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  • 1. Ball, William T.
    et al.
    Alsing, Justin
    Mortlock, Daniel J.
    Stockholm University, Faculty of Science, Department of Astronomy. Imperial College London, UK .
    Rozanov, Eugene V.
    Tummon, Fiona
    Haigh, Joanna D.
    Reconciling differences in stratospheric ozone composites2017In: Atmospheric Chemistry And Physics, ISSN 1680-7316, E-ISSN 1680-7324, Vol. 17, no 20, p. 12269-12302Article in journal (Refereed)
    Abstract [en]

    Observations of stratospheric ozone from multiple instruments now span three decades; combining these into composite datasets allows long-term ozone trends to be estimated. Recently, several ozone composites have been published, but trends disagree by latitude and altitude, even between composites built upon the same instrument data. We confirm that the main causes of differences in decadal trend estimates lie in (i) steps in the composite time series when the instrument source data changes and (ii) artificial sub-decadal trends in the underlying instrument data. These artefacts introduce features that can alias with regressors in multiple linear regression (MLR) analysis; both can lead to inaccurate trend estimates. Here, we aim to remove these artefacts using Bayesian methods to infer the underlying ozone time series from a set of composites by building a joint-likelihood function using a Gaussian-mixture density to model outliers introduced by data artefacts, together with a data-driven prior on ozone variability that incorporates knowledge of problems during instrument operation. We apply this Bayesian self-calibration approach to stratospheric ozone in 10 degrees bands from 60 degrees S to 60 degrees N and from 46 to 1 hPa (similar to 21-48 km) for 1985-2012. There are two main outcomes: (i) we independently identify and confirm many of the data problems previously identified, but which remain unaccounted for in existing composites; (ii) we construct an ozone composite, with uncertainties, that is free from most of these problems - we call this the BAyeSian Integrated and Consolidated (BASIC) composite. To analyse the new BASIC composite, we use dynamical linear modelling (DLM), which provides a more robust estimate of long-term changes through Bayesian inference than MLR. BASIC and DLM, together, provide a step forward in improving estimates of decadal trends. Our results indicate a significant recovery of ozone since 1998 in the upper stratosphere, of both northern and southern midlatitudes, in all four composites analysed, and particularly in the BASIC composite. The BASIC results also show no hemispheric difference in the recovery at midlatitudes, in contrast to an apparent feature that is present, but not consistent, in the four composites. Our overall conclusion is that it is possible to effectively combine different ozone composites and account for artefacts and drifts, and that this leads to a clear and significant result that upper stratospheric ozone levels have increased since 1998, following an earlier decline.

  • 2. Jungclaus, Johann H.
    et al.
    Bard, Edouard
    Baroni, Mélanie
    Braconnot, Pascale
    Cao, Jian
    Chini, Louise P.
    Egorova, Tania
    Evans, Michael
    González-Rouco, J. Fidel
    Goosse, Hugues
    Hurtt, George C.
    Joos, Fortunat
    Kaplan, Jed O.
    Khodri, Myriam
    Goldewijk, Kees Klein
    Krivova, Natalie
    LeGrande, Allegra N.
    Lorenz, Stephan J.
    Luterbacher, Jürg
    Man, Wenmin
    Maycock, Amanda C.
    Meinshausen, Malte
    Moberg, Anders
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Muscheler, Raimund
    Nehrbass-Ahles, Christoph
    Otto-Bliesner, Bette I.
    Phipps, Steven J.
    Pongratz, Julia
    Rozanov, Eugene
    Schmidt, Gavin A.
    Schmidt, Hauke
    Schmutz, Werner
    Schurer, Andrew
    Shapiro, Alexander I.
    Sigl, Michael
    Smerdon, Jason E.
    Solanki, Sami K.
    Timmreck, Claudia
    Toohey, Matthew
    Usoskin, Ilya G.
    Wagner, Sebastian
    Wu, Chi-Ju
    Yeo, Kok Leng
    Zanchettin, Davide
    Zhang, Qiong
    Stockholm University, Faculty of Science, Department of Physical Geography.
    Zorita, Eduardo
    The PMIP4 contribution to CMIP6-Part 3: The last millennium, scientific objective, and experimental design for the PMIP4 past1000 simulations2017In: Geoscientific Model Development, ISSN 1991-959X, E-ISSN 1991-9603, Vol. 10, no 11, p. 4005-4033Article in journal (Refereed)
    Abstract [en]

    The pre-industrial millennium is among the periods selected by the Paleoclimate Model Intercomparison Project (PMIP) for experiments contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and the fourth phase of the PMIP (PMIP4). The past1000 transient simulations serve to investigate the response to (mainly) natural forcing under background conditions not too different from today, and to discriminate between forced and internally generated variability on interannual to centennial timescales. This paper describes the motivation and the experimental set-ups for the PMIP4-CMIP6 past1000 simulations, and discusses the forcing agents orbital, solar, volcanic, and land use/land cover changes, and variations in greenhouse gas concentrations. The past1000 simulations covering the pre-industrial millennium from 850 Common Era (CE) to 1849 CE have to be complemented by historical simulations (1850 to 2014 CE) following the CMIP6 protocol. The external forcings for the past1000 experiments have been adapted to provide a seamless transition across these time periods. Protocols for the past1000 simulations have been divided into three tiers. A default forcing data set has been defined for the Tier 1 (the CMIP6 past1000) experiment. However, the PMIP community has maintained the flexibility to conduct coordinated sensitivity experiments to explore uncertainty in forcing reconstructions as well as parameter uncertainty in dedicated Tier 2 simulations. Additional experiments (Tier 3) are defined to foster collaborative model experiments focusing on the early instrumental period and to extend the temporal range and the scope of the simulations. This paper outlines current and future research foci and common analyses for collaborative work between the PMIP and the observational communities (reconstructions, instrumental data).

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