Preprints
https://doi.org/10.5194/egusphere-2022-769
https://doi.org/10.5194/egusphere-2022-769
 
07 Sep 2022
07 Sep 2022
Status: this preprint is open for discussion.

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

Zun Yin1, Kirsten Findell2, Paul Dirmeyer3, Elena Shevliakova2, Sergey Malyshev2, Khaled Ghannam1, Nina Raoult4, and Zhihong Tan1 Zun Yin et al.
  • 1Program in Atmospheric and Oceanic Sciences, Princeton University, Princeton, 08540, New Jersey, USA
  • 2Geophysical Fluid Dynamics Laboratory, NOAA/OAR, Princeton, 08540, New Jersey, USA
  • 3Center for Ocean-Land-Atmosphere Studies, George Mason University, Fairfax, 22030, Virginia, USA
  • 4Laboratoire des Sciences du Climat et de l’Environnement, IPSL, CNRS-CEA-UVSQ, Gif-sur-Yvette, 91191, Essonne, France

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.

Zun Yin et al.

Status: open (until 02 Nov 2022)

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 reply
  • RC2: 'Comment on egusphere-2022-769', Anonymous Referee #2, 28 Sep 2022 reply

Zun Yin et al.

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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.