Publications by authors named "J M Neelin"
Tropical marine low cloud feedback is key to the uncertainty in climate sensitivity, and it depends on the warming pattern of sea surface temperatures (SSTs). Here, we empirically constrain this feedback in two major low cloud regions, the tropical Pacific and Atlantic, using interannual variability. Low cloud sensitivities to local SST and to remote SST, represented by lower-troposphere temperature, are poorly captured in many models of the latest global climate model ensemble, especially in the less-studied tropical Atlantic.
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- Climate change projections rely on physical models that struggle with small-scale processes, leading to uncertainties in predictions.
- Recent machine learning algorithms show potential for improving these models but often fail when applied to new climate conditions they weren't originally trained on.
- The proposed "climate-invariant" ML framework integrates physical knowledge into machine learning, enhancing accuracy and adaptability across various climate scenarios and improving Earth system modeling.
Article Synopsis
- - The study highlights a significant variation in climate models' estimates of equilibrium climate sensitivity (ECS), which affects effective climate change policy and strategy.
- - The researchers found that higher ECS models (over 4.75 K) show a substantial decrease in extratropical low-cloud fraction from winter to summer, aligning with predicted declines in cloud coverage due to climate warming.
- - In contrast, models predicting lower ECS (under 3.3 K) do not exhibit the same seasonal pattern in extratropical low-cloud fraction, indicating differing cloud behaviors that influence climate variability.
Accurate precipitation monitoring is crucial for understanding climate change and rainfall-driven hazards at a local scale. However, the current suite of monitoring approaches, including weather radar and rain gauges, have different insufficiencies such as low spatial and temporal resolution and difficulty in accurately detecting potentially destructive precipitation events such as hailstorms. In this study, we develop an array-based method to monitor rainfall with seismic nodal stations, offering both high spatial and temporal resolution.
View Article and Find Full Text PDFDaily precipitation extremes are projected to intensify with increasing moisture under global warming following the Clausius-Clapeyron (CC) relationship at about [Formula: see text]. However, this increase is not spatially homogeneous. Projections in individual models exhibit regions with substantially larger increases than expected from the CC scaling.
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