Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model

The Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ) combines process‐based, large‐scale representations of terrestrial vegetation dynamics and land‐atmosphere carbon and water exchanges in a modular framework. Features include feedback through canopy conductance between photosynthesis and transpiration and interactive coupling between these ‘fast’ processes and other ecosystem processes including resource competition, tissue turnover, population dynamics, soil organic matter and litter dynamics and fire disturbance. Ten plants functional types (PFTs) are differentiated by physiological, morphological, phenological, bioclimatic and fire‐response attributes. Resource competition and differential responses to fire between PFTs influence their relative fractional cover from year to year. Photosynthesis, evapotranspiration and soil water dynamics are modelled on a daily time step, while vegetation structure and PFT population densities are updated annually. Simulations have been made over the industrial period both for specific sites where field measurements were available for model evaluation, and globally on a 0.5°° × 0.5°° grid. Modelled vegetation patterns are consistent with observations, including remotely sensed vegetation structure and phenology. Seasonal cycles of net ecosystem exchange and soil moisture compare well with local measurements. Global carbon exchange fields used as input to an atmospheric tracer transport model (TM2) provided a good fit to observed seasonal cycles of CO₂ concentration at all latitudes. Simulated inter‐annual variability of the global terrestrial carbon balance is in phase with and comparable in amplitude to observed variability in the growth rate of atmospheric CO₂. Global terrestrial carbon and water cycle parameters (pool sizes and fluxes) lie within their accepted ranges. The model is being used to study past, present and future terrestrial ecosystem dynamics, biochemical and biophysical interactions between ecosystems and the atmosphere, and as a component of coupled Earth system models.     

Coordinated approaches to quantify long‐term ecosystem dynamics in response to global change

Many serious ecosystem consequences of climate change will take decades or even centuries to emerge. Long‐term ecological responses to global change are strongly regulated by slow processes, such as changes in species composition, carbon dynamics in soil and by long‐lived plants, and accumulation of nutrient capitals. Understanding and predicting these processes require experiments on decadal time scales. But decadal experiments by themselves may not be adequate because many of the slow processes have characteristic time scales much longer than experiments can be maintained. This article promotes a coordinated approach that combines long‐term, large‐scale global change experiments with process studies and modeling. Long‐term global change manipulative experiments, especially in high‐priority ecosystems such as tropical forests and high‐latitude regions, are essential to maximize information gain concerning future states of the earth system. The long‐term experiments should be conducted in tandem with complementary process studies, such as those using model ecosystems, species replacements, laboratory incubations, isotope tracers, and greenhouse facilities. Models are essential to assimilate data from long‐term experiments and process studies together with information from long‐term observations, surveys, and space‐for‐time studies along environmental and biological gradients. Future research programs with coordinated long‐term experiments, process studies, and modeling have the potential to be the most effective strategy to gain the best information on long‐term ecosystem dynamics in response to global change.     

Potentially complex biosphere responses to transient global warming

Feedback interactions between terrestrial vegetation and climate could alter predictions of the responses of both systems to a doubling of atmospheric CO₂. Most previous analyses of biosphere responses to global warming have used output from equilibrium simulations of current and future climate, as compared to more recently available transient GCM simulations. We compared the vegetation responses to these two different classes of GCM simulation (equilibrium and transient) using an equilibrium vegetation distribution model, MAPSS. Average climatologies were extracted from the transient GCM simulations for current and doubled (2×) CO₂ concentrations (taken to be 2070–2099) for use by the equilibrium vegetation model. However, the 2 × CO₂ climates extracted from the transient GCM simulations were not in equilibrium, having attained only about 65% of their eventual 2 × CO₂ equilibrium temperature change. Most of the differences in global vegetation response appeared to be related to a very different simulated change in the pole to tropic temperature gradient. Also, the transient scenarios produced much larger increases of precipitation in temperate latitudes, commensurate with a minimum in the latitudinal temperature change. Thus, the (equilibrium) global vegetation response, under the transient scenarios, tends more to a greening than a decline in vegetation density, as often previously simulated. It may be that much of the world could become greener during the early phases of global warming, only to reverse in later, more equilibrial stages. However, whether or not the world's vegetation experiences large drought-induced declines or perhaps large vegetation expansions in early stages could be determined by the degree to which elevated CO₂ will actually benefit natural vegetation, an issue still under debate. There may occur oscillations, perhaps on long timescales, between greener and drier phases, due to different frequency responses of the coupled ocean–atmosphere–biosphere interactions. Such oscillations would likely, of themselves, impart further reverberations to the coupled Earth System.     

The effects of land use and climate change on the carbon cycle of Europe over the past 500 years

The long residence time of carbon in forests and soils means that both the current state and future behavior of the terrestrial biosphere are influenced by past variability in climate and anthropogenic land use. Over the last half‐millennium, European terrestrial ecosystems were affected by the cool temperatures of the Little Ice Age, rising CO₂ concentrations, and human induced deforestation and land abandonment. To quantify the importance of these processes, we performed a series of simulations with the LPJ dynamic vegetation model driven by reconstructed climate, land use, and CO₂ concentrations. Although land use change was the major control on the carbon inventory of Europe over the last 500 years, the current state of the terrestrial biosphere is largely controlled by land use change during the past century. Between 1500 and 2000, climate variability led to temporary sequestration events of up to 3 Pg, whereas increasing atmospheric CO₂ concentrations during the 20th century led to an increase in carbon storage of up to 15 Pg. Anthropogenic land use caused between 25 Pg of carbon emissions and 5 Pg of uptake over the same time period, depending on the historical and spatial pattern of past land use and the timing of the reversal from deforestation to afforestation during the last two centuries. None of the currently existing anthropogenic land use change datasets adequately capture the timing of the forest transition in most European countries as recorded in historical observations. Despite considerable uncertainty, our scenarios indicate that with limited management, extant European forests have the potential to absorb between 5 and 12 Pg of carbon at the present day.     

全球变化下海岸带微生物生态研究进展

海岸带地区是元素循环最活跃的自然区域之一,微生物作为地球元素循环的主要驱动者,对该区域生态系统中物质转化与能量流动起着至关重要的作用.本文从典型海岸带生态系统:海岸带湿地、海草床与海藻森林、近岸水体出发,围绕微生物参与的碳、氮循环过程以及其中的温室气体排放情况,综述了在全球气候变化与人为活动干扰的作用下,海岸带地区的微...     

Global ecosystems and fire: Multi‐model assessment of fire‐induced tree‐cover and carbon storage reduction

In this study, we use simulations from seven global vegetation models to provide the first multi‐model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001–2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire‐induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state‐of‐the‐art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems.     

Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models

The possible responses of ecosystem processes to rising atmospheric CO₂ concentration and climate change are illustrated using six dynamic global vegetation models that explicitly represent the interactions of ecosystem carbon and water exchanges with vegetation dynamics. The models are driven by the IPCC IS92a scenario of rising CO₂ ( Wigley et al. 1991 ), and by climate changes resulting from effective CO₂ concentrations corresponding to IS92a, simulated by the coupled ocean atmosphere model HadCM2‐SUL. Simulations with changing CO₂ alone show a widely distributed terrestrial carbon sink of 1.4–3.8 Pg C y^?1 during the 1990s, rising to 3.7–8.6 Pg C y^?1 a century later. Simulations including climate change show a reduced sink both today (0.6–3.0 Pg C y^?1) and a century later (0.3–6.6 Pg C y^?1) as a result of the impacts of climate change on NEP of tropical and southern hemisphere ecosystems. In all models, the rate of increase of NEP begins to level off around 2030 as a consequence of the ‘diminishing return’ of physiological CO₂ effects at high CO₂ concentrations. Four out of the six models show a further, climate‐induced decline in NEP resulting from increased heterotrophic respiration and declining tropical NPP after 2050. Changes in vegetation structure influence the magnitude and spatial pattern of the carbon sink and, in combination with changing climate, also freshwater availability (runoff). It is shown that these changes, once set in motion, would continue to evolve for at least a century even if atmospheric CO₂ concentration and climate could be instantaneously stabilized. The results should be considered illustrative in the sense that the choice of CO₂ concentration scenario was arbitrary and only one climate model scenario was used. However, the results serve to indicate a range of possible biospheric responses to CO₂ and climate change. They reveal major uncertainties about the response of NEP to climate change resulting, primarily, from differences in the way that modelled global NPP responds to a changing climate. The simulations illustrate, however, that the magnitude of possible biospheric influences on the carbon balance requires that this factor is taken into account for future scenarios of atmospheric CO₂ and climate change.     

CO2 emissions from land‐use change affected more by nitrogen cycle,than by the choice of land‐cover data

The high uncertainty in land‐based CO₂ fluxes estimates is thought to be mainly due to uncertainty in not only quantifying historical changes among forests, croplands, and grassland, but also due to different processes included in calculation methods. Inclusion of a nitrogen (N) cycle in models is fairly recent and strongly affects carbon (C) fluxes. In this study, for the first time, we use a model with C and N dynamics with three distinct historical reconstructions of land‐use and land‐use change (LULUC) to quantify LULUC emissions and uncertainty that includes the integrated effects of not only climate and CO₂ but also N. The modeled global average emissions including N dynamics for the 1980s, 1990s, and 2000–2005 were 1.8 ± 0.2, 1.7 ± 0.2, and 1.4 ± 0.2 GtC yr^?1, respectively, (mean and range across LULUC data sets). The emissions from tropics were 0.8 ± 0.2, 0.8 ± 0.2, and 0.7 ± 0.3 GtC yr^?1, and the non tropics were 1.1 ± 0.5, 0.9 ± 0.2, and 0.7 ± 0.1 GtC yr^?1. Compared to previous studies that did not include N dynamics, modeled net LULUC emissions were higher, particularly in the non tropics. In the model, N limitation reduces regrowth rates of vegetation in temperate areas resulting in higher net emissions. Our results indicate that exclusion of N dynamics leads to an underestimation of LULUC emissions by around 70% in the non tropics, 10% in the tropics, and 40% globally in the 1990s. The differences due to inclusion/exclusion of the N cycle of 0.1 GtC yr^?1 in the tropics, 0.6 GtC yr^?1 in the non tropics, and 0.7 GtC yr^?1 globally (mean across land‐cover data sets) in the 1990s were greater than differences due to the land‐cover data in the non tropics and globally (0.2 GtC yr^?1). While land‐cover information is improving with satellite and inventory data, this study indicates the importance of accounting for different processes, in particular the N cycle.     

Seasonal responses of terrestrial ecosystem water‐use efficiency to climate change

Ecosystem water‐use efficiency (EWUE) is an indicator of carbon–water interactions and is defined as the ratio of carbon assimilation (GPP) to evapotranspiration (ET). Previous research suggests an increasing long‐term trend in annual EWUE over many regions and is largely attributed to the physiological effects of rising CO₂. The seasonal trends in EWUE, however, have not yet been analyzed. In this study, we investigate seasonal EWUE trends and responses to various drivers during 1982–2008. The seasonal cycle for two variants of EWUE, water‐use efficiency (WUE, GPP/ET), and transpiration‐based WUE (WUE_t, the ratio of GPP and transpiration), is analyzed from 0.5° gridded fields from four process‐based models and satellite‐based products, as well as a network of 63 local flux tower observations. WUE derived from flux tower observations shows moderate seasonal variation for most latitude bands, which is in agreement with satellite‐based products. In contrast, the seasonal EWUE trends are not well captured by the same satellite‐based products. Trend analysis, based on process‐model factorial simulations separating effects of climate, CO₂, and nitrogen deposition (NDEP), further suggests that the seasonal EWUE trends are mainly associated with seasonal trends of climate, whereas CO₂ and NDEP do not show obvious seasonal difference in EWUE trends. About 66% grid cells show positive annual WUE trends, mainly over mid‐ and high northern latitudes. In these regions, spring climate change has amplified the effect of CO₂ in increasing WUE by more than 0.005 gC m⁻² mm⁻¹ yr⁻¹ for 41% pixels. Multiple regression analysis further shows that the increase in springtime WUE in the northern hemisphere is the result of GPP increasing faster than ET because of the higher temperature sensitivity of GPP relative to ET. The partitioning of annual EWUE to seasonal components provides new insight into the relative sensitivities of GPP and ET to climate, CO_2, and NDEP.     

Robust dynamics of Amazon dieback to climate change with perturbed ecosystem model parameters

Climate change science is increasingly concerned with methods for managing and integrating sources of uncertainty from emission storylines, climate model projections, and ecosystem model parameterizations. In tropical ecosystems, regional climate projections and modeled ecosystem responses vary greatly, leading to a significant source of uncertainty in global biogeochemical accounting and possible future climate feedbacks. Here, we combine an ensemble of IPCC‐AR4 climate change projections for the Amazon Basin (eight general circulation models) with alternative ecosystem parameter sets for the dynamic global vegetation model, LPJmL. We evaluate LPJmL simulations of carbon stocks and fluxes against flux tower and aboveground biomass datasets for individual sites and the entire basin. Variability in LPJmL model sensitivity to future climate change is primarily related to light and water limitations through biochemical and water‐balance‐related parameters. Temperature‐dependent parameters related to plant respiration and photosynthesis appear to be less important than vegetation dynamics (and their parameters) for determining the magnitude of ecosystem response to climate change. Variance partitioning approaches reveal that relationships between uncertainty from ecosystem dynamics and climate projections are dependent on geographic location and the targeted ecosystem process. Parameter uncertainty from the LPJmL model does not affect the trajectory of ecosystem response for a given climate change scenario and the primary source of uncertainty for Amazon ‘dieback’ results from the uncertainty among climate projections. Our approach for describing uncertainty is applicable for informing and prioritizing policy options related to mitigation and adaptation where long‐term investments are required.     

Temporal response of soil organic carbon after grassland‐related land‐use change

The net flux of CO₂ exchanged with the atmosphere following grassland‐related land‐use change (LUC) depends on the subsequent temporal dynamics of soil organic carbon (SOC). Yet, the magnitude and timing of these dynamics are still unclear. We compiled a global data set of 836 paired‐sites to quantify temporal SOC changes after grassland‐related LUC. In order to discriminate between SOC losses from the initial ecosystem and gains from the secondary one, the post‐LUC time series of SOC data was combined with satellite‐based net primary production observations as a proxy of carbon input to the soil. Globally, land conversion from either cropland or forest into grassland leads to SOC accumulation; the reverse shows net SOC loss. The SOC response curves vary between different regions. Conversion of cropland to managed grassland results in more SOC accumulation than natural grassland recovery from abandoned cropland. We did not consider the biophysical variables (e.g., climate conditions and soil properties) when fitting the SOC turnover rate into the observation data but analyzed the relationships between the fitted turnover rate and these variables. The SOC turnover rate is significantly correlated with temperature and precipitation (p < 0.05), but not with the clay fraction of soils (p > 0.05). Comparing our results with predictions from bookkeeping models, we found that bookkeeping models overestimate by 56% of the long‐term (100 years horizon) cumulative SOC emissions for grassland‐related LUC types in tropical and temperate regions since 2000. We also tested the spatial representativeness of our data set and calculated SOC response curves using the representative subset of sites in each region. Our study provides new insight into the impact grassland‐related LUC on the global carbon budget and sheds light on the potential of grassland conservation for climate mitigation.     

Long‐term terrestrial carbon dynamics in the Midwestern United States during 1850–2015: Roles of land use and cover change and agricultural management

To meet the increasing food and biofuel demand, the Midwestern United States has become one of the most intensively human‐disturbed hotspots, characterized by widespread cropland expansion and various management practices. However, the role of human activities in the carbon (C) cycling across managed landscape remains far from certain. In this study, based on state‐ and national census, field experiments, and model simulation, we comprehensively examined long‐term carbon storage change in response to land use and cover change (LUCC) and agricultural management in the Midwest from 1850 to 2015. We also quantified estimation uncertainties related to key parameter values. Model estimation showed LUCC led to a reduction of 1.35 Pg (with a range of 1.3–1.4 Pg) in vegetation C pool of the Midwest, yet agricultural management barely affected vegetation C change. In comparison, LUCC reduced SOC by 4.5 Pg (3.1 to 6.2 Pg), while agricultural management practices increased SOC stock by 0.9 Pg. Moreover, we found 45% of the study area was characterized by continuously decreasing SOC caused by LUCC, and SOC in 13% and 31% of the area was fully and partially recovered, respectively, since 1850. Agricultural management was estimated to increase the area of full recovery and partial recovery by 8.5% and 1.1%. Our results imply that LUCC plays an essential role in regional C balance, and more importantly, sustainable land management can be beneficial for strengthening C sequestration of the agroecosystems in the Midwestern US, which may serve as an important contributor to C sinks in the US.     

Land use for animal production in global change studies: Defining and characterizing a framework

Land use for animal production influences the earth system in a variety of ways, including local‐scale modification to biodiversity, soils, and nutrient cycling; regional changes in albedo and hydrology; and global‐scale changes in greenhouse gas and aerosol concentrations. Pasture is furthermore the single most extensive form of land cover, currently comprising about 22–26% of the earth's ice‐free land surface. Despite the importance and variable expressions of animal production, distinctions among different systems are effectively absent from studies of land use and land cover change. This deficiency is improving; however, livestock production system classifications are rarely applied in this context, and the most popular global land cover inventories still present only a single, usually poorly defined category of “pasture” or “rangeland” with no characterization of land use. There is a marked lack of bottom‐up, evidence‐based methodology, creating a pressing need to incorporate cross‐disciplinary evidence of past and present animal production systems into global change studies. Here, we present a framework, modified from existing livestock production systems, that is rooted in sociocultural, socioeconomic, and ecological contexts. The framework defines and characterizes the range of land usage pertaining to animal production, and is suitable for application in land use inventories and scenarios, land cover modeling, and studies on sustainable land use in the past, present, and future.     

Time‐lag effects of global vegetation responses to climate change

Climate conditions significantly affect vegetation growth in terrestrial ecosystems. Due to the spatial heterogeneity of ecosystems, the vegetation responses to climate vary considerably with the diverse spatial patterns and the time‐lag effects, which are the most important mechanism of climate–vegetation interactive effects. Extensive studies focused on large‐scale vegetation–climate interactions use the simultaneous meteorological and vegetation indicators to develop models; however, the time‐lag effects are less considered, which tends to increase uncertainty. In this study, we aim to quantitatively determine the time‐lag effects of global vegetation responses to different climatic factors using the GIMMS3g NDVI time series and the CRU temperature, precipitation, and solar radiation datasets. First, this study analyzed the time‐lag effects of global vegetation responses to different climatic factors. Then, a multiple linear regression model and partial correlation model were established to statistically analyze the roles of different climatic factors on vegetation responses, from which the primary climate‐driving factors for different vegetation types were determined. The results showed that (i) both the time‐lag effects of the vegetation responses and the major climate‐driving factors that significantly affect vegetation growth varied significantly at the global scale, which was related to the diverse vegetation and climate characteristics; (ii) regarding the time‐lag effects, the climatic factors explained 64% variation of the global vegetation growth, which was 11% relatively higher than the model ignoring the time‐lag effects; (iii) for the area with a significant change trend (for the period 1982–2008) in the global GIMMS3g NDVI (P < 0.05), the primary driving factor was temperature; and (iv) at the regional scale, the variation in vegetation growth was also related to human activities and natural disturbances. Considering the time‐lag effects is quite important for better predicting and evaluating the vegetation dynamics under the background of global climate change.     

Land use change and the global carbon cycle: the role of tropical soils

Millions of hectares of tropical forest are cleared annually for agriculture, pasture, shifting cultivation and timber. One result of these changes in land use is the release of CO₂ from the cleared vegetation and soils. Although there is uncertainty as to the size of this release, it appears to be a major source of atmospheric CO₂, second only to the release from the combustion of fossil fuels. This study estimates the release of CO₂ from tropical soils using a computer model that simulates land use change in the tropics and data on (1) the carbon content of forest soils before clearing; (2) the changes in the carbon content under the various types of land use; and (3) the area of forest converted to each use. It appears that the clearing and use of tropical soils affects their carbon content to a depth of about 40 cm. Soils of tropical closed forests contain approximately 6.7 kg C · m^-2; soils of tropical open forests contain approximately 5.2 kg C · m^-2 to this depth. The cultivation of tropical soils reduces their carbon content by 40% 5 yr after clearing; the use of these soils for pasture reduces it by about 20%. Logging in tropical forests appears to have little effect on soil carbon. The carbon content of soils used by shifting cultivators returns to the level found under primary forest about 35 yr after abandonment. The estimated net release of carbon from tropical soils due to land use change was 0.11–0.26 × 10¹⁵ g in 1980.     

A Land System representation for global assessments and land‐use modeling

Current global scale land‐change models used for integrated assessments and climate modeling are based on classifications of land cover. However, land‐use management intensity and livestock keeping are also important aspects of land use, and are an integrated part of land systems. This article aims to classify, map, and to characterize Land Systems (LS) at a global scale and analyze the spatial determinants of these systems. Besides proposing such a classification, the article tests if global assessments can be based on globally uniform allocation rules. Land cover, livestock, and agricultural intensity data are used to map LS using a hierarchical classification method. Logistic regressions are used to analyze variation in spatial determinants of LS. The analysis of the spatial determinants of LS indicates strong associations between LS and a range of socioeconomic and biophysical indicators of human‐environment interactions. The set of identified spatial determinants of a LS differs among regions and scales, especially for (mosaic) cropland systems, grassland systems with livestock, and settlements. (Semi‐)Natural LS have more similar spatial determinants across regions and scales. Using LS in global models is expected to result in a more accurate representation of land use capturing important aspects of land systems and land architecture: the variation in land cover and the link between land‐use intensity and landscape composition. Because the set of most important spatial determinants of LS varies among regions and scales, land‐change models that include the human drivers of land change are best parameterized at sub‐global level, where similar biophysical, socioeconomic and cultural conditions prevail in the specific regions.     

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.     

Sensitivity of a dynamic global vegetation model to climate and atmospheric CO2

The equilibrium carbon storage capacity of the terrestrial biosphere has been investigated by running the Lund–Potsdam–Jena Dynamic Global Vegetation Model to equilibrium for a range of CO₂ concentrations and idealized climate states. Local climate is defined by the combination of an observation-based climatology and perturbation patterns derived from a 4 × CO₂ warming simulations, which are linearly scaled to global mean temperature deviations, Δ T _glob. Global carbon storage remains close to its optimum for Δ T _glob in the range of ±3°C in simulations with constant atmospheric CO₂. The magnitude of the carbon loss to the atmosphere per unit change in global average surface temperature shows a pronounced nonlinear threshold behavior. About twice as much carbon is lost per degree warming for Δ T _glob above 3°C than for present climate. Tropical, temperate, and boreal trees spread poleward with global warming. Vegetation dynamics govern the distribution of soil carbon storage and turnover in the climate space. For cold climate conditions, the global average decomposition rate of litter and soil decreases with warming, despite local increases in turnover rates. This result is not compatible with the assumption, commonly made in global box models, that soil turnover increases exponentially with global average surface temperature, over a wide temperature range.