BrGDGT-based palaeothermometer in drylands: the necessity to constrain aridity and salinity as confounding factors to ensure the robustness of calibrations

<p>Past temperature reconstructions offer valuable insights into the impact of climate change on the global climate-human-vegetation system. Branched glycerol dialkyl glycerol tetraethers (brGDGTs) are recognized as effective temperature proxies, particularly in lakes and peatlands, where they are well preserved. However, their reliability as palaeothermometers can be compromised by factors beyond air temperature, especially in drylands. This study investigates the recently compiled Arid Central Asian (ACA) brGDGT surface Data Base, a regional dataset consisting of 753 surface samples from the drylands of ACA. The distribution of brGDGTs in relation to climate and environmental variables was analysed to explore their potential as reliable temperature proxies, mainly focusing on brGDGTs methylation (MBT), cyclisation (CBT), and isomer (IR) indices. The brGDGT-based palaeothermometer is a promising tool for understanding past climates, but our comparison between an ACA-centred database and a worldwide continental surface sample database reveals several challenges. Drylands exhibit extreme climate and soil/lacustrine properties, amplifying the impact of confounding factors on brGDGT-based relationships with mean annual air temperature. Salinity emerges as the dominant factor influencing brGDGT variance, followed by sample type, pH, and aridity, all of which contribute significantly. These factors interact in complex ways, with the salinity effect varying between soil and lacustrine deposits. For sample physicochemical conditions, the <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M1" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi mathvariant="normal">IR</mi><mrow><mn mathvariant="normal">6</mn><mo>+</mo><mn mathvariant="normal">7</mn><mi mathvariant="normal">Me</mi></mrow><mo>′</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="39pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="404d15cb413636fa72b31d758eb2b8e4"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-23-1013-2026-ie00001.svg" width="39pt" height="15pt" src="bg-23-1013-2026-ie00001.png"/></svg:svg></span></span> index is best for salinity, and <span class="inline-formula">IR_6Me</span> is most suitable for pH reconstruction. Thus, the <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M3" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi mathvariant="normal">MBT</mi><mrow><mn mathvariant="normal">5</mn><mi mathvariant="normal">Me</mi></mrow><mo>′</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="41pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="d2673fdb423b1b85cfcf4412eef2b9e6"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-23-1013-2026-ie00002.svg" width="41pt" height="15pt" src="bg-23-1013-2026-ie00002.png"/></svg:svg></span></span>-temperature relationship is limited in ACA, particularly for lacustrine samples, and <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M4" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi mathvariant="normal">MBT</mi><mrow><mn mathvariant="normal">6</mn><mi mathvariant="normal">Me</mi></mrow><mo>′</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="41pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="f4039b5ba56e3721568134c666667476"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-23-1013-2026-ie00003.svg" width="41pt" height="15pt" src="bg-23-1013-2026-ie00003.png"/></svg:svg></span></span> does not offer a better solution under hyper- to semi-arid conditions. Sub-calibrating models for specific environmental conditions such as salinity and aridity improves the accuracy of temperature reconstructions. Furthermore, the difference between <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M5" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi mathvariant="normal">MBT</mi><mrow><mn mathvariant="normal">5</mn><mi mathvariant="normal">Me</mi></mrow><mo>′</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="41pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="0a684ebc852fb94664967095c4a428eb"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-23-1013-2026-ie00004.svg" width="41pt" height="15pt" src="bg-23-1013-2026-ie00004.png"/></svg:svg></span></span> and <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M6" display="inline" overflow="scroll" dspmath="mathml"><mrow><msubsup><mi mathvariant="normal">MBT</mi><mrow><mn mathvariant="normal">6</mn><mi mathvariant="normal">Me</mi></mrow><mo>′</mo></msubsup></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="41pt" height="15pt" class="svg-formula" dspmath="mathimg" md5hash="6bb188ac6753569566e722fa36cae38a"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-23-1013-2026-ie00005.svg" width="41pt" height="15pt" src="bg-23-1013-2026-ie00005.png"/></svg:svg></span></span> provides a promising proxy to assess aridity. Although the brGDGT signal in drylands is influenced by multiple controlling factors, it remains a valuable tool for understanding past climate and environmental conditions, especially when accounting for the complex interactions between these factors based on each study's unique physicochemical and bioclimatic context. Further research, incorporating a broader range of surface samples alongside comprehensive soil and climate data, holds the potential to enhance the accuracy of brGDGT-based climate reconstructions.</p>