Top PDF An ensemble approach to simulate CO<sub>2</sub> emissions from natural fires

An ensemble approach to simulate CO<sub>2</sub> emissions from natural fires

An ensemble approach to simulate CO<sub>2</sub> emissions from natural fires

In particular, the synoptic-scale processes are parametrised in our model (Petoukhov et al., 1998). This leads to the un- derestimation of weather variability. This is important be- cause fire development depends on climate state in a strongly non-linear fashion. Because our model is only able to repro- duce a “smoothed” curve of seasonal changes of the state of the atmosphere and the soil, we could argue that this might affect the results of our calibration. Moreover, such an impact of this parametrisation might be hidden for a present-day cli- mate but affect projections for the 21st–23rd centuries, when climate state is markedly different from the present-day one. In addition, the parametrised synoptic-scale processes lead to the underestimated interannual variability of climate and fire activity. However, the latter is not a major issue for the purpose of this paper, because its focus (together with the presentation of the ensemble approach for simulating natural fires by using the coupled Earth system model) is on climato- logical means of such characteristics and their sensitivity to climate change. Moreover, our future projections generally agree with the offline simulations reported by Kloster et al. (2012).
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Constraining CO<sub>2</sub> emissions from open biomass burning  by satellite observations of co-emitted species: a method and  its application to wildfires in Siberia

Constraining CO<sub>2</sub> emissions from open biomass burning by satellite observations of co-emitted species: a method and its application to wildfires in Siberia

The simulations were performed with a horizontal resolu- tion of 1 ◦ × 1 ◦ for 12 layers in the vertical (up to the 200 hPa pressure level). The main model domain (35.5–136.5 ◦ E; 38.5–75.5 ◦ N) covered a major part of northern Eurasia, in- cluding Siberia and parts of eastern Europe and the far east (see Fig. 1). Note that the inclusion of a part of European Russia allowed us to take into account anthropogenic emis- sions from the major Russian industrial regions. In addition, we used the nested domain (86.2–92.4 ◦ E; 57.6–63.9 ◦ N) covering a central part of Siberia with a higher resolution of 0.2 ◦ × 0.1 ◦ to simulate the evolution of the near-surface CO concentration at the ZOTTO site. Meteorological data were obtained from the WRF-ARW (advanced research weather research and forecasting) model (Skamarock et al., 2005), which was run with a horizontal resolution of 90 km × 90 km and driven with the NCEP Reanalysis-2 data. Chemical pro- cesses were simulated with the simplified MELCHIOR2 chemical mechanism (Schmidt et al., 2001) with recent up- dates. The main model runs were performed for the period from 18 April to 30 September 2012 by using the initial and boundary conditions for gases and aerosols from climatolog- ical runs of the MOZART (Horowitz et al., 2003) and GO- CART (Ginoux et al., 2001) models, respectively. Addition- ally, the simulations were done for the periods covered by CO measurements at the ZOTTO site in 2007 and 2008. An- thropogenic emissions were specified using the EDGAR ver- sion 4.2 data (EC-JCR/PBL, 2010), and biogenic emissions were calculated “online” by using biogenic emission poten- tials from the MEGAN global inventory (Guenther et al., 2012).
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N<sub>2</sub>O, NO, N<sub>2</sub> and CO<sub>2</sub> emissions from tropical savanna and grassland of northern Australia: an incubation experiment with intact soil cores

N<sub>2</sub>O, NO, N<sub>2</sub> and CO<sub>2</sub> emissions from tropical savanna and grassland of northern Australia: an incubation experiment with intact soil cores

Tropical savanna ecosystems are one of the most impor- tant biomes of the earth and cover approximately 27.6 mil- lion km 2 (Hutley and Setterfield, 2008) or 11.5 % of the global surface (Scholes and Hall, 1996). However, it is of- ten difficult to find concise and clear criteria to define the spatial extent of this biome, and thus scientists often use a wide range of areal estimates leading to substantial uncer- tainties in calculating the total contribution of this biome to the global soil–atmosphere exchange of nitrogen (David- son and Kingerlee, 1997). On a continental scale these areas can be constrained much better. Northern Australian tropi- cal savannas cover about 2 million km 2 (approximately 12 % of the global extent of the biome) and are located north of 20 ◦ S in Western Australia, the northern part of the North- ern Territory and northern Queensland (e.g., Lehmann et al., 2009; Williams et al., 2005). In addition to these natural and semi-natural landscapes, farmers have introduced im- proved pastures in northern Australia since the 1880s to increase productivity and their area expanded by approxi- mately 2500 km −2 yr −1 during the 1980s (Lonsdale, 1994).
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Global atmospheric carbon budget: results from an ensemble of atmospheric CO<sub>2</sub> inversions

Global atmospheric carbon budget: results from an ensemble of atmospheric CO<sub>2</sub> inversions

To further investigate the general mean behavior of the participating inversions, Fig. 3 displays the zonally inte- grated total fluxes (natural land and ocean plus fossil), inte- grated from south to north for each inversion over the period 2001–2004. This zonally integrated cumulative flux reveals key characteristics of the inverse systems in general and in particular of the transport model used by each inversion. First one can notice that even for a 4 yr period (2001–2004) the to- tal net surface fluxes (values at the North Pole in Fig. 3) differ by up to 0.5 Pg C yr −1 (see Sect. 4.2 below). More interest- ingly, if we assume that all systems provide a reasonable fit to the atmospheric growth rate at all stations, the differences be- tween the shapes of the curve in Fig. 3 could reveal structural differences between the transport models and/or the longitu- dinal distribution of the total fluxes. For example, the much larger slope between 25 ◦ S and 25 ◦ N in RIGC, NICAM and MATCH may indicate that their transport models have dif- ferent atmospheric mixing over the tropics (stronger) than the other models or that their flux spatial distributions dif- fer. Large differences between the slopes of the integrated fluxes over the tropics (30 ◦ S to 30 ◦ N) reflect the poor at- mospheric constraint over this latitudinal band, while north of 30 ◦ N the results are in much closer agreement. Overall, the zonally integrated flux diagnostic helps to differentiate and group the participating inversions. For instance, RIGC, NICAM and JMA and to a lesser extent MATCH and TrC systems have a different north to south flux behavior com- pared to the other systems.
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Uncertainties in assessing the environmental impact of amine emissions from a CO<sub>2</sub> capture plant

Uncertainties in assessing the environmental impact of amine emissions from a CO<sub>2</sub> capture plant

Fugacity models are routinely applied to investigate the fate of compounds in a multimedia context (Mackay, 2001). The fugacity level III model was used to simulate concentrations of nitrosamines and nitramines in lake water. The model has four bulk media compartments; air, soil, water, and sedi- ments. The model includes quantitative advective and dif- fusive transport processes between these compartments pa- rameterized with mass transfer coefficients and transport ve- locities. Loss processes are by advection (e.g. movement of air and water to outside the model domain in addition to per- manent removal of sediment) and degradation of the com- pound. Deposition is assumed to be constant and the steady- state distribution of the compounds is achieved with equi- librium within the compartments (e.g. between pore water and sediments), but not between bulk media (i.e. sediment and water have different fugacities). Given a parameteri- zation of the evaluative environment, i.e. area and volume of compartments as well as transport coefficients, there is a linear relationship between deposition/emission and con- centration in the water phase for a given compound. Fugac- ity level III models have successfully been applied to a wide range of compounds and environments (Mackay et al., 1996; MacLeod and Mackay, 1999) and are an integrated part of the US EPA software for environmental fate estimation (US EPA, 2012).
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Field test of available methods to measure remotely SO<sub>x</sub> and NO<sub>x</sub> emissions from ships

Field test of available methods to measure remotely SO<sub>x</sub> and NO<sub>x</sub> emissions from ships

high because of high average sulphur in marine heavy fu- els. Emissions from ships are characterized by their distri- bution along typical shipping routes, connecting the network of world ports. According to different studies (e.g. Endresen et al., 2003; Eyring et al., 2005), 70 % or more of emissions by international shipping occur within 400 km off land and they can consequently be transported hundreds of kilome- tres inland. This pathway is especially relevant for deposi- tion of sulphur and nitrogen compounds, which cause acid- ification/eutrophication of natural ecosystems and freshwa- ter bodies and threaten biodiversity through excessive nitro- gen input (Isakson et al., 2001; Galloway et al., 2003). At the local and regional scales, the impact on human health occurs through the formation and transport of ground-level ozone, sulphur emissions and particulate matter. In cities with large ports, ship emissions are in many cases a dominant source of urban pollution. Corbett et al. (2007) demonstrated that PM emissions from ocean-going ships could cause ap- proximately 60 000 premature mortalities annually from car- diopulmonary disease and lung cancer, particularly in Europe and Southeast Asia. In addition, ship emissions will have an impact on climate change both as positive radiative forcing due to greenhouse gases like CO 2 and the secondarily formed
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Inverse modeling of CO<sub>2</sub> sources and sinks using satellite observations of CO<sub>2</sub> from TES and surface flask measurements

Inverse modeling of CO<sub>2</sub> sources and sinks using satellite observations of CO<sub>2</sub> from TES and surface flask measurements

Ramonet, M., Randa, B., Reichelt, M., Rhee, T. S., Rohwer, J., Rosenfeld, K., Scharffe, D., Schlager, H., Schumann, U., Slemr, F., Sprung, D., Stock, P., Thaler, R., Valentino, F., van Velthoven, P., Waibel, A., Wandel, A., Waschitschek, K., Wiedensohler, A., Xueref-Remy, I., Zahn, A., Zech, U., and Ziereis, H.: Civil Aircraft for the regular investigation of the at- mosphere based on an instrumented container: The new CARIBIC system, Atmos. Chem.

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Scaling laws for perturbations in the ocean–atmosphere system following large CO<sub>2</sub> emissions

Scaling laws for perturbations in the ocean–atmosphere system following large CO<sub>2</sub> emissions

LOSCAR is a box model designed for these objectives. It has been employed to investigate a range of problems for both paleo- and modern-climate applications. LOSCAR al- lows for easy switching between modern and Paleocene and Eocene ocean configurations. It has specifically been used to study the impacts of large transient emissions such as those found during the Paleocene–Eocene Thermal Maxi- mum (PETM), as well as modern anthropogenic emissions. For the modern Earth, LOSCAR components include the at- mosphere and a three-layer representation of the Atlantic, In- dian, and Pacific (and Tethys for the paleo-version) ocean basins, coupled to a marine-sediment component (Zeebe, 2012b). The marine-sediment component consists of sedi- ment boxes in each of the major ocean basins arranged as functions of depth. The ocean component includes a repre- sentation of the mean overturning circulation as well as mix- ing. Biological cycling is parameterized by restoring surface nutrients to fixed values. In the simulations described here, the circulation and target surface nutrients are kept indepen- dent of climate change, so that we focus solely on contrasting surface weathering and sedimentary responses. Biogeochem- ical cycling in LOSCAR also includes calcium carbonate (CaCO 3 ) dissolution, weathering and burial, silicate weath-
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The relationship between 0.25–2.5 μm aerosol and CO<sub>2</sub> emissions over a city

The relationship between 0.25–2.5 μm aerosol and CO<sub>2</sub> emissions over a city

A linear correlation between particle number flux and CO 2 flux was used to deter- mine an emission factor (Dp, size range 0.25–2.5 µm) in units of particles/mmol CO 2 . Figure 4 shows the linear fit to the data. The data has been divided into 15 concentra- tion intervals. The interval width was chosen so that in each interval at least 20 or more half hour values were present. The linear fit was made to the median value of each size

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Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO<sub>2</sub> emissions

Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO<sub>2</sub> emissions

Acknowledgements. A portion of this work was performed at the Jet Propulsion Laboratory, California Institute of Technology, under contract with NASA. The Megacities Carbon Project is sponsored in part by the National Institute of Standards and Technology (NIST). Sally Newman acknowledges funding from the Caltech/JPL President & Director’s Research and Development Fund. Kevin R. Gurney thanks NIST grant 70NANB14H321. Ravan Ahmadov was supported by the US Weather Research Program within the NOAA/OAR Office of Weather and Air Quality. Seongeun Jeong and Marc L. Fischer acknowledge the support by the Laboratory Directed Research and Development Program, Office of Science, of the US Department of Energy under contract no. DE-AC02-05CH11231. Thanks to W. Angevine at NOAA for radar wind profiler data, K. Aikin at NOAA for Aircraft WP-3D data, and B. Lefer at University of Houston for ceilometer data.
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TransCom N<sub>2</sub>O model inter-comparison – Part 2:  Atmospheric inversion estimates of N<sub>2</sub>O emissions

TransCom N<sub>2</sub>O model inter-comparison – Part 2: Atmospheric inversion estimates of N<sub>2</sub>O emissions

The number of degrees of freedom in the inversion is an important factor for determining how closely the poste- rior fluxes resemble the prior ones. For MOZART4-I and ACTMt42l67-I, which solve the inversion using coarse re- gions, the number of degrees of freedom is substantially re- duced, representing a strong constraint on the inversion as only the mean flux in each region is optimized and the flux pattern within each region remains as described a priori. On the other hand, solving for fine regions i.e. at the resolution of the transport model, as in TM5-I, TM3-I and LMDZ4- I, benefits from additional regularization constraints, such as spatial correlations of the prior flux errors (used in the defini- tion of B). For TM5-I the spatial correlation length (200 km) means that the grid cells are only weakly correlated to one another resulting in a weak constraint, whereas in LMDZ4-I, longer scale lengths are used (500 km for land and 1000 km for ocean) resulting in a stronger constraint (see Table 2).
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Ten years of CO<sub>2</sub>, CH<sub>4</sub>, CO and N<sub>2</sub>O fluxes over Western Europe inferred from atmospheric measurements at Mace Head, Ireland

Ten years of CO<sub>2</sub>, CH<sub>4</sub>, CO and N<sub>2</sub>O fluxes over Western Europe inferred from atmospheric measurements at Mace Head, Ireland

Head between 1992 and 2005. In these long-term series, we can see a trend in base- lines, as well as in seasonal cycles. For example in the CO 2 time series, we observe a long-term concentration increase (at a mean rate of 2 ppm per yr) and a seasonal cycle with minimum summertime value in August and a broad wintertime maximum from December. Superimposed on this signal are synoptic peaks generally associated

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Fluxes of CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O from soil of burned grassland savannah of central Africa

Fluxes of CO<sub>2</sub>, CH<sub>4</sub> and N<sub>2</sub>O from soil of burned grassland savannah of central Africa

neutral carbon balance (Saarnak, 2001; Bombelli et al., 2009). High uncertainty is associated to this value due to the lack of sufficient studies which also include the overall balance of GHG in unburned and burned conditions. Data on post burning variations of soil greenhouse gas (GHG) fluxes from savannahs are relatively few and do not give a clear and univocal answer. Few of these studies have been conducted

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Assessing the potential long-term increase of oceanic fossil fuel CO<sub>2</sub> uptake due to CO<sub>2</sub>-calcification feedback

Assessing the potential long-term increase of oceanic fossil fuel CO<sub>2</sub> uptake due to CO<sub>2</sub>-calcification feedback

model thus increases with a greater ambient environmental degree of super-saturation with respect to the solid carbon- ate phase (>1.0). Carbonate production is zero at ≤1 – i.e., we implicitly assume that super-saturation and ther- modynamically favorable environmental conditions are re- quired for pelagic carbonate production. However, while this assumption appears valid for corals, it may not hold for foraminifera. We discuss the implications of this later. In ad- dition, although coccolithophorid and foraminiferal calcifi- cation rates have been observed to respond to changes in sat- uration (e.g., Bijma et al., 1999; Riebesell et al., 2000; Zon- dervan et al., 2001; Delille et al., 2005), we do not explicitly capture other important controls in our formulation of car- bonate production. Instead, we have implicitly collapsed the (poorly understood) ecological and physical oceanographic controls on marine carbonate production onto a single, purely thermodynamic dependence on . Furthermore, while POC production is affected by changes in climate (such as strat- ification) in the GENIE-1 model, for simplicity, we do not additionally modify POC production in response to changes in pH (Zondervan et al., 2001).
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Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation

Technical Note: Comparison of ensemble Kalman filter and variational approaches for CO<sub>2</sub> data assimilation

For experiments AR–CR, the posterior uncertainty esti- mates for all the three approaches are higher compared to experiments A–C, as expected due to the higher prescribed model–data mismatch error. Similarly to experiments A– C, the 4D-VAR uncertainty estimates for individual loca- tions/times are too variable relative to BIM (Fig. 5). Aver- aged over time and space, the 4D-VAR uncertainty estimates underestimate the BIM uncertainty estimates by approxi- mately 25 % (Fig. 5ar–cr). Thus, even though the 4D-VAR uncertainty estimates for experiments AR–CR are higher than the corresponding uncertainty estimates for experiments A–C, they fail to capture the full magnitude of the BIM un- certainty estimates. This makes intuitive sense due to the indirect approach adopted for generating the 4D-VAR un- certainty estimates. Conversely, as the observational net- work becomes sparser and more heterogeneous, the EnSRF slightly overestimates the BIM average uncertainties by 3 % (HM; Fig. 5br) and 5 % (HT; Fig. 5cr), while it underesti- mates the uncertainty by only 1 % for the reference network (Fig. 5ar). The EnSRF uncertainty estimates for individual locations/times are more closely distributed around the BIM estimates (Fig. 5). The better performance of EnSRF in terms of the uncertainty estimation can be directly related to the en- semble spread. Relative to experiments A–C, when the pre- scribed model–data mismatch error is high in experiments AR–CR, the initial ensemble spread is reduced by a lower amount as observations are now being given less weight and hence have lower impact on the ensemble spread. Con- sequently, the ensemble members maintain a large spread throughout the analysis and results in large posterior uncer- tainty estimates that are more realistic relative to 4D-VAR.
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Modeling global atmospheric CO<sub>2</sub> with improved emission inventories and CO<sub>2</sub> production from the oxidation of other carbon species

Modeling global atmospheric CO<sub>2</sub> with improved emission inventories and CO<sub>2</sub> production from the oxidation of other carbon species

Fig. 13. The upper panels show the annual average CO 2 as a function of latitude for 2005–2007 from GEOS-Chem with the chemical source (red) and without (blue). The zonal average at the surface (solid line) and the zonal average at the model level near 5 km (dotted line) are shown along with single pixels compared with 18 selected GLOBALVIEW stations (black) denoted in Table 2. The lower panels show the model – GLOBALVIEW difference at the surface for the two simulations (colored as above), both of which started from a uniform 3-D global CO 2 field
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PREPARATION OF ACTIVATED CARBON FROM PALM OIL SHELL BY CHEMICAL ACTIVATION WITH Na<sub>2</sub>CO<sub>3</sub> AND ZnCl<sub>2</sub> AS IMPRENATED AGENTS FOR H<sub>2</sub>S ADSORPTION

PREPARATION OF ACTIVATED CARBON FROM PALM OIL SHELL BY CHEMICAL ACTIVATION WITH Na<sub>2</sub>CO<sub>3</sub> AND ZnCl<sub>2</sub> AS IMPRENATED AGENTS FOR H<sub>2</sub>S ADSORPTION

The surface area of powdered or porous solid can be calculated from the volume of gas absorbed onto the surface of the solid. In general, solids adsorb gases weakly bound due to Van der Waal’s forces only, to cause sufficient gas to be absorbed for surface area measurement. The volume of gas absorbed increases with increasing pressure. The physical absorption of gases by solids increases with decreasing temperature and with increasing pressure. The process is exothermic, i.e., energy is released. The investigative procedure has first to establish what is known as an absorption (or desorption) isotherm. This, quite simply, is a measure of the molar quantity of gas n (or standard volume Va, or general quantity q) taken up, or released, at a constant temperature usually T by an initially clean solid surface as a function of gas pressure P. Most frequently the test is conducted at a low temperature, usually that of Liquid Nitrogen (LN2) at one atmosphere pressure) (Hussar et al., 2011). Convention has established that the quantity of gas adsorbed is expressed as its volume at standard conditions of temperature and pressure (0°C and 760 torr and signified by STP) while the pressure is expressed as a relative pressure which is the actual gas pressure P divided by the vapor pressure P 0 of adsorbing
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Molecular hydrogen (H<sub>2</sub>) emissions and their isotopic signatures (H/D) from a motor vehicle: implications on atmospheric H<sub>2</sub>

Molecular hydrogen (H<sub>2</sub>) emissions and their isotopic signatures (H/D) from a motor vehicle: implications on atmospheric H<sub>2</sub>

air samples in the Los Angeles basin in 2000, from which they derived a integrated pure anthropogenic isotopic sig- nature (end-member) of −270‰. This extrapolated value is bracketed by our pre-TWC and fuel-rich post-TWC results. Earlier results from Gerst and Quay (2001) from samples taken in 1999 in a parking garage in Seattle yielded higher concentration samples than those by Rahn et al. (2002), and were less depleted in D/H with results ranging from −173‰ to −183‰. The insert of Fig. 5 also includes the samples with very low concentrations from the fuel-lean range. As these concentrations are sub-ambient, they cannot be quanti- tatively investigated in the Keeling diagram. Still, the isotope signatures are in the same range as the very high concentra- tion samples. This suggests that in fact all of our samples are “pure” end-member samples (not mixtures of produced H 2 with ambient background H 2 ), but with largely differing
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Simulating the integrated summertime Δ<sup>14</sup>CO<sub>2</sub> signature from anthropogenic emissions over Western Europe

Simulating the integrated summertime Δ<sup>14</sup>CO<sub>2</sub> signature from anthropogenic emissions over Western Europe

ing the SiBCASA model (Schaefer et al., 2008; van der Velde et al., 2014), which used meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF). It provides us with monthly averaged gross pho- tosynthetic production (GPP) and terrestrial ecosystem res- piration (TER) at 1 ◦ × 1 ◦ resolution. Due to the coarse res- olution of the SiBCASA model, we find land-use categories in the higher resolution map of WRF that are not in the nat- ural land-use map of SiBCASA. To address this issue, we ran 9 simulations with SiBCASA prescribing a single veg- etation category, alternating through all the vegetation cate- gories to produce biospheric fluxes for the different land-use categories within the resolution of WRF. For temporal inter- polation of the monthly fluxes, we scale the GPP and TER with the instantaneous WRF meteorological variables (tem- perature at 2 m and shortwave solar radiation) following the method described in Olsen and Randerson (2004).
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Oceanic N<sub>2</sub>O emissions in the 21st century

Oceanic N<sub>2</sub>O emissions in the 21st century

ner, G., Labetoulle, S., Lahellec, A., Lefebvre, M. P., Lefevre, F., Levy, C., Li, Z. X., Lloyd, J., Lott, F., Madec, G., Mancip, M., Marchand, M., Masson, S., Meurdesoif, Y., Mignot, J., Musat, I., Parouty, S., Polcher, J., Rio, C., Schulz, M., Swingedouw, D., Szopa, S., Ta- landier, C., Terray, P., Viovy, N., and Vuichard, N.: Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5, Clim. Dynam., 40, 2123–2165,

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