Monthly Gross Primary Productivity Research Articles

Exploring the Spatiotemporal Alterations in China’s GPP Based on the DTEC Model

Gross primary productivity (GPP) is a reliable measure of the carbon sink potential of terrestrial ecosystems and is an essential element of terrestrial carbon cycle research. This study employs the diffuse fraction-based two-leaf light-use efficiency (DTEC) model to imitate China’s monthly GPP from 2001 to 2020. We studied the trend of GPP, investigated its relationship with climatic factors, and separated the contributions of climate change and human activities. The findings showed that the DTEC model was widely applicable in China. During the study period, China’s average GPP increased significantly, by 9.77 g C m−2 yr−1 (p < 0.001). The detrimental effect of aerosol optical depth (AOD) on GPP was more widespread than that of total precipitation, temperature, and solar radiation. Areas that benefited from AOD, such as Northwest China, experienced significant increases in GPP. Climate change and human activities had a primary and positive influence on GPP during the study period, accounting for 28% and 72% of the increase, respectively. Human activities, particularly ecological restoration projects and the adoption of advanced agricultural technologies, played a significant role in China’s GPP growth. China’s afforestation plan was particularly notable, with the GPP increasing in afforestation areas at a rate greater than 10 g C m−2 yr−1. This research provides a theoretical foundation for the long-term management of China’s terrestrial ecosystems and helps develop adaptive ecological restoration tactics.

Global prediction of gross primary productivity under future climate change

The ecosystem gross primary productivity (GPP) is crucial to land-atmosphere carbon exchanges, and changes in global GPP as well as its influencing factors have been well studied in recent years. However, identifying the spatio-temporal variations of global GPP under future climate changes is still a challenging issue. This study aims to develop data-driven approach for predicting the global GPP as well as its monthly and annual variations up to the year 2100 under changing climate. Specifically, Catboost was employed to examine the potential relationship between the GPP and environmental factors, with climate variables, CO2 concentration and terrain attributes being selected as environmental factors. The predicted monthly and annual GPP from Coupled Model Intercomparison Project phase 6 (CMIP6) under future SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5 scenarios were analyzed. The results indicate that the global GPP is predicted to increase under the future climate change in the 21st century. The annual GPP is expected to be 115.122 Pg C, 116.537 Pg C, 117.626 Pg C, and 120.097 Pg C in 2100 under four future scenarios, and the predicted monthly GPP shows seasonal difference. Meanwhile, GPP tends to increase in the northern mid–high latitude regions and decrease in the equatorial regions. For the climate zones form Köppen-Geiger classification, the arid, cold, and polar zones present increased GPP, while GPP in the tropical zone will decrease in the future. Moreover, the high importance of climate variables in GPP prediction illustrates that the future climate change is the main driver of the global GPP dynamics. This study provides a basis for predicting how global GPP responds to future climate change in the coming decades, which contribute to understanding the interactions between vegetation and climate.

Estimating Global GPP From the Plant Functional Type Perspective Using a Machine Learning Approach

AbstractThe long‐term monitoring of gross primary production (GPP) is crucial to the assessment of the carbon cycle of terrestrial ecosystems. In this study, a well‐known machine learning model (random forest, RF) is established to reconstruct the global GPP data set named ECGC_GPP. The model distinguished nine functional plant types, including C3 and C4 crops, using eddy fluxes, meteorological variables, and leaf area index (LAI) as training data of RF model. Based on ERA5_Land and the corrected GEOV2 data, global monthly GPP data set at a 0.05° resolution from 1999 to 2019 was estimated. The results showed that the RF model could explain 74.81% of the monthly variation of GPP in the testing data set, of which the average contribution of LAI reached 41.73%. The average annual and standard deviation of GPP during 1999–2019 were 117.14 ± 1.51 Pg C yr−1, with an upward trend of 0.21 Pg C yr−2 (p < 0.01). By using the plant functional type classification, the underestimation of cropland is improved. Therefore, ECGC_GPP provides reasonable global spatial pattern and long‐term trend of annual GPP.

Interannual characteristics and driving mechanism of CO2 fluxes during the growing season in an alpine wetland ecosystem at the southern foot of the Qilian Mountains.

The carbon process of the alpine ecosystem is complex and sensitive in the face of continuous global warming. However, the long-term dynamics of carbon budget and its driving mechanism of alpine ecosystem remain unclear. Using the eddy covariance (EC) technique-a fast and direct method of measuring carbon dioxide (CO2) fluxes, we analyzed the dynamics of CO2 fluxes and their driving mechanism in an alpine wetland in the northeastern Qinghai-Tibet Plateau (QTP) during the growing season (May-September) from 2004-2016. The results show that the monthly gross primary productivity (GPP) and ecosystem respiration (Re) showed a unimodal pattern, and the monthly net ecosystem CO2 exchange (NEE) showed a V-shaped trend. With the alpine wetland ecosystem being a carbon sink during the growing season, that is, a reservoir that absorbs more atmospheric carbon than it releases, the annual NEE, GPP, and Re reached -67.5 ± 10.2, 473.4 ± 19.1, and 405.9 ± 8.9 gCm-2, respectively. At the monthly scale, the classification and regression tree (CART) analysis revealed air temperature (Ta) to be the main determinant of variations in the monthly NEE and GPP. Soil temperature (Ts) largely determined the changes in the monthly Re. The linear regression analysis confirmed that thermal conditions (Ta, Ts) were crucial determinants of the dynamics of monthly CO2 fluxes during the growing season. At the interannual scale, the variations of CO2 fluxes were affected mainly by precipitation and thermal conditions. The annual GPP and Re were positively correlated with Ta and Ts, and were negatively correlated with precipitation. However, hydrothermal conditions (Ta, Ts, and precipitation) had no significant effect on annual NEE. Our results indicated that climate warming would be beneficial to the improvement of GPP and Re in the alpine wetland, while the increase of precipitation can weaken this effect.

Three Decades of Gross Primary Production (GPP) in China: Variations, Trends, Attributions, and Prediction Inferred from Multiple Datasets and Time Series Modeling

The accurate estimation of gross primary production (GPP) is crucial to understanding plant carbon sequestration and grasping the quality of the ecological environment. Nevertheless, due to the inconsistencies of current GPP products, the variations, trends and short-term predictions of GPP have not been sufficiently well studied. In this study, we explore the spatiotemporal variability and trends of GPP and its associated climatic and anthropogenic factors in China from 1982 to 2015, mainly based on the optimum light use efficiency (LUEopt) product. We also employ an autoregressive integrated moving average (ARIMA) model to forecast the monthly GPP for a one-year lead time. The results show that GPP experienced an upward trend of 2.268 g C/m2 per year during the studied period, that is, an increasing rate of 3.9% per decade since 1982. However, these trend changes revealed distinct heterogeneity across space and time. The positive trends were mainly distributed in the Yellow River and Huaihe River out of the nine major river basins in China. We found that the dynamics of GPP were concurrently affected by climate factors and human activities. While air temperature and leaf area index (LAI) played dominant roles at a national level, the effects of precipitation, downward shortwave radiation (SRAD), carbon dioxide (CO2) and aerosol optical depth (AOD) exhibited discrepancies in terms of degree and scope. The ARIMA model achieved satisfactory prediction performance in most areas, though the accuracy was influenced by both data values and data quality. The model can potentially be generalized for other biophysical parameters with distinct seasonality. Our findings are further verified and corroborated by four widely used GPP products, demonstrating a good consistency of GPP trends and prediction. Our analysis provides a robust framework for characterizing long-term GPP dynamics that shed light on the improved assessment of the environmental quality of terrestrial ecosystems.

Improved estimation of global gross primary productivity during 1981–2020 using the optimized P model

Accurate estimation of terrestrial gross primary productivity (GPP) is essential for quantifying the net carbon exchange between the atmosphere and biosphere. Light use efficiency (LUE) models are widely used to estimate GPP at different spatial scales. However, difficulties in proper determination of maximum LUE (LUEmax) and downregulation of LUEmax into actual LUE result in uncertainties in GPP estimated by LUE models. The recently developed P model, as a LUE-like model, captures the deep mechanism of photosynthesis and simplifies parameterization. Site level studies have proved the outperformance of P model over LUE models. However, the global application of the P model is still lacking. Thus, the effectiveness of 5 water stress factors integrated into the P model was compared. The optimal P model was used to generate a new long-term (1981–2020) global monthly GPP dataset at a spatial resolution of 0.1° × 0.1°, called PGPP. Validation at globally distributed 109 FLUXNET sites indicated that PGPP is better than three widely-used GPP products. R2 between PGPP and observed GPP equals to 0.75, the corresponding root mean squared error (RMSE) and mean absolute error (MAE) equal to 1.77 g C m−2 d−1 and 1.28 g C m−2 d−1. During the period from 1981 to 2020, PGPP significantly increased in 69.02% of global vegetated regions (p < 0.05). Overall, PGPP provides a new GPP product choice for global ecology studies and the comparison of various water stress factors provides a new idea for the improvement of GPP model in the future.

COS-derived GPP relationships with temperature and light help explain high-latitude atmospheric CO2 seasonal cycle amplification

In the Arctic and Boreal region (ABR) where warming is especially pronounced, the increase of gross primary production (GPP) has been suggested as an important driver for the increase of the atmospheric CO2 seasonal cycle amplitude (SCA). However, the role of GPP relative to changes in ecosystem respiration (ER) remains unclear, largely due to our inability to quantify these gross fluxes on regional scales. Here, we use atmospheric carbonyl sulfide (COS) measurements to provide observation-based estimates of GPP over the North American ABR. Our annual GPP estimate is 3.6 (2.4 to 5.5) PgC · y-1 between 2009 and 2013, the uncertainty of which is smaller than the range of GPP estimated from terrestrial ecosystem models (1.5 to 9.8 PgC · y-1). Our COS-derived monthly GPP shows significant correlations in space and time with satellite-based GPP proxies, solar-induced chlorophyll fluorescence, and near-infrared reflectance of vegetation. Furthermore, the derived monthly GPP displays two different linear relationships with soil temperature in spring versus autumn, whereas the relationship between monthly ER and soil temperature is best described by a single quadratic relationship throughout the year. In spring to midsummer, when GPP is most strongly correlated with soil temperature, our results suggest the warming-induced increases of GPP likely exceeded the increases of ER over the past four decades. In autumn, however, increases of ER were likely greater than GPP due to light limitations on GPP, thereby enhancing autumn net carbon emissions. Both effects have likely contributed to the atmospheric CO2 SCA amplification observed in the ABR.

Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability

Water stress is one of the primary environmental factors that limits terrestrial ecosystems’ productivity. Hense, the way to quantify gobal vegetation productivity’s vulnerability under water stress and reveal its seasonal dynamics in response to drought is of great significance in mitigating and adapting to global changes. Here, we estimated monthly gross primary productivity (GPP) first based on light-use efficiency (LUE) models for 1982–2015. GPP’s response time to water availability can be determined by correlating the monthly GPP series with the multiple timescale Standardized Precipitation Evapotranspiration Index (SPEI). Thereafter, we developed an optimal bivariate probabilistic model to derive the vegetation productivity loss probabilities under different drought scenarios using the copula method. The results showed that LUE models have a good fit and estimate GPP well (R2 exceeded 0.7). GPP is expected to decrease in 71.91% of the global land vegetation area because of increases in radiation and temperature and decreases in soil moisture during drought periods. Largely, we found that vegetation productivity and water availability are correlated positively globally. The vegetation productivity in arid and semiarid areas depends considerably upon water availability compared to that in humid and semi-humid areas. Weak drought resistance often characterizes the land cover types that water availability influences more. In addition, under the scenario of the same level of GPP damage with different drought degrees, as droughts increase in severity, GPP loss probabilities increase as well. Further, under the same drought severity with different levels of GPP damage, drought’s effect on GPP loss probabilities weaken gradually as the GPP damage level increaes. Similar patterns were observed in different seasons. Our results showed that arid and semiarid areas have higher conditional probabilities of vegetation productivity losses under different drought scenarios.

Importance of Shaded Leaf Contribution to the Total GPP of Canadian Terrestrial Ecosystems: Evaluation of MODIS GPP

AbstractTerrestrial gross primary productivity (GPP) quantifies the photosynthetic uptake by vegetation and it is the largest component of the terrestrial carbon cycle. The separation of sunlit and shaded leaves has been shown to be an effective leaf‐to‐canopy upscaling method for modeling vegetation GPP. In this study, the performance of the Integrated Carbon‐Canadian Land Surface Scheme (IC‐CLASS) based on Farquhar's photosynthetic model and the two‐leaf approach was compared against that of Moderate Resolution Imaging Spectroradiometer (MODIS) GPP algorithm using the light use efficiency (LUE) approach, validated with eddy covariance (EC) measured GPP over a variety of terrestrial ecosystems in Canada. There were systematic differences between the IC‐CLASS simulated GPP and the MODIS GPP product in spatial distribution patterns. The differences were due to inherent shortcomings of the LUE modeling approach where a constant maximum LUE value is specified for each biome type, ignoring the variation of shaded leaf contribution to total GPP. The IC‐CLASS model separates the sunlit and shaded leaves and the bias in simulating GPP was minimized. The IC‐CLASS performed better than the MODIS GPP algorithm compared with monthly and annual GPP derived from EC flux data at 13 Canadian Carbon Program sites. The differences between the IC‐CLASS and the MODIS GPP estimates were larger in more clumped canopies (i.e. forests), because of the increase in the shaded leaf fraction. Different LUEs in sunlit and shaded portions of the canopy should be considered for effective and reliable estimation of GPP at regional scale.

Carbon assimilation, water consumption and water use efficiency under different land use types in subtropical ecosystems: from native forests to pine plantations

The effects of selective logging in Argentinean native subtropical forests or its replacement by pine plantations on carbon and hydrological cycles is of importance for developing strategies for a sustainable use of water resources and climate mitigation in subtropical humid regions. Ecosystem-level gross primary productivity (GPP), evapotranspiration (ET) and water use efficiency (WUE) were assessed using multi-temporal MODIS remote sensing data and its response to climatic variables were analyzed in well-preserved native forests, degraded native forests by selective harvesting and pine plantations. Field measurements of leaf area index (LAI), tree density, basal area, tree high, diameter at breast height and understory cover was implemented. The GPP was high throughout the year across all three-land use types. Selective harvesting of commercial trees resulted in a significant decrease in tree density and in basal area compared to well-preserved native forests but did not have a negative impact on GPP. Mean annual ET was similar between both native forests because although the degraded forest had a lower tree density they had a greater maximun DBH per tree than the well-preserved forests and therefore high transpiration. The WUE was similar across the ecosystem types. Removal of 5.5 m2 of canopy trees produced a compensatory change in ecosystem structure and function resulting in the maintenance of GPP and ET. Replacement of native forests by pine plantations for wood production did not change annual GPP compared to native forests, but the final carbon assimilation should be lower due to the CO2 released before canopy closure and after harvesting. All climatic variables were linearly and significantly related to monthly GPP, ET and WUE with the exception of precipitation with monthly GPP. Finding forest management techniques for increasing carbon assimilation is of importance for limiting the rise of global CO2 emissions and for regulation of the water cycle.

Trends and controls of terrestrial gross primary productivity of China during 2000–2016

Terrestrial gross primary productivity (GPP) is an important flux that drives the global carbon cycle. However, quantifying the trend and the control factor of GPP from the pixel level to the regional level is still a challenge. We generated monthly GPP dataset using the vegetation photosynthesis model and calculated the interannual linear trend for China during 2000–2016. The Breaks For Additive Seasonal and Trend method was applied to detect the timing of breakpoint and trends shift of monthly GPP, while boosted regression tree analysis was used to identify the most important factor and its relative influence on GPP based on gridded leaf area index (LAI), aerosol optical thickness, and NCEP-DOE Reanalysis II meteorological data. The results show that annual mean GPP was significantly (P < 0.001, R2 = 0.78) increased, especially in the Loess Plateau and South China, from 2000 to 2016. The change rate of annual mean GPP declined from 18.82 g C m−2 yr−1 in 2000–2008 to 3.48 g C m−2 yr−1 in 2008–2016. About 55.4% of the breakpoints occur between 2009 and 2011 and was mainly distributed in Qinghai-Tibet Plateau, Central China, Southwestern China, and South China, and negative oriented GPP trends variation type still accounts for about 28.76%. LAI and temperature related factors generally had the highest relative influence on GPP in the north part and south part of China, respectively. Our study indicates that the ecological restoration projects and rapid urbanization have respectively induced the most obvious increase and decrease trends of GPP in China. Land cover change and climate change are the main reasons for GPP dynamics in the north and south part of China, respectively.

Biotic and abiotic influences on monthly variation in carbon fluxes in on-year and off-year Moso bamboo forest

Monthly variation in gross primary productivity (GPP) between on-years and off-years were different. Main drivers of GPP in on-years were abiotic. In off-years drivers were biotic and abiotic. Understanding biotic (living or once-living organisms) and abiotic (non-living physical and chemical elements) influences on seasonal variation in carbon fluxes in Moso bamboo forest is important for predicting future carbon sequestration under climate change. Although differing physiological and ecological characteristics of Moso bamboo forest between on-years and off-years have been observed, the drivers of annual differences in carbon fluxes remain unknown. In this study, drivers of variation in carbon fluxes were analyzed based on gross primary productivity (GPP) and biotic factors (leaf area and chlorophyll content here, represented by vegetation indices—VIs) and abiotic factors. Results showed that average monthly GPP between on-years and off-years was significantly different from January to June, mainly due to natural variation in biotic factors. The monthly variation in GPP during on-years was mainly influenced by abiotic factors, whereas that in off-years was determined by the combination of biotic and abiotic factors. Monthly variation and differences in GPP between on-years and off-years were well represented by VIs. The GPP was more strongly correlated with VIs in off-years than in on-years, owing to large seasonal variation in canopy chlorophyll content. Hence, GPP estimated from both air temperature and simple ratio was more accurate than that estimated from air temperature alone. Overall, the difference in GPP between on-years and off-years and its underlying mechanisms can be used to accurately estimate carbon fluxes in Moso forest and predict carbon fluxes under future climate warming.

Disentangling Climate and LAI Effects on Seasonal Variability in Water Use Efficiency Across Terrestrial Ecosystems in China

AbstractWater use efficiency (WUE), the ratio of gross primary productivity (GPP) over evapotranspiration (ET), is a critical ecosystem function. However, it is difficult to distinguish the individual effects of climatic variables and leaf area index (LAI) on WUE, mainly due to the high collinearity among these factors. Here we proposed a partial least squares regression‐based sensitivity algorithm to confront the issue, which was first verified at seven ChinaFlux sites and then applied across China. The results showed that across all biomes in China, monthly GPP (0.42–0.65), ET (0.33–0.56), and WUE (0.01–0.31) showed positive sensitivities to air temperature, particularly in croplands in northeast China and forests in southwest China. Radiation exerted stronger effects on ET (0.55–0.78) than GPP (0.19–0.65), resulting in negative responses (−0.44 to 0.04) of WUE to increased radiation among most biomes. Increasing precipitation stimulated both GPP (0.06–0.17) and ET (0.05–0.12) at the biome level, but spatially negative effects of excessive precipitation were also found in some grasslands. Both monthly GPP (−0.01 to 0.29) and ET (0.02–0.12) showed weak or moderate responses to vapor pressure deficit among biomes, resulting in weak response of monthly WUE to vapor pressure deficit (−0.04 to 0.08). LAI showed positive effects on GPP (0.18–0.60), ET (0–0.23), and WUE (0.13–0.42) across biomes, particularly on WUE in grasslands (0.42 ± 0.30). Our results highlighted the importance of LAI in influencing WUE against climatic variables. Furthermore, the sensitivity algorithm can be used to inform the design of manipulative experiments and compare with factorial simulations for discerning effects of various variables on ecosystem functions.

Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: Climatic factors, CO2 concentration and land-cover change, 1982–2015

Quantitative estimation of spatial pattern of gross primary production (GPP) trends and its drivers plays a crucial role in global change research. This study applied C-Fix model to estimate the net effect of each factor on GPP trends of 1982–2015, used an unsupervised classifier to group similar GPP trend behaviors, and analyzed the responses of GPP to changes in climatic, atmospheric and environmental drivers. According to the features of monthly GPP trends and the patterns of growing season, we presented nine categories as aids in interpreting large-scale behavior. Land-cover change (LCC), rising CO2, temperature and water conditions changes have the positive overall effect on GPP over the entire world, contrary to radiation change effects. The global average contributions of LCC, CO2, temperature, radiation and water on GPP trend are 4.57%, 65.73%, 13.07%, −7.24 and 11.74%, respectively. LCC and climatic factors changes have had a greater impact on GPP in terms of a specific location or regional rather than globally, and the interactions between factors are positive on GPP. The effects of climatic factors trends on GPP in different locations can be opposite, in general: regionally, GPP changes at middle and high latitudes are likely dominated by rises in radiation and temperature; at lower latitudes, GPP changes are likely to be driven by shifts in water conditions; at high altitudes, GPP changes are probably caused by changes in temperature and water conditions. These results will increase the understanding of the variations of carbon flux under future CO2, LCC and climate conditions.

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.

Global evaluation of gross primary productivity in the JULES land surface model v3.4.1

Abstract. This study evaluates the ability of the JULES land surface model (LSM) to simulate gross primary productivity (GPP) on regional and global scales for 2001–2010. Model simulations, performed at various spatial resolutions and driven with a variety of meteorological datasets (WFDEI-GPCC, WFDEI-CRU and PRINCETON), were compared to the MODIS GPP product, spatially gridded estimates of upscaled GPP from the FLUXNET network (FLUXNET-MTE) and the CARDAMOM terrestrial carbon cycle analysis. Firstly, when JULES was driven with the WFDEI-GPCC dataset (at 0. 5° × 0. 5° spatial resolution), the annual average global GPP simulated by JULES for 2001–2010 was higher than the observation-based estimates (MODIS and FLUXNET-MTE), by 25 and 8 %, respectively, and CARDAMOM estimates by 23 %. JULES was able to simulate the standard deviation of monthly GPP fluxes compared to CARDAMOM and the observation-based estimates on global scales. Secondly, GPP simulated by JULES for various biomes (forests, grasslands and shrubs) on global and regional scales were compared. Differences among JULES, MODIS, FLUXNET-MTE and CARDAMOM on global scales were due to differences in simulated GPP in the tropics. Thirdly, it was shown that spatial resolution (0. 5° × 0. 5°, 1° × 1° and 2° × 2°) had little impact on simulated GPP on these large scales, with global GPP ranging from 140 to 142 PgC year−1. Finally, the sensitivity of JULES to meteorological driving data, a major source of model uncertainty, was examined. Estimates of annual average global GPP were higher when JULES was driven with the PRINCETON meteorological dataset than when driven with the WFDEI-GPCC dataset by 3 PgC year−1. On regional scales, differences between the two were observed, with the WFDEI-GPCC-driven model simulations estimating higher GPP in the tropics (5° N–5° S) and the PRINCETON-driven model simulations estimating higher GPP in the extratropics (30–60° N).

Alpine wetland ecosystem carbon sink and its controls at the Qinghai Lake

Wetland ecosystems play an important role in regulating the Earth’s climate. This paper presents the results of a study on the alpine wetland ecosystem CO2 fluxes at the Qinghai Lake at different vegetative development stages and CO2 controlling factors measured by eddy covariance system. The results showed that the alpine wetland ecosystem at the Qinghai Lake area was a carbon sink on the whole year basis. The average net ecosystem CO2 exchange (NEE) amount was −904.42 g CO2/m2; the average amount of the ecosystem reparation (Re) was 1450 g CO2/m2; the average gross primary productivity (GPP) was 2354.42 g CO2/m2 from July 2011 to June 2013 year. The NEE, Re and GPP amounts were the maximum during the growing stage of the whole year. There were significant negative linear correlations between monthly NEE amount and monthly average total solar radiation (SR) (R 2 = 0.27, p < 0.05), incident photosynthetically active radiation (PAR) (R 2 = 0.28, p < 0.05), ambient vapor pressure deficit (VPD) (R 2 = 0.38, p < 0.01), monthly total precipitation (P) (R 2 = 0.65, p < 0.0001), monthly mean leaf area index (LAI) (R 2 = 0.85, p < 0.0001) and enhanced vegetation index (EVI) (R 2 = 0.84, p < 0.0001). Extreme significant polynomial nonlinear negative correlations were found between NEE amounts and monthly average air temperature (T a ) (R 2 = 0.84, p < 0.0001), soil temperature at 0–5 cm depth (T s ) (R 2 = 0.90, p < 0.0001) and air relative humidity (RH) (R 2 = 0.76, p < 0.0001). The monthly Re amount of alpine wetland ecosystem at the Qinghai Lake at different stages showed significant nonlinear positive correlations with all the identified affecting factors except for monthly mean EVI, which showed a linear relationship (R 2 = 0.72, p < 0.0001). The monthly GPP amount at different vegetative development stages showed a significant exponential correlation with monthly average T a (R 2 = 0.92, p < 0.0001), a power function with VPD (R 2 = 0.24, p < 0.05), quadratic polynomial ones with monthly average T s (R 2 = 0.93, p < 0.0001), RH (R 2 = 0.74, p < 0.0001) and monthly P (R 2 = 0.55, p < 0.05). Extremely significant linear correlations of the monthly GPP amount at different developing stages with monthly average LAI (R 2 = 0.85, p < 0.0001) and EVI (R 2 = 0.92, p < 0.0001) were also found.

Water-use efficiency of an old-growth forest in lower subtropical China

Carbon and water fluxes are key properties of ecosystem processes and functions. A better understanding of their temporal dynamics and coupling mechanism between these fluxes will help us improve ecosystem management for mitigation as well as adaption to future climatic change. From 2003 to 2009, carbon and water flux data were obtained by the eddy covariance method over an old-growth forest in the lower subtropical China. The 7 years of observational data indicated that the water-use efficiency (WUE) of the old-growth forest exhibited weak inter-annual variability. The mean annual WUE ranged from 1.70 to 1.98 g C kg−1 H2O. An analysis of the effects of environmental variables on the monthly gross primary productivity (GPP) and evapotranspiration (ET) indicated that solar radiation, air temperature, precipitation and vapor pressure deficit (VPD) produced similar effects on the monthly GPP and ET, which suggests that photosynthesis and ET were similarly driven by the climatic variables. At the monthly scale, the WUE decreased significantly as the precipitation and soil moisture content increased. However, a significant correlation was not detected between the WUE and the VPD at the monthly scale. Moisture conditions tend to be major drivers of the ecosystem WUE.

Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region

AbstractRealistic projection of future climate‐carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower‐based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.

Sensitivity of terrestrial water and carbon fluxes to climate variability in semi-humid basins of Haihe River, China

Inter-annual climate variability dominates the evolution of eco-hydrological processes over the water limited basins. Haihe Basin in semi-humid northern China is under significant climate change and serious human disturbance during the recent decades. Integrated with NASA Terra-Modis remote sensing LAI data products, the VIP eco-hydrological model is used to predict the spatiotemporal variations of evapotranspiration (ET) and vegetation gross primary production (GPP) over two catchments of Haihe River branches from 2000 to 2013. Along with precipitation increasing, average annual ET and GPP are significantly increasing. The trends are more distinct over the mountainous upstream areas. The inter-annual variations of ET and GPP are highly correlated with the 12-month SPI (Standardized Precipitation Index), while the monthly GPP is related clearly with 3-month SPI. Sensitivity analysis shows that there are remarkable differences in the elasticity coefficients of both ET and GPP to the climatic factors. It is found that precipitation, solar radiation and leaf area index are the most sensitive factors to ET and GPP in the study basin. The results are helpful to understand the catchment eco-hydrological mechanisms and their responses to climate change.