Global Vegetation Model Research Articles

The Response of Vegetation to Regional Climate Change on the Tibetan Plateau Based on Remote Sensing Products and the Dynamic Global Vegetation Model

Changes in vegetation dynamics play a critical role in terrestrial ecosystems and environments. Remote sensing products and dynamic global vegetation models (DGVMs) are useful for studying vegetation dynamics. In this study, we revised the Community Land Surface Biogeochemical Dynamic Vegetation Model (referred to as the BGCDV_CTL experiment) and validated it for the Tibetan Plateau (TP) by comparing vegetation distribution and carbon flux simulations against observations. Then, seasonal–deciduous phenology parameterization was adopted according to the observed parameters (referred to as the BGCDV_NEW experiment). Compared to the observed parameters, monthly variations in gross primary productivity (GPP) showed that the BGCDV_NEW experiment had the best performance against the in situ observations on the TP. The climatology from the remote sensing and simulated GPPs showed similar patterns, with GPP increasing from northwest to southeast, although the BGCDV_NEW experiment overestimated GPP in the semi-arid and arid regions of the TP. The results show that temperature warming was the dominant factor resulting in the increase in GPP based on the remote sensing products, while precipitation enhancement was the reason for the GPP increase in the model simulation.

How more sophisticated leaf biomass simulations can increase the realism of modelled animal populations

Animal biodiversity, and its key roles in ecosystem state and functioning, is facing critical challenges in the wake of anthropogenic activities. It is urgently necessary to improve understanding of the interconnections between animals and the vegetation within ecosystems. Process-based modelling has shown to be a mighty tool in making assessments on ecological processes. We assess the effect of different vegetation models on simulated animal biodiversity by replacing the vegetation module within Madingley, a multi-trophic model of functional diversity with LPJ-GUESS, a dynamic global vegetation model. We compare the output metrics of the model system to Madingley’s default version for four ecosystem types around the globe and analyse whether the realism of the simulation results increased as a result of the coupling between Madingley and LPJ-GUESS. Simulated animal populations react to the coupling by shifting towards smaller individuals with a higher abundance. General shifts in body mass and animal distributions can be traced back to ecological processes, allowing in-depth analysis of heterotrophic responses to changes in leaf biomass. We also derive power-law relationships for herbivory to NPP and herbivore biomass to NPP and conclude that the coupled model system simulates animal populations that follow reasonable power-laws which are similar to power-laws derived from empirical data. Our results indicate that developing process-based model systems is a viable way to assess multi-trophic interconnections between animal populations and the ecosystems vegetation.

Carbon removals from nature restoration are no substitute for steep emission reductions

Carbon removals from nature restoration are no substitute for steep emission reductions

Tree migration in the dynamic, global vegetation model LPJ-GM 1.1: efficient uncertainty assessment and improved dispersal kernels of European trees

Abstract. The prediction of species geographic redistribution under climate change (i.e. range shifts) has been addressed by both experimental and modelling approaches and can be used to inform efficient policy measures on the functioning and services of future ecosystems. Dynamic global vegetation models (DGVMs) are considered state-of-the art tools to understand and quantify the spatio-temporal dynamics of ecosystems at large scales and their response to changing environments. They can explicitly include local vegetation dynamics relevant to migration (establishment, growth, seed (propagule) production), species-specific dispersal abilities and the competitive interactions with other species in the new environment. However, the inclusion of more detailed mechanistic formulations of range shift processes may also widen the overall uncertainty of the model. Thus, a quantification of these uncertainties is needed to evaluate and improve our confidence in the model predictions. In this study, we present an efficient assessment of parameter and model uncertainties combining low-cost analyses in successive steps: local sensitivity analysis, exploration of the performance landscape at extreme parameter values, and inclusion of relevant ecological processes in the model structure. This approach was tested on the newly implemented migration module of the state-of-the-art DGVM LPJ-GM, focusing on European forests. Estimates of post-glacial migration rates obtained from pollen and macrofossil records of dominant European tree taxa were used to test the model performance. The results indicate higher sensitivity of migration rates to parameters associated with the dispersal kernel (dispersal distances and kernel shape) compared to plant traits (germination rate and maximum fecundity) and highlight the importance of representing rare long-distance dispersal events via fat-tailed kernels. Overall, the successful parametrization and model selection of LPJ-GM will allow plant migration to be simulated with a more mechanistic approach at larger spatial and temporal scales, thus improving our efforts to understand past vegetation dynamics and predict future range shifts in a context of global change.

Human Fire Use and Management: A Global Database of Anthropogenic Fire Impacts for Modelling

Human use and management of fire in landscapes have a long history and vary globally in purpose and impact. Existing local research on how people use and manage fire is fragmented across multiple disciplines and is diverse in methods of data collection and analysis. If progress is to be made on systematic understanding of human fire use and management globally, so that it might be better represented in dynamic global vegetation models, for example, we need improved synthesis of existing local research and literature. The database of anthropogenic fire impacts (DAFI) presented here is a response to this challenge. We use a conceptual framework that accounts for categorical differences in the land system and socio-economic context of human fire to structure a meta-study for developing the database. From the data collated, we find that our defined anthropogenic fire regimes have distinct quantitative signatures and identify seven main modes of fire use that account for 93% of fire instance records. We describe the underlying rationales of these seven modes of fire use, map their spatial distribution and summarise their quantitative characteristics, providing a new understanding that could become the basis of improved representation of anthropogenic fire in global process-based models. Our analysis highlights the generally small size of human fires (60% of DAFI records for mean size of deliberately started fires are <21 ha) and the need for continuing improvements in methods for observing small fires via remote sensing. Future efforts to model anthropogenic fire should avoid assuming that drivers are uniform globally and will be assisted by aligning remotely sensed data with field-based data and process understanding of human fire use and management.

Deficient precipitation sensitivity to Sahel land surface forcings among CMIP5 models

AbstractThe overall performance of the simulated seasonal precipitation response to local terrestrial forcings, namely vegetation abundance and soil moisture, in the Sahel among the Coupled Model Intercomparison Project Phase Five (CMIP5) Earth System Models (ESMs) is systematically investigated and compared with its observational counterpart using a multivariate statistical method. The observed seasonal precipitation response is evaluated against a large ensemble of observational, reanalysis, and satellite data sets to provide quantification of uncertainties. The behaviour of models with and without a Dynamic Global Vegetation Model (DGVM) component is also explored, along with the mechanisms responsible for terrestrial feedback on rainfall. In general, the CMIP5 models can reasonably capture the seasonal evolution of Sahel precipitation and soil moisture, albeit with wet biases during the pre‐monsoon period and dry biases during the peak monsoon period. The non‐DGVM ESMs simulate comparable leaf area indices (LAIs) with observations, while DGVM‐enabled ESMs simulate too much year‐round LAI. The variance of precipitation that is attributed to oceanic forcings in CMIP5 is comparable with observations; however, the variance of precipitation that is attributed to terrestrial forcings is smaller in CMIP5 models than observed, especially for non‐DGVM ESMs. CMIP5 models, especially those without DGVMs, undervalue precipitation's observed response strength to soil moisture anomalies. In both observations and CMIP5 models, none of the atmospheric variables show significant responses to direct vegetation forcing, except for the response in transpiration. Although vegetation has minimal direct effect on the atmospheric state, it can affect the atmosphere by modifying soil moisture and transpiration rate indirectly, which helps explain the more realistic simulation of rainfall in DGVM‐enabled ESMs than non‐DGVM ESMs. Coupling of an ESM to a DGVM is critical in generating reasonable land–atmosphere feedback and examining future ecological and climatic changes over the Sahel.

Attribution of NDVI Dynamics over the Globe from 1982 to 2015

Satellite remote sensing has witnessed a global widespread vegetation greening since the 1980s. However, reliable observation-based quantitative knowledge on global greening remains obscure due to uncertainties in model simulations and the contribution of natural variability is largely unknown. Here, we revisit the attribution of global vegetation changes using the Time Series Segment and Residual Trend (TSS-RESTREND) method. Results showed global vegetation significantly greening over 40.6% of the vegetated grids, whereas vegetation significantly browning over 11.6% of the vegetated grids. The attribution results based on the TSS-RESTREND method show that CO2 fertilization (CO2) plays an influential role in vegetation changes over 61.4% of the global vegetated areas, followed by land use (LU, 23.5%), climate change (CC, 7.3%), and climate variability (CV, 1.5%). The vegetation greening can be largely attributed to CO2 fertilization while the vegetation browning is mainly caused by LU. Meanwhile, we also identify positive impacts of LU and CC on vegetation change in arid regions but negative impacts in humid regions. Our findings indicate spatial heterogeneity in causes behind global vegetation changes, providing more detailed references for global vegetation modeling.

Global Sensitivity Analysis of the LPJ Model for Larix olgensis Henry Forests NPP in Jilin Province, China

Parameter sensitivity analysis can determine the influence of the input parameters on the model output. Identification and calibration of critical parameters are the crucial points of the process model optimization. Based on the Extended Fourier Amplitude Sensitivity Test (EFAST) and the Morris method, this paper analyzes and compares the parameter sensitivity of the annual mean net primary productivity (NPP) of Larix olgensis Henry forests in Jilin Province simulated by the Lund–Potsdam–Jena dynamic global vegetation model (LPJ model) in 2009–2014 and 2000–2019, and deeply examines the sensitivity and influence of the two methods to each parameter and their respective influence on the model’s output. Moreover, it optimizes some selected parameters and re-simulates the NPP of Larix olgensis forests in Jilin Province from 2010 to 2019. The conclusions are the following: (1) For the LPJ model, the sensitive and non-influential parameters could be identified, which could guide the optimization order of the model and was valuable for model area applications. (2) The results of the two methods were similar but not identical. The sensitivity parameters were significantly correlated (p < 0.05); parameter krp was the most sensitive parameter, followed by parameters αm, αa and gm. These sensitive parameters were mainly found in the photosynthesis, water balance, and allometric growth modules. (3) The EFAST method had a higher precision than the Morris method, which could calculate quantitatively the contribution rate of each parameter to the variances of the model results; however, the Morris method involved fewer model running times and higher efficiency. (4) The mean relative error (MRE) and mean absolute error (MAE) of the simulated value of LPJ model after parameter optimization decreases. The optimized annual mean value of NPP from 2010 to 2019 was 580 g C m−2 a−1, with a mean annual growth rate of 2.13%, exhibiting a fluctuating growth trend. The MAE of the simulated value of LPJ model after parameter optimization decreases.

Towards a global behavioural model of anthropogenic fire: The spatiotemporal distribution of land-fire systems

Landscape fire regimes are created through socio-ecological processes, yet in current global models the representation of anthropogenic impacts on fire regimes is restricted to simplistic functions derived from coarse measures such as GDP and population density. As a result, fire-enabled dynamic global vegetation models (DGVMs) have limited ability to reproduce observed patterns of fire, and limited prognostic value. At the heart of this challenge is a failure to represent human agency and decision-making related to fire. This paper outlines progress towards a global behavioural model that captures the categorical differences in human fire use and management that arise from diverse land use objectives under varying socio-ecological contexts. We present a modelled global spatiotemporal distribution of what we term ‘land-fire systems’ (LFSs), a classification that combines land use systems and anthropogenic fire regimes. Our model simulates competition between LFSs with a novel bootstrapped classification tree approach that performs favourably against reference multinomial regressions. We evaluate model outputs with the human appropriation of net primary production (HANPP) framework and find good overall agreement. We discuss limitations to our methods, as well as remaining challenges to the integration of behavioural modelling in DGVMs and associated model-intercomparison protocols.

Projected global warming-induced terrestrial ecosystem carbon across China under SSP scenarios

Terrestrial ecosystem carbon sequestration is one of the most economically feasible and important ways to mitigate the increase of atmospheric CO2 concentration. China's terrestrial ecosystems have a huge carbon sequestration potential. Therefore, the study on the carbon sequestration potential of vegetation ecosystems is related to the smooth implementation of China's carbon neutrality strategy in 2060. Based on the CMIP6 shared socioeconomic pathways (SSPs) scenarios, changes in the vegetation aboveground biomass carbon (ABC) were estimated using the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ) model (1981–2060) for China. Subsequently, the vegetation ABC dynamics were analyzed under three future scenarios based on the traditional Multi-Model Ensemble Mean (MME) method for different eco-regions in China. We found that the vegetation ABC density was 32.38 Mg/ha in China with an increasing trend of 53.99% from 1981 to 2014. The vegetation ABC density was higher in sub-regions I, II, V, and VI. Due to the main forest carbon sinks was appeared in Southwest, Northeast and Southeast of China. However, the water conditions are poor in sub-regions III, VII, and VIII, and mostly covered by desert and steppe vegetation where the productivity level is low. Under SSP1-2.6 and SSP2-4.5 scenarios, the vegetation ABC would show a decreasing trend from 2015 to 2040 with a reduction rate of 4.34% and 4.60%, but exhibit an increasing trend from 2041 to 2060 with a growth rate of 1.46% and 0.97%. While the vegetation ABC would show a decreasing trend during 2015 to 2060 with a reduction rate of 14.78% under SSP5-8.5 scenarios. Additionally, temperature and precipitation are main factors influencing the vegetation ABC. In the future scenario, due to higher temperatures resulted in the decrease of vegetation ABC in the cold temperate humid and temperate humid/sub-humid regions. However, in the northwestern arid region and Qinghai-Tibet Plateau region may have been due to relatively low temperatures in the region, warming may address this limitation and lead to a general increase in ABC.

Global environmental controls on wildfire burnt area, size, and intensity

Fire is an important influence on the global patterns of vegetation structure and composition. Wildfire is included as a distinct process in many dynamic global vegetation models but limited current understanding of fire regimes restricts these models’ ability to reproduce more than the broadest geographic patterns. Here we present a statistical analysis of the global controls of remotely sensed burnt area (BA), fire size (FS), and a derived metric related to fire intensity (FI). Separate generalized linear models were fitted to observed monthly fractional BA from the Global Fire Emissions Database (GFEDv4), median FS from the Global Fire Atlas, and median fire radiative power from the MCD14ML dataset normalized by the square root of median FS. The three models were initially constructed from a common set of 16 predictors; only the strongest predictors for each model were retained in the final models. It is shown that BA is primarily driven by fuel availability and dryness; FS by conditions promoting fire spread; and FI by fractional tree cover and road density. Both BA and FS are constrained by landscape fragmentation, whereas FI is constrained by fuel moisture. Ignition sources (lightning and human population) were positively related to BA (after accounting for road density), but negatively to FI. These findings imply that the different controls on BA, FS and FI need to be considered in process-based models. They highlight the need to include measures of landscape fragmentation as well as fuel load and dryness, and to pay close attention to the controls of fire spread.

Remotely sensed carotenoid dynamics improve modelling photosynthetic phenology in conifer and deciduous forests

Detecting the phenology of photosynthesis, which conveys the length of the growing season, is key for terrestrial ecosystem models to constrain total annual carbon uptake and estimate gross primary productivity (GPP). However, some of the vegetation indices that are widely used for modelling GPP lack the ability to represent changes in the magnitude of photosynthesis, leading to errors in detecting phenology and large uncertainties. Crucially, remotely sensed vegetation indices such as the photochemical reflectance index (PRI) and chlorophyll/carotenoid index (CCI) can detect changes in foliar carotenoid composition that represent adjustments in photosynthetic light-use-efficiency (LUE) and changes in phenology. We modeled GPP from remote sensing data using PRI and CCI to represent foliar carotenoid changes as proxies for LUE. GPP values estimated from these PRI and CCI modified LUE models were compared against GPP from eddy covariance flux tower measurements, MODerate Resolution Imaging Spectroradiometer (MODIS) GPP product, conventional meteorological driven LUE-model, and process-based dynamic global vegetation model (ie. JULES) in an evergreen needleleaf and a deciduous broadleaf forest in the Great Lakes region. Overall, and in particular for evergreen needleleaf forests, estimates of start and end of growing season using PRI and CCI LUE-models showed less year-to-year variability than estimates obtained by process-based meteorological models. Although many process-based models provide reasonable estimates of start and end of growing season, our results demonstrate that using regulatory carotenoids and photosynthetic efficiency can improve remote monitoring of the phenology of forest vegetation.

Exploring complex water stress–gross primary production relationships: Impact of climatic drivers, main effects, and interactive effects

The dominance of vapor pressure deficit (VPD) and soil water content (SWC) for plant water stress is still under debate. These two variables are strongly coupled and influenced by climatic drivers. The impacts of climatic drivers on the relationships between gross primary production (GPP) and water stress from VPD/SWC and the interaction between VPD and SWC are not fully understood. Here, applying statistical methods and extreme gradient boosting models-Shapley additive explanations framework to eddy-covariance observations from the global FLUXNET2015 data set, we found that the VPD-GPP relationship was strongly influenced by climatic interactions and that VPD was more important for plant water stress than SWC across most plant functional types when we removed the effect of main climatic drivers, e.g. air temperature, incoming shortwave radiation and wind speed. However, we found no evidence for a significant influence of elevated CO2 on stress alleviation, possibly because of the short duration of the records (approximately one decade). Additionally, the interactive effect between VPD and SWC differed from their individual effect. When SWC was high, the SHAP interaction value of SWC and VPD on GPP was decreased with increasing VPD, but when SWC was low, the trend was the opposite. Additionally, we revealed a threshold effect for VPD stress on GPP loss; above the threshold value, the stress on GPP was flattened off. Our results have important implications for independently identifying VPD and SWC limitations on plant productivity, which is meaningful for capturing the magnitude of ecosystem responses to water stress in dynamic global vegetation models.

The influence of thinning and prescribed burning on future forest fires in fire-prone regions of Europe

Climate change is expected to increase fire risk in many forested regions, posing a potential threat to forest functioning (i.e. carbon pools and fluxes). At the same time, expansion of the wildland-urban interface threatens to bring more and more people, property, and infrastructure into contact with wildfire events. It is critical that fire be managed in a way that minimizes risk to human health and well-being and maintains forest climate change mitigation potential without affecting the important ecological role fire plays in many ecosystems. Dynamic global vegetation models (DGVMs) simulate processes over large geographic regions and long time periods and could provide information that supports fire and fuel management programs by assessing performance of such measures under different climate change scenarios in different regions. However, thus far DGVMs have not been put to this use. In this work, we introduce a novel prescribed burning (PB) module to the LPJ-GUESS DGVM. Focusing on two regions (Eastern Europe and the Iberian Peninsula), we compare the effectiveness of PB and mechanical thinning on various aspects of the fire regime under two climate change scenarios through the end of the 21st century. We find that PB and thinning, by reducing fuel load, reduce fireline intensity; this suggests that what wildfires do occur could be more easily controlled. While this would reduce risks to human health and well-being, PB comes with the tradeoff of increased fire emissions, which could contribute to respiratory problems. Mechanical thinning reduces fireline intensity by as much or more while also reducing emissions. While net primary production remained unaffected by fire management, cumulative net biome production until the end of the 21st century declined especially under the influence of thinning. While these results are based on stylized management treatments, this work shows the potential of DGVMs in exploring fire management options.

Investigating the response of leaf area index to droughts in southern African vegetation using observations and model simulations

Abstract. In many regions of the world, frequent and continual dry spells are exacerbating drought conditions, which have severe impacts on vegetation biomes. Vegetation in southern Africa is among the most affected by drought. Here, we assessed the spatiotemporal characteristics of meteorological drought in southern Africa using the standardized precipitation evapotranspiration index (SPEI) over a 30-year period (1982–2011). The severity and the effects of droughts on vegetation productiveness were examined at different drought timescales (1- to 24-month timescales). In this study, we characterized vegetation using the leaf area index (LAI) after evaluating its relationship with the normalized difference vegetation index (NDVI). Correlating the LAI with the SPEI, we found that the LAI responds strongly (r=0.6) to drought over the central and southeastern parts of the region, with weaker impacts (r<0.4) over parts of Madagascar, Angola, and the western parts of South Africa. Furthermore, the latitudinal distribution of LAI responses to drought indicates a similar temporal pattern but different magnitudes across timescales. The results of the study also showed that the seasonal response across different southern African biomes varies in magnitude and occurs mostly at shorter to intermediate timescales. The semi-desert biome strongly correlates (r=0.95) to drought as characterized by the SPEI at a 6-month timescale in the MAM (March–May; summer) season, while the tropical forest biome shows the weakest response (r=0.35) at a 6-month timescale in the DJF (December–February; hot and rainy) season. In addition, we found that the spatial pattern of change of LAI and SPEI are mostly similar during extremely dry and wet years, with the highest anomaly observed in the dry year of 1991, and we found different temporal variability in global and regional responses across different biomes. We also examined how well an ensemble of state-of-the-art dynamic global vegetation models (DGVMs) simulate the LAI and its response to drought. The spatial and seasonal response of the LAI to drought is mostly overestimated in the DGVM multimodel ensemble compared to the response calculated for the observation-based data. The correlation coefficient values for the multimodel ensemble are as high as 0.76 (annual) over South Africa and 0.98 in the MAM season over the temperate grassland biome. Furthermore, the DGVM model ensemble shows positive biases (3 months or longer) in the simulation of spatial distribution of drought timescales and overestimates the seasonal distribution timescales. The results of this study highlight the areas to target for further development of DGVMs and can be used to improve the models' capability in simulating the drought–vegetation relationship.

Global Carbon Budget 2021

Abstract. Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFOS) are based on energy statistics and cement production data, while emissions from land-use change (ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly, and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) is estimated with global ocean biogeochemistry models and observation-based data products. The terrestrial CO2 sink (SLAND) is estimated with dynamic global vegetation models. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the first time, an approach is shown to reconcile the difference in our ELUC estimate with the one from national greenhouse gas inventories, supporting the assessment of collective countries' climate progress. For the year 2020, EFOS declined by 5.4 % relative to 2019, with fossil emissions at 9.5 ± 0.5 GtC yr−1 (9.3 ± 0.5 GtC yr−1 when the cement carbonation sink is included), and ELUC was 0.9 ± 0.7 GtC yr−1, for a total anthropogenic CO2 emission of 10.2 ± 0.8 GtC yr−1 (37.4 ± 2.9 GtCO2). Also, for 2020, GATM was 5.0 ± 0.2 GtC yr−1 (2.4 ± 0.1 ppm yr−1), SOCEAN was 3.0 ± 0.4 GtC yr−1, and SLAND was 2.9 ± 1 GtC yr−1, with a BIM of −0.8 GtC yr−1. The global atmospheric CO2 concentration averaged over 2020 reached 412.45 ± 0.1 ppm. Preliminary data for 2021 suggest a rebound in EFOS relative to 2020 of +4.8 % (4.2 % to 5.4 %) globally. Overall, the mean and trend in the components of the global carbon budget are consistently estimated over the period 1959–2020, but discrepancies of up to 1 GtC yr−1 persist for the representation of annual to semi-decadal variability in CO2 fluxes. Comparison of estimates from multiple approaches and observations shows (1) a persistent large uncertainty in the estimate of land-use changes emissions, (2) a low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) a discrepancy between the different methods on the strength of the ocean sink over the last decade. This living data update documents changes in the methods and datasets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this dataset (Friedlingstein et al., 2020, 2019; Le Quéré et al., 2018b, a, 2016, 2015b, a, 2014, 2013). The data presented in this work are available at https://doi.org/10.18160/gcp-2021 (Friedlingstein et al., 2021).

Field-based tree mortality constraint reduces estimates of model-projected forest carbon sinks

Considerable uncertainty and debate exist in projecting the future capacity of forests to sequester atmospheric CO2. Here we estimate spatially explicit patterns of biomass loss by tree mortality (LOSS) from largely unmanaged forest plots to constrain projected (2015–2099) net primary productivity (NPP), heterotrophic respiration (HR) and net carbon sink in six dynamic global vegetation models (DGVMs) across continents. This approach relies on a strong relationship among LOSS, NPP, and HR at continental or biome scales. The DGVMs overestimated historical LOSS, particularly in tropical regions and eastern North America by as much as 5 Mg ha−1 y−1. The modeled spread of DGVM-projected NPP and HR uncertainties was substantially reduced in tropical regions after incorporating the field-based mortality constraint. The observation-constrained models show a decrease in the tropical forest carbon sink by the end of the century, particularly across South America (from 2 to 1.4 PgC y−1), and an increase in the sink in North America (from 0.8 to 1.1 PgC y−1). These results highlight the feasibility of using forest demographic data to empirically constrain forest carbon sink projections and the potential overestimation of projected tropical forest carbon sinks.

Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products

Abstract. Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, due to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, dynamic global vegetation model (DGVM) simulations, and an upscaled product from eddy covariance (EC) measurements, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r>0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r=0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extratropical Southern Hemisphere (Trop+SH) and extratropical Northern Hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual global total GPP was dominated by the CO2 fertilization effect (83.9 % contribution), however, with the largest uncertainty in magnitude in individual simulations among the three drivers of CO2 fertilization, climate, and land-use change. Interestingly, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. After 2000, trends from satellite-based GPP products were different from the full time series, suggesting weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs and NIRv kept increasing. The inconsistencies of GPP trends are very likely caused by the contrasting performance between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. The higher consistency between DGVM GPP and NIRv suggests that the trends from a DGVM ensemble might even have better performance than satellite-based GPP products.

Impacts of Land Use Change and Atmospheric CO2 on Gross Primary Productivity (GPP), Evaporation, and Climate in Southern Amazon

AbstractRecent publications indicate that the Amazon may be acting more as a carbon source than a sink in some regions. Moreover, the Amazon is a source of moisture for other regions in the continent, and deforestation over the years may be reducing this function. In this work, we analyze the impacts of elevated CO2 (eCO2) and land use change (LUC) on gross primary productivity (GPP) and evaporation in the southern Amazon (7°S 14°S, 66°W 51°W), which suffered strong anthropogenic influence in the period of 1981‒2010. We ran four dynamic global vegetation models (DGVMs), isolating historical CO2, constant CO2, LUC, and potential natural vegetation scenarios with three climate variable data sets: precipitation, temperature, and shortwave radiation. We compared the outputs to five “observational” data sets obtained through eddy covariance, remote sensing, meteorological measurements, and machine learning. The results indicate that eCO2 may have offset deforestation, with GPP increasing by ∼13.5% and 9.3% (dry and rainy seasons, respectively). After isolating the LUC effect, a reduction in evaporation of ∼4% and ∼1.2% (dry and rainy seasons, respectively) was observed. The analysis of forcings in subregions under strong anthropogenic impact revealed a reduction in precipitation of ∼15 and 30 mm, and a temperature rise of 1°C and 0.6°C (dry and rainy seasons, respectively). Differences in the implementation of plant physiology and leaf area index in the DGVMs introduced some uncertainties in the interpretation of the results. Nevertheless, we consider that it was an important exercise given the relevance.

Effect of tree demography and flexible root water uptake for modeling the carbon and water cycles of Amazonia

Amazonian forest plays a crucial role in regulating the carbon and water cycles in the global climate system. However, the representation of biogeochemical fluxes and forest structure in dynamic global vegetation models (DGVMs) remains challenging. This situation has considerable implications to simulate the state and dynamics of Amazonian forest. This study aims at simulating the dynamic of the evapotranspiration (ET), productivity (GPP), biomass (AGB) and forest structure of wet tropical forests in the Amazon basin using the updated ORCHIDEE land surface model. The latter is improved for two processes: stand structure and demography, and plant water uptake by roots. Stand structure is simulated by adapting the CAN version of ORCHIDEE, originally developed for temperate forests. Here, we account for the permanent recruitment of young individual trees, the distribution of stand level growth into 20 different cohorts of variable diameter classes, and mortality due to asymmetric competition for light. Plant water uptake is simulated by including soil-to-root hydraulic resistance (RS). To evaluate the effect of the soil resistance alone, we performed factorial simulations with demography only (CAN) and both demography and resistance (CAN-RS). AGB, ET and GPP outputs of CAN-RS are also compared with the standard version of ORCHIDEE (TRUNK) for which eco-hydrological parameters were tuned globally to fit GPP and evapotranspiration at flux tower sites. All the model versions are benchmarked against in situ and regional datasets. We show that CAN-RS correctly reproduce stand level structural variables (as CAN) like diameter classes and tree densities when validated using in-situ data. Besides offering the key advantage to simulate forest's structure, it also correctly simulates ET and GPP and improves fluxes spatial patterns when compared to TRUNK. With the new formulation of soil water uptake, which is driven by soil water availability rather than root-biomass, the simulated trees preferentially use water in the deepest soil layers during the dry seasons. This improves the seasonality of ET and GPP compared to CAN, especially on clay soils for which the soil moisture potential drops rapidly in the dry season. Nevertheless, since demography parameters in CAN-RS are constant for all evergreen tropical forests, spatial variability of AGB and basal area across the Amazon remains too uniform compared to observations, and are very comparable to the TRUNK. Additional processes such as climate driven mortality and phosphorus limitation on growth leading to the prevalence of species with different functional traits across the Amazon need to be included in the future development of this model.