2024
Research News
Scientists Sort out Uncertainties in Sea Level Projections
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As global temperatures continue to rise, coastal communities face the pressing challenge of rising sea levels. The need to provide decision makers with reliable predictions of future sea levels is becoming increasingly urgent. At the forefront of these forecasting efforts lies Dynamic Sea Level (DSL), a nuanced variable intricately linked to seawater density and ocean circulation that is currently under intense scrutiny in climate models.
Researchers from the Institute of Atmospheric Physics of the Chinese Academy of Sciences have conducted a comprehensive study that highlights the uncertainties surrounding DSL projections using the cutting-edge Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble and the extensive FGOALS-g3 super-large ensemble.
Their study shows that with respect to basin-scale dyenamics, intermodel uncertainty plays a leading role, contributing over 55%, 80%, and 70% to the total uncertainty of the DSL projections in the near term (2021-2040), midterm (2041-2060), and long term (2081-2100), respectively. This is closely followed by internal variability, which accounts for 10-42% in the near term and less than 20% in the midterm. While the impact of scenario uncertainty is initially minimal, it gradually increases and exceeds the contribution of internal variability in the long term.
"There are also regional nuances. At the regional scale, internal variability dominates in the near term for the Pacific Ocean, the Indian Ocean, and the western boundary of the Atlantic Ocean. Conversely, intermodel uncertainty is dominant in other regions. Contributions evolve over time, with scenario uncertainty becoming significant in the Southern, Pacific, and Atlantic Oceans in the long term," said Prof. LIU Hailong, corresponding author of the series of published studies.
The researchers also observed that anthropogenic DSL signals are expected to appear in certain regions by the end of this century. Refining the CMIP6 ensemble by eliminating model differences improves our ability to detect these signals in advance.
"Imagine trying to understand the Earth's climate using a computer model. Instead of running the model just once, we run it many times with slight variations in the initial conditions. This allows us to see how the model responds to different situations," said Prof. LIU. "In this way, we can better measure how the Earth's climate responds to external factors, such as changes in greenhouse gases, and also understand the natural ups and downs that happen on their own. This gives us a clearer and more reliable picture of how our climate works."
The team is therefore gaining insights from the FGOALS-g3 Super-Large Ensemble of 110 model members, which seamlessly aligns with CMIP6 members in basin-mean DSL projections. A comparative analysis with the CMIP6 ensemble reveals larger estimates of internal variability in the FGOALS-g3 super-large ensemble.
What are the implications for the future? The team's research not only deepens our understanding of sea-level rise, but also lays the groundwork for more accurate and informed climate models. These findings are critical to securing the future of our coastal communities.
The results were published in the Journal of Climate, Advances in Atmospheric Sciences, and Geoscience Letters.
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