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This commentary considers the benefits of a new framework to incorporate ecological processes in Earth System Models (ESMs) to both Earth system science and to ecology. Adding ecological processes to ESMs skillfully will likely improve the long-term performance of these models. The rigor required to achieve this will prompt ecologists to test complex ecological hypotheses on regional and global scales. Some candidate processes are suggested.

Plant activity in semi-arid ecosystems is largely controlled by pulses of precipitation, making them particularly vulnerable to increased aridity that is expected with climate change. Simple bucket-model hydrology schemes in land surface models (LSMs) have had limited ability in accurately capturing semi-arid water stores and fluxes. Recent, more complex, LSM hydrology models have not been widely evaluated against semi-arid ecosystem in situ data. We hypothesize that the failure of older LSM versions to represent evapotranspiration, ET, in arid lands is because simple bucket models do not capture realistic fluctuations in upper-layer soil moisture. We therefore predict that including a discretized soil hydrology scheme based on a mechanistic description of moisture diffusion will result in an improvement in model ET when compared to data because the temporal variability of upper-layer soil moisture content better corresponds to that of precipitation inputs. To test this prediction, we compared ORCHIDEE LSM simulations from (1) a simple conceptual 2-layer bucket scheme with fixed hydraulic parameters and (2) an 11-layer discretized mechanistic scheme of moisture diffusion in unsaturated soil based on Richards equations, against daily and monthly soil moisture and ET observations, together with data-derived estimates of transpiration / evapotranspiration, T∕ET, ratios, from six semi-arid grass, shrub, and forest sites in the south-western USA. The 11-layer scheme also has modified calculations of surface runoff, water limitation, and resistance to bare soil evaporation, E, to be compatible with the more complex hydrology configuration. To diagnose remaining discrepancies in the 11-layer model, we tested two further configurations: (i) the addition of a term that captures bare soil evaporation resistance to dry soil; and (ii) reduced bare soil fractional vegetation cover. We found that the more mechanistic 11-layer model results in a better representation of the daily and monthly ET observations. We show that, as predicted, this is because of improved simulation of soil moisture in the upper layers of soil (top ∼ 10 cm). Some discrepancies between observed and modelled soil moisture and ET may allow us to prioritize future model development and the collection of additional data. Biases in winter and spring soil moisture at the forest sites could be explained by inaccurate soil moisture data during periods of soil freezing and/or underestimated snow forcing data. Although ET is generally well captured by the 11-layer model, modelled T∕ET ratios were generally lower than estimated values across all sites, particularly during the monsoon season. Adding a soil resistance term generally decreased simulated bare soil evaporation, E, and increased soil moisture content, thus increasing transpiration, T, and reducing the negative bias between modelled and estimated monsoon T∕ET ratios. This negative bias could also be accounted for at the low-elevation sites by decreasing the model bare soil fraction, thus increasing the amount of transpiring leaf area. However, adding the bare soil resistance term and decreasing the bare soil fraction both degraded the model fit to ET observations. Furthermore, remaining discrepancies in the timing of the transition from minimum T∕ET ratios during the hot, dry May–June period to high values at the start of the monsoon in July–August may also point towards incorrect modelling of leaf phenology and vegetation growth in response to monsoon rains. We conclude that a discretized soil hydrology scheme and associated developments improve estimates of ET by allowing the modelled upper-layer soil moisture to more closely match the pulse precipitation dynamics of these semi-arid ecosystems; however, the partitioning of T from E is not solved by this modification alone.

Semiarid forests in the southwestern USA are generally restricted to mountain regions where complex terrain adds to the challenge of characterizing stand productivity. Among the heterogeneous features of these ecosystems, topography represents an important control on system-level processes including snow accumulation and melt. This basic relationship between geology and hydrology affects radiation and water balances within the forests, with implications for canopy structure and function across a range of spatial scales. In this study, we quantify the effect of topographic aspect on primary productivity by observing the response of two codominant native tree species to seasonal changes in the timing and magnitude of energy and water inputs throughout a montane headwater catchment in Arizona, USA. On average, soil moisture on north-facing aspects remained higher during the spring and early summer compared with south-facing aspects. Repeated measurements of net carbon assimilation (Anet) showed that Pinus ponderosa C. Lawson was sensitive to this difference, while Pseudotsuga menziesii (Mirb.) Franco was not. Irrespective of aspect, we observed seasonally divergent patterns at the species level where P. ponderosa maintained significantly greater Anet into the fall despite more efficient water use by P. menziesii individuals during that time. As a result, this study at the southern extent of the geographical P. menziesii distribution suggests that this species could increase water-use efficiency as a response to future warming and/or drying, but at lower rates of production relative to the more drought-adapted P. ponderosa. At the sub-landscape scale, opposing aspects served as a mesocosm of current versus anticipated climate conditions. In this way, these results also constrain the potential for changing carbon sequestration patterns from Pinus-dominated landscapes due to forecasted changes in seasonal moisture availability.

BACKGROUND: To investigate whether meteorological factors (temperature, barometric pressure, relative humidity, ultraviolet index [UVI], and seasons) trigger flares in male and female urologic chronic pelvic pain patients. METHODS: We assessed flare status every two weeks in our case-crossover study of flare triggers in the Multidisciplinary Approach to the Study of Chronic Pelvic Pain one-year longitudinal study. Flare symptoms, flare start date, and exposures in the three days preceding a flare or the date of questionnaire completion were assessed for the first three flares and at three randomly selected non-flare times. We linked these data to daily temperature, barometric pressure, relative humidity, and UVI values by participants’ first 3 zip code digits. Values in the three days before and the day of a flare, as well as changes in these values, were compared to non-flare values by conditional logistic regression. Differences in flare rates by astronomical and growing seasons were investigated by Poisson regression in the full study population. RESULTS: 574 flare and 792 non-flare assessments (290 participants) were included in the case-crossover analysis, and 966 flare and 5,389 non-flare (409 participants) were included in the full study analysis. Overall, no statistically significant associations were observed for daily weather, no patterns of associations were observed for weather changes, and no differences in flare rates were observed by season. CONCLUSIONS: We found minimal evidence to suggest that weather triggers flares, although we cannot rule out the possibility that a small subset of patients are susceptible.

Dryland ecosystems are dominant influences on both the trend and interannual variability of the terrestrial carbon sink. Despite their importance, dryland carbon dynamics are not well-characterized by current models. Here, we present DryFlux, an upscaled product built on a dense network of eddy covariance sites in the North American Southwest. To estimate dryland gross primary productivity, we fuse in situ fluxes with remote sensing and meteorological observations using machine learning. DryFlux explicitly accounts for intra-annual variation in water availability, and accurately predicts interannual and seasonal variability in carbon uptake. Applying DryFlux globally indicates existing products may underestimate impacts of large-scale climate patterns on the interannual variability of dryland carbon uptake. We anticipate DryFlux will be an improved benchmark for earth system models in drylands, and prompt a more sensitive accounting of water limitation on the carbon cycle.

The Arctic and Boreal Region (ABR) is subject to extensive land cover change (LCC) due to elements such as wildfire, permafrost thaw, and shrubification. The natural and anthropogenic ecosystem transitions (i.e. LCC) alter key ecosystem characteristics including land surface temperature (LST), albedo, and evapotranspiration (ET). These biophysical variables are important in controlling surface energy balance, water exchange, and carbon uptake which are important factors influencing the warming trend over the ABR. However, to what extent these variables are sensitive to various LCC in heterogeneous systems such as ABR is still an open question. In this study, we use a novel data-driven approach based on high-resolution land cover data (2003 and 2013) over four million km2 to estimate the impact of multiple types of ecosystem transitions on LST, albedo, and ET. We also disentangle the contribution of LCC vs. natural variability of the system in changes in biophysical variables. Our results indicate that from 2003 to 2013 about 46% (∼2 million km2) of the region experienced LCC, which drove measurable changes to the biophysical environment across ABR over the study period. In almost half of the cases, LCC imposes a change in biophysical variables against the natural variability of the system. For example, in ∼35% of cases, natural variability led to −1.4 ± 0.9 K annual LST reduction, while LCC resulted in a 0.9 ± 0.6 K LST increase, which dampened the decrease in LST due to natural variability. In some cases, the impact of LCC was strong enough to reverse the sign of the overall change. Our results further demonstrate the contrasting sensitivity of biophysical variables to specific LCC. For instance, conversion of sparsely vegetated land to a shrub (i.e. shrubification) significantly decreased annual LST (−2.2 ± 0.1 K); whereas sparsely vegetated land to bare ground increased annual LST (1.6 ± 0.06 K). We additionally highlight the interplay between albedo and ET in driving changes in annual and seasonal LST. Whether our findings are generalizable to the spatial and temporal domain outside of our data used here is unknown, but merits future research due to the importance of the interactions between LCC and biophysical variables.

Hybrid-poplar tree plantations provide a source for biofuel and biomass, but they also increase forest isoprene emissions. The consequences of increased isoprene emissions include higher rates of tropospheric ozone production, increases in the lifetime of methane, and increases in atmospheric aerosol production, all of which affect the global energy budget and/or lead to the degradation of air quality. Using RNA interference (RNAi) to suppress isoprene emission, we show that this trait, which is thought to be required for the tolerance of abiotic stress, is not required for high rates of photosynthesis and woody biomass production in the agroforest plantation environment, even in areas with high levels of climatic stress. Biomass production over 4 y in plantations in Arizona and Oregon was similar among genetic lines that emitted or did not emit significant amounts of isoprene. Lines that had substantially reduced isoprene emission rates also showed decreases in flavonol pigments, which reduce oxidative damage during extremes of abiotic stress, a pattern that would be expected to amplify metabolic dysfunction in the absence of isoprene production in stress-prone climate regimes. However, compensatory increases in the expression of other proteomic components, especially those associated with the production of protective compounds, such as carotenoids and terpenoids, and the fact that most biomass is produced prior to the hottest and driest part of the growing season explain the observed pattern of high biomass production with low isoprene emission. Our results show that it is possible to reduce the deleterious influences of isoprene on the atmosphere, while sustaining woody biomass production in temperate agroforest plantations.

The response of terrestrial carbon uptake to increasing atmospheric [CO2 ], that is the CO2 fertilization effect (CFE), remains a key area of uncertainty in carbon cycle science. Here we provide a perspective on how satellite observations could be better used to understand and constrain CFE. We then highlight data assimilation (DA) as an effective way to reconcile different satellite datasets and systematically constrain carbon uptake trends in Earth System Models. As a proof-of-concept, we show that joint DA of multiple independent satellite datasets reduced model ensemble error by better constraining unobservable processes and variables, including those directly impacted by CFE. DA of multiple satellite datasets offers a powerful technique that could improve understanding of CFE and enable more accurate forecasts of terrestrial carbon uptake.

Severe drought can cause lagged effects on tree physiology that negatively impact forest functioning for years. These "drought legacy effects" have been widely documented in tree-ring records and could have important implications for our understanding of broader scale forest carbon cycling. However, legacy effects in tree-ring increments may be decoupled from ecosystem fluxes due to (a) postdrought alterations in carbon allocation patterns; (b) temporal asynchrony between radial growth and carbon uptake; and (c) dendrochronological sampling biases. In order to link legacy effects from tree rings to whole forests, we leveraged a rich dataset from a Midwestern US forest that was severely impacted by a drought in 2012. At this site, we compiled tree-ring records, leaf-level gas exchange, eddy flux measurements, dendrometer band data, and satellite remote sensing estimates of greenness and leaf area before, during, and after the 2012 drought. After accounting for the relative abundance of tree species in the stand, we estimate that legacy effects led to ~10% reductions in tree-ring width increments in the year following the severe drought. Despite this stand-scale reduction in radial growth, we found that leaf-level photosynthesis, gross primary productivity (GPP), and vegetation greenness were not suppressed in the year following the 2012 drought. Neither temporal asynchrony between radial growth and carbon uptake nor sampling biases could explain our observations of legacy effects in tree rings but not in GPP. Instead, elevated leaf-level photosynthesis co-occurred with reduced leaf area in early 2013, indicating that resources may have been allocated away from radial growth in conjunction with postdrought upregulation of photosynthesis and repair of canopy damage. Collectively, our results indicate that tree-ring legacy effects were not observed in other canopy processes, and that postdrought canopy allocation could be an important mechanism that decouples tree-ring signals from GPP.

Drylands account for approximately 40% of the global land surface and play a dominant role in the trend and variability of terrestrial carbon uptake and storage. Gross ecosystem photosynthesis – termed gross primary productivity (GPP) – is a critical driver of terrestrial carbon uptake and remains challenging to be observed directly. Currently, vegetation indices that largely capture changes in greenness are the most commonly used datasets in satellite-based GPP modeling. However, there remains significant uncertainty in the spatiotemporal relationship between greenness indices and GPP, especially for relatively heterogeneous dryland ecosystems. In this paper, we compared vegetation greenness indices from PhenoCam and satellite (Landsat and MODIS) observations against GPP estimates from the eddy covariance technique, across three representative ecosystem types of the southwestern United States. We systematically evaluated the changes in the relationship between vegetation greenness indices and GPP: i) across spatial scales of canopy-level, 30-meter, and 500-meter resolution; and ii) across temporal scale of daily, 8-day, 16-day, and monthly resolution. We found that greenness-GPP relationships were independent of spatial scales as long as land cover type and composition remained relatively constant. We also found that the greenness-GPP relationships became stronger as the time interval increased, with the strongest relationships observed at the monthly resolution. We posit that the greenness-GPP relationship breaks down at short timescales because greenness changes more slowly than plant physiological function, which responds rapidly to changes in key biophysical drivers. These findings provide insights into the potential for and limitations of modeling GPP using remotely sensed greenness indices across dryland ecosystem types.

Despite their sparse vegetation, dryland regions exert a huge influence over global biogeochemical cycles because they cover more than 40% of the world surface (Schimel 2010 Science 327 418–9). It is thought that drylands dominate the inter-annual variability (IAV) and long-term trend in the global carbon (C) cycle (Poulter et al 2014 Nature 509 600–3, Ahlstrom et al 2015 Science 348 895–9, Zhang et al 2018 Glob. Change Biol. 24 3954–68). Projections of the global land C sink therefore rely on accurate representation of dryland C cycle processes; however, the dynamic global vegetation models (DGVMs) used in future projections have rarely been evaluated against dryland C flux data. Here, we carried out an evaluation of 14 DGVMs (TRENDY v7) against net ecosystem exchange (NEE) data from 12 dryland flux sites in the southwestern US encompassing a range of ecosystem types (forests, shrub- and grasslands). We find that all the models underestimate both mean annual C uptake/release as well as the magnitude of NEE IAV, suggesting that improvements in representing dryland regions may improve global C cycle projections. Across all models, the sensitivity and timing of ecosystem C uptake to plant available moisture was at fault. Spring biases in gross primary production (GPP) dominate the underestimate of mean annual NEE, whereas models’ lack of GPP response to water availability in both spring and summer monsoon are responsible for inability to capture NEE IAV. Errors in GPP moisture sensitivity at high elevation forested sites were more prominent during the spring, while errors at the low elevation shrub and grass-dominated sites were more important during the monsoon. We propose a range of hypotheses for why model GPP does not respond sufficiently to changing water availability that can serve as a guide for future dryland DGVM developments. Our analysis suggests that improvements in modeling C cycle processes across more than a quarter of the Earth’s land surface could be achieved by addressing the moisture sensitivity of dryland C uptake.

Increasing water-use efficiency (WUE), the ratio of carbon gain to water loss, is a key mechanism that enhances carbon uptake by terrestrial vegetation under rising atmospheric CO2 (ca). Existing theory and empirical evidence suggest a proportional increase of WUE in response to rising ca as plants maintain a relatively constant ratio between the leaf internal (ci) and ambient (ca) partial CO2 pressure (ci/ca). This has been hypothesized as the main driver of the strengthening of the terrestrial carbon sink over the recent decades. However, proportionality may not characterize CO2 effects on WUE on longer time-scales and the role of climate in modulating these effects is uncertain. We evaluated the long-term WUE responses to ca and climate from 1901-2012 CE by reconstructing intrinsic WUE (iWUE, the ratio of photosynthesis to stomatal conductance) using carbon isotopes in tree rings across temperate forests in the northeastern USA. We further replicated iWUE reconstructions at eight additional sites for the 1992-2012 period-overlapping with the common period of the longest flux-tower record at Harvard Forest to evaluate the spatial coherence of recent iWUE variation across the region. Finally, we compared tree-ring based and modelled ci/ca over the 1901-2012 period to examine whether temporal patterns of ci/ca reconstructions are consistent with predictions based on the optimality principle of balancing the costs of water loss and carbon gain.We found that iWUE increased steadily from 1901 to 1975 CE but remained constant thereafter despite continuously rising ca. This finding is consistent with a passive physiological response to ca and coincides with a shift to significantly wetter conditions across the region. Tree physiology was driven by summer moisture at multi-decadal time-scales and did not maintain a constant ci/ca in response to rising ca indicating that a point was reached where rising CO2 had a diminishing effect on tree iWUE. The ci/ca derived from tree-ring d13C and the predicted values based on the optimality theory model had similar median values over the 1901-2012 CE period, though with a modest agreement (R2adj = 0.22, p < 0.001). The reconstructed and predicted ci/ca trends were not statistically different from 0 when estimated over the 1901-2012 CE period; however, isotope-based reconstruction of the ci/ca trendshowed distinct multidecadal variation while the predicted ci/ca remained nearly constant. Our results challenge the mechanism, magnitude, and persistence of CO2’s effect on iWUE with significant implications for projections of terrestrial productivity under a changing climate.

Increasing water-use efficiency (WUE), the ratio of carbon gain to water loss, is a key mechanism that enhances carbon uptake by terrestrial vegetation under rising atmospheric CO

Forecasts of forest responses to climate variability are governed by climate exposure and ecosystem sensitivity, but ecosystem model projections and process representations are under-constrained by data at multidecadal and longer timescales. Here, we assess ecosystem sensitivity to centennial-scale hydroclimate variability, by comparing dendroclimatic and pollen-inferred reconstructions of drought, forest composition and biomass for the last millennium with five ecosystem model simulations. In both observations and models, spatial patterns in ecosystem responses to hydroclimate variability are strongly governed by ecosystem sensitivity rather than climate exposure. Ecosystem sensitivity was highest in simpler models and higher than observations, suggesting that interactions among biodiversity, demography, and ecophysiology processes dampen the sensitivity of forest composition and biomass to climate variability and change. By integrating ecosystem models with observations from timescales extending beyond the instrumental record, we can better understand and forecast the mechanisms regulating forest sensitivity to climate variability in a complex and changing world.

Atmospheric carbon dioxide concentration ([CO

The magnitude and persistence of land carbon (C) pools influence long‐term climate feedbacks. Interactive ecological processes influence land C pools and our understanding of these processes is imperfect so land surface models have errors and biases when compared to each other and to real observations. Here we implement an Ensemble Adjustment Kalman Filter (EAKF), a sequential state data assimilation technique to reduce these errors and biases. We implement the EAKF using the Data Assimilation Research Testbed coupled with the Community Land Model (CLM 4.5 in CESM 1.2). We assimilated simulated and real satellite observations for a site in central New Mexico, United States. A series of observing system simulation experiments allowed assessment of the data assimilation system without model error. This showed that assimilating biomass and leaf area index observations decreased model error in C dynamics forecasts (29% using biomass observations and 40% using leaf area index observations) and that assimilation in combination shows greater improvement (51% using both observation streams). Assimilating real observations highlighted likely model structural errors and we implemented an adaptive model‐variance‐inflation technique to allow the model to track the observations. Monthly and longer model forecasts using real observations were improved relative to forecasts without data assimilation. The reliable forecast lead‐time varied by model pool and is dependent on how tightly the C pool is coupled to meteorologically driven processes. The EAKF and similar state data assimilation techniques could reduce errors in projections of the land C sink and provide more robust forecasts of C pools and land‐atmosphere exchanges.

Coarse woody debris (CWD; i.e. downed limbs and boles) serves numerous ecosystem functions, which vary according to the degree of decay. CWD decay is often described using five categories based on readily observed physical characteristics ranging from freshly fallen (Class I) to advanced decay with little structural integrity (Class V). Though useful in categorizing downed wood in a forest, these categories do not necessarily provide information about time since death or the decay process. Dendrochronology can be used to assign death dates to CWD and begin to provide a temporal description of the decay process. We used standard dendrochronological techniques to determine the death dates of 94 CWD samples from five common hardwood taxa in southern Indiana. Across taxa, the time since death of Class I (1.4 ± 1.7 years; mean ± SD; least decayed class) was significantly shorter than Class II (5.2 ± 3.6 years), which was shorter than the more decayed classes (Class III: 11.5 ± 4.9, and Class IV: 11.2 ...

DOE's Office of Science; NASA [NNH16ZDA001N]; USDA [58-0111-17-013]; DOE's Regional and Global Climate Modeling Program [DE-SC0016011]

The response of understory trees to climate variability is key to understanding current and future forest dynamics. However, analyses of climatic effects on tree growth have primarily focused on the upper canopy, leaving understory dynamics unresolved. We analyzed differences in climate sensitivity based on canopy position of four common tree species (Acer rubrum, Fagus grandifolia, Quercus rubra, and Tsuga canadensis) using growth information from 1,084 trees across eight sites in the northeastern United States. Effects of canopy position on climate response varied, but were significant and often nonlinear, for all four species. Compared to overstory trees, understory trees showed stronger reductions in growth at high temperatures and varied shifts in precipitation response. This contradicts the prevailing assumption that climate responses, particularly to temperature, of understory trees are buffered by the overstory. Forest growth trajectories are uncertain in compositionally and structurally complex forests, and future demography and regeneration dynamics may be misinferred if not all canopy levels are represented in future forecasts.

Ecosystem models show divergent responses of the terrestrial carbon cycle to global change over the next century. Individual model evaluation and multi-model comparisons with data have largely focused on individual processes at sub-annual to decadal scales. Thus far, data-based evaluations of emergent ecosystem responses to climate and CO2 at multi-decadal and centennial time scales have been rare. We compared the sensitivity of net primary productivity (NPP) to temperature, precipitation, and CO2 in ten ecosystem models with the sensitivities found in tree-ring reconstructions of NPP and raw ring-width series at six temperate forest sites. These model-data comparisons were evaluated at three temporal extents to determine whether the rapid, directional changes in temperature and CO2 in the recent past skew our observed responses to multiple drivers of change. All models tested here were more sensitive to low growing season precipitation than tree-ring NPP and ring widths in the past 30 years, although some model precipitation responses were more consistent with tree rings when evaluated over a full century. Similarly, all models had negative or no response to warm growing season temperatures while tree-ring data showed consistently positive effects of temperature. Although precipitation responses were least consistent among models, differences among models to CO2 drive divergence and ensemble uncertainty in relative change in NPP over the past century. Changes in forest composition within models had no effect on climate or CO2 sensitivity. Fire in model simulations reduced model sensitivity to climate and CO2, but only over the course of multiple centuries. Formal evaluation of emergent model behavior at multi-decadal and multi-centennial time scales is essential to reconciling model projections with observed ecosystem responses to past climate change. Future evaluation should focus on improved representation of disturbance and biomass change as well as the feedbacks with moisture balance and CO2 in individual models.

Plant isoprene emissions have been linked to several reaction pathways involved in atmospheric photochemistry. Evidence exists from a limited set of past observations that isoprene emission rate (I(sub s)) decreases as a function of increasing atmospheric CO2 concentration, and that increased temperature suppresses the CO2 effect. We studied interactions between intercellular CO2 concentration (C(sub I)) and temperature as they affect I(sub s) in field-grown hybrid poplar trees in one of the warmest climates on earth - the Sonoran Desert of the southwestern United States. We observed an unexpected midsummer down regulation of I(sub s) despite the persistence of relatively high temperatures. High temperature suppression of the I(sub s):C(sub I) relation occurred at all times during the growing season, but sensitivity of I(sub s) to increased C(sub I) was greatest during the midsummer period when I(subs) was lowest. We interpret the seasonal down regulation of I(sub s) and increased sensitivity of I(sub s) to C(sub I) as being caused by weather changes associated with the onset of a regional monsoon system. Our observations on the temperature suppression of the I(sub s):C(sub I) relation are best explained by the existence of a small pool of chloroplastic inorganic phosphate, balanced by several large, connected metabolic fluxes, which together, determine the C(sub I) and temperature dependencies of phosphoenolpyruvate import into the chloroplast.

Bark beetle outbreaks are widespread in western North American forests, reducing primary productivity and transpiration, leading to forest mortality across large areas and altering ecosystem carbon cycling. Here the carbon isotope composition (δ(13) C) of soil respiration (δJ ) was monitored in the decade after disturbance for forests affected naturally by mountain pine beetle infestation and artificially by stem girdling. The seasonal mean δJ changed along both chronosequences. We found (a) enrichment of δJ relative to controls (

Rapid changes to the biosphere are altering ecological processes worldwide. Developing informed policies for mitigating the impacts of environmental change requires an exponential increase in the quantity, diversity, and resolution of field-collected data, which, in turn, necessitates greater reliance on innovative technologies to monitor ecological processes across local to global scales. Automated digital time-lapse cameras – “phenocams” – can monitor vegetation status and environmental changes over long periods of time. Phenocams are ideal for documenting changes in phenology, snow cover, fire frequency, and other disturbance events. However, effective monitoring of global environmental change with phenocams requires adoption of data standards. New continental-scale ecological research networks, such as the US National Ecological Observatory Network (NEON) and the European Union's Integrated Carbon Observation System (ICOS), can serve as templates for developing rigorous data standards and extending the utility of phenocam data through standardized ground-truthing. Open-source tools for analysis, visualization, and collaboration will make phenocam data more widely usable.

© 2017 Elsevier B.V. AmeriFlux scientists were early adopters of a network-enabled approach to ecosystem science that continues to transform the study of land-atmosphere interactions. In the 20 years since its formation, AmeriFlux has grown to include more than 260 flux tower sites in the Americas that support continuous observation of ecosystem carbon, water, and energy fluxes. Many of these sites are co-located within a similar climate regime, and more than 50 have data records that exceed 10 years in length. In this prospective assessment of AmeriFlux's strengths in a new era of network-enabled ecosystem science, we discuss how the longevity and spatial distribution of AmeriFlux data make them exceptionally well suited for disentangling ecosystem response to slowly evolving changes in climate and land-cover, and to rare events like droughts and biological disturbances. More recently, flux towers have also been integrated into environmental observation networks that have broader scientific goals; in North America these include the National Ecological Observatory Network (NEON), Critical Zone Observatory network (CZO), and Long-Term Ecological Research network (LTER). AmeriFlux stands apart from these other networks in its reliance on voluntary participation of individual sites, which receive funding from diverse sources to pursue a wide, transdisciplinary array of research topics. This diffuse, grassroots approach fosters methodological and theoretical innovation, but also challenges network-level data synthesis and data sharing to the network. While AmeriFlux has had strong ties to other regional flux networks and FLUXNET, better integration with networks like NEON, CZO and LTER provides opportunities for new types of cooperation and synergies that could strengthen the scientific output of all these networks.

A recent unprecedented epidemic of beetle-induced tree mortality has occurred in the lodgepole pine forests of Western North America. Here, we present the results of studies in two subalpine forests in the Rocky Mountains, one that experienced natural pine beetle disturbance and one that experienced simulated disturbance imposed through bole girdling. We assessed changes to soil microclimate and biogeochemical pools in plots representing different post-disturbance chronosequences. High plot tree mortality, whether due to girdling or beetle infestation, caused similar alterations in soil nutrient pools. During the first 4 years after disturbance, sharp declines were observed in the soil dissolved organic carbon (DOC) concentration (45-51 %), microbial biomass carbon concentration (33-39 %), dissolved organic nitrogen (DON) concentration (31-42%), and inorganic phosphorus (PO4(3-)) concentration (53-55%). Five to six years after disturbance, concentrations of DOC, DON, and PO4(3-) recovered to 71-140 % of those measured in undisturbed plots. Recovery was coincident with observed increases in litter depth and the sublitter, soil O-horizon. During the 4 years following disturbance, soil ammonium, but not nitrate, increased to 2-3 times the levels measured in undisturbed plots. Microbial biomass N increased in plots where increased ammonium was available. Our results show that previously observed declines in soil respiration following beetle-induced disturbance are accompanied by losses in key soil nutrients. Recovery of the soil nutrient pool occurs only after several years following disturbance, and is correlated with progressive mineralization of dead tree litter.

In the United States’ (US) Northern Rockies, synoptic pressure systems and atmospheric circulation drive interannual variation in seasonal temperature and precipitation. The radial growth of high-elevation trees in this semi-arid region captures this temperature and precipitation variability and provides long time series to contextualize instrumental-era variability in synoptic-scale climate patterns. Such variability in climate patterns can trigger extreme climate events, such as droughts, floods, and forest fires, which have a damaging impact on human and natural systems. We developed 11 tree-ring width (TRW) chronologies from multiple species and sites to investigate the seasonal climatic drivers of tree growth in the Bighorn Mountains, WY. A principal component analysis of the chronologies identified 54% of shared common variance (1894-2014). Tree growth (expressed by PC1) was driven by multiple seasonal climate variables: previous October and current July temperatures, as well as previous December and current April precipitation, had a positive influence on growth, whereas growth was limited by July precipitation. These seasonal growth-climate relationships corresponded to circulation patterns at higher atmospheric levels over the Bighorn Mountains. Tree growth was enhanced when the winter jet stream was in a northward position, which led to warmer winters, and when the spring jet stream was further south, which led to wetter springs. The second principal component, explaining 19% of the variance, clustered sites by elevation and was strongly related to summer temperature. We leverage this summer temperature signal in our TRW chronologies by combining it with an existing maximum latewood density (MXD) chronology in a nested approach. This allowed us to reconstruct Bighorn Mountains summer (June, July, and August) temperature (BMST) back to 1654, thus extending the instrumental temperature record by 250 years. Our BMST reconstruction explains 39-53% of the variance in regional summer temperature variability. The 1830s were the relatively coolest decade and the 1930s were the warmest decade over the reconstructed period (1654-1983 CE) – which excludes the most recent 3 decades. Our results contextualize recent drivers and trends of climate variability in the US Northern Rockies, which contributes to the information that managers of human and natural systems need in order to prepare for potential future variability.

Drylands make up roughly 40% of the Earth's land surface, and billions of people depend on services provided by these critically important ecosystems. Despite their relatively sparse vegetation, dryland ecosystems are structurally and functionally diverse, and emerging evidence suggests that these ecosystems play a dominant role in the trend and variability of the terrestrial carbon sink. More, drylands are highly sensitive to climate and are likely to have large, non-linear responses to hydroclimatic change. Monitoring the spatiotemporal dynamics of dryland ecosystem structure (e.g., leaf area index) and function (e.g., primary production and evapotranspiration) is therefore a high research priority. Yet, dryland remote sensing is defined by unique challenges not typically encountered in mesic or humid regions. Major challenges include low vegetation signal-to-noise ratios, high soil background reflectance, presence of photosynthetic soils (i.e., biological soil crusts), high spatial heterogeneity from plot to regional scales, and irregular growing seasons due to unpredictable seasonal rainfall and frequent periods of drought. Additionally, there is a relative paucity of continuous, long-term measurements in drylands, which impedes robust calibration and evaluation of remotely-sensed dryland data products. Due to these issues, remote sensing techniques developed in other ecosystems or for global application often result in inaccurate, poorly constrained estimates of dryland ecosystem structural and functional dynamics. Here, we review past achievements and current progress in remote sensing of dryland ecosystems, including a detailed discussion of the major challenges associated with remote sensing of key dryland structural and functional dynamics. We then identify strategies aimed at leveraging new and emerging opportunities in remote sensing to overcome previous challenges and more accurately contextualize drylands within the broader Earth system. Specifically, we recommend: 1) Exploring novel combinations of sensors and techniques (e.g., solar-induced fluorescence, thermal, microwave, hyperspectral, and LiDAR) across a range of spatiotemporal scales to gain new insights into dryland structural and functional dynamics; 2) utilizing near-continuous observations from new-and-improved geostationary satellites to capture the rapid responses of dryland ecosystems to diurnal variation in water stress; 3) expanding ground observational networks to better represent the heterogeneity of dryland systems and enable robust calibration and evaluation; 4) developing algorithms that are specifically tuned to dryland ecosystems by utilizing expanded ground observational network data; and 5) coupling remote sensing observations with process-based models using data assimilation to improve mechanistic understanding of dryland ecosystem dynamics and to better constrain ecological forecasts and long-term projections.

Estimating the exchanges of carbon and water between vegetation and the atmosphere requires process-based ecosystem models; however, uncertainty in model predictions is inevitable due to the uncertainties in model structure, model parameters, and driving variables. This paper proposes a methodological framework for analyzing prediction uncertainty of ecosystem models caused by parameters and applies it in Qianyanzhou subtropical coniferous plantation using the Simplified Photosynthesis and Evapotranspiration model. We selected 20 key parameters from 42 parameters of the model using one-at-a-time sensitivity analysis method and estimated their posterior distributions using Markov Chain Monte Carlo technique. Prediction uncertainty was quantified through Monte Carlo method and partitioned using Sobol' method by decomposing the total variance of model predictions into different components. The uncertainty in predicted net ecosystem CO2 exchange (NEE), gross primary production (GPP), ecosystem respiration (RE), evapotranspiration (ET), and transpiration (T), defined as the coefficient of variation, was 61.0%, 20.6%, 12.7%, 14.2%, and 19.9%, respectively. Modeled carbon and water fluxes were highly sensitive to two parameters, maximum net CO2 assimilation rate (A(max)) and specific leaf weight (SLWC). They contributed more than two thirds of the uncertainty in predicted NEE, GPP, ET, and T and almost one third of the uncertainty in predicted RE, which should be focused on in further efforts to reduce uncertainty. The results indicated a direction for future model development and data collection. Although there were still limitations in the framework illustrated here, it did provide a paradigm for systematic quantification of ecosystem model prediction uncertainty.

[1] Forecasting the carbon uptake potential of terrestrial ecosystems in the face of future climate change has proven challenging. Process models, which have been increasingly used to study ecosystem-atmosphere carbon and water exchanges when conditioned with tower-based eddy covariance data, have the potential to inform us about biogeochemical processes in future climate regimes, but only if we can reconcile the spatial and temporal scales used for observed fluxes and projected climate. Here, we used weather generator and ecosystem process models conditioned on observed weather dynamics and carbon/water fluxes, and embedded them within climate projections from a suite of six Earth Systems Models. Using this combination of models, we studied carbon cycle processes in a subalpine forest within the context of future (2080–2099) climate regimes. The assimilation of daily averaged, observed net ecosystem CO2 exchange (NEE) and evapotranspiration (ET) into the ecosystem process model resulted in retrieval of projected NEE with a level of accuracy that was similar to that following the assimilation of half-daily averaged observations; the assimilation of 30 min averaged fluxes or monthly averaged fluxes caused degradation in the model's capacity to accurately simulate seasonal patterns in observed NEE. Using daily averaged flux data with daily averaged weather data projected for the period 2080–2099, we predicted greater forest net CO2 uptake in response to a lengthening of the growing season. These results contradict our previous observations of reduced CO2 uptake in response to longer growing seasons in the current (1999–2008) climate regime. The difference between these analyses is due to a projected increase in the frequency of rain versus snow during warmer winters of the future. Our results demonstrate the sensitivity of modeled processes to local variation in meteorology, which is often left unresolved in traditional approaches to earth systems modeling, and the importance of maintaining similarity in the timescales used in ecosystem process models driven by downscaled climate projections.

Many ecosystem processes that influence Earth system feedbacks, including vegetation growth, water and nutrient cycling, and disturbance regimes, are strongly influenced by multi-decadal to millennial-scale variations in climate that cannot be captured by instrumental climate observations. Paleoclimate information is therefore essential for understanding contemporary ecosystems and their potential trajectories under a variety of future climate conditions. With the exception of fossil pollen records, there are a limited number of northeastern US (NE US) paleoclimate archives that can provide constraints on its temperature and hydroclimate history. Moreover, the records that do exist have not been considered together. Tree-ring data indicate that the 20th century was one of the wettest of the past 500 years in the eastern US (Pederson et al., 2014), and lake-level records suggest it was one of the wettest in the Holocene (Newby et al., 2014); how such results compare with other available data remains unclear, however. Here we conduct a systematic review, assessment, and comparison of paleotemperature and paleohydrological proxies from the NE US for the last 3000 years. Regional temperature reconstructions are consistent with the long-term cooling trend (1000 BCE–1700 CE) evident in hemispheric-scale reconstructions, but hydroclimate reconstructions reveal new information, including an abrupt transition from wet to dry conditions around 550–750 CE. NE US paleo data suggest that conditions during the Medieval Climate Anomaly were warmer and drier than during the Little Ice Age, and drier than today. There is some evidence for an acceleration over the past century of a longer-term wetting trend in the NE US, and coupled with the abrupt shift from a cooling trend to a warming trend from increased greenhouse gases, may have wide-ranging implications for species distributions, ecosystem dynamics, and extreme weather events. More work is needed to gather paleoclimate data in the NE US, make inter-proxy comparisons, and improve estimates of uncertainty in the reconstructions.

Ecosystem models show divergent responses of the terrestrial carbon cycle to global change over the next century. Individual model evaluation and multimodel comparisons with data have largely focused on individual processes at subannual to decadal scales. Thus far, data-based evaluations of emergent ecosystem responses to climate and CO

© 2016 Elmendorf et al. Phenology is an integrative science that comprises the study of recurring biological activities or events. In an era of rapidly changing climate, the relationship between the timing of those events and environmental cues such as temperature, snowmelt, water availability, or day length are of particular interest. This article provides an overview of the observer-based plant phenology sampling conducted by the U.S. National Ecological Observatory Network (NEON), the resulting data, and the rationale behind the design. Trained technicians will conduct regular in situ observations of plant phenology at all terrestrial NEON sites for the 30-yr life of the observatory. Standardized and coordinated data across the network of sites can be used to quantify the direction and magnitude of the relationships between phenology and environmental forcings, as well as the degree to which these relationships vary among sites, among species, among phenophases, and through time. Vegetation at NEON sites will also be monitored with tower-based cameras, satellite remote sensing, and annual high-resolution airborne remote sensing. Ground-based measurements can be used to calibrate and improve satellite-derived phenometrics. NEON's phenology monitoring design is complementary to existing phenology research efforts and citizen science initiatives throughout the world and will produce interoperable data. By collocating plant phenology observations with a suite of additional meteorological, biophysical, and ecological measurements (e.g., climate, carbon flux, plant productivity, population dynamics of consumers) at 47 terrestrial sites, the NEON design will enable continental-scale inference about the status, trends, causes, and ecological consequences of phenological change.

Plant isoprene emissions have been linked to several reaction pathways involved in atmospheric photochemistry. Evidence exists from a limited set of past observations that isoprene emission rate (I

Forest insects and pathogens are major disturbance agents that have affected millions of hectares in North America in recent decades, implying significant impacts to the carbon (C) cycle. Here, we review and synthesize published studies of the effects of biotic disturbances on forest C cycling in the United States and Canada. Primary productivity in stands was reduced, sometimes considerably, immediately following insect or pathogen attack. After repeated growth reductions caused by some insects or pathogens or a single infestation by some bark beetle species, tree mortality occurred, altering productivity and decomposition. In the years following disturbance, primary productivity in some cases increased rapidly as a result of enhanced growth by surviving vegetation, and in other cases increased slowly because of lower forest regrowth. In the decades following tree mortality, decomposition increased as a result of the large amount of dead organic matter. Net ecosystem productivity decreased immediately following attack, with some studies reporting a switch to a C source to the atmosphere, and increased afterward as the forest regrew and dead organic matter decomposed. Large variability in C cycle responses arose from several factors, including type of insect or pathogen, time since disturbance, number of trees affected, and capacity of remaining vegetation to increase growth rates following outbreak. We identified significant knowledge gaps, including limited understanding of carbon cycle impacts among different biotic disturbance types (particularly pathogens), their impacts at landscape and regional scales, and limited capacity to predict disturbance events and their consequences for carbon cycling. We conclude that biotic disturbances can have major impacts on forest C stocks and fluxes and can be large enough to affect regional C cycling. However, additional research is needed to reduce the uncertainties associated with quantifying biotic disturbance effects on the North American C budget.