Research Products
All Products Are For Educational and Research Purposes Only !
4. SNICARv2: An updated stand-alone version of SNow, ICe, Aerosol Radiative (SNICAR) model.
Model codes & more details are available here: https://github.com/EarthSciCode/SNICARv2
Contact Info: Cenlin He (cenlinhe@ucar.edu), Mark G. Flanner (flanner@umich.edu)
Original version: written by Mark G. Flanner (flanner@umich.edu), see Flanner et al. 2007 JGR.
Current version: updated by Cenlin He (cenlinhe@ucar.edu), see He et al. 2018 ACP.
The original version assumes aerosols externally mixed with spherical snow grains.
The updated version includes non-spherical snow grains & black carbon (BC)-snow internal mixing.
The SNICAR model is the snow module of Community Land Model (CLM) embedded in CESM GCM.
3. Aerosol-Snow Parameterizations:
Parameterizations & more details are available here:
He et al. (2017, J. Climate), He et al. (2018, JGR), He et al. (2019, JAMES)
Contact Info: Cenlin He (cenlinhe@ucar.edu)
This is a set of new parameterizations for spectral single-scattering properties and albedos of clean and black
carbon (BC)/dust-contaminated snow for application in climate models, which resolve various snow grain
shapes and BC/dust-snow internal/external mixing states with high accuracies.
The parameterizations have been implemented into the SNICAR snow model (see Product #4).
2. Microphysics-based BC aging scheme:
Scheme & more details are available here: He et al. (2016, ACP)
Contact Info: Cenlin He (cenlinhe@ucar.edu)
This is a "hybrid" microphysics-based black carbon (BC) aerosol aging scheme that accounts for
condensation, coagulation, and heterogeneous chemical oxidation processes for application in global
chemical transport models, without sacrificing . The above paper shows an application in GEOS-Chem.
1. Parameterization for SOA production from cloud processes:
Parameterization & more details are available here: He et al. (2013, ACP)
Contact Info: Cenlin He (cenlinhe@ucar.edu), Junfeng Liu (jfliu@pku.edu.cn)
This parameterization quantitatively relates SOA mass production rate from cloud processes to cloud liquid
water content and total carbon chemical loss rate. It reasonably well captures spatiotemporal variability of
the process-based SOA formation in clouds.
