An El Niño Mode in the Glacial Indian Ocean?

Abstract

Despite minor variations in sea surface temperature (SST) compared to other tropical regions, coupled ocean‐atmosphere dynamics in the Indian Ocean cause widespread drought, wildfires, and flooding. It is unclear whether changes in the Indian Ocean mean state can support stronger SST variability and climatic extremes. Here we focus on the Last Glacial Maximum (19,000–21,000 years before present) when background oceanic conditions could have been favorable for stronger variability. Using individual foraminiferal analyses and climate model simulations, we find that seasonal and interannual SST variations in the eastern equatorial Indian Ocean were much larger during this glacial period relative to modern conditions. The increase in year‐to‐year variance is consistent with the emergence of an equatorial mode of climate variability, which strongly resembles the Pacific El Niño and is currently not active in the Indian Ocean.

Emergence of an equatorial mode of climate variability in the Indian Ocean

“…The emergence of the equatorial mode is supported by a consistent link between changes in variability and mean state across climate models, although a sufficiently large change is required for its activation. These predictions are supported by paleoclimate data from the LGM, which show mean state changes of a magnitude comparable to those predicted under high emissions (24) along with an active equatorial mode (27). Furthermore, the activation of the equatorial mode appears to be less sensitive to common biases in the simulation of seasonal climate by CMIP models ( fig.…”

“…S2). Furthermore, multiple paleoclimate datasets from the LGM show large changes in mean state potentially amplified by coupled feedbacks (24) along with much stronger climate variability (27), attesting to this ocean's ability to experience large changes in mean state and variability via coupled feedbacks.…”

Estimating the timescale-dependent uncertainty of paleoclimate records – a spectral approach. Part II: Application and interpretation

“…Beyond modelling errors, PSEM also facilitates the use of the proxy variability itself to make inferences about the climate system. It allows prediction of the variability observed in individual foraminifera assemblages (IFA) (e.g., Groeneveld et al, 2019;Thirumalai et al, 2019) and thus to directly test the sensitivity of IFA statistics on the sedimentation rate, seasonality or the spectrum of climate variability. Finally, PSEM provides the basis to develop spectral correction approaches that infer the climate spectrum from the corrupted and distorted proxy spectrum, building on the approaches previously proposed for simpler sediment models (Laepple and Huybers, 2013) or for aliasing only (Kirchner, 2005).…”

Modeling the impact of bioturbation and species abundance upon discrete-depth individual foraminifera analysis used in ENSO-type climate reconstructions.

“…Many IFA studies have gone beyond studying a discrete depth's 1σ SST value and have branched into more forensic studies of a discrete depth's IFA-derived SST distribution. These studies have focussed on analysing the shape of said distribution using various statistical tools, including histograms to infer distribution skewness analysis of histograms (Khider et al, 2011;Leduc et al, 2009), as well as quantilequantile (Q-Q) plots (Ford et al, 2015;Rongstad et al, 2020;Thirumalai et al, 2019;White et al, 2018;White & Ravelo, 2020). Such analysis can reveal apparent shifts in the shape of the downcore, IFAderived SST distribution, which the aforementioned studies have attributed to changes in ENSO-type climate variability.…”

“…In practical terms, these results suggest that if one were to, at the same coring location, retrieve multiple sediment cores and carry out discrete-depth IFA, it is likely that markedly different outcomes would be produced each time, each with poor correspondence to the true SST distribution. Furthermore, as the level of noise increases with lower SAR, one has to be additionally careful when comparing IFA results from sites with markedly different SAR (Thirumalai et al, 2019).…”