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In the Amazon, deforestation and climate change lead to increased vulnerability to forest degradation, threatening its existing carbon stocks and its capacity as a carbon sink. We use satellite L-Band Vegetation Optical Depth (L-VOD) data that provide an integrated (top-down) estimate of biomass carbon to track changes over 2011–2019. Because the spatial resolution of L-VOD is coarse (0.25°), it allows limited attribution of the observed changes. We therefore combined high-resolution annual maps of forest cover and disturbances with biomass maps to model carbon losses (bottom- up) from deforestation and degradation, and gains from regrowing secondary forests. We show an increase of deforestation and associated degradation losses since 2012 which greatly outweigh secondary forest gains. Degradation accounted for 40% of gross losses. After an increase in 2011, old-growth forests show a net loss of above-ground carbon between 2012 and 2019. The sum of component carbon fluxes in our model is consistent with the total biomass change from L-VOD of 1.3 Pg C over 2012-2019. Across nine Amazon countries, we found that while Brazil contains the majority of biomass stocks (64%), its losses from disturbances were disproportionately high (79% of gross losses). Our multi-source analysis provides a pessimistic assessment of the Amazon carbon balance and highlights the urgent need to stop the recent rise of deforestation and degradation, particularly in the Brazilian Amazon. [ABSTRACT FROM AUTHOR]

Uncovering the mechanisms that lead to Amazon forest resilience variations is crucial to predict the impact of future climatic and anthropogenic disturbances. Here, we apply a previously used empirical resilience metrics, lag‐1 month temporal autocorrelation (TAC), to vegetation optical depth data in C‐band (a good proxy of the whole canopy water content) in order to explore how forest resilience variations are impacted by human disturbances and environmental drivers in the Brazilian Amazon. We found that human disturbances significantly increase the risk of critical transitions, and that the median TAC value is ~2.4 times higher in human‐disturbed forests than that in intact forests, suggesting a much lower resilience in disturbed forests. Additionally, human‐disturbed forests are less resilient to land surface heat stress and atmospheric water stress than intact forests. Among human‐disturbed forests, forests with a more closed and thicker canopy structure, which is linked to a higher forest cover and a lower disturbance fraction, are comparably more resilient. These results further emphasize the urgent need to limit deforestation and degradation through policy intervention to maintain the resilience of the Amazon rainforests. [ABSTRACT FROM AUTHOR]

Strategies to mitigate climate change by reducing deforestation and forest degradation (e.g. REDD+) require country- or region-specific information on temporal changes in forest carbon (C) pools to develop accurate emission factors. The soil C pool is one of the most important C reservoirs, but is rarely included in national forest reference emission levels due to a lack of data. Here, we present the soil organic C ( SOC) dynamics along 20 years of forest-to-pasture conversion in two subregions with different management practices during pasture establishment in the Colombian Amazon: high-grazing intensity ( HG) and low-grazing intensity ( LG) subregions. We determined the pattern of SOC change resulting from the conversion from forest (C3 plants) to pasture (C4 plants) by analysing total SOC stocks and the natural abundance of the stable isotopes 13C along two 20-year chronosequences identified in each subregion. We also analysed soil N stocks and the natural abundance of 15N during pasture establishment. In general, total SOC stocks at 30 cm depth in the forest were similar for both subregions, with an average of 47.1 ± 1.8 Mg C ha−1 in HG and 48.7 ± 3.1 Mg C ha−1 in LG. However, 20 years after forest-to-pasture conversion SOC in HG decreased by 20%, whereas in LG SOC increased by 41%. This net SOC decrease in HG was due to a larger reduction in C3-derived input and to a comparatively smaller increase in C4-derived C input. In LG both C3- and C4-derived C input increased along the chronosequence. N stocks were generally similar in both subregions and soil N stock changes during pasture establishment were correlated with SOC changes. These results emphasize the importance of management practices involving low-grazing intensity in cattle activities to preserve SOC stocks and to reduce C emissions after land-cover change from forest to pasture in the Colombian Amazon. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate 10 process-based terrestrial biosphere models that were used for the IPCC fifth Assessment Report. The simulated gross primary productivity ( GPP) is compared with flux-tower-based estimates by Jung et al. [ Journal of Geophysical Research 116 (2011) G00J07] ( JU11). The net primary productivity ( NPP) apparent sensitivity to climate variability and atmospheric CO2 trends is diagnosed from each model output, using statistical functions. The temperature sensitivity is compared against ecosystem field warming experiments results. The CO2 sensitivity of NPP is compared to the results from four Free-Air CO2 Enrichment ( FACE) experiments. The simulated global net biome productivity ( NBP) is compared with the residual land sink ( RLS) of the global carbon budget from Friedlingstein et al. [ Nature Geoscience 3 (2010) 811] ( FR10). We found that models produce a higher GPP (133 ± 15 Pg C yr−1) than JU11 (118 ± 6 Pg C yr−1). In response to rising atmospheric CO2 concentration, modeled NPP increases on average by 16% (5-20%) per 100 ppm, a slightly larger apparent sensitivity of NPP to CO2 than that measured at the FACE experiment locations (13% per 100 ppm). Global NBP differs markedly among individual models, although the mean value of 2.0 ± 0.8 Pg C yr−1 is remarkably close to the mean value of RLS (2.1 ± 1.2 Pg C yr−1). The interannual variability in modeled NBP is significantly correlated with that of RLS for the period 1980-2009. Both model-to-model and interannual variation in model GPP is larger than that in model NBP due to the strong coupling causing a positive correlation between ecosystem respiration and GPP in the model. The average linear regression slope of global NBP vs. temperature across the 10 models is −3.0 ± 1.5 Pg C yr−1 °C−1, within the uncertainty of what derived from RLS (−3.9 ± 1.1 Pg C yr−1 °C−1). However, 9 of 10 models overestimate the regression slope of NBP vs. precipitation, compared with the slope of the observed RLS vs. precipitation. With most models lacking processes that control GPP and NBP in addition to CO2 and climate, the agreement between modeled and observation-based GPP and NBP can be fortuitous. Carbon-nitrogen interactions (only separable in one model) significantly influence the simulated response of carbon cycle to temperature and atmospheric CO2 concentration, suggesting that nutrients limitations should be included in the next generation of terrestrial biosphere models. [ABSTRACT FROM AUTHOR]

We model future changes in land biogeochemistry and biogeography across East Africa. East Africa is one of few tropical regions where general circulation model (GCM) future climate projections exhibit a robust response of strong future warming and general annual-mean rainfall increases. Eighteen future climate projections from nine GCMs participating in the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment were used as input to the LPJ dynamic global vegetation model (DGVM), which predicted vegetation patterns and carbon storage in agreement with satellite observations and forest inventory data under the present-day climate. All simulations showed future increases in tropical woody vegetation over the region at the expense of grasslands. Regional increases in net primary productivity (NPP) (18–36%) and total carbon storage (3–13%) by 2080–2099 compared with the present-day were common to all simulations. Despite decreases in soil carbon after 2050, seven out of nine simulations continued to show an annual net land carbon sink in the final decades of the 21st century because vegetation biomass continued to increase. The seasonal cycles of rainfall and soil moisture show future increases in wet season rainfall across the GCMs with generally little change in dry season rainfall. Based on the simulated present-day climate and its future trends, the GCMs can be grouped into four broad categories. Overall, our model results suggest that East Africa, a populous and economically poor region, is likely to experience some ecosystem service benefits through increased precipitation, river runoff and fresh water availability. Resulting enhancements in NPP may lead to improved crop yields in some areas. Our results stand in partial contradiction to other studies that suggest possible negative consequences for agriculture, biodiversity and other ecosystem services caused by temperature increases. [ABSTRACT FROM AUTHOR]

We assess the role of changing natural (volcanic, aerosol, insolation) and anthropogenic (CO2 emissions, land cover) forcings on the global climate system over the last 150 years using an earth system model of intermediate complexity, CLIMBER-2. We apply several datasets of historical land-use reconstructions: the cropland dataset by (R&F), the HYDE land cover dataset of , and the land-use emissions data from . Comparison between the simulated and observed temporal evolution of atmospheric CO2 and δ13CO2 are used to evaluate these datasets. To check model uncertainty, CLIMBER-2 was coupled to the more complex Lund–Potsdam–Jena (LPJ) dynamic global vegetation model. In simulation with R&F dataset, biogeophysical mechanisms due to land cover changes tend to decrease global air temperature by 0.26°C, while biogeochemical mechanisms act to warm the climate by 0.18°C. The net effect on climate is negligible on a global scale, but pronounced over the land in the temperate and high northern latitudes where a cooling due to an increase in land surface albedo offsets the warming due to land-use CO2 emissions. Land cover changes led to estimated increases in atmospheric CO2 of between 22 and 43 ppmv. Over the entire period 1800–2000, simulated δ13CO2 with HYDE compares most favourably with ice core during 1850–1950 and Cape Grim data, indicating preference of earlier land clearance in HYDE over R&F. In relative terms, land cover forcing corresponds to 25–49% of the observed growth in atmospheric CO2. This contribution declined from 36–60% during 1850–1960 to 4–35% during 1960–2000. CLIMBER-2-LPJ simulates the land cover contribution to atmospheric CO2 growth to decrease from 68% during 1900–1960 to 12% in the 1980s. Overall, our simulations show a decline in the relative role of land cover changes for atmospheric CO2 increase during the last 150 years. [ABSTRACT FROM AUTHOR]

Robust estimates of CO2 budget, CO2 exchanged between the atmosphere and terrestrial biosphere, are necessary to better understand the role of the terrestrial biosphere in mitigating anthropogenic CO2 emissions. Over the past decade, this field of research has advanced through understanding of the differences and similarities of two fundamentally different approaches: "top‐down" atmospheric inversions and "bottom‐up" biosphere models. Since the first studies were undertaken, these approaches have shown an increasing level of agreement, but disagreements in some regions still persist, in part because they do not estimate the same quantity of atmosphere–biosphere CO2 exchange. Here, we conducted a thorough comparison of CO2 budgets at multiple scales and from multiple methods to assess the current state of the science in estimating CO2 budgets. Our set of atmospheric inversions and biosphere models, which were adjusted for a consistent flux definition, showed a high level of agreement for global and hemispheric CO2 budgets in the 2000s. Regionally, improved agreement in CO2 budgets was notable for North America and Southeast Asia. However, large gaps between the two methods remained in East Asia and South America. In other regions, Europe, boreal Asia, Africa, South Asia, and Oceania, it was difficult to determine whether those regions act as a net sink or source because of the large spread in estimates from atmospheric inversions. These results highlight two research directions to improve the robustness of CO2 budgets: (a) to increase representation of processes in biosphere models that could contribute to fill the budget gaps, such as forest regrowth and forest degradation; and (b) to reduce sink–source compensation between regions (dipoles) in atmospheric inversion so that their estimates become more comparable. Advancements on both research areas will increase the level of agreement between the top‐down and bottom‐up approaches and yield more robust knowledge of regional CO2 budgets. [ABSTRACT FROM AUTHOR]

Tropical forests play a pivotal role in regulating the global carbon cycle. However, the response of these forests to changes in absorbed solar energy and water supply under the changing climate is highly uncertain. Three‐year (2018–2021) spaceborne high‐resolution measurements of solar‐induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) provide a new opportunity to study the response of gross primary production (GPP) and more broadly tropical forest carbon dynamics to differences in climate. SIF has been shown to be a good proxy for GPP on monthly and regional scales. Combining tropical climate reanalysis records and other contemporary satellite products, we find that on the seasonal timescale, the dependence of GPP on climate variables is highly heterogeneous. Following the principal component analyses and correlation comparisons, two regimes are identified: water limited and energy limited. GPP variations over tropical Africa are more correlated with water‐related factors such as vapor pressure deficit (VPD) and soil moisture, while in tropical Southeast Asia, GPP is more correlated with energy‐related factors such as photosynthetically active radiation (PAR) and surface temperature. Amazonia is itself heterogeneous: with an energy‐limited regime in the north and water‐limited regime in the south. The correlations of GPP with climate variables are supported by other observation‐based products, such as Orbiting Carbon Observatory‐2 (OCO2) SIF and FluxSat GPP. In each tropical continent, the coupling between SIF and VPD increases with the mean VPD. Even on the interannual timescale, the correlation of GPP with VPD is still discernable, but the sensitivity is smaller than the intra‐annual correlation. By and large, the dynamic global vegetation models in the TRENDY v8 project do not capture the high GPP seasonal sensitivity to VPD in dry tropics. The complex interactions between carbon and water cycles in the tropics illustrated in this study and the poor representation of this coupling in the current suite of vegetation models suggest that projections of future changes in carbon dynamics based on these models may not be robust. [ABSTRACT FROM AUTHOR]

Summary: Climate warming is causing compositional changes in Andean tropical montane forests (TMFs). These shifts are hypothesised to result from differential responses to warming of cold‐ and warm‐affiliated species, with the former experiencing mortality and the latter migrating upslope. The thermal acclimation potential of Andean TMFs remains unknown.Along a 2000 m Andean altitudinal gradient, we planted individuals of cold‐ and warm‐affiliated species (under common soil and irrigation), exposing them to the hot and cold extremes of their thermal niches, respectively. We measured the response of net photosynthesis (Anet), photosynthetic capacity and leaf dark respiration (Rdark) to warming/cooling, 5 months after planting.In all species, Anet and photosynthetic capacity at 25°C were highest when growing at growth temperatures (Tg) closest to their thermal means, declining with warming and cooling in cold‐affiliated and warm‐affiliated species, respectively. When expressed at Tg, photosynthetic capacity and Rdark remained unchanged in cold‐affiliated species, but the latter decreased in warm‐affiliated counterparts. Rdark at 25°C increased with temperature in all species, but remained unchanged when expressed at Tg.Both species groups acclimated to temperature, but only warm‐affiliated species decreased Rdark to photosynthetic capacity ratio at Tg as temperature increased. This could confer them a competitive advantage under future warming. [ABSTRACT FROM AUTHOR]

Significant land greening since the 1980s has been detected through satellite observation, forest inventory, and Earth system modeling. However, whether and to what extent global land greening enhances ecosystem carbon stock remains uncertain. Here, using 40 global models, we first detected a positive correlation between the terrestrial ecosystem carbon stock and leaf area index (LAI) over time. Then, we diagnose the source of uncertainty of simulated the sensitivities of ecosystem carbon stock to LAI based on a traceability analysis. We found that the sensitivity of gross primary productivity (GPP) to LAI is the largest contributor to the model uncertainty in more than 60% of the vegetated grids. Using the ensemble of four long‐term global data sets of GPP and three satellite LAI products from 1982 to 2014, we provided an emergent constraint on the ecosystem carbon stock increase as 0.75 ± 0.46 kg C m−2 per unit LAI over global land areas. Furthermore, the biome‐based results reveal that the tropical forest regions have the highest inter‐model variation and model bias. Overall, this study identifies the uncertainty source and provides constrained estimates of the greening effect on ecosystem carbon stock at the global scale. Plain Language Summary: In the last few decades, we can see that there has been more green land from looking at satellite images, checking forest records, and using computer models. However, we do not know if having more green land is helping the ecosystem store more carbon. This study investigated the greening effect on ecosystem carbon stock among the 40 global models, but this effect varied between models. Therefore, we diagnosed the simulated source of uncertainty based on the traceability analysis framework. Based on satellite‐derived data sets, we further constrained the greening effect on ecosystem carbon stock at the global scale. Land greening enhances ecosystem carbon stock, although this increase is highly heterogeneous on a global scale. Key Points: A globally enhanced ecosystem C stock (ECS) by land greening has been detected since the 1980s from 40 global modelsThe sensitivity of gross primary productivity to leaf area index (LAI) is the largest contributor to the modeled greening effect on ECS uncertaintyThis study estimated the effect of global greening on ECS with the emergent constraint method [ABSTRACT FROM AUTHOR]

Satellite XCO2 retrievals could improve the estimates of surface carbon fluxes, but it remains unknown on what scales these estimates are robust. Here, we use the time‐dependent Bayesian synthesis top‐down method and prior net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) to infer the monthly carbon fluxes of 51 land regions with constraints by GOSAT XCO2 retrievals. We find that the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) decrease significantly at decreasing spatial scales. On the continental‐scale, the mean URR is about 57%, and the annual and seasonal cycle estimates of NEE are rather robust. The evaluation shows that the posterior CO2 concentrations are significantly improved at the continental scale. Our study suggests that the GOSAT XCO2 can only promise a robust continental‐scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current spatial coverage. Plain Language Summary: Remotesensing‐based CO2 measurement can improve the estimates of surface carbon fluxes due to its relatively well global coverage, but it remains unknown on what spatial scales the satellite observation could provide a robust estimate. Here, net ecosystem exchanges (NEEs) from 12 terrestrial biosphere models (TBMs) of 51 land regions for the period of 2011–2014 are constrained using GOSAT XCO2 retrievals, and the uncertainty (standard deviation of 12 TBMs) reduction rates (uncertainty reduction rate (URR)) at different spatial scales are analyzed. We find that (a) from the whole globe to the mean of 51 regions, the URR decreases from 95% to 16%. (b) On the continental‐scale, the mean URR is about 57%, and the annual NEEs in Asia, N. America, Europe, S. America, Africa, and Australia are estimated to be −2.07 ± 0.25, −0.85 ± 0.09, −0.76 ± 0.21, −0.36 ± 0.17, −0.36 ± 0.30, and −0.15 ± 0.12 PgC yr−1, respectively. Our study suggests that the GOSAT XCO2 can only promise a robust continental‐scale NEE estimate, and improving the XCO2 accuracy is an effective way to achieve robust estimates on smaller scales under current satellite observing capacity. Key Points: Terrestrial carbon sinks estimated based on GOSAT XCO2 and net ecosystem exchanges (NEE) from 12 terrestrial biosphere models using atmospheric inverse methodThe uncertainty reduction rates decrease significantly at decreasing spatial scalesThe GOSAT XCO2 can only promise a robust continental‐scale NEE estimate [ABSTRACT FROM AUTHOR]

The 2019 extreme positive Indian Ocean dipole drove climate extremes over Indian Ocean rim countries with unclear carbon‐cycle responses. We investigated its impact on net biome productivity (NBP) and its constituent fluxes, using the Global Carbon Assimilation System (GCASv2) product, process‐based model simulations from TRENDYv9, and satellite‐based gross primary productivity (GPP). By distinguishing two separate regions, the India‐Africa and Asia‐Pacific, GCASv2 indicated enhanced terrestrial carbon uptake of 0.23 ± 0.20 PgC and release of 0.38 ± 0.15 PgC, respectively, during September–December (SOND) 2019. These NBP anomalies had comparable magnitudes to those following the 2015 extreme El Niño which, however, caused the consistent carbon release in both regions. The TRENDYv9 model ensemble confirmed these NBP responses, albeit with smaller magnitudes. These regional NBP anomalies were related to soil moisture variations with a dominant role of GPP. Understanding the impact of IOD provides new insights into mechanisms driving interannual variations in regional carbon cycling. Plain Language Summary: The extreme Indian Ocean Dipole (IOD) can drive climate extremes, such as floods, heatwaves, droughts, and wildfires, over the Indian Ocean rim countries. However, responses of regional terrestrial carbon cycling to IOD remained unclear. We used the net biome productivity (NBP) from an atmospheric inversion and multiple terrestrial biosphere models to demonstrate an enhanced terrestrial carbon uptake and release over the India‐Africa and Asia‐Pacific regions, respectively, during the extreme positive IOD (September–December) in 2019. These IOD‐induced regional NBP anomalies showed comparable magnitudes but different patterns to those following the 2015 extreme El Niño. Along with the more frequent extreme IOD under future greenhouse warming, IOD will be an important mechanism driving interannual variations in regional carbon cycling. Key Points: The 2019 extreme positive Indian Ocean Dipole caused the enhanced land carbon uptake over India‐Africa and release over Asia‐Pacific during September–DecemberThese regional net biome productivity (NBP) anomalies were closely related to soil moisture variations with a dominant role of gross primary productivityThese Indian Ocean Dipole‐induced regional NBP anomalies showed comparable magnitudes but different patterns to those following the 2015 extreme El Niño [ABSTRACT FROM AUTHOR]

Recent wildfire outbreaks around the world have prompted concern that climate change is increasing fire incidence, threatening human livelihood and biodiversity, and perpetuating climate change. Here, we review current understanding of the impacts of climate change on fire weather (weather conditions conducive to the ignition and spread of wildfires) and the consequences for regional fire activity as mediated by a range of other bioclimatic factors (including vegetation biogeography, productivity and lightning) and human factors (including ignition, suppression, and land use). Through supplemental analyses, we present a stocktake of regional trends in fire weather and burned area (BA) during recent decades, and we examine how fire activity relates to its bioclimatic and human drivers. Fire weather controls the annual timing of fires in most world regions and also drives inter‐annual variability in BA in the Mediterranean, the Pacific US and high latitude forests. Increases in the frequency and extremity of fire weather have been globally pervasive due to climate change during 1979–2019, meaning that landscapes are primed to burn more frequently. Correspondingly, increases in BA of ∼50% or higher have been seen in some extratropical forest ecoregions including in the Pacific US and high‐latitude forests during 2001–2019, though interannual variability remains large in these regions. Nonetheless, other bioclimatic and human factors can override the relationship between BA and fire weather. For example, BA in savannahs relates more strongly to patterns of fuel production or to the fragmentation of naturally fire‐prone landscapes by agriculture. Similarly, BA trends in tropical forests relate more strongly to deforestation rates and forest degradation than to changing fire weather. Overall, BA has reduced by 27% globally in the past two decades, due in large part to a decline in BA in African savannahs. According to climate models, the prevalence and extremity of fire weather has already emerged beyond its pre‐industrial variability in the Mediterranean due to climate change, and emergence will become increasingly widespread at additional levels of warming. Moreover, several of the major wildfires experienced in recent years, including the Australian bushfires of 2019/2020, have occurred amidst fire weather conditions that were considerably more likely due to climate change. Current fire models incompletely reproduce the observed spatial patterns of BA based on their existing representations of the relationships between fire and its bioclimatic and human controls, and historical trends in BA also vary considerably across models. Advances in the observation of fire and understanding of its controlling factors are supporting the addition or optimization of a range of processes in models. Overall, climate change is exerting a pervasive upwards pressure on fire globally by increasing the frequency and intensity of fire weather, and this upwards pressure will escalate with each increment of global warming. Improvements to fire models and a better understanding of the interactions between climate, climate extremes, humans and fire are required to predict future fire activity and to mitigate against its consequences. Plain Language Summary: In this review, with supplemental data analyses, we focus on the global and regional impacts of climate change on the frequency and intensity of fire weather (conditions conducive to fire ignition and spread) and the consequences for fire activity. We find that significant increases in fire weather have occurred in most world regions during recent decades due to climate change. Corresponding increases in the area burned by fires have been seen in some regions, most notably in mesic forests, however, in many regions fire is controlled by a range of other bioclimatic and human factors whose influences mediate or override those of fire weather. Weather conditions affecting vegetation growth and the build‐up of fuels, the presence of human ignitions in regions that are not naturally fire‐prone, and the fragmentation of fire‐prone landscapes by agriculture are key examples of factors that can locally or regionally outweigh fire weather as controls on fire activity. Climate models project that fire weather will become increasingly frequent and intense under future warming, and at an increasing rate with each additional increment of warming. The outcomes for fire activity in future will depend on other regionally important factors that control fire ignition and spread. Existing fire models represent the controls on fire incompletely and so they reproduce observed patterns of fire with only limited success. Models also disagree on historical trends, leading to low confidence in their simulations of future fire activity. Various efforts to improve the representation of fire in models are underway and should yield greater capacity to predict the future of fire activity. Key Points: The frequency and severity of fire weather has increased in recent decades and is projected to escalate with each added increment of warmingFire weather is one of the major controls on fire activity, and is the dominant control on variability in burned area (BA) in many mesic forest ecoregionsVarious human and bioclimatic factors also control fire, modulating the relationship between BA and fire weather in many regions [ABSTRACT FROM AUTHOR]

Fossil fuel combustion, land use change and other human activities have increased the atmospheric carbon dioxide (CO2) abundance by about 50% since the beginning of the industrial age. The atmospheric CO2 growth rates would have been much larger if natural sinks in the land biosphere and ocean had not removed over half of this anthropogenic CO2. As these CO2 emissions grew, uptake by the ocean increased in response to increases in atmospheric CO2 partial pressure (pCO2). On land, gross primary production also increased, but the dynamics of other key aspects of the land carbon cycle varied regionally. Over the past three decades, CO2 uptake by intact tropical humid forests declined, but these changes are offset by increased uptake across mid- and high-latitudes. While there have been substantial improvements in our ability to study the carbon cycle, measurement and modeling gaps still limit our understanding of the processes driving its evolution. Continued ship-based observations combined with expanded deployments of autonomous platforms are needed to quantify ocean-atmosphere fluxes and interior ocean carbon storage on policy-relevant spatial and temporal scales. There is also an urgent need for more comprehensive measurements of stocks, fluxes and atmospheric CO2 in humid tropical forests and across the Arctic and boreal regions, which are experiencing rapid change. Here, we review our understanding of the atmosphere, ocean, and land carbon cycles and their interactions, identify emerging measurement and modeling capabilities and gaps and the need for a sustainable, operational framework to ensure a scientific basis for carbon management. [ABSTRACT FROM AUTHOR]

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one‐third of anthropogenic CO2 emissions during the 1959–2019 period. This sink‐estimate is produced by an ensemble of terrestrial biosphere models and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well terrestrial biosphere models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation‐based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference data sets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter‐model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties. Plain Language Summary: Earth's natural vegetation absorbs about one‐third of CO2 emissions caused by human activities. This value is produced by a group of models rather than through direct observations. Our study assesses how well models reproduce the processes that drive the CO2 exchange between land and atmosphere using a wide range of data sets that are mainly derived from field measurements and satellite images. These reference data sets are prone to errors that are not quantified in a consistent manner. To account for such errors, we first compare different reference data sets against each other. We then compare model output against reference data and assess whether the differences are comparable to the differences among the reference data sets. We conclude that the performance of models is encouraging given how uncertain reference data are, but that ample potential for improvements remains. Key Points: Differences between model and observations are often similar compared to differences between independently derived observation‐based dataWe quantify differences between independently derived observations to disentangle model deficiencies from observational uncertaintiesFuture work should address biases in soil organic carbon, leaf area index, and the large spread of gross primary productivity among models [ABSTRACT FROM AUTHOR]

•The large-scale loss of Amazonian rainforest under some future climate scenarios has generally been considered to be driven by increased drying over Amazonia predicted by some general circulation models (GCMs). However, the importance of rainfall relative to other drivers has never been formally examined. •Here, we conducted factorial simulations to ascertain the contributions of four environmental drivers (precipitation, temperature, humidity and CO2) to simulated changes in Amazonian vegetation carbon ( Cveg), in three dynamic global vegetation models (DGVMs) forced with climate data based on HadCM3 for four SRES scenarios. •Increased temperature was found to be more important than precipitation reduction in causing losses of Amazonian Cveg in two DGVMs (Hyland and TRIFFID), and as important as precipitation reduction in a third DGVM (LPJ). Increases in plant respiration, direct declines in photosynthesis and increases in vapour pressure deficit (VPD) all contributed to reduce Cveg under high temperature, but the contribution of each mechanism varied greatly across models. Rising CO2 mitigated much of the climate-driven biomass losses in the models. •Additional work is required to constrain model behaviour with experimental data under conditions of high temperature and drought. Current models may be overly sensitive to long-term elevated temperatures as they do not account for physiological acclimation. [ABSTRACT FROM AUTHOR]

Abstract A new fire model is proposed which estimates areas burnt on a macro-scale (10–100 km). It consists of three parts: evaluation of fire danger due to climatic conditions, estimation of the number of fires and the extent of the area burnt. The model can operate on three time steps, daily, monthly and yearly, and interacts with a Dynamic Global Vegetation Model (DGVM), thereby providing an important forcing for natural competition. Fire danger is related to number of dry days and amplitude of daily temperature during these days. The number of fires during fire days varies with human population density. Areas burnt are calculated based on average wind speed, available fuel and fire duration. The model has been incorporated into the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) and has been tested for peninsular Spain. LPJ-DGVM was modified to allow bi-directional feedback between fire disturbance and vegetation dynamics. The number of fires and areas burnt were simulated for the period 1974–94 and compared against observations. The model produced realistic results, which are well correlated, both spatially and temporally, with the fire statistics. Therefore, a relatively simple mechanistic fire model can be used to reproduce fire regime patterns in human- dominated ecosystems over a large region and a long time period. [ABSTRACT FROM AUTHOR]

Abstract 1Disturbances from fire, wind-throw, insects and other herbivores are, besides climate, CO2, and soils, critical factors for composition, structure and dynamics of most vegetation. To simulate the influence of fire on the dynamic equilibrium, as well as on potential change, of vegetation at the global scale, we have developed a fire model, running inside the modular framework of the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ-DGVM). 2Estimated litter moisture is the main driver of day-to-day fire probability. The length of the fire season is used to estimate the fractional area of a grid cell which is burnt in a given year. This affected area is converted into an average fire return interval which can be compared to observations. 3When driven by observed climate for the 20th century (at a 0.5° longitude/latitude resolution), the model yielded fire return intervals in good agreement with observations for many regions (except parts of semiarid Africa and boreal Siberia). We suggest that further improvement for these regions must involve additional process descriptions such as permafrost and fuel/fire dynamics. [ABSTRACT FROM AUTHOR]

The rising atmospheric CO2 concentration leads to a CO2 fertilization effect on plants—that is, increased photosynthetic uptake of CO2 by leaves and enhanced water‐use efficiency (WUE). Yet, the resulting net impact of CO2 fertilization on plant growth and soil moisture (SM) savings at large scale is poorly understood. Drylands provide a natural experimental setting to detect the CO2 fertilization effect on plant growth since foliage amount, plant water‐use and photosynthesis are all tightly coupled in water‐limited ecosystems. A long‐term change in the response of leaf area index (LAI, a measure of foliage amount) to changes in SM is likely to stem from changing water demand of primary productivity in water‐limited ecosystems and is a proxy for changes in WUE. Using 34‐year satellite observations of LAI and SM over tropical and subtropical drylands, we identify that a 1% increment in SM leads to 0.15% (±0.008, 95% confidence interval) and 0.51% (±0.01, 95% confidence interval) increments in LAI during 1982‒1998 and 1999‒2015, respectively. The increasing response of LAI to SM has contributed 7.2% (±3.0%, 95% confidence interval) to total dryland greening during 1999‒2015 compared to 1982‒1998. The increasing response of LAI to SM is consistent with the CO2 fertilization effect on WUE in water‐limited ecosystems, indicating that a given amount of SM has sustained greater amounts of photosynthetic foliage over time. The LAI responses to changes in SM from seven dynamic global vegetation models are not always consistent with observations, highlighting the need for improved process knowledge of terrestrial ecosystem responses to rising atmospheric CO2 concentration. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Changing amplitude of the seasonal cycle of atmospheric CO2 (SCA) in the northern hemisphere is an emerging carbon cycle property. Mauna Loa (MLO) station (20°N, 156°W), which has the longest continuous northern hemisphere CO2 record, shows an increasing SCA before the 1980s (p <.01), followed by no significant change thereafter. We analyzed the potential driving factors of SCA slowing‐down, with an ensemble of dynamic global vegetation models (DGVMs) coupled with an atmospheric transport model. We found that slowing‐down of SCA at MLO is primarily explained by response of net biome productivity (NBP) to climate change, and by changes in atmospheric circulations. Through NBP, climate change increases SCA at MLO before the 1980s and decreases it afterwards. The effect of climate change on the slowing‐down of SCA at MLO is mainly exerted by intensified drought stress acting to offset the acceleration driven by CO2 fertilization. This challenges the view that CO2 fertilization is the dominant cause of emergent SCA trends at northern sites south of 40°N. The contribution of agricultural intensification on the deceleration of SCA at MLO was elusive according to land–atmosphere CO2 flux estimated by DGVMs and atmospheric inversions. Our results also show the necessity to adequately account for changing circulation patterns in understanding carbon cycle dynamics observed from atmospheric observations and in using these observations to benchmark DGVMs. [ABSTRACT FROM AUTHOR]

Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above‐ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model‐data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB95) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB95, particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB. [ABSTRACT FROM AUTHOR]

Summary: Land surface models (LSMs) typically use empirical functions to represent vegetation responses to soil drought. These functions largely neglect recent advances in plant ecophysiology that link xylem hydraulic functioning with stomatal responses to climate.We developed an analytical stomatal optimization model based on xylem hydraulics (SOX) to predict plant responses to drought. Coupling SOX to the Joint UK Land Environment Simulator (JULES) LSM, we conducted a global evaluation of SOX against leaf‐ and ecosystem‐level observations.SOX simulates leaf stomatal conductance responses to climate for woody plants more accurately and parsimoniously than the existing JULES stomatal conductance model. An ecosystem‐level evaluation at 70 eddy flux sites shows that SOX decreases the sensitivity of gross primary productivity (GPP) to soil moisture, which improves the model agreement with observations and increases the predicted annual GPP by 30% in relation to JULES. SOX decreases JULES root‐mean‐square error in GPP by up to 45% in evergreen tropical forests, and can simulate realistic patterns of canopy water potential and soil water dynamics at the studied sites.SOX provides a parsimonious way to incorporate recent advances in plant hydraulics and optimality theory into LSMs, and an alternative to empirical stress factors. See also the Commentary on this article by Anderegg & Venturas, 226: 1535–1538. [ABSTRACT FROM AUTHOR]

Understanding changes in terrestrial carbon balance is important to improve our knowledge of the regional carbon cycle and climate change. However, evaluating regional changes in the terrestrial carbon balance is challenging due to the lack of surface flux measurements. This study reveals that the terrestrial carbon uptake over the Republic of Korea has been enhanced from 1999 to 2017 by analyzing long‐term atmospheric CO2 concentration measurements at the Anmyeondo Station (36.53°N, 126.32°E) located in the western coast. The influence of terrestrial carbon flux on atmospheric CO2 concentrations (ΔCO2) is estimated from the difference of CO2 concentrations that were influenced by the land sector (through easterly winds) and the Yellow Sea sector (through westerly winds). We find a significant trend in ΔCO2 of −4.75 ppm per decade (p <.05) during the vegetation growing season (May through October), suggesting that the regional terrestrial carbon uptake has increased relative to the surrounding ocean areas. Combined analysis with satellite measured normalized difference vegetation index and gross primary production shows that the enhanced carbon uptake is associated with significant nationwide increases in vegetation and its production. Process‐based terrestrial model and inverse model simulations estimate that regional terrestrial carbon uptake increases by up to 18.9 and 8.0 Tg C for the study period, accounting for 13.4% and 5.7% of the average annual domestic carbon emissions, respectively. Atmospheric chemical transport model simulations indicate that the enhanced terrestrial carbon sink is the primary reason for the observed ΔCO2 trend rather than anthropogenic emissions and atmospheric circulation changes. Our results highlight the fact that atmospheric CO2 measurements could open up the possibility of detecting regional changes in the terrestrial carbon cycle even where anthropogenic emissions are not negligible. [ABSTRACT FROM AUTHOR]

With accumulation of carbon cycle observations and model developments over the past decades, exploring interannual variation (IAV) of terrestrial carbon cycle offers the opportunity to better understand climate–carbon cycle relationships. However, despite growing research interest, uncertainties remain on some fundamental issues, such as the contributions of different regions, constituent fluxes and climatic factors to carbon cycle IAV. Here we overviewed the literature on carbon cycle IAV about current understanding of these issues. Observations and models of the carbon cycle unanimously show the dominance of tropical land ecosystems to the signal of global carbon cycle IAV, where tropical semiarid ecosystems contribute as much as the combination of all other tropical ecosystems. Vegetation photosynthesis contributes more than ecosystem respiration to IAV of the global net land carbon flux, but large uncertainties remain on the contribution of fires and other disturbance fluxes. Climatic variations are the major drivers to the IAV of net land carbon flux. Although debate remains on whether the dominant driver is temperature or moisture variability, their interaction,that is, the dependence of carbon cycle sensitivity to temperature on moisture conditions, is emerging as key regulators of the carbon cycle IAV. On timescales from the interannual to the centennial, global carbon cycle variability will be increasingly contributed by northern land ecosystems and oceans. Therefore, both improving Earth system models (ESMs) with the progressive understanding on the fast processes manifested at interannual timescale and expanding carbon cycle observations at broader spatial and longer temporal scales are critical to better prediction on evolution of the carbon–climate system. [ABSTRACT FROM AUTHOR]

Abstract: 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. [ABSTRACT FROM AUTHOR]

Summary: Plant temperature responses vary geographically, reflecting thermally contrasting habitats and long‐term species adaptations to their climate of origin. Plants also can acclimate to fast temporal changes in temperature regime to mitigate stress. Although plant photosynthetic responses are known to acclimate to temperature, many global models used to predict future vegetation and climate–carbon interactions do not include this process. We quantify the global and regional impacts of biogeographical variability and thermal acclimation of temperature response of photosynthetic capacity on the terrestrial carbon (C) cycle between 1860 and 2100 within a coupled climate–carbon cycle model, that emulates 22 global climate models. Results indicate that inclusion of biogeographical variation in photosynthetic temperature response is most important for present‐day and future C uptake, with increasing importance of thermal acclimation under future warming. Accounting for both effects narrows the range of predictions of the simulated global land C storage in 2100 across climate projections (29% and 43% globally and in the tropics, respectively). Contrary to earlier studies, our results suggest that thermal acclimation of photosynthetic capacity makes tropical and temperate C less vulnerable to warming, but reduces the warming‐induced C uptake in the boreal region under elevated CO2. [ABSTRACT FROM AUTHOR]

Abstract: Land Surface Models (LSMs) are essential to reproduce biophysical processes modulated by vegetation and to predict the future evolution of the land‐climate system. To assess the performance of an ensemble of LSMs (JSBACH, JULES, ORCHIDEE, CLM, and LPJ‐GUESS) a consistent set of land surface energy fluxes and leaf area index (LAI) has been generated. Relationships of interannual variations of modeled surface fluxes and LAI changes have been analyzed at global scale across climatological gradients and compared with those obtained from satellite‐based products. Model‐specific strengths and deficiencies were diagnosed for tree and grass biomes. Results show that the responses of grasses are generally well represented in models with respect to the observed interplay between turbulent fluxes and LAI, increasing the confidence on how the LAI‐dependent partition of net radiation into latent and sensible heat are simulated. On the contrary, modeled forest responses are characterized by systematic bias in the relation between the year‐to‐year variability in LAI and net radiation in cold and temperate climates, ultimately affecting the amount of absorbed radiation due to LAI‐related effects on surface albedo. In addition, for tree biomes, the relationships between LAI and turbulent fluxes appear to contradict the experimental evidences. The dominance of the transpiration‐driven over the observed albedo‐driven effects might suggest that LSMs have the incorrect balance of these two processes. Such mismatches shed light on the limitations of our current understanding and process representation of the vegetation control on the surface energy balance and help to identify critical areas for model improvement. [ABSTRACT FROM AUTHOR]

Abstract: No consensus has yet been reached on the major factors driving the observed increase in the seasonal amplitude of atmospheric CO2 in the northern latitudes. In this study, we used atmospheric CO2 records from 26 northern hemisphere stations with a temporal coverage longer than 15 years, and an atmospheric transport model prescribed with net biome productivity (NBP) from an ensemble of nine terrestrial ecosystem models, to attribute change in the seasonal amplitude of atmospheric CO2. We found significant (p < .05) increases in seasonal peak‐to‐trough CO2 amplitude (AMPP‐T) at nine stations, and in trough‐to‐peak amplitude (AMPT‐P) at eight stations over the last three decades. Most of the stations that recorded increasing amplitudes are in Arctic and boreal regions (>50°N), consistent with previous observations that the amplitude increased faster at Barrow (Arctic) than at Mauna Loa (subtropics). The multi‐model ensemble mean (MMEM) shows that the response of ecosystem carbon cycling to rising CO2 concentration (eCO2) and climate change are dominant drivers of the increase in AMPP‐T and AMPT‐P in the high latitudes. At the Barrow station, the observed increase of AMPP‐T and AMPT‐P over the last 33 years is explained by eCO2 (39% and 42%) almost equally than by climate change (32% and 35%). The increased carbon losses during the months with a net carbon release in response to eCO2 are associated with higher ecosystem respiration due to the increase in carbon storage caused by eCO2 during carbon uptake period. Air‐sea CO2 fluxes (10% for AMPP‐T and 11% for AMPT‐P) and the impacts of land‐use change (marginally significant 3% for AMPP‐T and 4% for AMPT‐P) also contributed to the CO2 measured at Barrow, highlighting the role of these factors in regulating seasonal changes in the global carbon cycle. [ABSTRACT FROM AUTHOR]

Accurate representation of photosynthesis in terrestrial biosphere models ( TBMs) is essential for robust projections of global change. However, current representations vary markedly between TBMs, contributing uncertainty to projections of global carbon fluxes. Here we compared the representation of photosynthesis in seven TBMs by examining leaf and canopy level responses of photosynthetic CO2 assimilation ( A) to key environmental variables: light, temperature, CO2 concentration, vapor pressure deficit and soil water content. We identified research areas where limited process knowledge prevents inclusion of physiological phenomena in current TBMs and research areas where data are urgently needed for model parameterization or evaluation. We provide a roadmap for new science needed to improve the representation of photosynthesis in the next generation of terrestrial biosphere and Earth system models. [ABSTRACT FROM AUTHOR]

Tropospheric ozone (O3) produces harmful effects to forests and crops, leading to a reduction of land carbon assimilation that, consequently, influences the land sink and the crop yield production. To assess the potential negative O3 impacts to vegetation, the European Union uses the Accumulated Ozone over Threshold of 40 ppb (AOT40). This index has been chosen for its simplicity and flexibility in handling different ecosystems as well as for its linear relationships with yield or biomass loss. However, AOT40 does not give any information on the physiological O3 uptake into the leaves since it does not include any environmental constraints to O3 uptake through stomata. Therefore, an index based on stomatal O3 uptake (i.e. PODY), which describes the amount of O3 entering into the leaves, would be more appropriate. Specifically, the PODY metric considers the effects of multiple climatic factors, vegetation characteristics and local and phenological inputs rather than the only atmospheric O3 concentration. For this reason, the use of PODY in the O3 risk assessment for vegetation is becoming recommended. We compare different potential O3 risk assessments based on two methodologies (i.e. AOT40 and stomatal O3 uptake) using a framework of mesoscale models that produces hourly meteorological and O3 data at high spatial resolution (12 km) over Europe for the time period 2000-2005. Results indicate a remarkable spatial and temporal inconsistency between the two indices, suggesting that a new definition of European legislative standard is needed in the near future. Besides, our risk assessment based on AOT40 shows a good consistency compared to both in-situ data and other model-based datasets. Conversely, risk assessment based on stomatal O3 uptake shows different spatial patterns compared to other model-based datasets. This strong inconsistency can be likely related to a different vegetation cover and its associated parameterizations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

•Second-generation Dynamic Global Vegetation Models (DGVMs) have recently been developed that explicitly represent the ecological dynamics of disturbance, vertical competition for light, and succession. Here, we introduce a modified second-generation DGVM and examine how the representation of demographic processes operating at two-dimensional spatial scales not represented by these models can influence predicted community structure, and responses of ecosystems to climate change. •The key demographic processes we investigated were seed advection, seed mixing, sapling survival, competitive exclusion and plant mortality. We varied these parameters in the context of a simulated Amazon rainforest ecosystem containing seven plant functional types (PFTs) that varied along a trade-off surface between growth and the risk of starvation induced mortality. •Varying the five unconstrained parameters generated community structures ranging from monocultures to equal co-dominance of the seven PFTs. When exposed to a climate change scenario, the competing impacts of CO2 fertilization and increasing plant mortality caused ecosystem biomass to diverge substantially between simulations, with mid-21st century biomass predictions ranging from 1.5 to 27.0 kg C m−2. •Filtering the results using contemporary observation ranges of biomass, leaf area index (LAI), gross primary productivity (GPP) and net primary productivity (NPP) did not substantially constrain the potential outcomes. We conclude that demographic processes represent a large source of uncertainty in DGVM predictions. [ABSTRACT FROM AUTHOR]

Measurements of the net CO2 flux between terrestrial ecosystems and the atmosphere using the eddy covariance technique have the potential to underpin our interpretation of regional CO2 source–sink patterns, CO2 flux responses to forcings, and predictions of the future terrestrial C balance. Information contained in FLUXNET eddy covariance data has multiple uses for the development and application of global carbon models, including evaluation/validation, calibration, process parameterization, and data assimilation. This paper reviews examples of these uses, compares global estimates of the dynamics of the global carbon cycle, and suggests ways of improving the utility of such data for global carbon modelling. Net ecosystem exchange of CO2 (NEE) predicted by different terrestrial biosphere models compares favourably with FLUXNET observations at diurnal and seasonal timescales. However, complete model validation, particularly over the full annual cycle, requires information on the balance between assimilation and decomposition processes, information not readily available for most FLUXNET sites. Site history, when known, can greatly help constrain the model-data comparison. Flux measurements made over four vegetation types were used to calibrate the land-surface scheme of the Goddard Institute for Space Studies global climate model, significantly improving simulated climate and demonstrating the utility of diurnal FLUXNET data for climate modelling. Land-surface temperatures in many regions cool due to higher canopy conductances and latent heat fluxes, and the spatial distribution of CO2 uptake provides a significant additional constraint on the realism of simulated surface fluxes. FLUXNET data are used to calibrate a global production efficiency model (PEM). [ABSTRACT FROM AUTHOR]

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 ( Rh). 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. [ABSTRACT FROM AUTHOR]

Climate change effects on seasonal activity in terrestrial ecosystems are significant and well documented, especially in the middle and higher latitudes. Temperature is a main driver of many plant developmental processes, and in many cases higher temperatures have been shown to speed up plant development and lead to earlier switching to the next ontogenetic stage. Qualitatively consistent advancement of vegetation activity in spring has been documented using three independent methods, based on ground observations, remote sensing, and analysis of the atmospheric CO2 signal. However, estimates of the trends for advancement obtained using the same method differ substantially. We propose that a high fraction of this uncertainty is related to the time frame analysed and changes in trends at decadal time scales. Furthermore, the correlation between estimates of the initiation of spring activity derived from ground observations and remote sensing at interannual time scales is often weak. We propose that this is caused by qualitative differences in the traits observed using the two methods, as well as the mixture of different ecosystems and species within the satellite scenes. © New Phytologist (2004) doi: 10.1111/j.1469-8137.2004.01059.x [ABSTRACT FROM AUTHOR]

Abstract. We develop and evaluate a large-scale dynamic vegetation model, TEM-LPJ, which considers interactions among water, light and nitrogen in simulating ecosystem function and structure. We parameterized the model for three plant functional types (PFTs): a temperate deciduous forest, a temperate coniferous forest, and a temperate C[sub 3]; grassland. Model parameters were determined using data from forest stands at the Harvard Forest in Massachusetts. Applications of the model reasonably simulated stand development over 120 yr for Populus tremuloides in Wisconsin and for Pinus elliottii in Florida. Our evaluation of tree-grass interactions simulated by the model indicated that competition for light led to dominance by the deciduous forest PFT in moist regions of eastern United States and that water competition led to dominance by the grass PFT in dry regions of the central United States. Along a moisture transect at 41.5° N in the eastern United States, simulations by TEM-LPJ reproduced the composition of potential temperate deciduous forest, temperate savanna, and C3 grassland located along the transect. [ABSTRACT FROM AUTHOR]

Summary 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 (), 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. [ABSTRACT FROM AUTHOR]