Paleotemperature records from lake sediment archives are highly sought after in studies of high latitude terrestrial paleoclimate. Branched glycerol dialkyl glycerol tetraether (brGDGT) lipids have solidified themselves as an important tool in this pursuit. Although brGDGTs are globally ubiquitous, the bacterial producers of these membrane lipids remain poorly identified, precluding a full understanding of the ways in which a range of environmental parameters control their production and distribution.

Mean annual air temperature (MAT) has been the traditional target of brGDGT calibrations in lake sediments. However, it was recognized early on that brGDGT-derived temperatures in cold regions may more accurately reflect warm-season temperatures, an hypothesis that was strongly supported in high-latitude lake sediments (e.g. Shanahan et al., 2013). Since the methodological advances that allowed for the separation of brGDGT isomers and the development of new calibrations, both modern and paleo studies have continued to support a warm-season bias. Additionally, a recent Bayesian calibration found the mean temperature of Months Above Freezing (MAF, which is increasingly close to MAT at lower latitudes) to be the only mode to significantly correlate with brGDGT distributions in a global lake sediment dataset (Martínez-Sosa et al., 2020). However, this warm-season bias has yet to be tested thoroughly in the regions in which it is most pronounced – namely, those with low MAT and high seasonality. As these are the regions that are currently experiencing the most rapid climate change, their temperature histories are of high interest and the quantification of the brGDGT warm-season bias is an important target of study.

Here, we outline the results of new work (Raberg et al., 2021) that advances brGDGT paleoclimate proxies in three ways. First, we present 43 new high-latitude lake sites from the Eastern Canadian Arctic and Iceland that are characterized by low mean annual air temperatures (MATs) and high seasonality, filling an important gap in the global dataset. Second, we introduce a new approach for analyzing brGDGT data in which compound fractional abundances (FAs) are calculated within structural groups based on methylation number, methylation position, and cyclization number. Finally, we perform linear and nonlinear regressions of the resulting FAs against a suite of environmental parameters in a compiled global lake sediment dataset (n = 182). We find that our approach deconvolves temperature, lake water conductivity, and pH trends in brGDGTs without increasing calibration errors from the standard approach. We also find that it reveals novel patterns in brGDGT distributions and provides a methodology for investigating the biological underpinnings of their structural diversity. Warm-season temperature indices outperformed MAT in our regressions, with MAF yielding the highest-performing model (adjusted R2 = 0.91, RMSE = 1.97°C, n = 182). The natural logarithm of conductivity had the second-strongest relationship to brGDGT distributions (adjusted R2 = 0.83, RMSE = 0.66, n = 143), notably outperforming pH in our dataset (adjusted R2 = 0.73, RMSE = 0.57, n = 154) and providing a potential new proxy for paleohydrology applications. We recommend these calibrations for use in lake sediments globally, including at high latitudes, and detail the advantages and disadvantages of each.