Interannual Variability Of Gross Primary Production Research Articles

Drought in the middle growing season inhibited carbon uptake more critical in an anthropogenic shrub ecosystem of Northwest China

Large-scale ecological restoration efforts of the degraded desert steppe ecosystem in northwestern China have effectively combated desertification but have also resulted in the encroachment of shrubs. However, the function of the anthropogenic shrub ecosystem is still unknown under climate change, especially the response of carbon fluxes to drought, which hinders our further understanding of its sustainability in the future. Hence, based on the eddy covariance and environment sensors, continuous carbon and water fluxes and biophysical factors were monitored during 2019–2022 to investigate the characteristics of carbon fluxes and their response to seasonal drought in an anthropogenic shrub ecosystem dominated by Caragana liouana in the southern Mu Us sandy land, northwestern China. The results showed the shrub ecosystem was a stable carbon sink over the 4 years despite large interannual variability, with annual net ecosystem production (NEP) ranging from 54.37 to 150.35 g C·m−2·y−1. Interannual variability in NEP was mainly due to variation in gross primary productivity (GPP), which is driven by the fact that interannual variability in GPP was higher than interannual variability in ecosystem respiration (Reco). While drought inhibited GPP more than Reco, thus weakening the carbon sequestration ability of the ecosystem during the middle growing season, and thereby carbon fluxes in the shrub ecosystem were significantly suppressed by drought in the middle growing season but slightly in the early growing season. Furthermore, the magnitude and variation of carbon fluxes were determined by the maximum apparent photosynthetic capacity of the canopy (Pmax), leaf area index (LAI), and canopy conductance (gc), all of which are limited by water stress during drought. The work suggests that to keep the carbon sink of anthropogenic shrub ecosystems in arid and semi-arid areas, managers should focus on drought-relief measures in the middle growing season that are better adapted to future climate change.

Underestimated Interannual Variability of Terrestrial Vegetation Production by Terrestrial Ecosystem Models

AbstractVegetation gross primary production (GPP) is the largest terrestrial carbon flux and plays an important role in regulating the carbon sink. Current terrestrial ecosystem models (TEMs) are indispensable tools for evaluating and predicting GPP. However, to which degree the TEMs can capture the interannual variability (IAV) of GPP remains unclear. With large data sets of remote sensing, in situ observations, and predictions of TEMs at a global scale, this study found that the current TEMs substantially underestimate the GPP IAV in comparison to observations at global flux towers. Our results also showed the larger underestimations of IAV in GPP at nonforest ecosystem types than forest types, especially in arid and semiarid grassland and shrubland. One cause of the underestimation is that the IAV in GPP predicted by models is strongly dependent on canopy structure, that is, leaf area index (LAI), and the models underestimate the changes of canopy physiology responding to climate change. On the other hand, the simulated interannual variations of LAI are much less than the observed. Our results highlight the importance of improving TEMs by precisely characterizing the contribution of canopy physiological changes on the IAV in GPP and of clarifying the reason for the underestimated IAV in LAI. With these efforts, it may be possible to accurately predict the IAV in GPP and the stability of the global carbon sink in the context of global climate change.

Functional diversity and soil nutrients regulate the interannual variability in gross primary productivity

Abstract Global change, encompassing rising temperatures and an increase in extreme precipitation events, has influenced vegetation photosynthesis; this can be seen in the gross primary productivity (GPP) of terrestrial ecosystems, which, over time affects the global carbon cycle. The impact of climate on interannual variability in GPP (GPPIAV) has been extensively explored in the literature. Other changing factors driven by global change, such as biodiversity and soil nutrient availability, are vital in predicting the future of the biosphere. However, the roles of these factors remain unclear. We combined (i) data from 454 community plots collected using standard protocols from 2013 to 2019 across China, (ii) plant trait data and phylogenetic information of more than 2500 plant species, and (iii) soil nutrient data that we measured. Using these data from 72 “real‐world” ecosystems located across a range of environmental conditions and species pools, we investigated the role of environmental factors including temperature, precipitation and soil nutrients and multifaceted diversity (i.e. species richness, hypervolume‐based functional diversity, and phylogenetic diversity) in mediating the magnitude of GPPIAV using multi‐model averaging and structural equation modelling. We found that soil nutrients and functional diversity are the main determinants of the magnitude of GPPIAV and that climate effects are predominantly mediated by multifaceted diversity. Synthesis. We provide strong evidence that ecosystems with higher biodiversity have less variable annual biomass production and decrease the extent of GPPIAV through compensatory effects across diverse ecosystems. Nutrient‐rich ecosystems are likely to buffer the impact of climate variability on ecosystem carbon uptake better than nutrient‐poor ecosystems. Our results demonstrate that biodiversity plays a crucial role in buffering the effects of environmental variability on carbon uptake in terrestrial ecosystems.

What plant–pollinator network structure tells us about the mechanisms underlying the bidirectional biodiversity productivity relationship?

The Biodiversity – Ecosystem Functioning (B–EF) relationship remains a topic of ongoing debate with most studies focusing on primary productivity, and documenting that this relationship takes many forms. It remains unclear if biodiversity drives productivity or productivity shapes biodiversity or the relationship is bidirectional. B-EF studies explore almost exclusively the relationship between species richness and ecosystem functioning, while the role of biotic interactions, a key component of ecosystem functioning, has been neglected. Here, using data of 80 local plant–pollinator networks on 20 Aegean islands, and of gross primary productivity (GPP) from the MODIS satellite, we explored the bidirectional relationship between interaction network structure (nestedness and specialization), species richness (plants and pollinators) and mean and inter-annual variability of GPP. We found that nestedness and specialisation of plant–pollinator networks is driven by mean GPP. However, specialisation alone was a significant predictor of mean GPP, implying that networks tend to be more specialised in low-productivity areas. Pollinator species richness exerted a strong effect on mean GPP with the remaining factors playing a minor role, while the effect of mean GPP on pollinator species richness was weaker. Furthermore, the nestedness of plant–pollinator networks drives inter-annual variability of GPP with more nested networks displaying less variability, which is in accordance with the predictions of the insurance hypothesis. Plant and pollinator species richness were also associated with inter-annual variability of GPP.

Spatiotemporal Variation in Gross Primary Productivity and Their Responses to Climate in the Great Lakes Region of Sub-Saharan Africa during 2001–2020

The impacts of climate on spatiotemporal variations of eco-physiological and bio-physical factors have been widely explored in previous research, especially in dry areas. However, the understanding of gross primary productivity (GPP) variations and its interactions with climate in humid and semi-humid areas remains unclear. Based on hyperspectral satellite remotely sensed vegetation phenology processes and related indices and the re-analysed climate datasets, we investigated the seasonal and inter-annual variability of GPP by using different light-use efficiency (LUE) models including the Carnegie-Ames-Stanford Approaches (CASA) model, vegetation photosynthesis models (VPMChl and VPMCanopy) and Moderate Resolution Imaging Spectroradiometer (MODIS) GPP products (MOD17A2H) during 2001–2020 over the Great Lakes region of Sub-Saharan Africa (GLR-SSA). The models’ validation against the in situ GPP-based upscaled observations (GPP-EC) indicated that these three models can explain 82%, 79% and 80% of GPP variations with root mean square error (RMSE) values of 5.7, 8.82 and 10.12 g C·m−2·yr−1, respectively. The spatiotemporal variations of GPP showed that the GLR-SSA experienced: (i) high GPP values during December-May; (ii) high annual GPP increase during 2002–2003, 2011–2013 and 2015–2016 and annual decreasing with a marked alternation in other years; (iii) evergreen broadleaf forests having the highest GPP values while grasslands and croplands showing lower GPP values. The spatial correlation between GPP and climate factors indicated 60% relative correlation between precipitation and GPP and 65% correction between surface air temperature and GPP. The results also showed high GPP values under wet conditions (in rainy seasons and humid areas) that significantly fell by the rise of dry conditions (in long dry season and arid areas). Therefore, these results showed that climate factors have potential impact on GPP variability in this region. However, these findings may provide a better understanding of climate implications on GPP variability in the GLR-SSA and other tropical climate zones.

Inter-comparisons of mean, trend and interannual variability of global terrestrial gross primary production retrieved from remote sensing approach

Many models were established to estimate gross primary production (GPP) of terrestrial ecosystems based on vegetation light use efficiency (LUE). Analysing the spatial-temporal variations of global terrestrial GPP became capable with the increasing length of satellite data. Previous studies mainly focused on evaluating the model performance or investigating the mean, the temporal trend or the interannual variability (IAV) of global terrestrial GPP based on one single or multiple models, which is difficult to identify common merits of a same cluster of GPP models. This study compared eight satellite-based LEU-type GPP models in capturing the mean, temporal trend and IAV of global GPP concurrently. Our results showed that current common-used models based on LUE methodology estimated global mean GPP ranging from 128.5 to 158.3 Pg C year−1, and global mean IAV ranging from 0.1 to 0.35, but the trends ranging from −0.22 to 0.51 Pg C year−1. In the context of plant functional types (PFTs) and climate classifications, no consistent feature for either of the mean, trend or IAV of GPP are identified among eight models. Future studies should integrate the latest advances on the mechanisms and associated environmental factors into models and consolidate performance of models to better understand the evolutions of terrestrial ecosystem functioning.

Environmental and biotic controls on the interannual variations in CO2 fluxes of a continental monsoon temperate forest

The interannual variability (IAV) of CO2 fluxes of temperate forests in continental East Asia was rarely explored via direct measurements. To explore the IAV of net ecosystem exchange of CO2 (NEE) and its driving factors, we continuously measured the CO2 fluxes of a temperate deciduous broadleaved forest at the Maoershan site, northeast China with a continental monsoon climate, by eddy covariance over 11 years (2008 – 2018). The means of NEE, gross primary production (GPP), and ecosystem respiration (Re) were −157 ± 64 (mean ± standard deviation), 1356 ± 148, and 1200 ± 138 g C m−2 yr−1, respectively. For annual fluxes, the IAV of NEE was jointly controlled by the length of net CO2 uptake period and the summer peak of net CO2 uptake, whereas those of GPP and Re were predominated by the summer peaks (GPPmax and Remax). Meanwhile, the dominant role of GPPmax on the IAV of GPP was attributed to the leaf-level photosynthetic capacity rather than the maximum leaf area index. However, the environmental factors showed weak impacts on NEE, largely because of the offset between the positive responses of GPP and Re to the spring and autumn soil water content, respectively. For particular seasons, phenology was the dominant biotic driver of the spring and autumn NEE, whereas spring precipitation and autumn photosynthetically active radiation were the main environmental drivers of the spring and autumn NEE, respectively. Overall, this work filled the gap of the relatively long-term CO2 fluxes measured in the temperate forests of continental East Asia. Taking the influences of ecosystem- and leaf-level physiology, canopy structure, and phenology, together with their environmental interactions, on the CO2 fluxes into account will improve the understanding and prediction of the forest carbon dynanics.

Contrasting Regional Carbon Cycle Responses to Seasonal Climate Anomalies Across the East-West Divide of Temperate North America.

Across temperate North America, interannual variability (IAV) in gross primary production (GPP) and net ecosystem exchange (NEE) and their relationship with environmental drivers are poorly understood. Here, we examine IAV in GPP and NEE and their relationship to environmental drivers using two state‐of‐the‐science flux products: NEE constrained by surface and space‐based atmospheric CO measurements over 2010–2015 and satellite up‐scaled GPP from FluxSat over 2001–2017. We show that the arid western half of temperate North America provides a larger contribution to IAV in GPP (104% of east) and NEE (127% of east) than the eastern half, in spite of smaller magnitude of annual mean GPP and NEE. This occurs because anomalies in western ecosystems are temporally coherent across the growing season leading to an amplification of GPP and NEE. In contrast, IAV in GPP and NEE in eastern ecosystems is dominated by seasonal compensation effects, associated with opposite responses to temperature anomalies in spring and summer. Terrestrial biosphere models in the MsTMIP ensemble generally capture these differences between eastern and western temperate North America, although there is considerable spread between models.

Contribution of different external forcings to the terrestrial carbon cycle variability in extratropical Eurasia in the last millennium

The simulations for the last millennium with an Earth system model developed at the A. M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences (IAP RAS CM) are performed. These simulations are forced by changes of the parameters of the Earth orbit, total solar irradiance, volcanic (stratospheric) aerosols optical depth (only since 500 C. E.), atmospheric concentrations of greenhouse gases (CO2, CH4, and N2O), land use, tropospheric sulphate burden, and population density. It is found that the externally forced part of the terrestrial carbon cycle (TCC) interannual variability (IAV) was mostly driven by volcanic activity in the preindustrial part of the last millennium with an increase of importance of anthropogenic forcing during the 20th century. The latter enhanced IAV in the 20th century. For different time intervals and for different kinds of external forcing, coefficient of variation of IAV in different TCC characteristics is smaller (typically, up to few percent) in forested regions and larger in the regions covered by grasses (e.g., in tundra), where it could be as large as several tens of per cents for fire return interval. We show that the externally forced IAV of gross primary production during the 20th century dramatically increased as compared to that during the preindustrial period. In addition, the land use activity increases the relaxation time scale of the vegetation carbon stock by one order of magnitude.

Spatiotemporal differentiation of the terrestrial gross primary production response to climate constraints in a dryland mountain ecosystem of northwestern China

Monitoring seasonal and interannual variability in gross primary production (GPP) and attributing these changes to climate change across various ecosystems helps to predict the future climate-carbon cycle feedback. However, such studies are scarce in dryland mountain ecosystems, possibly because of high spatial heterogeneity in landscapes and terrain. To better understand how carbon fluxes of the dryland mountain ecosystem respond to meteorology, we identified the trend and driving mechanism related to GPP in the Qilian Mountains (QLMs) of northwestern China from 2000 to 2016 by adopting the vegetation photosynthesis model that incorporates satellite and meteorological data. Our results revealed contrasting GPP trends in the growing season (May–September) between 2000–2010 and 2010–2016. In the later period, widespread GPP reductions were found across almost the whole area, especially at the middle and end of the growing season. In the central part of the QLMs, GPP reductions were induced by warming hiatus in contrast to drought in the western and eastern parts. Responses of GPP to temperature, precipitation and solar radiation differed in seasons and biomes. The positive effect of rising temperature that increased GPP was dominant during the growing season. The interannual variability in GPP was positively related to precipitation in June and July, but was negatively related to precipitation in other months. A positive correlation between forest GPP and solar radiation occurred in all months but July. Desert GPP responded negatively to solar radiation in all months but September. Temperature and solar radiation accounted for most of the interannual variability in forest GPP. Temperature was the major climate constraints on the interannual variability in grassland GPP. Precipitation and solar radiation primarily controlled the interannual variability in desert GPP from July to September, while temperature became more limited than precipitation and solar radiation for desert GPP in May and June.

Regional contribution to variability and trends of global gross primary productivity

Terrestrial gross primary productivity (GPP) is the largest component of the global carbon cycle and a key process for understanding land ecosystems dynamics. In this study, we used GPP estimates from a combination of eight global biome models participating in the Inter-Sectoral Impact-Model Intercomparison Project phase 2a (ISIMIP2a), the Moderate Resolution Spectroradiometer (MODIS) GPP product, and a data-driven product (Model Tree Ensemble, MTE) to study the spatiotemporal variability of GPP at the regional and global levels. We found the 2000–2010 total global GPP estimated from the model ensemble to be 117 ± 13 Pg C yr−1 (mean ± 1 standard deviation), which was higher than MODIS (112 Pg C yr−1), and close to the MTE (120 Pg C yr−1). The spatial patterns of MODIS, MTE and ISIMIP2a GPP generally agree well, but their temporal trends are different, and the seasonality and inter-annual variability of GPP at the regional and global levels are not completely consistent. For the model ensemble, Tropical Latin America contributes the most to global GPP, Asian regions contribute the most to the global GPP trend, the Northern Hemisphere regions dominate the global GPP seasonal variations, and Oceania is likely the largest contributor to inter-annual variability of global GPP. However, we observed large uncertainties across the eight ISIMIP2a models, which are probably due to the differences in the formulation of underlying photosynthetic processes. The results of this study are useful in understanding the contributions of different regions to global GPP and its spatiotemporal variability, how the model- and observational-based GPP estimates differ from each other in time and space, and the relative strength of the eight models. Our results also highlight the models’ ability to capture the seasonality of GPP that are essential for understanding the inter-annual and seasonal variability of GPP as a major component of the carbon cycle.

Compensatory water effects link yearly global land CO2 sink changes to temperature.

Large interannual variations in the measured growth rate of atmospheric carbon dioxide (CO2) originate primarily from fluctuations in carbon uptake by land ecosystems. It remains uncertain, however, to what extent temperature and water availability control the carbon balance of land ecosystems across spatial and temporal scales. Here we use empirical models based on eddy covariance data and process-based models to investigate the effect of changes in temperature and water availability on gross primary productivity (GPP), terrestrial ecosystem respiration (TER) and net ecosystem exchange (NEE) at local and global scales. We find that water availability is the dominant driver of the local interannual variability in GPP and TER. To a lesser extent this is true also for NEE at the local scale, but when integrated globally, temporal NEE variability is mostly driven by temperature fluctuations. We suggest that this apparent paradox can be explained by two compensatory water effects. Temporal water-driven GPP and TER variations compensate locally, dampening water-driven NEE variability. Spatial water availability anomalies also compensate, leaving a dominant temperature signal in the year-to-year fluctuations of the land carbon sink. These findings help to reconcile seemingly contradictory reports regarding the importance of temperature and water in controlling the interannual variability of the terrestrial carbon balance. Our study indicates that spatial climate covariation drives the global carbon cycle response.

Direct and indirect effects of climatic variations on the interannual variability in net ecosystem exchange across terrestrial ecosystems

Climatic variables not only directly affect the interannual variability (IAV) in net ecosystem exchange of CO2 (NEE) but also indirectly drive it by changing the physiological parameters. Identifying these direct and indirect paths can reveal the underlying mechanisms of carbon (C) dynamics. In this study, we applied a path analysis using flux data from 65 sites to quantify the direct and indirect climatic effects on IAV in NEE and to evaluate the potential relationships among the climatic variables and physiological parameters that represent physiology and phenology of ecosystems. We found that the maximum photosynthetic rate was the most important factor for the IAV in gross primary productivity (GPP), which was mainly induced by the variation in vapour pressure deficit. For ecosystem respiration (RE), the most important drivers were GPP and the reference respiratory rate. The biome type regulated the direct and indirect paths, with distinctive differences between forests and non-forests, evergreen needleleaf forests and deciduous broadleaf forests, and between grasslands and croplands. Different paths were also found among wet, moist and dry ecosystems. However, the climatic variables can only partly explain the IAV in physiological parameters, suggesting that the latter may also result from other biotic and disturbance factors. In addition, the climatic variables related to NEE were not necessarily the same as those related to GPP and RE, indicating the emerging difficulty encountered when studying the IAV in NEE. Overall, our results highlight the contribution of certain physiological parameters to the IAV in C fluxes and the importance of biome type and multi-year water conditions, which should receive more attention in future experimental and modelling research.

Contrasting changes in gross primary productivity of different regions of North America as affected by warming in recent decades

Ecosystem responses to the increasing warming in recent decades across North America (NA) are spatially heterogeneous and partly uncertain. Here we examined the spatial and temporal variability of warming across different eco-regions of NA using long-term (1979–2010) climate data (North America Regional Reanalysis (NARR)) with 3-hourly time-step and 0.25°×0.25° spatial resolution and run a comprehensive mathematical process model, ecosys to study the impacts of this variability in warming on gross primary productivity (GPP). In a site scale test of model results, annual GPP modeled for pixels which corresponded to the locations of 20 eddy covariance flux towers correlated well (R2=0.76) with annual GPP derived from the towers in 2005. At continental scale, long-term annual average modeled GPP correlated well (geographically weighed regression R2=0.8) with MODIS GPP. GPP modeled in eastern temperate forests and most areas with lower mean annual air temperature (Ta), such as those in northern forests and Taiga, increased due to early spring and late autumn warming observed in NARR and these eco-regions contributed 92% of the increases in NA GPP over the last three decades. However, modeled GPP declined in most southwestern regions of NA (accounting >50% of the ecosystems with declining GPP), due to water stress from rising Ta and declining precipitation, implying that further warming and projected dryness in this region could further reduce NA carbon uptake. Overall, NA modeled GPP increased by 5.8% in the last 30 years, with a positive trend of +0.012±0.01PgCyr−1 and a range of −1.16 to +0.87PgCyr−1 caused by interannual variability of GPP from the long-term (1980–2010) mean. This variability was the greatest in southwest of US and part of the Great Plains, which could be as a result of frequent El Niño–Southern Oscillation’ events that led to major droughts.

Evaluation of the ORCHIDEE ecosystem model over Africa against 25 years of satellite-based water and carbon measurements

Few studies have evaluated land surface models for African ecosystems. Here we evaluate the Organizing Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) process-based model for the interannual variability (IAV) of the fraction of absorbed active radiation, the gross primary productivity (GPP), soil moisture, and evapotranspiration (ET). Two ORCHIDEE versions are tested, which differ by their soil hydrology parameterization, one with a two-layer simple bucket and the other a more complex 11-layer soil-water diffusion. In addition, we evaluate the sensitivity of climate forcing data, atmospheric CO2, and soil depth. Beside a very generic vegetation parameterization, ORCHIDEE simulates rather well the IAV of GPP and ET (0.5 < r < 0.9 interannual correlation) over Africa except in forestlands. The ORCHIDEE 11-layer version outperforms the two-layer version for simulating IAV of soil moisture, whereas both versions have similar performance of GPP and ET. Effects of CO2 trends, and of variable soil depth on the IAV of GPP, ET, and soil moisture are small, although these drivers influence the trends of these variables. The meteorological forcing data appear to be quite important for faithfully reproducing the IAV of simulated variables, suggesting that in regions with sparse weather station data, the model uncertainty is strongly related to uncertain meteorological forcing. Simulated variables are positively and strongly correlated with precipitation but negatively and weakly correlated with temperature and solar radiation. Model-derived and observation-based sensitivities are in agreement for the driving role of precipitation. However, the modeled GPP is too sensitive to precipitation, suggesting that processes such as increased water use efficiency during drought need to be incorporated in ORCHIDEE.

Carbon dioxide fluxes over a temperate meadow in eastern Inner Mongolia, China

Understanding the carbon dynamics in grassland is essential to precisely estimate global atmospheric carbon budget in response to climatic change. Eddy flux measurements were carried out during 2011 and 2012 to characterize seasonal and annual variability of carbon exchanges above a temperate meadow in eastern Inner Mongolia, China. The CO2 flux showed obvious diurnal variations and the monthly mean amplitudes of diurnal course followed June/July > August > May > September. The daily maximum NEE reached up to −8.0 and −7.7 g C m−2 for 2011 and 2012, respectively. CO2 uptake was mainly from May to August, with seasonal peaks of −16.0 g C m−2 day−1 in both two years. Gross primary production (GPP) and ecosystem respiration (Re) were −1,084.5, 987.1 g C m−2 year−1 in 2011, and −1,123.3, 1,040.2 g C m−2 year−1 in 2012, respectively. The meadow acted as a stable carbon sink, with integrated net ecosystem exchange (NEE) of −97.4 and −83.1 g C m−2 year−1 for 2011 and 2012, respectively. Compared with 2011, the ecosystem assimilated more carbon and meanwhile respired even more, leading to a less carbon sequestration in 2012. PAR and leaf area index (LAI) dominated the seasonal variations in NEE, with PAR explaining 61–69 % of the variance in NEE as LAI maintaining the plateau during June to July. Harvest significantly decreased ecosystem carbon uptake. The interannual variability in GPP and Re resulted primarily from the variations in temperature and its effect on biomass growth.

Remote sensing of annual terrestrial gross primary productivity from MODIS: an assessment using the FLUXNET La Thuile data set

Abstract. Gross primary productivity (GPP) is the largest and most variable component of the global terrestrial carbon cycle. Repeatable and accurate monitoring of terrestrial GPP is therefore critical for quantifying dynamics in regional-to-global carbon budgets. Remote sensing provides high frequency observations of terrestrial ecosystems and is widely used to monitor and model spatiotemporal variability in ecosystem properties and processes that affect terrestrial GPP. We used data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and FLUXNET to assess how well four metrics derived from remotely sensed vegetation indices (hereafter referred to as proxies) and six remote sensing-based models capture spatial and temporal variations in annual GPP. Specifically, we used the FLUXNET La Thuile data set, which includes several times more sites (144) and site years (422) than previous studies have used. Our results show that remotely sensed proxies and modeled GPP are able to capture significant spatial variation in mean annual GPP in every biome except croplands, but that the percentage of explained variance differed substantially across biomes (10–80%). The ability of remotely sensed proxies and models to explain interannual variability in GPP was even more limited. Remotely sensed proxies explained 40–60% of interannual variance in annual GPP in moisture-limited biomes, including grasslands and shrublands. However, none of the models or remotely sensed proxies explained statistically significant amounts of interannual variation in GPP in croplands, evergreen needleleaf forests, or deciduous broadleaf forests. Robust and repeatable characterization of spatiotemporal variability in carbon budgets is critically important and the carbon cycle science community is increasingly relying on remotely sensing data. Our analyses highlight the power of remote sensing-based models, but also provide bounds on the uncertainties associated with these models. Uncertainty in flux tower GPP, and difference between the footprints of MODIS pixels and flux tower measurements are acknowledged as unresolved challenges.

Technical Note: The Simple Diagnostic Photosynthesis and Respiration Model (SDPRM)

Abstract. We present a Simple Diagnostic Photosynthesis and Respiration Model (SDPRM) that has been developed based on pre-existing formulations. The photosynthesis model is based on the light use efficiency logic for calculating the gross primary production (GPP), while the ecosystem respiration (Reco) is a modified version of an Arrhenius-type equation. SDPRM is driven by satellite-derived fAPAR (fraction of Absorbed Photosynthetically Active Radiation) and climate data from the National Center for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP/NCAR). The model estimates 3-hourly values of GPP for seven major biomes and daily Reco. The motivation is to provide a priori fields of surface CO2 fluxes with fine temporal and spatial scales for atmospheric CO2 inversions. The estimated fluxes from SDPRM showed that the model is capable of producing flux estimates consistent with the ones inferred from atmospheric CO2 inversion or simulated from process-based models. In this Technical Note, different analyses were carried out to test the sensitivity of the estimated fluxes of GPP and CO2 to their driving forces. The spatial patterns of the climatic controls (temperature, precipitation, water) on the interannual variability of GPP are consistent with previous studies, even though SDPRM has a very simple structure and few adjustable parameters and hence it is much easier to modify in an inversion than more sophisticated process-based models. In SDPRM, temperature is a limiting factor for the interannual variability of Reco over cold boreal forest, while precipitation is the main limiting factor of Reco over the tropics and the southern hemisphere, consistent with previous regional studies.

Response of savanna gross primary productivity to interannual variability in rainfall

Studying the temporal pattern of savanna gross primary productivity (GPP) is essential for predicting the response of the biome to global environmental changes. In this study, MODIS satellite data coupled with eddy covariance based flux measurements were used to estimate GPP using a remote sensing based light use efficiency model across a significant rainfall gradient in the Northern Territory (NT) region of Australia. Closed forest that occurred in wet and often fireproof environments assimilated (GPP) 4–6 times more carbon than grasslands and Acacia woodlands that grow in arid environments (&lt;600 mm annual rainfall). However, due to their small spatial extent, closed forests contributed &lt;0.5% of the regional budget compared to savanna woodlands (86%) and grasslands (32%). Annual rainfall was found to exert a significant influence on GPP for different vegetation types except for closed forest which was less sensitive to above-average rainfall. Interannual variability in GPP showed that arid ecosystems had a higher variation (&gt;20%) compared to woodlands and forest (∼5%). This variation in GPP was correlated with that of rainfall (R2 = 0.88, p&lt;0.05). Analysis of the impact of wettest and driest years on GPP showed a strong positive correlation between the magnitude of the relative maxima in rainfall and maxima in GPP (R2 = 0.89, p&lt;0.05). In contrast, the relative rainfall minima exhibited an insignificant relationship with relative GPP minima (R2 = 0.45, p = 0.07). These findings provide valuable information on the carbon uptake across the savanna biome and show the sensitivity of different vegetation systems to rainfall, a variable that may change in quantity and variability with projected climate change. Such data also show regions of high levels of carbon that could be linked with savanna management to protect the resources in the Australian savannas.

Estimation of gross primary production over the terrestrial ecosystems in China

Gross primary production (GPP) is of significant importance for the terrestrial carbon budget and climate change, but large uncertainties in the regional estimation of GPP still remain over the terrestrial ecosystems in China. Eddy covariance (EC) flux towers measure continuous ecosystem-level exchange of carbon dioxide (CO2) and provide a promising way to estimate GPP. We used the measurements from 32 EC sites to examine the performance of a light use efficiency model (i.e., EC-LUE) at various ecosystem types, including 23 sites in China and 9 sites in adjacent areas with the similar climate environments. No significant systematic error was found in the EC-LUE model predictions, which explained 79% and 62% of the GPP variation at the validation sites with C3 and C4 vegetation, respectively. Regional patterns of GPP at a spatial resolution of 10km×10km from 2000 to 2009 were determined using the MERRA (Modern Era Retrospective-analysis for Research and Applications) reanalysis dataset and MODIS (MODerate resolution Imaging Spectroradiometer). China's terrestrial GPP decreased from southeast toward the northwest, with the highest values occurring over tropical forests areas, and the lowest values in dry regions. The annual GPP of land in China varied between 5.63PgC and 6.39PgC, with a mean value of 6.04PgC, which accounted for 4.90–6.29% of the world's total terrestrial GPP. The GPP densities of most vegetation types in China such as evergreen needleleaf forests, deciduous needleleaf forests, mixed forests, woody savannas, and permanent wetlands were much higher than the respective global GPP densities. However, a high proportion of sparsely vegetated area in China resulted in the overall low GPP. The inter-annual variability in GPP was significantly influenced by air temperature (R2=0.66, P<0.05), precipitation (R2=0.71, P<0.05), and normalized difference vegetation index (NDVI) (R2=0.83, P<0.05), respectively.