Joint structural and physiological control on the interannual variation in productivity in a temperate grassland: A data‐model comparison

Given the important contributions of semiarid region to global land carbon cycle, accurate modeling of the interannual variability (IAV) of terrestrial gross primary productivity (GPP) is important but remains challenging. By decomposing GPP into leaf area index (LAI) and photosynthesis per leaf area (i.e., GPP_leaf), we investigated the IAV of GPP and the mechanisms responsible in a temperate grassland of northwestern China. We further assessed six ecosystem models for their capabilities in reproducing the observed IAV of GPP in a temperate grassland from 2004 to 2011 in China. We observed that the responses to LAI and GPP_leaf to soil water significantly contributed to IAV of GPP at the grassland ecosystem. Two of six models with prescribed LAI simulated of the observed IAV of GPP quite well, but still underestimated the variance of GPP_leaf, therefore the variance of GPP. In comparison, simulated pattern by the other four models with prognostic LAI differed significantly from the observed IAV of GPP. Only some models with prognostic LAI can capture the observed sharp decline of GPP in drought years. Further analysis indicated that accurately representing the responses of GPP_leaf and leaf stomatal conductance to soil moisture are critical for the models to reproduce the observed IAV of GPP_leaf. Our framework also identified that the contributions of LAI and GPP_leaf to the observed IAV of GPP were relatively independent. We conclude that our framework of decomposing GPP into LAI and GPP_leaf has a significant potential for facilitating future model intercomparison, benchmarking and optimization should be adopted for future data‐model comparisons.     

Carbon dioxide exchange above a Mediterranean C3/C4 grassland during two climatologically contrasting years

Eddy‐covariance measurements of net ecosystem carbon exchange (NEE) were carried out above a grazed Mediterranean C3/C4 grassland in southern Portugal, during two hydrological years, 2004–2005 and 2005–2006, of contrasting rainfall. Here, we examine the seasonal and interannual variation in NEE and its major components, gross primary production (GPP) and ecosystem respiration (R_eco), in terms of the relevant biophysical controls. The first hydrological year was dry, with total precipitation 45% below the long‐term mean (669 mm) and the second was normal, with total precipitation only 12% above the long‐term mean. The drought conditions during the winter and early spring of the dry year limited grass production and the leaf area index (LAI) was very low. Hence, during the peak of the growth period, the maximum daily rate of NEE and the light‐use and water‐use efficiencies were approximately half of those observed in the normal year. In the summer of 2006, the warm‐season C4 grass, Cynodon dactylon L., exerted an evident positive effect on NEE by converting the ecosystem into a carbon sink after strong rain events and extending the carbon sequestration for several days, after the end of senescence of the C3 grasses. On an annual basis, the GPP and NEE were 524 and 49 g C m^?2, respectively, for the dry year, and 1261 and ?190 g C m^?2 for the normal year. Therefore, the grassland was a moderate net source of carbon to the atmosphere, in the dry year, and a considerable net carbon sink, in the normal year. In these 2 years of experiment the total amount of precipitation was the main factor determining the interannual variation in NEE. In terms of relevant controls, GPP and NEE were strongly related to incident photosynthetic photon flux density on short‐term time scales. Changes in LAI explained 84% and 77% of the variation found in GPP and NEE, respectively. Variations in R_eco were mainly controlled by canopy photosynthesis. After each grazing event, the reduction in LAI affected negatively the NEE.     

Interannual variability of plant phenology in tussock tundra: modelling interactions of plant productivity, plant phenology, snowmelt and soil thaw

We present a linked model of plant productivity, plant phenology, snowmelt and soil thaw in order to estimate interannual variability of arctic plant phenology and its effects on plant productivity. The model is tested using 8 years of soil temperature data, and three years of bud break data of Betula nana. Because the factors that trigger the end of the growing season of arctic vegetation are less well known than those of the start of the growing season, three hypotheses were formulated and tested for their effects on productivity and its sensitivity to climate change; the hypothesised factors determining the end of the growing season were frost, photoperiod and periodic constraints. The performance of the soil thermal model was good; both the onset of soil thaw in spring and the initiation of freezing in autumn were predicted correctly in most cases. The phenology model predicted the bud break date of Betula nana closely for the three different years. The soil thaw model predicted similar growing season start dates under current climate as the models based on sum of temperatures, but it made significantly different predictions under climate change scenarios, probably because of the non‐linear interactions between snowmelt and soil thaw. The uncertainty about the driving factors for the end of the growing season, in turn, resulted in uncertainty in the interannual variability of the simulated annual gross primary productivity (GPP). The interannual variability ranged from ? 25 to + 26% of the mean annual GPP for the frost hypothesis, from ? 20 to + 20% for the photoperiod hypothesis and only from ? 7 to + 7% for the periodic hypothesis. The different hypotheses also resulted in different sensitivity to climate change, with the frost hypothesis resulting in 30% higher annual GPP values than the periodic hypothesis when air temperatures were increased by 3 °C.     

Spatiotemporal pattern of gross primary productivity and its covariation with climate in China over the last thirty years

The uncertainties of China's gross primary productivity (GPP) estimates by global data‐oriented products and ecosystem models justify a development of high‐resolution data‐oriented GPP dataset over China. We applied a machine learning algorithm developing a new GPP dataset for China with 0.1° spatial resolution and monthly temporal frequency based on eddy flux measurements from 40 sites in China and surrounding countries, most of which have not been explored in previous global GPP datasets. According to our estimates, mean annual GPP over China is 6.62 ± 0.23 PgC/year during 1982–2015 with a clear gradient from southeast to northwest. The trend of GPP estimated by this study (0.020 ± 0.002 PgC/year² from 1982 to 2015) is almost two times of that estimated by the previous global dataset. The GPP increment is widely spread with 60% area showing significant increasing trend (p < .05), except for Inner Mongolia. Most ecosystem models overestimated the GPP magnitudes but underestimated the temporal trend of GPP. The monsoon affected eastern China, in particular the area surrounding Qinling Mountain, seems having larger contribution to interannual variability (IAV) of China's GPP than the semiarid northwestern China and Tibetan Plateau. At country scale, temperature is the dominant climatic driver for IAV of GPP. The area where IAV of GPP dominated by temperature is about 42%, while precipitation and solar radiation dominate 31% and 27% respectively over semiarid area and cold‐wet area. Such spatial pattern was generally consistent with global GPP dataset, except over the Tibetan Plateau and northeastern forests, but not captured by most ecosystem models, highlighting future research needs to improve the modeling of ecosystem response to climate variations.     

基于碳-水-氮耦合过程改进模型的温带草地生态系统生产力模拟研究

基于碳-水-氮耦合过程改进模型的温带草地生态系统生产力模拟研究 预测气候变化背景下生态系统总初级生产力的响应是全球变化生态学研究领域的一项核心任务。然而,对模型研究领域来说,准确模拟干旱生态系统总初级生产力的年际变异仍然是一个巨大的挑战。土壤含水量和总初级生产力对土壤水敏感性的精确模拟,是预测干旱生态系统中总初级生产力年际变异的两个关键方面。为此,本研究以一个广泛应用的生态系统模型(Biome-BGC模型)为例,旨在改进温带草地生态系统的模型模拟效果。一方面,通过对蒸散模块、土壤水沿剖面的垂直分布和田间持水量计算的改进和调整,模型实现了对土壤水模拟的更新。另一方面,我们改进了影响水-氮关系的函数,从而调节了总初级生产力对土壤水的敏感性。研究结果表明,原有模型高估了土壤含水量,低估了总初级生产力敏感性的年际变异,从而导致模拟总初级生产力的年际变异低于观测值。例如,原模型严重低估了总初级生产力在干旱年份的减少。相比之下,改进后的模型准确地模拟了观测土壤水的季节和年际变化,特别是表层土壤水。通过优化影响氮矿化的参数,改进后的模型改善了总初级生产力对土壤水敏感性的模拟,使其更接近观测值。因此,改进后模型对总初级生产力年际变异的模拟得到了很大程度的提高。我们的结果表明,在对干旱生态系统总初级生产力年际变异进行模拟时,应优先考虑表层土壤水及其对氮有效性的影响。     

The effect of a dry spring on seasonal carbon allocation and vegetation dynamics in a poplar bioenergy plantation

In this study the seasonal variation in carbon, water and energy fluxes as well as in net primary productivity (NPP) of different tree components is presented for a 2‐year‐old poplar (Populus spp.) plantation. A thorough ecophysiological study was performed at ecosystem scale, at tree and at leaf level, in this high‐density bioenergy plantation. Seasonal variation in NPP and fluxes was analysed in relation to meteorological parameters at the field site. The growing season length in terms of carbon uptake was controlled by leaf area development until the maximum leaf area index (LAI_max) was reached. Afterwards, a shift to belowground carbon allocation was observed. A dry period in spring caused a reduced leaf area production as well as a decrease in net ecosystem exchange and gross primary production (GPP) due to stomatal closure. Water use efficiency and fine root growth increased in response to limiting soil water availability in the root zone. When soil water availability was not limiting, GPP was controlled by a decrease in solar radiation and air temperature. The results of this study indicate that the productivity of recently established bioenergy plantations with fast‐growing trees is very sensitive to drought. The interaction between soil water availability and factors controlling ecosystem GPP is crucial in assessing the CO₂ mitigation potential under future climate conditions.     

Autumn warming reduces the CO2 sink of a black spruce forest in interior Alaska based on a nine‐year eddy covariance measurement

Nine years (2003–2011) of carbon dioxide (CO₂) flux were measured at a black spruce forest in interior Alaska using the eddy covariance method. Seasonal and interannual variations in the gross primary productivity (GPP) and ecosystem respiration (RE) were associated primarily with air temperature: warmer conditions enhanced GPP and RE. Meanwhile, interannual variation in annual CO₂ balance was controlled predominantly by RE, and not GPP. During these 9 years of measurement, the annual CO₂ balance shifted from a CO₂ sink to a CO₂ source, with a 9‐year average near zero. The increase in autumn RE was associated with autumn warming and was mostly attributed to a shift in the annual CO₂ balance. The increase in autumn air temperature (0.22 °C yr^?1) during the 9 years of study was 15 times greater than the long‐term warming trend between 1905 and 2011 (0.015 °C yr^?1) due to decadal climate oscillation. This result indicates that most of the shifts in observed CO₂ fluxes were associated with decadal climate variability. Because the natural climate varies in a cycle of 10–30 years, a long‐term study covering at least one full cycle of decadal climate oscillation is important to quantify the CO₂ balance and its interaction with the climate.     

Interannual variations in GPP in forest ecosystems in Southwest China and regional differences in the climatic contributions

Forest ecosystem plays an important role as carbon sinks in Southwest China. Currently, remote sensing technology has been widely used to substantially model the high temporal and spatial variation in gross primary production (GPP) at a site or regional scale. However, during the growing season, the regional uncertainty of GPP in the forest ecosystem and the relative contributions of climate variations to interannual variation (IAV) of GPP are not well understood across Southwest China. Our research focuses on the joint analysis of the three-cornered hat (TCH) algorithm and uses the contribution index to analyse the model's uncertainties varying with plant functional types (PFTs), climate zones, and the contribution of climate variabilities to GPP IAV. Here, three GPP datasets are used to investigate how climate variabilities contribute to the GPP IAV during the growing season. The uncertainties in GPP vary from 829.33 g C m⁻² year⁻¹ to 2031.86 g C m⁻² year⁻¹ for different models in different climate zones and different PFTs. Additionally, the results highlight that precipitation dominates the interannual variation in GPP in forest ecosystem during the growing season in Southwest China. It makes the largest contribution (34.46%) to the IAV of GPP in the climate zone of E (cold subtropical highland area) and the largest contribution (80.83%) to PFTs of the MF (mixed forest). Our study suggests the availability and applicability of GPP products can be used to assess GPP uncertainties and analyse the contributions of climate factors to GPP IAV in forest ecosystem or other ecosystems.     

Self‐thinning and Tree Competition in Savannas

This paper examines the feasibility of applying self‐thinning concepts to savannas and how competition with herbaceous vegetation may modify self‐thinning patterns among woody plants in these ecosystems. Competition among woody plants has seldom been invoked as a major explanation for the persistence of herbaceous vegetation in mixed tree–grass ecosystems. On the contrary, the primary resource‐based explanations for tree–grass coexistence are based on tree–grass competition (niche‐separation) that assumes that trees are inferior competitors unless deeper rooting depths provide them exclusive access to water. Alternative nonresource‐based hypotheses postulate that trees are the better competitors, but that tree populations are suppressed by mortality related to fire, herbivores, and other disturbances. If self‐thinning of woody plants can be detected in savannas, stronger evidence for resource‐limitation and competitive interactions among woody plants would suggest that the primary models of savannas need to be adjusted. We present data from savanna sites in South Africa to suggest that self‐thinning among woody plants can be detected in low‐disturbance situations, while also showing signs that juvenile trees, more so than adults, are suppressed when growing with herbaceous vegetation in these ecosystems. This finding we suggest is evidence for size‐asymmetric competition in savannas.     

Interannual variability in carbon dioxide fluxes and flux–climate relationships on grazed and ungrazed northern mixed-grass prairie

The annual carbon (C) budget of grasslands is highly dynamic, dependent on grazing history and on effects of interannual variability (IAV) in climate on carbon dioxide (CO₂) fluxes. Variability in climatic drivers may directly affect fluxes, but also may indirectly affect fluxes by altering the response of the biota to the environment, an effect termed ‘functional change’. We measured net ecosystem exchange of CO₂ (NEE) and its diurnal components, daytime ecosystem CO₂ exchange (P_D) and night‐time respiration (R_E), on grazed and ungrazed mixed‐grass prairie in North Dakota, USA, for five growing seasons. Our primary objective was to determine how climatic anomalies influence variability in CO₂ exchange. We used regression analysis to distinguish direct effects of IAV in climate on fluxes from functional change. Functional change was quantified as the improvement in regression on fitting a model in which slopes of flux–climate relationships vary among years rather than remain invariant. Functional change and direct effects of climatic variation together explained about 20% of variance in weekly means of NEE, P_D, and R_E. Functional change accounted for more than twice the variance in fluxes of direct effects of climatic variability. Grazing did not consistently influence the contribution of functional change to flux variability, but altered which environmental variable best explained year‐to‐year differences in flux–climate slopes, reduced IAV in seasonal means of fluxes, lessened the strength of flux–climate correlations, and increased NEE by reducing R_E relatively more than P_D. Most of these trends are consistent with the interpretation that grazing reduced the influence of plants on ecosystem fluxes. Because relationships between weekly values of fluxes and climatic regulators changed annually, year‐to‐year differences in the C balance of these ecosystems cannot be predicted from knowledge of IAV in climate alone.     

Abiotic drivers of interannual phytoplankton variability and a 1999–2000 regime shift in the North Sea examined by multivariate statistics

The Dutch coastal zone is a region of the North Sea with a marked interannual and long‐term abiotic and phytoplankton variability. To investigate the relationship between abiotic variability and phytoplankton composition, two routine water monitoring data sets (1991–2005) were examined. Multivariate statistics revealed two significant partitions in the data. The first consisted of interannual abiotic fluctuations that were correlated to Rhine discharge that affected the abundance of summer and autumn diatom species. The second partition was caused by a shift in the abiotic data from 1998 to 1999 that was followed by a shift in phytoplankton composition from 1999 to 2000. Important factors in the abiotic shift were decreases in suspended matter (SPM) and phosphate (DIP) concentrations, as well as in pH. The decrease in SPM was caused by a reduction in wind speed. The increase in water column daily irradiance from the decrease in SPM led to increases in the abundance of winter–spring species, notably the prymnesiophyte Phaeocystis globosa. Because wind speed is related to the North Atlantic Oscillation (NAO) index it was possible to correlate NAO index and P. globosa abundance. Only five abiotic variables representing interannual and long‐term variability, including Rhine discharge and NAO index, were needed to model the observed partitions in phytoplankton composition. It was concluded that interannual variability in the coastal phytoplankton composition was related to year‐to‐year changes in river discharge while the long‐term shift was caused by an alternating large‐scale meteorological phenomenon.     

Tree–grass coexistence in savannas revisited – insights from an examination of assumptions and mechanisms invoked in existing models

Several explanations for the persistence of tree–grass mixtures in savannas have been advanced thus far. In general, these either concentrate on competition‐based mechanisms, where niche separation with respect to limiting resources such as water lead to tree–grass coexistence, or demographic mechanisms, where factors such as fire, herbivory and rainfall variability promote tree–grass persistence through their dissimilar effects on different life‐history stages of trees. Tests of these models have been largely site‐specific, and although different models find support in empirical data from some savanna sites, enough dissenting evidence exists from others to question their validity as general mechanisms of tree–grass coexistence. This lack of consensus on determinants of savanna structure and function arises because different models: (i) focus on different demographic stages of trees, (ii) focus on different limiting factors of tree establishment, and (iii) emphasize different subsets of the potential interactions between trees and grasses. Furthermore, models differ in terms of the most basic assumptions as to whether trees or grasses are the better competitors. We believe an integration of competition‐based and demographic approaches is required if a comprehensive model that explains both coexistence and the relative productivity of the tree and grass components across the diverse savannas of the world is to emerge. As a first step towards this end, we outline a conceptual framework that integrates existing approaches and applies them explicitly to different life‐history stage of trees.     

Seasonal controls on interannual variability in carbon dioxide exchange of a near-end-of rotation Douglas-fir stand in the Pacific Northwest, 1997–2006

This study analyzes 9 years of eddy‐covariance (EC) data carried out in a Pacific Northwest Douglas‐fir (Pseudotsuga menzesii) forest (58‐year old in 2007) on the east coast of Vancouver Island, Canada, and characterizes the seasonal and interannual variability in net ecosystem productivity (NEP), gross primary productivity (GPP), and ecosystem respiration (R_e) and primary climatic controls on these fluxes. The annual values (± SD) of NEP, GPP and R_e were 357 ± 51, 2124 ± 125, and 1767 ± 146 g C m^?2 yr^?1, respectively, with ranges of 267–410, 1592–2338, and 1642–2071 g C m^?2 yr^?1, respectively. Spring to early summer (March–June) accounted for more than 80% of annual NEP while late spring to early autumn (May–August) was mainly responsible for its interannual variability (～80%). The major drivers of interannual variability in annual carbon (C) fluxes were annual and spring mean air temperatures (T_a) and water deficiency during late summer and autumn (July–October) when this Douglas‐fir forest growth was often water‐limited. Photosynthetically active radiation (Q), and the combination of Q and soil water content (θ) explained 85% and 91% of the variance of monthly GPP, respectively; and 91% and 96% of the variance of monthly R_e was explained by T_a and the combination of T_a and θ, respectively. Annual net C sequestration was high during optimally warm and normal precipitation years, but low in unusually warm or severely dry years. Excluding 1998 and 1999, the 2 years strongly affected by an El Niño/La Niña cycle, annual NEP significantly decreased with increasing annual mean T_a. Annual NEP will likely decrease whereas both annual GPP and R_e will likely increase if the future climate at the site follows a trend similar to that of the past 40 years.     

Nitrogen controls the net primary production of an alpine Kobresia meadow in the northern Qinghai‐Tibet Plateau

Net primary production (NPP) is a fundamental property of natural ecosystems. Understanding the temporal variations of NPP could provide new insights into the responses of communities to environmental factors. However, few studies based on long‐term field biomass measurements have directly addressed this subject in the unique environment of the Qinghai‐Tibet plateau (QTP). We examined the interannual variations of NPP during 2008–2015 by monitoring both aboveground net primary productivity (ANPP) and belowground net primary productivity (BNPP), and identified their relationships with environmental factors with the general linear model (GLM) and structural equation model (SEM). In addition, the interannual variation of root turnover and its controls were also investigated. The results show that the ANPP and BNPP increased by rates of 15.01 and 143.09 g/m² per year during 2008–2015, respectively. BNPP was mainly affected by growing season air temperature (GST) and growing season precipitation (GSP) rather than mean annual air temperature (MAT) or mean annual precipitation (MAP), while ANPP was only controlled by GST. In addition, available nitrogen (AN) was significantly positively associated with BNPP and ANPP. Root turnover rate averaged 30%/year, increased with soil depth, and was largely controlled by GST. Our results suggest that alpine Kobresia meadow was an N‐limited ecosystem, and the NPP on the QTP might increase further in the future in the context of global warming and nitrogen deposition.     

Comparison of phenology trends by land cover class: a case study in the Great Basin, USA

Direct impacts of human land use and indirect impacts of anthropogenic climate change may alter land cover and associated ecosystem function, affecting ecological goods and services. Considerable work has been done to identify long‐term global trends in vegetation greenness, which is associated with primary productivity, using remote sensing. Trend analysis of satellite observations is subject to error, and ecosystem change can be confused with interannual variability. However, the relative trends of land cover classes may hold clues about differential ecosystem response to environmental forcing. Our aim was to identify phenological variability and 10‐year trends for the major land cover classes in the Great Basin. This case study involved two steps: a regional, phenology‐based land cover classification and an identification of phenological variability and 10‐year trends stratified by land cover class. The analysis used a 10‐year time series of Advanced Very High Resolution Radiometer satellite data to assess regional scale land cover variability and identify change. The phenology‐based regional classification was more detailed and accurate than national or global products. Phenological variability over the 10‐year period was high, with substantial shifts in timing of start of season of up to 9 weeks. The mean long‐term trends of montane land cover classes were significantly different from valley land cover classes due to a poor response of montane shrubland and pinyon‐juniper woodland to the early 1990s drought. The differential response during the 1990s suggests that valley ecosystems may be more resilient and montane ecosystems more susceptible to prolonged drought. This type of regional‐scale land cover analysis is necessary to characterize current patterns of land cover phenology, distinguish between anthropogenically driven land cover change and interannual variability, and identify ecosystems potentially susceptible to regional and global change.     

Contrasting demographics of tropical savanna and temperate forest eucalypts provide insight into how savannas and forests function. A case study using Corymbia clarksoniana from north‐eastern Australia

Eucalypts (Eucalyptus spp. and Corymbia spp.) dominate many communities across Australia, including frequently burnt tropical savannas and temperate forests, which receive less frequent but more intense fires. Understanding the demographic characteristics that allow related trees to persist in tropical savannas and temperate forest ecosystems can provide insight into how savannas and forests function, including grass–tree coexistence. This study reviews differences in critical stages in the life cycle of savanna and temperate forest eucalypts, especially in relation to fire. It adds to the limited data on tropical eucalypts, by evaluating the effect of fire regimes on the population biology of Corymbia clarksoniana, a tree that dominates some tropical savannas of north‐eastern Australia. Corymbia clarksoniana displays similar demographic characteristics to other tropical savanna species, except that seedling emergence is enhanced when seed falls onto recently burnt ground during a high rainfall period. In contrast to many temperate forest eucalypts, tropical savanna eucalypts lack canopy‐stored seed banks; time annual seed fall to coincide with the onset of predictable wet season rain; have very rare seedling emergence events, including a lack of mass germination after each fire; possess an abundant sapling bank; and every tropical eucalypt species has the ability to maintain canopy structure by epicormically resprouting after all but the most intense fires. The combination of poor seedling recruitment strategies, coupled with characteristics allowing long‐term persistence of established plants, indicate tropical savanna eucalypts function through the persistence niche rather than the regeneration niche. The high rainfall‐promoted seedling emergence of C. clarksoniana and the reduction of seedling survival and sapling growth by fire, support the predictions that grass–tree coexistence in savannas is governed by rainfall limiting tree seedling recruitment and regular fires limiting the growth of juvenile trees to the canopy.     

Cultivation of a perennial grass for bioenergy on a boreal organic soil – carbon sink or source?

The area under the cultivation of perennial bioenergy crops on organic soils in the northern countries is fast increasing. To understand the impact of reed canary grass (RCG, Phalaris arundinaceae L.) cultivation on the carbon dioxide (CO₂) balance of an organic soil, net ecosystem CO₂ exchange (NEE) was measured for four years in a RCG cultivated cutover peatland in eastern Finland using the eddy covariance technique. There were striking differences among the years in the annual precipitation. The annual precipitation was higher during 2004 and 2007 and lower during 2005 and 2006 than the 1971–2000 regional mean. During wet growing seasons, moderate temperatures, high surface soil moisture and low evaporative demand favoured high CO₂ uptake. During dry seasons, owing to soil moisture and atmospheric stress, photosynthetic activity was severely restricted. The CO₂ uptake [gross primary productivity (GPP)] was positively correlated with soil moisture, air temperature and inversely with vapour pressure deficit. Total ecosystem respiration (TER) increased with increasing soil temperature but decreased with increasing soil moisture. The relative responses of GPP and TER to moisture stress were different. While changes in TER for a given change in soil moisture were moderate, variations in GPP were drastic. Also, the seasonal variations in TER were not as conspicuous as those in GPP implying that GPP is the primary regulator of the interannual variability in NEE in this ecosystem. The ecosystem accumulated a total of 398 g C m^?2 from the beginning of 2004 until the end of 2007. It retained some carbon during a wet year such as 2004 even after accounting for the loss of carbon in the form of harvested biomass. Based on this CO₂ balance analysis, RCG cultivation is found to be a promising after‐use option on an organic soil.     

Negative impacts of high temperatures on growth of black spruce forests intensify with the anticipated climate warming

An increasing number of studies conclude that water limitations and heat stress may hinder the capacity of black spruce (Picea mariana (Mill.) B.S.P.) trees, a dominant species of Canada's boreal forests, to grow and assimilate atmospheric carbon. However, there is currently no scientific consensus on the future of these forests over the next century in the context of widespread climate warming. The large spatial extent of black spruce forests across the Canadian boreal forest and associated variability in climate, demography, and site conditions pose challenges for projecting future climate change responses. Here we provide an evaluation of the impacts of climate warming and drying, as well as increasing [CO₂], on the aboveground productivity of black spruce forests across Canada south of 60°N for the period 1971 to 2100. We use a new extensive network of tree‐ring data obtained from Canada's National Forest Inventory, spatially explicit simulations of net primary productivity (NPP) and its drivers, and multivariate statistical modeling. We found that soil water availability is a significant driver of black spruce interannual variability in productivity across broad areas of the western to eastern Canadian boreal forest. Interannual variability in productivity was also found to be driven by autotrophic respiration in the warmest regions. In most regions, the impacts of soil water availability and respiration on interannual variability in productivity occurred during the phase of carbohydrate accumulation the year preceding tree‐ring formation. Results from projections suggest an increase in the importance of soil water availability and respiration as limiting factors on NPP over the next century due to warming, but this response may vary to the extent that other factors such as carbon dioxide fertilization, and respiration acclimation to high temperature, contribute to dampening these limitations.     

Do dynamic global vegetation models capture the seasonality of carbon fluxes in the Amazon basin? A data‐model intercomparison

To predict forest response to long‐term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short‐term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry‐season intensities and lengths, to determine how well four state‐of‐the‐art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry‐season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry‐season GPP reductions were driven by an external environmental factor, ‘soil water stress’ and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry‐season GPP resulted from a combination of internal biological (leaf‐flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (R_e) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry‐season declines in GPP and R_e consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light‐harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf‐level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.     

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.