Continuous sterane and phytane δ^13C record reveals a substantial pCO_2 decline since the mid-Miocene

Domain: earth_science
Published: 2024-06-18
Editorial outcome: major_revision

Abstract
Constraining the relationship between temperature and atmospheric concentrations of carbon dioxide (pCO2) is essential to model near-future climate. Here, we reconstruct pCO2 values over the past 15 million years (Myr), providing a series of analogues for possible near-future temperatures and pCO2, from a single continuous site (DSDP Site 467, California coast). We reconstruct pCO2 values using sterane and phytane, compounds that many phytoplankton produce and then become fossilised in sediment. From 15.0-0.3 Myr ago, our reconstructed pCO2 values steadily decline from 650 ± 150 to 280 ± 75 ppmv, mirroring global temperature decline. Using our new range of pCO2 values, we calculate average Earth system sensitivity and equilibrium climate sensitivity, resulting in 13.9 °C and 7.2 °C per doubling of pCO2, respectively. These values are significantly higher than IPCC global warming estimations, consistent or higher than some recent state-of-the-art climate models, and consistent with other proxy-based estimates.

Introduction
Defining the relationship between the atmospheric concentration of carbon dioxide (pCO2) and temperature is essential for understanding present environmental changes and modelling future climate trends. Geologic data can provide critical context, as well as possible analogues, for our future. For example, as compared to today’s global annual temperatures of 14.5 °C1, the middle Miocene (ca. 15 million years ago; Ma) was 18.4 °C2–4, equivalent to that predicted for the year 2100 using the IPCC RCP8.5 scenario5,6. Thus, studying the past 15 million years (Myr) may provide a series of climate analogues relevant for possible near-future climates.

Over the past 15 million years, pCO2 values have steadily declined, according to the latest pCO2 compilation7. Within this revised compilation, however, estimates widely range both among proxies and within a single proxy (i.e., >500 ppmv difference) and include some unrealistically low values (i.e., <120 ppmv), providing the need for additional independent proxy records. Furthermore, our current understanding of the past 15 Myr is comprised of compilations7–10 of much shorter intervals, e.g., the recent study by Brown et al.11 covering 5–7 Ma; no single site covers the entirety of the past 15 Myr, which introduces concerns in stitching disparate sections together (e.g., regional and latitudinal influences). Thus, pCO2 reconstructions over the past 15 Myr require further clarification from an additional independent proxy record from a single site that spans this whole period, especially given the importance of defining the relationship between pCO2 and temperature for future climate scenarios.

Here, we aim to better define the relationship between pCO2 and temperature from the mid Miocene to late Pleistocene by using a single site that covers this entire 15-Myr time interval, supplemented with additional sites that cover shorter timespans within the same time interval. We use a refreshed approach to estimate pCO2 from the stable carbon isotopic fractionation that occurs during photosynthetic CO2-fixation (Ɛp). This isotopic fractionation occurs in photoautotrophs as their CO2-fixing enzyme Rubisco selects 12C over 13C, resulting in isotopically more negative biomass than the dissolved inorganic carbon source (e.g., growth water). The Ɛp framework assumes passive diffusion of CO2 into photoautotroph cells and so the utilization of carbon-concentrating mechanisms by photoautotrophs in past oceans is an unavoidable limitation. However, three decades of field observations and laboratory cultures (e.g.12–20,) have attributed ambient pCO2 as the primary controlling factor for Ɛp, in which higher pCO2 results in higher Ɛp values. Ɛp can be calculated from the δ13C of phytoplanktonic biomass corrected for the δ13C of dissolved CO2 (CO2[aq]). Ɛp is then used to estimate the concentration of CO2 via CO2[aq] = b/(Ɛf - Ɛp), where Ɛf is the maximum potential fractionation due to CO2-fixation and b represents carbon demand per supply for phytoplankton. Finally, CO2[aq] is converted to atmospheric pCO2 via Henry’s law assuming atmosphere-ocean equilibrium. This b parameter represents physiological factors that may impact CO2 uptake, e.g., growth rate, cell radius, and cell membrane permeability e.g.21,22,. Recent culture experiments suggest that light energy, independent of its effect on growth rate, may also be an important control, with higher irradiance resulting in higher Ɛp values15,19,23,24, but has not yet resulted in revisions to the pCO2 proxy calculation. This recent work provides new and exciting questions to explore, making the application of Ɛp for reconstructing pCO2 in different settings and time periods timely, especially in context of the latest pCO2 compilation7. By using a diversity of independent proxy methodologies, ideally with consistent deployment of methods between groups, with honest and robust modelling of uncertainty within each system, we can then challenge and scrutinize persistent proxy outliers that range outside the uncertainty bands of multiple other proxies.

Whereas previous work has relied on the δ13C of alkenones i.e., compounds produced by species within the Haptophyte clade, we expand the Ɛp approach to the δ13C of general phytoplankton biomarkers (GPBs) i.e., compounds produced by the majority of photoautotrophs in sea surface waters and have subsequently become fossilized in marine sediments25,26. Several recent studies explored the potential of GPBs across a modern environmental transect from high pCO2 near a naturally-occurring marine CO2 seep towards control values in two drastically different geographic locations (i.e., off the coasts of Vulcano Island, Italy25 and Shikine Island, Japan26). The applied GPBs known as phytol (i.e., the side-chain of the vital photoautotrophic pigment chlorophyll-a27) and cholesterol (i.e., a sterol that all eukaryotes synthesize or produce from ingested sterols with minimal isotopic fractionation28,29) demonstrate that mixed phytoplankton communities with varying cell sizes and growth rates still exhibit a strong isotopic response to CO2[aq]. Phytol has further been tested across glacial-interglacial cycles, which suggest phytol reconstructions were within error of the ice core-based CO2 records and showed nearly identical values as the alkenone-based reconstructions30. Phytol and sterols have less well-constrained sources than alkenones, possibly leading to more uncertainty in absolute pCO2 estimates. However, because GPBs are produced by a large number of species, they may have several benefits over alkenones: (i) GPBs have greater spatial and temporal distribution (spanning at least 10x deeper in the geologic record)31 and (ii) GPBs have the potential to curb species-specific concerns and environmental effects by averaging the whole phytoplankton community, while also being much more specific than the δ13C of bulk sedimentary OM that has been used to this end32. Because GBPs are far more ubiquitous than alkenones, they provide more extensive coverage (both spatially and temporally) to generate a continuous record of pCO2, overcoming a major hurdle with previous proxy-based pCO2 reconstructions. As such, GPBs have the potential to span the Phanerozoic, whereas alkenones are limited to the Cenozoic, which would extend Ɛp-based pCO2 proxies by nearly ten-fold. Thus, this general phytoplankton biomarker approach is a promising tool to obtain paleo pCO2 records.

Here, we estimate paleo pCO2 from the δ13C of GPBs over the past 15 Myr, as well as from the δ13C of alkenones for proxy comparison.

Results
δ13C values of GPBs over the past 15 Myr at DSDP Site 467
Marine sediments retrieved by drilling DSDP Site 467 (33.8495, −120.757833) off the coast of California are remarkably unique in that they contain OM-rich sediments over this entire timeframe (details in “Methods”). The most abundant and ubiquitous GPBs in these sediments are phytane, 5α-cholestane, 24-ethyl-5α-cholestane, and 24-methyl-5α-cholestane, the diagenetic products of our target GPBs (i.e., phytol and sterols). These GPBs occurred in organic sulphur (S)-rich macromolecules and were recovered by desulfurization. Because reduced inorganic S species rapidly react with functionalized labile lipids in an oxic surface sediments, S-bound molecules reflect in situ produced lipids. The low abundance of higher plant-derived long-chain n-alkanes and terpenoids from terrestrial inputs indicate that sedimentary OM at Site 467 is predominantly derived from marine sources. Thus, these phytane and steranes originate primarily from phytoplankton that are photosynthetically fixing dissolved CO2 in the upper part of the water column33. Details on methods in Methods.

The δ13C values of the GPBs steadily increase from 15.0 to 0.3 Ma (Fig. 1a; Supplementary Fig. 1; Supplementary Dataset 1–6). Here, we use a weighted average for the δ13C of steranes, based on their fractional abundances (Supplementary Dataset 3–6). The δ13C of phytane (ranging from –26.8 to –23.7‰) and weighted steranes (from –28.2 to –24.3‰) show statistically similar δ13C trends throughout the record (Fig. 1, S3, S4), consistent with a similar general source, i.e., phytoplankton. These δ13C records are consistent with the much shorter δ13C records for GPBs (Supplementary Data 2) reported for the Monterey Formation at Naples Beach in the Santa Barbara basin33 and Shell Beach in the Pismo basin34, as well as for the δ13C of phytane record from DSDP Site 608 in King’s Trough in the eastern North Atlantic3,35. Although these three latter sections only span ca. 11–18 Ma, their corresponding results strongly suggest that the δ13C records at DSDP Site 467 reflect a global (and not local) signal for pCO2. Alkenones were present in only the most recent 4 Myr and their δ13C values range from -21.4 to -23.8‰ (Fig. 1a; Supplementary Data 7), consistent with the δ13C trends for the GBPs during that time.

Calculations for Ɛp based on δ13C values of GPBs
Ɛp is calculated from the δ13C of phytoplanktonic biomass (δp) and the δ13C of aqueous carbon dioxide (CO2[aq]) in the photic zone (δd):

εp = 1000 * [(δd + 1000)/(δp + 1000) - 1] (1)

To determine δp, we use the δ13C of a phytoplanktonic biomarker lipid (Supplementary Fig. 1) corrected for the isotopic offset between the specific biomarker lipid and biomass (Fig. 1a). This offset is calculated based on compiled laboratory cultures, with the isotopic offset between biomass from phytane as 3.5±1.3SD ‰ based on its precursor phytol (compiled in ref. 36), steranes as 4.5±3.0SD ‰ based on their precursor sterols37, and alkenones as 3.9±0.4SD ‰ (compiled in ref. 36). δd was estimated from a global compilation of δ13C of planktonic foraminiferal shells38 (Fig. 1b) and was corrected for the temperature-dependent carbon isotopic fractionation of CO2[aq] with respect to HCO3- ref. 39. Sea surface temperatures (SST) were calculated using the TEX86 proxy40,41 based on the ratio of cyclopentane rings in glycerol dibiphytanyl glycerol tetraethers (GDGTs) in the same sediments as our GPBs (Fig. 1c; Supplementary Data S8) and assigned an uncertainty of ±4°C SD40 caused by potential calibration errors.

The estimated values for Ɛp are compiled in Supplementary Fig. 2. pCO2 was then calculated using 22,42:

pCO2 = [b / (εf - εp)] / K0 (2)

where K0 reflects the Henry’s Law constant that is used to convert CO2[aq] to pCO2 based on temperature and salinity43. Within the brackets of Eq. (2), Ɛf reflects the maximum potential isotopic fractionation due to CO2-fixation by the enzyme Rubisco44, which is 26.5±1.5 ‰ uniformly distributed uncertainty to reflect the full potential range reported in algal cultures (compiled in ref. 36). The b parameter reflects species carbon demand per supply22,42, which was back-calculated from bulk OM and phytol in a compilation of modern surface sediments worldwide36 (i.e., 28 sites at different latitudes with known environmental parameters): the average is 168±43SD ‰ kg µM-1. This value for b is further supported by two phytol studies across two naturally occurring steep CO2 gradients25,26 and a phytol study in the equatorial Pacific Ocean45, as well as previous paleoclimate studies using phytane where a b value of 170 ‰ kg µM-1 was used46–48. Thus, we apply 168 ‰ kg µM-1 in all our calculations.

pCO2 estimation over the past 15 Ma
Expectedly, Ɛp calculated from the δ13C of GPBs also all share similar values, ranges, and declining trends: 15.8 to 11.2‰ for phytane and 17.0 to 11.0‰ for steranes (Supplementary Fig. 2; Supplementary Dataset 1–6). This similarity among the GPBs is likewise reflected in the resulting pCO2 estimations (Fig. 2). pCO2 is highest at 15.0 Ma with values of 620 ppmv and 655 ppmv using phytane and steranes, respectively. These estimates tightly follow each other throughout the record, with the exception of the data point at 9.8 Ma, where phytane suggests a continued decline (435 ppmv) but steranes suggests a singular spike in pCO2 (540 ppmv). By 8.7 Ma, phytane and sterane estimates converge (400 ppmv) for the rest of the record. Alkenone-based pCO2 estimations, which could only be determined for the most recent 4 Myr period, where alkenones were present, were almost identical to those obtained with the GPBs.

The similarity in pCO2 estimations between these GPBs is reassuring. However, we consider that these estimations may be influenced by the same factors, such as constraints on calculation parameters or upwelling. One of the more-difficult-to-constrain parameters in our calculation is factor b. Although it may change over time21, it is not possible to constrain this value in most geologic settings; thus, maintaining b as a constant is the most reasonable approach. Sensitivity tests demonstrate that the uncertainty within the b value could lead up to a maximum of 25% change in pCO2 estimation36, which is still too small to account for the consistent decline over the studied time interval. Furthermore, the overlap between our GPB-based pCO2 estimates with the more conventional alkenone-based pCO2 estimates, for which substantial research on the b-value has been conducted20,42, suggests that b values for our GPB-based reconstructions are realistic.

Another potential factor to consider is change in upwelling intensity. Upwelling may mask the phytoplankton response to a changing pCO2, bringing the ocean out of equilibrium with the atmosphere as upwelling brings more 13C-depleted CO2 from cold bottom waters to the surface45. Radiolarian evidence49 at the nearby ODP Site 1021 suggest enhanced coastal upwelling is unlikely in this region due to the timing of biosiliceous sedimentation in relation to known changes in the California Current. A potential increased production of North Atlantic Deep Water (NADW), the source of upwelling in this region, may have occurred between 11.5 to 10.0 Ma and from 7.6 to 6.5 Ma49 (marked in Fig. 2). Notably, during these potential periods of increased NADW (Fig. 2), our biomarker-based pCO2 values do not deviate from the overall downward trend. To further explore the potential impact of upwelling from a biomarker-perspective, we also analyzed the C25 highly branched isoprenoids (HBI), a biomarker produced by specific diatoms that thrive in upwelling regions, as seen in the Arabian Sea over the past 0.3 Myr50. Although the diatom-produced C25 HBI is present in our sediments, the δ13C of the C25 HBI varies greatly throughout the record and has no correlation with the δ13C of the GPBs (Supplementary Fig. 1, 3). This lack of correlation suggests that these upwelling-related diatom species do not significantly contribute to the overall phytoplankton lipid pool and thus the effect of upwelling is likely minimal. We also considered the relative contribution of the C28 sterane (24-methyl-5a-cholestane), which tends to be more dominant in diatoms; however, again, there is no relationship between the fractionation abundance of these diatom produced biomarkers with these periods of potential upwelling. Overall, although productivity changes or upwelling could play some role, these factors alone cannot account for our reconstructed ca. 350 ppmv decline in pCO2 over 15 Myr.

Here, we put our results into context of earlier reports. We find that as compared with 19,51,52 the recently revised alkenone- and boron-based pCO2 proxies compiled in Rae et al.7 (Fig. 3a), our pCO2 estimations follow similar trends and fall within error of estimate in Rae et al.7, with absolute values closely matching throughout the record, especially the boron-based pCO2 estimations. In the most recent 4 Myr of our record where our sediments contained alkenones, our pCO2 values closely match the boron-based pCO2 in Rae et al.7, but tend to be slightly higher (ca. 50 ppmv) than the 7 alkenone-based pCO2 estimations in Rae et al.7. This overall alignment with the most up-to-date records is promising; this suggests that our GPB-based values are likely producing reasonable pCO2 estimations, but in this case, from a single continuous proxy record.

Furthermore, it is notable that our GPB-based pCO2 estimates are consistent with the pCO2 required by the majority of climate models in order to agree with the proxy-derived temperature estimates (and the generally accepted sensitivity to pCO2). For the mid-Miocene Climate Optimum (17-15 Ma), two different versions of the National Centre for Atmospheric Research model (NCAR) indicate that pCO2 needs to be within the range 460–580 ppmv2,51, the Max-Planck Institute Earth System Model (MPI-ESM) suggest that pCO2 should be around 720 ppmv52, and Community Earth System Model (CESM1.0) requires around 800 ppmv51. Knorr et al.53 did succeed in simulating the warmth of the mid-Miocene Climatic Optimum with relatively low pCO2 but only by adopting large changes to the vegetation distribution, reducing planetary albedo, and having a strong positive water vapour feedback in their climate model. Therefore, our pCO2 estimates are aligned with model-based interpretations of what reasonable pCO2 should be across this period.

We also considered how our GPB-based pCO2 estimations relate to proxy-based temperature. Based on visual comparison over this period (Fig. 3), it is clear that our pCO2 record shows a similar declining trend as two different temperature proxies (Fig. 3b): the alkenone derived UK´37 proxy10, which represents SST, and the δ18O derived from benthic foraminifera54, which represents oceanic bottom water temperature, as well as the build-up of the continental ice volume. The remarkably similar trends of our pCO2 estimates and independent temperature records from the Miocene through to the Pliocene therefore suggest that pCO2 and temperature are closely coupled. A potential caveat for this observed correlation is the fact that SST is used twice in our paleo pCO2 estimation (although notably, we have used the SST proxy TEX86 to calculate our pCO2, whereas our temperature comparisons in Fig. 3b are derived from different temperature proxies). Sensitivity tests over the Phanerozoic show negligible effects of SST on the first use in the equations, when calculating Ɛp (±0.5‰). In the second use of SST in the calculations (converting CO2[aq] to pCO2), sensitivity tests show that SST may potentially affect pCO2 estimations up to ±50 ppmv36. Although a sizeable error, this potential ±50 ppmv is too small to suggest SST alone is driving the declining trend ranging from ca. 630 to 280 ppmv over the entire record.

Climate sensitivity
To explore the precise relationship between pCO2 and global temperature, we calculated climate sensitivity, which refers to the impact of radiative forcing (which is primarily impacted by pCO2) on temperature. Here, we calculate both Earth system sensitivity (ESS) and equilibrium climate sensitivity (ECS), respectively representing slow and fast climate feedback responses, using our new pCO2 dataset from DSDP 467 (Table 1). We calculate an average sensitivity over 15 Myr and a range of pCO2 values. Any variations over time could come from subsets of these points but given that there are a limited number (i.e., 30) of different (unequally spaced) time values, subdivisions and new regressions with all uncertainties would most likely give non-significant fits. The temporal dependence of climate sensitivity can only be determined with higher temporal resolution records for specific time intervals.

First, we calculate ESS, i.e., the response to CO2 including (slow) climate feedbacks. ESS was estimated for each latitudinal region using a linear regression between the change in mean annual SST relative to modern SST (ΔSST) and radiative forcing due to CO2 (ΔRCO2 [Wm–2]). ΔSST is based on the UK´37 proxy for SST, which were compiled10 by latitude and hemisphere into 0.125 Ma bins (Fig. 3b) and linearly interpolated for the age of our sediments (Fig. 4). Tropical SSTs may be underestimated from 15 to 8 Ma, given that the UK´37 ratio is approaching saturation at these sites, and so to maintain consistency in proxy method but avoid bias in the tropic SSTs, we have not included UK´37 values when it approaches 1.0 in our calculations. ΔRCO2 was calculated using only the phytane-based pCO2 record of DSDP 467, given that the GPBs and alkenones show similar pCO2 values throughout this record and because the phytane record is most complete. Furthermore, phytane has yielded secular trends in pCO2 comparable to other proxies in the Cretaceous31,48 and over the Phanerozoic36. Monte Carlo simulations were used to propagate uncertainty for each equation parameter in these calculations36.

The resulting ESS shows 18.8 °C (per CO2-doubling) for NH high latitudes, 16.0 °C for the mid-latitudes, and 11.1 °C for the tropics (Supplementary Fig. 5), with respective values in K/Wm-2 shown in Table 1 and are based on the slope of a linear fit of the data. When we weigh each sensitivity by the percent-area for the Earth: tropics (30°N-30°S, 50.0%), mid-latitudes (30–60°, 36.6%), and high latitudes (60–90°, 13.4%), our global average ESS amounts to 13.9 °C per doubling of CO2. These values are considerably higher than the global ESS of 2.2 to 5.6 °C per CO2-doubling calculated for the Plio-Pleistocene55, although ESS from the same data (taking into account individual shifts in time) has been estimated as 9.0±2.7 °C per CO2-doubling (68% confidence level)56,57 which is more consistent with our values, even though we use a similar approach (long-time average) as ref. 55. Our mid- and high-latitude ESS estimates suggest significant polar amplification despite generally less-than-present ice cover. Recent modelling efforts have highlighted the importance of cloud feedbacks in explaining very high polar sensitivity58,59. Even in largely ice-free climates of the Cenozoic, models suggest strong polar amplification due to cloud and land-surface feedbacks60,61. That said, the Cenozoic CO2PIP7 estimations for ESS exceed ca. 8 °C per CO2-doubling for the past 20 Myr, reaching ca. 13 °C per CO2-doubling in the early Cenozoic.

Next, we calculate ECS, i.e., fast climate feedback and the quantity generally used in policy discussions. Given that our record spans 15.0 to 0.3 Ma, during which there is large variability in ice sheet coverage, we additionally consider radiative forcing due to land ice change (ΔRLI) based on earlier work55,62,63, with results shown in Fig. 4. ECS was determined by a linear regression of ΔSST versus ΔRCO2+LI and is estimated to be 11.6 °C (per CO2 doubling) for NH high latitudes, 8.6 °C for the mid-latitudes, and 5.0 °C for the tropics, with respective values in K/Wm-2 and r2 values shown in Table 1. When we again weigh each sensitivity by the percent-area for the Earth, our global average ECS is 7.2 °C per doubling of CO2, much higher than the most recent IPCC estimates of 2.3 to 4.5 °C6 and consistent with some of the latest state-of-the-art models which suggest ca. 5.2 °C64. It should be noted that our ECS is not the same as the ECS used by the IPCC, given that it represents specific climate sensitivity S[CO2,LI] (i.e., ESS corrected for potential slow land ice feedback) and does not consider changes in other greenhouse gases (e.g., methane), paleogeography, nor solar luminosity; we are currently unable to conduct these additional considerations65. The impact of additional methane and water would bring down ECS, which likely explains why paleo ECS is generally higher than modern models.

Our work represents the application of general phytoplankton biomarkers (GPB) to reconstruct pCO2 values, offering a refreshed approach to the pCO2 proxy based on photosynthetic isotopic fraction that may enable reconstructions over longer timescales where other existing proxies are lacking (e.g., the Phanerozoic). Our reconstructed pCO2 values across the past 15 million years suggest Earth system sensitivity averages 13.9 °C per doubling of pCO2 and equilibrium climate sensitivity averages 7.2 °C per doubling of pCO2. Although these values are significantly higher than IPCC global warming estimations, they are consistent or higher than some recent state-of-the-art climate models and consistent with other proxy-based estimates.

Methods
Study site
Site 467 (33.8495, -120.757833) was collected by Deep Sea Drilling Project Leg 63 at the San Miguel Gap off the coast of California, USA. The total length of the core section is 1041.5 m and 426.3 m were recovered. The core has the best-preserved organic matter of Leg 6366, likely due to incorporation of abiotic sulphur species into labile functionalized lipids, a process which occurs rapidly during very early diagenesis67–69. The age model is based on diatom, coccolith, and radiolarian events66, which we have revised for every reported species using first and last occurrence, checked against the most up-to-date period tie points54, and reported alongside mbsf from the core (details and references within Supplementary Data 9). The present-day oceanic regime of this region comprises of the California Current, a part of the North Pacific subtropical gyre which carries cold, fresher surface water from the North Pacific into the warmer, more saline surface water of the subtropical regions. Over long timescales, orbital forces impact the latitudinal changes, strength, and mean transport of the California Current flow.

Analytical methodology
Thirty-five marine sediments, depths ranging from 9 to 1038 mbsf, were sampled approximately every 30 m from the Site 467 core (Dataset S1). 15–20 g of homogenized sediments were extracted on a Dionex 250 accelerated solvent extractor at 100°C, 7.6×106 Pa, using dichloromethane (DCM):methanol (MeOH) (9:1 v/v) and the extracts were dried over NaSO4. The extracts were eluted over an alumina packed column and separated into an apolar (hexane:DCM, 9:1 v/v), ketone (DCM), and apolar fraction (DCM:MeOH, 1:1 v/v). Polar fractions were desulfurized using Raney-nickel, eluted over an alumina packed column into an apolar fraction (hexane:DCM, 9:1 v/v) and hydrogenated using acetic acid and platinum oxide67,68. These were left overnight and then cleaned over a small column of magnesium sulphate and sodium carbonate with DCM. To obtain baseline separation of the targeted biomarkers, n-alkanes were removed using vacuum-oven prepared 5Å molecular sieve added to the samples, dissolved in cyclohexane, and left overnight; the supernatant was then removed and analyzed.

An Agilent 7890A gas chromatograph-mass spectrometer (GC-MS) was used to identify GPBs (i.e., phytane and steranes), and the C25 HBI alkane in the resulting apolar fraction from the desulfurized polar fraction, as well as alkenones in the ketone fractions. An Agilent 7890B GC with flame ion detector (FID) was used to determine compound quantities prior to injection on a Thermo Trace 1310 GC coupled to a Thermo Delta V-isotope ratio mass spectrometer (IRMS). GC-MS, GC-FID, and GC-IRMS measurements were conducted on a CP-Sil 5 column (25 m × 0.32 mm; df 0.12 μm). GC-MS and GC-FID used constant pressure and IRMS used constant flow of He carrier gas. All three instruments used the same GC program with starting oven temperatures of 70°C ramped at 20°C/min to 130°C and then ramped at 4°C/min to 320°C for 10 min. For IRMS measurements, a standard with n-alkanes (C20 and C24) with known isotopic values (–32.7 and –27.0‰, respectively) was run at the start of each day and then co-injected with samples to monitor the integrity of the instrument (within 0.5‰). At the start of each day, the IRMS underwent an oxidation sequence for 10 min, He backflushed after oxidation for 5 min, and conditioning line purged for 5 min; a shorter version of this sequence is conducted in a post-sample seed oxidation, which includes 2 min oxidation, 2 min He backflush, and 2 min purge conditioning line.

An Agilent 1260 ultra-high-performance liquid chromatography (UHPLC) coupled to a 6130 quadrupole MSD in selected ion monitoring mode was used to identify and integrate glycerol dibiphytanyl glycerol tetraethers (GDGTs) in the polar fraction. Separation was achieved on two UHPLC silica columns (BEH HILIC columns, 2.1×150 mm, 1.7 μm; Waters) in series, fitted with a 2.1×5 mm pre-column of the same material (Waters) and maintained at 30°C according to previously established methods69–71. With these GDGTs, we then apply the SST proxy known as TEX86 (TetraEther indeX of tetraethers consisting of 86 carbon atoms), where the number of cyclopentane moieties increases along with SST40. Here, we use the modified version known as TEX86-H, modified for (sub)tropical oceans and greenhouse periods where the function excludes crenarchaeol regio-isomer for (sub)polar oceans40,72,73. Because several minor isoGDGT were below the detection level in the deepest part of the studied section, it was not possible to obtain TEX86 values. To accommodate for this, we compared the overall records from Site 467 to the TEX86 values at Site 608 at the same latitude, as well as U K´37 values from the nearby Site 1010 (directly south of DSDP Site 467) and Site 1021 (directly north of DSDP 467); all four sites have near-identical SST values throughout the past 15 Myr, so we use these other sites to linearly extrapolate the several missing SST at Site 467. All raw data is available in Supplementary Data 8.

Estimating climate sensitivity
ESS was then estimated for each latitude region using a linear regression of radiative forcing due to CO2 (ΔRCO2) versus ΔSST. ΔRCO2 was calculated using:

ΔRCO2 = [a(C - C0)2 + b1(C - C0) + cN + 5.36] × ln(C/C0) (3)

where C is pCO2 at the time of forcing (our phytane-based pCO2), C0 is a reference pCO2 (280 ppm), N is the average concentration of N2O, and the constant coefficients a, b1, and c are 2.4×10−7, 7.2×10-4, and 2.1×10–4 Wm–2 ppm–1, respectively, based on previously established methods72. For ΔSST, we used the previously compiled10 UK´37-based SSTs (expressed relative to the modern SST at each site) by latitude: northern hemisphere (NH) high-latitudes (Ocean Drilling Program Sites 883, 907, 982, 983), NH mid-latitudes (ODP Sites 1010, 1021, 1208), southern hemisphere (SH) mid-latitudes (ODP Sites 594, 1085, 1088, 1125), and tropics (ODP Sites 722, 846, 850, 1241, U1338) from the UK´37-proxy.

Given that this record spans 15.0 to 0.3 Ma, during which the ice sheet cover varied to a large extent, we also consider radiative forcing due to land ice change (ΔRLI) which we estimated by multiplying reconstructed sea level (m) by 0.0308 Wm–3, based on earlier work64,74. Sea level over the last 16 Ma is estimated at a few instances (0 Ma = 0 m change in sea level relative to present72 and 3.2 Ma = 24 m, 10.0 Ma = 67 m, 14.9 Ma = 66 m, and 19.5 Ma = 105 m)73 and then linearly interpolated. ECS is then approximated by the specific climate sensitivity S[CO2,LI] (nomenclature is in Palaeosens62), which we determine by a linear regression of SST anomaly versus ΔRCO2 + ΔRLI.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Witkowski and colleagues present a new record of CO2 over the past 15 Myrs and pair these data with compiled temperature data in order to estimate climate sensitivity. It is a strong and well-written study that will be of interest to many. I start my review with some larger items, followed by a longer list of finer-level comments.\n1) The authors describe at some length the strengths and weaknesses of their proxy. I encourage them to push further on this. My colleagues who specialize in the alkenone CO2 proxy say that the proxy is undergoing a lot of growing pains, both in terms of understanding the mechanistic basis of the proxy but also in terms of parameterizing key variables related to growth rate (b, cell size, etc.). Many of the most recent alkenone papers publish a ‘family’ of plausible CO2 curves. I’m not necessarily advocating such an approach here, but the bottom line is that whatever one thinks of the alkenone method, the phytane method is less robust (not as accurate, not as precise). It has less taxonomic control and incorporates fewer variables (the key advantage is that it is more widely applicable in time and space). There are good reasons why folks moved away from the phytane-style approach (like in Freeman and Hayes 1990) to alkenones. Now we’ve come full circle, in a way. The relative trends in reconstructed CO2 should be quite robust, much more so than the absolute values. I encourage the authors to think about their data in these terms. For climate sensitivity, at least, relative changes are still extremely helpful!\n2) Please describe how you combined the CO2 and temperature data to create the individual data points in Figure 3. The CO2 and temperature data in Figure 2 are—by my eye—diachronous, underscoring the importance of how you went about constructing the data points in Figure 3.\n3) The ‘global’ climate sensitivities for ECS and ESS (5.0 and 11.1 oC per CO2 doubling) seem to be on the high end. That is, not “consistent” (line 10) with many existing estimates. For example, your 5.0 oC ECS falls outside the 95% confidence window from the IPCC (1.5-4.5 oC). In addition, your estimates are based on surface temperatures of tropical oceans. The global ECS will be higher both because higher latitudes and land surfaces have higher ECS values. The newest IPCC report probably has the most up-to-date amplification factor between tropical oceans and the globe for the present-day Earth.\nYour ESS value (11 oC) is consistent with some of the other proxy-constrained estimates of ESS. But if memory serves me right, ESS values derived from global climate models tend to be lower—on the order of 50% higher than ECS for this general time period (papers by Valdes, Lunt, and others).\n4) I have concerns with the regression approach in Figure 3. Now, absolutely, the major advantage of this approach is that it smooths out the uncertainty of individual data points, which is admittedly quite large. But one key drawback is that the time element stitching together your data has been completely lost. There is no way to tell if climate sensitivity has changed over the captured time interval. Moreover, using a regression can mask the true relationship between CO2 and temperature. It is possible, to give one example, for the slopes of all time-adjacent datapoints to be higher (or lower) than the regression slope. Or, half are steeper and half are shallower. These kinds of things would be really important to know. Yes, the uncertainty in each of the slopes connecting time-adjacent points is high, but in aggregate they should be very useful. A second advantage to analyzing time-adjacent data is that changes in other radiative forcings (for example, due to changes in paleogeography, the brightening of the sun, plant evolution, etc., etc.) should be small. This is not necessarily the case when you apply a single regression to an entire data set.\nI also have some concerns about the type of regression used, which I am assuming is an ordinary least-squares regression. This type of regression assumes that the x-dimension has no uncertainty. That is, one is minimizing the residuals only in the y-dimension. I wonder if a major-axis regression is more appropriate (or one of its cousins), which incorporates variance in both dimensions. In most cases, major axis regressions are steeper. I’ve looked into the literature a bit on this topic, and I didn’t see an easy answer. I’ll end by adding that this is yet another reason for avoiding the regression approach—if you instead analyze time-adjacent data points, there is only one way to draw a straight line between two points (!).\nOther comments:\nLine 10: Need to say that these estimates are for tropical latitudes (not global).\nLines 28-30: “The most recent pCO2 compilation (7) suggests that pCO2 did indeed change over =45< &)#,@; :1;590\" .>= ?-6>1< -;1 :99;6@ /98<=;-5810\" 5$1$\" ;-83583 2;97 '+% =9 &(*% A-=7 -= - single time-point. Thus, pCO2 over the past 15 Myr is no less confusing…” Yes, this is true, but most of the variability is across proxy methods. In most cases, individual studies across the MCO show a compelling fall in CO2, in keeping with the patterns of the current study. It’s just that the absolute values vary across studies. Thus, in this sense, the current study doesn’t address/rectify the problem posed in the quotation. I would argue that the MCO is one of the best pre-Pleistocene examples we have where almost all of the CO2 data tell the same story (in terms of relative change).\nLine 98: Citation to source #9 doesn’t seem right. May be good to double-check the numbering throughout.\nLines 169-171: Are you talking about the MCO time slice?\nLine 179: I’m not sure why you assume SST is a good proxy for global temperature.\nLines 186-190: You should estimate CO2 assuming a fixed temperature, and then compare this to Figure 1. Then you will know, for your study, how important the confounding influence of temperature is. It’s not clear if reference 23 looked at the same ranges of temperature and CO2, etc.\nLine 223: And Royer (2016, Figure 1 and Table 1) as well.\nRoyer, D. L., 2016, Climate sensitivity in the geologic past: Annual Review of Earth and Planetary Sciences, v. 44, p. 277-293", "reviewer_doi": "10.1146/annurev-earth-100815-024150.\nLines 223-224: The numbers you cite for reference #52 are in units of oC per CO2 doubling, not oC per W/m^2. And, these are numbers for a non-glacial state. During glacial periods (which includes the last 34 Myrs) ESS is twice as high, according to #52.\nLine 500: The variable “N” should really be called “N_0” so that it is clear you are talking about present-day concentrations of N2O (at least, that is what I infer you are doing). And, why stop at N2O? Why not include other greenhouse gases like methane?\nLine 503: The citation to source #66 can’t be right.", "reviewer_2": "This is a well-constucted study, building on the group's work on new pCO2 proxies based on phytane and sterane biomarkers. I have no issues with the findings or the science.\nI do, however, take issue with the set-up of the paper, which is that there is big uncertainty in late Miocene to Recent CO2 estimations that they have now resolved - see the abstract. Given that their new estimates look (they don't reference or plot this study) very similar in trend to the best recent review and compilation of Cenozoic CO2 (Rae et al. 2021) this is simply a confirmation (which is useful) rather than the resolution of a major problem. Although the Rae paper looks somewhat higher in absolute values, which again might be why they didn't plot or engage with that study - this is just not good enough.\nIf they'd referenced Rae et al. 2021 and factored this into their manuscript, some novelty may have come across, but the fact that they didn't means either:\n1) they are willfully ignoring the main paper in this area to be published in the last couple of years (and thought no one would notice)", "reviewer_or": "n2) they are not aware of it - which, given the author list, I really can't believe.\nThe results are not of a novelty or importance sufficient to be published in Nature Communications. And I'm quite disappointed in the authors, if willfully ignoring key literature is their approach to \"doing science\".\nThis paper is suitable for publication with minor revisions in a diciplinary journal (e.g. P&P or CoP), but please, in the rewrite, cite and integrate all the relevent literature."}, "author_response": "We would like to thank both the reviewers and the editor for their comments and suggestions, which have undoubtedly improved this manuscript. We respond to each individual comment below in bold font and the line numbers refer to the “clean” version of the manuscript (without track changes).\n______________________________\nReviewer #1:\nWitkowski and colleagues present a new record of CO2 over the past 15 Myrs and pair these data with compiled temperature data in order to estimate climate sensitivity. It is a strong and well-written study that will be of interest to many. I start my review with some larger items, followed by a longer list of finer-level comments.\nWe thank the reviewer for their positive comments.\n1) The authors describe at some length the strengths and weaknesses of their proxy. I encourage them to push further on this.\nWe have further expanded the discussion around the strengths and weaknesses of the proxy in the Introduction Line 46-62.\nMy colleagues who specialize in the alkenone CO2 proxy say that the proxy is undergoing a lot of growing pains, both in terms of understanding the mechanistic basis of the proxy but also in terms of parameterizing key variables related to growth rate (b, cell size, etc.). Many of the most recent alkenone papers publish a ‘family’ of plausible CO2 curves. I’m not necessarily advocating such an approach here, but the bottom line is that whatever one thinks of the alkenone method, the phytane method is less robust (not as accurate, not as precise). It has less taxonomic control and incorporates fewer variables (the key advantage is that it is more widely applicable in time and space). There are good reasons why folks moved away from the phytane-style approach (like in Freeman and Hayes 1990) to alkenones. Now we’ve come full circle, in a way. The relative trends in reconstructed CO2 should be quite robust, much more so than the absolute values. I encourage the authors to think about their data in these terms. For climate sensitivity, at least, relative changes are still extremely helpful!\nWe more explicitly acknowledge the weakness in phytane versus alkenones on Line 80-84, “Phytol and sterols have less well-constrained sources than alkenones, possibly leading to more uncertainty in absolute pCO2 estimates. However, general phytoplankton biomarkers are far more ubiquitous than alkenones, providing more extensive coverage (both spatially and temporally) to generate a continuous record of pCO2, overcoming a hurdle with previous proxy-based pCO2 reconstructions.”\nInterestingly, our alkenone-based, phytane-based, and sterane-based pCO2 estimates yield near-identical absolute values when found in the same sediments. The benefit of using a general biomarker is clear: phytane and sterane appear throughout the section, whereas alkenones are confined to the final 4 million years of the record.\nThe broad paleo-pCO2 community would agree that all pCO2 proxies have issues; because of this (and not despite this) we should continue reconstructing pCO2 in context of multiple proxies, where similar values provide higher confidence for pCO2 estimates and unlike values identify areas that require further investigation. We acknowledge that relative changes are more robust, and this is why we used climate sensitivity analysis based on relative change, not absolute numbers.\n2) Please describe how you combined the CO2 and temperature data to create the individual data points in Figure 3. The CO2 and temperature data in Figure 2 are—by my eye—diachronous, underscoring the importance of how you went about constructing the data points in Figure 3.\nThe methods for Fig. 3 are described in the section “Climate sensitivity”, with additional details in the Supplemental Material in the section “Estimating climate sensitivity”. On the y-axis of Fig. 3, we show mean annual SST relative to modern SST \"F112#8A?= ,7A47AC7C 3<&\")'(*#$C:7B3=763C36;B@<3E76 ;> +;9& )4&/> C:7D%3D;B ?8 Fig. 3, we show radiative forcing due to CO \"F0 [Wm–2]) which is calculated from 2 CO2 our phytane-based pCO2 estimates in Fig. 2a, with additional considerations for land ;575:3>97\"F0.-#&\n3) The ‘global’ climate sensitivities for ECS and ESS (5.0 and 11.1 oC per CO2 doubling) seem to be on the high end. That is, not “consistent” (line 10) with many existing estimates. For example, your 5.0 oC ECS falls outside the 95% confidence window from the IPCC (1.5-4.5 oC). In addition, your estimates are based on surface temperatures of tropical oceans. The global ECS will be higher both because higher latitudes and land surfaces have higher ECS values. The newest IPCC report probably has the most up-to-date amplification factor between tropical oceans and the globe for the present-day Earth. Your ESS value (11 oC) is consistent with some of the other proxy-constrained estimates of ESS. But if memory serves me right, ESS values derived from global climate models tend to be lower—on the order of 50% higher than ECS for this general time period (papers by Valdes, Lunt, and others).\nWe have revised the ECS and ESS paragraphs for clarity. We agree and now explicitly state that, “Our estimate of the tropical ECS of 5.0°C suggests that the global ECS is just above the most recent IPCC estimates of 2.3 to 4.5°C per CO2-doubling (Sherwood et al., 2020) and consistent with the latest state-of-the-art models which suggest ca. 5.2°C (Forster et al., 2020).” Regarding ESS, there is no general range for ESS due to time-specific changes (e.g., land ice distribution, paleogeography). We have now expanded this section to include some examples from the Pliocene and Phanerozoic to illustrate this point.\n4) I have concerns with the regression approach in Figure 3. Now, absolutely, the major advantage of this approach is that it smooths out the uncertainty of individual data points, which is admittedly quite large. But one key drawback is that the time element stitching together your data has been completely lost. There is no way to tell if climate sensitivity has changed over the captured time interval. Moreover, using a regression can mask the true relationship between CO2 and temperature. It is possible, to give one example, for the slopes of all time-adjacent datapoints to be higher (or lower) than the regression slope. Or, half are steeper and half are shallower. These kinds of things would be really important to know. Yes, the uncertainty in each of the slopes connecting time-adjacent points is high, but in aggregate they should be very useful. A second advantage to analyzing time-adjacent data is that changes in other radiative forcings (for example, due to changes in paleogeography, the brightening of the sun, plant evolution, etc., etc.) should be small. This is not necessarily the case when you apply a single regression to an entire data set.\nI also have some concerns about the type of regression used, which I am assuming is an ordinary least-squares regression. This type of regression assumes that the x-dimension has no uncertainty. That is, one is minimizing the residuals only in the y-dimension. I wonder if a major-axis regression is more appropriate (or one of its cousins), which incorporates variance in both dimensions. In most cases, major axis regressions are steeper. I’ve looked into the literature a bit on this topic, and I didn’t see an easy answer. I’ll end by adding that this is yet another reason for avoiding the regression approach—if you instead analyze time-adjacent data points, there is only one way to draw a straight line between two points (!).\nWe had conducted a linear fit with errors in x and y (not ordinary least-squares regression), as described in the Supplement, which we now explicitly state in the main text line 225, Table 1 captions, and Fig. 3 captions.\nOther comments:\nLine 10: Need to say that these estimates are for tropical latitudes (not global).\nWe have added that these are for tropical latitudes.\nLines 28-30: “The most recent pCO2 compilation (7) suggests that pCO2 did indeed change over this 15-Myr period, but values are poorly constrained, i.e., ranging from 280 to 1460 A-;5-; -:3614/;35/#8736;$+2<:\"8(*&7=/9;2/8-:; %')>9 3: 674/:: .760<:361?@ ,/:\", this is true, but most of the variability is across proxy methods. In most cases, individual studies across the MCO show a compelling fall in CO2, in keeping with the patterns of the current study. It’s just that the absolute values vary across studies. Thus, in this sense, the current study doesn’t address/rectify the problem posed in the quotation. I would argue that the MCO is one of the best pre-Pleistocene examples we have where almost all of the CO2 data tell the same story (in terms of relative change).\nWe have revised this paragraph (Lines 28-39). First, we have added the most recent compilation (Rae et al., 2021) which shows a clearer decline in pCO2. Notably, even this most up-to-date version shows wide ranges in values among proxies and within a single proxy (i.e., >500 µatm difference) and some unrealistically low values (i.e., <120 µatm). Here, we also add that, “no single site covers the entirety of the past 15 Myr, which introduces concerns in stitching disparate sections together (e.g., regional and latitudinal influences)”; this is a major advantage of our study here, in which we show a continuous reconstruction over the full 15-myr timespan from a single site.\nLine 98: Citation to source #9 doesn’t seem right. May be good to double-check the numbering throughout.\nWe have checked/revised all references throughout the manuscript.\nLines 169-171: Are you talking about the MCO time slice?\nYes, we have now added “For the mid-Miocene Climate Optimum” at the start of the sentence (line 193).\nLine 179: I’m not sure why you assume SST is a good proxy for global temperature.\nWe have removed the phrase “global temperatures” for clarity. We do not assume SST is a good proxy for global temperature, which is why we latitudinal ESS and ECS instead of global ESS and ECS.\nLines 186-190: You should estimate CO2 assuming a fixed temperature, and then compare this to Figure 1. Then you will know, for your study, how important the confounding influence of temperature is. It’s not clear if reference 23 looked at the same ranges of temperature and CO2, etc.\nTo clarify, we have added “Phanerozoic” to this sentence. The influence of temperature on this proxy has been calculated in Witkowski et al. (2018, see Supplement) for the Phanerozoic, which includes a much larger range of CO2 and temperature. Temperature impacts every marine-based CO2 proxy, as it is a critical parameter to calculate Henry’s Law from dissolved CO2 to atmospheric CO2 concentrations.\nLine 223: And Royer (2016, Figure 1 and Table 1) as well.\nRoyer, D. L., 2016, Climate sensitivity in the geologic past: Annual Review of Earth and Planetary Sciences, v. 44, p. 277-293"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed many of my concerns, but some remain. I start with the two larger issues, followed by more minor comments.\n1) I do not agree—for two reasons—with the authors’ interpretation for a constant climate sensitivity (in title, abstract, and main text). First, their interpreted ECS and ESS values are for the tropics, which will be lower than the global-temperature-based estimates they are comparing against because half of the world’s surface area is not in the tropics, and one-third of the world’s surface area is on land. Both of these categories have higher regional climate sensitivities, meaning global-based climate sensitivities calculated from the authors’ data will be higher than their reported tropical estimate of 5.0 oC. IPCC AR6 reports an ECS of 2-5 oC at 90% confidence. After adjusting the authors’ estimate of 5 oC to account for the tropical bias, I wouldn’t say that this is only “slightly higher” (remembering that the IPCC considers 5 oC only 5% likely). For ESS, there is a large body of literature pointing to a ~6 oC ESS for the Pleistocene, which is the closest we can get to the notion of “present-day”. Is 11 oC “consistent” with 6 oC ???? I find it interesting that your estimates of ECS and ESS are higher than the present-day—indeed, it makes for an interesting story!\nSecond, the authors’ data analysis is not sufficient to test if climate sensitivity has remained constant “since the mid-Miocene” (to use the language from the Title). The authors estimate climate sensitivity by calculating the regression slope through a cross-plot of radiative forcing vs. temperature. This tells you the *average* climate sensitivity for the chosen interval (15 Ma to near-present-day). It is possible that the average climate sensitivity is close to the present-day value but that it fluctuated a lot within the chosen interval. These *temporal* associations are lost when relying on a single regression. What does Figure 3 look like when you include the temporal associations (in other words, add lines connecting the time-adjacent data points)? This will give you a lot more information about if climate sensitivity has indeed remained constant.\n2) The authors have not adequately addressed my question about combining the CO2 radiative forcing and temperature data in Figure 2 to make the cross-plots in Figure 3. To re-state: the ages of the CO2 and temperature data are not identical (at least not to my eye, looking at Figure 2). So, if this is true, how did you modify radiative forcing or temperature so that you could plot them directly against each other? If you simply took the temperature data closest in age to each single estimate of radiative forcing, this should be stated.\n---------\nLine 16: *concentration* of CO2 (ppmv) is a much more useful concept in this context than partial pressure (for example, during the Miocene the total pressure of the atmosphere at sea level may have been somewhat different). And, most CO2 proxies are sensitive to concentration, not partial pressure (and so in contrast to what is said in line 24).\nLine 25: This is a strawman argument. Foster et al. use a low-pass filter over a 400 million year long record, so it is not an apples-to-apples comparison to your record. If you look at the *raw* data in the Foster compilation (a better apples-to-apples comparison), there is a definite peak of ~600 ppm during the MCO. This peak is one of the best-resolved features of proxy-reconstructed Cenozoic CO2, present in multiple studies and multiple proxies. So it’s odd (and wrong) to imply that the existing CO2 record is weak here.\nLines 244-245: Sure, but you use the same regression approach of Martinez-Boti, so the best comparison is with that study.\nLines 257-259: It’s important to say here, in the main text, that your ECS is not the same as the ECS’s in the IPCC; most importantly, you don’t consider the potential for changes in other GHGs. You also don’t consider changes in paleogeography or solar luminosity (short-term wiggles or long-term secular increase).", "reviewer_2": "I appreciate the efforts of the authors to now include the Rae et al. 2021 CO2 compilation, but even if this manuscript had been submitted to another journal beforehand, when the manuscript was submitted to Nature Communications the Rae et al. 2021 paper was published, well known and should have been included in the original submission.\nIn the new introduction, lines 22-39, they include Rae et al. but still start from an argument of the Foster et al. compilation and the ‘problem’ of low middle Miocene estimated CO2 concentrations. In my view they still need to accept that the Foster compilation is superseded and that there’s no longer a significant middle Miocene problem. If they did this, I think they could make a better argument for the novelty of their approach and its importance – I think it’s more significant as a new method that can extend CO2 records further back in time than the alkenone CO2 proxy (and a study that tests the robustness of this method).\nI appreciate the work that has gone into the modern calibration and ground-truthing of this new CO2 proxy approach, and also the detailed organic geochemistry to carefully extract and analyse these GPBs from DPDP 467 sediments – including the desulfurization steps – that are a significant benchmark and reference for such analyses. With more of a refocus on the importance (a revised introduction – see comments above) and details of the new methods (see comments below), I think this could be considered for publication.\nGiven the novelty of these records, I would like to see more of the primary data in the main text. At present they go straight to a CO2 record in their first figure – it looks good and makes sense considering the long-term Neogene climate evolution, but the fact that it arrives “fully formed” as the first figure in the paper prompts questions as to the underlying assumptions and data – is it really as good as it looks (and I have serious concerns about the SST data – outlined below)? I would like to see the primary d13C of the biomarkers, the long-term planktic foraminiferal d13C compilation they use, the sea surface temperature records and the calculated Ep all plotted in the main text. Some of these are plotted in the SI but not all. All these data combine to generate the final CO2 record, and I would like to see the trends, magnitudes of change and variability in these records in order to understand the components that contribute to the final CO2 record. I would also like further justification of:\n- The use of the global compilation of planktic foraminiferal d13C – how was the uncertainty on this propagated through into CO2 estimates? How well is this expected to represent local conditions at the study site? How do California Margin seawater DIC d13C compare to the “global ocean” today?\n- SST records - these should be plotted in the main text. How sensitive is the final CO2 estimate to the choice of GDGT SST proxy? E.g. does the use of BAYSPAR substantially alter CO2 records? I would like to see a sensitivity test with CO2 curves plotted for at least the BAYSPAR SST estimations as well as the Kim et al. (2010) calibration of TEX86. Checking the data tables I’m also not happy with two things:\n1) The SST used for the CO2 estimations (S1) say that they are Uk37 based temperatures not TEX86 based temperatures. What is going on here? The calculations in the tables don’t match the methodologies outlined in the text?\n2) And further, the actual TEX86 data is not available for 10 samples at the base of the record because GDGT abundances are too low. Where is this mentioned in the methods? And what did they do without TEX (although as noted in point 1 – it actually looks like it's Uk37 that is being used). If one of the global records for SST from Uk37 is being used (I can’t see it being from the actual site – as there are no alkenones recorded in the older part of the section), then I am very concerned about circularity here – driving the CO2 proxy with a global SST signal and then comparing it back against that same SST curve and finding a strong correlation…. This may not be what they are doing, but I’d like a very good explanation of what is happening here, as the methods in the text don’t seem to match the data tables. As with the failure to present the Rae et al. 2021 compilation, the jump straight to a finished CO2 curve in the main text with none of the underlying data clearly shown in the main text, and then when the working is checked, to find it doesn’t match the stated methods in the paper, and further to discover substantial chunks of (GDGT SST) data missing, is very concerning.\n- The ‘b’ term. Although this is based on modern calibration studies, which look to be convergent on a similar number, I’m surprised at the “sensitivity” test of a “1% variability” in b which gives only a “1% change in CO2” (lines 152 -156). This 1% variability doesn’t even span the difference between the two figures they quote for b in the text (168 and 170 per mil kg µM-1). I think those estimating CO2 from the alkenone-based proxies would be over-joyed to know the ‘b’ term for the coccolithophores to within 1%. In the case of the alkenones it’s clear that estimates of the ‘b’ term in the modern ocean do not translate into an accurate representation of the b term on even glacial-interglacial timescales (e.g. see discussion in Rae et al. 2021; Badger et al. 2020; Zhang et al 2019). In the case of the coccolithophore alkenone-producers, this variation in the b term is within relatively few species and just through the late Pleistocene. So, I am sceptical that a 1% variation in the b term is an appropriate sensitivity test for general (unconstrained) phytoplankton biomarkers on significantly longer (~15 Ma) timescales. Not only could there be significant evolutionary adaptation to long-term declining CO2 concentrations (as seen in the coccolithophores – see Bolton, Stoll and many other papers), in the single site there are likely to be significant changes in dominant phytoplankton community, growth rates and size distributions. I do appreciate the efforts to test community change through the cross-comparison of the d13C data with biomarker indications for upwelling, but I don’t agree with the assumption that the b term is well-constrained (<1% variation!) in this application or that it is stable on long-timescales.\nParagraph from Line 191 onwards – sounds like confirmation bias. That you get the answer for CO2 from a proxy that fits with what the models need to reach Miocene temperatures is not the way to reassure yourselves that you have a robust CO2 proxy – or that the models are robust. Keeping clear blue water between these two is important to provide confidence that they are independent, and we are properly testing model behaviour with the proxy data. Getting the ‘right answer’ is no guarantee that one’s methods are sound, or, in this case, that one has actually arrived at the correct answer.\nLine 213 – 215; why don’t they do the sensitivity test with at least two common approaches to GDGT-based SSTs? This would give at least some representation of what (minimum) SST uncertainty in their records might translate to in terms of CO2 rather than relying on other studies. I also don’t understand why they don’t plot their own SST record from this site as well as the existing alkenone compilations? – the local SST records may not be the most helpful for the Fig 2 comparison of CO2 against global SST records, but the fact that the data is not plotted anywhere is not helpful. See also the comments above about which SST they actually have from the site, and which data they use to calculate CO2.\nFinally, if I were Reviewer 1, I would not be satisfied that the revised manuscript has properly engaged with their comments and concerns on several fronts. One aspect that overlaps with my comments above is the discussion and presentation of the 'b' term. As reviewer 1 notes, we seem to be coming \"full circle\" and somehow finding that phytane CO2 records are better constrained than the alkenone CO2 records. I agree that they might be more widely applicable, but like Reviewer 1 I'm sceptical that d13C fractionation factors from biomarkers that are more poorly constrained in terms of their biological source and growth environment can do better than ones that are well-constrained."}, "author_response": "Reviewer#1\nTheauthorshaveaddressedmanyofmyconcerns,butsomeremain.Istartbymoreminorcomments.\nNXg[Ta^g[XeXi\\XjXeYbeg[XTWW\\g\baT_Vb``Xagf(j[\\V[jXTWWeXffUX_bj*\n1)Idonotagree—fortworeasons—withtheauthors’interpretationforaconstantclimatesensitivity(intitle,abstract,andmaintext).First,theirinterpretedECSandESSvaluesareforthetropics,whichwillbelowerthantheglobal-temperature-basedestimatestheyarecomparingagainstbecausehalfoftheworld’ssurfaceareaisnotinthetropics,andone-thirdoftheworld’ssurfaceareaisonland.Bothofthesecategorieshavehigherregionalclimatesensitivities,meaningglobal-basedclimatesensitivitiescalculatedfromtheauthors’datawillbehigherthantheirreportedtropicalestimateof5.0°C.IPCCAR6reportsanECSof2-5°Cat90%confidence.Afteradjustingtheauthors’estimateof5°Ctoaccountforthetropicalbias,Iwouldn’tsaythatthisisonly“slightlyhigher”(rememberingthattheIPCCconsiders5°Conly5%likely).ForESS,thereisalargebodyofliteraturepointingtoa~6°CESSforthePleistocene,whichistheclosestwecangettothenotionof“present-day”.Is11°C“consistent”with6°C????IfinditinterestingthatyourestimatesofECSandESSarehigherthanthepresent-day—indeed,itmakesforaninterestingstory!\nNXTZeXXj\\g[g[XeXi\\XjXe*NXeX`biXWtVbafgTagV_\\`TgXfXaf\\g\\i\\gluYeb`g[X`TahfVe\\cg(\\aV_hW\\aZg[Xg\\g_X(TUfgeTVg(TaW`T\\agXkg*\nNXV[TaZXWbhec[eTf\\aZ gbtf\\Za\\Y\\VTag_l[\\Z[Xeg[Tag[X@G::su%C\\aX-0)-4&TaWt`hV[[\\Z[Xeg[Tag[X`bfgeXVXag@G::su%C\\aX.45&*\nNXeXVT_Vh_TgXWbhe<JJTaW<:JgbVbaf\\WXeT`beXZ_bUT_iT_hX%C\\aXf-0)-4(.24).3,(TaW.43).44&(j[XeXjXjX\\Z[XTV[fXaf\\g\\i\\glUlg[XcXeVXag)TeXTYbeg[X<Teg[6gebc\\Vf%/,oE)/,oJ(1,*,$&(`\\W)_Tg\\ghWXf%/,)2,o(/2*2$&(TaW[\\Z[_Tg\\ghWXf%2,)5,o(-/*0$&*\n=beXkT`c_X(g[XTUfgeTVgeXi\\f\bafeXTW Tf6tNXVT_Vh_TgXTaTiXeTZX<Teg[flfgX`fXaf\\g\\i\\glTaWTiXeTZXXdh\\_\\Ue\\h`V_\\`TgXfXaf\\g\\i\\glbiXe-1Dlehf\\aZbheaXjeTaZXbYp:F iT_hXf%jX\\Z[XWUl.cXeVXag)TeXTYbeg[X<Teg[&(eXfh_g\\aZ\\a-/*5TaW3*.o:cXeWbhU_\\aZbYp:F (eXfcXVg\\iX_l*K[XfX.iT_hXfTeXVbaf\\fgXagj\\g[bg[Xecebkl)UTfXW Xfg\\`TgXf(Vbaf\\fgXagbe[\\Z[Xeg[Tafb`XeXVXagfgTgX)bY)g[X)TegV_\\`TgX`bWX_f(TaWf\\Za\\Y\\VTag_l[\\Z[Xeg[Ta@G::Z_bUT_jTe`\\aZXfg\\`Tg\baf*u\nSecond,theauthors’dataanalysisis notsufficienttotestifclimatesensitivity hasremainedconstant“since themid-Miocene”(tousethelanguagefromtheTitle).Theauthorsestimateclimatesensitivitybycalculatingtheregressionslopethroughacross-plotofradiativeforcingvs.temperature.Thistellsyouthe*average*climatesensitivityforthechoseninterval(15Matonear-present-day).Itispossiblethattheaverageclimatesensitivityisclosetothepresent-dayvaluebutthatitfluctuatedalotwithinthechoseninterval.These*temporal*associationsarelostwhenrelyingonasingleregression.WhatdoesFigure3looklikewhenyouincludethetemporalassociations(inotherwords,addlinesconnectingthetime-adjacentdatapoints)?Thiswillgiveyoualotmoreinformationaboutifclimatesensitivityhasindeedremainedconstant.\nNXTZeXXj\\g[g[XeXi\\XjXeg[Tgg[\\ff[bjfg[XTiXeTZXV_\\`TgXfXaf\\g\\i\\gl*@ag[XTUfgeTVg(jXV_Te\\Ylg[TgtNXVT_Vh_TgXTaTiXeTZX<Teg[flfgX`fXaf\\g\\i\\glTaWTiXeTZXXdh\\_\\Ue\\h`V_\\`TgXfXaf\\g\\i\\glbiXe-1Dlesu\nNX[TiXTWWXWC\\aXf.01).1,gbTWWeXffg[XgX`cbeT_TffbV\\Tg\baf(tNXVT_Vh_TgXTaTiXeTZXfXaf\\g\\i\\glbiXe-1DleTaWTeTaZXbYp:F iT_hXf*8aliTe\\Tg\bafbiXeg\\`XVbh_WVb`XYeb`fhUfXgf.bYg[XfXcb\\agfUhgZ\\iXag[Tgg[XeXTeXT_\\`\\gXWah`UXe%\\*X*(/,&bYW\\YYXeXag%haXdhT__lfcTVXW&g\\`XiT_hXf(fhUW\\i\\f\bafTaWaXjeXZeXff\bafj\\g[T__haVXegT\\ag\\Xfjbh_W`bfg_\\^X_lZ\\iXaba)f\\Za\\Y\\VTagY\\gf*K[XgX`cbeT_WXcXaWXaVXbYV_\\`TgXfXaf\\g\\i\\glVTaba_lUXWXgXe`\\aXWj\\g[[\\Z[XegX`cbeT_eXfb_hg\baeXVbeWfYbefcXV\\Y\\Vg\\`X\\agXeiT_f*u\n2)TheauthorshavenotadequatelyaddressedmyquestionaboutcombiningtheCO2radiativeforcingandtemperaturedatainFigure2tomakethecross-plots inFigure3.Tore-state:theagesoftheCO2andtemperaturedataarenotidentical(atleastnottomyeye,lookingatFigure2).So,ifthisistrue,howdidyoumodifyradiativeforcingortemperaturesothatyoucouldplotthemdirectlyagainsteachother?Ifyousimplytookthetemperaturedataclosestinagetoeachsingleestimateofradiativeforcing,thisshouldbestated.\nNXTcb_bZ\\mXYbeabgTWXdhTgX_lTWWeXff\\aZlbhedhXfg\ba*NX[TiXeXi\\fXWC\\aX.10).11(t€JJK\\fUTfXWbag[XLBp cebklYbeJJK(j[\\V[jXeXVb`c\\_XW5Ul_Tg\\ghWXTaW[X`\\fc[XeX\\agb,*-.1DTU\\af/3%=\\Z*/U&TaW_\\aXTe_l\\agXecb_TgXWYbeg[XTZXbYbhefXW\\`Xagf%=\\Z*0&*u\nLine16:*concentration*ofCO2(ppmv)isamuchmoreusefulconceptinthiscontextthanpartialpressure(forexample,duringtheMiocenethetotalpressureoftheatmosphereatsealevelmayhavebeensomewhatdifferent).And,mostCO2proxiesaresensitivetoconcentration,notpartialpressure(andsoincontrastto whatissaidinline24).\nNX[TiXeXc_TVXWT__rTg`j\\g[cc`iTaWeXi\\fXW C\\aX..tTg`bfc[Xe\\VVbaVXageTg\babYVTeUbaW\bk\\WXsu\nLine25:Thisisastrawmanargument.Fosteretal.usealow-passfilter overa400millionyearlongrecord,soitisnotanapples-to-applescomparisontoyourrecord.Ifyoulookatthe*raw*dataintheFostercompilation(abetterapples-to-applescomparison),thereisadefinitepeak of~600ppmduringtheMCO.Thispeakisoneofthebest-resolvedfeaturesofproxy-reconstructedCenozoicCO2,presentinmultiple studiesandmultipleproxies.Soit’sodd(andwrong)toimplythattheexistingCO2recordisweakhere.\nNXTZeXXj\\g[g[XeXi\\XjXe*NXjXeXabgeXYXee\\aZgbg[XD:F(Uhggbg[XXag\\eXglbYg[XcTfg-1`\\__\ba lXTef%\\*X*(-1DTg[ebhZ[gb,DT&*K[XeTjWTgT\\a =bfgXeXgT_*%.,-3&WbXf\\aWXXWf[bjWTgTfcTefX\\agXeiT_f%X*Z*(-.)4DT&*IXZTeW_Xff(jX[TiXeX`biXW=bfgXeXgT_*%.,-3&Yeb`g[X@agebWhVg\baZ\\iXa g[Tg\\g[TfUXXa fhcXefXWXWUlITXXgT_*%.,.-&%fXXVb``XagfIXi\\XjXe#.&*\nLines244-245:Sure,butyouusethesameregressionapproachofMartinez-Boti,sothebestcomparisoniswiththatstudy.\nPXf(g[XeXYbeX\\g\\fXiXa `beXfhece\\f\\aZg[TgjXY\\aW[\\Z[XeiT_hXfYbe<JJ(j[XaVbaf\\WXe\\aZaXTeUl g\\`X\\agXeiT_fTfIblXe%.,-2&*KbV_Te\\Yl(jX[TiXTWWXW%C\\aX.31&(tXiXag[bhZ[jXhfXTf\\`\\_Te TccebTV[%_baZ)g\\`XTiXeTZX&TfeXY*12*u\nLines257-259:It’simportanttosay here,inthemaintext,thatyour ECSisnotthesameastheECS’sintheIPCC;mostimportantly,youdon’tconsiderthepotentialforchangesinotherGHGs.Youalsodon’tconsiderchanges inpaleogeographyorsolarluminosity(short-termwigglesor long-termsecularincrease).\nNXTZeXX*NX[TiXTWWXWC\\aX.5.).53(t@gf[bh_W UXabgXWg[Tgbhe<:J\\fabgg[XfT`XTfg[X<:J hfXWUlg[X@G::(Z\\iXag[Tg\\geXceXfXagffcXV\\Y\\VV_\\`TgXfXaf\\g\\i\\glJ %\\*X*(<JJVbeeXVgXWYbe R:F.(C@S cbgXag\\T_f_bj_TaW\\VXYXXWUTV^&TaWWbXfabgVbaf\\WXeV[TaZXf\\abg[XeZeXXa[bhfXZTfXf%X*Z*( `Xg[TaX&(cT_XbZXbZeTc[l(abefb_Te_h`\\abf\\gl7jXTeXVheeXag_lhaTU_XgbVbaWhVgg[XfXTWW\\g\baT_ `Xg[TaXTaWjTgXejbh_WUe\\aZWbja<:J(j[\\V[_\\^X_l Xkc_T\\afj[lcT_Xb<:J\\fZXaXeT__l[\\Z[Xeg[Ta `bWXea`bWX_f*u\nReviewer#2\nIappreciatetheeffortsoftheauthorstonowincludetheRaeetal.2021CO2compilation,butevenifthismanuscripthadbeensubmittedtoanotherjournalbeforehand,whenthemanuscriptwassubmittedtoNature CommunicationstheRaeetal.2021paper waspublished,wellknownandshouldhavebeenincludedintheoriginalsubmission.\nNXTZeXXj\\g[g[XeXi\\XjXe*8fXkc_T\\aXWceXi\bhf_l(bhe`TahfVe\\cgVT`XUTV^Yeb`TiXel_baZ eXi\\XjTaWhaYbeghaTgX_ljTfW\\eXVg_lWXV_\\aXW*8gg[Tgg\\`X(Tfg[XVbeeXfcbaW\\aZThg[bejTf`bi\\aZ Yeb`[XeG[;cbf\\g\ba\\ag[XEXg[Xe_TaWfgbTcbfgWbVcbf\\g\ba\\ag[XLB(\\gjTfWXV\\WXWgbba_l`T^X fb`X`\\abe`bW\\Y\\VTg\bafTaWgbeXfhU`\\gdh\\V^_l(\\ai\\XjbYg[Xg\\`X_\\aXffbYg[X`TahfVe\\cg*\nInthenewintroduction,lines22-39,theyincludeRaeetal.butstillstartfromanargumentoftheFosteretal.compilationandthe‘problem’oflowmiddleMioceneestimatedCO2concentrations.InmyviewtheystillneedtoacceptthattheFostercompilationissupersededandthatthere’snolongerasignificantmiddleMiocene problem.Iftheydidthis,Ithinktheycouldmakeabetterargumentforthenoveltyoftheirapproachanditsimportance–Ithinkit’smoresignificantasanewmethodthatcanextendCO2recordsfurtherbackintime thanthealkenoneCO2proxy(andastudythatteststherobustnessofthismethod).Iappreciatetheworkthat hasgoneintothemoderncalibrationandground-truthingofthisnewCO2proxyapproach,andalsothe detailedorganicgeochemistrytocarefullyextractandanalysetheseGPBsfromDPDP467sediments– includingthedesulfurizationsteps–thatareasignificantbenchmarkandreferenceforsuchanalyses.With moreofarefocusontheimportance(arevisedintroduction–seecommentsabove)anddetailsofthenew methods(seecommentsbelow),Ithinkthiscouldbeconsideredfor publication.\nNX[TiXeX`biXWg[X=bfgXeXg T_*Vb`c\\_Tg\baYeb`g[X`TahfVe\\cg*NX[TiXT_fb`TWXTabgXbYg[X XiXa`beXeXVXagp:F Vb`c\\_Tg\ba\\aJV\\XaVX%C\\aX/3)/5&(tg[Xp:F Vb`c\\_Tg\baUlITXXgT_*3. .j[\\V[cebi\\WXfeXi\\fXWeXi\\f\bafgbg[XUbeba)UTfXWTaWT_^XabaX)UTfXWp:F cebk\\Xf(Vbaf\\WXeXW.g[X`bfgebUhfgcebk\\XfT`baZg[X:Xabmb\\Vp:F Vb`c\\_Tg\ba4*u.KbYb__bjg[XeXi\\XjXewffhZZXfg\baeXZTeW\\aZg[Xcebk\\Xf(jX[TiXV[TaZXWg[Xg\\g_Xgbt:bag\\ahbhf fgXeTaXTaWc[lgTaX(cid:127)-/:eXVbeWeXiXT_fTfhUfgTag\\T_p:F WXV_\\aXf\\aVXg[X`\\W)D\bVXaXu*@ag[X.TUfgeTVg(jXTWWXWC\\aX5)-,ts[XeXhf\\aZg[XY\\efg ZXaXeT_c[lgbc_Ta^gbaU\b`Te^Xef%c[lgTaXTaW fgXeTaXf&Tcc_\\XWgbg[XD\bVXaXu TaW_TgXeC\\aX-,,)-,-t=heg[Xe`beX(>G9f[TiXg[XcbgXag\\T_gb fcTag[XG[TaXebmb\\V(j[XeXTfT_^XabaXfTeX_\\`\\gXWgbg[X:Xabmb\\V(j[\\V[jbh_WXkgXaW† )UTfXW c c:F cebk\\XfUlaXTe_lgXa)Yb_W*uNX[TiXT_fb TWWXWg[XeTjWTgTYbeg[XVT_Vh_Tg\baf\\agbg[X`T\\a.gXkgTaWY\\ZheXf*\nGiventhenoveltyoftheserecords,Iwouldliketoseemoreoftheprimarydatainthemaintext.Atpresent theygostraighttoaCO2recordintheirfirstfigure–itlooksgoodandmakessenseconsideringthelong-term Neogeneclimateevolution,butthefactthatitarrives“fullyformed”asthefirstfigureinthepaperprompts questionsastotheunderlyingassumptionsanddata–isitreallyasgoodasit looks(andIhaveserious concernsabouttheSST data–outlinedbelow)?Iwouldliketoseetheprimaryd13Cofthebiomarkers,the long-termplankticforaminiferald13C compilationthey use,theseasurfacetemperaturerecordsandthe calculatedEpallplottedinthemaintext.SomeoftheseareplottedintheSIbutnotall.Allthesedata combinetogeneratethefinalCO2record,andIwouldliketoseethetrends,magnitudesofchangeand variabilityintheserecordsinordertounderstandthecomponentsthatcontributetothefinalCO2record.\nNX[TiXTWWXWTaXjY\\ZheX\\ag[X`T\\agXkg%=\\Z*-&gb\\aV_hWXT__WTgThfXWTaWeXYXe\\ag[X`T\\agXkg [bjjXTee\\iXYeb`g[XfXWTgTeXVbeWgbg[XeXVbafgehVgXWp:F ceXfXagXW\\a=\\Z*.%ceXi\bhf_l=\\Z*-&* .Iwouldalsolikefurtherjustificationof:\n-Theuseoftheglobalcompilationofplankticforaminiferald13C–howwastheuncertaintyonthis propagatedthroughintoCO2estimates?Howwellisthisexpectedtorepresentlocalconditionsatthestudy site?HowdoCaliforniaMarginseawaterDICd13Ccomparetothe“globalocean”today?\nGeXYXeXag\\T__l((cid:127)-/:Ybe;@:jbh_WUXWXe\\iXWYeb`c_Ta^g\\VYbeT`\\a\\YXeTYeb`g[XfT`Xf\\gXbeaXTeUl f\\gX7[bjXiXe(g[\\fjTfabgcbff\\U_XZ\\iXag[XWXcbf\\g\baT_VbaW\\g\bafTgg[\\ff\\gX%j[\\V[jXeXiXel YTibeTU_XYbebeZTa\\V`TggXeUhgabgYbeVTeUbaTgXceXfXeiTg\ba&*K[TgfT\\W(`bWXeaiT_hXfTgg[Xf\\gX V_bfX_leXY_XVgg[XZ_bUT_TiXeTZX%fXX`TcUX_bj&*\nK[XhaVXegT\\agl\\aXTV[\\aW\\i\\WhT_cTeT`XgXej\\g[\\aT__bYg[XXdhTg\bafjTfVbaf\\WXeXW\\aDbagX :Te_bf\\`h_Tg\bafgbcebcTZTgXTW\\fge\\Uhg\babYhaVXegT\\aglbaXTV[p:F Xfg\\`TgX*K[XWXgT\\_fbYg[\\f .`Xg[bWb_bZl%T_baZj\\g[g[XGlg[baVbWX&TeXTiT\\_TU_X\\aN\\g^bjf^\\Xg T_*%.,-4&* K[XhaVXegT\\aglbY ,*0cXe`\\_Ybeg[X(cid:127)-/:bY c_Ta^g\\VYbeT`\\a\\YXeTjTfUTfXWbaTaT_lg\\VT_\\afgeh`XagTg\baT_XeebebY,*. cXe`\\_j[\\V[jTfWbhU_XWYbeg[XVbaf\\WXeTg\babYg[X_bVTg\ba)UTfXWhaVXegT\\agl7g[\\f,*0cXe`\\_jTf g[XacebcTZTgXWg[ebhZ[bhgg[XDbagX:Te_behaXdhTg\baf*\n-SSTrecords-theseshouldbeplottedinthemaintext.HowsensitiveisthefinalCO2estimatetothechoice ofGDGTSSTproxy?E.g.doestheuseofBAYSPARsubstantiallyalterCO2records?Iwouldliketoseea sensitivitytestwithCO2curvesplottedfor atleasttheBAYSPARSSTestimationsaswellastheKimetal. (2010)calibrationofTEX86.\nK[XJJKeXVbeWfTeXabjc_bggXW\\ag[X`T\\agXkg*\nNX\\aV_hWXT'+)0o:haVXegT\\aglbaJJKg[ebhZ[bhgg[XXdhTg\baf%fXXceXi\bhfVb``Xagba(cid:127)-/:bY ;@:&*p:F fXaf\\g\\i\\glgXfgfgbgX`cXeTgheXTeXWXfVe\\UXW\\aWXgT\\_\\ag[XJhcc_X`XagbYN\\g^bjf^\\Xg .T_*%.,-4&*K[\\fgX`cXeTgheXVb`cbaXag[TfUXXaTYhaWT`XagT_cTegbYg[XYeTVg\baTg\ba)UTfXWp:F .cebklf\\aVX\\gf\\aVXcg\ba\\ag[X_TgX-54,f(`bfg\\`cbegTag_l\\aVbaiXeg\\aZW\\ffb_iXW:F \\agb .RTdS Tg`bfc[Xe\\Vp:F i\\T?XaelwfCTj* .NX[TiXabg\\aV_hWXW98PJG8IVT_Vh_Tg\bafUXVThfXg[XhaVXegT\\aglW\\YYXeXaVXUXgjXXag[Xgjb >;>K`Xg[bWb_bZ\\Xfj\\__YT__jX__j\\g[\\ag[\\f'+)0o:haVXegT\\agl*\nCheckingthedatatablesI’malsonothappywithtwothings:\n1)TheSSTusedfortheCO2estimations(S1)saythattheyareUk37basedtemperaturesnotTEX86based temperatures.Whatisgoingonhere?Thecalculationsinthetablesdon’tmatchthemethodologiesoutlinedin thetext?\nK[Ta^lbhYbecb\\ag\\aZbhgg[\\f_TUX__\\aZ`\\fgT^X\\ag[XJhcc_X`XagT_KTU_XWXfVe\\cg\baf*NXW\\W \\aWXXWhfXK<O JJKfYbeeXVbafgehVg\\aZp:F (TfWXfVe\\UXW\\ag[X`TahfVe\\cgbaC\\aX-0-)-006tJXT 42 .fheYTVXgX`cXeTgheXf%JJK&jXeXVT_Vh_Tg\\Whf\\aZg[XK<O cebklUTfXWbag[XeTg\bbYVlV_bcXagTaX 42 e\\aZf\\aZ_lVXeb_W\\U\\c[lgTal_Z_lVXeb_gXgeTXg[Xef%>;>Kf&\\ag[XfT`XfXW\\`XagfTfbhe>G9f%=\\Z* -V7KTU_XJ4&TaWTff\\ZaXWTahaVXegT\\aglbYq0o:J;VThfXWUlcbgXag\\T_VT_\\UeTg\baXeebef*u\nNXhfXLBp )UTfXWZ_bUT_JJKVb`c\\_Tg\baYbeV_\\`TgXfXaf\\g\\i\\gl(C\\aX.11).126t€JJK\\fUTfXWba /3 g[XLBp cebklYbeJJK(j[\\V[jXeXVb`c\\_XW5Ul_Tg\\ghWXTaW[X`\\fc[XeX\\agb,*-.1DTU\\af5%=\\Z*/9& /3 TaW_\\aXTe_l\\agXecb_TgXWYbeg[XTZXbYbhefXW\\`Xagf%=\\Z*0&*u\n2)Andfurther,theactualTEX86dataisnotavailablefor10samplesatthebaseoftherecordbecauseGDGT abundancesaretoolow.Whereisthismentionedinthemethods?AndwhatdidtheydowithoutTEX (althoughasnotedinpoint1–itactuallylookslikeit'sUk37thatisbeingused).Ifoneoftheglobalrecordsfor SSTfromUk37isbeingused(Ican’tseeitbeingfromtheactualsite–astherearenoalkenonesrecordedin theolderpartofthesection),thenIamveryconcernedaboutcircularityhere–drivingtheCO2proxywitha globalSSTsignalandthencomparingitbackagainstthatsameSSTcurveandfindingastrongcorrelation…. Thismaynotbewhatthey aredoing,butI’dlikeaverygoodexplanationofwhatishappeninghere,asthe methodsinthetextdon’tseemtomatchthedatatables.AswiththefailuretopresenttheRaeetal.2021 compilation,thejumpstraighttoafinishedCO2curveinthemaintextwithnoneoftheunderlyingdata clearlyshowninthemaintext,andthenwhentheworkingischecked,tofinditdoesn’tmatchthestated methodsinthepaper,andfurthertodiscoversubstantialchunksof(GDGTSST)datamissing,isvery concerning.\nNXhfXf\\gX)fcXV\\Y\\VK<O Ybeg[Xp:F eXVbafgehVg\bafTaWchU_\\f[XW LBp eXVbeWfYeb`TiTe\\XglbY 42 . /3 Z_bUT__lW\\fcXefXf\\gXfYbehaWXefgTaW\\aZV_\\`TgXfXaf\\g\\i\\gl(\\aWXXWgb^XXcg[XfXgjbfXcTeTgX%fXX ceXi\bhfeXfcbafX&*\nNX[TiXV_Te\\Y\\XWg[\\fK<O \\ffhXbaC\\aX355)4,1(t9XVThfXfXiXeT_`\\abe\\fb>;>KjXeXUX_bjg[X 42 WXgXVg\ba_XiX_\\ag[XWXXcXfgcTegbYg[XfghW\\XWfXVg\ba(\\gjTfabgcbff\\U_XgbbUgT\\aK<O iT_hXf* 42 KbTVVb``bWTgXYbeg[\\f(jXVb`cTeXWg[XbiXeT__eXVbeWfYeb`J\\gX023gbg[XK<O iT_hXfTgJ\\gX 2,4Tgg[XfT`X_Tg\\ghWX( TfjX__TfLBp iT_hXfYeb`g[XaXTeUlJ\\gX-,-,%W\\eXVg_lfbhg[bY;J;GJ\\gX /3 023&TaWJ\\gX-,.-%W\\eXVg_labeg[bY;J;G023&7T__ Ybhef\\gXf[TiXaXTe)\\WXag\\VT_JJK iT_hXf g[ebhZ[bhgg[XcTfg-1Dle(fbjXhfXg[XfXbg[Xef\\gXfgb_\\aXTe_lXkgeTcb_TgXg[XfXiXeT_`\\ff\\aZ JJKfTgJ\\gX023*u\n@ag[XY\\ZheXUX_bj(jXf[bj;J;G023cebkl)WXe\\iXWgX`cXeTgheXf\\abeTaZX%L w/3_\\Z[gbeTaZX( B K<O `\\WbeTaZX(K<O XkgeTcb_TgXWWTe^beTaZX&(T_baZf\\WXg[XfT`X_Tg\\ghWXK<O J\\gX2,4 42 42 42 %chec_X&TaWYbeYheg[XeVb`cTe\\fba(g[XaXTeUlJ\\gX-,-,%ZeXl&TaWJ\\gX-,.-%U_hX&* \n-The‘b’term.Althoughthisisbasedonmoderncalibrationstudies,whichlooktobeconvergentonasimilar number,I’msurprisedatthe“sensitivity”testofa“1%variability”inbwhichgivesonlya“1%changeinCO2” (lines152-156).This1% variabilitydoesn’tevenspanthedifferencebetweenthetwofigurestheyquoteforb inthetext(168and170permilkgµM-1).IthinkthoseestimatingCO2fromthealkenone-basedproxies wouldbeover-joyedtoknowthe‘b’termforthecoccolithophorestowithin1%.Inthecaseofthealkenones it’sclearthatestimatesofthe‘b’terminthemodernoceandonottranslateintoanaccuraterepresentationof thebtermonevenglacial-interglacialtimescales(e.g.seediscussioninRaeetal.2021;Badgeretal.2020; Zhangetal2019).Inthecaseofthecoccolithophorealkenone-producers,thisvariationinthebtermiswithin relativelyfewspeciesandjustthroughthelatePleistocene.So,Iamscepticalthata1%variationinthebterm isanappropriatesensitivitytestforgeneral(unconstrained)phytoplanktonbiomarkersonsignificantlylonger (~15Ma)timescales.Notonlycouldtherebesignificantevolutionaryadaptationtolong-termdecliningCO2 concentrations(asseeninthecoccolithophores–seeBolton,Stollandmanyotherpapers),inthesinglesite therearelikelytobesignificantchangesindominantphytoplanktoncommunity,growthratesandsize distributions.Idoappreciatetheeffortstotestcommunitychangethroughthecross-comparisonofthed13C datawithbiomarkerindicationsforupwelling,butIdon’tagreewiththeassumptionthatthebtermiswell- constrained(<1%variation!)inthisapplicationorthatitisstableonlong-timescales.\nNXjbh_W_\\^XgbfgeXffg[TgjXWbabghfXTeTaZXbY-$TfbhehaVXegT\\aglYbeg[XvUwgXe`*@aWXXW(Tf fgTgXWUlg[XeXi\\XjXe(g[Tgjbh_WUXYTegbbf`T__* NXXkc_\\V\\g_lhfXg[XeTaZX-24q0/J;y^ZrD)-( j[\\V[eXY_XVgfTn.1$J;haVXegT\\agl*K[X_\\aXftJXaf\\g\\i\\glgXfgfWX`bafgeTgXg[Tg-$V[TaZX\\ab eXfh_gf\\aba_lT-$V[TaZX\\ap:F Xfg\\`Tg\bau`XTafg[Tg\\Yg[XTiXeTZXYbetUuYbeg[XfXceXfXeiXW .T_ZTXjTf-$W\\YYXeXagYeb`g[XTiXeTZX-24y^ZrD)-(\\gjbh_WV[TaZXg[Xp:F Xfg\\`TgXUl-$7\\Y\\g .jTf-,$W\\YYXeXag(\\gjbh_WV[TaZXg[Xp:F Xfg\\`TgXUl-,$7\\Y\\gjTfW\\YYXeXagUl0,$g[Xa\\gjbh_W .V[TaZX:F.Ul0,$7TaWfbba*\nNXeXYXegbg[XtUuYTVgbeTftbaXbYg[X`beX)W\\YY\\Vh_g)gb)VbafgeT\\acTeT`XgXef\\abheVT_Vh_Tg\bau %C\\aX-3.&TaWjbh_W_\\^XgbX`c[Tf\\mXg[TgjXTeX baXbYg[XYXjeXfXTeV[Zebhcfj[bTVghT__l \\aV_hWXTtUueTaZX\\ag[X\\ehaVXegT\\aglTaT_lfXf(_XgT_baX\\aV_hWXTtUuiT_hXhaVXegT\\aglj\\g[fhV[ _TeZXeTaZXf*?XaVX(jXYXX_g[TgjX[TiXTWWeXffXWg[XhaVXegT\\agl\\ag[XtUuYTVgbeTfjX__TfjX VTa*\nParagraphfromLine191onwards–soundslikeconfirmationbias.ThatyougettheanswerforCO2froma proxythatfitswithwhatthemodelsneedtoreachMiocenetemperatures isnotthewaytoreassure yourselvesthatyouhavea robustCO2proxy–orthatthemodelsarerobust.Keepingclearbluewater betweenthesetwoisimportanttoprovideconfidencethattheyareindependent,andweareproperlytesting modelbehaviourwiththeproxydata.Gettingthe‘rightanswer’isnoguaranteethatone’smethodsaresound, or,inthiscase,thatonehasactuallyarrivedatthecorrectanswer.\nNXTZeXXj\\g[g[XeXi\\XjXe*KbV_Te\\Ylg[\\f(jX[TiXTWWXWC\\aX.,-(tgbchgbheeXfh_gf\\agbVbagXkgbY g[X_\\gXeTgheXsuTaWC\\aX.-/(ts\\g\\fabgTU_Xg[Tgbhe>G9)UTfXWp:F Xfg\\`TgXfTeXVbaf\\fgXagj\\g[ .g[Xp:F eXdh\\eXWUlg[X`T]be\\glbYV_\\`TgX`bWX_fsu .Line213–215;whydon’ttheydothesensitivitytestwithatleasttwocommonapproachestoGDGT-based SSTs?Thiswouldgiveatleastsomerepresentationofwhat(minimum) SST uncertaintyintheirrecordsmight translatetointermsofCO2ratherthanrelyingonotherstudies.Ialsodon’tunderstandwhytheydon’tplot theirownSSTrecordfromthissiteaswellastheexistingalkenonecompilations?–thelocalSSTrecords maynotbethemosthelpfulfortheFig2comparisonofCO2againstglobalSSTrecords,butthefactthatthe dataisnotplottedanywhereisnothelpful.SeealsothecommentsaboveaboutwhichSSTtheyactuallyhave fromthesite,andwhichdatatheyusetocalculateCO2.\nK[XJJKWTgT\\fc_bggXW\\a g[XaXj_lTWWXW=\\Z*-*NXTeXVheeXag_lhf\\aZJJKhaVXegT\\aglg[Tg\\f`hV[ _TeZXeg[Tag[XhaVXegT\\aglg[Tgjbh_WUXWXe\\iXWYeb`g[XfhZZXfgXWhaVXegT\\aglgXfg%fXXceXi\bhf eXfcbafX&*NXWbabghfXg[XLBp eXVbeWfTgg[\\ff\\gX\\abeWXegbceXiXagV\\eVh_Te\\gl(Z\\iXag[TgjXTeX /3 T_fbVT_Vh_Tg\\aZZ_bUT_V_\\`TgXfXaf\\g\\i\\gl\\ag[\\f`TahfVe\\cg%fXXceXi\bhfeXfcbafX&*\nFinally,ifIwereReviewer1,Iwouldnotbesatisfiedthattherevisedmanuscripthasproperlyengagedwith theircommentsandconcernsonseveralfronts.Oneaspectthatoverlapswithmycommentsaboveisthe discussionandpresentationofthe'b'term.Asreviewer1notes,weseemtobecoming\"fullcircle\"and somehowfindingthatphytaneCO2recordsarebetterconstrainedthanthealkenoneCO2records.Iagree thattheymightbemorewidelyapplicable,butlikeReviewer1I'mscepticalthatd13Cfractionationfactors frombiomarkersthataremorepoorlyconstrainedintermsoftheirbiologicalsourceandgrowthenvironment candobetterthanonesthatarewell-constrained.\nNXg[Ta^g[XeXi\\XjXeYbeg[\\feX`Te^UhgIXi\\XjXe-fgTgXf\\ag[X\\eeXi\\XjeXcbegg[Tgtg[XThg[bef [TiXTWWeXffXW`TalbY`lVbaVXeafuTaWg[\\f\\aV_hWXWg[XW\\fVhff\ba TaWceXfXagTg\babYg[XvUw gXe`*"}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have addressed my concerns satisfactorily, thank you. Two small items:\nLine 38: change to “considered the most robust proxies”. It’s not reasonable to judge what the two best proxies are---they all have their warts. If you read through the Cenozoic CO2PIP supplement, you wouldn’t come away thinking that the alkenone proxy is the best (or second best) proxy.\n%--B2D2:D2B;2FF;63AIA?A7BWedV1ALD2M6D2:6%--A7egWmA$<E@AFFAA5<86D6@F7DA?F;6 $6@AQA<4$+f,(,6EG?2F6\\<7PAL>AA=2FF;6<D&<:LD6gTF;62M6D2:6%--7DA?fd*2FABD6E6@F 569@<F6>P6O4665ElA$W(F;<@=F;<E<ENADF;E2P<@:W(@4A@FD2EFTF;6/A@:6F2>W6EG?2F6^PALDD67Wik_ <E@AFF;636EF4A?B2D<EA@FAPALDEFL5P3642LE6F;6P?2562E<@:>66EG?2F67ADF;66@GD6FPA7F;6 >2EFhddq*PDEUEA(EL::6EF4LJ@:^2@5T7ADN;2F<F`ENADF;T5LD<@:G?6EN<F;<46E;66FE/A@: 2EEL?6E2f0%--TEA7ADF;6*<A46@6PALE;AL>55AL3>6F;6gWhA$6EG?2F6F;2FPALE66<@F;6<D abstract).", "reviewer_2": "(F;2@=F;62LF;ADE7ADF;6<D4A@G@L656@:2:6?6@FN<F;D6M<6N6DE`4A??6@FE2@5F;6<D6OF6@E<M6 NAD=A@F;<E?2@LE4D<BFW/<F;F;6255<GA@2>4>2D<942GA@ET56F2<>E2@5D6M<E<A@EBDAM<565T(F;<@=<F`E <@2EF2F67ADF;6N<56D4A??L@<FPFAF2=62M<6N2@5D64A??6@5BL3><42GA@W.;6LE6A7F;6 sterane and phytane approaches should be picked up by other groups, further tested in the modern, 2EN6>>2E<@B2>6AD64AD5E7DA?2D2@:6A7E<F6E2@524DAEEG?6E42>6EW'AB67L>>P<FN<>>367AL@5FA36 DA3LEF2@5BDAM<562M2>L23>6255<GA@FAF;64D<G42>68ADFEFAL@56DEF2@5B2EF2F?AEB;6D<4$+f M2D<2GA@EW\nSome minor points:\nLines 68 – 71: I don’t understand the use of the word “momentous” in this context. Adding an 077&)0&4(*(54975254#6(5:2)'*;0*<*)&83&104.&(53620(&9*)&4))0-(:29B95B(54897&04'9*73? *;*4357*)0-(:29@$)54A99/041$A).7**99/08&8353*495:8@\nAlso, the second part of this passage is troubling – it appears to be arguing that if we get the answer <*A7*255104.+57&2208,4*?0+459<*(&4<**)5:99/*675'2*38@$)54A99/0419/0808&.55)&6675&(/ FAEL4;2@<?BADF2@FCL6EGA@2EB$+f6EG?2GA@]2@5(F;<@=E25>P>625EFA2_4A@9D?2GA@3<2EA7 EFL5<6EA@>P36<@:BL3><E;65F;2F?66FF;6Z6OB64F2GA@[^2E<:@<942@FBDA3>6?_U2@53_<E@AF2:AA5 approach when parameters within the proxy system are poorly constrained – the risk is that these 2D64;AE6@<@5<86D6@F42E6E2@55<86D6@FEFL5<6EFA24;<6M62X:AA59FYFA6OB64F2GA@T2@5EA ;<@56DE2BDAB6DDA3LEFLE62@56M2>L2GA@A72BDAOPL@56DF;6E2?62EEL?BGA@E36FN66@EFL5<6E 2@5F;DAL:;G?6]AF;6DN<E6F;6PD62>>P2D6@AFF;6E2?6ZBDAOP[W,DAB6D>PTF;6L@46DF2<@FPA@ poorly constrained parameters should be modelled in the deployment of the proxy, as is done in this study.\nI think I see what they are trying to say - we need a diversity of independent proxy methodologies, ideally with consistent deployment of methods bewteen groups, with honest and robust modelling 5+:4(*79&049=<09/04*&(/8=89*3?95(53*95&4=(54,)*4(*049/*&(9:&297*4)04&93586/*70(\"%E F;DAL:;G?6W(@F;<E4A@F6OFTN642@F;6@4;2>>6@:62@5E4DLG@<Q6B6DE<EF6@FBDAOPALF><6DEF;2F D2@:6ALFE<56F;6L@46DF2<@FP32@5EA7?L>GB>6AF;6DBDAO<6EW\nLine 73 – “few Haptophyte species” – maybe a few in the modern, but across the Cenozoic, there are many tens of named Noelaerhabdaceae species (even back to the mid-Miocene). Given the A44LDD6@46A7?A56D@\\FPB62>=6@A@6E324=FAF;6%A46@6TF;64A@E6DM2GM62EEL?BGA@NAL>536 F;2F2>=6@A@6\\BDA5L4GA@<E24A??A@762FLD6A7?AEFA7F;672?<>PW\"@5T:<M6@^BE6L5A\\_4DPBG4 diversity in modern species, the number of named fossil morpho-species is likely a very large L@56D6EG?2F6A7`24FL2>`EB64<6ED<4;@6EEW\">=6@A@6BDA5L4GA@<EF;LE2762FLD6A7M6DP?2@P 86*(0*8C<09/04&<*22B)*,4*)(2&)*D049/*\"*45>50(?459&+*<@ )<@6eki\\a-6@E<GM<FPF6EFE56?A@EFD2F6F;2F2ep4;2@:6<@3D6EL>FE<@A@>P2ep4;2@:6<@B$+f 6EG?2GA@gjgjTN;<4;<EFAAE?2>>FA244AL@F7ADF;64A@E<EF6@F564><@6AM6DF;6EFL5<65G?6 interval.\"\n(`?:A<@:324=FAF;<E2E(EG>>5A@`F><=6F;67AD?L>2GA@W(E66F;62LF;ADED6EBA@E6FAF;<E\\2@5(:6F N;2FF;6PE2PW#LF<@F;6<D424L>2GA@EF;6P2>>AN7ADfipM2D<2GA@<@3TN;<4;NAL>5362EEA4<2F65 with 25% uncertainty in CO2, and as shown on their plots. 25% is the uncertainty in b in the modern, so I really don't understand why they use an example of a \"1% change in b\". OK, so a 1% change in b is associated with a 1% change in CO2, but then see how that statement stands up with, \"our L@46DF2<@FPA@3<Eq\\fipaS\"@5F;2F`E<@F;6?A56D@EPEF6?T<@D6>2GM6>P76NEFL5<6EW(`536?AD6 42LGALEA7;ANTN;6@2@5N;6D63?<:;FM2DPE<:<@<942@F>PT2@5(5A@`FF;<@=F2>=<@:23ALF2ep M2D<2GA@<@3<E2@2BBDABD<2F66EG?2F6A7F;6BAF6@G2>4;2@:6EF;2FF;6D6?<:;F36<@F;<E parameter through their record."}, "author_response": "Reviewer #1 (Remarks to the Author):\nThe authors have addressed my concerns satisfactorily, thank you. Two small items:\nLine 38: change to “considered the most robust proxies”. It’s not reasonable to judge what the two best proxies are---they all have their warts. If you read through the Cenozoic CO2PIP supplement, you wouldn’t come away thinking that the alkenone proxy is the best (or second best) proxy.\nWe have removed “robust”.\nESS paragraph at the bottom of p. 10: Your average ESS of 13.9 oC is not too different from the Cenozoic CO2PIP estimate---if you look at their Figure 3, the average ESS from 20 Ma to present definitely exceeds 8 oC. I think this is worth saying. In contrast, the Wong et al. estimate (your ref. 57) is not the best comparison to your study because they made a single estimate for the entirety of the last 400+ Myrs; so I suggest cutting (and, for what it's worth, during times with ice sheets Wong assumes a 2X ESS, so for the Miocene you should double the 3.4 oC estimate that you see in their abstract).\nWe have added, “The Cenozoic CO2PIP7 estimations for ESS exceed ca. 8°C per CO2-doubling for the past 20 Myr, reaching ca. 13°C per CO2-doubling in the early Cenozoic.”\nReviewer #2 (Remarks to the Author):\nI thank the authors for their continued engagement with reviewers' comments and their extensive work on this manuscript. With the additional clarifications, details and revisions provided, I think it's in a state for the wider community to take a view and recommend publication. The use of the sterane and phytane approaches should be picked up by other groups, further tested in the modern, as well as in paleorecords from a range of sites and across timescales. Hopefully it will be found to be robust and provide a valuable addition to the critical efforts to understand past atmospheric CO2 variations.\nSome minor points:\nLines 68 – 71: I don’t understand the use of the word “momentous” in this context. Adding an irradiance control on Ep could be viewed as making a complicated and difficult-to-constrain b term, even more difficult. I don’t think I’d greet this as momentous.\nWe have changed “momentous” to “timely” to provide a more neutral term.\nAlso, the second part of this passage is troubling – it appears to be arguing that if we get the answer we’re looking for all is fine, if not we can weed out the problems. I don’t think this is a good approach to such an important question as pCO2 estimation – and I think sadly leads to a) confirmation bias of studies only being published that meet the “expectation” (a significant problem); and b) is not a good approach when parameters within the proxy system are poorly constrained – the risk is that these are chosen in different cases and different studies to achieve a ‘good fit’ to expectation, and so hinders a proper robust use and evaluation of a proxy under the same assumptions between studies and through time – otherwise they really are not the same “proxy”.\nProperly, the uncertainty on poorly constrained parameters should be modelled in the deployment of the proxy, as is done in this study.\nI think I see what they are trying to say - we need a diversity of independent proxy methodologies, ideally with consistent deployment of methods between groups, with honest and robust modelling of uncertainty within each system, to come to any confidence in the actual trend in atmospheric CO2 through time. In this context, we can then challenge and scrutinize persistent proxy outliers that range outside the uncertainty bands of multiple other proxies.\nWe agree with the reviewer. We have changed the second part of this passage to, Line 53-56, “By using a diversity of independent proxy methodologies, ideally with consistent deployment of methods between groups, with honest and robust modelling of uncertainty within each system, we can then challenge and scrutinize persistent proxy outliers that range outside the uncertainty bands of multiple other proxies.” which more accurately reflects what we were trying to express in the text.\nLine 73 – “few Haptophyte species” – maybe a few in the modern, but across the Cenozoic, there are many tens of named Noelaerhabdaceae species (even back to the mid-Miocene). Given the occurrence of modern-type alkenones back to the Eocene, the conservative assumption would be that alkenone-production is a common feature of most of the family. And, given (pseudo-)cryptic diversity in modern species, the number of named fossil morpho-species is likely a very large underestimate of 'actual' species richness. Alkenone production is thus a feature of very many species (within a well-defined clade) in the Cenozoic, not a few.\nWe have changed this to “compounds produced by species within the Haptophyte clade”. Given there is no definitive amount, we agree it is prudent to remove “few” (as we had originally written) and also prudent to exclude “many” (as the reviewer suggests).\nLine 175 - \"Sensitivity tests demonstrate that a 1% change in b results in only a 1% change in pCO2 estimation3636, which is too small to account for the consistent decline over the studied time interval.\" I'm going back to this as I still don't like the formulation. I see the authors response to this - and I get what they say. But in their calculations they allow for 25% variation in b, which would be associated with 25% uncertainty in CO2, and as shown on their plots. 25% is the uncertainty in b in the modern, so I really don't understand why they use an example of a \"1% change in b\". OK, so a 1% change in b is associated with a 1% change in CO2, but then see how that statement stands up with, \"our uncertainty on b is +-25%\"! And that's in the modern system, in relatively few studies. I'd be more cautious of how, when and where b might vary significantly, and I don't think talking about a 1% variation in b is an appropriate estimate of the potential changes that there might be in this parameter through their record.\nWe have changed this to “Sensitivity tests demonstrate that the uncertainty within the b value could lead up to a maximum of 25% change in pCO2 estimation.”"}]

Decision letter