Comment on egusphere-2023-2773 DOI Creative Commons

Fani Alexandri,

Felix MÃ ⁄ ller,

Goutam Choudhury

et al.

Published: Dec. 9, 2023

Abstract. The effective radiative forcing (ERF) due to aerosol-cloud interactions (ACI) and rapid adjustments (ERFaci) still causes the largest uncertainty in assessment of climate change. It is understood only with medium confidence studied primarily for warm clouds. Here, we present a novel cloud-by-cloud (C×C) approach studying ACI satellite observations that combines concentration cloud condensation nuclei (nCCN) ice nucleating particles (nINP) from polar-orbiting lidar measurements development properties individual clouds tracking them geostationary observations. We step-by-step description obtaining matched cases. application over Central Europe Northern Africa during 2014 together rigorous quality assurance leads 399 liquid-only 95 ice-containing can be surrounding nCCN nINP, respectively, at level. use this initial data set assessing impact changes cloud-relevant aerosol concentrations on droplet number (Nd) radius (reff) liquid phase regime heterogeneous formation. find Δ ln Nd/Δ 0.13 0.30 which lower end commonly inferred values 0.3 0.8. reff/Δ between -0.09 -0.21 suggests reff decreases by -0.81 -3.78 nm per increase 1 cm-3. also tendency towards more fully glaciated increasing nINP cannot explained increasingly cloud-top temperature super-cooled liquid, mixed-phase, alone. Applied larger amount observations, C×C has potential enable systematic investigation cold This marks step change quantification ERFaci space.

Language: Английский

A cloud-by-cloud approach for studying aerosol–cloud interaction in satellite observations DOI Creative Commons

Fani Alexandri,

Felix Müller, Goutam Choudhury

et al.

Atmospheric measurement techniques, Journal Year: 2024, Volume and Issue: 17(6), P. 1739 - 1757

Published: March 26, 2024

Abstract. The effective radiative forcing (ERF) due to aerosol–cloud interactions (ACIs) and rapid adjustments (ERFaci) still causes the largest uncertainty in assessment of climate change. It is understood only with medium confidence studied primarily for warm clouds. Here, we present a novel cloud-by-cloud (C×C) approach studying ACI satellite observations that combines concentration cloud condensation nuclei (nCCN) ice nucleating particles (nINP) from polar-orbiting lidar measurements development properties individual clouds by tracking them geostationary observations. We step-by-step description obtaining matched cases. application over central Europe northern Africa during 2014, together rigorous quality assurance, leads 399 liquid-only 95 ice-containing can be surrounding nCCN nINP respectively at level. use this initial data set assessing impact changes cloud-relevant aerosol concentrations on droplet number (Nd) radius (reff) liquid phase regime heterogeneous formation. find Δln⁡Nd/Δln⁡nCCN 0.13 0.30, which lower end commonly inferred values 0.3 0.8. Δln⁡reff/Δln⁡nCCN between −0.09 −0.21 suggests reff decreases −0.81 −3.78 nm per increase 1 cm−3. also tendency towards more fully glaciated increasing cannot explained increasingly top temperature supercooled-liquid, mixed-phase, alone. Applied larger observations, C×C has potential enable systematic investigation cold This marks step change quantification ERFaci space.

Language: Английский

Citations

1
Comment on egusphere-2023-2773 DOI Creative Commons

Fani Alexandri,

Felix MÃ ⁄ ller,

Goutam Choudhury

et al.

Published: Feb. 2, 2024

Abstract. The effective radiative forcing (ERF) due to aerosol-cloud interactions (ACI) and rapid adjustments (ERFaci) still causes the largest uncertainty in assessment of climate change. It is understood only with medium confidence studied primarily for warm clouds. Here, we present a novel cloud-by-cloud (C×C) approach studying ACI satellite observations that combines concentration cloud condensation nuclei (nCCN) ice nucleating particles (nINP) from polar-orbiting lidar measurements development properties individual clouds tracking them geostationary observations. We step-by-step description obtaining matched cases. application over Central Europe Northern Africa during 2014 together rigorous quality assurance leads 399 liquid-only 95 ice-containing can be surrounding nCCN nINP, respectively, at level. use this initial data set assessing impact changes cloud-relevant aerosol concentrations on droplet number (Nd) radius (reff) liquid phase regime heterogeneous formation. find Δ ln Nd/Δ 0.13 0.30 which lower end commonly inferred values 0.3 0.8. reff/Δ between -0.09 -0.21 suggests reff decreases by -0.81 -3.78 nm per increase 1 cm-3. also tendency towards more fully glaciated increasing nINP cannot explained increasingly cloud-top temperature super-cooled liquid, mixed-phase, alone. Applied larger amount observations, C×C has potential enable systematic investigation cold This marks step change quantification ERFaci space.

Language: Английский

Citations

0
Comment on egusphere-2023-2773 DOI Creative Commons
Matthias Tesche

Published: Feb. 12, 2024

Abstract. The effective radiative forcing (ERF) due to aerosol-cloud interactions (ACI) and rapid adjustments (ERFaci) still causes the largest uncertainty in assessment of climate change. It is understood only with medium confidence studied primarily for warm clouds. Here, we present a novel cloud-by-cloud (C×C) approach studying ACI satellite observations that combines concentration cloud condensation nuclei (nCCN) ice nucleating particles (nINP) from polar-orbiting lidar measurements development properties individual clouds tracking them geostationary observations. We step-by-step description obtaining matched cases. application over Central Europe Northern Africa during 2014 together rigorous quality assurance leads 399 liquid-only 95 ice-containing can be surrounding nCCN nINP, respectively, at level. use this initial data set assessing impact changes cloud-relevant aerosol concentrations on droplet number (Nd) radius (reff) liquid phase regime heterogeneous formation. find Δ ln Nd/Δ 0.13 0.30 which lower end commonly inferred values 0.3 0.8. reff/Δ between -0.09 -0.21 suggests reff decreases by -0.81 -3.78 nm per increase 1 cm-3. also tendency towards more fully glaciated increasing nINP cannot explained increasingly cloud-top temperature super-cooled liquid, mixed-phase, alone. Applied larger amount observations, C×C has potential enable systematic investigation cold This marks step change quantification ERFaci space.

Language: Английский

Citations

0
A cloud-by-cloud approach for studying aerosol-cloud interaction in satellite observations DOI Creative Commons

Fani Alexandri,

Felix Müller, Goutam Choudhury

et al.

Published: Nov. 23, 2023

Abstract. The effective radiative forcing (ERF) due to aerosol-cloud interactions (ACI) and rapid adjustments (ERFaci) still causes the largest uncertainty in assessment of climate change. It is understood only with medium confidence studied primarily for warm clouds. Here, we present a novel cloud-by-cloud (C×C) approach studying ACI satellite observations that combines concentration cloud condensation nuclei (nCCN) ice nucleating particles (nINP) from polar-orbiting lidar measurements development properties individual clouds tracking them geostationary observations. We step-by-step description obtaining matched cases. application over Central Europe Northern Africa during 2014 together rigorous quality assurance leads 399 liquid-only 95 ice-containing can be surrounding nCCN nINP, respectively, at level. use this initial data set assessing impact changes cloud-relevant aerosol concentrations on droplet number (Nd) radius (reff) liquid phase regime heterogeneous formation. find Δ ln Nd/Δ 0.13 0.30 which lower end commonly inferred values 0.3 0.8. reff/Δ between -0.09 -0.21 suggests reff decreases by -0.81 -3.78 nm per increase 1 cm-3. also tendency towards more fully glaciated increasing nINP cannot explained increasingly cloud-top temperature super-cooled liquid, mixed-phase, alone. Applied larger amount observations, C×C has potential enable systematic investigation cold This marks step change quantification ERFaci space.

Language: Английский

Citations

0
Comment on egusphere-2023-2773 DOI Creative Commons

Fani Alexandri,

Felix MÃ ⁄ ller,

Goutam Choudhury

et al.

Published: Dec. 9, 2023

Abstract. The effective radiative forcing (ERF) due to aerosol-cloud interactions (ACI) and rapid adjustments (ERFaci) still causes the largest uncertainty in assessment of climate change. It is understood only with medium confidence studied primarily for warm clouds. Here, we present a novel cloud-by-cloud (C×C) approach studying ACI satellite observations that combines concentration cloud condensation nuclei (nCCN) ice nucleating particles (nINP) from polar-orbiting lidar measurements development properties individual clouds tracking them geostationary observations. We step-by-step description obtaining matched cases. application over Central Europe Northern Africa during 2014 together rigorous quality assurance leads 399 liquid-only 95 ice-containing can be surrounding nCCN nINP, respectively, at level. use this initial data set assessing impact changes cloud-relevant aerosol concentrations on droplet number (Nd) radius (reff) liquid phase regime heterogeneous formation. find Δ ln Nd/Δ 0.13 0.30 which lower end commonly inferred values 0.3 0.8. reff/Δ between -0.09 -0.21 suggests reff decreases by -0.81 -3.78 nm per increase 1 cm-3. also tendency towards more fully glaciated increasing nINP cannot explained increasingly cloud-top temperature super-cooled liquid, mixed-phase, alone. Applied larger amount observations, C×C has potential enable systematic investigation cold This marks step change quantification ERFaci space.

Language: Английский

Citations

0