Preprints
https://doi.org/10.5194/egusphere-2022-769
https://doi.org/10.5194/egusphere-2022-769
07 Sep 2022
 | 07 Sep 2022

Daytime-only-mean data can enhance understanding of land-atmosphere coupling

Zun Yin, Kirsten Findell, Paul Dirmeyer, Elena Shevliakova, Sergey Malyshev, Khaled Ghannam, Nina Raoult, and Zhihong Tan

Abstract. Land-atmosphere (L-A) interactions encompass the co-evolution of the land surface and overlying planetary boundary layer, primarily during daylight hours. However, many studies have been conducted using monthly or entire-day-mean time series due to the lack of sub-daily data. It has been unclear whether the inclusion of nighttime data alters the assessment of L-A coupling or obscures L-A interactive processes. To address this question, we generate monthly (M), entire-day-mean (E), and daytime-only-mean (D) data based on the ERA5 (5th European Centre for Medium-Range Weather Forecasts reanalysis) product, and evaluate the strength of L-A coupling through two-legged metrics, which partition the impact of the land states on surface fluxes (the land leg) from the impact of surface fluxes on the atmospheric states (the atmospheric leg). Here we show that the spatial patterns of strong L-A coupling regions among the M-, D- and E-based diagnoses can differ by as much as 84.8 %. The signal loss from E- to M-based diagnoses is determined by the memory of local L-A states. The differences between E- and D-based diagnoses can be driven by physical mechanisms or the averaging algorithms. To improve understanding of L-A interactions, we call attention to the urgent need for more high-frequency data from both simulations and observations for relevant diagnoses. Regarding model outputs, two approaches are proposed to resolve the storage dilemma for high-frequency data: (1) integration of L-A metrics within Earth System Models, and (2) producing alternative daily datasets based on different averaging algorithms.

Journal article(s) based on this preprint

23 Feb 2023
Daytime-only mean data enhance understanding of land–atmosphere coupling
Zun Yin, Kirsten L. Findell, Paul Dirmeyer, Elena Shevliakova, Sergey Malyshev, Khaled Ghannam, Nina Raoult, and Zhihong Tan
Hydrol. Earth Syst. Sci., 27, 861–872, https://doi.org/10.5194/hess-27-861-2023,https://doi.org/10.5194/hess-27-861-2023, 2023
Short summary

Zun Yin et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-769', Anonymous Referee #1, 19 Sep 2022
  • RC2: 'Comment on egusphere-2022-769', Anonymous Referee #2, 28 Sep 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-769', Anonymous Referee #1, 19 Sep 2022
  • RC2: 'Comment on egusphere-2022-769', Anonymous Referee #2, 28 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
ED: Publish subject to revisions (further review by editor and referees) (18 Nov 2022) by Alexander Gruber
AR by Zun Yin on behalf of the Authors (19 Nov 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (29 Nov 2022) by Alexander Gruber
RR by Anonymous Referee #2 (05 Jan 2023)
ED: Publish subject to minor revisions (review by editor) (10 Jan 2023) by Alexander Gruber
AR by Zun Yin on behalf of the Authors (21 Jan 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (06 Feb 2023) by Alexander Gruber
AR by Zun Yin on behalf of the Authors (08 Feb 2023)

Journal article(s) based on this preprint

23 Feb 2023
Daytime-only mean data enhance understanding of land–atmosphere coupling
Zun Yin, Kirsten L. Findell, Paul Dirmeyer, Elena Shevliakova, Sergey Malyshev, Khaled Ghannam, Nina Raoult, and Zhihong Tan
Hydrol. Earth Syst. Sci., 27, 861–872, https://doi.org/10.5194/hess-27-861-2023,https://doi.org/10.5194/hess-27-861-2023, 2023
Short summary

Zun Yin et al.

Viewed

Total article views: 481 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
360 106 15 481 32 4 2
  • HTML: 360
  • PDF: 106
  • XML: 15
  • Total: 481
  • Supplement: 32
  • BibTeX: 4
  • EndNote: 2
Views and downloads (calculated since 07 Sep 2022)
Cumulative views and downloads (calculated since 07 Sep 2022)

Viewed (geographical distribution)

Total article views: 478 (including HTML, PDF, and XML) Thereof 478 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Mar 2023
Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
Land-atmosphere (L-A) interactions concerns daytime process. However, most studies used monthly (M) or entire-day-mean (E) data, due to the lack of daytime-only data. We questioned if M and E are sufficient for assessing L-A coupling strength. Via this study, we found that the evaluation is biased by integrating nighttime or by monthly smoothing. We propose either integrating L-A metrics within models or providing daily products based on optimized averaging algorithms.