The Impact of Regional Multidecadal and Century-Scale Internal Climate Variability on Sea Level Trends in CMIP5 Models

Abstract

Regional sea surface height variability due to internal climate fluctuations is estimated using preindustrial control runs of 21 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Projected sea level trends of the representative concentration pathway 4.5 (RCP4.5) scenario for 20-, 50-, and 100-yr intervals grow from being largely dominated by internal variability on shorter time scales to being the dominant sea level signal on long time scales. The internal variability is estimated by calculating overlapping trends for the various time scales on the regional sea level control run output from each model. When compared to the ensemble spread of the RCP4.5 scenario trends, the internal variability remains a substantial portion of the spread even after 50 years. The regional ensemble mean trends are mostly larger than the ensemble spread for the 50-yr interval and are larger everywhere, except for part of the central Arctic and the Southern Ocean for the 100-yr projection. Although it is unclear whether the model internal variability estimate will be comparable to long-term variability in the real ocean, the authors compare the strength of the estimate to satellite altimetry and find that altimetry-based trends may be larger in tropical ocean regions, with only limited extratropical regions rising above the internal variability. The authors also analyze a single model’s internal variability against its future RCP4.5-projected sea level and show that, by 50 years, many regional sea level trends are larger than the underlying internal variability, though this variability still accounts for more than a third of the trend magnitude for almost half of the extratropical ocean.

Recent Progress in Understanding and Projecting Regional and Global Mean Sea Level Change

“…The east-west Pacific pattern (Fig. 3) does not appear to be anthropogenic in CMIP5 historical simulations, but on the other hand, it is too large to be consistent with unforced variability as simulated by the AOGCMs [144,156,157]. Consequently, this remains an important phenomenon that still requires an explanation.…”

“…Instead of using a reference level, a comparison of trends in projections of forced sea level change starting in 1990 with simulated unforced trends of the same length shows a detectable signal by the early 2030s in half of the ocean area [165]. In the MPI-ESM-LR AOGCM, chosen as an example, trends of 20 years starting in 2006 exceed one standard deviation of unforced variability in more than half of the area, and trends of 50 years in more than 90 % [157]. With both techniques, the region of earliest emergence is the low-latitude Atlantic, where unforced variability is particularly small [132,144,157,164,165].…”

“…In the MPI-ESM-LR AOGCM, chosen as an example, trends of 20 years starting in 2006 exceed one standard deviation of unforced variability in more than half of the area, and trends of 50 years in more than 90 % [157]. With both techniques, the region of earliest emergence is the low-latitude Atlantic, where unforced variability is particularly small [132,144,157,164,165]. Early emergence is also noted in some coastal areas, especially the Atlantic coast of North America [157,165].…”

“…With both techniques, the region of earliest emergence is the low-latitude Atlantic, where unforced variability is particularly small [132,144,157,164,165]. Early emergence is also noted in some coastal areas, especially the Atlantic coast of North America [157,165]. By contrast, in much the Southern Ocean, where variability is high and the forced response is small, the signal of change may not be detectable until late in the twenty-first century or afterwards [144,164,165].…”

The Sea Level Response to External Forcings in Historical Simulations of CMIP5 Climate Models*

“…While the internal climate variability has a relatively small effect on GMTSL, it can be larger regionally (Hu and Deser 2013), mainly on shorter (up to multidecadal) time scales, but also on centennial time scales (Carson et al 2015;Bordbar et al 2015;Monselesan et al 2015). We have compared DSL trends for the five different forcing experiments over various periods and find that, while the overall response to the external forcings is consistent, the patterns show more variability over shorter (decadal to multidecadal) periods because of the larger influence of internal climate variability.…”