Increase in sea level variability with ocean warming associated with the nonlinear thermal expansion of seawater

Widlansky, M. J.; Long, Xiaoyu; Schloesser, Fabian

doi:10.1038/s43247-020-0008-8

Cited by 59 publications

(46 citation statements)

References 42 publications

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“…S3 ), compared against the observational-based sea level estimates for the west Pacific Ocean region (see Supplementary Information for the other regions). We chose this region for illustration purposes as it shows large sea level variations linked to oceanic mixed-layer heat content 8 , 24 . This region, which includes Indonesia's islands, is also among the locations most threatened by rising sea levels 1 .…”

Section: Resultsmentioning

confidence: 99%

“…Internal climate variability due to natural fluctuations of the ocean–atmosphere coupled system (such as the Pacific Decadal Oscillation) can lead to periods when the upper ocean warms more slowly or more rapidly, which has direct implications for sea level change 6 . Nevertheless, climate is a highly complex and networked dynamical system (with no simple relationships) that can change naturally in unexpected ways 7 , 8 . From this perspective, advanced statistical analyses, including machine learning (ML) methods, can provide useful insight.…”

Section: Introductionmentioning

confidence: 99%

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Predicting regional coastal sea level changes with machine learning

Nieves

Radin

Camps‐Valls

2021

Sci Rep

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All ocean basins have been experiencing significant warming and rising sea levels in recent decades. There are, however, important regional differences, resulting from distinct processes at different timescales (temperature-driven changes being a major contributor on multi-year timescales). In view of this complexity, it deems essential to move towards more sophisticated data-driven techniques as well as diagnostic and prognostic prediction models to interpret observations of ocean warming and sea level variations at local or regional sea basins. In this context, we present a machine learning approach that exploits key ocean temperature estimates (as proxies for the regional thermosteric sea level component) to model coastal sea level variability and associated uncertainty across a range of timescales (from months to several years). Our findings also demonstrate the utility of machine learning to estimate the possible tendency of near-future regional sea levels. When compared to actual sea-level records, our models perform particularly well in the coastal areas most influenced by internal climate variability. Yet, the models are widely applicable to evaluate the patterns of rising and falling sea levels across many places around the globe. Thus, our approach is a promising tool to model and anticipate sea level changes in the coming (1–3) years, which is crucial for near-term decision making and strategic planning about coastal protection measures.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting regional coastal sea level changes with machine learning

Nieves

Radin

Camps‐Valls

2021

Sci Rep

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“…A noteworthy point is that the sea level heights annual trend line shows a similar possible oscillation to the COSMIC mixing ratio (Figure 3). This observation was crucial for showing a potential link between sea level heights and COSMIC mixing ratio due to the thermal expansion/compression of the ocean (e.g., Widlansky et al 2020) [30]. In order to explain the variations of SLH anomalies, a deeper look into the Oceanic Niño Index (Climate Prediction Center 2020) [24] was required.…”

Section: Sea Level Heightsmentioning

confidence: 99%

Climate Variability of Atmospheric Rivers and Droughts over the West Coast of the United States from 2006 to 2019

Zechiel

Chiao

2021

Atmosphere

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Water resources are crucial to the livelihood and sustainability of the general public across the western United States. This study covers the timespan of both the third driest drought in Californian history between 2012 and 2015 as well as the extreme atmospheric river year in 2016–2017. The evaluation of vertical moisture profiles using Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Radio Occultation (RO) data, National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis of 500 hPa geopotential heights, 1000–500 hPa thickness, Optimum Interpolation (OI) Sea Surface Temperature (SST), NOAA/NDBC buoy data, and NASA, MEaSUREs, Gridded Sea Surface Height Anomalies (SSHA) were performed. The daily COSMIC time evolution from 2006 through 2015 showed a flat to slightly upward trend of both temperature and water vapor profiles through the entirety of the western US drought. Subsequently, a significant increase of temperatures and water vapor were recorded in early 2016 before the extreme Atmospheric River (AR) season of 2016–2017. The quantitative analyses suggest that warmer SST and higher SSHA lead to an increase of heat fluxes from the ocean into the troposphere, which forces thickness changes and thus the position of troughs in the geopotential height field changes afterwards, consequently pushing the trough eastward over the Pacific Northwest and potentially leading to an active AR year in the western US. It appears that regional COSMIC RO moisture profiles, seasonal SST, and SLH anomalies may serve as a precursor for seasonal or sub-seasonal precipitation outlook along the western US.

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“…The oceans have a critical role in climate change, acting as a heat sink and being responsible for the uptake of more than 90% of the excess heat in our climate system between 1971 and 2010 (Pörtner et al, 2019;Johnson and Lyman, 2020). Warming ocean temperatures are intrinsically linked to sea level rise and projections show the rise accelerating because of non-linear thermal expansion (Widlansky et al, 2020). In addition, the number and severity of occurrences of extreme events linked to increased sea temperatures, such as heat waves, are expected to increase with global warming (Bindoff et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Divers as Citizen Scientists: Response Time, Accuracy and Precision of Water Temperature Measurement Using Dive Computers

et al. 2021

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There is a lack of depth-resolved temperature data, especially in coastal areas, which are often commonly dived by SCUBA divers. Many case studies have demonstrated that citizen science can provide high quality data, although users require more confidence in the accuracy of these data. This study examined the response time, accuracy and precision of water temperature measurement in 28 dive computers plus three underwater cameras, from 12 models. A total of 239 temperature response times (τ) were collected from 29 devices over 11 chamber dives. Mean τ by device ranged from (17 ± 6) to (341 ± 69) s, with significant between-model differences found for τ across all models. Clear differences were found in τ by pressure sensor location and material, but not by size. Two models had comparable τ to designed-for-purpose aquatic temperature loggers. 337 mean data points were collected from equilibrated temperatures in hyperbaric chamber (n = 185) and sea (n = 152) dives, compared with baseline mean temperature from Castaway CTDs over the same time period. Mean bias, defined as mean device temperature minus baseline temperature, by model ranged from (0.0 ± 0.5) to (−1.4 ± 2.1) °C and by device from (0.0 ± 0.6) to (−3.4 ± 1.0) °C. Nine of the twelve models were found to have “good” accuracy (≤0.5 °C) overall. Irrespective of model, the overall mean bias of (−0.2 ± 1.1) °C is comparable with existing commonly used coastal temperature data sets, and within global ocean observing system accuracy requirements for in situ temperature. Our research shows that the quality of temperature data in dive computers could be improved, but, with collection of appropriate metadata to allow assessment of data quality, some models of dive computers have a role in future oceanographic monitoring.

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Increase in sea level variability with ocean warming associated with the nonlinear thermal expansion of seawater

Cited by 59 publications

References 42 publications

Predicting regional coastal sea level changes with machine learning

Predicting regional coastal sea level changes with machine learning

Climate Variability of Atmospheric Rivers and Droughts over the West Coast of the United States from 2006 to 2019

Divers as Citizen Scientists: Response Time, Accuracy and Precision of Water Temperature Measurement Using Dive Computers

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