Effect of climate warming on the annual terrestrial net ecosystem CO2 exchange globally in the boreal and temperate regions

The net ecosystem CO2 exchange is the result of the imbalance between the assimilation process (gross primary production, GPP) and ecosystem respiration (RE). The aim of this study was to investigate temperature sensitivities of these processes and the effect of climate warming on the annual terrestrial net ecosystem CO2 exchange globally in the boreal and temperate regions. A database of 403 site-years of ecosystem flux data at 101 sites in the world was collected and analyzed. Temperature sensitivities of rates of RE and GPP were quantified with Q 10, defined as the increase of RE (or GPP) rates with a temperature rise of 10 °C. Results showed that on the annual time scale, the intrinsic temperature sensitivity of GPP (Q 10sG ) was higher than or equivalent to the intrinsic temperature sensitivity of RE (Q 10sR ). Q 10sG was negatively correlated to the mean annual temperature (MAT), whereas Q 10sR was independent of MAT. The analysis of the current temperature sensitivities and net ecosystem production suggested that temperature rise might enhance the CO2 sink of terrestrial ecosystems both in the boreal and temperate regions. In addition, ecosystems in these regions with different plant functional types should sequester more CO2 with climate warming.

Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake

Terrestrial ecosystems play a significant role in the global carbon cycle and offset a large fraction of anthropogenic CO₂ emissions. The terrestrial carbon sink is increasing, yet the mechanisms responsible for its enhancement, and implications for the growth rate of atmospheric CO₂, remain unclear. Here using global carbon budget estimates, ground, atmospheric and satellite observations, and multiple global vegetation models, we report a recent pause in the growth rate of atmospheric CO₂, and a decline in the fraction of anthropogenic emissions that remain in the atmosphere, despite increasing anthropogenic emissions. We attribute the observed decline to increases in the terrestrial sink during the past decade, associated with the effects of rising atmospheric CO₂ on vegetation and the slowdown in the rate of warming on global respiration. The pause in the atmospheric CO₂ growth rate provides further evidence of the roles of CO₂ fertilization and warming-induced respiration, and highlights the need to protect both existing carbon stocks and regions, where the sink is growing rapidly.

Diagnosing the Dynamics of Observed and Simulated Ecosystem Gross Primary Productivity with Time Causal Information Theory Quantifiers

Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. We demonstrate here that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide data-analytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.

Permafrost carbon-climate feedback is sensitive to deep soil carbon decomposability but not deep soil nitrogen dynamics

Permafrost soils contain enormous amounts of organic carbon whose stability is contingent on remaining frozen. With future warming, these soils may release carbon to the atmosphere and act as a positive feedback to climate change. Significant uncertainty remains on the postthaw carbon dynamics of permafrost-affected ecosystems, in particular since most of the carbon resides at depth where decomposition dynamics may differ from surface soils, and since nitrogen mineralized by decomposition may enhance plant growth. Here we show, using a carbon-nitrogen model that includes permafrost processes forced in an unmitigated warming scenario, that the future carbon balance of the permafrost region is highly sensitive to the decomposability of deeper carbon, with the net balance ranging from 21 Pg C to 164 Pg C losses by 2300. Increased soil nitrogen mineralization reduces nutrient limitations, but the impact of deep nitrogen on the carbon budget is small due to enhanced nitrogen availability from warming surface soils and seasonal asynchrony between deeper nitrogen availability and plant nitrogen demands. Although nitrogen dynamics are highly uncertain, the future carbon balance of this region is projected to hinge more on the rate and extent of permafrost thaw and soil decomposition than on enhanced nitrogen availability for vegetation growth resulting from permafrost thaw.

Influence of physiological phenology on the seasonal pattern of ecosystem respiration in deciduous forests

Understanding the environmental and biotic drivers of respiration at the ecosystem level is a prerequisite to further improve scenarios of the global carbon cycle. In this study we investigated the relevance of physiological phenology, defined as seasonal changes in plant physiological properties, for explaining the temporal dynamics of ecosystem respiration (RECO) in deciduous forests. Previous studies showed that empirical RECO models can be substantially improved by considering the biotic dependency of RECO on the short-term productivity (e.g., daily gross primary production, GPP) in addition to the well-known environmental controls of temperature and water availability. Here, we use a model-data integration approach to investigate the added value of physiological phenology, represented by the first temporal derivative of GPP, or alternatively of the fraction of absorbed photosynthetically active radiation, for modeling RECO at 19 deciduous broadleaved forests in the FLUXNET La Thuile database. The new data-oriented semiempirical model leads to an 8% decrease in root mean square error (RMSE) and a 6% increase in the modeling efficiency (EF) of modeled RECO when compared to a version of the model that does not consider the physiological phenology. The reduction of the model-observation bias occurred mainly at the monthly time scale, and in spring and summer, while a smaller reduction was observed at the annual time scale. The proposed approach did not improve the model performance at several sites, and we identified as potential causes the plant canopy heterogeneity and the use of air temperature as a driver of ecosystem respiration instead of soil temperature. However, in the majority of sites the model-error remained unchanged regardless of the driving temperature. Overall, our results point toward the potential for improving current approaches for modeling RECO in deciduous forests by including the phenological cycle of the canopy.

Measuring fluxes of trace gases and energy between ecosystems and the atmosphere - the state and future of the eddy covariance method

The application of the eddy covariance flux method to measure fluxes of trace gas and energy between ecosystems and the atmosphere has exploded over the past 25 years. This opinion paper provides a perspective on the contributions and future opportunities of the eddy covariance method. First, the paper discusses the pros and cons of this method relative to other methods used to measure the exchange of trace gases between ecosystems and the atmosphere. Second, it discusses how the use of eddy covariance method has grown and evolved. Today, more than 400 flux measurement sites are operating world-wide and the duration of the time series exceed a decade at dozens of sites. Networks of tower sites now enable scientists to ask scientific questions related to climatic and ecological gradients, disturbance, changes in land use, and management. The paper ends with discussions on where the field of flux measurement is heading. Topics discussed include role of open access data sharing and data mining, in this new era of big data, and opportunities new sensors that measure a variety of trace gases, like volatile organic carbon compounds, methane and nitrous oxide, and aerosols, may yield.

Influence and predictive capacity of climate anomalies on daily to decadal extremes in canopy photosynthesis

Significant advances have been made over the past decades in capabilities to simulate diurnal and seasonal variation of leaf-level and canopy-scale photosynthesis in temperate and boreal forests. However, long-term prediction of future forest productivity in a changing climate may be more dependent on how climate and biological anomalies influence extremes in interannual to decadal variability of canopy ecosystem carbon exchanges. These exchanges can differ markedly from leaf level responses, especially owing to the prevalence of long lags in nutrient and water cycling. Until recently, multiple long-term (10+ year) high temporal frequency (daily) observations of canopy exchange were not available to reliably assess this claim. An analysis of one of the longest running North American eddy covariance flux towers reveals that single climate variables do not adequately explain carbon exchange anomalies beyond the seasonal timescale. Daily to weekly lagged anomalies of photosynthesis positively autocorrelate with daily photosynthesis. This effect suggests a negative feedback in photosynthetic response to climate extremes, such as anomalies in evapotranspiration and maximum temperature. Moisture stress in the prior season did inhibit photosynthesis, but mechanisms are difficult to assess. A complex interplay of integrated and lagged productivity and moisture-limiting factors indicate a critical role of seasonal thresholds that limit growing season length and peak productivity. These results lead toward a new conceptual framework for improving earth system models with long-term flux tower observations.

Light inhibition of leaf respiration in field-grown Eucalyptus saligna in whole-tree chambers under elevated atmospheric CO2 and summer drought

We investigated whether the degree of light inhibition of leaf respiration (R) differs among large Eucalyptus saligna grown in whole-tree chambers and exposed to present and future atmospheric [CO(2) ] and summer drought. Associated with month-to-month changes in temperature were concomitant changes in R in the light (R(light) ) and darkness (R(dark) ), with both processes being more temperature dependent in well-watered trees than under drought. Overall rates of R(light) and R(dark) were not significantly affected by [CO(2) ]. By contrast, overall rates of R(dark) (averaged across both [CO(2) ]) were ca. 25% lower under drought than in well-watered trees. During summer, the degree of light inhibition of leaf R was greater in droughted (ca. 80% inhibition) than well-watered trees (ca. 50% inhibition). Notwithstanding these treatment differences, an overall positive relationship was observed between R(light) and R(dark) when data from all months/treatments were combined (R(2)  = 0.8). Variations in R(light) were also positively correlated with rates of Rubisco activity and nitrogen concentration. Light inhibition resulted in a marked decrease in the proportion of light-saturated photosynthesis respired (i.e. reduced R/A(sat) ). Collectively, these results highlight the need to account for light inhibition when assessing impacts of global change drivers on the carbon economy of tree canopies.