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

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

Long‐term variability and rainfall control of savanna fire regimes in equatorial East Africa

Fires burning the vast grasslands and savannas of Africa significantly influence the global carbon cycle. Projecting the impacts of future climate change on fire‐mediated biogeochemical processes in these dry tropical ecosystems requires understanding of how various climate factors influence regional fire regimes. To examine climate–vegetation–fire linkages in dry savanna, we conducted macroscopic and microscopic charcoal analysis on the sediments of the past 25 000 years from Lake Challa, a deep crater lake in equatorial East Africa. The charcoal‐inferred shifts in local and regional fire regimes were compared with previously published reconstructions of temperature, rainfall, seasonal drought severity, and vegetation dynamics to evaluate millennial‐scale drivers of fire occurrence. Our charcoal data indicate that fire in the dry lowland savanna of southeastern Kenya was not fuel‐limited during the Last Glacial Maximum (LGM) and Late Glacial, in contrast to many other regions throughout the world. Fire activity remained high at Lake Challa probably because the relatively high mean‐annual temperature (~22 °C) allowed productive C₄ grasses with high water‐use efficiency to dominate the landscape. From the LGM through the middle Holocene, the relative importance of savanna burning in the region varied primarily in response to changes in rainfall and dry‐season length, which were controlled by orbital insolation forcing of tropical monsoon dynamics. The fuel limitation that characterizes the region's fire regime today appears to have begun around 5000–6000 years ago, when warmer interglacial conditions coincided with prolonged seasonal drought. Thus, insolation‐driven variation in the amount and seasonality of rainfall during the past 25 000 years altered the immediate controls on fire occurrence in the grass‐dominated savannas of eastern equatorial Africa. These results show that climatic impacts on dry‐savanna burning are heterogeneous through time, with important implications for efforts to anticipate future shifts in fire‐mediated ecosystem processes.     

Large‐scale prerain vegetation green‐up across Africa

Information on the response of vegetation to different environmental drivers, including rainfall, forms a critical input to ecosystem models. Currently, such models are run based on parameters that, in some cases, are either assumed or lack supporting evidence (e.g., that vegetation growth across Africa is rainfall‐driven). A limited number of studies have reported that the onset of rain across Africa does not fully explain the onset of vegetation growth, for example, drawing on the observation of prerain flush effects in some parts of Africa. The spatial extent of this prerain green‐up effect, however, remains unknown, leaving a large gap in our understanding that may bias ecosystem modelling. This paper provides the most comprehensive spatial assessment to‐date of the magnitude and frequency of the different patterns of phenology response to rainfall across Africa and for different vegetation types. To define the relations between phenology and rainfall, we investigated the spatial variation in the difference, in number of days, between the start of rainy season (SRS) and start of vegetation growing season (SOS); and between the end of rainy season (ERS) and end of vegetation growing season (EOS). We reveal a much more extensive spread of prerain green‐up over Africa than previously reported, with prerain green‐up being the norm rather than the exception. We also show the relative sparsity of postrain green‐up, confined largely to the Sudano‐Sahel region. While the prerain green‐up phenomenon is well documented, its large spatial extent was not anticipated. Our results, thus, contrast with the widely held view that rainfall drives the onset and end of the vegetation growing season across Africa. Our findings point to a much more nuanced role of rainfall in Africa's vegetation growth cycle than previously thought, specifically as one of a set of several drivers, with important implications for ecosystem modelling.     

Impacts of climate change on the vegetation of Africa: an adaptive dynamic vegetation modelling approach

Recent IPCC projections suggest that Africa will be subject to particularly severe changes in atmospheric conditions. How the vegetation of Africa and particularly the grassland–savanna–forest complex will respond to these changes has rarely been investigated. Most studies on global carbon cycles use vegetation models that do not adequately account for the complexity of the interactions that shape the distribution of tropical grasslands, savannas and forests. This casts doubt on their ability to reliably simulate the future vegetation of Africa. We present a new vegetation model, the adaptive dynamic global vegetation model (aDGVM) that was specifically developed for tropical vegetation. The aDGVM combines established components from existing DGVMs with novel process‐based and adaptive modules for phenology, carbon allocation and fire within an individual‐based framework. Thus, the model allows vegetation to adapt phenology, allocation and physiology to changing environmental conditions and disturbances in a way not possible in models based on fixed functional types. We used the model to simulate the current vegetation patterns of Africa and found good agreement between model projections and vegetation maps. We simulated vegetation in absence of fire and found that fire suppression strongly influences tree dominance at the regional scale while at a continental scale fire suppression increases biomass in vegetation by a more modest 13%. Simulations under elevated temperature and atmospheric CO₂ concentrations predicted longer growing periods, higher allocation to roots, higher fecundity, more biomass and a dramatic shift toward tree dominated biomes. Our analyses suggest that the CO₂ fertilization effect is not saturated at ambient CO₂ levels and will strongly increase in response to further increases in CO₂ levels. The model provides a general and flexible framework for describing vegetation response to the interactive effects of climate and disturbances.     

Back to the future: using historical climate variation to project near‐term shifts in habitat suitable for coast redwood

Studies that model the effect of climate change on terrestrial ecosystems often use climate projections from downscaled global climate models (GCMs). These simulations are generally too coarse to capture patterns of fine‐scale climate variation, such as the sharp coastal energy and moisture gradients associated with wind‐driven upwelling of cold water. Coastal upwelling may limit future increases in coastal temperatures, compromising GCMs’ ability to provide realistic scenarios of future climate in these coastal ecosystems. Taking advantage of naturally occurring variability in the high‐resolution historic climatic record, we developed multiple fine‐scale scenarios of California climate that maintain coherent relationships between regional climate and coastal upwelling. We compared these scenarios against coarse resolution GCM projections at a regional scale to evaluate their temporal equivalency. We used these historically based scenarios to estimate potential suitable habitat for coast redwood (Sequoia sempervirens D. Don) under ‘normal’ combinations of temperature and precipitation, and under anomalous combinations representative of potential future climates. We found that a scenario of warmer temperature with historically normal precipitation is equivalent to climate projected by GCMs for California by 2020–2030 and that under these conditions, climatically suitable habitat for coast redwood significantly contracts at the southern end of its current range. Our results suggest that historical climate data provide a high‐resolution alternative to downscaled GCM outputs for near‐term ecological forecasts. This method may be particularly useful in other regions where local climate is strongly influenced by ocean–atmosphere dynamics that are not represented by coarse‐scale GCMs.     

Modelling biome shifts and tree cover change for 2050 in West Africa

Aim Africa is expected to face severe changes in climatic conditions. Our objectives are: (1) to model trends and the extent of future biome shifts that may occur by 2050, (2) to model a trend in tree cover change, while accounting for human impact, and (3) to evaluate uncertainty in future climate projections. Location West Africa. Methods We modelled the potential future spatial distribution of desert, grassland, savanna, deciduous and evergreen forest in West Africa using six bioclimatic models. Future tree cover change was analysed with generalized additive models (GAMs). We used climate data from 17 general circulation models (GCMs) and included human population density and fire intensity to model tree cover. Consensus projections were derived via weighted averages to: (1) reduce inter‐model variability, and (2) describe trends extracted from different GCM projections. Results The strongest predicted effect of climate change was on desert and grasslands, where the bioclimatic envelope of grassland is projected to expand into the desert by an area of 2 million km². While savannas are predicted to contract in the south (by 54 ± 22 × 10⁴ km²), deciduous and evergreen forest biomes are expected to expand (64 ± 13 × 10⁴ km² and 77 ± 26 × 10⁴ km²). However, uncertainty due to different GCMs was particularly high for the grassland and the evergreen biome shift. Increasing tree cover (1–10%) was projected for large parts of Benin, Burkina Faso, Côte d’Ivoire, Ghana and Togo, but a decrease was projected for coastal areas (1–20%). Furthermore, human impact negatively affected tree cover and partly changed the direction of the projected change from increase to decrease. Main conclusions Considering climate change alone, the model results of potential vegetation (biomes) show a ‘greening’ trend by 2050. However, the modelled effects of human impact suggest future forest degradation. Thus, it is essential to consider both climate change and human impact in order to generate realistic future tree cover projections.     

Predicting potential climate change impacts with bioclimate envelope models: a palaeoecological perspective

Aim We assess the realism of bioclimate envelope model projections for anticipated future climates by validating ecosystem reconstructions for the late Quaternary with fossil and pollen data. Specifically, we ask: (1) do climate conditions with no modern analogue negatively affect the accuracy of ecosystem reconstructions? (2) are bioclimate envelope model projections biased towards under‐predicting forested ecosystems? (3) given a palaeoecological perspective, are potential habitat projections for the 21st century within model capabilities? Location Western North America. Methods We used an ensemble classifier modelling approach (RandomForest) to spatially project the climate space of modern ecosystem classes throughout the Holocene (at 6000, 9000, 11,000, 14,000, 16,000, and 21,000 YBP) using palaeoclimate surfaces generated by two general circulation models (GFDL and CCM1). The degree of novel arrangement of climate variables was quantified with the multivariate Mahalanobis distance to the nearest modern climatic equivalent. Model projections were validated against biome classifications inferred from 1460 palaeoecological records. Results Model accuracy assessed against independent palaeoecology data is generally low for the present day, increases for 6000 YBP, and then rapidly declines towards the last glacial maximum, primarily due to the under‐prediction of forested biomes. Misclassifications were closely correlated with the degree of climate dissimilarity from the present day. For future projections, no‐analogue climates unexpectedly emerged in the coastal Pacific Northwest but were absent throughout the rest of the study area. Main conclusions Bioclimate envelope models could approximately reconstruct ecosystem distributions for the mid‐ to late‐Holocene but proved unreliable in the Late Pleistocene. We attribute this failure to a combination of no‐analogue climates and a potential lack of niche conservatism in tree species. However, climate dissimilarities in future projections are comparatively minor (similar to those of the mid‐Holocene), and we conclude that no‐analogue climates should not compromise the accuracy of model predictions for the next century.     

Abrupt changes in Great Britain vegetation carbon projected under climate change

Past abrupt ‘regime shifts’ have been observed in a range of ecosystems due to various forcing factors. Large‐scale abrupt shifts are projected for some terrestrial ecosystems under climate change, particularly in tropical and high‐latitude regions. However, there is very little high‐resolution modelling of smaller‐scale future projected abrupt shifts in ecosystems, and relatively less focus on the potential for abrupt shifts in temperate terrestrial ecosystems. Here, we show that numerous climate‐driven abrupt shifts in vegetation carbon are projected in a high‐resolution model of Great Britain's land surface driven by two different climate change scenarios. In each scenario, the effects of climate and CO₂ combined are isolated from the effects of climate change alone. We use a new algorithm to detect and classify abrupt shifts in model time series, assessing the sign and strength of the non‐linear responses. The abrupt ecosystem changes projected are non‐linear responses to climate change, not simply driven by abrupt shifts in climate. Depending on the scenario, 374–1,144 grid cells of 1.5 km × 1.5 km each, comprising 0.5%–1.5% of Great Britain's land area show abrupt shifts in vegetation carbon. We find that abrupt ecosystem shifts associated with increases (rather than decreases) in vegetation carbon, show the greatest potential for early warning signals (rising autocorrelation and variance beforehand). In one scenario, 89% of abrupt increases in vegetation carbon show increasing autocorrelation and variance beforehand. Across the scenarios, 81% of abrupt increases in vegetation carbon have increasing autocorrelation and 74% increasing variance beforehand, whereas for decreases in vegetation carbon these figures are 56% and 47% respectively. Our results should not be taken as specific spatial or temporal predictions of abrupt ecosystem change. However, they serve to illustrate that numerous abrupt shifts in temperate terrestrial ecosystems could occur in a changing climate, with some early warning signals detectable beforehand.     

Emergent climate and CO2 sensitivities of net primary productivity in ecosystem models do not agree with empirical data in temperate forests of eastern North America

Ecosystem models show divergent responses of the terrestrial carbon cycle to global change over the next century. Individual model evaluation and multimodel comparisons with data have largely focused on individual processes at subannual to decadal scales. Thus far, data‐based evaluations of emergent ecosystem responses to climate and CO₂ at multidecadal and centennial timescales have been rare. We compared the sensitivity of net primary productivity (NPP) to temperature, precipitation, and CO₂ in ten ecosystem models with the sensitivities found in tree‐ring reconstructions of NPP and raw ring‐width series at six temperate forest sites. These model‐data comparisons were evaluated at three temporal extents to determine whether the rapid, directional changes in temperature and CO₂ in the recent past skew our observed responses to multiple drivers of change. All models tested here were more sensitive to low growing season precipitation than tree‐ring NPP and ring widths in the past 30 years, although some model precipitation responses were more consistent with tree rings when evaluated over a full century. Similarly, all models had negative or no response to warm‐growing season temperatures, while tree‐ring data showed consistently positive effects of temperature. Although precipitation responses were least consistent among models, differences among models to CO₂ drive divergence and ensemble uncertainty in relative change in NPP over the past century. Changes in forest composition within models had no effect on climate or CO₂ sensitivity. Fire in model simulations reduced model sensitivity to climate and CO₂, but only over the course of multiple centuries. Formal evaluation of emergent model behavior at multidecadal and multicentennial timescales is essential to reconciling model projections with observed ecosystem responses to past climate change. Future evaluation should focus on improved representation of disturbance and biomass change as well as the feedbacks with moisture balance and CO₂ in individual models.     

Historical climate‐human‐ecosystem interaction in East Africa: a review

The palaeoecological visibility of historical human impact on natural ecosystems in tropical East Africa is strongly impeded by an overriding dominant signature of climate change at decadal‐to‐millennial time scales. Better knowledge of the relative magnitude and timing of present and past human impact and climate variability is, however, instrumental to properly assess the resilience, and recovery potential, of East Africa's natural ecosystems. Here, we briefly review comprehensive previous attempts to assess past ecosystem responses to climate change and human impact. We further discuss some key issues of climate‐human‐ecosystem relationships in a multidisciplinary framework and address some future challenges and outcomes, which may pave the way to a better understanding of past climate‐human‐ecosystem interaction‐ in tropical Africa.     

Vulnerability of rangeland beef cattle production to climate‐induced NPP fluctuations in the US Great Plains

The vulnerability of rangeland beef cattle production to increasing climate variability in the US Great Plains has received minimal attention in spite of potentially adverse socioeconomic and ecological consequences. Vulnerability was assessed as the frequency and magnitude of years in which net primary production (NPP) deviated >±25% from mean values, to represent major forage surplus and deficit years, for a historic reference period (1981–2010), mid‐century (2041–2065), and late‐century (2075–2099) periods. NPP was simulated by MC2, a dynamic global vegetation model, driven by five climate projections for representative concentration pathway (RCP) 4.5 and 8.5. Historically, 4–4.7 years per decade showed either NPP surpluses or deficits. The future number of extreme years increased to 5.4–6.4 and 5.9–6.9 per decade for RCP 4.5 and 8.5, respectively, which represents an increase of 33%–56% and 38%–73%, respectively. Future simulations exhibited increases in surplus years to between 3 and 5 years in the Northern Plains and 3–3.5 in the Southern Plains. The number of deficit years remained near historic values of 2 in the Northern Plains, but increased in the Southern Plains from 2.5 to 3.3 per decade. Historically, NPP in extreme surplus and deficit years both deviated 40% from mean NPP in all three regions. The magnitude of deficit years increased by 6%–17% in future simulations for all three regions, while the magnitude of surplus years decreased 16% in the Northern Plains and increased 16% in the Southern Plains. The Southern Plains was the only region to exhibit an increase in the magnitude of both surplus and deficit years. Unprecedented future variability of NPP may surpass the existing adaptive capacity of beef producers and adversely impact the economic viability of rangeland cattle production and ecological sustainability of rangeland resources.     

Net primary and ecosystem production and carbon stocks of terrestrial ecosystems and their responses to climate change

Evaluating the role of terrestrial ecosystems in the global carbon cycle requires a detailed understanding of carbon exchange between vegetation, soil, and the atmosphere. Global climatic change may modify the net carbon balance of terrestrial ecosystems, causing feedbacks on atmospheric CO₂ and climate. We describe a model for investigating terrestrial carbon exchange and its response to climatic variation based on the processes of plant photosynthesis, carbon allocation, litter production, and soil organic carbon decomposition. The model is used to produce geographical patterns of net primary production (NPP), carbon stocks in vegetation and soils, and the seasonal variations in net ecosystem production (NEP) under both contemporary and future climates. For contemporary climate, the estimated global NPP is 57.0 Gt C y^–1, carbon stocks in vegetation and soils are 640 Gt C and 1358 Gt C, respectively, and NEP varies from –0.5 Gt C in October to 1.6 Gt C in July. For a doubled atmospheric CO₂ concentration and the corresponding climate, we predict that global NPP will rise to 69.6 Gt C y^–1, carbon stocks in vegetation and soils will increase by, respectively, 133 Gt C and 160 Gt C, and the seasonal amplitude of NEP will increase by 76%. A doubling of atmospheric CO₂ without climate change may enhance NPP by 25% and result in a substantial increase in carbon stocks in vegetation and soils. Climate change without CO₂ elevation will reduce the global NPP and soil carbon stocks, but leads to an increase in vegetation carbon because of a forest extension and NPP enhancement in the north. By combining the effects of CO₂ doubling, climate change, and the consequent redistribution of vegetation, we predict a strong enhancement in NPP and carbon stocks of terrestrial ecosystems. This study simulates the possible variation in the carbon exchange at equilibrium state. We anticipate to investigate the dynamic responses in the carbon exchange to atmospheric CO₂ elevation and climate change in the past and future.     

Response of Amazonian forests to mid-Holocene drought: A model-data comparison

There is a major concern for the fate of Amazonia over the coming century in the face of anthropogenic climate change. A key area of uncertainty is the scale of rainforest dieback to be expected under a future, drier climate. In this study, we use the middle Holocene (ca. 6000 years before present) as an approximate analogue for a drier future, given that palaeoclimate data show much of Amazonia was significantly drier than present at this time. Here, we use an ensemble of climate and vegetation models to explore the sensitivity of Amazonian biomes to mid-Holocene climate change. For this, we employ three dynamic vegetation models (JULES, IBIS, and SDGVM) forced by the bias-corrected mid-Holocene climate simulations from seven models that participated in the Palaeoclimate Modelling Intercomparison Project 3 (PMIP3). These model outputs are compared with a multi-proxy palaeoecological dataset to gain a better understanding of where in Amazonia we have most confidence in the mid-Holocene vegetation simulations. A robust feature of all simulations and palaeodata is that the central Amazonian rainforest biome is unaffected by mid-Holocene drought. Greater divergence in mid-Holocene simulations exists in ecotonal eastern and southern Amazonia. Vegetation models driven with climate models that simulate a drier mid-Holocene (100–150 mm per year decrease) better capture the observed (palaeodata) tropical forest dieback in these areas. Based on the relationship between simulated rainfall decrease and vegetation change, we find indications that in southern Amazonia the rate of tropical forest dieback was ~125,000 km² per 100 mm rainfall decrease in the mid-Holocene. This provides a baseline sensitivity of tropical forests to drought for this region (without human-driven changes to greenhouse gases, fire, and deforestation). We highlight the need for more palaeoecological and palaeoclimate data across lowland Amazonia to constrain model responses.     

Response of terrestrial carbon uptake to climate interannual variability in China

The interest in national terrestrial ecosystem carbon budgets has been increasing because the Kyoto Protocol has included some terrestrial carbon sinks in a legally binding framework for controlling greenhouse gases emissions. Accurate quantification of the terrestrial carbon sink must account the interannual variations associated with climate variability and change. This study used a process‐based biogeochemical model and a remote sensing‐based production efficiency model to estimate the variations in net primary production (NPP), soil heterotrophic respiration (HR), and net ecosystem production (NEP) caused by climate variability and atmospheric CO₂ increases in China during the period 1981–2000. The results show that China's terrestrial NPP varied between 2.86 and 3.37 Gt C yr^?1 with a growth rate of 0.32% year^?1 and HR varied between 2.89 and 3.21 Gt C yr^?1 with a growth rate of 0.40% year^?1 in the period 1981–1998. Whereas the increases in HR were related mainly to warming, the increases in NPP were attributed to increases in precipitation and atmospheric CO₂. Net ecosystem production (NEP) varied between ?0.32 and 0.25 Gt C yr^?1 with a mean value of 0.07 Gt C yr^?1, leading to carbon accumulation of 0.79 Gt in vegetation and 0.43 Gt in soils during the period. To the interannual variations in NEP changes in NPP contributed more than HR in arid northern China but less in moist southern China. NEP had no a statistically significant trend, but the mean annual NEP for the 1990s was lower than for the 1980s as the increases in NEP in southern China were offset by the decreases in northern China. These estimates indicate that China's terrestrial ecosystems were taking up carbon but the capacity was undermined by the ongoing climate change. The estimated NEP related to climate variation and atmospheric CO₂ increases may account for from 40 to 80% to the total terrestrial carbon sink in China.     

Importance of recent shifts in soil thermal dynamics on growing season length, productivity, and carbon sequestration in terrestrial high-latitude ecosystems

In terrestrial high‐latitude regions, observations indicate recent changes in snow cover, permafrost, and soil freeze–thaw transitions due to climate change. These modifications may result in temporal shifts in the growing season and the associated rates of terrestrial productivity. Changes in productivity will influence the ability of these ecosystems to sequester atmospheric CO₂. We use the terrestrial ecosystem model (TEM), which simulates the soil thermal regime, in addition to terrestrial carbon (C), nitrogen and water dynamics, to explore these issues over the years 1960–2100 in extratropical regions (30–90°N). Our model simulations show decreases in snow cover and permafrost stability from 1960 to 2100. Decreases in snow cover agree well with National Oceanic and Atmospheric Administration satellite observations collected between the years 1972 and 2000, with Pearson rank correlation coefficients between 0.58 and 0.65. Model analyses also indicate a trend towards an earlier thaw date of frozen soils and the onset of the growing season in the spring by approximately 2–4 days from 1988 to 2000. Between 1988 and 2000, satellite records yield a slightly stronger trend in thaw and the onset of the growing season, averaging between 5 and 8 days earlier. In both, the TEM simulations and satellite records, trends in day of freeze in the autumn are weaker, such that overall increases in growing season length are due primarily to earlier thaw. Although regions with the longest snow cover duration displayed the greatest increase in growing season length, these regions maintained smaller increases in productivity and heterotrophic respiration than those regions with shorter duration of snow cover and less of an increase in growing season length. Concurrent with increases in growing season length, we found a reduction in soil C and increases in vegetation C, with greatest losses of soil C occurring in those areas with more vegetation, but simulations also suggest that this trend could reverse in the future. Our results reveal noteworthy changes in snow, permafrost, growing season length, productivity, and net C uptake, indicating that prediction of terrestrial C dynamics from one decade to the next will require that large‐scale models adequately take into account the corresponding changes in soil thermal regimes.     

CO2 stabilization,climate change and the terrestrial carbon sink

A nonequilibrium, dynamic, global vegetation model, Hybrid v4.1, with a subdaily timestep, was driven by increasing CO₂ and transient climate output from the UK Hadley Centre GCM (HadCM2) with simulated daily and interannual variability. Three IPCC emission scenarios were used: (i) IS92a, giving 790 ppm CO₂ by 2100, (ii) CO₂ stabilization at 750 ppm by 2225, and (iii) CO₂ stabilization at 550 ppm by 2150. Land use and future N deposition were not included. In the IS92a scenario, boreal and tropical lands warmed 4.5 °C by 2100 with rainfall decreased in parts of the tropics, where temperatures increased over 6 °C in some years and vapour pressure deficits (VPD) doubled. Stabilization at 750 ppm CO₂ delayed these changes by about 100 years while stabilization at 550 ppm limited the rise in global land surface temperature to 2.5 °C and lessened the appearance of relatively hot, dry areas in the tropics. Present‐day global predictions were 645 PgC in vegetation, 1190 PgC in soils, a mean carbon residence time of 40 years, NPP 47 PgC y^?1 and NEP (the terrestrial sink) about 1 PgC y^?1, distributed at both high and tropical latitudes. With IS92a emissions, the high latitude sink increased to the year 2100, as forest NPP accelerated and forest vegetation carbon stocks increased. The tropics became a source of CO₂ as forest dieback occurred in relatively hot, dry areas in 2060–2080. High VPDs and temperatures reduced NPP in tropical forests, primarily by reducing stomatal conductance and increasing maintenance respiration. Global NEP peaked at 3–4 PgC y^?1 in 2020–2050 and then decreased abruptly to near zero by 2100 as the tropical source offset the high‐latitude sink. The pattern of change in NEP was similar with CO₂ stabilization at 750 ppm, but was delayed by about 100 years and with a less abrupt collapse in global NEP. CO₂ stabilization at 550 ppm prevented sustained tropical forest dieback and enabled recovery to occur in favourable years, while maintaining a similar time course of global NEP as occurred with 750 ppm stabilization. By lessening dieback, stabilization increased the fraction of carbon emissions taken up by the land. Comparable studies and other evidence are discussed: climate‐induced tropical forest dieback is considered a plausible risk of following an unmitigated emissions scenario.     

Elucidating climatic drivers of photosynthesis by tropical forests

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.     

Sensitivity of African biomes to changes in the precipitation regime

Aim Africa is identified by the Inter‐governmental Panel on Climate Change (IPCC) as the least studied continent in terms of ecosystem dynamics and climate variability. The aim of this study was (1) to adapt the Lund‐Postdam‐Jena‐GUESS (LPJ‐GUESS) ecological modelling framework to Africa by providing new parameter values for tropical plant functional types (PFT), and (2) to assess the sensitivity of some African biomes to changes in precipitation regime. Location The study area was a representative transect (0–22° N and 7–18° E) through the transition from equatorial evergreen forests to savannas, steppes and desert northwards. The transect showed large latitudinal variation in precipitation (mean rainfall ranged from 50 to 2300 mm year^?1). Methods New PFT parameters used to calibrate LPJ‐GUESS were based on modern pollen PFTs and remote sensed leaf area index (LAI). The model was validated using independent modern pollen assemblages, LAI and through comparison with White's modern potential vegetation map. Several scenarios were developed by combining changes in total rainfall amount with variation in the length of the dry season in order to test the sensitivity of African biomes. Results Simulated vegetation compared well to observed data at local and regional scales, in terms of ecosystem functioning (LAI), and composition (pollen and White's vegetation map). The assessment of the sensitivity of biomes to changes in precipitation showed that none of the ecosystems would shift towards a new type under the range of precipitation increases suggested by the IPCC (increases from 5 to 20%). However, deciduous and semi‐deciduous forests may be very sensitive to small reductions in both the amount and seasonality of precipitation. Main conclusions This version of LPJ‐GUESS parameterized for Africa simulated correctly the vegetation present over a wide precipitation gradient. The biome sensitivity assessment showed that, compared with savannas and grasslands, closed canopy forests may be more sensitive to change in precipitation regime due to the synergetic effects of changed rainfall amounts and seasonality on vegetation functioning.     

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

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

Moisture‐induced greening of the South Asia over the past three decades

South Asia experienced a weakening of summer monsoon circulation in the past several decades, resulting in rainfall decline in wet regions. In comparison with other tropical ecosystems, quantitative assessments of the extent and triggers of vegetation change are lacking in assessing climate‐change impacts over South Asia dominated by crops. Here, we use satellite‐based Normalized Difference Vegetation Index (NDVI) to quantify spatial–temporal changes in vegetation greenness, and find a widespread annual greening trend that stands in contrast to the weakening of summer monsoon circulation particularly over the last decade. We further show that moisture supply is the primary factor limiting vegetation activity during dry season or in dry region, and cloud cover or temperature would become increasingly important in wet region. Enhanced moisture conditions over dry region, coinciding with the decline in monsoon, are mainly responsible for the widespread greening trend. This result thereby cautions the use of a unified monsoon index to predict South Asia's vegetation dynamics. Current climate–carbon models in general correctly reproduce the dominant control of moisture in the temporal characteristics of vegetation productivity. But the model ensemble cannot exactly reproduce the spatial pattern of satellite‐based vegetation change mainly because of biases in climate simulations. The moisture‐induced greening over South Asia, which is likely to persist into the wetter future, has significant implications for regional carbon cycling and maintaining food security.