M. Altaf Arain

Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate

Carbon Cycle and Climate Change As climate change accelerates, it is important to know the likely impact of climate change on the carbon cycle (see the Perspective by Reich). Gross primary production (GPP) is a measure of the amount of CO2 removed from the atmosphere every year to fuel photosynthesis. Beer et al. (p. 834, published online 5 July) used a combination of observation and calculation to estimate that the total GPP by terrestrial plants is around 122 billion tons per year; in comparison, burning fossil fuels emits about 7 billion tons annually. Thirty-two percent of this uptake occurs in tropical forests, and precipitation controls carbon uptake in more than 40% of vegetated land. The temperature sensitivity (Q10) of ecosystem respiratory processes is a key determinant of the interaction between climate and the carbon cycle. Mahecha et al. (p. 838, published online 5 July) now show that the Q10 of ecosystem respiration is invariant with respect to mean annual temperature, independent of the analyzed ecosystem type, with a global mean value for Q10 of 1.6. This level of temperature sensitivity suggests a less-pronounced climate sensitivity of the carbon cycle than assumed by recent climate models. A combination of data and models provides an estimate of how much photosynthesis by all the world’s plants occurs each year. Terrestrial gross primary production (GPP) is the largest global CO2 flux driving several ecosystem functions. We provide an observation-based estimate of this flux at 123 ± 8 petagrams of carbon per year (Pg C year−1) using eddy covariance flux data and various diagnostic models. Tropical forests and savannahs account for 60%. GPP over 40% of the vegetated land is associated with precipitation. State-of-the-art process-oriented biosphere models used for climate predictions exhibit a large between-model variation of GPP’s latitudinal patterns and show higher spatial correlations between GPP and precipitation, suggesting the existence of missing processes or feedback mechanisms which attenuate the vegetation response to climate. Our estimates of spatially distributed GPP and its covariation with climate can help improve coupled climate–carbon cycle process models.

Global patterns of land‐atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations

We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.

Global Convergence in the Temperature Sensitivity of Respiration at Ecosystem Level

Carbon Cycle and Climate Change As climate change accelerates, it is important to know the likely impact of climate change on the carbon cycle (see the Perspective by Reich). Gross primary production (GPP) is a measure of the amount of CO2 removed from the atmosphere every year to fuel photosynthesis. Beer et al. (p. 834, published online 5 July) used a combination of observation and calculation to estimate that the total GPP by terrestrial plants is around 122 billion tons per year; in comparison, burning fossil fuels emits about 7 billion tons annually. Thirty-two percent of this uptake occurs in tropical forests, and precipitation controls carbon uptake in more than 40% of vegetated land. The temperature sensitivity (Q10) of ecosystem respiratory processes is a key determinant of the interaction between climate and the carbon cycle. Mahecha et al. (p. 838, published online 5 July) now show that the Q10 of ecosystem respiration is invariant with respect to mean annual temperature, independent of the analyzed ecosystem type, with a global mean value for Q10 of 1.6. This level of temperature sensitivity suggests a less-pronounced climate sensitivity of the carbon cycle than assumed by recent climate models. The long-standing discrepancy between modeled and empirical measures of ecosystem temperature sensitivity is resolved. The respiratory release of carbon dioxide (CO2) from the land surface is a major flux in the global carbon cycle, antipodal to photosynthetic CO2 uptake. Understanding the sensitivity of respiratory processes to temperature is central for quantifying the climate–carbon cycle feedback. We approximated the sensitivity of terrestrial ecosystem respiration to air temperature (Q10) across 60 FLUXNET sites with the use of a methodology that circumvents confounding effects. Contrary to previous findings, our results suggest that Q10 is independent of mean annual temperature, does not differ among biomes, and is confined to values around 1.4 ± 0.1. The strong relation between photosynthesis and respiration, by contrast, is highly variable among sites. The results may partly explain a less pronounced climate–carbon cycle feedback than suggested by current carbon cycle climate models.

A cohort study of traffic-related air pollution and mortality in Toronto, Ontario, Canada.

Background Chronic exposure to traffic-related air pollution (TRAP) may contribute to premature mortality, but few studies to date have addressed this topic. Objectives In this study we assessed the association between TRAP and mortality in Toronto, Ontario, Canada. Methods We collected nitrogen dioxide samples over two seasons using duplicate two-sided Ogawa passive diffusion samplers at 143 locations across Toronto. We calibrated land use regressions to predict NO2 exposure on a fine scale within Toronto. We used interpolations to predict levels of particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) and ozone levels. We assigned predicted pollution exposures to 2,360 subjects from a respiratory clinic, and abstracted health data on these subjects from medical billings, lung function tests, and diagnoses by pulmonologists. We tracked mortality between 1992 and 2002. We used standard and multilevel Cox proportional hazard models to test associations between air pollution and mortality. Results After controlling for age, sex, lung function, obesity, smoking, and neighborhood deprivation, we observed a 17% increase in all-cause mortality and a 40% increase in circulatory mortality from an exposure contrast across the interquartile range of 4 ppb NO2. We observed no significant associations with other pollutants. Conclusions Exposure to TRAP was significantly associated with increased all-cause and circulatory mortality in this cohort. A high prevalence of cardiopulmonary disease in the cohort probably limits inference of the findings to populations with a substantial proportion of susceptible individuals.

Impacts of peat and vegetation on permafrost degradation under climate warming

[1] Simulations of maximum annual thaw at a continuous and a discontinuous permafrost site in Canada were performed using Community Land Model version 3 (CLM3) and randomized historical climate records from these sites, superimposed with United Nations Intergovernmental Panel on Climate Change (IPCC), Report on Emission Scenarios (SRES) A2 scenario of climate change. A positive trend in permafrost degradation was simulated for the 2000 to 2100 period in response to climate warming. Surface cover condition and soil properties play a dominant role in affecting ground thaw. In particular, a thin peat layer or surface organic cover can significantly buffer the permafrost against severe degradation. The occurrence of vegetation and extensive presence of a peat and organic layer in the circumpolar areas will modulate the regional impact of climate warming on permafrost thaw.

The association between chronic exposure to traffic-related air pollution and ischemic heart disease.

Increasing evidence links air pollution to the risk of cardiovascular disease. This study investigated the association between ischemic heart disease (IHD) prevalence and exposure to traffic-related air pollution (nitrogen dioxide [NO2], fine particulate matter [PM2.5], and ozone [O3]) in a population of susceptible subjects in Toronto. Local (NO2) exposures were modeled using land use regression based on extensive field monitoring. Regional exposures (PM2.5, O3) were modeled as confounders using inverse distance weighted interpolation based on government monitoring data. The study sample consisted of 2360 patients referred during 1992 to 1999 to a pulmonary clinic at the Toronto Western Hospital in Toronto, Ontario, Canada, to diagnose or manage a respiratory complaint. IHD status was determined by clinical database linkages (ICD-9-CM 412–414). The association between IHD and air pollutants was assessed with a modified Poisson regression resulting in relative risk estimates. Confounding was controlled with individual and neighborhood-level covariates. After adjusting for multiple covariates, NO2 was significantly associated with increased IHD risk, relative risk (RR) = 1.33 (95% confidence interval [CI]: 1.2, 1.47). Subjects living near major roads and highways had a trend toward an elevated risk of IHD, RR = 1.08 (95% CI: 0.99, 1.18). Regional PM2.5 and O3 were not associated with risk of IHD.

Thermal optimality of net ecosystem exchange of carbon dioxide and underlying mechanisms.

• It is well established that individual organisms can acclimate and adapt to temperature to optimize their functioning. However, thermal optimization of ecosystems, as an assemblage of organisms, has not been examined at broad spatial and temporal scales. • Here, we compiled data from 169 globally distributed sites of eddy covariance and quantified the temperature response functions of net ecosystem exchange (NEE), an ecosystem-level property, to determine whether NEE shows thermal optimality and to explore the underlying mechanisms. • We found that the temperature response of NEE followed a peak curve, with the optimum temperature (corresponding to the maximum magnitude of NEE) being positively correlated with annual mean temperature over years and across sites. Shifts of the optimum temperature of NEE were mostly a result of temperature acclimation of gross primary productivity (upward shift of optimum temperature) rather than changes in the temperature sensitivity of ecosystem respiration. • Ecosystem-level thermal optimality is a newly revealed ecosystem property, presumably reflecting associated evolutionary adaptation of organisms within ecosystems, and has the potential to significantly regulate ecosystem-climate change feedbacks. The thermal optimality of NEE has implications for understanding fundamental properties of ecosystems in changing environments and benchmarking global models.

Modifications of a land surface scheme for improved simulation of ground freeze-thaw in northern environments

[1] Freezing and thawing front (FTF) depths have implications for surface and subsurface exchanges of energy and water, vegetation growth and organic matter decomposition. Long-term changes in FTF depths are an important indicator of climate change. The FTF is seldom represented explicitly in land surface schemes, but the 0°C isotherm is used as a surrogate for the front. However, when multiple FTFs occur within a soil column or when soil temperature hovers around the freezing point in the spring, the simulated 0°C isotherm exhibits large fluctuations though in reality, the fronts develop rather smoothly. To explicitly simulate the FTF depths, this study couples a Two-Direction Stefan Algorithm (TDSA) in the Community Land Model 3 (CLM3). Several modifications are also introduced to adapt the CLM3 to the northern region, including the addition of a peat cover to the soil column, retention of a minimum unfrozen water content in the frozen soil, and implementation of canopy heat storage. The modified scheme was tested using field data from a boreal forest site. The TDSA enables the simulated FTF to be defined properly. Sensitivity tests demonstrate that the modified scheme (addition of a peat cover, unfrozen water and canopy heat storage) greatly improves the match between the simulated fronts and the 0°C isotherm derived from measured soil temperatures. These modifications and the coupling of the TDSA are applicable to other lands surface schemes for the simulation of ground frost in the cold regions.

Linking variability in soil solution dissolved organic carbon to climate, soil type, and vegetation type

Lateral transport of carbon plays an important role in linking the carbon cycles of terrestrial and aquatic ecosystems. There is, however, a lack of information on the factors controlling one of the main C sources of this lateral flux, i.e., the concentration of dissolved organic carbon (DOC) in soil solution across large spatial scales and under different soil, vegetation, and climate conditions. We compiled a database on DOC in soil solution down to 80 cm and analyzed it with the aim, first, to quantify the differences in DOC concentrations among terrestrial ecosystems, climate zones, soil, and vegetation types at global scale and second, to identify potential determinants of the site-to-site variability of DOC concentration in soil solution across European broadleaved and coniferous forests. We found that DOC concentrations were 75% lower in mineral than in organic soil, and temperate sites showed higher DOC concentrations than boreal and tropical sites. The majority of the variation (R2 = 0.67–0.99) in DOC concentrations in mineral European forest soils correlates with NH4+, C/N, Al, and Fe as the most important predictors. Overall, our results show that the magnitude (23% lower in broadleaved than in coniferous forests) and the controlling factors of DOC in soil solution differ between forest types, with site productivity being more important in broadleaved forests and water balance in coniferous stands.

What eddy-covariance measurements tell us about prior land flux errors in CO2-flux inversion schemes

To guide the future development of CO2-atmospheric inversion modeling systems, we analyzed the errors arising from prior information about terrestrial ecosystem fluxes. We compared the surface fluxes calculated by a process-based terrestrial ecosystem model with daily averages of CO2 flux measurements at 156 sites across the world in the FLUXNET network. At the daily scale, the standard deviation of the model-data fit was 2.5 gC*m−2*d−1; temporal autocorrelations were significant at the weekly scale (>0.3 for lags less than four weeks), while spatial correlations were confined to within the first few hundred kilometers (<0.2 after 200 km). Separating out the plant functional types did not increase the spatial correlations, except for the deciduous broad-leaved forests. Using the statistics of the flux measurements as a proxy for the statistics of the prior flux errors was shown not to be a viable approach. A statistical model allowed us to upscale the site-level flux error statistics to the coarser spatial and temporal resolutions used in regional or global models. This approach allowed us to quantify how aggregation reduces error variances, while increasing correlations. As an example, for a typical inversion of grid point (300 km × 300 km) monthly fluxes, we found that the prior flux error follows an approximate e-folding correlation length of 500 km only, with correlations from one month to the next as large as 0.6.