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Climate Change

On the Verge: Sea Level to Emerge


Lyu, K., Zhang, X., Church, J.A., Slangen, A.B.A., Hu, J., (2014).  Time of emergence for regional sea-level change.  Nature Climate Change 4, 1006-1010.  doi:10.1038/nclimate2397



Human induced climate change and natural climate variability can be difficult to separate. For example, one summer of record setting high temperatures can be expected with natural climate variability. However, increasingly warm summers over the course of decades falls outside the range of natural variability and is more likely human induced, due to increasing greenhouse gas concentrations in the atmosphere. This study by Lyu et al. focuses on time of emergence (ToE), which simply represents the moment when the signal of human induced climate change is strong enough to be distinguished from the signal of natural climate variability. According to this study, the time of emergence occurs when the ratio of the signal (climate change) to noise (natural climate variability) exceeds a defined threshold for two decades.

In this study, researchers estimate the time of emergence of regional sea-level change by comparing three sea-level projections through the year 2100 to a reference period (1986 – 2005). The global mean sea-level has been steadily rising and is predicted to continue rising. Sea-level is not rising uniformly across the globe. Local sea-level can vary greatly due to numerous factors, and detecting changes are further complicated by natural variability.



This study relies on the use of climate models (17 Coupled Model Intercomparison Project Phase 5 [CMIP5]). The models allow scientists to simulate climatic conditions into the future. To estimate time of emergence of regional sea-level, three sea-level projections to the year 2100 were used:

  1. Regional Dynamic Sea Level (DSL)
  2. DSL + Global Mean Thermosteric Sea Level (GMTSL)
  3. DSL + GMTSL + Total Sea Level (TSL)

DSL is the simplest sea-level projection. GMTSL accounts for the volumetric expansion of seawater as the ocean warms, which is expected to greatly contribute to global sea-level rise. For example, picture a bathtub filled to the brim with cold water. If the water in the bathtub were heated, the water molecules would expand, perhaps enough to overtop the bathtub. In this example, the overflowing of the bathtub is representative of a rising sea-level due to the thermal expansion of water. Although the change in volume is miniscule, the immense volume of the water in the ocean would mean significant changes in sea-level. The TSL projection builds on the GMTSL projection including the change in vertical elevations of land relative to sea level due to processes such as the addition or loss of regional glacial ice.

The main objective of this study is to determine when a human induced climate change signal (S) can be identified amidst a noisy natural climate variability signal (N). The time at which the climate change signal can undoubtedly be observed is considered the time of emergence. The CMIP5 climate simulations represent the natural climate variability signal whereas the sea-level projections are the climate change signal.



The simplest sea-level projection estimates that the climate change signal emerges over 8.8 to 18.4% of the global ocean by the year 2080. When adding in the thermal expansion of seawater as is the case with the GMTSL projection, emergence occurs in 92.1 to 95% of the global ocean by the year 2080. The most comprehensive TSL projection estimates emergence of the climate change signal in the entire global ocean by the year 2080, with emergence over 50% of the global ocean occurring between the years 2017 and 2019 (Fig 1).


Figure 1. Time of emergence of human induced sea level rise using three sea level projections: a,d DSL; b,e GMTSL; c,f TSL.  DSL, the simplest projection shows very little emergence of sea level rise outside of natural variability.  GMTSL and TSL show respectively earlier and more prevalent emergence of sea level rise.

Figure 1.  Time of emergence of human induced sea level rise using three sea-level projections: a,d. DSL; b,e. GMTSL; c,f. TSL. DSL, the simplest projection shows very little emergence of sea-level rise outside of natural variability. GMTSL and TSL are respectively more comprehensive projections and show earlier and more prevalent emergence of sea-level rise.


Late time of emergence often occurs in places that have two or more competing factors that create variability in changes in sea-level. For example, the Southern Ocean near Antarctica experiences a decreasing DSL projection, yet is countered by an increasing GMTSL due to the thermal expansion of seawater. For this reason, the time of emergence of the climate change signal is highly variable, and often later than other regions of the ocean.

When considering the time of emergence of the TSL projections, the factors that contribute the greatest to early emergence are the loss of land ice and the glacial isostatic adjustment. A good way to think of how sea-level changes with the loss of glacial ice is to envision a heavily loaded boat. Think of glacial ice as the heavy load in a boat. The weight of the load overwhelms the buoyancy force, causing boat to float lower in the water, and thus causes the water level to appear to rise relative to a fixed position on the hull of the boat. Remove the heavy load (ice) and the boat floats higher in the water, with that fixed position on the hull of the boat experiencing a lowering water level. The removal of heavy glacial ice allows the land surface to “float” higher since the crust of the earth sits above a deformable lower layer called the mantle.

Previous time of emergence studies focused on the emergence of a climate change surface air warming signal. In this same study, the authors used the same methodology to estimate time of emergence of regional sea-level to estimate time of emergence for surface air warming (Fig 2). The results of the earliest emergences occurred in the tropical regions and was consistent with previous studies.


Figure 2.

Figure 2.  Three sea level projections (DSL, DSL + GMTSL and Total sea level) and a surface air temperature projection, showing the cumulative total area over which the climate change signal emerges (y-axis) from 2010 to 2080 (x-axis).  Dashed vs solid lines represent different modeling scenarios.  


Considering such a large proportion of the world’s population live in coastal cities and communities, understanding sea-level changes due to human induced climate change is a critical problem. This study demonstrates that in less than a decade more than half of the global ocean is expected to experience measurable sea-level rise outside of natural climate variability. The results and conclusions of this study should be considered for future policy decisions when assessing coastline vulnerability to rising sea-level as well as mitigating the human influence on climate change. The authors do acknowledge that improved distribution and observations of sea-level will only benefit future climate models upon which sea-level projections are based, which in turn will improve sea-level rise estimates and the assessment of coastal vulnerability.


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