Net primary production and net ecosystem production of a boreal black spruce wildfire chronosequence

Net primary production (NPP) was measured in seven black spruce (Picea mariana (Mill.) BSP)‐dominated sites comprising a boreal forest chronosequence near Thompson, Man., Canada. The sites burned between 1998 and 1850, and each contained separate well‐ and poorly drained stands. All components of NPP were measured, most for 3 consecutive years. Total NPP was low (50–100 g C m^?2 yr^?1) immediately after fire, highest 12–20 years after fire (332 and 521 g C m^?2 yr^?1 in the dry and wet stands, respectively) but 50% lower than this in the oldest stands. Tree NPP was highest 37 years after fire but 16–39% lower in older stands, and was dominated by deciduous seedlings in the young stands and by black spruce trees (>85%) in the older stands. The chronosequence was unreplicated but these results were consistent with 14 secondary sites sampled across the landscape. Bryophytes comprised a large percentage of aboveground NPP in the poorly drained stands, while belowground NPP was 0–40% of total NPP. Interannual NPP variability was greater in the youngest stands, the poorly drained stands, and for understory and detritus production. Net ecosystem production (NEP), calculated using heterotrophic soil and woody debris respiration data from previous studies in this chronosequence, implied that the youngest stands were moderate C sources (roughly, 100 g C m^?2 yr^?1), the middle‐aged stands relatively strong sinks (100–300 g C m^?2 yr^?1), and the oldest stands about neutral with respect to the atmosphere. The ecosystem approach employed in this study provided realistic estimates of chronosequence NPP and NEP, demonstrated the profound impact of wildfire on forest–atmosphere C exchange, and emphasized the need to account for soil drainage, bryophyte production, and species succession when modeling boreal forest C fluxes.     

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.     

Forest carbon use efficiency: is respiration a constant fraction of gross primary production?

Carbon‐use efficiency (CUE), the ratio of net primary production (NPP) to gross primary production (GPP), describes the capacity of forests to transfer carbon (C) from the atmosphere to terrestrial biomass. It is widely assumed in many landscape‐scale carbon‐cycling models that CUE for forests is a constant value of ∼0.5. To achieve a constant CUE, tree respiration must be a constant fraction of canopy photosynthesis. We conducted a literature survey to test the hypothesis that CUE is constant and universal among forest ecosystems. Of the 60 data points obtained from 26 papers published since 1975, more than half reported values of GPP that were not estimated independently from NPP; values of CUE calculated from independent estimates of GPP were greater than those calculated from estimates of GPP derived from NPP. The slope of the relationship between NPP and GPP for all forests was 0.53, but values of CUE varied from 0.23 to 0.83 for different forest types. CUE decreased with increasing age, and a substantial portion of the variation among forest types was caused by differences in stand age. When corrected for age the mean value of CUE was greatest for temperate deciduous forests and lowest for boreal forests. CUE also increased as the ratio of leaf mass‐to‐total mass increased. Contrary to the assumption of constancy, substantial variation in CUE has been reported in the literature. It may be inappropriate to assume that respiration is a constant fraction of GPP as adhering to this assumption may contribute to incorrect estimates of C cycles. A 20% error in current estimates of CUE used in landscape models (i.e. ranging from 0.4 to 0.6) could misrepresent an amount of C equal to total anthropogenic emissions of CO₂ when scaled to the terrestrial biosphere.     

Climate‐driven uncertainties in modeling terrestrial gross primary production: a site level to global‐scale analysis

We used a land surface model to quantify the causes and extents of biases in terrestrial gross primary production (GPP) due to the use of meteorological reanalysis datasets. We first calibrated the model using meteorology and eddy covariance data from 25 flux tower sites ranging from the tropics to the northern high latitudes and subsequently repeated the site simulations using two reanalysis datasets: NCEP/NCAR and CRUNCEP. The results show that at most sites, the reanalysis‐driven GPP bias was significantly positive with respect to the observed meteorology‐driven simulations. Notably, the absolute GPP bias was highest at the tropical evergreen tree sites, averaging up to ca. 0.45 kg C m^?2 yr^?1 across sites (ca. 15% of site level GPP). At the northern mid‐/high‐latitude broadleaf deciduous and the needleleaf evergreen tree sites, the corresponding annual GPP biases were up to 20%. For the nontree sites, average annual biases of up to ca. 20–30% were simulated within savanna, grassland, and shrubland vegetation types. At the tree sites, the biases in short‐wave radiation and humidity strongly influenced the GPP biases, while the nontree sites were more affected by biases in factors controlling water stress (precipitation, humidity, and air temperature). In this study, we also discuss the influence of seasonal patterns of meteorological biases on GPP. Finally, using model simulations for the global land surface, we discuss the potential impacts of site‐level reanalysis‐driven biases on the global estimates of GPP. In a broader context, our results can have important consequences on other terrestrial ecosystem fluxes (e.g., net primary production, net ecosystem production, energy/water fluxes) and reservoirs (e.g., soil carbon stocks). In a complementary study (Barman et al., 2013 ), we extend the present analysis for latent and sensible heat fluxes, thus consistently integrating the analysis of climate‐driven uncertainties in carbon, energy, and water fluxes using a single modeling framework.     

Estimates of soil respiration and net primary production of three forests at different succession stages in South China

Soil respiration (heterotropic and autotropic respiration, R_g) and aboveground litter fall carbon were measured at three forests at different succession (early, middle and advanced) stages in Dinghushan Biosphere Reserve, Southern China. It was found that the soil respiration increases exponentially with soil temperature at 5 cm depth (T_s) according to the relation R_g=a exp(bT_s), and the more advanced forest community during succession has a higher value of a because of higher litter carbon input than the forests at early or middle succession stages. It was also found that the monthly soil respiration is linearly correlated with the aboveground litter carbon input of the previous month. Using measurements of aboveground litter and soil respiration, the net primary productions (NPPs) of three forests were estimated using nonlinear inversion. They are 475, 678 and 1148 g C m^?2 yr^?1 for the Masson pine forest (MPF), coniferous and broad‐leaf mixed forest (MF) and subtropical monsoon evergreen broad‐leaf forest (MEBF), respectively, in year 2003/2004, of which 54%, 37% and 62% are belowground NPP for those three respective forests if no change in live plant biomass is assumed. After taking account of the decrease in live plant biomass, we estimated the NPP of the subtropical MEBF is 970 g C m^?2 yr^?1 in year 2003/2004. Total amount of carbon allocated below ground for plant roots is 388 g C m^?2 yr^?1 for the MPF, 504 g C m^?2 yr^?1 for the coniferous and broad‐leaf MF and 1254 g C m^?2 yr^?1 for the subtropical MEBF in 2003/2004. Our results support the hypothesis that the amount of carbon allocation belowground increases during forest succession.     

A cross-biome comparison of daily light use efficiency for gross primary production

Vegetation light use efficiency is a key physiological parameter at the canopy scale, and at the daily time step is a component of remote sensing algorithms for scaling gross primary production (GPP) and net primary production (NPP) over regional to global domains. For the purposes of calibrating and validating the light use efficiency ( ε _g) algorithms, the components of ε _g– absorbed photosynthetically active radiation (APAR) and ecosystem GPP – must be measured in a variety of environments. Micrometeorological and mass flux measurements at eddy covariance flux towers can be used to estimate APAR and GPP, and the emerging network of flux tower sites offers the opportunity to investigate spatial and temporal patterns in ε _g at the daily time step. In this study, we examined the relationship of daily GPP to APAR, and relationships of ε _g to climatic variables, at four micrometeorological flux tower sites – an agricultural field, a tallgrass prairie, a deciduous forest, and a boreal forest. The relationship of GPP to APAR was close to linear at the tallgrass prairie site but more nearly hyperbolic at the other sites. The sites differed in the mean and range of daily ε _g, with higher values associated with the agricultural field than the boreal forest. ε_g decreased with increasing APAR at all sites, a function of mid‐day saturation of GPP and higher ε _g under overcast conditions. ε _g was generally not well correlated with vapor pressure deficit or maximum daily temperature. At the agricultural site, a ε _g decline towards the end of the growing season was associated with a decrease in foliar nitrogen concentration. At the tallgrass prairie site, a decline in ε _g in August was associated with soil drought. These results support inclusion of parameters for cloudiness and the phenological status of the vegetation, as well as use of biome‐specific parameterization, in operational ε _g algorithms.     

Biomass carbon storage and net primary production in different habitats of Hunshandake Sandland, China

Terrestrial ecosystems are playing important roles in global carbon cycling. However, the information is still limited with regard to the semi-arid sandland or desert area, compared with the thorough studies on forest and grassland. We here estimated the biomass carbon storage, net primary production (NPP) and rain use efficiency (RUE) of Hunshandake Sandland, a semi-arid sandy region in Inner Mongolia covered with vegetation of Siberian elm (Ulmus pumila L.) sparse forest grassland. Five main habitats, i.e. fixed dunes, semi-fixed dunes, shifting dunes, lowland, and wetland, were compared to analyze the patterns of carbon storage and NPP distribution. The average biomass (9.19 Mg C ha^?1) and NPP (4.79 Mg C ha^?1 yr^?1) of the sparse forest grassland were respectively 82% and 54% higher than the mean level of the surrounding temperate grassland. Governed by the same climate, sparse forest grassland ecosystem had RUE almost twice that of surrounding grassland. The ratio of below to aboveground biomass was 3.5: 1 in the sandland, indicating that most of the vegetational carbon was stored in belowground pool. Although trees were functionally critical in maintaining the integrity of sparse forest grassland, they accounted for only 10.6% and 1.2% of the biomass and NPP, respectively. The sparse forest grassland in Hunshandake Sandland should be recognized as a temperate savanna ecosystem which is distinctively different from typical temperate grassland in the same region as evidenced by the higher NPP and vegetation carbon storage. Well designed management and restoration efforts can potentially sustain ecosystem services in both forage production and carbon sequestration.     

Biomass carbon storage and net primary production in different habitats of Hunshandake Sandland, China

Terrestrial ecosystems are playing important roles in global carbon cycling. However, the information is still limited with regard to the semi-arid sandland or desert area, compared with the thorough studies on forest and grassland. We here estimated the biomass carbon storage, net primary production (NPP) and rain use efficiency (RUE) of Hunshandake Sandland, a semi-arid sandy region in Inner Mongolia covered with vegetation of Siberian elm (Ulmus pumila L.) sparse forest grassland. Five main habitats, i.e. fixed dunes, semi-fixed dunes, shifting dunes, lowland, and wetland, were compared to analyze the patterns of carbon storage and NPP distribution. The average biomass (9.19 Mg C ha^?1) and NPP (4.79 Mg C ha^?1 yr^?1) of the sparse forest grassland were respectively 82% and 54% higher than the mean level of the surrounding temperate grassland. Governed by the same climate, sparse forest grassland ecosystem had RUE almost twice that of surrounding grassland. The ratio of below to aboveground biomass was 3.5: 1 in the sandland, indicating that most of the vegetational carbon was stored in belowground pool. Although trees were functionally critical in maintaining the integrity of sparse forest grassland, they accounted for only 10.6% and 1.2% of the biomass and NPP, respectively. The sparse forest grassland in Hunshandake Sandland should be recognized as a temperate savanna ecosystem which is distinctively different from typical temperate grassland in the same region as evidenced by the higher NPP and vegetation carbon storage. Well designed management and restoration efforts can potentially sustain ecosystem services in both forage production and carbon sequestration.     

Diagnosing and assessing uncertainties of terrestrial ecosystem models in a multimodel ensemble experiment: 1. Primary production

We conducted an ensemble modeling exercise using the Terrestrial Observation and Prediction System (TOPS) to evaluate sources of uncertainty in carbon flux estimates resulting from structural differences among ecosystem models. The experiment ran public‐domain versions of biome‐bgc, lpj, casa , and tops‐bgc over North America at 8 km resolution and for the period of 1982–2006. We developed the Hierarchical Framework for Diagnosing Ecosystem Models (HFDEM) to separate the simulated biogeochemistry into a cascade of three functional tiers and sequentially examine their characteristics in climate (temperature–precipitation) and other spaces. Analysis of the simulated annual gross primary production (GPP) in the climate domain indicates a general agreement among the models, all showing optimal GPP in regions where the relationship between annual average temperature (T, °C) and annual total precipitation (P, mm) is defined by P=50T+500. However, differences in simulated GPP are identified in magnitudes and distribution patterns. For forests, the GPP gradient along P=50T+500 ranges from ～50 g C yr^?1 m^?2 °C^?1 (casa ) to ～125 g C yr^?1 m^?2 °C^?1 (biome‐bgc ) in cold/temperate regions; for nonforests, the diversity among GPP distributions is even larger. Positive linear relationships are found between annual GPP and annual mean leaf area index (LAI) in all models. For biome‐bgc and lpj , such relationships lead to a positive feedback from LAI growth to GPP enhancement. Different approaches to constrain this feedback lead to different sensitivity of the models to disturbances such as fire, which contribute significantly to the diversity in GPP stated above. The ratios between independently simulated NPP and GPP are close to 50% on average; however, their distribution patterns vary significantly between models, reflecting the difficulties in estimating autotrophic respiration across various climate regimes. Although these results are drawn from our experiments with the tested model versions, the developed methodology has potential for other model exercises.     

Terrestrial gross primary production inferred from satellite fluorescence and vegetation models

Determining the spatial and temporal distribution of terrestrial gross primary production (GPP) is a critical step in closing the Earth's carbon budget. Dynamical global vegetation models (DGVMs) provide mechanistic insight into GPP variability but diverge in predicting the response to climate in poorly investigated regions. Recent advances in the remote sensing of solar‐induced chlorophyll fluorescence (SIF) opens up a new possibility to provide direct global observational constraints for GPP. Here, we apply an optimal estimation approach to infer the global distribution of GPP from an ensemble of eight DGVMs constrained by global measurements of SIF from the Greenhouse Gases Observing SATellite (GOSAT). These estimates are compared to flux tower data in N. America, Europe, and tropical S. America, with careful consideration of scale differences between models, GOSAT, and flux towers. Assimilation of GOSAT SIF with DGVMs causes a redistribution of global productivity from northern latitudes to the tropics of 7–8 Pg C yr^?1 from 2010 to 2012, with reduced GPP in northern forests (~3.6 Pg C yr^?1) and enhanced GPP in tropical forests (~3.7 Pg C yr^?1). This leads to improvements in the structure of the seasonal cycle, including earlier dry season GPP loss and enhanced peak‐to‐trough GPP in tropical forests within the Amazon Basin and reduced growing season length in northern croplands and deciduous forests. Uncertainty in predicted GPP (estimated from the spread of DGVMs) is reduced by 40–70% during peak productivity suggesting the assimilation of GOSAT SIF with models is well‐suited for benchmarking. We conclude that satellite fluorescence augurs a new opportunity to quantify the GPP response to climate drivers and the potential to constrain predictions of carbon cycle evolution.     

Primary production and carbon allocation in relation to nutrient supply in a tropical experimental forest

Nutrient supply commonly limits aboveground plant productivity in forests, but the effects of an altered nutrient supply on gross primary production (GPP) and patterns of carbon (C) allocation remain poorly characterized. Increased nutrient supply may lead to a higher aboveground net primary production (ANPP), but a lower total belowground carbon allocation (TBCA), with little change in either aboveground plant respiration (APR) or GPP. Alternatively, increases in nutrient supply may increase GPP, with the quantity of GPP allocated aboveground increasing more steeply than the quantity of GPP allocated belowground. To examine the effects of an elevated nutrient supply on the C allocation patterns in forests, we determined whole‐ecosystem C budgets in unfertilized plots of Eucalyptus saligna and in adjacent plots receiving regular additions of 65 kg N ha^?1, 31 kg P ha^?1, 46 kg K ha^?1, and macro‐ and micronutrients. We measured the absolute flux of C allocated to the components of GPP (ANPP, TBCA and APR), as well as the fraction of GPP allocated to these components. Fertilization dramatically increased GPP. Averaged over 3 years, GPP in the fertilized plots was 34% higher than that in the unfertilized controls (3.95 vs. 2.95 kg C m^?2 yr^?1). Fertilization‐related increases in GPP were allocated entirely aboveground – ANPP was 85% higher and APR was 57% higher in the fertilized than in the control plots, while TBCA did not differ significantly between treatments. Carbon use efficiency (NPP/GPP) was slightly higher in the fertilized (0.53) compared with the control plots (0.51). Overall, fertilization increased ANPP and APR, and these increases were related to a greater GPP and an increase in the fraction of GPP allocated aboveground.     

Satellite estimates of productivity and light use efficiency in United States agriculture, 1982–98

Remote sensing of net primary production (NPP) is a critical tool for assessing spatial and temporal patterns of carbon exchange between the atmosphere and biosphere. However, satellite estimates suffer from a lack of large‐scale field data needed for validation, as well as the need to parameterize plant light‐use efficiencies (LUEs). In this study, we estimated cropland NPP with the Carnegie‐Ames‐Stanford‐Approach (CASA), a biogeochemical model driven by satellite observations, and then compared these results with field estimates based on harvest data from United States Department of Agriculture National Agriculture Statistics Service (NASS) county statistics. Observed interannual variations in NPP over a 17‐year period were well modelled by CASA, with exceptions mainly due to occasional difficulties in estimating NPP from harvest yields. The role of environmental stressors in agriculture was investigated by running CASA with and without temperature and moisture down‐regulators, which are used in the model to simulate climate impacts on plant LUE. In most cases, correlations with NASS data were highest with modelled stresses, while the opposite was true for irrigated and temperature resistant crops. Analysis of the spatial variability in computed LUE revealed significantly higher values for corn than for other crops, suggesting a simple parameterization of LUE for future studies based on the fraction of area with corn. Absolute values of LUE were much lower than those reported in field trials, due to uncommonly high yields in most field trials, as well as overestimates of absorbed radiation in CASA attributed to bias from temporal compositing of satellite data. Total NPP for US croplands, excluding Alaska and Hawaii, was estimated as 0.62 Pg C year^?1, representing ～20% of total US NPP, and exhibited a positive trend of 3.7 Tg C year^?2. These results have several implications for large‐scale carbon cycle research that are discussed, and are especially relevant for studies of the role of agriculture in the global carbon balance.     

An improved indicator of simulated grassland production based on MODIS NDVI and GPP data: A case study in the Sichuan province,China

Grassland monitoring is important for both global change research and regional sustainable development. Gross primary production (GPP) is one of the key factors for understanding grass growing conditions. Methods for estimating GPP are plentiful, and the light use efficiency (LUE) model based on remote sensing data is widely used. The MODIS GPP product, which is employed by the National Aeronautics and Space Administration (NASA), is calculated using the LUE model and the surface reflection data from the Moderate Resolution Imaging Spectroradiometer onboard the Terra/Aqua satellite. The MODIS GPP product harbors its own uncertainties arising from the sources and parameters, such as FPAR and light use efficiency (ɛ). In this study, we propose an improved indicator for monitoring grassland based on MODIS GPP and NDVI data. Fractional vegetation coverage and the percentage of grass area (1 km²) were used to reduce the mixed pixel effect. A function of NDVI was used to simulate the light use efficiency and FPAR. The modified GPP data were calculated and validated with in situ measured data from the Sichuan province, China, 2011. The results indicated that the modified GPP data were a more accurate indicator for monitoring grassland than previous indicators, and the precision of grass production simulated by SsGPP_ndvi reached 85.6%. Spatial statistic results were consistent with the practical condition in most cases. Since MODIS data are available twice a day, the improved indicator can meet the actual requirement of grassland monitoring at regional scale.     

Modelling the vegetation of China using the process-based equilibrium terrestrial biosphere model BIOME3

1 We model the potential vegetation and annual net primary production (NPP) of China on a 10′ grid under the present climate using the processed‐based equilibrium terrestrial biosphere model BIOME3. The simulated distribution of the vegetation was in general in good agreement with the potential natural vegetation based on a numerical comparison between the two maps using the ΔV statistic (ΔV = 0.23). Predicted and measured NPP were also similar, especially in terms of biome‐averages. 2 A coupled ocean–atmosphere general circulation model including sulphate aerosols was used to drive a double greenhouse gas scenario for 2070–2099. Simulated vegetation maps from two different CO₂ scenarios (340 and 500 p.p.m.v.) were compared to the baseline biome map using ΔV. Climate change alone produced a large reduction in desert, alpine tundra and ice/polar desert, and a general pole‐ward shift of the boreal, temperate deciduous, warm–temperate evergreen and tropical forest belts, a decline in boreal deciduous forest and the appearance of tropical deciduous forest. The inclusion of CO₂ physiological effects led to a marked decrease in moist savannas and desert, a general decrease for grasslands and steppe, and disappearance of xeric woodland/scrub. Temperate deciduous broadleaved forest, however, shifted north to occupy nearly half the area of previously temperate mixed forest. 3 The impact of climate change and increasing CO₂ is not only on biogeography, but also on potential NPP. The NPP values for most of the biomes in the scenarios with CO₂ set at 340 p.p.m.v. and 500 p.p.m.v. are greater than those under the current climate, except for the temperate deciduous forest, temperate evergreen broadleaved forest, tropical rain forest, tropical seasonal forest, and xeric woodland/scrub biomes. Total vegetation and total carbon is simulated to increase significantly in the future climate scenario, both with and without the CO₂ direct physiological effect. 4 Our results show that the global process‐based equilibrium terrestrial biosphere model BIOME3 can be used successfully at a regional scale.     

Postfire response of North American boreal forest net primary productivity analyzed with satellite observations

Fire is a major disturbance in the boreal forest, and has been shown to release significant amounts of carbon (C) to the atmosphere through combustion. However, less is known about the effects on ecosystems following fire, which include reduced productivity and changes in decomposition in the decade immediately following the disturbance. In this study, we assessed the impact of fire on net primary productivity (NPP) in the North American boreal forest using a 17‐year record of satellite NDVI observations at 8‐ km spatial resolution together with a light‐use efficiency model. We identified 61 fire scars in the satellite observations using digitized fire burn perimeters from a database of large fires. We studied the postfire response of NPP by analyzing the most impacted pixel within each burned area. NPP decreased in the year following the fire by 60–260 g C m^?2 yr^?1 (30–80%). By comparing pre‐ and postfire observations, we estimated a mean NPP recovery period for boreal forests of about 9 years, with substantial variability among fires. We incorporated this behavior into a carbon cycle model simulation to demonstrate these effects on net ecosystem production. The disturbance resulted in a release of C to the atmosphere during the first 8 years, followed by a small, but long‐lived, sink lasting 150 years. Postfire net emissions were three times as large as from a model run without changing NPP. However, only small differences in the C cycle occurred between runs after 8 years due to the rapid recovery of NPP. We conclude by discussing the effects of fire on the long‐term continental trends in satellite NDVI observed across boreal North America during the 1980s and 1990s.     

Impacts of Diffuse Radiation on Light Use Efficiency across Terrestrial Ecosystems Based on Eddy Covariance Observation in China

Ecosystem light use efficiency (LUE) is a key factor of production models for gross primary production (GPP) predictions. Previous studies revealed that ecosystem LUE could be significantly enhanced by an increase on diffuse radiation. Under large spatial heterogeneity and increasing annual diffuse radiation in China, eddy covariance flux data at 6 sites across different ecosystems from 2003 to 2007 were used to investigate the impacts of diffuse radiation indicated by the cloudiness index (CI) on ecosystem LUE in grassland and forest ecosystems. Our results showed that the ecosystem LUE at the six sites was significantly correlated with the cloudiness variation (0.24≤R²≤0.85), especially at the Changbaishan temperate forest ecosystem (R² = 0.85). Meanwhile, the CI values appeared more frequently between 0.8 and 1.0 in two subtropical forest ecosystems (Qianyanzhou and Dinghushan) and were much larger than those in temperate ecosystems. Besides, cloudiness thresholds which were favorable for enhancing ecosystem carbon sequestration existed at the three forest sites, respectively. Our research confirmed that the ecosystem LUE at the six sites in China was positively responsive to the diffuse radiation, and the cloudiness index could be used as an environmental regulator for LUE modeling in regional GPP prediction.     

Strong links between teleconnections and ecosystem exchange found at a Pacific Northwest old-growth forest from flux tower and MODIS EVI data

Variability in three Pacific teleconnection patterns are examined to see if net carbon exchange at a low‐elevation, old‐growth forest is affected by climatic changes associated with these periodicities. Examined are the Pacific Decadal Oscillation (PDO), Pacific/North American Oscillation (PNA) and El Niño‐Southern Oscillation (ENSO). We use 9 years of eddy covariance CO₂, H₂O and energy fluxes measured at the Wind River AmeriFlux site, Washington, USA and 8 years of tower‐pixel remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to address this question. We compute a new Composite Climate Index (CCI) based on the three Pacific Oscillations to divide the measurement period into positive‐ (2003 and 2005), negative‐ (1999 and 2000) and neutral‐phase climate years (2001, 2002, 2004, 2006 and 2007). The forest transitioned from an annual net carbon sink (NEP=+217 g C m^?2 yr^?1, 1999) to a source (NEP=?100 g C m^?2 yr^?1, 2003) during two dominant teleconnection patterns. Net ecosystem productivity (NEP), water use efficiency (WUE) and light use efficiency (LUE) were significantly different (P<0.01) during positive (NEP=?0.27 g C m^?2 day^?1, WUE=4.1 mg C g^?1 H₂O, LUE=0.94 g C MJ^?1) and negative (NEP=+0.37 g C m^?2 day^?1, WUE=3.4 mg C g^?1 H₂O, LUE=0.83 g C MJ^?1) climate phases. The CCI was linked to variability in the MODIS Enhanced Vegetation Index (EVI) but not to MODIS Fraction of absorbed Photosynthetically Active Radiation (FPAR). EVI was highest during negative climate phases (1999 and 2000) and was positively correlated with NEP and showed potential for using MODIS to estimate teleconnection‐driven anomalies in ecosystem CO₂ exchange in old‐growth forests. This work suggests that any increase in the strength or frequency of ENSO coinciding with in‐phase, low frequency Pacific oscillations (PDO and PNA) will likely increase CO₂ uptake variability in Pacific Northwest conifer forests.     

Importance of disturbance history on net primary productivity in the world's most productive forests and implications for the global carbon cycle

Analysis of growth and biomass turnover in natural forests of Eucalyptus regnans, the world's tallest angiosperm, reveals it is also the world's most productive forest type, with fire disturbance an important mediator of net primary productivity (NPP). A comprehensive empirical database was used to calculate the averaged temporal pattern of NPP from regeneration to 250 years age. NPP peaks at 23.1 ± 3.8 (95% interquantile range) Mg C ha^?1 year^?1 at age 14 years, and declines gradually to about 9.2 ± 0.8 Mg C ha^?1 year^?1 at 130 years, with an average NPP over 250 years of 11.4 ± 1.1 Mg C ha^?1 year^?1, a value similar to the most productive temperate and tropical forests around the world. We then applied the age‐class distribution of E. regnans resulting from relatively recent historical fires to estimate current NPP for the forest estate. Values of NPP were 40% higher (13 Mg C ha^?1 year^?1) than if forests were assumed to be at maturity (9.2 Mg C ha^?1 year^?1). The empirically derived NPP time series for the E. regnans estate was then compared against predictions from 21 global circulation models, showing that none of them had the capacity to simulate a post‐disturbance peak in NPP, as found in E. regnans. The potential importance of disturbance impacts on NPP was further tested by applying a similar approach to the temperate forests of conterminous United States and of China. Allowing for the effects of disturbance, NPP summed across both regions was on average 11% (or 194 Tg C/year) greater than if all forests were assumed to be in a mature state. The results illustrate the importance of accounting for past disturbance history and growth stage when estimating forest primary productivity, with implications for carbon balance modelling at local to global scales.     

Net primary production, carbon storage and climate change in Chinese biomes

Net primary production (NPP) and leaf area index (LAI) of Chinese biomes were simulated by BIOME3 under the present climate, and their responses to climate change and doubled CO₂ under a future climatic scenario using output from Hadley Center coupled ocean‐atmosphere general circulation model with CO₂ modelled at 340 and 500 ppmv. The model estimated annual mean NPP of the biomes in China to be between 0 and 1270.7 gC m^‐2 yr^‐1 at present. The highest productivity was found in tropical seasonal and rain forests while temperate forests had an intermediate NPP, which is higher than a lower NPP of temperate savannas, grasslands and steppes. The lowest NPP occurred in desert, alpine tundra and ice/polar desert in cold or arid regions, especially on the Tibetan Plateau. The lowest monthly NPP of each biome occurred generally in February and the highest monthly NPP occurred during the summer (June to August). The annual mean NPP and LAI of most of biomes at changed climate with CO₂ at 340 and 500 ppmv (direct effects on physiology) would be greater than present. The direct effects of carbon dioxide on plant physiology result in significant increase of LAI and NPP. The carbon storage of Chinese biomes at present and changed climates was calculated by the carbon density and vegetation area method. The present estimates of carbon storage are totally 175.83 × 10¹² gC (57.57 × 10¹² gC in vegetation and 118.28 × 10¹² gC in soils). Changed climate without and with the CO₂ direct physiological effects will result in an increase of carbon storage of 5.1 and 16.33 × 10¹², gC compared to present, respectively. The interaction between elevated CO₂ and climate change plays an important role in the overall responses of NPP and carbon to climate change.     

A new seasonal‐deciduous spring phenology submodel in the Community Land Model 4.5: impacts on carbon and water cycling under future climate scenarios

A spring phenology model that combines photoperiod with accumulated heating and chilling to predict spring leaf‐out dates is optimized using PhenoCam observations and coupled into the Community Land Model (CLM) 4.5. In head‐to‐head comparison (using satellite data from 2003 to 2013 for validation) for model grid cells over the Northern Hemisphere deciduous broadleaf forests (5.5 million km²), we found that the revised model substantially outperformed the standard CLM seasonal‐deciduous spring phenology submodel at both coarse (0.9 × 1.25°) and fine (1 km) scales. The revised model also does a better job of representing recent (decadal) phenological trends observed globally by MODIS, as well as long‐term trends (1950–2014) in the PEP725 European phenology dataset. Moreover, forward model runs suggested a stronger advancement (up to 11 days) of spring leaf‐out by the end of the 21st century for the revised model. Trends toward earlier advancement are predicted for deciduous forests across the whole Northern Hemisphere boreal and temperate deciduous forest region for the revised model, whereas the standard model predicts earlier leaf‐out in colder regions, but later leaf‐out in warmer regions, and no trend globally. The earlier spring leaf‐out predicted by the revised model resulted in enhanced gross primary production (up to 0.6 Pg C yr^?1) and evapotranspiration (up to 24 mm yr^?1) when results were integrated across the study region. These results suggest that the standard seasonal‐deciduous submodel in CLM should be reconsidered, otherwise substantial errors in predictions of key land–atmosphere interactions and feedbacks may result.