Dynamic Global Vegetation Model Research Articles

The key role of forest disturbance in reconciling estimates of the northern carbon sink

AbstractNorthern forests are an important carbon sink, but our understanding of the driving factors is limited due to discrepancies between dynamic global vegetation models (DGVMs) and atmospheric inversions. We show that DGVMs simulate a 50% lower sink (1.1 ± 0.5 PgC yr−1 over 2001–2021) across North America, Europe, Russia, and China compared to atmospheric inversions (2.2 ± 0.6 PgC yr−1). We explain why DGVMs underestimate the carbon sink by considering how they represent disturbance processes, specifically the overestimation of fire emissions, and the lack of robust forest demography resulting in lower forest regrowth rates than observed. We reconcile net sink estimates by using alternative disturbance-related fluxes. We estimate carbon uptake through forest regrowth by combining satellite-derived forest age and biomass maps. We calculate a regrowth flux of 1.1 ± 0.1 PgC yr−1, and combine this with satellite-derived estimates of fire emissions (0.4 ± 0.1 PgC yr−1), land-use change emissions from bookkeeping models (0.9 ± 0.2 PgC yr−1), and the DGVM-estimated sink from CO2 fertilisation, nitrogen deposition, and climate change (2.2 ± 0.9 PgC yr−1). The resulting ‘bottom-up’ net flux of 2.1 ± 0.9 PgC yr−1 agrees with atmospheric inversions. The reconciliation holds at regional scales, increasing confidence in our results.

Analysis of Quantitative Estimates of the Greenhouse Gases Net Flow in the Russian Land Use Sector

A comparative analysis of the actual greenhouse gas net runoff by the Russian land use sector has been carried out, which showed that depending on the methodologies and approaches used, as well as the type of initial data, information on the absorptive capacity can be aggregated into three groups: 1) studies based on process (inverse) modeling and VNIILM methodology; 2) studies with statistical net runoff modeling and approaches regulated by the IPCC on the basis of the state forest registry; 3) studies based on dynamic global vegetation models (DGVM) and ROBUL methodology, verified by the IPCC, but using as input data information from the state forest registry. An assumption is made that the existing official Russian ROBUL methodology, which coincides with the IPCC methodology, currently reflects a conservative estimate of the absorption capacity of the Russian land use sector and can be adjusted with a potential increase to 35-45% of the current values due to additional accounting of reserve forests in the calculations, as well as the results of the state forest inventory and a new state forest registry with a high degree of spatial resolution.

An Evaluation of Evapotranspiration Products over the Tibetan Plateau

Abstract In situ observations from 13 sites located over the Tibetan Plateau (TP) are used to evaluate evapotranspiration (ET) products, including remote sensing–based, land surface–modeled, and reanalysis products. It is found that the Global Land Surface Satellite (GLASS) product, the Global Land Evaporation Amsterdam Model (GLEAM) product, and the simulations by the Community Land Model–dynamic global vegetation model [biogeochemical dynamic vegetation (CLM–BGCDV)] are the top-ranked products measured by the percentage bias, root-mean-square error, and correlation coefficient against in situ observations. The evaluated data are then used to examine the consistency in spatial and temporal variability of summer ET and its controlling factors on the TP and the three-river source region (TRSR). All products show consistently that precipitation in the central semiarid part of TP is the dominant factor influencing summer ET, while air temperature plays a certain role in the southeastern and eastern TP. Uncertainties exist in the western TP, possibly due to the lack of observations or gaps in the satellite data. Some suggestions for improving ET product development based on models and satellite retrievals, particularly for the cold and complex surface of the TP, are also given. Significance Statement Evapotranspiration (ET) is a crucial water cycle component, but accurately assessing it remains difficult. In situ measurements are scarce, while other products have large uncertainties, such as satellite remote sensing retrievals, land surface model simulations, and reanalyses. This is particularly a problem for the Tibetan Plateau (TP). With some considerable effort, ET fluxes from 13 sites and reasonable length in the TP have become available. These in situ data are used to evaluate gridded ET products. Based on an overall score, they are ranked as the Global Land Surface Satellite (GLASS) product, the Global Land Evaporation Amsterdam Model (GLEAM) product, and the model simulation by the Community Land Model–dynamic global vegetation model [biogeochemical dynamic vegetation (CLM–BGCDV)]. Then, we used these top-ranked products to detect the spatial and trend consistencies and the environmental impacts to further discuss the product conformance. It was shown that all products show precipitation dominated the summer ET variation in the semiarid region in the central TP, while air temperature had more impact on the relatively humid region in the east and southeast of the TP. Uncertainties exist in the west TP possibly due to the lack of observations in the assimilation product or missing gaps in the satellite product. Specifically, we conclude that dynamic vegetation could play an increasing role under the background of climate warming, which should be considered in the ET model–based product generation. The suggestions for ET generation based on models and satellite retrievals, particularly for the special cold and complex surface of the TP, are also provided.

The decline in tropical land carbon sink drives high atmospheric CO2 growth rate in 2023

Abstract Atmospheric CO2 growth rate (CGR), reflecting the carbon balance between anthropogenic emissions and net uptake from land and ocean, largely determines the magnitude and speed of global warming. CGR at Mauna Loa Baseline Observatory reached record high in 2023. Here we quantified major components of global carbon balance for 2023, by developing a framework which integrated fossil fuel CO2 emissions data, an atmospheric inversion from Global ObservatioN-based system for monitoring Greenhouse GAses (GONGGA) with two artificial intelligence (AI) models derived from dynamic global vegetation models. We attributed the record high CGR increase in 2023 compared to 2022 primarily to the large decline in land carbon sink (1803 ± 197 TgC year−1), with minor contributions from a small reduction in ocean carbon sink (184 TgC year−1) and a slight increase in fossil fuel emissions (24 TgC year−1). At least 78% of the global decline in land carbon sink was contributed by the decline in tropical sink, with GONGGA inversion (1354 TgC year−1) and AI simulations (1578 ± 666 TgC year−1) showing similar declines in the tropics. We further linked this tropical decline to the detrimental impact of El Niño-induced anomalous warming and drying on vegetation productivity in water-limited Sahel and southern Africa. Our successful attribution of CGR increase within the framework combining atmospheric inversion with AI simulations enabled near-real-time tracking of the global carbon budget which had a one-year reporting lag.

Low latency carbon budget analysis reveals a large decline of the land carbon sink in 2023

Abstract In 2023, the CO2 growth rate was 3.37 ± 0.11 ppm at Mauna Loa, 86% above the previous year, and hitting a record high since observations began in 1958, while global fossil fuel CO2 emissions only increased by 0.6 ± 0.5%. This implies an unprecedented weakening of land and ocean sinks, and raises the question of where and why this reduction happened. Here we show a global net land CO2 sink of 0.44 ± 0.21 GtC yr−1, the weakest since 2003. We used dynamic global vegetation models, satellites fire emissions, an atmospheric inversion based on OCO-2 measurements, and emulators of ocean biogeochemical and data driven models to deliver a fast-track carbon budget in 2023. Those models ensured consistency with previous carbon budgets. Regional flux anomalies from 2015–2022 are consistent between top-down and bottom-up approaches, with the largest abnormal carbon loss in the Amazon during the drought in the second half of 2023 (0.31 ± 0.19 GtC yr−1), extreme fire emissions of 0.58 ± 0.10 GtC yr−1 in Canada and a loss in South-East Asia (0.13 ± 0.12 GtC yr−1). Since 2015, land CO2 uptake north of 20°N declined by half to 1.13 ± 0.24 GtC yr−1 in 2023. Meanwhile, the tropics recovered from the 2015–16 El Niño carbon loss, gained carbon during the La Niña years (2020–2023), then switched to a carbon loss during the 2023 El Niño (0.56 ± 0.23 GtC yr−1). The ocean sink was stronger than normal in the equatorial eastern Pacific due to reduced upwelling from La Niña's retreat in early 2023 and the development of El Niño later. Land regions exposed to extreme heat in 2023 contributed a gross carbon loss of 1.73 GtC yr−1, indicating that record warming in 2023 had a strong negative impact on the capacity of terrestrial ecosystems to mitigate climate change.

Carbon dioxide fertilization enhanced carbon sink offset by climate change and land use in Amazonia on a centennial scale

The Amazon, the Earth's largest tropical forest, plays a critical role in the global carbon cycle, acting as a significant carbon sink. Recent studies, however, indicate a decline in its carbon sequestration capacity due to climate variability, intensive deforestation, and fires. This study aims to examine the impacts of these factors on the carbon dynamics of the Amazon over a centennial scale based on dynamic global vegetation models (DGVMs) of Trendy-v11. It was found that the Amazon region exhibited significant spatiotemporal variations in net land carbon (C) fluxes, and was a net C sink (40.02 ± 242.64 Tg C yr−1) during 1901–2021. The Amazonian net biome productivity (NBP) showed a 6-decades-scale shift from a decreasing trend (−3.78 Tg C yr−2) during 1901–1959 to an increasing trend (2.39 Tg C yr−2) during 1960–2021. The Amazonian NBP was negatively related to air temperature while positively related to dry-season precipitation during 1901–2021. Furthermore, the increase of atmospheric CO2 concentration during 1901–2020 enhanced Amazonian NBP by 36.40 ± 8.39 Pg C, which was largely offset by land use change (−18.84 ± 12.02 Pg C) and climate change (−10.03 ± 5.00 Pg C). Our findings underscore the critical need for sustainable management practices in the Amazon to enhance its C sink and preserve its function in the global climate system.

Change trend and attribution analysis of leaf area index in the East African Plateau from 1982 to 2020.

The ecosystems on the East African Plateau are crucial for maintaining the biodiversity, water resource balance, and ecological equilibrium of the African continent. However, the spatiotemporal variations of vegetation and the driving factors remain unclear. We analyzed leaf area index (LAI) change trends in the East African Plateau based on the GIMMS LAI4g dataset and further conducted attribution analysis combining temperature and precipitation data, as well as 10 Dynamic Global Vegetation Models (DGVMs) in TRNEDY v9. The results showed that LAI of the East African Plateau had a modest change trend from 1982 to 1999 (2.5×10-3 m2·m-2·a-1), but significantly increased from 2000 to 2020 (5.2×10-3 m2·m-2·a-1), which was 2.1 times faster than that during 1982-1999. Temperature and precipitation had weak correlations with LAI from 1982 to 1999, but showed significant correlations from 2000 to 2020. The DGVMs demonstrated consistent attribution results, with temperature and precipitation contributing significantly more to the LAI variations from 2000 to 2020 compared to the period from 1982 to 1999. The results highlighted the key role of climate change in driving vegetation greening on the East African Plateau during 2000-2020, which could provide important evidence for ecological conservation and sustainable development strategies in the region.

Reduced productivity and carbon drawdown of tropical forests from ground-level ozone exposure

AbstractElevated ground-level ozone, a result of human activity, is known to reduce plant productivity, but its influence on tropical forests remains unclear. Here we estimate how increased ozone exposure has affected tropical-forest productivity and the global carbon cycle. We experimentally measure the ozone susceptibility of various tropical tree species, and then incorporate these data into a dynamic global vegetation model. We find that current anthropogenic-derived ozone results in a substantial decline in annual net primary productivity (NPP) across all tropical forests, with some areas being particularly impacted. For example, Asia sees losses of 10.9% (7.2–19.7%) NPP. We calculate that this productivity decline has resulted in a cumulative loss in carbon drawdown of 0.29 PgC per year since 2000, equating to ~17% of the tropical contemporary annual land carbon sink in the twenty-first century. We also find that areas of current and future forest restoration are disproportionately affected by elevated ozone. Future socioeconomic pathways that reduce ozone formation in the tropics will incur benefits to the global carbon budget by relieving the current ozone impacts seen across both intact forest and areas of forest restoration, which are critical terrestrial regions for mitigation of rising atmospheric carbon dioxide.

Dissecting the characteristics and driver factors of potential vegetation water use efficiency in China

While large-scale vegetation greening in China has substantially influenced global vegetation dynamics, the specific impact of this restoration on water use efficiency (WUE) remained inadequately understood. This study employed both the Geodetector and structural equation modeling (SEM) methods, utilizing the Lund-Potsdam-Jena (LPJ) Global Dynamic Vegetation Model, to explore the contributions of various driving factors to China's potential vegetation WUE from 1982 to 2019. The results indicated: (1) there existed considerable further potential for vegetation recovery nationwide. Among them, the Loess Plateau, Inner Mongolia Plateau, and northern Xinjiang had relatively high potential for vegetation recovery. This potential was further amplified by the significant prospects for enhancing WUE in these areas; (2) The application of the Geodetector method revealed that the normalized difference vegetation index (NDVI) explained over 40 % of the variation in potential vegetation WUE in China, exerting a greater influence than climatic factors. In arid/semi-arid regions, precipitation (PRE), NDVI, and vapor pressure deficit (VPD) significantly influenced WUE. Temperature (TEM) was the dominant factor affecting WUE in humid and humid/semi-humid regions; (3) Utilizing the SEM analysis method, it was evident that NDVI exerted the most substantial direct positive influence on potential vegetation WUE in China, whereas VPD and PRE had notable negative impacts. In arid/semi-arid regions, PRE emerged as the primary determinant of WUE. Conversely, in regions where water resources were not limiting, TEM and VPD exerted a more pronounced influence on potential vegetation WUE. This indicated that while vegetation restoration generally enhanced potential vegetation WUE, other factors such as PRE, TEM, and VPD played critical roles in different climatic zones, shaping the regional variations in WUE.

Increasing Sensitivity of Tree Radial Growth to Precipitation

AbstractThe sensitivity of tree growth to precipitation regulates their responses to drought, and is a crucial metric for predicting ecosystem dynamics and vulnerability. Sensitivity may be changing with continuing climate change, yet a comprehensive assessment of its change is still lacking. We utilized tree ring measurements from 3,044 sites, climate data and CO2 concentrations obtained from monitoring stations, combined with dynamic global vegetation models to investigate spatiotemporal changes in the sensitivity over the past century. We observed an increasing sensitivity since around 1950. This increased sensitivity was particularly pronounced in arid biomes due to the combined effect of increased precipitation and elevated CO2. While elevated CO2 reduced the sensitivity of the humid regions, the intensified water pressure caused by decreased precipitation still increased the sensitivity. Our findings suggest an escalating vulnerability of tree growth to precipitation change, which may increase the risk of tree mortality under future intensified drought.

Earlier peak photosynthesis timing potentially escalates global wildfires.

More intense fire weather due to climate change is implicated as a key driver of recent extreme wildfire events. As fuel stock, the role of vegetation and its phenology changes in wildfire dynamics, however is not fully appreciated. Using long-term satellite-based burned areas and photosynthesis observations, we reveal that an earlier peak photosynthesis timing (PPT) potentially acts to escalate subsequent wildfires, with an increase in the global average burned fraction of 0.021% (∼2.20 Mha) for every additional day of PPT advancement. Satellite observations and Earth System modeling consistently show that this fire escalation is likely due to intensified drought conditions and increased fuel availability associated with the climate feedback arising from earlier PPT. Current fire-enabled dynamic global vegetation models can reproduce the observed negative correlation between PPT and burned area but underestimate the strength of the relationship notably. Given the continued PPT advancement owing to climate change, the bioclimatic effects of vegetation phenology change suggest a potentially pervasive upward pressure on future wildfires.

Trigger thresholds and their dynamics of vegetation production loss under different atmospheric and soil drought conditions

Many evidences have shown that both atmospheric and soil droughts can constrain vegetation growth and further threaten its ability to sequester carbon. However, the trigger thresholds of vegetation production loss under different atmospheric and soil drought conditions are still unknown. In this study, we proposed a Copula and Bayesian equations-based framework to investigate trigger thresholds of various vegetation production losses under different atmospheric and soil drought conditions. The trigger thresholds dynamics and their possible causes were also investigated. To achieve this goal, we first simulated the gross primary production, soil moisture, and vapor pressure deficit over China during 1961–2018 using an individual-based, spatially explicit dynamic global vegetation model. The main drivers of the dynamic change in trigger thresholds were then explored by Random Forest model. We found that soil drought caused greater stress on gross primary production loss than atmospheric drought, with a larger impact area and higher probability of damage. In terms of spatial distribution, the risk probability of gross primary production loss was higher in eastern China than in western China, and the drought trigger threshold was also smaller in eastern China. In addition, the trigger thresholds for atmospheric and soil drought in most regions exhibited a decreasing trend from 1961 to 2018, while the CO2 fertilization enhanced the drought tolerance of vegetation. The reduction in CO2 fertilization effect slowed down the downward trend of trigger threshold for soil drought, while the increase in temperature exacerbated the downward trend of trigger threshold for atmospheric drought. This study highlighted the larger effect of soil drought on vegetation production loss than atmospheric drought and implied that climate change can modulate the trigger threshold of vegetation production losses under drought conditions. These findings provide scientific guidance for managing the increasing risk of drought on vegetation and optimizing watershed water allocation.

Rainfall seasonality dominates critical precipitation threshold for the Amazon forest in the LPJmL vegetation model

Understanding the Amazon Rainforest's response to shifts in precipitation is paramount with regard to its sensitivity to climate change and deforestation. Studies using Dynamic Global Vegetation Models (DGVMs) typically only explore a range of socio-economically plausible pathways. In this study, we applied the state-of-the-art DGVM LPJmL to simulate the Amazon forest's response under idealized scenarios where precipitation is linearly decreased and subsequently increased between current levels and zero. Our results indicate a nonlinear but reversible relationship between vegetation Above Ground Biomass (AGB) and Mean Annual Precipitation (MAP), suggesting a threshold at a critical MAP value, below which vegetation biomass decline accelerates with decreasing MAP. We find that approaching this critical threshold is accompanied by critical slowing down, which can hence be expected to warn of accelerating biomass decline with decreasing rainfall. The critical precipitation threshold is lowest in the northwestern Amazon, whereas the eastern and southern regions may already be below their critical MAP thresholds. Overall, we identify the seasonality of precipitation and the potential evapotranspiration (PET) as the most important parameters determining the threshold value. While vegetation fires show little effect on the critical threshold and the biomass pattern in general, the ability of trees to adapt to water stress by investing in deep roots leads to increased biomass and a lower critical threshold in some areas in the eastern and southern Amazon where seasonality and PET are high. Our findings underscore the risk of Amazon forest degradation due to changes in the water cycle, and imply that regions that are currently characterized by higher water availability may exhibit heightened vulnerability to future drying.

Climate and vegetation change impacts on future conterminous United States water yield

Future water availability is influenced by both climate and associated vegetation dynamics. This study coupled vegetation projections from a dynamic global vegetation model (MC2) with an ecohydrological model, Water Supply Stress Index (WaSSI), to predict water yield at the 8-digit Hydrologic Unit Code (HUC8) watershed level for the conterminous United States (CONUS) for the 21st century. We considered two contrasting warming scenarios (Representative Concentration Pathways 8.5 and 4.5) and accounted for simulation uncertainty by using a large ensemble of climate model outputs. The coupled model projects a decrease in water yield across much of CONUS, especially towards end-century (2080–2099) under RCP 8.5 (warmer scenario), reaching up to −30% at the regional level, relative to the 2008–2027 baseline period. Overall, the projected water yield reduction under RCP 8.5 is roughly twice as high as under RCP 4.5. Substantial changes in water yield for watersheds in the central and southeastern United States are expected by mid-century (2040–2059), reaching up to −40% (RCP 4.5) and −75% (RCP 8.5) at at the century’s end (2080–2099), relative to 2008–2027, respectively. Climate change, rather than vegetation change, strongly dominates the projected future changes in water yield, with contributions typically one order of magnitude higher. For a small number of watersheds, the effects of vegetation change can mitigate or exacerbate the effects of climate change on water yield. Our simulation results suggest a widespread increase in aridity and evaporative indices and a decrease in soil moisture, especially under RCP 8.5. Our integrated modeling results can inform policy makers and resource development planners quantitative information of future water availability.

Consistent time allocation fraction to vegetation green-up versus senescence across northern ecosystems despite recent climate change

Extended growing season lengths under climatic warming suggest increased time for plant growth. However, research has focused on climatic impacts to the timing or duration of distinct phenological events. Comparatively little is known about impacts to the relative time allocation to distinct phenological events, for example, the proportion of time dedicated to leaf growth versus senescence. We use multiple satellite and ground-based observations to show that, despite recent climate change during 2001 to 2020, the ratio of time allocated to vegetation green-up over senescence has remained stable [1.27 (± 0.92)] across more than 83% of northern ecosystems. This stability is independent of changes in growing season lengths and is caused by widespread positive relationships among vegetation phenological events; longer vegetation green-up results in longer vegetation senescence. These empirical observations were also partly reproduced by 13 dynamic global vegetation models. Our work demonstrates an intrinsic biotic control to vegetation phenology that could explain the timing of vegetation senescence under climate change.

A global behavioural model of human fire use and management: WHAM! v1.0

Abstract. Fire is an integral ecosystem process and a major natural source of vegetation disturbance globally. Yet at the same time, humans use and manage fire in diverse ways and for a huge range of purposes. Therefore, it is perhaps unsurprising that a central finding of the first Fire Model Intercomparison Project was simplistic representation of humans is a substantial shortcoming in the fire modules of dynamic global vegetation models (DGVMs). In response to this challenge, we present a novel, global geospatial model that seeks to capture the diversity of human–fire interactions. Empirically grounded with a global database of anthropogenic fire impacts, WHAM! (the Wildfire Human Agency Model) represents the underlying behavioural and land system drivers of human approaches to fire management and their impact on fire regimes. WHAM! is designed to be coupled with DGVMs (JULES-INFERNO in the current instance), such that human and biophysical drivers of fire on Earth, and their interactions, can be captured in process-based models for the first time. Initial outputs from WHAM! presented here are in line with previous evidence suggesting managed anthropogenic fire use is decreasing globally and point to land use intensification as the underlying reason for this phenomenon.

Climate change critically affects the status of the land-system change planetary boundary

The planetary boundaries framework defines a safe operating space for humanity. To date, these boundaries have mostly been investigated separately, and it is unclear whether breaching one boundary can lead to the transgression of another. By employing a dynamic global vegetation model, we systematically simulate the strength and direction of the effects of different transgression levels of the climate change boundary (using climate output from ten phase 6 of the Coupled Model Intercomparison Project models for CO2 levels ranging from 350 ppm to 1000 ppm). We focus on climate change-induced shifts of Earth’s major forest biomes, the control variable for the land-system change boundary, both by the end of this century and, to account for the long-term legacy effect, by the end of the millennium. Our simulations show that while staying within the 350 ppm climate change boundary co-stabilizes the land-system change boundary, breaching it (>450 ppm) leads to critical transgression of the latter, with greater severity the higher the ppm level rises and the more time passes. Specifically, this involves a poleward treeline shift, boreal forest dieback (nearly completely within its current area under extreme climate scenarios), competitive expansion of temperate forest into today’s boreal zone, and a slight tropical forest extension. These interacting changes also affect other planetary boundaries (freshwater change and biosphere integrity) and provide feedback to the climate change boundary itself. Our quantitative process-based study highlights the need for interactions to be studied for a systemic operationalization of the planetary boundaries framework.

Conflicting Changes of Vegetation Greenness Interannual Variability on Half of the Global Vegetated Surface

AbstractChanges in the interannual variability (IAV) of vegetation greenness and carbon sequestration are key indicators of the stability and climate sensitivities of terrestrial ecosystems. Recent studies have examined the changes in the vegetation IAV using atmospheric CO2 observations and dynamic global vegetation models (DGVMs), however, reported different and even contradictory IAV trends. Here, we investigate the changes in the IAV of vegetation greenness, quantified as coefficient of variability (CV), over the past few decades based on multiple satellite remote sensing products and DGVMs. Our results suggested that, on half of the global vegetated surface (mostly in the tropics), the CV trends detected by different satellite remote sensing products are conflicting. We found that 22.20% and 28.20% of the global vegetated surface (mostly in the non‐tropical land surface) show significant positive and negative CV trends (p ≤ 0.1), respectively. Regions with higher air temperature and greater aridity tend to have increasing CV trends, whereas greater vegetation greening trend and higher nitrogen deposition lead to smaller CV trends. DGVMs generally cannot capture the CV trends obtained from satellite remote sensing products, while the inconsistency among satellite remote sensing products is likely caused by their process algorithms rather than the sensors utilized. Our study closely examines the changes in the IAV of global vegetation greenness, and highlights substantial uncertainty when using satellite remote sensing to study the response of terrestrial ecosystems to climate change.

Biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)

Abstract. Ecosystems are under multiple stressors, and impacts can be measured with multiple variables. Humans have altered mass and energy flows of basically all ecosystems on Earth towards dangerous levels. However, integrating the data and synthesizing conclusions is becoming more and more complicated. Here we present an automated and easy-to-apply R package to assess terrestrial biosphere integrity that combines two complementary metrics. (i) The BioCol metric that quantifies the human colonization pressure exerted on the biosphere through alteration and extraction (appropriation) of net primary productivity.(ii) The EcoRisk metric that quantifies biogeochemical and vegetation structural changes as a proxy for the risk of ecosystem destabilization. Applied to simulations with the dynamic global vegetation model LPJmL5 for 1500–2016, we find that large regions presently (period 2007–2016) show modification and extraction of >20 % of the preindustrial potential net primary production. The modification (degradation) of net primary production (NPP) as a result of land use change and extraction in terms of biomass removal (e.g., from harvest) leads to drastic alterations in key ecosystem properties, which suggests a high risk of ecosystem destabilization. As a consequence of these dynamics, EcoRisk shows particularly high values in regions with intense land use and deforestation and in regions prone to impacts of climate change, such as the Arctic and boreal zone. The metrics presented here enable spatially explicit global-scale evaluation of historical and future states of the biosphere and are designed for use by the wider scientific community, being applicable not only to assessing biosphere integrity but also to benchmarking model performance. The package will be maintained on GitHub and through that we encourage its future application to other models and data sets.

CLASH – Climate-responsive Land Allocation model with carbon Storage and Harvests

Abstract. The Climate-responsive Land Allocation model with carbon Storage and Harvests (CLASH) is a global, biophysical land-use model that can be embedded into integrated assessment models (IAMs). CLASH represents vegetation growth, terrestrial carbon stocks, and production from agriculture and forestry for different land uses in a changing climate. Connecting CLASH to an IAM would allow the consideration of terrestrial carbon stocks, agriculture and forestry in global climate policy analyses. All terrestrial ecosystems and their carbon dynamics are comprehensively described at a coarse resolution. Special emphasis is placed on representing the world's forests. Vegetation growth, soil carbon stocks, agricultural yields and natural disturbance frequencies react to changing climatic conditions, emulating the dynamic global vegetation model LPJ-GUESS. Land is divided into 10 biomes with six land-use classes (including forests and agricultural classes). Secondary forests are age structured. The timing of forest harvests affects forest carbon stocks, and, hence, carbon storage per forest area can be increased through forest management. In addition to secondary forests, CLASH also includes primary ecosystems, cropland and pastures. The comprehensive inclusion of all land-use classes and their main functions allows representing the global land-use competition. In this article, we present, calibrate and validate the model; demonstrate its use; and discuss how it can be integrated into IAMs.