à une vitesse accrue en devenant mature et au cours des saisons de croissance chaudes. Il est en outre possible que les changements dans le relief dynamique en bosses et en creux de la tourbière basse bloquent la photosynthèse

Multiscale analyses of solar‐induced florescence and gross primary production

Solar‐induced fluorescence (SIF) has shown great promise for probing spatiotemporal variations in terrestrial gross primary production (GPP), the largest component flux of the global carbon cycle. However, scale mismatches between SIF and ground‐based GPP have posed challenges toward fully exploiting these data. We used SIF obtained at high spatial sampling rates and resolution by NASA’s Orbiting Carbon Observatory‐2 satellite to elucidate GPP‐SIF relationships across space and time in the U.S. Corn Belt. Strong linear scaling functions (R2 ≥ 0.79) that were consistent across instantaneous to monthly time scales were obtained for corn ecosystems and for a heterogeneous landscape based on tall tower observations. Although the slope of the corn function was ~56% higher than for the landscape, SIF was similar for corn (C4) and soybean (C3). Taken together, there is strong observational evidence showing robust linear GPP‐SIF scaling that is sensitive to plant physiology but insensitive to the spatial or temporal scale.Key PointsGPP scales linearly with SIF from instantaneous to monthly time scalesAggregating ecosystem GPP‐SIF functions yield a representative landscape relation that matched one obtained directly using tall tower GPPGPP‐SIF relations showed sensitivity to plant physiology but not spatiotemporal scalePeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135999/1/grl55274_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135999/2/grl55274.pd

Comparing crop growth and carbon budgets simulated across AmeriFlux agricultural sites using the Community Land Model (CLM)

Improvement of process-based crop models is needed to achieve high fidelity forecasts of regional energy, water, and carbon exchanges. However, most state-of-the-art Land Surface Models (LSMs) assessed in the fifth phase of the Coupled Model Inter-comparison project (CMIP5) simulated crops as unmanaged C3 or C4 grasses. This study evaluated the crop-enabled version of one of the most widely used LSMs, the Community Land Model (CLM4- Crop), for simulating corn and soybean agro-ecosystems at relatively long-time scales (up to 11 years) using 54 site-years of data. We found that CLM4-Crop had a biased phenology during the early growing season and that carbon emissions from corn and soybean were underestimated. The model adopts universal physiological parameters for all crop types neglecting the fact that different crops have different specific leaf area, leaf nitrogen content and vcmax25, etc. As a result, model performance varied considerably according to crop type. Overall, the energy and carbon exchange of corn systems were better simulated than soybean systems. Long-term simulations at multiple sites showed that gross primary production (GPP) was consistently over-estimated at soybean sites leading to very large short and long-term biases. A modified model, CLM4-CropM’, with optimized phenology and calibrated crop physiological parameters yielded significantly better simulations of gross primary production (GPP), ecosystem respiration (ER) and leaf area index (LAI) at both short (hourly) and long-term (annual to decadal) timescales for both soybean and corn

Multiscale analyses of solar-induced florescence and gross primary production

Solar‐induced fluorescence (SIF) has shown great promise for probing spatiotemporal variations in terrestrial gross primary production (GPP), the largest component flux of the global carbon cycle. However, scale mismatches between SIF and ground‐based GPP have posed challenges toward fully exploiting these data. We used SIF obtained at high spatial sampling rates and resolution by NASA's Orbiting Carbon Observatory‐2 satellite to elucidate GPP‐SIF relationships across space and time in the U.S. Corn Belt. Strong linear scaling functions (R^2 ≥ 0.79) that were consistent across instantaneous to monthly time scales were obtained for corn ecosystems and for a heterogeneous landscape based on tall tower observations. Although the slope of the corn function was ~56% higher than for the landscape, SIF was similar for corn (C_4) and soybean (C_3). Taken together, there is strong observational evidence showing robust linear GPP‐SIF scaling that is sensitive to plant physiology but insensitive to the spatial or temporal scale

Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions

We apply airborne measurements across three seasons (summer, winter and spring 2017-2018) in a multi-inversion framework to quantify methane emissions from the US Corn Belt and Upper Midwest, a key agricultural and wetland source region. Combing our seasonal results with prior fall values we find that wetlands are the largest regional methane source (32 %, 20 [16-23] Gg/d), while livestock (enteric/manure; 25 %, 15 [14-17] Gg/d) are the largest anthropogenic source. Natural gas/petroleum, waste/landfills, and coal mines collectively make up the remainder. Optimized fluxes improve model agreement with independent datasets within and beyond the study timeframe. Inversions reveal coherent and seasonally dependent spatial errors in the WetCHARTs ensemble mean wetland emissions, with an underestimate for the Prairie Pothole region but an overestimate for Great Lakes coastal wetlands. Wetland extent and emission temperature dependence have the largest influence on prediction accuracy; better representation of coupled soil temperature-hydrology effects is therefore needed. Our optimized regional livestock emissions agree well with the Gridded EPA estimates during spring (to within 7 %) but are g≈ 25 % higher during summer and winter. Spatial analysis further shows good top-down and bottom-up agreement for beef facilities (with mainly enteric emissions) but larger (g≈ 30 %) seasonal discrepancies for dairies and hog farms (with \u3e 40 % manure emissions). Findings thus support bottom-up enteric emission estimates but suggest errors for manure; we propose that the latter reflects inadequate treatment of management factors including field application. Overall, our results confirm the importance of intensive animal agriculture for regional methane emissions, implying substantial mitigation opportunities through improved management

Representativeness of Eddy-Covariance flux footprints for areas surrounding AmeriFlux sites

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

ECOSTRESS: NASA's next generation mission to measure evapotranspiration from the International Space Station

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station ECOSTRESS) was launched to the International Space Station on June 29, 2018. The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as level‐3 (L3) latent heat flux (LE) data products. These data are generated from the level‐2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear‐sky ET product (L3_ET_PT‐JPL, version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear‐sky instantaneous/time of overpass: r2 = 0.88; overall bias = 8%; normalized RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are over‐represented. The 70 m high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1 km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data

Reply to Magnani et al.: Linking large-scale chlorophyll fluorescence observations with cropland gross primary production

Guanter, Luis et al.The derivation of the first global maps of sun-induced chlorophyll fluorescence (SIF) from Greenhouse Gases Observing Satellite (GOSAT) data in 2011 (1, 2), and later from Global Ozone Monitoring Experiment-2 (GOME-2) (3), was perceived as a milestone in the fields of vegetation remote sensing and carbon modeling.Peer Reviewe