Terrestrial Gross Primary Production Research Articles

First Investigation of the Relationship Between Solar-Induced Chlorophyll Fluorescence Observed by TanSat and Gross Primary Productivity

Reliable estimation of gross primary production (GPP) of terrestrial ecosystems is crucial in the global carbon cycle and ecosystem functioning studies. Solar-induced chlorophyll fluorescence (SIF) has emerged as an unprecedented proxy for terrestrial GPP estimation. TanSat SIF with a 2-km fine spatial resolution provides an excellent opportunity to investigate the relationships of satellite SIF and eddy covariance (EC) GPP at ecosystem or site scale, while has not been investigated. In this study, we investigated the relationship between TanSat SIF and EC GPP based on flux tower sites encompassing different biomes over the globe. TanSat SIF exhibited strong relationships with EC GPP at Fe and KI bands for instantaneous and daily timescales. The correlations between EC GPP and TanSat SIF (R2 = 0.61) were much better than moderate resolution imaging spectroradiometer (MODIS) bidirectional reflectance distribution function (BRDF)- corrected vegetation indices (VIs) (R2 = 0.480.58). Further comparisons between SIF and GPP with absorbed photosynthetically active radiation (APAR) and two environmental factors (fTmin and fVPD) showed that SIF responded to APAR and was similarly affected by environmental factors as GPP. GPP derived from SIF (R2 = 0.64) also performed slightly better than enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) and even light use efficiency MODIS GPP algorithm (R2 = 0.580.60). Therefore, our results demonstrated the feasibility of SIF on estimating GPP at sites or ecosystem levels; in addition, incorporating TanSat SIF with other satellite SIF products will open a new era for carbon cycling and ecosystem functioning studies.

Peak growing season patterns and climate extremes-driven responses of gross primary production estimated by satellite and process based models over North America

Representations of the seasonal peak uptake of CO2 and climate extremes effects have important implications for accurately estimating annual magnitude and inter-annual variations of terrestrial carbon fluxes, however the consistency of such representations among different satellite models and process-based (PB) models remain poorly known. Here we investigated these issues over North America based on a large ensemble of state-of-the-art gross primary production (GPP) models, including two solar-induced chlorophyll fluorescence (SIF)-based models (WECANN and GOPT), three remote sensing driven light-use efficiency (LUE) models, and 10 PB models. We found that the two SIF-based GPP estimates were bilaterally consistent in spatial patterns of peak growing season GPP (GPPPGS; with the largest uptake at the Corn-Belt area in the United States) and climate extremes-driven responses. The simulations from three LUE models showed relatively consistent spatial patterns of GPPPGS and climate extremes-driven responses, which agreed well with SIF-based estimates and satellite based metrics. Obviously differed from SIF and LUE based estimates, the simulations from PB models exhibited noticeable divergences and mostly failed to reasonably replicate the spatial pattern of GPPPGS. In addition, satellite models and PB models were comparably able to capture the effects of climate extremes on GPP, but showing obvious divergences in the magnitude of impacts among different models, and the former outperformed the latter in locating GPP changes caused by climate extremes. We discussed the possible origins of such discrepancies in state-of-the-art models with focus on PB models. Improving the parameterizations of critical variables (e.g. leaf area index) and better characterizing environmental stresses could lead to more robust estimates of large-scale terrestrial GPP with PB models, thus serving for accurately assessing global carbon budget and better understanding the impacts of climate change on the terrestrial carbon cycle. Our study offers a baseline for improving large-scale estimation of terrestrial GPP.

Vegetation structural change and CO2 fertilization more than offset gross primary production decline caused by reduced solar radiation in China

Terrestrial gross primary production (GPP) is the largest carbon flux from the atmosphere to the terrestrial ecosystems. However, how GPP dynamics respond to vegetation structural change (VSC), climate change, and rising CO2 concentration are still unclear. In this study, we developed a process-based model (i.e., Farquhar GPP Model) to characterize GPP dynamics in China from 2001 to 2016, and further identify the dominant drivers for GPP variation related to VSC, climate change, and rising atmospheric CO2 through model scenario design. During the study period, VSC showed an overwhelmingly positive impact on annual total GPP (i.e., 39.2 Tg C yr−1, P<0.001), followed by the fertilization effect of rising CO2 (i.e., 11.9 Tg C yr−1, P<0.001). However, climate variation would be expected to have an overall negative effect (i.e., -9.9 Tg C yr−1, P<0.001), which is primarily related to the decline of the incoming solar radiation over this period. Consequently, China's GPP showed a remarkable upward trend (i.e., 40.5 Tg C yr−1, P<0.001) during the study period. Our study highlighted the strong positive GPP contribution from VSC, which is mainly related to forest increase under the background of forest cover changes primarily from widespread forest conservation and restoration programs implemented in China since the late 1990s. Therefore, our study suggests that forest conservation and restoration could effectively increase terrestrial ecosystem GPP, despite the negative effects of climate change.

How Simulations of the Land Carbon Sink Are Biased by Ignoring Fluvial Carbon Transfers: A Case Study for the Amazon Basin

Summary Land-surface models are important tools for simulation of the past, present, and future capacity of terrestrial ecosystems to absorb anthropogenic CO2 emissions. However, fluvial carbon (C) transfers are presently neglected in these models. Using the Amazon basin as a case study, we show that this negligence leads to significant underestimation of the net uptake of atmospheric C while terrestrial C storage changes are overestimated. These biases arise from the fact that C—in reality, leached from soils and exported through the river network—is instead represented as partly being respired and partly being stored in soils. Moreover, these biases scale mainly to the fluvial C export to the coast, despite aquatic CO2 emission to the atmosphere being the major pathway of riverine C exports. We further show that fluvial C transfers may change significantly in response to changes in either hydrology or in atmospheric C uptake by vegetation.

Estimating site-specific optimum air temperature and assessing its effect on the photosynthesis of grasslands in mid- to high-latitudes

The effect of air temperature on photosynthesis is important for the terrestrial carbon cycle. The optimum air temperature for photosynthesis is one of the major parameters in data-driven and process-based photosynthesis models that estimate the gross primary production (GPP) of vegetation under a changing climate. To date, most models use the biome-specific optimum air temperature () parameter. To what degree will the site-specific optimum air temperature () affect GPP simulation results remains unclear. In this study, we estimated by using GPP data from 11 grassland eddy flux tower sites (GPPEC) and satellite vegetation indices (NDVI and EVI). We found that Topt-s parameter values estimated from EVI have good consistency with those from GPPEC at individual sites. We also evaluated the effects of site-specific and biome-specific optimum air temperature parameters on grassland photosynthesis. The results showed that the use of in the Vegetation Photosynthesis Model improved to various degrees in both daily and annual GPP estimates in those grassland flux tower sites. Our results highlight the necessity and potential for the use of in terrestrial GPP models, especially in those situations with large temperature variation (heatwave and cold spill events).

Reduction of structural impacts and distinction of photosynthetic pathways in a global estimation of GPP from space-borne solar-induced chlorophyll fluorescence

Quantifying global photosynthesis remains a challenge due to a lack of accurate remote sensing proxies. Solar-induced chlorophyll fluorescence (SIF) has been shown to be a good indicator of photosynthetic activity across various spatial scales. However, a global and spatially challenging estimate of terrestrial gross primary production (GPP) based on satellite SIF remains unresolved due to the confounding effects of species-specific physical and physiological traits and external factors, such as canopy structure or photosynthetic pathway (C3 or C4). Here we analyze an ensemble of far-red SIF data from OCO-2 satellite and ground observations at multiple sites, using the spectral invariant theory to reduce the effects of canopy structure and to retrieve a structure-corrected total canopy SIF emission (SIFtotal). We find that the relationships between observed canopy-leaving SIF and ecosystem GPP vary significantly among biomes. In contrast, the relationships between SIFtotal and GPP converge around two unique models, one for C3 and one for C4 plants. We show that the two single empirical models can be used to globally scale satellite SIF observations to terrestrial GPP. We obtain an independent estimate of global terrestrial GPP of 129.56 ± 6.54 PgC/year for the 2015–2017 period, which is consistent with the state-of-the-art data- and process-oriented models. The new GPP product shows improved sensitivity to previously undetected ‘hotspots’ of productivity, being able to resolve the double-peak in GPP due to rotational cropping systems. We suggest that the direct scheme to estimate GPP presented here, which is based on satellite SIF, may open up new possibilities to resolve the dynamics of global terrestrial GPP across space and time.

Annual and seasonal variations in gross primary productivity across the agro-climatic regions in India.

Gross primary productivity (GPP) is a vital ecosystem variable that is used as a proxy to study the functional behaviour of a terrestrial ecosystem and its ability to regulate atmospheric CO2 by working as a carbon pool. India, having the potential terrestrial ecosystem dynamics to absorb the atmospheric carbon dioxide to some extent, is one of the least-explored regions in terms of carbon monitoring studies. The current study evaluates the applicability of a newly developed, quantum yield-based, remote sensing data-driven diagnostic model called the Southampton Carbon Flux (SCARF). This model was used to estimate the annual and seasonal variability of the terrestrial GPP over the Indian region with a spatial resolution of 1 km during 2008. This modified version of the conventional production efficiency model successfully predicted GPP using meteorological variables (PAR, air temperature and dew point temperature), the fraction of photosynthetically active radiation and quantum yield of C3 and C4 plants as the key input parameters. The annual GPP values were in the range from 0 to 4147.55 g C m-2 year-1, with a mean value of 1507.32 g C m-2 year-1. The maximum and minimum GPP were during the summer monsoon and pre-monsoon, respectively. The seasonal and annual distributions of GPP over the study area obtained using the SCARF model, and the MODIS GPP product (MOD17A2H) were similar. However, MODIS was found to underestimate the GPP in all regions and an overestimation in eastern Himalaya region. The study reveals that environmental scalars, specifically water stress, are the pivotal controlling variables responsible for the variation of GPP in India. The estimates of the GPP in different regions of the study area were made using SCARF, and an eddy covariance technique was similar. The SCARF model can be used to estimate GPP on a global scale. SCARF appears to be a better model in terms of the simplicity of the algorithm, performance and resolution. Thus, it may give higher accuracy in carbon monitoring studies.

Contrasting Performance of the Remotely-Derived GPP Products over Different Climate Zones across China

Precise quantification of terrestrial gross primary production (GPP) has been recognized as one of the most important components in understanding the carbon balance between the biosphere and the atmosphere. In recent years, although many large-scale GPP estimates from satellite data and ecosystem models have been generated, few attempts have been made to compare the different GPP products at national scales, particularly for various climate zones. In this study, two of the most widely-used GPP datasets were systematically compared over the eight climate zones across China’s terrestrial ecosystems from 2001 to 2015, which included the moderate resolution imaging spectroradiometer (MODIS) GPP and the breathing Earth system simulator (BESS) GPP products. Additionally, the coarse (0.05o) GPP estimates from the vegetation photosynthesis model (VPM) at the same time scale were used for auxiliary analysis with the two products. Both MODIS and BESS products exhibited a decreasing trend from the southeast region to the northwest inland. The largest GPP was found in the tropical humid region with 5.49 g C m−2 d−1 and 5.07 g C m−2 d−1 for MODIS and BESS, respectively, while the lowest GPP was distributed in the warm temperate arid region, midtemperate semiarid region and plateau zone. Meanwhile, the work confirmed that all these GPP products showed apparent seasonality with the peaks in the summertime. However, large differences were found in the interannual variations across the three GPP products over different climate regions. Generally, the BESS GPP agreed better than the MODIS GPP when compared to the seasonal and interannual variations of VPM GPP. Furthermore, the spatial correlation analysis between terrestrial GPP and the climatic factors, including temperature and precipitation, indicated that natural rainfall dominated the variability in GPP of Northern China, such as the midtemperate semiarid region, while temperature was a key controlling factor in the Southern China and the Tibet Plateau area.

El Niño‐Southern Oscillation‐Induced Variability of Terrestrial Gross Primary Production During the Satellite Era

AbstractTerrestrial gross primary production (GPP) is the largest carbon flux entering the biosphere from the atmosphere, which serves as a key driver of global carbon cycle and provides essential matter and energy for life on land. However, terrestrial GPP variability is still poorly understood and difficult to predict, especially at the annual scale. As a major internal climate oscillation, El Niño‐Southern Oscillation (ENSO) influences global climate patterns and thus may strongly alter interannual terrestrial GPP variation. Using a remote sensing‐driven ecosystem model with long‐term satellite and climate data, we comprehensively examined the impacts of ENSO on global GPP dynamics from 1982 to 2016, focusing on lag effects of ENSO and their spatial heterogeneity. We found a clear seasonal lag effect of previous‐year ENSO indices on current‐year global GPP variability. The composite Oceanic Niño Index in the previous‐year's August‐October showed the strongest correlation with global annual GPP (R = −0.51, p &lt; 0.01). Spatially, 20.1% and 11.7% of vegetated land area showed significant negative and positive correlations with the ENSO cycle, respectively. ENSO effects on annual GPP exhibited diverse seasonal evolutions, and the timings of peak ENSO influences were heterogeneous across the globe. Annual GPP from TRENDY land surface model ensemble did not capture the major lag effects of ENSO identified in the satellite‐derived GPP and top‐down‐based land sink. Despite the complexity of the climate system, our efforts linking ENSO with global GPP dynamics provide a simple framework to understand and project climatic influences on the terrestrial carbon cycle.

No Proportional Increase of Terrestrial Gross Carbon Sequestration From the Greening Earth

AbstractTerrestrial vegetation, as the key component of the biosphere, has a greening trend since the beginning of this century. However, how this substantial greening translated to global gross carbon sequestration or gross primary production (GPP) is not clear. Here we investigated terrestrial GPP dynamics and the respective contributions of climate change and vegetation cover change (VCC) from 2000 to 2015. We adopted a remote sensing based data‐driven model, which was calibrated based on the global eddy flux data set (FLUXNET2015) and Moderate Resolution Imaging Spectroradiometer vegetation index data (Collection 6). A series of simulation experiments were conducted to disaggregate the effects of climate and VCC. We found a much weaker increase in global GPP (0.08%/year; P = 0.07) when compared with the global greening rate (0.23%/year; P &lt; 0.001). The positive effect of VCC on GPP was reduced by 53% due to climate stress. Enhanced global GPP were largely contributed by nonforests, especially croplands. However, tropical forests, once a major driver of the global GPP increase, negatively contributed to global GPP trend due to warming‐induced moisture stress and deforestation. Given the limited potential of cropland carbon storage due to harvest and consumption, the contrasting GPP changes (i.e., cropland GPP increase vs. forest GPP reduction) may have shifted the distribution of the land carbon sink. Our study highlights the potential vulnerability of terrestrial gross carbon sequestration under climate and land use changes and has important implications in the global carbon cycle and climate warming mitigation.

Improving Estimation of Gross Primary Production in Dryland Ecosystems by a Model-Data Fusion Approach

Accurate and continuous monitoring of the production of arid ecosystems is of great importance for global and regional carbon cycle estimation. However, the magnitude of carbon sequestration in arid regions and its contribution to the global carbon cycle is poorly understood due to the worldwide paucity of measurements of carbon exchange in arid ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity (GPP) product provides worldwide high-frequency monitoring of terrestrial GPP. While there have been a large number of studies to validate the MODIS GPP product with ground-based measurements over a range of biome types. Few studies have comprehensively validated the performance of MODIS estimates in arid and semi-arid ecosystems, especially for the newly released Collection 6 GPP products, whose resolution have been improved from 1000 m to 500 m. Thus, this study examined the performance of MODIS-derived GPP by compared with eddy covariance (EC)-observed GPP at different timescales for the main ecosystems in arid and semi-arid regions of China. Meanwhile, we also improved the estimation of MODIS GPP by using in situ meteorological forcing data and optimization of biome-specific parameters with the Bayesian approach. Our results revealed that the current MOD17A2H GPP algorithm could, on the whole, capture the broad trends of GPP at eight-day time scales for the most investigated sites. However, GPP was underestimated in some ecosystems in the arid region, especially for the irrigated cropland and forest ecosystems (with R2 = 0.80, RMSE = 2.66 gC/m2/day and R2 = 0.53, RMSE = 2.12 gC/m2/day, respectively). At the eight-day time scale, the slope of the original MOD17A2H GPP relative to the EC-based GPP was only 0.49, which showed significant underestimation compared with tower-based GPP. However, after using in situ meteorological data to optimize the biome-based parameters of MODIS GPP algorithm, the model could explain 91% of the EC-observed GPP of the sites. Our study revealed that the current MODIS GPP model works well after improving the maximum light-use efficiency (εmax or LUEmax), as well as the temperature and water-constrained parameters of the main ecosystems in the arid region. Nevertheless, there are still large uncertainties surrounding GPP modelling in dryland ecosystems, especially for desert ecosystems. Further improvements in GPP simulation in dryland ecosystems are needed in future studies, for example, improvements of remote sensing products and the GPP estimation algorithm, implementation of data-driven methods, or physiology models.

Improving satellite-based modelling of gross primary production in deciduous broadleaf forests by accounting for seasonality in light use efficiency

ABSTRACTVegetation gross primary production (GPP), the photosynthetic yields by green plants per unit area per unit time, is a key metric of carbon flux in understanding the land–atmosphere interactions and terrestrial carbon cycles. Satellite-based light use efficiency (LUE) models are valuable methods to retrieve large-scale terrestrial GPP using remote sensing data. As studies have reported that maximum light use efficiency, a key parameter that is often assumed to be constant in the LUE models, there is a need to explore the effects of LUE seasonality on GPP simulation and ways for correction. This study proposes a method based on leaf area index to account for LUE seasonality and applies it to four different light use efficiency models (i.e., the MOD17 algorithm, the vegetation photosynthesis model, the radiation partitioning model, and the vegetation index model) for comparisons. Based on 59 site-years flux tower data from deciduous broadleaf forest sites in the United States, the results show that all models could simulate daily GPP time series well and explain more than 85.0% variance of tower-based GPP. There is, however, a tendency to overestimate GPP during the non-growing season but underestimate GPP during the growing season. By applying the correction function, GPP simulation using the LUE models improved in all experiments as indicated by increased correlation coefficients, the index of agreement and decreased root-mean-square errors. Among all models, the radiation partitioning model achieves the highest correlation coefficients between modelled and observed daily GPP likely because it considers the influences of direct and diffuse radiation partitioning on daily canopy photosynthesis. Our study indicates that satellite-based light use efficiency models could be successfully applied for deriving daily vegetation GPP and potentially producing daily routine satellite products, while considering the effects of LUE seasonality on canopy could help improve significantly the simulation accuracy of daily GPP in phenology.

Effect of climate dataset selection on simulations of terrestrial GPP: Highest uncertainty for tropical regions.

Biogeochemical models use meteorological forcing data derived with different approaches (e.g. based on interpolation or reanalysis of observation data or a hybrid hereof) to simulate ecosystem processes such as gross primary productivity (GPP). This study assesses the impact of different widely used climate datasets on simulated gross primary productivity and evaluates the suitability of them for reproducing the global and regional carbon cycle as mapped from independent GPP data. We simulate GPP with the biogeochemical model LPJ-GUESS using six historical climate datasets (CRU, CRUNCEP, ECMWF, NCEP, PRINCETON, and WFDEI). The simulated GPP is evaluated using an observation-based GPP product derived from eddy covariance measurements in combination with remotely sensed data. Our results show that all datasets tested produce relatively similar GPP simulations at a global scale, corresponding fairly well to the observation-based data with a difference between simulations and observations ranging from -50 to 60 g m-2 yr-1. However, all simulations also show a strong underestimation of GPP (ranging from -533 to -870 g m-2 yr-1) and low temporal agreement (r < 0.4) with observations over tropical areas. As the shortwave radiation for tropical areas was found to have the highest uncertainty in the analyzed historical climate datasets, we test whether simulation results could be improved by a correction of the tested shortwave radiation for tropical areas using a new radiation product from the International Satellite Cloud Climatology Project (ISCCP). A large improvement (up to 48%) in simulated GPP magnitude was observed with bias corrected shortwave radiation, as well as an increase in spatio-temporal agreement between the simulated GPP and observation-based GPP. This study conducts a spatial inter-comparison and quantification of the performances of climate datasets and can thereby facilitate the selection of climate forcing data over any given study area for modelling purposes.

Changes in Gross Primary Production (GPP) over the Past Two Decades Due to Land Use Conversion in a Tourism City

Understanding the changes in gross primary production (GPP), which is the total carbon fixation by terrestrial ecosystems through vegetation photosynthesis, due to land use conversion in a tourism city is important for carbon cycle studies. Satellite data from Landsat 5, Landsat 7 and Landsat 8 and meteorological data are used to calculate annual GPP for 1995, 2003 and 2014, respectively, using the vegetation production model (VPM) in the tourism city Denpasar, Bali, Indonesia. Five land use types generated from topographic maps in three different years over the past two decades are used to quantify the impacts of land use changes on GPP estimation values. Analysis was performed for two periods to determine changes in land use and GPP value as well as their speed. The results demonstrated that urban land development, namely, the increase of settlement areas due to tourism activity, had overall negative effects on terrestrial GPP. The total GPP of the whole area decreased by 7793.96 tC year−1 (12.65%) during the study period. The decline is due to the conversion of agriculture and grassland area into settlements, which caused the city to lose half of its ability to uptake carbon through vegetation. However, although forest area is declining, forest maintenance and restoration by making them protection areas has been helpful in preventing a drastic decline in GPP value over the past two decades. This study provides information that is useful for carbon resource management, tourism, policy making and scholars concerned about carbon reduction in a tourism city.

An attempt to introduce atmospheric CO2 concentration data to estimate the gross primary production by the terrestrial biosphere and analyze its effects

Quantitative estimations of the GPP (gross primary production) and its variations at spatial scales are important issues with future significance due to the increasing atmospheric CO2 levels. However, the effects of the spatiotemporal variability in the atmospheric CO2 concentrations on GPP estimations are challenging with respect to the terrestrial ecosystem due to land cover component characteristics and difficulties associated with measuring CO2 concentrations over large spatial areas. The development of remote sensing offer a means to routinely monitor CO2 concentrations both spatially and temporally from space. To introduce continuous spatial CO2 data as an indicator for the estimation of the terrestrial biosphere GPP, we used the decoupling coefficients to evaluate the canopy CO2 concentrations, photosynthetic biochemical models to calculate the photosynthetic rate, and Big-leaf model to scale up to a global scale. The GPPs estimated by this method are relatively consistent with the GPP derived from Flux tower sites. Thus, the method proposed in this study utilizing continuous spatial CO2 data to estimate the GPP is practicable and feasible. Finally, we compared the GPPs under different atmospheric CO2 concentrations conditions between 2000 and 2014 and analyzed the effects of the spatiotemporal variability in the atmospheric CO2 concentrations on the GPP estimates. The results show that, in general, the terrestrial GPP increases as atmospheric CO2 concentrations increase, and the increases in the lower latitudes are more significant than those in the middle and high latitudes; by comparing the annual GPP estimates in 2000 with those in 2014, it was observed that the increases in forest GPP is greater than that of other functional types. The effects of the variations in the spatial distribution of atmospheric CO2 concentrations on the terrestrial GPP distribution vary based on time and location. Regarding the annual GPP estimates, without considering the CO2 spatial distribution, the estimates overestimate the GPP in the lower latitudes and underestimate those in the middle and high latitudes. Regarding the monthly GPP estimates, using the annual averages caused the GPP estimates of the Northern Hemisphere to be overestimated during the first half of the year, while those during the second half of the year were underestimated; the GPP estimates for the Southern Hemisphere were underestimated each month. However, using monthly averages caused the GPP estimates for the Northern Hemisphere to be overestimated in summer and underestimated in spring and autumn, which are opposite to the estimates for the Southern Hemisphere.

Robustness and Uncertainties of the \u201cTemperature and Greenness\u201d Model for Estimating Terrestrial Gross Primary Production

Terrestrial gross primary production (GPP) plays a vital role in offsetting anthropogenic CO2 emission and regulating global carbon cycle. Various remote sensing approaches for estimating GPP have attracted considerable scientific attentions, yet their robustness and uncertainties remain unclear. Here we evaluate the performance of the “temperature and greenness” (TG) model, a representative remote sensing model in estimating GPP, using the global FLUXNET GPP based on parameter sensitive analysis and optimization strategies. The results show that the minimum (xn) and optimum (xo) temperatures for photosynthesis are sensitive parameters but maximum temperature (xm) not. Optimized xn and xo differ largely from their defaults for more than half of 12 plant functional types (PFTs). Parameter optimization significantly improves the TG’s performance in forest ecosystems where temperature or solar radiation has significant contribution to GPP. For water-limited ecosystems where GPP are strongly dependent of EVI and EVI are sensitive to precipitation, parameter optimization has limited effects. These results imply that the TG model, and most likely for other kind of GPP models using same methodology, can’t be significantly improved for all PFTs through parameter optimization only, and other key climatic variables should be incorporated into the model for better predicting terrestrial ecosystem GPP.

Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2.

Uncertainties in the response of vegetation to rising atmospheric CO2 concentrations contribute to the large spread in projections of future climate change. Climate-carbon cycle models generally agree that elevated atmospheric CO2 concentrations will enhance terrestrial gross primary productivity (GPP). However, the magnitude of this CO2 fertilization effect varies from a 20 per cent to a 60 per cent increase in GPP for a doubling of atmospheric CO2 concentrations in model studies. Here we demonstrate emergent constraints on large-scale CO2 fertilization using observed changes in the amplitude of the atmospheric CO2 seasonal cycle that are thought to be the result of increasing terrestrial GPP. Our comparison of atmospheric CO2 measurements from Point Barrow in Alaska and Cape Kumukahi in Hawaii with historical simulations of the latest climate-carbon cycle models demonstrates that the increase in the amplitude of the CO2 seasonal cycle at both measurement sites is consistent with increasing annual mean GPP, driven in part by climate warming, but with differences in CO2 fertilization controlling the spread among the model trends. As a result, the relationship between the amplitude of the CO2 seasonal cycle and the magnitude of CO2 fertilization of GPP is almost linear across the entire ensemble of models. When combined with the observed trends in the seasonal CO2 amplitude, these relationships lead to consistent emergent constraints on the CO2 fertilization of GPP. Overall, we estimate a GPP increase of 37 ± 9 per cent for high-latitude ecosystems and 32 ± 9 per cent for extratropical ecosystems under a doubling of atmospheric CO2 concentrations on the basis of the Point Barrow and Cape Kumukahi records, respectively.

Consistency between sun-induced chlorophyll fluorescence and gross primary production of vegetation in North America

Accurate estimation of the gross primary production (GPP) of terrestrial ecosystems is vital for a better understanding of the spatial-temporal patterns of the global carbon cycle. In this study,we estimate GPP in North America (NA) using the satellite-based Vegetation Photosynthesis Model (VPM), MODIS (Moderate Resolution Imaging Spectrometer) images at 8-day temporal and 500 meter spatial resolutions, and NCEP-NARR (National Center for Environmental Prediction-North America Regional Reanalysis) climate data. The simulated GPP (GPP (sub VPM)) agrees well with the flux tower derived GPP (GPPEC) at 39 AmeriFlux sites (155 site-years). The GPP (sub VPM) in 2010 is spatially aggregated to 0.5 by 0.5-degree grid cells and then compared with sun-induced chlorophyll fluorescence (SIF) data from Global Ozone Monitoring Instrument 2 (GOME-2), which is directly related to vegetation photosynthesis. Spatial distribution and seasonal dynamics of GPP (sub VPM) and GOME-2 SIF show good consistency. At the biome scale, GPP (sub VPM) and SIF shows strong linear relationships (R (sup 2) is greater than 0.95) and small variations in regression slopes ((4.60-5.55 grams Carbon per square meter per day) divided by (milliwatts per square meter per nanometer per square radian)). The total annual GPP (sub VPM) in NA in 2010 is approximately 13.53 petagrams Carbon per year, which accounts for approximately 11.0 percent of the global terrestrial GPP and is within the range of annual GPP estimates from six other process-based and data-driven models (11.35-22.23 petagrams Carbon per year). Among the seven models, some models did not capture the spatial pattern of GOME-2 SIF data at annual scale, especially in Midwest cropland region. The results from this study demonstrate the reliable performance of VPM at the continental scale, and the potential of SIF data being used as a benchmark to compare with GPP models.

A new model of the global biogeochemical cycle of carbonyl sulfide – Part 2: Use of carbonyl sulfide to constrain gross primary productivity in current vegetation models

Abstract. Clear analogies between carbonyl sulfide (OCS) and carbon dioxide (CO2) diffusion pathways through leaves have been revealed by experimental studies, with plant uptake playing an important role for the atmospheric budget of both species. Here we use atmospheric OCS to evaluate the gross primary production (GPP) of three dynamic global vegetation models (Lund–Potsdam–Jena, LPJ; National Center for Atmospheric Research – Community Land Model 4, NCAR-CLM4; and Organising Carbon and Hydrology In Dynamic Ecosystems, ORCHIDEE). Vegetation uptake of OCS is modeled as a linear function of GPP and leaf relative uptake (LRU), the ratio of OCS to CO2 deposition velocities of plants. New parameterizations for the non-photosynthetic sinks (oxic soils, atmospheric oxidation) and biogenic sources (oceans and anoxic soils) of OCS are also provided. Despite new large oceanic emissions, global OCS budgets created with each vegetation model show exceeding sinks by several hundred Gg S yr−1. An inversion of the surface fluxes (optimization of a global scalar which accounts for flux uncertainties) led to balanced OCS global budgets, as atmospheric measurements suggest, mainly by drastic reduction (up to −50 %) in soil and vegetation uptakes. The amplitude of variations in atmospheric OCS mixing ratios is mainly dictated by the vegetation sink over the Northern Hemisphere. This allows for bias recognition in the GPP representations of the three selected models. The main bias patterns are (i) the terrestrial GPP of ORCHIDEE at high northern latitudes is currently overestimated, (ii) the seasonal variations of the GPP are out of phase in the NCAR-CLM4 model, showing a maximum carbon uptake too early in spring in the northernmost ecosystems, (iii) the overall amplitude of the seasonal variations of GPP in NCAR-CLM4 is too small, and (iv) for the LPJ model, the GPP is slightly out of phase for the northernmost ecosystems and the respiration fluxes might be too large in summer in the Northern Hemisphere. These results rely on the robustness of the OCS modeling framework and, in particular, the choice of the LRU values (assumed constant in time) and the parameterization of soil OCS uptake with small seasonal variations. Refined optimization with regional-scale and seasonally varying coefficients might help to test some of these hypothesis.

Improving the light use efficiency model for simulating terrestrial vegetation gross primary production by the inclusion of diffuse radiation across ecosystems in China

Qualification of gross primary production (GPP) of terrestrial ecosystem over large areas is important in understanding the response of terrestrial ecosystem to global climate change. While light use efficiency (LUE) models were widely used in regional carbon budget estimates, few studies consider the effect of diffuse radiation on LUE caused by clouds using a big leaf model. Here we developed a cloudiness index light use efficiency (CI-LUE) model based on the MOD17 model algorithm to estimate the terrestrial ecosystem GPP, in which the base light use efficiency encompassed the cloudiness index, maximum LUE and clear sky LUE. GPP measured at seven sites from 2003 to 2007 in China were used to calibrate and validate the CI-LUE model. The results showed that at forest sites and cropland site the CI-LUE model outperformed the Vegetation Photosynthesis Model (VPM), Terrestrial Ecosystem Carbon flux model (TEC), MOD17 model algorithm driven by in situ meteorological measurements and MODIS GPP products, especially the R2 of simulated GPP against flux measurements at Dinghushan forest site increased from 0.17 (MODIS GPP products) to 0.61 (CI-LUE). Instead, VPM model had the best agreement with GPP measurements followed by CI-LUE model and lastly TEC model at two grassland sites. Meanwhile, GPP calculated by CI-LUE model has less underestimation under cloudy skies in comparison with MOD17 model. This study demonstrated the potential of the CI-LUE model in improving GPP simulations resulting from the inclusion of diffuse radiation in regulating the base light use efficiency and maximum light use efficiency.