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- ItemThe Gaia -ESO Survey: Lithium measurements and new curves of growth(Les Ulis : EDP Sciences, 2022) Franciosini, E.; Randich, S.; de Laverny, P.; Biazzo, K.; Feuillet, D.K.; Frasca, A.; Lind, K.; Prisinzano, L.; Tautvaišiene, G.; Lanzafame, A.C.; Smiljanic, R.; Gonneau, A.; Magrini, L.; Pancino, E.; Guiglion, G.; Sacco, G.G.; Sanna, N.; Gilmore, G.; Bonifacio, P.; Jeffries, R.D.; Micela, G.; Prusti, T.; Alfaro, E.J.; Bensby, T.; Bragaglia, A.; François, P.; Korn, A.J.; Van Eck, S.; Bayo, A.; Bergemann, M.; Carraro, G.; Heiter, U.; Hourihane, A.; Jofré, P.; Lewis, J.; Martayan, C.; Monaco, L.; Morbidelli, L.; Worley, C.C.; Zaggia, S.Context. The Gaia-ESO Survey (GES) is a large public spectroscopic survey that was carried out using the multi-object FLAMES spectrograph at the Very Large Telescope. The survey provides accurate radial velocities, stellar parameters, and elemental abundances for ~115 000 stars in all Milky Way components. Aims. In this paper, we describe the method adopted in the final data release to derive lithium equivalent widths (EWs) and abundances. Methods. Lithium EWs were measured using two different approaches for FGK and M-type stars, to account for the intrinsic differences in the spectra. For FGK stars, we fitted the lithium line using Gaussian components, while direct integration over a predefined interval was adopted for M-type stars. Care was taken to ensure continuity between the two regimes. Abundances were derived using a new set of homogeneous curves of growth that were derived specifically for GES, and which were measured on a synthetic spectral grid consistently with the way the EWs were measured. The derived abundances were validated by comparison with those measured by other analysis groups using different methods. Results. Lithium EWs were measured for ~40 000 stars, and abundances could be derived for ~38 000 of them. The vast majority of the measures (80%) have been obtained for stars in open cluster fields. The remaining objects are stars in globular clusters, or field stars in the Milky Way disc, bulge, and halo. Conclusions. The GES dataset of homogeneous lithium abundances described here will be valuable for our understanding of several processes, from stellar evolution and internal mixing in stars at different evolutionary stages to Galactic evolution.
- ItemImplications of non-linearities between cumulative CO2 emissions and CO2-induced warming for assessing the remaining carbon budget(Bristol : IOP Publ., 2020) Nicholls, Z.R.J.; Gieseke, R.; Lewis, J.; Nauels, A.; Meinshausen, M.To determine the remaining carbon budget, a new framework was introduced in the Intergovernmental Panel on Climate Change's Special Report on Global Warming of 1.5 °C (SR1.5). We refer to this as a 'segmented' framework because it considers the various components of the carbon budget derivation independently from one another. Whilst implementing this segmented framework, in SR1.5 the assumption was that there is a strictly linear relationship between cumulative CO2 emissions and CO2-induced warming i.e. the TCRE is constant and can be applied to a range of emissions scenarios. Here we test whether such an approach is able to replicate results from model simulations that take the climate system's internal feedbacks and non-linearities into account. Within our modelling framework, following the SR1.5's choices leads to smaller carbon budgets than using simulations with interacting climate components. For 1.5 °C and 2 °C warming targets, the differences are 50 GtCO2 (or 10%) and 260 GtCO2 (or 17%), respectively. However, by relaxing the assumption of strict linearity, we find that this difference can be reduced to around 0 GtCO2 for 1.5 °C of warming and 80 GtCO2 (or 5%) for 2.0 °C of warming (for middle of the range estimates of the carbon cycle and warming response to anthropogenic emissions). We propose an updated implementation of the segmented framework that allows for the consideration of non-linearities between cumulative CO2 emissions and CO2-induced warming.
- ItemReduced Complexity Model Intercomparison Project Phase 2: Synthesizing Earth System Knowledge for Probabilistic Climate Projections(Hoboken, NJ : Wiley-Blackwell, 2021) Nicholls, Z.; Meinshausen, M.; Lewis, J.; Corradi, M. Rojas; Dorheim, K.; Gasser, T.; Gieseke, R.; Hope, A.P.; Leach, N.J.; McBride, L.A.; Quilcaille, Y.; Rogelj, J.; Salawitch, R.J.; Samset, B.H.; Sandstad, M.; Shiklomanov, A.; Skeie, R.B.; Smith, C.J.; Smith, S.J.; Su, X.; Tsutsui, J.; Vega-Westhoff, B.; Woodard, D.L.Over the last decades, climate science has evolved rapidly across multiple expert domains. Our best tools to capture state-of-the-art knowledge in an internally self-consistent modeling framework are the increasingly complex fully coupled Earth System Models (ESMs). However, computational limitations and the structural rigidity of ESMs mean that the full range of uncertainties across multiple domains are difficult to capture with ESMs alone. The tools of choice are instead more computationally efficient reduced complexity models (RCMs), which are structurally flexible and can span the response dynamics across a range of domain-specific models and ESM experiments. Here we present Phase 2 of the Reduced Complexity Model Intercomparison Project (RCMIP Phase 2), the first comprehensive intercomparison of RCMs that are probabilistically calibrated with key benchmark ranges from specialized research communities. Unsurprisingly, but crucially, we find that models which have been constrained to reflect the key benchmarks better reflect the key benchmarks. Under the low-emissions SSP1-1.9 scenario, across the RCMs, median peak warming projections range from 1.3 to 1.7°C (relative to 1850-1900, using an observationally based historical warming estimate of 0.8°C between 1850-1900 and 1995-2014). Further developing methodologies to constrain these projection uncertainties seems paramount given the international community's goal to contain warming to below 1.5°C above preindustrial in the long-term. Our findings suggest that users of RCMs should carefully evaluate their RCM, specifically its skill against key benchmarks and consider the need to include projections benchmarks either from ESM results or other assessments to reduce divergence in future projections.