dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods To understand the impact of CIMR the future observability of SST, we have undertaken a simulation study that provides synthetic observations to the OSTIA system. By providing input data with and without CIMR observations and with and without realistic observational errors, we can test the effect of the increased coverage provided by adding CIMR data to the IR constellation and whether this effect remains when allowance is made for the increased uncertainties associated with PMW-derived SSTs. To achieve this, a "truth" dataset was required from which simulated observations for CIMR, SLSTR and AVHRR could subsequently be derived and to which analysis fields could be compared. To act as SST truth, we obtained data generated as part of the North Atlantic Climate System Integrated Study (ACSIS)  [15] , consisting of the output of a free-running coupled general-circulation simulation using the Nucleus for European Modelling of the Ocean (NEMO) model  [16].
These data were provided on the tripolar, curvilinear, ORCA12 grid  [17, 18] , with approximate 1/12° resolution, and regridded here to an equiangular 0. 05° grid for input to OSTIA. As the ORCA grid is slightly coarser than the target OSTIA grid, there is the potential for loss of feature resolution at the smallest scales. Consequently, sharpening was applied, using a 2-D Laplacian filter, to increase the power at shorter length scales to match the -11/3 power law expected for sea surface height and SST in the open ocean  [19] [20] [21]. In order to simulate IR observations of the "truth" dataset with appropriate spatial coverage, it was necessary to apply realistic masking to the data. The largest single cause of invalid IR-based SST values over the ocean, within the satellite swath, is the presence of cloud.
Suitable masking was achieved using L3U data for the two IR instruments from 2018 generated with the processor (GBCS) used for the Sea Surface Temperature Climate Change Initiative project  [7, [22] [23] [24]  and the Copernicus Climate Change Service Climate Data Store  [25, 26]. L3U data were generated at 0. 05 resolution with multiple files a day, each corresponding to an input L1 file. The SST in each L3U file was replaced with the value from the "truth" dataset for that day of the year at all locations with a valid SST value and with a quality level of 4 or higher. In January, this results in 14. 0±0. 6% of global ocean cells having at least one AVHRR observation on a given day and 12. 4±0. 5% having at least one SLSTR observation. Being based on a free-running model, the choice of year for the "truth" data was arbitrary rather than specifically 2018.
Model cells with SST less than -1. 7°C were interpreted as the locations of sea ice. The resulting dataset constituted the SSTs that would be derived in the case of a perfect observing and retrieval system for the two infrared instruments, given the cloud distribution of the target days. To provide a more realistic observational dataset, synthetic noise was added to the SST in the "perfect" files based on the uncertainty terms provided for each cell in the L3U files. These are (with typically values for AVHRR) large-scale correlated uncertainty (~0. 1 K), adjustment uncertainty (0. 05-0. 1 K), synoptically correlated uncertainty (0. 1-1. 0 K) and uncorrelated uncertainty (0. 05-0. 2 K). Large-scale correlated uncertainties are those that arise from effects that can be assumed to be correlated everywhere and over long time scales (such as calibration of the satellite sensor).
The adjustment uncertainty arises from the process of adjusting SSTs to a standard time and depth. The synoptically correlated uncertainty arises from correlations in the atmospheric conditions between SST retrievals. Both the adjustment and synoptic uncertainties are assumed to be correlated over 100 km for IR sensors and 1 day. Uncorrelated uncertainties arise from effects that are not correlated from location to location such as random noise in the satellite sensors  [27, 28]. Each of these contributions was considered in turn and, for each, a value was added to the SST in each cell in the perfect dataset of with an appropriate magnitude and correlation properties. A single large-scale noise value was applied to all cells in a given L3U file by selecting a random number from the Gaussian distribution with a standard deviation given by the mean of the largescale correlated uncertainty values in the corresponding file.
Noise values corresponding to the adjustment and synoptically correlated uncertainties were generated using a common method. Here a value was chosen at random for each cell on a 1° resolution grid from a Gaussian distribution with a standard deviation given by the mean of these uncertainties over all of the 0. 05° resolution cells falling within the 1° cell. These values were then interpolated back down to 0. 05° resolution to give a noise value to add to each cell of an appropriate size, that varied smoothly and had correlations over the correct lengthscale (~100 km) for synoptic weather systems. The uncorrelated uncertainty values for each cell were used directly by choosing a random number chosen from a Gaussian distribution with the given standard deviation. Representative daily L3C CIMR data were also generated based on the model "truth" dataset.
An initial sea ice mask was applied on the same basis as for the IR data. The edges of the mask were extended by two additional cells in both latitude and longitude to represent the need to avoid sidelobe contamination when measuring SST. A median filter was subsequently applied over a 3x3 box to obtain a SST value as seen by an instrument with a 15 km footprint. These data were then subset to every third pixel in each direction to ensure the independence of each cell being fed to the OSTIA system and a mask was applied to eliminate cells within 25 km of land. Finally, a mask was applied based on the daily coverage expected for the instrument. Analogously to the IR instruments, the resulting data represents the SSTs that would be derived in the case of a perfect observing and retrieval system for CIMR.
The above datasets were used as input to the OSTIA system in four configurations: "perfect" IRonly, "perfect" combined IR and CIMR, IR-only with realistic errors, and combined IR and CIMR with realistic errors. SST analysis fields were generated for January and July for each configuration. In each case, the first two weeks of the analysis were discarded as a spin-up period and the rest of the month used for assessing the error characteristics of the analysis outputs for each configuration. The data processing methods are summarized in Figure  2  and Figure  3.
Error Statistics The mean and standard deviation of the error in the daily analysis SST were formed by comparison with the original "truth" dataset for the day. Histograms of the distribution and global maps of these statistics are shown for January in Figure  4  and Figure  5  respectively for the perfect input data cases. Similar comparisons using input data with realistic errors are shown in Figure  6  and Both Figure  4  and Figure  6  show a clear improvement in the error distributions with the addition of CIMR data and imply an improved ability of the analyses to reproduce the "truth" dataset. As might be expected, the OSTIA analyses in Figure  5 , Figure  7  and Figure  9  show patches of larger error standard deviations in regions of dynamical SST such as around the Aghulas current and the Gulf Stream.
Reconstruction in Dynamical regions Figure  11  shows spatial power spectra of the SST fields from the January analysis using the data with realistic errors for the region of the Aghulas current and Gulf Stream. These are plotted as a ratio to the "truth" power spectra in these regions and so perfect replication of the truth field in these plots would give a ratio of 1:1 across the whole spectrum. In both cases there is a marked improvement in the agreement with the original "truth" dataset by the addition of CIMR data, as shown by the blue lines being closer to the 1:1 ratio across almost all spatial frequencies than the red lines. The structure inherent in the SST fields in these regions is shown in Figure  12. This shows a map of the mean magnitude of the gradient of the SST field in the second-half of January.
trations. It reinforces the lower limit from a previous databased approach (2. 5 to 3. 3 Pg C). Independent of model resolution, there was roughly 3 times as much C ant that entered the Arctic Ocean through lateral transport than via the flux of CO 2 across the air-sea interface. Wider comparison to nine earth system models that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5) reveals much larger diversity of stored C ant and lateral transport. Only the CMIP5 models with higher lateral transport obtain C ant inventories that are close to the data-based estimates. Increasing resolution also enhances acidification, e. g. , with greater shoaling of the Arctic's average depth of the aragonite saturation horizon during 1960-2012, from 50 m in ORCA2 to 210 m in ORCA025. Even higher model resolution would likely further improve such estimates, but its prohibitive costs also call for other more practical avenues for improvement, e.
Models For our study, we used the global ocean circulation model NEMO-v3. 2 (Nucleus for European Modeling of the Ocean -version 3. 2). The NEMO model has three parts: (1) the ocean dynamics and thermodynamics model Océan Parallélisé (OPA)  (Madec, 2008) , (2) the Louvain-la-Neuve Sea Ice Model (LIM)  (Vancoppenolle et al. , 2009) , and (3) the passive tracer module TOP. This physical model is coupled via TOP to version 1 of PISCES (Pelagic Interactions Scheme for Carbon and Ecosystem Studies)  (Aumont and Bopp, 2006). For this study we used NEMO at three resolutions: a laminar 2 • configuration (ORCA2) typical of coarse-resolution ocean models  (Madec et al. , 1998) , which does not resolve eddies; an intermediate 0. 5 • configuration (ORCA05) that just begins to allow eddies to appear spontaneously  (Bourgeois et al.
, 2000)  and Antarctic Bedrock Mapping (BEDMAP) bathymetric data are used for the Southern Ocean south of 72 • S  (Lythe and Vaughan, 2001). To interpolate the bathymetry on the model grid, the median of all data points in one model grid cell was computed. NEMO uses the partial-step approach for the model to better match the observed topography. In this approach, the bathymetry of the model is not tied directly to the bottom edge of the deepest ocean grid level, which varies with latitude and longitude; rather, the deepest ocean grid level for each column of grid cells is partially filled in to better match the observed ocean bathymetry  (Barnier et al. , 2006). The lateral isopycnal diffusion and viscosity coefficients were chosen depending on the resolution (Table  2 ). In ORCA2, a Laplacian viscosity operator was used, whereas a bi-Laplacian operator was used in ORCA05 and ORCA025.
Biogeochemical simulations For initial conditions, we used observational climatologies for (1) temperature and salinity (a combined product from  Barnier et al. , 2006) , (2) dissolved oxygen and nutrients (nitrate, P T , and Si T ) from the 2001 World Ocean Atlas  (Conkright et al. , 2002) , and (3) preindustrial C T and A T from the observation-based Global Data Analysis Product (GLODAP)  (Key et al. , 2004). As comparable observational climatologies for DOC and iron are lacking, those variables were initialized from the output of a 3000-year spin-up of an ORCA2 simulation including PISCES. Other tracers have short recycling times and were thus initialized with globally uniform constants. For physical boundary conditions, all simulations were forced with the same DRAKKAR forcing set (DFS) constructed originally by  Brodeau et al.
(2010)  and routinely updated. This historical reanalysis-based forcing data set provides surface air temperature and humidity at 2 m, wind fields at 10 m, shortwave and longwave radiation, and the net surface freshwater flux (evaporation minus precipitation). It covers  55 years, including 1958-2001 from version 4. 2 and 2002-2012 from version 4. 4. A 50-year spin-up was first made from rest in the ORCA05 NEMO-PISCES model (coupled circulationbiogeochemistry), after initializing the model variables with the abovementioned fields. The resulting simulated physical and biogeochemical fields were then used to initialize the ORCA05 NEMO-PISCES simulations in 1870, and that model was subsequently integrated over 1870-1957. Since no atmospheric reanalysis is available during that period, we simply looped the DRAKKAR Forcing Set.
Anthropogenic carbon inventories and concentrations Simulated global ocean C ant inventories are 152 Pg C in ORCA2, 146 Pg C in ORCA05, and 148 Pg C in ORCA025 in 2008, after accounting for corrections for the earlier starting date of 1765 using our perturbation simulations (P1765-P1870). The correction is similar for each resolution, e. g. , 24-25 Pg C in 1995, and is consistent with our biogeochemical model simulation strategy (all three resolutions initialized with the ORCA05 output in 1958). Furthermore, these model-based corrections fall within the 29 ± 5 Pg C correction calculated for the same 1765-1995 period with a databased approach  (Bronselaer et al. , 2017). For the 1765-2008 period, the data-based global C ant inventory estimate from  (Khatiwala et al. , 2009 ) is 140 ± 24 Pg C, the range of which encompasses the results from all three model resolutions.
By 150 m, the simulated concentrations in all resolutions have dropped below the databased estimates and remain so, except for ORCA025, down to the ocean bottom. Data-model differences are largest at 400 m, with all resolutions underestimating data-based C ant estimates by up to ∼ 28 % (9 µmol kg -1 ). Below that depth, results from the three resolutions differ more. The C ant concentration in ORCA2 decreases monotonically reaching 11 µmol kg -1 at 1000 m and essentially zero by 2300 m. The vertical penetration of C ant in ORCA2 * (the simulation without branching from ORCA05 in 1958) is shallower, reaching zero by 1400 m. In ORCA05, C ant concentrations decrease slowly to 19 µmol kg -1 at 1000 m, below which they  decline rapidly, reaching zero at 2300 m. Only in ORCA025 do C ant concentrations increase again below 400 m, reaching a local maximum at 900 m, an increase that causes the ORCA025 results to exceed data-based estimates by up to 2 µmol kg -1 (∼ 11 %) at 1100 m.
Lateral flux In our model, about three-fourths of the net total mass of C ant that accumulates in the Arctic Ocean enters laterally from the Atlantic and Pacific oceans, independent of model resolution. Our simulated lateral fluxes of C ant in ORCA025 were compared to data-based estimates from studies that multiply databased C ant concentrations (TTD estimates) along the Arctic boundaries by corresponding observation-based estimates of water transport. The simulated lateral transport of C ant in ORCA025 generally agrees with data-based estimates within their large uncertainties. These uncertainties result from uncertainties in data-based estimates of C ant and from uncertainties in observational constraints on water flow, which also varies interannually  (Jeansson et al. , 2011). For the Fram Strait,  Jeansson et al.
(2011)  estimated a net C ant outflux (from the Arctic) of 1±17 Tg C yr -1 in 2002, while for 2012  Stöven et al. (2016)  estimate an outflux of 12 Tg C yr -1 without indicating uncertainties. For the same years, ORCA025 simulates a net outflux of 8 Tg C yr -1 in 2002 but a net influx (to the Arctic) of 5 Tg C yr -1 in 2012. Both model and data-based estimates vary greatly between 2002 and 2012. Across the Barents Sea Opening, there is a consistent net influx from the Atlantic to the Arctic Ocean, i. e. , 41 ± 8 Tg C yr -1 in 2002 for the databased estimate  (Jeansson et al. , 2011)  and 50 Tg C yr -1 for ORCA025 in the same year. More recently,  Olsen et al. (2015)  added data-based estimates of lateral fluxes of C ant across the two other major Arctic Ocean boundaries, completing the set of four boundaries that define the perimeter.
Weighing in at about one-fourth of the lateral flux is the simulated air-sea flux of C ant in ORCA025 of 10 Tg C yr -1 when both are averaged over 2000-2010. That simulated estimate is only about 40 % of the data-based estimate of 26 Tg C yr -1 from  Olsen et al. (2015). Although no uncertainty is provided with that data-based air-sea flux estimate, it too must be at least ±18 Tg C yr -1 given that it is calculated as the difference between the data-based storage estimate  (Tanhua et al. , 2009)  and the  Olsen et al. (2015)  databased net lateral flux. The simulated air-sea flux of C ant falls within that assigned uncertainty range for the data-based estimate. In any case, both the model and data-based estimates suggest that the air-sea flux of C ant is not the dominant contributor to the anthropogenic carbon budget of the Arctic Ocean, respectively representing 21 % and 47 % of the total C ant input averaged over 2000-2010.
CMIP5 comparison For a wider perspective, we compared results from our forced NEMO-PISCES simulations to those from nine ocean biogeochemical models that were coupled within different earth system modeling frameworks as part of CMIP5 (Table  8 , Fig. 9 ). When the CMIP5 models are compared to the databased estimate of the C ant inventory, only the MIROC-ESM with its inventory of 2. 7 Pg C falls within the data-based uncertainty estimate What is perhaps most surprising are the large differences between our forced ORCA2 * model and the IPSL-CM5A-LR and IPSL-CM5A-MR ESMs. All three of those models use the same coarse-resolution ocean model, although both ESMs rely on an earlier version with a different vertical resolution (31 instead of 46 vertical levels). The contrast in vertical resolution may explain part of the large difference in inventories (1.
Acknowledgements. We thank  Jean-Marc Molines ,  Laurent Brodeau , and  Bernard Barnier  for developing the DRAKKAR  ORCA05  and  ORCA025  global configurations of NEMO and  Jennifer Simeon  for the implementations of those configurations and ORCA2L46 with PISCES. We acknowledge the  World Climate Research Programme 's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups (listed in Table 8 of this paper) for producing and making available their model output. For CMIP the  U. S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison  provides coordinating support and led the development of software infrastructure in partnership with the  Global Organization for Earth System Science Portals. The research was funded by the  EU   H2020  project  C-CASCADES (Marie Sklodowska-Curie  grant  643052 ).
The coauthors also acknowledge support from the  EU H2020   CRESCENDO  project (grant  641816 ), the  ANR   SOBUMS  project ( ANR-16-CE01-0014 ), and the  MTES   Acidoscope  project. Simulations were made using HPC resources from the  French GENCI-IDRIS program  (grant  x2015010040 ). Model output was stored and analyzed on the Ciclad platform at IPSL. Review statement. This paper was edited by  Marilaure Grégoire  and reviewed by two anonymous referees.
Code availability. The code for the NEMO ocean model version 3. 2 is available under CeCILL license at http://www. nemo-ocean. eu (Aumont et al. , 2006). Data availability. The data from the AOS94 section are available at https://data. nodc. noaa. gov/cgi-bin/iso?id=gov. noaa. nodc: 0113983  (Jones et al. , 2007)  and the data from the Beringia section 2005 are available at https://data. nodc. noaa. gov/cgi-bin/iso?id=gov. noaa. nodc:0108129 (Anderson et al. , 2011).
Data analysis Analysis of variance (ANOVA) in R (R Core Team, 2018) was used to determine whether the treatments had any effect on the biological endpoints that were measured. In cases where the data were nonlinear, General Additive Models (GAM) were applied using the -mgcv package  (Wood, 2015). In those cases, the GAM smoothed the time term degree day so that a hypothesis test could be performed on the fitted models of differing treatments.
Experimental conditions During the 3-week experiment, mean salinity was 34 PSU and mean temperature was 10. 7 • C. A gradual increase in the temperature of the source water led to a difference between treatment tanks of up to ± 0. 3 • C. There were no significant differences in degree days between treatments (Table  1 ). Carbonate chemistry in the treatment tanks was calculated using pH measurements and Alkalinity. The pH was not significantly different between replicate tanks within a treatment but was significantly different between treatments with a pH T of 7. 81, 7. 60, 7. 51 in the Control, Mid and High pCO 2 treatments (Table  1 ). There were no significant differences in the nutrient concentration or total alkalinity across treatments (Table  1 ); therefore, the data were pooled to calculate daily carbonate chemistry.
Treatment effects of elevated pCO 2 There were no differences in MMP between treatments. Specifically, when analyzed by degree day, there was no significant difference between the Control and Mid (GAM, P = 0. 873), nor the Control and High (GAM, P = 0. 952) pCO 2 treatments (Fig. 7 ). OCR in the High pCO 2 treatment was significantly higher than in the Control (GAM, P <0. 001) (Fig. 6 ). Control pCO 2 had the lowest coefficient of OCR at 0. 48 ± 0. 03 nmol hr -1 followed by Mid with 0. 54 ± 0. 05 nmol hr -1 , and High pCO 2 oxygen consumption was greatest with 0. 69 ± 0. 05 nmol hr -1. Since the OCR measurements of egg strings were conducted on a different cohort of animals, we applied a GAM to the dataset without those measurements-when this was done, OCR in the High pCO 2 treatment was still significantly higher than in the Control (GAM, P <0.
Data Availability The following information was supplied regarding data availability: Raw data is available in the Supplemental File and at Zenodo: Thompson, Cameron RS, Fields, David M, Bjelland, Reidun M, Chan, Vera BS, Durif, Caroline MF, Mount, Andrew,. Browman, Howard. (2019). The planktonic stages of the salmon louse (Lepeophtheirus salmonis) are tolerant of end-of-century pCO2 concentrations [Data set]. PeerJ. Zenodo. http://doi. org/10. 5281/zenodo. 3406147.
Each spot and related colour coding refers to the measured H 2 O content, including the crystal lattice and grain boundaries. All analyses surrounded by a dotted circle are used in Fig. 3. (c) Mapping of the SIMS dataset across the strain gradient. The interpolation was processed using the griddata function of MATLAB with isocontours increment of 125 ppm. The white line gives the isocontour of added water before experiment. Ol = olivine; Di = diopside; FIB = Focused Ion Beam zone documented in Précigout and Stünitz  25. Distributions of H 2 O and grain size. In Fig. 1c , we have mapped our SIMS data to show the distribution of H 2 O (see methods). The data interpolation indicates ~1500 ppm and locally more than 2000 ppm where strain is highest. These values are far above the 1330 ppm of bulk water added before the experiment, particularly if we consider the loss of hydrogen during hot-pressing and deformation, here estimated at ~550 ppm H 2 O from a hot-pressed, undeformed sample (see Supplementary Fig.
Evidence of excess water storage. In Fig. 3a , we plot our SIMS dataset with respect to the interface density in three dimensions (3-D). While the grain boundary areas have been estimated from the density of grain boundary length on EBSD maps multiplied by 4/π 27 (see Supplementary Fig. S5 ), each value of H 2 O content is shown for grain boundaries only, i. e. , the measured amount minus the potential one stored in the olivine structure (~150 ppm)  26. For comparison, we also estimate the amount of fluid that grain boundaries can contain for a given interface density and grain boundary thickness (Fig. 3a ). Based on previous studies that documented grain boundary thicknesses lower than 1 nanometer at pressures higher than 1 GPa 20,28 , we used a thickness of 0. 8 to 1 nanometre within a volume of 10 × 10 × 4 µm 3 , which corresponds to the ionized volume of a SIMS analysis.