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Uncovering the mechanisms that lead to Amazon forest resilience variations is crucial to predict the impact of future climatic and anthropogenic disturbances. Here, we apply a previously used empirical resilience metrics, lag-1 month temporal autocorrelation (TAC), to vegetation optical depth data in C-band (a good proxy of the whole canopy water content) in order to explore how forest resilience variations are impacted by human disturbances and environmental drivers in the Brazilian Amazon. We found that human disturbances significantly increase the risk of critical transitions, and that the median TAC value is ~2.4 times higher in human-disturbed forests than that in intact forests, suggesting a much lower resilience in disturbed forests. Additionally, human-disturbed forests are less resilient to land surface heat stress and atmospheric water stress than intact forests. Among human-disturbed forests, forests with a more closed and thicker canopy structure, which is linked to a higher forest cover and a lower disturbance fraction, are comparably more resilient. These results further emphasize the urgent need to limit deforestation and degradation through policy intervention to maintain the resilience of the Amazon rainforests., (© 2023 John Wiley & Sons Ltd.)

Tropical forests play a pivotal role in regulating the global carbon cycle. However, the response of these forests to changes in absorbed solar energy and water supply under the changing climate is highly uncertain. Three-year (2018-2021) spaceborne high-resolution measurements of solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI) provide a new opportunity to study the response of gross primary production (GPP) and more broadly tropical forest carbon dynamics to differences in climate. SIF has been shown to be a good proxy for GPP on monthly and regional scales. Combining tropical climate reanalysis records and other contemporary satellite products, we find that on the seasonal timescale, the dependence of GPP on climate variables is highly heterogeneous. Following the principal component analyses and correlation comparisons, two regimes are identified: water limited and energy limited. GPP variations over tropical Africa are more correlated with water-related factors such as vapor pressure deficit (VPD) and soil moisture, while in tropical Southeast Asia, GPP is more correlated with energy-related factors such as photosynthetically active radiation (PAR) and surface temperature. Amazonia is itself heterogeneous: with an energy-limited regime in the north and water-limited regime in the south. The correlations of GPP with climate variables are supported by other observation-based products, such as Orbiting Carbon Observatory-2 (OCO2) SIF and FluxSat GPP. In each tropical continent, the coupling between SIF and VPD increases with the mean VPD. Even on the interannual timescale, the correlation of GPP with VPD is still discernable, but the sensitivity is smaller than the intra-annual correlation. By and large, the dynamic global vegetation models in the TRENDY v8 project do not capture the high GPP seasonal sensitivity to VPD in dry tropics. The complex interactions between carbon and water cycles in the tropics illustrated in this study and the poor representation of this coupling in the current suite of vegetation models suggest that projections of future changes in carbon dynamics based on these models may not be robust., (© 2023 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

In the Amazon, deforestation and climate change lead to increased vulnerability to forest degradation, threatening its existing carbon stocks and its capacity as a carbon sink. We use satellite L-Band Vegetation Optical Depth (L-VOD) data that provide an integrated (top-down) estimate of biomass carbon to track changes over 2011-2019. Because the spatial resolution of L-VOD is coarse (0.25°), it allows limited attribution of the observed changes. We therefore combined high-resolution annual maps of forest cover and disturbances with biomass maps to model carbon losses (bottom-up) from deforestation and degradation, and gains from regrowing secondary forests. We show an increase of deforestation and associated degradation losses since 2012 which greatly outweigh secondary forest gains. Degradation accounted for 40% of gross losses. After an increase in 2011, old-growth forests show a net loss of above-ground carbon between 2012 and 2019. The sum of component carbon fluxes in our model is consistent with the total biomass change from L-VOD of 1.3 Pg C over 2012-2019. Across nine Amazon countries, we found that while Brazil contains the majority of biomass stocks (64%), its losses from disturbances were disproportionately high (79% of gross losses). Our multi-source analysis provides a pessimistic assessment of the Amazon carbon balance and highlights the urgent need to stop the recent rise of deforestation and degradation, particularly in the Brazilian Amazon., (© 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

The rising atmospheric CO 2 concentration leads to a CO 2 fertilization effect on plants-that is, increased photosynthetic uptake of CO 2 by leaves and enhanced water-use efficiency (WUE). Yet, the resulting net impact of CO 2 fertilization on plant growth and soil moisture (SM) savings at large scale is poorly understood. Drylands provide a natural experimental setting to detect the CO 2 fertilization effect on plant growth since foliage amount, plant water-use and photosynthesis are all tightly coupled in water-limited ecosystems. A long-term change in the response of leaf area index (LAI, a measure of foliage amount) to changes in SM is likely to stem from changing water demand of primary productivity in water-limited ecosystems and is a proxy for changes in WUE. Using 34-year satellite observations of LAI and SM over tropical and subtropical drylands, we identify that a 1% increment in SM leads to 0.15% (±0.008, 95% confidence interval) and 0.51% (±0.01, 95% confidence interval) increments in LAI during 1982-1998 and 1999-2015, respectively. The increasing response of LAI to SM has contributed 7.2% (±3.0%, 95% confidence interval) to total dryland greening during 1999-2015 compared to 1982-1998. The increasing response of LAI to SM is consistent with the CO 2 fertilization effect on WUE in water-limited ecosystems, indicating that a given amount of SM has sustained greater amounts of photosynthetic foliage over time. The LAI responses to changes in SM from seven dynamic global vegetation models are not always consistent with observations, highlighting the need for improved process knowledge of terrestrial ecosystem responses to rising atmospheric CO 2 concentration., (© 2021 John Wiley & Sons Ltd.)

In this study, we use simulations from seven global vegetation models to provide the first multi-model estimate of fire impacts on global tree cover and the carbon cycle under current climate and anthropogenic land use conditions, averaged for the years 2001-2012. Fire globally reduces the tree covered area and vegetation carbon storage by 10%. Regionally, the effects are much stronger, up to 20% for certain latitudinal bands, and 17% in savanna regions. Global fire effects on total carbon storage and carbon turnover times are lower with the effect on gross primary productivity (GPP) close to 0. We find the strongest impacts of fire in savanna regions. Climatic conditions in regions with the highest burned area differ from regions with highest absolute fire impact, which are characterized by higher precipitation. Our estimates of fire-induced vegetation change are lower than previous studies. We attribute these differences to different definitions of vegetation change and effects of anthropogenic land use, which were not considered in previous studies and decreases the impact of fire on tree cover. Accounting for fires significantly improves the spatial patterns of simulated tree cover, which demonstrates the need to represent fire in dynamic vegetation models. Based upon comparisons between models and observations, process understanding and representation in models, we assess a higher confidence in the fire impact on tree cover and vegetation carbon compared to GPP, total carbon storage and turnover times. We have higher confidence in the spatial patterns compared to the global totals of the simulated fire impact. As we used an ensemble of state-of-the-art fire models, including effects of land use and the ensemble median or mean compares better to observational datasets than any individual model, we consider the here presented results to be the current best estimate of global fire effects on ecosystems., (© 2020 The Authors. Global Change Biology published by John Wiley & Sons Ltd.)

Changing amplitude of the seasonal cycle of atmospheric CO 2 (SCA) in the northern hemisphere is an emerging carbon cycle property. Mauna Loa (MLO) station (20°N, 156°W), which has the longest continuous northern hemisphere CO 2 record, shows an increasing SCA before the 1980s (p < .01), followed by no significant change thereafter. We analyzed the potential driving factors of SCA slowing-down, with an ensemble of dynamic global vegetation models (DGVMs) coupled with an atmospheric transport model. We found that slowing-down of SCA at MLO is primarily explained by response of net biome productivity (NBP) to climate change, and by changes in atmospheric circulations. Through NBP, climate change increases SCA at MLO before the 1980s and decreases it afterwards. The effect of climate change on the slowing-down of SCA at MLO is mainly exerted by intensified drought stress acting to offset the acceleration driven by CO 2 fertilization. This challenges the view that CO 2 fertilization is the dominant cause of emergent SCA trends at northern sites south of 40°N. The contribution of agricultural intensification on the deceleration of SCA at MLO was elusive according to land-atmosphere CO 2 flux estimated by DGVMs and atmospheric inversions. Our results also show the necessity to adequately account for changing circulation patterns in understanding carbon cycle dynamics observed from atmospheric observations and in using these observations to benchmark DGVMs., (© 2020 John Wiley & Sons Ltd.)

Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above-ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model-data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB 95 ) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB 95 , particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB., (© 2020 John Wiley & Sons Ltd.)

Understanding changes in terrestrial carbon balance is important to improve our knowledge of the regional carbon cycle and climate change. However, evaluating regional changes in the terrestrial carbon balance is challenging due to the lack of surface flux measurements. This study reveals that the terrestrial carbon uptake over the Republic of Korea has been enhanced from 1999 to 2017 by analyzing long-term atmospheric CO 2 concentration measurements at the Anmyeondo Station (36.53°N, 126.32°E) located in the western coast. The influence of terrestrial carbon flux on atmospheric CO 2 concentrations (ΔCO 2 ) is estimated from the difference of CO 2 concentrations that were influenced by the land sector (through easterly winds) and the Yellow Sea sector (through westerly winds). We find a significant trend in ΔCO 2 of -4.75 ppm per decade (p < .05) during the vegetation growing season (May through October), suggesting that the regional terrestrial carbon uptake has increased relative to the surrounding ocean areas. Combined analysis with satellite measured normalized difference vegetation index and gross primary production shows that the enhanced carbon uptake is associated with significant nationwide increases in vegetation and its production. Process-based terrestrial model and inverse model simulations estimate that regional terrestrial carbon uptake increases by up to 18.9 and 8.0 Tg C for the study period, accounting for 13.4% and 5.7% of the average annual domestic carbon emissions, respectively. Atmospheric chemical transport model simulations indicate that the enhanced terrestrial carbon sink is the primary reason for the observed ΔCO 2 trend rather than anthropogenic emissions and atmospheric circulation changes. Our results highlight the fact that atmospheric CO 2 measurements could open up the possibility of detecting regional changes in the terrestrial carbon cycle even where anthropogenic emissions are not negligible., (© 2020 John Wiley & Sons Ltd.)

Robust estimates of CO 2 budget, CO 2 exchanged between the atmosphere and terrestrial biosphere, are necessary to better understand the role of the terrestrial biosphere in mitigating anthropogenic CO 2 emissions. Over the past decade, this field of research has advanced through understanding of the differences and similarities of two fundamentally different approaches: "top-down" atmospheric inversions and "bottom-up" biosphere models. Since the first studies were undertaken, these approaches have shown an increasing level of agreement, but disagreements in some regions still persist, in part because they do not estimate the same quantity of atmosphere-biosphere CO 2 exchange. Here, we conducted a thorough comparison of CO 2 budgets at multiple scales and from multiple methods to assess the current state of the science in estimating CO 2 budgets. Our set of atmospheric inversions and biosphere models, which were adjusted for a consistent flux definition, showed a high level of agreement for global and hemispheric CO 2 budgets in the 2000s. Regionally, improved agreement in CO 2 budgets was notable for North America and Southeast Asia. However, large gaps between the two methods remained in East Asia and South America. In other regions, Europe, boreal Asia, Africa, South Asia, and Oceania, it was difficult to determine whether those regions act as a net sink or source because of the large spread in estimates from atmospheric inversions. These results highlight two research directions to improve the robustness of CO 2 budgets: (a) to increase representation of processes in biosphere models that could contribute to fill the budget gaps, such as forest regrowth and forest degradation; and (b) to reduce sink-source compensation between regions (dipoles) in atmospheric inversion so that their estimates become more comparable. Advancements on both research areas will increase the level of agreement between the top-down and bottom-up approaches and yield more robust knowledge of regional CO 2 budgets., (© 2019 John Wiley & Sons Ltd.)

With accumulation of carbon cycle observations and model developments over the past decades, exploring interannual variation (IAV) of terrestrial carbon cycle offers the opportunity to better understand climate-carbon cycle relationships. However, despite growing research interest, uncertainties remain on some fundamental issues, such as the contributions of different regions, constituent fluxes and climatic factors to carbon cycle IAV. Here we overviewed the literature on carbon cycle IAV about current understanding of these issues. Observations and models of the carbon cycle unanimously show the dominance of tropical land ecosystems to the signal of global carbon cycle IAV, where tropical semiarid ecosystems contribute as much as the combination of all other tropical ecosystems. Vegetation photosynthesis contributes more than ecosystem respiration to IAV of the global net land carbon flux, but large uncertainties remain on the contribution of fires and other disturbance fluxes. Climatic variations are the major drivers to the IAV of net land carbon flux. Although debate remains on whether the dominant driver is temperature or moisture variability, their interaction,that is, the dependence of carbon cycle sensitivity to temperature on moisture conditions, is emerging as key regulators of the carbon cycle IAV. On timescales from the interannual to the centennial, global carbon cycle variability will be increasingly contributed by northern land ecosystems and oceans. Therefore, both improving Earth system models (ESMs) with the progressive understanding on the fast processes manifested at interannual timescale and expanding carbon cycle observations at broader spatial and longer temporal scales are critical to better prediction on evolution of the carbon-climate system., (© 2019 John Wiley & Sons Ltd.)

Most climate mitigation scenarios involve negative emissions, especially those that aim to limit global temperature increase to 2°C or less. However, the carbon uptake potential in land-based climate change mitigation efforts is highly uncertain. Here, we address this uncertainty by using two land-based mitigation scenarios from two land-use models (IMAGE and MAgPIE) as input to four dynamic global vegetation models (DGVMs; LPJ-GUESS, ORCHIDEE, JULES, LPJmL). Each of the four combinations of land-use models and mitigation scenarios aimed for a cumulative carbon uptake of ~130 GtC by the end of the century, achieved either via the cultivation of bioenergy crops combined with carbon capture and storage (BECCS) or avoided deforestation and afforestation (ADAFF). Results suggest large uncertainty in simulated future land demand and carbon uptake rates, depending on the assumptions related to land use and land management in the models. Total cumulative carbon uptake in the DGVMs is highly variable across mitigation scenarios, ranging between 19 and 130 GtC by year 2099. Only one out of the 16 combinations of mitigation scenarios and DGVMs achieves an equivalent or higher carbon uptake than achieved in the land-use models. The large differences in carbon uptake between the DGVMs and their discrepancy against the carbon uptake in IMAGE and MAgPIE are mainly due to different model assumptions regarding bioenergy crop yields and due to the simulation of soil carbon response to land-use change. Differences between land-use models and DGVMs regarding forest biomass and the rate of forest regrowth also have an impact, albeit smaller, on the results. Given the low confidence in simulated carbon uptake for a given land-based mitigation scenario, and that negative emissions simulated by the DGVMs are typically lower than assumed in scenarios consistent with the 2°C target, relying on negative emissions to mitigate climate change is a highly uncertain strategy., (© 2018 John Wiley & Sons Ltd.)

No consensus has yet been reached on the major factors driving the observed increase in the seasonal amplitude of atmospheric CO 2 in the northern latitudes. In this study, we used atmospheric CO 2 records from 26 northern hemisphere stations with a temporal coverage longer than 15 years, and an atmospheric transport model prescribed with net biome productivity (NBP) from an ensemble of nine terrestrial ecosystem models, to attribute change in the seasonal amplitude of atmospheric CO 2 . We found significant (p < .05) increases in seasonal peak-to-trough CO 2 amplitude (AMP P -T ) at nine stations, and in trough-to-peak amplitude (AMP T -P ) at eight stations over the last three decades. Most of the stations that recorded increasing amplitudes are in Arctic and boreal regions (>50°N), consistent with previous observations that the amplitude increased faster at Barrow (Arctic) than at Mauna Loa (subtropics). The multi-model ensemble mean (MMEM) shows that the response of ecosystem carbon cycling to rising CO 2 concentration (eCO 2 ) and climate change are dominant drivers of the increase in AMP P -T and AMP T -P in the high latitudes. At the Barrow station, the observed increase of AMP P -T and AMP T -P over the last 33 years is explained by eCO 2 (39% and 42%) almost equally than by climate change (32% and 35%). The increased carbon losses during the months with a net carbon release in response to eCO 2 are associated with higher ecosystem respiration due to the increase in carbon storage caused by eCO 2 during carbon uptake period. Air-sea CO 2 fluxes (10% for AMP P -T and 11% for AMP T -P ) and the impacts of land-use change (marginally significant 3% for AMP P -T and 4% for AMP T -P ) also contributed to the CO 2 measured at Barrow, highlighting the role of these factors in regulating seasonal changes in the global carbon cycle., (© 2017 John Wiley & Sons Ltd.)

Strategies to mitigate climate change by reducing deforestation and forest degradation (e.g. REDD+) require country- or region-specific information on temporal changes in forest carbon (C) pools to develop accurate emission factors. The soil C pool is one of the most important C reservoirs, but is rarely included in national forest reference emission levels due to a lack of data. Here, we present the soil organic C (SOC) dynamics along 20 years of forest-to-pasture conversion in two subregions with different management practices during pasture establishment in the Colombian Amazon: high-grazing intensity (HG) and low-grazing intensity (LG) subregions. We determined the pattern of SOC change resulting from the conversion from forest (C3 plants) to pasture (C4 plants) by analysing total SOC stocks and the natural abundance of the stable isotopes (13) C along two 20-year chronosequences identified in each subregion. We also analysed soil N stocks and the natural abundance of (15) N during pasture establishment. In general, total SOC stocks at 30 cm depth in the forest were similar for both subregions, with an average of 47.1 ± 1.8 Mg C ha(-1) in HG and 48.7 ± 3.1 Mg C ha(-1) in LG. However, 20 years after forest-to-pasture conversion SOC in HG decreased by 20%, whereas in LG SOC increased by 41%. This net SOC decrease in HG was due to a larger reduction in C3-derived input and to a comparatively smaller increase in C4-derived C input. In LG both C3- and C4-derived C input increased along the chronosequence. N stocks were generally similar in both subregions and soil N stock changes during pasture establishment were correlated with SOC changes. These results emphasize the importance of management practices involving low-grazing intensity in cattle activities to preserve SOC stocks and to reduce C emissions after land-cover change from forest to pasture in the Colombian Amazon., (© 2016 John Wiley & Sons Ltd.)

Tropospheric ozone (O3) produces harmful effects to forests and crops, leading to a reduction of land carbon assimilation that, consequently, influences the land sink and the crop yield production. To assess the potential negative O3 impacts to vegetation, the European Union uses the Accumulated Ozone over Threshold of 40 ppb (AOT40). This index has been chosen for its simplicity and flexibility in handling different ecosystems as well as for its linear relationships with yield or biomass loss. However, AOT40 does not give any information on the physiological O3 uptake into the leaves since it does not include any environmental constraints to O3 uptake through stomata. Therefore, an index based on stomatal O3 uptake (i.e. PODY), which describes the amount of O3 entering into the leaves, would be more appropriate. Specifically, the PODY metric considers the effects of multiple climatic factors, vegetation characteristics and local and phenological inputs rather than the only atmospheric O3 concentration. For this reason, the use of PODY in the O3 risk assessment for vegetation is becoming recommended. We compare different potential O3 risk assessments based on two methodologies (i.e. AOT40 and stomatal O3 uptake) using a framework of mesoscale models that produces hourly meteorological and O3 data at high spatial resolution (12 km) over Europe for the time period 2000-2005. Results indicate a remarkable spatial and temporal inconsistency between the two indices, suggesting that a new definition of European legislative standard is needed in the near future. Besides, our risk assessment based on AOT40 shows a good consistency compared to both in-situ data and other model-based datasets. Conversely, risk assessment based on stomatal O3 uptake shows different spatial patterns compared to other model-based datasets. This strong inconsistency can be likely related to a different vegetation cover and its associated parameterizations., (© 2015 John Wiley & Sons Ltd.)

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., (© 2013 Blackwell Publishing Ltd.)

Process-based models can be classified into: (a) terrestrial biogeochemical models (TBMs), which simulate fluxes of carbon, water and nitrogen coupled within terrestrial ecosystems, and (b) dynamic global vegetation models (DGVMs), which further couple these processes interactively with changes in slow ecosystem processes depending on resource competition, establishment, growth and mortality of different vegetation types. In this study, four models - RHESSys, GOTILWA+, LPJ-GUESS and ORCHIDEE - representing both modelling approaches were compared and evaluated against benchmarks provided by eddy-covariance measurements of carbon and water fluxes at 15 forest sites within the EUROFLUX project. Overall, model-measurement agreement varied greatly among sites. Both modelling approaches have somewhat different strengths, but there was no model among those tested that universally performed well on the two variables evaluated. Small biases and errors suggest that ORCHIDEE and GOTILWA+ performed better in simulating carbon fluxes while LPJ-GUESS and RHESSys did a better job in simulating water fluxes. In general, the models can be considered as useful tools for studies of climate change impacts on carbon and water cycling in forests. However, the various sources of variation among models simulations and between models simulations and observed data described in this study place some constraints on the results and to some extent reduce their reliability. For example, at most sites in the Mediterranean region all models generally performed poorly most likely because of problems in the representation of water stress effects on both carbon uptake by photosynthesis and carbon release by heterotrophic respiration (R h ). The use of flux data as a means of assessing key processes in models of this type is an important approach to improving model performance. Our results show that the models have value but that further model development is necessary with regard to the representation of the some of the key ecosystem processes.