The tropical forest carbon (C) balance threatened by extensive socio-economic development in the Greater Mekong Subregion (GMS) in Asia is a notable data gap and remains contentious. Here we generated a long-term spatially quantified assessment of changes in forests and C stocks from 1999 to 2019 at a spatial resolution of 30 m, based on multiple streams of state-of-the-art high-resolution satellite imagery and in situ observations. Our results show that (i) about 0.54 million square kilometers (21.0% of the region) experienced forest cover transitions with a net increase in forest cover by 4.3% (0.11 million square kilometers, equivalent to 0.31 petagram of C [Pg C] stocks); (ii) forest losses mainly in Cambodia, Thailand, and in the south of Vietnam, were also counteracted by forest gains in China due mainly to afforestation; and (iii) at the national level during the study period an increase in both C stocks and C sequestration (net C gain of 0.087 Pg C) in China from new plantation, offset anthropogenetic emissions (net C loss of 0.074 Pg C) mainly in Cambodia and Thailand from deforestation. Political, social, and economic factors significantly influenced forest cover change and C sequestration in the GMS, positively in China while negatively in other countries, especially in Cambodia and Thailand. These findings have implications on national strategies for climate change mitigation and adaptation in other hotspots of tropical forests.

A meta-analysis of 95 published research articles and compiled a database comprised of 144 K fertilization experiments.

Abstract Global terrestrial vegetation dynamics have been rapidly altered by climate change. A widespread vegetation greenness over a large part of the planet from the 1980s to early this century has been reported, whereas weakening of CO2 fertilization effects and increasing climate extremes and the adverse impact of increasing rate of warming and severity of drought on vegetation growth were also reported. Earth system models project that the land carbon sink will decrease in size in response to an increase in warming during this century. How global vegetation is changing during this century in response to global warming and water availability across spatial and temporal scales remains uncertain. Our understanding of the widespread vegetation greening or browning processes and identifying the biogeochemical mechanisms remain incomplete. Here we use multiple long‐term satellite leaf area index (LAI) records to investigate vegetation growth trends from 1982 to 2018. We find that the widespread increase of growing‐season integrated LAI (greening) since 1980s was reversed (p‐value < 0.05) around the year 2000 over 90% of the global vegetated area, and continued in only 10% of the global vegetated area. The reversal of greening trend was largely explained by the inhibitive effects of excessive optimal temperature on photosynthesis in most of the tropics and low latitudes, and by increasing water limitation (increasing in atmospheric vapor pressure deficit and decreasing in soil water availability) in the northern high latitudes (>45°N). Overall, the reversal of greening trend since 2000 weakened the negative feedback of carbon sequestration on the climatic system and should be considered in the strategies for climate warming mitigation and adaptation. Our findings of the diversity of processes that drive browning across bioclimatic‐zones and ecosystems and of how those driving processes are changing would enhance our ability to project global future vegetation change and its climatic and abiotic consequences.

Ecosystem respiration is a major component of the global terrestrial carbon cycle and is strongly influenced by temperature. The global extent of the temperature-ecosystem respiration relationship, however, has not been fully explored. Here, we test linear and threshold models of ecosystem respiration across 210 globally distributed eddy covariance sites over an extensive temperature range. We find thresholds to the global temperature-ecosystem respiration relationship at high and low air temperatures and mid soil temperatures, which represent transitions in the temperature dependence and sensitivity of ecosystem respiration. Annual ecosystem respiration rates show a markedly reduced temperature dependence and sensitivity compared to half-hourly rates, and a single mid-temperature threshold for both air and soil temperature. Our study indicates a distinction in the influence of environmental factors, including temperature, on ecosystem respiration between latitudinal and climate gradients at short (half-hourly) and long (annual) timescales. Such climatological differences in the temperature sensitivity of ecosystem respiration have important consequences for the terrestrial net carbon sink under ongoing climate change.

Leaf area index (LAI), defined as one half the total leaf area per unit ground area, and Vcmax, representing the maximal carboxylation rate of leaves, are two most significant parameters used in most Terrestrial Biosphere Models (TBMs). The ability of TBMs to simulate gross primary productivity (GPP) and evapotranspiration (ET) for evergreen needle-leave forests (ENF) can be significantly hampered by uncertainties in LAI and Vcmax. Remotely sensed (RS) LAI for ENF is generally underestimated in winter, early spring and late autumn. Although constant Vcmax throughout the growing season is often used in TBMs for GPP and ET modeling, it could vary significantly under leaf aging and stressed conditions. There were recent studies that apply seasonal leaf chlorophyll constraints on GPP modeling for croplands and deciduous forests, but little attention is given to the influence of the seasonality of either LAI or Vcmax on GPP or ET estimations for the ENF biome. In this study, we pay special attention to this biome, with the purpose of investigating if the representations of seasonal LAI and Vmax variations are essential in TBMs. To serve this purpose, the University of Toronto LAI product Version 2 was corrected for its seasonal variation using leaf lifespan and in-situ measurements at eight ENF sites in Canada. Seasonal Vcmax variation was derived from the MERIS Terrestrial Chlorophyll Index (MTCI) through downscaling it to the leaf level using a scheme with a general vertical nitrogen distribution within the canopy. Leaf chlorophyll content (LCC) is thus derived from MTCI and converted to Vcmax using empirical equations. Four model cases with and without considerations of the seasonal LAI and Vcmax variations were tested and compared. Validation against eddy covariance measurements indicates that the case with both LAI and Vcmax variations produced the highest R-2, lowest root mean square error (RMSE) and lowest mean absolute error (MAE) for both GPP and ET simulations, and thus outperforms all other cases without considering the variations or with consideration of one of the variations only. In this best case, the simulated daily GPP yields R-2 of 0.91, RMSE of 0.91 g C m(-2) and MAE of 0.65 g C m(-2), while the simulated daily ET yields R-2 of 0.8, RMSE of 0.52 mm and MAE of 0.34 mm. Most improvements were found in spring and autumn. Not only the correlations between the seasonal trajectories of model simulation and observation were improved, but also the annual total GPP and ET were more accurately estimated. The smallest mean absolute relative bias to eddy covariance measurements is 9% for GPP and 15% for ET, both were found in the best case. Moreover, improvements in GPP were more pronounced than in ET. Our results highlight the significance of considering both seasonal structural and physiological characteristics of leaves in TBMs. Considering the important role that evergreen coniferous forests play in global terrestrial ecosystems, global simulations of GPP and ET in space and time can benefit from the proper representation of seasonal variations in canopy structure and leaf physiology as represented by LAI and Vcmax, respectively.

The Quest Carbon Capture and Storage (CCS) project in Alberta, Canada, is a fully integrated project, as it involves the capture, transport, injection, storage of CO2, and a measurement, monitoring and verification (MMV) program. The MMV program has two key objectives: a) to ensure containment and b) to ensure conformance. Prior to the start of CO2 injection at the end of August 2015, a number of projects were undertaken to gather data from various domains, namely the atmosphere, biosphere, hydrosphere and geosphere, to provide input to the Quest MMV program. The focus of this paper is on monitoring activities undertaken in relation to the atmosphere and biosphere domains. Activities undertaken across the Quest sequestration lease area (SLA) included an eddy covariance system, soil gas probes, soil flux chambers, and walk-over surveys. In conclusion, understanding the spatial and temporal variability of CO2 levels prior to start of CO2 injection represents an important activity of a CCS MMV program. It provides technical input to the development of such a program, but also provides knowledge for communication to and awareness of project stakeholders (e.g. landowners) regarding CO2 levels within the atmosphere and biosphere across a SLA.

In this study latent heat flux ( λE ) measurements made at 65 boreal and arctic eddy‐covariance ( EC ) sites were analyses by using the Penman–Monteith equation. Sites were stratified into nine different ecosystem types: harvested and burnt forest areas, pine forests, spruce or fir forests, Douglas‐fir forests, broadleaf deciduous forests, larch forests, wetlands, tundra and natural grasslands. The Penman–Monteith equation was calibrated with variable surface resistances against half‐hourly eddy‐covariance data and clear differences between ecosystem types were observed. Based on the modeled behavior of surface and aerodynamic resistances, surface resistance tightly control λE in most mature forests, while it had less importance in ecosystems having shorter vegetation like young or recently harvested forests, grasslands, wetlands and tundra. The parameters of the Penman–Monteith equation were clearly different for winter and summer conditions, indicating that phenological effects on surface resistance are important. We also compared the simulated λE of different ecosystem types under meteorological conditions at one site. Values of λE varied between 15% and 38% of the net radiation in the simulations with mean ecosystem parameters. In general, the simulations suggest that λE is higher from forested ecosystems than from grasslands, wetlands or tundra‐type ecosystems. Forests showed usually a tighter stomatal control of λE as indicated by a pronounced sensitivity of surface resistance to atmospheric vapor pressure deficit. Nevertheless, the surface resistance of forests was lower than for open vegetation types including wetlands. Tundra and wetlands had higher surface resistances, which were less sensitive to vapor pressure deficits. The results indicate that the variation in surface resistance within and between different vegetation types might play a significant role in energy exchange between terrestrial ecosystems and atmosphere. These results suggest the need to take into account vegetation type and phenology in energy exchange modeling.

Simulating gross primary productivity (GPP) of terrestrial ecosystems has been a major challenge in quantifying the global carbon cycle. Many different light use efficiency (LUE) models have been developed recently, but our understanding of the relative merits of different models remains limited. Using CO2 flux measurements from multiple eddy covariance sites, we here compared and assessed major algorithms and performance of seven LUE models (CASA, CFix, CFlux, EC-LUE, MODIS, VPM and VPRM). Comparison between simulated GPP and estimated GPP from flux measurements showed that model performance differed substantially among ecosystem types. In general, most models performed better in capturing the temporal changes and magnitude of GPP in deciduous broadleaf forests and mixed forests than in evergreen broadleaf forests and shrublands. Six of the seven LUE models significantly underestimated GPP during cloudy days because the impacts of diffuse radiation on light use efficiency were ignored in the models. CFlux and EC-LUE exhibited the lowest root mean square error among all models at 80% and 75% of the sites, respectively. Moreover, these two models showed better performance than others in simulating interannual variability of GPP. Two pairwise comparisons revealed that the seven models differed substantially in algorithms describing the environmental regulations, particularly water stress, on GPP. This analysis highlights the need to improve representation of the impacts of diffuse radiation and water stress in the LUE models. (C) 2014 Elsevier B.V. All rights reserved.

Land‐surface models use different formulations of stomatal conductance and plant hydraulics, and it is unclear which type of model best matches the observed surface‐atmosphere water flux. We use the North American Carbon Program data set of latent heat flux (LE) measurements from 25 sites and predictions from 9 models to evaluate models' ability to resolve subdaily dynamics of transpiration. Despite overall good forecast at the seasonal scale, the models have difficulty resolving the dynamics of intradaily hysteresis. The majority of models tend to underestimate LE in the prenoon hours and overestimate in the evening. We hypothesize that this is a result of unresolved afternoon stomatal closure due to hydrodynamic stresses. Although no model or stomata parameterization was consistently best or worst in terms of ability to predict LE, errors in model‐simulated LE were consistently largest and most variable when soil moisture was moderate and vapor pressure deficit was moderate to limiting. Nearly all models demonstrate a tendency to underestimate the degree of maximum hysteresis which, across all sites studied, is most pronounced during moisture‐limited conditions. These diurnal error patterns are consistent with models' diminished ability to accurately simulate the natural hysteresis of transpiration. We propose that the lack of representation of plant hydrodynamics is, in part, responsible for these error patterns.

More accurate estimation of the carbon dioxide flux depends on the improved scientific understanding of the terrestrial carbon cycle. Remote-sensing-based approaches to continental-scale estimation of net ecosystem exchange (NEE) have been developed but coarse spatial resolution is a source of errors. Here we demonstrate a satellite-based method of estimating NEE using Landsat TM/ETM + data and an upscaling framework. The upscaling framework contains flux-footprint climatology modeling, modified regression tree (MRT) analysis and image fusion. By scaling NEE measured at flux towers to landscape and regional scales, this satellite-based method can improve NEE estimation at high spatial-temporal resolution at the landscape scale relative to methods based on MODIS data with coarser spatial–temporal resolution. This method was applied to sixteen flux sites from the Canadian Carbon Program and AmeriFlux networks located in North America, covering forest, grass, and cropland biomes. Compared to a similar method using MODIS data, our estimation is more effective for diagnosing landscape NEE with the same temporal resolution and higher spatial resolution (30 m versus 1 km) (r2 = 0.7548 vs. 0.5868, RMSE = 1.3979 vs. 1.7497 g C m− 2 day− 1, average error = 0.8950 vs. 1.0178 g C m− 2 day− 1, relative error = 0.47 vs. 0.54 for fused Landsat and MODIS imagery, respectively). We also compared the regional NEE estimations using Carbon Tracker, our method and eddy-covariance observations. This study demonstrates that the data-driven satellite-based NEE diagnosed model can be used to upscale eddy-flux observations to landscape scales with high spatial–temporal resolutions.

Reducing the large uncertainties in current estimates of CO2 sources and sinks at regional scales (102–105 km2) is fundamental to improving our understanding of the terrestrial carbon cycle. Continuous high-precision CO2 concentration measurements on a tower within the planetary boundary layer contain information on regional carbon fluxes; however, its spatial representativeness is generally unknown. In this study, we developed a footprint model (Simple Analytical Footprint model based on Eulerian coordinates for scalar Concentration [SAFE-C]) and applied it to two CO2 concentration towers in central Canada: the East Trout Lake 106-m-tall tower (54°21′N, 104°59′W) and the Candle Lake 28-m-high tower (53°59′N, 105°07′W). Results show that the ETL tower’s annual concentration footprints were around 103–105 km2. The monthly footprint climatologies in summer were 1.5–2 times larger than in winter. The impacts of land surface carbon flux associated with heterogeneous distribution of vegetation types on the CO2 concentration measurements were different for the different heights, varied with a range of ±5 % to ±10 % among four heights. This study indicates that concentration footprint climatology analysis is important in interpreting the seasonal, annual and inter-annual variations of tower measured CO2 concentration data and is essential for comparing and scaling regional carbon flux estimates using top-down or bottom-up approaches.

To understand how environmental changes have influenced forest productivity, stemwood biomass ( B ) dynamics were analyzed at 1267 permanent inventory plots, covering a combined 209 ha area of unmanaged temperate‐maritime forest in southwest British Columbia, Canada. Net stemwood production (Δ B ) was derived from periodic remeasurements of B collected over a 40‐year measurement period (1959–1998) in stands ranging from 20 to 150 years old. Comparison between the integrated age response of net stemwood production, Δ B ( A ), and the age response of stemwood biomass, B ( A ), suggested a 58 ± 11% increase in Δ B between the first 40 years of the chronosequence period (1859–1898) and the measurement period. To estimate extrinsic forcing on Δ B , several different candidate models were developed to remove variation explained by intrinsic factors. All models exhibited temporal bias, with positive trends in (observed minus predicted) residual Δ B ranging between of 0.40 and 0.64% yr −1 . Applying the same methods to stemwood growth ( G ) indicated residual increases ranging from 0.43 and 0.67% yr −1 . Higher trend estimates corresponded with models that included site index ( SI ) as a predictor, which may reflect exaggeration of the age‐decline in SI tables. Choosing a model that excluded SI , suggested that Δ B increased by 0.40 ± 0.18% yr −1 , while G increased by 0.43 ± 0.12% yr −1 over the measurement period. Residual G was significantly correlated with atmospheric carbon dioxide ( CO 2 ), temperature ( T ), and climate moisture index ( CMI ). However, models driven with climate and CO 2 , alone, could not simultaneously explain long‐term and measurement‐period trends without additional representation of indirect effects, perhaps reflecting compound interest on direct physiological responses to environmental change. Evidence of accelerating forest regrowth highlights the value of permanent inventories to detect and understand systematic changes in forest productivity caused by environmental change.

Basal ecosystem respiration rate (BR), the ecosystem respiration rate at a given temperature, is a common and important parameter in empirical models for quantifying ecosystem respiration (ER) globally. Numerous studies have indicated that BR varies in space. However, many empirical ER models still use a global constant BR largely due to the lack of a functional description for BR. In this study, we redefined BR to be ecosystem respiration rate at the mean annual temperature. To test the validity of this concept, we conducted a synthesis analysis using 276 site-years of eddy covariance data, from 79 research sites located at latitudes ranging from similar to 3 degrees S to similar to 70 degrees N. Results showed that mean annual ER rate closely matches ER rate at mean annual temperature. Incorporation of site-specific BR into global ER model substantially improved simulated ER compared to an invariant BR at all sites. These results confirm that ER at the mean annual temperature can be considered as BR in empirical models. A strong correlation was found between the mean annual ER and mean annual gross primary production (GPP). Consequently, GPP, which is typically more accurately modeled, can be used to estimate BR. A light use efficiency GPP model (i.e., EC-LUE) was applied to estimate global GPP, BR and ER with input data from MERRA (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate resolution Imaging Spectroradiometer). The global ER was 103 Pg C yr (-1), with the highest respiration rate over tropical forests and the lowest value in dry and high-latitude areas.

We propose the Breathing Earth System Simulator (BESS), an upscaling approach to quantify global gross primary productivity and evapotranspiration using MODIS with a spatial resolution of 1-5 km and a temporal resolution of 8 days. This effort is novel because it is the first system that harmonizes and utilizes MODIS Atmosphere and Land products on the same projection and spatial resolution over the global land. This enabled us to use the MODIS Atmosphere products to calculate atmospheric radiative transfer for visual and near infrared radiation wave bands. Then we coupled atmospheric and canopy radiative transfer processes, with models that computed leaf photosynthesis, stomatal conductance and transpiration on the sunlit and shaded portions of the vegetation and soil. At the annual time step, the mass and energy fluxes derived from BESS showed strong linear relations with measurements of solar irradiance (r(2) = 0.95, relative bias: 8%), gross primary productivity (r(2) = 0.86, relative bias: 5%) and evapotranspiration (r(2) = 0.86, relative bias: 15%) in data from 33 flux towers that cover seven plant functional types across arctic to tropical climatic zones. A sensitivity analysis revealed that the gross primary productivity and evapotranspiration computed in BESS were most sensitive to leaf area index and solar irradiance, respectively. We quantified the mean global terrestrial estimates of gross primary productivity and evapotranpiration between 2001 and 2003 as 118 +/- 26 PgC yr(-1) and 500 +/- 104 mm yr(-1) (equivalent to 63,000 +/- 13,100 km(3) yr(-1)), respectively. BESS-derived gross primary productivity and evapotranspiration estimates were consistent with the estimates from independent machine-learning, data-driven products, but the process-oriented structure has the advantage of diagnosing sensitivity of mechanisms. The process-based BESS is able to offer gridded biophysical variables everywhere from local to the total global land scales with an 8-day interval over multiple years.

This study investigated how nitrogen (N) fertilization with 200 kg N ha−1 of urea affected ecosystem carbon (C) sequestration in the first‐postfertilization year in a Pacific Northwest Douglas‐fir (Pseudotsuga menziesii) stand on the basis of multiyear eddy‐covariance (EC) and soil‐chamber measurements before and after fertilization in combination with ecosystem modeling. The approach uses a data‐model fusion technique which encompasses both model parameter optimization and data assimilation and minimizes the effects of interannual climatic perturbations and focuses on the biotic and abiotic factors controlling seasonal C fluxes using a prefertilization 9‐year‐long time series of EC data (1998–2006). A process‐based ecosystem model was optimized using the half‐hourly data measured during 1998–2005, and the optimized model was validated using measurements made in 2006 and further applied to predict C fluxes for 2007 assuming the stand was not fertilized. The N fertilization effects on C sequestration were then obtained as differences between modeled (unfertilized stand) and EC or soil‐chamber measured (fertilized stand) C component fluxes. Results indicate that annual net ecosystem productivity in the first‐post‐N fertilization year increased by∼83%, from 302 ± 19 to 552 ± 36 g m−2 yr−1, which resulted primarily from an increase in annual gross primary productivity of∼8%, from 1938 ± 22 to 2095 ± 29 g m−2 yr−1 concurrent with a decrease in annual ecosystem respiration (Re) of∼5.7%, from 1636 ± 17 to 1543 ± 31 g m−2 yr−1. Moreover, with respect to respiration, model results showed that the fertilizer‐induced reduction in Re (∼93 g m−2 yr−1) principally resulted from the decrease in soil respiration Rs (∼62 g m−2 yr−1).

Lianhong Gu, Wilfred M. Post, Dennis Baldocchi, T. Andy Black, Shashi B. Verma, Timo Vesala, and Steve C. Wofsy Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA; Department of Environmental Science, Policy & Management, University of California, Berkeley, CA, USA; Faculty of Agricultural Sciences, University of British Columbia, Vancouver, Canada; School of Natural Resource Sciences, University of Nebraska, Lincoln NE, USA; Department of Physical Sciences, University of Helsinki, Helsinki, Finland; Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA