Analysis of ENSO’s response to unforced variability and anthropogenic forcing using CESM

Vega-Westhoff, Benjamin Aaron; Sriver, R. L.

doi:10.1038/s41598-017-18459-8

Cited by 30 publications

(26 citation statements)

References 51 publications

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“…While the sequence of internal variability differs from simulation to simulation, external forcing is consistent for each member of the ensemble: CMIP5 historical radiative forcings from 1920 to 2005 and RCP8.5 from 2006 to 2100. This fact can be exploited to assess the strength of the externally forced signal relative to the uncertainty associated with internal variability within CESM‐LE (i.e., Deser et al, 2012; Vega‐Westhoff & Sriver, 2017). Further elaboration of the ensemble is discussed in Kay et al (2015).…”

Section: Methodsmentioning

confidence: 99%

Historical and Future Roles of Internal Atmospheric Variability in Modulating Summertime Greenland Ice Sheet Melt

Sherman

Tziperman

Deser

et al. 2020

Geophysical Research Letters

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Understanding how internal atmospheric variability affects Greenland ice sheet (GrIS) summertime melting would improve understanding of future sea level rise. We analyze the Community Earth System Model Large Ensemble (CESM‐LE) over 1951–2000 and 2051–2100. We find that internal variability dominates the forced response on short timescales (~20 years) and that the area impacted by internal variability grows in the future, connecting internal variability and climate change. Unlike prior studies, we do not assume specific patterns of internal variability to affect GrIS melting but derive them from maximum covariance analysis. We find that the North Atlantic Oscillation (NAO) is the major source of internal atmospheric variability associated with GrIS melt conditions in CESM‐LE and reanalysis, with the positive phase (NAO+) linked to widespread cooling over the ice sheet. CESM‐LE and CMIP5 project an increase in the frequency of NAO+ events, suggesting a negative feedback to the GrIS under future climate change.

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Section: Methodsmentioning

confidence: 99%

Historical and Future Roles of Internal Atmospheric Variability in Modulating Summertime Greenland Ice Sheet Melt

Sherman

Tziperman

Deser

et al. 2020

Geophysical Research Letters

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“…We present results from two climate change ensemble experiments using the low‐resolution version of CESM (Shields et al, ), introduced by Sriver et al (), Hogan and Sriver (), and Vega‐Westhoff and Sriver (). Each ensemble uses the same preindustrial control simulation with constant preindustrial radiative forcing, which was spun up with a full‐coupled atmosphere–ocean component, until the deep ocean had reached approximate dynamic equilibrium (by year 4200).…”

Section: Methodsmentioning

confidence: 99%

“…Climate models are often evaluated to provide information on changes in the deep ocean, and studies have found that ocean heat uptake contributes considerably to climate model spread (Bóe et al, 2009). Here we focus on quantifying the time scales of adjustment in the ocean using fully coupled, comprehensive model ensemble runs simulated by the Community Earth System Model (CESM; Sriver et al, 2015;Hogan & Sriver, 2017;Vega-Westhoff & Sriver, 2017;Haugen et al, 2018; -https://journals.ametsoc.org/doi/abs/ 10.1175/JCLI-D-17-0782.1). We evaluate ocean internal variability in temperature as a proxy for ocean adjustment and assess this variability on both global-and basin-scale spatial regions, and throughout different ocean depths.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Internal Variability on Ocean Temperature Adjustment in a Low‐Resolution CESM Initial Condition Ensemble

Hogan

Sriver

2019

JGR Oceans

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Due to its large heat capacity and circulation, the ocean contributes significantly to global heat uptake, global heat transport, spatial temperature patterns, and variability. Quantifying ocean heat uptake across different temporal and spatial scales is important to quantify Earth's climate response to anthropogenic warming. Here we evaluate ocean adjustment time scales from two different fully coupled climate model ensembles using the Community Earth System Model. Both ensembles use the same model version, anthropogenic and natural forcings, and coupling configurations, but we initialize the ensembles in two different ways: (1) sampling joint internal variability of the ocean–atmosphere system (unique atmosphere and ocean conditions) and (2) sampling the internal variability of the atmosphere only (unique atmosphere, identical ocean conditions). Uncertainty due to internal variability is used as a proxy to quantify the time scales of ocean temperature adjustment at different depths and basins in Community Earth System Model. Time scales of equilibration are longer in the deep ocean than the upper ocean, highlighting the vertical structure of dynamic adjustment. The Atlantic equilibrates on shorter time scales (82 years above 1,000 m, 140 years below 1,000 m) relative to the Pacific (106 years above 1,000 m, 444 years below 1,000 m) in Community Earth System Model due to the large North Atlantic Deep Water formation and strong overturning circulation in the Atlantic. These results have broad implications for analyzing internal climate variability, ocean adjustment, and drift in global coupled model experiments and intercomparisons.

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“…Similarly, the El Niño-Southern Oscillation (ENSO) can influence the atmospheric flow with its warmer than normal sea surface temperatures (SST) in the eastern central Pacific during its El Niño phase, or cooler than normal SST during its La Niña phase. These impacts will likely persist into the future, thus analysis of ENSO variability in climate models is a major area of current research (Bellenger et al, 2014), including estimating ENSO changes under parametric model uncertainties (Sriver et al, 2014) and internal variability (Vega-Westhoff & Sriver, 2017). ENSO can also influence severe and hazardous weather activity, such as tropical cyclones and tornadoes (Allen et al, 2015;Pielke & Landsea, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…ENSO can also influence severe and hazardous weather activity, such as tropical cyclones and tornadoes (Allen et al, 2015;Pielke & Landsea, 1999). These impacts will likely persist into the future, thus analysis of ENSO variability in climate models is a major area of current research (Bellenger et al, 2014), including estimating ENSO changes under parametric model uncertainties (Sriver et al, 2014) and internal variability (Vega-Westhoff & Sriver, 2017). Overall, knowledge acquired from these types of studies can then be applied to improve the operational models that forecast ENSO phases from the next month to the next year, ultimately helping countries prepare for the impending impacts (Chapman et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Analyzing El Niño–Southern Oscillation Predictability Using Long‐Short‐Term‐Memory Models

Huang

Vega-Westhoff

Sriver

2019

Earth and Space Science

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El Niño–Southern Oscillation (ENSO) can have global impacts, affecting daily temperature and precipitation, and extreme weather, such as hurricanes and tornadoes. Because of its importance, scientists strive to understand the processes that govern ENSO and develop models to predict its evolution and changes in variability. Here long‐short‐term‐memory models (LSTMs) were compared to linear regression models (LR) to explore the benefits of simple, deep neural networks in predicting ENSO, in addition to quantifying the relative importance of the sources of ENSO's predictability. The models use central Pacific sea surface temperatures (SST), equatorial Pacific warm water volumes, and western Pacific zonal winds as predictors, individually and in combinations, on monthly and daily resolutions, from 1‐ to 11‐month leads. By using these predictors, many characteristic time scales are encompassed—from days‐to‐weeks in the atmosphere, to months‐to‐seasons in the coupled system, and interseasonal‐to‐interannual in the subsurface ocean. Results show, with monthly input, predictions from LSTM were like predictions from LR. However, with daily SST at longer leads, LSTM exhibited some advantage over LR in terms of the correlation coefficient. This suggests that daily SST may contain some nonlinear element that improves LSTM predictability compared to LR. In addition, this suggests that more information, such as gridded data and additional variables, would likely improve predictability using LSTM, but results would be more difficult to interpret. Overall, LSTM may be appealing because once the computationally expensive training of LSTM is complete, the predictions employing the trained model can be relatively cheap to perform thereafter.

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Analysis of ENSO’s response to unforced variability and anthropogenic forcing using CESM

Cited by 30 publications

References 51 publications

Historical and Future Roles of Internal Atmospheric Variability in Modulating Summertime Greenland Ice Sheet Melt

Historical and Future Roles of Internal Atmospheric Variability in Modulating Summertime Greenland Ice Sheet Melt

The Effect of Internal Variability on Ocean Temperature Adjustment in a Low‐Resolution CESM Initial Condition Ensemble

Analyzing El Niño–Southern Oscillation Predictability Using Long‐Short‐Term‐Memory Models

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