Sheffield Dynamic Global Vegetation Model Research Articles

Tropical vegetation productivity and atmospheric methane over the last 40,000 years from model simulations and stalagmites in Sulawesi, Indonesia

AbstractRecent research has shown the potential of speleothem δ13C to record a range of environmental processes. Here, we report on 230Th-dated stalagmite δ13C records for southwest Sulawesi, Indonesia, over the last 40,000 yr to investigate the relationship between tropical vegetation productivity and atmospheric methane concentrations. We demonstrate that the Sulawesi stalagmite δ13C record is driven by changes in vegetation productivity and soil respiration and explore the link between soil respiration and tropical methane emissions using HadCM3 and the Sheffield Dynamic Global Vegetation Model. The model indicates that changes in soil respiration are primarily driven by changes in temperature and CO2, in line with our interpretation of stalagmite δ13C. In turn, modelled methane emissions are driven by soil respiration, providing a mechanism that links methane to stalagmite δ13C. This relationship is particularly strong during the last glaciation, indicating a key role for the tropics in controlling atmospheric methane when emissions from high-latitude boreal wetlands were suppressed. With further investigation, the link between δ13C in stalagmites and tropical methane could provide a low-latitude proxy complementary to polar ice core records to improve our understanding of the glacial–interglacial methane budget.

Climatic Controls on C4 Grassland Distributions During the Neogene: A Model-Data Comparison

Grasslands dominated by taxa using the C4photosynthetic pathway first developed on several continents during the Neogene and Quaternary, long after C4photosynthesis first evolved among grasses. The histories of these ecosystems are relatively well-documented in the geological record from stable carbon isotope measurements (of fossil vertebrate herbivores and paleosols) and the plant microfossil record (pollen and/or phytolith assemblages). The distinct biogeography and ecophysiology of modern C3and C4grasses have led to hypotheses explaining the origins of C4grasslands in terms of long-term changes in the Earth system, such as increased aridity and decreasing atmospheric pCO2. However, quantitative proxies for key abiotic drivers of these hypotheses (e.g., temperature, precipitation, pCO2) are still in development, not yet widely applied at the continental or global scale or throughout the late Cenozoic, and/or remain contentious. Testing these hypotheses globally therefore remains difficult. To understand better the potential links between changes in the Earth system and the origin of C4grasslands, we undertook a global-scale comparison between observational records of C4plant abundances in Miocene and Pliocene localities compiled from the literature and three increasingly complex models of C4physiology, dominance, and abundance. The literature compilation comprises > 2,600 δ13C-values each of fossil terrestrial vertebrates and of paleosol carbonates, which we interpret as primarily proxies for the abundance of C4grasses, based on the modern contribution of C4grasses to terrestrial net primary productivity. We forced the vegetation models with simulated monthly climates from the HadCM3 family of coupled ocean-atmosphere general circulation models (OAGCMs) over a range of pCO2-values for each epoch to model C4dominance or abundance in grid cells as: (1) months per year exceeding the temperature at which net carbon assimilation is greater for C4than C3photosynthesis (crossover temperature model); (2) the number of months per year exceeding the crossover temperature and having sufficient precipitation for growth (≥25 mm/month; Collatz model); and (3) the Sheffield Dynamic Global Vegetation Model (SDGVM), which models multiple plant functional types (PFTs) (C3and C4grasses, evergreen, and deciduous trees). Model-data agreement is generally weak, although statistically significant for many comparisons, suggesting that regional to local ecological interactions, continent-specific plant evolutionary histories, and/or regional to local climatic conditions not represented in global scale OAGCMs may have been equally strong or stronger in driving the evolution of C4grasslands as global changes in the Earth system such as decreases in atmospheric pCO2and late Cenozoic global cooling and/or aridification.

The impact of alternative trait-scaling hypotheses for the maximum photosynthetic carboxylation rate (Vcmax ) on global gross primary production.

The maximum photosynthetic carboxylation rate (Vcmax ) is an influential plant trait that has multiple scaling hypotheses, which is a source of uncertainty in predictive understanding of global gross primary production (GPP). Four trait-scaling hypotheses (plant functional type, nutrient limitation, environmental filtering, and plant plasticity) with nine specific implementations were used to predict global Vcmax distributions and their impact on global GPP in the Sheffield Dynamic Global Vegetation Model (SDGVM). Global GPP varied from 108.1 to 128.2PgCyr-1 , 65% of the range of a recent model intercomparison of global GPP. The variation in GPP propagated through to a 27% coefficient of variation in net biome productivity (NBP). All hypotheses produced global GPP that was highly correlated (r=0.85-0.91) with three proxies of global GPP. Plant functional type-based nutrient limitation, underpinned by a core SDGVM hypothesis that plant nitrogen (N) status is inversely related to increasing costs of N acquisition with increasing soil carbon, adequately reproduced global GPP distributions. Further improvement could be achieved with accurate representation of water sensitivity and agriculture in SDGVM. Mismatch between environmental filtering (the most data-driven hypothesis) and GPP suggested that greater effort is needed understand Vcmax variation in the field, particularly in northern latitudes.

Three dimensional coupled model approaches to terrestrial methane cycling during Paleogene greenhouse climates

The biogeochemical cycling of methane presents a major challenge for paleoclimate scientists, as although this critical greenhouse gas (GHG) has the forcing potential to drive significant changes in the Earth System, there are no proxy methods for reconstructing it’s ancient atmospheric concentration. This is especially important as biogenic methane emissions are controlled by environmental conditions such as temperature and precipitation, and so methane has significant power as a positive or negative feedback on global climate change. Understanding how methane emissions and cycling acted in the high pCO2 greenhouse worlds of the Paleogene potentially bridges the gap between our understanding of other, better (although arguably still poorly-) constrained GHGs and global temperature. The recent application of advanced three dimensional global modelling strategies to the problem of Eocene trace GHG concentrations has begun to show how important these may be in high-CO2 worlds (Valdes et al, 2005; Singarayer et al, 2011; Beerling et al, 2011), suggesting that as much as 2.7 °C of global warming may be contributed. This approach couples the unified Hadley Centre climate model (HadCM3L) with Sheffield Dynamic Global Vegetation Model (SDGVM; Beerling et al, 1997) to simulate trace gas emissions, and the atmospheric chemistry model STOCHEM to simulate the concentration of methane and ozone in the troposphere Here we present early results of a project to extend and develop the results of these earlier works, with revised and improved biogeochemistry and vegetation models, and broader consideration of boundary and initial conditions. These new results will be compared to indirect proxy methods to determine Paleogene methane emissions using lipid biomarker carbon isotopes in Eocene wetland settings (preserved as lignites) to probe past methanogenic and methanotrophic populations and constrain ancient methane emissions in high-CO2 worlds.

Influence of future climate and cropland expansion on isoprene emissions and tropospheric ozone

Abstract. Over the 21st century, changes in CO2 levels, climate and land use are expected to alter the global distribution of vegetation, leading to changes in trace gas emissions from plants, including, importantly, the emissions of isoprene. This, combined with changes in anthropogenic emissions, has the potential to impact tropospheric ozone levels, which above a certain level are harmful to animals and vegetation. In this study we use a biogenic emissions model following the empirical parameterisation of the MEGAN model, with vegetation distributions calculated by the Sheffield Dynamic Global Vegetation Model (SDGVM) to explore a range of potential future (2095) changes in isoprene emissions caused by changes in climate (including natural land use changes), land use, and the inhibition of isoprene emissions by CO2. From the present-day (2000) value of 467 Tg C yr−1, we find that the combined impact of these factors could cause a net decrease in isoprene emissions of 259 Tg C yr−1 (55%) with individual contributions of +78 Tg C yr−1 (climate change), −190 Tg C yr−1 (land use) and −147 Tg C yr−1 (CO2 inhibition). Using these isoprene emissions and changes in anthropogenic emissions, a series of integrations is conducted with the UM-UKCA chemistry-climate model with the aim of examining changes in ozone over the 21st century. Globally, all combined future changes cause a decrease in the tropospheric ozone burden of 27 Tg (7%) from 379 Tg in the present-day. At the surface, decreases in ozone of 6–10 ppb are calculated over the oceans and developed northern hemispheric regions, due to reduced NOx transport by PAN and reductions in NOx emissions in these areas respectively. Increases of 4–6 ppb are calculated in the continental tropics due to cropland expansion in these regions, increased CO2 inhibition of isoprene emissions, and higher temperatures due to climate change. These effects outweigh the decreases in tropical ozone caused by increased tropical isoprene emissions with climate change. Our land use change scenario consists of cropland expansion, which is most pronounced in the tropics. The tropics are also where land use change causes the greatest increases in ozone. As such there is potential for increased crop exposure to harmful levels of ozone. However, we find that these ozone increases are still not large enough to raise ozone to such damaging levels.

Limited response of peatland CH<sub>4</sub> emissions to abrupt Atlantic Ocean circulation changes in glacial climates

Abstract. Ice-core records show that abrupt Dansgaard–Oeschger (D–O) climatic warming events of the last glacial period were accompanied by large increases in the atmospheric CH4 concentration (up to 200 ppbv). These abrupt changes are generally regarded as arising from the effects of changes in the Atlantic Ocean meridional overturning circulation and the resultant climatic impact on natural CH4 sources, in particular wetlands. We use two different ecosystem models of wetland CH4 emissions to simulate northern CH4 sources forced with coupled general circulation model simulations of five different time periods during the last glacial to investigate the potential influence of abrupt ocean circulation changes on atmospheric CH4 levels during D–O events. The simulated warming over Greenland of 7–9 °C in the different time periods is at the lower end of the range of 11–15 °C derived from ice cores, but is associated with strong impacts on the hydrological cycle, especially over the North Atlantic and Europe during winter. We find that although the sensitivity of CH4 emissions to the imposed climate varies significantly between the two ecosystem emissions models, the model simulations do not reproduce sufficient emission changes to satisfy ice-core observations of CH4 increases during abrupt events. The inclusion of permafrost physics and peatland carbon cycling in one model (LPJ-WHyMe) increases the climatic sensitivity of CH44 emissions relative to the Sheffield Dynamic Global Vegetation Model (SDGVM) model, which does not incorporate these processes. For equilibrium conditions this additional sensitivity is mostly due to differences in carbon cycle processes, whilst the increased sensitivity to the imposed abrupt warmings is also partly due to the effects of freezing on soil thermodynamics. These results suggest that alternative scenarios of climatic change could be required to explain the abrupt glacial CH4 variations, perhaps with a more dominant role for tropical wetland CH4 sources.

Probabilistic uncertainty specification: Overview, elaboration techniques and their application to a mechanistic model of carbon flux

It is widely recognised that the appropriate representation for expert judgements of uncertainty is as a probability distribution for the unknown quantity of interest. However, formal elicitation of probability distributions is a non-trivial task. We provide an overview of this field, including an outline of the process of eliciting knowledge from experts in probabilistic form. We explore approaches to probabilistic uncertainty specification including direct elicitation and Bayesian analysis. In particular, we introduce the generic technique of elaboration and present a variety of forms of elaboration, illustrated with a series of examples.The methods are applied to the expression of uncertainty in a case study. Mechanistic models are built in just about every area of science and technology, to represent complex physical processes. They are used to predict, understand and control those processes, and increasingly play a role in national and international policy making. As such models gain higher prominence, recipients of their forecasts are increasingly demanding to know how accurate they are. There is therefore a growing interest in quantifying the uncertainties in model predictions.Uncertainty in model outputs, as representations of reality, arise from uncertainty about model inputs (such as initial conditions, external forcing variables and parameters in model equations) and from uncertainty about model structure.Our case study is based on the Sheffield Dynamic Global Vegetation Model (SDGVM), which is used to estimate the combined carbon flux from vegetation in England and Wales in a given year. The extent to which vegetation acts as a carbon sink is an important component of the debate about climate change. We show how different approaches were used to characterise uncertainty in vegetation model parameters, soil conditions and land cover.

Simulation and evaluation of terrestrial ecosystem NPP with M-SDGVM over continental China

Using the regional terrestrial Net Primary Production (NPP) from different observations and models over China, we validated the NPP simulations and explored the relationship between NPP and climate variation at interannual and decadal scales in the Modified Sheffield Dynamic Global Vegetation Model (M-SDGVM) during 1981–2000. M-SDGVM shows agreement with the NPP data from 743 sites under the Global Primary Production Data Initiative (GPPDI). The spatial and the zonal averaged NPP of M-SDGVM agree well with different historic datasets and are closest to the IGBP NPP. Compared to the 1980s, NPP in the 1990s increases in most of China with a high degree of spatial heterogeneity. The multi-year mean NPP of forest types is reasonably modeled (above 500 g C m−2 yr−1) while that of C3 path of photosynthesis (C3) grasslands is underestimated. The NPP of 7 M-SDGVM main plant functional types (PFTs) increases and the increment of the broad-leaved deciduous forest is the most obvious (5.05 g C m−2 yr−1). During the studied period, the annual NPP of M-SDGVM over China increases, with significant fluctuations, at an average rate of 0.0164 Gt C yr−1. Regulated by annual temperature and precipitation, the interannual variation of the total NPP shows more significant correlation with temperature (relativity and probability are R= 0.61, P = 0.00403) than precipitation (R = 0.40, P = 0.08352). CO2 fertilization may play a key role in the increase of terrestrial ecosystem NPP over continental China, and CO2 stimulation increases with CO2 concentrations, and also with the climate variability of the 1980s and 1990s.

Bayesian emulation of complex multi-output and dynamic computer models

Computer models are widely used in scientific research to study and predict the behaviour of complex systems. The run times of computer-intensive simulators are often such that it is impractical to make the thousands of model runs that are conventionally required for sensitivity analysis, uncertainty analysis or calibration. In response to this problem, highly efficient techniques have recently been developed based on a statistical meta-model (the emulator) that is built to approximate the computer model. The approach, however, is less straightforward for dynamic simulators, designed to represent time-evolving systems. Generalisations of the established methodology to allow for dynamic emulation are here proposed and contrasted. Advantages and difficulties are discussed and illustrated with an application to the Sheffield Dynamic Global Vegetation Model, developed within the UK Centre for Terrestrial Carbon Dynamics.

Sensitivity of the carbon storage of potential vegetation to historical climate variability and CO2 in continental China

The interest in the national levels of the terrestrial carbon sink and its spatial and temporal variability with the climate and CO2 concentrations has been increasing. How the climate and the increasing atmospheric CO2 concentrations in the last century affect the carbon storage in continental China was investigated in this study by using the Modified Sheffield Dynamic Global Vegetation Model (M-SDGVM). The estimates of the M-SDGVM indicated that during the past 100 years a combination of increasing CO2 with historical temperature and precipitation variability in continental China have caused the total vegetation carbon storage to increase by 2.04 Pg C, with 2.07 Pg C gained in the vegetation biomass but 0.03 Pg C lost from the organic soil carbon matter. The increasing CO2 concentration in the 20th century is primarily responsible for the increase of the total potential vegetation carbon. These factorial experiments show that temperature variability alone decreases the total carbon storage by 1.36 Pg C and precipitation variability alone causes a loss of 1.99 Pg C. The effect of the increasing CO2 concentration alone increased the total carbon storage in the potential vegetation of China by 3.22 Pg C over the past 100 years. With the changing of the climate, the CO2 fertilization on China’s ecosystems is the result of the enhanced net biome production (NBP), which is caused by a greater stimulation of the gross primary production (GPP) than the total soil-vegetation respiration. Our study also shows notable interannual and decadal variations in the net carbon exchange between the atmosphere and terrestrial ecosystems in China due to the historical climate variability.

Impact of land cover uncertainties on estimates of biospheric carbon fluxes

Large‐scale bottom‐up estimates of terrestrial carbon fluxes, whether based on models or inventory, are highly dependent on the assumed land cover. Most current land cover and land cover change maps are based on satellite data and are likely to be so for the foreseeable future. However, these maps show large differences, both at the class level and when transformed into Plant Functional Types (PFTs), and these can lead to large differences in terrestrial CO2 fluxes estimated by Dynamic Vegetation Models. In this study the Sheffield Dynamic Global Vegetation Model is used. We compare PFT maps and the resulting fluxes arising from the use of widely available moderate (1 km) resolution satellite‐derived land cover maps (the Global Land Cover 2000 and several MODIS classification schemes), with fluxes calculated using a reference high (25 m) resolution land cover map specific to Great Britain (the Land Cover Map 2000). We demonstrate that uncertainty is introduced into carbon flux calculations by (1) incorrect or uncertain assignment of land cover classes to PFTs; (2) information loss at coarser resolutions; (3) difficulty in discriminating some vegetation types from satellite data. When averaged over Great Britain, modeled CO2 fluxes derived using the different 1 km resolution maps differ from estimates made using the reference map. The ranges of these differences are 254 gC m−2 a−1 in Gross Primary Production (GPP); 133 gC m−2 a−1 in Net Primary Production (NPP); and 43 gC m−2 a−1 in Net Ecosystem Production (NEP). In GPP this accounts for differences of −15.8% to 8.8%. Results for living biomass exhibit a range of 1109 gC m−2. The types of uncertainties due to land cover confusion are likely to be representative of many parts of the world, especially heterogeneous landscapes such as those found in western Europe.

Effects of vegetation map change in MM5 simulations of southern Africa's summer climate

AbstractA regional climate model is used to assess potential climatic impacts of altering the vegetation map of southern Africa from an estimated natural state to present‐day conditions. The Pennsylvania State University‐National Center for Atmospheric Research Mesoscale Model (MM5) is integrated from 1 August to 28 February for 1988/1989, 1991/1992 and 1995/1996 for two spatial vegetation representations: (1) potential natural vegetation as predicted by the Sheffield Dynamic Global Vegetation Model, and (2) current conditions given by the United States Geological Survey land‐surface classification. Significant impacts on mean September–November (SON) and December–February (DJF) surface climate arise from the change in vegetation, the most notable of which is cooling over large parts of the continent in SON, which gives rise to increased large‐scale subsidence and decreased moisture convergence. Resultant decreases in rainfall causes a hydrological feedback through reduced latent heat flux which mitigates the initial cooling and weakens the positive geopotential height bias in DJF. The increase in geopotential height extends vertically up to 500 hPa, which potentially has important implications for regional moisture transport over southern Africa. Copyright © 2008 Royal Meteorological Society

Improvements of a dynamic global vegetation model and simulations of carbon and water at an upland-oak forest

The interest in the development and improvement of dynamic global vegetation models (DGVMs), which have the potential to simulate fluxes of carbon, water and nitrogen, along with changes in the vegetation dynamics, within an integrated system, has been increasing. In this paper, some numerical schemes and a higher resolution soil texture dataset were employed to improve the Sheffield dynamic Global Vegetation Model (SDGVM). Using eddy covariance-based measurements, we then tested the standard version of the SDGVM and the modified version of the SDGVM. Detailed observations of daily carbon and water fluxes made at the upland oak forest on the Walker Branch Watershed in Tennessee, USA offered a unique opportunity for these comparisons. The results revealed that the modified version of the SDGVM did a reasonable job of simulating the carbon and water flux and the variation of soil water content (SWC). However, at the end of the growing season, it failed to simulate the effect of the limitations on the soil respiration dynamics and as a result underestimated this respiration. It was also noted that the modified version overestimated the increase in the SWC following summer rainfall, which was attributed to an inadequate representation of the ground water and thermal cycle.

Constraining the Sheffield dynamic global vegetation model using stream‐flow measurements in the United Kingdom

The biospheric water and carbon cycles are intimately coupled, so simulating carbon fluxes by vegetation also requires modelling of the water fluxes, with each component influencing the other. Observations of river streamflow integrate information at the catchment scale and are widely available over a long period; they therefore provide an important source of information for validating or calibrating vegetation models. In this paper, we analyse the performance of the Sheffield dynamic global vegetation model (SDGVM) for predicting river streamflow and quantifying how this information helps to constrain carbon flux predictions. The SDGVM is run for 29 large catchments in the United Kingdom. Annual streamflow estimates are compared with long time-series observations. In 23 out of the 29 catchments, the bias between model and observations is less than 50 mm, equivalent to less than 10% of precipitation. In the remaining catchments, larger errors are because of combinations of unpredictable causes, in particular various human activities and measurement issues and, in two cases, unidentified causes. In one of the catchments, we assess to what extent a knowledge of annual streamflow can constrain model parameters and in turn constrain estimates of gross primary production (GPP). For this purpose, we assume the model parameters are uncertain and constrain them by the streamflow observations using the generalized likelihood uncertainty estimation method. Comparing the probability density function of GPP with and without constraint shows that streamflow effectively constrains GPP, mainly by setting a low probability to GPP values below about 1100 g C-1 m2 yr-1 . In other words, streamflow observations allow the rejection of low values of GPP, so that the potential range of possible GPP values is almost halved.

Bud-burst modelling in Siberia and its impact on quantifying the carbon budget.

Vegetation phenology is affected by climate change and in turn feeds back on climate by affecting the annual carbon uptake by vegetation. To quantify the impact of phenology on terrestrial carbon fluxes, we calibrate a bud-burst model and embed it in the Sheffield dynamic global vegetation model (SDGVM) in order to perform carbon budget calculations. Bud-burst dates derived from the VEGETATION sensor onboard the SPOT-4 satellite are used to calibrate a range of bud-burst models. This dataset has been recently developed using a new methodology based on the normalized difference water index, which is able to distinguish snowmelt from the onset of vegetation activity after winter. After calibration, a simple spring warming model was found to perform as well as more complex models accounting for a chilling requirement, and hence it was used for the carbon flux calculations. The root mean square difference (RMSD) between the calibrated model and the VEGETATION dataset was 6.5 days, and was 6.9 days between the calibrated model and independent ground observations of bud-burst available at nine locations over Siberia. The effects of bud-burst model uncertainties on the carbon budget were evaluated using the SDGVM. The 6.5 days RMSD in the bud-burst date (a 6% variation in the growing season length), treated as a random noise, translates into about 41 g cm-2 yr-1 in net primary production (NPP), which corresponds to 8% of the mean NPP. This is a moderate impact and suggests the calibrated model is accurate enough for carbon budget calculations. In addition to random differences between the calibrated model and VEGETATION data, systematic errors between the calibrated bud-burst model and true ground behaviour may occur, because of bias in the temperature dataset or because the bud-burst detected by VEGETATION is because of some other phenological indicator. A systematic error of 1 day in bud-burst translates into a 10 g cm-2 yr-1 error in NPP (about 2%). Based on the limited available ground data, any systematic error because of the use of VEGETATION data should not lead to significant errors in the calculated carbon flux. In contrast, widely used methods based on the normalized difference vegetation index from the advanced very high resolution radiometer satellite are likely to confuse snowmelt and vegetation greening, leading to errors of up to 15 days in bud-burst date, with consequent large errors in carbon flux calculations.

Relating Radar Remote Sensing of Biomass to Modelling of Forest Carbon Budgets

This paper addresses the use of radar remote sensing to map forest above-ground biomass, and discusses the use of biomass maps to test a dynamic vegetation model that identifies carbon sources and sinks and predicts their variation over time. For current radar satellite data, only the biomass of young/sparse forests or regrowth after disturbances can be recovered. An example from central Siberia illustrates that biomass can be measured by radar at a continental scale, and that a significant proportion of the Siberian forests have biomass values less than 50 tonnes/ha. Comparison between the radar map and calculations by the Sheffield Dynamic Global Vegetation Model (SDGVM) indicates that the model considerably overestimates biomass; under-representation of managed areas, disturbed areas and areas of low site quality in the model are suggested reasons for this effect. A case study carried out at the Budingen plantation forest in Germany supports the argument that inadequate representations of site quality and forest management may cause model overestimates of biomass. Comparison of the calculated biomass of stands planted after 1990 with biomass estimates by radar allows identification of forest stands where the growth conditions assumed by the model are not valid. This allows a quality check on model calculations of carbon fluxes: only calculations for stands where there is good agreement between the data and the model predictions should be accepted. Although the paper only uses the SDGVM model, similar effects are likely in other dynamic vegetation models, and the results show that model calculations attempting to quantify the role of forests as carbon sources or sinks could be qualified and potentially improved by exploiting remotely sensed measurements of biomass.

Vegetation dynamics--simulating responses to climatic change.

A modelling approach to simulating vegetation dynamics is described, incorporating critical processes of carbon sequestration, growth, mortality and distribution. The model has been developed to investigate the responses of vegetation to environmental change, at time scales from days to centuries and from the local to the global scale. The model is outlined and subsequent tests, against independent data sources, are relatively successful, from the small scale to the global scale. Tests against eddy covariance observations of carbon exchange by vegetation indicated significant differences between measured and simulated net ecosystem production (NEP). NEP is the net of large fluxes due to gross primary production and respiration, which are not directly measured and so there is some uncertainty in explaining differences between observations and simulations. In addition it was noted that closer agreement of fluxes was achieved for natural, or long-lived managed vegetation than for recently managed vegetation. The discrepancies appear to be most closely related to respiratory carbon losses from the soil, but this area needs further exploration. The differences do not scale up to the global scale, where simulated and measured global net biome production were similar, indicating that fluxes measured at the managed observed sites are not typical globally. The model (the Sheffield Dynamic Global Vegetation Model, SDGVM) has been applied to contemporary vegetation dynamics and indicates a significant CO2 fertilisation effect on the sequestration of atmospheric CO2. The terrestrial carbon sink for the 20th century is simulated to be widespread between latitudes 40 degrees S and 65 degrees N, but is greatest between 10 degrees S and 6 degrees N, excluding the effects of human deforestation. The mean maximum sink capacity over the 20th century is small, at 25 gC m(-2) year(-1), or approximately 1% of gross primary production. Simulations of vegetation dynamics under a scenario of future global warming indicate a gradual decline in the terrestrial carbon sink, with the capacity to absorb human emissions of CO2 being reduced from 20% in 2000 to approximately 2% between 2075 and 2100. The responses of carbon sequestration and vegetation structure and distribution to stabilisation of climate and CO2 may extend for up to 50 years after stabilisation has occurred.

Simulating vegetation processes along the Kalahari transect

AbstractThe Sheffield Dynamic Global Vegetation Model has simulated the structure and net carbon exchange of vegetation at five sites along the Kalahari transect where there is a strong gradient in precipitation from 299 to 918 mm yr−1. There has been a decline in precipitation of 8 mm yr−1 along the whole of the transect since about 1970. Simulations of vegetation dynamics and structure indicate that this decline has exerted a notable effect on the vegetation, with reductions in woody plant cover at the dry end of the transect and reductions in tree density at the wetter end. These changes were driven primarily by reductions in the net primary production and increased rates of mortality, with rather small impacts of fire.

Global terrestrial productivity in the Mesozoic era

Abstract This paper describes a global-scale modelling approach for investigating the structure and productivity of terrestrial vegetation in the Mesozoic era. General circulation model (GCM) palaeoclimate simulations (by the University of Reading) for Kimmeridgian and Cenomanian time are described as driving datasets for the University of Sheffield Dynamic Global Vegetation Model (SDGVM). The validity of these GCM palaeoclimates is reviewed. Global patterns of terrestrial net primary productivity (NPP) and leaf area index (LAI) have been simulated with the SDGVM, which models plant physiological processes and the biogeochemical cycling of carbon and nitrogen in soils and vegetation. An important feature of this modelling approach is that it requires no underlying map of soils or vegetation type. Global NPP in Kimmeridgian and Cenomanian time was high (117.6 and 106.8 Gt Ca −1 , respectively) compared with the present-day level (57.0 Gt Ca −1 ). The high concentrations of atmospheric CO 2 at each interval significantly influenced the NPP and LAI of Mesozoic vegetation, to an extent dependent on climate. CO 2 effects on the structure of vegetation in Kimmeridgian and Cenomanian time were sufficient to markedly influence key land surface variables required by GCMs for climate predictions, and point to the need to include direct CO 2 -vegetation interactions in climate models. Two approaches to testing the modelled NPP patterns were devised, one using palaeobotanical information on tree growth, and the other based on a comparison of measurements and predictions of the stable carbon isotope ratios of fossil plants. Both approaches provided some support for the global-scale simulations, indicating the feasibility of modelling vegetation activity in ancient climates from a knowledge of present-day processes.