University of Oulu
Oulu University Library
OuluCRIS
Laturi
Muuntaja

Similar Items

Cover Image

Sharma, P., Ganguly, D., Sharma, A. K., Kant, S., & Mishra, S. (2023). Assessing the aerosols, clouds and their relationship over the northern bay of bengal using a global climate model. Earth and Space Science, 10(2). https://doi.org/10.1029/2022EA002706

Assessing the aerosols, clouds and their relationship over the northern Bay of Bengal using a global climate model

Saved in:
Author: Sharma, Puneet1,2; Ganguly, Dilip2; Sharma, Amit Kumar2;
Organizations: 1School of Interdisciplinary Research, Indian Institute of Technology Delhi, New Delhi, India
2Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, India
3Center for Atmospheric Research, University of Oulu, Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.8 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2023032733298
Language: English
Published: American Geophysical Union, 2023
Publish Date: 2023-03-27
Description:

Abstract

Comprehensive evaluation of aerosol-cloud interactions (ACI) simulated by climate models using observations is crucial for advancing model development. Here, we use Moderate Resolution Imaging Spectroradiometer (MODIS) data to evaluate aerosol and cloud properties obtained from Community Atmosphere Model 5 (CAM5) over northern Bay of Bengal during winter season. We conduct simulations for default model setup as well as for prescribed, and nudged meteorology using Goddard Earth Observing System (GEOS5) reanalysis dataset in order to study the impact of meteorological parameters on simulated ACI. CAM5 captures the spatial variability of cloud optical depth (τc), cloud droplet number concentration (Nc), and liquid water path (LWP), although the values are overestimated in the model. Default model strongly simulates observed negative cloud effective radius (re)-Nc susceptibility but fails to reproduce the observed positive LWP-Nc susceptibility possibly due to evaporative cooling of the large number of smaller droplets. Compared to default and prescribed meteorology simulations, nudging specific humidity (Q) at 6-hr relaxation time scale leads to a positive LWP-Nc susceptibility, and an overall improved simulation of aerosol indirect effects. Increasing the relaxation time scale beyond 6-hr degrade the simulation of indirect effects suggesting high sensitivity of indirect effects to Q and serious deficiencies in Q simulated by the model. Improvement in simulation of aerosol and cloud characteristics are also noted when winds (UV) are nudged but it worsens some of the simulated ACI sensitivities due to increased transport of absorbing aerosols over the study region and a dominant semi-direct effect in the model.

see all
Series: Earth and space science
ISSN: 2333-5084
ISSN-E: 2333-5084
ISSN-L: 2333-5084
Volume: 10
Issue: 2
Article number: e2022EA002706
DOI: 10.1029/2022EA002706
OADOI: https://oadoi.org/10.1029/2022EA002706
Type of Publication: A1 Journal article – refereed
Field of Science: 1172 Environmental sciences
Subjects:
CAM5
aerosol indirect effect
cloud susceptibility
nudging
Article
Funding: Sunny Kant is grateful for the fellowship support from Science and Engineering Research Board, Department of Science and Technology, India under the National Post-Doctoral scheme (File number: PDF/2019/003444).
Dataset Reference: The model code used for our simulations using CESM-CAM5 can be accessed from https://www.cesm.ucar.edu/models/cesm1.2/. The CESM-CAM5 model experiment output datasets (Sharma et al., 2023) can be accessed from https://doi.org/10.5281/zenodo.7506927. The satellite datasets for CERES (Rutan et al., 2015), MODIS (Levy et al., 2013; Remer et al., 2005, 2008) and CloudSat (Stephens et al., 2008) can be downloaded from https://ceres.larc.nasa.gov/data/#syn1deg-level-3, https://modis.gsfc.nasa.gov/data/ and https://www.cloudsat.cira.colostate.edu/ respectively. Meteorological variables prescribed and nudged into the model simulations can be downloaded from Earth System Grid Federation (https://www.earthsystemgrid.org). For data processing and visualization, we used matplotlib (Hunter, 2007) and PyNgl/PyNio developed at UCAR, Boulder, Colorado, United States available from https://www.pyngl.ucar.edu/.
  https://www.cesm.ucar.edu/models/cesm1.2/
https://doi.org/10.5281/zenodo.7506927
https://ceres.larc.nasa.gov/data/#syn1deg-level-3
https://modis.gsfc.nasa.gov/data/
https://www.cloudsat.cira.colostate.edu/
https://www.earthsystemgrid.org
https://www.pyngl.ucar.edu/
Copyright information: © 2023 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
  https://creativecommons.org/licenses/by-nc-nd/4.0/