Terrestrial Carbon Cycle Models Research Articles

Vertical and seasonal variations in temperature responses of leaf respiration in a Chamaecyparis obtusa canopy.

Leaf respiration (R) is a major component of carbon balance in forest ecosystems. Clarifying the variability of leaf R within a canopy is essential for predicting the impact of global warming on forest productivity and the potential future function of the forest ecosystem as a carbon sink. We examined vertical and seasonal variations in short-term temperature responses of leaf R as well as environmental factors (light and mean air temperature) and physiological factors [leaf nitrogen (N), leaf mass per area (LMA), and shoot growth] in the canopy of a 10-year-old stand of hinoki cypress [Chamaecyparis obtusa (Sieb. et Zucc.) Endl.] in Kyushu, Japan. Leaf respiration rate adjusted to 20 °C (R20) exhibited evident vertical gradients in each season and was correlated with light, LMA and leaf N. In contrast, the temperature sensitivity of leaf R (Q10) did not vary vertically throughout the seasons. Seasonally, Q10 was higher in winter than in summer and was strongly negatively correlated to mean air temperature. A negative correlation of R20 with mean air temperature was also observed for each of the three canopy layers. These results clearly indicate that leaf R was able to adjust to seasonal changes in ambient temperature under field conditions and down-regulate during warmer periods. We also found that the degree of thermal acclimation did not vary with canopy position. Overall, our results suggest that vertical and seasonal variations in temperature responses of leaf R within a hinoki cypress canopy could be predicted by relatively simple parameters (light and temperature). There was an exception of extremely high R20 values in April that may have been due to the onset of shoot growth in spring. Understanding thermal acclimation and variations in leaf R within forest canopies will improve global terrestrial carbon cycle models.

The large influence of climate model bias on terrestrial carbon cycle simulations

Global vegetation models and terrestrial carbon cycle models are widely used for projecting the carbon balance of terrestrial ecosystems. Ensembles of such models show a large spread in carbon balance predictions, ranging from a large uptake to a release of carbon by the terrestrial biosphere, constituting a large uncertainty in the associated feedback to atmospheric CO2 concentrations under global climate change. Errors and biases that may contribute to such uncertainty include ecosystem model structure, parameters and forcing by climate output from general circulation models (GCMs) or the atmospheric components of Earth system models (ESMs), e.g. as prepared for use in IPCC climate change assessments. The relative importance of these contributing factors to the overall uncertainty in carbon cycle projections is not well characterised. Here we investigate the role of climate model-derived biases by forcing a single global ecosystem-carbon cycle model, with original climate outputs from 15 ESMs and GCMs from the CMIP5 ensemble. We show that variation among the resulting ensemble of present and future carbon cycle simulations propagates from biases in annual means of temperature, precipitation and incoming shortwave radiation. Future changes in carbon pools, and thus land carbon sink trends, are also affected by climate biases, although to a smaller extent than the absolute size of carbon pools. Our results suggest that climate biases could be responsible for a considerable fraction of the large uncertainties in ESM simulations of land carbon fluxes and pools, amounting to about 40% of the range reported for ESMs. We conclude that climate bias-induced uncertainties must be decreased to make accurate coupled atmosphere-carbon cycle projections.

Interannual variability of the atmospheric CO<sub>2</sub> growth rate: roles of precipitation and temperature

Abstract. The interannual variability (IAV) in atmospheric CO2 growth rate (CGR) is closely connected with the El Niño–Southern Oscillation. However, sensitivities of CGR to temperature and precipitation remain largely uncertain. This paper analyzed the relationship between Mauna Loa CGR and tropical land climatic elements. We find that Mauna Loa CGR lags precipitation by 4 months with a correlation coefficient of −0.63, leads temperature by 1 month (0.77), and correlates with soil moisture (−0.65) with zero lag. Additionally, precipitation and temperature are highly correlated (−0.66), with precipitation leading by 4–5 months. Regression analysis shows that sensitivities of Mauna Loa CGR to temperature and precipitation are 2.92 ± 0.20 PgC yr−1 K−1 and −0.46 ± 0.07 PgC yr−1 100 mm−1, respectively. Unlike some recent suggestions, these empirical relationships favor neither temperature nor precipitation as the dominant factor of CGR IAV. We further analyzed seven terrestrial carbon cycle models, from the TRENDY project, to study the processes underlying CGR IAV. All models capture well the IAV of tropical land–atmosphere carbon flux (CFTA). Sensitivities of the ensemble mean CFTA to temperature and precipitation are 3.18 ± 0.11 PgC yr−1 K−1 and −0.67 ± 0.04 PgC yr−1 100 mm−1, close to Mauna Loa CGR. Importantly, the models consistently show the variability in net primary productivity (NPP) dominates CGR, rather than heterotrophic respiration. Because previous studies have proved that NPP is largely driven by precipitation in tropics, it suggests a key role of precipitation in CGR IAV despite the higher CGR correlation with temperature. Understanding the relative contribution of CO2 sensitivity to precipitation and temperature has important implications for future carbon-climate feedback using such ''emergent constraint''.

Microbial assimilation of atmospheric CO2 into soil organic matter revealed by the incubation of paddy soils under 14C-CO2 atmosphere

ABSTRACTSimilar to higher plants, microbial autotrophs possess photosynthetic systems that enable them to fix CO2. To measure the activity of microbial autotrophs in assimilating atmospheric CO2, five paddy soils were incubated with 14C-labeled CO2 for 45 days to determine the amount of 14C-labeled organic C being synthesized. The results showed that a significant amount of 14C-labeled CO2 incorporated into microbial biomass was soil specific, accounting for 0.37%–1.18% of soil organic carbon (14C-labeled organic C range: 81.6–156.9 mg C kg−1 of the soil after 45 days). Consequently, high amounts of C-labeled organic C were synthesized (the synthesis rates ranged from 86 to 166 mg C m−2 d−1). The amount of atmospheric 14CO2 incorporated into microbial biomass (14C-labeled microbial biomass) was significantly correlated with organic C components (14C-labeled organic C) in the soil (r = 0.80, p < 0.0001). Our results indicate that the microbial assimilation of atmospheric CO2 is an important process for the sequestration and cycling of terrestrial C. Our results showed that microbial assimilation of atmospheric CO2 has been underestimated by researchers globally, and that it should be accounted for in global terrestrial carbon cycle models.

Spatiotemporal patterns of terrestrial gross primary production: A review

AbstractGreat advances have been made in the last decade in quantifying and understanding the spatiotemporal patterns of terrestrial gross primary production (GPP) with ground, atmospheric, and space observations. However, although global GPP estimates exist, each data set relies upon assumptions and none of the available data are based only on measurements. Consequently, there is no consensus on the global total GPP and large uncertainties exist in its benchmarking. The objective of this review is to assess how the different available data sets predict the spatiotemporal patterns of GPP, identify the differences among data sets, and highlight the main advantages/disadvantages of each data set. We compare GPP estimates for the historical period (1990–2009) from two observation‐based data sets (Model Tree Ensemble and Moderate Resolution Imaging Spectroradiometer) to coupled carbon‐climate models and terrestrial carbon cycle models from the Fifth Climate Model Intercomparison Project and TRENDY projects and to a new hybrid data set (CARBONES). Results show a large range in the mean global GPP estimates. The different data sets broadly agree on GPP seasonal cycle in terms of phasing, while there is still discrepancy on the amplitude. For interannual variability (IAV) and trends, there is a clear separation between the observation‐based data that show little IAV and trend, while the process‐based models have large GPP variability and significant trends. These results suggest that there is an urgent need to improve observation‐based data sets and develop carbon cycle modeling with processes that are currently treated either very simplistically to correctly estimate present GPP and better quantify the future uptake of carbon dioxide by the world's vegetation.

Including an ocean carbon cycle model into &amp;lt;i&amp;gt;i&amp;lt;/i&amp;gt;LOVECLIM (v1.0)

Abstract. The atmospheric carbon dioxide concentration plays a crucial role in the radiative balance and as such has a strong influence on the evolution of climate. Because of the numerous interactions between climate and the carbon cycle, it is necessary to include a model of the carbon cycle within a climate model to understand and simulate past and future changes of the carbon cycle. In particular, natural variations of atmospheric CO2 have happened in the past, while anthropogenic carbon emissions are likely to continue in the future. To study changes of the carbon cycle and climate on timescales of a few hundred to a few thousand years, we have included a simple carbon cycle model into the iLOVECLIM Earth System Model. In this study, we describe the ocean and terrestrial biosphere carbon cycle models and their performance relative to observational data. We focus on the main carbon cycle variables including the carbon isotope ratios δ13C and the Δ14C. We show that the model results are in good agreement with modern observations both at the surface and in the deep ocean for the main variables, in particular phosphates, dissolved inorganic carbon and the carbon isotopes.

Observations of 14CO2 in ecosystem respiration from a temperate deciduous forest in Northern Wisconsin

AbstractThe 14CO2 composition of plant and soil respiration can be used to determine the residence time of photosynthetically fixed carbon before it is released back to the atmosphere. To estimate the residence time of actively cycled carbon in a temperate forest, we employed two approaches for estimating the Δ14CO2 of ecosystem respiration (Δ14C‐Reco) at the Willow Creek AmeriFlux site in Northern Wisconsin, USA. Our first approach was to construct nighttime Keeling plots from subcanopy profiles of Δ14CO2 and CO2, providing estimates of Δ14C‐Reco of 121.7‰ in June and 42.0‰ in August 2012. These measurements are likely dominated by soil fluxes due to proximity to the ground level. Our second approach utilized samples taken over 20 months within the forest canopy and from 396 m above ground level at the nearby LEF NOAA tall tower site (Park Falls, WI). In this canopy‐minus‐background approach we employed a mixing model described by Miller and Tans (2003) for estimating isotopic sources by subtracting time‐varying background conditions. For the period from May 2011 to December 2012 the estimated Δ14C‐Reco using the Miller‐Tans model was 76.8‰. Together, these Δ14C‐Reco values represent mean Reco carbon ages of approximately 1–19 years. We also found that heterotrophic soil‐respired Δ 14C at Willow Creek was 5–38‰ higher (i.e., 1–10 years older) than predicted by the Carnegie‐Ames‐Stanford Approach global biosphere carbon model for the 1 × 1 pixel nearest to the site. This study provides much needed observational constraints of ecosystem carbon residence times, which are a major source of uncertainty in terrestrial carbon cycle models.

Last glacial maximum constraints on the Earth System model HadGEM2-ES

We investigate the response of the atmospheric and land surface components of the CMIP5/AR5 Earth System model HadGEM2-ES to pre-industrial (PI: AD 1860) and last glacial maximum (LGM: 21 kyr) boundary conditions. HadGEM2-ES comprises atmosphere, ocean and sea-ice components which are interactively coupled to representations of the carbon cycle, aerosols including mineral dust and tropospheric chemistry. In this study, we focus on the atmosphere-only model HadGEM2-A coupled to terrestrial carbon cycle and aerosol models. This configuration is forced with monthly sea surface temperature and sea-ice fields from equivalent coupled simulations with an older version of the Hadley Centre model, HadCM3. HadGEM2-A simulates extreme cooling over northern continents and nearly complete die back of vegetation in Asia, giving a poor representation of the LGM environment compared with reconstructions of surface temperatures and biome distributions. The model also performs significantly worse for the LGM in comparison with its precursor AR4 model HadCM3M2. Detailed analysis shows that the major factor behind the vegetation die off in HadGEM2-A is a subtle change to the temperature dependence of leaf mortality within the phenology model of HadGEM2. This impacts on both snow-vegetation albedo and vegetation dynamics. A new set of parameters is tested for both the pre-industrial and LGM, showing much improved coverage of vegetation in both time periods, including an improved representation of the needle-leaf forest coverage in Siberia for the pre-industrial. The new parameters and the resulting changes in global vegetation distribution strongly impact the simulated loading of mineral dust, an important aerosol for the LGM. The climate response in an abrupt 4× pre-industrial CO2 simulation is also analysed and shows modest regional impacts on surface temperatures across the Boreal zone.

Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude.

The atmospheric carbon dioxide (CO2) record displays a prominent seasonal cycle that arises mainly from changes in vegetation growth and the corresponding CO2 uptake during the boreal spring and summer growing seasons and CO2 release during the autumn and winter seasons. The CO2 seasonal amplitude has increased over the past five decades, suggesting an increase in Northern Hemisphere biospheric activity. It has been proposed that vegetation growth may have been stimulated by higher concentrations of CO2 as well as by warming in recent decades, but such mechanisms have been unable to explain the full range and magnitude of the observed increase in CO2 seasonal amplitude. Here we suggest that the intensification of agriculture (the Green Revolution, in which much greater crop yield per unit area was achieved by hybridization, irrigation and fertilization) during the past five decades is a driver of changes in the seasonal characteristics of the global carbon cycle. Our analysis of CO2 data and atmospheric inversions shows a robust 15 per cent long-term increase in CO2 seasonal amplitude from 1961 to 2010, punctuated by large decadal and interannual variations. Using a terrestrial carbon cycle model that takes into account high-yield cultivars, fertilizer use and irrigation, we find that the long-term increase in CO2 seasonal amplitude arises from two major regions: the mid-latitude cropland between 25°N and 60°N and the high-latitude natural vegetation between 50°N and 70°N. The long-term trend of seasonal amplitude increase is 0.311±0.027 per cent per year, of which sensitivity experiments attribute 45, 29 and 26 per cent to land-use change, climate variability and change, and increased productivity due to CO2 fertilization, respectively. Vegetation growth was earlier by one to two weeks, as measured by the mid-point of vegetation carbon uptake, and took up 0.5 petagrams more carbon in July, the height of the growing season, during 2001-2010 than in 1961-1970, suggesting that human land use and management contribute to seasonal changes in the CO2 exchange between the biosphere and the atmosphere.

A two-fold increase of carbon cycle sensitivity to tropical temperature variations

Earth system models project that the tropical land carbon sink will decrease in size in response to an increase in warming and drought during this century, probably causing a positive climate feedback. But available data are too limited at present to test the predicted changes in the tropical carbon balance in response to climate change. Long-term atmospheric carbon dioxide data provide a global record that integrates the interannual variability of the global carbon balance. Multiple lines of evidence demonstrate that most of this variability originates in the terrestrial biosphere. In particular, the year-to-year variations in the atmospheric carbon dioxide growth rate (CGR) are thought to be the result of fluctuations in the carbon fluxes of tropical land areas. Recently, the response of CGR to tropical climate interannual variability was used to put a constraint on the sensitivity of tropical land carbon to climate change. Here we use the long-term CGR record from Mauna Loa and the South Pole to show that the sensitivity of CGR to tropical temperature interannual variability has increased by a factor of 1.9 ± 0.3 in the past five decades. We find that this sensitivity was greater when tropical land regions experienced drier conditions. This suggests that the sensitivity of CGR to interannual temperature variations is regulated by moisture conditions, even though the direct correlation between CGR and tropical precipitation is weak. We also find that present terrestrial carbon cycle models do not capture the observed enhancement in CGR sensitivity in the past five decades. More realistic model predictions of future carbon cycle and climate feedbacks require a better understanding of the processes driving the response of tropical ecosystems to drought and warming.

Variations in the relationship between maximum leaf carboxylation rate and leaf nitrogen concentration

Aims Maximum leaf carboxylation rate is one of the key parameters determining the photosynthetic capacity of plants. It is affected by irradiance, temperature, moisture, atmospheric CO2 concentration, leaf nitrogen content, and some other factors. Accurate simulation of the responses of the maximum leaf carboxylation rate to varying environmental conditions is the premise for predicting the changes in vegetation productivity and carbon cycle in future environments. Most of the process-based terrestrial carbon cycle models use the Farqhuar photosynthesis model to simulate plant photosynthesis. However, the methods in simulating the relationship between maximum leaf carboxylation rate and leaf nitrogen content differ from each other. Methods We collected data on maximum leaf carboxylation rate and leaf nitrogen content from literature published during 1990–2013, and analyzed the variations in the relationship between maximum leaf carboxylation rate at 25 °C(Vcmax,25) and area-based leaf nitrogen concentration(Na) across different plant functional types and seasons, and in responses to rising atmospheric CO2 and nitrogen supply. Moreover, we reviewed possible causes of those variations and the influencing factors. Important findings The results showed that: 1) the relationship between Vcmax,25 and Na varied with plantfunctional types, and the average values of the slope ranged from 16.29 to 50.25 μmol CO2·g N–1·s–1. Deciduous trees generally showed a steeper slope and greater photosynthetic nitrogen use efficiency than evergreen trees due to the differences in leaf mass per area(LMA) and nitrogen allocation to Rubisco. 2) The relationship between Vcmax,25 and Na had seasonal and annual variations. In years without water stress, the highest value of the slope mostly occurred in spring or summer. A change of the slope was related to seasonal variations in LMA and nitrogen allocation to Rubisco. The slope increased in drought seasons or years. 3) The slope of the linear relationship between Vcmax,25 and Na for perennial needle leaf was reduced due to a decrease in Rubisco content in response to elevated CO2. The maximum leaf carboxylation rate, nitrogen content, and the slope of their linear relationship increased with increment of nitrogen application rate. On the basis of these analyses, we suggest that simulating the relationship between maximum leaf carboxylation and leaf nitrogen should consider seasonal variations in LMA and nitrogen allocation to Rubisco, the influences of water stress, atmospheric CO2 concentration, and nitrogen supply level. More multi-factor experimental studies are needed to further investigate the underlying mechanisms of the variations in the relationship between maximum leaf carboxylation rate and leaf nitrogen content, to obtain more observational data with systematic approaches, and thus to further improve ecosystem process-based models.

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.

Traceable components of terrestrial carbon storage capacity in biogeochemical models

Biogeochemical models have been developed to account for more and more processes, making their complex structures difficult to be understood and evaluated. Here, we introduce a framework to decompose a complex land model into traceable components based on mutually independent properties of modeled biogeochemical processes. The framework traces modeled ecosystem carbon storage capacity (Xss ) to (i) a product of net primary productivity (NPP) and ecosystem residence time (τE ). The latter τE can be further traced to (ii) baseline carbon residence times (τ'E ), which are usually preset in a model according to vegetation characteristics and soil types, (iii) environmental scalars (ξ), including temperature and water scalars, and (iv) environmental forcings. We applied the framework to the Australian Community Atmosphere Biosphere Land Exchange (CABLE) model to help understand differences in modeled carbon processes among biomes and as influenced by nitrogen processes. With the climate forcings of 1990, modeled evergreen broadleaf forest had the highest NPP among the nine biomes and moderate residence times, leading to a relatively high carbon storage capacity (31.5 kg cm(-2) ). Deciduous needle leaf forest had the longest residence time (163.3 years) and low NPP, leading to moderate carbon storage (18.3 kg cm(-2) ). The longest τE in deciduous needle leaf forest was ascribed to its longest τ'E (43.6 years) and small ξ (0.14 on litter/soil carbon decay rates). Incorporation of nitrogen processes into the CABLE model decreased Xss in all biomes via reduced NPP (e.g., -12.1% in shrub land) or decreased τE or both. The decreases in τE resulted from nitrogen-induced changes in τ'E (e.g., -26.7% in C3 grassland) through carbon allocation among plant pools and transfers from plant to litter and soil pools. Our framework can be used to facilitate data model comparisons and model intercomparisons via tracking a few traceable components for all terrestrial carbon cycle models. Nevertheless, more research is needed to develop tools to decompose NPP and transient dynamics of the modeled carbon cycle into traceable components for structural analysis of land models.

Evaluation of the carbon cycle components in the Norwegian Earth System Model (NorESM)

Abstract. The recently developed Norwegian Earth System Model (NorESM) is employed for simulations contributing to the CMIP5 (Coupled Model Intercomparison Project phase 5) experiments and the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC-AR5). In this manuscript, we focus on evaluating the ocean and land carbon cycle components of the NorESM, based on the preindustrial control and historical simulations. Many of the observed large scale ocean biogeochemical features are reproduced satisfactorily by the NorESM. When compared to the climatological estimates from the World Ocean Atlas (WOA), the model simulated temperature, salinity, oxygen, and phosphate distributions agree reasonably well in both the surface layer and deep water structure. However, the model simulates a relatively strong overturning circulation strength that leads to noticeable model-data bias, especially within the North Atlantic Deep Water (NADW). This strong overturning circulation slightly distorts the structure of the biogeochemical tracers at depth. Advancements in simulating the oceanic mixed layer depth with respect to the previous generation model particularly improve the surface tracer distribution as well as the upper ocean biogeochemical processes, particularly in the Southern Ocean. Consequently, near-surface ocean processes such as biological production and air–sea gas exchange, are in good agreement with climatological observations. The NorESM adopts the same terrestrial model as the Community Earth System Model (CESM1). It reproduces the general pattern of land-vegetation gross primary productivity (GPP) when compared to the observationally based values derived from the FLUXNET network of eddy covariance towers. While the model simulates well the vegetation carbon pool, the soil carbon pool is smaller by a factor of three relative to the observational based estimates. The simulated annual mean terrestrial GPP and total respiration are slightly larger than observed, but the difference between the global GPP and respiration is comparable. Model-data bias in GPP is mainly simulated in the tropics (overestimation) and in high latitudes (underestimation). Within the NorESM framework, both the ocean and terrestrial carbon cycle models simulate a steady increase in carbon uptake from the preindustrial period to the present-day. The land carbon uptake is noticeably smaller than the observations, which is attributed to the strong nitrogen limitation formulated by the land model.

The climate dependence of the terrestrial carbon cycle, including parameter and structural uncertainties

Abstract. The feedback between climate and the terrestrial carbon cycle will be a key determinant of the dynamics of the Earth System (the thin layer that contains and supports life) over the coming decades and centuries. However, Earth System Model projections of the terrestrial carbon-balance vary widely over these timescales. This is largely due to differences in their terrestrial carbon cycle models. A major goal in biogeosciences is therefore to improve understanding of the terrestrial carbon cycle to enable better constrained projections. Utilising empirical data to constrain and assess component processes in terrestrial carbon cycle models will be essential to achieving this goal. We used a new model construction method to data-constrain all parameters of all component processes within a global terrestrial carbon model, employing as data constraints a collection of 12 empirical data sets characterising global patterns of carbon stocks and flows. Our goals were to assess the climate dependencies inferred for all component processes, assess whether these were consistent with current knowledge and understanding, assess the importance of different data sets and the model structure for inferring those dependencies, assess the predictive accuracy of the model and ultimately to identify a methodology by which alternative component models could be compared within the same framework in the future. Although formulated as differential equations describing carbon fluxes through plant and soil pools, the model was fitted assuming the carbon pools were in states of dynamic equilibrium (input rates equal output rates). Thus, the parameterised model is of the equilibrium terrestrial carbon cycle. All but 2 of the 12 component processes to the model were inferred to have strong climate dependencies, although it was not possible to data-constrain all parameters, indicating some potentially redundant details. Similar climate dependencies were obtained for most processes, whether inferred individually from their corresponding data sets or using the full terrestrial carbon model and all available data sets, indicating a strong overall consistency in the information provided by different data sets under the assumed model formulation. A notable exception was plant mortality, in which qualitatively different climate dependencies were inferred depending on the model formulation and data sets used, highlighting this component as the major structural uncertainty in the model. All but two component processes predicted empirical data better than a null model in which no climate dependency was assumed. Equilibrium plant carbon was predicted especially well (explaining around 70% of the variation in the withheld evaluation data). We discuss the advantages of our approach in relation to advancing our understanding of the carbon cycle and enabling Earth System Models to make better constrained projections.

The carbon budget of terrestrial ecosystems in East Asia over the last two decades

Abstract. This REgional Carbon Cycle Assessment and Processes regional study provides a synthesis of the carbon balance of terrestrial ecosystems in East Asia, a region comprised of China, Japan, North and South Korea, and Mongolia. We estimate the current terrestrial carbon balance of East Asia and its driving mechanisms during 1990–2009 using three different approaches: inventories combined with satellite greenness measurements, terrestrial ecosystem carbon cycle models and atmospheric inversion models. The magnitudes of East Asia's terrestrial carbon sink from these three approaches are comparable: −0.293±0.033 PgC yr−1 from inventory–remote sensing model–data fusion approach, −0.413±0.141 PgC yr−1 (not considering biofuel emissions) or −0.224±0.141 PgC yr−1 (considering biofuel emissions) for carbon cycle models, and −0.270±0.507 PgC yr−1 for atmospheric inverse models. Here and in the following, the numbers behind ± signs are standard deviations. The ensemble of ecosystem modeling based analyses further suggests that at the regional scale, climate change and rising atmospheric CO2 together resulted in a carbon sink of −0.289±0.135 PgC yr−1, while land-use change and nitrogen deposition had a contribution of −0.013±0.029 PgC yr−1 and −0.107±0.025 PgC yr−1, respectively. Although the magnitude of climate change effects on the carbon balance varies among different models, all models agree that in response to climate change alone, southern China experienced an increase in carbon storage from 1990 to 2009, while northern East Asia including Mongolia and north China showed a decrease in carbon storage. Overall, our results suggest that about 13–27% of East Asia's CO2 emissions from fossil fuel burning have been offset by carbon accumulation in its terrestrial territory over the period from 1990 to 2009. The underlying mechanisms of carbon sink over East Asia still remain largely uncertain, given the diversity and intensity of land management processes, and the regional conjunction of many drivers such as nutrient deposition, climate, atmospheric pollution and CO2 changes, which cannot be considered as independent for their effects on carbon storage.

Nutrient limitation reduces land carbon uptake in simulations with a model of combined carbon, nitrogen and phosphorus cycling

Abstract. Terrestrial carbon (C) cycle models applied for climate projections simulate a strong increase in net primary productivity (NPP) due to elevated atmospheric CO2 concentration during the 21st century. These models usually neglect the limited availability of nitrogen (N) and phosphorus (P), nutrients that commonly limit plant growth and soil carbon turnover. To investigate how the projected C sequestration is altered when stoichiometric constraints on C cycling are considered, we incorporated a P cycle into the land surface model JSBACH (Jena Scheme for Biosphere–Atmosphere Coupling in Hamburg), which already includes representations of coupled C and N cycles. The model reveals a distinct geographic pattern of P and N limitation. Under the SRES (Special Report on Emissions Scenarios) A1B scenario, the accumulated land C uptake between 1860 and 2100 is 13% (particularly at high latitudes) and 16% (particularly at low latitudes) lower in simulations with N and P cycling, respectively, than in simulations without nutrient cycles. The combined effect of both nutrients reduces land C uptake by 25% compared to simulations without N or P cycling. Nutrient limitation in general may be biased by the model simplicity, but the ranking of limitations is robust against the parameterization and the inflexibility of stoichiometry. After 2100, increased temperature and high CO2 concentration cause a shift from N to P limitation at high latitudes, while nutrient limitation in the tropics declines. The increase in P limitation at high-latitudes is induced by a strong increase in NPP and the low P sorption capacity of soils, while a decline in tropical NPP due to high autotrophic respiration rates alleviates N and P limitations. The quantification of P limitation remains challenging. The poorly constrained processes of soil P sorption and biochemical mineralization are identified as the main uncertainties in the strength of P limitation. Even so, our findings indicate that global land C uptake in the 21st century is likely overestimated in models that neglect P and N limitations. In the long term, insufficient P availability might become an important constraint on C cycling at high latitudes. Accordingly, we argue that the P cycle must be included in global models used for C cycle projections.

Photoperiodic regulation of the seasonal pattern of photosynthetic capacity and the implications for carbon cycling

Although temperature is an important driver of seasonal changes in photosynthetic physiology, photoperiod also regulates leaf activity. Climate change will extend growing seasons if temperature cues predominate, but photoperiod-controlled species will show limited responsiveness to warming. We show that photoperiod explains more seasonal variation in photosynthetic activity across 23 tree species than temperature. Although leaves remain green, photosynthetic capacity peaks just after summer solstice and declines with decreasing photoperiod, before air temperatures peak. In support of these findings, saplings grown at constant temperature but exposed to an extended photoperiod maintained high photosynthetic capacity, but photosynthetic activity declined in saplings experiencing a naturally shortening photoperiod; leaves remained equally green in both treatments. Incorporating a photoperiodic correction of photosynthetic physiology into a global-scale terrestrial carbon-cycle model significantly improves predictions of seasonal atmospheric CO(2) cycling, demonstrating the benefit of such a function in coupled climate system models. Accounting for photoperiod-induced seasonality in photosynthetic parameters reduces modeled global gross primary production 2.5% (∼4 PgC y(-1)), resulting in a >3% (∼2 PgC y(-1)) decrease of net primary production. Such a correction is also needed in models estimating current carbon uptake based on remotely sensed greenness. Photoperiod-associated declines in photosynthetic capacity could limit autumn carbon gain in forests, even if warming delays leaf senescence.

Parameter and prediction uncertainty in an optimized terrestrial carbon cycle model: Effects of constraining variables and data record length

[1] Many parameters in terrestrial biogeochemical models are inherently uncertain, leading to uncertainty in predictions of key carbon cycle variables. At observation sites, this uncertainty can be quantified by applying model-data fusion techniques to estimate model parameters using eddy covariance observations and associated biometric data sets as constraints. Uncertainty is reduced as data records become longer and different types of observations are added. We estimate parametric and associated predictive uncertainty at the Morgan Monroe State Forest in Indiana, USA. Parameters in the Local Terrestrial Ecosystem Carbon (LoTEC) are estimated using both synthetic and actual constraints. These model parameters and uncertainties are then used to make predictions of carbon flux for up to 20 years. We find a strong dependence of both parametric and prediction uncertainty on the length of the data record used in the model-data fusion. In this model framework, this dependence is strongly reduced as the data record length increases beyond 5 years. If synthetic initial biomass pool constraints with realistic uncertainties are included in the model-data fusion, prediction uncertainty is reduced by more than 25% when constraining flux records are less than 3 years. If synthetic annual aboveground woody biomass increment constraints are also included, uncertainty is similarly reduced by an additional 25%. When actual observed eddy covariance data are used as constraints, there is still a strong dependence of parameter and prediction uncertainty on data record length, but the results are harder to interpret because of the inability of LoTEC to reproduce observed interannual variations and the confounding effects of model structural error.

Deciphering the components of regional net ecosystem fluxes following a bottom-up approach for the Iberian Peninsula

Abstract. Quantification of ecosystem carbon pools is a fundamental requirement for estimating carbon fluxes and for addressing the dynamics and responses of the terrestrial carbon cycle to environmental drivers. The initial estimates of carbon pools in terrestrial carbon cycle models often rely on the ecosystem steady state assumption, leading to initial equilibrium conditions. In this study, we investigate how trends and inter-annual variability of net ecosystem fluxes are affected by initial non-steady state conditions. Further, we examine how modeled ecosystem responses induced exclusively by the model drivers can be separated from the initial conditions. For this, the Carnegie-Ames-Stanford Approach (CASA) model is optimized at set of European eddy covariance sites, which support the parameterization of regional simulations of ecosystem fluxes for the Iberian Peninsula, between 1982 and 2006. The presented analysis stands on a credible model performance for a set of sites, that represent generally well the plant functional types and selected descriptors of climate and phenology present in the Iberian region – except for a limited Northwestern area. The effects of initial conditions on inter-annual variability and on trends, results mostly from the recovery of pools to equilibrium conditions; which control most of the inter-annual variability (IAV) and both the magnitude and sign of most of the trends. However, by removing the time series of pure model recovery from the time series of the overall fluxes, we are able to retrieve estimates of inter-annual variability and trends in net ecosystem fluxes that are quasi-independent from the initial conditions. This approach reduced the sensitivity of the net fluxes to initial conditions from 47% and 174% to −3% and 7%, for strong initial sink and source conditions, respectively. With the aim to identify and improve understanding of the component fluxes that drive the observed trends, the net ecosystem production (NEP) trends are decomposed into net primary production (NPP) and heterotrophic respiration (RH) trends. The majority (~97%) of the positive trends in NEP is observed in regions where both NPP and RH fluxes show significant increases, although the magnitude of NPP trends is higher. Analogously, ~83% of the negative trends in NEP are also associated with negative trends in NPP. The spatial patterns of NPP trends are mainly explained by the trends in fAPAR (r=0.79) and are only marginally explained by trends in temperature and water stress scalars (r=0.10 and r=0.25, respectively). Further, we observe the significant role of substrate availability (r=0.25) and temperature (r=0.23) in explaining the spatial patterns of trends in RH. These results highlight the role of primary production in driving ecosystem fluxes. Overall, our study illustrates an approach for removing the confounding effects of initial conditions and emphasizes the need to decompose the ecosystem fluxes into its components and drivers for more mechanistic interpretations of modeling results. We expect that our results are not only specific for the CASA model since it incorporates concepts of ecosystem functioning and modeling assumptions common to biogeochemical models. A direct implication of these results is the ability of this approach to detect climate and phenology induced trends regardless of the initial conditions.