dataesr/scientific-paragraphs-categorization huggingface dataset:
We generate our ensemble of 500 BICYCLE simulations across the plausible ranges of its key inputs and present the resultant Marine20 curve in Section 5. Here we also discuss differences with the previous Marine13 curve. In particular, we note that Marine20 estimates a significant, and consistent, increase in the globally averaged MRA when compared to Marine13. With a sensitivity analysis we also document how much the different processes contribute to the overall uncertainty in Marine20. Section 6 provides a comparison against simulations with the more complex LSG OGCM and against both current day (pre-bomb) global marine data and older marine data from the IntCal20 database. Here we show that the increase, when compared to Marine13, in the global-average MRA in Marine20 is in agreement with average pre-bomb observations and the LSG OGCM.
Finally, in Section 7 we discuss the limitations of our approach alongside areas needed for future work and development. For those seeking further details on the model output, including the final Marine20 radiocarbon age calibration curve, the spatially resolved regional open-ocean MRA estimates from the LSG OGCM, as well as the 500 NH atmospheric Δ 14 C realizations used as inputs for our Monte-Carlo ensemble, see PANGAEA (https://doi. org/ 10. 1594/PANGAEA. 914500). For a calibration end-user, it is key to note that the update from Marine13 to Marine20 is accompanied by updates to the regional ΔR corrections which can be found at http://calib. org/marine/. This is particularly critical since Marine20 estimates a significantly increased globally averaged MRA. Calibration of local reservoirs using values of ΔR based on Marine13 will give incorrect calendar age estimates.
Construction of such polar curves would be a valuable area for future study which we discuss in more detail within Section 7. For current users wishing to calibrate marine 14 C samples from polar locations where variability in sea ice and ocean circulation may have significantly affected MRAs, we direct them to the regional LSG OGCM output as discussed in Section 3 and available on PANGAEA. Such users should however be aware that each individual LSG OGCM simulation only considers a single climate scenario. Due to the very considerable uncertainty on the appropriate historic polar climatic conditions, and since the impact of different climate scenarios on MRA in these regions is very large, resultant polar calibration based upon such fixed scenarios should be treated with extreme caution. The calibrated age estimates obtained under any single fixed scenario are likely to be considerably over-precise.
To begin, an interim atmospheric 14 C curve based upon only the Hulu Cave speleothems  (Southon et al. 2012; Cheng et al. 2018 ) was constructed using the same Bayesian spline with errors-in-variables methodology as the final IntCal20 curve  (Heaton et al. 2020 in this issue). This interim Hulu-based 14 C history was then entered as a forcing to the LSG OGCM to get the coarse regional MRA estimates under its GS scenario  (Butzin et al. 2020  in this issue) shown in Figure  3  of  Reimer et al. (2020 in this issue). Since LSG OGCM estimates are intended for the open-ocean as opposed to the more coastal locations of the marine IntCal20 data, a further constant coastal radiocarbon reservoir age adjustment/shift was however required. For each marine dataset, an independent prior was placed on this required coastal shift based upon the observed offset between the 14 C ages of the more recent observations from that specific marine dataset and IntCal20's atmospheric dendrodated trees (which extend back to ca.
14,190 cal BP 2  ). Intuitively, this coastal shift approach used the relative changes (i. e. the shape) in the regional LSG OGCM open-ocean MRA estimates but not their absolute values. The internally estimated coastal shift is equivalent to estimating a temporally invariant pseudo-ΔR for the specific coastal location relative to the nearby open-ocean. These coastal pseudo-ΔR shifts from the GS scenario of LSG OGCM ranged from approximately 12 14 C yr in Vanuatu up to over 280 14 C yr in Kiritimati indicating the difficulty in using open-ocean OGCM estimates directly for coastal data. These preliminary Hulu-Cave based MRA estimates are expected to be overly smooth (since they are based upon the relatively smooth Hulu Cave record) however they should provide robust first-order approximations that enable marine data to contribute to IntCal20.
The region-specific prior MRA estimates were then used as inputs for the Bayesian approach to IntCal20's curve construction allowing for some further fine-scale MRA variability not captured by the coarse preliminary Hulu-based estimates. In this curve construction process, the coastal shift priors were further updated to resolve differences between the diverse pre-Holocene data sets, see  Heaton et al. (2020 in this issue)  for complete details. Importantly, as shown in Figure  3  of  Reimer et al. (2020 in this issue) , for locations of the marine data used for making IntCal20, the relative differences in the shapes of the Hulubased regional open-ocean MRA estimates provided by the LSG OGCM were small (in the order of ±5% of the absolute value of MRA). Furthermore, these regional open-ocean MRA estimates had similar shapes to the non-polar global-average MRA.
Hence, in terms of the final MRAs used for the marine data contributing to IntCal20, there would be little difference in having applied individual temporally invariant coastal shifts to the non-polar global-average MRA (i. e. the approach we recommend for Marine20 to restrict temporal changes in MRA to the global-average) of the LSG OGCM rather than the region-specific output. Our proposed global-average Marine20 approach to calibration is therefore consistent with that taken to integrate the marine data into IntCal20. Why Not Use OGCM Output Directly to Provide Regional Calibration Curves? Much of the marine data which users wish to calibrate come from coastal regions. With our current state of knowledge, most global OGCMs do not have the resolution to simulate these coastal regions accurately and their results may be flawed at such sites.
Also shown are the LSG OGCM (50°S-50°N) globally averaged estimate with the same IntCal20 pointwise mean forcing and GS scenario, and also the Monte-Carlo mean of our BICYCLE/Marine20 globally averaged MRA discussed in detail later. A potential approach to adapt this open-ocean LSG OGCM output for calibration in wider and/or coastal locations would be that taken for inclusion of marine data in IntCal20. One would initially identify the regional open-ocean LSG OGCM site that lies closest to the particular location of interest, before then applying a constant, baseline-specific, pseudo-ΔR shift from this specific regional open-ocean LSG OGCM estimate. However, extending this approach to Marine20 would be complex and reliant upon users identifying both the correct LSG OGCM regional baseline and the matching pseudo-ΔR shift.
With BICYCLE we can more easily investigate the effect of such scenario changes. Our BICYCLE approach attempts to minimise potential inaccuracies in marine radiocarbon variability that stem from strictly unknown changes in the marine carbon cycle by exploring a wide range of possible carbon cycle states, providing a trade-off between expected accuracy and precision in resulting calibrated ages. As we describe later in Section 7, key future work lies in developing 3D OGCM models which can provide region-specific output further and understanding their inherent uncertainties. Work towards this goal would be invaluable in improving both marine calibration and our insight into the global carbon cycle. Users calibrating open-ocean samples who wish to use individual regional MRA estimates provided by the LSG OGCM can find them on PANGAEA (https://doi.
org/10. 1594/ PANGAEA. 914500). This may also be relevant for users wishing to calibrate data outside Marine20's intended range, i. e. where sea ice may have been present (north of 40/50°N or south of 40°S), especially if the LSG OGCM estimates exhibit significantly different relative changes. One approach to incorporate LSG OGCM MRA estimates, for example into OxCal, can be found via  Alves et al. (2019).
Atmospheric CO 2 : our record of past atmospheric CO 2 (Figure  5 ) is taken from a spline through ice core data as described in  Köhler et al. (2017). The spline estimate combines raw data from several different ice cores (Talos Dome, Siple Dome, WAIS Divide (WDC), EPICA Dome C (EDC), EPICA Dronning Maud Land (EDML) and Law Dome) each on the most recent age scale, e. g. AICC2012, GICC05, WD2014  (Ahn and Brook 2014; Ahn et al. 2012; Bauska et al. 2015; Bereiter et al. 2012; Lüthi et al. 2010; MacFarling-Meure et al. 2006; Marcott et al. 2014; Monnin et al. 2001 Monnin et al. , 2004;; Rubino et al. 2013). This fitted spline is accompanied by uncertainties that consider data resolution errors, Monte Carlo errors, and error due to a chosen cutoff period during smoothing leading to combined uncertainties in the spline, σ CO 2 θ.
COMPARISONS WITH A MORE COMPLEX MODEL, RECENT PRE-BOMB OBSERVATIONS AND OLDER CORALS AND FORAMS To provide further support for Marine20's robustness and suitability we compare it to the output of a set of simulations provided by the LSG OGCM  (Butzin et al. 2017 (Butzin et al. , 2020  in this issue) and against a collection of pre-bomb marine observations which are primarily from surface water, coastal samples  (Reimer and Reimer 2001) , as well as the marine observations from corals and forams within the IntCal20 database (http://intcal. org) extending back to 55 cal kBP.
In (C) the globalaverage MRA from the LSG OGCM output (restricted to 50ºN-50ºS) for the scenario PD is also given for comparison. IntCal20 is shown with its 95% predictive intervals, while Marine13 and Marine20 their 95% probability intervals. 804 T J Heaton et al. uncertainty and so no adjustments of the BICYCLE simulations have been applied which might have led to a complete vanishing of the model/data offset.
Comparison with Corals and Foraminifera in IntCal20 Database Figure 10 plots the complete Marine20 curve, in radiocarbon age space and from 0-55 cal kBP, against all the raw coral and foraminifera data available in the IntCal20 database, http://intcal. org. As described in the IntCal20 paper  (Reimer et al. 2020  in this issue), corals which are older than 25 cal kBP have been excluded in view of potential diagenesis. Further, we do not include observations from the Cariaco Basin for comparison here since this basin appears to be a unique marine environment  (Hughen and Heaton 2020  in this issue) which no model is currently able to resolve, see Section 7 describing the limitations of our model. We plot here the raw 14 C ages of the observations, with no MRA or ΔR correction, along with their 95% probability intervals in both radiocarbon and calendar age.
Since we have made no marine radiocarbon age correction for the observations in the plot, we would not expect the Marine20 curve to necessarily fit the raw data themselves. Rather our interest is in whether the offset from the curve (i. e. ΔR) is consistent for each dataset or location. Since marine data from older than 13. 91 cal kBP does influence the construction of IntCal20, and hence also Marine20, the Marine20 curve is not entirely independent of the plotted data for this time periodalthough the large volume of other data informing IntCal20 still mean it is significantly so. From 0-13. 91 cal kBP, the Marine20 curve is almost entirely independent of the plotted data since no marine data informed IntCal20 in this more recent period. As with the present-day observations discussed in the previous section, most of this marine data is coastal and hence may not be representative of open-ocean.
LIMITATIONS AND FUTURE WORK While the Marine20 radiocarbon calibration curve already offers many improvements over previous marine calibration curves there are still a number of areas for future refinements. Perhaps the clearest limitation in our current work is our inability to accurately reproduce the apparent MRA within the Cariaco Basin. This lack of reproduction is however not only an issue for BICYCLE and the LSG OGCM but also, to our knowledge, any current carbon cycle model. Based upon comparison with other IntCal20 data, the Cariaco Basin (Hughen Figure  10  Plot of observed marine 14 C ages (with no MRA or ΔR correction) against the Marine20 and IntCal20 curves 0-55 cal kBP. The datasets shown consist of: foraminifera (forams) from the Iberian and Pakistan Margins  (Bard et al. 2013) ; corals from Tahiti and Barbados  (Bard et al.
This drop occurs over a relatively short period of 200-300 calendar years. The older three drops (ca. 17, 23, and 31 cal kBP) potentially appear on longer, multi-millennia, scales. Due to the inability of our current carbon-cycle models to reproduce Cariaco's MRA, in order to include the Cariaco data within IntCal20, the basin's MRA beyond ca. 13. 91 cal kBP was modeled as an additional flexible Bayesian spline simultaneously to the construction of the IntCal20 calibration curve  (Heaton et al. 2020  in this issue; Hughen and Heaton 2020 in this issue). Intuitively with this approach, deviations in 14 C levels within Cariaco that were also seen in other atmosphere-adjusted IntCal20 records would likely be considered genuine atmospheric signal. Conversely, deviations seen in Cariaco 14 C levels alone would likely be modeled as changes in the basin's MRA.
The consequent Cariaco MRA estimate from 13. 91-55 cal kBP is therefore predominantly influenced by the offset between the observed Cariaco 14 C data and the Hulu Cave speleothems, for which we assumed that the dead carbon fraction (DCF) remains approximately constant. The mechanism responsible for such MRA reductions is unknown and not seen in any of our model reproductions. Work to understand, and resolve, the differences between the estimate one obtains for the Cariaco Basin's MRA based upon observed data and that provided by carbon-cycle models would therefore be very valuable. Regarding future potential areas of development, most directly, new records at key locations in the oceans would help to improve the resolution and accuracy of existing marine records. This might be further refined by integrating marine records for the sea surface, intermediate and deep-water masses through parallel studies of planktonic and benthic foraminifera, warm and cold water corals, studying and correcting the influence of signal perturbations (e.
Figure1Schematic diagram of IntCal20 and Marine20 age calibration curve construction. IntCal20 is based solely upon tree-ring measurements from 0 to ca. 13. 9 cal kBP. Beyond this a variety of data are used, including marine records which require an initial transformation to an atmospheric equivalent. To achieve this, a preliminary Δ 14 C history is estimated based upon Hulu Cave speleothems (Southon et al. 2012; Cheng et al. 2018). This preliminary Δ 14 C curve is used to drive the LSG OGCM and provide prior, first-order, estimates of regional MRAs (Butzin et al. 2020  in this issue) for each IntCal20 marine dataset, with the exception of the Cariaco Basin which is adjusted adaptively during curve construction (Hughen and Heaton 2020  in this issue). The adjusted marine data are then combined with speleothems, macrofossils from Lake Suigetsu, and floating tree-ring sequences to constitute a mixed, atmospheric and atmospheric-adjusted, dataset extending from ca.
ACKNOWLEDGMENTS We would like to thank  Jeremy Oakley  and  Richard Bintanja  for informative discussions during the development of this work. T. J. Heaton  is supported by a  Leverhulme Trust  Fellowship  RF-2019-140\9 , " Improving the Measurement of Time Using Radiocarbon ". M Butzin  is supported by the  German Federal Ministry of Education and Research (BMBF), as Research for Sustainability initiative (FONA) ; www. fona. dethrough the  PalMod  project (grant numbers:  01LP1505B ,  01LP1919A ). E. Bard is supported by  EQUIPEX ASTER-CEREGE  and  ANR CARBOTRYDH. Meetings of the IntCal Marine Focus  group have been supported by  Collège de France. Data are available on the PANGAEA database at doi: 10. 1594/PANGAEA. 914500.
Within the IntCal20 database, NH tree-ring 14 C determinations extend back to ca. 14,190 cal BP. However, beyond approximately 13,910 cal BP these tree-ring measurements alone are not of sufficient density to estimate the IntCal20 calibration curve precisely. Further back in time than 13,913 cal BP other types of 14 C material including macrofossils, speleothems and marine-based samples are therefore also used to construct the curve. See Heaton et al. (2020 in  this issue) for more details. 790 T J Heaton et al. https://www. cambridge. org/core/terms. https://doi. org/10. 1017/RDC. 2020. 68 Downloaded from https://www. cambridge. org/core. MBLWHOI Library, on.
5-year time span with an average photon-noise precision of 1. 07 m/s. We performed an extensive analysis of the HARPS and ESPRESSO datasets and also analyzed them together with the new TESS photometry. We analyzed the Bayesian evidence of several models with different numbers of planets and orbital configurations. Results. We significantly improve our knowledge of the properties of the known planets LHS 1140 b (P b ∼ 24. 7 days) and LHS 1140 c (P c ∼ 3. 77 days). We determine new masses with a precision of 6% for LHS 1140 b (6. 48 ± 0. 46 M ⊕ ) and 9% for LHS 1140 c (m c = 1. 78 ± 0. 17 M ⊕ ). This reduces the uncertainties relative to previously published values by half. Although both planets have Earth-like bulk compositions, the internal structure analysis suggests that LHS 1140 b might be iron-enriched and LHS 1140 c might be a true Earth twin.
In both cases, the water content is compatible to a maximum fraction of 10-12% in mass, which is equivalent to a deep ocean layer of 779 ± 650 km for the habitable-zone planet LHS 1140 b. Our results also provide evidence for a new planet candidate in the system (m d = 4. 8 ± 1. 1M ⊕ ) on a 78. 9-day orbital period, which is detected through three independent methods. The analysis also allows us to discard other planets above 0. 5 M ⊕ for periods shorter than 10 days and above 2 M ⊕ for periods up to one year. Finally, our co-orbital analysis discards co-orbital planets in the tadpole and horseshoe configurations of LHS 1140 b down to 1 M ⊕ with a 95% confidence level (twice better than with the previous HARPS dataset). Indications for a possible co-orbital signal in LHS 1140 c are detected in both radial velocity (alternatively explained by a high eccentricity) and photometric data (alternatively explained by systematics), however.
6. We discuss the result in Sect. 7 and provide the final conclusions in Sect. 8. Astudillo-Defru). In total, this radial velocity dataset comprised 293 HARPS radial velocity measurements that were presented in  Ment et al. (2019) , spanning 783 days between November 2015 (JD = 2457349. 65) and January 2018 (JD = 2458133. 54). The average cadence was one epoch every 1. 8 days, but normally, two spectra were obtained every night with separations of a few hours. We performed a night binning of the dataset, as suggested in  Dumusque et al. (2012). This simple strategy has proved to be efficient to reduce the effect of short-term correlated noises on orbital elements (e. g. ,  Hara et al. 2019). In total, 145 individual Article number, page ii of xxii measurements were available. The corresponding uncertainties per binned data point are distributed around 3.
56), and a mean cadence of one spectrum was obtained every 2. 2 days (median of one spectrum per night). Each spectrum was obtained with an exposure time of 1820 s in P102 and 1915 s in P103 and P104, and produced a median signal-to-noise ratio (S/N) S/N = 80 at 700 nm. We processed the whole dataset using version v2. 0. 0 of the ESPRESSO Data reduction Software 2 (DRS) pipeline  (Pepe et al. 2020). We selected a binary mask corresponding to an M5 spectral type for this M4. 5 star to perform the cross-correlation and obtain the final radial velocities  (Baranne et al. 1996; Pepe et al. 2003). The resulting radial velocities have a mean photon-noise uncertainty of 1. 07 m/s. In June 2019, ESPRESSO suffered a major intervention, and the chamber and the vacuum vessel were opened to perform a fiberlink exchange.
This introduced a small jump in the radial velocity datasets taken before and after the intervention, and the actual value of this jump is not constant for the different spectral types. The data taken before (labeled ESPRESSOpre in this paper) and after (ESPRESSOpost) should therefore be treated as coming from different instruments. This fiber-link exchange occurred between our first and second observation semester. The mean precision corresponding to the data obtained before the fiber-link exchange is 1. 16 m/s, and the post-change data have an average uncertainty of 1. 02 m/s. This improvement goes in line with the increase in transmission measured after the intervention  (Pepe et al. 2020 ) and a slight increase in exposure time from 1820 s in P102 to 1915 s in P103 and P104. We removed two data points with uncertainties above 3 m/s because low clouds caused a low S/N (the observations were aborted after half the requested exposure time).
The final dataset including the radial velocities, activity indicators, S/N, and exposure time for each spectrum is presented in Table  B. 1. Figure  2  shows the time-series radial velocity for both HARPS and ESPRESSO, and Fig. 3  shows the phase-folded curves for the two known planets in the system. The periodogram of the full dataset (including HARPS and ESPRESSO measurements, see Fig. 1 ) shows clear signals of the known planets LHS 1140 b and LHS 1140 c. The two signals are detectable independently in the two instrument datasets (ESPRESSO and HARPS), and their significance is boosted when they are combined by only including a 26 m/s radial velocity offset between the two (see Sect. 3. 2). In addition to the 1 Prog. IDs: 0102. C-0294(A) , 0103. C-0219(A) and 0104. C-0316(A), PI: J. Lillo-Box 2 https://www.
eso. org/sci/software/pipelines/espresso/ espresso-pipe-recipes. html signals of these two planets, other signals appear at a significant level. The rotation period of the star at P rot = 131 days is clearly detected in each of the two datasets and in the combined set. Interestingly, both datasets also show a peak at half the rotation period P rot /2 ∼ 65 days, which is not as evident in the HARPS radial velocity dataset. However, the rotation period and its alias are clearly visible in the activity index corresponding to the full width at half maximum (FWHM) of the cross-correlation function (CCF) in both datasets, see Fig. 1. Added to this, additional signals stand out in the region around 70-100 days. This is further investigated in Sect. 3. 2. Taking advantage of the large number of ESPRESSO spectra, we used ODUSSEAS (Antoniadis-Karnavas et al.
2020) to estimate the effective temperature and metallicity of this M star. This machine-learning tool measures pseudo-equivalent widths for more than 4000 spectral lines and compares them to a training dataset composed of HARPS spectra of reference Mdwarf stars. Although the code was originally tested for spectra with resolutions from 48 000 to 115 000, we used the combined ESPRESSO spectrum directly with a higher resolution of 140 000. Although the resolution is slightly higher than the highest resolution grid of the code (115 000), we can safely assume that the method that measures the pseudo-equivalent widths provides compatible values when a spectrum with higher resolution is used 3. With this code we derived an effective temper-   For each planet, the upper panel shows the radial velocity data (color-coded as in the legend), binned radial velocities (open black symbols) with a bin size corresponding to 15% of the phase, the median model for each planet obtained from the joint fit analysis (see Sect.