dataesr/scientific-paragraphs-categorization huggingface dataset:
Ice sheet geometry The DATED-1 database is based on evidence found in the existing literature and retrieved from various geological materials (e. g. , terrestrial plant macrofossils, foraminifera, speleothems, bones…) analyzed with a range of dating methods. Based on these data, the DATED-1 compilation provides three different scenarios for the maximal, minimal and most credible EIS extent. The GLAC-1D, ICE-6G_C, and ANU reconstructions are based on inverse modeling approaches constrained by GPS data, relative sea level and geomorphological data. The main differences in the three DATED-1 scenarios at the LGM  (Hughes et al. , 2016 ) are related to the potential BIIS-FIS connection (or disconnection), the southern continental limit of the FIS and the eastern limit of BKIS (Fig. 2a ). Only the minimum scenario suggests the absence of ice between the BIIS and FIS.
Acknowledgements. Victor van Aalderen  is funded by the  French National Research Agency  (Grant:  ANR-19-CE01-15 ). We acknowledge the  World Climate Research Programme's Working Group on Coupled Model ling , which is responsible for the  Paleoclimate Modelling Intercomparison Project (PMIP ) and we thank the climate modeling groups (listed in Table 1 of this paper) for producing and making available their model output. This work benefited from productive exchanges with  N. Jourdain  and  D. Swingedouw. https://doi. org/10. 5194/egusphere-2023-34 Preprint. Discussion started: 10 February 2023 c Author(s) 2023. CC  BY 4. 0 License.
Following the filtering of the profiles based on the depth-interval criterion, the dataset contains 14 686 light profiles (from which K L is derived, see equation 1), which includes 1 387 concomitant L and Fluo (i. e. [F Fluo ] or [Chla Fluo ]) profiles. The number of L profiles following the application of the successive selection criteria is summarized in Table  2. In the present study, the LFM approach was used to predict either [F Fluo ] or [Chla Fluo ] from K L , with different objectives, described hereafter (see Sections 2. 4 and 2. 5).
[Chla] prediction Following the calibration procedure described in the previous section, the LFM approach was applied for inference purposes. A new LFM was designed to infer [Chla Fluo ] from K L. Being constructed with [Chla Fluo ] profiles, the resulting LFM model hence inherently contains the calibration of the [F Fluo ] data (i. e. G LFM e and G Sat coefficients). The output variable is denoted [Chla LFM ] (see flowchart, Figure  1 ). The objective of the prediction phase is to increase the spatial resolution of the [Chla Fluo ] field description with [Chla LFM ]. The prediction is made on the basis of the 14 686 available light profiles (see Table  2 ). Prior to the retrieval of the [Chla Fluo ] field at (sub)mesoscale, the performance of the LFM was assessed. Performance assessment The sample of concomitant [Chla Fluo ] and K L observations was likewise randomly split into two subsets: 70% of the profiles for the construction of the LFM (970 profiles) and 30% to assess the performance of the LFM (validation sample, 417 profiles).
Transect of a SES equipped in Kerguelen A subset of the SES dataset corresponding to a single individual transect (Tag 3) is shown in Figure  7. The transect is 5 746 km long and covers 70 days at sea, between 25-Oct-2019 and 02-Jan-2020 (Figure  7A ). The transect comprises 234 profiles of [Chla Fluo ] and 879 light profiles. One notable detail regarding the transect of Tag 3 is the malfunctioning of the fluorescence sensor during a certain period of the deployment. As a consequence, no Fluo data were available for the time interval extending from 09-Nov-2019 to 02-Dec-2019 (over the same period, 342 T, S and light profiles were sampled by the SRDL). The comparison of <Chla Fluo > and <Chla LFM > along the animal's trajectory clearly reveals that the <Chla> signal is well captured by the model.
The signal gets much noisier after the energy drop. A similar behavior was observed for every tag included in the present study, namely a pronounced energy drop materializing a spatial scale threshold below which the spectrum follows a power-law behavior, and above which the signal loses coherency (i. e. gets much noisier). It is consequently reasonable to consider that the interpretations of the structures depicted by the <Chla LFM > signal are valid up to the scale of the energy-drop threshold and should be discarded for higher spatial frequencies. In the case of Tag 11 (specifically highlighted in Figure  8 ), the spatial resolution of the observations hence extends from ~21 km with Fluo to at least ~7 km with LFM predictions. The energy-drop threshold was different for each tag. The shaded gray area in Figure  8  represents the envelope of the <Chla LFM > spectra, encompassing the minimum and the maximum variances per waveband obtained in the dataset of the 18 SES tags included in the present study.
Exploiting light-Fluo synergies through LFM: Data intercalibration and homogenization Including data from multiple tags in a study raises the issue of the intercalibration of the fluorescence sensors, a critical point when  [Chla]  estimates are to be derived from Fluo measurements. Evidences of inter-sensor variability have already been pointed out for different fluorometer models (e. g. Guinet et al. , 2013; Xing et al. , 2014; Keates et al. , 2020) , emphasizing the necessity of homogenizing the Fluo data from one tag to another before any further analysis. A common benchmark which provides an absolute Fluo to [Chla] conversion theoretically resolves the inter-tag calibration issue. However, the fluorescence sensors embedded in the tags examined in the present study did not undergo any in-situ calibration process and not all of them could be successfully independently calibrated with concomitant satellite estimates of  [Chla]  (see Section 4.
e. conversion from [F] into  [Chla] , see flowchart, Figure  1 ) could subsequently be established. The merging of all the intercalibrated tags indeed reinforces the quality of the comparison with satellite data and increases the robustness of the calibration procedure. The second step of the Fluo calibration therefore converts the fluorescence signal into an estimate of [Chla], based on concomitant satellite measurements. Satellite-based ocean-color algorithms for the retrieval of  [Chla]  do not perform equally in Variance spectrum of <Chla> from observations and model predictions along the trajectory of Tag 11. Increasing wave numbers correspond to smaller spatial scales (see Section 2. 6). The green (black) solid line represents the variance per waveband of the <Chla Fluo > (<Chla LFM >) signal.
The blue solid line represents the variance spectrum of dark KL. The green (gray) shaded area represents the envelope of the <Chla Fluo > (<Chla LFM >) spectra. The envelope encompasses the minimum and the maximum variances per waveband obtained in the dataset of the 18 SES tags included in the present study. The green (black) dashed line represents the linear regression of the variance spectra of <Chla Fluo > (<Chla LFM >) on the spatial scale interval between ~2 000 km and ~21 km, with a spectral slope equal to -1. 22 (-1. 17). The dashed (dotted) red line represents the linear regression of the variance spectrum of <Chla LFM > on the spatial scale interval between ~21 km and ~7 km (~7 km and ~2 km). The exact corresponding wave numbers are 5 10 -7 m -1 (~2 000 km), 4. 7 10 -5 m -1 (~21 km), 1.
Assessment of the method 4. 3. 1 Retrieval of the vertical distribution of Chla Following the constitution of calibrated [Chla] datasets, the LFM was developed such that the modeled [Chla LFM ] matched as closely as possible the targeted [Chla Fluo ]. The retrieval of <Chla Fluo > was generally well achieved with the LFM predictions (see Section 3. 2). The model however lacks accuracy along the vertical dimension. In the SO, Z MLD is a major driver in the vertical distribution of Chla. For a large majority of profiles, the mixed layer contains most of the Chla biomass and [Chla] is homogeneous within this layer  (Cornec et al. , 2021). Yet a remainder of the vertically-integrated Chla can be present below Z MLD. Since the model also proved to successfully estimate <Chla LFM > %ML (Figure  5A ), an estimate of this remainder is achievable.
, 2016). The energy-drop threshold materializing the validity domain of the predictions (see Section 3. 4. 2) corresponded for each tag to twice the mean spatial frequency of the tags' light measurements (for detailed theoretical interpretation of the validity domain, see supplementary material, Text S7 and Figures  S8-S9 ). The energydrop threshold is hence directly dependent on the inherent properties of the corresponding transect. In the dataset of the 18 SES tags included in the present study, the mean distance between two valid consecutive Fluo profiles is 14. 9 km +-4. 1, whereas for light profiles this distance is reduced to 5. 9 km +-3. 1. As a result, while on average the SRDL Fluo measurements enable observation of phytoplankton dynamics at spatial scales up to ~30 km, LFM predictions extend the spatial scale of the observations up to ~12 km.
Conclusion and perspectives The present study highlights the benefits of using the LFM both to homogenize the Fluo data from different sensors and to infer the Chla content in the water column. The interest of a model such as the LFM using K L to describe the dynamics of Chla along the trajectory of an equipped SES stands out especially for a device with severe power consumption constraints such as the SRDL. The substantially low mean error associated with LFM predictions (see Section 3. 2) emphasizes the accuracy of the LFM-based method for retrieving the variability of the <Chla> field and extending the spatio-temporal scale of observations (see Section 4. 3. 4). While the sole use of fluorescence measurements might not be sufficient to access (sub)mesoscale processes, the finer horizontal resolution achievable with LFM predictions unlocks the (sub)mesoscale observation gap of SRDLs.
, 2018). The improvements brought by the LFM in terms of spatial scales hence contribute key elements for deepening study of the coupling between phytoplankton distribution and the ocean's physical structure at (sub)mesoscale (including Lagrangian studies,  Lehahn et al. , 2018) , and also provide novel data for studying SES behavior and the horizontal exploration of the ocean by such marine predators. In this way, the dynamics of phytoplankton along the trajectories of SESs are optimally described by merging satellite-calibrated [Chla Fluo ] data derived from the tags' fluorescence measurements (covering both day and night periods) with [Chla LFM ] estimates (only available during daylight hours), which improves the spatio-temporal resolution of the data. In the present study, the data from tag deployment campaigns performed between 2018 and 2020 were included.
The predictive capabilities of the LFM can possibly be extended towards a larger range of tags, e. g. tags deployed in the past measuring light but not Fluo. Indeed, bio-logging data have proved to be a considerable source of in-situ data at (sub)mesoscale in the SO in the past two decades. Every light profile, despite not providing truly reliable metrics on the vertical, is useful, under the method developed in the present study, to feed biogeochemical models with an estimate of the vertically-integrated Chla amount and the proportion of the Chla amount present within the mixed layer. Datasets comprising hundreds of thousands of in-situ vertical profiles sampled by equipped animals in the SO hence constitute a possible insight into the ocean subsurface to extend the quasi-synoptic -but surface-onlyvision provided by satellite data.
Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: The marine mammal data were collected as part of the Système National d'Observation Mammifères Echantillonneurs du Milieu Oceánique (SNO-MEMO) and made freely available by the International MEOP Consortium and the national programs that contribute to it. The MEOP data are available at www. meop. net/ database/meop-databases. The computer codes produced for the present study are available at https://doi. org/10. 17882/93268. The GlobColor products were produced and distributed by the Copernicus Marine Service with support from CNES and ACRI-ST, and are available at http://marine. copernicus. eu/services-portfolio/ access-to-products/.
Funding This work was partly funded by the  CNES  and  REFINE (European Research Council , Grant agreement  834177 ) project. LLS is supported by a  joint CNES-CNRS doctoral grant. The elephant seal work was supported as part of the  SNO-MEMO  and by the  CNES - TOSCA  project Elephant seals as Oceanographic Samplers of submesoscale features led by  C. Guinet  with support of the  French Polar Institute  (programmes 109 and 1201). This research was carried out, in part, at the  Centre d'E ́tudes Biologiques de Chize , under a contract with the  Centre National d'E ́tudes Spatiales (CNES)  and at the  Laboratoire d'Oceánographie de Villefranche.
GEM measurements GEM was continuously measured using a cold vapor atomic fluorescence spectroscopic (CVAFS) Hg analyzer (Tekran TM 2537B). The sampling inlet of the Hg analyzer was mounted at the front of the Research Vessel Polarstern to minimize the influence of the ship exhaust. The flow rate of the sample air was set as 0. 7 L•min -1 , and Hg in ambient air was alternatively trapped on two gold cartridges with a cycle time of 5 min. The trapped Hg on the cartridge was then thermally desorbed at 550°C and detected by CVAFS. The inlet system was equipped with two soda lime tubes and two 0. 45 μm Teflon filters to remove moisture and coarse particles. The observed data were carefully screened for local contamination as discussed in Angot et al. 69  and Beck et al. 70. Calibration using a built-in internal Hg permeation source was conducted on a regular basis.
In addition, external calibration through manual injections of known GEM contents using a Tekran 2505 unit was performed before and after the cruise, and the accuracy of both calibrations was better than 95%. The detection limit (DL) for GEM measurements using the 2537B unit is lower than 0. 10 ng•m -3. In addition, GEM measurements were also independently performed in the University of Colorado (CU) sea-laboratory container during the whole expedition  69, 71. Briefly, a Tekran 2537B analyzer was used to analyze 15-min integrated samples. Millipore 0. 45 µm polyether sulfone cation-exchange membranes were used to remove Hg(II) species, and the instrument was automatically calibrated every 25 h. Data for this analysis were intercompared and adjusted for calibration offset using CU measurements as reference.
Other ancillary data We use DMS, CO, and SO 2 datasets collected during the MOSAiC expedition as ancillary data in this analysis. These datasets are described by ref. 69. DMS measurements were conducted using an Atmospheric Pressure Ionization Mass Spectrometer with an Isotopically Labeled Standard (APIMS-ILS)  72. This instrument monitors the DMS mole fraction of a dried sample air stream at 10 Hz, averaged to 10 s, and the detection limit is typically <5 ppt  73. CO measurements were performed in two independent containers on the D-deck. Here, we use hourly averaged merged datasets that combine the cross-evaluated independent measurements  74. SO 2 measurements were performed in the Swiss container on the D-deck of Research Vessel Polarstern using a Thermo Fisher Scientific instrument (model 43i)  75.
Furthermore, surface ocean chlorophyll a (Chla) was measured daily from the ships underway system (11 m depth). Two liters of seawater were filtered onto GF/F filters (Whatman®) in duplicate or triplicate and frozen at -80 °C until further analyses at the Alfred Wegener Institute in Germany. Samples were extracted at 4 °C in 90% acetone overnight and subsequently analyzed on a fluorometer (TD-700; Turner Designs, USA), including an acidification step (1 M HCl) to determine phaeopigments. Navigation parameters and a subset of meteorological data, including wind speed (WS), surface air pressure (P) and air temperature (Temp), were acquired from the ship's scientific data system. Shortwave radiation (RD), ocean fraction (ocean coverage fraction per grid cell) and sea-ice fraction data were extracted from Goddard Earth Observing System-Forward Processing (GEOS-FP) assimilated hourly meteorological data (with a horizontal resolution of 2°× 2.
Model validation We further systematically evaluated the quality of the GAM using different methods. We used a 5-fold cross-validation test to assess the accuracy of GAM simulations. The principle of this method is to randomly divide the whole dataset into 5 subsets, and in each crossvalidation round, four subsets are used to fit the model, and the remaining subset is used to make a prediction. This process was repeated 5 times to ensure that every subset was tested  77, 81. The 5-fold crossvalidation results displayed a good coincidence between the GAM and cross-validated results (slope = 1. 00, R 2 = 0. 99), demonstrating good reliability of the model (Supplementary Fig. 11 )  82. In order to test the underlying assumptions of homogeneity, normality, and independence and ensure the validity and accuracy of the model, we used the following methods: (1) quantile-quantile (Q-Q) plot (sample quantiles against theoretical quantiles), (2) scatterplots of residuals against linear predictors, and (3) histograms of the residuals  36, 77.
Acknowledgements Data reported in this manuscript were produced as part of the international Multidisciplinary drifting Observatory for the Study of  Arctic Climate (MOSAiC)  expedition with the tag MOSAiC20192020, with activities supported by  Polarstern  expedition  AWI_PS122_00  and with the help of many people 84. We thank the  China Arctic and Antarctic Administration  for fieldwork support. Some observations used here were provided by the  Atmospheric Radiation Measurement (ARM) Climate Research Facility , a  US Department of Energy (DOE) Office of Science User Facility  sponsored by the  Office of Biological and Environmental Research. We thank the many ARM operators who supported the field observations. We thank  Hui Kang ,  Jianfang Chen  and  Songtao Ai  for help in field work. We thank  Weihua Gu ,  Hongwei Liu , and  Lei Zhang  for helpful discussion.
Data availability The GEM and the corresponding meteorological (including air temperature, atmospheric pressure, solar radiation and wind speed) and hydrologic data (including Open-water fraction, sea-ice fraction) of this study have been merged and are available at figshare (https://doi. org/10. 6084/m9. figshare. 23614494)  83. SO 2 data is available at https:// doi. org/10. 1594/PANGAEA. 944270. CO data is available at https://doi. org/10. 1594/PANGAEA. 944389.
Their inter-model mean importance of -36% of IRF suggests a corresponding overestimation of net radiation change in Fig. 2. A reduction of this magnitude is plausible given that e. g. forests mostly stimulated low-level clouds in boreal and temperate regions, which dominate our dataset, in a recent study  32. The dominant stimulated cloud types have net cooling effects at the TOA  33  and would thus counteract the heating effect of lower forest albedo. However, the forest breeze circulation contributing to such cloud stimulation  34  can also reduce cloudiness over nearby non-forest surfaces. Assuming that albedo strongly contributes to the land-use effects inhibited according to Smith, et al. 31  , while the effect of CO 2 alone has been shown to be slightly reinforced by tropospheric adjustments by approximately 5% (ref.
Likewise, satellite-derived fAPAR of our study sites shows negative relationships with their respective α s (Fig. 3a ). However, photosynthesis is known to consume only a small fraction of incoming radiation, even when focusing on its photosynthetically active part PAR or its absorbed part. In our dataset, this fraction could be estimated by applying the energy intensity of photosynthesis (0. 469 J μmol -1 CO 2  38, 39  ) to gross primary productivity GPP and comparing to non-reflected incoming shortwave radiation SW net. While the resulting fraction of radiation energy used for photosynthesis can vary depending on ecosystem and cloudiness  40  , its site-averaged long-term value was nowhere above 2. 7% and on average (both arithmetic mean and median) 1. 2% (standard deviation 0. 6%). This fraction showed no clear relation to α s (see Fig.
Case studies  36, 37  found negative correlations of reflectance with various biomass-density measures. Forests are an extreme case, but as suggested for energy crops  20  , the causal relation as such can be expected to apply to non-woody canopies as well. In our dataset, canopy height was negatively related to albedo (Fig. 3c ); significantly (p < 0. 05) negative Spearman correlation coefficients of 0. 37 to 0. 83 were also found when analysing snow-affected and snow-free ecosystems, or forests, non-woody and intermediate ecosystems separately. Respiration. NEP is determined by ecosystem respiration as well as by photosynthesis. Small heterotrophic respiration losses of CO 2 can be favoured by water excess (such as in peatlands), seasonal temperature and moisture limitation dynamics (such as on chernozems), or chemical composition of plant material and resulting litter.
Components resisting decomposition, such as bark or multi-annual leaves, or humus, might generally exhibit a lower albedo than others. We are not aware of published studies on such a general relation, except for biochar application that caused decrease in surface albedo  21, 44  and a weak relation between albedo and soil organic carbon found by Post, et al. 45. Our dataset, too, allows only limited insights, since the included inferred values of ecosystem respiration R eco can be expected to scale with the amount of already accumulated carbon, and thus need to be normalized by it before resistance of an ecosystem or its components to respiratory losses can be detected. However, tentatively using canopy height h c as a proxy of accumulated carbon, based on its close relation to at least aboveground biomass  46  , indeed suggests the possible existence of such a relation (Fig.
Methods Dataset. We compared joint in-situ measurements of landatmosphere fluxes of CO 2 and radiation by compiling and   1  (rescaled to units of gC m -2 month -1 ). Blue broken line: Theoretical maximum if all absorbed PAR energy (of an assumed 100 W m -2 ) was used for CO 2 sequestration; Grey broken line: Theoretical maximum to annual effective values if monthly averages could be freely combined under constant SW in. postprocessing half-hourly time series from a global network of eddy-covariance stations  72  ; in particular, the FLUXNET2015  73  dataset, and recent compatible updates to its European and American branches of ICOS  74  and AMERIFLUX  75  , respectively. All stations with an open data policy and at least one calendar year of measurements of the core variables net ecosystem productivity (NEP based on dataset variable NEE_VUT_REF), incoming and outgoing shortwave radiation at the land surface (SW in , SW out ), were included (Supplementary Methods 1).