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Croplands are highly productive ecosystems that contribute to land–atmosphere exchange of carbon, energy, and water during their short growing seasons. We evaluated and compared net ecosystem exchange (NEE), latent heat flux (LE), and sensible heat flux (H) simulated by a suite of ecosystem models at five agricultural eddy covariance flux tower sites in the central United States as part of the North American Carbon Program Site Synthesis project. Most of the models overestimated H and underestimated LE during the growing season, leading to overall higher Bowen ratios compared to the observations. Most models systematically under predicted NEE, especially at rain-fed sites. Certain crop-specific models that were developed considering the high productivity and associated physiological changes in specific crops better predicted the NEE and LE at both rain-fed and irrigated sites. Models with specific parameterization for different crops better simulated the inter-annual variability of NEE for maize-soybean rotation compared to those models with a single generic crop type. Stratification according to basic model formulation and phenological methodology did not explain significant variation in model performance across these sites and crops. The under prediction of NEE and LE and over prediction of H by most of the models suggests that models developed and parameterized for natural ecosystems cannot accurately predict the more robust physiology of highly bred and intensively managed crop ecosystems. When coupled in Earth System Models, it is likely that the excessive physiological stress simulated in many land surface component models leads to overestimation of temperature and atmospheric boundary layer depth, and underestimation of humidity and CO2 seasonal uptake over agricultural regions.

Efforts to control climate change require the stabilization of atmospheric CO 2 concentrations. This can only be achieved through a drastic reduction of global CO 2 emissions. Yet fossil fuel emissions increased by 29% between 2000 and 2008, in conjunction with increased contributions from emerging economies, from the production and international trade of goods and services, and from the use of coal as a fuel source. In contrast, emissions from land-use changes were nearly constant. Between 1959 and 2008, 43% of each year's CO 2 emissions remained in the atmosphere on average; the rest was absorbed by carbon sinks on land and in the oceans. In the past 50 years, the fraction of CO 2 emissions that remains in the atmosphere each year has likely increased, from about 40% to 45%, and models suggest that this trend was caused by a decrease in the uptake of CO 2 by the carbon sinks in response to climate change and variability. Changes in the CO 2 sinks are highly uncertain, but they could have a significant influence on future atmospheric CO 2 levels. It is therefore crucial to reduce the uncertainties. © 2009 Macmillan Publishers Limited. All rights reserved.

With representation of the global carbon cycle becoming increasingly complex in climate models, it is important to develop ways to quantitatively evaluate model performance against in situ and remote sensing observations. Here we present a systematic framework, the Carbon-LAnd Model Intercomparison Project (C-LAMP), for assessing terrestrial biogeochemistry models coupled to climate models using observations that span a wide range of temporal and spatial scales. As an example of the value of such comparisons, we used this framework to evaluate two biogeochemistry models that are integrated within the Community Climate System Model (CCSM) -Carnegie-Ames-Stanford Approach′ (CASA′) and carbon-nitrogen (CN). Both models underestimated the magnitude of net carbon uptake during the growing season in temperate and boreal forest ecosystems, based on comparison with atmospheric CO 2 measurements and eddy covariance measurements of net ecosystem exchange. Comparison with MODerate Resolution Imaging Spectroradiometer (MODIS) measurements show that this low bias in model fluxes was caused, at least in part, by 1-3 month delays in the timing of maximum leaf area. In the tropics, the models overestimated carbon storage in woody biomass based on comparison with datasets from the Amazon. Reducing this model bias will probably weaken the sensitivity of terrestrial carbon fluxes to both atmospheric CO2 and climate. Global carbon sinks during the 1990s differed by a factor of two (2.4PgCyr-1 for CASA′ vs. 1.2PgCyr-1 for CN), with fluxes from both models compatible with the atmospheric budget given uncertainties in other terms. The models captured some of the timing of interannual global terrestrial carbon exchange during 1988-2004 based on comparison with atmospheric inversion results from TRANSCOM (r =0.66 for CASA′ and r =0.73 for CN). Adding (CASA′) or improving (CN) the representation of deforestation fires may further increase agreement with the atmospheric record. Information from C-LAMP has enhanced model performance within CCSM and serves as a benchmark for future development. We propose that an open source, community-wide platform for model-data intercomparison is needed to speed model development and to strengthen ties between modeling and measurement communities. Important next steps include the design and analysis of land use change simulations (in both uncoupled and coupled modes), and the entrainment of additional ecological and earth system observations. Model results from C-LAMP are publicly available on the Earth System Grid. © 2009 Blackwell Publishing Ltd.

This paper describes the Carbon-Land Model Intercomparison Project (C-LAMP) being carried out through a collaboration between the Community Climate System Model (CCSM) Biogeochemistry Working Group, a DOE SciDAC-2 project, and the DOE Program for Climate Model Diagnosis and Intercomparison (PCMDI). The goal of the project is to intercompare terrestrial biogeochemistry models running within the CCSM framework to determine the best set of processes to include in future versions of CCSM. As a part of the project, observational datasets are being collected and used to score the scientific performance of these models following a well-defined set of metrics. In addition, metadata standards for terrestrial biosphere models are being developed to support archival and distribution of the C-LAMP model output via the Earth System Grid (ESG). Progress toward completion of this project and preliminary results from the first set of experiments are reported. © 2007 IOP Publishing Ltd.

Synthesis of results from several Arctic and boreal research programmes provides evidence for the strong role of high-latitude ecosystems in the climate system. Average surface air temperature has increased 0.3 °C per decade during the twentieth century in the western North American Arctic and boreal forest zones. Precipitation has also increased, but changes in soil moisture are uncertain. Disturbance rates have increased in the boreal forest; for example, there has been a doubling of the area burned in North America in the past 20 years. The disturbance regime in tundra may not have changed. Tundra has a 3-6-fold higher winter albedo than boreal forest, but summer albedo and energy partitioning differ more strongly among ecosystems within either tundra or boreal forest than between these two biomes. This indicates a need to improve our understanding of vegetation dynamics within, as well as between, biomes. If regional surface warming were to continue, changes in albedo and energy absorption would likely act as a positive feedback to regional warming due to earlier melting of snow and, over the long term, the northward movement of treeline. Surface drying and a change in dominance from mosses to vascular plants would also enhance sensible heat flux and regional warming in tundra. In the boreal forest of western North America, deciduous forests have twice the albedo of conifer forests in both winter and summer, 50-80% higher evapotranspiration, and therefore only 30-50% of the sensible heat flux of conifers in summer. Therefore, a warming-induced increase in fire frequency that increased the proportion of deciduous forests in the landscape, would act as a negative feedback to regional warming. Changes in thermokarst and the aerial extent of wetlands, lakes, and ponds would alter high-latitude methane flux. There is currently a wide discrepancy among estimates of the size and direction of CO2 flux between high-latitude ecosystems and the atmosphere. These discrepancies relate more strongly to the approach and assumptions for extrapolation than to inconsistencies in the underlying data. Inverse modelling from atmospheric CO2 concentrations suggests that high latitudes are neutral or net sinks for atmospheric CO2 , whereas field measurements suggest that high latitudes are neutral or a net CO2 source. Both approaches rely on assumptions that are difficult to verify. The most parsimonious explanation of the available data is that drying in tundra and disturbance in boreal forest enhance CO2 efflux. Nevertheless, many areas of both tundra and boreal forests remain net sinks due to regional variation in climate and local variation in topographically determined soil moisture. Improved understanding of the role of high-latitude ecosystems in the climate system requires a concerted research effort that focuses on geographical variation in the processes controlling land-atmosphere exchange, species composition, and ecosystem structure. Future studies must be conducted over a long enough time-period to detect and quantify ecosystem feedbacks.

Understanding terrestrial carbon metabolism is critical because terrestrial ecosystems play a major role in the global carbon cycle. Furthermore, humans have severely disrupted the carbon cycle in ways that will alter the climate system and directly affect terrestrial metabolism. Changes in terrestrial metabolism may well be as important an indicator of global change as the changing temperature signal. Improving our understanding of the carbon cycle at various spatial and temporal scales will require the integration of multiple, complementary and independent methods that are used by different research communities. Tools such as air sampling networks, inverse numerical methods, and satellite data (top-down approaches) allow us to study the strength and location of the global- and continental-scale carbon sources and sinks. Bottom-up studies provide estimates of carbon fluxes at finer spatial scales and examine the mechanisms that control fluxes at the ecosystem, landscape, and regional scales. Bottom-up approaches include comparative and process studies (for example, ecosystem manipulative experiments) that provide the necessary mechanistic information to develop and validate terrestrial biospheric models. An iteration and reiteration of top-down and bottom-up approaches will be necessary to help constrain measurements at various scales. We propose a major international effort to coordinate and lead research programs of global scope of the carbon cycle.

Understanding terrestrial carbon metabolism is critical because terrestrial ecosystems play a major role in the global carbon cycle. Furthermore, humans have severely disrupted the carbon cycle in ways that will alter the climate system and directly affect terrestrial metabolism. Changes in terrestrial metabolism may well be as important an indicator of global change as the changing temperature signal. Improving our understanding of the carbon cycle at various spatial and temporal scales will require the integration of multiple, complementary and independent methods that are used by different research communities. Tools such as air sampling networks, inverse numerical methods, and satellite data (top-down approaches) allow us to study the strength and location of the global- and continental-scale carbon sources and sinks. Bottom-up studies provide estimates of carbon fluxes at finer spatial scales and examine the mechanisms that control fluxes at the ecosystem, landscape, and regional scales. Bottom-up approaches include comparative and process studies (for example, ecosystem manipulative experiments) that provide the necessary mechanistic information to develop and validate terrestrial biospheric models. An iteration and reiteration of top-down and bottom-up approaches will be necessary to help constrain measurements at various scales. We propose a major international effort to coordinate and lead research programs of global scope of the carbon cycle.

Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO 2 and mitigating global climate change., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Remote sensing optical sensors onboard operational satellites cannot have high spectral, spatial and temporal resolutions simultaneously. In addition, clouds and aerosols can adversely affect the signal contaminating the land surface observations. We present a HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to combine multispectral images of different sensors to reduce noise and produce monthly gap free high resolution (30 m) observations over land. Our approach uses images from the Landsat (30 m spatial resolution and 16 day revisit cycle) and the MODIS missions, both from Terra and Aqua platforms (500 m spatial resolution and daily revisit cycle). We implement a bias-aware Kalman filter method in the Google Earth Engine (GEE) platform to obtain fused images at the Landsat spatial-resolution. The added bias correction in the Kalman filter estimates accounts for the fact that both model and observation errors are temporally auto-correlated and may have a non-zero mean. This approach also enables reliable estimation of the uncertainty associated with the final reflectance estimates, allowing for error propagation analyses in higher level remote sensing products. Quantitative and qualitative evaluations of the generated products through comparison with other state-of-the-art methods confirm the validity of the approach, and open the door to operational applications at enhanced spatio-temporal resolutions at broad continental scales., (© 2020 The Authors.)

Plant traits are both responsive to local climate and strong predictors of primary productivity. We hypothesized that future climate change might promote a shift in global plant traits resulting in changes in Gross Primary Productivity (GPP). We characterized the relationship between key plant traits, namely Specific Leaf Area (SLA), height, and seed mass, and local climate and primary productivity. We found that by 2070, tropical and arid ecosystems will be more suitable for plants with relatively lower canopy height, SLA and seed mass, while far northern latitudes will favor woody and taller plants than at present. Using a network of tower eddy covariance CO2 flux measurements and the extrapolated plant trait maps, we estimated the global distribution of annual GPP under current and projected future plant community distribution. We predict that annual GPP in northern biomes (≥45 °N) will increase by 31% (+8.1 ± 0.5 Pg C), but this will be offset by a 17.9% GPP decline in the tropics (-11.8 ± 0.84 Pg C). These findings suggest that regional climate changes will affect plant trait distributions, which may in turn affect global productivity patterns.

[This corrects the article DOI: 10.1371/journal.pone.0184466.].

Gross primary production (GPP) is the Earth's largest carbon flux into the terrestrial biosphere and plays a critical role in regulating atmospheric chemistry and global climate. The Moderate Resolution Imaging Spectrometer (MODIS)-MOD17 data product is a widely used remote sensing-based model that provides global estimates of spatiotemporal trends in GPP. When the MOD17 algorithm is applied to regional scale heterogeneous landscapes, input data from coarse resolution land cover and climate products may increase uncertainty in GPP estimates, especially in high productivity tropical ecosystems. We examined the influence of using locally specific land cover and high-resolution local climate input data on MOD17 estimates of GPP for the State of Hawaii, a heterogeneous and discontinuous tropical landscape. Replacing the global land cover data input product (MOD12Q1) with Hawaii-specific land cover data reduced statewide GPP estimates by ~8%, primarily because the Hawaii-specific land cover map had less vegetated land area compared to the global land cover product. Replacing coarse resolution GMAO climate data with Hawaii-specific high-resolution climate data also reduced statewide GPP estimates by ~8% because of the higher spatial variability of photosynthetically active radiation (PAR) in the Hawaii-specific climate data. The combined use of both Hawaii-specific land cover and high-resolution Hawaii climate data inputs reduced statewide GPP by ~16%, suggesting equal and independent influence on MOD17 GPP estimates. Our sensitivity analyses within a heterogeneous tropical landscape suggest that refined global land cover and climate data sets may contribute to an enhanced MOD17 product at a variety of spatial scales.

Large-scale climate patterns control variability in the global carbon sink. In Europe, the North-Atlantic Oscillation (NAO) influences vegetation activity, however the East-Atlantic (EA) pattern is known to modulate NAO strength and location. Using observation-driven and modelled data sets, we show that multi-annual variability patterns of European Net Biome Productivity (NBP) are linked to anomalies in heat and water transport controlled by the NAO-EA interplay. Enhanced NBP occurs when NAO and EA are both in negative phase, associated with cool summers with wet soils which enhance photosynthesis. During anti-phase periods, NBP is reduced through distinct impacts of climate anomalies in photosynthesis and respiration. The predominance of anti-phase years in the early 2000s may explain the European-wide reduction of carbon uptake during this period, reported in previous studies. Results show that improving the capability of simulating atmospheric circulation patterns may better constrain regional carbon sink variability in coupled carbon-climate models.

Recent studies showed that anomalous dry conditions and limited moisture supply roughly between 1998 and 2008, especially in the Southern Hemisphere, led to reduced vegetation productivity and ceased growth in land evapotranspiration (ET). However, natural variability of Earth's climate system can degrade capabilities for identifying climate trends. Here we produced a long-term (1982-2013) remote sensing based land ET record and investigated multidecadal changes in global ET and underlying causes. The ET record shows a significant upward global trend of 0.88 mm yr(-2) (P < 0.001) over the 32-year period, mainly driven by vegetation greening (0.018% per year; P < 0.001) and rising atmosphere moisture demand (0.75 mm yr(-2); P = 0.016). Our results indicate that reduced ET growth between 1998 and 2008 was an episodic phenomenon, with subsequent recovery of the ET growth rate after 2008. Terrestrial precipitation also shows a positive trend of 0.66 mm yr(-2) (P = 0.08) over the same period consistent with expected water cycle intensification, but this trend is lower than coincident increases in evaporative demand and ET, implying a possibility of cumulative water supply constraint to ET. Continuation of these trends will likely exacerbate regional drought-induced disturbances, especially during regional dry climate phases associated with strong El Niño events.

Ongoing climate change can alter conditions for plant growth, in turn affecting ecological and social systems. While there have been considerable advances in understanding the physical aspects of climate change, comprehensive analyses integrating climate, biological, and social sciences are less common. Here we use climate projections under alternative mitigation scenarios to show how changes in environmental variables that limit plant growth could impact ecosystems and people. We show that although the global mean number of days above freezing will increase by up to 7% by 2100 under "business as usual" (representative concentration pathway [RCP] 8.5), suitable growing days will actually decrease globally by up to 11% when other climatic variables that limit plant growth are considered (i.e., temperature, water availability, and solar radiation). Areas in Russia, China, and Canada are projected to gain suitable plant growing days, but the rest of the world will experience losses. Notably, tropical areas could lose up to 200 suitable plant growing days per year. These changes will impact most of the world's terrestrial ecosystems, potentially triggering climate feedbacks. Human populations will also be affected, with up to ~2,100 million of the poorest people in the world (~30% of the world's population) highly vulnerable to changes in the supply of plant-related goods and services. These impacts will be spatially variable, indicating regions where adaptations will be necessary. Changes in suitable plant growing days are projected to be less severe under strong and moderate mitigation scenarios (i.e., RCP 2.6 and RCP 4.5), underscoring the importance of reducing emissions to avoid such disproportionate impacts on ecosystems and people.

Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The land and ocean act as a sink for fossil-fuel emissions, thereby slowing the rise of atmospheric carbon dioxide concentrations. Although the uptake of carbon by oceanic and terrestrial processes has kept pace with accelerating carbon dioxide emissions until now, atmospheric carbon dioxide concentrations exhibit a large variability on interannual timescales, considered to be driven primarily by terrestrial ecosystem processes dominated by tropical rainforests. We use a terrestrial biogeochemical model, atmospheric carbon dioxide inversion and global carbon budget accounting methods to investigate the evolution of the terrestrial carbon sink over the past 30 years, with a focus on the underlying mechanisms responsible for the exceptionally large land carbon sink reported in 2011 (ref. 2). Here we show that our three terrestrial carbon sink estimates are in good agreement and support the finding of a 2011 record land carbon sink. Surprisingly, we find that the global carbon sink anomaly was driven by growth of semi-arid vegetation in the Southern Hemisphere, with almost 60 per cent of carbon uptake attributed to Australian ecosystems, where prevalent La Niña conditions caused up to six consecutive seasons of increased precipitation. In addition, since 1981, a six per cent expansion of vegetation cover over Australia was associated with a fourfold increase in the sensitivity of continental net carbon uptake to precipitation. Our findings suggest that the higher turnover rates of carbon pools in semi-arid biomes are an increasingly important driver of global carbon cycle inter-annual variability and that tropical rainforests may become less relevant drivers in the future. More research is needed to identify to what extent the carbon stocks accumulated during wet years are vulnerable to rapid decomposition or loss through fire in subsequent years.

Many protected areas may not be adequately safeguarding biodiversity from human activities on surrounding lands and global change. The magnitude of such change agents and the sensitivity of ecosystems to these agents vary among protected areas. Thus, there is a need to assess vulnerability across networks of protected areas to determine those most at risk and to lay the basis for developing effective adaptation strategies. We conducted an assessment of exposure of U.S. National Parks to climate and land use change and consequences for vegetation communities. We first defined park protected-area centered ecosystems (PACEs) based on ecological principles. We then drew on existing land use, invasive species, climate, and biome data sets and models to quantify exposure of PACEs from 1900 through 2100. Most PACEs experienced substantial change over the 20th century (> 740% average increase in housing density since 1940, 13% of vascular plants are presently nonnative, temperature increase of 1 degree C/100 yr since 1895 in 80% of PACEs), and projections suggest that many of these trends will continue at similar or increasingly greater rates (255% increase in housing density by 2100, temperature increase of 2.5 degrees-4.5 degrees C/100 yr, 30% of PACE areas may lose their current biomes by 2030). In the coming century, housing densities are projected to increase in PACEs at about 82% of the rate of since 1940. The rate of climate warming in the coming century is projected to be 2.5-5.8 times higher than that measured in the past century. Underlying these averages, exposure of individual park PACEs to change agents differ in important ways. For example, parks such as Great Smoky Mountains exhibit high land use and low climate exposure, others such as Great Sand Dunes exhibit low land use and high climate exposure, and a few such as Point Reyes exhibit high exposure on both axes. The cumulative and synergistic effects of such changes in land use, invasives, and climate are expected to dramatically impact ecosystem function and biodiversity in national parks. These results are foundational to developing effective adaptation strategies and suggest policies to better safeguard parks under broad-scale environmental change.

Nitrogen (N) and phosphorus (P) availability regulate plant productivity throughout the terrestrial biosphere, influencing the patterns and magnitude of net primary production (NPP) by land plants both now and into the future. These nutrients enter ecosystems via geologic and atmospheric pathways and are recycled to varying degrees through the plant-soil-microbe system via organic matter decay processes. However, the proportion of global NPP that can be attributed to new nutrient inputs versus recycled nutrients is unresolved, as are the large-scale patterns of variation across terrestrial ecosystems. Here, we combined satellite imagery, biogeochemical modeling, and empirical observations to identify previously unrecognized patterns of new versus recycled nutrient (N and P) productivity on land. Our analysis points to tropical forests as a hotspot of new NPP fueled by new N (accounting for 45% of total new NPP globally), much higher than previous estimates from temperate and high-latitude regions. The large fraction of tropical forest NPP resulting from new N is driven by the high capacity for N fixation, although this varies considerably within this diverse biome; N deposition explains a much smaller proportion of new NPP. By contrast, the contribution of new N to primary productivity is lower outside the tropics, and worldwide, new P inputs are uniformly low relative to plant demands. These results imply that new N inputs have the greatest capacity to fuel additional NPP by terrestrial plants, whereas low P availability may ultimately constrain NPP across much of the terrestrial biosphere.

United States (U.S.) energy policy includes an expectation that bioenergy will be a substantial future energy source. In particular, the Energy Independence and Security Act of 2007 (EISA) aims to increase annual U.S. biofuel (secondary bioenergy) production by more than 3-fold, from 40 to 136 billion liters ethanol, which implies an even larger increase in biomass demand (primary energy), from roughly 2.9 to 7.4 EJ yr(-1). However, our understanding of many of the factors used to establish such energy targets is far from complete, introducing significgant uncertainty into the feasibility of current estimates of bioenergy potential. Here, we utilized satellite-derived net primary productivity (NPP) data-measured for every 1 km(2) of the 7.2 million km(2) of vegetated land in the conterminous U.S.-to estimate primary bioenergy potential (PBP). Our results indicate that PBP of the conterminous U.S. ranges from roughly 5.9 to 22.2 EJ yr(-1), depending on land use. The low end of this range represents the potential when harvesting residues only, while the high end would require an annual biomass harvest over an area more than three times current U.S. agricultural extent. While EISA energy targets are theoretically achievable, we show that meeting these targets utilizing current technology would require either an 80% displacement of current crop harvest or the conversion of 60% of rangeland productivity. Accordingly, realistically constrained estimates of bioenergy potential are critical for effective incorporation of bioenergy into the national energy portfolio.

Terrestrial net primary production (NPP) quantifies the amount of atmospheric carbon fixed by plants and accumulated as biomass. Previous studies have shown that climate constraints were relaxing with increasing temperature and solar radiation, allowing an upward trend in NPP from 1982 through 1999. The past decade (2000 to 2009) has been the warmest since instrumental measurements began, which could imply continued increases in NPP; however, our estimates suggest a reduction in the global NPP of 0.55 petagrams of carbon. Large-scale droughts have reduced regional NPP, and a drying trend in the Southern Hemisphere has decreased NPP in that area, counteracting the increased NPP over the Northern Hemisphere. A continued decline in NPP would not only weaken the terrestrial carbon sink, but it would also intensify future competition between food demand and proposed biofuel production.

Climate change has altered the environment in which forests grow, and climate change models predict more severe alterations to come. Forests have already responded to these changes, and the future temperature and precipitation scenarios are of foremost concern, especially in the mountainous western United States, where forests occur in the dry environments that interface with grasslands. The objective of this study was to understand the trade-offs between temperature and water controls on these forested sites in the context of available climate projections. Three temperature and precipitation scenarios from IPCC AR4 AOGCMs ranging in precipitation levels were input to the process model Biome-BGC for key forested sites in the northern U.S. Rocky Mountains. Despite the omission of natural and human-caused disturbances in our simulations, our results show consequential effects from these conservative future temperature and precipitation scenarios. According to these projections, if future precipitation and temperatures are similar to or drier than the dry scenario depicted here, high-elevation forests on both the drier and wetter sites, which have in the absence of disturbance accumulated carbon, will reduce their carbon accumulation. Under the marginally drier climate projections, most forests became carbon sources by the end of the simulation horizon (2089). Under all three scenarios, growing season lengthened, the number of days with snow on the ground decreased, peak snow occurred earlier, and water stress increased through the projection horizon (1950-2089) for all sites, which represent the temperature and precipitation spectrum of forests in this region. The quantity, form, and timing of precipitation ultimately drive the carbon accumulation trajectory of forests in this region.

Regional mountain block recharge (MBR) is a key component of alluvial basin aquifer systems typical of the western United States. Yet neither water scientists nor resource managers have a commonly available and reasonably invoked quantitative method to constrain MBR rates. Recent advances in landscape-scale ecohydrologic process modeling offer the possibility that meteorological data and land surface physical and vegetative conditions can be used to generate estimates of MBR. A water balance was generated for a temperate 24,600-ha mountain watershed, elevation 1565 to 3207 m, using the ecosystem process model Biome-BGC (BioGeochemical Cycles) (Running and Hunt 1993). Input data included remotely sensed landscape information and climate data generated with the Mountain Climate Simulator (MT-CLIM) (Running et al. 1987). Estimated mean annual MBR flux into the crystalline bedrock terrain is 99,000 m(3) /d, or approximately 19% of annual precipitation for the 2003 water year. Controls on MBR predictions include evapotranspiration (radiation limited in wet years and moisture limited in dry years), soil properties, vegetative ecotones (significant at lower elevations), and snowmelt (dominant recharge process). The ecohydrologic model is also used to investigate how climatic and vegetative controls influence recharge dynamics within three elevation zones. The ecohydrologic model proves useful for investigating controls on recharge to mountain blocks as a function of climate and vegetation. Future efforts will need to investigate the uncertainty in the modeled water balance by incorporating an advanced understanding of mountain recharge processes, an ability to simulate those processes at varying scales, and independent approaches to calibrating MBR estimates., (Copyright © 2009 The Author(s). Journal compilation © 2009 National Ground Water Association.)

Despite early speculation to the contrary, all tropical forests studied to date display seasonal variations in the presence of new leaves, flowers, and fruits. Past studies were focused on the timing of phenological events and their cues but not on the accompanying changes in leaf area that regulate vegetation-atmosphere exchanges of energy, momentum, and mass. Here we report, from analysis of 5 years of recent satellite data, seasonal swings in green leaf area of approximately 25% in a majority of the Amazon rainforests. This seasonal cycle is timed to the seasonality of solar radiation in a manner that is suggestive of anticipatory and opportunistic patterns of net leaf flushing during the early to mid part of the light-rich dry season and net leaf abscission during the cloudy wet season. These seasonal swings in leaf area may be critical to initiation of the transition from dry to wet season, seasonal carbon balance between photosynthetic gains and respiratory losses, and litterfall nutrient cycling in moist tropical forests.

The timing, location, and magnitude of major disturbance events are currently major uncertainties in the global carbon cycle. Accurate information on the location, spatial extent, and duration of disturbance at the continental scale is needed to evaluate the ecosystem impacts of land cover changes due to wildfire, insect epidemics, flooding, climate change, and human-triggered land use. This paper describes an algorithm developed to serve as an automated, economical, systematic disturbance detection index for global application using Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua Land Surface Temperature (LST) and Terra/MODIS Enhanced Vegetation Index (EVI) data from 2003 to 2004. The algorithm is based on the consistent radiometric relationship between LST and EVI computed on a pixel-by-pixel basis. We used annual maximum composite LST data to detect fundamental changes in land-surface energy partitioning, while avoiding the high natural variability associated with tracking LST at daily, weekly, or seasonal time frames. Verification of potential disturbance events from our algorithm was carried out by demonstration of close association with independently confirmed, well-documented historical wildfire events throughout the study domain. We also examined the response of the disturbance index to irrigation by comparing a heavily irrigated poplar tree farm to the adjacent semiarid vegetation. Anomalous disturbance results were further examined by association with precipitation variability across areas of the study domain known for large interannual vegetation variability. The results illustrate that our algorithm is capable of detecting the location and spatial extent of wildfire with precision, is sensitive to the incremental process of recovery of disturbed landscapes, and shows strong sensitivity to irrigation. Disturbance detection in areas with high interannual variability of precipitation will benefit from a multiyear data set to better separate natural variability from true disturbance.

We used a terrestrial ecosystem process model, BIOME-BGC, to investigate historical climate change and fire disturbance effects on regional carbon and water budgets within a 357,500 km(2) portion of the Canadian boreal forest. Historical patterns of increasing atmospheric CO2, climate change, and regional fire activity were used as model drivers to evaluate the relative effects of these impacts to spatial patterns and temporal trends in forest net primary production (NPP) and evapotranspiration (ET). Historical trends of increasing atmospheric CO2 resulted in overall 13% and 5% increases in annual NPP and ET from 1994 to 1996, respectively. NPP was found to be relatively sensitive to changes in air temperature (T(a)), while ET was more sensitive to precipitation (P) change within the ranges of observed climate variability (e.g., +/-2 degrees C for T(a) and +/-20% for P). In addition, the potential effect of climate change related warming on NPP is exacerbated or offset depending on whether these changes are accompanied by respective decreases or increases in precipitation. Historical fire activity generally resulted in reductions of both NPP and ET, which consumed an average of approximately 6% of annual NPP from 1959 to 1996. Areas currently occupied by dry conifer forests were found to be subject to more frequent fire activity, which consumed approximately 8% of annual NPP. The results of this study show that the North American boreal ecosystem is sensitive to historical patterns of increasing atmospheric CO2, climate change and regional fire activity. The relative impacts of these disturbances on NPP and ET interact in complex ways and are spatially variable depending on regional land cover and climate gradients.

Turf grasses are ubiquitous in the urban landscape of the United States and are often associated with various types of environmental impacts, especially on water resources, yet there have been limited efforts to quantify their total surface and ecosystem functioning, such as their total impact on the continental water budget and potential net ecosystem exchange (NEE). In this study, relating turf grass area to an estimate of fractional impervious surface area, it was calculated that potentially 163,800 km2 (+/- 35,850 km2) of land are cultivated with turf grasses in the continental United States, an area three times larger than that of any irrigated crop. Using the Biome-BGC ecosystem process model, the growth of warm-season and cool-season turf grasses was modeled at a number of sites across the 48 conterminous states under different management scenarios, simulating potential carbon and water fluxes as if the entire turf surface was to be managed like a well-maintained lawn. The results indicate that well-watered and fertilized turf grasses act as a carbon sink. The potential NEE that could derive from the total surface potentially under turf (up to 17 Tg C/yr with the simulated scenarios) would require up to 695 to 900 liters of water per person per day, depending on the modeled water irrigation practices, suggesting that outdoor water conservation practices such as xeriscaping and irrigation with recycled waste-water may need to be extended as many municipalities continue to face increasing pressures on freshwater.

The relationship between photosynthesis and accumulated cold degree days (CDD) over the late growing season was examined at the shoot, ecosystem and landscape scales in a boreal cutover in eastern Canada predominated by black spruce (Picea mariana Mill. BSP), lowbush blueberry (Vaccinium angustifolium Ait.) and sheep laurel (Kalmia angustifolia L.). We calculated CDD as the sum of minimum daily temperatures below a 5 degrees C threshold. Light-saturated photosynthesis at the shoot level (A(max)) of black spruce and V. angustifolium decreased steadily with increasing CDD once temperatures below the CDD threshold value became frequent in mid-September, whereas K. angustifolia showed a more irregular pattern. Tissue acclimation played an important role in the decrease in A(max) as the season progressed, but only V. angustifolium showed decreasing foliar nitrogen concentrations. Based on eddy covariance flux tower data, maximum daily gross primary productivity (GPP(max)-tower) at the ecosystem level was more strongly related to CDD (r(2) = 0.59) than was maximum daily net ecosystem exchange (r(2) = 0.32). The GPP(max) was likely influenced by both tissue acclimation and the direct effects of changing temperatures and irradiances on physiological rates. Mean daily GPP, calculated for consecutive 8-day periods for a 25 km(2) area around the tower by the MODIS MOD17A2 Collection 4 satellite algorithm (GPP- MODIS), decreased more rapidly with increasing CDD than did GPP(max)-tower. Although GPP-MODIS was closely correlated with mean daily GPP from the tower (GPP(daily)-tower, r(2) = 0.95) over the late growing season, the former was about twice as high. Although MODIS estimates of air temperature closely tracked the ground data, the maximum light-use efficiency parameter used by the MODIS algorithm was much higher than that indicated by the tower measurements. There was a 3% decline in GPP(max)-tower with an increase of 10 CDD, corresponding to the percent decline in branch-level A(max) of black spruce and V. angustifolium.

Some saplings and shrubs growing in the understory of temperate deciduous forests extend their periods of leaf display beyond that of the overstory, resulting in periods when understory radiation, and hence productivity, are not limited by the overstory canopy. To assess the importance of the duration of leaf display on the productivity of understory and overstory trees of deciduous forests in the north eastern United States, we applied the simulation model, BIOME-BGC with climate data for Hubbard Brook Experimental Forest, New Hampshire, USA and mean ecophysiological data for species of deciduous, temperate forests. Extension of the overstory leaf display period increased overstory leaf area index (LAI) by only 3 to 4% and productivity by only 2 to 4%. In contrast, extending the growing season of the understory relative to the overstory by one week in both spring and fall, increased understory LAI by 35% and productivity by 32%. A 2-week extension of the growing period in both spring and fall increased understory LAI by 53% and productivity by 55%.

Recent climatic changes have enhanced plant growth in northern mid-latitudes and high latitudes. However, a comprehensive analysis of the impact of global climatic changes on vegetation productivity has not before been expressed in the context of variable limiting factors to plant growth. We present a global investigation of vegetation responses to climatic changes by analyzing 18 years (1982 to 1999) of both climatic data and satellite observations of vegetation activity. Our results indicate that global changes in climate have eased several critical climatic constraints to plant growth, such that net primary production increased 6% (3.4 petagrams of carbon over 18 years) globally. The largest increase was in tropical ecosystems. Amazon rain forests accounted for 42% of the global increase in net primary production, owing mainly to decreased cloud cover and the resulting increase in solar radiation.

Our paper documents that land-use change impacts regional and global climate through the surface-energy budget, as well as through the carbon cycle. The surface-energy budget effects may be more important than the carbon-cycle effects. However, land-use impacts on climate cannot be adequately quantified with the usual metric of 'global warming potential'. A new metric is needed to quantify the human disturbance of the Earth's surface-energy budget. This 'regional climate change potential' could offer a new metric for developing a more inclusive climate protocol. This concept would also implicitly provide a mechanism to monitor potential local-scale environmental changes that could influence biodiversity.

The large magnitude of predicted warming at high latitudes and the potential feedback of ecosystems to atmospheric CO 2 concentrations make it important to quantify both warming and its effects on high-latitude carbon balance. We analysed long-term, daily surface meteorological records for 13 sites in Alaska and north-western Canada and an 82-y record of river ice breakup date for the Tanana River in interior Alaska. We found increases in winter and spring temperature extrema for all sites, with the greatest increases in spring minimum temperature, average 0.47 °C per 10 y, and a 0.7-day per 10 y advance in ice breakup on the Tanana River. We used the climate records to drive an ecosystem process model, BIOME&#95;BGC, to simulate the effects of climate change on the carbon and water balances of boreal forest ecosystems. The growing season has lengthened by an average of 2.6 days per 10 y with an advance in average leaf onset date of 1.10 days per 10 y. This advance in the start of the active growing season correlates positively with progressively earlier ice breakup on the Tanana River in interior Alaska. The advance in the start of the growing season resulted in a 20% increase in net primary production for both aspen (Populus tremuloides) and white spruce (Picea glauca) stands. Aspen had a greater mean increase in maintenance respiration than spruce, whereas spruce had a greater mean increase in evapotranspiration. Average decomposition rates also increased for both species. Both net primary production and decomposition are enhanced in our simulations, suggesting that productive forest types may not experience a significant shift in net carbon flux as a result of climate warming.

An ecological process model (BIOME-BGC) was used to assess boreal forest regional net primary production (NPP) and response to short-term, year-to-year weather fluctuations based on spatially explicit, land cover and biomass maps derived by radar remote sensing, as well as soil, terrain and daily weather information. Simulations were conducted at a 30-m spatial resolution, over a 1205 km(2) portion of the BOREAS Southern Study Area of central Saskatchewan, Canada, over a 3-year period (1994-1996). Simulations of NPP for the study region were spatially and temporally complex, averaging 2.2 (+/- 0.6), 1.8 (+/- 0.5) and 1.7 (+/- 0.5) Mg C ha(-1) year(-1) for 1994, 1995 and 1996, respectively. Spatial variability of NPP was strongly controlled by the amount of aboveground biomass, particularly photosynthetic leaf area, whereas biophysical differences between broadleaf deciduous and evergreen coniferous vegetation were of secondary importance. Simulations of NPP were strongly sensitive to year-to-year variations in seasonal weather patterns, which influenced the timing of spring thaw and deciduous bud-burst. Reductions in annual NPP of approximately 17 and 22% for 1995 and 1996, respectively, were attributed to 3- and 5-week delays in spring thaw relative to 1994. Boreal forest stands with greater proportions of deciduous vegetation were more sensitive to the timing of spring thaw than evergreen coniferous stands. Similar relationships were found by comparing simulated snow depth records with 10-year records of aboveground NPP measurements obtained from biomass harvest plots within the BOREAS region. These results highlight the importance of sub-grid scale land cover complexity in controlling boreal forest regional productivity, the dynamic response of the biome to short-term interannual climate variations, and the potential implications of climate change and other large-scale disturbances.

Recent research suggests that increases in growing-season length (GSL) in mid-northern latitudes may be partially responsible for increased forest growth and carbon sequestration. We used the BIOME-BGC ecosystem model to investigate the impacts of including a dynamically regulated GSL on simulated carbon and water balance over a historical 88-year record (1900-1987) for 12 sites in the eastern USA deciduous broadleaf forest. For individual sites, the predicted GSL regularly varied by more than 15 days. When grouped into three climatic zones, GSL variability was still large and rapid. There is a recent trend in colder, northern sites toward a longer GSL, but not in moderate and warm climates. The results show that, for all sites, prediction of a long GSL versus using the mean GSL increased net ecosystem production (NEP), gross primary production (GPP), and evapotranspiration (ET); conversely a short GSL is predicted to decrease these parameters. On an absolute basis, differences in GPP between the dynamic and mean GSL simulations were larger than the differences in NEP. As a percentage difference, though, NEP was much more sensitive to changes in GSL than were either GPP or ET. On average, a 1-day change in GSL changed NEP by 1.6%, GPP by 0.5%, and ET by 0.2%. Predictions of NEP and GPP in cold climates were more sensitive to changes in GSL than were predictions in warm climates. ET was not similarly sensitive. First, our results strongly agree with field measurements showing a high correlation between NEP and dates of spring growth, and second they suggest that persistent increases in GSL may lead to long-term increases in carbon storage.

Although there is a great deal of information concerning responses to increases in atmospheric CO 2 at the tissue and plant levels, there are substantially fewer studies that have investigated ecosystem-level responses in the context of integrated carbon, water, and nutrient cycles. Because our understanding of ecosystem responses to elevated CO 2 is incomplete, modeling is a tool that can be used to investigate the role of plant and soil interactions in the response of terrestrial ecosystems to elevated CO 2 . In this study, we analyze the responses of net primary production (NPP) to doubled CO 2 from 355 to 710 ppmv among three biogeochemistry models in the Vegetation/Ecosystem Modeling and Analysis Project (VEMAP): BIOME-BGC (BioGeochemical Cycles), Century, and the Terrestrial Ecosystem Model (TEM). For the conterminous United States, doubled atmospheric CO 2 causes NPP to increase by 5% in Century, 8% in TEM, and 11% in BIOME-BGC. Multiple regression analyses between the NPP response to doubled CO 2 and the mean annual temperature and annual precipitation of biomes or grid cells indicate that there are negative relationships between precipitation and the response of NPP to doubled CO 2 for all three models. In contrast, there are different relationships between temperature and the response of NPP to doubled CO 2 for the three models: there is a negative relationship in the responses of BIOME-BGC, no relationship in the responses of Century, and a positive relationship in the responses of TEM. In BIOME-BGC, the NPP response to doubled CO 2 is controlled by the change in transpiration associated with reduced leaf conductance to water vapor. This change affects soil water, then leaf area development and, finally, NPP. In Century, the response of NPP to doubled CO 2 is controlled by changes in decomposition rates associated with increased soil moisture that results from reduced evapotranspiration. This change affects nitrogen availability for plants, which influences NPP. In TEM, the NPP response to doubled CO 2 is controlled by increased carboxylation which is modified by canopy conductance and the degree to which nitrogen constraints cause down-regulation of photosynthesis. The implementation of these different mechanisms has consequences for the spatial pattern of NPP responses, and represents, in part, conceptual uncertainty about controls over NPP responses. Progress in reducing these uncertainties requires research focused at the ecosystem level to understand how interactions between the carbon, nitrogen, and water cycles influence the response of NPP to elevated atmospheric CO 2 .

A process-based, general ecosystem model (BIOME-BGC) was used to simulate daily gross primary production, maintenance and heterotrophic respiration, net primary production and net ecosystem carbon exchange of boreal aspen, jack pine and black spruce stands. Model simulations of daily net carbon exchange of the ecosystem (NEE) explained 51.7% (SE = 1.32 g C m(-2) day(-1)) of the variance in daily NEE derived from stand eddy flux measurements of CO(2) during 1994. Differences between measured and simulated results were attributed to several factors including difficulties associated with measuring nighttime CO(2) fluxes and model assumptions of site homogeneity. However, comparisons between simulations and field data improved markedly at coarser time-scales. Model simulations explained 66.1% (SE = 0.97 g C m(-2) day(-1)) of the variance in measured NEE when 5-day means of daily results were compared. Annual simulations of aboveground net primary production ranged from 0.6-2.4 Mg C ha(-1) year(-1) and were concurrent with results derived from tree increment core measurements and allometric equations. Model simulations showed that all of the sites were net sinks (0.1-4.1 Mg C ha(-1) year(-1)) of atmospheric carbon for 1994. Older conifer stands showed narrow margins between uptake of carbon by net photosynthesis and carbon release through respiration. Younger stands were more productive than older stands, primarily because of lower maintenance respiration costs. However, all sites appeared to be less productive than temperate forests. Productivity simulations were strongly linked to stand morphology and site conditions. Old jack pine and aspen stands showed decreased productivity in response to simulated low soil water contents near the end of the 1994 growing season. Compared with the aspen stand, the jack pine stand appeared better adapted to conserve soil water through lower daily evapotranspiration losses but also exhibited a narrower margin between daily net photosynthesis and respiration. Stands subjected to water stress during the growing season may exist on the edge between being annual sources or sinks for atmospheric carbon.

A mechanistic, biogeochemical succession model, FIRE-BGC, was used to investigate the role of fire on long-term landscape dynamics in northern Rocky Mountain coniferous forests of Glacier National Park, Montana, USA. FIRE-BGC is an individual-tree model-created by merging the gap-phase process-based model FIRESUM with the mechanistic ecosystem biogeochemical model FOREST-BGC-that has mixed spatial and temporal resolution in its simulation architecture. Ecological processes that act at a landscape level, such as fire and seed dispersal, are simulated annually from stand and topographic information. Stand-level processes, such as tree establishment, growth and mortality, organic matter accumulation and decomposition, and undergrowth plant dynamics are simulated both daily and annually. Tree growth is mechanistically modeled based on the ecosystem process approach of FOREST-BGC where carbon is fixed daily by forest canopy photosynthesis at the stand level. Carbon allocated to the tree stem at the end of the year generates the corresponding diameter and height growth. The model also explicitly simulates fire behavior and effects on landscape characteristics. We simulated the effects of fire on ecosystem characteristics of net primary productivity, evapotranspiration, standing crop biomass, nitrogen cycling and leaf area index over 200 years for the 50,000-ha McDonald Drainage in Glacier National Park. Results show increases in net primary productivity and available nitrogen when fires are included in the simulation. Standing crop biomass and evapotranspiration decrease under a fire regime. Shade-intolerant species dominate the landscape when fires are excluded. Model tree increment predictions compared well with field data.