Reconstruction and attribution of the carbon sink of European forests between 1950 and 2000

European forests are an important carbon sink; however, the relative contributions to this sink of climate, atmospheric CO₂ concentration ([CO₂]), nitrogen deposition and forest management are under debate. We attributed the European carbon sink in forests using ORCHIDEE‐FM, a process‐based vegetation model that differs from earlier versions of ORCHIDEE by its explicit representation of stand growth and idealized forest management. The model was applied on a grid across Europe to simulate changes in the net ecosystem productivity (NEP) of forests with and without changes in climate, [CO₂] and age structure, the three drivers represented in ORCHIDEE‐FM. The model simulates carbon stocks and volume increment that are comparable – root mean square error of 2 m³ ha^?1 yr^?1 and 1.7 kg C m^?2 respectively – with inventory‐derived estimates at country level for 20 European countries. Our simulations estimate a mean European forest NEP of 175 ± 52 g C m^?2 yr^?1 in the 1990s. The model simulation that is most consistent with inventory records provides an upwards trend of forest NEP of 1 ± 0.5 g C m^?2 yr^?2 between 1950 and 2000 across the EU 25. Furthermore, the method used for reconstructing past age structure was found to dominate its contribution to temporal trends in NEP. The potentially large fertilizing effect of nitrogen deposition cannot be told apart, as the model does not explicitly simulate the nitrogen cycle. Among the three drivers that were considered in this study, the fertilizing effect of increasing [CO₂] explains about 61% of the simulated trend, against 26% to changes in climate and 13% only to changes in forest age structure. The major role of [CO₂] at the continental scale is due to its homogeneous impact on net primary productivity (NPP). At the local scale, however, changes in climate and forest age structure often dominate trends in NEP by affecting NPP and heterotrophic respiration.     

Large uncertainty in carbon uptake potential of land‐based climate‐change mitigation efforts

Most climate mitigation scenarios involve negative emissions, especially those that aim to limit global temperature increase to 2°C or less. However, the carbon uptake potential in land‐based climate change mitigation efforts is highly uncertain. Here, we address this uncertainty by using two land‐based mitigation scenarios from two land‐use models (IMAGE and MAgPIE) as input to four dynamic global vegetation models (DGVMs; LPJ‐GUESS, ORCHIDEE, JULES, LPJmL). Each of the four combinations of land‐use models and mitigation scenarios aimed for a cumulative carbon uptake of ~130 GtC by the end of the century, achieved either via the cultivation of bioenergy crops combined with carbon capture and storage (BECCS) or avoided deforestation and afforestation (ADAFF). Results suggest large uncertainty in simulated future land demand and carbon uptake rates, depending on the assumptions related to land use and land management in the models. Total cumulative carbon uptake in the DGVMs is highly variable across mitigation scenarios, ranging between 19 and 130 GtC by year 2099. Only one out of the 16 combinations of mitigation scenarios and DGVMs achieves an equivalent or higher carbon uptake than achieved in the land‐use models. The large differences in carbon uptake between the DGVMs and their discrepancy against the carbon uptake in IMAGE and MAgPIE are mainly due to different model assumptions regarding bioenergy crop yields and due to the simulation of soil carbon response to land‐use change. Differences between land‐use models and DGVMs regarding forest biomass and the rate of forest regrowth also have an impact, albeit smaller, on the results. Given the low confidence in simulated carbon uptake for a given land‐based mitigation scenario, and that negative emissions simulated by the DGVMs are typically lower than assumed in scenarios consistent with the 2°C target, relying on negative emissions to mitigate climate change is a highly uncertain strategy.     

Tree‐ring analysis and modeling approaches yield contrary response of circumboreal forest productivity to climate change

Circumboreal forest ecosystems are exposed to a larger magnitude of warming in comparison with the global average, as a result of warming‐induced environmental changes. However, it is not clear how tree growth in these ecosystems responds to these changes. In this study, we investigated the sensitivity of forest productivity to climate change using ring width indices (RWI) from a tree‐ring width dataset accessed from the International Tree‐Ring Data Bank and gridded climate datasets from the Climate Research Unit. A negative relationship of RWI with summer temperature and recent reductions in RWI were typically observed in continental dry regions, such as inner Alaska and Canada, southern Europe, and the southern part of eastern Siberia. We then developed a multiple regression model with regional meteorological parameters to predict RWI, and then applied to these models to predict how tree growth will respond to twenty‐first‐century climate change (RCP8.5 scenario). The projections showed a spatial variation and future continuous reduction in tree growth in those continental dry regions. The spatial variation, however, could not be reproduced by a dynamic global vegetation model (DGVM). The DGVM projected a generally positive trend in future tree growth all over the circumboreal region. These results indicate that DGVMs may overestimate future wood net primary productivity (NPP) in continental dry regions such as these; this seems to be common feature of current DGVMs. DGVMs should be able to express the negative effect of warming on tree growth, so that they simulate the observed recent reduction in tree growth in continental dry regions.     

Climate-growth relations of congeneric tree species vary across a tropical vegetation gradient in Brazil

Seasonally dry tropical forests are an important global climatic regulator, a main driver of the global carbon sink dynamics and are predicted to suffer future reductions in their productivity due to climate change. Yet, little is known about how interannual climate variability affects tree growth and how climate-growth responses vary across rainfall gradients in these forests. Here we evaluate changes in climate sensitivity of tree growth along an environmental gradient of seasonally dry tropical vegetation types (evergreen forest – savannah – dry forest) in Northeastern Brazil, using congeneric species of two common neotropical genera: Aspidosperma and Handroanthus. We built tree-ring width chronologies for each species × forest type combinations and explored how growth variability correlated with local (precipitation, temperature) and global (the El Niño Southern Oscillation - ENSO) climatic factors. We also assessed how growth sensitivity to climate and the presence of growth deviations varied along the gradient. Precipitation stimulates tree growth and was the main growth-influencing factor across vegetation types. Trees in the dry forest site showed highest growth sensitivity to interannual variation in precipitation. Temperature and ENSO phenomena correlated negatively with growth and sensitivity to both climatic factors were similar across sites. Negative growth deviations were present and found mostly in the dry-forest species. Our results reveal a dominant effect of precipitation on tree growth in seasonally dry tropical forests and suggest that along the gradient, dry forests are the most sensitivity to drought. These forests may therefore be the most vulnerable to the deleterious effects of future climatic changes. These results highlight the importance of understanding the climatic sensitivity of different tropical forests. This understanding is key to predict the carbon dynamics in tropical regions, and sensitivity differences should be considered when prioritizing conservation measures of seasonally dry topical forests.     

Contrasting responses of net primary productivity to inter-annual variability and changes of climate among three forest types in northern China

Aims A lack of explicit information on differential controls on net primary productivity (NPP) across regions and ecosystem types is largely responsible for uncertainties in global trajectories of terrestrial carbon balance with changing environment. The objectives of this study were to determine how NPP of different forest types would respond to inter-annual variability of climate and to examine the responses of NPP to future climate change scenarios across contrasting forest types in northern China.Methods We investigated inter-annual variations of NPP in relation to climate variability across three forest types in northern China, including a boreal forest dominated by Larix gmelinii Rupr., and two temperate forests dominated by Pinus tabulaeformis Carr. and Quercus wutaishanica Mayr., respectively, and studied the responses of NPP in these forests to predicted changes in climate for the periods 2011–40, 2041–70 and 2070–100 under carbon emission scenarios A2 and B2 of Intergovernmental Panel on Climate Change. We simulated the responses of NPP to predicted changes in future climate as well as inter-annual variability of the present climate with the Biome-BGC version 4.2 based on site- and species-specific parameters. The modeled forest NPP data were validated against values in literature for similar types of forests and compared with inter-annual growth variations reflected by tree-ring width index (RWI) at the study sites.Important findings Inter-annual variations in modeled NPP during the period 1960–06 were mostly consistent with the temporal patterns in RWI. There were contrasting responses of modeled NPP among the three forest types to inter-annual variability of the present climate as well as to predicted changes in future climate. The modeled NPP was positively related to annual mean air temperature in the L. gmelinii forest (P < 0.001), but negatively in the P. tabulaeformis forest (P = 0.05) and the Q. wutaishanica forest (P = 0.03), while the relationships of modeled NPP with annual precipitation for the three forest types were all positive. Multiple stepwise regression analyses showed that temperature was a more important constraint of NPP than precipitation in the L. gmelinii forest, whereas precipitation appeared to be a prominent factor limiting the growth in P. tabulaeformis and Q. wutaishanica. Model simulations suggest marked, but differential increases in NPP across the three forest types with predicted changes in future climate.     

Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes

Process‐based models can be classified into: (a) terrestrial biogeochemical models (TBMs), which simulate fluxes of carbon, water and nitrogen coupled within terrestrial ecosystems, and (b) dynamic global vegetation models (DGVMs), which further couple these processes interactively with changes in slow ecosystem processes depending on resource competition, establishment, growth and mortality of different vegetation types. In this study, four models – RHESSys, GOTILWA+, LPJ‐GUESS and ORCHIDEE – representing both modelling approaches were compared and evaluated against benchmarks provided by eddy‐covariance measurements of carbon and water fluxes at 15 forest sites within the EUROFLUX project. Overall, model‐measurement agreement varied greatly among sites. Both modelling approaches have somewhat different strengths, but there was no model among those tested that universally performed well on the two variables evaluated. Small biases and errors suggest that ORCHIDEE and GOTILWA+ performed better in simulating carbon fluxes while LPJ‐GUESS and RHESSys did a better job in simulating water fluxes. In general, the models can be considered as useful tools for studies of climate change impacts on carbon and water cycling in forests. However, the various sources of variation among models simulations and between models simulations and observed data described in this study place some constraints on the results and to some extent reduce their reliability. For example, at most sites in the Mediterranean region all models generally performed poorly most likely because of problems in the representation of water stress effects on both carbon uptake by photosynthesis and carbon release by heterotrophic respiration (R_h). The use of flux data as a means of assessing key processes in models of this type is an important approach to improving model performance. Our results show that the models have value but that further model development is necessary with regard to the representation of the some of the key ecosystem processes.     

Modeling Productivity in Mangrove Forests as Impacted by Effective Soil Water Availability and Its Sensitivity to Climate Change Using Biome-BGC

Ecosystem dynamics and the responses to climate change in mangrove forests are poorly understood. We applied the biogeochemical process model Biome-BGC to simulate the dynamics of net primary productivity (NPP) and leaf area index (LAI) under the present and future climate conditions in mangrove forests in Shenzhen, Zhanjiang, and Qiongshan across the southern coast of China, and in three monocultural mangrove stands of two native species, Avicennia marina and Kandelia obovata, and one exotic species, Sonneratia apetala, in Shenzhen. The soil hydrological process of the model was modified by incorporating a soil water (SW) stress index to account for the impact of the effective SW availability in the coastal wetland. Our modified Biome-BGC well predicted the dynamics of NPP and LAI in the mangrove forests at the study sites. We found that the six mangrove systems differed in sensitivity to variations in the effective SW availability. At the ecosystem level, however, soil salinity alone could not entirely explain the limitation of the effective SW availability on the productivity of mangrove forests. Increasing atmospheric CO₂ concentration differentially affected growth of different mangrove species but only had a small impact on NPP (<7%); whereas a doubling of atmospheric CO₂ concentration associated with a 2°C temperature rise would increase NPP by 14–19% across the three geographically separate mangrove forests and by 12% to as much as 68% across the three monocultural mangrove stands. Our simulation analysis indicates that temperature change is more important than increasing CO₂ concentration in affecting productivity of mangroves at the ecosystem level, and that different mangrove species differ in sensitivity to increases in temperature and CO₂ concentration.     

Evaluation of climate‐related carbon turnover processes in global vegetation models for boreal and temperate forests

Turnover concepts in state‐of‐the‐art global vegetation models (GVMs) account for various processes, but are often highly simplified and may not include an adequate representation of the dominant processes that shape vegetation carbon turnover rates in real forest ecosystems at a large spatial scale. Here, we evaluate vegetation carbon turnover processes in GVMs participating in the Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP, including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT) using estimates of vegetation carbon turnover rate (k) derived from a combination of remote sensing based products of biomass and net primary production (NPP). We find that current model limitations lead to considerable biases in the simulated biomass and in k (severe underestimations by all models except JeDi and VISIT compared to observation‐based average k), likely contributing to underestimation of positive feedbacks of the northern forest carbon balance to climate change caused by changes in forest mortality. A need for improved turnover concepts related to frost damage, drought, and insect outbreaks to better reproduce observation‐based spatial patterns in k is identified. As direct frost damage effects on mortality are usually not accounted for in these GVMs, simulated relationships between k and winter length in boreal forests are not consistent between different regions and strongly biased compared to the observation‐based relationships. Some models show a response of k to drought in temperate forests as a result of impacts of water availability on NPP, growth efficiency or carbon balance dependent mortality as well as soil or litter moisture effects on leaf turnover or fire. However, further direct drought effects such as carbon starvation (only in HYBRID4) or hydraulic failure are usually not taken into account by the investigated GVMs. While they are considered dominant large‐scale mortality agents, mortality mechanisms related to insects and pathogens are not explicitly treated in these models.     

Microbial dynamics and soil physicochemical properties explain large‐scale variations in soil organic carbon

First‐order organic matter decomposition models are used within most Earth System Models (ESMs) to project future global carbon cycling; these models have been criticized for not accurately representing mechanisms of soil organic carbon (SOC) stabilization and SOC response to climate change. New soil biogeochemical models have been developed, but their evaluation is limited to observations from laboratory incubations or few field experiments. Given the global scope of ESMs, a comprehensive evaluation of such models is essential using in situ observations of a wide range of SOC stocks over large spatial scales before their introduction to ESMs. In this study, we collected a set of in situ observations of SOC, litterfall and soil properties from 206 sites covering different forest and soil types in Europe and China. These data were used to calibrate the model MIMICS (The MIcrobial‐MIneral Carbon Stabilization model), which we compared to the widely used first‐order model CENTURY. We show that, compared to CENTURY, MIMICS more accurately estimates forest SOC concentrations and the sensitivities of SOC to variation in soil temperature, clay content and litter input. The ratios of microbial biomass to total SOC predicted by MIMICS agree well with independent observations from globally distributed forest sites. By testing different hypotheses regarding (using alternative process representations) the physicochemical constraints on SOC deprotection and microbial turnover in MIMICS, the errors of simulated SOC concentrations across sites were further decreased. We show that MIMICS can resolve the dominant mechanisms of SOC decomposition and stabilization and that it can be a reliable tool for predictions of terrestrial SOC dynamics under future climate change. It also allows us to evaluate at large scale the rapidly evolving understanding of SOC formation and stabilization based on laboratory and limited filed observation.     

Multiscale modeling of spring phenology across Deciduous Forests in the Eastern United States

Phenological events, such as bud burst, are strongly linked to ecosystem processes in temperate deciduous forests. However, the exact nature and magnitude of how seasonal and interannual variation in air temperatures influence phenology is poorly understood, and model‐based phenology representations fail to capture local‐ to regional‐scale variability arising from differences in species composition. In this paper, we use a combination of surface meteorological data, species composition maps, remote sensing, and ground‐based observations to estimate models that better represent how community‐level species composition affects the phenological response of deciduous broadleaf forests to climate forcing at spatial scales that are typically used in ecosystem models. Using time series of canopy greenness from repeat digital photography, citizen science data from the USA National Phenology Network, and satellite remote sensing‐based observations of phenology, we estimated and tested models that predict the timing of spring leaf emergence across five different deciduous broadleaf forest types in the eastern United States. Specifically, we evaluated two different approaches: (i) using species‐specific models in combination with species composition information to ‘upscale’ model predictions and (ii) using repeat digital photography of forest canopies that observe and integrate the phenological behavior of multiple representative species at each camera site to calibrate a single model for all deciduous broadleaf forests. Our results demonstrate variability in cumulative forcing requirements and photoperiod cues across species and forest types, and show how community composition influences phenological dynamics over large areas. At the same time, the response of different species to spatial and interannual variation in weather is, under the current climate regime, sufficiently similar that the generic deciduous forest model based on repeat digital photography performed comparably to the upscaled species‐specific models. More generally, results from this analysis demonstrate how in situ observation networks and remote sensing data can be used to synergistically calibrate and assess regional parameterizations of phenology in models.     

Globally consistent climate sensitivity of natural disturbances across boreal and temperate forest ecosystems

Disturbance regimes are changing in forests across the world in response to global climate change. Despite the profound impacts of disturbances on ecosystem services and biodiversity, assessments of disturbances at the global scale remain scarce. Here, we analyzed natural disturbances in boreal and temperate forest ecosystems for the period 2001–2014, aiming to 1) quantify their within- and between-biome variation and 2) compare the climate sensitivity of disturbances across biomes. We studied 103 unmanaged forest landscapes with a total land area of 28.2 × 10⁶ ha, distributed across five continents. A consistent and comprehensive quantification of disturbances was derived by combining satellite-based disturbance maps with local expert knowledge of disturbance agents. We used Gaussian finite mixture models to identify clusters of landscapes with similar disturbance activity as indicated by the percent forest area disturbed as well as the size, edge density and perimeter–area-ratio of disturbed patches. The climate sensitivity of disturbances was analyzed using Bayesian generalized linear mixed effect models and a globally consistent climate dataset. Within-biome variation in natural disturbances was high in both boreal and temperate biomes, and disturbance patterns did not vary systematically with latitude or biome. The emergent clusters of disturbance activity in the boreal zone were similar to those in the temperate zone, but boreal landscapes were more likely to experience high disturbance activity than their temperate counterparts. Across both biomes high disturbance activity was particularly associated with wildfire, and was consistently linked to years with warmer and drier than average conditions. Natural disturbances are a key driver of variability in boreal and temperate forest ecosystems, with high similarity in the disturbance patterns between both biomes. The universally high climate sensitivity of disturbances across boreal and temperate ecosystems indicates that future climate change could substantially increase disturbance activity.     

Terrestrial gross primary production inferred from satellite fluorescence and vegetation models

Determining the spatial and temporal distribution of terrestrial gross primary production (GPP) is a critical step in closing the Earth's carbon budget. Dynamical global vegetation models (DGVMs) provide mechanistic insight into GPP variability but diverge in predicting the response to climate in poorly investigated regions. Recent advances in the remote sensing of solar‐induced chlorophyll fluorescence (SIF) opens up a new possibility to provide direct global observational constraints for GPP. Here, we apply an optimal estimation approach to infer the global distribution of GPP from an ensemble of eight DGVMs constrained by global measurements of SIF from the Greenhouse Gases Observing SATellite (GOSAT). These estimates are compared to flux tower data in N. America, Europe, and tropical S. America, with careful consideration of scale differences between models, GOSAT, and flux towers. Assimilation of GOSAT SIF with DGVMs causes a redistribution of global productivity from northern latitudes to the tropics of 7–8 Pg C yr^?1 from 2010 to 2012, with reduced GPP in northern forests (~3.6 Pg C yr^?1) and enhanced GPP in tropical forests (~3.7 Pg C yr^?1). This leads to improvements in the structure of the seasonal cycle, including earlier dry season GPP loss and enhanced peak‐to‐trough GPP in tropical forests within the Amazon Basin and reduced growing season length in northern croplands and deciduous forests. Uncertainty in predicted GPP (estimated from the spread of DGVMs) is reduced by 40–70% during peak productivity suggesting the assimilation of GOSAT SIF with models is well‐suited for benchmarking. We conclude that satellite fluorescence augurs a new opportunity to quantify the GPP response to climate drivers and the potential to constrain predictions of carbon cycle evolution.     

Large‐scale disturbance legacies and the climate sensitivity of primary Picea abies forests

Determining the drivers of shifting forest disturbance rates remains a pressing global change issue. Large‐scale forest dynamics are commonly assumed to be climate driven, but appropriately scaled disturbance histories are rarely available to assess how disturbance legacies alter subsequent disturbance rates and the climate sensitivity of disturbance. We compiled multiple tree ring‐based disturbance histories from primary Picea abies forest fragments distributed throughout five European landscapes spanning the Bohemian Forest and the Carpathian Mountains. The regional chronology includes 11,595 tree cores, with ring dates spanning the years 1750–2000, collected from 560 inventory plots in 37 stands distributed across a 1,000 km geographic gradient, amounting to the largest disturbance chronology yet constructed in Europe. Decadal disturbance rates varied significantly through time and declined after 1920, resulting in widespread increases in canopy tree age. Approximately 75% of current canopy area recruited prior to 1900. Long‐term disturbance patterns were compared to an historical drought reconstruction, and further linked to spatial variation in stand structure and contemporary disturbance patterns derived from LANDSAT imagery. Historically, decadal Palmer drought severity index minima corresponded to higher rates of canopy removal. The severity of contemporary disturbances increased with each stand's estimated time since last major disturbance, increased with mean diameter, and declined with increasing within‐stand structural variability. Reconstructed spatial patterns suggest that high small‐scale structural variability has historically acted to reduce large‐scale susceptibility and climate sensitivity of disturbance. Reduced disturbance rates since 1920, a potential legacy of high 19th century disturbance rates, have contributed to a recent region‐wide increase in disturbance susceptibility. Increasingly common high‐severity disturbances throughout primary Picea forests of Central Europe should be reinterpreted in light of both legacy effects (resulting in increased susceptibility) and climate change (resulting in increased exposure to extreme events).     

Interannual NPP variation and trend of Picea schrenkiana forests under changing climate conditions in the Tianshan Mountains, Xinjiang, China

The interannual net primary production variation and trends of a Picea schrenkiana forest were investigated in the context of historical changes in climate and increased atmospheric CO₂ concentration at four sites in the Tianshan Mountain range, China. Historical changes in climate and atmospheric CO₂ concentration were used as Biome–BGC model drivers to evaluate the spatial patterns and temporal trends of net primary production (NPP). The temporal dynamics of NPP of P. schrenkiana forests were different in the western, middle and eastern sites of Tianshan, which showed substantial interannual variation. Climate changes would result in increased NPP at all study sites, but only the change in NPP in the western forest (3.186 gC m⁻² year⁻¹, P < 0.05) was statistically significant. Our study also showed a higher increase in the air temperature, precipitation and NPP during 1987–2000 than 1961–1986. Statistical analysis indicates that changes in NPP are positively correlated with annual precipitation (R = 0.77–0.92) but that NPP was less sensitive to changes in air temperature. According to the simulation, increases in atmospheric CO₂ increased NPP by improving the water use efficiency. The results of this study show that the Tianshan Mount boreal forest ecosystem is sensitive to historical changes in climate and increasing atmospheric CO₂. The relative impacts of these variations on NPP interact in complex ways and are spatially variable, depending on local conditions and climate gradients. W. Sang and H. Su contributed equally to this paper, arranged in alphabetical order by surnames.     

Responses of vegetation structure and primary production of a forest transect in eastern China to global change

Aim A regional model of vegetation dynamics was enhanced to include biogeochemical cycling of nitrogen and was then applied to a forest transect in east China (FTEC) in order to investigate the responses of the transect to possible global change. Location Eastern China. Methods Biomass and nitrogen concentration of green and nongreen portions of vegetation, moisture contents of three soil layers, and total and available soil nitrogen are included as state variables in the enhanced model. The model was parameterized and validated against field observations of biomass, productivity, plant and soil nitrogen concentration, nitrogen uptake, a vegetation index derived from satellite remote sensing and digital maps of vegetation and soil distributions along a forest transect in eastern China (FTEC). The model was applied to FTEC in order to investigate the responsive characteristics of the ecosystems to global climatic change. Scenarios of climate change under doubled CO₂ produced by seven general circulation models (GCM) were used to drive the model. Results The simulations indicated that the model is capable of simulating accurately potential vegetation distribution and net primary productivity under contemporary climatic conditions. The simulations for GCM‐projected future climate scenarios with doubled atmospheric CO₂ concentration predicted that broadleaf forests would increase, but conifer forests, shrubs and grasses would decrease; and that deciduous forests would have the largest relative increase, but evergreen shrubs would have the largest decrease. Conclusions The overall effects of doubling CO₂ and climatic changes on FTEC were to produce an increased net primary productivity (NPP) at equilibrium for all seven GCM scenarios. The inclusion of nitrogen dynamics in the model imposes more constraint on the responses of FTEC to climatic change than the previous version of the model without nitrogen dynamics. Temperature exerts a stronger control on NPP than precipitation, as indicated by the negative correlations between NPP and temperature. The southern portion of FTEC, at latitudes less than 33 °N, show much larger increases in annual NPP than in the north. However, the predicted range of NPP increases is much larger in the north than in the south.     

Assessing the response of forest productivity to climate extremes in Switzerland using model–data fusion

The response of forest productivity to climate extremes strongly depends on ambient environmental and site conditions. To better understand these relationships at a regional scale, we used nearly 800 observation years from 271 permanent long‐term forest monitoring plots across Switzerland, obtained between 1980 and 2017. We assimilated these data into the 3‐PG forest ecosystem model using Bayesian inference, reducing the bias of model predictions from 14% to 5% for forest stem carbon stocks and from 45% to 9% for stem carbon stock changes. We then estimated the productivity of forests dominated by Picea abies and Fagus sylvatica for the period of 1960–2018, and tested for productivity shifts in response to climate along elevational gradient and in extreme years. Simulated net primary productivity (NPP) decreased with elevation (2.86 ± 0.006 Mg C ha^?1 year^?1 km^?1 for P. abies and 0.93 ± 0.010 Mg C ha^?1 year^?1 km^?1 for F. sylvatica). During warm–dry extremes, simulated NPP for both species increased at higher and decreased at lower elevations, with reductions in NPP of more than 25% for up to 21% of the potential species distribution range in Switzerland. Reduced plant water availability had a stronger effect on NPP than temperature during warm‐dry extremes. Importantly, cold–dry extremes had negative impacts on regional forest NPP comparable to warm–dry extremes. Overall, our calibrated model suggests that the response of forest productivity to climate extremes is more complex than simple shift toward higher elevation. Such robust estimates of NPP are key for increasing our understanding of forests ecosystems carbon dynamics under climate extremes.     

Increased topsoil carbon stock across China's forests

Biomass carbon accumulation in forest ecosystems is a widespread phenomenon at both regional and global scales. However, as coupled carbon–climate models predicted, a positive feedback could be triggered if accelerated soil carbon decomposition offsets enhanced vegetation growth under a warming climate. It is thus crucial to reveal whether and how soil carbon stock in forest ecosystems has changed over recent decades. However, large‐scale changes in soil carbon stock across forest ecosystems have not yet been carefully examined at both regional and global scales, which have been widely perceived as a big bottleneck in untangling carbon–climate feedback. Using newly developed database and sophisticated data mining approach, here we evaluated temporal changes in topsoil carbon stock across major forest ecosystem in China and analysed potential drivers in soil carbon dynamics over broad geographical scale. Our results indicated that topsoil carbon stock increased significantly within all of five major forest types during the period of 1980s–2000s, with an overall rate of 20.0 g C m^?2 yr^?1 (95% confidence interval, 14.1–25.5). The magnitude of soil carbon accumulation across coniferous forests and coniferous/broadleaved mixed forests exhibited meaningful increases with both mean annual temperature and precipitation. Moreover, soil carbon dynamics across these forest ecosystems were positively associated with clay content, with a larger amount of SOC accumulation occurring in fine‐textured soils. In contrast, changes in soil carbon stock across broadleaved forests were insensitive to either climatic or edaphic variables. Overall, these results suggest that soil carbon accumulation does not counteract vegetation carbon sequestration across China's forest ecosystems. The combination of soil carbon accumulation and vegetation carbon sequestration triggers a negative feedback to climate warming, rather than a positive feedback predicted by coupled carbon–climate models.     

不同菌根类型的森林净初级生产力对气温变化的响应

菌根是由土壤中的菌根菌与植物根系形成的互惠共生体, 在植物生产力和生态系统碳循环过程中发挥着重要的作用。该文基于全球森林数据库, 建立了包括全球森林菌根类型、净初级生产力(net primary productivity, NPP)和年平均气温等指标的新数据库。在此基础上, 分析了6种菌根类型(丛枝菌根(arbuscular mycorrhiza, AM)、AM +外生菌根(ectomycorrhiza, ECM)、AM + ECM +内外生菌根(ectendomycorrhiza, EEM)、ECM、ECM + EEM和ECM + EEM +无菌根(nonmycorrhiza, NM))森林的总NPP、地上和地下NPP、树木主干NPP、树叶NPP, 以及树木细根NPP对年平均气温变化的响应。结果表明, 不同菌根类型的森林总NPP、地上和地下NPP虽然都随气温的升高呈现上升的趋势, 但其响应程度因菌根类型的不同而有所差异。除AM和AM + ECM + EEM类型的森林外, 其他4种菌根类型的森林总NPP都随年平均气温的增加而显著增加; 随着菌根类型的不同, 地上和地下NPP对年平均气温变化的响应程度也存在差异, 在AM + ECM类型的森林中, 气温对地上NPP变异的解释率最高, 达到57.27%, 而地下NPP仅在ECM类型和ECM + EEM类型的森林中呈现出与年平均气温显著的回归关系。树木主干、树叶和细根的NPP则随菌根类型的不同而变化, 与气温变化呈现正、负相关关系。从AM与ECM类型的森林的NPP来看, 无论是总NPP还是各个组成部分的NPP, ECM类型的森林的NPP对气温的响应总是较AM类型更为敏感。可见, 不同类型的菌根通过影响森林不同部分的NPP对气温变化的响应程度而影响到森林NPP对气温变化的响应。这表明菌根类型是预测气温变化对森林NPP影响的重要指标。     

Impacts of tropospheric ozone and climate change on net primary productivity and net carbon exchange of China's forest ecosystems

Aim We investigated how ozone pollution and climate change/variability have interactively affected net primary productivity (NPP) and net carbon exchange (NCE) across China's forest ecosystem in the past half century. Location Continental China. Methods Using the dynamic land ecosystem model (DLEM) in conjunction with 10‐km‐resolution gridded historical data sets (tropospheric O₃ concentrations, climate variability/change, and other environmental factors such as land‐cover/land‐use change (LCLUC), increasing CO₂ and nitrogen deposition), we conducted nine simulation experiments to: (1) investigate the temporo‐spatial patterns of NPP and NCE in China's forest ecosystems from 1961–2005; and (2) quantify the effects of tropospheric O₃ pollution alone or in combination with climate variability and other environmental stresses on forests' NPP and NCE. Results China's forests acted as a carbon sink during 1961–2005 as a result of the combined effects of O₃, climate, CO₂, nitrogen deposition and LCLUC. However, simulated results indicated that elevated O₃ caused a 7.7% decrease in national carbon storage, with O₃‐induced reductions in NCE (Pg C year^?1) ranging from 0.4–43.1% among different forest types. Sensitivity experiments showed that climate change was the dominant factor in controlling changes in temporo‐spatial patterns of annual NPP. The combined negative effects of O₃ pollution and climate change on NPP and NCE could be largely offset by the positive fertilization effects of nitrogen deposition and CO₂. Main conclusions In the future, tropospheric O₃ should be taken into account in order to fully understand the variations of carbon sequestration capacity of forests and assess the vulnerability of forest ecosystems to climate change and air pollution. Reducing air pollution in China is likely to increase the resilience of forests to climate change. This paper offers the first estimate of how prevention of air pollution can help to increase forest productivity and carbon sequestration in China's forested ecosystems.     

Changes in European ecosystem productivity and carbon balance driven by regional climate model output

Climate change resulting from the enhanced greenhouse effect together with the direct effect of increased atmospheric CO₂ concentrations on vegetation growth are expected to produce changes in the cycling of carbon in terrestrial ecosystems. Impacts will vary across Europe, and regional-scale studies are needed to resolve this variability. In this study, we used the LPJ-GUESS ecosystem model driven by a suite of regional climate model (RCM) scenarios from the European Union (EU) project PRUDENCE to estimate climate impacts on carbon cycling across Europe. We identified similarities and discrepancies in simulated climate impacts across scenarios, particularly analyzing the uncertainties arising from the range of climate models and emissions scenarios considered. Our results suggest that net primary production (NPP) and heterotrophic respiration (Rh) will generally increase throughout Europe, but with considerable variation between European subregions. The smallest NPP increases, and in some cases decreases, occurred in the Mediterranean, where many ecosystems switched from sinks to sources of carbon by 2100, mainly as a result of deteriorating water balance. Over the period 1991–2100, modeled climate change impacts on the European carbon balance ranged from a sink of 11.6 Gt C to a source of 3.3 Gt C, the average annual sink corresponding with 1.85% of the current EU anthropogenic emissions. Projected changes in carbon balance were more dependent on the choice of the general circulation model (GCM) providing boundary conditions to the RCM than the choice of RCM or the level of anthropogenic greenhouse gases emissions.