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Vegetation productivity first increases and then decreases with temperature; and temperature corresponding to the maximum productivity is called optimal temperature (Topt). In this study, we used satellite derived near‐infrared reflectance of vegetation (NIRv) data to map Topt of vegetation productivity at the spatial resolution of 0.1° on the Tibetan Plateau (TP), one of most sensitive regions in the climate system. The average Topt of non‐forest vegetation on the TP is about 14.7°C, significantly lower than the Topt value used in current ecosystem models. A remarkable geographical heterogeneity in Topt is observed over the TP. Higher Topt values generally appear in the north‐eastern TP, while the south‐western TP has relatively lower Topt (<10°C), in line with the difference of climate conditions and topography across different regions. Spatially, Topt tends to decrease by 0.41°C per 100 m increase in elevation, faster than the elevational elapse rate of growing season temperature, implying a potential CO2 regulation of Topt in addition to temperature acclimation. Topt increases by 0.66°C for each 1°C of rising mean annual temperature as a result of vegetation acclimation to climate change. However, at least at the decadal scale, there is no significant change in Topt between 2000s and 2010s, suggesting that the Topt climate acclimation may not keep up with the warming rate. Finally, future (2091–2100) warming could be close to and even surpass Topt on the TP under different RCP scenarios without considering potential climate acclimation. Our analyses imply that the temperature tipping point when the impact of future warming shifts from positive to negative on the TP is greatly overestimated by current vegetation models. Future research needs to include varying thermal and CO2 acclimation effects on Topt across different time scales in vegetation models. [ABSTRACT FROM AUTHOR]

Effective use of solar‐induced chlorophyll fluorescence (SIF) to estimate and monitor gross primary production (GPP) in terrestrial ecosystems requires a comprehensive understanding and quantification of the relationship between SIF and GPP. To date, this understanding is incomplete and somewhat controversial in the literature. Here we derived the GPP/SIF ratio from multiple data sources as a diagnostic metric to explore its global‐scale patterns of spatial variation and potential climatic dependence. We found that the growing season GPP/SIF ratio varied substantially across global land surfaces, with the highest ratios consistently found in boreal regions. Spatial variation in GPP/SIF was strongly modulated by climate variables. The most striking pattern was a consistent decrease in GPP/SIF from cold‐and‐wet climates to hot‐and‐dry climates. We propose that the reduction in GPP/SIF with decreasing moisture availability may be related to stomatal responses to aridity. Furthermore, we show that GPP/SIF can be empirically modeled from climate variables using a machine learning (random forest) framework, which can improve the modeling of ecosystem production and quantify its uncertainty in global terrestrial biosphere models. Our results point to the need for targeted field and experimental studies to better understand the patterns observed and to improve the modeling of the relationship between SIF and GPP over broad scales. [ABSTRACT FROM AUTHOR]

Reactive gaseous nitrogen (Ngr) emissions significantly affect Earth's climate system. Disagreement exists, however, over Ngr contributions to short‐ versus long‐term climate forcing, from local to global scales and among different gaseous forms, including NH3, NOx, and N2O. Here, we provide a comprehensive inventory of Ngr from China's croplands based on a new bottom‐up, mass flow‐based approach integrated with fine‐resolution agricultural activity data and nitrogen emission factors. We demonstrate that China's croplands emit about 8.87 Tg N to the atmosphere in 2014. Across different prefectures, Ngr emission per capita conforms to a "Kuznets curve," that is, first increases then decreases, along the gradient of increasing urbanization. Ngr emission per gross domestic productivity (GDP) decreases exponentially with increasing urbanization or per capita GDP. Furthermore, climate change impact analyses suggest that the global‐scale warming effect of China's cropland N2O emissions dominate over local cooling effects ascribed to its NH3 and NOx emissions. Plain Language Summary: Reactive gaseous nitrogen, sourced to the world's highest fertilizer application rates in China, imparts local cooling effects on short time scales. The effects are ultimately counterbalanced by nitrous oxide emissions over the long term, leading to substantial warming effects on the global climate system. We base our conclusion on the first‐ever high‐resolution assessment of reactive nitrogen emissions to the atmosphere from China's croplands. Owing to limited high‐resolution reactive gaseous nitrogen emissions data, hitherto, large uncertainties have existed in the detailing the role of China's cropland reactive gaseous nitrogen emissions on climate forcing. This paucity of information poses a challenge for informing China's agricultural sustainability policies with regard to maintaining/increasing agricultural yields while reduces reactive gaseous nitrogen emissions and their warming potential. Our analyses provide essential information for further sustainable N management and the interactions between agriculture and climate change. We provide policy‐relevant information for mitigation targets, including the 2°C limit goal set by the 2015 Paris Agreement of the United Nations Framework Convention on Climate Change, enabling progress related to multiple United Nations Sustainable Development Goals. Key Points: We provide new high‐resolution estimates of reactive gaseous nitrogen (Ngr) emissions from China's croplands and their seasonal dynamicsAcross different prefectures, Ngr emission per capita follows an environmental Kuznets curve against urbanization rateThe overall warming effect of N2O emissions dominate over local cooling effects ascribed to NH3 and NOx emissions each year [ABSTRACT FROM AUTHOR]

The ability to down-regulate leaf maximum net photosynthetic capacity (Amax) and dark respiration rate (Rdark) in response to shading is thought to be an important adaptation of trees to the wide range of light environments that they are exposed to across space and time. A simple, general rule that accurately described this down-regulation would improve carbon cycle models and enhance our understanding of how forest successional diversity is maintained. In this paper, we investigated the light response of Amax and Rdark for saplings of six temperate forest tree species in New Jersey, USA, and formulated a simple model of down-regulation that could be incorporated into carbon cycle models. We found that full-sun values of Amax and Rdark differed significantly among species, but the rate of down-regulation (proportional decrease in Amax or Rdark relative to the full-sun value) in response to shade was not significantly species- or taxon-specific. Shade leaves of sun-grown plants appear to follow the same pattern of down-regulation in response to shade as leaves of shade-grown plants. Given the light level above a leaf and one species-specific number (either the full-sun Amax or full-sun Rdark), we provide a formula that can accurately predict the leaf's Amax and Rdark. We further show that most of the down regulation of per unit area Rdark and Amax is caused by reductions in leaf mass per unit area (LMA): as light decreases, leaves get thinner, while per unit mass Amax and Rdark remain approximately constant. [ABSTRACT FROM AUTHOR]

The sensitivity of vegetation productivity to precipitation (Sppt) is a key metric for understanding the variations in vegetation productivity under changing precipitation and predicting future changes in ecosystem functions. However, a comprehensive assessment of Sppt over all the global land is lacking. Here, we investigated spatial patterns and temporal changes of Sppt across the global land from 2001 to 2018 with multiple streams of satellite observations. We found consistent spatial patterns of Sppt with different satellite products: Sppt was highest in dry regions while low in humid regions. Grassland and shrubland showed the highest Sppt, and evergreen needle‐leaf forest and wetland showed the lowest. Temporally, Sppt showed a generally declining trend over the past two decades (p <.05), yet with clear spatial heterogeneities. The decline in Sppt was especially noticeable in North America and Europe, likely due to the increase in precipitation. In central Russia and Australia, however, Sppt showed an increasing trend. Biome‐wise, most ecosystem types exhibited significant decrease in Sppt, while grassland, evergreen broadleaf forest, and mixed forest showed slight increases or non‐significant changes in Sppt. Our finding of the overall decline in Sppt implies a potential stabilization mechanism for ecosystem productivity under climate change. However, the revealed Sppt increase for some regions and ecosystem types, in particular global grasslands, suggests that grasslands might be increasingly vulnerable to climatic variability with continuing global climate change. [ABSTRACT FROM AUTHOR]

Plant phenology, the annually recurring sequence of plant developmental stages, is important for plant functioning and ecosystem services and their biophysical and biogeochemical feedbacks to the climate system. Plant phenology depends on temperature, and the current rapid climate change has revived interest in understanding and modeling the responses of plant phenology to the warming trend and the consequences thereof for ecosystems. Here, we review recent progresses in plant phenology and its interactions with climate change. Focusing on the start (leaf unfolding) and end (leaf coloring) of plant growing seasons, we show that the recent rapid expansion in ground‐ and remote sensing‐ based phenology data acquisition has been highly beneficial and has supported major advances in plant phenology research. Studies using multiple data sources and methods generally agree on the trends of advanced leaf unfolding and delayed leaf coloring due to climate change, yet these trends appear to have decelerated or even reversed in recent years. Our understanding of the mechanisms underlying the plant phenology responses to climate warming is still limited. The interactions between multiple drivers complicate the modeling and prediction of plant phenology changes. Furthermore, changes in plant phenology have important implications for ecosystem carbon cycles and ecosystem feedbacks to climate, yet the quantification of such impacts remains challenging. We suggest that future studies should primarily focus on using new observation tools to improve the understanding of tropical plant phenology, on improving process‐based phenology modeling, and on the scaling of phenology from species to landscape‐level. [ABSTRACT FROM AUTHOR]

This study develops a convolutional recurrent deep learning model to accurately predict fine‐resolution spatiotemporal changes in grass coverage in arid regions. Applying the model to the Gobi Desert reveals that ecological flow regulation contributes to 61.8% of the total increase in grass cover (130.6 km2) in the study area (40,423 km2) over 2005–2015, nearly triple the contribution of local climate change (+23.0%). The transboundary hydrological impact (+32.4%) and interactions between drivers (−17.2%) are also significant. In an intermediate future climate change scenario, we found no statistically significant trend for the total grass‐covered area due to the counteracting effects among different drivers. The study findings suggest that timely, adaptive and spatially heterogeneous ecological flow management is crucial for addressing grassland degradation in arid regions. This study provides a promising approach to land surface modeling under climate change and human disturbance and expands the existing understanding of the global greening process. Plain Language Summary: The Earth's recent greening (i.e., vegetation increase) has boosted the terrestrial carbon sink, intensified the hydrologic cycle, and contributed to mitigating global warming. CO2 fertilization has been identified as the primary cause at the global scale, while climate change, land use changes, and nitrogen deposition also contribute. However, there are still many gaps in our understanding of how grasslands in arid areas are being affected. Studies in Central Asia identified precipitation as the dominant factor affecting grassland change and grazing as the main influence from humans. Based on satellite data, we employ a novel deep learning (DL) approach to emulate spatiotemporal changes in grass coverage at a 1‐km resolution over an area of 40,423 km2 in the Gobi Desert in northwestern China. Our study identified previously overlooked mechanisms contributing to the increase in grass coverage, including ecological flow management and transboundary hydrological impact. Ecological flow management was found to be responsible for almost three times the increase in grass coverage over 2005–2015 than that of local climate changes. Our findings suggest that timely, adaptive, and spatially targeted ecological flow management is urgently needed to prevent further grassland degradation in arid regions, and DL‐based artificial intelligence can offer valuable management suggestions. Key Points: Deep learning (DL) precisely emulates spatiotemporal changes in grass coverage at a 1‐km resolution over an area of 40,423 km2DL interprets ecological flow regulation (EFR) as the leading cause of grassland greening in the Gobi DesertTimely, adaptive and spatially heterogeneous ecological flow management is desired to prevent grassland degradation in arid regions [ABSTRACT FROM AUTHOR]

Global greening, characterized by an increase in leaf area index (LAI), implies an increase in foliar carbon (C). Whether this increase in foliar C under climate change is due to higher photosynthesis or to higher allocation of C to leaves remains unknown. Here, we explored the trends in foliar C accumulation and allocation during leaf green‐up from 2000 to 2017 using satellite‐derived LAI and solar‐induced chlorophyll fluorescence (SIF) across the Northern Hemisphere. The accumulation of foliar C accelerated in the early green‐up period due to both increased photosynthesis and higher foliar C allocation driven by climate change. In the late stage of green‐up, however, we detected decreasing trends in foliar C accumulation and foliar C allocation. Such stage‐dependent trends in the accumulation and allocation of foliar C are not represented in current terrestrial biosphere models. Our results highlight that a better representation of C allocation should be incorporated into models. [ABSTRACT FROM AUTHOR]

Negative extreme anomalies in vegetation growth (NEGs) usually indicate severely impaired ecosystem services. These NEGs can result from diverse natural and anthropogenic causes, especially climate extremes (CEs). However, the relationship between NEGs and many types of CEs remains largely unknown at regional and global scales. Here, with satellite‐derived vegetation index data and supporting tree‐ring chronologies, we identify periods of NEGs from 1981 to 2015 across the global land surface. We find 70% of these NEGs are attributable to five types of CEs and their combinations, with compound CEs generally more detrimental than individual ones. More importantly, we find that dominant CEs for NEGs vary by biome and region. Specifically, cold and/or wet extremes dominate NEGs in temperate mountains and high latitudes, whereas soil drought and related compound extremes are primarily responsible for NEGs in wet tropical, arid and semi‐arid regions. Key characteristics (e.g., the frequency, intensity and duration of CEs, and the vulnerability of vegetation) that determine the dominance of CEs are also region‐ and biome‐dependent. For example, in the wet tropics, dominant individual CEs have both higher intensity and longer duration than non‐dominant ones. However, in the dry tropics and some temperate regions, a longer CE duration is more important than higher intensity. Our work provides the first global accounting of the attribution of NEGs to diverse climatic extremes. Our analysis has important implications for developing climate‐specific disaster prevention and mitigation plans among different regions of the globe in a changing climate. [ABSTRACT FROM AUTHOR]

Abstract: Phenological changes are closely related to the carbon cycle of terrestrial ecosystems, and satellite data have been widely used in large scale phenological research. Numerous methods have been developed to reconstruct distinct satellite derived vegetation signals from continuous vegetation index time series and to track the points corresponding to important phenological events. In this study, we perform a multiple-method investigation of the spring vegetation growth onset phenology in temperate China north of 30°N with NDVI (normalized difference vegetation index) data produced from SPOT satellites. The results indicated that the spring onset dates estimated from five different methods show similar spatial pattern along latitudinal or altitudinal gradients, but with significant variances in the dates. In most areas (76% of pixels), the standard deviation (SD) of the estimated spring onset dates from different methods is within one month, while in a few places (2% of pixels), mostly agricultural cropland, the SD is more than 60 days. Comparing the satellite derived green-up onset dates against the gross primary productivity (GPP) time series curves acquired from six eddy covariance towers further highlights the strengths and limits of different methods in estimating phenological events. In a vast region with diverse vegetation types and physical environment, it is critical to choose the “right” method for the “right” place. Generally climate factors such as temperature and precipitation play an important role in controlling the start of vegetation growth in temperate China. Yet this climate-phenology relationship varies largely across different regions and vegetation types. [Copyright &y& Elsevier]

Abstract: Research in phenology change has been one heated topic of current ecological and climate change study. In this study, we use satellite derived NDVI (Normalized Difference Vegetation Index) data to explore the spatio-temporal changes in the timing of spring vegetation green-up in the Qinghai-Xizang (Tibetan) Plateau from 1982 to 2006 and to characterize their relationship with elevation and temperature using concurrent satellite and climate data sets. At the regional scale, no statistically significant trend of the vegetation green-up date is observed during the whole study period (R 2 =0.00, P =0.95). Two distinct periods of green-up changes are identified. From 1982 to 1999, the vegetation green-up significantly advanced by 0.88daysyear−1 (R 2 =0.56, P <0.001). In contrast, from 1999 to 2006, a marginal delaying trend is evidenced (R 2 =0.44, P =0.07), suggesting that the persistent trend towards earlier vegetation green-up in spring between 1980s and 1990s was stalled during the first decade of this century. This shift in the tendency of the vegetation green-up seems to be related to differing temperature trends between these two periods. Statistical analysis shows that the average onset of vegetation green-up over the Qinghai-Xizang Plateau would advance by about 4.1 days in response to 1°C increase of spring temperature. In addition, results from our analysis indicate that the spatial patterns of the vegetation green-up date and its change since 1982 are altitude dependent. The magnitude of the vegetation green-up advancement during 1982–1999, and of its postponement from 1999 to 2006 significantly increases along an increasing elevation gradient. [Copyright &y& Elsevier]

Increasing anthropogenic nitrogen (N) deposition will affect the global carbon (C) cycle, although the mechanisms and magnitudes of its effects are not yet fully understood. Soil respiration (Rs) and its components (i.e., heterotrophic (Rh) and autotrophic (Ra) respiration) represent the largest flux of carbon dioxide (CO 2 ) from terrestrial ecosystems to the atmosphere. However, the potentially interactive effect of N addition and stand age on Rs in forests remains unclear. We conducted a 7-year (2010–2016) field experiment in three differently-aged larch plantations (11-, 20-, and 45-year-old, representing sapling, intermediate, and mature stands) to investigate the effects of different N addition rates (control: no N addition; low-N addition, N20: 20 kg N ha −1  year −1 ; and high-N addition, N50: 50 kg N ha −1  year −1 ) on Rs. We found significant seasonal variation in Rs, Rh, and Ra, with levels highest in summer and lowest in autumn. Rs correlated strongly with soil temperature and weakly with soil moisture, and Rs, Rh, and Ra increased exponentially with soil temperature. The temperature sensitivity (Q 10 ) of Rs was lowest in the mature stand, which suggests that it will sequester relatively more C in a warmer climate. The Q 10 of Ra was higher than that of Rh, indicating that it contributed proportionally more to the total soil CO 2 efflux. N addition had no significant effect on Q 10 . Rs in the intermediate stand was significantly lower than Rs in the sapling and mature stands, which may be due to its lower soil temperature and poorer substrate quality. N addition decreased Rs by an average of 9%, but significant effects were only observed in the sapling and intermediate stands. The negative influence of N addition on Rs was largely dependent on soil temperature, with more negative effects observed in the summer when temperatures were higher, and attributable to a reduction in either Ra in the intermediate stand or Rh in the sapling stand. We hypothesize that N limitation in the young trees was alleviated by fertilization, which decreased C allocation to roots, whereas N addition may have exerted a more negative influence on soil microbial activity in the sapling stand. In summary, our results highlight the importance of stand age in regulating the negative effects of N addition on Rs and its components, which has implications for projecting the global C–climate feedback in the future. [ABSTRACT FROM AUTHOR]

The methane (CH4) cycle on the Qinghai-Tibet Plateau (QTP), the world's largest high-elevation permafrost region, is sensitive to climate change and subsequent freezing and thawing dynamics. Yet, its magnitudes, patterns, and environmental controls are still poorly understood. Here, we report results from five continuous year-round CH4 observations from a typical alpine steppe ecosystem in the QTP permafrost region. Our results suggest that the QTP permafrost region was a CH4 sink of -0.86±0.23 g CH4- Cm-2 yr-1 over 2012-2016, a rate higher than that of many other permafrost areas, such as the Arctic tundra in northern Greenland, Alaska, and western Siberia. Soil temperature and soil water content were dominant factors controlling CH4 fluxes; however, their correlations changed with soil depths due to freezing and thawing dynamics. This region was a net CH4 sink in autumn, but a net source in spring, despite both seasons experiencing similar top soil thawing and freezing dynamics. The opposite CH4 source-sink function in spring versus in autumn was likely caused by the respective seasons' specialized freezing and thawing processes, which modified the vertical distribution of soil layers that are highly mixed in autumn, but not in spring. Furthermore, the traditional definition of four seasons failed to capture the pattern of the annual CH4 cycle. We developed a new seasonal division method based on soil temperature, bacterial activity, and permafrost active layer thickness, which significantly improved the modeling of the annual CH4 cycle. Collectively, our findings highlight the critical role of fine-scale climate freezing and thawing dynamics in driving permafrost CH4 dynamics, which needs to be better monitored and modeled in Earth system models. [ABSTRACT FROM AUTHOR]

Carbon use efficiency (CUE), defined as the ratio of net primary production (NPP) to gross primary production (GPP), represents the capacity of plants in converting assimilated atmospheric carbon dioxide to ecosystem carbon storage. Process-based models are important tools for simulating NPP and GPP; yet the model performance in simulating vegetation CUE has not been fully explored. The goal of this paper is thus to investigate the spatial variations in CUE from different process-based carbon cycle models in comparison with that from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data, and to analyze their linkage with climate factors. The global average CUE derived from the five process-based models is 0.45 ± 0.05 (range from 0.38 to 0.52), slightly lower than the value of 0.48 obtained from MODIS data. A strong latitudinal gradient of CUE, with greater CUE at high latitudes, is well agreed by these different datasets. However, there also exist considerable discrepancies in CUE estimations among those products, especially in temperate Northern Hemisphere. Furthermore, for both the satellite-based dataset and results from process-based models, vegetation CUE declines non-linearly with increase in temperature, but remains relatively stable with enhanced precipitation. Our results also indicate that the differences in global patterns of CUE estimated by different approaches could be primarily resulted from their systematic differences in autotrophic respiration (R a ) rather than in GPP. Understanding mechanisms behind spatio-temporal changes in R a is therefore a critical step towards better quantifying global CUE. [ABSTRACT FROM AUTHOR]

Accurate assessment of the strength of China’s terrestrial ecosystem carbon sink is key to understanding its regional carbon budget. However, large uncertainties in current carbon sink estimations still exist, which hinder the prediction of future climate change trajectories. In this study, we generated a high-resolution (1 km × 1 km) dataset of China’s net ecosystem productivity (NEP) in the last decade via a model tree ensemble approach combined with data from 46 flux sites in China and neighboring regions. The upscaling also included detailed information on nitrogen (N) deposition and forest age that have often been neglected in previous studies. The performance of MTE algorithm in simulating NEP at the site level is relatively high for both training (R 2  = 0.81, RMSE = 0.73 gC m −2  day −1 ) and validation datasets (R 2  = 0.76, RMSE = 0.81 gC m −2  day −1 ). Our data-driven estimation showed that roughly 70% of the area is a carbon sink, and the largest carbon sinks are found in the southeast and southwest monsoon regions. The total annual NEP in China in the last decade was 1.18 ± 0.05 Pg C yr −1 , which is similar to the results found by another foundational global-scale study. Yet, the two studies significantly differ in the spatial distribution of carbon sink density. The seasonality of China’s NEP is characterized by region-specific kurtosis and skewness in most areas. Furthermore, ecosystem carbon use efficiency (CUE), defined as the annual NEP/GPP ratio, also showed high spatial variation. For example, the Xiaoxing’anling and Changbai Mountains in northeastern China, the eastern edge of the Tibetan Plateau, and bordering areas of the southeast and southwest monsoon regions have a larger CUE than the rest of China. On average, China’s terrestrial ecosystem CUE is approximately 0.17. Our data-driven NEP and CUE estimates provide a new tool for assessing China’s carbon dioxide flux. Our study also highlights the necessity to incorporate more environmental variables related to vegetation growth and more data derived from flux sites into NEP upscaling to reduce uncertainties in carbon budget estimations. [ABSTRACT FROM AUTHOR]

Earth system models project that the tropical land carbon sink will decrease in size in response to an increase in warming and drought during this century, probably causing a positive climate feedback. But available data are too limited at present to test the predicted changes in the tropical carbon balance in response to climate change. Long-term atmospheric carbon dioxide data provide a global record that integrates the interannual variability of the global carbon balance. Multiple lines of evidence demonstrate that most of this variability originates in the terrestrial biosphere. In particular, the year-to-year variations in the atmospheric carbon dioxide growth rate (CGR) are thought to be the result of fluctuations in the carbon fluxes of tropical land areas. Recently, the response of CGR to tropical climate interannual variability was used to put a constraint on the sensitivity of tropical land carbon to climate change. Here we use the long-term CGR record from Mauna Loa and the South Pole to show that the sensitivity of CGR to tropical temperature interannual variability has increased by a factor of 1.9 ± 0.3 in the past five decades. We find that this sensitivity was greater when tropical land regions experienced drier conditions. This suggests that the sensitivity of CGR to interannual temperature variations is regulated by moisture conditions, even though the direct correlation between CGR and tropical precipitation is weak. We also find that present terrestrial carbon cycle models do not capture the observed enhancement in CGR sensitivity in the past five decades. More realistic model predictions of future carbon cycle and climate feedbacks require a better understanding of the processes driving the response of tropical ecosystems to drought and warming. [ABSTRACT FROM AUTHOR]

Climate warming is likely inducing carbon loss from soils of northern ecosystems, but little evidence comes from large-scale observations. Here we used data from a repeated soil survey and remote sensing vegetation index to explore changes in soil organic carbon (SOC) stock on the Tibetan Plateau during the past two decades. Our results showed that SOC stock in the top 30 cm depth in alpine grasslands on the plateau amounted to 4.4 Pg C (1 Pg=1015 g), with an overall average of 3.9 kg C m−2. SOC changes during 1980s–2004 were estimated at −0.6 g C m−2 yr−1, ranging from −36.5 to 35.8 g C m−2 yr−1 at 95% confidence, indicating that SOC stock in the Tibetan alpine grasslands remained relatively stable over the sampling periods. Our findings are nonconsistent with previous reports of loss of soil C in grassland ecosystems due to the accelerated decomposition with warming. In the case of the alpine grasslands on the Tibetan Plateau studied here, we speculate that increased rates of decomposition as soils warmed during the last two decades may have been compensated by increased soil C inputs due to increased grass productivity. These results suggest that soil C stock in terrestrial ecosystems may respond differently to climate change depending on ecosystem type, regional climate pattern, and intensity of human disturbance. [ABSTRACT FROM AUTHOR]

The carbon balance of terrestrial ecosystems is particularly sensitive to climatic changes in autumn and spring, with spring and autumn temperatures over northern latitudes having risen by about 1.1 °C and 0.8 °C, respectively, over the past two decades. A simultaneous greening trend has also been observed, characterized by a longer growing season and greater photosynthetic activity. These observations have led to speculation that spring and autumn warming could enhance carbon sequestration and extend the period of net carbon uptake in the future. Here we analyse interannual variations in atmospheric carbon dioxide concentration data and ecosystem carbon dioxide fluxes. We find that atmospheric records from the past 20 years show a trend towards an earlier autumn-to-winter carbon dioxide build-up, suggesting a shorter net carbon uptake period. This trend cannot be explained by changes in atmospheric transport alone and, together with the ecosystem flux data, suggest increasing carbon losses in autumn. We use a process-based terrestrial biosphere model and satellite vegetation greenness index observations to investigate further the observed seasonal response of northern ecosystems to autumnal warming. We find that both photosynthesis and respiration increase during autumn warming, but the increase in respiration is greater. In contrast, warming increases photosynthesis more than respiration in spring. Our simulations and observations indicate that northern terrestrial ecosystems may currently lose carbon dioxide in response to autumn warming, with a sensitivity of about 0.2 PgC °C-1, offsetting 90% of the increased carbon dioxide uptake during spring. If future autumn warming occurs at a faster rate than in spring, the ability of northern ecosystems to sequester carbon may be diminished earlier than previously suggested. [ABSTRACT FROM AUTHOR]