Lund-Potsdam-Jena Dynamic Global Vegetation Model Research Articles

Increasing sensitivity of dryland water use efficiency to soil water content due to rising atmospheric CO2

Examining the intricate interplay between ecosystem carbon-water coupling and soil moisture sensitivity serves as a crucial approach to effectively assess the dilemma arising from escalating global carbon emissions and concomitant water scarcity. Using the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ), this study investigated the potential effects of climate change and soil water content (SWC) on terrestrial ecosystem water use efficiency (WUE) across China from 1982 to 2060. The results revealed that: (1) WUE was higher in South China and Northeast China, but lower in Northwest China and it had shown a significant upward trend in the past 40 years, especially in Northwest China where grasslands were widely distributed. The increase in WUE was mainly closely related to the greening of vegetation. In the past 40 years, the area of net primary productivity (NPP), evapotranspiration (ET), and WUE showing an upward trend accounted for 85.85 %, 63.66 %, and 83.88 % of the total area of the country, respectively. Although ET also showed an increasing trend nationwide, the increase of NPP was more obvious; (2) The control experiment showed that WUE showed a significant increase trend in arid and semi-arid areas of Northwest China with the increase of CO2 concentration, while SWC showed a significant drying trend, but both WUE and SWC showed an increasing trend in humid areas. The sensitivity of WUE to SWC was enhanced in arid and semi-arid areas, and the effect of soil drought was partially offset by the increase of WUE; (3) Future climate projections also indicated that the CO2 fertilization effect will contribute to an increase in WUE while causing drier soil moisture conditions in the arid and semi-arid regions. Especially under the SSP5-8.5 scenario, CO2 fertilization in Northwest China contributed more than 14 % to WUE from 2015 to 2060, while the impact on SWC depletion exceeded 3 %. This highlights the potential implications of rising atmospheric CO2 concentration, as it may promote a significant rise in WUE and exacerbate the drying of soil moisture in these areas. These findings emphasize the need for careful attention and consideration in managing water resources in arid and semi-arid regions in the face of future climate change.

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.

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.

Historical and future global burned area with changing climate and human demography

Summary Wildfires influence terrestrial carbon cycling and represent a safety risk, and yet a process-based understanding of their frequency and spatial distributions remains elusive. We combine satellite-based observations with an enhanced dynamic global vegetation model to make regionally resolved global assessments of burned area (BA) responses to changing climate, derived from 34 Earth system models and human demographics for 1860–2100. Limited by climate and socioeconomics, recent BA has decreased, especially in central South America and mesic African savannas. However, future simulations predict increasing BA due to changing climate, rapid population density growth, and urbanization. BA increases are especially notable at high latitudes, due to accelerated warming, and over the tropics and subtropics, due to drying and human ignitions. Conversely, rapid urbanization also limits BA via enhanced fire suppression in the immediate vicinity of settlements, offsetting the potential for dramatic future increases, depending on warming extent. Our analysis provides further insight into regional and global BA trends, highlighting the importance of including human demographic change in models for wildfire under changing climate.

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.

Changes in vegetation and surface water balance at basin-scale in Central China with rising atmospheric CO2

Elevated atmospheric CO2 concentration alters vegetation growth and composition, increases plant water use efficiency (WUE), and changes surface water balance. These changes and their differences between wet and dry climate are studied at a mid-latitude experiment site in the Loess Plateau of China. The study site, the Jinghe River basin (JRB), covers an area of 43,216 km2 and has a semiarid climate in the north and a semi-humid climate in the south. Two simulations from 1965 to 2012 are made using a site-calibrated Lund–Potsdam–Jena dynamic global vegetation model: one with the observed rise of the atmospheric CO2 from 319.7–391.2 ppmv, and the other with a fixed CO2 at the level of 1964 (318.9 ppmv). Analyses of the model results show that the elevated atmospheric CO2 promotes growth of woody vegetation (trees) and causes a 6.0% increase in basin-wide net primary production (NPP). The NPP increase uses little extra water however because of higher WUE. Further examination of the surface water budget reveals opposite CO2 effects between semiarid and semi-humid climates in the JRB. In the semiarid climate, plants sustain growth in higher CO2 because of the higher level of intracellular CO2 and therefore WUE, thus consuming more water and causing a greater decrease of surface runoff than in the fixed-lower CO2 case. In the semi-humid climate, NPP also increases but by a smaller amount than in the semiarid climate. Plant transpiration (ET) and total evapotranspiration (E) decrease in the elevated CO2 environment, yielding the increase of runoff. This asymmetry of the effects of elevated atmospheric CO2 exacerbates drying in the semiarid climate and enhances wetness in the semi-humid climate. Furthermore, plant WUE (=NPP/ET) is found to be nearly invariant to climate but primarily a function of the atmospheric CO2 concentration, a result suggesting a strong constraint of atmospheric CO2 on biophysical properties of the Earth system.

Assimilation of Remotely-Sensed Leaf Area Index into a Dynamic Vegetation Model for Gross Primary Productivity Estimation

Quantitative estimation of the magnitude and variability of gross primary productivity (GPP) is required to study the carbon cycle of the terrestrial ecosystem. Using ecosystem models and remotely-sensed data is a practical method for accurately estimating GPP. This study presents a method for assimilating high-quality leaf area index (LAI) products retrieved from satellite data into a process-oriented Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM) to acquire accurate GPP. The assimilation methods, including the Ensemble Kalman Filter (EnKF) and a proper orthogonal decomposition (POD)-based ensemble four-dimensional (4D) variational assimilation method (PODEn4DVar), incorporate information provided by observations into the model to achieve a better agreement between the model-estimated and observed GPP. The LPJ-POD scheme performs better with a correlation coefficient of r = 0.923 and RMSD of 32.676 gC/m2/month compared with the LPJ-EnKF scheme (r = 0.887, RMSD = 38.531 gC/m2/month) and with no data assimilation (r = 0.840, RMSD = 45.410 gC/m2/month). Applying the PODEn4DVar method into LPJ-DGVM for simulating GPP in China shows that the annual amount of GPP in China varied between 5.92 PgC and 6.67 PgC during 2003–2012 with an annual mean of 6.35 PgC/yr. This study demonstrates that integrating remotely-sensed data with dynamic global vegetation models through data assimilation methods has potential in optimizing the simulation and that the LPJ-POD scheme shows better performance in improving GPP estimates, which can provide a favorable way for accurately estimating dynamics of ecosystems.

Simulated annual changes in plant functional types and their responses to climate change on the northern Tibetan Plateau

Abstract. Changes in plant functional types (PFTs) have important implications for both climate and water resources. Still, little is known about whether and how PFTs have changed over the past decades on the northern Tibetan Plateau (NTP) where several of the top largest rivers in the world are originated. Also, the relative importance of atmospheric conditions vs. soil physical conditions in affecting PFTs is unknown on the NTP. In this study, we used the improved Lund–Potsdam–Jena Dynamic Global Vegetation Model to investigate PFT changes through examining the changes in foliar projective coverages (FPCs) during 1957–2009 and their responses to changes in root zone soil temperature, soil moisture, air temperature, precipitation and CO2 concentrations. The results show spatially heterogeneous changes in FPCs across the NTP during 1957–2009, with 34 % (13 %) of the region showing increasing (decreasing) trends. Dominant drivers responsible for the observed FPC changes vary with regions and vegetation types, but overall, precipitation is the major factor in determining FPC changes on the NTP with positive impacts. Soil temperature increase exhibits small but negative impacts on FPCs. Different responses of individual FPCs to regionally varying climate change result in spatially heterogeneous patterns of vegetation changes on the NTP. The implication of the study is that fresh water resources in one of the world's largest and most important headwater basins and the onset and intensity of Asian monsoon circulations could be affected because of the changes in FPCs on the NTP.

Influence of dynamic vegetation on climate change and terrestrial carbon storage in the Last Glacial Maximum

Abstract. When the climate is reconstructed from paleoevidence, it shows that the Last Glacial Maximum (LGM, ca. 21 000 yr ago) is cold and dry compared to the present-day. Reconstruction also shows that compared to today, the vegetation of the LGM is less active and the distribution of vegetation was drastically different, due to cold temperature, dryness, and a lower level of atmospheric CO2 concentration (185 ppm compared to a preindustrial level of 285 ppm). In the present paper, we investigate the influence of vegetation change on the climate of the LGM by using a coupled atmosphere-ocean-vegetation general circulation model (AOVGCM, the MIROC-LPJ). The MIROC-LPJ is different from earlier studies in the introduction of a bias correction method in individual running GCM experiments. We examined four GCM experiments (LGM and preindustrial, with and without vegetation feedback) and quantified the strength of the vegetation feedback during the LGM. The result shows that global-averaged cooling during the LGM is amplified by +13.5 % due to the introduction of vegetation feedback. This is mainly caused by the increase of land surface albedo due to the expansion of tundra in northern high latitudes and the desertification in northern middle latitudes around 30° N to 60° N. We also investigated how this change in climate affected the total terrestrial carbon storage by using offline Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM). Our result shows that the total terrestrial carbon storage was reduced by 597 PgC during the LGM, which corresponds to the emission of 282 ppm atmospheric CO2. In the LGM experiments, the global carbon distribution is generally the same whether the vegetation feedback to the atmosphere is included or not. However, the inclusion of vegetation feedback causes substantial terrestrial carbon storage change, especially in explaining the lowering of atmospheric CO2 during the LGM.

Neotropical C3/C4 grass distributions – present, past and future

AbstractChanges in C4 grass distribution and abundance are frequently observed in Quaternary, Holocene and future environmental‐change scenarios. However, the factors driving these dynamics are not fully understood, and conflicting theories have been reported. In this paper, we present a very large dataset of modern altitudinal distribution profiles of C3 and C4 grasses covering the entire Neotropical Andes, which was compared with actual climate data. The results of multivariate analysis demonstrate that, in the Neotropical Andes, mean annual temperature is the main factor governing the modern altitudinal distribution of C3 and C4 grass species. The C3 and C4 grass distributions were compared with simulations based on the Lund‐Potsdam‐Jena dynamic global vegetation model (LPJ‐DGVM), which allowed the present grass distribution to be estimated. Finally, the DGVM was employed to simulate past and future scenarios, using the IPCC's climate projections for 2100 and PMIP2 models for the Holocene Optimum (HO, 6000 years bp) and the Last Glacial Maximum (LGM, 21 000 years bp). The results were found to be significantly different from those obtained using a simple photosynthetic model. According to LPJ forced with the PMIP2 models for the LGM, during the LGM, the C4 grasses would not have reached higher altitudes than found in the present day.

Uncertainty analysis of vegetation distribution in the northern high latitudes during the 21st century with a dynamic vegetation model.

This study aims to assess how high-latitude vegetation may respond under various climate scenarios during the 21st century with a focus on analyzing model parameters induced uncertainty and how this uncertainty compares to the uncertainty induced by various climates. The analysis was based on a set of 10,000 Monte Carlo ensemble Lund-Potsdam-Jena (LPJ) simulations for the northern high latitudes (45oN and polewards) for the period 1900–2100. The LPJ Dynamic Global Vegetation Model (LPJ-DGVM) was run under contemporary and future climates from four Special Report Emission Scenarios (SRES), A1FI, A2, B1, and B2, based on the Hadley Centre General Circulation Model (GCM), and six climate scenarios, X901M, X902L, X903H, X904M, X905L, and X906H from the Integrated Global System Model (IGSM) at the Massachusetts Institute of Technology (MIT). In the current dynamic vegetation model, some parameters are more important than others in determining the vegetation distribution. Parameters that control plant carbon uptake and light-use efficiency have the predominant influence on the vegetation distribution of both woody and herbaceous plant functional types. The relative importance of different parameters varies temporally and spatially and is influenced by climate inputs. In addition to climate, these parameters play an important role in determining the vegetation distribution in the region. The parameter-based uncertainties contribute most to the total uncertainty. The current warming conditions lead to a complexity of vegetation responses in the region. Temperate trees will be more sensitive to climate variability, compared with boreal forest trees and C3 perennial grasses. This sensitivity would result in a unanimous northward greenness migration due to anomalous warming in the northern high latitudes. Temporally, boreal needleleaved evergreen plants are projected to decline considerably, and a large portion of C3 perennial grass is projected to disappear by the end of the 21st century. In contrast, the area of temperate trees would increase, especially under the most extreme A1FI scenario. As the warming continues, the northward greenness expansion in the Arctic region could continue.

Coupled simulation of climate and vegetation dynamics

Experiments are performed with the climate model of the Institute of Numerical Mathematics, Russian Academy of Sciences (INM RAS), integrated jointly with the Lund-Potsdam-Jena dynamic global vegetation model (LPJ-DGVM). It is shown that the coupled model reproduces the distribution of basic plant functional types around the world quite well. In simulations of climate for the 21st century, this model predicts changes in dominant plant types and in the total area occupied by vegetation regionally. However, it does not reproduce the significant inverse influence that vegetation succession has on the simulated climate.

Dynamic Response of Terrestrial Hydrological Cycles and Plant Water Stress to Climate Change in China

Abstract Rising atmospheric CO2 concentration CO2 and climate change are expected to have a major effect on terrestrial ecosystem hydrological cycles and plant water stress in the coming decades. The present study investigates the potential responses of terrestrial ecosystem hydrological cycles and plant water stress across China to elevated CO2 and climate change in the twentieth and twenty-first centuries using the calibrated and validated Lund–Potsdam–Jena dynamic global vegetation model (LPJ-DGVM) and eight climate change scenarios. The spatiotemporal change patterns of estimated evapotranspiration (ET), soil moisture, runoff, and plant water stress due to climate change and elevated CO2 are plotted singly and in combination. Positive future trends in ET, soil moisture, and runoff—although differing greatly among regions—are projected. Resultant plant water stress over China’s terrestrial ecosystem generally could be eased substantially through the twenty-first century under the climate scenarios driven by emission scenarios that consider economic concerns. By contrast, under the climate scenarios driven by emission scenarios that consider environmental concerns, plant water stress could be eased until 2060, then begin to fluctuate until 2100. The net impact of physiological and structural vegetation responses to elevated CO2 could result in an increasing trend in runoff in southern and northeastern China, and a decreasing trend in runoff in northern and northwestern China in the twentieth century. It is projected to reduce ET by 1.5 × 109 to 6.5 × 109 m3 yr−1 on average, and increase runoff by 1.0 × 109 to 5.4 × 109 m3 yr−1 during 2001–2100 across China’s terrestrial ecosystems, although the spatial change pattern could be quite diverse. These findings, in partial contradiction to previous results, present an improved understanding of transient responses of China’s terrestrial ecosystem hydrological cycles and plant water stress to climate change and elevated CO2 in the twentieth and twenty-first centuries.

Impact of changing wood demand, climate and land use on European forest resources and carbon stocks during the 21st century

AbstractWe used the European Forest Information Scenario Model (EFISCEN) to project the development of forest resources for 15 European countries from 2000 to 2100 under a broad range of climate scenarios, which were based on the a1fi, a2, b1 and b2 storylines of the Special Report on Emissions Scenarios of the Intergovernmental Panel on Climate Change. Each climate scenario was associated with consistent land‐use change and wood demand assumptions. Climate change‐induced growth changes were incorporated into the calculations by scaling inventory‐based stem growth in EFISCEN by net primary productivity estimated from the Lund–Potsdam–Jena dynamic global vegetation model. The impact of changes in wood demand, climate and forest area were studied separately, and in combination, in order to assess their respective effects. For all climate scenarios under consideration, climate change resulted in increased forest growth, especially in Northern Europe. In Southern Europe, higher precipitation in spring and the projected increased water‐use efficiency in response to rising atmospheric CO2concentrations mitigated the effects of increasing summer drought. Climate change enhanced carbon sequestration in tree biomass. The climate change‐induced increase in tree growth led to a faster increase in growing stocks compared with the simulation using current climate. As productivity decreased in higher stocked forests, the positive impact of climate change began to level off during the second half of the 21st century in the scenarios where wood demand was low. Afforestation measures had the potential to increase growing stock and annual increment; however, large areas were needed to obtain notable effects. Despite noticeable differences in the growth response between the climate scenarios, changes in wood demand proved to be the crucial driving force in forest resource development. Tree carbon stocks increased by 33–114% between 2000 and 2100 when only changes in wood demand were regarded. Climate change added another 23–31% increase, while changes in forest area accounted for an additional increase of 2–40%. Our results highlight potential future pathways of forest resource development resulting from different scenarios of wood demand, land use and climate changes, and stress the importance of resource utilization in the European forest carbon balance.

Estimating the evolution of vegetation cover and its hydrological impact in the Mekong River basin in the 21st century

AbstractThe terrestrial biosphere plays a key role in regional energy and water cycles. Thus, for long‐term hydrological predictions, possible future changes in vegetation cover must be understood. This study examined the evolution of vegetation cover in the 21st century and its estimated impact on river discharge in the Mekong River basin. Based on climatic predictions (TYN SC 2·03) under the Intergovernmental Panel on Climate Change Special Report on Emissions Scenarios (IPCC SRES) A1FI, A2, B1, and B2, changes in vegetation type and the leaf area index (LAI) were simulated using a Lund‐Potsdam‐Jena‐Dynamic Global Vegetation Model (LPJ‐DGVM) and Terrestrial Biogeochemical Cycle Model (BIOME‐BGC). The estimated LAI was then used in the rainfall‐runoff analysis in the Yamanashi Distributed Hydrological Model (YHyM).The simulation results indicated a significant change in vegetation type mainly on the Tibetan Plateau and in mountainous areas, with the degree of change differing for each SRES scenario; LAI increases around the edge of the Tibetan Plateau and decreases in the lower reaches of the basin; and more conspicuous changes in river discharge in upstream areas than in the middle to lower reaches, mainly due to increases in precipitation in the plateau region. After the 2050s, the results suggested changes in river discharge will be slowed due to changes in evapotranspiration. Copyright © 2008 John Wiley & Sons, Ltd.

Effects of human land-use on the global carbon cycle during the last 6,000 years

Humanity has become a major player within the Earth system, particularly by transforming large parts of the land surface and by altering the gaseous composition of the atmosphere. Deforestation for agricultural purposes started thousands of years ago and might have resulted in a detectable human influence on climate much earlier than the industrial revolution. This study presents a first attempt to estimate the impact of human land-use on the global carbon cycle over the last 6,000 years. A global gridded data set for the spread of permanent and non-permanent agriculture over this time period was developed and integrated within the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). The model was run with and without human land-use, and the difference in terrestrial carbon storage was calculated as an estimate of anthropogenic carbon release to the atmosphere. The modelled total carbon release during the industrial period (a.d. 1850–1990) was 148 gigatons of carbon (GtC), of which 33 GtC originated from non-permanent agriculture. For pre-industrial times (4000 b.c.–a.d. 1850), the net carbon release was 79 GtC from permanent agriculture with an additional 35 GtC from non-permanent agriculture. The modelled pre-industrial carbon release was considerably lower than would be required for a substantial influence on the climate system.

Effects of soil freezing and thawing on vegetation carbon density in Siberia: A modeling analysis with the Lund‐Potsdam‐Jena Dynamic Global Vegetation Model (LPJ‐DGVM)

The current latitudinal gradient in biomass suggests a climate‐driven limitation of biomass in high latitudes. Understanding of the underlying processes, and quantification of their relative importance, is required to assess the potential carbon uptake of the biosphere in response to anticipated warming and related changes in tree growth and forest extent in these regions. We analyze the hydrological effects of thawing and freezing of soil on vegetation carbon density (VCD) in permafrost‐dominated regions of Siberia using a process‐based biogeochemistry‐biogeography model, the Lund‐Potsdam‐Jena Dynamic Global Vegetation Model (LPJ‐DGVM). The analysis is based on spatially explicit simulations of coupled daily thaw depth, site hydrology, vegetation distribution, and carbon fluxes influencing VCD subject to climate, soil texture, and atmospheric CO2 concentration. LPJ represents the observed high spring peak of runoff of large Arctic rivers, and simulates a realistic fire return interval of 100 to 200 years in Siberia. The simulated VCD changeover from taiga to tundra is comparable to inventory‐based information. Without the consideration of freeze‐thaw processes VCD would be overestimated by a factor of 2 in southern taiga to a factor of 5 in northern forest tundra, mainly because available soil water would be overestimated with major effects on fire occurrence and net primary productivity. This suggests that forest growth in high latitudes is not only limited by temperature, radiation, and nutrient availability but also by the availability of liquid soil water.

Implementing plant hydraulic architecture within the LPJ Dynamic Global Vegetation Model

ABSTRACTAim To implement plant hydraulic architecture within the Lund–Potsdam–Jena Dynamic Global Vegetation Model (LPJ–DGVM), and to test the model against a set of observational data. If the model can reproduce major patterns in vegetation and ecosystem processes, we consider this to be an important linkage between plant physiology and larger‐scale ecosystem dynamics.Location The location is global, geographically distributed.Methods A literature review was carried out to derive model formulations and parameter values for representing the hydraulic characteristics of major global plant functional types (PFTs) in a DGVM. After implementing the corresponding formulations within the LPJ–DGVM, present‐day model output was compared to observational data.Results The model reproduced observed broad‐scale patterns in potential natural vegetation, but it failed to distinguish accurately between different types of grassland and savanna vegetation, possibly related to inadequate model representations of water fluxes in the soil and wildfire effects. Compared to a version of the model using an empirical formulation for calculating plant water supply without considering plant hydraulic architecture, the new formulation improved simulated patterns of vegetation in particular for dry shrublands. Global‐scale simulation results for runoff and actual evapotranspiration (AET) corresponded well to available data. The model also successfully reproduced the magnitude and seasonal cycle of AET for most EUROFLUX forests, while modelled variation in NPP across a large number of sites spanning several biomes showed a strong correlation with estimates from field measurements.Main conclusions The model was generally confirmed by comparison to observational data. The novel model representation of water flow within plants makes it possible to resolve mechanistically the effects of hydraulic differences between plant functional groups on vegetation structure, water cycling, and competition. This may be an advantage when predicting ecosystem responses to nonextant climates, in particular in areas dominated by dry shrubland vegetation.

Terrestrial biosphere carbon storage under alternative climate projections

This study investigates commonalities and differences in projected land biosphere carbon storage among climate change projections derived from one emission scenario by five different general circulation models (GCMs). Carbon storage is studied using a global biogeochemical process model of vegetation and soil that includes dynamic treatment of changes in vegetation composition, a recently enhanced version of the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM). Uncertainty in future terrestrial carbon storage due to differences in the climate projections is large. Changes by the end of the century range from −106 to +201 PgC, thus, even the sign of the response whether source or sink, is uncertain. Three out of five climate projections produce a land carbon source by the year 2100, one is approximately neutral and one a sink. A regional breakdown shows some robust qualitative features. Large areas of the boreal forest are shown as a future CO2 source, while a sink appears in the arctic. The sign of the response in tropical and sub-tropical ecosystems differs among models, due to the large variations in simulated precipitation patterns. The largest uncertainty is in the response of tropical rainforests of South America and Central Africa.

Simulated changes in vegetation distribution, land carbon storage, and atmospheric CO2 in response to a collapse of the North Atlantic thermohaline circulation

It is investigated how abrupt changes in the North Atlantic (NA) thermohaline circulation (THC) affect the terrestrial carbon cycle. The Lund–Potsdam–Jena Dynamic Global Vegetation Model is forced with climate perturbations from glacial freshwater experiments with the ECBILT-CLIO ocean–atmosphere–sea ice model. A reorganisation of the marine carbon cycle is not addressed. Modelled NA THC collapses and recovers after about a millennium in response to prescribed freshwater forcing. The initial cooling of several Kelvin over Eurasia causes a reduction of extant boreal and temperate forests and a decrease in carbon storage in high northern latitudes, whereas improved growing conditions and slower soil decomposition rates lead to enhanced storage in mid-latitudes. The magnitude and evolution of global terrestrial carbon storage in response to abrupt THC changes depends sensitively on the initial climate conditions. These were varied using results from time slice simulations with the Hadley Centre model HadSM3 for different periods over the past 21 kyr. Changes in terrestrial storage vary between −67 and +50 PgC for the range of experiments with different initial conditions. Simulated peak-to-peak differences in atmospheric CO2 are 6 and 13 ppmv for glacial and late Holocene conditions. Simulated changes in δ13C are between 0.15 and 0.25‰. These simulated carbon storage anomalies during a NA THC collapse depend on their magnitude on the CO2 fertilisation feedback mechanism. The CO2 changes simulated for glacial conditions are compatible with available evidence from marine studies and the ice core CO2 record. The latter shows multi-millennial CO2 variations of up to 20 ppmv broadly in parallel with the Antarctic warm events A1 to A4 in the South and cooling in the North.