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We describe the physiological responses of boreal conifers to climate change for the past 112 years using ring width and carbon isotope ratios (δ13C) chronologies at six forest sites in northern Eurasia and Canada. Responses differed among regions, depending on their climatic and/or geographic characteristics. Tree radial growth decreased over the past 52 years in central eastern Siberia with the higher rate of summer temperature increase than other regions, as indicated by the negative correlation between radial growth and summer temperature, but increased in northern Europe and Canada. Changes in tree-ring δ13C indicated that recent climatic conditions have induced stronger drought stress for trees from central eastern Siberia than for those from other regions. The observed tree growth trends were compared to those simulated using a dynamic global vegetation model. Although the modeled annual net primary production (NPP) for trees generally exhibited similar decadal variation to radial growth, simulations did not show a recent decrease in tree growth, even in central eastern Siberia. This was probably due to an overestimation of the sensitivity of modeled tree NPP to precipitation. Our results suggest that the tree NPP forecasted under the expected future increases in temperature and average precipitation might be overestimated, especially in severely dry regions such as central eastern Siberia.

Woody residence time (τw) is an important parameter that expresses the balance between mature forest recruitment/growth and mortality. Using field data collected from the literature, this study explored the global forest τw and investigated its influence on model simulations of aboveground biomass (AGB) at a global scale. Specifically, τw was found to be related to forest age, annual temperature, and precipitation at a global scale, but its determinants were different among various plant function types. The estimated global forest τw based on the filed data showed large spatial heterogeneity, which plays an important role in model simulation of AGB by a dynamic global vegetation model (DGVM). The τw could change the resulting AGB in tenfold based on a site-level test using the Monte Carlo method. At the global level, different parameterization schemes of the Integrated Biosphere Simulator using the estimated τw resulted in a twofold change in the AGB simulation for 2100. Our results highlight the influences of various biotic and abiotic variables on forest τw. The estimation of τw in our study may help improve the model simulations and reduce the parameter's uncertainty over the projection of future AGB in the current DGVM or Earth System Models. A clearer understanding of the responses of τw to climate change and the corresponding sophisticated description of forest growth/mortality in model structure is also needed for the improvement of carbon stock prediction in future studies.

Large-scale atmospheric circulation patterns (i.e., teleconnections) influence global climate variability patterns and can be studied to provide a simple framework for relating the complex response of ecosystems to climate. This study analyzes the effects of 15 major teleconnections on terrestrial ecosystem carbon fluxes during 1951–2012 using an ensemble of nine dynamic global vegetation models. We map the global pattern of the dominant teleconnections and find that these teleconnections significantly affect gross primary productivity variations over more than 82.1% of the global vegetated area, through mediating the global temperature and regional precipitation and cloud cover. The El Nino–Southern Oscillation, the Pacific Decadal Oscillation, and the Atlantic Multidecadal Oscillation are strongly correlated with global, hemispherical, and continental carbon fluxes and climatic variables, while the Northern Hemisphere teleconnections have only regional influences. Further research regarding the interactions among the teleconnections and the nonstationarity of the relationship between teleconnections and carbon fluxes is needed.

Realistic representation of vegetation's response to drought is important for understanding terrestrial carbon cycling. We evaluated 9 Earth System Models from the historical experiment of the Coupled Model Intercomparison Project Phase 5 (CMIP5) for the response of gross primary productivity (GPP) and leaf area index (LAI) to hydrological anomalies. Hydrological anomalies were characterized by the standardized precipitation index (SPI) and surface soil moisture anomalies (SMA). GPP and LAI in models were on average more responsive to SPI than in observations revealed through several indicators. First, we find higher mean correlations between global annual anomalies of GPP and SPI in models than observations. Second, the maximum correlation between GPP and SPI across 1-24 month timescales is higher in models than observations. And finally we found stronger excursions of GPP to extreme dry or wet events. Similar to GPP, LAI responded more to SPI in models than observations. The over-response of models is smaller if evaluated based on SMA instead of SPI. LAI responses to SMA are inconsistent among models, showing both higher and lower LAI when soil moisture is reduced. The timescale of maximum correlation is shorter in models than the observation for GPP and the markedly different response timescales among models for LAI indicate gaps in understanding how variability of water availability affects foliar cover. The discrepancy of responses derived from SPI and SMA among models, and between models and observations calls for improvement in understanding the dynamics of plant-available water in addition to how vegetation responds to these anomalies.

Nitrogen (N) cycle dynamics and N deposition play an important role in determining the terrestrial biosphere's carbon (C) balance. We assess global and biome-specific N deposition effects on C sequestration rates with the dynamic global vegetation model LPJ-GUESS. Modeled CN interactions are evaluated by comparing predictions of the C and CN version of the model with direct observations of C fluxes from 68 forest FLUXNET sites. N limitation on C uptake reduced overestimation of gross primary productivity for boreal evergreen needleleaf forests from 56% to 18%, presenting the greatest improvement among forest types. Relative N deposition effects on C sequestration (dC/dN) in boreal, temperate, and tropical sites ranged from 17 to 26 kg C kg N-1 when modeled at site scale and were reduced to 12-22 kg C kg N-1 at global scale. We find that 19% of the recent (1990-2007) and 24% of the historical global C sink (1900-2006) was driven by N deposition effects. While boreal forests exhibit highest dC/dN, their N deposition-induced C sink was relatively low and is suspected to stay low in the future as no major changes in N deposition rates are expected in the boreal zone. N deposition induced a greater C sink in temperate and tropical forests, while predicted C fluxes and N-induced C sink response in tropical forests were associated with greatest uncertainties. Future work should be directed at improving the ability of LPJ-GUESS and other process-based ecosystem models to reproduce C cycle dynamics in the tropics, facilitated by more benchmarking data sets. Furthermore, efforts should aim to improve understanding and model representations of N availability (e.g., N fixation and organic N uptake), N limitation, P cycle dynamics, and effects of anthropogenic land use and land cover changes.

General circulation models (GCMs) forecast higher global vapor pressure deficit (VPD) but unchanged global relative humidity (RH) in future climates. A literature survey revealed that 50% of Earth system models and land surface models embedded within GCMs employ RH as an atmospheric aridity index when describing stomatal conductance (gs), whereas the remaining 50% employ VPD. The consequences of using RH or VPD in gs models for water cycling and vegetation productivity in future climates on large spatial and temporal scales remain to be explored. Process-based global dynamic vegetation model runs, changes in the hydrological cycle, and concomitant vegetation productivity for the 21st century projected climate were conducted by altering only gs responses to VPD or RH and not changing any other formulations. In the simulations of the African continent under a 21st century warming trend, both stomatal functions of VPD and RH resulted in similar geographic patterns in gross primary production (GPP). However, continental total GPP was larger for the VPD response than that for the RH response. Transpiration rates were lower, resulting in a 13% increase in water-use efficiency for the VPD response compared with its RH counterpart.

The coarse-grained spatial representation of many terrestrial ecosystem models hampers the importance of local-scale heterogeneities. To address this issue, we combine a range of observations (forest inventories, eddy flux tower data, and remote sensing products) and modeling approaches with contrasting degrees of abstraction. The following models are selected: (i) Lund-Potsdam-Jena (LPJ), a well-established, area-based, dynamic global vegetation model (DGVM); (ii) LPJ-General Ecosystem Simulator, a hybrid, individual-based approach that additionally considers plant population dynamics in greater detail; and (iii) distributed in space-LPJ, a spatially explicit version of LPJ, operating at a fine spatial resolution (100 m × 100 m), which uses an enhanced hydrological representation accounting for lateral connectivity of surface and subsurface water fluxes. By comparing model simulations with a multivariate data set available at the catchment scale, we argue that (i) local environmental and topographic attributes that are often ignored or crudely represented in DGVM applications exert a strong control on terrestrial ecosystem response; (ii) the assumption of steady state vegetation and soil carbon pools at the beginning of simulation studies (e.g., under “current conditions”), as embedded in many DGVM applications, is in contradiction with the current state of many forests that are often out of equilibrium; and (iii) model evaluation against vegetation carbon fluxes does not imply an accurate simulation of vegetation carbon stocks. Having gained insights about the magnitude of aggregation-induced biases due to smoothing of spatial variability at the catchment scale, we discuss the implications of our findings with respect to the global-scale modeling studies of carbon cycle and we illustrate alternative ways forward.

Fire plays an important role in terrestrial ecosystems by regulating biogeochemistry, biogeography, and energy budgets, yet despite the importance of fire as an integral ecosystem process, significant advances remain to improve its prognostic representation in carbon cycle models. To recommend and to help prioritize model improvements, this study investigates the sensitivity of a coupled global biogeography and biogeochemistry model, LPJ, to observed burned area measured by three independent satellite-derived products, GFED v3.1, L3JRC, and GlobCarbon. Model variables are compared with benchmarks that include pantropical aboveground biomass, global tree cover, and CO2 and CO trace gas concentrations. Depending on prescribed burned area product, global aboveground carbon stocks varied by 300 Pg C, and woody cover ranged from 50 to 73 Mkm2. Tree cover and biomass were both reduced linearly with increasing burned area, i.e., at regional scales, a 10% reduction in tree cover per 1000 km2, and 0.04-to-0.40 Mg C reduction per 1000 km2. In boreal regions, satellite burned area improved simulated tree cover and biomass distributions, but in savanna regions, model-data correlations decreased. Global net biome production was relatively insensitive to burned area, and the long-term land carbon sink was robust, ~2.5 Pg C yr−1, suggesting that feedbacks from ecosystem respiration compensated for reductions in fuel consumption via fire. CO2 transport provided further evidence that heterotrophic respiration compensated any emission reductions in the absence of fire, with minor differences in modeled CO2 fluxes among burned area products. CO was a more sensitive indicator for evaluating fire emissions, with MODIS-GFED burned area producing CO concentrations largely in agreement with independent observations in high latitudes. This study illustrates how ensembles of burned area data sets can be used to diagnose model structures and parameters for further improvement and also highlights the importance in considering uncertainties and variability in observed burned area data products for model applications.

[1] Phenological dynamics and their related processes strongly constrain land-atmosphere interactions, but their relative importance vis-a-vis meteorological forcing within general circulation models (GCMs) is still uncertain. Using an off-line land surface model, we evaluate leaf area and meteorological controls on gross primary productivity, evapotranspiration, transpiration, and runoff at four North American sites, representing different vegetation types and background climates. Our results demonstrate that compared to meteorological controls, variation in leaf area has a dominant control on gross primary productivity, a comparable but smaller influence on transpiration, a weak influence on total evapotranspiration, and a negligible impact on runoff. Climate regime and characteristic variations in leaf area have important modulating effects on these relative controls, which vary depending on the fluxes and timescales of interest. We find that leaf area in energy-limited evaporative regimes tends to exhibit greater control on annual gross primary productivity than in moisture-limited regimes, except when vegetation exhibits little inter-annual variation in leaf area. For transpiration, leaf area control is somewhat less in energy-limited regimes and greater in moisture-limited regimes for maximum pentad and annual fluxes. These modulating effects of climate and leaf area were less clear for other fluxes and at other timescales. Our findings are relevant to land-atmosphere coupling in GCMs, especially considering that leaf area variations are a fundamental element of land use and land cover change simulations.

While a new class of Dynamic Global Ecosystem Models (DGEMs) has emerged in the past few years as an important tool for describing global biogeochemical cycles and atmosphere-biosphere interactions, these models are still largely untested. Here we analyze the behavior of a new DGEM and compare the results to global-scale observations of water balance, carbon balance, and vegetation structure. In this study, we use version 2 of the Integrated Biosphere Simulator (IBIS), which includes several major improvements and additions to the prototype model developed by Foley et al. [1996]. IBIS is designed to be a comprehensive model of the terrestrial biosphere; the model represents a wide range of processes, including land surface physics, canopy physiology, plant phenology, vegetation dynamics and competition, and carbon and nutrient cycling. The model generates global simulations of the surface water balance (e.g., runoff), the terrestrial carbon balance (e.g., net primary production, net ecosystem exchange, soil carbon, aboveground and belowground litter, and soil CO2 fluxes), and vegetation structure (e.g., biomass, leaf area index, and vegetation composition). In order to test the performance of the model, we have assembled a wide range of continental and global-scale data, including measurements of river discharge, net primary production, vegetation structure, root biomass, soil carbon, litter carbon, and soil CO2 flux. Using these field data and model results for the contemporary biosphere (1965–1994), our evaluation shows that simulated patterns of runoff, NPP, biomass, leaf area index, soil carbon, and total soil CO2 flux agree reasonably well with measurements that have been compiled from numerous ecosystems. These results also compare favorably to other global model results.

The equilibrium terrestrial biosphere model BIOME3 simulates vegetation distribution and biogeochemistry, and couples vegetation distribution directly to biogeochemistry. Model inputs consist of latitude, soil texture class, and monthly climate (temperature, precipitation, and sunshine) data on a 0.5 degrees grid. Ecophysiological constraints determine which plant functional types (PFTs) may potentially occur. A coupled carbon and water flux model is then used to calculate, for each PFT, the leaf area index (LAI) that maximizes net primary production (NPP), subject to the constraint that NPP must be sufficient to maintain this LAI. Competition between PFTs is simulated by using the optimal NPP of each PFT as an index of competitiveness, with additional rules to approximate the dynamic equilibrium between natural disturbance and succession driven by light competition. Model output consists of a quantitative vegetation state description in terms of the dominant PFT, secondary PFTs present, and the total LAI and NPP for the ecosystem. Canopy conductance is treated as a function of the calculated optimal photosynthetic rate and water stress. Regional evapotranspiration is calculated as a function of canopy conductance, equilibrium evapotranspiration rate, and soil moisture using a simple planetary boundary layer parameterization. This scheme results in a two-way coupling of the carbon and water fluxes through canopy conductance, allowing simulation of the response of photosynthesis, stomatal conductance, and leaf area to environmental factors including atmospheric CO2. Comparison with the mapped distribution of global vegetation shows that the model successfully reproduces the broad-scale patterns in potential natural vegetation distribution. Comparison with NPP measurements, and with an FPAR (fractional absorbed photosynthetically active radiation) climatology based on remotely sensed greenness measurements, provides further checks on the model's internal logic. The model is envisaged as a tool for integrated analysis of the impacts of changes in climate and CO2 on ecosystem structure and function. (Less)

Here we present a new terrestrial biosphere model (the Integrated Biosphere Simulator - IBIS) which demonstrates how land surface biophysics, terrestrial carbon fluxes, and global vegetation dynamics can be represented in a single, physically consistent modeling framework. In order to integrate a wide range of biophysical, physiological, and ecological processes, the model is designed around a hierarchical, modular structure and uses a common state description throughout. First, a coupled simulation of the surface water, energy, and carbon fluxes is performed on hourly timesteps and is integrated over the year to estimate the annual water and carbon balance. Next, the annual carbon balance is used to predict changes in the leaf area index and biomass for each of nine plant functional types, which compete for light and water using different ecological strategies. The resulting patterns of annual evapotranspiration, runoff, and net primary productivity are in good agreement with observations. In addition, the model simulates patterns of vegetation dynamics that qualitatively agree with features of the natural process of secondary succession. Comparison of the model's inferred near-equilibrium vegetation categories with a potential natural vegetation map shows a fair degree of agreement. This integrated modeling framework provides a means of simulating both rapid biophysical processes and long-term ecosystem dynamics that can be directly incorporated within atmospheric models.

[1] A simple process-based model for the consumption of atmospheric hydrogen (H2) has been developed. The model includes a description of diffusion and biological processes which together control H2flux into the soil. The model was incorporated into the LPJ-WHyMe Dynamic Global Vegetation Model, and used to simulate H2 fluxes over the 1988–2006 period. The model results have been confronted with field and laboratory measurements. The model reproduces observed seasonal cycles of H2 uptake at different sites and shows a realistic sensitivity to changes in soil temperature and soil water content in comparisons with field and laboratory measurements. A recent study, based on 3D atmospheric model inversion, found an increase of the global H2 sink from soils, with a trend of −0.77 Tg a−2 for the 1992–2004 period (fluxes are negative as soils act as a sink for atmospheric H2). For the same period, however, our process-based model calculates a trend of only −0.04 Tg a−2. Even when forced with drastic changes in soil water content, soil temperature and snow cover depth, our model is unable to reproduce the trend found in the inversion-based study, questioning the realism of such a large trend.

[1] We applied a dynamic global vegetation model (DGVM) to the African continent. After calibration, the model reproduced geographical distributions of the continent's biomes, annual gross primary productivity (GPP), and biomass under current climatic conditions. The model is driven by the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenarios (SRES) A1B scenario of rising CO2, and by climate changes during the twenty-first century resulting from the change in CO2concentrations, simulated by a coupled Model for Interdisciplinary Research on Climate (MIROC) ocean atmosphere model. Simulations under this condition revealed time lags between environmental change and biome change, with the extent of these lags depending largely on the type of biome change. A switch in forest type was accompanied by the longest delay in biome change among all changes classified, indicating that resident trees largely prevent the establishment of nonresident tree types adapted to the new environment, and that tree growth requires additional years after successful establishment. In addition, assumptions for tree dispersal, which determine whether nonresident tree types can be established, modified the patterns of biome change under the twenty-first-century environment: under the assumption that nonresident tree types cannot be established even if environmental conditions change, the extent of the forest type switch and the development of forest and savanna were suppressed, while forest dieback was enhanced. These changes accompanied a slowing of the increasing trend in net primary productivity (NPP), biomass, and soil carbon during the twenty-first century and in subsequent years. These results quantitatively demonstrate that both patch dynamics and invasive tree recruitment significantly modify the transient change in vegetation distribution and function under a changing environment on the African continent.

[1] Four CO2 concentration inversions and the Global Fire Emissions Database (GFED) versions 2.1 and 3 are used to provide benchmarks for climate-driven modeling of the global land-atmosphere CO2 flux and the contribution of wildfire to this flux. The Land surface Processes and exchanges (LPX) model is introduced. LPX is based on the Lund-Potsdam-Jena Spread and Intensity of FIRE (LPJ-SPITFIRE) model with amended fire probability calculations. LPX omits human ignition sources yet simulates many aspects of global fire adequately. It captures the major features of observed geographic pattern in burnt area and its seasonal timing and the unimodal relationship of burnt area to precipitation. It simulates features of geographic variation in the sign of the interannual correlations of burnt area with antecedent dryness and precipitation. It simulates well the interannual variability of the global total land-atmosphere CO2 flux. There are differences among the global burnt area time series from GFED2.1, GFED3 and LPX, but some features are common to all. GFED3 fire CO2 fluxes account for only about 1/3 of the variation in total CO2 flux during 1997–2005. This relationship appears to be dominated by the strong climatic dependence of deforestation fires. The relationship of LPX-modeled fire CO2 fluxes to total CO2 fluxes is weak. Observed and modeled total CO2 fluxes track the El Nino–Southern Oscillation (ENSO) closely; GFED3 burnt area and global fire CO2 flux track the ENSO much less so. The GFED3 fire CO2 flux-ENSO connection is most prominent for the El Nino of 1997–1998, which produced exceptional burning conditions in several regions, especially equatorial Asia. The sign of the observed relationship between ENSO and fire varies regionally, and LPX captures the broad features of this variation. These complexities underscore the need for process-based modeling to assess the consequences of global change for fire and its implications for the carbon cycle.

[1] Our understanding of the structure and function of China’s terrestrial ecosystems, particularly their responses to transient climate change on timescales of decades to centuries, can be further improved by dynamic vegetation models that include vegetation dynamics as well as biogeochemical processes. Here, the Lund‐Potsdam‐Jena dynamic global vegetation model was calibrated to reasonably capture the general patterns of vegetation distribution across China’s terrestrial ecosystems. New parameter values were used for bioclimatic limits, and the model was validated against satellite, in situ, and inventory data for net primary production (NPP), leaf area index, and carbon storage simulations. Dynamic responses of China’s terrestrial ecosystems to rising atmospheric CO2 concentration ([CO2]) and climate change in the 20th and 21st centuries were then simulated. Simulations were driven by eight climate scenarios consisting of combinations of two general circulation models and four emission scenarios from Intergovernmental Panel on Climate Change Special Report Emission Scenarios ranging from low emission to fossil intensive emission. We derived possible temporal and spatial change patterns of vegetation distribution, carbon flux, and carbon pools across China. Simulations showed that regional changes in temperature and precipitation would cause substantial vegetation changes in northern, northeastern, and western China. Such vegetation dynamics, and associated mechanisms such as responses of NPP and heterotrophic respiration to rising [CO2] and regional climate change, would lead to corresponding changes in temporal and spatial patterns of carbon flux and carbon pools. As a result, some regional ecosystems could switch from carbon sinks to carbon sources or vice versa by the end of the 21st century. China’s terrestrial ecosystems have an average carbon uptake of 0.12 to 0.20 Gt C yr −1 over the period of 1981–2100; however, the rate of increase in carbon sinks is projected to decrease after about 2040, particularly under the fossil intensive emission scenario.

[1] In this contribution, a reactive-transport model describing weathering in soil profiles and at the watershed scale is coupled to a dynamic global vegetation model to calculate the dissolved load of continental waters on a 0.5° latitude × 0.5° longitude grid. The so-called Biosphere-Weathering at the Catchment Scale (B-WITCH) model is applied to the Orinoco watershed (South America). We show that B-WITCH is able to reproduce the main cation composition of the surface waters over the watershed. Sensitivity tests demonstrate that clay mineral reactivities are key factors controlling the calculated discharge of dissolved species. More specifically, our simulations show that the dissolution and precipitation rates of clay minerals in the weathering profiles are strongly intertwined, and that this coupling must be accurately described when modeling the weathering fluxes at the continental scale. A second set of sensitivity tests show that, for the tropical environment, land plants control the total base cation discharge through their impact on the soil hydrology, rather than through enhanced soil CO2 pressures. Indeed, the complete removal of the continental vegetation leads to an increase in the dissolved fluxes to the ocean by 80% because of the collapse in the evapotranspiration, resulting in a more efficient drainage of the weathering profiles. On the other hand, neglecting the root respiration and setting the soil CO2 pressure to the atmospheric level forces the total base cation discharge to decrease by only 20%.

[1] Peatlands and permafrost are important components of the carbon cycle in the northern high latitudes. The inclusion of these components into a dynamic global vegetation model required changes to physical land surface routines, the addition of two new peatland-specific plant functional types, incorporation of an inundation stress mechanism, and deceleration of decomposition under inundation. The new model, LPJ-WHy v1.2, was used to simulate net ecosystem production (NEP), net primary production (NPP), heterotrophic respiration (HR), and soil carbon content. Annual peatland NEP matches observations even though the seasonal amplitude is overestimated. This overestimation is caused by excessive NPP values, probably due to the lack of nitrogen or phosphorus limitation in LPJ-WHy. Introduction of permafrost reduces circumpolar (45–90N) NEP from 1.65 to 0.96 Pg C a � 1 and leads to an increase in soil carbon content of almost 40 Pg C; adding peatlands doubles this soil carbon increase. Peatland soil carbon content and hence HR depend on model spin-up duration and are crucial for simulating NEP. These results highlight the need for a regional peatland age map to help determine spin-up times. A sensitivity experiment revealed that under future climate conditions, NPP may rise more rapidly than HR resulting in increases in NEP.

Received 25 October 2008; revised 3 April 2009; accepted 7 May 2009; published 22 August 2009. [1] Northern peatlands and permafrost soils are associated with large carbon stocks. Rising temperatures are likely to affect the carbon balance in high-latitude ecosystems, but to what degree is uncertain. We have enhanced the Lund-Potsdam-Jena (LPJ) dynamic global vegetation model by introducing processes necessary to simulate permafrost dynamics, peatland hydrology, and peatland vegetation. The new version, LPJ-WHy v1.2, was used to study soil temperature, active layer depth, permafrost distribution, and water table position. Modeled soil temperatures agreed well with observations, apart from a Siberian site where the soil is insulated by an extensive shrub layer. Water table positions were generally in the range of observations, with some exceptions. Simulated active layer depth showed a mean absolute error of 44 cm when compared to observations, but the error was reduced to 25 cm when the soil type for seven sites was manually corrected to mirror local conditions. A sensitivity test, in which temperature and precipitation were varied independently, showed that soil temperatures and active layer depths increased more under higher temperatures when precipitation was increased at the same time. The sensitivity experiment suggested persisting wet conditions in peatlands even under temperature increases of up to 9C as long as annual precipitation is allowed to increase with temperature to the extent indicated by climate model experiments.

Large-scale bottom-up estimates of terrestrial carbon fluxes, whether based on models or inventory, are highly dependent on the assumed land cover. Most current land cover and land cover change maps are based on satellite data and are likely to be so for the foreseeable future. However, these maps show large differences, both at the class level and when transformed into Plant Functional Types (PFTs), and these can lead to large differences in terrestrial CO2 fluxes estimated by Dynamic Vegetation Models. In this study the Sheffield Dynamic Global Vegetation Model is used. We compare PFT maps and the resulting fluxes arising from the use of widely available moderate (1 km) resolution satellite-derived land cover maps (the Global Land Cover 2000 and several MODIS classification schemes), with fluxes calculated using a reference high (25 m) resolution land cover map specific to Great Britain (the Land Cover Map 2000). We demonstrate that uncertainty is introduced into carbon flux calculations by (1) incorrect or uncertain assignment of land cover classes to PFTs; (2) information loss at coarser resolutions; (3) difficulty in discriminating some vegetation types from satellite data. When averaged over Great Britain, modeled CO2 fluxes derived using the different 1 km resolution maps differ from estimates made using the reference map. The ranges of these differences are 254 gC m−2 a−1 in Gross Primary Production (GPP); 133 gC m−2 a−1 in Net Primary Production (NPP); and 43 gC m−2 a−1 in Net Ecosystem Production (NEP). In GPP this accounts for differences of −15.8% to 8.8%. Results for living biomass exhibit a range of 1109 gC m−2. The types of uncertainties due to land cover confusion are likely to be representative of many parts of the world, especially heterogeneous landscapes such as those found in western Europe.

[1] This study uses offline simulations with a land surface model to explore how the future response of potential vegetation to elevated CO2 and attendant climate changes feeds back to influence surface hydrological processes. Climate changes are those projected by eight General Circulation Models (GCMs) under the SRESA1B, and the potential natural vegetation structure corresponding to the Preindustrial control and SRESA1B 2100 climate of the 8 GCMs are simulated by a dynamic global vegetation model integrated to equilibrium. For climate change forcing from each GCM, comparisons are made among three surface hydrology simulations using CLM3.0 driven with different combinations of climate forcing, atmospheric CO2 concentration, and potential natural vegetation. These simulations are designed to separate the effect of structural vegetation feedback from the combined influence of climate and CO2 changes. With the exception of the HadCM scenario, all other GCM scenarios broadly agree on the spatial patterns of structural vegetation feedbacks on surface temperature and surface water budget, although the response of soil moisture varies considerably among the GCM scenarios especially in the tropics. With the HadCM excluded, averages over the seven GCM scenarios indicate that the CO2-induced warming in winter is stronger than in summer in the northern mid and high latitudes, and structural vegetation feedback enhances the winter warming and reduces the summer warming over a large portion of these regions; the global hydrological cycle is expected to accelerate in a warmer future climate, while the structural vegetation feedback further increases evapotranspiration in a major portion of the globe, including parts of South America, tropical Africa, Southeast Asia, and regions north of approximately 45°N, suggesting that vegetation feedback could further accelerate the hydrological cycle. Averaged over the globe, this increase in evapotranspiration due to structural vegetation feedback is equivalent to 78% of that due to climate and CO2 changes. When changes in vegetation structure are not considered, the 7-model average response of soil moisture to climate and CO2 changes is characterized by wetter soil conditions in the northern high latitudes and parts of the midlatitudes. Structural vegetation feedback, however, causes strong midlatitude dryness both in the winter and in the summer. The impact of vegetation changes corresponding to the HadCM-projected climate changes is markedly different, being either more extreme or in a different direction than that corresponding to the other GCMs examined. Our results are constrained by the lack of consideration for human land use changes and vegetation feedback to climate, as well as the uncertainty related to the highly disputed physiological response of ecosystems to elevated CO2. Nevertheless, this study demonstrates that climate- and CO2-induced changes in potential vegetation structure substantially influence the surface hydrological processes, thus emphasizing the importance of including vegetation feedback in future climate change predictions.

This study explores the applicability of data-driven clustering analysis in predictingvegetation distribution over two continents where water is an important controlling factorfor vegetation growth, South America and Africa, and compares the ability ofclustering analysis with that of a physically based dynamic vegetation model to predictvegetation distribution. A clustering analysis algorithm based on the genetic-algorithm-based K-means is tested, with the number of clusters determined a priori according tothe primary plant functional types observed to exist in the study domain. The mostimportant variables upon which the clustering analysis is based include available water, itsseasonality, and evaporative demand. The dynamic vegetation model used is theCommunity Land Model version 3 coupled with a Dynamic Global Vegetation Model(CLM3-DGVM) with modifications targeted to address some known biases of the model.Results from both the clustering analysis and the modified CLM3-DGVM are comparedagainst observations derived from the Moderate Resolution Imaging Spectroradiometer(MODIS). Both methods reasonably reproduced the general pattern of dominant plantfunctional type distribution. There is no clear winner between the two methods, asthe DGVM outperforms the clustering analysis approach in some aspects and isoutperformed in others. It is therefore suggested that clustering analysis can be a usefultool in biogeography estimation, although it cannot be used in mechanistic studies asthe process-based DGVMs are.

[1] Arid and semiarid regions represent a large fraction of global land, but most of the existing dynamic global vegetation models (DGVMs) do not include shrubs or do not effectively distinguish shrubs from grasses, and hence cannot realistically reproduce the ecosystem formation and variability there. A shrub submodel is developed here for the Community Land Model-DGVM (CLM-DGVM), and the major revisions include (1) explicit consideration of shrubs' drought tolerance in the photosynthesis computation; (2) use of appropriate phenology type and morphology parameters for shrubs; (3) consistent treatment of fractional vegetation coverage; (4) development of tree/grass/ shrub hierarchy for light competition; and (5) improvement of the allocation scheme to avoid unrealistic behaviors. Preliminary global offline CLM-DGVM simulations for 400 years show that, with the shrub submodel, the simulated global distribution of temperate shrubs agrees with Moderate Resolution Imaging Spectroradiometer (MODIS) data. The simulated shrub coverage reaches its peak around annual precipitation (P ann ) of 300 mm, the grass coverage reaches its peak over a broad range of P ann (from 400 to 1100 mm), and the tree coverage reaches its peak for P ann = 1500 mm or higher, all in good agreement with MODIS data.

[1] The Community Land Model version 3 (CLM3) is the land component of the Community Climate System Model (CCSM). CLM3 has energy and water biases resulting from deficiencies in some of its canopy and soil parameterizations related to hydrological processes. Recent research by the community that utilizes CLM3 and the family of CCSM models has indicated several promising approaches to alleviating these biases. This paper describes the implementation of a selected set of these parameterizations and their effects on the simulated hydrological cycle. The modifications consist of surface data sets based on Moderate Resolution Imaging Spectroradiometer products, new parameterizations for canopy integration, canopy interception, frozen soil, soil water availability, and soil evaporation, a TOPMODEL-based model for surface and subsurface runoff, a groundwater model for determining water table depth, and the introduction of a factor to simulate nitrogen limitation on plant productivity. The results from a set of offline simulations were compared with observed data for runoff, river discharge, soil moisture, and total water storage to assess the performance of the new model (referred to as CLM3.5). CLM3.5 exhibits significant improvements in its partitioning of global evapotranspiration (ET) which result in wetter soils, less plant water stress, increased transpiration and photosynthesis, and an improved annual cycle of total water storage. Phase and amplitude of the runoff annual cycle is generally improved. Dramatic improvements in vegetation biogeography result when CLM3.5 is coupled to a dynamic global vegetation model. Lower than observed soil moisture variability in the rooting zone is noted as a remaining deficiency.

[1] A number of previous modeling studies have assessed the implications of projected CO2-induced climate change for future terrestrial ecosystems. However, although current understanding of possible long-term response of vegetation to elevated CO2 and CO2-induced climate change in some geographical areas (e.g., the high-latitude regions) has been strengthened by dint of accumulating evidence from these past studies, it is still weak in others. This study examines the responses of global potential natural vegetation distribution, net primary production (NPP), and fire emissions to future changes in atmospheric CO2 concentration and climate using the National Center for Atmospheric Research Community Land Model's dynamic global vegetation model. The model is run to vegetative equilibrium (i.e., with respect to leaf area index (LAI) and vegetation coverage) driven with preindustrial climate and future climate near 2100, respectively, simulated by eight general circulation models (GCMs). The simulated potential vegetation under the preindustrial control mean climate (CO2 concentration held at 275 ppm) is compared with that under the SRESA1B 2100 mean climate (CO2 concentration stabilizes at 720 ppm beyond 2100). Simulated vegetation response ranges from mild changes of the fractional coverage of different plant functional types to the rather dramatic changes of dominant plant functional types. Although such response differs significantly across different GCM climate projections, a quite consistent spatial pattern emerges, characterized by a considerable poleward spread or shift of temperate and boreal forests in the Northern Hemisphere high latitudes, and a substantial degradation of vegetation type in the tropics (e.g., increase of drought deciduous trees coverage at the expense of evergreen trees) especially in portions of West and southern Africa and South America. Despite the widespread degradation of vegetation type in the tropics, NPP, and growing season LAI are predicted to increase under most GCM scenarios over most of the globe. Carbon fluxes to the atmosphere due to fire generally increase too across the globe. Such responses of NPP and fire occurrence result from the synergistic effects of CO2 concentration changes, climate changes, and vegetation changes. In the HadCM-driven simulation, however, extreme responses are shown in some regions: Deciduous forest is replaced by grasses in large areas in the middle latitudes, and substantial areas in northern South America and southern Africa predominantly covered by evergreen forest are replaced with grasses while NPP and fire emissions reduce drastically (by more than 100%). A future paper will examine how the biosphere response documented here influences the impact of climate change on surface hydrological conditions.

[1] We investigate the capability of global leaf area index (LAI) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) to be assimilated within a process-oriented biosphere model in order to constrain the distribution of carbon fluxes. This is achieved by implementing a sequential data assimilation procedure within a Dynamic Global Vegetation Model. Assimilating two years (2000–2001) of satellite LAI retrievals advances the onset and the end of the growing season at high northern latitudes, by 20 and 40 days respectively. This reduces the growing season length and leads to lower estimates of global annual gross primary productivity (GPP) and net primary productivity (NPP), respectively by 5 and 3% with large variations from one vegetation biome to another. In situ measurements of monthly GPP from eddy flux towers provide an independent check on the performance of the assimilation procedure, resulting in an improvement of 25% in terms of root mean square error between modelled and observed GPP over the model grid points where the flux towers are located.

[1] The current latitudinal gradient in biomass suggests a climate-driven limitation of biomass in high latitudes. Understanding of the underlying processes, and quantification of their relative importance, is required to assess the potential carbon uptake of the biosphere in response to anticipated warming and related changes in tree growth and forest extent in these regions. We analyze the hydrological effects of thawing and freezing of soil on vegetation carbon density (VCD) in permafrost-dominated regions of Siberia using a process-based biogeochemistry-biogeography model, the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). The analysis is based on spatially explicit simulations of coupled daily thaw depth, site hydrology, vegetation distribution, and carbon fluxes influencing VCD subject to climate, soil texture, and atmospheric CO2 concentration. LPJ represents the observed high spring peak of runoff of large Arctic rivers, and simulates a realistic fire return interval of 100 to 200 years in Siberia. The simulated VCD changeover from taiga to tundra is comparable to inventory-based information. Without the consideration of freeze-thaw processes VCD would be overestimated by a factor of 2 in southern taiga to a factor of 5 in northern forest tundra, mainly because available soil water would be overestimated with major effects on fire occurrence and net primary productivity. This suggests that forest growth in high latitudes is not only limited by temperature, radiation, and nutrient availability but also by the availability of liquid soil water.

[1] The coupled global carbon and water cycles are influenced by multiple factors of human activity such as fossil-fuel emissions and land use change. We used the LPJmL Dynamic Global Vegetation Model (DGVM) to quantify the potential influences of human demography, diet, and land allocation, and compare these to the effects of fossil-fuel emissions and corresponding climate change. For this purpose, we generate 12 land use patterns in which these factors are analyzed in a comparative static setting, providing information on their relative importance and the range of potential impacts on the terrestrial carbon and water balance. We show that these aspects of human interference are equally important to climate change and historic fossil-fuel emissions for global carbon stocks but less important for net primary production (NPP). Demand for agricultural area and thus the magnitude of impacts on the carbon and water cycles are mainly determined by constraints on localizing agricultural production and modulated by total demand for agricultural products.

[1] Climate at the Last Glacial Maximum (LGM) together with low atmospheric CO2 concentration forced a shift in vegetation zones, generally favored grasses over woody plants and allowed the colonization of continental shelves. Many studies using models and/or palaeo data have focused on reconstructing climate and vegetation changes between LGM and present, but the implications for changes in fire regime and atmospheric chemistry have not previously been analyzed. We have investigated possible global changes in fire regime using climate model simulations of the LGM to drive the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ) with its embedded fire model, Glob-FIRM. Simulation results reveal a pronounced shift of pyrogenic emission sources to lower latitudes. Global total emissions were slightly reduced. Enhanced nitrogen oxides emissions in the tropics could potentially have increased the oxidizing capacity of the atmosphere, helping to explain the low atmospheric methane concentrations during glacial periods as observed in the ice core records.

This work presents a new dynamic global vegetation model designed as an extension of an existing surface-vegetation-atmosphere transfer scheme which is included in a coupled ocean-atmosphere general circulation model. The new dynamic global vegetation model simulates the principal processes of the continental biosphere influencing the global carbon cycle (photosynthesis, autotrophic and heterotrophic respiration of plants and in soils, fire, etc.) as well as latent, sensible, and kinetic energy exchanges at the surface of soils and plants. As a dynamic vegetation model, it explicitly represents competitive processes such as light competition, sapling establishment, etc. It can thus be used in simulations for the study of feedbacks between transient climate and vegetation cover changes, but it can also be used with a prescribed vegetation distribution. The whole seasonal phenological cycle is prognostically calculated without any prescribed dates or use of satellite data. The model is coupled to the IPSL-CM4 coupled atmosphere-ocean-vegetation model. Carbon and surface energy fluxes from the coupled hydrology-vegetation model compare well with observations at FluxNet sites. Simulated vegetation distribution and leaf density in a global simulation are evaluated against observations, and carbon stocks and fluxes are compared to available estimates, with satisfying results.

[1] We quantify the extent to which net primary production (NPP) of terrestrial ecosystems is water-limited under future atmospheric CO2 enrichment and climate change. Analysis is based on spatially explicit simulations with a Dynamic Global Vegetation Model (DGVM) forced by climate projections from five different General Circulation Models (GCMs) under one emission scenario. We find for many but not all regions that NPP will be less water-limited despite concurrent declines in soil moisture, owing to a frail balance of unfavorable climate effects on soil moisture, reduced transpiration by CO2-induced lower stomatal aperture, and continuous acclimation of vegetation.

[1] Observed evaporative demand has decreased worldwide during the past several decades. This trend is also noted on the Tibetan Plateau, a region that is particularly sensitive to climate change. However, patterns and trends of evapotranspiration and their relationship to drought stress on the Tibetan Plateau are complex and poorly understood. Here, we analyze spatiotemporal changes in evapotranspiration and effective moisture (defined as the ratio of actual evapotranspiration (ETa) to reference crop evapotranspiration (ETo)) based on the modified Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ). Climate data from 80 meteorological stations on the Tibetan Plateau were compiled for the period 1981–2010 and future climate projections were generated by a regional climate model through the 21st century. The results show regional trends towards decreasing ETo and statistically significant increases in ETa (p

[1] The new Global Wildland Fire Emission Model (GWEM) has been developed on the basis of data from the European Space Agency’s monthly Global Burnt Scar satellite product (GLOBSCAR) and results from the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). GWEM computes monthly emissions of more than 40 chemical compounds and aerosols from forest and savanna fires. This study focuses on an evaluation of the GLOBSCAR data set. The GWEM version presented here makes use of the Moderate-Resolution Imaging Spectroradiometer (MODIS) land cover map. Emission totals for the year 2000 are 1741 Tg C, 5716 Tg CO2, 271 Tg CO, 12.52 Tg CH4, 9.09 Tg C (as nonmethane hydrocarbons), 8.08 Tg NOx (as NO), 24.30 Tg PM2.5, 15.80 Tg OC, and 1.84 Tg black carbon. These emissions are lower than other estimates found in literature. An evaluation assesses the uncertainties of the individual input data. The GLOBSCAR product yields reasonable estimates of burnt area for large wildland fires in most parts of the globe but experiences problems in some regions where small fires dominate. The seasonality derived from GLOBSCAR differs from other satellite products detecting active fires owing to the different algorithms applied. Application of the presented GWEM results in global chemistry transport modeling will require additional treatment of small deforestation fires in the tropical rain forest regions and small savanna fires, mainly in subequatorial Africa. Further improvements are expected from a more detailed description of the carbon pools and the inclusion of anthropogenic disturbances in the LPJ model. INDEX TERMS: 0315 Atmospheric Composition and Structure: Biosphere/atmosphere interactions; 0368 Atmospheric Composition and Structure: Troposphere—constituent transport and chemistry; 0322 Atmospheric Composition and Structure: Constituent sources and sinks; 1615 Global Change: Biogeochemical processes (4805); KEYWORDS: vegetation fire emissions, global area burnt satellite products, tropospheric chemistry

We present a bottom-up approach to simulate the terrestrial isotopic carbon variations using the Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM). LPJ is extended to include isotopic fractionation of C-13 at the leaf level during assimilation and includes a full isotopic terrestrial carbon cycle. The model thus allows a quantitative analysis of the net biosphere exchange of CO2 and (CO2)-C-13 with the atmosphere as a function of changes in climate, atmospheric CO2, and the isotope ratio of CO2. LPJ simulates a global mean isotopic fractionation of 17.7% at the leaf level with interannual variations of ca. 0.3%. Interannual variability in the net (CO2)-C-13 flux between atmosphere and terrestrial biosphere is of the order of 15 PgC% yr(-1). It is reduced to 4 PgC% yr(-1) if the leaf-level fractionation factor is held constant at the long term mean. Taking climate driven variable fractionation effects into account in double deconvolution studies we estimate that this could imply shifts of up to 0.8 PgC yr(-1) in the inferred partitioning between terrestrial and oceanic carbon sinks.

From a national collaboration among citizen scientists, educational institutions, environmental organizations, and federal agencies, Jeong et al. developed the first countrywide assessment of springtime tree budding based on in-the-field measurements. The phenological assessment, which measured the timing of bud burst for five tree species, was used to build a model of species-specific budding across the continental United States. Using their tree budding model in forecasts of future climate change, the authors found that by the end of the century, tree budding across the United States will occur more than 1 month earlier in some places.