Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest

Accurate estimation of terrestrial gross primary productivity (GPP) remains a challenge despite its importance in the global carbon cycle. Chlorophyll fluorescence (ChlF) has been recently adopted to understand photosynthesis and its response to the environment, particularly with remote sensing data. However, it remains unclear how ChlF and photosynthesis are linked at different spatial scales across the growing season. We examined seasonal relationships between ChlF and photosynthesis at the leaf, canopy, and ecosystem scales and explored how leaf‐level ChlF was linked with canopy‐scale solar‐induced chlorophyll fluorescence (SIF) in a temperate deciduous forest at Harvard Forest, Massachusetts, USA. Our results show that ChlF captured the seasonal variations of photosynthesis with significant linear relationships between ChlF and photosynthesis across the growing season over different spatial scales (R² = 0.73, 0.77, and 0.86 at leaf, canopy, and satellite scales, respectively; P < 0.0001). We developed a model to estimate GPP from the tower‐based measurement of SIF and leaf‐level ChlF parameters. The estimation of GPP from this model agreed well with flux tower observations of GPP (R² = 0.68; P < 0.0001), demonstrating the potential of SIF for modeling GPP. At the leaf scale, we found that leaf F_q’/F_m’, the fraction of absorbed photons that are used for photochemistry for a light‐adapted measurement from a pulse amplitude modulation fluorometer, was the best leaf fluorescence parameter to correlate with canopy SIF yield (SIF/APAR, R² = 0.79; P < 0.0001). We also found that canopy SIF and SIF‐derived GPP (GPP_SIF) were strongly correlated to leaf‐level biochemistry and canopy structure, including chlorophyll content (R² = 0.65 for canopy GPP_SIF and chlorophyll content; P < 0.0001), leaf area index (LAI) (R² = 0.35 for canopy GPP_SIF and LAI; P < 0.0001), and normalized difference vegetation index (NDVI) (R² = 0.36 for canopy GPP_SIF and NDVI; P < 0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales.     

Estimation of vegetation photosynthetic capacity from space‐based measurements of chlorophyll fluorescence for terrestrial biosphere models

Photosynthesis simulations by terrestrial biosphere models are usually based on the Farquhar's model, in which the maximum rate of carboxylation (V_cmax) is a key control parameter of photosynthetic capacity. Even though V_cmax is known to vary substantially in space and time in response to environmental controls, it is typically parameterized in models with tabulated values associated to plant functional types. Remote sensing can be used to produce a spatially continuous and temporally resolved view on photosynthetic efficiency, but traditional vegetation observations based on spectral reflectance lack a direct link to plant photochemical processes. Alternatively, recent space‐borne measurements of sun‐induced chlorophyll fluorescence (SIF) can offer an observational constraint on photosynthesis simulations. Here, we show that top‐of‐canopy SIF measurements from space are sensitive to V_cmax at the ecosystem level, and present an approach to invert V_cmax from SIF data. We use the Soil‐Canopy Observation of Photosynthesis and Energy (SCOPE) balance model to derive empirical relationships between seasonal V_cmax and SIF which are used to solve the inverse problem. We evaluate our V_cmax estimation method at six agricultural flux tower sites in the midwestern US using spaced‐based SIF retrievals. Our V_cmax estimates agree well with literature values for corn and soybean plants (average values of 37 and 101 μmol m^?2 s^?1, respectively) and show plausible seasonal patterns. The effect of the updated seasonally varying V_cmax parameterization on simulated gross primary productivity (GPP) is tested by comparing to simulations with fixed V_cmax values. Validation against flux tower observations demonstrate that simulations of GPP and light use efficiency improve significantly when our time‐resolved V_cmax estimates from SIF are used, with R² for GPP comparisons increasing from 0.85 to 0.93, and for light use efficiency from 0.44 to 0.83. Our results support the use of space‐based SIF data as a proxy for photosynthetic capacity and suggest the potential for global, time‐resolved estimates of V_cmax.     

Carbon cycling and storage in world forests: biome patterns related to forest age

Forest age, which is affected by stand‐replacing ecosystem disturbances (such as forest fires, harvesting, or insects), plays a distinguishing role in determining the distribution of carbon (C) pools and fluxes in different forested ecosystems. In this synthesis, net primary productivity (NPP), net ecosystem productivity (NEP), and five pools of C (living biomass, coarse woody debris, organic soil horizons, soil, and total ecosystem) are summarized by age class for tropical, temperate, and boreal forest biomes. Estimates of variability in NPP, NEP, and C pools are provided for each biome‐age class combination and the sources of variability are discussed. Aggregated biome‐level estimates of NPP and NEP were higher in intermediate‐aged forests (e.g., 30–120 years), while older forests (e.g., >120 years) were generally less productive. The mean NEP in the youngest forests (0–10 years) was negative (source to the atmosphere) in both boreal and temperate biomes (?0.1 and –1.9 Mg C ha^?1 yr^?1, respectively). Forest age is a highly significant source of variability in NEP at the biome scale; for example, mean temperate forest NEP was ?1.9, 4.5, 2.4, 1.9 and 1.7 Mg C ha^?1 yr^?1 across five age classes (0–10, 11–30, 31–70, 71–120, 121–200 years, respectively). In general, median NPP and NEP are strongly correlated (R²=0.83) across all biomes and age classes, with the exception of the youngest temperate forests. Using the information gained from calculating the summary statistics for NPP and NEP, we calculated heterotrophic soil respiration (R_h) for each age class in each biome. The mean R_h was high in the youngest temperate age class (9.7 Mg C ha^?1 yr^?1) and declined with age, implying that forest ecosystem respiration peaks when forests are young, not old. With notable exceptions, carbon pool sizes increased with age in all biomes, including soil C. Age trends in C cycling and storage are very apparent in all three biomes and it is clear that a better understanding of how forest age and disturbance history interact will greatly improve our fundamental knowledge of the terrestrial C cycle.     

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.     

Seasonal responses of terrestrial ecosystem water‐use efficiency to climate change

Ecosystem water‐use efficiency (EWUE) is an indicator of carbon–water interactions and is defined as the ratio of carbon assimilation (GPP) to evapotranspiration (ET). Previous research suggests an increasing long‐term trend in annual EWUE over many regions and is largely attributed to the physiological effects of rising CO₂. The seasonal trends in EWUE, however, have not yet been analyzed. In this study, we investigate seasonal EWUE trends and responses to various drivers during 1982–2008. The seasonal cycle for two variants of EWUE, water‐use efficiency (WUE, GPP/ET), and transpiration‐based WUE (WUE_t, the ratio of GPP and transpiration), is analyzed from 0.5° gridded fields from four process‐based models and satellite‐based products, as well as a network of 63 local flux tower observations. WUE derived from flux tower observations shows moderate seasonal variation for most latitude bands, which is in agreement with satellite‐based products. In contrast, the seasonal EWUE trends are not well captured by the same satellite‐based products. Trend analysis, based on process‐model factorial simulations separating effects of climate, CO₂, and nitrogen deposition (NDEP), further suggests that the seasonal EWUE trends are mainly associated with seasonal trends of climate, whereas CO₂ and NDEP do not show obvious seasonal difference in EWUE trends. About 66% grid cells show positive annual WUE trends, mainly over mid‐ and high northern latitudes. In these regions, spring climate change has amplified the effect of CO₂ in increasing WUE by more than 0.005 gC m⁻² mm⁻¹ yr⁻¹ for 41% pixels. Multiple regression analysis further shows that the increase in springtime WUE in the northern hemisphere is the result of GPP increasing faster than ET because of the higher temperature sensitivity of GPP relative to ET. The partitioning of annual EWUE to seasonal components provides new insight into the relative sensitivities of GPP and ET to climate, CO_2, and NDEP.     

Gross primary production responses to warming,elevated CO2, and irrigation: quantifying the drivers of ecosystem physiology in a semiarid grassland

Determining whether the terrestrial biosphere will be a source or sink of carbon (C) under a future climate of elevated CO₂ (eCO₂) and warming requires accurate quantification of gross primary production (GPP), the largest flux of C in the global C cycle. We evaluated 6 years (2007–2012) of flux‐derived GPP data from the Prairie Heating and CO₂ Enrichment (PHACE) experiment, situated in a grassland in Wyoming, USA. The GPP data were used to calibrate a light response model whose basic formulation has been successfully used in a variety of ecosystems. The model was extended by modeling maximum photosynthetic rate (A_max) and light‐use efficiency (Q) as functions of soil water, air temperature, vapor pressure deficit, vegetation greenness, and nitrogen at current and antecedent (past) timescales. The model fits the observed GPP well (R² = 0.79), which was confirmed by other model performance checks that compared different variants of the model (e.g. with and without antecedent effects). Stimulation of cumulative 6‐year GPP by warming (29%, P = 0.02) and eCO₂ (26%, P = 0.07) was primarily driven by enhanced C uptake during spring (129%, P = 0.001) and fall (124%, P = 0.001), respectively, which was consistent across years. Antecedent air temperature (Tair_ant) and vapor pressure deficit (VPD_ant) effects on A_max (over the past 3–4 days and 1–3 days, respectively) were the most significant predictors of temporal variability in GPP among most treatments. The importance of VPD_ant suggests that atmospheric drought is important for predicting GPP under current and future climate; we highlight the need for experimental studies to identify the mechanisms underlying such antecedent effects. Finally, posterior estimates of cumulative GPP under control and eCO₂ treatments were tested as a benchmark against 12 terrestrial biosphere models (TBMs). The narrow uncertainties of these data‐driven GPP estimates suggest that they could be useful semi‐independent data streams for validating TBMs.     

Pathways regulating decreased soil respiration with warming in a biocrust‐dominated dryland

A positive soil carbon (C)‐climate feedback is embedded into the climatic models of the IPCC. However, recent global syntheses indicate that the temperature sensitivity of soil respiration (R_S) in drylands, the largest biome on Earth, is actually lower in warmed than in control plots. Consequently, soil C losses with future warming are expected to be low compared with other biomes. Nevertheless, the empirical basis for these global extrapolations is still poor in drylands, due to the low number of field experiments testing the pathways behind the long‐term responses of soil respiration (R_S) to warming. Importantly, global drylands are covered with biocrusts (communities formed by bryophytes, lichens, cyanobacteria, fungi, and bacteria), and thus, R_S responses to warming may be driven by both autotrophic and heterotrophic pathways. Here, we evaluated the effects of 8‐year experimental warming on R_S, and the different pathways involved, in a biocrust‐dominated dryland in southern Spain. We also assessed the overall impacts on soil organic C (SOC) accumulation over time. Across the years and biocrust cover levels, warming reduced R_S by 0.30 μmol CO₂ m^?2 s^?1 (95% CI = ?0.24 to 0.84), although the negative warming effects were only significant after 3 years of elevated temperatures in areas with low initial biocrust cover. We found support for different pathways regulating the warming‐induced reduction in R_S at areas with low (microbial thermal acclimation via reduced soil mass‐specific respiration and β‐glucosidase enzymatic activity) vs. high (microbial thermal acclimation jointly with a reduction in autotrophic respiration from decreased lichen cover) initial biocrust cover. Our 8‐year experimental study shows a reduction in soil respiration with warming and highlights that biocrusts should be explicitly included in modeling efforts aimed to quantify the soil C–climate feedback in drylands.     

Assimilation exceeds respiration sensitivity to drought: A FLUXNET synthesis

The intensification of the hydrological cycle, with an observed and modeled increase in drought incidence and severity, underscores the need to quantify drought effects on carbon cycling and the terrestrial sink. FLUXNET, a global network of eddy covariance towers, provides dense data streams of meteorological data, and through flux partitioning and gap filling algorithms, estimates of net ecosystem productivity (F_NEP), gross ecosystem productivity (P), and ecosystem respiration (R). We analyzed the functional relationship of these three carbon fluxes relative to evaporative fraction (EF), an index of drought and site water status, using monthly data records from 238 micrometeorological tower sites distributed globally across 11 biomes. The analysis was based on relative anomalies of both EF and carbon fluxes and focused on drought episodes by biome and climatic season. Globally P was ≈50% more sensitive to a drought event than R. Network‐wide drought‐induced decreases in carbon flux averaged ?16.6 and ?9.3 g C m^?2 month^?1 for P and R, i.e., drought events induced a net decline in the terrestrial sink. However, in evergreen forests and wetlands drought was coincident with an increase in P or R during parts of the growing season. The most robust relationships between carbon flux and EF occurred during climatic spring for F_NEP and in climatic summer for P and R. Upscaling flux sensitivities to a global map showed that spatial patterns for all three carbon fluxes were linked to the distribution of croplands. Agricultural areas exhibited the highest sensitivity whereas the tropical region had minimal sensitivity to drought. Combining gridded flux sensitivities with their uncertainties and the spatial grid of FLUXNET revealed that a more robust quantification of carbon flux response to drought requires additional towers in all biomes of Africa and Asia as well as in the cropland, shrubland, savannah, and wetland biomes globally.     

Developing a diagnostic model for estimating terrestrial vegetation gross primary productivity using the photosynthetic quantum yield and Earth Observation data

This article develops a new carbon exchange diagnostic model [i.e. Southampton CARbon Flux (SCARF) model] for estimating daily gross primary productivity (GPP). The model exploits the maximum quantum yields of two key photosynthetic pathways (i.e. C₃ and C₄) to estimate the conversion of absorbed photosynthetically active radiation into GPP. Furthermore, this is the first model to use only the fraction of photosynthetically active radiation absorbed by photosynthetic elements of the canopy (i.e. FAPAR_ps) rather than total canopy, to predict GPP. The GPP predicted by the SCARF model was comparable to in situ GPP measurements (R² > 0.7) in most of the evaluated biomes. Overall, the SCARF model predicted high GPP in regions dominated by forests and croplands, and low GPP in shrublands and dry‐grasslands across USA and Europe. The spatial distribution of GPP from the SCARF model over Europe and conterminous USA was comparable to those from the MOD17 GPP product except in regions dominated by croplands. The SCARF model GPP predictions were positively correlated (R² > 0.5) to climatic and biophysical input variables indicating its sensitivity to factors controlling vegetation productivity. The new model has three advantages, first, it prescribes only two quantum yield terms rather than species specific light use efficiency terms; second, it uses only the fraction of PAR absorbed by photosynthetic elements of the canopy (FAPAR_ps) hence capturing the actual PAR used in photosynthesis; and third, it does not need a detailed land cover map that is a major source of uncertainty in most remote sensing based GPP models. The Sentinel satellites planned for launch in 2014 by the European Space Agency have adequate spectral channels to derive FAPAR_ps at relatively high spatial resolution (20 m). This provides a unique opportunity to produce global GPP operationally using the Southampton CARbon Flux (SCARF) model at high spatial resolution.     

Carbon uptake in Eurasian boreal forests dominates the high-latitude net ecosystem carbon budget

Arctic-boreal landscapes are experiencing profound warming, along with changes in ecosystem moisture status and disturbance from fire. This region is of global importance in terms of carbon feedbacks to climate, yet the sign (sink or source) and magnitude of the Arctic-boreal carbon budget within recent years remains highly uncertain. Here, we provide new estimates of recent (2003–2015) vegetation gross primary productivity (GPP), ecosystem respiration (R_eco), net ecosystem CO₂ exchange (NEE; R_eco − GPP), and terrestrial methane (CH₄) emissions for the Arctic-boreal zone using a satellite data-driven process-model for northern ecosystems (TCFM-Arctic), calibrated and evaluated using measurements from >60 tower eddy covariance (EC) sites. We used TCFM-Arctic to obtain daily 1-km² flux estimates and annual carbon budgets for the pan-Arctic-boreal region. Across the domain, the model indicated an overall average NEE sink of −850 Tg CO₂-C year⁻¹. Eurasian boreal zones, especially those in Siberia, contributed to a majority of the net sink. In contrast, the tundra biome was relatively carbon neutral (ranging from small sink to source). Regional CH₄ emissions from tundra and boreal wetlands (not accounting for aquatic CH₄) were estimated at 35 Tg CH₄-C year⁻¹. Accounting for additional emissions from open water aquatic bodies and from fire, using available estimates from the literature, reduced the total regional NEE sink by 21% and shifted many far northern tundra landscapes, and some boreal forests, to a net carbon source. This assessment, based on in situ observations and models, improves our understanding of the high-latitude carbon status and also indicates a continued need for integrated site-to-regional assessments to monitor the vulnerability of these ecosystems to climate change.     

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.     

Global patterns and predictors of stem CO2 efflux in forest ecosystems

Stem CO₂ efflux (E_S) plays an important role in the carbon balance of forest ecosystems. However, its primary controls at the global scale are poorly understood and observation‐based global estimates are lacking. We synthesized data from 121 published studies across global forest ecosystems and examined the relationships between annual E_S and biotic and abiotic factors at individual, biome, and global scales, and developed a global gridded estimate of annual E_S. We tested the following hypotheses: (1) Leaf area index (LAI) will be highly correlated with annual E_S at biome and global scales; (2) there will be parallel patterns in stem and root CO₂ effluxes (R_A) in all forests; (3) annual E_S will decline with forest age; and (4) LAI coupled with mean annual temperature (MAT) and mean annual precipitation (MAP) will be sufficient to predict annual E_S across forests in different regions. Positive linear relationships were found between E_S and LAI, as well as gross primary production (GPP), net primary production (NPP), wood NPP, soil CO₂ efflux (R_S), and R_A. Annual E_S was correlated with R_A in temperate forests after controlling for GPP and MAT, suggesting other additional factors contributed to the relationship. Annual E_S tended to decrease with stand age. Leaf area index, MAT and MAP, predicted 74% of variation in E_S at global scales. Our statistical model estimated a global annual E_S of 6.7 ± 1.1 Pg C yr⁻¹ over the period of 2000–2012 with little interannual variability. Modeled mean annual E_S was 71 ± 43, 270 ± 103, and 420 ± 134 g C m² yr⁻¹ for boreal, temperate, and tropical forests, respectively. We recommend that future studies report E_S at a standardized constant temperature, incorporate more manipulative treatments, such as fertilization and drought, and whenever possible, simultaneously measure both aboveground and belowground CO₂ fluxes.     

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.     

Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO₂ between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO₂ release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO₂ fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co‐acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data‐driven approach based on combined neural networks (NN_C‐part). NN_C‐part incorporates process knowledge by introducing a photosynthetic response based on the light‐use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R² > .94) and RECO (R² > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NN_C‐part performed more realistic than the traditional methods for predicting additional patterns of gross CO₂ fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO₂ fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NN_C‐part is a valid data‐driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.     

Synthesis of Novel Chiral Sulfonamide‐Bearing 1,2,4‐Triazole‐3‐thione Analogs Derived from D‐ and L‐Phenylalanine Esters as Potential Anti‐Influenza Agents

Novel enantiopure 1,2,4‐trizole‐3‐thiones containing a benzensulfonamide moiety were synthesized via multistep reaction sequence starting with D‐phenylalanine methyl ester and L‐phenylalanine ethyl ester as a source of chirality. The chemical structures of all compounds were characterized by elemental analysis, UV, IR, ¹H NMR, ¹³C NMR, 2D NMR (HETCOR), and mass spectral data. All compounds were tested in vitro antiviral activity against a broad variety of DNA and RNA viruses and in vitro cytostatic activity against murine leukemia (L1210), human T‐lymphocyte (CEM) and human cervix carcinoma (HeLa) cell lines. Although enantiopure 1,2,4‐triazole‐3‐thione analogs in (R) configuration emerged as promising anti‐influenza A H1N1 subtype in Madin Darby canine kidney cell cultures (MDCK), their enantiomers exhibited no activity. Especially compounds 18a , 21a , 22a , 23a , and 24a (EC₅₀: 6.5, 6.1, 2.4, 1.6, 1.7 μM, respectively) had excellent activity against influenza A H1N1 subtype compared to the reference drug ribavirin (EC₅₀: 8.0 μM). Several compounds have been found to inhibit proliferation of L1210, CEM and HeLa cell cultures with IC₅₀ in the 12–53 μM range. Compound 5a and 27a in (R) configuration were the most active compounds (IC₅₀: 12–22 μM for 5a and IC₅₀: 19–23 μM for 27a ). Chirality 28:495–513, 2016. © 2016 Wiley Periodicals, Inc.     

Concentration‐dependent studies of Nd3+‐doped zinc phosphate glasses for NIR photoluminescence at 1.05 μm

Nd³⁺‐doped lead‐free zinc phosphate glasses with the chemical compositions (60‐x) NH₄H₂PO₄ + 20ZnO + 10BaF₂ + 10NaF + xNd₂O₃ (where x = 0.5, 1.0, 1.5, 2.0 and 2.5 mol%) were prepared using a melt quenching technique. Vibrational bands were assigned and clearly elucidated by Raman spectral profiles for all the glass samples. Judd–Ofelt (J–O) intensity parameters (Ω_λ: λ = 2, 4, 6) were obtained from the spectral intensities of different absorption bands of Nd³⁺ ions. Radiative properties such as radiative transition probabilities (A_R), radiative lifetimes (τ_R) and branching ratios (β_R) for different excited states were calculated using J–O parameters. The near infrared (NIR) photoluminescence spectra exhibited three emission bands (⁴F_3/2 level to ⁴I_13/2, ⁴I_11/2 and ⁴I_9/2 states) for all the concentrations of Nd³⁺ ions. Various luminescence properties were studied by varying the Nd³⁺ concentration for the three spectral profiles. Fluorescence decay curves of the ⁴F_3/2 level were recorded. The energy transfer mechanism that leads to quenching of the ⁴F_3/2 state lifetimes was discussed at higher concentration of Nd³⁺ ions. These glasses are suggested as suitable hosts to produce efficient lasing action in NIR region at 1.05 μm.     

Spatial variation in landscape‐level CO2 and CH4 fluxes from arctic coastal tundra: influence from vegetation,wetness, and the thaw lake cycle

Regional quantification of arctic CO₂ and CH₄ fluxes remains difficult due to high landscape heterogeneity coupled with a sparse measurement network. Most of the arctic coastal tundra near Barrow, Alaska is part of the thaw lake cycle, which includes current thaw lakes and a 5500‐year chronosequence of vegetated thaw lake basins. However, spatial variability in carbon fluxes from these features remains grossly understudied. Here, we present an analysis of whole‐ecosystem CO₂ and CH₄ fluxes from 20 thaw lake cycle features during the 2011 growing season. We found that the thaw lake cycle was largely responsible for spatial variation in CO₂ flux, mostly due to its control on gross primary productivity (GPP). Current lakes were significant CO₂ sources that varied little. Vegetated basins showed declining GPP and CO₂ sink with age (R² = 67% and 57%, respectively). CH₄ fluxes measured from a subset of 12 vegetated basins showed no relationship with age or CO₂ flux components. Instead, higher CH₄ fluxes were related to greater landscape wetness (R² = 57%) and thaw depth (additional R² = 28%). Spatial variation in CO₂ and CH₄ fluxes had good satellite remote sensing indicators, and we estimated the region to be a small CO₂ sink of ?4.9 ± 2.4 (SE) g C m^?2 between 11 June and 25 August, which was countered by a CH₄ source of 2.1 ± 0.2 (SE) g C m^?2. Results from our scaling exercise showed that developing or validating regional estimates based on single tower sites can result in significant bias, on average by a factor 4 for CO₂ flux and 30% for CH₄ flux. Although our results are specific to the Arctic Coastal Plain of Alaska, the degree of landscape‐scale variability, large‐scale controls on carbon exchange, and implications for regional estimation seen here likely have wide relevance to other arctic landscapes.     

Simulations of chlorophyll fluorescence incorporated into the Community Land Model version 4

Several studies have shown that satellite retrievals of solar‐induced chlorophyll fluorescence (SIF) provide useful information on terrestrial photosynthesis or gross primary production (GPP). Here, we have incorporated equations coupling SIF to photosynthesis in a land surface model, the National Center for Atmospheric Research Community Land Model version 4 (NCAR CLM4), and have demonstrated its use as a diagnostic tool for evaluating the calculation of photosynthesis, a key process in a land surface model that strongly influences the carbon, water, and energy cycles. By comparing forward simulations of SIF, essentially as a byproduct of photosynthesis, in CLM4 with observations of actual SIF, it is possible to check whether the model is accurately representing photosynthesis and the processes coupled to it. We provide some background on how SIF is coupled to photosynthesis, describe how SIF was incorporated into CLM4, and demonstrate that our simulated relationship between SIF and GPP values are reasonable when compared with satellite (Greenhouse gases Observing SATellite; GOSAT) and in situ flux‐tower measurements. CLM4 overestimates SIF in tropical forests, and we show that this error can be corrected by adjusting the maximum carboxylation rate (V_max) specified for tropical forests in CLM4. Our study confirms that SIF has the potential to improve photosynthesis simulation and thereby can play a critical role in improving land surface and carbon cycle models.     

The Net Landscape Carbon Balance—Integrating terrestrial and aquatic carbon fluxes in a managed boreal forest landscape in Sweden

The boreal biome exchanges large amounts of carbon (C) and greenhouse gases (GHGs) with the atmosphere and thus significantly affects the global climate. A managed boreal landscape consists of various sinks and sources of carbon dioxide (CO₂), methane (CH₄), and dissolved organic and inorganic carbon (DOC and DIC) across forests, mires, lakes, and streams. Due to the spatial heterogeneity, large uncertainties exist regarding the net landscape carbon balance (NLCB). In this study, we compiled terrestrial and aquatic fluxes of CO₂, CH₄, DOC, DIC, and harvested C obtained from tall‐tower eddy covariance measurements, stream monitoring, and remote sensing of biomass stocks for an entire boreal catchment (~68 km²) in Sweden to estimate the NLCB across the land–water–atmosphere continuum. Our results showed that this managed boreal forest landscape was a net C sink (NLCB = 39 g C m^?2 year^?1) with the landscape–atmosphere CO₂ exchange being the dominant component, followed by the C export via harvest and streams. Accounting for the global warming potential of CH₄, the landscape was a GHG sink of 237 g CO₂‐eq m^?2 year^?1, thus providing a climate‐cooling effect. The CH₄ flux contribution to the annual GHG budget increased from 0.6% during spring to 3.2% during winter. The aquatic C loss was most significant during spring contributing 8% to the annual NLCB. We further found that abiotic controls (e.g., air temperature and incoming radiation) regulated the temporal variability of the NLCB whereas land cover types (e.g., mire vs. forest) and management practices (e.g., clear‐cutting) determined their spatial variability. Our study advocates the need for integrating terrestrial and aquatic fluxes at the landscape scale based on tall‐tower eddy covariance measurements combined with biomass stock and stream monitoring to develop a holistic understanding of the NLCB of managed boreal forest landscapes and to better evaluate their potential for mitigating climate change.     

Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends

The purpose of this study was to evaluate 10 process‐based terrestrial biosphere models that were used for the IPCC fifth Assessment Report. The simulated gross primary productivity (GPP) is compared with flux‐tower‐based estimates by Jung et al. [Journal of Geophysical Research 116 (2011) G00J07] (JU11). The net primary productivity (NPP) apparent sensitivity to climate variability and atmospheric CO₂ trends is diagnosed from each model output, using statistical functions. The temperature sensitivity is compared against ecosystem field warming experiments results. The CO₂ sensitivity of NPP is compared to the results from four Free‐Air CO₂ Enrichment (FACE) experiments. The simulated global net biome productivity (NBP) is compared with the residual land sink (RLS) of the global carbon budget from Friedlingstein et al. [Nature Geoscience 3 (2010) 811] (FR10). We found that models produce a higher GPP (133 ± 15 Pg C yr^?1) than JU11 (118 ± 6 Pg C yr^?1). In response to rising atmospheric CO₂ concentration, modeled NPP increases on average by 16% (5–20%) per 100 ppm, a slightly larger apparent sensitivity of NPP to CO₂ than that measured at the FACE experiment locations (13% per 100 ppm). Global NBP differs markedly among individual models, although the mean value of 2.0 ± 0.8 Pg C yr^?1 is remarkably close to the mean value of RLS (2.1 ± 1.2 Pg C yr^?1). The interannual variability in modeled NBP is significantly correlated with that of RLS for the period 1980–2009. Both model‐to‐model and interannual variation in model GPP is larger than that in model NBP due to the strong coupling causing a positive correlation between ecosystem respiration and GPP in the model. The average linear regression slope of global NBP vs. temperature across the 10 models is ?3.0 ± 1.5 Pg C yr^?1 °C^?1, within the uncertainty of what derived from RLS (?3.9 ± 1.1 Pg C yr^?1 °C^?1). However, 9 of 10 models overestimate the regression slope of NBP vs. precipitation, compared with the slope of the observed RLS vs. precipitation. With most models lacking processes that control GPP and NBP in addition to CO₂ and climate, the agreement between modeled and observation‐based GPP and NBP can be fortuitous. Carbon–nitrogen interactions (only separable in one model) significantly influence the simulated response of carbon cycle to temperature and atmospheric CO₂ concentration, suggesting that nutrients limitations should be included in the next generation of terrestrial biosphere models.