Terrestrial Gross Primary Production Research Articles

The impact of drought time scales and characteristics on gross primary productivity in China from 2001 to 2020

ABSTRACT Drought events have occurred frequently in the past and have had profound effects on Gross Primary Productivity (GPP). However, the role of different drought event characteristics in the response of GPP to drought is not well understood. To address this issue, we investigated the spatiotemporal response patterns of Chinese terrestrial ecosystem GPP to different time scales of meteorological droughts, and explored the difference of GPP response to drought events under various drought characteristics. Our research revealed that: Different time scales of meteorological droughts significantly impacted the GPP of 17.3%–25.6% area of China. Among them, the impact range of 1-month and 12-month meteorological drought was the smallest and largest respectively. GPP along the 400 mm isohyet line was more susceptible to drought. Second, grasslands and shrubs are more susceptible to drought than forested areas. Among the characteristics of drought events, drought severity had the greatest impact on GPP changes, while drought frequency had the least impact. An increase in drought frequency intensified the impact of 1-month meteorological drought on GPP. Increased drought duration and severity weakened the impact of 3-month meteorological drought on GPP and intensified the impact of 1-month and 12-month meteorological droughts on GPP. Our study reveals the impact of different time scales drought and drought characteristics on plant productivity, and provides more possibilities for understanding the effect of drought on plant productivity.

Vegetation canopy structure mediates the response of gross primary production to environmental drivers across multiple temporal scales

Gross primary production (GPP) is a critical component of the global carbon cycle and plays a significant role in the terrestrial carbon budget. The impact of environmental factors on GPP can occur through both direct (by influencing photosynthetic efficiency) and indirect (through the modulation of vegetation structure) pathways, but the extent to which these mechanisms contribute has been seldom quantified. In this study, we used structural equation modeling and observations from the FLUXNET network to investigate the direct and indirect effects of environmental factors on terrestrial ecosystem GPP at multiple temporal scales. We found that canopy structure, represented by leaf area index (LAI), is a crucial intermediate factor in the GPP response to environmental drivers. Environmental factors affect GPP indirectly by altering canopy structure, and the relative proportion of indirect effects decreased with increasing LAI. The study also identified different effects of environmental factors on GPP across time scales. At the half-hourly time scale, radiation was the primary driver of GPP. In contrast, the influences of temperature and vapor pressure deficit took on greater prominence at longer time scales. About half of the total effect of temperature on GPP was indirect through the regulation of canopy structure, and the indirect effect increased with increasing time scale (GPPNT-based models: 0.135 (half-hourly) vs. 0.171 (daily) vs. 0.189 (weekly) vs. 0.217 (monthly); GPPDT-based models: 0.139 vs. 0.170 vs. 0.187 vs. 0.215; all values were reported in gC m−2 d−1 °C−1, P < 0.001); while the indirect effect of radiation on GPP was comparatively lower, accounting for less than a quarter of the total effect. Furthermore, we observed a direct, negative-to-positive impact of precipitation on GPP across timescales. These findings provide crucial information on the interplay between environmental factors and LAI on GPP and enable a deeper understanding of the driving mechanisms of GPP.

Joint improvement on absorbed photosynthetically active radiation and intrinsic quantum yield efficiency algorithms in the P model betters the estimate of terrestrial gross primary productivity

Terrestrial gross primary productivity (GPP) plays a crucial role in global carbon cycle and budget. A range of light use efficiency (LUE) models have been developed to estimate GPP at different spatial scales. However, large uncertainties still remain in GPP output from such models, mainly owing to the difficulty in the proper determination of maximum light use efficiency (LUEmax). The recently developed P model directly quantifies actual LUE based on environmental and physiological factors, reducing the uncertainty in GPP estimation caused by the ambiguous LUEmax. However, the existing P models still suffer from a potential underestimation in global validation, which might be related to the calculation of absorbed photosynthetically active radiation (APAR) and intrinsic quantum yield efficiency (φ0) in the P model. In this study, we improved the P model by differentiating sunlit and shaded leaves for APAR calculation and by considering the spatial variability of the optimal temperature for φ0 (Topt_phi0). The roles of modified APAR and φ0 calculations in reducing the uncertainty of simulated GPP were assessed through model simulation experiments with monthly tower-based GPP from 120 flux sites globally distributed as the benchmark. The validation indicates that simultaneous improvements on APAR and φ0 algorithms are able to better the performance of the P model. Monthly GPP simulated with this improved P model (i.e., IP model) is in good agreement with tower-based values, with a linear R2, root mean squared error, mean error, mean absolute error, and DISO of 0.76, 1.88 g C m−2d−1, -0.14 g C m−2d−1, 1.77 g C m−2d−1 and 0.60. The total global terrestrial GPP output from the IP model (IPGPP) averaged 134.3 Pg C yr−1 during 1981—2021, in the upper range of existing GPP products.

Advanced time-lagged effects of drought on global vegetation growth and its social risk in the 21st century

Extensive studies have demonstrated the restricting effect of past and present drought conditions on vegetation growth over the past three decades. However, the underlying mechanism of the impact of prior drought on vegetation growth – along with the magnitude of its impact over the rest of the 21st century – remains uncertain. Herein, we examined the evolution and characteristics of global vegetation growth and drought for both baseline (1982–2014) and future (2015–2100) periods under four representative pathways using the gross primary productivity (GPP) and the Standardized Precipitation Evapotranspiration Index from the CMIP6. Further, we investigated the time-lagged effects of drought on vegetation growth and the intensity of population and economy exposure to drought by identifying drought-threatened areas under four emission scenarios. The results show that, at the end of the 21st century, the global terrestrial GPP will experience an increasing trend under four scenarios, especially in SSP5-8.5, with a growth rate of 0.032 kg C m−2/decade, which is 10 times higher than that in SSP1-2.6. From the SSP1-2.6 to the SSP5-8.5 scenario, the SPEI change rates are −0.03, −0.01, −0.017, and −0.018/decade, respectively, indicating that the intensity of global drought events will rise with increases in CO2 emissions. 28.3%, 24.7%, 30.4%, and 35% of global land exhibit downward mean time-lagged months in four scenarios, especially in the middle–high latitudes of the northern hemisphere (>45°N), indicating an advanced response of vegetation to drought. Nearly 8, 9.1, 12.9, and 11.5 billion people – valued at 94,138 (SSP1-2.6), 976,020 (SSP2-4.5), 526,595 (SSP3-7.0), and 204,728 (SSP5-8.5) billion US$, respectively – will be threatened by continuous drought. Globally, the population and economy exposure to moderate and extreme drought zones is larger, and the economic risk from extreme droughts is 8 times greater under the high emissions scenario than the low emissions scenario.

Soil moisture content-based analysis of terrestrial ecosystems in China: Water use efficiency of vegetation systems

Water is a prerequisite for the formation of earth-biochemical-ecological systems. Differences in the spatial and temporal distribution of water resources are important factors in the formation of differences in the distribution of vegetation in terrestrial ecosystems and are key to the differences in vegetation productivity. Vegetation water use efficiency is calculated by the total amount of fixed biogenic carbon per unit mass of water consumed in photosynthesis and can be used to assess the intensity and capacity of an ecosystem to use water biomass. Based on remote satellite sensing data, this study proposes a new water use efficiency assessment model. The model was validated using flux site data, and we analyzed the relative contribution of climate factors to soil moisture use efficiency using a first-difference method. The results revealed the following: (1) The soil moisture use efficiencies (SUE) of remote sensing data inversions that were evaluated using flux site data based on correlation coefficients and Nash coefficients showed high reliability, and only the NMG (Inner Mongolia) station showed low correlation among the nine sites. (2) Among the nine agricultural sub-regions in China, only the SUE of the Qinghai-Tibet Plateau region showed a decreasing trend (−1.08 g C/m−2 kg H2O yr), while all other regions showed an increasing trend. (3) The highest vegetation soil moisture use efficiency (1.83 g C/m−2 kg H2O) was found in ferralisols, while the lowest vegetation SUE (0.17 g C/m−2 kg H2O) was found in arid soils. The SUE of different vegetation types showed the characteristics of forest > scrub > cultivated vegetation > wetland > grassland. (4) The relative contribution of gross primary productivity (GPP) to the change in SUE was 37.53%, while the relative contribution of soil moisture content to the change in SUE was −26.71%. Among the five climatic factors, temperature was the most dominant factor affecting the change in SUE, followed by precipitation, net radiation, leaf area index, and potential evapotranspiration. Revealing the relationship between terrestrial ecosystem GPP, soil moisture content, and their responses to climate factors is a prerequisite for understanding the adaptation strategies of regional terrestrial ecosystems to global climate change, which can help to inform decision-making for the sustainable development of ecosystems.

Light use efficiency models incorporating diffuse radiation impacts for simulating terrestrial ecosystem gross primary productivity: A global comparison

Estimating dynamic changes in gross primary productivity (GPP) of terrestrial ecosystems has always been challenging. Indeed, light use efficiency (LUE) models are extensively employed to capture GPP dynamics. Improved big-leaf LUE models by introducing a cloudiness scalar and two-leaf LUE models by dividing the canopy into shaded and sunlit leaves are two improved families for addressing the widely-recognized effects of diffuse radiation on terrestrial ecosystem GPP. However, the global performance in simulating GPP dynamics between such models has not been evaluated comprehensively. Here, we assess and compare the global performance of the ten LUE models considering diffuse radiation impacts (CFLUX, DIFFUSE, CI-LUE, CCW, Wang's Model, CI-EF, TL-LUE, TL-LUEn, DTEC, and RTL-LUE) at 102 flux sites in a standardized framework. Results indicate that LUE models considering diffuse radiation impacts can explain 46.7–63.6% of daily GPP variability obtained by the eddy covariance technique. Seven of the ten models show a better performance across all ecosystems than MOD17, despite the relatively poor performance of three (i.e., DIFFUSE, Wang's Model, and DTEC). The seven improved models exhibiting a similar explanation of GPPEC perform poorly in evergreen broadleaf forests and croplands but well in deciduous broadleaf forests. Although two-leaf models with higher leaf area index (LAI) sensitivity and better model structure have greater potential to describe diffuse radiation fertilization (DRF), their performance in capturing GPP dynamics is limited by the uncertainties of remote sensing data. This study indicates the importance of accurate LAI for two-leaf models and emphasizes the necessity of incorporating diffuse radiation impacts into the global GPP estimates in the future.

2020-2050年CO2施肥效应促进全球陆地生态系统碳吸收

PDF HTML阅读 XML下载 导出引用 引用提醒 2020-2050年CO2施肥效应促进全球陆地生态系统碳吸收 DOI: 10.5846/stxb202107051782 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 福建省自然科学基金面上项目(2021J01502);福建省农业科学院自由探索科技创新项目(ZYTS202233);福建省农业高质量发展协同创新工程(XTCXGC2021015);福建省智慧农业科技创新团队 (CXTD2021013-1) CO2 fertilization promoting the carbon uptake of global terrestrial ecosystems in 2020-2050 Author: Affiliation: Fund Project: the Natural Science Foundation of Fujian Province (2021J01502)，Fujian Academy of Agricultural Sciences Free Exploration Science and Technology Innovation Project (ZYTS202233); Collaborative Innovation Project of High-Quality Development of Agriculture in Fujian Province (XTCXGC2021015), and Fujian Intelligent Agricultural Science and Technology Innovation Team (CXTD2021013-1) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:CO2施肥效应是全球变绿的主要原因,随着大气中CO2浓度的持续增加,预估未来气候变化条件下,CO2施肥效应对陆地生态系统的影响对减缓全球气候变化具有重大意义。基于未来气候情景数据和Farquhar模型,并结合生态过程模型BEPS(Boreal Ecosystem Productivity Simulator),定量化研究2020-2050年CO2施肥效应对全球叶面积指数(LAI)和总初级生产力(GPP)的影响。研究结果显示2020-2050年,在RCP2.6、RCP4.5和RCP8.5气候情景下,CO2施肥效应导致的LAI年际变化趋势分别为0.002、0.003和0.005 m-2m-2a-1;三个气候情景下CO2施肥效应对LAI的影响为CO2每增加0.1%,LAI平均增加约8.1%-9.2%,由此导致GPP对应增加7.9%-14.6%;由CO2施肥效应导致的全球LAI的增加对未来GPP年际变化趋势的贡献分别为66.7%、48.7%和57.1%。表明在未来气候变化情景下,LAI的增加仍然主要受CO2施肥效应的影响,CO2施肥效应导致的LAI的增加将显著促进全球陆地生态系统碳吸收。 Abstract:CO2 fertilization is the main reason for global greening. With the persistent increase in CO2 concentration, it is critically significant to evaluate the effect of CO2 fertilization on the global terrestrial ecosystem. Gross primary productivity (GPP) quantifies the photosynthetic uptake of carbon by terrestrial ecosystems, and it is the basis of the global carbon cycle. Leaf area index (LAI) is a vegetation structural parameter that modulates the interaction between the land surface and the atmosphere and therefore is used in many terrestrial biosphere models. LAI is a critical parameter for leaf-to-canopy upscaling in the terrestrial biosphere models at the regional and global scales. Therefore, a reliable estimation of CO2 fertilization on LAI is crucial for understanding and predicting the terrestrial carbon cycle under future climate change. Due to the complexity and spatiotemporal difference in the global terrestrial ecosystems, there are still large uncertainties in simulating global LAI based on phenology and carbon dynamic allocation in Earth system models. Thus, in this study, we used the future scenario climate data, combined with the Farquhar and Boreal Ecosystem Productivity Simulator (BEPS) model to investigate the effect of CO2 fertilization on the global LAI and GPP during 2020-2050. The results showed that for RCP2.6, RCP4.5, and RCP8.5 scenarios, the CO2 fertilization led to the global LAI interannual trends were 0.002, 0.003, and 0.005 m-2m-2a-1, respectively. The LAI increased 8.1%-9.2%, resulting in a corresponding increase in GPP of 7.9%-14.6% per 0.1% CO2 concentration. The contributions of LAI to the global terrestrial ecosystem GPP were 66.7%, 48.7%, and 57.1%, respectively. It shows that CO2 fertilization is still the primary reason for increasing LAI under future climate scenarios. The increase of LAI caused by CO2 fertilization will significantly promote the carbon uptake of global terrestrial ecosystems. 参考文献 相似文献 引证文献

Future Land Use/Land Cover Change Has Nontrivial and Potentially Dominant Impact on Global Gross Primary Productivity

AbstractAnthropogenic land use/land cover (LULC) change alters terrestrial gross primary productivity (GPP), the largest land‐atmosphere carbon exchanges. Identifying the impacts of LULC changes on future terrestrial GPP has been challenging due to the scarcity of standardized future LULC for ecosystem models. Here, we present eight scenario‐based projections of global spatially explicit LULC at 1‐km resolution over the period 2015–2100 with a Future Land Use Simulation model—consistent with the Shared Socioeconomic Pathways and Representative Concentration Pathways. Twenty computational experiments with different LULC patterns, climate forcing, and CO2 concentrations were conducted to quantify their contributions to future GPP dynamics. Results show that the global terrestrial GPP would decline in the 21st century in most LULC scenarios due to urbanization, agricultural expansion, and deforestation. Moreover, the contribution of LULC changes to global GPP dynamics ranges from 3.43% to 10.78% when CO2 fertilization effect (CFE) is not modeled during 2000–2100 (7%–9% of the terrestrial area is dominated by LULC change). However, this value may range from 10.92% to 16.16% during 2000–2050 and 1.41%–14.57% during 2050–2100. The contribution of LULC even reached 56.08% during 2050–2100 in Southeast Asia due to deforestation. Despite the relatively important role of LULC to GPP dynamics, it becomes trivial globally when incorporating CFE into the model (i.e., LULC accounts for 1.24%–2.51% during 2000–2100). Our findings emphasize the strategic role of CFE in enhancing global GPP and highlight the quantitatively nontrivial role of LULC at the regional scale.

Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products

Abstract. Understanding historical changes in gross primary productivity (GPP) is essential for better predicting the future global carbon cycle. However, the historical trends of terrestrial GPP, due to the CO2 fertilization effect, climate, and land-use change, remain largely uncertain. Using long-term satellite-based near-infrared radiance of vegetation (NIRv), a proxy for GPP, and multiple GPP datasets derived from satellite-based products, dynamic global vegetation model (DGVM) simulations, and an upscaled product from eddy covariance (EC) measurements, here we comprehensively investigated their trends and analyzed the causes for any discrepancies during 1982–2015. Although spatial patterns of climatological annual GPP from all products and NIRv are highly correlated (r&gt;0.84), the spatial correlation coefficients of trends between DGVM GPP and NIRv significantly decreased (with the ensemble mean of r=0.49) and even the spatial correlation coefficients of trends between other GPP products and NIRv became negative. By separating the global land into the tropics plus extratropical Southern Hemisphere (Trop+SH) and extratropical Northern Hemisphere (NH), we found that, during 1982–2015, simulated GPP from most of the models showed a stronger increasing trend over Trop+SH than NH. In contrast, the satellite-based GPP products indicated a substantial increase over NH. Mechanistically, model sensitivity experiments indicated that the increase of annual global total GPP was dominated by the CO2 fertilization effect (83.9 % contribution), however, with the largest uncertainty in magnitude in individual simulations among the three drivers of CO2 fertilization, climate, and land-use change. Interestingly, the spatial distribution of inter-model spreads of GPP trends resulted mainly from climate and land-use change rather than CO2 fertilization effect. After 2000, trends from satellite-based GPP products were different from the full time series, suggesting weakened rising trends over NH and even significantly decreasing trends over Trop+SH, while the trends from DGVMs and NIRv kept increasing. The inconsistencies of GPP trends are very likely caused by the contrasting performance between satellite-derived and DGVM simulated vegetation structure parameter (leaf area index, LAI). Therefore, the uncertainty in satellite-based GPP products induced by highly uncertain LAI data in the tropics undermines their roles in assessing the performance of DGVM simulations and understanding the changes of global carbon sinks. The higher consistency between DGVM GPP and NIRv suggests that the trends from a DGVM ensemble might even have better performance than satellite-based GPP products.

SIF-Based GPP Is a Useful Index for Assessing Impacts of Drought on Vegetation: An Example of a Mega-Drought in Yunnan Province, China

The impact of drought on terrestrial ecosystem Gross Primary Productivity (GPP) is strong and widespread; therefore, it is important to study the response of terrestrial ecosystem GPP to drought. In this paper, we compared the correlations of Sun-induced Chlorophyll fluorescence (SIF), Enhanced Vegetation Index (EVI), and Normalized Differential Vegetation Index (NDVI) with the drought index sc_PDSI, estimated GPP in Yunnan Province, China, based on SIFTOTAL data (SIF data with canopy effects eliminated), and analyzed the response characteristics of GPP to drought for one mega-drought event (2009–2011) in combination with the sc_PDSI drought index. The results show that SIF is more sensitive to drought than the NDVI and EVI; the correlation between the GPP estimated based on SIF data (GPPSIF) and the actual observed flux values (R2 = 0.83) is better than GPPGLASS and GPPLUE, and the RMSE is also lower than those two products. This drought has a serious impact on GPP, and the monthly average values of the effect of drought on GPP (GPPd) in Yunnan Province in 2009, 2010, and 2011 are −11.37 gC·m−2·month−1, −23.48 gC·m−2·month−1 and −17.92 gC·m−2·month−1, which are 8.6%, 17.48% and 13.85% of the monthly average in a normal year, respectively. The spatial variability of GPP response to drought is significant, which is mainly determined by the degree, and duration of the drought, the vegetation type, the topography, and anthropogenic factors. In conclusion, GPPSIF quickly and accurately reflects the process of this drought, and this study helps to elucidate the response of GPP to drought conditions and provides more scientific information for drought prediction and ecosystem management.

Improving global gross primary productivity estimation by fusing multi-source data products

A reliable estimate of the gross primary productivity (GPP) of terrestrial vegetation is essential for both making decisions to address global climate change and understanding the global carbon balance. The lack of consistency in global terrestrial GPP estimates across various products leads to great uncertainty. In this study, we improve the quantification of global gross primary productivity by integrating multiple source GPP products without using any prior knowledge through the Bayesian-based Three-Cornered Hat (BTCH) method to generate a new weighted GPP data set. The fusion results demonstrate the superiority of weighted GPP, which greatly reduces the random error of individual datasets and fully takes advantage of the characteristics of multi-source data products. The weighted dataset can largely reproduce the interannual variation of regional GPP. Overall, the merging scheme based on the BTCH method can effectively generate a new GPP dataset that integrates information from multiple products and provides new ideas for GPP estimation on a global scale.

Probabilistic impacts of compound dry and hot events on global gross primary production

As the basis of food and fiber production, gross primary production (GPP) plays a critical role in the growth of vegetation. Understanding the response of GPP to climate extremes is important for ensuring food security under ongoing global warming. Plenty of evidence shows that the recent widespread dry or hot events across the globe have significant influences on GPP, yet little is known about their joint impacts. Here, we reveal a high risk of compound dry and hot events globally, in response to the strong negative dependence of precipitation and temperature, which leads to a substantial negative impact on GPP for both crop and pasture ecosystems. Using a meta-Gaussian model, we show that the probability of a reduction in global terrestrial GPP increases significantly under compound dry and hot conditions relative to their individual counterparts. Further, the risk of GPP reductions increases with the intensified severity of compound dry and hot events across the globe. These results unravel the sensitivity of GPP to compound dry and hot conditions and highlight the need to account for the influence of compound events when assessing the carbon budget.

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.

Satellite evidence for China's leading role in restoring vegetation productivity over global karst ecosystems

Karst ecosystems occupy approximately 20% of the Earth's land surface with the unique and vulnerable geomorphological and hydrogeological characteristics. To date, it remains a challenge to accurately monitor ecosystem productivity from space, as well as their responses to the environmental conditions due to climate change and anthropogenic pressure, which is pivotal to the sustainable development strategies in global karst areas. Here we use a reconstructed long-term solar-induced chlorophyll fluorescence dataset (SIF) and two satellite-based gross primary productivity (GPP) products to examine the patterns and trends of vegetation productivity within global karst ecosystems, and to assess the relative contributions of different countries to the restoration of these fragile ecosystems over the period 2001–2016. As an effective proxy for terrestrial GPP, SIF reveals a greening trend across most of the world's karst areas. China and the European Union (EU) lead the world in vegetation greening within their karst areas by 78.02% and 42.44%, respectively. The total net increase in SIF shows that China alone accounted for 43.66% with just 7.0% of global karst area. Brazil is the only country with a negative greening trend. Recent land cover changes caused by the grain-for-green programme in China and deforestation in Brazil account for 36.93% and 64.71% of the increases and decreases, respectively. Our results have significant implications for restoring ecosystem productivity in global karst areas.

Thermal optima of gross primary productivity are closely aligned with mean air temperatures across Australian wooded ecosystems

Gross primary productivity (GPP) of wooded ecosystems (forests and savannas) is central to the global carbon cycle, comprising 67%-75% of total global terrestrial GPP. Climate change may alter this flux by increasing the frequency of temperatures beyond the thermal optimum of GPP (Topt ). We examined the relationship between GPP and air temperature (Ta) in 17 wooded ecosystems dominated by a single plant functional type (broadleaf evergreen trees) occurring over a broad climatic gradient encompassing five ecoregions across Australia ranging from tropical in the north to Mediterranean and temperate in the south. We applied a novel boundary-line analysis to eddy covariance flux observations to (a) derive ecosystem GPP-Ta relationships and Topt (including seasonal analyses for five tropical savannas); (b) quantitatively and qualitatively assess GPP-Ta relationships within and among ecoregions; (c) examine the relationship between Topt and mean daytime air temperature (MDTa) across all ecosystems; and (d) examine how down-welling short-wave radiation (Fsd) and vapour pressure deficit (VPD) influence the GPP-Ta relationship. GPP-Ta relationships were convex parabolas with narrow curves in tropical forests, tropical savannas (wet season), and temperate forests, and wider curves in temperate woodlands, Mediterranean woodlands, and tropical savannas (dry season). Ecosystem Topt ranged from 15℃ (temperate forest) to 32℃ (tropical savanna-wet and dry seasons). The shape of GPP-Ta curves was largely determined by daytime Ta range, MDTa, and maximum GPP with the upslope influenced by Fsd and the downslope influenced by VPD. Across all ecosystems, there was a strong positive linear relationship between Topt and MDTa (Adjusted R2 : 0.81; Slope: 1.08) with Topt exceeding MDTa by >1℃ at all but two sites. We conclude that ecosystem GPP has adjusted to local MDTa within Australian broadleaf evergreen forests and that GPP is buffered against small Ta increases in the majority of these ecosystems.

The spatial heterogeneity of the relationship between gross primary production and sun-induced chlorophyll fluorescence regulated by climate conditions during 2007–2018

The strong relationship between gross primary productivity (GPP) and sun-induced chlorophyll fluorescence (SIF) provided a novel perspective to estimate the terrestrial GPP based on satellite SIF. However, the influence of environmental conditions on the relationship between GPP and SIF is still unclear. In this study, we synthesized GOME-2 SIF and FLUXCOM GPP coupled with climate data to explore the spatial pattern of the GPP-SIF relationship and its sensitivity to climate conditions at global scale during 2007–2018. The slope (GPP/SIF) of the intercept-free linear regression, which contained information about the allocation of light energy for fluorescence and photosynthesis, was used to represent the GPP-SIF relationship. Our study found that the slope and R2 of the GPP-SIF relationship were spatially heterogeneous, with high slope mainly distributed in the tropical regions and boreal regions and the high R2 mainly concentrated in temperate ecosystems of the Northern Hemisphere. In climate regimes, we found that the GPP-SIF relationship was jointly constrained by environmental variables and the slope had a significant decreasing trend in climate regions from high mean annual temperature (MAT) and low mean annual photosynthetically active radiation (MAR) to low MAT and high MAR. Furthermore, the slope and mean annual precipitation (MAP) have a positive correlation, which indicated the GPP-SIF relationship was climate-dependent. Environmental stress may destroy the relationship between fluorescence and photosynthesis by increasing non-photochemical quenching (NPQ). Our research showed that environmental conditions regulated the light energy distribution for fluorescence and photosynthesis, so accurate estimation of terrestrial ecosystem productivity based on SIF should consider the constraints of climate variables.

Constraining global terrestrial gross primary productivity in a global carbon assimilation system with OCO-2 chlorophyll fluorescence data

The gross primary productivity (GPP) is the largest carbon flux in the terrestrial carbon cycle. Constraining GPP is critical for understanding the terrestrial carbon sources and sinks. In this study, we attempted to constrain the terrestrial GPP at regional to global scales through optimizing the key photosynthetic parameter (the carboxylation capacity at 25 °C, Vcmax25) using solar-induced chlorophyll fluorescence (SIF) observations from the Orbiting Carbon Observatory-2 (OCO-2). The optimization was made within the Global Carbon Assimilation System (GCAS), in which the Boreal Ecosystem Productivity Simulator (BEPS) model was used to simultaneously simulate the global GPP and SIF in the process-based manner. Optimized Vcmax25 shows a distinguishable spatial pattern, with the largest values over the crop regions. After optimization, Vcmax25 of crop is significantly increased. Importantly, the optimized Vcmax25 of different plant functional types (PFTs) show unambiguous seasonal variations. With these optimized Vcmax25, the simulated global GPP in 2015–2016 amounts to 119.1 PgC yr−1, close to the median value (121.3 PgC yr−1) of the observation-based estimates. Global GPP decreases by 8.3% relative to the value simulated using prior Vcmax25. In detail, GPP of crops increases by 16.4%, but it decreases over the other PFT regions, ranging from −4.4% over grasses to −34.0% over deciduous needleleaf forests. The spatiotemporal variations in the optimized PFT-dependent Vcmax25 also reshape the seasonal cycle in GPP. We regard that it is an effective pathway to constrain GPP based on the satellite SIF and the process-based assimilation system, which can provide us an opportunity to better understand the terrestrial and global carbon cycle.

Potential of hotspot solar-induced chlorophyll fluorescence for better tracking terrestrial photosynthesis.

Remote sensing of solar-induced fluorescence (SIF) opens a new window for quantifying a key ecological variable, the terrestrial ecosystem gross primary production (GPP), because of the revealed strong SIF-GPP correlation. However, similar to many other remotely sensed metrics, SIF observations suffer from the sun-sensor geometry effects, which may have important impacts on the SIF-GPP relationship but remain poorly understood. Here we used remotely sensed SIF, globally distributed tower GPP data, and a mechanistic model to provide a systematic analysis. Our results reveal that leaf physiology, canopy structure, and sun-sensor geometries all affect the SIF-GPP relationship. In particular, we found that SIF observations in the sun-tracking hotspot direction can be a better proxy of GPP due to the similar responses of light use efficiency and SIF escaping probability in the hotspot direction to the increasing incoming solar radiation. Such conclusions are supported by a variety of modeling simulations and satellite observations over various plant function types, at different time scales and with satellite observational modes. This study demonstrates the potential and advantage of normalizing SIF observations to the hotspot direction for better global GPP estimations. This study also demonstrates the great potentials of current and future spaceborne sun-tracking satellite missions for a significant improvement in measuring and monitoring, at a wide range of spatial and temporal scales, the changes in terrestrial ecosystem GPP in response to anticipated changes in the Earth's environmental conditions.

Global positive gross primary productivity extremes and climate contributions during 1982–2016

Gross primary production (GPP) quantifies the photosynthetic uptake of carbon by the terrestrial ecosystem. Positive GPP extremes represent the potential capacity of the terrestrial ecosystem to uptake carbon dioxide. Studying the positive GPP extreme is vital for the global carbon cycle and mitigation of global warming. With increasing climate extreme events, many kinds of research focus on studying negative GPP and the negative impact of climatic extremes on GPP. There is still a lack of research on positive GPP extremes and whether climatic extremes could be beneficial to global carbon uptake. In this study, we used daily Boreal Ecosystem Productivity Simulator (BEPS) to simulate GPP of the global terrestrial ecosystem during 1982–2016 and combined TRENDY models to detect positive GPP extremes and investigate the effects of climate extremes on GPP. We found the results of the TRENDY models have large differences in some areas of the globe, and the BEPS model driven by remote sensing data could be more suitable for simulating the long-term time series of global terrestrial GPP. Compared to other plant functional types, grasslands contributed the most to positive GPP extremes, accounting for approximately 41.6% (TRENDY) and 34.8% (BEPS) of the global positive GPP extremes. The probabilities of positive GPP extremes caused by positive precipitation extremes were significantly higher than those caused by temperature and radiation in most areas of the globe, indicating that sufficient precipitation (not a flood) would boost the carbon uptake ability of the global terrestrial ecosystem to form positive GPP extremes. On the contrary, the partial correlation coefficients between temperature and GPP were negative in most areas of globe, suggesting that global warming will not be conducive to carbon uptake of the terrestrial ecosystem. This study may provide new knowledge on the global positive GPP extremes.

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

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