Water vapour variability in the high-latitude upper troposphere – Part  2: Impact  of volcanic eruptions

Sioris, Christopher E.; Zou, Jason; McElroy, C. Thomas; Boone, Chris D.; Sheese, Patrick E.; Bernath, Peter F.

doi:https://doi.org/10.5194/acp-16-2207-2016

Articles | Volume 16, issue 4

https://doi.org/10.5194/acp-16-2207-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/acp-16-2207-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 16, issue 4

Research article

|

25 Feb 2016

Research article |

| 25 Feb 2016

Water vapour variability in the high-latitude upper troposphere – Part 2: Impact of volcanic eruptions

Christopher E. Sioris, Jason Zou, C. Thomas McElroy, Chris D. Boone, Patrick E. Sheese, and Peter F. Bernath

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Feb 2016	109	43	4	156
Mar 2016	73	41	4	118
Apr 2016	19	14	0	33
May 2016	17	12	1	30
Jun 2016	16	10	1	27
Jul 2016	13	8	1	22
Aug 2016	11	3	1	15
Sep 2016	13	5	1	19
Oct 2016	13	5	4	22
Nov 2016	7	5	12
Dec 2016	4	3	0	7
Jan 2017	20	4	1	25
Feb 2017	13	1	1	15
Mar 2017	24	15	1	40
Apr 2017	25	11	0	36
May 2017	26	10	0	36
Jun 2017	28	4	1	33
Jul 2017	23	6	0	29
Aug 2017	14	11	1	26
Sep 2017	11	9	1	21
Oct 2017	9	7	1	17
Nov 2017	12	6	1	19
Dec 2017	4	6	1	11
Jan 2018	8	4	0	12
Feb 2018	5	2	0	7
Mar 2018	11	6	0	17
Apr 2018	9	4	2	15
May 2018	13	7	0	20
Jun 2018	8	12	1	21
Jul 2018	10	4	0	14
Aug 2018	13	6	0	19
Sep 2018	7	8	2	17
Oct 2018	17	5	2	24
Nov 2018	18	13	1	32
Dec 2018	10	4	2	16
Jan 2019	5	6	1	12
Feb 2019	2	1	0	3
Mar 2019	5	5	1	11
Apr 2019	7	4	0	11
May 2019	10	6	0	16
Jun 2019	6	6	1	13
Jul 2019	8	7	0	15
Aug 2019	8	6	0	14
Sep 2019	8	4	3	15
Oct 2019	7	2	1	10
Nov 2019	8	5	0	13
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Jan 2020	12	2	0	14
Feb 2020	12	4	2	18
Mar 2020	2	5	1	8
Apr 2020	4	2	0	6
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Jul 2020	26	12	4	42
Aug 2020	8	7	4	19
Sep 2020	7	6	1	14
Oct 2020	7	7	0	14
Nov 2020	5	4	1	10
Dec 2020	6	4	0	10
Jan 2021	15	5	1	21
Feb 2021	12	4	0	16
Mar 2021	6	4	1	11
Apr 2021	7	5	1	13
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Jun 2021	4	7	0	11
Jul 2021	7	10	0	17
Aug 2021	3	13	0	16
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Oct 2021	5	34	0	39
Nov 2021	9	25	1	35
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Mar 2022	6	3	2	11
Apr 2022	7	5	1	13
May 2022	12	3	0	15
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Jul 2022	3	3	4	10
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Nov 2022	23	3	0	26
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Jan 2023	8	4	1	13
Feb 2023	9	6	0	15
Mar 2023	8	3	1	12
Apr 2023	2	4	0	6
May 2023	35	12	0	47
Jun 2023	41	6	3	50
Jul 2023	38	3	1	42
Aug 2023	34	7	2	43
Sep 2023	35	7	1	43
Oct 2023	38	6	3	47
Nov 2023	24	1	25
Dec 2023	41	6	0	47
Jan 2024	41	4	0	45
Feb 2024	39	7	0	46
Mar 2024	41	15	3	59
Apr 2024	24	6	2	32

Cumulative views and downloads (calculated since 24 Sep 2015)

Month	HTML	PDF	XML	Total
Sep 2015	59	44	2	105
Oct 2015	44	27	9	80
Nov 2015	20	15	6	41
Dec 2015	14	7	5	26
Jan 2016	25	9	3	37
Feb 2016	109	43	4	156
Mar 2016	73	41	4	118
Apr 2016	19	14	0	33
May 2016	17	12	1	30
Jun 2016	16	10	1	27
Jul 2016	13	8	1	22
Aug 2016	11	3	1	15
Sep 2016	13	5	1	19
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Nov 2016	7	5	12
Dec 2016	4	3	0	7
Jan 2017	20	4	1	25
Feb 2017	13	1	1	15
Mar 2017	24	15	1	40
Apr 2017	25	11	0	36
May 2017	26	10	0	36
Jun 2017	28	4	1	33
Jul 2017	23	6	0	29
Aug 2017	14	11	1	26
Sep 2017	11	9	1	21
Oct 2017	9	7	1	17
Nov 2017	12	6	1	19
Dec 2017	4	6	1	11
Jan 2018	8	4	0	12
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Mar 2018	11	6	0	17
Apr 2018	9	4	2	15
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Jul 2020	26	12	4	42
Aug 2020	8	7	4	19
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Oct 2020	7	7	0	14
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Oct 2021	5	34	0	39
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Apr 2022	7	5	1	13
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Jun 2022	6	3	0	9
Jul 2022	3	3	4	10
Aug 2022	13	6	0	19
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Oct 2022	10	1	0	11
Nov 2022	23	3	0	26
Dec 2022	10	5	2	17
Jan 2023	8	4	1	13
Feb 2023	9	6	0	15
Mar 2023	8	3	1	12
Apr 2023	2	4	0	6
May 2023	35	12	0	47
Jun 2023	41	6	3	50
Jul 2023	38	3	1	42
Aug 2023	34	7	2	43
Sep 2023	35	7	1	43
Oct 2023	38	6	3	47
Nov 2023	24	1	25
Dec 2023	41	6	0	47
Jan 2024	41	4	0	45
Feb 2024	39	7	0	46
Mar 2024	41	15	3	59
Apr 2024	24	6	2	32

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Short summary

This paper shows that volcanic eruptions occurring at higher latitudes in windy environments can lead to significant perturbations to upper tropospheric (UT) humidity mostly due to entrainment of lower tropospheric moisture by wind-blown plumes. This research was performed for the purpose of determining long-term trends in high-latitude UT water vapour. The steps involve building a monthly climatology and using it to deseasonalize the time series. Large observed anomalies are then studied.