A Review of the Factors Influencing Arctic Mixed‐Phase Clouds: Progress and Outlook

Tan, Ivy; Sotiropoulou, Georgia; Taylor, Patrick C.; Zamora, Lauren; Wendisch, Manfred

doi:10.1002/9781119700357.ch5

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The Influence Of Sea Ice Anomaly On the Cloud Feedback1

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2024

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“…Conversely, sea ice loss can result in increases in cloud fraction and cloud optical depth (Tan et al, 2023) via local aerosol production (Ridley et al, 2016), or decreases in lower tropospheric stability (P. C. Taylor et al, 2022). However, the disparate representations of these processes in models (Schmale et al, 2021;P.…”

Section: The Influence Of Sea Ice Anomaly On the Cloud Feedbackmentioning

confidence: 99%

Top of the Atmosphere Shortwave Arctic Cloud Feedbacks: A Comparison of Diagnostic Methods

Coulbury,

Tan

2024

Geophysical Research Letters

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The cloud feedback may result in amplification or damping of Arctic warming. Two common techniques used to diagnose the top‐of‐the‐atmosphere cloud feedback are the Adjusted Cloud Radiative Effect (AdjCRE) method and the Cloud Radiative Kernel (CRK) method. We apply both to CMIP5 and CMIP6 model data, finding that the AdjCRE calculated Arctic shortwave cloud feedback is twice as correlated with sea ice loss in CMIP5, and four times in CMIP6, as the CRK method. We find that the CRK method produces Arctic all‐sky residual percentages exceeding 20% in 15 of 18 models. We use the CRK method to decompose the feedback in CMIP5 and CMIP6 finding that its median value changed from negative to positive driven by a less‐negative cloud optical depth feedback. Despite its lack of closure, we conclude that the CRK method is better suited for Arctic SW feedbacks as it is less impacted by surface albedo changes.

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