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Dimethylsulfoniopropionate (DMSP) and dimethylsulfide (DMS) sea surface distributions simulated from a global three-dimensional ocean carbon cycle model

Dimethylsulfoniopropionate (DMSP) and dimethylsulfide (DMS) sea surface distributions simulated from a global three-dimensional ocean carbon cycle model

Received 18 October 1999; revised 8 September 2000; accepted 8 February 2001; published 24 April 2002. [ 1 ] A global model for surface dimethylsulfide (DMS) and particulate dimethylsulfoniopropionate (DMSP) (pDMS) distributions is presented. The main goals of this work were to be able to predict the regional distribution of the air-sea fluxes of DMS and to predict eventually their future evolution with climate change. Diagnostic relationships have been established from data sets obtained during the ALBATROSS and EUMELI cruises carried out in the Atlantic Ocean. These equations nonlinearly relate DMS and pDMSP concentrations to chlorophyll concentrations and to the trophic status of surface waters. This model has been embedded in the global ocean carbon cycle model Institut Pierre et Simon Laplace-Ocean Carbon Cycle Model version 2 (ISPL-OCCM2), a simple plankton model coupled to a global three-dimensional ocean general circulation model. Predicted global distributions and seasonal variations of surface chlorophyll are in good agreement with the observations, except in the equatorial Pacific Ocean and, to a lesser extent, in the Southern Ocean. In these regions, simulated surface chlorophyll concentrations are strongly overestimated, most likely because limitations of the biological production by nutrients like iron or silicate are not considered. The model predicts surface DMS and pDMSP concentrations, which compare reasonably well with the observations. However, in the high latitudes, seasonal variations are underestimated, especially in the Ross and Weddell Seas where observed very elevated
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Global response of the terrestrial biosphere to CO 2 and climate change using a coupled climate-carbon cycle model

Global response of the terrestrial biosphere to CO 2 and climate change using a coupled climate-carbon cycle model

tion and land cover changes are also believed to play a large role. Several studies estimated the importance of anthropo- genic nitrogen deposition on terrestrial carbon cycle [Hol- land et al., 1997; Nadelhoffer et al., 1999]. The role played by anthropogenic nitrogen remains largely uncertain but could be significant and should be included in future climate-carbon cycle studies. Another potentially large component of the present-day terrestrial uptake is due to forest regrowth in the temperate regions Caspersen et al., 2000; Joos et al., 2002; Pacala et al., 2001; Schimel et al., 2001. Such processes are also not included in the present Figure 11. Time series of the evolution of the onset and of the offset of NEP growing season for the
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C4MIP – The Coupled Climate–Carbon Cycle Model Intercomparison Project: experimental protocol for CMIP6

C4MIP – The Coupled Climate–Carbon Cycle Model Intercomparison Project: experimental protocol for CMIP6

nitrogen availability for carboxylation enzymes and new tissue construction; (2) it allows for changes in plant al- location in response to changing nutrient availability; (3) it generally decreases net ecosystem C losses as- sociated with soil warming, because increased decom- position leads to increased plant N availability, which can potentially increase plant productivity and C stor- age in N-limited ecosystems; and (4) it alters primary production due to anthropogenic N deposition and fer- tilizer application, which may regionally enhance net C uptake. The magnitude of each of these processes is un- certain given strong natural gradients in the natural N availability in ecosystems and sparse ecosystem data to constrain these models (Thornton et al., 2009; Zaehle et al., 2014; Meyerholt and Zaehle, 2015) but offline anal- ysis of CMIP5 simulations suggests significant overes- timation of terrestrial carbon uptake in models that ne- glect the role of nitrogen (Wieder et al., 2015; Zaehle et al., 2015). The new generation of models will provide a more comprehensive assessment of the attenuating ef- fect of nitrogen on carbon cycle dynamics compared to CMIP5 and in particular provide a better constrained estimate of the carbon storage capacity of land ecosys- tems.
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Quantifying the model structural error in carbon cycle data assimilation systems

Quantifying the model structural error in carbon cycle data assimilation systems

The present study aims at providing a method to explic- itly quantify the error of process-based terrestrial models, in particular, for global CCDASs. The conclusions also apply to site-scale parameter optimisation schemes. Denoting as “prior” the state of the carbon-cycle model before any obser- vational constraint, we propose to analyse the statistics of the prior residuals (observations-minus-prior simulations) with the help of the assigned prior parameter uncertainties pro- jected in the observation space. Within the Bayesian frame- work, these two pieces of information and the observation er- ror, which is the summed contribution of model and measure- ment errors, are linked together. We apply this method to the global biosphere model ORganising Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE, Krinner et al., 2005) in temperate deciduous broadleaf forests, using measure- ments of the daily net ecosystem exchange (NEE) flux at twelve eddy-covariance flux measurement sites as observ- able quantity. We take advantage from the previous studies that have characterised the uncertainties of these measure- ments (e.g., Richardson et al., 2008). The inferred structure of the observation (model + measurement) error on the mod- elled net carbon fluxes is then projected in the space of atmo- spheric concentrations in order to characterise its structure when assimilating concentration measurements with a CC- DAS.
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Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system

Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system

While the requirements and scope of collaboration for the proposed monitoring system may seem daunting, there are two factors that indicate this problem is eminently tractable. First, many elements of a global carbon monitoring sys- tem exist today in the form of various surface observ- ing networks, data synthesis, community models, and data- assimilation systems, and coordinated use of ground and satellite observations. Indeed, the carbon-cycle science com- munity has for some time been executing these kind of coordinated observations, data-model integration, and top- down/bottom-up reconciliation, albeit in a research mode with limited persistence or geospatial coverage and/or reso- lution. Second, at scales larger than ∼ 10 km, the relationship between monitoring system performance and policy-support value may be monotonic (i.e. there does not exist a sharp breaking point between a flux map with 100 km resolution and one with 200 km resolution). This suggests that an in- cremental or phased deployment process can be employed. Such an approach would allow a first generation system to be deployed based strictly on existing observational assets, and therefore focus the initial investment on the development of the necessary data integration system. However, there cer- tainly is a strong break towards increased policy support if the scales addressed can be of less 10 km, the scale at which emissions can begin to be monitored (Duren and Miller, 2012). Given the uncertainties in both the carbon-cycle and socio-economic drivers, it is very likely that climate policies will continue to evolve in an organic fashion over the next decades. A monitoring system put in place sooner rather than later can provide baseline information for comparisons, and both inform and grow with new policy developments.
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Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system

Current systematic carbon-cycle observations and the need for implementing a policy-relevant carbon observing system

While there is currently no satellite instrument in space specifically designed to map global forest biomass, recent advances in active remote-sensing technologies demonstrate the possibility of high-resolution, globally consistent esti- mates of aboveground biomass and carbon stocks with sig- nificantly reduced uncertainties in the estimates. Remote- sensing techniques integrating space-borne imaging and airborne lidar with pattern recognition methods (e.g. the Carnegie LANDSAT Analysis System Lite – CLASLITE; www.claslite.ciw.edu/) have demonstrated a strong capa- bility for tracking and quantifying biomass and structural changes in forest undergoing deforestation at the national and county scale (Asner et al., 2010). Forest height and canopy profile metrics have been derived from the Geoscience Laser Altimeter System (GLAS) on the ICESat satellite and used to estimate aboveground biomass (Lefsky et al., 2005). ICE- Sat height samples and MODIS data have been merged to create the first global canopy height product (Lefsky, 2010). ICESat, MODIS, QuikScat, and Shuttle Radar Topography Mission (SRTM) data have been used to spatially extrapolate ICESat observations and create a benchmark map of carbon storage, along with uncertainties, for tropical forests (Baccini et al., 2012; Saatchi et al., 2011). Comparison of these maps, however, shows significant differences indicating uncertainty in the data processing methods. Further, these maps are not temporally discrete (data from multiple years were used), and the main source of data, from ICEsat, no longer exists. A replacement mission may be launched in 2016. Preliminary results using polarimetric interferometric SAR (PolInSAR) approaches have demonstrated sensitivity to biomass in some high-biomass ecosystems (Treuhaft et al., 2003; Hajnsek and Papathannassiou, 2009).
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Roofing system product model: life cycle economic implications

Roofing system product model: life cycle economic implications

expenditures. Markov Chain modeling (Lounis et al. 1998) not only predicts the remaining service life of roofing systems, but also can predict their probability of failure. Having calculated the remaining service life, and knowing the risks of failure, these data along with the costs of renewal can be used for calculating the life cycle costs of the roofing system. Computer modeling using BLCC, alongside data from condition assessment surveys, risk analysis and a costing database, makes it possible to calculate the life cycle costs of various MRandR strategies and present these to the user. This capability permits the BELCAM project to meet its second goal.
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Response of the carbon cycle in an intermediate complexity model to the different climate configurations of the last nine interglacials

Response of the carbon cycle in an intermediate complexity model to the different climate configurations of the last nine interglacials

a ∼ 14-fold underestimation by the simulations. Hence the ocean carbon uptake in the simulations is not sufficient to drive a significant lowering of atmospheric CO 2 . Either the change in global ocean circulation and SST should be larger or another mechanism and feedbacks need to be taken into account to modify the biological or physical car- bon uptake and amplify the initial change. Since the repre- sentation of bottom water formation in the Southern Ocean is biased in the model, with an over-representation of open ocean convection, as is also the case for many more complex general circulation models (Heuzé et al., 2013), it is possi- ble that this hinders simulating the full range of carbon stor- age due to ocean circulation changes, as it is suspected for colder periods such as the Last Glacial Maximum (around 21 000 years ago) (Fischer et al., 2010).
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Spatiotemporal patterns of terrestrial carbon cycle during the 20th century

Spatiotemporal patterns of terrestrial carbon cycle during the 20th century

suggested that the land use source in the 1980s was nearly doubled when applying the cropland area of Houghton [2003] instead of the one of Ramankutty and Foley [1999], but their estimation (0.6 – 1.3 Pg C yr 1 ) was still not as large as estimated by Houghton [2003]. Our calcu- lated land use flux of 1.0 Pg C yr 1 , is considerably larger than that of Jain and Yang [2005] (0.67 Pg yr 1 ), and larger than that of McGuire et al. [2001], although all three carbon models were prescribed the same Ramankutty and Foley [1999] cropland data set. Such larger C emission in response to land use change may be caused by our set up and dif- ferent simulation scenarios (Table 1). Because most defor- estation has occurred in moist tropical forests with a very long natural fire return interval, we calculated the effect of forest clearing on C balance without considering natural fire disturbances. To check on this indirect effect of fires on the land use flux, we also integrated ORCHIDEE by activating natural fire disturbances. The results indicate that in com- parison with the simulation results without fire disturbance, annually about 0.3 Pg (or 30%) of less C emission from land use change is estimated when fires are included (Figure 12). This finding illustrates the indirect importance of natural fires in the calculation of C emissions associated with land use change.
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Impact of strong deep ocean stratification on the glacial carbon cycle

Impact of strong deep ocean stratification on the glacial carbon cycle

[20] We can then consider the vertical distribution of the stratification. To investigate the impact of the deep stratifi- cation according to the depth we analyze three simulations with very idealized Kz coefficients (Kz 5, 6, and 7). They take low values from 2500 m and then high values below in the very deep ocean from 4000 m to the bottom (Kz 5), 4500 m to the bottom (Kz 6), or stay at low value (Kz 7). It appears that when Kz becomes greater in the very deep ocean (Kz 5 and Kz 6) it does not change drastically the results. The surface-deep d 13 C gradient is slightly smaller compared to the fully stratified case (Kz 7), but not significantly. Indeed, despite the higher Kz in the very deep part of the ocean, as long as the ocean above has a small Kz the deep waters below remain isolated, which is the funda- mental mechanism, and therefore does not greatly change the results. Results are similar for pCO 2 , though the greater very deep Kz has a more effective impact on pCO 2 , and lessens the pCO 2 . Atmospheric pCO 2 has a more linear response to the deep Kz. A greater deep ocean Kz also improves the horizontal deep ocean d 13 C gradient (Kz 5 and Kz 6). As can be seen in Figure 8, the abyssal ocean becomes more homogeneous with a high Kz (Kz 5) than with a low Kz (Kz 7), because a high Kz implies a higher vertical mixing than with a low Kz, i.e., a better mixing. The d 13 C horizontal gradient is diminished as it is better mixed, and gets closer to the data reconstruction [Curry and Oppo, 2005]. Such a higher Kz in the very deep ocean could physically come from the geothermal heating which can play a substantial role in bringing energy in the deep ocean, as recently pointed out [Adcroft et al., 2001; Emile-Geay and Madec, 2008]. Finally, The main mechanism is clearly the isolation of the deep ocean that can store carbon with low d 13 C, which is obtained by the deep stratification, and not drastically changed when the very deep ocean is better mixed as long as it is highly stratified just above.
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Positive feedback between future climate change and the carbon cycle

Positive feedback between future climate change and the carbon cycle

on Figure 3, the reduction in the oceanic uptake of carbon, as discussed in earlier studies, results from the combination of three effects: impact of increased sea-surface temp[r]

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Does GOSAT capture the true seasonal cycle of carbon dioxide?

Does GOSAT capture the true seasonal cycle of carbon dioxide?

TCCON improved notably when the co-location criteria were made sufficiently tight to not include soundings taken too far from the basin. Model comparisons at latitudes 0–70 ◦ N revealed that qualitatively the models and satellite observations agreed well, but also that the model-to-model differences were (at most latitude bands studied) larger than model-to-ACOS dif- ferences. From the tropics up to 50 ◦ N, the zonally averaged seasonal cycle amplitude of ACOS was in very good agree- ment with MACC 13.1, while between 50 and 60 ◦ N, ACOS agreed better with the University of Edinburgh model and CarbonTracker CT2013B. Both of the latter models had sea- sonal cycle amplitudes shallower than ACOS or MACC at tropical and subtropical latitudes, where the models lack di- rect constraints from measurements over land and are thus more affected by their prior fluxes (or by extra-tropical or ocean measurements through long-range transport). There- fore, the shallower seasonal cycle amplitude might be con- nected to their prior land surface models that are different variants of CASA. However, to verify this, one should inves- tigate also the impact of transport, data assimilation, and in- version system differences. We also found that the longitudi- nal changes in the seasonal cycle amplitude at mid-latitudes can be notable. In particular, we showed that at 45–50 ◦ N latitudes, the amplitude of the GOSAT XCO 2 seasonal cy-
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Coordinated regulation of glycolysis and the folate one-carbon cycle

Coordinated regulation of glycolysis and the folate one-carbon cycle

In support of a metabolic role for pyruvate kinase in proliferation the glycolytic enzyme activity of PKM2, but not PKM1, can be inhibited by growth signaling in rapidly pr[r]

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The global carbon cycle and its changes over glacial–interglacial cycles

The global carbon cycle and its changes over glacial–interglacial cycles

increasing rapidly. This trend will continue in the future and, according to scenarios recently established by the IPCC working group I (Houghton et al., 2001), will even be reinforced at least until the second half of the 21st century. As a result of this change in the radiative forcing of the planet, the global mean surface air temperature has increased by approximately 0.6 jC over the 20th century and is projected to rise by 1.4 to 5.8 jC between 1990 and 2100. Important changes in precipitation and extreme events are also expected. Precipitation will increase globally, but regionally substantial reductions are forecasted in some areas, as in the Mediterranean basin. These climate changes will impact the carbon cycle, including terrestrial vegetation and marine biology, possibly leading to unexpected feedback on the carbon and, hence, green- house gas budgets of the atmosphere. Consequently, the reliability of future climate projections heavily depends on our understanding of the carbon cycle and its two-way interaction with the climate system. This understanding is still limited, especially regarding biological subsystems or side processes which have been disregarded up to now, because they are thought to operate at longer timescales. The models currently used for future projections are calibrated on the present-day system and validated at most on the period for which we have instrumental climatic data, mostly the 20th century. This means that we are actually extrapolating climate from two know points (the end of the 19th and 20th centuries) which are very close
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Simulating the carbon cycle in a high latitude shelf sea (North Sea) — evidence for decoupled carbon and nutrient cycles

Simulating the carbon cycle in a high latitude shelf sea (North Sea) — evidence for decoupled carbon and nutrient cycles

Simulating the carbon cycle in a high latitude shelf sea (North Sea) — evidence for decoupled carbon and nutrient cycles A. E. F. Prowe (1,2), H. Thomas (1,3), J. Pätsch (4), W. Kühn (4), Y. Bozec (3,5), L.-S. Schiettecatte (6), A. V. Borges (6)

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A comprehensive reduced model of the mammalian cell cycle

A comprehensive reduced model of the mammalian cell cycle

[M P F ] n + k n n (11) where the cdc25 equation (10) is that of an activator or promoter and wee1 equation (11) represents a repressor. Next, we observe that cdc20 isn’t an essential variable for the oscillary behavior and we can make it constant. Now focusing on the APC equation, we study the varia- tions on parameters in equation (5). We verify that the parameter k7 can be decreased to very low values without changing the output of the model: k 7 ' 0, implying that the first Michaelis-Menten term of equation (5) is satu- rated and can be approximated by a constant. Further- more, we also verify that almost all the time k8 > [AP C] and k8 can be very large without dramatically affecting the system, which in its turn implies that the second Michaelis- Menten term of equation (5) can be approximated by a linear function. Thus,the equation for APC becomes:
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The impact of different external sources of iron on the global carbon cycle

The impact of different external sources of iron on the global carbon cycle

ample, accounting for dust mineralogy and associated variability in Fe content/solubility should be addressed, although this will conceivably have a greater impact on local biogeochemistry and ∫Fe than on CO 2atm . In addition, recent work has highlighted variability in sediment [Homoky et al., 2013] and hydro- thermal [Saito et al., 2013] inputs that would be important to constrain in future models. Our prior under- standing, and its inclusion in our model, was that shelf depth, and in particular, the degree of carbon oxidation was the main driver of Fe ef flux [Elrod et al., 2004]. However, Homoky et al. [2013] have noted that some shelves can be less important sources of Fe than their depth and oxygen content would indicate. In a similar fashion, we assume that the hydrothermal Fe flux is regulated by ridge spreading rate, as parameterized by a constant DFe/Helium ratio [Tagliabue et al., 2010]. Yet recent observations [Saito et al., 2013] suggest that there might be less variability in Fe input from hydrothermal vents than there is for helium. To respond to this, we have upscaled the DFe/Helium ratio in this study, but the connection to ridge spreading rate remains. All sources clearly also do not only supply DFe, and although our model simulates particulate Fe, we do not consider unique sources of particulate Fe. More observational and speci fic modeling work is therefore needed to better understand how shelf depth and other factors interact to regulate sedimentary Fe input and how
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Early Triassic fluctuations of the global carbon cycle: New evidence from paired carbon isotopes in the western USA basin

Early Triassic fluctuations of the global carbon cycle: New evidence from paired carbon isotopes in the western USA basin

The siltstones and carbonates of the Early Triassic Dinwoody, Woodside and Thaynes formations at the HS section were deposited in proximal marine environments ( Kummel, 1954 ) and illustrate the global rise in sea level recorded for the Early Triassic ( Haq et al., 1987; Embry, 1997 ). No marked unconformity can be observed between each successive formation along the section and at the scale of southeastern Idaho (e.g., Kummel, 1957; Paull and Paull, 1993 ) and no major erosion nor reworked material can be observed within these levels. The upper part of the HS section is biostratigraphically well calibrated using the regional synthetic frame including the successive middle Smithian to lower Spathian ammonoid Meekoceras, “Bajarunia”, “Tirolites” and Columbites beds, which are commonly found in the SFB ( Kummel, 1954, 1957; Guex et al., 2010; Brayard et al., 2013; Jenks et al., 2013; Jattiot et al. in press ; Fig. 2 ). Lower strata of the Dinwoody Fm. are Griesbachian to Dienerian based on the occurrence of Claraia and lingulid beds (e.g., Kummel, 1954, 1957 ). This is supported by the presence of conodonts Hindeodus typicalis and Isarcella isarca in the basal beds indicating a mid-Griesbachian age ( Paull et al., 1985 ). Usually, the lower boundary of the Thaynes Fm. is regionally identi fied at the base of the Smithian ledge-forming Meekoceras beds for con- venience. However, these limestone deposits are included within a high-order sequence in continuity with underlying rocks (e.g., Embry, 1997 ) and only represent part of the early-middle Smithian ( Jattiot et al. in press ). It thus implies that the Dienerian/Smithian boundary (DSB) as well as the lower to middle Smithian transition at HS are mainly recognized using regional lithostratigraphy (after e.g. Kummel, 1954, 1957 ) and also by chemostratigraphy using comparisons with known global carbon isotopic signals (e.g., Payne et al., 2004; Galfetti et al., 2007a; Richoz et al., 2007; Horacek et al., 2009; Hermann et al., 2011; Grasby et al., 2013 ). Comparisons with other known carbon isotopic signals worldwide indicate that the Dienerian to middle Smithian transition is probably an expanded succession at HS. This is in agreement with the high sedimentation rates reported for this place and time interval (~ 650 m/Myr, Caravaca et al., in press ). As the DSB is not yet formally de fined based on biostratigraphical markers (see proposals of Tong et al., 2004; Krystyn et al., 2007 and Richoz et al., 2007 ), we determined its approximate position by using a positive δ 13
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The contribution of weathering of the main Alpine rivers on the global carbon cycle

The contribution of weathering of the main Alpine rivers on the global carbon cycle

The contribution of weathering of the main Alpine rivers on the global carbon cycle Abstract On geological time-scales the carbon fluxes from the solid Earth to the atmosphere mainly result from vol- canism and metamorphic-decarbonation processes, whereas the carbon fluxes from atmosphere to solid Earth mainly depend on weathering of silicates and carbonates, biogenic precipitation and removal of CaCO 3 in the

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Comparative Dynamics of the Standard Real Business Cycle Model

Comparative Dynamics of the Standard Real Business Cycle Model

L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignemen[r]

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