Global Vegetation Model Research Articles

Opening Pandora's box: reducing global circulation model uncertainty in Australian simulations of the carbon cycle

Abstract. Climate projections from global circulation models (GCMs), part of the Coupled Model Intercomparison Project 6 (CMIP6), are often employed to study the impact of future climate on ecosystems. However, especially at regional scales, climate projections display large biases in key forcing variables such as temperature and precipitation. These biases have been identified as a major source of uncertainty in carbon cycle projections, hampering predictive capacity. In this study, we open the proverbial Pandora's box and peer under the lid of strategies to tackle climate model ensemble uncertainty. We employ a dynamic global vegetation model (LPJ-GUESS) and force it with raw output from CMIP6 to assess the uncertainty associated with the choice of climate forcing. We then test different methods to either bias-correct or calculate ensemble averages over the original forcing data to reduce the climate-driven uncertainty in the regional projection of the Australian carbon cycle. We find that all bias correction methods reduce the bias of continental averages of steady-state carbon variables. Bias correction can improve model carbon outputs, but carbon pools are insensitive to the type of bias correction method applied for both individual GCMs and the arithmetic ensemble average across all corrected models. None of the bias correction methods consistently improve the change in simulated carbon over time compared to the target dataset, highlighting the need to account for temporal properties in correction or ensemble-averaging methods. Multivariate bias correction methods tend to reduce the uncertainty more than univariate approaches, although the overall magnitude is similar. Even after correcting the bias in the meteorological forcing dataset, the simulated vegetation distribution presents different patterns when different GCMs are used to drive LPJ-GUESS. Additionally, we found that both the weighted ensemble-averaging and random forest approach reduce the bias in total ecosystem carbon to almost zero, clearly outperforming the arithmetic ensemble-averaging method. The random forest approach also produces the results closest to the target dataset for the change in the total carbon pool, seasonal carbon fluxes, emphasizing that machine learning approaches are promising tools for future studies. This highlights that, where possible, an arithmetic ensemble average should be avoided. However, potential target datasets that would facilitate the application of machine learning approaches, i.e., that cover both the spatial and temporal domain required to derive a robust informed ensemble average, are sparse for ecosystem variables.

CO2 fertilization effect may balance climate change impacts on oil palm cultivation

Oil palm cultivation has become one of the world’s most important drivers of land use change in the tropics causing biodiversity loss and greenhouse gas emissions. The impact of climate change and rising carbon dioxide (CO2) concentrations in the atmosphere on oil palm productivity is not well understood. If environmental change leads to declining palm oil yields in existing cultivation areas, cultivation areas may expand or shift to other regions. Here we assess climate change impacts on palm oil production using an extended version of the dynamic global vegetation model with managed land, LPJmL4, and a range of climate scenarios from the inter-sectoral impact model intercomparison project. We find increasing average yields under all future climate scenarios. This contradicts earlier studies, which did not consider the potential positive effect of CO2 fertilization. If we do not account for CO2 fertilization, future yields also decrease in our simulations. Our results indicate the potentially large role of rising CO2 levels on oil palm cultivation. This highlights the importance of further applied plant science to better understand the impact of climate change and elevated CO2 levels on oil palm growth and productivity.

Estimating the Global Influence of Cover Crops on Ecosystem Service Indicators in Croplands With the LPJ‐GUESS Model

AbstractCover crops (CCs) can improve soil nutrient retention and crop production while providing climate change mitigation co‐benefits. However, quantifying these ecosystem services across global agricultural lands remains inadequate. Here, we assess how the use of herbaceous CCs with and without biological nitrogen (N) fixation affects agricultural soil carbon stocks, N leaching, and crop yields, using the dynamic global vegetation model LPJ‐GUESS. The model performance is evaluated with observations from worldwide field trials and modeled output further compared against previously published large‐scale estimates. LPJ‐GUESS broadly captures the enhanced soil carbon, reduced N leaching, and yield changes that are observed in the field. Globally, we found that combining N‐fixing CCs with no‐tillage technique could potentially increase soil carbon levels by 7% (+0.32 Pg C yr−1 in global croplands) while reducing N leaching loss by 41% (−7.3 Tg N yr−1) compared with fallow controls after 36 years of simulation since 2015. This integrated practice is accompanied by a 2% of increase in total crop production (+37 million tonnes yr−1 including wheat, maize, rice, and soybean) in the last decade of the simulation. The identified effects of CCs on crop productivity vary widely among main crop types and N fertilizer applications, with small yield changes found in soybean systems and highly fertilized agricultural soils. Our results demonstrate the possibility of conservation agriculture when targeting long‐term environmental sustainability without compromising crop production in global croplands.

A multi-satellite framework to rapidly evaluate extreme biosphere cascades: The Western US 2021 drought and heatwave.

The increasing frequency and intensity of climate extremes and complex ecosystem responses motivate the need for integrated observational studies at low latency to determine biosphere responses and carbon-climate feedbacks. Here, we develop a satellite-based rapid attribution workflow and demonstrate its use at a 1-2-month latency to attribute drivers of the carbon cycle feedbacks during the 2020-2021 Western US drought and heatwave. In the first half of 2021, concurrent negative photosynthesis anomalies and large positive column CO2 anomalies were detected with satellites. Using a simple atmospheric mass balance approach, we estimate a surface carbon efflux anomaly of 132 TgC in June 2021, a magnitude corroborated independently with a dynamic global vegetation model. Integrated satellite observations of hydrologic processes, representing the soil-plant-atmosphere continuum (SPAC), show that these surface carbon flux anomalies are largely due to substantial reductions in photosynthesis because of a spatially widespread moisture-deficit propagation through the SPAC between 2020 and 2021. A causal model indicates deep soil moisture stores partially drove photosynthesis, maintaining its values in 2020 and driving its declines throughout 2021. The causal model also suggests legacy effects may have amplified photosynthesis deficits in 2021 beyond the direct effects of environmental forcing. The integrated, observation framework presented here provides a valuable first assessment of a biosphere extreme response and an independent testbed for improving drought propagation and mechanisms in models. The rapid identification of extreme carbon anomalies and hotspots can also aid mitigation and adaptation decisions.

Implementation of trait-based ozone plant sensitivity in the Yale Interactive terrestrial Biosphere model v1.0 to assess global vegetation damage

Abstract. A major limitation in modeling global ozone (O3) vegetation damage has long been the reliance on empirical O3 sensitivity parameters derived from a limited number of species and applied at the level of plant functional types (PFTs), which ignore the large interspecific variations within the same PFT. Here, we present a major advance in large-scale assessments of O3 plant injury by linking the trait leaf mass per area (LMA) and plant O3 sensitivity in a broad and global perspective. Application of the new approach and a global LMA map in a dynamic global vegetation model reasonably represents the observed interspecific responses to O3 with a unified sensitivity parameter for all plant species. Simulations suggest a contemporary global mean reduction of 4.8 % in gross primary productivity by O3, with a range of 1.1 %–12.6 % for varied PFTs. Hotspots with damage >10 % are found in agricultural areas in the eastern US, western Europe, eastern China, and India, accompanied by moderate to high levels of surface O3. Furthermore, we simulate the distribution of plant sensitivity to O3, which is highly linked with the inherent leaf trait trade-off strategies of plants, revealing high risks for fast-growing species with low LMA, such as crops, grasses, and deciduous trees.

Large Variability in Simulated Response of Vegetation Composition and Carbon Dynamics to Variations in Drought‐Heat Occurrence

AbstractThe frequency of heatwaves, droughts and their co‐occurrence vary greatly in simulations of different climate models. Since these extremes are expected to become more frequent with climate change, it is important to understand how vegetation models respond to different climatologies in heatwave and drought occurrence. In previous work, six climate scenarios featuring different drought‐heat signatures have been developed to investigate how single versus compound extremes affect vegetation and carbon dynamics. Here, we use these scenarios to force six dynamic global vegetation models to investigate model agreement in vegetation and carbon cycle response to these scenarios. We find that global responses to different drought‐heat signatures vary considerably across models. Models agree that frequent compound hot‐dry events lead to a reduction in tree cover and vegetation carbon stocks. However, models show opposite responses in vegetation changes for the scenario with no extremes. We find a strong relationship between the frequency of concurrent hot‐dry conditions and the total carbon pool, suggesting a reduction of the natural land carbon sink for increasing occurrence of hot‐dry events. The effect of frequent compound hot and dry extremes is larger than the sum of the effects when only one extreme occurs, highlighting the importance of studying compound events. Our results demonstrate that uncertainties in the representation of compound hot‐dry event occurrence in climate models propagate to uncertainties in the simulation of vegetation distribution and carbon pools. Therefore, to reduce uncertainties in future carbon cycle projections, the representation of compound events in climate models needs to be improved.

South American mountain ecosystems and global change – a case study for integrating theory and field observations for land surface modelling and ecosystem management

ABSTRACT Background Plot-based monitoring has yielded much information on the taxonomic diversity and carbon (C) storage in tropical lowland forests of the Amazon basin. This has resulted in an improved understanding of the relationship between lowland forest biomass dynamics and global change drivers, such as climate change and atmospheric CO2 concentration. Much less attention has been paid to the mountain ecosystems of South America that comprise montane forests and alpine vegetation (páramo, puna, high Andean grasslands, wetlands, and alpine heath). This vegetation complex provides a variety of ecosystem services and forms a natural laboratory along various physiographic, geological and evolutionary history/biogeography, and land use history gradients. Aims Here, we review existing empirical understanding and model-based approaches to quantify the contribution of mountain ecosystems to ecosystem service provision in the rapidly changing socioecological setting of the South American mountains. The objective of this paper is to outline a broad road map for the implementation of mountain vegetation into dynamic global vegetation models (DGVM) for use in Earth System Models (ESM), based on our current understanding of their structure and function and of their responsiveness to global change drivers. We also identify treeline processes, critical in mountain ecosystems, as key missing elements in DGVMs/ESMs, and thus explore in addition a treeline model. Methods Stocktaking of the availability of empirical data was undertaken from eight research sites along the Andes and in south-eastern Brazil. Out of eight sites, two (one each in Venezuela and Brazil) had some climate, ecological and ecophysiological data potentially suitable to parametrise a DGVM. Tree biomass data were available for six sites. A preliminary assessment of the Joint UK Land Environment Simulator (JULES) DGVM was made to identify gaps in available data and their impacts on model parametrisation and calibration. Additionally, the potential climate-determined elevation of the treeline was modelled to check the DGVM for its ability to identify the transition between the montane forest and alpine vegetation. Results Outcomes of the evaluation of the JULES land surface model identified the following key processes in montane forests: temperature-related decrease in net primary production, respiration, and allocation to above-ground biomass and increase in soil C stocks with elevation. There was a variable agreement between simulated biomass and those derived from field measurements via allometric equations. Conclusions We identified major gaps between data availability and the needs for process-based modelling of South American mountain vegetation and its dynamics in DGVMs. To bridge this gap, we propose a transdisciplinary network, composed of members of the theoretical/modelling and empirical scientific communities, to study the natural dynamics of mountain ecosystems and their responses to global change drivers locally, regionally and at the continental scale, within a social-ecological system framework. The work presented here forms the basis for the design of data collection from field measurements and instrumental monitoring stations to parametrise and verify DGVMs. The network is designed to collaborate with and complement existing long-term research initiatives in the region and will adopt existing standard field protocols. Complementary protocols will ensure compatibility between field data collection and data needed for process-based and empirical models.

Progress in the remote sensing of veld fire occurrence and detection: A review

AbstractOur research provides a detailed overview of the progress in remotely sensed fire monitoring techniques, which have been developed and employed for fire occurrence and detection. Our overview is provided from a literature search of English‐peer reviewed articles, conference proceedings and scientific book sections published between the periods of 1980 and 2019. Literature reveals that historically, fire detection through remotely sensed techniques has mainly occurred through ground‐based, airborne and satellite systems. Mathematical models, such as decision tree models, Species Distribution Models, Dynamic Global Vegetation Models and Global Climate Models, have also been employed alongside satellite systems to facilitate a greater understanding of fire dynamics and its susceptibility to changes in ecological and climatic variables. Fire frequency and severity are known to be influenced by atmospheric conditions, fuel load and ignitions. However, the literature suggests that targeting inappropriate wildfires with these techniques may still result in wildfires outside of the natural fire regime. Most studies regarding fire occurrence and/or monitoring focus on satellite‐based techniques as they provide the greatest coverage of wildfires at varying spatial and temporal resolutions depending on the sensor used. Satellite systems are advantageous for fire monitoring as they provide extensive coverage inexpensively. Finally, fire occurrence is explicitly influenced by moisture‐limited climatic conditions and/or fuel load in the form of leaf‐litter or water‐stressed plants.

Harmonising the land-use flux estimates of global models and national inventories for 2000–2020

Abstract. As the focus of climate policy shifts from pledges to implementation, there is a growing need to track progress on climate change mitigation at the country level, particularly for the land-use sector. Despite new tools and models providing unprecedented monitoring opportunities, striking differences remain in estimations of anthropogenic land-use CO2 fluxes between, on the one hand, the national greenhouse gas inventories (NGHGIs) used to assess compliance with national climate targets under the Paris Agreement and, on the other hand, the Global Carbon Budget and Intergovernmental Panel on Climate Change (IPCC) assessment reports, both based on global bookkeeping models (BMs). Recent studies have shown that these differences are mainly due to inconsistent definitions of anthropogenic CO2 fluxes in managed forests. Countries assume larger areas of forest to be managed than BMs do, due to a broader definition of managed land in NGHGIs. Additionally, the fraction of the land sink caused by indirect effects of human-induced environmental change (e.g. fertilisation effect on vegetation growth due to increased atmospheric CO2 concentration) on managed lands is treated as non-anthropogenic by BMs but as anthropogenic in most NGHGIs. We implement an approach that adds the CO2 sink caused by environmental change in countries' managed forests (estimated by 16 dynamic global vegetation models, DGVMs) to the land-use fluxes from three BMs. This sum is conceptually more comparable to NGHGIs and is thus expected to be quantitatively more similar. Our analysis uses updated and more comprehensive data from NGHGIs than previous studies and provides model results at a greater level of disaggregation in terms of regions, countries and land categories (i.e. forest land, deforestation, organic soils, other land uses). Our results confirm a large difference (6.7 GtCO2 yr−1) in global land-use CO2 fluxes between the ensemble mean of the BMs, which estimate a source of 4.8 GtCO2 yr−1 for the period 2000–2020, and NGHGIs, which estimate a sink of −1.9 GtCO2 yr−1 in the same period. Most of the gap is found on forest land (3.5 GtCO2 yr−1), with differences also for deforestation (2.4 GtCO2 yr−1), for fluxes from other land uses (1.0 GtCO2 yr−1) and to a lesser extent for fluxes from organic soils (0.2 GtCO2 yr−1). By adding the DGVM ensemble mean sink arising from environmental change in managed forests (−6.4 GtCO2 yr−1) to BM estimates, the gap between BMs and NGHGIs becomes substantially smaller both globally (residual gap: 0.3 GtCO2 yr−1) and in most regions and countries. However, some discrepancies remain and deserve further investigation. For example, the BMs generally provide higher emissions from deforestation than NGHGIs and, when adjusted with the sink in managed forests estimated by DGVMs, yield a sink that is often greater than NGHGIs. In summary, this study provides a blueprint for harmonising the estimations of anthropogenic land-use fluxes, allowing for detailed comparisons between global models and national inventories at global, regional and country levels. This is crucial to increase confidence in land-use emissions estimates, support investments in land-based mitigation strategies and assess the countries' collective progress under the Global Stocktake of the Paris Agreement. Data from this study are openly available online via the Zenodo portal (Grassi et al., 2023) at https://doi.org/10.5281/zenodo.7650360.

The effect of charcoal production on carbon cycling in African biomes

AbstractUsing biomass for charcoal production in sub‐Saharan Africa (SSA) may change carbon stock dynamics and lead to irreversible changes in the carbon balance, yet we have little understanding of whether these dynamics vary by biome in this region. Currently, charcoal production contributes up to 7% of yearly deforestation in tropical regions, with carbon emissions corresponding to 71.2 million tonnes of CO2 and 1.3 million tonnes of CH4. With a projected increased demand for charcoal in the coming decades, even low harvest rates may throw the carbon budget off‐balance due to legacy effects. Here, we parameterized the dynamic global vegetation model LPJ‐GUESS for six SSA biomes and examined the effect of charcoal production on net ecosystem exchange (NEE), carbon stock sizes and recovery time for tropical rain forest, montane forest, moist savanna, dry savanna, temperate grassland and semi‐desert. Under historical charcoal regimes, tropical rain forests and montane forests transitioned from net carbon sinks to net sources, that is, mean cumulative NEE from −3.56 ± 2.59 kg C/m2 to 2.46 ± 3.43 kg C/m2 and −2.73 ± 2.80 kg C/m2 to 1.87 ± 4.94 kg C/m2 respectively. Varying charcoal production intensities resulted in tropical rain forests showing at least two times higher carbon losses than the other biomes. Biome recovery time varied by carbon stock, with tropical and montane forests taking about 10 times longer than the fast recovery observed for semi‐desert and temperate grasslands. Our findings show that high biomass biomes are disproportionately affected by biomass harvesting for charcoal, and even low harvesting rates strongly affect vegetation and litter carbon and their contribution to the carbon budget. Therefore, the prolonged biome recoveries imply that current charcoal production practices in SSA are not sustainable, especially in tropical rain forests and montane forests, where we observe longer recovery for vegetation and litter carbon stocks.

Climate Change and CO2 Fertilization Have Played Important Roles in the Recent Decadal Vegetation Greening Trend on the Chinese Loess Plateau

Vegetation greening has been widely occurring on the Chinese Loess Plateau, and the contributions of human land-use management have been well-understood. However, the influences of climatic change and CO2 fertilization on reported vegetation variations remain difficult to determine. Therefore, we quantified the impacts of multiple factors on vegetation changes for the Chinese Loess Plateau from 2000 to 2019 by integrating satellite-based leaf area index (LAI) and simulated LAI from dynamic global vegetation models. More than 96% of the vegetated areas of the Loess Plateau exhibited greening trends, with an annually averaged satellite-based LAI rate of 0.037 ± 0.006 m2 m−2 a−1 (P < 0.01). Human land-use management and environmental change have jointly accelerated vegetation growth, explaining 54% and 46% of the overall greening trend, respectively. CO2 fertilization and climate change explain 55% and 45% of the greening trend due to environmental change, respectively; solar radiation and precipitation were the main driving factors for climate-induced vegetation greenness (P < 0.05). Spatially, the eastern part of the Loess Plateau was dominated by CO2 fertilization, while the western part was mainly affected by climate change. Furthermore, solar radiation was the key limiting factor affecting LAI variations in the relatively humid area, while precipitation was the major influencing factor in relatively arid areas. This study highlights the important roles that climate change and CO2 fertilization have played in vegetation greenness in recent decades of the Loess Plateau, despite strong influences of anthropogenic footprint.

Diagnosing destabilization risk in global land carbon sinks.

Global net land carbon uptake or net biome production (NBP) has increased during recent decades1. Whether its temporal variability and autocorrelation have changed during this period, however, remains elusive, even though an increase in both could indicate an increased potential for a destabilized carbon sink2,3. Here, we investigate the trends and controls of net terrestrial carbon uptake and its temporal variability and autocorrelation from 1981 to 2018 using two atmospheric-inversion models, the amplitude of the seasonal cycle of atmospheric CO2 concentration derived from nine monitoring stations distributed across the Pacific Ocean and dynamic global vegetation models. We find that annual NBP and its interdecadal variability increased globally whereas temporal autocorrelation decreased. We observe a separation of regions characterized by increasingly variable NBP, associated with warm regions and increasingly variable temperatures, lower and weaker positivetrends in NBP and regions where NBP became stronger and less variable. Plant species richness presented a concave-down parabolic spatial relationship with NBP and its variability at the global scale whereas nitrogen deposition generally increased NBP. Increasing temperature and its increasing variability appear as the most important drivers of declining and increasingly variable NBP. Our results show increasing variability of NBP regionally that can be mostly attributed to climate change and that may point to destabilization of the coupled carbon-climate system.

Isoprene and monoterpene simulations using the chemistry–climate model EMAC (v2.55) with interactive vegetation from LPJ-GUESS (v4.0)

Abstract. Earth system models (ESMs) integrate previously separate models of the ocean, atmosphere and vegetation into one comprehensive modelling system enabling the investigation of interactions between different components of the Earth system. Global isoprene and monoterpene emissions from terrestrial vegetation, which represent the most important source of volatile organic compounds (VOCs) in the Earth system, need to be included in global and regional chemical transport models given their major chemical impacts on the atmosphere. Due to the feedback of vegetation activity involving interactions with weather and climate, a coupled modelling system between vegetation and atmospheric chemistry is recommended to address the fate of biogenic volatile organic compounds (BVOCs). In this work, further development in linking LPJ-GUESS, a global dynamic vegetation model, to the atmospheric-chemistry-enabled atmosphere–ocean general circulation model EMAC is presented. New parameterisations are included to calculate the foliar density and leaf area density (LAD) distribution from LPJ-GUESS information. The new vegetation parameters are combined with existing LPJ-GUESS output (i.e. leaf area index and cover fractions) and used in empirically based BVOC modules in EMAC. Estimates of terrestrial BVOC emissions from EMAC's submodels ONEMIS and MEGAN are evaluated using (1) prescribed climatological vegetation boundary conditions at the land–atmosphere interface and (2) dynamic vegetation states calculated in LPJ-GUESS (replacing the “offline” vegetation inputs). LPJ-GUESS-driven global emission estimates for isoprene and monoterpenes from the submodel ONEMIS were 546 and 102 Tg yr−1, respectively. MEGAN determines 657 and 55 Tg of isoprene and monoterpene emissions annually. The new vegetation-sensitive BVOC fluxes in EMAC are in good agreement with emissions from the semi-process-based module in LPJ-GUESS. The new coupled system is used to evaluate the temperature and vegetation sensitivity of BVOC fluxes in doubling CO2 scenarios. This work provides evidence that the new coupled model yields suitable estimates for global BVOC emissions that are responsive to vegetation dynamics. It is concluded that the proposed model set-up is useful for studying land–biosphere–atmosphere interactions in the Earth system.

Uncertain effectiveness of Miscanthus bioenergy expansion for climate change mitigation explored using land surface, agronomic and integrated assessment models

AbstractLarge‐scale bioenergy plays a key role in climate change mitigation scenarios, but its efficacy is uncertain. This study aims to quantify that uncertainty by contrasting the results of three different types of models under the same mitigation scenario (RCP2.6‐SSP2), consistent with a 2°C temperature target. This analysis focuses on a single bioenergy feedstock, Miscanthus × giganteus, and contrasts projections for its yields and environmental effects from an integrated assessment model (IMAGE), a land surface and dynamic global vegetation model tailored to Miscanthus bioenergy (JULES) and a bioenergy crop model (MiscanFor). Under the present climate, JULES, IMAGE and MiscanFor capture the observed magnitude and variability in Miscanthus yields across Europe; yet in the tropics JULES and IMAGE predict high yields, whereas MiscanFor predicts widespread drought‐related diebacks. 2040–2049 projections show there is a rapid scale up of over 200 Mha bioenergy cropping area in the tropics. Resulting biomass yield ranges from 12 (MiscanFor) to 39 (JULES) Gt dry matter over that decade. Change in soil carbon ranges from +0.7 Pg C (MiscanFor) to −2.8 Pg C (JULES), depending on preceding land cover and soil carbon.2090–99 projections show large‐scale biomass energy with carbon capture and storage (BECCS) is projected in Europe. The models agree that <2°C global warming will increase yields in the higher latitudes, but drought stress in the Mediterranean region could produce low yields (MiscanFor), and significant losses of soil carbon (JULES and IMAGE). These results highlight the uncertainty in rapidly scaling‐up biomass energy supply, especially in dry tropical climates and in regions where future climate change could result in drier conditions. This has important policy implications—because prominently used scenarios to limit warming to ‘well below 2°C’ (including the one explored here) depend upon its effectiveness.

Accelerated photosynthesis routine in LPJmL4

Abstract. The increasing impacts of climate change require strategies for climate adaptation. Dynamic global vegetation models (DGVMs) are one type of multi-sectorial impact model with which the effects of multiple interacting processes in the terrestrial biosphere under climate change can be studied. The complexity of DGVMs is increasing as more and more processes, especially for plant physiology, are implemented. Therefore, there is a growing demand for increasing the computational performance of the underlying algorithms as well as ensuring their numerical accuracy. One way to approach this issue is to analyse the routines which have the potential for improved computational efficiency and/or increased accuracy when applying sophisticated mathematical methods. In this paper, the Farquhar–Collatz photosynthesis model under water stress as implemented in the Lund–Potsdam–Jena managed Land DGVM (4.0.002) was examined. We additionally tested the uncertainty of most important parameter of photosynthesis as an additional approach to improve model quality. We found that the numerical solution of a nonlinear equation, so far solved with the bisection method, could be significantly improved by using Newton's method instead. The latter requires the computation of the derivative of the underlying function which is presented. Model simulations show a significantly lower number of iterations to solve the equation numerically and an overall run time reduction of the model of about 16 % depending on the chosen accuracy. Increasing the parameters θ and αC3 by 10 %, respectively, while keeping all other parameters at their original value, increased global gross primary production (GPP) by 2.384 and 9.542 GtC yr−1, respectively. The Farquhar–Collatz photosynthesis model forms the core component in many DGVMs and land surface models. An update in the numerical solution of the nonlinear equation in connection with adjusting globally important parameters to best known values can therefore be applied to similar photosynthesis models. Furthermore, this exercise can serve as an example for improving computationally costly routines while improving their mathematical accuracy.

Simulating Global Dynamic Surface Reflectances for Imaging Spectroscopy Spaceborne Missions: LPJ‐PROSAIL

AbstractSpectroscopic reflectance data provide novel information on the properties of the Earth's terrestrial and aquatic surfaces. Until recently, imaging spectroscopy missions were dependent mainly on airborne instruments, such as the Next Generation Airborne Visible InfraRed Imaging Spectrometer (AVIRIS‐NG), providing limited spatial and temporal observations. Currently, there is an emergence of spaceborne imaging spectroscopy missions, which require advances in end‐to‐end model support for traceability studies. To provide this support, the LPJ‐wsl dynamic global vegetation model is coupled with the canopy radiative transfer model, PROSAIL, to generate global, gridded, daily visible to shortwave infrared (VSWIR) spectra (400–2,500 nm). LPJ‐wsl variables are cross‐walked to meet required PROSAIL parameters, which include leaf structure, chlorophyll a + b, brown pigment, equivalent water thickness, and dry matter content. Simulated spectra are compared to a boreal forest site, a temperate forest, managed grassland, a dryland and a tropical forest site using reflectance data from tower‐mounted, aircraft, and spaceborne imagers. We find that canopy nitrogen and leaf‐area index are the most uncertain variables in translating LPJ‐wsl to PROSAIL parameters but at first order, LPJ‐PROSAIL successfully simulates surface reflectance dynamics. Future work will optimize functional relationships required for improving PROSAIL parameters and include the development of the LPJ‐model to represent improvements in leaf water content and canopy nitrogen. The LPJ‐PROSAIL model is intended to support missions such as NASA's Surface Biology and Geology and subsequent modeled products related to the carbon cycle and hydrology.

Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil moisture

Abstract. Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We designed an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The integrated model, LPJ-PM (LPJ-PT-JPLSM Model) as the underlying model, was coupled from the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ-DGVM version 3.01) and a hydrology module (i.e., the updated Priestley–Taylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the Global LAnd and Surface Satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 ( coefficient of determination) of 0.91 and 0.81 and an ubRMSD (unbiased root mean square deviation) reduced by 40.3 % and 29.9 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The GPP and ET resulting from the assimilation demonstrated a better performance in the arid and semi-arid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m−2 d−1; ET: R2 = 0.73, ubRMSD = 0.61 mm d−1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m−2 d−1; ET: R2 = 0.66; ubRMSD = 0.67 mm d−1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS data had a slight difference, and the SMAP soil moisture data performed better than SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, and GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (μ): 3.4 mm per month; estimated standard deviation of μ: 1.91 mm per month). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.

Using a Simple Representation of Non‐Structural Carbohydrates Allocation to Predict Wood Growth in Temperate Forests

AbstractWood growth is an important process of carbon sequestration and plant physiology in forest ecosystems. Hence, understanding and predicting wood growth is central to quantifying forest carbon cycling. Current dynamic global vegetation models (DGVMs) are mainly driven by carbon inputs (e.g., Gross Primary Production, GPP). However, observations indicate that wood growth is often independent of carbon flux at the annual scale. Here, we revised a DGVM, FORCCHN2 model, to use the active non‐structural carbohydrates (NSC) pool and slow NSC pool (i.e., represented temporary and long‐term NSC storage, respectively), and to integrate the stored NSC with annual wood growth. For the diameter increments (ΔDBH) and aboveground wood growth of 506 trees in 32 plots of Harvard Forest, we tested the NSC allocation coefficients from 5% to 95%. The predictions reproduced 31.2%–55.1% of individual‐tree ΔDBH and 36.8%–43.0% of the aboveground wood increment. Trees of shade‐intolerant species invested more of their available carbon resources (i.e., NSC storage) into wood growth than shade‐tolerant species. Specifically, the shade‐tolerant trees consumed approximately 32% NSC storage by wood growth at the annual scale, while the shade‐intolerant trees consumed about 90%. Our study provides a simple framework that constructs a direct link between annual wood growth and NSC dynamics. The findings highlight the need for research into NSC storage and allocation in trees, particularly in considering NSC allocation strategies of different tree species.

Future tree survival in European forests depends on understorey tree diversity

Climate change heavily threatens forest ecosystems worldwide and there is urgent need to understand what controls tree survival and forests stability. There is evidence that biodiversity can enhance ecosystem stability (Loreau and de Mazancourt in Ecol Lett 16:106–115, 2013; McCann in Nature 405:228–233, 2000), however it remains largely unclear whether this also holds for climate change and what aspects of biodiversity might be most important. Here we apply machine learning to outputs of a flexible-trait Dynamic Global Vegetation Model to unravel the effects of enhanced functional tree trait diversity and its sub-components on climate-change resistance of temperate forests (http://www.pik-potsdam.de/~billing/video/Forest_Resistance_LPJmLFIT.mp4). We find that functional tree trait diversity enhances forest resistance. We explain this with 1. stronger complementarity effects (~ 25% importance) especially improving the survival of trees in the understorey of up to + 16.8% (± 1.6%) and 2. environmental and competitive filtering of trees better adapted to future climate (40–87% importance). We conclude that forests containing functionally diverse trees better resist and adapt to future conditions. In this context, we especially highlight the role of functionally diverse understorey trees as they provide the fundament for better survival of young trees and filtering of resistant tree individuals in the future.

Increased fire activity under high atmospheric oxygen concentrations is compatible with the presence of forests

Throughout Earth’s history, the abundance of oxygen in our atmosphere has varied, but by how much remains debated. Previously, an upper limit for atmospheric oxygen has been bounded by assumptions made regarding the fire window: atmospheric oxygen concentrations higher than 30–40% would threaten the regeneration of forests in the present world. Here we have tested these assumptions by adapting a Dynamic Global Vegetation Model to run over high atmospheric oxygen concentrations. Our results show that whilst global tree cover is significantly reduced under high O2 concentrations, forests persist in the wettest parts of the low and high latitudes and fire is more dependent on fuel moisture than O2 levels. This implies that the effect of fire on suppressing global vegetation under high O2 may be lower than previously assumed and questions our understanding of the mechanisms involved in regulating the abundance of oxygen in our atmosphere, with moisture as a potentially important factor.