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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 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 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 https://doi.org/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.

Soil health is a key element in enhancing agricultural production, environmental sustainability, and food system resilience (Farnsworth 2015; Stevens 2018). The USDA Natural Resources Conservation Service (NRCS) defines soil health as “the continued capacity of soil to function as a vital living ecosystem that sustains plants, animals, and humans” (Pankhurst et al. 1997; Farnsworth 2015; Bowman et al. 2016). A common theme points to soil health as an indicator of the soil’s ability to support life, withstand environmental stresses, and endure as an important part of a resilient ecosystem. Because one of the key individual components of soil health is carbon (C) in organic matter (Idowu et al. 2009; Morrow et al. 2016), enhancing soil health also has a prominent role to play in addressing climate change (Stockmann et al. 2013; Schipanski et al. 2014; Paustian et al. 2016). Improving soil health is a natural climate solution (NCS) that increases C storage in soils (Poeplau and Don 2015; Griscom et al. 2017; Jian et al. 2020). Given soil health’s key role in sustaining agricultural productivity and enhancing C storage, there is significant interest in promoting …

Experiments show that elevated atmospheric CO2 (eCO2) often enhances plant photosynthesis and productivity, yet this effect varies substantially and may be climate sensitive. Understanding if, where and how water supply regulates CO2 enhancement is critical for projecting terrestrial responses to increasing atmospheric CO2 and climate change. Here, using data from 14 long-term ecosystem-scale CO2 experiments, we show that the eCO2 enhancement of annual aboveground net primary productivity is sensitive to annual precipitation and that this sensitivity differs between woody and grassland ecosystems. During wetter years, CO2 enhancement increases in woody ecosystems but declines in grass-dominated systems. Consistent with this difference, woody ecosystems can increase leaf area index in wetter years more effectively under eCO2 than can grassland ecosystems. Overall, and across different precipitation regimes, woody systems had markedly stronger CO2 enhancement (24%) than grasslands (13%). We developed an empirical relationship to quantify aboveground net primary productivity enhancement on the basis of changes in leaf area index, providing a new approach for evaluating eCO2 impacts on the productivity of terrestrial ecosystems.

Nature-based Climate Solutions (NbCS) are managed alterations to ecosystems designed to increase carbon sequestration or reduce greenhouse gas emissions. While they have growing public and private support, the realizable benefits and unintended consequences of NbCS are not well understood. At regional scales where policy decisions are often made, NbCS benefits are estimated from soil and tree survey data that can miss important carbon sources and sinks within an ecosystem, and do not reveal the biophysical impacts of NbCS for local water and energy cycles. The only direct observations of ecosystem-scale carbon fluxes, for example, by eddy covariance flux towers, have not yet been systematically assessed for what they can tell us about NbCS potentials, and state-of-the-art remote sensing products and land-surface models are not yet being widely used to inform NbCS policymaking or implementation. As a result, there is a critical mismatch between the point- and tree-scale data most often used to assess NbCS benefits and impacts, the ecosystem and landscape scales where NbCS projects are implemented, and the regional to continental scales most relevant to policymaking. Here, we propose a research agenda to confront these gaps using data and tools that have long been used to understand the mechanisms driving ecosystem carbon and energy cycling, but have not yet been widely applied to NbCS. We outline steps for creating robust NbCS assessments at both local to regional scales that are informed by ecosystem-scale observations, and which consider concurrent biophysical impacts, future climate feedbacks, and the need for equitable and inclusive NbCS implementation strategies. We contend that these research goals can largely be accomplished by shifting the scales at which pre-existing tools are applied and blended together, although we also highlight some opportunities for more radical shifts in approach.

A long-standing goal of ecology has been to understand the cycling of carbon in forests. This has taken on new urgency with the need to address a rapidly changing climate. Forests serve as longterm stores for atmospheric CO2, but their continued ability to take up new carbon is dependent on future changes in climate and other factors such as age. We have been measuring many aspects of carbon cycling at an unmanaged evergreen forest in central Maine, USA, for over 25 years. Here we use these data to address questions about the magnitude and control of carbon fluxes and quantify flows and uncertainties between the different pools. A key issue was to assess whether recent climate change and an aging tree population were reducing annual C storage. Total ecosystem C stocks determined from inventory and quantitative soil pits were about 23,300 g C m−2 with 46% in live trees, and 48% in the soil. Annual biomass increment in trees at Howland Forest averaged 161 ± 23 g C m−2 yr−1, not significantly different from annual net ecosystem production (NEP = −NEE) of 211 ± 40 g C m−2 y−1 measured by eddy covariance. Unexpectedly, there was a small but significant trend of increasing C uptake through time in the eddy flux data. This was despite the period of record including some of the most climate-extreme years in the last 125. We find a surprising lack of influence of climate variability on annual carbon storage in this mature forest.

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.

Experiments show that elevated atmospheric CO

Significance Forests remove about 30% of anthropogenic CO2 emissions through photosynthesis and return almost 40% of incident precipitation back to the atmosphere via transpiration. The trade-off between photosynthesis and transpiration through stomata, the water-use efficiency (WUE), is an important driver of plant evolution and ecosystem functioning, and has profound effects on climate. Using stable carbon and oxygen isotope ratios in tree rings, we found that WUE has increased by a magnitude consistent with estimates from atmospheric measurements and model predictions. Enhanced photosynthesis was widespread, while reductions in stomatal conductance were modest and restricted to moisture-limited forests. This result points to smaller reductions in transpiration in response to increasing atmospheric CO2, with important implications for forest–climate interactions, which remain to be explored., Multiple lines of evidence suggest that plant water-use efficiency (WUE)—the ratio of carbon assimilation to water loss—has increased in recent decades. Although rising atmospheric CO2 has been proposed as the principal cause, the underlying physiological mechanisms are still being debated, and implications for the global water cycle remain uncertain. Here, we addressed this gap using 30-y tree ring records of carbon and oxygen isotope measurements and basal area increment from 12 species in 8 North American mature temperate forests. Our goal was to separate the contributions of enhanced photosynthesis and reduced stomatal conductance to WUE trends and to assess consistency between multiple commonly used methods for estimating WUE. Our results show that tree ring-derived estimates of increases in WUE are consistent with estimates from atmospheric measurements and predictions based on an optimal balancing of carbon gains and water costs, but are lower than those based on ecosystem-scale flux observations. Although both physiological mechanisms contributed to rising WUE, enhanced photosynthesis was widespread, while reductions in stomatal conductance were modest and restricted to species that experienced moisture limitations. This finding challenges the hypothesis that rising WUE in forests is primarily the result of widespread, CO2-induced reductions in stomatal conductance.

The atmospheric boundary layer mediates the exchange of energy, matter, and momentum between the land surface and the free troposphere, integrating a range of physical, chemical, and biological processes and is defined as the lowest layer of the atmosphere (ranging from a few meters to 3 km). In this review, we investigate how continuous, automated observations of the atmospheric boundary layer can enhance the scientific value of co-located eddy covariance measurements of land-atmosphere fluxes of carbon, water, and energy, as are being made at FLUXNET sites worldwide. We highlight four key opportunities to integrate tower-based flux measurements with continuous, long-term atmospheric boundary layer measurements: (1) to interpret surface flux and atmospheric boundary layer exchange dynamics and feedbacks at flux tower sites, (2) to support flux footprint modelling, the interpretation of surface fluxes in heterogeneous and mountainous terrain, and quality control of eddy covariance flux measurements, (3) to support regional-scale modeling and upscaling of surface fluxes to continental scales, and (4) to quantify land-atmosphere coupling and validate its representation in Earth system models. Adding a suite of atmospheric boundary layer measurements to eddy covariance flux tower sites, and supporting the sharing of these data to tower networks, would allow the Earth science community to address new emerging research questions, better interpret ongoing flux tower measurements, and would present novel opportunities for collaborations between FLUXNET scientists and atmospheric and remote sensing scientists.

Key Messages: * Soil salinization in the coastal Southeastern United States is becoming more prevalent as storm surges increase in frequency,and sea levels rise. * Salinization reduces the productivity of working lands and can prevent crops from growing. * Resources are lacking for landowners to understand coastal salinization and how to manage for resilience. * Action must be taken if the land is to remain profitable as conditions change. This manual describes the impacts and includes adaptation measures that can be taken to maintain productivity in working lands.

Monitoring drought impacts in forest ecosystems is a complex process, because forest ecosystems are composed of different species with heterogeneous structural compositions. Even though forest drought status is a key control on the carbon cycle, very few indices exist to monitor and predict forest drought stress. The Forest Drought Indicator (ForDRI) is a new monitoring tool developed by the National Drought Mitigation Center (NDMC) to identify forest drought stress. ForDRI integrates 12 types of data, including satellite, climate, evaporative demand, ground water, and soil moisture, into a single hybrid index to estimate tree stress. The model uses Principal Component Analysis (PCA) to determine the contribution of each input variable based on its covariance in the historical records (2003–2017). A 15-year time series of 780 ForDRI maps at a weekly interval were produced. The ForDRI values at a 12.5km spatial resolution were compared with normalized weekly Bowen ratio data, a biophysically based indicator of stress, from nine AmeriFlux sites. There were strong and significant correlations between Bowen ratio data and ForDRI at sites that had experienced intense drought. In addition, tree ring annual increment data at eight sites in four eastern U.S. national parks were compared with ForDRI values at the corresponding sites. The correlation between ForDRI and tree ring increments at the selected eight sites during the summer season ranged between 0.46 and 0.75. Generally, the correlation between the ForDRI and normalized Bowen ratio or tree ring increment are reasonably good and indicate the usefulness of the ForDRI model for estimating drought stress and providing decision support on forest drought management.

Forest resource use efficiencies (RUEs) can vary with tree age, but the nature of these trends and their underlying mechanisms are not well understood. Understanding the age dynamics of forest RUEs and their drivers is vital for assessing the trade-offs between forest functions and resource consumption, making rational management policy, and projecting ecosystem carbon dynamics. Here we used the FLUXNET2015 and AmeriFlux datasets and published literature to explore the age-dependent variability of forest light use efficiency (LUE) and inherent water use efficiency as well as their main regulatory variables in temperate regions. Our results showed that evergreen forest RUEs initially increased before reaching the mature stage (i.e., around 90 years old), and then gradually declined; in contrast, RUEs continuously increased with age for mature deciduous forests. Changing canopy photosynthetic capacity (Amax) was the primary cause of age-related changes in RUEs across temperate forest sites. More importantly, soil nitrogen (N) increased in mature deciduous forests through time but decreased in older evergreen forests. The age-dependent changes in soil N were closely linked with the age dynamics of Amax for mature temperate forests. Additionally, soil nutrient conditions played a greater role in deciduous forest RUEs than evergreen forest RUEs. This study highlights the importance of stand age and forest type on temperate forest RUEs over the long term.

There are few whole-canopy or ecosystem scale assessments of the interplay between canopy temperature and photosynthesis across both spatial and temporal scales. The stable oxygen isotope ratio (δ18O) of plant cellulose can be used to resolve a photosynthesis-weighted estimate of canopy temperature, but the method requires independent confirmation. We compare isotope-resolved canopy temperatures derived from multi-year homogenization of tree cellulose δ18O to canopy-air temperatures weighted by gross primary productivity (GPP) at multiple sites, ranging from warm temperate to boreal and subalpine forests. We also perform a sensitivity analysis for isotope-resolved canopy temperatures that showed errors in plant source water δ18O lead to the largest errors in canopy temperature estimation. The relationship between isotope-resolved canopy temperatures and GPP-weighted air temperatures was highly significant across sites (p

Despite decades of research, gaining a comprehensive understanding of carbon (C) cycling in forests remains a considerable challenge. Uncertainties stem from persistent methodological limitations and the difficulty of resolving top-down estimates of ecosystem C exchange with bottom-up measurements of individual pools and fluxes. To address this, we derived estimates and associated uncertainties of ecosystem C fluxes for a 100–125 year old mixed temperate forest stand at the Bartlett Experimental Forest, New Hampshire, USA, using three different approaches: (1) tower-based eddy covariance, (2) a biometric approach involving C flux measurements of individual ecosystem subcomponents, and (3) an inventory approach involving changes in major C stocks over time. Our analysis made use of 13 years of data, collected over the period from 2004 to 2016.Estimates of mean annual net ecosystem production (NEP) ranged from 120 to 133 g C m−2, demonstrating strong agreement among methods and suggesting that this aging forest acts as a moderate C sink. The use of multiple approaches to measure C fluxes and their uncertainties helped place constraints on difficult-to-measure processes such as aboveground contributions to ecosystem respiration and belowground allocation to mycorrhizal fungal biomass (which was estimated at 20% of net primary production).Analysis of interannual variability in C fluxes revealed a decoupling between annual wood growth and either current year or lagged NEP or GPP, suggesting that source limitation (C supply) is likely not controlling rates of wood production, at least on an interannual scale. Results also demonstrated a strong association between the maximum rate of C uptake during the growing season (Amax) and the length of the vernal window, defined as the period of time between soil thaw and the onset of photosynthesis. This suggests an important, but poorly understood, influence of winter and spring climate on mid-summer canopy physiology. Efforts to resolve the mechanisms responsible should be prioritized in light of ongoing and predicted changes in climate for the northeastern U.S. region, particularly during the winter and winter-spring transition period.

A range of environmental factors regulate tree growth; however, climate is generally thought to most strongly influence year-to-year variability in growth. Numerous dendrochronological (tree-ring) studies have identified climate factors that influence year-to-year variability in growth for given tree species and location. However, traditional dendrochronology methods have limitations that prevent them from adequately assessing stand-level (as opposed to species-level) growth. We argue that stand-level growth analyses provide a more meaningful assessment of forest response to climate fluctuations, as well as the management options that may be employed to sustain forest productivity. Working in a mature, mixed-species stand at the Howland Research Forest of central Maine, USA, we used two alternatives to traditional dendrochronological analyses by (1) selecting trees for coring using a stratified (by size and species), random sampling method that ensures a representative sample of the stand, and (2) converting ring widths to biomass increments, which once summed, produced a representation of stand-level growth, while maintaining species identities or canopy position if needed. We then tested the relative influence of seasonal climate variables on year-to-year variability in the biomass increment using generalized least squares regression, while accounting for temporal autocorrelation. Our results indicate that stand-level growth responded most strongly to previous summer and current spring climate variables, resulting from a combination of individualistic climate responses occurring at the species- and canopy-position level. Our climate models were better fit to stand-level biomass increment than to species-level or canopy-position summaries. The relative growth responses (i.e., percent change) predicted from the most influential climate variables indicate stand-level growth varies less from to year-to-year than species-level or canopy-position growth responses. By assessing stand-level growth response to climate, we provide an alternative perspective on climate-growth relationships of forests, improving our understanding of forest growth dynamics under a fluctuating climate.

Heterotrophic respiration (Rh), microbial processing of soil organic matter to carbon dioxide (CO 2 ), is a major, yet highly uncertain, carbon (C) flux from terrestrial systems to the atmosphere. Temperature sensitivity of Rh is often represented with a simple Q 10 function in ecosystem models and earth system models (ESMs), sometimes accompanied by an empirical soil moisture modifier. More explicit representation of the effects of soil moisture, substrate supply, and their interactions with temperature has been proposed as a way to disentangle the confounding factors of apparent temperature sensitivity of Rh and improve the performance of ecosystem models and ESMs. The objective of this work was to insert into an ecosystem model a more mechanistic, but still parsimonious, model of environmental factors controlling Rh and evaluate the model performance in terms of soil and ecosystem respiration. The Dual Arrhenius and Michaelis-Menten (DAMM) model simulates Rh using Michaelis-Menten, Arrhenius, and diffusion functions. Soil moisture affects Rh and its apparent temperature sensitivity in DAMM by regulating the diffusion of oxygen, soluble C substrates, and extracellular enzymes to the enzymatic reaction site. Here, we merged the DAMM soil flux model with a parsimonious ecosystem flux model, FoBAAR (Forest Biomass, Assimilation, Allocation and Respiration). We used high-frequency soil flux data from automated soil chambers and landscape-scale ecosystem fluxes from eddy covariance towers at two AmeriFlux sites (Harvard Forest, MA and Howland Forest, ME) in the northeastern USA to estimate parameters, validate the merged model, and to quantify the uncertainties in a multiple constraints approach. The optimized DAMM-FoBAAR model better captured the seasonal and inter-annual dynamics of soil respiration (Soil R) compared to the FoBAAR-only model for the Harvard Forest, where higher frequency and duration of drying events significantly regulate substrate supply to heterotrophs. However, DAMM-FoBAAR showed improvement over FoBAAR-only at the boreal transition Howland Forest only in unusually dry years. The frequency of synoptic-scale dry periods is lower at Howland, resulting in only brief water limitation of Rh in some years. At both sites, the declining trend of soil R during drying events was captured by the DAMM-FoBAAR model; however, model performance was also contingent on site conditions, climate, and the temporal scale of interest. While the DAMM functions require a few more parameters than a simple Q 10 function, we have demonstrated that they can be included in an ecosystem model and reduce the model-data mismatch. Moreover, the mechanistic structure of the soil moisture effects using DAMM functions should be more generalizable than the wide variety of empirical functions that are commonly used, and these DAMM functions could be readily incorporated into other ecosystem models and ESMs.

Agricultural service providers often work closely with producers, and are well positioned to include weather and climate change information in the services they provide. By doing so, they can help producers reduce risks due to climate variability and change. A national survey of United States Department of Agriculture Farm Service Agency (FSA) field staff (n = 4621) was conducted in 2016. The survey was designed to assess FSA employees’ use of climate and weather-related data and explore their perspectives on climate change, attitudes toward adaptation and concerns regarding climate- and weather-driven risks. Two structural equation models were developed to explore relationships between these factors, and to predict respondents’ willingness to integrate climate and weather data into their professional services in the future. The two models were compared with assess the relative influence of respondents’ current use of weather and climate information. Findings suggest that respondents’ perceptions of weather-related risk in combination with their personal observations of weather variability help predict whether an individual intends to use weather and climate information in the future. Importantly, climate change belief is not a significant predictor of this intention; however, the belief that producers will have to adapt to climate change in order to remain viable is. Surprisingly, whether or not an individual currently uses weather and climate information is not a good predictor of whether they intend to in the future. This suggests that there are opportunities to increase employee exposure and proficiency with weather and climate information to meet the needs of American farmers by helping them to reduce risk.

Clouds and aerosols increase the fraction of global solar irradiance that is diffuse light. This phenomenon is known to increase the photosynthetic light use efficiency (LUE) of closed-canopy vegetation by redistributing photosynthetic photon flux density (400–700 nm) from saturated, sunlit leaves at the top of the canopy, to shaded leaves deeper in the canopy. We combined a process-based carbon cycle model with 10 years of eddy covariance carbon flux measurements and other ancillary data sets to assess 1) how this LUE enhancement influences interannual variation in carbon uptake, and 2) how errors in modeling diffuse fraction affect predictions of carbon uptake. Modeled annual gross primary productivity (GPP) increased by ≈0.94% when observed levels of diffuse fraction were increased by 0.01 (holding total irradiance constant). The sensitivity of GPP to increases in diffuse fraction was highest when the diffuse fraction was low to begin with, and lowest when the diffuse fraction was already high. Diffuse fraction also explained significantly more of the interannual variability of modeled net ecosystem exchange (NEE), than did total irradiance. Two tested radiation partitioning models yielded over- and underestimates of diffuse fraction at our site, which propagated to over- and underestimates of annual NEE, respectively. Our findings highlight the importance of incorporating LUE enhancement under diffuse light into models of global primary production, and improving models of diffuse fraction.

Eddy covariance methodologies have greatly improved our understanding of the forest carbon cycle, including controls over year-to-year variability in productivity (measured as net ecosystem productivity, NEP, where NEP is the difference between the mass of carbon fixed by photosynthesis and that lost by ecosystem respiration). However, establishing and maintaining eddy covariance towers requires sizeable financial and logistical investments. Tree-ring methods, which can produce annual estimates of tree biomass increment from individual trees, provide an alternative approach for assessing forest productivity. Attempts to link these measures of productivity (i.e., NEP and tree biomass increment) have produced inconsistent results, in part because NEP time series are typically too short to provide robust comparisons. We here use a relatively long (20-year) NEP time series together with annual tree biomass increment (derived from tree-ring data) from the same site to determine to what extent the two productivity measures relate to each other. We conducted this study at the Howland Research Forest, central Maine USA, which supports a mature, mixed-species conifer forest. We expressed stand-level tree biomass increment on a per-area basis, which allowed direct comparisons with NEP data. Our results revealed a strong relationship between tree biomass increment and annual NEP measurements when the latter are summarized from previous-year fall to current-year fall, a marked improvement over more typical calendar-year summaries. Further, our results suggest tree biomass increment lagged one year behind NEP (i.e., assimilated carbon was not allocated to wood formation until the following year) for roughly the first half of the time-series, but later became synchronized with current-year NEP. This shift to synchrony may reflect a change in stand-level carbon allocation and growth dynamics. The apparent shift in carbon allocation from storage into current-year wood formation is most evident in two recent years with above-average spring temperatures. Although our results demonstrate a link between annual tree biomass increment and NEP, they also point to complexities that may confound our interpretation of these productivity measures.

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 (z0m) and heat (z0h) using the kB−1 parameter (ln(z0m/z0h)). 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 (Rah). This seasonality tracked PhenoCam-derived start-of-season green-up transitions in PFTs displaying the most significant seasonal changes in canopy structure, with Rah decreasing near green-up transitions. Conversely, in woody savanna sites and evergreen needleleaf forests, patterns in Rah were not linked to green-up. Our findings highlight that decreases in kB−1 are an important control over Rah, explaining > 50% of seasonal variation in Rah across most sites. Decreases in kB−1 during green-up are likely caused by increasing z0h 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.

Climate change may both exacerbate the vulnerabilities and open up new opportunities for farming in the Northeastern USA. Among the opportunities are double-cropping and new crop options that may come with warmer temperatures and a longer frost-free period. However, prolonged periods of spring rains in recent years have delayed planting and offset the potentially beneficial longer frost-free period. Water management will be a serious challenge for Northeast farmers in the future, with projections for increased frequency of heavy rainfall events, as well as projections for more frequent summer water deficits than this historically humid region has experienced in the past. Adaptations to increase resilience to such changes include expanded irrigation capacity, modernized water monitoring and irrigation scheduling, farm drainage systems that collect excess rain into ponds for use as a water source during dry periods, and improved soil water holding capacity and drainage. Among the greatest vulnerabilities over the next several decades for the economically important perennial fruit crop industry of the region is an extended period of spring frost risk associated with warmer winter and early spring temperatures. Improved real-time frost warning systems, careful site selection for new plantings, and use of misting, wind machine, or other frost protection measures will be important adaptation strategies. Increased weed and pest pressure associated with longer growing seasons and warmer winters is another increasingly important challenge. Pro-active development of non-chemical control strategies, improved regional monitoring, and rapid-response plans for targeted control of invasive weeds and pests will be necessary.

The livestock industries are a major contributor to the economy of the northeastern United States. Climate models predict increased average maximum temperatures, days with temperatures exceeding 25 °C, and higher annual precipitation in the Northeast. These environmental changes combined with increased atmospheric CO2 concentration are expected to either increase or decrease forage productivity depending on the crop, and may decrease protein content and forage digestibility. Winter damage to sensitive forage species may also increase. Predicted temperature increases are expected to reduce fertility in dairy cattle and heat stress-induced inflammation may limit energy available for productive functions. Additional loss in milk production due to decreased feed intake is estimated to be up to 1% of the projected annual milk production through 2100. The effects of climate change on the beef industry in the Northeast are expected to be minimal. Broiler production in the region may benefit from warmer winter and summer temperatures, but future housing will require greater insulation and ventilation fan capacity. Providing adequate housing and ventilation to offset climate changes will also be important for the layer industry and will likely increase the price of eggs. Climate change is expected to have an economic impact on the horse industry in the region through additional management of land and forage resources, building of shelters, and heat abatement at equine events. Increased temperatures and more intense storms will increase nutrient losses and gaseous emissions from animal manure. Uncertainties about how host animals, pathogens, and disease vectors will respond to climate change highlight the need for continued animal health monitoring.

Carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) are the greenhouse gases largely responsible for anthropogenic climate change. Natural plant and microbial metabolic processes play a major role in the global atmospheric budget of each. We have been studying ecosystem-atmosphere trace gas exchange at a sub-boreal forest in the northeastern United States for over two decades. Historically our emphasis was on turbulent fluxes of CO2 and water vapor. In 2012 we embarked on an expanded campaign to also measure CH4 and N2O. Here we present continuous tower-based measurements of the ecosystem-atmosphere exchange of CO2 and CH4, recorded over the period 2012–2018 and reported at a 30-minute time step. Additionally, we describe a five-year (2012–2016) dataset of chamber-based measurements of soil fluxes of CO2, CH4, and N2O (2013–2016 only), conducted each year from May to November. These data can be used for process studies, for biogeochemical and land surface model validation and benchmarking, and for regional-to-global upscaling and budgeting analyses., Design Type(s)observational design • data collection and processing objectiveMeasurement Type(s)greenhouse gasTechnology Type(s)environmental monitoringFactor Type(s)greenhouse gas • temporal_intervalSample Characteristic(s)Howland Forest • forest ecosystem Machine-accessible metadata file describing the reported data (ISA-Tab format)

We partitioned the soil carbon dioxide flux (Rs) into its respective autotrophic and heterotrophic components in a mature temperate-boreal forest (Howland Forest in Maine, USA). We combined automated chamber measurements of Rs with two different partitioning methods: (1) a classic root trenching experiment and (2) a radiocarbon (14C) mass balance approach. With a model-data fusion approach, we used these data to constrain a parsimonious ecosystem model (FoBAAR), and we investigated differences in modeled C fluxes and pools under both current and future climate scenarios. The trenching experiment indicated that heterotrophic respiration accounted for 53 ± 11% of total Rs. In comparison, using the 14C method, the heterotrophic contribution was 42 ± 9%. For both current and future model runs, incorporating the partitioning data as constraints substantially reduced the uncertainties of autotrophic and heterotrophic respiration fluxes. Moreover, with best-fit model parameters, the two partitioning methods yielded fundamentally different estimates of the relative contributions of autotrophic and heterotrophic respiration to total Rs, especially at the annual time scale. Surprisingly, however, modeled soil C and biomass C pool size trajectories did not differ significantly between model runs based on the different methods. Instead, model differences in partitioning were compensated for by changes in C allocation, resulting in similar, but still highly uncertain, soil C pool trajectories. Our findings show that incorporating constraints on the partitioning of Rs can reduce model uncertainties of fluxes but not pools, and the results are sensitive to the partitioning method used.

Ecosystem models often perform poorly in reproducing interannual variability in carbon and water fluxes, resulting in considerable uncertainty when estimating the land-carbon sink. While many aggregated variables (growing season length, seasonal precipitation, or temperature) have been suggested as predictors for interannual variability in carbon fluxes, their explanatory power is limited and uncertainties remain as to their relative contributions. Recent results show that the annual count of hours where evapotranspiration (ET) is larger than its 95th percentile is strongly correlated with the annual variability of ET and gross primary production (GPP) in an ecosystem model. This suggests that the occurrence of favorable conditions has a strong influence on the annual carbon budget. Here we analyzed data from eight forest sites of the AmeriFlux network with at least 7 years of continuous measurements. We show that for ET and the carbon fluxes GPP, ecosystem respiration (RE), and net ecosystem production, counting the "most active hours/days" (i.e., hours/days when the flux exceeds a high percentile) correlates well with the respective annual sums, with correlation coefficients generally larger than 0.8. Phenological transitions have much weaker explanatory power. By exploiting the relationship between most active hours and interannual variability, we classify hours as most active or less active and largely explain interannual variability in ecosystem fluxes, particularly for GPP and RE. Our results suggest that a better understanding and modeling of the occurrence of large values in high-frequency ecosystem fluxes will result in a better understanding of interannual variability of these fluxes.

Clouds scatter direct solar radiation, generating diffuse radiation and altering the ratio of direct to diffuse light. If diffuse light increases plant canopy CO2 uptake, clouds may indirectly influence climate by altering the terrestrial carbon cycle. However, past research primarily uses proxies or qualitative categories of clouds to connect the effect of diffuse light on CO2 uptake to sky conditions. We mechanistically link and quantify effects of cloud optical thickness (τc) to surface light and plant canopy CO2 uptake by comparing satellite retrievals of τc to ground-based measurements of diffuse and total photosynthetically active radiation (PAR; 400–700 nm) and gross primary production (GPP) in forests and croplands. Overall, total PAR decreased with τc, while diffuse PAR increased until an average τc of 6.8 and decreased with larger τc. When diffuse PAR increased with τc, 7–24% of variation in diffuse PAR was explained by τc. Light-use efficiency (LUE) in this range increased 0.001–0.002 per unit increase in τc. Although τc explained 10–20% of the variation in LUE, there was no significant relationship between τc and GPP (p > 0.05) when diffuse PAR increased. We conclude that diffuse PAR increases under a narrow range of optically thin clouds and the dominant effect of clouds is to reduce total plant-available PAR. This decrease in total PAR offsets the increase in LUE under increasing diffuse PAR, providing evidence that changes within this range of low cloud optical thickness are unlikely to alter the magnitude of terrestrial CO2 fluxes.

In our recent study in Global Change Biology (Li et al., ), we examined the relationship between solar-induced chlorophyll fluorescence (SIF) measured from the Orbiting Carbon Observatory-2 (OCO-2) and gross primary productivity (GPP) derived from eddy covariance flux towers across the globe, and we discovered that there is a nearly universal relationship between SIF and GPP across a wide variety of biomes. This finding reveals the tremendous potential of SIF for accurately mapping terrestrial photosynthesis globally.

Forests and their products provide many benefits including clean water, recreation, wildlife habitat, wood products, energy, as well as carbon sequestration and climate change mitigation. This project assesses past and future carbon sequestration and mitigation potential across the forest sector of Pennsylvania with a focus on State Forest lands. This research resulted from a collaboration between the U.S. Forest Service and the Pennsylvania Department of Conservation and Natural Resources (PA DCNR).

article i nfo Albedo products from the MODerate resolution Imaging Spectroradiometer (MODIS) have been validated extensively over spatially homogeneous sites with snow-cover. This study evaluated the spatial scaling of albedo and related reflectance-based quantities over areas of high spatial heterogeneity in temperate mixed forest, de- ciduous forest, urban and built-up, and cropland/natural mosaic lands under snow covered conditions. Reflectance-based quantities evaluated included spectral radiance, surface directional reflectance, and spectral and broadband albedo derived from ground- and tower-based measurements and high-resolution (5 m) hyperspectral imagery (HSI) to coarse resolution (~500 m) MODIS satellite data. Ourapproach firstcomparedground-andtower-basedspectralandbroadbandreflectancequantitiestoHSIdata, then to evaluate HSI with MODIS reflectance-based products. Over snow-covered pasture, HSI directional reflec- tance was biased lower than ground-based measurements collected using a spectroradiometer, and greatly underestimated at wavelengths less than 450 nm. Tower-based shortwave broadband albedo (280-2800 nm) andHSIalbedoagreedwithin±0.04.HSIdirectional spectral reflectance agreedwell withtower-basedmeasure- ments of spectral albedo collected using a spectroradiometer and remote cosine receptor above a mixed forest canopy with underlying snow cover. Spectral albedo collected over a dormant deciduous broadleaf canopy increased from 0.10 for snow-free conditions to 0.14-0.18 when snow-cover was present under the canopy. Canopy shortwave broadband albedo was not very sensitive to underlying snowpack depth, indicative of strong vegetation masking. Next,HSIdatawerespatiallyaggregatedandaveragedto500mMODISgridsandcomparedtotwoMODISalbedo

Changes in climate and extreme weather are already increasing challenges for agriculture nationally and globally, and many of these impacts will continue into the future. This technical bulletin contains information and resources designed to help agricultural producers, service providers, and educators in the Midwest and Northeast regions of the United States integrate climate change considerations and action-oriented decisions into existing farm and conservation plans. An Adaptation Workbook provides producers a flexible, structured process to identify and assess climate change impacts, challenges, opportunities, and farm-level adaptation tactics and continuously evaluate adaptation actions for improving responses to extreme and uncertain conditions. A synthesis of Adaptation Strategies and Approaches serves as a “menu” of potential responses organized to provide a clear rationale for making decisions by connecting planned actions to broad adaptation concepts. Responses address both short- and long-range timeframes and extend from incremental adjustments of existing practices to major alterations that transform the entire farm operation. Example adaptation tactics—prescriptive actions for agricultural production systems common in the region—for each approach guide producers, service providers, and educators to develop appropriate responses for their farms and location. Four Adaptation Examples demonstrate how these adaptation process resources are used.

Solar-induced chlorophyll fluorescence (SIF) has been increasingly used as a proxy for terrestrial gross primary productivity (GPP). Previous work mainly evaluated the relationship between satellite-observed SIF and gridded GPP products both based on coarse spatial resolutions. Finer resolution SIF (1.3 km × 2.25 km) measured from the Orbiting Carbon Observatory-2 (OCO-2) provides the first opportunity to examine the SIF-GPP relationship at the ecosystem scale using flux tower GPP data. However, it remains unclear how strong the relationship is for each biome and whether a robust, universal relationship exists across a variety of biomes. Here we conducted the first global analysis of the relationship between OCO-2 SIF and tower GPP for a total of 64 flux sites across the globe encompassing eight major biomes. OCO-2 SIF showed strong correlations with tower GPP at both midday and daily timescales, with the strongest relationship observed for daily SIF at the 757 nm (R2 = 0.72, p

Forest ecosystems will face direct and indirect impacts from a changing climate over the 21st century. This assessment evaluates the vulnerability of forest ecosystems across the New England region (Connecticut, Maine, Massachusetts, New Hampshire, northern New York, Rhode Island, and Vermont) under a range of future climates. We synthesized and summarized information on the contemporary landscape, provided information on past climate trends, and described a range of projected future climates. This information was used to parameterize and run multiple vegetation impact models, which provided a range of potential vegetative responses to climate. Finally, we brought these results before a multidisciplinary panel of scientists and natural resource professionals familiar with the forests of this region to assess ecosystem vulnerability through a formal consensus-based expert elicitation process. Observed trends in climate over the historical record from 1901 through 2011 show that the mean annual temperature has increased across the region by 2.4 °F, with even greater warming during winter. Precipitation patterns also changed during this time, with a slight trend toward greater annual precipitation and a substantial increase in extreme precipitation events. Projected climate trends using downscaled global climate model data indicate a potential increase in mean annual temperature of 3 to 8 °F for the assessment area by 2100. Projections for precipitation indicate an increase in fall and winter precipitation, and spring and summer precipitation projections vary by scenario. We identified potential impacts on forests by incorporating these future climate projections into three forest impact models (DISTRIB, LINKAGES, and LANDIS PRO). Model projections suggest that many northern and boreal species, including balsam fir, red spruce, and black spruce, may fare worse under future conditions, but other species may benefit from projected changes in climate. Published literature on climate impacts related to wildfire, invasive species, and forest pests and diseases also contributed to the overall determination of climate change vulnerability. We assessed vulnerability for eight forest communities in the assessment area. The assessment was conducted through a formal elicitation process with 20 scientists and resource managers from across the area, who considered vulnerability in terms of the potential impacts and the adaptive capacity for an individual community. Montane spruce-fir, low-elevation spruce-fir, and lowland mixed conifer forests were determined to be the most vulnerable communities. Central hardwoods, transition hardwoods, and pitch pine-scrub oak forests were perceived as having lower vulnerability to projected changes in climate. These projected changes in climate and the associated impacts and vulnerabilities will have important implications for economically valuable timber species, forest-dependent animals and plants, recreation, and long-term natural resource planning.

a b s t r a c t The carbon storage potential of terrestrial ecosystems depends in part on how atmospheric conditions influence the type and amount of surface radiation available for photosynthesis. Diffuse light, result- ing from interactions between incident solar radiation and atmospheric aerosols and clouds, has been postulated to increase carbon uptake in terrestrial ecosystems. However, the magnitude of the diffuse light effect is unclear because existing studies use different methods to derive above-canopy diffuse light conditions. We used site-based, above-canopy measurements of diffuse light and gross primary produc- tivity (GPP) from 10 temperate ecosystems (including mixed conifer forests, deciduous broadleaf forests, and croplands) to quantify the GPP variation explained by diffuse photosynthetically active radiation (PAR) and to calculate increases in GPP as a function of diffuse light. Our analyses show that diffuse PAR explained up to 41% of variation in GPP in croplands and up to 17% in forests, independent of direct light levels. Carbon enhancement rates in response to diffuse PAR (calculated after accounting for vapor pressure deficit and air temperature) were also higher in croplands (0.011-0.050 mol CO2 per mol photons of diffuse PAR) than in forests (0.003-0.018 mol CO2 per mol photons of diffuse PAR). The amount of variation in GPP and carbon enhancement rate both differed with solar zenith angle and across sites for the same plant functional type. At crop sites, diffuse PAR had the strongest influence and the largest carbon enhancement rate during early mornings and late afternoons when zenith angles were large, with greater enhancement in the afternoons. In forests, diffuse PAR had the strongest influence at small zenith angles, but the largest carbon enhancement rate at large zenith angles, with a trend in ecosystem-specific responses. These results highlight the influence of zenith angle and the role of plant community composition in modifying diffuse light enhancement in terrestrial ecosystems, which will be important in scaling this effect from individual sites to the globe.

The exchange of carbon dioxide is a key measure of ecosystem metabolism and a critical intersection between the terrestrial biosphere and the Earth's climate. Despite the general agreement that the terrestrial ecosystems in North America provide a sizeable carbon sink, the size and distribution of the sink remain uncertain. We use a data-driven approach to upscale eddy covariance flux observations from towers to the continental scale by integrating flux observations, meteorology, stand age, aboveground biomass, and a proxy for canopy nitrogen concentrations from AmeriFlux and Fluxnet-Canada Research Network as well as a variety of satellite data streams from the MODIS sensors. We then use the resulting gridded flux estimates from March 2000 to December 2012 to assess the magnitude, distribution, and interannual variability of carbon fluxes for the U.S. and Canada. The mean annual gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP) of the U.S. over the period 2001–2012 were 6.84, 5.31, and 1.10 Pg C yr−1, respectively; the mean annual GPP, ER, and NEP of Canada over the same 12-year period were 3.91, 3.26, and 0.60 Pg C yr−1, respectively. The mean nationwide annual NEP of natural ecosystems over the period 2001–2012 was 0.53 Pg C yr−1 for the U.S. and 0.49 Pg C yr−1 for the conterminous U.S. Our estimate of the carbon sink for the conterminous U.S. was almost identical with the estimate of the First State of the Carbon Cycle Report (SOCCR). The carbon fluxes exhibited relatively large interannual variability over the study period. The main sources of the interannual variability in carbon fluxes included drought and disturbance. The annual GPP and NEP were strongly related to annual evapotranspiration (ET) for both the U.S. and Canada, showing that the carbon and water cycles were closely coupled. Our gridded flux estimates provided an independent, alternative perspective on ecosystem carbon exchange over North America.