Dynamic Global Vegetation Model Research Articles

Improved ET assimilation through incorporating SMAP soil moisture observations using a coupled process model: A study of U.S. arid and semiarid regions

An accurate estimation of evapotranspiration (ET) is essential for characterizing the water budget in arid and semiarid ecosystems. Although various soil moisture data have been used to improve the hydrological process modeling, only a few studies improved ET estimation at a global scale by utilizing satellite soil moisture active passive SMAP data, particularly targeting arid and semiarid areas. To address this issue, this paper proposes a process-based assimilation scheme (LPJ-SMA) to simulate daily ET at 0.25° spatial resolution for the water limited areas. First, an integrated model (LPJ-PM) is constructed by the updated Priestley–Taylor Jet Propulsion Laboratory model (PT-JPLSM) and the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). As the PT-JPLSM model establishes a connection between soil moisture (SM) and ET, the SMAP data could be integrated into LPJ-PM. Second, with the estimated ETPM (3-day interval) in LPJ-PM as “observations”, the original ETLPJ (daily) estimated by LPJ-DGVM could be well constrained in a water-limited area through data assimilation. The results showed that: (1) the ETPM with SMAP information performed better and had a higher accuracy than ETLPJ. (2) After assimilating ETPM into LPJ-DGVM, the assimilated-ET (ETDA) showed a superior performance (R = 0.75, RMSD = 0.72 mm/d) to ETLPJ (R = 0.55, RMSD = 1.02 mm/d) and ETPM (R = 0.70, RMSD = 0.93 mm/d) when evaluated against in situ observations at a 95% significance level. Our proposed assimilation system (LPJ-SMA) was applied to arid and semiarid regions in the United States, and the results illustrate that the spatial distribution and annual value of the LPJ-SMA ET was very similar to NLDAS-2 ET products, which have a higher precision over North America than other global ‘reference’ products. The proposed LPJ-SMA system can be used to optimize model simulation performance and effectively improve ET prediction accuracy. This method can be used as an alternative to estimate global ET, especially in water-limited regions.

Moving toward Net-Zero Emissions Requires New Alliances for Carbon Dioxide Removal

The 1.5°C target will require removing at least some of the carbon dioxide (CO2) previously emitted. Knowledge on how this can be done has been increasing, though barriers remain concerning governance, policy, and acceptability. For the 26th session of the Conference of the Parties (COP26) to move beyond an academic debate on CO2 removal (CDR), a broader alliance of research and policy communities, industry, and the public is needed.

Drought-modulated allometric patterns of trees in semi-arid forests

Tree allometry in semi-arid forests is characterized by short height but large canopy. This pattern may be important for maintaining water-use efficiency and carbon sequestration simultaneously, but still lacks quantification. Here we use terrestrial laser scanning to quantify allometry variations of Quercus mongolica in semi-arid forests. With tree height (Height) declining, canopy area (CA) decreases with substantial variations. The theoretical CA-Height relationship in dynamic global vegetation models (DGVMs) matches only the 5th percentile of our results because of CA underestimation and Height overestimation by breast height diameter (DBH). Water supply determines Height variation (P = 0.000) but not CA (P = 0.2 in partial correlation). The decoupled functions of stem, hydraulic conductance and leaf spatial arrangement, may explain the inconsistency, which may further ensure hydraulic safety and carbon assimilation, avoiding forest dieback. Works on tree allometry pattern and determinant will effectively supply tree drought tolerance studying and support DGVM improvements.

Global vegetation patterns of the past 140,000 years

AbstractAimInsight into global biome responses to climatic and other environmental changes is essential to address key questions about past and future impacts of such changes. By simulating global biome patterns 140 ka to present, we aimed to address important questions about biome changes during this interval.LocationGlobal.TaxonSpermatophyta.MethodsUsing the LPJ‐GUESS dynamic global vegetation model, we made 89 simulations driven using ice‐core atmospheric CO2 concentrations, Earth's obliquity, and outputs from a pre‐industrial and 88 palaeoclimate experiments run using HadCM3. Experiments were run for 81 time slices between 1 and 140 ka, seven ‘hosing’ experiments also being run, using a 1‐Sv freshwater flux to the North Atlantic, for time slices corresponding to Heinrich Events H0–H6. Using a rule‐based approach, based on carbon mass and leaf area index of the LPJ‐GUESS plant functional types, the biome was inferred for each grid cell. Biomes were mapped, and the extent and total vegetation biomass of each biome, and total global vegetation biomass, estimated.ResultsSubstantial changes in biome extents and locations were found on all vegetated continents. Although the largest magnitude changes were in Eurasia, important changes were seen in tropical latitudes and the Southern Hemisphere. Total global extent of most biomes varied on multi‐millennial (orbital) time scales, although some (e.g. Tropical Raingreen Forest) responded principally to the c. 100‐kyr glacial–interglacial cycle and others (e.g. Temperate Broad‐leaved Evergreen Forest) mainly to the c. 20‐kyr precession cycle. Many also responded to millennial contrasts between stadial (‘hosing’) and interstadial climates, with some (e.g. Tropical Evergreen Forest) showing stronger responses than to the multi‐millennial changes.Main conclusionsNo two time slices had identical biome patterns. Even equivalent Holocene and last interglacial time slices, and the last and penultimate glacial maxima, showed important differences. Only a small proportion of global land area experienced no biome change since 140 ka; many places experienced multiple biome changes. These modelling experiments provided little evidence for long‐term biome stability.

Climate change promotes transitions to tall evergreen vegetation in tropical Asia.

Vegetation in tropical Asia is highly diverse due to large environmental gradients and heterogeneity of landscapes. This biodiversity is threatened by intense land use and climate change. However, despite the rich biodiversity and the dense human population, tropical Asia is often underrepresented in global biodiversity assessments. Understanding how climate change influences the remaining areas of natural vegetation is therefore highly important for conservation planning. Here, we used the adaptive Dynamic Global Vegetation Model version 2 (aDGVM2) to simulate impacts of climate change and elevated CO2 on vegetation formations in tropical Asia for an ensemble of climate change scenarios. We used climate forcing from five different climate models for representative concentration pathways RCP4.5 and RCP8.5. We found that vegetation in tropical Asia will remain a carbon sink until 2099, and that vegetation biomass increases of up to 28% by 2099 are associated with transitions from small to tall woody vegetation and from deciduous to evergreen vegetation. Patterns of phenology were less responsive to climate change and elevated CO2 than biomes and biomass, indicating that the selection of variables and methods used to detect vegetation changes is crucial. Model simulations revealed substantial variation within the ensemble, both in biomass increases and in distributions of different biome types. Our results have important implications for management policy, because they suggest that large ensembles of climate models and scenarios are required to assess a wide range of potential future trajectories of vegetation change and to develop robust management plans. Furthermore, our results highlight open ecosystems with low tree cover as most threatened by climate change, indicating potential conflicts of interest between biodiversity conservation in open ecosystems and active afforestation to enhance carbon sequestration.

N<sub>2</sub>O changes from the Last Glacial Maximum to the preindustrial – Part 2: terrestrial N<sub>2</sub>O emissions and carbon–nitrogen cycle interactions

Abstract. Carbon–nitrogen (C–N) interactions regulate N availability for plant growth and for emissions of nitrous oxide (N2O) and the uptake of carbon dioxide. Future projections of these terrestrial greenhouse gas fluxes are strikingly divergent, leading to major uncertainties in projected global warming. Here we analyse the large increase in terrestrial N2O emissions over the past 21 000 years as reconstructed from ice-core isotopic data and presented in part 1 of this study. Remarkably, the increase occurred in two steps, each realized over decades and within a maximum of 2 centuries, at the onsets of the major deglacial Northern Hemisphere warming events. The data suggest a highly dynamic and responsive global N cycle. The increase may be explained by an increase in the flux of reactive N entering and leaving ecosystems or by an increase in N2O yield per unit N converted. We applied the LPX-Bern dynamic global vegetation model in deglacial simulations forced with Earth system model climate data to investigate N2O emission patterns, mechanisms, and C–N coupling. The N2O emission changes are mainly attributed to changes in temperature and precipitation and the loss of land due to sea-level rise. LPX-Bern simulates a deglacial increase in N2O emissions but underestimates the reconstructed increase by 47 %. Assuming time-independent N sources in the model to mimic progressive N limitation of plant growth results in a decrease in N2O emissions in contrast to the reconstruction. Our results appear consistent with suggestions of (a) biological controls on ecosystem N acquisition and (b) flexibility in the coupling of the C and N cycles during periods of rapid environmental change. A dominant uncertainty in the explanation of the reconstructed N2O emissions is the poorly known N2O yield per N lost through gaseous pathways and its sensitivity to soil conditions. The deglacial N2O record provides a constraint for future studies.

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

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

A Semiprognostic Phenology Model for Simulating Multidecadal Dynamics of Global Vegetation Leaf Area Index

AbstractVegetation leaf phenology, often reflected by the dynamics in leaf area index (LAI), influences a variety of land surface processes. Robust models of vegetation phenology are pivot components in both land surface models and dynamic global vegetation models but remain challenging in terms of the model accuracy. This study develops a semiprognostic phenology model that is suitable for simulating time series of vegetation LAI. This method establishes a linear relationship between the steady‐state LAI (i.e., the LAI when the environment conditions remain unchanging) and gross primary productivity, meaning that the LAI an unchanging environment can carry is proportional to the photosynthetic products produced by plant leaves and implements with a simple light use efficiency algorithm of MOD17 to form a closed set of equations. We derive an analytical solution based on the Lambert W function to the closed equations and then apply a simple restricted growth process model to simulate the time series of actual LAI. The results modeled using global climate data demonstrate that the model is able to capture both the spatial pattern and intra‐annual and interannual variation of LAI derived from the satellite‐based product on a global scale. The results modeled using the flux tower data suggest that the developed model is able to explain over 70% variation in daily LAI for each plant functional type except evergreen broadleaf forest. The developed semiprognostic approach provides a simple solution to modeling the spatiotemporal variation in vegetation LAI across plant functional types on the global scale.

Causes of slowing-down seasonal CO2 amplitude at Mauna Loa.

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

Periodic Relations between Terrestrial Vegetation and Climate Factors across the Globe

In this paper, cross-spectrum analysis was used to verify the agreement of periodicity between the global LAI (leaf area index) and climate factors. The results demonstrated that the LAI of deciduous forests and permanent wetlands have high agreement with temperature, rainfall and radiation over annual cycles. A low agreement between the LAI and seasonal climate variables was observed for some of the temperate and tropical vegetation types including shrublands and evergreen broadleaf forests, possibly due to the diversity of vegetation and human activities. Across all vegetation types, the LAI demonstrated a large time lag following variation in radiation (&gt;1 month), whereas relatively short lag periods were observed between the LAI and annual temperature (around 2 weeks)/rainfall patterns (less than 10 days), suggesting that the impact of radiation on global vegetation growth is relatively slow, which is in accord with the results of previous studies. This work can provide a benchmark of the phenological drivers in global vegetation, from the perspective of periodicity, as well as helping to parameterize and refine the DGVMs (Dynamic Global Vegetation Models) for different vegetation types.

Using a Vegetation Model and Stakeholder Input to Assess the Climate Change Vulnerability of Tribally Important Ecosystem Services

We demonstrate a generalizable approach for assessing climate change effects on tribally important ecosystem goods and services. Indigenous peoples may be highly vulnerable to the impacts of climate change because they rely on ecosystem goods and services, such as traditional foods, hunting, timber production, nontimber forest resources, and cultural resources. However, there are few assessments that have examined the potential impact of climate change on these goods and services and even less that examine ecological, socio-economic, and cultural resources in the Pacific Northwest, USA. Our approach uses four basic steps: (1) identify 78 tribally important ecosystem services (species and resources), (2) relate those ecosystem services with biologically relevant vegetation projections from a dynamic global vegetation model, (3) identify appropriate timeframes and future climate scenarios, and (4) assess future changes for vegetation types and ecosystem services. We then highlight how model uncertainty can be explored to better inform resilience building and adaptation planning. We found that more than half of the species and resources analyzed may be vulnerable to climate change due to loss of potential habitat, including aridland species and grazing quality. We further highlight our findings for tribally important species, huckleberries (genus Vaccinium) and bitterbrush (Purshia tridentate (Pursh) DC.), and show how this information can be applied to help inform resource management and adaptation planning. We have demonstrated a generalizable approach that identified tribally important ecosystem services and related them with biologically relevant vegetation projections from a Dynamic Global Vegetation Model. Although our assessment is focused in the Pacific Northwest, our approach can be applied in other regions for which model data is available. We recognize that there is some inherent uncertainty associated with using model output for future scenario planning; however, if that uncertainty is addressed and applied as demonstrated by our approach, it then can be explored to help inform resource management and adaptation planning.

Foreign demand for agricultural commodities drives virtual carbon exports from Cambodia

Rapid deforestation is a major sustainability challenge, partly as the loss of carbon sinks exacerbates global climate change. In Cambodia, more than 13% of the total land area has been contracted out to foreign and domestic agribusinesses in the form of economic land concessions, causing rapid large-scale land use change and deforestation. Additionally, the distant drivers of local and global environmental change often remain invisible. Here, we identify hotspots of carbon loss between 1987–2017 using the dynamic global vegetation model LPJ-GUESS and by comparing past and present land use and land cover. We also link global consumption and production patterns to their environmental effects in Cambodia by mapping the countries to which land-use embedded carbon are exported. We find that natural forests have decreased from 54%–21% between 1987 and 2017, mainly for the expansion of farmland and orchards, translating into 300 million tons of carbon lost, with loss rates over twice as high within economic land concessions. China is the largest importer of embedded carbon, mainly for rubber and sugarcane from Chinese agribusinesses. Cambodian investors have also negatively affected carbon pools through export-oriented products like rubber. The combined understanding of environmental change and trade flows makes it possible to identify distant drivers of deforestation, which is important for crafting more environmentally and socially responsible policies on national and transnational scales.

Differential effects of drought on nonstructural carbohydrate storage in seedlings and mature trees of four species in a subtropical forest

Nonstructural carbohydrates (NSC) play important roles in forest vulnerability to climate change, especially under increasing drought intensity and frequency. Understanding NSC dynamics is essential for accurately predicting the resistance and resilience of forests in response to drought. However, our knowledge of NSC responses to drought is still limited due to the lack of research in trees of different life stages. In this study, we conducted a throughfall exclusion experiment (TFE) with four subtropical evergreen tree species to examine drought effects on NSC in mature trees and understory seedlings. Our results showed the differential effects of drought on NSC dynamics of understory seedlings and mature trees. In the TFE experiment, mature trees of all four species were relatively homoeostatic with the insignificantly changed NSC pools, photosynthesis, and growth under the drought treatment compared to the control. In contrast, understory seedlings displayed significant decreases in total NSC and soluble sugars (−14.70 ± 3.66% and −16.93 ± 3.85%, respectively) with the exception of Lithocarpus glaber (L. glaber). The seedlings of L. glaber with the highest hydraulic resistance maintained or slightly increased NSC and its components in response to drought. Our study highlights the importance of life stage in assessing drought effects of trees on NSC storage and then forest C cycling, which could be incorporated into the dynamic global vegetation models (DGVMs) to better understand drought effects on forest C balance in the future.

Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates.

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

Modelling past and future peatland carbon dynamics across the pan-Arctic.

The majority of northern peatlands were initiated during the Holocene. Owing to their mass imbalance, they have sequestered huge amounts of carbon in terrestrial ecosystems. Although recent syntheses have filled some knowledge gaps, the extent and remoteness of many peatlands pose challenges to developing reliable regional carbon accumulation estimates from observations. In this work, we employed an individual- and patch-based dynamic global vegetation model (LPJ-GUESS) with peatland and permafrost functionality to quantify long-term carbon accumulation rates in northern peatlands and to assess the effects of historical and projected future climate change on peatland carbon balance. We combined published datasets of peat basal age to form an up-to-date peat inception surface for the pan-Arctic region which we then used to constrain the model. We divided our analysis into two parts, with a focus both on the carbon accumulation changes detected within the observed peatland boundary and at pan-Arctic scale under two contrasting warming scenarios (representative concentration pathway-RCP8.5 and RCP2.6). We found that peatlands continue to act as carbon sinks under both warming scenarios, but their sink capacity will be substantially reduced under the high-warming (RCP8.5) scenario after 2050. Areas where peat production was initially hampered by permafrost and low productivity were found to accumulate more carbon because of the initial warming and moisture-rich environment due to permafrost thaw, higher precipitation and elevated CO2 levels. On the other hand, we project that areas which will experience reduced precipitation rates and those without permafrost will lose more carbon in the near future, particularly peatlands located in the European region and between 45 and 55°N latitude. Overall, we found that rapid global warming could reduce the carbon sink capacity of the northern peatlands in the coming decades.

Contrasting acclimation responses to elevated CO2 and warming between an evergreen and a deciduous boreal conifer

Rising atmospheric carbon dioxide (CO2 ) concentrations may warm northern latitudes up to 8°C by the end of the century. Boreal forests play a large role in the global carbon cycle, and the responses of northern trees to climate change will thus impact the trajectory of future CO2 increases. We grew two North American boreal tree species at a range of future climate conditions to assess how growth and carbon fluxes were altered by high CO2 and warming. Black spruce (Picea mariana, an evergreen conifer) and tamarack (Larix laricina, a deciduous conifer) were grown under ambient (407ppm) or elevated CO2 (750ppm) and either ambient temperatures, a 4°C warming, or an 8°C warming. In both species, the thermal optimum of net photosynthesis (ToptA ) increased and maximum photosynthetic rates declined in warm-grown seedlings, but the strength of these changes varied between species. Photosynthetic capacity (maximum rates of Rubisco carboxylation, Vcmax , and of electron transport, Jmax ) was reduced in warm-grown seedlings, correlating with reductions in leaf N and chlorophyll concentrations. Warming increased the activation energy for Vcmax and Jmax (EaV and EaJ , respectively) and the thermal optimum for Jmax . In both species, the ToptA was positively correlated with both EaV and EaJ , but negatively correlated with the ratio of Jmax /Vcmax . Respiration acclimated to elevated temperatures, but there were no treatment effects on the Q10 of respiration (the increase in respiration for a 10°C increase in leaf temperature). A warming of 4°C increased biomass in tamarack, while warming reduced biomass in spruce. We show that climate change is likely to negatively affect photosynthesis and growth in black spruce more than in tamarack, and that parameters used to model photosynthesis in dynamic global vegetation models (EaV and EaJ ) show no response to elevated CO2 .

Critical land change information enhances the understanding of carbon balance in the United States.

Large-scale terrestrial carbon (C) estimating studies using methods such as atmospheric inversion, biogeochemical modeling, and field inventories have produced different results. The goal of this study was to integrate fine-scale processes including land use and land cover change into a large-scale ecosystem framework. We analyzed the terrestrial C budget of the conterminous United States from 1971 to 2015 at 1-km resolution using an enhanced dynamic global vegetation model and comprehensive land cover change data. Effects of atmospheric CO2 fertilization, nitrogen deposition, climate, wildland fire, harvest, and land use/land cover change (LUCC) were considered. We estimate annual C losses from cropland harvest, forest clearcut and thinning, fire, and LUCC were 436.8, 117.9, 10.5, and 10.4 TgC/year, respectively. C stored in ecosystems increased from 119,494 to 127,157 TgC between 1971 and 2015, indicating a mean annual net C sink of 170.3 TgC/year. Although ecosystem net primary production increased by approximately 12.3 TgC/year, most of it was offset by increased C loss from harvest and natural disturbance and increased ecosystem respiration related to forest aging. As a result, the strength of the overall ecosystem C sink did not increase over time. Our modeled results indicate the conterminous US C sink was about 30% smaller than previous modeling studies, but converged more closely with inventory data.

Including vegetation dynamics in an atmospheric chemistry-enabled general circulation model: linking LPJ-GUESS (v4.0) with the EMAC modelling system (v2.53)

Abstract. Central to the development of Earth system models (ESMs) has been the coupling of previously separate model types, such as ocean, atmospheric, and vegetation models, to address interactive feedbacks between the system components. A modelling framework which combines a detailed representation of these components, including vegetation and other land surface processes, enables the study of land–atmosphere feedbacks under global climate change. Here we present the initial steps of coupling LPJ-GUESS, a dynamic global vegetation model, to the atmospheric chemistry-enabled atmosphere–ocean general circulation model EMAC. The LPJ-GUESS framework is based on ecophysiological processes, such as photosynthesis; plant and soil respiration; and ecosystem carbon, nitrogen, and water cycling, and it includes a comparatively detailed individual-based representation of resource competition, plant growth, and vegetation dynamics as well as fire disturbance. Although not enabled here, the model framework also includes a crop and managed-land scheme, a representation of arctic methane and permafrost, and a choice of fire models; and hence it represents many important terrestrial biosphere processes and provides a wide range of prognostic trace-gas emissions from vegetation, soil, and fire. We evaluated an online one-way-coupled model configuration (with climate variable being passed from EMAC to LPJ-GUESS but no return information flow) by conducting simulations at three spatial resolutions (T42, T63, and T85). These were compared to an expert-derived map of potential natural vegetation and four global gridded data products: tree cover, biomass, canopy height, and gross primary productivity (GPP). We also applied a post hoc land use correction to account for human land use. The simulations give a good description of the global potential natural vegetation distribution, although there are some regional discrepancies. In particular, at the lower spatial resolutions, a combination of low-temperature and low-radiation biases in the growing season of the EMAC climate at high latitudes causes an underestimation of vegetation extent. Quantification of the agreement with the gridded datasets using the normalised mean error (NME) averaged over all datasets shows that increasing the spatial resolution from T42 to T63 improved the agreement by 10 %, and going from T63 to T85 improved the agreement by a further 4 %. The highest-resolution simulation gave NME scores of 0.63, 0.66, 0.84, and 0.53 for tree cover, biomass, canopy height, and GPP, respectively (after correcting tree cover and biomass for human-caused deforestation which was not present in the simulations). These scores are just 4 % worse on average than an offline LPJ-GUESS simulation using observed climate data and corrected for deforestation by the same method. However, it should be noted that the offline LPJ-GUESS simulation used a higher spatial resolution, which makes the evaluation more rigorous, and that excluding GPP from the datasets (which was anomalously better in the EMAC simulations) gave 10 % worse agreement for the EMAC simulation than the offline simulation. Gross primary productivity was best simulated by the coupled simulations, and canopy height was the worst. Based on this first evaluation, we conclude that the coupled model provides a suitable means to simulate dynamic vegetation processes in EMAC.

Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach

Abstract. FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere–atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM ensembles (R2&gt;0.94 at 1∘ spatial resolution) while the majority of DGVMs show, for 70 % of the land surface, values outside the FLUXCOM range. Global mean GPP magnitudes for 2008–2010 from FLUXCOM members vary within 106 and 130 PgC yr−1 with the largest uncertainty in the tropics. Seasonal variations in independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise R2∼0.75) than with GPP from DGVMs (mean global pixel-wise R2∼0.6). Seasonal variations in FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions (R2&gt;0.92). Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which also uses meteorological inputs shows a strong co-variation in interannual patterns with inversions (R2=0.87 for 2001–2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred grammes of carbon per square metre per year. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured by the flux tower measurements such as carbon emissions from fires and water bodies. We hypothesize that a combination of systematic biases in the underlying eddy covariance data, in particular in tall tropical forests, and a lack of site history effects on NEE in FLUXCOM are likely responsible for the too strong tropical carbon sink estimated by FLUXCOM. Furthermore, as FLUXCOM does not account for CO2 fertilization effects, carbon flux trends are not realistic. Overall, current FLUXCOM estimates of mean annual and seasonal cycles of GPP as well as seasonal NEE variations provide useful constraints of global carbon cycling, while interannual variability patterns from FLUXCOM are valuable but require cautious interpretation. Exploring the diversity of Earth observation data and of machine learning concepts along with improved quality and quantity of flux tower measurements will facilitate further improvements of the FLUXCOM approach overall.

JULES-BE: representation of bioenergy crops and harvesting in the Joint UK Land Environment Simulator vn5.1

Abstract. We describe developments to the land surface model JULES, allowing for flexible user-prescribed harvest regimes of various perennial bioenergy crops or natural vegetation types. Our aim is to integrate the most useful aspects of dedicated bioenergy models into dynamic global vegetation models, in order that assessment of bioenergy options can benefit from state-of-the-art Earth system modelling. A new plant functional type (PFT) representing Miscanthus is also presented. The Miscanthus PFT fits well with growth parameters observed at a site in Lincolnshire, UK; however, global observed yields of Miscanthus are far more variable than is captured by the model, primarily owing to the model's lack of representation of crop age and establishment time. Global expansion of bioenergy crop areas under a 2 ∘C emissions scenario and balanced greenhouse gas mitigation strategy from the IMAGE integrated assessment model (RCP2.6-SSP2) achieves a mean yield of 4.3 billion tonnes of dry matter per year over 2040–2099, around 30 % higher than the biomass availability projected by IMAGE. In addition to perennial grasses, JULES-BE can also be used to represent short-rotation coppicing, residue harvesting from cropland or forestry and rotation forestry.