The importance of temporal collocation for the evaluation of aerosol models with observations

Schutgens, N. A. J.; Partridge, D. G.; Stier, P.

doi:10.5194/acp-16-1065-2016

Articles | Volume 16, issue 2

https://doi.org/10.5194/acp-16-1065-2016

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

https://doi.org/10.5194/acp-16-1065-2016

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

Articles | Volume 16, issue 2

Research article

|

29 Jan 2016

Research article |

| 29 Jan 2016

The importance of temporal collocation for the evaluation of aerosol models with observations

N. A. J. Schutgens, D. G. Partridge, and P. Stier

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Total article views: 4,694 (including HTML, PDF, and XML)

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HTML: 2,902
PDF: 1,666
XML: 126
Total: 4,694
BibTeX: 169
EndNote: 184

Views and downloads (calculated since 25 Sep 2015)

Month	HTML	PDF	XML	Total
Sep 2015	60	60	5	125
Oct 2015	37	43	10	90
Nov 2015	22	20	8	50
Dec 2015	4	10	8	22
Jan 2016	28	12	2	42
Feb 2016	157	92	2	251
Mar 2016	45	25	0	70
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May 2016	22	19	0	41
Jun 2016	13	6	1	20
Jul 2016	16	15	2	33
Aug 2016	16	9	0	25
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Oct 2016	13	12	0	25
Nov 2016	14	5	0	19
Dec 2016	8	8	0	16
Jan 2017	12	11	0	23
Feb 2017	10	8	0	18
Mar 2017	27	21	1	49
Apr 2017	28	16	1	45
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Sep 2017	15	17	1	33
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Sep 2019	6	18	0	24
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Jan 2024	53	6	2	61
Feb 2024	34	3	0	37
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Apr 2024	43	17	6	66
May 2024	16	19	1	36
Jun 2024	9	8	0	17
Jul 2024	20	11	3	34
Aug 2024	40	6	3	49
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Oct 2024	14	5	1	20
Nov 2024	9	5	4	18
Dec 2024	8	7	3	18
Jan 2025	22	6	0	28
Feb 2025	35	6	4	45
Mar 2025	20	12	2	34
Apr 2025	24	15	2	41
May 2025	37	4	0	41
Jun 2025	62	14	5	81
Jul 2025	54	16	4	74
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Sep 2025	69	12	2	83
Oct 2025	20	30	0	50
Nov 2025	41	23	2	66
Dec 2025	19	7	0	26

Cumulative views and downloads (calculated since 25 Sep 2015)

Month	HTML	PDF	XML	Total
Sep 2015	60	60	5	125
Oct 2015	37	43	10	90
Nov 2015	22	20	8	50
Dec 2015	4	10	8	22
Jan 2016	28	12	2	42
Feb 2016	157	92	2	251
Mar 2016	45	25	0	70
Apr 2016	20	13	0	33
May 2016	22	19	0	41
Jun 2016	13	6	1	20
Jul 2016	16	15	2	33
Aug 2016	16	9	0	25
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Oct 2016	13	12	0	25
Nov 2016	14	5	0	19
Dec 2016	8	8	0	16
Jan 2017	12	11	0	23
Feb 2017	10	8	0	18
Mar 2017	27	21	1	49
Apr 2017	28	16	1	45
May 2017	26	12	0	38
Jun 2017	26	10	3	39
Jul 2017	23	11	1	35
Aug 2017	13	18	0	31
Sep 2017	15	17	1	33
Oct 2017	25	26	0	51
Nov 2017	17	41	0	58
Dec 2017	15	21	0	36
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Apr 2023	7	12	0	19
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Jun 2023	50	8	2	60
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Aug 2023	52	4	1	57
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Nov 2023	189	6	1	196
Dec 2023	57	15	1	73
Jan 2024	53	6	2	61
Feb 2024	34	3	0	37
Mar 2024	44	13	0	57
Apr 2024	43	17	6	66
May 2024	16	19	1	36
Jun 2024	9	8	0	17
Jul 2024	20	11	3	34
Aug 2024	40	6	3	49
Sep 2024	9	5	0	14
Oct 2024	14	5	1	20
Nov 2024	9	5	4	18
Dec 2024	8	7	3	18
Jan 2025	22	6	0	28
Feb 2025	35	6	4	45
Mar 2025	20	12	2	34
Apr 2025	24	15	2	41
May 2025	37	4	0	41
Jun 2025	62	14	5	81
Jul 2025	54	16	4	74
Aug 2025	57	7	1	65
Sep 2025	69	12	2	83
Oct 2025	20	30	0	50
Nov 2025	41	23	2	66
Dec 2025	19	7	0	26

Latest update: 06 Dec 2025

Short summary

When comparing models against observations, researchers often use long-term averages without due regard for the temporal sampling of the underlying data sets. We study the errors introduced by this practice and show they are often larger than observational errors and comparable to model errors. We further analyse what causes these errors and suggest best practices for eliminating them.