Terrestrial gross primary production: Using NIRV to scale from site to globe

Terrestrial photosynthesis is the largest and one of the most uncertain fluxes in the global carbon cycle. We find that near‐infrared reflectance of vegetation (NIR_V), a remotely sensed measure of canopy structure, accurately predicts photosynthesis at FLUXNET validation sites at monthly to annual timescales (R² = 0.68), without the need for difficult to acquire information about environmental factors that constrain photosynthesis at short timescales. Scaling the relationship between gross primary production (GPP) and NIR_V from FLUXNET eddy covariance sites, we estimate global annual terrestrial photosynthesis to be 147 Pg C/year (95% credible interval 131–163 Pg C/year), which falls between bottom‐up GPP estimates and the top‐down global constraint on GPP from oxygen isotopes. NIR_V‐derived estimates of GPP are systematically higher than existing bottom‐up estimates, especially throughout the midlatitudes. Progress in improving estimated GPP from NIR_V can come from improved cloud screening in satellite data and increased resolution of vegetation characteristics, especially details about plant photosynthetic pathway.     

Gross primary productivity and transpiration flux of the Australian vegetation from 1788 to 1988 AD: effects of CO2 and land use change

We present a novel approach to estimating the transpiration flux and gross primary productivity (GPP) from Normalized Difference Vegetation Index, leaf functional types, and readily available climate data. We use this approach to explore the impact of variations in the concentration of carbon dioxide in the atmosphere ([CO₂]) and consequent predicted changes in vegetation cover, on the transpiration flux and GPP. There was a near 1 : 1 relationship between GPP estimated with this transpiration flux approach and that estimated using a radiation‐use efficiency (RUE) approach. Model estimates are presented for the Australian continent under three vegetation–[CO₂] scenarios: the present vegetation and hypothetical ‘natural’ vegetation cover with atmospheric CO₂ concentration ([CO₂]) of 350 μmol mol^?1 (pveg₃₅₀ and nveg₃₅₀), and for the ‘natural’ vegetation with [CO₂] 280 μmol mol^?1 (nveg₂₈₀). Estimated continental GPP is 6.5, 6.3 and 4.3 Gt C yr^?1 for pveg₃₅₀, nveg₃₅₀ and nveg₂₈₀, respectively_. The corresponding transpiration fluxes are 232, 224 and 190 mm H₂O yr^?1. The contribution of the raingreen and evergreen components of the canopy to these fluxes are also estimated.     

Bud-burst modelling in Siberia and its impact on quantifying the carbon budget

Vegetation phenology is affected by climate change and in turn feeds back on climate by affecting the annual carbon uptake by vegetation. To quantify the impact of phenology on terrestrial carbon fluxes, we calibrate a bud‐burst model and embed it in the Sheffield dynamic global vegetation model (SDGVM) in order to perform carbon budget calculations. Bud‐burst dates derived from the VEGETATION sensor onboard the SPOT‐4 satellite are used to calibrate a range of bud‐burst models. This dataset has been recently developed using a new methodology based on the normalized difference water index, which is able to distinguish snowmelt from the onset of vegetation activity after winter. After calibration, a simple spring warming model was found to perform as well as more complex models accounting for a chilling requirement, and hence it was used for the carbon flux calculations. The root mean square difference (RMSD) between the calibrated model and the VEGETATION dataset was 6.5 days, and was 6.9 days between the calibrated model and independent ground observations of bud‐burst available at nine locations over Siberia. The effects of bud‐burst model uncertainties on the carbon budget were evaluated using the SDGVM. The 6.5 days RMSD in the bud‐burst date (a 6% variation in the growing season length), treated as a random noise, translates into about 41 g cm^?2 yr^?1 in net primary production (NPP), which corresponds to 8% of the mean NPP. This is a moderate impact and suggests the calibrated model is accurate enough for carbon budget calculations. In addition to random differences between the calibrated model and VEGETATION data, systematic errors between the calibrated bud‐burst model and true ground behaviour may occur, because of bias in the temperature dataset or because the bud‐burst detected by VEGETATION is because of some other phenological indicator. A systematic error of 1 day in bud‐burst translates into a 10 g cm^?2 yr^?1 error in NPP (about 2%). Based on the limited available ground data, any systematic error because of the use of VEGETATION data should not lead to significant errors in the calculated carbon flux. In contrast, widely used methods based on the normalized difference vegetation index from the advanced very high resolution radiometer satellite are likely to confuse snowmelt and vegetation greening, leading to errors of up to 15 days in bud‐burst date, with consequent large errors in carbon flux calculations.     

Annual balances and extended seasonal modelling of carbon fluxes from a temperate fen cropped to festulolium and tall fescue under two‐cut and three‐cut harvesting regimes

This study reports the annual carbon balance of a drained riparian fen under two‐cut or three‐cut managements of festulolium and tall fescue. CO₂ fluxes measured with closed chambers were partitioned into gross primary production (GPP) and ecosystem respiration (ER) for modelling according to environmental factors (light and temperature) and canopy reflectance (ratio vegetation index, RVI). Methodological assessments were made of (i) GPP models with or without temperature functions (Ft) to adjust GPP constraints imposed by low temperature (<10 °C) and (ii) ER models with RVI or GPP parameters as biomass proxies. The sensitivity of the models was also tested on partial datasets including only alternate measurement campaigns and on datasets only from the crop growing period. Use of Ft in GPP models effectively corrected GPP overestimation in cold periods, and this approach was used throughout. Annual fluxes obtained with ER models including RVI or GPP parameters were similar, and also annual GPP and ER fluxes obtained with full and partial datasets were similar. Annual CO₂ fluxes and biomass yield were not significantly different in the crop/management combinations although the individual collars (n = 12) showed some variations in GPP (?1818 to ?2409 g CO₂‐C m^?2), ER (1071 to 1738 g CO₂‐C m^?2), net ecosystem exchange (NEE, ?669 to ?949 g CO₂‐C m^?2) and biomass yield (556 to 1044 g CO₂‐C m^?2). Net ecosystem carbon balance (NECB), as the sum of NEE and biomass carbon export, was only slightly negative to positive in all crop/management combinations. NECBs, interpreted as emission factors, tended to favour the least biomass producing systems as the best management options in relation to climate saving carbon balances. Yet, considering the down‐stream advantages of biomass for fossil fuel replacement, yield‐scaled carbon fluxes are suggested to be given additional considerations for comparison of management options in terms of atmospheric impact.     

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.     

Climate‐driven uncertainties in modeling terrestrial gross primary production: a site level to global‐scale analysis

We used a land surface model to quantify the causes and extents of biases in terrestrial gross primary production (GPP) due to the use of meteorological reanalysis datasets. We first calibrated the model using meteorology and eddy covariance data from 25 flux tower sites ranging from the tropics to the northern high latitudes and subsequently repeated the site simulations using two reanalysis datasets: NCEP/NCAR and CRUNCEP. The results show that at most sites, the reanalysis‐driven GPP bias was significantly positive with respect to the observed meteorology‐driven simulations. Notably, the absolute GPP bias was highest at the tropical evergreen tree sites, averaging up to ca. 0.45 kg C m^?2 yr^?1 across sites (ca. 15% of site level GPP). At the northern mid‐/high‐latitude broadleaf deciduous and the needleleaf evergreen tree sites, the corresponding annual GPP biases were up to 20%. For the nontree sites, average annual biases of up to ca. 20–30% were simulated within savanna, grassland, and shrubland vegetation types. At the tree sites, the biases in short‐wave radiation and humidity strongly influenced the GPP biases, while the nontree sites were more affected by biases in factors controlling water stress (precipitation, humidity, and air temperature). In this study, we also discuss the influence of seasonal patterns of meteorological biases on GPP. Finally, using model simulations for the global land surface, we discuss the potential impacts of site‐level reanalysis‐driven biases on the global estimates of GPP. In a broader context, our results can have important consequences on other terrestrial ecosystem fluxes (e.g., net primary production, net ecosystem production, energy/water fluxes) and reservoirs (e.g., soil carbon stocks). In a complementary study (Barman et al., 2013 ), we extend the present analysis for latent and sensible heat fluxes, thus consistently integrating the analysis of climate‐driven uncertainties in carbon, energy, and water fluxes using a single modeling framework.     

Site-level evaluation of satellite-based global terrestrial gross primary production and net primary production monitoring

Operational monitoring of global terrestrial gross primary production (GPP) and net primary production (NPP) is now underway using imagery from the satellite‐borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Evaluation of MODIS GPP and NPP products will require site‐level studies across a range of biomes, with close attention to numerous scaling issues that must be addressed to link ground measurements to the satellite‐based carbon flux estimates. Here, we report results of a study aimed at evaluating MODIS NPP/GPP products at six sites varying widely in climate, land use, and vegetation physiognomy. Comparisons were made for twenty‐five 1 km² cells at each site, with 8‐day averages for GPP and an annual value for NPP. The validation data layers were made with a combination of ground measurements, relatively high resolution satellite data (Landsat Enhanced Thematic Mapper Plus at ～30 m resolution), and process‐based modeling. There was strong seasonality in the MODIS GPP at all sites, and mean NPP ranged from 80 g C m^?2 yr^?1 at an arctic tundra site to 550 g C m^?2 yr^?1 at a temperate deciduous forest site. There was not a consistent over‐ or underprediction of NPP across sites relative to the validation estimates. The closest agreements in NPP and GPP were at the temperate deciduous forest, arctic tundra, and boreal forest sites. There was moderate underestimation in the MODIS products at the agricultural field site, and strong overestimation at the desert grassland and at the dry coniferous forest sites. Analyses of specific inputs to the MODIS NPP/GPP algorithm – notably the fraction of photosynthetically active radiation absorbed by the vegetation canopy, the maximum light use efficiency (LUE), and the climate data – revealed the causes of the over‐ and underestimates. Suggestions for algorithm improvement include selectively altering values for maximum LUE (based on observations at eddy covariance flux towers) and parameters regulating autotrophic respiration.     

Metabolism,Gas Exchange,and Carbon Spiraling in Rivers

Ecosystem metabolism, that is, gross primary productivity (GPP) and ecosystem respiration (ER), controls organic carbon (OC) cycling in stream and river networks and is expected to vary predictably with network position. However, estimates of metabolism in small streams outnumber those from rivers such that there are limited empirical data comparing metabolism across a range of stream and river sizes. We measured metabolism in 14 rivers (discharge range 14–84 m³ s^?1) in the Western and Midwestern United States (US). We estimated GPP, ER, and gas exchange rates using a Lagrangian, 2-station oxygen model solved in a Bayesian framework. GPP ranged from 0.6–22 g O₂ m^?2 d^?1 and ER tracked GPP, suggesting that autotrophic production supports much of riverine ER in summer. Net ecosystem production, the balance between GPP and ER was 0 or greater in 4 rivers showing autotrophy on that day. River velocity and slope predicted gas exchange estimates from these 14 rivers in agreement with empirical models. Carbon turnover lengths (that is, the distance traveled before OC is mineralized to CO₂) ranged from 38 to 1190 km, with the longest turnover lengths in high-sediment, arid-land rivers. We also compared estimated turnover lengths with the relative length of the river segment between major tributaries or lakes; the mean ratio of carbon turnover length to river length was 1.6, demonstrating that rivers can mineralize much of the OC load along their length at baseflow. Carbon mineralization velocities ranged from 0.05 to 0.81 m d^?1, and were not different than measurements from small streams. Given high GPP relative to ER, combined with generally short OC spiraling lengths, rivers can be highly reactive with regard to OC cycling.     

Seasonal and interannual variations in carbon dioxide exchange over a cropland in the North China Plain

In China, croplands account for a relatively large form of vegetation cover. Quantifying carbon dioxide exchange and understanding the environmental controls on carbon fluxes over croplands are critical in understanding regional carbon budgets and ecosystem behaviors. In this study, the net ecosystem exchange (NEE) at a winter wheat/summer maize rotation cropping site, representative of the main cropping system in the North China Plain, was continuously measured using the eddy covariance technique from 2005 to 2009. In order to interpret the abiotic factors regulating NEE, NEE was partitioned into gross primary production (GPP) and ecosystem respiration (R_eco). Daytime R_eco was extrapolated from the relationship between nighttime NEE and soil temperature under high turbulent conditions. GPP was then estimated by subtracting daytime NEE from the daytime estimates of R_eco. Results show that the seasonal patterns of the temperature responses of R_eco and light‐response parameters are closely related to the crop phenology. Daily R_eco was highly dependent on both daily GPP and air temperature. Interannual variability showed that GPP and R_eco were mainly controlled by temperature. Water availability also exerted a limit on R_eco. The annual NEE was ?585 and ?533 g C m^?2 for two seasons of 2006–2007 and 2007–2008, respectively, and the wheat field absorbed more carbon than the maize field. Thus, we concluded that this cropland was a strong carbon sink. However, when the grain harvest was taken into account, the wheat field was diminished into a weak carbon sink, whereas the maize field was converted into a weak carbon source. The observations showed that severe drought occurring during winter did not reduce wheat yield (or integrated NEE) when sufficient irrigation was carried out during spring.     

Greenhouse gas fluxes of grazed and hayed wetland catchments in the U.S. Prairie Pothole Ecoregion

Wetland catchments are major ecosystems in the Prairie Pothole Region (PPR) and play an important role in greenhouse gases (GHG) flux. However, there is limited information regarding effects of land-use on GHG fluxes from these wetland systems. We examined the effects of grazing and haying, two common land-use practices in the region, on GHG fluxes from wetland catchments during 2007 and 2008. Fluxes of methane (CH₄), nitrous oxide (N₂O), and carbon dioxide (CO₂), along with soil water content and temperature, were measured along a topographic gradient every other week during the growing season near Ipswich, SD, USA. Closed, opaque chambers were used to measure fluxes of soil and plant respiration from native sod catchments that were grazed or left idle, and from recently restored catchments which were seeded with native plant species; half of these catchments were hayed once during the growing season. Catchments were adjacent to each other and had similar soils, soil nitrogen and organic carbon content, precipitation, and vegetation. When compared with idle catchments, grazing as a land-use had little effect on GHG fluxes. Likewise, haying had little effect on fluxes of CH₄ and N₂O compared with non-hayed catchments. Haying, however, did have a significant effect on combined soil and vegetative CO₂ flux in restored wetland catchments owing to the immediate and comprehensive effect haying has on plant productivity. This study also examined soil conditions that affect GHG fluxes and provides cumulative annual estimates of GHG fluxes from wetland catchment in the PPR.     

Ecosystem‐scale volatile organic compound fluxes during an extreme drought in a broadleaf temperate forest of the Missouri Ozarks (central USA)

Considerable amounts and varieties of biogenic volatile organic compounds (BVOCs) are exchanged between vegetation and the surrounding air. These BVOCs play key ecological and atmospheric roles that must be adequately represented for accurately modeling the coupled biosphere–atmosphere–climate earth system. One key uncertainty in existing models is the response of BVOC fluxes to an important global change process: drought. We describe the diurnal and seasonal variation in isoprene, monoterpene, and methanol fluxes from a temperate forest ecosystem before, during, and after an extreme 2012 drought event in the Ozark region of the central USA. BVOC fluxes were dominated by isoprene, which attained high emission rates of up to 35.4 mg m^?2 h^?1 at midday. Methanol fluxes were characterized by net deposition in the morning, changing to a net emission flux through the rest of the daylight hours. Net flux of CO₂ reached its seasonal maximum approximately a month earlier than isoprenoid fluxes, which highlights the differential response of photosynthesis and isoprenoid emissions to progressing drought conditions. Nevertheless, both processes were strongly suppressed under extreme drought, although isoprene fluxes remained relatively high compared to reported fluxes from other ecosystems. Methanol exchange was less affected by drought throughout the season, confirming the complex processes driving biogenic methanol fluxes. The fraction of daytime (7–17 h) assimilated carbon released back to the atmosphere combining the three BVOCs measured was 2% of gross primary productivity (GPP) and 4.9% of net ecosystem exchange (NEE) on average for our whole measurement campaign, while exceeding 5% of GPP and 10% of NEE just before the strongest drought phase. The megan v2.1 model correctly predicted diurnal variations in fluxes driven mainly by light and temperature, although further research is needed to address model BVOC fluxes during drought events.     

The use of CO2 flux measurements in models of the global terrestrial carbon budget

CO₂ flux measurements give access to two critical terms of the carbon budget of terrestrial ecosystems, the gross primary productivity (GPP) and the net ecosystem productivity (NEP). CO₂ fluxes measured by micrometeorological methods have spatial and temporal characteristics that make them potentially useful in modelling the global terrestrial carbon budget. The first use is in parameterizing ecosystem physiological processes. We present an example, based on parameterizing the mean light response of GPP. This parameterization can be used in diagnostic, satellite-based GPP models. A global application leads to realistic estimates of global GPP. The second use is in testing the seasonality of fluxes predicted by global models. Our example of this use tests two global GPP models. One is a diagnostic, satellite-based model, and one is a prognostic, process-based model. Despite the limitations of the models, both agree reasonably well with the measurements. The agreements and disagreements are useful in addressing the problems of available input datasets and representation of processes, in global models. Long-term CO₂ flux measurements give access to key variables of terrestrial vegetation models and therefore offer exciting perspectives.     

Climate-Induced Changes in Spring Snowmelt Impact Ecosystem Metabolism and Carbon Fluxes in an Alpine Stream Network

Although stream ecosystems are recognized as an important component of the global carbon cycle, the impacts of climate-induced hydrological extremes on carbon fluxes in stream networks remain unclear. Using continuous measurements of ecosystem metabolism, we report on the effects of changes in snowmelt hydrology during the anomalously warm winter 2013/2014 on gross primary production (GPP), ecosystem respiration (ER), and net ecosystem production (NEP) in an Alpine stream network. We estimated ecosystem metabolism across 12 study reaches of the 254 km² subalpine Ybbs River Network (YRN), Austria, for 18 months. During spring snowmelt, GPP peaked in 10 of our 12 study reaches, which appeared to be driven by PAR and catchment area. In contrast, the winter precipitation shift from snow to rain following the low-snow winter in 2013/2014 increased spring ER in upper elevation catchments, causing spring NEP to shift from autotrophy to heterotrophy. Our findings suggest that the YRN transitioned from a transient sink to a source of carbon dioxide (CO₂) in spring as snowmelt hydrology differed following the high-snow versus low-snow winter. This shift toward increased heterotrophy during spring snowmelt following a warm winter has potential consequences for annual ecosystem metabolism, as spring GPP contributed on average 33% to annual GPP fluxes compared to spring ER, which averaged 21% of annual ER fluxes. We propose that Alpine headwaters will emit more within-stream respiratory CO₂ to the atmosphere while providing less autochthonous organic energy to downstream ecosystems as the climate gets warmer.     

An improved analysis of forest carbon dynamics using data assimilation

There are two broad approaches to quantifying landscape C dynamics – by measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process‐based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation (DA) techniques – which combine stock and flux observations with a dynamic model – improve estimates of, and provide insights into, ecosystem carbon (C) exchanges. We use an ensemble Kalman filter (EnKF) to link a series of measurements with a simple box model of C transformations. Measurements were collected at a young ponderosa pine stand in central Oregon over a 3‐year period, and include eddy flux and soil CO₂ efflux data, litterfall collections, stem surveys, root and soil cores, and leaf area index data. The simple C model is a mass balance model with nine unknown parameters, tracking changes in C storage among five pools; foliar, wood and fine root pools in vegetation, and also fresh litter and soil organic matter (SOM) plus coarse woody debris pools. We nested the EnKF within an optimization routine to generate estimates from the data of the unknown parameters and the five initial conditions for the pools. The efficacy of the DA process can be judged by comparing the probability distributions of estimates produced with the EnKF analysis vs. those produced with reduced data or model alone. Using the model alone, estimated net ecosystem exchange of C (NEE)=?251±197 g C m^?2 over the 3 years, compared with an estimate of ?419±29 g C m^?2 when all observations were assimilated into the model. The uncertainty on daily measurements of NEE via eddy fluxes was estimated at 0.5 g C m^?2 day^?1, but the uncertainty on assimilated estimates averaged 0.47 g C m^?2 day^?1, and only exceeded 0.5 g C m^?2 day^?1 on days where neither eddy flux nor soil efflux data were available. In generating C budgets, the assimilation process reduced the uncertainties associated with using data or model alone and the forecasts of NEE were statistically unbiased estimates. The results of the analysis emphasize the importance of time series as constraints. Occasional, rare measurements of stocks have limited use in constraining the estimates of other components of the C cycle. Long time series are particularly crucial for improving the analysis of pools with long time constants, such as SOM, woody biomass, and woody debris. Long‐running forest stem surveys, and tree ring data, offer a rich resource that could be assimilated to provide an important constraint on C cycling of slow pools. For extending estimates of NEE across regions, DA can play a further important role, by assimilating remote‐sensing data into the analysis of C cycles. We show, via sensitivity analysis, how assimilating an estimate of photosynthesis – which might be provided indirectly by remotely sensed data – improves the analysis of NEE.     

A high‐resolution approach to estimating ecosystem respiration at continental scales using operational satellite data

A better understanding of the local variability in land‐atmosphere carbon fluxes is crucial to improving the accuracy of global carbon budgets. Operational satellite data backed by ground measurements at Fluxnet sites proved valuable in monitoring local variability of gross primary production at highly resolved spatio‐temporal resolutions. Yet, we lack similar operational estimates of ecosystem respiration (Re) to calculate net carbon fluxes. If successful, carbon fluxes from such a remote sensing approach would form an independent and sought after measure to complement widely used dynamic global vegetation models (DGVMs). Here, we establish an operational semi‐empirical Re model, based only on data from the Moderate Resolution Imaging Spectroradiometer (MODIS) with a resolution of 1 km and 8 days. Fluxnet measurements between 2000 and 2009 from 100 sites across North America and Europe are used for parameterization and validation. Our analysis shows that Re is closely tied to temperature and plant productivity. By separating temporal and intersite variation, we find that MODIS land surface temperature (LST) and enhanced vegetation index (EVI) are sufficient to explain observed Re across most major biomes with a negligible bias [R² = 0.62, RMSE = 1.32 (g C m^?2 d^?1), MBE = 0.05 (g C m^?2 d^?1)]. A comparison of such satellite‐derived Re with those simulated by the DGVM LPJmL reveals similar spatial patterns. However, LPJmL shows higher temperature sensitivities and consistently simulates higher Re values, in high‐latitude and subtropical regions. These differences remain difficult to explain and they are likely associated either with LPJmL parameterization or with systematic errors in the Fluxnet sampling technique. While uncertainties remain with Re estimates, the model formulated in this study provides an operational, cross‐validated and unbiased approach to scale Fluxnet Re to the continental scale and advances knowledge of spatio‐temporal Re variability.     

遥感GPP模型在中亚干旱区4个典型生态系统的适用性评价

总初级生产力(GPP)是全球生态系统碳循环的重要组成部分,对全球气候变化有重要影响。目前有多种遥感模型可以模拟总初级生产力,比较不同遥感模型在中亚干旱区上的适用性对推进全球干旱区碳收支估算具有重要意义。基于涡度协相关技术观测的四个地面站数据验证MOD17、VODCA2、VPM、TG、SANIRv五种模型的模拟精度。结果表明：(1)基于光能利用率理论的MOD17、VPM模型模拟咸海荒漠植被和阜康荒漠植被GPP的精度最高(R²分别为0.52和0.80),但在模拟草地、农田生态系统生产力时存在较明显的低估(RE>20%);基于植被指数的遥感模型TG模型、SANIRv模型模拟巴尔喀什湖草地生态系统和乌兰乌苏农田生态系统GPP的精度最高(R²分别为0.91和0.81),同时模拟值与实测值的相对误差也较低;基于微波的VODCA2模型模拟各生态系统生产力的效果最差。(2)水分亏缺是限制植被GPP的主要因素,因此是否合理考虑水分胁迫是影响GPP模型在中亚干旱区适用性的重要因素。研究揭示了遥感GPP模型在中亚干旱区的应用潜力,为推进全球植被碳通量的准确估...     

Regional atmospheric CO2 inversion reveals seasonal and geographic differences in Amazon net biome exchange

Understanding tropical rainforest carbon exchange and its response to heat and drought is critical for quantifying the effects of climate change on tropical ecosystems, including global climate–carbon feedbacks. Of particular importance for the global carbon budget is net biome exchange of CO₂ with the atmosphere (NBE), which represents nonfire carbon fluxes into and out of biomass and soils. Subannual and sub‐Basin Amazon NBE estimates have relied heavily on process‐based biosphere models, despite lack of model agreement with plot‐scale observations. We present a new analysis of airborne measurements that reveals monthly, regional‐scale (~1–8 × 10⁶ km²) NBE variations. We develop a regional atmospheric CO₂ inversion that provides the first analysis of geographic and temporal variability in Amazon biosphere–atmosphere carbon exchange and that is minimally influenced by biosphere model‐based first guesses of seasonal and annual mean fluxes. We find little evidence for a clear seasonal cycle in Amazon NBE but do find NBE sensitivity to aberrations from long‐term mean climate. In particular, we observe increased NBE (more carbon emitted to the atmosphere) associated with heat and drought in 2010, and correlations between wet season NBE and precipitation (negative correlation) and temperature (positive correlation). In the eastern Amazon, pulses of increased NBE persisted through 2011, suggesting legacy effects of 2010 heat and drought. We also identify regional differences in postdrought NBE that appear related to long‐term water availability. We examine satellite proxies and find evidence for higher gross primary productivity (GPP) during a pulse of increased carbon uptake in 2011, and lower GPP during a period of increased NBE in the 2010 dry season drought, but links between GPP and NBE changes are not conclusive. These results provide novel evidence of NBE sensitivity to short‐term temperature and moisture extremes in the Amazon, where monthly and sub‐Basin estimates have not been previously available.     

Diagnosing and assessing uncertainties of terrestrial ecosystem models in a multimodel ensemble experiment: 1. Primary production

We conducted an ensemble modeling exercise using the Terrestrial Observation and Prediction System (TOPS) to evaluate sources of uncertainty in carbon flux estimates resulting from structural differences among ecosystem models. The experiment ran public‐domain versions of biome‐bgc, lpj, casa , and tops‐bgc over North America at 8 km resolution and for the period of 1982–2006. We developed the Hierarchical Framework for Diagnosing Ecosystem Models (HFDEM) to separate the simulated biogeochemistry into a cascade of three functional tiers and sequentially examine their characteristics in climate (temperature–precipitation) and other spaces. Analysis of the simulated annual gross primary production (GPP) in the climate domain indicates a general agreement among the models, all showing optimal GPP in regions where the relationship between annual average temperature (T, °C) and annual total precipitation (P, mm) is defined by P=50T+500. However, differences in simulated GPP are identified in magnitudes and distribution patterns. For forests, the GPP gradient along P=50T+500 ranges from ～50 g C yr^?1 m^?2 °C^?1 (casa ) to ～125 g C yr^?1 m^?2 °C^?1 (biome‐bgc ) in cold/temperate regions; for nonforests, the diversity among GPP distributions is even larger. Positive linear relationships are found between annual GPP and annual mean leaf area index (LAI) in all models. For biome‐bgc and lpj , such relationships lead to a positive feedback from LAI growth to GPP enhancement. Different approaches to constrain this feedback lead to different sensitivity of the models to disturbances such as fire, which contribute significantly to the diversity in GPP stated above. The ratios between independently simulated NPP and GPP are close to 50% on average; however, their distribution patterns vary significantly between models, reflecting the difficulties in estimating autotrophic respiration across various climate regimes. Although these results are drawn from our experiments with the tested model versions, the developed methodology has potential for other model exercises.     

Canopy Carbon Gain and Water Use: Analysis of Old-growth Conifers in the Pacific Northwest

This report summarizes our current knowledge of leaf-level physiological processes that regulate carbon gain and water loss of the dominant tree species in an old-growth forest at the Wind River Canopy Crane Research Facility. Analysis includes measurements of photosynthesis, respiration, stomatal conductance, water potential, stable carbon isotope values, and biogenic hydrocarbon emissions from Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla), and western red cedar (Thuja plicata). Leaf-level information is used to scale fluxes up to the canopy to estimate gross primary production using a physiology-based process model. Both light-saturated and in situ photosynthesis exhibit pronounced vertical gradients through the canopy, but are consistently highest in Douglas-fir, intermediate in western hemlock, and lowest in western red cedar. Net photosynthesis and stomatal conductance are strongly dependent on vapor-pressure deficit in Douglas-fir, and decline through the course of a seasonal drought. Foliar respiration is similar for Douglas-fir and western hemlock, and lowest for western red cedar. Water-use efficiency varied with species and tree height, as indexed using stable carbon isotopes values for foliage. Leaf water potential is most negative for Douglas-fir and similar for western hemlock and western red cedar. Terpene fluxes from foliage equal approximately 1% of the net carbon loss from the forest. Modeled estimates based on physiological measurements show gross primary productivity (GPP) to be about 22 Mg C m^–2 y^–1. Physiological studies will be necessary to further refine estimates of stand-level carbon balance and to make long-term predictions of changes in carbon balance due to changes in forest structure, species composition, and climate.