First measurements of the Twomey indirect effect using ground‐based remote sensors

By G. Feingold, W. Eberhard, D. Veron, M. Previdi. 2003

344 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/bbe570cea9a95e3bbf53c940ba9a65d302ffe00f

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. Hugh Morrison
2. Andrew Gettelman
3. C. Hoose
4. Ulrike Lohmann
5. G. Feingold
6. Steven Ghan
7. Johannes Quaas
8. P. Stier

In Global and Planetary Change

1. Hugh Morrison
2. Andrew Gettelman
3. C. Hoose
4. Ulrike Lohmann
5. G. Feingold
6. Steven Ghan
7. Johannes Quaas
8. P. Stier

Citations in the corpus

• The scale problem in quantifying aerosol indirect effects (2011, background): “…remote sensing provide high temporal resolution (order 20 s), co-located data for aerosol and cloud properties in a single column of air (e.g., Feingold et al., 2003; Kim et al., 2008) and improve confidence that the aerosol measured is that with the potential to impact the cloud properties…”
• Mesoscale modeling of springtime Arctic mixed-phase stratiform clouds using a new two-moment bulk microphysics scheme (2005, background): “…focused on aerosol modification of the droplet population (e.g., Albrecht 1989; Coakley and Walsh 2002; Ghan et al. 2001; Rotstayn and Penner 2001; Feingold et al. 2003), but aerosol–ice interactions may also have a significant impact on ice- and mixed-phase clouds (e.g., DeMott et al. 1997;…”
• Effect of aerosol on warm convective clouds: Aerosol‐cloud‐surface flux feedbacks in a new coupled large eddy model (2006, background): “…et al., 2000], by satellite remote sensors [Kaufman and Nakajima, 1993; Han et al., 1998; Bréon et al., 2002; Nakajima et al., 2001] and surface-based remote sensors [e.g., Feingold et al., 2003; Kim et al., 2003] but quantification of the magnitude of this effect remains an elusive goal.”
• Global indirect aerosol effects: a review (2004, unknown): “The estimate of the indirect effect by Feingold et al. (2003) at the ARM site is larger than estimated from POLDER satellite data by Br´eon et al. (2002).”
• Global observations of aerosol‐cloud‐precipitation‐climate interactions (2014, methodology): “where α represents the CCN concentration, often estimated using satellite-measured aerosol optical depth (AOD) [Feingold et al., 2003; Nakajima and Schulz, 2009; Sorooshian et al., 2010].”
• Bounding Global Aerosol Radiative Forcing of Climate Change (2020, methodology): “The sensitivity βlnN−ln a is often evaluated using linear regressions, with var-891 ious choices for the aerosol metric a (Feingold et al., 2003; McComiskey et al., 2009).”
• Increase of cloud droplet size with aerosol optical depth: An observation and modeling study (2008, methodology): “While some of the differences are related to the use of different analysis methods and/or observational data [Rosenfeld and Feingold, 2003], natural variation is observed [Feingold et al., 2003; Kim et al., 2003].”
• Large-Eddy Simulations of Trade Wind Cumuli: Investigation of Aerosol Indirect Effects (2006, background): “Satellite- and surface-based remote sensing has also been widely used to identify aerosol effects on clouds (e.g., Kaufman and Nakajima 1993; Feingold et al. 2003), but questions remain regarding the ability of these methods to quantify the indirect effects (Feingold 2003).”
• Aerosol indirect effects – general circulation model intercomparison and evaluation with satellite data (2009, background): “Following Feingold et al. (2003), the strength of the aerosol-cloud interactions may be quantified as the relative change in cloud droplet number concentration ( N d ) with a relative change in τ a .”
• Interpreting the cloud cover – aerosol optical depth relationship found in satellite data using a general circulation model (2009, methodology): “Following Feingold et al. (2003), the strength of the aerosol-cloud cover relationship is quantified here as the slope of the linear regression between the natural logarithm of TCC and the natural logarithm of AOD b = 1lnTCC/1lnAOD.”
• Modeling of the first indirect effect: Analysis of measurement requirements (2003, methodology): “The constraint of constant LWC as a basis for comparison is practically difficult to achieve although attempts in this direction have been made using models [Schwartz et al., 2002] and surfacebased remote sensors [Feingold et al., 2003].”