Global Terrestrial Biosphere Models Research Articles

Optimizing Carbon Cycle Parameters Drastically Improves Terrestrial Biosphere Model Underestimates of Dryland Mean Net CO2 Flux and its Inter‐Annual Variability

AbstractDrylands occupy ∼40% of the land surface and are thought to dominate global carbon (C) cycle inter‐annual variability (IAV). Therefore, it is imperative that global terrestrial biosphere models (TBMs), which form the land component of IPCC earth system models, are able to accurately simulate dryland vegetation and biogeochemical processes. However, compared to more mesic ecosystems, TBMs have not been widely tested or optimized using in situ dryland CO2 fluxes. Here, we address this gap using a Bayesian data assimilation system and 89 site‐years of daily net ecosystem exchange (NEE) data from 12 southwest US Ameriflux sites to optimize the C cycle parameters of the ORCHIDEE TBM. The sites span high elevation forest ecosystems, which are a mean sink of C, and low elevation shrub and grass ecosystems that are either a mean C sink or “pivot” between an annual C sink and source. We find that using the default (prior) model parameters drastically underestimates both the mean annual NEE at the forested mean C sink sites and the NEE IAV across all sites. Our analysis demonstrated that optimizing phenology parameters are particularly useful in improving the model's ability to capture both the magnitude and sign of the NEE IAV. At the forest sites, optimizing C allocation, respiration, and biomass and soil C turnover parameters reduces the underestimate in simulated mean annual NEE. Our study demonstrates that all TBMs need to be calibrated for dryland ecosystems before they are used to determine dryland contributions to global C cycle variability and long‐term carbon‐climate feedbacks.

Global hunter-gatherer population densities constrained by influence of seasonality on diet composition.

The dependence of hunter-gatherers on local net primary production (NPP) to provide food played a major role in shaping long-term human population dynamics. Observations of contemporary hunter-gatherers have shown an overall correlation between population density and annual NPP, but with a thousand-fold variation in population density per unit NPP that remains unexplained. Here we build a process-based hunter-gatherer population model embedded within a global terrestrial biosphere model, which explicitly addresses the extraction of NPP through dynamically-allocated hunting and gathering activities. The emergent results reveal a strong, previously unrecognized effect of seasonality on population density via diet composition, whereby hunter-gatherers consume high fractions of meat in regions where growing seasons are short, leading to greatly reduced population density due to trophic inefficiency. This seasonal carnivory bottleneck largely explains the wide variation in population density per unit NPP, and questions the prevailing usage of annual NPP as the proxy of carrying capacity for ancient humans. Our process-based approach has the potential to greatly refine our understanding of dynamical responses of ancient human populations to past environmental changes.

Moisture availability mediates the relationship between terrestrial gross primary production and solar-induced chlorophyll fluorescence: Insights from global-scale variations.

Effective use of solar-induced chlorophyll fluorescence (SIF) to estimate and monitor gross primary production (GPP) in terrestrial ecosystems requires a comprehensive understanding and quantification of the relationship between SIF and GPP. To date, this understanding is incomplete and somewhat controversial in the literature. Here we derived the GPP/SIF ratio from multiple data sources as a diagnostic metric to explore its global-scale patterns of spatial variation and potential climatic dependence. We found that the growing season GPP/SIF ratio varied substantially across global land surfaces, with the highest ratios consistently found in boreal regions. Spatial variation in GPP/SIF was strongly modulated by climate variables. The most striking pattern was a consistent decrease in GPP/SIF from cold-and-wet climates to hot-and-dry climates. We propose that the reduction in GPP/SIF with decreasing moisture availability may be related to stomatal responses to aridity. Furthermore, we show that GPP/SIF can be empirically modeled from climate variables using a machine learning (random forest) framework, which can improve the modeling of ecosystem production and quantify its uncertainty in global terrestrial biosphere models. Our results point to the need for targeted field and experimental studies to better understand the patterns observed and to improve the modeling of the relationship between SIF and GPP over broad scales.

Simulating spatially distributed solar-induced chlorophyll fluorescence using a BEPS-SCOPE coupling framework

Remotely sensed solar-induced chlorophyll fluorescence (SIF) has been increasingly used to probe photosynthesis and model the gross primary productivity (GPP). Although SIF at the top of canopy (TOC) can be simulated using the coupled photosynthesis-fluorescence model SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes), simulating spatially distributed TOC SIF usually requires extensive calculations, entailing some challenges when applying the model to the regional and the global scales. This study puts forward a coupling framework that combines SIF and global terrestrial biosphere models (TBMs). The theory for fluorescence emissions and the fluorescence radiative transfer algorithm described in the SCOPE model were integrated with the “two-leaf”-based BEPS (Boreal Ecosystem Productivity Simulator) model. To simplify the fluorescence radiative transfer physics, we put forward a canopy-averaged leaf-level fluorescence to represent the fluorescence emitted from sunlit and shaded leaf groups and performed a sensitivity analysis to assess the determining factors in upscaling fluorescence from leaf scale to canopy scale. We found that the relationship between the leaf and canopy fluorescence at 740 nm was mainly affected by LAI. Although brown pigments and leaf inclination angle demonstrate some impacts on the scaling process, an LAI-based coefficient can well characterize the upscaling from leaf to canopy scale. Since our BEPS-SCOPE coupling model deploys the sunlit-shaded leaf separation strategy, we expect that it can efficiently characterize the nonlinear responses of photosynthesis and the associated fluorescence to environmental factors. The performance of our model was evaluated at both site and global scales, which demonstrated a good performance for most plant functional types (PFTs) except for needleleaf types that have a more clumped nature. Apart from these limitations, the presented model can contribute to efficiently simulating SIF at regional and global scales, and has the potential to reduce uncertainties in GPP estimation.

Wide discrepancies in the magnitude and direction of modeled solar-induced chlorophyll fluorescence in response to light conditions

Abstract. Recent successes in passive remote sensing of far-red solar-induced chlorophyll fluorescence (SIF) have spurred the development and integration of canopy-level fluorescence models in global terrestrial biosphere models (TBMs) for climate and carbon cycle research. The interaction of fluorescence with photochemistry at the leaf and canopy scales provides opportunities to diagnose and constrain model simulations of photosynthesis and related processes, through direct comparison to and assimilation of tower, airborne, and satellite data. TBMs describe key processes related to the absorption of sunlight, leaf-level fluorescence emission, scattering, and reabsorption throughout the canopy. Here, we analyze simulations from an ensemble of process-based TBM–SIF models (SiB3 – Simple Biosphere Model, SiB4, CLM4.5 – Community Land Model, CLM5.0, BETHY – Biosphere Energy Transfer Hydrology, ORCHIDEE – Organizing Carbon and Hydrology In Dynamic Ecosystems, and BEPS – Boreal Ecosystems Productivity Simulator) and the SCOPE (Soil Canopy Observation Photosynthesis Energy) canopy radiation and vegetation model at a subalpine evergreen needleleaf forest near Niwot Ridge, Colorado. These models are forced with local meteorology and analyzed against tower-based continuous far-red SIF and gross-primary-productivity-partitioned (GPP) eddy covariance data at diurnal and synoptic scales during the growing season (July–August 2017). Our primary objective is to summarize the site-level state of the art in TBM–SIF modeling over a relatively short time period (summer) when light, canopy structure, and pigments are similar, setting the stage for regional- to global-scale analyses. We find that these models are generally well constrained in simulating photosynthetic yield but show strongly divergent patterns in the simulation of absorbed photosynthetic active radiation (PAR), absolute GPP and fluorescence, quantum yields, and light response at the leaf and canopy scales. This study highlights the need for mechanistic modeling of nonphotochemical quenching in stressed and unstressed environments and improved the representation of light absorption (APAR), distribution of light across sunlit and shaded leaves, and radiative transfer from the leaf to the canopy scale.

Wintertime grassland dynamics may influence belowground biomass under climate change: a model analysis

Abstract. Rising temperatures and changes in snow cover, as can be expected under a warmer global climate, may have large impacts on mountain grassland productivity limited by cold and long winters. Here, we combined two existing models, the multi-layer atmosphere-SOiL-VEGetation model (SOLVEG) and the BASic GRAssland model (BASGRA), which accounts for snow, freeze–thaw events, grass growth, and soil carbon balance. The model was applied to simulate the responses of managed grasslands to anomalously warm winter conditions. The grass growth module considered key ecological processes under a cold environment, such as leaf formation, elongation and death, tillering, carbon allocation, and cold acclimation, in terms of photosynthetic activity. Input parameters were derived for two pre-Alpine grassland sites in Germany, for which the model was run using 3 years of data that included a winter with an exceptionally small amount of snow. The model reproduced the temporal variability of observed daily mean heat fluxes, soil temperatures, and snow depth throughout the study period. High physiological activity levels during the extremely warm winter led to a simulated CO2 uptake of 100 gC m−2, which was mainly allocated into the belowground biomass and only to a minor extent used for additional plant growth during early spring. If these temporary dynamics are representative of long-term changes, this process, which is so far largely unaccounted for in scenario analysis using global terrestrial biosphere models, may lead to carbon accumulation in the soil and/or carbon loss from the soil as a response to global warming.

Improving Estimates of Gross Primary Productivity by Assimilating Solar‐Induced Fluorescence Satellite Retrievals in a Terrestrial Biosphere Model Using a Process‐Based SIF Model

AbstractOver the last few years, solar‐induced chlorophyll fluorescence (SIF) observations from space have emerged as a promising resource for evaluating the spatio‐temporal distribution of gross primary productivity (GPP) simulated by global terrestrial biosphere models. SIF can be used to improve GPP simulations by optimizing critical model parameters through statistical Bayesian data assimilation techniques. A prerequisite is the availability of a functional link between GPP and SIF in terrestrial biosphere models. Here we present the development of a mechanistic SIF observation operator in the ORCHIDEE (Organizing Carbon and Hydrology In Dynamic Ecosystems) terrestrial biosphere model. It simulates the regulation of photosystem II fluorescence quantum yield at the leaf level thanks to a novel parameterization of non‐photochemical quenching as a function of temperature, photosynthetically active radiation, and normalized quantum yield of photochemistry. It emulates the radiative transfer of chlorophyll fluorescence to the top of the canopy using a parametric simplification of the SCOPE (Soil Canopy Observation Photosynthesis Energy) model. We assimilate two years of monthly OCO‐2 (Orbiting Carbon Observatory‐2) SIF product at 0.5° (2015–2016) to optimize ORCHIDEE photosynthesis and phenological parameters over an ensemble of grid points for all plant functional types. The impact on the simulated GPP is considerable with a large decrease of the global scale budget by 28 GtC/year over the period 1990–2009. The optimized GPP budget (134/136 GtC/year over 1990–2009/2001–2009) remarkably agrees with independent GPP estimates, FLUXSAT (137 GtC/year over 2001–2009) in particular and FLUXCOM (121 GtC/year over 1990–2009). Our results also suggest a biome dependency of the SIF‐GPP relationship that needs to be improved for some plant functional types.

A successful prediction of the record CO2 rise associated with the 2015/2016 El Niño.

In early 2016, we predicted that the annual rise in carbon dioxide concentration at Mauna Loa would be the largest on record. Our forecast used a statistical relationship between observed and forecast sea surface temperatures in the Niño 3.4 region and the annual CO2 rise. Here, we provide a formal verification of that forecast. The observed rise of 3.4 ppm relative to 2015 was within the forecast range of 3.15 ± 0.53 ppm, so the prediction was successful. A global terrestrial biosphere model supports the expectation that the El Niño weakened the tropical land carbon sink. We estimate that the El Niño contributed approximately 25% to the record rise in CO2, with 75% due to anthropogenic emissions. The 2015/2016 CO2 rise was greater than that following the previous large El Niño in 1997/1998, because anthropogenic emissions had increased. We had also correctly predicted that 2016 would be the first year with monthly mean CO2 above 400 ppm all year round. We now estimate that atmospheric CO2 at Mauna Loa would have remained above 400 ppm all year round in 2016 even if the El Niño had not occurred, contrary to our previous expectations based on a simple extrapolation of previous trends.This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

Satellite Chlorophyll Fluorescence and Soil Moisture Observations Lead to Advances in the Predictive Understanding of Global Terrestrial Coupled Carbon‐Water Cycles

AbstractThe terrestrial carbon and water cycles are coupled through a multitude of connected processes among soil, roots, leaves, and the atmosphere. The strength and sensitivity of these couplings are not yet well known at the global scale, which contributes to uncertainty in predicting the terrestrial water and carbon budgets. We now have synchronous, global‐scale satellite observations of critical terrestrial carbon and water cycle components: solar‐induced chlorophyll fluorescence (SIF) and soil moisture. We used these observations within the framework of a global terrestrial biosphere model (Simplified Simple Biosphere Model version 2.0, SSiB2) to investigate carbon‐water coupling processes. We updated SSiB2 to include a mechanistic representation of SIF and tested the sensitivity of model parameters to improve the simulation of both SIF and soil moisture with the ultimate objective of improving the first‐order terrestrial carbon component, gross primary production. Although several vegetation parameters, such as leaf area index and the green leaf fraction, improved the simulated SIF, and several soil parameters, such as hydraulic conductivity, improved simulated soil moisture, their effects were mainly limited to their respective cycles. One root‐mean‐square error parameter emerged as the key coupler between the carbon and water cycles: the wilting point. Updates to the wilting point significantly improved the simulations for SIF and gross primary production although substantial mismatches with the satellite data still existed. This study demonstrates the value of synchronous global measurements of the terrestrial carbon and water cycles in improving the understanding of coupled carbon‐water cycles.

Development and evaluation of an ozone deposition scheme for coupling to a terrestrial biosphere model

Abstract. Ozone (O3) is a toxic air pollutant that can damage plant leaves and substantially affect the plant's gross primary production (GPP) and health. Realistic estimates of the effects of tropospheric anthropogenic O3 on GPP are thus potentially important to assess the strength of the terrestrial biosphere as a carbon sink. To better understand the impact of ozone damage on the terrestrial carbon cycle, we developed a module to estimate O3 uptake and damage of plants for a state-of-the-art global terrestrial biosphere model called OCN. Our approach accounts for ozone damage by calculating (a) O3 transport from 45 m height to leaf level, (b) O3 flux into the leaf, and (c) ozone damage of photosynthesis as a function of the accumulated O3 uptake over the lifetime of a leaf. A comparison of modelled canopy conductance, GPP, and latent heat to FLUXNET data across European forest and grassland sites shows a general good performance of OCN including ozone damage. This comparison provides a good baseline on top of which ozone damage can be evaluated. In comparison to literature values, we demonstrate that the new model version produces realistic O3 surface resistances, O3 deposition velocities, and stomatal to total O3 flux ratios. A sensitivity study reveals that key metrics of the air-to-leaf O3 transport and O3 deposition, in particular the stomatal O3 uptake, are reasonably robust against uncertainty in the underlying parameterisation of the deposition scheme. Nevertheless, correctly estimating canopy conductance plays a pivotal role in the estimate of cumulative O3 uptake. We further find that accounting for stomatal and non-stomatal uptake processes substantially affects simulated plant O3 uptake and accumulation, because aerodynamic resistance and non-stomatal O3 destruction reduce the predicted leaf-level O3 concentrations. Ozone impacts on GPP and transpiration in a Europe-wide simulation indicate that tropospheric O3 impacts the regional carbon and water cycling less than expected from previous studies. This study presents a first step towards the integration of atmospheric chemistry and ecosystem dynamics modelling, which would allow for assessing the wider feedbacks between vegetation ozone uptake and tropospheric ozone burden.

Carbon cost of plant nitrogen acquisition: global carbon cycle impact from an improved plant nitrogen cycle in theCommunity Land Model.

Plants typically expend a significant portion of their available carbon (C) on nutrient acquisition - C that could otherwise support growth. However, given that most global terrestrial biosphere models (TBMs) do not include the C cost of nutrient acquisition, these models fail to represent current and future constraints to the land C sink. Here, we integrated a plant productivity-optimized nutrient acquisition model - the Fixation and Uptake of Nitrogen Model - into one of the most widely used TBMs, the Community Land Model. Global plant nitrogen (N) uptake is dynamically simulated in the coupled model based on the C costs of N acquisition from mycorrhizal roots, nonmycorrhizal roots,N-fixing microbes, and retranslocation (from senescing leaves). We find that at the global scale, plants spend 2.4Pg C yr(-1) to acquire 1.0 Pg Nyr(-1) , and that the C cost of N acquisition leads to a downregulation of global net primary production (NPP) by 13%. Mycorrhizal uptake represented the dominant pathway by which N is acquired, accounting for ~66% of the N uptake by plants. Notably, roots associating with arbuscular mycorrhizal (AM) fungi - generally considered for their role in phosphorus (P) acquisition - are estimated to be the primary source of global plant N uptake owing to the dominance of AM-associated plants in mid- and low-latitude biomes. Overall, our coupled model improves the representations of NPP downregulation globally and generates spatially explicit patterns of belowground C allocation, soil N uptake, and N retranslocation at the global scale. Such model improvements are critical for predicting how plant responses to altered N availability (owing to N deposition, rising atmospheric CO2 , and warming temperatures) may impact the land C sink.

Using satellite data to improve the leaf phenology of a global terrestrial biosphere model

Abstract. Correct representation of seasonal leaf dynamics is crucial for terrestrial biosphere models (TBMs), but many such models cannot accurately reproduce observations of leaf onset and senescence. Here we optimised the phenology-related parameters of the ORCHIDEE TBM using satellite-derived Normalized Difference Vegetation Index data (MODIS NDVI v5) that are linearly related to the model fAPAR. We found the misfit between the observations and the model decreased after optimisation for all boreal and temperate deciduous plant functional types, primarily due to an earlier onset of leaf senescence. The model bias was only partially reduced for tropical deciduous trees and no improvement was seen for natural C4 grasses. Spatial validation demonstrated the generality of the posterior parameters for use in global simulations, with an increase in global median correlation of 0.56 to 0.67. The simulated global mean annual gross primary productivity (GPP) decreased by ~ 10 PgC yr−1 over the 1990–2010 period due to the substantially shortened growing season length (GSL – by up to 30 days in the Northern Hemisphere), thus reducing the positive bias and improving the seasonal dynamics of ORCHIDEE compared to independent data-based estimates. Finally, the optimisations led to changes in the strength and location of the trends in the simulated vegetation productivity as represented by the GSL and mean annual fraction of absorbed photosynthetically active radiation (fAPAR), suggesting care should be taken when using un-calibrated models in attribution studies. We suggest that the framework presented here can be applied for improving the phenology of all global TBMs.

Does the integration of the dynamic nitrogen cycle in a terrestrial biosphere model improve the long-term trend of the leaf area index?

The carbon cycle strongly interacts with the nitrogen cycle. Several observations show that the effects of global change on primary production and carbon storage in plant biomass and soils are partially controlled by N availability. Nevertheless, only a small number of terres- trial biosphere models represent explicitly the nitrogen cycle, despite its importance on the carbon cycle and on climate. These models are difficult to evaluate at large spatiotemporal scales because of the scarcity of data at the global scale over a long time period. In this study, we benchmark the capacity of the O-CN global terrestrial biosphere model to reproduce temporal changes in leaf area index (LAI) at the global scale observed by NOAA_AV- HRR satellites over the period 1982-2002. Using a satellite LAI product based on the normalized difference vegetation index of global inventory monitoring and modelling studies dataset, we estimate the long-term trend of LAI and we compare it with the results from the terrestrial biosphere models, either with (O-CN) or without (O-C) a dynamic nitrogen cycle coupled to the carbon-water-energy cycles. In boreal and temperate regions, including a dynamic N cycle (O-CN) improved the fit between observed and modeled temporal changes in LAI. In contrast, in the tro- pics, simulated LAI from the model without the dynamic N cycle (O-C) better matched observed changes in LAI over time. Despite differential regional trends, the satellite estimate suggests an increase in the global average LAI during 1982-2002 by 0.0020 m 2 m -2 y -1 . Both versions of the model substantially overestimated the rate of change in LAI over time (0.0065 m 2 m -2 y -1 for O-C and 0.0057 m 2 m -2 y -1 for O-CN), suggesting that some additional limitation mechanisms are missing in the model. We also estimated the relative importance of climate, CO2 and N deposition as potential drivers of the temporal changes in LAI. We found that recent climate change better explained temporal changes in LAI when the dynamic N cycle was included in the model (higher ranked fit for O-CN vs. O-C). Using the O-C configuration to estimate the direct effect of climate on LAI, we quantified the importance of climate-N cycle feedbacks in explaining the LAI response. We found that the warming-induced release of N from soil organic matter decomposition explains 17.5 % of the global trend in LAI over time, however, reaching up to 40.9 % explained variance in the boreal zone, which is a more important contribution than increasing anthropogenic nitrogen deposition. Our analysis supports a strong connection between warming, N cycling, and vegetation productivity. These findings underscore the importance of including N cycling in global-scale models of vegetation response to environmental change.

THE IMPORTANCE OF AGE-RELATED DECLINE IN FOREST NPP FOR MODELING REGIONAL CARBON BALANCES

We show the implications of the commonly observed age-related decline in aboveground productivity of forests, and hence forest age structure, on the carbon dynamics of European forests in response to historical changes in environmental conditions. Size-dependent carbon allocation in trees to counteract increasing hydraulic resistance with tree height has been hypothesized to be responsible for this decline. Incorporated into a global terrestrial biosphere model (the Lund-Potsdam-Jena model, LPJ), this hypothesis improves the simulated increase in biomass with stand age. Application of the advanced model, including a generic representation of forest management in even-aged stands, for 77 European provinces shows that model-based estimates of biomass development with age compare favorably with inventory-based estimates for different tree species. Model estimates of biomass densities on province and country levels, and trends in growth increment along an annual mean temperature gradient are in broad agreement with inventory data. However, the level of agreement between modeled and inventory-based estimates varies markedly between countries and provinces. The model is able to reproduce the present-day age structure of forests and the ratio of biomass removals to increment on a European scale based on observed changes in climate, atmospheric CO2 concentration, forest area, and wood demand between 1948 and 2000. Vegetation in European forests is modeled to sequester carbon at a rate of 100 Tg C/yr, which corresponds well to forest inventory-based estimates.

Inter-annual variability of the atmospheric carbon dioxide concentrations as simulated with global terrestrial biosphere models and an atmospheric transport model

Seasonal and inter-annual variations of atmospheric CO2 for the period from 1961 to 1997 have been simulated using a global tracer transport model driven by a new version of the Biome BioGeochemical Cycle model (Biome-BGC). Biome-BGC was forced by daily temperature and precipitation from the NCEP reanalysis dataset, and the calculated monthly-averaged CO2 fluxes were used as input to the global transport model. Results from an inter-comparison with the Carnegie—Ames—Stanford Approach model (CASA) and the Simulation model of Carbon cYCle in Land Ecosystems (Sim-CYCLE) model are also reported. The phase of the seasonal cycle in the Northern Hemisphere was reproduced generally well by Biome-BGC, although the amplitude was smaller compared to the observations and to the other biosphere models. The CO2 time series simulated by Biome-BGC were compared to the global CO2 concentration anomalies from the observations at Mauna Loa and the South Pole. The modeled concentration anomalies matched the phase of the inter-annual variations in the atmospheric CO2 observations; however, the modeled amplitude was lower than the observed value in several cases. The result suggests that a significant part of the inter-annual variability in the global carbon cycle can be accounted for by the terrestrial biosphere models. Simulations performed with another climate-based model, Sim-CYCLE, produced a larger amplitude of inter-annual variability in atmospheric CO2, making the amplitude closer to the observed range, but with a more visible phase mismatch in a number of time periods. This may indicate the need to increase the Biome-BGC model sensitivity to seasonal and inter-annual changes in temperature and precipitation.

Inter-annual variability of the atmospheric carbon dioxide concentrations as simulated with global terrestrial biosphere models and an atmospheric transport model

Seasonal and inter-annual variations of atmospheric CO2 for the period from 1961 to 1997 have been simulated using a global tracer transport model driven by a new version of the Biome BioGeochemical Cycle model (Biome-BGC). Biome-BGC was forced by daily temperature and precipitation from the NCEP reanalysis dataset, and the calculated monthly-averaged CO2 fluxes were used as input to the global transport model. Results from an inter-comparison with the Carnegie—Ames—Stanford Approach model (CASA) and the Simulation model of Carbon cYCle in Land Ecosystems (Sim-CYCLE) model are also reported. The phase of the seasonal cycle in the Northern Hemisphere was reproduced generally well by Biome-BGC, although the amplitude was smaller compared to the observations and to the other biosphere models. The CO2 time series simulated by Biome-BGC were compared to the global CO2concentration anomalies from the observations at Mauna Loa and the South Pole. The modeled concentration anomalies matched the phase of the inter-annual variations in the atmospheric CO2 observations; however, the modeled amplitude was lower than the observed value in several cases. The result suggests that a significant part of the inter-annual variability in the global carbon cycle can be accounted for by the terrestrial biosphere models. Simulations performed with another climate-based model, Sim-CYCLE, produced a larger amplitude of inter-annual variability in atmospheric CO2, making the amplitude closer to the observed range, but with a more visible phase mismatch in a number of time periods. This may indicate the need to increase the Biome-BGC model sensitivity to seasonal and inter-annual changes in temperature and precipitation.

Sources of Variability in Net Primary Production Predictions at a Regional Scale: A Comparison Using PnET-II and TEM 4.0 in Northeastern US Forests

Because model predictions at continental and global scales are necessarily based on broad characterizations of vegetation, soils, and climate, estimates of carbon stocks and fluxes made by global terrestrial biosphere models may not be accurate for every region. At the regional scale, we suggest that attention can be focused more clearly on understanding the relative strengths of predicted net primary productivity (NPP) limitation by energy, water, and nutrients. We evaluate the sources of variability among model predictions of NPP with a regional-scale comparison between estimates made by PnET-II (a forest ecosystem process model previously applied to the northeastern region) and TEM 4.0 (a terrestrial biosphere model typically applied to the globe) for the northeastern US. When the same climate, vegetation, and soil data sets were used to drive both models, regional average NPP predictions made by PnET-II and TEM were remarkably similar, and at the biome level, model predictions agreed fairly well with NPP estimates developed from field measurements. However, TEM 4.0 predictions were more sensitive to regional variations in temperature as a result of feedbacks between temperature and belowground N availability. In PnET-II, the direct link between transpiration and photosynthesis caused substantial water stress in hardwood and pine forest types with increases in solar radiation; predicted water stress was relieved substantially when soil water holding capacity (WHC) was increased. Increasing soil WHC had little effect on TEM 4.0 predictions because soil water storage was already sufficient to meet plant demand with baseline WHC values, and because predicted N availability under baseline conditions in this region was not limited by water. Because NPP predictions were closely keyed to forest cover type, the relative coverage of low- versus high-productivity forests at both fine and coarse resolutions was an important determinant of regional NPP predictions. Therefore, changes in grid cell size and differences in the methods used to aggregate from fine to coarse resolution were important to NPP predictions insofar as they changed the relative proportions of forest cover. We suggest that because the small patches of high-elevation spruce-fir forest in this region are substantially less productive than forests in the remainder of the region, more accurate NPP predictions will result if models applied to this region use land cover input data sets that retain as much fine-resolution forest type variability as possible. The differences among model responses to variations in climate and soil WHC data sets suggest that the models will respond quite differently to scenarios of future climate. A better understanding of the dynamic interactions between water stress, N availability, and forest productivity in this region will enable models to make more accurate predictions of future carbon stocks and fluxes.

A method of determining rooting depth from a terrestrial biosphere model and its impacts on the global water and carbon cycle

AbstractWe outline a method of inferring rooting depth from a Terrestrial Biosphere Model by maximizing the benefit of the vegetation within the model. This corresponds to the evolutionary principle that vegetation has adapted to make best use of its local environment. We demonstrate this method with a simple coupled biosphere/soil hydrology model and find that deep rooted vegetation is predicted in most parts of the tropics. Even with a simple model like the one we use, it is possible to reproduce biome averages of observations fairly well. By using the optimized rooting depths global Annual Net Primary Production (and transpiration) increases substantially compared to a standard rooting depth of one meter, especially in tropical regions that have a dry season. The decreased river discharge due to the enhanced evaporation complies better with observations. We also found that the optimization process is primarily driven by the water deficit/surplus during the dry/wet season for humid and arid regions, respectively. Climate variability further enhances rooting depth estimates. In a sensitivity analysis where we simulate changes in the water use efficiency of the vegetation we find that vegetation with an optimized rooting depth is less vulnerable to variations in the forcing. We see the main application of this method in the modelling communities of land surface schemes of General Circulation Models and of global Terrestrial Biosphere Models. We conclude that in these models, the increased soil water storage is likely to have a significant impact on the simulated climate and the carbon budget, respectively. Also, effects of land use change like tropical deforestation are likely to be larger than previously thought.

Historical variations in terrestrial biospheric carbon storage

Changes in carbon storage in terrestrial ecosystems are a consequence of shifts in the balance between net primary production (NPP) and heterotrophic respiration (RH). Historical climatic variations which favored NPP over RH may have led to increased ecosystem carbon storage and might account for at least part of the “missing” sink required to balance the current century's global carbon budget. To test this hypothesis, we employed a georeferenced global terrestrial biosphere model of 0.5° spatial resolution. The model was driven from an assumed equilibrium in 1900 using gridded historical time series of monthly temperature and precipitation and the historical record of changes in atmospheric CO2 concentration. Interannual variability in climate induced interannual changes in terrestrial biospheric carbon storage and net carbon exchange with the atmosphere of the order of 1–2 Gt C yr−1. With climate change alone, global biospheric carbon storage declined by 1% (23 Gt C) over the period 1900–1988. With the addition of a moderate CO2 fertilization response, biospheric carbon storage increased by 3% (57 Gt C), primarily as a consequence of changes in NPP and litter inputs to the soil system. With CO2 fertilization, the model's cumulative carbon sink for the period 1900–1988 accounts for about 69% of the missing sink derived by deconvolution. For the period 1950–1988, the modeled sink is about 56% of the missing sink. Our results suggest that the temporal evolution of the missing sink over the period 1900–1988 could be a response of the terrestrial biosphere to changes in climate and atmospheric CO2 or perhaps climate change alone. The discrepancy in the magnitudes of the modeled and deconvolved sinks may be due to limitations of the biospheric model or to overestimates of the land‐use source flux.