Modeling of the first indirect effect: Analysis of measurement requirements

By G. Feingold. 2003

186 citations recorded by the source corpus.

Subfields: Atmospheric Science, Earth-Surface Processes, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/00f3ef6c1a4c2bce2377523ccdc7b6184b8c2537

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

1. Andrew Gettelman
2. Hugh Morrison
3. C. Hoose
4. Steven Ghan
5. G. Feingold
6. J. Kristjánsson
7. R. Easter
8. P. Stier

In Earth-Surface Processes

1. G. Feingold
2. H. Xue
3. Hongli Jiang
4. Allison C. McComiskey
5. Johannes Quaas
6. M. Andreae
7. Hugh Morrison
8. J. Kristjánsson

In Global and Planetary Change

1. Andrew Gettelman
2. Hugh Morrison
3. C. Hoose
4. Steven Ghan
5. G. Feingold
6. J. Kristjánsson
7. R. Easter
8. P. Stier

Citations in the corpus

• Large-Eddy Simulations of Trade Wind Cumuli: Investigation of Aerosol Indirect Effects (2006, background): “We are grateful to Bjorn Stevens for providing the LES model and helpful suggestions on this paper.”
• The scale problem in quantifying aerosol indirect effects (2011, methodology): “To quantify the microphysical component of the albedo effect, Feingold et al. (2001) proposed a metric IE=−d lnre/d lnτa, where re is the cloud drop effective radius and τa, the aerosol optical depth, holding cloud liquid water constant for all calculations.”
• Aerosol indirect effects – general circulation model intercomparison and evaluation with satellite data (2009, background): “McComiskey and Feingold (2008) investigated how the radiative forcing by the first aerosol indirect effect (cloud albedo effect) varies for a given variation in the slope of the Nd – τa relationship.”
• Effects of varying aerosol regimes on low‐level Arctic stratus (2004, background): “From the ground based retrievals, cloud droplet number concentration is inferred from N ¼ 3LWP 4prwr3eDZ exp 3s2d ð1Þ where, DZ is the cloud thickness derived from ceilometer and radar, rw is the bulk density of water, and sd is the assumed width of an log-normal droplet size distribution.”
• Aerosol- and updraft-limited regimes of cloud droplet formation: influence of particle number, size and hygroscopicity on the activation of cloud condensation nuclei (CCN) (2009, methodology): “Based on cloud parcel model simulations, Feingold (2003) proposed a linear regression method to calculate the relative sensitivity of one parameter (i.”
• Effect of aerosol on warm convective clouds: Aerosol‐cloud‐surface flux feedbacks in a new coupled large eddy model (2006, background): “The range of Nd versus Na relationships is due in varying, and uncertain degrees to aerosol concentration, size distribution, composition and updraft velocity [e.g., Twomey, 1959; Leaitch et al., 1996; Facchini et al., 1999; Nenes et al., 2002; Feingold, 2003].”