Trends and variability in the Southern Annular Mode over the Common Era

Abstract

The Southern Annular Mode (SAM) is the leading mode of atmospheric variability in the extratropical Southern Hemisphere and has wide ranging effects on ecosystems and societies. Despite the SAM’s importance, paleoclimate reconstructions disagree on its variability and trends. Here, we use data assimilation to reconstruct the SAM over the last 2000 years using temperature and drought-sensitive climate proxies. Our method does not assume a stationary relationship between proxy records and the SAM over an instrumental calibration period, so our reconstruction is less sensitive to the teleconnection variability that has hindered previous reconstructions. Our approach also allows us to identify critical paleoclimate records and quantify reconstruction uncertainty through time. We find no evidence for a forced response in SAM variability prior to the 20th century. We also find the modern positive trend is outside the range of the prior 2000 years, but only on multidecadal time scales.

DASH: A MATLAB Toolbox for Paleoclimate Data Assimilation

“…This is useful when assimilating gridded, spatially-covarying proxy networks and climate field reconstructions (e.g. King et al, 2022), such as drought atlases (e.g. Cook et al, 1999Cook et al, , 2010Morales et al, 2020).…”

“…Another potential approach involves running the forward models on instrumental data and comparing the resulting proxy estimates to the real proxy records (e.g. King et al, 2021King et al, , 2022. However, we note that these approaches are not applicable to all analyses, so users may need to develop additional methods to estimate proxy uncertainties.…”

“…Reconstructions of past climates also provide out-of-sample targets used to assess the skill of climate models, which in turn helps constrain future Recently, data assimilation (DA) methods have emerged as a novel approach to the problems and challenges of paleoclimate reconstruction (e.g. Bhend et al, 2012;Goosse et al, 2012b;Mairesse et al, 2013;Hakim et al, 2016;Steiger et al, 2017Steiger et al, , 2018Tardif et al, 2019;Tierney et al, 2020b;King et al, 2021;Osman et al, 2021;Zhu et al, 2022;King et al, 2022). Unlike the two independent approaches described above, DA methods integrate proxy data directly with climate model output and thereby leverage the strengths of both information sources.…”

“…DA analyses require numerous discrete tasks, including preparing and integrating the output from climate model simulations, proxy records, and possibly instrumental data, all of which may use different data formats, units, and metadata. The number of potential reconstruction targets and proxy variables is immense, and the choice of algorithm parameters will affect the implementation of any particular DA reconstruction (compare Tardif et al, 2019;Tierney et al, 2020b;King et al, 2021;Osman et al, 2021;King et al, 2022). Consequently, it can be difficult to adapt codes implementing an existing reconstruction to alternative applications.…”

“…Additionally, there are still relatively few paleoclimate applications in the mathematical DA literature, so DA descriptions may use a variety of mathematical notations. Finally, the diversity of algorithm variants potentially hinders transparency and accessibility, as studies using similarly named algorithms may implement different methods in practice (compare Tardif et al, 2019;Franke et al, 2020;Tierney et al, 2020b;King et al, 2022). Ultimately, there are limited frameworks for discussing DA within the paleoclimate literature, and the field as a whole would benefit from more transparent implementations that do not require additional specialized training.…”