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Simulation of the carbon cycle in climate models is important due to its impact on climate change, but many weaknesses in its reproduction were found in previous models. Improvements in the representation of the land carbon cycle in Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) include the interactive treatment of both the carbon and nitrogen cycles, improved photosynthesis, and soil hydrology. To assess the impact of these model developments on aspects of the global carbon cycle, the Earth System Model Evaluation Tool (ESMValTool) is expanded to compare CO2-concentration- and CO2-emission-driven historical simulations from CMIP5 and CMIP6 to observational data sets. A particular focus is on the differences in models with and without an interactive terrestrial nitrogen cycle. Overestimations of photosynthesis (gross primary productivity (GPP)) in CMIP5 were largely resolved in CMIP6 for participating models with an interactive nitrogen cycle but remaining for models without one. This points to the importance of including nutrient limitation. Simulating the leaf area index (LAI) remains challenging, with a large model spread in both CMIP5 and CMIP6. In ESMs, the global mean land carbon uptake (net biome productivity (NBP)) is well reproduced in the CMIP5 and CMIP6 multi-model means. However, this is the result of an underestimation of NBP in the Northern Hemisphere, which is compensated by an overestimation in the Southern Hemisphere and the tropics. Carbon stocks remain a large uncertainty in the models. While vegetation carbon content is slightly better represented in CMIP6, the inter-model range of soil carbon content remains the same between CMIP5 and CMIP6. Overall, a slight improvement in the simulation of land carbon cycle parameters is found in CMIP6 compared to CMIP5, but with many biases remaining, further improvements of models in particular for LAI and NBP is required. Models from modeling groups participating in both CMIP phases generally perform similarly or better in their CMIP6 compared to their CMIP5 models. This improvement is not as significant in the multi-model means due to more new models in CMIP6, especially those using older versions of the Community Land Model (CLM). Emission-driven simulations perform just as well as the concentration-driven models, despite the added process realism. Due to this, we recommend that ESMs in future Coupled Model Intercomparison Project (CMIP) phases perform emission-driven simulations as the standard so that climate–carbon cycle feedbacks are fully active. The inclusion of the nitrogen limitation led to a large improvement in photosynthesis compared to models not including this process, suggesting the need to view the nitrogen cycle as a necessary part of all future carbon cycle models. Possible benefits when including further limiting nutrients such as phosphorus should also be considered.

Nutrient resorption from senescing leaves can significantly affect ecosystem nutrient cycling, making it an essential process to better understand long-term plant productivity under environmental change that affects the balance between nutrient availability and demand. Although it is known that nutrient resorption rates vary strongly between different species and across environmental gradients, the underlying driving factors are insufficiently quantified. Here, we present an analysis of globally distributed observations of leaf nutrient resorption to investigate the factors driving resorption efficiencies for nitrogen (NRE) and phosphorus (PRE). Our results show that leaf structure and habit, together with indicators of nutrient availability, are the two most important factors driving spatial variation in NRE. Overall, we find higher NRE in deciduous plants (65.2 % ± 12.4 %, n=400) than in evergreen plants (57.9 % ± 11.4 %, n=551), likely associated with a higher share of metabolic N in leaves of deciduous plants. Tropical regions show the lowest resorption for N (NRE: 52.4 % ± 12.1 %), and tundra ecosystems in polar regions show the highest (NRE: 69.6 % ± 12.8 %). At the same time, the PRE is lowest in temperate regions (57.8 % ± 13.6 %) and highest in boreal regions (67.3 % ± 13.6 %). Soil clay content, N and P atmospheric deposition (globally available proxies for soil fertility), and mean annual precipitation (MAP) play an important role in this pattern. The statistical relationships developed in this analysis indicate the important role of leaf habit and type for nutrient cycling and guide improved representations of plant-internal nutrient recycling and nutrient conservation strategies in vegetation models.

Almost 95 % of all terrestrial plant species form symbioses with mycorrhizal fungi that mediate plant–soil interactions: mycorrhizae facilitate plant nitrogen (N) acquisition and are, therefore, vital for plant growth, but they also build a pathway for plant-assimilated carbon (C) into the rhizosphere. Therefore, mycorrhizae likely play an important role in shaping the response of ecosystems to environmental changes such as rising atmospheric carbon dioxide (CO2) concentrations, which can increase plant N demand and the transfer of plant C assimilation to the soil. While the importance of mycorrhizal fungi is widely recognised, they are rarely represented in current terrestrial biosphere models (TBMs) explicitly. Here, we present a novel, dynamic plant–mycorrhiza–soil model as part of the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) TBM. This new model is based on mycorrhizal functional types that either actively mine soil organic matter (SOM) for N or enhance soil microbial activity through increased transfer of labile C into the rhizosphere, thereby (passively) priming SOM decomposition. Using the Duke Free-Air CO2 Enrichment (FACE) experiment, we show that mycorrhizal fungi can have important effects on projected SOM turnover and plant nutrition under ambient as well as elevated-CO2 treatments. Specifically, we find that including enhanced active mining of SOM for N in the model allows one to more closely match the observations with respect to observed decadal responses of plant growth, plant N acquisition and soil C dynamics to elevated CO2, whereas a simple enhancement of SOM turnover by increased below-ground C transfer of mycorrhizae is unable to replicate the observed responses. We provide an extensive parameter uncertainty study to investigate the robustness of our findings with respect to model parameters that cannot readily be constrained by observations. Our study points to the importance of implementing mycorrhizal functionalities in TBMs as well as to further observational needs to better constrain mycorrhizal models and to close the existing major knowledge gaps in actual mycorrhizal functioning.

Phosphorus (P) availability affects the response of terrestrial ecosystems to environmental and climate change (e.g., elevated CO2), yet the magnitude of this effect remains uncertain. This uncertainty arises mainly from a lack of quantitative understanding of the soil biological and geochemical P cycling processes, particularly the P exchange with soil mineral surfaces, which is often described by a Langmuir sorption isotherm. We first conducted a literature review on P sorption experiments and terrestrial biosphere models (TBMs) using a Langmuir isotherm. We then developed a new algorithm to describe the inorganic P exchange between soil solution and soil matrix based on the double-surface Langmuir isotherm and extracted empirical equations to calculate the sorption capacity and Langmuir coefficient. We finally tested the conventional and new models of P sorption at five beech forest sites in Germany along a soil P stock gradient using the QUINCY (QUantifying Interactions between terrestrial Nutrient CYcles and the climate system) TBM. We found that the conventional (single-surface) Langmuir isotherm approach in most TBMs largely differed from P sorption experiments regarding the sorption capacities and Langmuir coefficients, and it simulated an overly low soil P-buffering capacity. Conversely, the double-surface Langmuir isotherm approach adequately reproduced the observed patterns of soil inorganic P pools. The better representation of inorganic P cycling using the double-surface Langmuir approach also improved simulated foliar N and P concentrations as well as the patterns of gross primary production and vegetation carbon across the soil P gradient. The novel model generally reduces the estimates of P limitation compared with the conventional model, particularly at the low-P site, as the model constraint of slow inorganic P exchange on plant productivity is reduced.

Biological nitrogen fixation is the main source of new nitrogen into natural terrestrial ecosystems and consequently in the nitrogen cycle in many earth system models. Representation of biological nitrogen fixation varies, and because of the tight coupling between the carbon and nitrogen cycles, previous studies have shown that this affects projected changes in net primary productivity. Here we present the first assessment of the performance of biological nitrogen fixation in models contributing to CMIP6 compared to observed and observation-constrained estimates of biological nitrogen fixation. We find that 9 out of 10 models represent global total biological nitrogen fixation within the uncertainty in recent global estimates. However, 6 out of 10 models overestimate the amount of fixation in the tropics and therefore the extent of the latitudinal gradient in the global distribution. For the SSP3-7.0 scenario of future climate change, models project increases in fixation over the 21st century of up to 80 %. However, while the historical range of biological nitrogen fixation amongst models is large (up to 140 kg N ha−1 yr−1 at the grid cell level and 43–208 Tg N yr−1 globally) this does not have explanatory power for variations within the model ensemble of net primary productivity or the coupled nitrogen–carbon cycle. Models with shared structures can have significant variations in both biological nitrogen fixation and other parts of the nitrogen cycle without differing in their net primary productivity. This points to systematic challenges in the representation of carbon–nitrogen model structures and the severe limitations of models using net primary productivity or evapotranspiration to project the biological nitrogen fixation response to elevated atmospheric carbon dioxide or other environmental changes.

Australia plays an important role in the global terrestrial carbon cycle on inter-annual timescales. While the Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations of net biome production (NBP) and the carbon stored in vegetation between 1901 to 2018 from 13 DGVMs (TRENDY v8 ensemble). We focused our analysis on Australia's short-term (inter-annual) and long-term (decadal to centennial) terrestrial carbon dynamics. The TRENDY models simulated differing magnitudes of NBP on inter-annual timescales, and these differences resulted in significant differences in long-term vegetation carbon accumulation (−4.7 to 9.5 Pg C). We compared the TRENDY ensemble to several satellite-derived datasets and showed that the spread in the models' simulated carbon storage resulted from varying changes in carbon residence time rather than differences in net carbon uptake. Differences in simulated long-term accumulated NBP between models were mostly due to model responses to land-use change. The DGVMs also simulated different sensitivities to atmospheric carbon dioxide (CO2) concentration, although notably, the models with nutrient cycles did not simulate the smallest NBP response to CO2. Our results suggest that a change in the climate forcing did not have a large impact on the carbon cycle on long timescales. However, the inter-annual variability in precipitation drives the year-to-year variability in NBP. We analysed the impact of key modes of climate variability, including the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD), on NBP. While the DGVMs agreed on sign of the response of NBP to El Niño and La Niña and to positive and negative IOD events, the magnitude of inter-annual variability in NBP differed strongly between models. In addition, we find that differences in the timing of simulated phenology and fire dynamics are associated with differences in simulated or prescribed vegetation cover and process representation. We further find model disagreement in simulated vegetation carbon, phenology, and apparent carbon residence time, indicating that the models have different types and coverage of vegetation across Australia (whether prescribed or emergent). Our study highlights the need to evaluate parameter assumptions and the key processes that drive vegetation dynamics, such as phenology, mortality, and fire, in an Australian context to reduce uncertainty across models.

Satellite data reveal widespread changes in Earth's vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the CO2 fertilization effect as the dominant driver in only two biomes, the temperate forests and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

Tropospheric ozone (O3) and nitrogen deposition affect vegetation growth and, thereby, the ability of the land biosphere to take up and store carbon. However, the magnitude of these effects on the contemporary and future terrestrial carbon balance is insufficiently understood. Here, we apply an extended version of the O–CN terrestrial biosphere model that simulates the atmosphere to canopy transport of O3, its surface and stomatal uptake, the O3-induced leaf injury, and the coupled terrestrial carbon and nitrogen cycles. We use this model to simulate past and future impacts of air pollution against a background of concurrent changes in climate and carbon dioxide concentrations (CO2) for two contrasting representative concentration pathway (RCP) scenarios (RCP2.6 and RCP8.5). The simulations show that O3-related damage considerably reduced northern hemispheric gross primary production (GPP) and long-term carbon storage between 1850 and the 2010s. The simulated O3 effect on GPP in the Northern Hemisphere peaked towards the end of the 20th century, with reductions of 4 %, causing a reduction in the northern hemispheric carbon sink of 0.4 Pg C yr−1. During the 21st century, O3-induced reductions in GPP and carbon storage are projected to decline, through a combination of direct air pollution control methods that reduce near-surface O3 and the indirect effects of rising atmospheric CO2, which reduces stomatal uptake of O3 concurrent with increases of leaf-level water use efficiency. However, in hot spot regions such as East Asia, the model simulations suggest a sustained decrease in GPP by more than 8 % throughout the 21st century. O3 exposure reduces projected carbon storage at the end of the 21st century by up to 15 % in parts of Europe, the US, and East Asia. Our simulations suggest that the stimulating effect of nitrogen deposition on regional GPP and carbon storage is lower in magnitude compared to the detrimental effect of O3 during most of the simulation period for both RCPs. In the second half of the 21st century, the detrimental effect of O3 on GPP is outweighed by nitrogen deposition, but the effect of nitrogen deposition on land carbon storage remains lower than the effect of O3. Accounting for the stimulating effects of nitrogen deposition but omitting the detrimental effect of O3 may lead to an overestimation of projected carbon uptake and storage.

Plant phenology plays a fundamental role in land–atmosphere interactions, and its variability and variations are an indicator of climate and environmental changes. For this reason, current land surface models include phenology parameterizations and related biophysical and biogeochemical processes. In this work, the climatology of the beginning and end of the growing season, simulated by the land component of seven state-of-the-art European Earth system models participating in the CMIP6, is evaluated globally against satellite observations. The assessment is performed using the vegetation metric leaf area index and a recently developed approach, named four growing season types. On average, the land surface models show a 0.6-month delay in the growing season start, while they are about 0.5 months earlier in the growing season end. The difference with observation tends to be higher in the Southern Hemisphere compared to the Northern Hemisphere. High agreement between land surface models and observations is exhibited in areas dominated by broadleaf deciduous trees, while high variability is noted in regions dominated by broadleaf deciduous shrubs. Generally, the timing of the growing season end is accurately simulated in about 25 % of global land grid points versus 16 % in the timing of growing season start. The refinement of phenology parameterization can lead to better representation of vegetation-related energy, water, and carbon cycles in land surface models, but plant phenology is also affected by plant physiology and soil hydrology processes. Consequently, phenology representation and, in general, vegetation modelling is a complex task, which still needs further improvement, evaluation, and multi-model comparison.

The trajectories of soil carbon in our changing climate are of the utmost importance as soil is a substantial carbon reservoir with a large potential to impact the atmospheric carbon dioxide (CO2) burden. Atmospheric CO2 observations integrate all processes affecting carbon exchange between the surface and the atmosphere and therefore are suitable for carbon cycle model evaluation. In this study, we present a framework for how to use atmospheric CO2 observations to evaluate two distinct soil carbon models (CBALANCE, CBA, and Yasso, YAS) that are implemented in a global land surface model (JSBACH). We transported the biospheric carbon fluxes obtained by JSBACH using the atmospheric transport model TM5 to obtain atmospheric CO2. We then compared these results with surface observations from Global Atmosphere Watch stations, as well as with column XCO2 retrievals from GOSAT (Greenhouse Gases Observing Satellite). The seasonal cycles of atmospheric CO2 estimated by the two different soil models differed. The estimates from the CBALANCE soil model were more in line with the surface observations at low latitudes (0–45∘ N) with only a 1 % bias in the seasonal cycle amplitude, whereas Yasso underestimated the seasonal cycle amplitude in this region by 32 %. Yasso, on the other hand, gave more realistic seasonal cycle amplitudes of CO2 at northern boreal sites (north of 45∘ N) with an underestimation of 15 % compared to a 30 % overestimation by CBALANCE. Generally, the estimates from CBALANCE were more successful in capturing the seasonal patterns and seasonal cycle amplitudes of atmospheric CO2 even though it overestimated soil carbon stocks by 225 % (compared to an underestimation of 36 % by Yasso), and its estimations of the global distribution of soil carbon stocks were unrealistic. The reasons for these differences in the results are related to the different environmental drivers and their functional dependencies on the two soil carbon models. In the tropics, heterotrophic respiration in the Yasso model increased earlier in the season since it is driven by precipitation instead of soil moisture, as in CBALANCE. In temperate and boreal regions, the role of temperature is more dominant. There, heterotrophic respiration from the Yasso model had a larger seasonal amplitude, which is driven by air temperature, compared to CBALANCE, which is driven by soil temperature. The results underline the importance of using sub-annual data in the development of soil carbon models when they are used at shorter than annual timescales.

The nitrogen cycle and its effect on carbon uptake in the terrestrial biosphere is a recent progression in earth system models. As with any new component of a model, it is important to understand the behaviour, strengths, and limitations of the various process representations. Here we assess and compare five land surface models with nitrogen cycles that are used as the terrestrial components of some of the earth system models in CMIP6. The land surface models were run offline with a common spin-up and forcing protocol. We use a historical control simulation and two perturbations to assess the model nitrogen-related performances: a simulation with atmospheric carbon dioxide increased by 200 ppm and one with nitrogen deposition increased by 50 kgN ha−1 yr−1. There is generally greater variability in productivity response between models to increased nitrogen than to carbon dioxide. Across the five models the response to carbon dioxide globally was 5 % to 20 % and the response to nitrogen was 2 % to 24 %. The models are not evenly distributed within the ensemble range, with two of the models having low productivity response to nitrogen and another one with low response to elevated atmospheric carbon dioxide, compared to the other models. In all five models individual grid cells tend to exhibit bimodality, with either a strong response to increased nitrogen or atmospheric carbon dioxide but rarely to both to an equal extent. However, this local effect does not scale to either the regional or global level. The global and tropical responses are generally more accurately modelled than boreal, tundra, or other high-latitude areas compared to observations. These results are due to divergent choices in the representation of key nitrogen cycle processes. They show the need for more observational studies to enhance understanding of nitrogen cycle processes, especially nitrogen-use efficiency and biological nitrogen fixation.

During the last decade, carbon cycle data assimilation systems (CCDAS) have focused on improving the simulation of seasonal and mean global carbon fluxes over a few years by simultaneous assimilation of multiple data streams. However, the ability of a CCDAS to predict longer-term trends and variability of the global carbon cycle and the constraint provided by the observations have not yet been assessed. Here, we evaluate two near-decade-long assimilation experiments of the Max Planck Institute – Carbon Cycle Data Assimilation System (MPI-CCDAS v1) using spaceborne estimates of the fraction of absorbed photosynthetic active radiation (FAPAR) and atmospheric CO2 concentrations from the global network of flask measurement sites from either 1982 to 1990 or 1990 to 2000. We contrast these simulations with independent observations from the period 1982–2010, as well as a third MPI-CCDAS assimilation run using data from the full 1982–2010 period, and an atmospheric inversion covering the same data and time. With 30 years of data, MPI-CCDAS is capable of representing land uptake to a sufficient degree to make it compatible with the atmospheric CO2 record. The long-term trend and seasonal amplitude of atmospheric CO2 concentrations at station level over the period 1982 to 2010 is considerably improved after assimilating only the first decade (1982–1990) of observations. After 15–19 years of prognostic simulation, the simulated CO2 mixing ratio in 2007–2010 diverges by only 2±1.3 ppm from the observations, the atmospheric inversion, and the MPI-CCDAS assimilation run using observations from the full period. The long-term trend, phenological seasonality, and interannual variability (IAV) of FAPAR in the Northern Hemisphere over the last 1 to 2 decades after the assimilation were also improved. Despite imperfections in the representation of the IAV in atmospheric CO2, model–data fusion for a decade of data can already contribute to the prognostic capacity of land carbon cycle models at relevant timescales.

Regional estimates of the effects of ozone pollution on forest growth depend on the availability of reliable injury functions that estimate a representative ecosystem response to ozone exposure. A number of such injury functions for forest tree species and forest functional types have recently been published and subsequently applied in terrestrial biosphere models to estimate regional or global effects of ozone on forest tree productivity and carbon storage in the living plant biomass. The resulting impacts estimated by these biosphere models show large uncertainty in the magnitude of ozone effects predicted. To understand the role that these injury functions play in determining the variability in estimated ozone impacts, we use the O-CN biosphere model to provide a standardised modelling framework. We test four published injury functions describing the leaf-level, photosynthetic response to ozone exposure (targeting the maximum carboxylation capacity of Rubisco (Vcmax) or net photosynthesis) in terms of their simulated whole-tree biomass responses against data from 23 ozone filtration/fumigation experiments conducted with young trees from European tree species at sites across Europe with a range of climatic conditions. Our results show that none of these previously published injury functions lead to simulated whole-tree biomass reductions in agreement with the observed dose–response relationships derived from these field experiments and instead lead to significant over- or underestimations of the ozone effect. By re-parameterising these photosynthetically based injury functions, we develop linear, plant-functional-type-specific dose–response relationships, which provide accurate simulations of the observed whole-tree biomass response across these 23 experiments.

The availability of nitrogen is one of the primary controls on plant growth. Terrestrial ecosystem nitrogen availability is not only determined by inputs from fixation, deposition, or weathering, but is also regulated by the rates with which nitrogen is lost through various pathways. Estimates of large-scale nitrogen loss rates have been associated with considerable uncertainty, as process rates and controlling factors of the different loss pathways have been difficult to characterize in the field. Therefore, the nitrogen loss representations in terrestrial biosphere models vary substantially, adding to nitrogen cycle-related uncertainty and resulting in varying predictions of how the biospheric carbon sink will evolve under future scenarios of elevated atmospheric CO2. Here, we test three commonly applied approaches to represent ecosystem-level nitrogen loss in a common carbon–nitrogen terrestrial biosphere model with respect to their impact on projections of the effect of elevated CO2. We find that despite differences in predicted responses of nitrogen loss rates to elevated CO2 and climate forcing, the variety of nitrogen loss representation between models only leads to small variety in carbon sink predictions. The nitrogen loss responses are particularly uncertain in the boreal and tropical regions, where plant growth is strongly nitrogen-limited or nitrogen turnover rates are usually high, respectively. This highlights the need for better representation of nitrogen loss fluxes through global measurements to inform models.

Wetlands of northern high latitudes are ecosystems highly vulnerable to climate change. Some degradation effects include soil hydrologic changes due to permafrost thaw, formation of deeper active layers, and rising topsoil temperatures that accelerate the degradation of permafrost carbon and increase in CO2 and CH4 emissions. In this work we present 2 years of modeled year-round CH4 emissions into the atmosphere from a Northeast Siberian region in the Russian Far East. We use a revisited version of the process-based JSBACH-methane model that includes four CH4 transport pathways: plant-mediated transport, ebullition and molecular diffusion in the presence or absence of snow. The gas is emitted through wetlands represented by grid cell inundated areas simulated with a TOPMODEL approach. The magnitude of the summertime modeled CH4 emissions is comparable to ground-based CH4 fluxes measured with the eddy covariance technique and flux chambers in the same area of study, whereas wintertime modeled values are underestimated by 1 order of magnitude. In an annual balance, the most important mechanism for transport of methane into the atmosphere is through plants (61 %). This is followed by ebullition ( ∼ 35 %), while summertime molecular diffusion is negligible (0.02 %) compared to the diffusion through the snow during winter ( ∼ 4 %). We investigate the relationship between temporal changes in the CH4 fluxes, soil temperature, and soil moisture content. Our results highlight the heterogeneity in CH4 emissions at landscape scale and suggest that further improvements to the representation of large-scale hydrological conditions in the model will facilitate a more process-oriented land surface scheme and better simulate CH4 emissions under climate change. This is especially necessary at regional scales in Arctic ecosystems influenced by permafrost thaw.

The response of the terrestrial net ecosystem exchange (NEE) of CO2 to climate variations and trends may crucially determine the future climate trajectory. Here we directly quantify this response on inter-annual timescales by building a linear regression of inter-annual NEE anomalies against observed air temperature anomalies into an atmospheric inverse calculation based on long-term atmospheric CO2 observations. This allows us to estimate the sensitivity of NEE to inter-annual variations in temperature (seen as a climate proxy) resolved in space and with season. As this sensitivity comprises both direct temperature effects and the effects of other climate variables co-varying with temperature, we interpret it as inter-annual climate sensitivity. We find distinct seasonal patterns of this sensitivity in the northern extratropics that are consistent with the expected seasonal responses of photosynthesis, respiration, and fire. Within uncertainties, these sensitivity patterns are consistent with independent inferences from eddy covariance data. On large spatial scales, northern extratropical and tropical inter-annual NEE variations inferred from the NEE–T regression are very similar to the estimates of an atmospheric inversion with explicit inter-annual degrees of freedom. The results of this study offer a way to benchmark ecosystem process models in more detail than existing effective global climate sensitivities. The results can also be used to gap-fill or extrapolate observational records or to separate inter-annual variations from longer-term trends.

The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions and for ecosystem processes affected by LULCC. One drawback of LULCC emissions from DGVMs, however, is lack of observation constraint. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (ELUCc) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and ELUCc. This method is applicable on the global and regional scale. The original DGVM estimates of ELUCc range from 94 to 273 PgC during 1901–2012. After constraining by current biomass observations, we derive a best estimate of 155 ± 50 PgC (1σ Gaussian error). The constrained LULCC emissions are higher than prior DGVM values in tropical regions but significantly lower in North America. Our emergent constraint approach independently verifies the median model estimate by biomass observations, giving support to the use of this estimate in carbon budget assessments. The uncertainty in the constrained ELUCc is still relatively large because of the uncertainty in the biomass observations, and thus reduced uncertainty in addition to increased accuracy in biomass observations in the future will help improve the constraint. This constraint method can also be applied to evaluate the impact of land-based mitigation activities.

Recent satellite observations of sun-induced chlorophyll fluorescence (SIF) are thought to provide a large-scale proxy for gross primary production (GPP), thus providing a new way to assess the performance of land surface models (LSMs). In this study, we assessed how well SIF is able to predict GPP in the Fenno-Scandinavian region and what potential limitations for its application exist. We implemented a SIF model into the JSBACH LSM and used active leaf-level chlorophyll fluorescence measurements (Chl F) to evaluate the performance of the SIF module at a coniferous forest at Hyytiälä, Finland. We also compared simulated GPP and SIF at four Finnish micrometeorological flux measurement sites to observed GPP as well as to satellite-observed SIF. Finally, we conducted a regional model simulation for the Fenno-Scandinavian region with JSBACH and compared the results to SIF retrievals from the GOME-2 (Global Ozone Monitoring Experiment-2) space-borne spectrometer and to observation-based regional GPP estimates. Both observations and simulations revealed that SIF can be used to estimate GPP at both site and regional scales. At regional scale the model was able to simulate observed SIF averaged over 5 years with r2 of 0.86. The GOME-2-based SIF was a better proxy for GPP than the remotely sensed fAPAR (fraction of absorbed photosynthetic active radiation by vegetation). The observed SIF captured the seasonality of the photosynthesis at site scale and showed feasibility for use in improving of model seasonality at site and regional scale.

Ozone (O3) is a toxic air pollutant that can damage plant leaves and substantially affect the plant's gross primary production (GPP) and health. Realistic estimates of the effects of tropospheric anthropogenic O3 on GPP are thus potentially important to assess the strength of the terrestrial biosphere as a carbon sink. To better understand the impact of ozone damage on the terrestrial carbon cycle, we developed a module to estimate O3 uptake and damage of plants for a state-of-the-art global terrestrial biosphere model called OCN. Our approach accounts for ozone damage by calculating (a) O3 transport from 45 m height to leaf level, (b) O3 flux into the leaf, and (c) ozone damage of photosynthesis as a function of the accumulated O3 uptake over the lifetime of a leaf. A comparison of modelled canopy conductance, GPP, and latent heat to FLUXNET data across European forest and grassland sites shows a general good performance of OCN including ozone damage. This comparison provides a good baseline on top of which ozone damage can be evaluated. In comparison to literature values, we demonstrate that the new model version produces realistic O3 surface resistances, O3 deposition velocities, and stomatal to total O3 flux ratios. A sensitivity study reveals that key metrics of the air-to-leaf O3 transport and O3 deposition, in particular the stomatal O3 uptake, are reasonably robust against uncertainty in the underlying parameterisation of the deposition scheme. Nevertheless, correctly estimating canopy conductance plays a pivotal role in the estimate of cumulative O3 uptake. We further find that accounting for stomatal and non-stomatal uptake processes substantially affects simulated plant O3 uptake and accumulation, because aerodynamic resistance and non-stomatal O3 destruction reduce the predicted leaf-level O3 concentrations. Ozone impacts on GPP and transpiration in a Europe-wide simulation indicate that tropospheric O3 impacts the regional carbon and water cycling less than expected from previous studies. This study presents a first step towards the integration of atmospheric chemistry and ecosystem dynamics modelling, which would allow for assessing the wider feedbacks between vegetation ozone uptake and tropospheric ozone burden.

We examined the net terrestrial carbon flux to the atmosphere (FTA) simulated by nine models from the TRENDY dynamic global vegetation model project for its seasonal cycle and amplitude trend during 1961–2012. While some models exhibit similar phase and amplitude compared to atmospheric inversions, with spring drawdown and autumn rebound, others tend to rebound early in summer. The model ensemble mean underestimates the magnitude of the seasonal cycle by 40 % compared to atmospheric inversions. Global FTA amplitude increase (19 ± 8 %) and its decadal variability from the model ensemble are generally consistent with constraints from surface atmosphere observations. However, models disagree on attribution of this long-term amplitude increase, with factorial experiments attributing 83 ± 56 %, −3 ± 74 and 20 ± 30 % to rising CO2, climate change and land use/cover change, respectively. Seven out of the nine models suggest that CO2 fertilization is the strongest control – with the notable exception of VEGAS, which attributes approximately equally to the three factors. Generally, all models display an enhanced seasonality over the boreal region in response to high-latitude warming, but a negative climate contribution from part of the Northern Hemisphere temperate region, and the net result is a divergence over climate change effect. Six of the nine models show that land use/cover change amplifies the seasonal cycle of global FTA: some are due to forest regrowth, while others are caused by crop expansion or agricultural intensification, as revealed by their divergent spatial patterns. We also discovered a moderate cross-model correlation between FTA amplitude increase and increase in land carbon sink (R2 = 0.61). Our results suggest that models can show similar results in some benchmarks with different underlying mechanisms; therefore, the spatial traits of CO2 fertilization, climate change and land use/cover changes are crucial in determining the right mechanisms in seasonal carbon cycle change as well as mean sink change.

Including a terrestrial nitrogen (N) cycle in Earth system models has led to substantial attenuation of predicted biosphere–climate feedbacks. However, the magnitude of this attenuation remains uncertain. A particularly important but highly uncertain process is biological nitrogen fixation (BNF), which is the largest natural input of N to land ecosystems globally. In order to quantify this uncertainty and estimate likely effects on terrestrial biosphere dynamics, we applied six alternative formulations of BNF spanning the range of process formulations in current state-of-the-art biosphere models within a common framework, the O-CN model: a global map of static BNF rates, two empirical relationships between BNF and other ecosystem variables (net primary productivity and evapotranspiration), two process-oriented formulations based on plant N status, and an optimality-based approach. We examined the resulting differences in model predictions under ambient and elevated atmospheric [CO2] and found that the predicted global BNF rates and their spatial distribution for contemporary conditions were broadly comparable, ranging from 108 to 148 Tg N yr−1 (median: 128 Tg N yr−1), despite distinct regional patterns associated with the assumptions of each approach. Notwithstanding, model responses in BNF rates to elevated levels of atmospheric [CO2] (+200 ppm) ranged between −4 Tg N yr−1 (−3 %) and 56 Tg N yr−1 (+42 %) (median: 7 Tg N yr−1 (+8 %)). As a consequence, future projections of global ecosystem carbon (C) storage (+281 to +353 Pg C, or +13 to +16 %) as well as N2O emission (−1.6 to +0.5 Tg N yr−1, or −19 to +7 %) differed significantly across the different model formulations. Our results emphasize the importance of better understanding the nature and magnitude of BNF responses to change-induced perturbations, particularly through new empirical perturbation experiments and improved model representation.

We modeled the carbon (C) cycle in Mexico with a process-based approach. We used different available products (satellite data, field measurements, models and flux towers) to estimate C stocks and fluxes in the country at three different time frames: present (defined as the period 2000–2005), the past century (1901–2000) and the remainder of this century (2010–2100). Our estimate of the gross primary productivity (GPP) for the country was 2137 ± 1023 TgC yr−1 and a total C stock of 34 506 ± 7483 TgC, with 20 347 ± 4622 TgC in vegetation and 14 159 ± 3861 in the soil.Contrary to other current estimates for recent decades, our results showed that Mexico was a C sink over the period 1990–2009 (+31 TgC yr−1) and that C accumulation over the last century amounted to 1210 ± 1040 TgC. We attributed this sink to the CO2 fertilization effect on GPP, which led to an increase of 3408 ± 1060 TgC, while both climate and land use reduced the country C stocks by −458 ± 1001 and −1740 ± 878 TgC, respectively. Under different future scenarios, the C sink will likely continue over the 21st century, with decreasing C uptake as the climate forcing becomes more extreme. Our work provides valuable insights on relevant driving processes of the C cycle such as the role of drought in drylands (e.g., grasslands and shrublands) and the impact of climate change on the mean residence time of soil C in tropical ecosystems.

The land and ocean absorb on average just over half of the anthropogenic emissions of carbon dioxide (CO2) every year. These CO2 "sinks" are modulated by climate change and variability. Here we use a suite of nine dynamic global vegetation models (DGVMs) and four ocean biogeochemical general circulation models (OBGCMs) to estimate trends driven by global and regional climate and atmospheric CO2 in land and oceanic CO2 exchanges with the atmosphere over the period 1990–2009, to attribute these trends to underlying processes in the models, and to quantify the uncertainty and level of inter-model agreement. The models were forced with reconstructed climate fields and observed global atmospheric CO2; land use and land cover changes are not included for the DGVMs. Over the period 1990–2009, the DGVMs simulate a mean global land carbon sink of −2.4 ± 0.7 Pg C yr−1 with a small significant trend of −0.06 ± 0.03 Pg C yr−2 (increasing sink). Over the more limited period 1990–2004, the ocean models simulate a mean ocean sink of −2.2 ± 0.2 Pg C yr−1 with a trend in the net C uptake that is indistinguishable from zero (−0.01 ± 0.02 Pg C yr−2). The two ocean models that extended the simulations until 2009 suggest a slightly stronger, but still small, trend of −0.02 ± 0.01 Pg C yr−2. Trends from land and ocean models compare favourably to the land greenness trends from remote sensing, atmospheric inversion results, and the residual land sink required to close the global carbon budget. Trends in the land sink are driven by increasing net primary production (NPP), whose statistically significant trend of 0.22 ± 0.08 Pg C yr−2 exceeds a significant trend in heterotrophic respiration of 0.16 ± 0.05 Pg C yr−2 – primarily as a consequence of widespread CO2 fertilisation of plant production. Most of the land-based trend in simulated net carbon uptake originates from natural ecosystems in the tropics (−0.04 ± 0.01 Pg C yr−2), with almost no trend over the northern land region, where recent warming and reduced rainfall offsets the positive impact of elevated atmospheric CO2 and changes in growing season length on carbon storage. The small uptake trend in the ocean models emerges because climate variability and change, and in particular increasing sea surface temperatures, tend to counter\-act the trend in ocean uptake driven by the increase in atmospheric CO2. Large uncertainty remains in the magnitude and sign of modelled carbon trends in several regions, as well as regarding the influence of land use and land cover changes on regional trends.

The LPJ-GUESS dynamic vegetation model uniquely combines an individual- and patch-based representation of vegetation dynamics with ecosystem biogeochemical cycling from regional to global scales. We present an updated version that includes plant and soil N dynamics, analysing the implications of accounting for C–N interactions on predictions and performance of the model. Stand structural dynamics and allometric scaling of tree growth suggested by global databases of forest stand structure and development were well reproduced by the model in comparison to an earlier multi-model study. Accounting for N cycle dynamics improved the goodness of fit for broadleaved forests. N limitation associated with low N-mineralisation rates reduces productivity of cold-climate and dry-climate ecosystems relative to mesic temperate and tropical ecosystems. In a model experiment emulating free-air CO2 enrichment (FACE) treatment for forests globally, N limitation associated with low N-mineralisation rates of colder soils reduces CO2 enhancement of net primary production (NPP) for boreal forests, while some temperate and tropical forests exhibit increased NPP enhancement. Under a business-as-usual future climate and emissions scenario, ecosystem C storage globally was projected to increase by ca. 10%; additional N requirements to match this increasing ecosystem C were within the high N supply limit estimated on stoichiometric grounds in an earlier study. Our results highlight the importance of accounting for C–N interactions in studies of global terrestrial N cycling, and as a basis for understanding mechanisms on local scales and in different regional contexts.

Earth system models (ESMs) are increasing in complexity by incorporating more processes than their predecessors, making them potentially important tools for studying the evolution of climate and associated biogeochemical cycles. However, their coupled behaviour has only recently been examined in any detail, and has yielded a very wide range of outcomes. For example, coupled climate–carbon cycle models that represent land-use change simulate total land carbon stores at 2100 that vary by as much as 600 Pg C, given the same emissions scenario. This large uncertainty is associated with differences in how key processes are simulated in different models, and illustrates the necessity of determining which models are most realistic using rigorous methods of model evaluation. Here we assess the state-of-the-art in evaluation of ESMs, with a particular emphasis on the simulation of the carbon cycle and associated biospheric processes. We examine some of the new advances and remaining uncertainties relating to (i) modern and palaeodata and (ii) metrics for evaluation. We note that the practice of averaging results from many models is unreliable and no substitute for proper evaluation of individual models. We discuss a range of strategies, such as the inclusion of pre-calibration, combined process- and system-level evaluation, and the use of emergent constraints, that can contribute to the development of more robust evaluation schemes. An increasingly data-rich environment offers more opportunities for model evaluation, but also presents a challenge. Improved knowledge of data uncertainties is still necessary to move the field of ESM evaluation away from a "beauty contest" towards the development of useful constraints on model outcomes.

Terrestrial ecosystem models used for Earth system modelling show a significant divergence in future patterns of ecosystem processes, in particular the net land–atmosphere carbon exchanges, despite a seemingly common behaviour for the contemporary period. An in-depth evaluation of these models is hence of high importance to better understand the reasons for this disagreement. Here, we develop an extension for existing benchmarking systems by making use of the complementary information contained in the observational records of atmospheric CO2 and remotely sensed vegetation activity to provide a novel set of diagnostics of ecosystem responses to climate variability in the last 30 yr at different temporal and spatial scales. The selection of observational characteristics (traits) specifically considers the robustness of information given that the uncertainty of both data and evaluation methodology is largely unknown or difficult to quantify. Based on these considerations, we introduce a baseline benchmark – a minimum test that any model has to pass – to provide a more objective, quantitative evaluation framework. The benchmarking strategy can be used for any land surface model, either driven by observed meteorology or coupled to a climate model. We apply this framework to evaluate the offline version of the MPI Earth System Model's land surface scheme JSBACH. We demonstrate that the complementary use of atmospheric CO2 and satellite-based vegetation activity data allows pinpointing of specific model deficiencies that would not be possible by the sole use of atmospheric CO2 observations.

We summarise the contemporary carbon budget of South America and relate it to its dominant controls: population and economic growth, changes in land use practices and a changing atmospheric environment and climate. Component flux estimate methods we consider sufficiently reliable for this purpose encompass fossil fuel emission inventories, biometric analysis of old-growth rainforests, estimation of carbon release associated with deforestation based on remote sensing and inventories, and agricultural export data. Alternative methods for the estimation of the continental-scale net land to atmosphere CO2 flux, such as atmospheric transport inverse modelling and terrestrial biosphere model predictions, are, we find, hampered by the data paucity, and improved parameterisation and validation exercises are required before reliable estimates can be obtained. From our analysis of available data, we suggest that South America was a net source to the atmosphere during the 1980s (~ 0.3–0.4 Pg C a−1) and close to neutral (~ 0.1 Pg C a−1) in the 1990s. During the latter period, carbon uptake in old-growth forests nearly compensated for the carbon release associated with fossil fuel burning and deforestation. Annual mean precipitation over tropical South America as inferred from Amazon River discharge shows a long-term upward trend. Although, over the last decade dry seasons have tended to be drier, with the years 2005 and 2010 in particular experiencing strong droughts. On the other hand, precipitation during the wet seasons also shows an increasing trend. Air temperatures have also increased slightly. Also with increases in atmospheric CO2 concentrations, it is currently unclear what effect these climate changes are having on the forest carbon balance of the region. Current indications are that the forests of the Amazon Basin have acted as a substantial long-term carbon sink, but with the most recent measurements suggesting that this sink may be weakening. Economic development of the tropical regions of the continent is advancing steadily, with exports of agricultural products being an important driver and witnessing a strong upturn over the last decade.

Land models, which have been developed by the modeling community in the past few decades to predict future states of ecosystems and climate, have to be critically evaluated for their performance skills of simulating ecosystem responses and feedback to climate change. Benchmarking is an emerging procedure to measure performance of models against a set of defined standards. This paper proposes a benchmarking framework for evaluation of land model performances and, meanwhile, highlights major challenges at this infant stage of benchmark analysis. The framework includes (1) targeted aspects of model performance to be evaluated, (2) a set of benchmarks as defined references to test model performance, (3) metrics to measure and compare performance skills among models so as to identify model strengths and deficiencies, and (4) model improvement. Land models are required to simulate exchange of water, energy, carbon and sometimes other trace gases between the atmosphere and land surface, and should be evaluated for their simulations of biophysical processes, biogeochemical cycles, and vegetation dynamics in response to climate change across broad temporal and spatial scales. Thus, one major challenge is to select and define a limited number of benchmarks to effectively evaluate land model performance. The second challenge is to develop metrics of measuring mismatches between models and benchmarks. The metrics may include (1) a priori thresholds of acceptable model performance and (2) a scoring system to combine data–model mismatches for various processes at different temporal and spatial scales. The benchmark analyses should identify clues of weak model performance to guide future development, thus enabling improved predictions of future states of ecosystems and climate. The near-future research effort should be on development of a set of widely acceptable benchmarks that can be used to objectively, effectively, and reliably evaluate fundamental properties of land models to improve their prediction performance skills.

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.

Globally, terrestrial ecosystems have absorbed about 30% of anthropogenic greenhouse gas emissions over the period 2000–2007 and inter-hemispheric gradients indicate that a significant fraction of terrestrial carbon sequestration must be north of the Equator. We present a compilation of the CO2, CO, CH4 and N2O balances of Europe following a dual constraint approach in which (1) a land-based balance derived mainly from ecosystem carbon inventories and (2) a land-based balance derived from flux measurements are compared to (3) the atmospheric data-based balance derived from inversions constrained by measurements of atmospheric GHG (greenhouse gas) concentrations. Good agreement between the GHG balances based on fluxes (1294 ± 545 Tg C in CO2-eq yr−1), inventories (1299 ± 200 Tg C in CO2-eq yr−1) and inversions (1210 ± 405 Tg C in CO2-eq yr−1) increases our confidence that the processes underlying the European GHG budget are well understood and reasonably sampled. However, the uncertainty remains large and largely lacks formal estimates. Given that European net land to atmosphere exchanges are determined by a few dominant fluxes, the uncertainty of these key components needs to be formally estimated before efforts could be made to reduce the overall uncertainty. The net land-to-atmosphere flux is a net source for CO2, CO, CH4 and N2O, because the anthropogenic emissions by far exceed the biogenic sink strength. The dual-constraint approach confirmed that the European biogenic sink removes as much as 205 ± 72 Tg C yr−1 from fossil fuel burning from the atmosphere. However, This C is being sequestered in both terrestrial and inland aquatic ecosystems. If the C-cost for ecosystem management is taken into account, the net uptake of ecosystems is estimated to decrease by 45% but still indicates substantial C-sequestration. However, when the balance is extended from CO2 towards the main GHGs, C-uptake by terrestrial and aquatic ecosystems is offset by emissions of non-CO2 GHGs. As such, the European ecosystems are unlikely to contribute to mitigating the effects of climate change.

The global geographic distribution of biodiversity and biomes is determined by species-specific physiological tolerances to climatic constraints. Current vegetation models employ empirical bioclimatic relationships to predict present-day vegetation patterns and to forecast biodiversity changes and biome shifts under climatic change. In this paper, we consider trade-offs in plant functioning and their responses under climatic changes to forecast and explain changes in plant functional richness and shifts in biome geographic distributions. The Jena Diversity model (JeDi) simulates plant survival according to essential plant functional trade-offs, including ecophysiological processes such as water uptake, photosynthesis, allocation, reproduction and phenology. We use JeDi to quantify changes in plant functional richness and biome shifts between present-day and a range of possible future climates from two SRES emission scenarios (A2 and B1) and seven global climate models using metrics of plant functional richness and functional identity. Our results show (i) a significant loss of plant functional richness in the tropics, (ii) an increase in plant functional richness at mid and high latitudes, and (iii) a pole-ward shift of biomes. While these results are consistent with the findings of empirical approaches, we are able to explain them in terms of the plant functional trade-offs involved in the allocation, metabolic and reproduction strategies of plants. We conclude that general aspects of plant physiological tolerances can be derived from functional trade-offs, which may provide a useful process- and trait-based alternative to bioclimatic relationships. Such a mechanistic approach may be particularly relevant when addressing vegetation responses to climatic changes that encounter novel combinations of climate parameters that do not exist under contemporary climate.

European ecosystems are thought to take up large amounts of carbon, but neither the rate nor the contributions of the underlying processes are well known. In the second half of the 20th century, carbon dioxide concentrations have risen by more that 100 ppm, atmospheric nitrogen deposition has more than doubled, and European mean temperatures were increasing by 0.02 °C yr−1. The extents of forest and grasslands have increased with the respective rates of 5800 km2 yr−1 and 1100 km2 yr−1 as agricultural land has been abandoned at a rate of 7000 km2 yr−1. In this study, we analyze the responses of European land ecosystems to the aforementioned environmental changes using results from four process-based ecosystem models: BIOME-BGC, JULES, ORCHIDEE, and O-CN. The models suggest that European ecosystems sequester carbon at a rate of 56 TgC yr−1 (mean of four models for 1951–2000) with strong interannual variability (±88 TgC yr−1, average across models) and substantial inter-model uncertainty (±39 TgC yr−1). Decadal budgets suggest that there has been a continuous increase in the mean net carbon storage of ecosystems from 85 TgC yr−1 in 1980s to 108 TgC yr−1 in 1990s, and to 114 TgC yr−1 in 2000–2007. The physiological effect of rising CO2 in combination with nitrogen deposition and forest re-growth have been identified as the important explanatory factors for this net carbon storage. Changes in the growth of woody vegetation are suggested as an important contributor to the European carbon sink. Simulated ecosystem responses were more consistent for the two models accounting for terrestrial carbon-nitrogen dynamics than for the two models which only accounted for carbon cycling and the effects of land cover change. Studies of the interactions of carbon-nitrogen dynamics with land use changes are needed to further improve the quantitative understanding of the driving forces of the European land carbon balance.

Exchange of non-CO2 trace gases between the land surface and the atmosphere plays an important role in atmospheric chemistry and climate. Recent studies have highlighted its importance for interpretation of glacial-interglacial ice-core records, the simulation of the pre-industrial and present atmosphere, and the potential for large climate-chemistry and climate-aerosol feedbacks in the coming century. However, spatial and temporal variations in trace gas emissions and the magnitude of future feedbacks are a major source of uncertainty in atmospheric chemistry, air quality and climate science. To reduce such uncertainties Dynamic Global Vegetation Models (DGVMs) are currently being expanded to mechanistically represent processes relevant to non-CO2 trace gas exchange between land biota and the atmosphere. In this paper we present a review of important non-CO2 trace gas emissions, the state-of-the-art in DGVM modelling of processes regulating these emissions, identify key uncertainties for global scale model applications, and discuss a methodology for model integration and evaluation.

Water stress is a defining characteristic of Mediterranean ecosystems, and is likely to become more severe in the coming decades. Simulation models are key tools for making predictions, but our current understanding of how soil moisture controls ecosystem functioning is not sufficient to adequately constrain parameterisations. Canopy-scale flux data from four forest ecosystems with Mediterranean-type climates were used in order to analyse the physiological controls on carbon and water flues through the year. Significant non-stomatal limitations on photosynthesis were detected, along with lesser changes in the conductance-assimilation relationship. New model parameterisations were derived and implemented in two contrasting modelling approaches. The effectiveness of two models, one a dynamic global vegetation model ("ORCHIDEE"), and the other a forest growth model particularly developed for Mediterranean simulations ("GOTILWA+"), was assessed and modelled canopy responses to seasonal changes in soil moisture were analysed in comparison with in situ flux measurements. In contrast to commonly held assumptions, we find that changing the ratio of conductance to assimilation under natural, seasonally-developing, soil moisture stress is not sufficient to reproduce forest canopy CO2 and water fluxes. However, accurate predictions of both CO2 and water fluxes under all soil moisture levels encountered in the field are obtained if photosynthetic capacity is assumed to vary with soil moisture. This new parameterisation has important consequences for simulated responses of carbon and water fluxes to seasonal soil moisture stress, and should greatly improve our ability to anticipate future impacts of climate changes on the functioning of ecosystems in Mediterranean-type climates.

Globally, the year 2003 is associated with one of the largest atmospheric CO2 rises on record. In the same year, Europe experienced an anomalously strong flux of CO2 from the land to the atmosphere associated with an exceptionally dry and hot summer in Western and Central Europe. In this study we analyze the magnitude of this carbon flux anomaly and key driving ecosystem processes using simulations of seven terrestrial ecosystem models of different complexity and types (process-oriented and diagnostic). We address the following questions: (1) how large were deviations in the net European carbon flux in 2003 relative to a short-term baseline (1998–2002) and to longer-term variations in annual fluxes (1980 to 2005), (2) which European regions exhibited the largest changes in carbon fluxes during the growing season 2003, and (3) which ecosystem processes controlled the carbon balance anomaly . In most models the prominence of 2003 anomaly in carbon fluxes declined with lengthening of the reference period from one year to 16 years. The 2003 anomaly for annual net carbon fluxes ranged between 0.35 and –0.63 Pg C for a reference period of one year and between 0.17 and –0.37 Pg C for a reference period of 16 years for the whole Europe. In Western and Central Europe, the anomaly in simulated net ecosystem productivity (NEP) over the growing season in 2003 was outside the 1σ variance bound of the carbon flux anomalies for 1980–2005 in all models. The estimated anomaly in net carbon flux ranged between –42 and –158 Tg C for Western Europe and between 24 and –129 Tg C for Central Europe depending on the model used. All models responded to a dipole pattern of the climate anomaly in 2003. In Western and Central Europe NEP was reduced due to heat and drought. In contrast, lower than normal temperatures and higher air humidity decreased NEP over Northeastern Europe. While models agree on the sign of changes in simulated NEP and gross primary productivity in 2003 over Western and Central Europe, models diverge in the estimates of anomalies in ecosystem respiration. Except for two process models which simulate respiration increase, most models simulated a decrease in ecosystem respiration in 2003. The diagnostic models showed a weaker decrease in ecosystem respiration than the process-oriented models. Based on the multi-model simulations we estimated the total carbon flux anomaly over the 2003 growing season in Europe to range between –0.02 and –0.27 Pg C relative to the net carbon flux in 1998–2002.

Three terrestrial biosphere models (LPJ, Orchidee, Biome-BGC) were evaluated with respect to their ability to simulate large-scale climate related trends in gross primary production (GPP) across European forests. Simulated GPP and leaf area index (LAI) were compared with GPP estimates based on flux separated eddy covariance measurements of net ecosystem exchange and LAI measurements along a temperature gradient ranging from the boreal to the Mediterranean region. The three models capture qualitatively the pattern suggested by the site data: an increase in GPP from boreal to temperate and a subsequent decline from temperate to Mediterranean climates. The models consistently predict higher GPP for boreal and lower GPP for Mediterranean forests. Based on a decomposition of GPP into absorbed photosynthetic active radiation (APAR) and radiation use efficiency (RUE), the overestimation of GPP for the boreal coniferous forests appears to be primarily related to too high simulated LAI - and thus light absorption (APAR) – rather than too high radiation use efficiency. We cannot attribute the tendency of the models to underestimate GPP in the water limited region to model structural deficiencies with confidence. A likely dry bias of the input meteorological data in southern Europe may create this pattern. On average, the models compare similarly well to the site GPP data (RMSE of ~30% or 420 gC/m2/yr) but differences are apparent for different ecosystem types. In terms of absolute values, we find the agreement between site based GPP estimates and simulations acceptable when we consider uncertainties about the accuracy in model drivers, a potential representation bias of the eddy covariance sites, and uncertainties related to the method of deriving GPP from eddy covariance measurements data. Continental to global data-model comparison studies should be fostered in the future since they are necessary to identify consistent model bias along environmental gradients.