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    Evaluating stratospheric ozone and water vapour changes in CMIP6 models from 1850 to 2100
    (Katlenburg-Lindau : European Geosciences Union, 2021) Keeble, James; Hassler, Birgit; Banerjee, Antara; Checa-Garcia, Ramiro; Chiodo, Gabriel; Davis, Sean; Eyring, Veronika; Griffiths, Paul T.; Morgenstern, Olaf; Nowack, Peer; Zeng, Guang; Zhang, Jiankai; Bodeker, Greg; Burrows, Susannah; Cameron-Smith, Philip; Cugnet, David; Danek, Christopher; Deushi, Makoto; Horowitz, Larry W.; Kubin, Anne; Li, Lijuan; Lohmann, Gerrit; Michou, Martine; Mills, Michael J.; Nabat, Pierre; Olivié, Dirk; Park, Sungsu; Seland, Øyvind; Stoll, Jens; Wieners, Karl-Hermann; Wu, Tongwen
    Stratospheric ozone and water vapour are key components of the Earth system, and past and future changes to both have important impacts on global and regional climate. Here, we evaluate long-term changes in these species from the pre-industrial period (1850) to the end of the 21st century in Coupled Model Intercomparison Project phase 6 (CMIP6) models under a range of future emissions scenarios. There is good agreement between the CMIP multi-model mean and observations for total column ozone (TCO), although there is substantial variation between the individual CMIP6 models. For the CMIP6 multi-model mean, global mean TCO has increased from ∼300 DU in 1850 to ∼ 305 DU in 1960, before rapidly declining in the 1970s and 1980s following the use and emission of halogenated ozone-depleting substances (ODSs). TCO is projected to return to 1960s values by the middle of the 21st century under the SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, and SSP5-8.5 scenarios, and under the SSP3-7.0 and SSP5-8.5 scenarios TCO values are projected to be ∼ 10 DU higher than the 1960s values by 2100. However, under the SSP1-1.9 and SSP1-1.6 scenarios, TCO is not projected to return to the 1960s values despite reductions in halogenated ODSs due to decreases in tropospheric ozone mixing ratios. This global pattern is similar to regional patterns, except in the tropics where TCO under most scenarios is not projected to return to 1960s values, either through reductions in tropospheric ozone under SSP1-1.9 and SSP1-2.6, or through reductions in lower stratospheric ozone resulting from an acceleration of the Brewer-Dobson circulation under other Shared Socioeconomic Pathways (SSPs). In contrast to TCO, there is poorer agreement between the CMIP6 multi-model mean and observed lower stratospheric water vapour mixing ratios, with the CMIP6 multi-model mean underestimating observed water vapour mixing ratios by ∼ 0.5 ppmv at 70 hPa. CMIP6 multi-model mean stratospheric water vapour mixing ratios in the tropical lower stratosphere have increased by ∼ 0.5 ppmv from the pre-industrial to the present-day period and are projected to increase further by the end of the 21st century. The largest increases (∼ 2 ppmv) are simulated under the future scenarios with the highest assumed forcing pathway (e.g. SSP5-8.5). Tropical lower stratospheric water vapour, and to a lesser extent TCO, shows large variations following explosive volcanic eruptions. © Author(s) 2021.
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    Detecting and quantifying causal associations in large nonlinear time series datasets
    (Washington, DC [u.a.] : Assoc., 2019) Runge, Jakob; Nowack, Peer; Kretschmer, Marlene; Flaxman, Seth; Sejdinovic, Dino
    Identifying causal relationships and quantifying their strength from observational time series data are key problems in disciplines dealing with complex dynamical systems such as the Earth system or the human body. Data-driven causal inference in such systems is challenging since datasets are often high dimensional and nonlinear with limited sample sizes. Here, we introduce a novel method that flexibly combines linear or nonlinear conditional independence tests with a causal discovery algorithm to estimate causal networks from large-scale time series datasets. We validate the method on time series of well-understood physical mechanisms in the climate system and the human heart and using large-scale synthetic datasets mimicking the typical properties of real-world data. The experiments demonstrate that our method outperforms state-of-the-art techniques in detection power, which opens up entirely new possibilities to discover and quantify causal networks from time series across a range of research fields. Copyright © 2019 The Authors,