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The eddy covariance technique has revolutionized our understanding of ecosystem-atmosphere interactions. Eddy covariance studies often use a “paired” tower design in which observations from nearby towers are used to understand how different vegetation, soils, hydrology, or experimental treatment shape ecosystem function and surface-atmosphere exchange. Paired towers have never been formally defined and their global distribution has not been quantified. We compiled eddy covariance tower information to find towers that could be considered paired. Of 1233 global eddy covariance towers, 692 (56%) were identified as paired by our criteria. Paired towers had cooler mean annual temperature (mean = 9.9 °C) than the entire eddy covariance network (10.5 °C) but warmer than the terrestrial surface (8.9 °C) from WorldClim 2.1, on average. The paired and entire tower networks had greater average soil nitrogen (0.57-0.58 g/kg) and more silt (36.0-36.4%) than terrestrial ecosystems (0.38 g/kg and 30.5%), suggesting that eddy covariance towers sample richer soils than the terrestrial surface as a whole. Paired towers existed in a climatic space that was more different from the global climate distribution sampled by the entire eddy covariance network, as revealed by an analysis of the Kullback-Leibler divergence, but the edaphic space sampled by the entire network and paired towers was similar. The lack of paired towers with available data across much of Africa, northern, central, southern, and western Asia, and Latin America with few towers in savannas, shrublands, and evergreen broadleaf forests point to key regions, ecosystems, and ecosystem transitions in need of additional research. Few if any paired towers study the flux of ozone and other atmospherically active trace gases at the present. By studying what paired towers measure – and what they do not – we can make infrastructural investments to further enhance the value of FLUXNET as it moves toward its fourth decade.

Heat and drought affect plant chemical defenses and thereby plant susceptibility to pests and pathogens. Monoterpenes are of particular importance for conifers as they play critical roles in defense against bark beetles. To date, work seeking to understand the impacts of heat and drought on monoterpenes has primarily focused on young potted seedlings, leaving it unclear how older age classes that are more vulnerable to bark beetles might respond to stress. Furthermore, we lack a clear picture of what carbon resources might be prioritized to support monoterpene synthesis under drought stress. To address this, we measured needle and woody tissue monoterpene concentrations and physiological variables simultaneously from mature piñon pines (Pinus edulis) from a unique temperature and drought manipulation field experiment. While heat had no effect on total monoterpene concentrations, trees under combined heat and drought stress exhibited ~ 85% and 35% increases in needle and woody tissue, respectively, over multiple years. Plant physiological variables like maximum photosynthesis each explained less than 10% of the variation in total monoterpenes for both tissue types while starch and glucose + fructose measured 1-month prior explained ~ 45% and 60% of the variation in woody tissue total monoterpene concentrations. Although total monoterpenes increased under combined stress, some key monoterpenes with known roles in bark beetle ecology decreased. These shifts may make trees more favorable for bark beetle attack rather than well defended, which one might conclude if only considering total monoterpene concentrations. Our results point to cumulative and synergistic effects of heat and drought that may reprioritize carbon allocation of specific non-structural carbohydrates toward defense. Supplementary Information The online version contains supplementary material available at 10.1007/s00442-021-05058-8.

Vegetation phenology—the seasonal timing and duration of vegetative phases—is controlled by spatiotemporally variable contributions of climatic and environmental factors plus additional potential influence from human management. We used land surface phenology derived from the Advanced Very High Resolution Radiometer and climate data to examine variability in vegetation productivity and phenological dates from 1989 to 2014 in the U.S. Northwestern Plains, a region with notable spatial heterogeneity in climate, vegetation, and land use. We first analyzed interannual trends in six phenological measures as a baseline. We then demonstrated how including annual‐resolution predictors can provide more nuanced insights into measures of phenology between plant communities and across the ecoregion. Across the study area, higher annual precipitation increased both peak and season‐long productivity. In contrast, higher mean annual temperatures tended to increase peak productivity but for the majority of the study area decreased season‐long productivity. Annual precipitation and temperature had strong explanatory power for productivity‐related phenology measures but predicted date‐based measures poorly. We found that relationships between climate and phenology varied across the region and among plant communities and that factors such as recovery from disturbance and anthropogenic management also contributed in certain regions. In sum, phenological measures did not respond ubiquitously nor covary in their responses. Nonclimatic dynamics can decouple phenology from climate; therefore, analyses including only interannual trends should not assume climate alone drives patterns. For example, models of areas exhibiting greening or browning should account for climate, anthropogenic influence, and natural disturbances. Investigating multiple aspects of phenology to describe growing‐season dynamics provides a richer understanding of spatiotemporal patterns that can be used for predicting ecosystem responses to future climates and land‐use change. Such understanding allows for clearer interpretation of results for conservation, wildlife, and land management., We compared interannual trends in six satellite‐derived phenological measures across seven vegetation community types to models incorporating climate in the U.S. Northwestern Plains. We found more nuanced insights by including climate over just intrannual phenological trends, that relationships between climate and phenology varied across the region, and responses of phenological measures did not covary with drivers. In sum, analyses should not assume climate alone drives patterns and areas may exhibit different patterns of greening or browning when using peak versus season‐long productivity.

Accurate measurements of actual evapotranspiration (ETa) and crop coefficients (Kc) are essential to know crop water requirements and to improve irrigation scheduling. The eddy covariance (EC) technique is increasingly being used to do so. Precise information on Kc for lowland rice is essential for local- and regional-scale irrigation planning but it is lacking for tropical humid climates such as those found in eastern India. We used the EC technique to measure ETa and Kc—the ratio of ETa to reference potential evapotranspiration (ET0)—of tropical lowland rice in eastern India over 2 years. ET0 was estimated by four different approaches—the Food and Agriculture Organization-Penman–Monteith (FAO-PM) method, the Hargreaves and Samani (HS) method, the Mahringer (MG) method, and pan evaporation (Epan) measurements. Measurements were taken when rice was grown in the dry season (January–May) and wet season (July–November) and in between growing seasons when the field was kept fallow. The magnitude of average ETa during dry seasons (2.86 and 3.32 mm d−1 in 2015 and 2016, respectively) was higher than that of the wet seasons (2.3 and 2.2 mm d−1) in both the study years. Of the four methods tested for ET0 estimation, the FAO-PM method best-represented ET0 in this region of India. The energy balance was found to be more closed in the dry seasons (75–84%) and dry fallow periods (73–81%) as compared to the wet season (42–48%) and wet fallow (33–69%) periods of both years of study, suggesting that lateral heat transport was an important term in the energy balance. The estimated Kc values for lowland rice in dry seasons by the FAO-PM method at the four crop growth stages, namely, initial, crop development, reproductive, and late-season, were 0.23, 0.42, 0.64, and 0.90, respectively, in 2015 and 0.32, 0.52, 0.76, and 0.88, respectively, in 2016. The FAO-PM, HS, and MG methods produced reliable estimates of Kc values in the dry seasons, whereas Epan performed better in wet seasons. The actual Kc values derived for tropical lowland rice in eastern India are different from those suggested by the FAO implying revision of Kc values for regional-scale irrigation scheduling.

Achieving food security is a critical challenge of the Anthropocene that may conflict with environmental and societal goals such as increased energy access. The “fuel versus food” debate coupled with climate mitigation efforts has given rise to next-generation biofuels. Findings of this systematic review indicate just over half of the studies (56% of 224 publications) reported a negative impact of bioenergy production on food security. However, no relationship was found between bioenergy feedstocks that are edible versus inedible and food security (P value = 0.15). A strong relationship was found between bioenergy and type of food security parameter (P value P value = 0.001), spatial scale (P value P value = 0.017). Programs and policies focused on bioenergy and climate mitigation should monitor multiple food security parameters at various scales over the long term toward achieving diverse sustainability goals.

Atmospheric carbon dioxide uptake is a critical ecosystem service that is highly sensitive to extreme events with important consequences for the human and natural systems that rely on it. We often monitor carbon dioxide uptake via gross primary productivity (GPP) at the ecosystem scale using the eddy covariance method, typically over half-hourly intervals. These observations are often ‘upscaled’ to regional or global scales using satellite observations, typically on time scales of weeks to years. These relatively infrequent estimates are due in part to intermittent overpasses from the polar-orbiting satellites like Landsat and MODIS that are commonly used for GPP monitoring.Geostationary satellites on the other hand have long observed the Earth’s surface and atmosphere on the order of minutes with a consistent viewing geometry. The new generation of imagers on many geostationary platforms like the Advanced Baseline Imager (ABI) onboard the GOES-R satellite series now have enhanced spectral resolution in the visible and near-infrared regions of the electromagnetic spectrum. This resolution is comparable to Landsat and MODIS and can now in principle estimate GPP using similar approaches. Therefore, geostationary satellites can now measure ecosystem carbon uptake in near-real time and radically improve our understanding of the interaction between the carbon cycle, climate, and extreme events.Here, we demonstrate an approach to estimate GPP using geostationary satellite observations. After correcting for atmospheric attenuation and applying a bidirectional reflectance distribution function, a model that uses the near-infrared reflectance of vegetation (NIRv) as a saturating function of GOES-derived photosynthetic photon flux density (PPFD) with adjustment for atmospheric vapor pressure deficit outperformed other models for simulating the diurnal pattern of eddy-covariance estimated GPP in crop, grass, savanna, and forested ecosystems. This model also captured the seasonal trend in the diurnal centroid of maximum diurnal GPP as it responds to seasonal drought stress. We describe current progress in upscaling geostationary GPP including machine learning algorithms to maximize computational efficiency and predictive skill. We also describe approaches to respect the data sovereignty of Tribal Nations while working with Tribal land managers to understand the consequences of ecosystem disturbances on natural resources. International collaboration is required to provide near-real-time GPP estimates across the globe, and our approach is applicable to European satellite systems like SEVIRI and other geostationary satellite systems like Himawari-8&9.

Understanding the critical soil moisture (SM) threshold (θcrit) of plant water stress and land surface energy partitioning is a basis to evaluate drought impacts and improve models for predicting future ecosystem condition and climate. Quantifying the θcrit across biomes and climates is challenging because observations of surface energy fluxes and SM remain sparse. Here, we used the latest database of eddy covariance measurements to estimate θcrit across Europe by evaluating evaporative fraction (EF)-SM relationships and investigating the covariance between vapor pressure deficit (VPD) and gross primary production (GPP) during SM dry-down periods. We found that the θcrit and soil matric potential threshold in Europe are 16.5% and −0.7 MPa, respectively. Surface energy partitioning characteristics varied among different vegetation types; EF in savannas had the highest sensitivities to SM in water-limited stage, and the lowest in forests. The sign of the covariance between daily VPD and GPP consistently changed from positive to negative during dry-down across all sites when EF shifted from relatively high to low values. This sign of the covariance changed after longer period of SM decline in forests than in grasslands and savannas. Estimated θcrit from the VPD–GPP covariance method match well with the EF–SM method, showing this covariance method can be used to detect the θcrit. We further found that soil texture dominates the spatial variability of θcrit while shortwave radiation and VPD are the major drivers in determining the spatial pattern of EF sensitivities. Our results highlight for the first time that the sign change of the covariance between daily VPD and GPP can be used as an indicator of how ecosystems transition from energy to SM limitation. We also characterized the corresponding θcrit and its drivers across diverse ecosystems in Europe, an essential variable to improve the representation of water stress in land surface models., Global Change Biology, 28 (6), ISSN:1354-1013, ISSN:1365-2486

Bioenergy with carbon capture and storage (BECCS) has been proposed as a potential climate mitigation strategy raising concerns over trade‐offs with existing ecosystem services. We evaluate the feasibility of BECCS in the Upper Missouri River Basin (UMRB), a landscape with diverse land use, ownership, and bioenergy potential. We develop land‐use change scenarios and a switchgrass (Panicum virgatum L.) crop functional type to use in a land‐surface model to simulate second‐generation bioenergy production. By the end of this century, average annual switchgrass production over the UMRB ranges from 60 to 210 Tg dry mass/year and is dependent on the Representative Concentration Pathway for greenhouse gas emissions and on land‐use change assumptions. Under our simple phase‐in assumptions this results in a cumulative total production of 2,000–6,000 Tg C over the study period with the upper estimates only possible in the absence of climate change. Switchgrass yields decreased as average CO2 concentrations and temperatures increased, suggesting the effect of elevated atmospheric CO2 was small because of its C4 photosynthetic pathway. By the end of the 21st century, the potential energy stored annually in harvested switchgrass averaged between 1 and 4 EJ/year assuming perfect conversion efficiency, or an annual electrical generation capacity of 7,000–28,000 MW assuming current bioenergy efficiency rates. Trade‐offs between bioenergy and ecosystem services were identified, including cumulative direct losses of 1,000–2,600 Tg C stored in natural ecosystems from land‐use change by 2090. Total cumulative losses of ecosystem carbon stocks were higher than the potential ~300 Tg C in fossil fuel emissions from the single largest power plant in the region over the same time period, and equivalent to potential carbon removal from the atmosphere from using biofuels grown in the same region. Numerous trade‐offs from BECCS expansion in the UMRB must be balanced against the potential benefits of a carbon‐negative energy system.

Land-use/cover change (LUCC) is an important driver of environmental change, occurring at the same time as, and often interacting with, global climate change. Reforestation and deforestation have been critical aspects of LUCC over the past two centuries and are widely studied for their potential to perturb the global carbon cycle. More recently, there has been keen interest in understanding the extent to which reforestation affects terrestrial energy cycling and thus surface temperature directly by altering surface physical properties (e.g., albedo and emissivity) and land-atmosphere energy exchange. The impacts of reforestation on land surface temperature and their mechanisms are relatively well understood in tropical and boreal climates, but the effects of reforestation on warming and/or cooling in temperate zones are less certain. This study is designed to elucidate the biophysical mechanisms that link land cover and surface temperature in temperate ecosystems. To achieve this goal, we used data from six paired eddy-covariance towers over co-located forests and grasslands in the temperate eastern United States, where radiation components, latent and sensible heat fluxes, and meteorological conditions were measured. The results show that, at the annual time scale, the surface of the forests is 1-2°C cooler than grasslands, indicating a substantial cooling effect of reforestation. The enhanced latent and sensible heat fluxes of forests have an average cooling effect of -2.5°C, which offsets the net warming effect (+1.5°C) of albedo warming (+2.3°C) and emissivity cooling effect (-0.8°C) associated with surface properties. Additional daytime cooling over forests is driven by local feedbacks to incoming radiation. We further show that the forest cooling effect is most pronounced when land surface temperature is higher, often exceeding -5°C. Our results contribute important observational evidence that reforestation in the temperate zone offers opportunities for local climate mitigation and adaptation.

We examined climate trends in the northern North American Great Plains (NNAGP) from 1970 to 2015, a period that aligns with widespread land-use changes in this globally important agricultural region. Trends were calculated from the Climatic Research Unit (CRU) and other climate datasets using a linear regression model that accounts for temporal autocorrelation. The NNAGP warmed on an annual basis, with the largest change occurring in winter (DJF) at 0.4°C decade−1. January in particular warmed at nearly 0.9°C decade−1. The NNAGP cooled by −0.18°C decade−1 during May and June, nearly the opposite of global warming trends during the study period. The atmospheric vapor pressure deficit (VPD), which can limit crop growth, decreased in excess of −0.4 hPa decade−1 during climatological summer in the southeastern part of the study domain. Precipitation P increased in the eastern portion of the NNAGP during all seasons except fall and increased during May and June in excess of 8 mm decade−1. Climate trends in the NNAGP largely followed global trends except during the early warm season (May and June) during which 2-m air temperature Tair became cooler, VPD lower, and P greater across large parts of the study region. These changes are consistent with observed agricultural intensification during the study period, namely the reduction of summer fallow and expansion of agricultural land use. Global climate model simulations indicate that observed Tair trends cannot be explained by natural climate variability. However, further climate attribution experiments are necessary to understand if observed changes are caused by increased agricultural intensity or other factors.

Feedbacks between atmospheric processes like precipitation and land surface fluxes including evapotranspiration are difficult to observe, but critical for understanding the role of the land surface in the Earth System. To quantify global surface-atmosphere feedbacks we use results of a process network (PN) applied to 251 eddy covariance sites from the LaThuile database to train a neural network across the global terrestrial surface. There is a strong land–atmosphere coupling between latent (LE) and sensible heat flux (H) and precipitation (P) during summer months in temperate regions, and between H and P during winter, whereas tropical rainforests show little coupling seasonality. Savanna, shrubland, and other semi-arid ecosystems exhibit strong responses in their coupling behavior based on water availability. Feedback couplings from surface fluxes to P peaks at aridity (P/potential evapotranspiration ETp) values near unity, whereas coupling with respect to clouds, inferred from reduced global radiation, increases as P/ETp approaches zero. Spatial patterns in feedback coupling strength are related to climatic zone and biome type. Information flow statistics highlight hotspots of (1) persistent land–atmosphere coupling in sub-Saharan Africa, (2) boreal summer coupling in the central and southwestern US, Brazil, and the Congo basin and (3) in the southern Andes, South Africa and Australia during austral summer. Our data-driven approach to quantifying land atmosphere coupling strength that leverages the global FLUXNET database and information flow statistics provides a basis for verification of feedback interactions in general circulation models and for predicting locations where land cover change will feedback to climate or weather. “Big data” methods reveal robust hotspots of land–atmosphere coupling. Sparse observations and inadequate analytical tools have hindered our understanding of land–atmosphere feedbacks, including the exchange of energy, water, and CO2. Emergent methods such as machine learning, however, offer new opportunities, as Tobias Gerken from Montana State University, USA, and colleagues, demonstrate. A “big data” approach is adopted to characterise the spatial and temporal variability of land–atmosphere coupling without a priori assumptions: information flows are computed from 251 FLUXNET sites which are subsequently used to train a neural network. Distinct regional differences in the magnitude of land–atmosphere feedbacks are found, related to climatic zone and biome type; coupling in semi-arid ecosystems, for example, are strongly related to seasonal water availability. Complementing model studies with such empirical approaches may assist in quantifying climate change impacts on ecosystem services.

Surface‐atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub‐grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub‐kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not continuously available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy‐balance Study Enabled by a High‐density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES‐16 and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station [ECOSTRESS]) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to independent observations from a network of 20 micrometeorological towers and piloted aircrafts in addition to Landsat‐based LST retrieval and drone‐based LST observed at one tower site. The downscaled 50‐m hourly LST showed good relationships with tower (r2 = 0.79, RMSE = 3.5 K) and airborne (r2 = 0.75, RMSE = 2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio‐temporal variation compared to a geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hot and cold spots across the landscape as evidenced by independent drone LST, with significant reduction in RMSE by 1.3 K. These results demonstrate a simple pathway for multi‐sensor retrieval of high space and time resolution LST.

Land management strategies can moderate or intensify the impacts of a warming atmosphere. Since the early 1980s, nearly 116,000 km2 of crop land that was once held in fallow during the summer is now planted in the northern North American Great Plains. To simulate the impacts of this substantial land cover change on regional climate processes, convection-permitting model experiments using the Weather Research and Forecasting (WRF) model were performed to simulate modern and historical amounts of summer fallow, and were extensively validated using multiple observational data products as well as eddy covariance tower observations. Results of these simulations show that the transition from summer fallow to modern land cover lead to ~1.5 °C cooler temperatures and decreased vapor pressure deficit by ~0.15 kPa during the growing season, which is consistent with observed cooling trends. The cooler and wetter land surface with vegetation leads to a shallower planetary boundary layer and lower lifted condensation level, creating conditions more conducive to convective cloud formation and precipitation. Our model simulations however show little widespread evidence of land surface changes effects on precipitation. The observed precipitation increase in this region is more likely related to increased moisture transport by way of the Great Plains Low Level Jet as suggested by the ERA5 reanalysis. Our results demonstrate that land cover change is consistent with observed regional cooling in the northern North American Great Plains but changes in precipitation cannot be explained by land management alone.

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.

Current in situ soil moisture monitoring networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical global surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100m and performed moderately well under cultivated, herbaceous, forest, and shrub soils (R2 = 0.524, RMSE = 0.07 m3 m−3). It has a relatively good transferability at the regional scale among different continental and regional networks (mean RMSE = 0.08–0.10 m3 m−3). The global model was then applied to map SSM dynamics at 30–100m across a field-scale network (TERENO-Wüstebach) in Germany and an 80-ha irrigated cropland in Wisconsin, USA. Without local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was also affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and high-resolution soil maps as well as assimilation with process-based water flow models.

Terrestrial ecosystems contribute most of the interannual variability (IAV) in atmospheric carbon dioxide (CO2) concentrations, but processes driving the IAV of net ecosystem CO2 exchange (NEE) remain elusive. For a predictive understanding of the global C cycle, it is imperative to identify indicators associated with ecological processes that determine the IAV of NEE. Here, we decompose the annual NEE of global terrestrial ecosystems into their phenological and physiological components, namely maximum carbon uptake (MCU) and release (MCR), the carbon uptake period (CUP), and two parameters, α and β, that describe the ratio between actual versus hypothetical maximum C sink and source, respectively. Using long‐term observed NEE from 66 eddy covariance sites and global products derived from FLUXNET observations, we found that the IAV of NEE is determined predominately by MCU at the global scale, which explains 48% of the IAV of NEE on average while α, CUP, β, and MCR explain 14%, 25%, 2%, and 8%, respectively. These patterns differ in water‐limited ecosystems versus temperature‐ and radiation‐limited ecosystems; 31% of the IAV of NEE is determined by the IAV of CUP in water‐limited ecosystems, and 60% of the IAV of NEE is determined by the IAV of MCU in temperature‐ and radiation‐limited ecosystems. The Lund‐Potsdam‐Jena (LPJ) model and the Multi‐scale Synthesis and Terrestrial Model Inter‐comparison Project (MsTMIP) models underestimate the contribution of MCU to the IAV of NEE by about 18% on average, and overestimate the contribution of CUP by about 25%. This study provides a new perspective on the proximate causes of the IAV of NEE, which suggest that capturing the variability of MCU is critical for modeling the IAV of NEE across most of the global land surface.

As human encroachment increases in the Amazon rain forest, it is important to determine how anthropogenic emissions of reactive gases affect regional atmospheric chemistry. In the present study, we investigate the extent to which urban air plumes modify the levels of ozone (O3) and nitrogen oxides (NOX) in the downwind rain forest. The median mixing ratios of the background O3, NOX, and NOX oxidation products (NOZ) were 20 (15 parts per billion on a volume basis, ppbv), 0.6 ppbv (0.6 ppbv), and 1.0 ppbv (0.5 ppbv) during the dry (wet) season at the study site. Compared to the background environment, air plumes from the city of Manaus had enhanced median mixing ratios for O3 and NOZ by 30–50% and 40–90%, respectively. However, the enhancements of NOX in the air plumes were less than 20%, indicating that the majority of NOX was chemically converted to O3 and NOZ during transport. Results from a photochemical model showed that an injection of 8 ppbv of NOX into the rain forest can cause up to 260% and 150% increases in O3 and hydroxyl radical (OH) levels compared to the background conditions, indicating the likely extent that NOX can modify the air quality and oxidative capacity in the Amazon rain forest. Slopes of the O3-temperature linear relationships increased with NOX levels from 3.7 to 6.5 ppbv per degree Kelvin during the dry season and 1.7–5.5 ppbv per degree Kelvin during the wet season. Average rates of change of the slope with respect to NOZ were approximately 1.8 and 2.3 times higher than those with respect to NOX for the dry and wet seasons. One key conclusion of this study is that NOZ substantially contributed to the O3 formation in response to temperature under enhanced NOX conditions in the forested environment.

The vegetated canopy plays a key role in regulating the surface fluxes and, therefore, the global energy, water and carbon cycles. In particular, vulnerable ecosystems like the Amazonia basin can be very sensitive to changes in vegetation that exert subsequent shifts in the partition of the energy, water and carbon in and above the canopy. Despite this relevance, most 3D atmospheric models represent the vegetated canopy as a flat 2D layer with, at most, a rough imitation of its effect in the atmospheric boundary layer through a modified roughness length. Thus, the representations often describe quite crudely the surface fluxes. In this work, particular emphasis is placed in the biophysical processes that take place within the canopy and its impact above. Our approach is to represent the coupling of the flow between the canopy and the atmosphere including the following processes: radiative transfer, photosynthesis, soil evaporation and CO2 respiration, combined with the mostly explicit atmospheric turbulence within and above the canopy. To this end, we implemented in LES a detailed multi-layer canopy model that solves the leaf energy balance for sunlit and shaded leaves independently, regulating the exchange of heat, moisture and carbon between the leaves and the air around. This allows us to connect the mechanistically represented processes occurring at the leaf level and strongly regulated by the transfer of diffuse and direct radiation within the canopy to the turbulent mixing explicitly resolved at the meter scale.We test and validate this combined photosynthesis-turbulence-canopy model by simulating a representative clear day transitioning to shallow cumulus. We based our evaluation on observations by the GoAmazon2014/5 campaign in Brazil in 2014. More specifically, we systematically validate the in-canopy radiation profiles; sources, sinks and turbulent fluxes of moisture, heat and CO2, and main state variables within the canopy, and also study the effects of these in the air above. Preliminary results show an encouraging satisfactory match to the observed evolution of the profiles. As a first exploration and demonstration of the capabilities of the model, we test the effects of a coarser in-canopy resolution, a different radiation scheme and the use of a more simple 2D canopy representation.

Global change is a change in the planetary energy balance. It is usually expressed as a change in near-surface (2 m) air temperature (Ta), but changes to Ta represent only part of the atmospheric e...

Understanding carbon (C) dynamics from ecosystem to global scales remains a challenge. Although expansion of global carbon dioxide (CO