Eleven coupled climate-carbon cycle models used a common protocol to study the coupling between climate change and the carbon cycle. The models were forced by historical emissions and the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A2 anthropogenic emissions of CO 2 for the 1850-2100 time period. For each model, two simulations were performed in order to isolate the impact of climate change on the land and ocean carbon cycle, and therefore the climate feedback on the atmospheric CO 2 concentration growth rate. There was unanimous agreement among the models that future climate change will reduce the efficiency of the earth system to absorb the anthropogenic carbon perturbation. A larger fraction of anthropogenic CO 2 will stay airborne if climate change is accounted for. By the end of the twenty-first century, this additional CO 2 varied between 20 and 200 ppm for the two extreme models, the majority of the models lying between 50 and 100 ppm. The higher CO 2 levels led to an additional climate warming ranging between 0.1°and 1.5°C.All models simulated a negative sensitivity for both the land and the ocean carbon cycle to future climate. However, there was still a large uncertainty on the magnitude of these sensitivities. Eight models attributed most of the changes to the land, while three attributed it to the ocean. Also, a majority of the models located the reduction of land carbon uptake in the Tropics. However, the attribution of the land sensitivity to changes in net primary productivity versus changes in respiration is still subject to debate; no consensus emerged among the models.

The Farquhar et al. model of C3 photosynthesis is frequently used to study the effect of global changes on the biosphere. Its two main parameters representing photosynthetic capacity, Vcmax and Jmax, have been observed to acclimate to plant growth temperature for single species, but a general formulation has never been derived. Here, we present a reanalysis of data from 36 plant species to quantify the temperature dependence of Vcmax and Jmax with a focus on plant growth temperature, i.e. the plants' average ambient temperature during the preceding month. The temperature dependence of Vcmax and Jmax within each data set was described very well by a modified Arrhenius function that accounts for a decrease of Vcmax and Jmax at high temperatures. Three parameters were optimized: base rate, activation energy and entropy term. An effect of plant growth temperature on base rate and activation energy could not be observed, but it significantly affected the entropy term. This caused the optimum temperature of Vcmax and Jmax to increase by 0.44°C and 0.33°C per 1°C increase of growth temperature. While the base rate of Vcmax and Jmax seemed not to be affected, the ratio Jmax : Vcmax at 25°C significantly decreased with increasing growth temperature. This moderate temperature acclimation is sufficient to doublemodelled photosynthesis at 40°C, if plants are grown at 25°C instead of 17°C.

The sensitivity of soil carbon to warming is a major uncertainty in projections of carbon dioxide concentration and climate. Experimental studies overwhelmingly indicate increased soil organic carbon (SOC) decomposition at higher temperatures, resulting in increased carbon dioxide emissions from soils. However, recent findings have been cited as evidence against increased soil carbon emissions in a warmer world. In soil warming experiments, the initially increased carbon dioxide efflux returns to pre-warming rates within one to three years, and apparent carbon pool turnover times are insensitive to temperature. It has already been suggested that the apparent lack of temperature dependence could be an artefact due to neglecting the extreme heterogeneity of soil carbon, but no explicit model has yet been presented that can reconcile all the above findings. Here we present a simple three-pool model that partitions SOC into components with different intrinsic turnover rates. Using this model, we show that the results of all the soil-warming experiments are compatible with long-term temperature sensitivity of SOC turnover: they can be explained by rapid depletion of labile SOC combined with the negligible response of non-labile SOC on experimental timescales. Furthermore, we present evidence that non-labile SOC is more sensitive to temperature than labile SOC, implying that the long-term positive feedback of soil decomposition in a warming world may be even stronger than predicted by global models.

Abstract. The Model of Emissions of Gases and Aerosols from Nature (MEGANv2.1) together with the ModernEra Retrospective Analysis for Research and Applications (MERRA) meteorological fields were used to create a global emission data set of biogenic volatile organic compounds (BVOC) available on a monthly basis for the time period of 1980-2010. This data set, developed under the Monitoring Atmospheric Composition and Climate project (MACC), is called MEGAN-MACC. The model estimated mean annual total BVOC emission of 760 Tg (C) yr −1 consisting of isoprene (70 %), monoterpenes (11 %), methanol (6 %), acetone (3 %), sesquiterpenes (2.5 %) and other BVOC species each contributing less than 2 %.Several sensitivity model runs were performed to study the impact of different model input and model settings on isoprene estimates and resulted in differences of up to ±17 % of the reference isoprene total. A greater impact was observed for a sensitivity run applying parameterization of soil moisture deficit that led to a 50 % reduction of isoprene emissions on a global scale, most significantly in specific regions of Africa, South America and Australia.MEGAN-MACC estimates are comparable to results of previous studies. More detailed comparison with other isoprene inventories indicated significant spatial and temporal differences between the data sets especially for Australia, Southeast Asia and South America. MEGAN-MACC estimates of isoprene, α-pinene and group of monoterpenes showed a reasonable agreement with surface flux measurements at sites located in tropical forests in the Amazon and Malaysia. The model was able to capture the seasonal variation of isoprene emissions in the Amazon forest.

Photosynthetic capacity and its relationship to leaf nitrogen content are two of the most sensitive parameters of terrestrial biosphere models (TBM) whose representation in globalscale simulations has been severely hampered by a lack of systematic analyses using a sufficiently broad database. Here, we use data of qualitative traits, climate and soil to subdivide the terrestrial vegetation into functional types (PFT), and then assimilate observations of carboxylation capacity, V max (723 data points), and maximum photosynthesis rates, A max (776 data points), into the C 3 photosynthesis model proposed by Farquhar et al. to constrain the relationship of V 25 max (V max normalised to 25 1C) to leaf nitrogen content per unit leaf area for each PFT. In a second step, the resulting functions are used to predict V 25 max per PFT from easily measurable values of leaf nitrogen content in natural vegetation (1966 data points). Mean values of V 25 max thus obtained are implemented into a TBM (BETHY within the coupled climate-vegetation model ECHAM5/JSBACH) and modelled gross primary production (GPP) is compared with independent observations on stand scale. Apart from providing parameter ranges per PFT constrained from much more comprehensive data, the results of this analysis enable several major improvements on previous parameterisations. (1) The range of mean V 25 max between PFTs is dominated by differences of photosynthetic nitrogen use efficiency (NUE, defined as V 25 max divided by leaf nitrogen content), while within each PFT, the scatter of V 25 max values is dominated by the high variability of leaf nitrogen content. (2) We find a systematic depression of NUE on certain tropical soils that are known to be deficient in phosphorous. (3) V 25 max of tropical trees derived by this study is substantially lower than earlier estimates currently used in TBMs, with an obvious effect on modelled GPP and surface temperature. (4) The rootmean-squared difference between modelled and observed GPP is substantially reduced.

We quantify the risks of climate-induced changes in key ecosystem processes during the 21st century by forcing a dynamic global vegetation model with multiple scenarios from 16 climate models and mapping the proportions of model runs showing forest͞ nonforest shifts or exceedance of natural variability in wildfire frequency and freshwater supply. Our analysis does not assign probabilities to scenarios or weights to models. Instead, we consider distribution of outcomes within three sets of model runs grouped by the amount of global warming they simulate: <2°C (including simulations in which atmospheric composition is held constant, i.e., in which the only climate change is due to greenhouse gases already emitted), 2-3°C, and >3°C. High risk of forest loss is shown for Eurasia, eastern China, Canada, Central America, and Amazonia, with forest extensions into the Arctic and semiarid savannas; more frequent wildfire in Amazonia, the far north, and many semiarid regions; more runoff north of 50°N and in tropical Africa and northwestern South America; and less runoff in West Africa, Central America, southern Europe, and the eastern U.S. Substantially larger areas are affected for global warming >3°C than for <2°C; some features appear only at higher warming levels. A land carbon sink of Ϸ1 Pg of C per yr is simulated for the late 20th century, but for >3°C this sink converts to a carbon source during the 21st century (implying a positive climate feedback) in 44% of cases. The risks continue increasing over the following 200 years, even with atmospheric composition held constant.climate change impacts ͉ dangerous climate change ͉ ecosystem vulnerability ͉ ecosystem modeling

No abstract

[1] This paper presents the space-time distribution of terrestrial carbon fluxes for the period 1979-1999 generated by a terrestrial carbon cycle data assimilation system (CCDAS). CCDAS is based around the Biosphere Energy Transfer Hydrology model. We assimilate satellite observations of photosynthetically active radiation and atmospheric CO 2 concentration observations in a two-step process. The control variables for the assimilation are the parameters of the carbon cycle model. The optimized model produces a moderate fit to the seasonal cycle of atmospheric CO 2 concentration and a good fit to its interannual variability. Long-term mean fluxes show large uptakes over the northern midlatitudes and uptakes over tropical continents partly offsetting the prescribed efflux due to land use change. Interannual variability is dominated by the tropics. On interannual timescales the controlling process is net primary productivity (NPP) while for decadal changes the main driver is changes in soil respiration. An adjoint sensitivity analysis reveals that uncertainty in long-term storage efficiency of soil carbon is the largest contributor to uncertainty in net flux.