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Developed regions of the world represent a major atmospheric methane (CH4) source, but these regional emissions remain poorly constrained. The Yangtze River Delta (YRD) region of China is densely populated (about 16% of China's total population) and consists of large anthropogenic and natural CH4 sources. Here, atmospheric CH4 concentrations measured at a 70-m tall tower in the YRD are combined with a scale factor Bayesian inverse (SFBI) modeling approach to constrain seasonal variations in CH4 emissions. Results indicate that in 2018 agricultural soils (AGS, rice production) were the main driver of seasonal variability in atmospheric CH4 concentration. There was an underestimation of emissions from AGS in the a priori inventories (EDGAR—Emissions Database for Global Atmospheric Research v432 or v50), especially during the growing seasons. Posteriori CH4 emissions from AGS accounted for 39% (4.58 Tg, EDGAR v432) to 47% (5.21 Tg, EDGAR v50) of the total CH4 emissions. The posteriori natural emissions (including wetlands and water bodies) were 1.21 Tg and 1.06 Tg, accounting for 10.1% (EDGAR v432) and 9.5% (EDGAR v50) of total emissions in the YRD in 2018. Results show that the dominant factor for seasonal variations in atmospheric concentration in the YRD was AGS, followed by natural sources. In summer, AGS contributed 42% (EDGAR v432) to 64% (EDGAR v50) of the CH4 concentration enhancement while natural sources only contributed about 10% (EDGAR v50) to 15% (EDGAR v432). In addition, the newer version of the EDGAR product (EDGAR v50) provided more reasonable seasonal distribution of CH4 emissions from rice cultivation than the old version (EDGAR v432). [ABSTRACT FROM AUTHOR]

The US Corn Belt is a global hotspot of atmospheric ammonia (NH3), a gas known to adversely impact the environment and human health. We combine hourly tall tower (100 m) measurements and bi‐weekly, spatially distributed, ground‐based observations from the Ammonia Monitoring Network with the US National Emissions Inventory (NEI) and WRF‐Chem simulations to constrain NH3 emissions from April to September 2017–2019. We show that: (1) NH3 emissions peaked from May to July and were 1.6–1.7 times the annual NEI average; (2) average growing season NH3 emissions from agricultural lands were remarkably similar across years (3.27–3.64 nmol m−2 s−1), yet showed substantial episodic variability driven by meteorology and land management; (3) dry deposition was 40% of gross emissions from agricultural lands and exceeded 100% of gross emissions in natural lands. Our findings provide an important benchmark for evaluating future NH3 emissions and mitigation efforts. Key Points: Rare multiyear tall tower and ground‐based ammonia measurements were combined with WRF‐Chem to constrain emissions from the US Corn BeltMonthly ammonia emissions peaked in May through July and were 1.6–1.7 times the annual NEI averageEpisodic low WRF‐Chem bias versus tall tower observations underscore the importance of weather and land management as emission drivers [ABSTRACT FROM AUTHOR]

• Developed an equilibrium boundary layer model for stable water isotope analyses. • Estimated a local water recycling ratio of 0.29 ± 0.12 for the Upper Midwest, US. • Local water recycling is shown to modify the continental isotope effect. • Future warming and elevated irrigation are expected to enhance local water recycling. The contribution of evapotranspiration (ET) to regional precipitation, known as "local water recycling", is a key process in the water cycle that can affect water management. However, the amount of planetary boundary layer (PBL) moisture that arises from ET is highly uncertain due to complex atmosphere and land surface conditions. In this study, an idealized two-layer equilibrium boundary layer model was coupled with a stable water isotope module including HDO and H 2 18O to constrain PBL growing season water transport processes. The model was validated using turbulent heat fluxes and isotope ratios of water vapor (δ m) and precipitation (δ P) measured at a cropland site and a nearby tall tower in the Upper Midwest, United States. The results show that the PBL equilibrium features of δ m and δ P are well-constrained by thermal and moisture equilibrium in the PBL. For this study region, the summer values of rain evaporation fraction (f) and local water recycling ratio (LRR) are estimated to be 0.09 and 0.29 ± 0.12, respectively. The summer LRR values for the years 2006–2010 were 0.35, 0.36, 0.17, 0.29, and 0.29, respectively. The small value of LRR in 2008 corresponded to a drought condition with the lowest precipitation and second lowest ET among the five years. The summer magnitude of the amount effect is −2.8‰ (mm day−1)−1 and −0.8‰ (mm day−1)−1 for HDO and H 2 18O, respectively. The local water recycling is identified as a significant factor influencing the continental isotope effect. Cropland has likely changed the regional LRR by −7.6 to 19.5% under different pre-agriculture land use scenarios. The feedback processes revealed here indicate that local water recycling is expected to be weakened under drought conditions, but it will be enhanced if irrigation is applied more intensely with more frequent drought events as the climate continues to warm. [ABSTRACT FROM AUTHOR]

Lakes provide enormous economic, recreational, and aesthetic benefits to citizens. These ecosystem services may be adversely impacted by climate change. In the Twin Cities Metropolitan Area of Minnesota, USA, many lakes have been at historic low levels and water augmentation strategies have been proposed to alleviate the problem. White Bear Lake (WBL) is a notable example. Its water level declined 1.5 m during 2003–2013 for reasons that are not fully understood. This study examined current, past, and future lake evaporation to better understand how climate will impact the water balance of lakes within this region. Evaporation from WBL was measured from July 2014 to February 2017 using two eddy covariance (EC) systems to provide better constraints on the water budget and to investigate the impact of evaporation on lake level. The estimated annual evaporation losses for years 2014 through 2016 were 559 ± 22 mm, 779 ± 81 mm, and 766 ± 11 mm, respectively. The higher evaporation in 2015 and 2016 was caused by the combined effects of larger average daily evaporation and a longer ice-free season. The EC measurements were used to tune the Community Land Model 4 – Lake, Ice, Snow and Sediment Simulator (CLM4-LISSS) to estimate lake evaporation over the period 1979–2016. Retrospective analyses indicate that WBL evaporation increased during this time by about 3.8 mm year −1 , which was driven by increased wind speed and lake-surface vapor pressure gradient. Using a business-as-usual greenhouse gas emission scenario (RCP8.5), lake evaporation was modeled forward in time from 2017 to 2100. Annual evaporation is expected to increase by 1.4 mm year −1 over this century, largely driven by lengthening ice-free periods. These changes in ice phenology and evaporation will have important implications for the regional water balance, and water management and water augmentation strategies that are being proposed for these Metropolitan lakes. [ABSTRACT FROM AUTHOR]

Nitrous oxide (N2O) has a global warming potential that is 300 times that of carbon dioxide on a 100-y timescale, and is of major importance for stratospheric ozone depletion. The climate sensitivity of N2O emissions is poorly known, which makes it difficult to project how changing fertilizer use and climate will impact radiative forcing and the ozone layer. Analysis of 6 y of hourly N2O mixing ratios from a very tall tower within the US Corn Belt--one of the most intensive agricultural regions of the world--combined with inverse modeling, shows large interannual variability in N2O emissions (316 Gg N2O-N⋅y-1 to 585 Gg N2O-N⋅y-1). This implies that the regional emission factor is highly sensitive to climate. In the warmest year and spring (2012) of the observational period, the emission factor was 7.5%, nearly double that of previous reports. Indirect emissions associated with runoff and leaching dominated the interannual variability of total emissions. Under current trends in climate and anthropogenic N use, we project a strong positive feedback to warmer and wetter conditions and unabated growth of regional N2O emissions that will exceed 600 Gg N2O-N⋅y-1, on average, by 2050. This increasing emission trend in the US Corn Belt may represent a harbinger of intensifying N2O emissions from other agricultural regions. Such feedbacks will pose a major challenge to the Paris Agreement, which requires large N2O emission mitigation efforts to achieve its goals. [ABSTRACT FROM AUTHOR]

N2O is an important greenhouse gas and the primary stratospheric ozone depleting substance. Its deleterious effects on the environment have prompted appeals to regulate emissions from agriculture, which represents the primary anthropogenic source in the global N2O budget. Successful implementation of mitigation strategies requires robust bottom-up inventories that are based on emission factors (EFs), simulation models, or a combination of the two. Top-down emission estimates, based on tall-tower and aircraft observations, indicate that bottom-up inventories severely underestimate regional and continental scale N2O emissions, implying that EFs may be biased low. Here, we measured N2O emissions from streams within the US Corn Belt using a chamber-based approach and analyzed the data as a function of Strahler stream order (S). N2O fluxes from headwater streams often exceeded 29 nmol N2O-N m-2.s-1 and decreased exponentially as a function of S. This relation was used to scale up riverine emissions and to assess the differences between bottom-up and top-down emission inventories at the local to regional scale. We found that the Intergovernmental Panel on Climate Change (IPCC) indirect EF for rivers (EF5r) is underestimated up to ninefold in southern Minnesota, which translates to a total tier 1 agricultural underestimation of N2O emissions by 40%.We show that accounting for zero-order streams as potential N2O hotspots can more than double the agricultural budget. Applying the same analysis to the US Corn Belt demonstrates that the IPCC EF5r underestimation explains the large differences observed between top-down and bottom-up emission estimates. [ABSTRACT FROM AUTHOR]

The effects of dams on carbon dioxide (CO2) fluxes in downstream lakes remain elusive. Here we combined eddy covariance observations and random forest models to examine multi‐decadal variations in CO2 fluxes in the Poyang Lake, the largest freshwater lake in China, and quantified the contribution of the Three Gorges Dam (TGD), the world's largest hydraulic project. We found the lake fluctuated between CO2 source and sink in 1961–2016, and tended to be CO2 sink in the post‐TGD period (2003–2016) when vegetation expanded early and spatially due to declining water level. TGD can explain approximately 6% of the total differences in annual CO2 fluxes, with major contributions in the impoundment period (up to 22% in middle September to October). The results show a positive side of operational major hydraulic projects on lake carbon sink, and probably caution the negative side of carbon release after dam removal. Plain Language Summary: In the past century, dams have significantly altered the hydrological connectivity between rivers and lakes, which affect CO2 exchange in the downstream lake systems. As the largest freshwater lake in China, Poyang Lake has also undergone drastic hydrological changes, attributable largely to the operation of the Three Gorges Dam (TGD), the world's largest hydraulic project ever, in 2003. Based on flux observations and machine learning method, we show that annual lake CO2 exchange shifted toward carbon sink during 1961–2016. The TGD has a major impact on lake CO2 fluxes, especially during the impoundment stage in middle September–October, explaining 22% of the flux differences between the pre‐ and post‐TGD period. The results show a positive side of hydraulic projects albeit their adverse impact on ecological protection. Key Points: Poyang Lake as a CO2 source or sink significantly depends on water levelPoyang Lake became a CO2 sink since the Three Gorges Dam operation in 2003Dam explains 22% of differences in CO2 fluxes in autumn impoundment period [ABSTRACT FROM AUTHOR]

Abstract: Development of optical isotope techniques over the last several years has provided scientists a set of tools for tracing the transport and cycling of CO2 and water vapor between the biosphere and atmosphere. Here, I take a micrometeorological perspective and review these technological advances, assess key instrument performance characteristics, examine how these techniques have been used in the field to improve our understanding of the processes governing the exchange of CO2 and water vapor, and discuss future research directions. Review of the recent literature indicates that: (1) optical techniques have been used to quantify the isotope composition of biosphere–atmosphere exchange using the traditional Keeling mixing line, flux–gradient, eddy covariance, and chamber approaches under a variety of field conditions with near-continuous data records now extending to more than 5-years; (2) high frequency and near continuous isotope measurements at the canopy scale have demonstrated important new insights regarding the behaviour of kinetic fractionation at the leaf versus canopy scales and the controls on ecosystem respiration that could not have been observed previously using traditional methods; (3) based on the assessment of instrument performance, carbon isotope disequilibrium (the difference between the isotope composition of photosynthesis and respiration), carbon turnover rates, and measurement uncertainties, 13C-CO2 investigations are best suited for examining the contributions and changes to ecosystem respiration with a need for more innovative 13C-isotope labeling experiments and compound specific isotope analyses under field conditions; (4) significant progress has been made in measuring the oxygen isotope composition of water vapor fluxes and canopy leaf water enrichment. These new data have provided an opportunity to evaluate models of leaf water enrichment and their application to the canopy scale; and (5) the use of 18O-H2O and 18O-CO2 as tracers of the coupled carbon–water cycle has matured significantly in recent years. Evidence from a range of ecosystems indicates that 18O-CO2 disequilibrium (ranging from 0 to 17‰) is much larger than for 13C-CO2 (typical less than 3‰ in natural C3 ecosystems) making it a useful tracer of coupled carbon and water cycle processes. However, a better understanding of the role of carbonic anhydrase in photosynthetic and respiratory processes under field conditions is now needed in order to make further progress. Finally, the increasing use and development of isotope-enabled land surface schemes along with the acquisition of high temporal resolution isotope data is providing a new opportunity to constrain the carbon and water cycle processes represented in these models. [Copyright &y& Elsevier]

Abstract: The stable carbon isotope ratio, , is a valuable tracer for studying the processes controlling the autotrophic (F Ra ) and heterotrophic (F Rh ) contributions to ecosystem respiration (F R ) and the influence of photosynthesis on F R . There is increasing interest in quantifying the temporal variability of the carbon isotope composition of ecosystem respiration (δ R ) because it contains information about the sources contributing to respiration and is an important parameter used for partitioning net ecosystem CO2 exchange using stable isotope methods. In this study, eddy covariance, flux gradient, automated chambers, and stable carbon isotope techniques were used to quantify and improve our understanding of the temporal variability in F R and δ R in a C3/C4 agricultural ecosystem. Six years (2004–2009) of isotope flux-gradient measurements indicated that δ R had a very consistent annual pattern during both C3 (soybean) and C4 (corn) growing seasons due to significant contributions from F Ra , which was strongly influenced by the isotope composition of the recent photosynthate. However, in the spring, δ R exhibited a C3 signal regardless of the crop grown in the previous season. One hypothesis for this anomaly is that at these low soil temperatures microbial activity relied predominantly on C3 substrates. Automated chamber measurements of soil respiration () and its isotope composition () were initiated in the early corn growing season of 2009 to help interpret the variability in δ R . These measurements showed good agreement with EC measurements of F R (within 0.5μmolm2 s−1) and isotope flux gradient measurements of δ R (within 2‰) at nighttime for near-bare soil conditions (LAI<0.1). At peak growth, nighttime δ R above the corn canopy was consistently 1–6‰ more enriched than . The relatively enriched signal above the canopy indicates that δ R was strongly influenced by aboveground plant respiration (F R,ag ), which accounted for about 40% of F R . The automated chamber data and analyses also revealed a strong diel pattern in . In the early growth period, showed a sharp morning enrichment of up to 4‰ followed by a gradual depletion throughout the afternoon and evening. Daytime enrichment in was most pronounced during dry conditions and was not observed when the upper soil was near saturation. We provide anecdotal evidence that the diel variability during early growth may have been influenced by turbulence (advection/non-diffusive transport), which reduced the kinetic fractionation effect. At peak growth, there is evidence that the sheltering effect of the corn plants diminished the influence of turbulence on the chamber measurement of . Further research is needed to evaluate and separate the contributions of biotic and abiotic (advection and non-steady state effects) influences on chamber observations. [Copyright &y& Elsevier]

Abstract: Separation of the photosynthetic (F P ) and respiratory (F R ) fluxes of net CO2 exchange (F N ) remains a necessary step toward understanding the biological and physical controls on carbon cycling between the soil, biomass, and atmosphere. Despite recent advancements in stable carbon isotope partitioning methodology, several potential limitations can cause uncertainty in the partitioned results. Here, we combined an automated chamber system with a tunable diode laser (TDL) to evaluate carbon isotope partitioning under controlled environmental conditions. Experiments were conducted in a climate controlled greenhouse utilizing both soybean (C3 pathway) and corn (C4 pathway) treatments. Under these conditions, net exchange of and was obtained with an improved signal to noise ratio. Further, the chamber system was used to estimate soil evaporation (E) and plant transpiration (T), allowing for an improved estimate of the total conductance to CO2 (g c ). This study found that the incorporation of short-term and diel variability in the isotope composition of respiration (δ R ) caused F P to nearly double in the corn system while only slightly increasing in the soybean system. Variability in both g c and the CO2 bundle sheath leakage factor for C4 plants (ϕ) also had a significant influence on F P . In addition, chamber measurements of F N and its isotope composition (δ N ) indicated that post-illumination processes caused a decrease in plant respiration for up to 3h following light termination. Finally, this study found systematic differences between the isotope and temperature-regression partitioning methods on the diel time scale. [Copyright &y& Elsevier]

Abstract: Routine field calibration of water vapor analyzers has always been a challenging problem for those making long-term flux measurements at remote sites. Automated sampling of standard gases from compressed tanks, the method of choice for CO2 calibration, cannot be used for H2O. Calibrations are typically done manually with a dew point generator, and as a consequence are done less frequently, degrading the accuracy and utility of H2O flux data. To remedy this, we have designed, built and tested a field-portable mixing ratio generator, also including features that facilitate its use in water vapor isotope research. It consists of an aluminum cell, with a usable capacity of 80cm3, that is coupled to a Peltier heater/cooler and encased in insulation. The temperature of water in the cell is monitored with a thermocouple and a pressure transducer is used to measure the cell pressure. A data logger uses this information to compute the mixing ratio in the cell and control the polarity and duty cycle of the power input to the Peltier block in order to drive the system toward the desired mixing ratio and to maintain it. Incoming air enters through a bubbler and the exiting air is directed to the gas analyzer. A separate fitting mates to a syringe, allowing easy filling and draining of the cell. The cost of the components, exclusive of the data logger, is approximately 1100 USD. Power consumption is low, on the order of 10W, facilitating use at remote sites. As a consequence, the response time of the instrument is relatively slow, but could be substantially improved for situations where dynamic response is more important than power consumption. Testing has shown that the unit is accurate over a broad range of mixing ratios, able to compensate for changes in ambient pressure, and stable for long periods of time. In addition, it has been used to generate Rayleigh distillation plots, demonstrating its utility for routinely checking the performance of water vapor isotope lasers. [Copyright &y& Elsevier]

Abstract: Climate change and economic concerns have motivated intense interest in the development of renewable energy sources, including fuels derived from plant biomass. However, the specter of massive biofuel production has raised other worries, specifically that by displacing food production it will lead to higher food prices, increased incidence of famine, and acceleration of undesirable land use change. One proposed solution is to increase the annual net primary productivity of the existing agricultural land base, so that it can sustainably produce both food and biofuel feedstocks. This might be possible in corn and soybean production regions through the use of winter cover crops, but the biophysical feasibility of this has not been systematically explored. We developed a model for this purpose that simulates the potential biomass production and water use of winter rye in continuous corn and corn–soybean rotations. The input data requirements represent an attempt to balance the demands of a physically and physiologically defensible simulation with the need for broad applicability in space and time. The necessary meteorological data are obtainable from standard agricultural weather stations, and the required management data are simply planting dates and harvest dates for corn and soybeans. Physiological parameters for rye were taken from the literature, supplemented by experimental data specifically collected for this project. The model was run for a number of growing seasons for 8 locations across the Midwestern USA. Results indicate potential rye biomass production of 1–8Mgha−1, with the lowest yields at the more northern sites, where both PAR and degree-days are limited in the interval between fall corn harvest and spring corn or soybean planting. At all sites rye yields are substantially greater when the following crop is soybean rather than corn, since soybean is planted later. Not surprisingly, soil moisture depletion is most likely in years and sites where rye biomass production is greatest. Consistent production of both food and biomass from corn/winter rye/soybean systems will probably require irrigation in many areas and additional N fertilizer, creating possible environmental concerns. Rye growth limitations in the northern portion of the corn belt may be partially mitigated with aerial seeding of rye into standing corn. [Copyright &y& Elsevier]

Ecosystem-scale estimation of photosynthesis and respiration using micrometeorological techniques remains an important, yet difficult, challenge. In this study, we combined micrometeorological and stable isotope methods to partition net ecosystem CO2 exchange ( FN) into photosynthesis ( FA) and respiration ( FR) in a corn–soybean rotation ecosystem during the summer 2003 corn phase. Mixing ratios of 12CO2 and 13CO2 were measured continuously using tunable diode laser (TDL) absorption spectroscopy. The dynamics of the isotope ratio of ecosystem respiration ( δR), net ecosystem CO2 exchange ( δN) and photosynthetic discrimination at the canopy scale (Δcanopy) were examined. During the period of full canopy closure, FN was partitioned into photosynthesis and respiration using both the isotopic approach and the conventional night-time-derived regression methodology. Results showed that δR had significant seasonal variation (−32 to −11‰) corresponding closely with canopy phenology. Daytime δN typically varied from −12 to −4‰, while Δcanopy remained relatively constant in the vicinity of 3‰. Compared with the regression approach, the isotopic flux partitioning showed more short-term variations and was considerably more symmetric about FN. In this experiment, the isotopic partitioning resulted in larger uncertainties, most of which were caused by the uncertainties in δN and the daytime estimate of δR. By sufficiently reducing these uncertainties, the tunable diode laser (TDL)–micrometeorological technique should yield a better understanding of the processes controlling photosynthesis, respiration and ecosystem-scale discrimination. [ABSTRACT FROM AUTHOR]

Abstract This paper presents an empirical model of net ecosystem CO2 exchange (NEE) developed for a subarctic fen near Churchill, Manitoba. The model with observed data helps explain the interannual variability in growing season NEE. Five years of tower-flux data are used to test and examine the seasonal behaviour of the model simulations. Processes controlling the observed interannual variability of CO2 exchange at the fen are examined by exploring the sensitivity of the model to changes in air temperature, precipitation and leaf area index. Results indicate that the sensitivity of NEE to changing environmental controls is complex and varies interannually depending on the initial conditions of the wetland. Changes in air temperature and the timing of precipitation events have a strong influence on NEE, which is largely manifest in gross ecosystem photosynthesis (GEP). Climate change scenarios indicate that warmer air temperatures will increase carbon acquisition during wet years but may act to reduce wetland carbon storage in years that experience a large water deficit early in the growing season. Model simulations for this subarctic sedge fen indicate that carbon acquisition is greatest during wet and warm conditions. This suggests therefore that carbon accumulation was greatest at this subarctic fen during its early developmental stages when hydroclimatic conditions were relatively wet and warm at approximately 2500 years before present. [ABSTRACT FROM AUTHOR]

SummaryLandscape- and community-level CO2 measurements were made at a subarctic sedge fen near Churchill Manitoba during the 1997 growing season. Climatic conditions were warmer and drier than the 30-y normal. Landscape-scale micrometeorological measurements indicated that the wetland gained 49 g CO2 m-2 during the growing season. Chamber-scale measurements from the main vegetation community types showed that small hummocks (Carex spp. sites) dominated the CO2 exchange, yielding an effective scaling factor of 70%. Scaled parameters of two algorithms describing photosynthesis and respiration for each community type show strong similarity to those derived at the landscape level. Scaling photosynthesis, respiration, and net ecosystem CO2 exchange from the community to landscape-level over the season is within the maximum probable error of each methodological approach and helps substantiate the 1997 CO2 budget. We explore the equilibrium response of net ecosystem CO2 exchange of this fen to climatic change by examining the feedback of water table position on vegetation distribution and nitrogen availability. Based on the effective scaling factors computed for each community type, we hypothesize that a small decrease in mean water table position could nearly triple the net uptake of CO2 at this wetland. [ABSTRACT FROM AUTHOR]

• 3.5 year-long study with simultaneous open- and closed path CH4 flux measurements. • Quantification of systematic, random and gap-filling errors and resulting uncertainty. • Average annual emissions of 18 g−CH4 m-2 yr-1 agreed within 0.6 g−CH4 m-2 yr-1 between analyzers. • Total flux uncertainty on annual fluxes was estimated to range from 7% to 17%. Wetlands represent the dominant natural source of methane (CH 4) to the atmosphere. Thus, substantial effort has been spent examining the CH 4 budgets of global wetlands via continuous ecosystem-scale measurements using the eddy covariance (EC) technique. Robust error characterization for such measurements, however, remains a major challenge. Here, we quantify systematic, random and gap-filling errors and the resulting uncertainty in CH 4 fluxes using a 3.5 year time series of simultaneous open- and closed path CH 4 flux measurements over a sub-boreal wetland. After correcting for high- and low frequency flux attenuation, the magnitude of systematic frequency response errors were negligible relative to other uncertainties. Based on three different random flux error estimations, we found that errors of the CH 4 flux measurement systems were smaller in magnitude than errors associated with the turbulent transport and flux footprint heterogeneity. Errors on individual half-hourly CH 4 fluxes were typically 6%–41%, but not normally distributed (leptokurtic), and thus need to be appropriately characterized when fluxes are compared to chamber-derived or modeled CH 4 fluxes. Integrated annual fluxes were only moderately sensitive to gap-filling, based on an evaluation of 4 different methods. Calculated budgets agreed on average to within 7% (≤1.5 g−CH 4 m−2 yr−1). Marginal distribution sampling using open source code was among the best-performing of all the evaluated gap-filling approaches and it is therefore recommended given its transparency and reproducibility. Overall, estimates of annual CH 4 emissions for both EC systems were in excellent agreement (within 0.6 g−CH 4 m−2 yr−1) and averaged 18 g−CH 4 m−2 yr−1. Total uncertainties on the annual fluxes were larger than the uncertainty of the flux measurement systems and estimated between 7–17%. Identifying trends and differences among sites or site years requires that the observed variability exceeds these uncertainties. [ABSTRACT FROM AUTHOR]

Abstract: It has long been suspected that root hydraulic redistribution (HR) affects the carbon and nitrogen cycles. Nitrous oxide (N2O) is an important greenhouse gas and is the primary stratospheric ozone‐depleting substance. To our knowledge, the influences of HR on N2O emissions have not been investigated. Here we use the HR schemes of Ryel et al. and Amenu and Kumar incorporated into CLM4.5 to examine N2O emissions at five AmeriFlux sites. The results show that HR reduced N2O emissions by 28–92% in the four natural ecosystems experiencing a dry season, whereas it had a very limited effect on the Corn Belt site that has strong emissions but with no distinct dry season. We hypothesize that N2O emissions in ecosystems with a distinct dry season are likely overestimated by CENTURY‐based Earth system models. [ABSTRACT FROM AUTHOR]

Nitrous oxide (N 2 O) has a high global warming potential and depletes stratospheric ozone. The U. S. Corn Belt plays an important role in the global anthropogenic N 2 O budget. To date, studies on local surface N 2 O emissions and the atmospheric N 2 O budget have commonly used Lagrangian models. In the present study, we used an Eulerian model - Weather Research and Forecasting Chemistry (WRF-Chem) model to investigate the relationships between N 2 O emissions in the Corn Belt and observed atmospheric N 2 O mixing ratios. We derived a simple equation to relate the emission strengths to atmospheric N 2 O mixing ratios, and used the derived equation and hourly atmospheric N 2 O measurements at the KCMP tall tower in Minnesota to constrain agricultural N 2 O emissions. The modeled spatial patterns of atmospheric N 2 O were evaluated against discrete observations at multiple tall towers in the NOAA flask network. After optimization of the surface flux, the model reproduced reasonably well the hourly N 2 O mixing ratios monitored at the KCMP tower. Agricultural N 2 O emissions in the EDGAR42 database needed to be scaled up by 19.0 to 28.1 fold to represent the true emissions in the Corn Belt for June 1–20, 2010 - a peak emission period. Optimized mean N 2 O emissions were 3.00–4.38, 1.52–2.08, 0.61–0.81 and 0.56–0.75 nmol m − 2 s − 1 for June 1–20, August 1–20, October 1–20 and December 1–20, 2010, respectively. The simulated spatial patterns of atmospheric N 2 O mixing ratios after optimization were in good agreement with the NOAA discrete observations during the strong emission peak in June. Such spatial patterns suggest that the underestimate of emissions using IPCC (Inter-governmental Panel on Climate Change) inventory methodology is not dependent on tower measurement location. [ABSTRACT FROM AUTHOR]

Aim: Fresh carbon (C) inputs to the soil can have important consequences for the decomposition rates of soil organic matter (priming effect), thereby impacting the delicate global C balance at the soil–atmosphere interface. Yet, the environmental factors that control soil priming effect intensity remain poorly understood at a global scale. Location: Global. Time period: 1980–2020. Major taxa studied: Soil priming effect intensity. Methods: We conducted a global dataset of CO2 effluxes in 711 pairwise soils with 13C or 14C simple C sources inputs and without C inputs from incubation experiments in which isotope‐labelled C was used to quantify fresh C‐induced rather than exudate‐induced priming. Results: Soil priming effect intensity is predominantly positive. Soil texture and C content were identified as the most important factors associated with priming effects, with sandy soils from tropical and mid‐latitudes supporting the highest soil priming effect intensity, and soils with greater C content and fine textures from high latitudes maintaining the lowest soil priming effects. The negative association between C content and soil priming effect intensity was also indirectly driven by changing mean annual temperature, net primary productivity, and fungi : bacteria ratio. Using this information, we generated a global map of soil priming effect intensity, and found that the priming was lower at high latitudes and higher at lower latitudes. Main conclusions: Global patterns of soil priming effect intensity can be predicted using environmental data, with soil texture and C content playing a predominant role in explaining in priming effects. These effects were also indirectly driven by climate, vegetation and soil microbial properties. We present the first global atlas of soil priming effect intensity and advance our knowledge on the potential mechanisms underlying soil priming effect intensity, which are integral to improving the climate change and soil C dynamics components of Earth System models. [ABSTRACT FROM AUTHOR]

Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the "black box" to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81 , and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes. [ABSTRACT FROM AUTHOR]

• We calculated field-scale net ecosystem C balances for forage and grain rotations. • C loss was threefold greater from continuous silage maize than soybean-grain maize. • Alfalfa reduced C loss by 23% relative to continuous silage maize. • Winter rye cover copping and liquid dairy manure improved C-balances marginally. • Every Mg of crop residue C left in-field improved C balances by +0.9 Mg C ha−1. Intensification of the US dairy industry has driven increased reliance on maize (Zea mays L.) silage as a primary forage source in place of perennial forages such as alfalfa (Medicago sativa L.). Using 29 site-years of eddy covariance, plant, and manure measurements, we calculated net ecosystem C balances (NECB) for two silage maize-based forage cropping systems and a soybean-maize grain rotation. We found that C losses were over threefold greater from continuous silage maize (-4.9 Mg C ha−1 yr−1) than from the predominant grain cropping system in the region, the soybean-maize rotation (-1.3 Mg C ha−1 yr−1). Including alfalfa in rotation reduced C losses by 23% relative to continuous silage maize, but net losses were still observed (-3.8 Mg C ha−1 yr−1). For every megagram of crop residue C left in-field, net C balances increased by +0.9 Mg C ha−1. A winter rye (Secale cereale L.) cover crop and applications of liquid dairy manure marginally improved C-balances but were insufficient to offset C losses in respiration and crop harvest. Increasing manure application rates could bring these systems to a net equilibrium C balance but would also result in soil N and P surpluses and unacceptable loss of nutrients to air and water. Since 1980, over 800,000 hectares of alfalfa have been lost across the Upper Midwest US, and C export in harvested maize grain and silage have increased dramatically. This shift implies a substantial reduction in SOC on forage cropped soils in the region. [ABSTRACT FROM AUTHOR]

Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gases (GHGs) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or constraints of model parameters, and to leverage recent advances in machine learning (ML) new method is needed to unlock the 'black box' to overcome its limitations due to low interpretability, out-of- sample failure and massive data demand. In this study, we developed a first of its kind knowledge-guided machine learning model for agroecosystems (KGML-ag), by incorporating biogeophysical/chemical domain knowledge from an advanced PB model, ecosys, and tested it by simulating daily N2O fluxes with real observed data from mesocosm experiments. The Gated Recurrent Unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including: 1) Using initials of intermediate variables (IMVs) instead of time series as model input to reduce data demand; 2) Building hierarchical structures to explicitly estimate IMVs for further N2O prediction; 3) Using multitask learning to balance the simultaneous training on multiple variables; and 4) Pretraining with millions of synthetic data generated from ecosys and fine tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2 = 0.81, and RMSE = 3.6 mg N m-2 day-1 from cross-validation). Importantly KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML- ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes. [ABSTRACT FROM AUTHOR]

Carbon monoxide (CO) is an ozone precursor, oxidant sink, and widely used pollution tracer. The importance of anthropogenic versus other CO sources in the US is uncertain. Here, we interpret extensive airborne measurements with an atmospheric model to constrain US fossil and nonfossil CO sources. Measurements reveal a low bias in the simulated CO background and a 30% overestimate of US fossil CO emissions in the 2016 National Emissions Inventory. After optimization we apply the model for source partitioning. During summer, regional fossil sources account for just 9%-16% of the sampled boundary layer CO, and 32%-38% of the North American enhancement--complicating use of CO as a fossil fuel tracer. The remainder predominantly reflects biogenic hydrocarbon oxidation plus fires. Fossil sources account for less domain-wide spatial variability at this time than nonfossil and background contributions. The regional fossil contribution rises in other seasons, and drives ambient variability downwind of urban areas. Plain Language Summary Carbon monoxide (CO) is an air pollutant emitted from fossil fuel combustion and from forest and agricultural fires. CO is also produced in the atmosphere through the oxidation of hydrocarbons from both natural and human-caused sources. US fossil fuel CO emissions have been declining in recent years, and their current importance relative to other regional sources is uncertain. Here, we interpreted a large group of aircraft-based CO measurements with a high-resolution atmospheric model to better quantify US fossil and nonfossil fuel CO sources over the eastern half of the US. We find that US fossil fuel CO emissions in the 2016 National Emissions Inventory are overestimated by ~30%. Furthermore, during summer regional fossil fuel sources account for only a small fraction of the CO over North America compared to the background concentrations already present in air entering North America, and compared to the regional source from natural hydrocarbon oxidation. This complicates the use of CO as a tracer for estimating fossil fuel sources of other pollutants such as carbon dioxide. [ABSTRACT FROM AUTHOR]

Peatlands are among the largest natural sources of atmospheric methane (CH4) worldwide. Peatland emissions are projected to increase under climate change, as rising temperatures and shifting precipitation accelerate microbial metabolic pathways favorable for CH4 production. However, how these changing environmental factors will impact peatland emissions over the long term remains unknown. Here, we investigate a novel data set spanning an exceptionally long 11 years to analyze the influence of soil temperature and water table elevation on peatland CH4 emissions. We show that higher water tables dampen the springtime increases in CH4 emissions as well as their subsequent decreases during late summer to fall. These results imply that any hydroclimatological changes in northern peatlands that shift seasonal water availability from winter to summer will increase annual CH4 emissions, even if temperature remains unchanged. Therefore, advancing hydrological understanding in peatland watersheds will be crucial for improving predictions of CH4 emissions. Plain Language Summary: Methane (CH4) emissions from wetlands are the largest natural source of atmospheric CH4 worldwide and are expected to increase under global warming. Because of a scarcity of field observations, we do not yet know how wetland CH4 emissions will be affected by future climates and under what conditions. In this study, we use a newly developed long‐term data set of CH4 flux measurements at a northern peatland to demonstrate the importance of seasonal water availability in controlling the sensitivity of CH4 emission increase to soil temperature. Our results suggest that a shift in water availability from winter to summer may result in higher annual CH4 emissions, even if soil temperatures remain the same. Key Points: A newly developed long‐term data set of methane (CH4) flux measurements at a northern peatland is analyzedSeasonal water availability is shown to control the sensitivity of CH4 emissions to increase in soil temperatureShifting water availability from winter to summer may result in higher annual CH4 emissions, even if soil temperatures remain the same [ABSTRACT FROM AUTHOR]

• First evaluation of the new E3SM land model (ELM) in an Amazonian peatland. • A surrogate-assisted Bayesian approach was implemented for parameter optimization. • Improved functions and optimized parameters greatly enhanced model performance. • Modeled energy, CO 2 and CH 4 fluxes were most sensitive to phenological parameters. • Parameter importance depended on biogeochemical process and shifted with season. Tropical peatlands are one of the largest natural sources of atmospheric methane (CH 4) and play a significant role in regional and global carbon budgets. However, large uncertainties persist regarding their feedbacks to climate variations. The Energy Exascale Earth System Model (E3SM) Land Model (ELM) is an ongoing state-of-the-science model, which has developed new representations of soil hydrology and biogeochemistry and includes a new microbial-functional-group-based CH 4 module. This model has been tested in boreal forest peatlands, but has not yet been evaluated for simulating energy and carbon exchange for tropical peatlands. Here, we evaluated the ELM performance in simulating energy, carbon dioxide (CO 2) and CH 4 fluxes of an Amazonian palm swamp peatland in Iquitos, Peru. ELM simulations using default parameter values resulted in poor performance of seasonal carbon dynamics. Several algorithms were improved according to site-specific characteristics and key parameters were optimized using an objective surrogate-assisted Bayesian approach. The modified algorithms included the soil water retention curve, a water coverage scalar function for CH 4 processes, and a seasonally varying leaf carbon-to-nitrogen ratio function. The revised tropics-specific model better simulated the diel and seasonal patterns of energy and carbon fluxes of the palm swamp peatland. Global sensitivity analyses indicated that the strong controls on energy and carbon fluxes were mainly attributed to the parameters associated with vegetation activities, such as plant carbon distribution, stomatal regulation, photosynthetic capacity, and leaf phenology. Parameter relative importance depended on biogeochemical processes and shifted significantly between wet and dry seasons. This modeling study advanced the understanding of biotic controls on the energy and carbon exchange in Amazonian palm swamp peatlands and identified knowledge gaps that need to be addressed for better prediction of carbon cycle processes and budgets for tropical peatlands. [ABSTRACT FROM AUTHOR]

• NGS N 2 O emissions accounted for 6–60% of the annual fluxes in the US Midwest. • NGS N 2 O emissions were controlled by distinct drivers in the southeast and northwest. • NGS precipitation 300 mm line was found to be a threshold for the divergence. • Intensive freezing effect is the dominant driver of NGS N 2 O emissions in the southeast. • The thawing effect is the dominant driver of NGS N 2 O emissions in the northwest. Agricultural N 2 O emission is a growing concern for climate change. Recent field evidence suggests that non-growing seasons (NGS) may contribute one-third to half of the annual N 2 O emissions, but implications on management adaptations remain unclear. Here we used an advanced process-based model, ecosys , to investigate the magnitude and drivers of NGS N 2 O emissions from the US Midwest. Results showed that simulated NGS N 2 O emissions accounted for 6–60% of the annual fluxes under continuous corn systems, peaking in counties with NGS precipitation (P NGS) around 300 mm. Divergent patterns of spatial-temporal correlations between NGS N 2 O emissions and environmental variables were shown in the southeast (P NGS > 300 mm) and the northwest (P NGS < 300 mm) of the study area by simulations. Causal analysis indicates that more intensive freezing caused by decreased air temperature (T a) is the dominant driver that leads to NGS N 2 O emissions increasing within the southeast of the study area, while increased P NGS and increased T a cooperatively result in soil moisture decreasing at soil thaws that enhances NGS N 2 O production within the northwest of the study area. Scenario simulations suggest that annual N 2 O emissions in the US Midwest are likely to reduce under climate change primarily due to the reduction of NGS N 2 O emissions. Our estimates on monetized social benefits inform the necessity to implement spatial-specific mitigation strategies, i.e. determining fertilizer timing and use of nitrification inhibitors (NI). Spring fertilizer application is more beneficial than fall fertilizer application for most counties, however, the latter can bring extra benefits to some counties in the west of the study area. Introducing NI with either spring or fall applications can greatly increase social benefits by reducing N 2 O emissions and N leaching. This study addresses possibly effective adaptations by providing seasonal- and spatial-explicit mitigation potentials. [Display omitted] [ABSTRACT FROM AUTHOR]

• NGS N 2 O emissions accounted for 6–60% of the annual fluxes in the US Midwest. • NGS N 2 O emissions were controlled by distinct drivers in the southeast and northwest. • NGS precipitation 300 mm line was found to be a threshold for the divergence. • Intensive freezing effect is the dominant driver of NGS N 2 O emissions in the southeast. • The thawing effect is the dominant driver of NGS N 2 O emissions in the northwest. Agricultural N 2 O emission is a growing concern for climate change. Recent field evidence suggests that non-growing seasons (NGS) may contribute one-third to half of the annual N 2 O emissions, but implications on management adaptations remain unclear. Here we used an advanced process-based model, ecosys , to investigate the magnitude and drivers of NGS N 2 O emissions from the US Midwest. Results showed that simulated NGS N 2 O emissions accounted for 6–60% of the annual fluxes under continuous corn systems, peaking in counties with NGS precipitation (P NGS) around 300 mm. Divergent patterns of spatial-temporal correlations between NGS N 2 O emissions and environmental variables were shown in the southeast (P NGS > 300 mm) and the northwest (P NGS < 300 mm) of the study area by simulations. Causal analysis indicates that more intensive freezing caused by decreased air temperature (T a) is the dominant driver that leads to NGS N 2 O emissions increasing within the southeast of the study area, while increased P NGS and increased T a cooperatively result in soil moisture decreasing at soil thaws that enhances NGS N 2 O production within the northwest of the study area. Scenario simulations suggest that annual N 2 O emissions in the US Midwest are likely to reduce under climate change primarily due to the reduction of NGS N 2 O emissions. Our estimates on monetized social benefits inform the necessity to implement spatial-specific mitigation strategies, i.e. determining fertilizer timing and use of nitrification inhibitors (NI). Spring fertilizer application is more beneficial than fall fertilizer application for most counties, however, the latter can bring extra benefits to some counties in the west of the study area. Introducing NI with either spring or fall applications can greatly increase social benefits by reducing N 2 O emissions and N leaching. This study addresses possibly effective adaptations by providing seasonal- and spatial-explicit mitigation potentials. [Display omitted] [ABSTRACT FROM AUTHOR]

The reliable simulation of gross primary productivity (GPP) at various spatial and temporal scales is of significance to quantifying the net exchange of carbon between terrestrial ecosystems and the atmosphere. This study aimed to verify the ability of a nonlinear two-leaf model (TL-LUEn), a linear two-leaf model (TL-LUE), and a big-leaf light use efficiency model (MOD17) to simulate GPP at half-hourly, daily and 8-day scales using GPP derived from 58 eddy-covariance flux sites in Asia, Europe and North America as benchmarks. Model evaluation showed that the overall performance of TL-LUEn was slightly but not significantly better than TL-LUE at half-hourly and daily scale, while the overall performance of both TL-LUEn and TL-LUE were significantly better (p < 0.0001) than MOD17 at the two temporal scales. The improvement of TL-LUEn over TL-LUE was relatively small in comparison with the improvement of TL-LUE over MOD17. However, the differences between TL-LUEn and MOD17, and TL-LUE and MOD17 became less distinct at the 8-day scale. As for different vegetation types, TL-LUEn and TL-LUE performed better than MOD17 for all vegetation types except crops at the half-hourly scale. At the daily and 8-day scales, both TL-LUEn and TL-LUE outperformed MOD17 for forests. However, TL-LUEn had a mixed performance for the three non-forest types while TL-LUE outperformed MOD17 slightly for all these non-forest types at daily and 8-day scales. The better performance of TL-LUEn and TL-LUE for forests was mainly achieved by the correction of the underestimation/overestimation of GPP simulated by MOD17 under low/high solar radiation and sky clearness conditions. TL-LUEn is more applicable at individual sites at the half-hourly scale while TL-LUE could be regionally used at half-hourly, daily and 8-day scales. MOD17 is also an applicable option regionally at the 8-day scale. [ABSTRACT FROM AUTHOR]

• Phenological controls over aerodynamic resistance (R ah) were investigated. • R ah exhibits significant seasonal variability across a wide range of sites. • These shifts in R ah were caused by phenology in some ecosystems. • Accounting for variation in kB −1 is important for improving predictions of H. Surface roughness – a key control on land-atmosphere exchanges of heat and momentum – differs between dormant and growing seasons. However, how surface roughness shifts seasonally at fine time scales (e.g., days) in response to changing canopy conditions is not well understood. This study: (1) explores how aerodynamic resistance changes seasonally; (2) investigates what drives these seasonal shifts, including the role of vegetation phenology; and (3) quantifies the importance of including seasonal changes of aerodynamic resistance in "big leaf" models of sensible heat flux (H). We evaluated aerodynamic resistance and surface roughness lengths for momentum (z 0m) and heat (z 0h) using the kB −1 parameter (ln(z 0m / z 0h)). We used AmeriFlux data to obtain surface-roughness estimates, and PhenoCam greenness data for phenology. This analysis included 23 sites and ∼190 site years from deciduous broadleaf, evergreen needleleaf, woody savanna, cropland, grassland, and shrubland plant-functional types (PFTs). Results indicated clear seasonal patterns in aerodynamic resistance to sensible heat transfer (R ah). This seasonality tracked PhenoCam-derived start-of-season green-up transitions in PFTs displaying the most significant seasonal changes in canopy structure, with R ah decreasing near green-up transitions. Conversely, in woody savanna sites and evergreen needleleaf forests, patterns in R ah were not linked to green-up. Our findings highlight that decreases in kB −1 are an important control over R ah , explaining > 50% of seasonal variation in R ah across most sites. Decreases in kB −1 during green-up are likely caused by increasing z 0h in response to higher leaf area index. Accounting for seasonal variation in kB −1 is key for predicting H as well; assuming kB −1 to be constant resulted in significant biases that also exhibited strong seasonal patterns. Overall, we found that aerodynamic resistance can be sensitive to phenology in ecosystems having strong seasonality in leaf area, and this linkage is critical for understanding land-atmosphere interactions at seasonal time scales. [ABSTRACT FROM AUTHOR]

Stable isotopes in water have the potential to diagnose changes in the earth's hydrological budget in response to climate change and land use change. However, there have been few measurements in the vapour phase. Here, we present high-frequency measurements of oxygen isotopic compositions of water vapour ( δv) and evapotranspiration ( δET) above a soybean canopy using the tunable diode laser (TDL) technique for the entire 2006 growing season in Minnesota, USA. We observed a large variability in surface δv from the daily to the seasonal timescales, largely explained by Rayleigh processes, but also influenced by vertical atmospheric mixing, local evapotranspiration (ET) and dew formation. We used δET measurements to calculate the isotopic composition at the sites of evaporative enrichment in leaves ( δL,e) and compared that with the commonly used steady-state prediction ( δL,s). There was generally a good agreement averaged over the season, but larger differences on individual days. We also found that vertical variability in relative humidity and temperature associated with canopy structure must be addressed in canopy-scale leaf water models. Finally, we explored this data set for direct evidence of the Péclet effect. [ABSTRACT FROM AUTHOR]

Abstract: It is widely believed that soil disturbance by tillage was a primary cause of the historical loss of soil organic carbon (SOC) in North America, and that substantial SOC sequestration can be accomplished by changing from conventional plowing to less intensive methods known as conservation tillage. This is based on experiments where changes in carbon storage have been estimated through soil sampling of tillage trials. However, sampling protocol may have biased the results. In essentially all cases where conservation tillage was found to sequester C, soils were only sampled to a depth of 30cm or less, even though crop roots often extend much deeper. In the few studies where sampling extended deeper than 30cm, conservation tillage has shown no consistent accrual of SOC, instead showing a difference in the distribution of SOC, with higher concentrations near the surface in conservation tillage and higher concentrations in deeper layers under conventional tillage. These contrasting results may be due to tillage-induced differences in thermal and physical conditions that affect root growth and distribution. Long-term, continuous gas exchange measurements have also been unable to detect C gain due to reduced tillage. Though there are other good reasons to use conservation tillage, evidence that it promotes C sequestration is not compelling. [Copyright &y& Elsevier]

The annual net ecosystem productivity (FNEP) of a second-growth Douglas-fir stand established in 1949 on the Canadian West Coast varied considerably over the 4-year period between 1998 and 2001. This period included the El Niño/La Niña cycle during the northern hemispheric winters of 1997/1998 and 1998/1999, offering a unique opportunity to study how a typical forest ecosystem in the Pacific Northwest reacts to interannual climate variability. This was possible even though annual FNEP values calculated from eddy covariance (EC) measurements of CO2 fluxes were subject to biases. These were largely due to the failure of the EC method to accurately measure losses of CO2 under low turbulence conditions at night, which caused FNEP overestimates of as much as 90 g C m-2 per year. As these biases were largely unaffected by interannual climate variability, it was possible to reliably quantify interannual differences in FNEP estimates if they were larger than random variability, which was estimated to be ±30 g C m-2 per year at most. Interannual differences were mainly due to differences in ecosystem respiration (R) between the 4 years. In the year following the 1997/1998 El Niño, high air temperatures led to the highest annual R of the 4 years, while annual gross ecosystem photosynthesis (P) was only slightly higher than normal. This resulted in 1998 having the lowest FNEP (270 g C m-2 per year) of the 4 years. For 1999, a cool and cloudy La Niña year, FNEP was 360 g C m-2 per year, much higher than 1998, but somewhat lower than the last 2 years, for which FNEP values were 390 and 420 g C m-2 per year, respectively. [Copyright &y& Elsevier]

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 1°C 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. [ABSTRACT FROM AUTHOR]

Compares two carbon budget estimates derived from long-term growth rates of organic soil and on shorter-term flux measurements, taken from research studies conducted in a wetland-peatland system in the northern Hudson Bay Lowland near the town of Churchill, Manitoba. Annual carbon budget of fen and forest; Average annual carbon budget of fen and forest; Net carbon fluxes from the fen for the detailed study period and their accompanying environmental energy and water budget parameters.

Eddy covariance (EC) measurements of ecosystem-atmosphere carbon dioxide (CO 2) exchange provide the most direct assessment of the terrestrial carbon cycle. Measurement biases for open-path (OP) CO 2 concentration and flux measurements have been reported for over 30 years, but their origin and appropriate correction approach remain unresolved. Here, we quantify the impacts of OP biases on carbon and radiative forcing budgets for a sub-boreal wetland. Comparison with a reference closed-path (CP) system indicates that a systematic OP flux bias (0.54 μ mol m − 2 s − 1 ) persists for all seasons leading to a 110% overestimate of the ecosystem CO 2 sink (cumulative error of 78 gC m−2). Two potential OP bias sources are considered: Sensor-path heat exchange (SPHE) and analyzer temperature sensitivity. We examined potential OP correction approaches including: i) Fast temperature measurements within the measurement path and sensor surfaces; ii) Previously published parameterizations; and iii) Optimization algorithms. The measurements revealed year-round average temperature and heat flux gradients of 2.9 °C and 16 W m−2 between the bottom sensor surfaces and atmosphere, indicating SPHE-induced OP bias. However, measured SPHE correlated poorly with the observed differences between OP and CP CO 2 fluxes. While previously proposed nominally universal corrections for SPHE reduced the cumulative OP bias, they led to either systematic under-correction (by 38.1 gC m−2) or to systematic over-correction (by 17-37 gC m−2). The resulting budget errors exceeded CP random uncertainty and change the sign of the overall carbon and radiative forcing budgets. Analysis of OP calibration residuals as a function of temperature revealed a sensitivity of 5 μ mol m − 3 K − 1 . This temperature sensitivity causes CO 2 calibration errors proportional to sample air fluctuations that can offset the observed growing season flux bias by 50%. Consequently, we call for a new OP correction framework that characterizes SPHE- and temperature-induced CO 2 measurement errors. [ABSTRACT FROM AUTHOR]

• We attributed the canopy conductance variability to VPD and soil moisture. • We obtained the canopy conductance using Ameriflux sites data in the U.S. Corn Belt. • High VPD dominantly controlled the canopy conductance variability. • Precipitation affects plant water stress more through the atmosphere than soil. The U.S. Corn Belt, the world's biggest production region for corn and soybean combined, is prone to droughts. Currently 92% of the U.S. Corn Belt croplands are rainfed, and thus are sensitive to interannual climate variability and future climate change. Most prior studies identify the lack of soil moisture as the primary cause of agricultural drought impacts, although water-related stresses are also induced by high atmospheric water demands (i.e., vapor pressure deficit, VPD). Here we empirically attributed the variability of canopy-level stomatal conductance (Gs) and gross primary productivity (GPP) to VPD and soil water supply (i.e. volumetric soil water content, SWC), using eddy-covariance data from seven AmeriFlux eddy covariance sites in maize and soybean fields across the U.S. Corn Belt, which are well represented for the current rainfed part of the Corn Belt croplands. We used three independent approaches, including two statistical models (i.e. a multiple-linear regression model and a semi-empirical, non-linear model) and information theory, to quantify the relationship of Gs (or GPP) with VPD and SWC. The attribution result from the two models shows that VPD explains most of Gs variability (91% and 89%, respectively), and mutual information also attributed 91% of GPP variability to VPD. This finding was robust over the gradients of rainfall and temperature, crop types (maize vs. soybean), and management practices (whether irrigated or not). We reconciled our finding with the previously emphasized importance of precipitation and SWC, by conducting a path analysis, which revealed the causal relationships between precipitation, air temperature (Ta), relative humidity (RH), VPD, SWC, and Gs. We find that precipitation impacts on Gs through reduced RH and Ta to VPD (rather than directly through SWC). With increased VPD robustly projected under climate change, we expect increased crop water stress in the future for the U.S. Corn Belt. Image, graphical abstract [ABSTRACT FROM AUTHOR]