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Clouds Are Crucial to Capture Antarctic Sea Ice Variability
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Clouds Are Crucial to Capture Antarctic Sea Ice Variability
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Journal Article
Clouds Are Crucial to Capture Antarctic Sea Ice Variability
2025
Overview
Models from the Coupled Model Intercomparison Project phase 6 (CMIP6) typically struggle to reproduce observed Antarctic sea ice trends, a bias that is substantially alleviated when constraining winds. We use wind‐nudged simulations from two CMIP models to investigate the influence of clouds on sea ice area (SIA). We find that nudging model winds in coupled simulations toward reanalysis, in addition to improving SIA variability, is crucial to reproduce realistic anomalies in cloud radiative effect (CRE) and cloud cover. Biases in the variability of cloud properties at sea ice edge—characterized by CRE anomalies—help explain the remaining discrepancies between simulated and observed SIA; a bias of 1 Wm−2${\\text{Wm}}^{-2}$in the CRE anomaly corresponds to a negative bias of 0.43 106km2${10}^{6}{\\text{km}}^{2}$in SIA anomaly. Finally, we find that most CMIP6 models show positive trends in CRE anomaly biases, which should contribute to enhanced SIA decline, a long‐standing bias in CMIP models. Plain Language Summary Climate models typically struggle to reproduce observed Antarctic sea ice trends. In climate simulations where the wind field is constrained to match the observations, that bias is substantially but not completely alleviated, prompting further investigation. Here we use wind‐nudged simulations to investigate the influence of clouds on Antarctic sea ice area (SIA). We find that prescribing the winds to match the observations in climate models, in addition to improving SIA variability, is crucial to realistically represent cloud variability compared to satellite observations. We then unveil a solid relationship between biases in cloud properties over the ocean near the sea ice edge and SIA, which helps explain the remaining discrepancies between simulated and observed SIA. An excess of absorbed radiation due to a lack of clouds compared to observations results in a warming at the surface and therefore a loss of SIA. Finally, we find that most climate models (10 of 12) show an excess of absorbed radiation due to cloud anomaly biases. Such biases should contribute to enhanced SIA decline, a long‐standing bias in CMIP models. Key Points Constraining winds produces more realistic variability in cloud radiative properties over the Southern Ocean Biases in shortwave cloud radiative effect variability explain the mismatch in sea ice variability between observations and wind‐nudged simulations A 1 Wm−2${\\text{Wm}}^{-2}$bias in the simulated SW CRE over the SO corresponds to a negative bias of 0.43×106$0.43\\times 1{0}^{6}$km2${\\text{km}}^{2}$in sea ice area
