Tide–surge interaction plays a substantial role in determining the characteristics of coastal water levels over shallow regions. We study the tide–surge interaction observed at seven tide gauges along Singapore and the east coast of Peninsular Malaysia, focusing on the timing of extreme non-tidal residuals relative to tidal high water. We propose a modified statistical framework using a no-tide–surge interaction (no-TSI) null distribution that accounts for asymmetry and variation in the duration of tidal cycles. We find that our modified framework can mitigate false-positive signals of tide–surge interaction in this region. We find evidence of tide–surge interaction at all seven locations, with characteristics varying smoothly along the coastline: the highest non-tidal residuals are found to occur most frequently before tidal high water in the south, both before and after tidal high water in the central region, and after tidal high water in the north. We also propose a semi-empirical model to investigate the effects of tidal-phase alteration, which is one mechanism of tide–surge interaction. Results of our semi-empirical model reveal that tidal-phase alteration caused by storm surges is substantial enough to generate significant change in the timing of extreme non-tidal residuals. To mitigate the effect of tidal-phase alteration on return level estimation, skew surge can be used. We conclude that (1) tide–surge interaction influences coastal water levels in this region, (2) our semi-empirical model provides insight into the mechanism of tidal-phase alteration, and (3) our no-TSI distribution should be used for similar studies globally.@en

A probabilistic projection of sea-level rise uses a probability distribution to represent scientific uncertainty. However, alternative probabilistic projections of sea-level rise differ markedly, revealing ambiguity, which poses a challenge to scientific assessment and decision-making. To address the challenge of ambiguity, we propose a new approach to quantify a best estimate of the scientific uncertainty associated with sea-level rise. Our proposed fusion combines the complementary strengths of the ice sheet models and expert elicitations that were used in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC). Under a low-emissions scenario, the fusion's very likely range (5th–95th percentiles) of global mean sea-level rise is 0.3–1.0 m by 2100. Under a high-emissions scenario, the very likely range is 0.5–1.9 m. The 95th percentile projection of 1.9 m can inform a high-end storyline, supporting decision-making for activities with low uncertainty tolerance. By quantifying a best estimate of scientific uncertainty, the fusion caters to diverse users.

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Global climate models are susceptible to drift, causing spurious trends in output variables. Drift is often corrected using data from a control simulation. However, internal climate variability within the control simulation introduces uncertainty to the drift correction process. To quantify this drift uncertainty, we develop a probabilistic technique: Monte Carlo drift correction (MCDC). MCDC samples the standard error associated with drift in the control time series. We apply MCDC to an ensemble of global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We find that drift correction partially addresses a problem related to drift: energy leakage. Nevertheless, the energy balance of several models remains suspect. We quantify the drift uncertainty of global quantities associated with the Earth's energy balance and thermal expansion of the ocean. When correcting drift in a cumulatively integrated energy flux, we find that it is preferable to integrate the flux before correcting the drift: An alternative method would be to correct the bias before integrating the flux, but this alternative method amplifies the drift uncertainty. Assuming that drift is linear likely leads to an underestimation of drift uncertainty. Time series with weak trends may be especially susceptible to drift uncertainty: for historical thermosteric sea level rise since the 1850s, the drift uncertainty can range from 3 to 24 mm, which is of comparable magnitude to the impact of omitting volcanic forcing in control simulations. Derived coefficients-such as the ocean's expansion efficiency of heat-can also be susceptible to drift uncertainty. When evaluating and analysing global climate model data that are susceptible to drift, researchers should consider drift uncertainty.

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