Vegetation controls on northern high latitude snow‐albedo feedback: observations and CMIP5 model simulations

The snow‐masking effect of vegetation exerts strong control on albedo in northern high latitude ecosystems. Large‐scale changes in the distribution and stature of vegetation in this region will thus have important feedbacks to climate. The snow‐albedo feedback is controlled largely by the contrast between snow‐covered and snow‐free albedo (Δα), which influences predictions of future warming in coupled climate models, despite being poorly constrained at seasonal and century time scales. Here, we compare satellite observations and coupled climate model representations of albedo and tree cover for the boreal and Arctic region. Our analyses reveal consistent declines in albedo with increasing tree cover, occurring south of latitudinal tree line, that are poorly represented in coupled climate models. Observed relationships between albedo and tree cover differ substantially between snow‐covered and snow‐free periods, and among plant functional type. Tree cover in models varies widely but surprisingly does not correlate well with model albedo. Furthermore, our results demonstrate a relationship between tree cover and snow‐albedo feedback that may be used to accurately constrain high latitude albedo feedbacks in coupled climate models under current and future vegetation distributions.     

Coupling dynamic models of climate and vegetation

Numerous studies have underscored the importance of terrestrial ecosystems as an integral component of the Earth's climate system. This realization has already led to efforts to link simple equilibrium vegetation models with Atmospheric General Circulation Models through iterative coupling procedures. While these linked models have pointed to several possible climate–vegetation feedback mechanisms, they have been limited by two shortcomings: (i) they only consider the equilibrium response of vegetation to shifting climatic conditions and therefore cannot be used to explore transient interactions between climate and vegetation; and (ii) the representations of vegetation processes and land-atmosphere exchange processes are still treated by two separate models and, as a result, may contain physical or ecological inconsistencies. Here we present, as a proof concept, a more tightly integrated framework for simulating global climate and vegetation interactions. The prototype coupled model consists of the GENESIS (version 2) Atmospheric General Circulation Model and the IBIS (version 1) Dynamic Global Vegetation Model. The two models are directly coupled through a common treatment of land surface and ecophysiological processes, which is used to calculate the energy, water, carbon, and momentum fluxes between vegetation, soils, and the atmosphere. On one side of the interface, GENESIS simulates the physics and general circulation of the atmosphere. On the other side, IBIS predicts transient changes in the vegetation structure through changes in the carbon balance and competition among plants within terrestrial ecosystems. As an initial test of this modelling framework, we perform a 30 year simulation in which the coupled model is supplied with modern CO₂ concentrations, observed ocean temperatures, and modern insolation. In this exploratory study, we run the GENESIS atmospheric model at relatively coarse horizontal resolution (4.5° latitude by 7.5° longitude) and IBIS at moderate resolution (2° latitude by 2° longitude). We initialize the models with globally uniform climatic conditions and the modern distribution of potential vegetation cover. While the simulation does not fully reach equilibrium by the end of the run, several general features of the coupled model behaviour emerge. We compare the results of the coupled model against the observed patterns of modern climate. The model correctly simulates the basic zonal distribution of temperature and precipitation, but several important regional biases remain. In particular, there is a significant warm bias in the high northern latitudes, and cooler than observed conditions over the Himalayas, central South America, and north-central Africa. In terms of precipitation, the model simulates drier than observed conditions in much of South America, equatorial Africa and Indonesia, with wetter than observed conditions in northern Africa and China. Comparing the model results against observed patterns of vegetation cover shows that the general placement of forests and grasslands is roughly captured by the model. In addition, the model simulates a roughly correct separation of evergreen and deciduous forests in the tropical, temperate and boreal zones. However, the general patterns of global vegetation cover are only approximately correct: there are still significant regional biases in the simulation. In particular, forest cover is not simulated correctly in large portions of central Canada and southern South America, and grasslands extend too far into northern Africa. These preliminary results demonstrate the feasibility of coupling climate models with fully dynamic representations of the terrestrial biosphere. Continued development of fully coupled climate-vegetation models will facilitate the exploration of a broad range of global change issues, including the potential role of vegetation feedbacks within the climate system, and the impact of climate variability and transient climate change on the terrestrial biosphere.     

Quantifying the biologically possible range of steady-state soil and surface climates with climate model simulations

The terrestrial biosphere shapes the exchange fluxes of energy and mass at the land surface. The diversity of plant form and functioning can potentially result in a wide variety of possible climatic conditions at the land surface and in the soil, which in turn feed back to more or less suitable conditions for terrestrial productivity. Here, I use sensitivity simulations to vegetation form and functioning with a global climate model to quantify this possible range of steady-states (“PROSS”) of the surface energy-and mass balances. The surface energy-and water balances over land are associated with substantial sensitivity to vegetation parameters, with precipitation varying by more than a factor of 2, and evapotranspiration by a factor of 5. This range in biologically possible climatic conditions is associated with drastically different levels of vegetation productivity. Optimum conditions for maximum productivity are close to the simulated climate of present-day conditions. These results suggest the conclusions that (a) climate does not determine vegetation form and function, but merely constrains it, and (b) the emergent climatic conditions at the land surface seem to be close to optimal for the functioning of the terrestrial biosphere.     

Towards a coupled paradigm of NH3‐CO2 biosphere–atmosphere exchange modelling

Stomatal conductance, one of the major plant physiological controls within NH₃ biosphere–atmosphere exchange models, is commonly estimated from semi‐empirical multiplicative schemes or simple light‐ and temperature‐response functions. However, due to their inherent parameterization on meteorological proxy variables, instead of a direct measure of stomatal opening, they are unfit for the use in climate change scenarios and of limited value for interpreting field‐scale measurements. Alternatives based on H₂O flux measurements suffer from uncertainties in the partitioning of evapotranspiration at humid sites, as well as a potential decoupling of transpiration from stomatal opening in the presence of hygroscopic particles on leaf surfaces. We argue that these problems may be avoided by directly deriving stomatal conductance from CO₂ fluxes instead. We reanalysed a data set of NH₃ flux measurements based on CO₂‐derived stomatal conductance, confirming the hypothesis that the increasing relevance of stomatal exchange with the onset of vegetation activity caused a rapid decrease of observed NH₃ deposition velocities. Finally, we argue that developing more mechanistic representations of NH₃ biosphere–atmosphere exchange can be of great benefit in many applications. These range from model‐based flux partitioning, over deposition monitoring using low‐cost samplers and inferential modelling, to a direct response of NH₃ exchange to climate change.     

A dynamic global vegetation model for use with climate models: concepts and description of simulated vegetation dynamics

Changes in vegetation structure and biogeography due to climate change feedback to alter climate by changing fluxes of energy, moisture, and momentum between land and atmosphere. While the current class of land process models used with climate models parameterizes these fluxes in detail, these models prescribe surface vegetation and leaf area from data sets. In this paper, we describe an approach in which ecological concepts from a global vegetation dynamics model are added to the land component of a climate model to grow plants interactively. The vegetation dynamics model is the Lund–Potsdam–Jena (LPJ) dynamic global vegetation model. The land model is the National Center for Atmospheric Research (NCAR) Land Surface Model (LSM). Vegetation is defined in terms of plant functional types. Each plant functional type is represented by an individual plant with the average biomass, crown area, height, and stem diameter (trees only) of its population, by the number of individuals in the population, and by the fractional cover in the grid cell. Three time‐scales (minutes, days, and years) govern the processes. Energy fluxes, the hydrologic cycle, and carbon assimilation, core processes in LSM, occur at a 20 min time step. Instantaneous net assimilated carbon is accumulated annually to update vegetation once a year. This is carried out with the addition of establishment, resource competition, growth, mortality, and fire parameterizations from LPJ. The leaf area index is updated daily based on prevailing environmental conditions, but the maximum value depends on the annual vegetation dynamics. The coupling approach is successful. The model simulates global biogeography, net primary production, and dynamics of tundra, boreal forest, northern hardwood forest, tropical rainforest, and savanna ecosystems, which are consistent with observations. This suggests that the model can be used with a climate model to study biogeophysical feedbacks in the climate system related to vegetation dynamics.     

Vegetation-atmosphere interactions and their role in global warming during the latest Cretaceous

Forest vegetation has the ability to warm Recent climate by its effects on albedo and atmospheric water vapour, but the role of vegetation in warming climates of the geologic past is poorly understood. This study evaluates the role of forest vegetation in maintaining warm climates of the Late Cretaceous by (1) reconstructing global palaeovegetation for the latest Cretaceous (Maastrichtian); (2) modelling latest Cretaceous climate under unvegetated conditions and different distributions of palaeovegetation; and (3) comparing model output with a global database of palaeoclimatic indicators. Simulation of Maastrichtian climate with the land surface coded as bare soil produces high-latitude temperatures that are too cold to explain the documented palaeogeographic distribution of forest and woodland vegetation. In contrast, simulations that include forest vegetation at high latitudes show significantly warmer temperatures that are sufficient to explain the widespread geographic distribution of high-latitude deciduous forests. These warmer temperatures result from decreased albedo and feedbacks between the land surface and adjacent oceans. Prescribing a realistic distribution of palaeovegetation in model simulations produces the best agreement between simulated climate and the geologic record of palaeoclimatic indicators. Positive feedbacks between high-latitude forests, the atmosphere, and ocean contributed significantly to high-latitude warming during the latest Cretaceous, and imply that high-latitude forest vegetation was an important source of polar warmth during other warm periods of geologic history.     

生态系统模拟模型的研究进展

从四个方面概述了生态系统模拟模型的发展现状：1)个体及种群,种群动态模型主要模拟在一个生境中单个种的动、植物个体出生或发芽、成长及其死亡过程,还有种内竞争和种间相互作用,主要分析生境中生物之间的相互作用。主要概述了林窗模型和土壤一植物一大气系统模型。2)群落与生态系统,概述了生态系统生产力模型、生物地球化学循环模型及演替模型。主要模拟植物种类在整个生态系统发展过程中的变化,以及植被类型的转变和相关的生物地球化学循环过程的改变,从而反映生物群落对气候变化的响应。3)景观生态系统,景观动态研究包含了时空两个方面的动态变化,一般可分为随机景观模型和基于过程的景观模型。随机模型用于模拟群落格局在演替过程中的动态变化等,基于过程的景观模型深入研究组成景观的各生态系统的空间结构。4)生物圈与地球生态系统,基于过程的陆地生物地球化学模式被用来研究自然生态系统中碳和其它矿物营养物质的潜在通量和蓄积量,较为流行的模式有陆地生态系统模式TEM、CENTURY、法兰克福生物圈模式FBM、Biome-BGC、卡内基-埃姆斯-斯坦福方法CASA等。这些模式己被用于估算自然生态系统对大气CO2加倍及相关气候变化在区域和全球尺度的平衡响应。最后,结合实际工作展望了生态系统模拟模型在各方面的发展方向。     

Development and assessment of a coupled crop–climate model

It is well established that crop production is inherently vulnerable to variations in the weather and climate. More recently the influence of vegetation on the state of the atmosphere has been recognized. The seasonal growth of crops can influence the atmosphere and have local impacts on the weather, which in turn affects the rate of seasonal crop growth and development. Considering the coupled nature of the crop–climate system, and the fact that a significant proportion of land is devoted to the cultivation of crops, important interactions may be missed when studying crops and the climate system in isolation, particularly in the context of land use and climate change.
To represent the two-way interactions between seasonal crop growth and atmospheric variability, we integrate a crop model developed specifically to operate at large spatial scales (General Large Area Model for annual crops) into the land surface component of a global climate model (GCM; HadAM3). In the new coupled crop–climate model, the simulated environment (atmosphere and soil states) influences growth and development of the crop, while simultaneously the temporal variations in crop leaf area and height across its growing season alter the characteristics of the land surface that are important determinants of surface fluxes of heat and moisture, as well as other aspects of the land-surface hydrological cycle. The coupled model realistically simulates the seasonal growth of a summer annual crop in response to the GCM's simulated weather and climate. The model also reproduces the observed relationship between seasonal rainfall and crop yield. The integration of a large-scale single crop model into a GCM, as described here, represents a first step towards the development of fully coupled crop and climate models. Future development priorities and challenges related to coupling crop and climate models are discussed.     

Why and how terrestrial plants exchange gases with air

This work is intended as a review of gas exchange processes between the atmosphere and the terrestrial vegetation, which have been known for more than two centuries since the discovery of photosynthesis. The physical and biological mechanisms of exchange of carbon dioxide, water vapour, volatile organic compounds emitted by plants and air pollutants taken up by them, is critically reviewed. The role of stomatal physiology is emphasised, as it controls most of these processes. The techniques used for measurement of gas exchange fluxes between the atmosphere and vegetation are outlined.     

Tropical forests and the global carbon cycle: impacts of atmospheric carbon dioxide, climate change and rate of deforestation

The remaining carbon stocks in wet tropical forests are currently at risk because of anthropogenic deforestation, but also because of the possibility of release driven by climate change. To identify the relative roles of CO2 increase, changing temperature and rainfall, and deforestation in the future, and the magnitude of their impact on atmospheric CO2 concentrations, we have applied a dynamic global vegetation model, using multiple scenarios of tropical deforestation (extrapolated from two estimates of current rates) and multiple scenarios of changing climate (derived from four independent offline general circulation model simulations). Results show that deforestation will probably produce large losses of carbon, despite the uncertainty about the deforestation rates. Some climate models produce additional large fluxes due to increased drought stress caused by rising temperature and decreasing rainfall. One climate model, however, produces an additional carbon sink. Taken together, our estimates of additional carbon emissions during the twenty-first century, for all climate and deforestation scenarios, range from 101 to 367 Gt C, resulting in CO2 concentration increases above background values between 29 and 129 p.p.m. An evaluation of the method indicates that better estimates of tropical carbon sources and sinks require improved assessments of current and future deforestation, and more consistent precipitation scenarios from climate models. Notwithstanding the uncertainties, continued tropical deforestation will most certainly play a very large role in the build-up of future greenhouse gas concentrations.     

Rainfall manipulation experiments as simulated by terrestrial biosphere models: Where do we stand?

Changes in rainfall amounts and patterns have been observed and are expected to continue in the near future with potentially significant ecological and societal consequences. Modelling vegetation responses to changes in rainfall is thus crucial to project water and carbon cycles in the future. In this study, we present the results of a new model‐data intercomparison project, where we tested the ability of 10 terrestrial biosphere models to reproduce the observed sensitivity of ecosystem productivity to rainfall changes at 10 sites across the globe, in nine of which, rainfall exclusion and/or irrigation experiments had been performed. The key results are as follows: (a) Inter‐model variation is generally large and model agreement varies with timescales. In severely water‐limited sites, models only agree on the interannual variability of evapotranspiration and to a smaller extent on gross primary productivity. In more mesic sites, model agreement for both water and carbon fluxes is typically higher on fine (daily–monthly) timescales and reduces on longer (seasonal–annual) scales. (b) Models on average overestimate the relationship between ecosystem productivity and mean rainfall amounts across sites (in space) and have a low capacity in reproducing the temporal (interannual) sensitivity of vegetation productivity to annual rainfall at a given site, even though observation uncertainty is comparable to inter‐model variability. (c) Most models reproduced the sign of the observed patterns in productivity changes in rainfall manipulation experiments but had a low capacity in reproducing the observed magnitude of productivity changes. Models better reproduced the observed productivity responses due to rainfall exclusion than addition. (d) All models attribute ecosystem productivity changes to the intensity of vegetation stress and peak leaf area, whereas the impact of the change in growing season length is negligible. The relative contribution of the peak leaf area and vegetation stress intensity was highly variable among models.     

Coupling microscale vegetation–soil water and macroscale vegetation–precipitation feedbacks in semiarid ecosystems

At macroscale, land–atmosphere exchange of energy and water in semiarid zones such as the Sahel constitutes a strong positive feedback between vegetation density and precipitation. At microscale, however, additional positive feedbacks between hydrology and vegetation such as increase of infiltration due to increase of vegetation, have been reported and have a large impact on vegetation distribution and spatial pattern formation. If both macroscale and microscale positive feedbacks are present in the same region, it is reasonable to assume that these feedback mechanisms are connected. In this study, we develop and analyse a soil‐vegetation‐atmosphere model coupling large‐scale evapotranspiration–precipitation feedback with a model of microscale vegetation–hydrology feedback to study the integration of these nonlinearities at disparate scales. From our results, two important conclusions can be drawn: (1) it is important to account for spatially explicit vegetation dynamics at the microscale in climate models (the strength of the precipitation feedback increased up to 35% by accounting for these microscale dynamics); (2) studies on resilience of ecosystems to climate change should always be cast within a framework of possible large‐scale atmospheric feedback mechanism (substantial changes in vegetation resilience resulted from incorporating macroscale precipitation feedback). Analysis of full‐coupled modelling shows that both type of feedbacks markedly influence each other and that they should both be accounted for in climate change models.     

Carbon cycle responses of semi‐arid ecosystems to positive asymmetry in rainfall

Recent evidence shows that warm semi‐arid ecosystems are playing a disproportionate role in the interannual variability and greening trend of the global carbon cycle given their mean lower productivity when compared with other biomes (Ahlström et al. 2015 Science, 348, 895). Using multiple observations (land‐atmosphere fluxes, biomass, streamflow and remotely sensed vegetation cover) and two state‐of‐the‐art biospheric models, we show that climate variability and extremes lead to positive or negative responses in the biosphere, depending on vegetation type. We find Australia to be a global hot spot for variability, with semi‐arid ecosystems in that country exhibiting increased carbon uptake due to both asymmetry in the interannual distribution of rainfall (extrinsic forcing), and asymmetry in the response of gross primary production (GPP) to rainfall change (intrinsic response). The latter is attributable to the pulse‐response behaviour of the drought‐adapted biota of these systems, a response that is estimated to be as much as half of that from the CO₂ fertilization effect during 1990–2013. Mesic ecosystems, lacking drought‐adapted species, did not show an intrinsic asymmetric response. Our findings suggest that a future more variable climate will induce large but contrasting ecosystem responses, differing among biomes globally, independent of changes in mean precipitation alone. The most significant changes are occurring in the extensive arid and semi‐arid regions, and we suggest that the reported increased carbon uptake in response to asymmetric responses might be contributing to the observed greening trends there.     

Regional atmospheric CO2 inversion reveals seasonal and geographic differences in Amazon net biome exchange

Understanding tropical rainforest carbon exchange and its response to heat and drought is critical for quantifying the effects of climate change on tropical ecosystems, including global climate–carbon feedbacks. Of particular importance for the global carbon budget is net biome exchange of CO₂ with the atmosphere (NBE), which represents nonfire carbon fluxes into and out of biomass and soils. Subannual and sub‐Basin Amazon NBE estimates have relied heavily on process‐based biosphere models, despite lack of model agreement with plot‐scale observations. We present a new analysis of airborne measurements that reveals monthly, regional‐scale (~1–8 × 10⁶ km²) NBE variations. We develop a regional atmospheric CO₂ inversion that provides the first analysis of geographic and temporal variability in Amazon biosphere–atmosphere carbon exchange and that is minimally influenced by biosphere model‐based first guesses of seasonal and annual mean fluxes. We find little evidence for a clear seasonal cycle in Amazon NBE but do find NBE sensitivity to aberrations from long‐term mean climate. In particular, we observe increased NBE (more carbon emitted to the atmosphere) associated with heat and drought in 2010, and correlations between wet season NBE and precipitation (negative correlation) and temperature (positive correlation). In the eastern Amazon, pulses of increased NBE persisted through 2011, suggesting legacy effects of 2010 heat and drought. We also identify regional differences in postdrought NBE that appear related to long‐term water availability. We examine satellite proxies and find evidence for higher gross primary productivity (GPP) during a pulse of increased carbon uptake in 2011, and lower GPP during a period of increased NBE in the 2010 dry season drought, but links between GPP and NBE changes are not conclusive. These results provide novel evidence of NBE sensitivity to short‐term temperature and moisture extremes in the Amazon, where monthly and sub‐Basin estimates have not been previously available.     

Spectral and Structural Measures of Northwest Forest Vegetation at Leaf to Landscape Scales

We report a multiscale study in the Wind River Valley in southwestern Washington, where we quantified leaf to stand scale variation in spectral reflectance for dominant species. Four remotely sensed structural measures, the normalized difference vegetation index (NDVI), cover fractions from spectral mixture analysis (SMA), equivalent water thickness (EWT), and albedo were investigated using Airborne Visible Infrared Imaging Spectrometer (AVIRIS) data. Discrimination of plant species varied with wavelength and scale, with deciduous species showing greater separability than conifers. Contrary to expectations, plant species were most distinct at the branch scale and least distinct at the stand scale. At the stand scale, broadleaf and conifer species were spectrally distinct, as were most conifer age classes. Intermediate separability occurred at the leaf scale. Reflectance decreased from leaf to stand scales except in the broadleaf species, which peaked in near-infrared reflectance at the branch scale. Important biochemical signatures became more pronounced spectrally progressing from leaf to stand scales. Recent regenerated clear-cuts (less than 10 years old) had the highest albedo and nonphotosynthetic vegetation (NPV). After 50 years, the stands showed significant decreases in albedo, NPV, and EWT and increases in shade. Albedo was lowest in old-growth forests. Peak EWT, a proxy measure for leaf area index (LAI), was observed in 11- to 30-year-old stands. When compared to LAI, EWT and NDVI showed exponentially decreasing, but distinctly different, relationships with increasing LAI. This difference is biologically important: at 95% of the maximum predicted NDVI and EWT, LAI was 5.17 and 9.08, respectively. Although these results confirm the stand structural variation expected with forest succession, remote-sensing images also provide a spatial context and establish a basis to evaluate variance within and between age classes. Landscape heterogeneity can thus be characterized over large areas—a critical and important step in scaling fluxes from stand-based towers to larger scales.     

Modelling the vegetation of China using the process-based equilibrium terrestrial biosphere model BIOME3

1 We model the potential vegetation and annual net primary production (NPP) of China on a 10′ grid under the present climate using the processed‐based equilibrium terrestrial biosphere model BIOME3. The simulated distribution of the vegetation was in general in good agreement with the potential natural vegetation based on a numerical comparison between the two maps using the ΔV statistic (ΔV = 0.23). Predicted and measured NPP were also similar, especially in terms of biome‐averages. 2 A coupled ocean–atmosphere general circulation model including sulphate aerosols was used to drive a double greenhouse gas scenario for 2070–2099. Simulated vegetation maps from two different CO₂ scenarios (340 and 500 p.p.m.v.) were compared to the baseline biome map using ΔV. Climate change alone produced a large reduction in desert, alpine tundra and ice/polar desert, and a general pole‐ward shift of the boreal, temperate deciduous, warm–temperate evergreen and tropical forest belts, a decline in boreal deciduous forest and the appearance of tropical deciduous forest. The inclusion of CO₂ physiological effects led to a marked decrease in moist savannas and desert, a general decrease for grasslands and steppe, and disappearance of xeric woodland/scrub. Temperate deciduous broadleaved forest, however, shifted north to occupy nearly half the area of previously temperate mixed forest. 3 The impact of climate change and increasing CO₂ is not only on biogeography, but also on potential NPP. The NPP values for most of the biomes in the scenarios with CO₂ set at 340 p.p.m.v. and 500 p.p.m.v. are greater than those under the current climate, except for the temperate deciduous forest, temperate evergreen broadleaved forest, tropical rain forest, tropical seasonal forest, and xeric woodland/scrub biomes. Total vegetation and total carbon is simulated to increase significantly in the future climate scenario, both with and without the CO₂ direct physiological effect. 4 Our results show that the global process‐based equilibrium terrestrial biosphere model BIOME3 can be used successfully at a regional scale.     

Landscape patterns and environmental controls of deciduousness in forests of central Panama

Aim Dry season deciduousness affects intra‐ and inter‐annual patterns of carbon, water and energy balance in seasonal tropical forests. Because it is affected by rainfall, temperature and solar radiation, deciduousness may be an indicator of the response of vegetation to climate change. Better understanding of how spatial patterns of deciduousness are affected by climate and other environmental gradients will improve the ability to predict responses to climate change. This study develops remote sensing methods for quantifying tropical forest deciduousness and examines the relationship between deciduousness and environmental factors in semi‐deciduous tropical forest. Location Central Panama. Methods I applied spectral mixture analysis (SMA) and the normalized difference vegetation index (NDVI) to Landsat images to predict deciduousness which was ground‐truthed with field observations of the percentage of overstorey deciduous trees. Using predicted deciduousness from SMA, patterns of deciduousness at three spatial scales were analysed. I determined how deciduousness varied spatially with rainfall and geological substrate. Results Both SMA and NDVI had strong correlations (r > 0.9) with field observations of deciduousness. On a landscape scale, deciduousness increased as rainfall decreased, but geological substrate altered this relationship. On some geological substrates, deciduousness was much greater than expected for a given rainfall total or showed a slight but significant increase with rainfall. At an intermediate spatial scale, there were highly deciduous patches from 3 to 250 ha in size embedded in non‐deciduous forest, which may have resulted from topography, soil variation or past land use. Main conclusions Dry season deciduousness can be accurately quantified using satellite images indicating that remote sensing can be a valuable tool for detecting change and understanding ecosystem processes in tropical forests from landscape to regional scales.     

Sources of Variability in Net Primary Production Predictions at a Regional Scale: A Comparison Using PnET-II and TEM 4.0 in Northeastern US Forests

Because model predictions at continental and global scales are necessarily based on broad characterizations of vegetation, soils, and climate, estimates of carbon stocks and fluxes made by global terrestrial biosphere models may not be accurate for every region. At the regional scale, we suggest that attention can be focused more clearly on understanding the relative strengths of predicted net primary productivity (NPP) limitation by energy, water, and nutrients. We evaluate the sources of variability among model predictions of NPP with a regional-scale comparison between estimates made by PnET-II (a forest ecosystem process model previously applied to the northeastern region) and TEM 4.0 (a terrestrial biosphere model typically applied to the globe) for the northeastern US. When the same climate, vegetation, and soil data sets were used to drive both models, regional average NPP predictions made by PnET-II and TEM were remarkably similar, and at the biome level, model predictions agreed fairly well with NPP estimates developed from field measurements. However, TEM 4.0 predictions were more sensitive to regional variations in temperature as a result of feedbacks between temperature and belowground N availability. In PnET-II, the direct link between transpiration and photosynthesis caused substantial water stress in hardwood and pine forest types with increases in solar radiation; predicted water stress was relieved substantially when soil water holding capacity (WHC) was increased. Increasing soil WHC had little effect on TEM 4.0 predictions because soil water storage was already sufficient to meet plant demand with baseline WHC values, and because predicted N availability under baseline conditions in this region was not limited by water. Because NPP predictions were closely keyed to forest cover type, the relative coverage of low- versus high-productivity forests at both fine and coarse resolutions was an important determinant of regional NPP predictions. Therefore, changes in grid cell size and differences in the methods used to aggregate from fine to coarse resolution were important to NPP predictions insofar as they changed the relative proportions of forest cover. We suggest that because the small patches of high-elevation spruce-fir forest in this region are substantially less productive than forests in the remainder of the region, more accurate NPP predictions will result if models applied to this region use land cover input data sets that retain as much fine-resolution forest type variability as possible. The differences among model responses to variations in climate and soil WHC data sets suggest that the models will respond quite differently to scenarios of future climate. A better understanding of the dynamic interactions between water stress, N availability, and forest productivity in this region will enable models to make more accurate predictions of future carbon stocks and fluxes. Received 19 June 1998; accepted 25 June 1999.     

Terrestrial biosphere model performance for inter‐annual variability of land‐atmosphere CO2 exchange

Interannual variability in biosphere‐atmosphere exchange of CO₂ is driven by a diverse range of biotic and abiotic factors. Replicating this variability thus represents the ‘acid test’ for terrestrial biosphere models. Although such models are commonly used to project responses to both normal and anomalous variability in climate, they are rarely tested explicitly against inter‐annual variability in observations. Herein, using standardized data from the North American Carbon Program, we assess the performance of 16 terrestrial biosphere models and 3 remote sensing products against long‐term measurements of biosphere‐atmosphere CO₂ exchange made with eddy‐covariance flux towers at 11 forested sites in North America. Instead of focusing on model‐data agreement we take a systematic, variability‐oriented approach and show that although the models tend to reproduce the mean magnitude of the observed annual flux variability, they fail to reproduce the timing. Large biases in modeled annual means are evident for all models. Observed interannual variability is found to commonly be on the order of magnitude of the mean fluxes. None of the models consistently reproduce observed interannual variability within measurement uncertainty. Underrepresentation of variability in spring phenology, soil thaw and snowpack melting, and difficulties in reproducing the lagged response to extreme climatic events are identified as systematic errors, common to all models included in this study.     

Ecosystem‐scale volatile organic compound fluxes during an extreme drought in a broadleaf temperate forest of the Missouri Ozarks (central USA)

Considerable amounts and varieties of biogenic volatile organic compounds (BVOCs) are exchanged between vegetation and the surrounding air. These BVOCs play key ecological and atmospheric roles that must be adequately represented for accurately modeling the coupled biosphere–atmosphere–climate earth system. One key uncertainty in existing models is the response of BVOC fluxes to an important global change process: drought. We describe the diurnal and seasonal variation in isoprene, monoterpene, and methanol fluxes from a temperate forest ecosystem before, during, and after an extreme 2012 drought event in the Ozark region of the central USA. BVOC fluxes were dominated by isoprene, which attained high emission rates of up to 35.4 mg m^?2 h^?1 at midday. Methanol fluxes were characterized by net deposition in the morning, changing to a net emission flux through the rest of the daylight hours. Net flux of CO₂ reached its seasonal maximum approximately a month earlier than isoprenoid fluxes, which highlights the differential response of photosynthesis and isoprenoid emissions to progressing drought conditions. Nevertheless, both processes were strongly suppressed under extreme drought, although isoprene fluxes remained relatively high compared to reported fluxes from other ecosystems. Methanol exchange was less affected by drought throughout the season, confirming the complex processes driving biogenic methanol fluxes. The fraction of daytime (7–17 h) assimilated carbon released back to the atmosphere combining the three BVOCs measured was 2% of gross primary productivity (GPP) and 4.9% of net ecosystem exchange (NEE) on average for our whole measurement campaign, while exceeding 5% of GPP and 10% of NEE just before the strongest drought phase. The megan v2.1 model correctly predicted diurnal variations in fluxes driven mainly by light and temperature, although further research is needed to address model BVOC fluxes during drought events.