Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 17
Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6787-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-6787-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 17
Development and technical paper
 | 
08 Sep 2022
Development and technical paper |  | 08 Sep 2022

Further improvement and evaluation of nudging in the E3SM Atmosphere Model version 1 (EAMv1): simulations of the mean climate, weather events, and anthropogenic aerosol effects

Shixuan Zhang, Kai Zhang, Hui Wan, and Jian Sun

Supplement

https://doi.org/10.5194/gmd-15-6787-2022-supplement

Data sets

Data used by figures in the manuscript Shixuan Zhang and Kai Zhang https://doi.org/10.5281/zenodo.6988262

Balloon-Borne Sounding System (SONDEWNPN). December 2009–-January 2011: Southern Great Plains (SGP) Central Facility (C1), North Slope Alaska (NSA) Central Facility (C1), Tropical Western Pacific (TWP) Central Facility (C1, C2, C3) Atmospheric Radiation Measurement (ARM) user facility https://doi.org/10.5439/1021460

Integrated Multi-satellitE Retrievals for GPM (IMERG) G. Huffman, D. Bolvin, D. Braithwaite, K. Hsu, R. Joyce, and P. Xie https://doi.org/10.5067/TRMM/TMPA/3H/7

NOAA Climate Data Record (CDR) of Daily Outgoing Longwave Radiation (OLR) Hai-Tien Lee and NOAA CDR Program https://doi.org/10.7289/V5SJ1HH2

NOAA Interpolated Outgoing Longwave Radiation (OLR) NOAA PSL https://psl.noaa. gov/data/gridded/data.olrcdr.interp.html

NOAA Climate Data Record (CDR) of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR) Soroosh Sorooshian, Kuolin Hsu, Dan Braithwaite, Hamed Ashouri, and NOAA CDR Program https://doi.org/10.7289/V51V5BWQ

Model code and software

EAM source codes and scripts for nudging experiments E3SM developers, Shixuan Zhang, Kai Zhang, and Jian Sun https://doi.org/10.5281/zenodo.5532606

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Short summary
This study investigates the nudging implementation in the EAMv1 model. We find that (1) revising the sequence of calculations and using higher-frequency constraining data to improve the performance of a simulation nudged to EAMv1’s own meteorology, (2) using the relocated nudging tendency and 3-hourly ERA5 reanalysis to obtain a better agreement between nudged simulations and observations, and (3) using wind-only nudging are recommended for the estimates of global mean aerosol effects.
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