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Reconciling differences in stratospheric ozone composites

机译：协调平流层臭氧复合材料的差异

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摘要

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.
机译：现在三十年来，多种仪器的平流层臭氧观察;将这些组合到复合数据集允许估计长期臭氧趋势。最近，已经发布了几种臭氧复合材料，但即使在基于相同仪器数据的复合材料之间，纬度和高度也不同意趋势不同意。我们证明，当仪器源数据变化和（ii）底层仪器数据中的人工次数趋势和（ii）人工子二数趋势时，Decadal趋势估计的差异的主要原因位于（i）复合时间序列中的步骤。这些人工制品介绍了多元线性回归（MLR）分析中的回归量可以别名的功能;两者都可以导致不准确的趋势估算。在这里，我们的目的是使用贝叶斯方法去除这些人工制品，通过使用高斯 - 混合密度建立一个复合材料来推断基础臭氧时间序列，使用高斯 - 混合密度与数据伪影引入的模拟异常值以及数据驱动的模拟异常值在臭氧变异之前，在仪器操作期间包含对问题的知识。我们将此贝叶斯自校准方法应用于10度条带的平流层臭氧，从60度到60度N，46到1 HPA（类似于21-48km），持续1985-2012。有两个主要结果：（i）我们独立地识别并确认先前已识别的许多数据问题，但其在现有复合材料中仍未考虑; （ii）我们构建一个臭氧综合，不确定，没有大多数这些问题 - 我们称之为贝叶斯综合和合并（基本）复合材料。为了分析新的基本复合材料，我们使用动态线性建模（DLM），通过贝叶斯推理比MLR提供更强大的长期变化的估计。基本和DLM在一起，在提高截止趋势估计方面向前迈进了一步。我们的结果表明，自1998年以来，臭氧在北部和南部的中间层，在所有四种复合材料中，特别是在基本复合材料中，臭氧。基本结果还显示了中间体在中间覆盖的中间差异，与四个复合材料中存在但不一致的表观特征相反。我们的总体结论是，可以有效地将不同的臭氧复合材料组合起来，并考虑人工制品和漂移，并且这导致较早的下降自1998年以来的上层散臭臭氧水平的明显显着效果。

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  • 来源
    《Atmospheric chemistry and physics》 |2017年第20期|共34页
  • 作者

    Ball William T.; Alsing Justin; Mortlock Daniel J.; Rozanov Eugene V.; Tummon Fiona; Haigh Joanna D.;

  • 作者单位

    Swiss Fed Inst Technol Zurich CHN Inst Atmospher &

    Climate Sci Univ Str 16 CH-8092 Zurich Switzerland;

    Flatiron Inst Ctr Computat Astrophys 162 5th Ave New York NY 10010 USA;

    Imperial Coll London Blackett Lab Phys Dept London SW7 2AZ England;

    Swiss Fed Inst Technol Zurich CHN Inst Atmospher &

    Climate Sci Univ Str 16 CH-8092 Zurich Switzerland;

    Swiss Fed Inst Technol Zurich CHN Inst Atmospher &

    Climate Sci Univ Str 16 CH-8092 Zurich Switzerland;

    Imperial Coll London Blackett Lab Phys Dept London SW7 2AZ England;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 大气科学（气象学）;
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