Reconstructing 15,000 years of southern France temperatures from coupled pollen and molecular (brGDGT) markers (Canroute, Massif Central)
Abstract. Climatic changes in southern Europe during the Holocene are characterised by a strong spatial and temporal heterogeneity whose patterns are still poorly understood, notably the presence or not of a Holocene thermal maximum (HTM; 10,000–6,000 cal. BP). The reconstructed climatic patterns also differ according to the proxies used (e.g., pollen, chironomid) and the latitude of the data. Here, a multi-proxy approach combining pollen and lipid biomarkers (branched Glycerol Dialkyl Glycerol Tetraethers, brGDGTs) is applied to the Canroute sedimentological sequence to reconstruct the climatic variation over the last 15,000 years in southern Massif Central, France. This area is poorly documented in terms of vegetation and climate. To provide reliable climate reconstructions, we have (1) performed a multi-method comparison based on transfer functions applied to pollen (MAT, WA-PLS, BRT) and molecular biomarkers (brGDGTs), (2) investigated the role of modern databases/calibrations in climate reconstructions. Three different calibration databases were tested for pollen data: one global based on a Eurasian Pollen Database, and two regional databases corresponding to Mediterranean/Temperate Europe and Temperate Europe/Scandinavian databases respectively. Nine global calibrations were tested for lipid biomarkers including eight for soil and one for peat. The use of different modern databases highlights the importance of considering environmental and ecological constraints when using transfer functions on pollen sequences. Pollen and brGDGT-inferred climate trends are consistent, notably for the Lateglacial, the Early and Late Holocene. However, the reconstructions notably differ concerning the presence of a Holocene thermal maximum with the Modern Analogue Technique (MAT) pollen-based method but not apparent with the BRT pollen method nor brGDGT. The temperature reconstructions estimated from the two independent pollen and lipid proxies are then compared to regional published climate signals (chironomids, pollen, molecular biomarkers) to better derive global regional climatic patterns in South Europe. Altogether, our results from the Canroute sequence and those already available in southern Europe reveal that for the Lateglacial and Early Holocene, the regional climate trends are coherent between sites and proxies, supporting the reliability of their reconstructions despite some discrepancies. During the Holocene, the temperature signal of Canroute does not indicate the clear presence of a pronounced mid-Holocene thermal maximum, but rather stable and warmer temperature compared to Lateglacial ones and overall negative anomalies compared to modern annual temperatures.
Léa d'Oliveira et al.
Status: final response (author comments only)
- RC1: 'Comment on cp-2023-15', Anonymous Referee #1, 10 May 2023
- RC2: 'Comment on cp-2023-15', Cindy De Jonge, 24 May 2023
Léa d'Oliveira et al.
Léa d'Oliveira et al.
Viewed (geographical distribution)
The authors present a multiproxy study for reconstructing past mean annual air temperatures based on two independent and complementary approaches based on pollen record and a specific bacterial-sourced biomarker based on brGDGTs for the last 15 kyr in the southern France. They have evaluated different existing temperature-calibrations for each proxy and some reasons for biases in derived-temperatures.
They have also documented other previously published studies from Europe using different proxies to derive temperature reconstruction including those studied, to explain similarities and differences with climate signal during the last deglaciation and the Early and Mid-Holocene. Therefore, the authors have made a nice work including different proxies and evaluating different calibrations, and an updated review of other works related with the topic. Thus, the idea and methods are well, I find the manuscript well-written, and figures are appropriated.
However, I have some general comments and some specific revisions that I suggest reviewing for the final version.
One concern is about the use of different modern pollen database. It is intriguing that you select the Scandinavian calibration, given the study site location. Do you have a climatically explanation for that?
It is true that the MEDTEMP calibration gives slightly low R2 values (0.91 for the BRT method), but the EAPDB calibration gives comparable R2 values for the BRT method (0.92), although with higher RMSE values. In my view, this EAPDB calibration gives a reasonable MAAT profile and is comparable to the TEMPSCAND one. Why do you do not include it in the MAAT profile with the brGDGTs-MAAT reconstructions? Please add some discussion about that.
In general, the climate variability during the Lateglacial has been characterized by warm conditions during the B-A (14.700-12.900 yr) and a cold YD period (12.900-11.700 yr) before the warm Holocene. This trend is likely showed by the MEDTEMP calibration but not really showed with the brGDGTs calibrations. The brGDGT-MAAT calibrations show similar trends to the EAPDB or TEMPSCAND calibrations. Do you have a hypothesis for that?
Line 37: delete nevertheless and replace fluctuations by oscillations
Line 38: at millennial timescale
Line 38: indicated
Line 74: please add after palaeotemperatures depending on the type of the archive and the region
Line 92: please add De Jonge et al., 2021 before Robles et al., 2022b
Please review references by Robles et al. along the text.
Robles et al., 2022a is now Robles et al., 2022
Robles et al., 2022b is now Robles et al., 2023
Line 96: there is other compared pollen- and brGDGT-based studies, please add for instance Watson et al., 2018
Line 107: Conroute peatland
Line 137: Add the acronym (OMC), and then use it in line 139
Line 151: Did you monitor the m/z 1303 instead of 1302?
Lines 160-161: I would denote mr and mrs as mr-1 and mr-2
Lines 161 and 162: Bayesian
Line 164: add the calibration error or RMSE for the Bayesian calibration
Line 261: isoGDGTs
Line 299: I would skip mr and mrs to refer multiple regressions, see my previous comment. You can refer as mr-1 and mr-2 and cite the corresponding reference (De Jonge et al., 2014b).
I find confusing the codes of the different soil and peat calibration used, and I would avoid the initial of the references, since they are indicated in Table 1 and along the text. Then I would replace some of them as:
Soil MBT’ (Peterse et al., 2012), Soil MBT (De Jonge et al., 2014b), Soil MBT’5Me-1 and Soil MBT’5Me-2 (Naafs et al., 2017b), Bog MBT’5Me (Naafs et al., 2017a), Index1 (De Jonge et al., 2014a), mr-1 and mr-2 (De Jonge et al., 2014b).
Line 338: WA-PLS
Line 339: 1.9 ºC
Line 548: the presence of the HTM in the Mediterranean region.
Line 570: a Late-Holocene
Table 1: Please add the calibration error of each equation before the reference.
Figure 4 Panel (a): Abundance (%), GDGT-0, GDGT-1, etc. Cren and Cren’ instead of GDGT4 and Crenach’, in consistence with the text, whereas it is referred as GDGT-0/Cren ratio.
Panel (b): you have identified some double prime isomers, please add some discussion about their significance in the manuscript. Are they detected along the whole record or just at some intervals, likely between 6.600-5.000 yr? Perhaps their distribution suggests one of the brGDGT-based calibration.
Figure 7: Please add different symbols for the different profile in each panel for better reading in a black and white printed version. In panel b, replace as WA-PLS
Figure 8: Please rename the codes of soil and peat calibrations avoiding the initial of each reference, as Bog, Index1, Soil, mr-2, Soil MBT, mr-1, mr-2, etc. See my previous comment.
Figure 9: Please add different symbols for the different profile in each panel for better reading in a black and white printed version. Also, in panel (b), I would rename as Soil Bayesian, mr-2 and Index1 (see my previous comment). Accordingly in the figure caption, please rewrite as: Soil Bayesian (XX symbol and dark blue line; Dearing Crampton-Flood et al., 2020), mr-2 (XX symbol and light blue line; De Jonge et al., 2014b), and Index1 (XX symbol and red line; De Jonge et al., 2014b).
Watson, B.I., Williams, J.W., Russell, J.M., Jackson, S.T., Shane, L., Lowell, T.V., 2018. Temperature variations in the southern Great Lakes during the last deglaciation: Comparison between pollen and GDGT proxies. Quaternary Science Reviews 182, 78–92. https://doi.org/10.1016/j.quascirev.2017.12.011