A GIS-based method for modeling methane emissions from paddy fields by fusing multiple sources of data

Quantification of regional methane (CH₄) gas emission in the paddy fields is critical under climate warming. Mechanism models generally require numerous parameters while empirical models are too coarse. Based on the mechanism and structure of the widely used model CH4MOD, a GIS-based Regional CH₄ Emission Calculation (GRMC) method was put forward by introducing multiple sources of remote sensing images, including MOD09A1, MOD11A2, MOD15A2H as well as local water management standards. The stress of soil moisture condition (f(water)) on CH₄ emissions was quantified by calculating the redox potential (Eh) from days after flooding or falling dry. The f(water)-t curve was calculated under different exogenous organic matter addition. Combining the f(water)-t curve with local water management standards, the seasonal variation of f(water) was obtained. It was proven that f(water) was effective in reflecting the regulation role of soil moisture condition. The GRMC was tested at four Eddy Covariance (EC) sites: Nanchang (NC) in China, Twitchell (TWT) in the USA, Castellaro (CAS) in Italy and Cheorwon (CRK) in Korea and has been proven to well track the seasonal dynamics of CH₄ emissions with R² ranges of 0.738-0.848, RMSE ranges of 31.94-149.22 mg C/m²d and MBE ranges of -66.42- -14.79 mg C/m²d. The parameters obtained in Nanchang (NC) site in China were then applied to the Ganfu Plain Irrigation System (GFPIS), a typical rice planting area of China, to analyse the spatial-temporal variations of CH₄ emissions. The total CH₄ emissions of late rice in the GFPIS from 2001 to 2013 was in the range of 14.47-20.48 (10³ t CH₄-C). Ts caused spatial variation of CH₄ production capacity, resulting in the spatial variability of CH4 emissions. Overall, the GRMC is effective in obtaining CH₄ emissions from rice fields on a regional scale.

Comparative Ecophysiology of Black Spruce between Lichen Woodlands and Feathermoss Stands in Eastern Canada

Climate change is likely to affect the growth, development and regeneration of the black spruce stands across the boreal forest. Regeneration failures cause gaps in the dense black spruce-feathermoss (SM) mosaic increasing the landscape proportion of open lichen woodland (LW). The aims of the study are to determine whether the contrasting characteristics of SM and LW induce different maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax) and light-saturated maximum photosynthesis (Amax) in black spruce trees across a latitudinal or seasonal gradient. Results show that the Vcmax and Jmax were higher in SM than in LW in western Quebec, at the ecotone of the closed-crown and open forest. Vcmax and Jmax were different between SM and LW mainly because nutrient acquisition seems different between stand types. Latitude affects values of Vcmax and Jmax, but the effect could be explained by soil and vegetation composition between experimental plots rather than by latitude. Physiological capacities do not match Amax values for stand types and latitude. Indeed, Amax rates suggest that black spruce in LWs perform as well as those in SMs at the needle scale because Amax would be limited by CO2 concentration which prevents saturation of Rubisco. Despite the lack of difference between the Amax of SM and LW stands, future increases in CO2 concentration and temperature could induce a gap between their respective photosynthesis rates because of their different physiological capacities.

Estimating mangrove forest gross primary production by quantifying environmental stressors in the coastal area

Mangrove ecosystems play an important role in global carbon budget, however, the quantitative relationships between environmental drivers and productivity in these forests remain poorly understood. This study presented a remote sensing (RS)-based productivity model to estimate the light use efficiency (LUE) and gross primary production (GPP) of mangrove forests in China. Firstly, LUE model considered the effects of tidal inundation and therefore involved sea surface temperature (SST) and salinity as environmental scalars. Secondly, the downscaling effect of photosynthetic active radiation (PAR) on the mangrove LUE was quantified according to different PAR values. Thirdly, the maximum LUE varied with temperature and was therefore determined based on the response of daytime net ecosystem exchange and PAR at different temperatures. Lastly, GPP was estimated by combining the LUE model with the fraction of absorbed photosynthetically active radiation from Sentinel-2 images. The results showed that the LUE model developed for mangrove forests has higher overall accuracy (RMSE = 0.0051, R² = 0.64) than the terrestrial model (RMSE = 0.0220, R² = 0.24). The main environmental stressor for the photosynthesis of mangrove forests in China was PAR. The estimated GPP was, in general, in agreement with the in-situ measurement from the two carbon flux towers. Compared to the MODIS GPP product, the derived GPP had higher accuracy, with RMSE improving from 39.09 to 19.05 g C/m²/8 days in 2012, and from 33.76 to 19.51 g C/m²/8 days in 2015. The spatiotemporal distributions of the mangrove GPP revealed that GPP was most strongly controlled by environmental conditions, especially temperature and PAR, as well as the distribution of mangroves. These results demonstrate the potential of the RS-based productivity model for scaling up GPP in mangrove forests, a key to explore the carbon cycle of mangrove ecosystems at national and global scales.

Environmental and developmental factors driving xylem anatomy and micro-density in black spruce

Wood density is the product of carbon allocation for structural growth and reflects the trade-off between mechanical support and water conductivity. We tested a conceptual framework based on the assumption that micro-density depends on direct and indirect relationships with endogenous and exogenous factors. The dynamics of wood formation, including timings and rates of cell division, cell enlargement, and secondary wall deposition, were assessed from microcores collected weekly between 2002 and 2016 from five black spruce stands located along a latitudinal gradient in Quebec, Canada. Cell anatomy and micro-density were recorded by anatomical analyses and X-ray measurements. Our structural equation model explained 80% of micro-density variation within the tree-ring with direct effects of wall thickness (σ = 0.61), cell diameter (σ = -0.51), and photoperiod (σ = -0.26). Wood formation dynamics had an indirect effect on micro-density. Micro-density increased under longer periods of cell-wall deposition and shorter durations of enlargement. Our results fill a critical gap in understanding the relationships underlying micro-density variation in conifers. We demonstrated that short-term responses to environmental variations could be overridden by plastic responses that modulate cell differentiation. Our results point to wood formation dynamics as a reliable predictor of carbon allocation in trees.

Thermal acclimation of leaf dark respiration of Larix gmelinii: A latitudinal transplant experiment

The response of tree leaf dark respiration (R_d) to temperature change is important in modeling and predicting forest carbon (C) cycling under climate change, but it has rarely been investigated in nature. We conducted a field experiment by transplanting the trees of Larix gmelinii - the dominant tree species in Chinese boreal forests from four latitudinal sites to a common garden near the warm border of its range. Our objective was to explore thermal acclimation of R_d and the underlying mechanisms by comparing the temperature-response curves of R_d and related leaf traits both in the common garden and at the original sites. We found that warming significantly decreased R_d and its temperature sensitivity (Q₁₀), which changed across the growing season and were correlated with the mean annual temperature of the original sites, reflecting a combination of both short- and long-term respiratory acclimation to warming. The trees from the southern sites tended to have higher thermal acclimation of R_d and lower Q₁₀ than that from the northern sites. R_d and Q₁₀ were highly correlated with the concentrations of leaf nitrogen and soluble sugars, which may be used as proxies for assessing thermal acclimation of respiration. Considering both short- and long-term thermal acclimation of R_d likely improves the prediction of forest C cycling in response to climate change.

Long-Term Grass Biomass Estimation of Pastures from Satellite Data

The general consensus on future climate projections poses new and increased concerns about climate change and its impacts. Droughts are primarily worrying, since they contribute to altering the composition, distribution, and abundance of species. Grasslands, for example, are the primary source for grazing mammals and modifications in climate determine variation in the available yields for cattle. To support the agriculture sector, international organizations such as the Food and Agriculture Organization (FAO) of the United Nations are promoting the development of dedicated monitoring initiatives, with particular attention for undeveloped and disadvantaged countries. The temporal scale is very important in this context, where long time series of data are required to compute consistent analyses. In this research, we discuss the results regarding long-term grass biomass estimation in an extended African region. The results are obtained by means of a procedure that is mostly automatic and replicable in other contexts. Zambia has been identified as a significant test area due to its vulnerability to the adverse impacts of climate change as a result of its geographic location, socioeconomic stresses, and low adaptive capacity. In fact, analysis and estimations were performed over a long time window (21 years) to identify correlations with climate variables, such as precipitation, to clarify sensitivity to climate change and possible effects already in place. From the analysis, decline in both grass quality and quantity was not currently evident in the study area. However, pastures in the considered area were found to be vulnerable to changing climate and, in particular, to the water shortages accompanying drought periods.

Integrating SIF and Clearness Index to Improve Maize GPP Estimation Using Continuous Tower-Based Observations

Solar-induced chlorophyll fluorescence (SIF) has been proven to be well correlated with vegetation photosynthesis. Although multiple studies have found that SIF demonstrates a strong correlation with gross primary production (GPP), SIF-based GPP estimation at different temporal scales has not been well explored. In this study, we aimed to investigate the quality of GPP estimates produced using the far-red SIF retrieved at 760 nm (SIF760) based on continuous tower-based observations of a maize field made during 2017 and 2018, and to explore the responses of GPP and SIF to different meteorological conditions, such as the amount of photosynthetically active radiation (PAR), the clearness index (CI, representing the weather condition), the air temperature (AT), and the vapor pressure deficit (VPD). Firstly, our results showed that the SIF760 tracked GPP well at both diurnal and seasonal scales, and that SIF760 was more linearly correlated to PAR than GPP was. Therefore, the SIF760-GPP relationship was clearly a hyperbolic relationship. For instantaneous observations made within a period of half an hour, the R2 value was 0.66 in 2017 and 2018. Based on daily mean observations, the R2 value was 0.82 and 0.76 in 2017 and 2018, respectively. and had an R2 value of 0.66 (2017) and 0.66 (2018) for instantaneous observations made within a period of half an hour and 0.82 (2017) and 0.76 (2018) for daily mean observations. Secondly, it was found that the SIF760-GPP relationship varied with the environmental conditions, with the CI being the dominant factor. At both diurnal and seasonal scales, the ratio of GPP to SIF760 decreased noticeably as the CI increased. Finally, the SIF760-based GPP models with and without the inclusion of CI were trained using 70% of daily observations from 2017 and 2018 and the models were validated using the remaining 30% of the dataset. For both linear and non-linear models, the inclusion of the CI greatly improved the SIF760-based GPP estimates based on daily mean observations: the value of R2 increased from 0.71 to 0.82 for the linear model and from 0.82 to 0.87 for the non-linear model. The validation results confirmed that the SIF760-based GPP estimation was improved greatly by including the CI, giving a higher R2 and a lower RMSE. These values improved from R2 = 0.66 and RMSE = 7.02 mw/m2/nm/sr to R2 = 0.76 and RMSE = 6.36 mw/m2/nm/sr for the linear model, and from R2 = 0.71 and RMSE = 4.76 mw/m2/nm/sr to R2 = 0.78 and RMSE = 3.50 mw/m2/nm/sr for the non-linear model. Therefore, our results demonstrated that SIF760 is a reliable proxy for GPP and that SIF760-based GPP estimation can be greatly improved by integrating the CI with SIF760. These findings will be useful in the remote sensing of vegetation GPP using satellite, airborne, and tower-based SIF data because the CI is usually an easily accessible meteorological variable.

Spatial variations and controls of carbon use efficiency in China's terrestrial ecosystems

Carbon use efficiency (CUE), one of the most important eco-physiological parameters, represents the capacity of plants to transform carbon into new biomass. Understanding the variations and controls of CUE is crucial for regional carbon assessment. Here, we used 15-years of continuous remote sensing data to examine the variations of CUE across broad geographic and climatic gradients in China. The results showed that the vegetation CUE was averaged to 0.54 ± 0.11 with minor interannual variation. However, the CUE greatly varied with geographic gradients and ecosystem types. Forests have a lower CUE than grasslands and croplands. Evergreen needleleaf forests have a higher CUE than other forest types. Climate factors (mean annual temperature (MAT), precipitation (MAP) and the index of water availability (IWA)) dominantly regulated the spatial variations of CUE. The CUE exhibited a linear decrease with enhanced MAT and MAP and a parabolic response to the IWA. Furthermore, the responses of CUE to environmental change varied with individual ecosystem type. In contrast, precipitation exerted strong control on CUE in grassland, while in forest and cropland, the CUE was mainly controlled by the available water. This study identifies the variations and response of CUE to environmental drivers in China, which will be valuable for the regional assessment of carbon cycling dynamics under future climate change.

A Forest Model Intercomparison Framework and Application at Two Temperate Forests Along the East Coast of the United States

State-of-the-art forest models are often complex, analytically intractable, and computationally expensive, due to the explicit representation of detailed biogeochemical and ecological processes. Different models often produce distinct results while predictions from the same model vary with parameter values. In this project, we developed a rigorous quantitative approach for conducting model intercomparisons and assessing model performance. We have applied our original methodology to compare two forest biogeochemistry models, the Perfect Plasticity Approximation with Simple Biogeochemistry (PPA-SiBGC) and Landscape Disturbance and Succession with Net Ecosystem Carbon and Nitrogen (LANDIS-II NECN). We simulated past-decade conditions at flux tower sites located within Harvard Forest, MA, USA (HF-EMS) and Jones Ecological Research Center, GA, USA (JERC-RD). We mined field data available from both sites to perform model parameterization, validation, and intercomparison. We assessed model performance using the following time-series metrics: Net ecosystem exchange, aboveground net primary production, aboveground biomass, C, and N, belowground biomass, C, and N, soil respiration, and species total biomass and relative abundance. We also assessed static observations of soil organic C and N, and concluded with an assessment of general model usability, performance, and transferability. Despite substantial differences in design, both models achieved good accuracy across the range of pool metrics. While LANDIS-II NECN showed better fidelity to interannual NEE fluxes, PPA-SiBGC indicated better overall performance for both sites across the 11 temporal and two static metrics tested (HF-EMS R 2 ¯ = 0.73 , + 0.07 , R M S E ¯ = 4.68 , − 9.96 ; JERC-RD R 2 ¯ = 0.73 , + 0.01 , R M S E ¯ = 2.18 , − 1.64 ). To facilitate further testing of forest models at the two sites, we provide pre-processed datasets and original software written in the R language of statistical computing. In addition to model intercomparisons, our approach may be employed to test modifications to forest models and their sensitivity to different parameterizations.

Underestimates of Grassland Gross Primary Production in MODIS Standard Products

As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPPMOD) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPPEC) from a global network of eddy covariance towers (FluxNet), we compared GPPMOD and GPPEC at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPPMOD in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (R2), and root mean square errors (RMSE). We found that GPPMOD generally underestimated GPP by about 34% across all biomes despite a significant relationship (R2 = 0.66 (CI, 0.63–0.69), RMSE = 2.46 (2.33–2.58) g Cm−2 day−1) for the three grassland biomes. GPPMOD had varied performances with R2 values of 0.72 (0.68–0.75) (temperate), 0.64 (0.59–0.68) (alpine), and 0.40 (0.27–0.52) (tropical). Thus, GPPMOD performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPPMOD underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (εmax) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in εmax, FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes.

Decadal Variations in Eastern Canada's Taiga Wood Biomass Production Forced by Ocean-Atmosphere Interactions

Across Eastern Canada (EC), taiga forests represent an important carbon reservoir, but the extent to which climate variability affects this ecosystem over decades remains uncertain. Here, we analyze an extensive network of black spruce (Picea mariana Mill.) ring width and wood density measurements and provide new evidence that wood biomass production is influenced by large-scale, internal ocean-atmosphere processes. We show that while black spruce wood biomass production is primarily governed by growing season temperatures, the Atlantic ocean conveys heat from the subtropics and influences the decadal persistence in taiga forests productivity. Indeed, we argue that 20-30 years periodicities in Sea Surface Temperatures (SSTs) as part of the the Atlantic Multi-decadal Oscillation (AMO) directly influence heat transfers to adjacent lands. Winter atmospheric conditions associated with the North Atlantic Oscillation (NAO) might also impact EC's taiga forests, albeit indirectly, through its effect on SSTs and sea ice conditions in surrounding seas. Our work emphasizes that taiga forests would benefit from the combined effects of a warmer atmosphere and stronger ocean-to-land heat transfers, whereas a weakening of these transfers could cancel out, for decades or longer, the positive effects of climate change on Eastern Canada's largest ecosystem.

Regional atmospheric cooling and wetting effect of permafrost thaw-induced boreal forest loss

In the sporadic permafrost zone of North America, thaw-induced boreal forest loss is leading to permafrost-free wetland expansion. These land cover changes alter landscape-scale surface properties with potentially large, however, still unknown impacts on regional climates. In this study, we combine nested eddy covariance flux tower measurements with satellite remote sensing to characterize the impacts of boreal forest loss on albedo, eco-physiological and aerodynamic surface properties, and turbulent energy fluxes of a lowland boreal forest region in the Northwest Territories, Canada. Planetary boundary layer modelling is used to estimate the potential forest loss impact on regional air temperature and atmospheric moisture. We show that thaw-induced conversion of forests to wetlands increases albedo: and bulk surface conductance for water vapour and decreases aerodynamic surface temperature. At the same time, heat transfer efficiency is reduced. These shifts in land surface properties increase latent at the expense of sensible heat fluxes, thus, drastically reducing Bowen ratios. Due to the lower albedo of forests and their masking effect of highly reflective snow, available energy is lower in wetlands, especially in late winter. Modelling results demonstrate that a conversion of a present-day boreal forest-wetland to a hypothetical homogeneous wetland landscape could induce a near-surface cooling effect on regional air temperatures of up to 3-4 °C in late winter and 1-2 °C in summer. An atmospheric wetting effect in summer is indicated by a maximum increase in water vapour mixing ratios of 2 mmol mol^-1 . At the same time, maximum boundary layer heights are reduced by about a third of the original height. In fall, simulated air temperature and atmospheric moisture between the two scenarios do not differ. Therefore, permafrost thaw-induced boreal forest loss may modify regional precipitation patterns and slow down regional warming trends.

Estimation of gross primary production of irrigated maize using Landsat-8 imagery and Eddy Covariance data

A study was conducted to understand the potential of Landsat-8 in the estimation of gross primary production (GPP) and to quantify the productivity of maize crop cultivated under hyper-arid conditions of Saudi Arabia. The GPP of maize crop was estimated by using the Vegetation Photosynthesis Model (VPM) utilizing remote sensing data from Landsat-8 reflectance (GPP_VPM) as well as the meteorological data provided by Eddy Covariance (EC) system (GPP_EC), for the period from August to November 2015. Results revealed that the cumulative GPP_EC for the entire growth period of maize crop was 1871 g C m^-2. However, the cumulative GPP determined as a function of the enhanced vegetation index - EVI (GPP_EVI) was 1979 g C m^-2, and that determined as a function of the normalized difference vegetation index - NDVI (GPP_NDVI) was 1754 g C m^-2. These results indicated that the GPP_EVI was significantly higher than the GPP_EC (R² = 0.96, P = 0.0241 and RMSE = 12.6%). While, the GPP_NDVI was significantly lower than the GPP_EC (R² = 0.93, P = 0.0384 and RMSE = 19.7%). However, the recorded relative error between the GPP_EC and both the GPP_EVI and the GPP_NDVI was -6.22% and 5.76%, respectively. These results demonstrated the potential of the landsat-8 driven VPM model for the estimation of GPP, which is relevant to the productivity and carbon fluxes.

Needle age and season influence photosynthetic temperature response and total annual carbon uptake in mature Picea mariana trees

BACKGROUND AND AIMS

The carbon (C) balance of boreal terrestrial ecosystems is sensitive to increasing temperature, but the direction and thresholds of responses are uncertain. Annual C uptake in Picea and other evergreen boreal conifers is dependent on seasonal- and cohort-specific photosynthetic and respiratory temperature response functions, so this study examined the physiological significance of maintaining multiple foliar cohorts for Picea mariana trees within an ombrotrophic bog ecosystem in Minnesota, USA.

METHODS

Measurements were taken on multiple cohorts of needles for photosynthetic capacity, foliar respiration (Rd) and leaf biochemistry and morphology of mature trees from April to October over 4 years. The results were applied to a simple model of canopy photosynthesis in order to simulate annual C uptake by cohort age under ambient and elevated temperature scenarios.

KEY RESULTS

Temperature responses of key photosynthetic parameters [i.e. light-saturated rate of CO2 assimilation (Asat), rate of Rubisco carboxylation (Vcmax) and electron transport rate (Jmax)] were dependent on season and generally less responsive in the developing current-year (Y0) needles compared with 1-year-old (Y1) or 2-year-old (Y2) foliage. Temperature optimums ranged from 18·7 to 23·7, 31·3 to 38·3 and 28·7 to 36·7 °C for Asat, Vcmax and Jmax, respectively. Foliar cohorts differed in their morphology and photosynthetic capacity, which resulted in 64 % of modelled annual stand C uptake from Y1&2 cohorts (LAI 0·67 m(2 )m(-2)) and just 36 % from Y0 cohorts (LAI 0·52 m(2 )m(-2)). Under warmer climate change scenarios, the contribution of Y0 cohorts was even less; e.g. 31 % of annual C uptake for a modelled 9 °C rise in mean summer temperatures. Results suggest that net annual C uptake by P. mariana could increase under elevated temperature, and become more dependent on older foliar cohorts.

CONCLUSIONS

Collectively, this study illustrates the physiological and ecological significance of different foliar cohorts, and indicates the need for seasonal- and cohort-specific model parameterization when estimating C uptake capacity of boreal forest ecosystems under ambient or future temperature scenarios.

Importance of natural cloud regimes to ecophysiology in the alpine species, Caltha leptosepala and Arnica parryi, Snowy Range Mountains, southeast Wyoming, USA

The south-central Rocky Mountains, USA, are characterised by a dry, continental mesoclimate with typical convective cloud formation during the afternoon. Little is known about the specific influence of such predictable cloud patterns on the microclimate and ecophysiology of associated species. During the summer of 2012, days with afternoon clouds were most common (50% of all days) compared with completely clear (24%) or cloudy days (6.5%). In two representative alpine species, Caltha leptosepala DC. and Arnica parryi A. Gray, fully overcast days reduced mean daily photosynthesis (A) by nearly 50% relative to fully clear days. Mean afternoon A was significantly lower on fully cloudy days relative to days with afternoon clouds only or no clouds in both species. Notably, A did not differ during afternoon cloud days relative to clear afternoons. Afternoon clouds significantly reduced transpiration (E) in C. leptosepala relative to clear days, and both species showed mean reductions in plant water stress (i.e. higher Ψ), though this difference was not significant. Water use efficiency (WUE) (A/E) decreased from morning to afternoon, especially on cloudy days, and the presence of clouds had a positive effect on the light reactions of photosynthesis based on fluorescence measurements (Fv'/Fm'), in both species. Cloudy days were characterised by higher Fv/Fm than afternoon clouds and clear days during both the morning and the afternoon (especially for A. parryi) and recovery to near pre-dawn values for cloudy and afternoon cloud day types, but not clear days. Overall, similar ecophysiological advantages of this typical afternoon cloud pattern was apparent in both species, although their spatial microsite differences related to winter snow accumulation may also play an important role.

Controls on seasonal patterns of maximum ecosystem carbon uptake and canopy-scale photosynthetic light response: contributions from both temperature and photoperiod

Most models of photosynthetic activity assume that temperature is the dominant control over physiological processes. Recent studies have found, however, that photoperiod is a better descriptor than temperature of the seasonal variability of photosynthetic physiology at the leaf scale. Incorporating photoperiodic control into global models consequently improves their representation of the seasonality and magnitude of atmospheric CO2 concentration. The role of photoperiod versus that of temperature in controlling the seasonal variability of photosynthetic function at the canopy scale remains unexplored. We quantified the seasonal variability of ecosystem-level light response curves using nearly 400 site years of eddy covariance data from over eighty Free Fair-Use sites in the FLUXNET database. Model parameters describing maximum canopy CO2 uptake and the initial slope of the light response curve peaked after peak temperature in about 2/3 of site years examined, emphasizing the important role of temperature in controlling seasonal photosynthetic function. Akaike's Information Criterion analyses indicated that photoperiod should be included in models of seasonal parameter variability in over 90% of the site years investigated here, demonstrating that photoperiod also plays an important role in controlling seasonal photosynthetic function. We also performed a Granger causality analysis on both gross ecosystem productivity (GEP) and GEP normalized by photosynthetic photon flux density (GEP n ). While photoperiod Granger-caused GEP and GEP n in 99 and 92% of all site years, respectively, air temperature Granger-caused GEP in a mere 32% of site years but Granger-caused GEP n in 81% of all site years. Results demonstrate that incorporating photoperiod may be a logical step toward improving models of ecosystem carbon uptake, but not at the expense of including enzyme kinetic-based temperature constraints on canopy-scale photosynthesis.

Climatic control of tracheid production of black spruce in dense mesic stands of eastern Canada

Inferences on climate change effects are reliable only if they are based on a causal relationship rather than simple statistical predictive capacity. To assess for causal links between climate and mature black spruce (Picea mariana (Mills.) BSP) radial growth, we combined the use of wood anatomy, cambium phenology, climate and soil measurements (air temperature and humidity, precipitations, soil temperature and water content, photosynthetically active radiation), and a model selection approach proceeding backwards from a full model. Results show that the number of tracheids is responsible for 88% of the variation in ring width whereas mean tracheid diameter accounts for the remaining 12%. The number of tracheids produced depends on factors related to photosynthesis during tracheid production, i.e., daily light intensity and maximum temperature between the day of initiation and the day of cessation of tracheid production, plus soil temperature during August of the previous year which is an important period for determining the number of new needles produced. It is also important to consider duration of the period for tracheid production. These results imply that short-term climate change should increase black spruce radial growth. They also suggest that the typical use of post-growth ring width sampling individually linked to air temperature and precipitations is not sufficient to infer climate change effects accurately on radial growth where there is no strong single climatic limitation but multiple limitations instead.

Increased resin flow in mature pine trees growing under elevated CO2 and moderate soil fertility

Warmer climates induced by elevated atmospheric CO(2) (eCO(2)) are expected to increase damaging bark beetle activity in pine forests, yet the effect of eCO(2) on resin production--the tree's primary defense against beetle attack--remains largely unknown. Following growth-differentiation balance theory, if extra carbohydrates produced under eCO(2) are not consumed by respiration or growth, resin production could increase. Here, the effect of eCO(2) on resin production of mature pines is assessed. As predicted, eCO(2) enhanced resin flow by an average of 140% (P=0.03) in canopy dominants growing in low-nitrogen soils, but did not affect resin flow in faster-growing fertilized canopy dominants or in carbohydrate-limited suppressed individuals. Thus, pine trees may become increasingly protected from bark beetle attacks in an eCO(2) climate, except where they are fertilized or are allowed to become overcrowded.

Constraining ecosystem processes from tower fluxes and atmospheric profiles

The planetary boundary layer (PBL) provides an important link between the scales and processes resolved by global atmospheric sampling/modeling and site-based flux measurements. The PBL is in direct contact with the land surface, both driving and responding to ecosystem processes. Measurements within the PBL (e.g., by radiosondes, aircraft profiles, and flask measurements) have a footprint, and thus an integrating scale, on the order of 1-100 km. We use the coupled atmosphere-biosphere model (CAB) and a Bayesian data assimilation framework to investigate the amount of biosphere process information that can be inferred from PBL measurements. We investigate the information content of PBL measurements in a two-stage study. First, we demonstrate consistency between the coupled model (CAB) and measurements, by comparing the model to eddy covariance flux tower measurements (i.e., water and carbon fluxes) and also PBL scalar profile measurements (i.e., water, carbon dioxide, and temperature) from Canadian boreal forest. Second, we use the CAB model in a set of Bayesian inversions experiments using synthetic data for a single day. In the synthetic experiment, leaf area and respiration were relatively well constrained, whereas surface albedo and plant hydraulic conductance were only moderately constrained. Finally, the abilities of the PBL profiles and the eddy covariance data to constrain the parameters were largely similar and only slightly lower than the combination of both observations.

Carbon dioxide exchange in a cool-temperate evergreen coniferous forest over complex topography in Japan during two years with contrasting climates

We investigated carbon dioxide (CO(2)) exchange and its environmental response during two years with contrasting climate (2006 and 2007) in a cool-temperate mixed evergreen coniferous forest dominated by Japanese cedar (Cryptomeria japonica) and Japanese cypress (Chamaecyparis obtusa). The study, which was conducted in a mountainous region of central Japan, used the eddy-covariance technique. Our results (crosschecked using the common u (*) approach and van Gorsel's alternative approach) showed that annual gross primary production (GPP) and ecosystem respiration (RE) were at least 6% higher in the dry year than in the wet year, whereas net ecosystem exchange (NEE) was similar in both years. Without soil water stress, strong light stress or seasonality of plant area index during most of the study period, the forest had high metabolic activity. GPP and RE differed greatly between the two years, especially in spring (April-May) and summer (July-September), respectively. The spring GPP difference (>20%) was influenced by different winter air temperatures and snow melt timing, which controlled photosynthetic capacity in spring, and by different spring light intensities. The annual NEE differed depending on the evaluation method used, but the mean 2-year NEE estimated by the u (*) threshold approach [-3.39 +/- 0.11 (SD) MgC ha(-1) year(-1)] appears more reasonable in comparison with results from other forests.

Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter

Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter performance, but it did not improve performance when used individually. The EnKF estimates of leaf area followed the expected springtime canopy phenology. However, there were also diel fluctuations in the leaf-area estimates; these are a clear indication of a model deficiency possibly related to vapor pressure effects on canopy conductance.

Ecosystem warming does not affect photosynthesis or aboveground autotrophic respiration for boreal black spruce

We measured light-saturated photosynthesis (A(net)), foliage respiration (R(fol)) and stem respiration (R(stem)) of black spruce (Picea mariana (Mill.) B.S.P.) in heated (+5 degrees C) and control plots during 2005, 2006 and 2007 in Thompson, MB, Canada. Large greenhouses and soil-heating cables were used to maintain air and soil temperature 5 degrees C above ambient air and soil temperature. Each greenhouse contained approximately nine black spruce trees and the majority of their fine root mass. Treatments were soil and air warming, soil-only warming, greenhouses maintained at ambient air temperature and control. Gas exchange rates ranged 0.71-4.66, 0.04-0.74 and 0.1-1.0 micromol m(-)(2) s(-)(1) for A(net), R(fol) and R(stem), respectively. Treatment differences for A(net), R(fol) and R(stem) were not significant in any of the 3 years of measurements. The results of this experiment suggest that in a warmer climate, black spruce may not have significant changes in the rate of photosynthesis or respiration.

Landscape heterogeneity, soil climate, and carbon exchange in a boreal black spruce forest

We measured soil climate and the turbulent fluxes of CO2, H2O, heat, and momentum on short towers (2 m) in a 160-yr-old boreal black spruce forest in Manitoba, Canada. Two distinct land cover types were studied: a Sphagnum-dominated wetland, and a feathermoss (Pleurozium and Hylocomium)-dominated upland, both lying within the footprint of a 30-m tower, which has measured whole-forest carbon exchange since 1994. Peak summertime uptake of CO2, was higher in the wetland than for the forest as a whole due to the influence of deciduous shrubs. Soil respiration rates in the wetland were approximately three times larger than in upland soils, and 30% greater than the mean of the whole forest, reflecting decomposition of soil organic matter. Soil respiration rates in the wetland were regulated by soil temperature, which was in turn influenced by water table depth through effects on soil heat capacity and conductivity. Warmer soil temperatures and deeper water tables favored increased heterotrophic respiration. Wetland drainage was limited by frost during the first half of the growing season, leading to high, perched water tables, cool soil temperatures, and much lower respiration rates than observed later in the growing season. Whole-forest evapotranspiration increased as water tables dropped, suggesting that photosynthesis in this forest was rarely subject to water stress. Our data indicate positive feedback between soil temperature, seasonal thawing, heterotrophic respiration, and evapotranspiration. As a result, climate warming could cause covariant changes in soil temperature and water table depths that may stimulate photosynthesis and strongly promote efflux of CO2 from peat soils in boreal wetlands.

Are ecosystem carbon inputs and outputs coupled at short time scales? A case study from adjacent pine and hardwood forests using impulse-response analysis

A number of recent studies have attributed a large proportion of soil respiration (R(soil)) to recently photoassimilated carbon (C). Time lags (tau(PR)) associated with these pulses of photosynthesis and responses of R(soil) have been found on time scales of hours to weeks for different ecosystems, but most studies find evidence for tau(PR) on the order of 1-5 d. We showed that such time scales are commensurate with CO(2) diffusion time scales from the roots to the soil surface, and may thus be independent from photosynthetic pulses. To further quantify the role of physical (i.e. edaphic) and biological (i.e. vegetative) controls on such lags, we investigated tau(PR) at adjacent planted pine (PP) and hardwood (HW) forest ecosystems over six and four measurement years, respectively, using both autocorrelation analysis on automated soil surface flux measurements and their lagged cross-correlations with drivers for and surrogates of photosynthesis. Evidence for tau(PR) on the order of 1-3 d was identified in both ecosystems and using both analyses, but this lag could not be attributed to recently photoassimilated C because the same analysis yielded comparable lags at HW during leaf-off periods. Future efforts to model ecosystem C inputs and outputs in a pulse-response framework must combine measurements of transport in the physical and biological components of terrestrial ecosystems.

Using continuous stable isotope measurements to partition net ecosystem CO2 exchange

Ecosystem-scale estimation of photosynthesis and respiration using micrometeorological techniques remains an important, yet difficult, challenge. In this study, we combined micrometeorological and stable isotope methods to partition net ecosystem CO2 exchange (FN) into photosynthesis (F(A)) and respiration (F(R)) in a corn-soybean rotation ecosystem during the summer 2003 corn phase. Mixing ratios of (12)CO2 and (13)CO2 were measured continuously using tunable diode laser (TDL) absorption spectroscopy. The dynamics of the isotope ratio of ecosystem respiration (R), net ecosystem CO2 exchange (deltaN) and photosynthetic discrimination at the canopy scale (delta canopy) were examined. During the period of full canopy closure, F(N) was partitioned into photosynthesis and respiration using both the isotopic approach and the conventional night-time-derived regression methodology. Results showed that deltaR had significant seasonal variation (-32 to -11% per hundred) corresponding closely with canopy phenology. Daytime deltaN typically varied from -12 to -4% per hundred, while delta canopy remained relatively constant in the vicinity of 3% per hundred. Compared with the regression approach, the isotopic flux partitioning showed more short-term variations and was considerably more symmetric about F(N). In this experiment, the isotopic partitioning resulted in larger uncertainties, most of which were caused by the uncertainties in deltaN. and the daytime estimate of deltaR. By sufficiently reducing these uncertainties, the tunable diode laser (TDL)-micrometeorological technique should yield a better understanding of the processes controlling photosynthesis, respiration and ecosystem-scale discrimination.