dataesr/scientific-paragraphs-categorization huggingface dataset:
Satellite observations are and will be key elements to understanding such a remote and harsh environment where in-situ observations are limited in space and frequency. In fact, most of our knowledge today about the surface properties, both for the Arctic and Antarctic, is derived from satellite data over the last 40 years, supplemented by sparse in-situ measurements derived largely from scientific expeditions. The continuity of satellite observations over several decades, with no or limited gaps between the satellite missions, is consequently key for the study of long-term changes. However, although the Earth Observation polar-orbiting satellite constellation is particularly dense today, gaps remain in the satellite observations, in terms of observed physical variables, spatial and temporal coverage, and resolution.
This choice is made to reduce the complexity and improve the readability of the figure presentations of the results, without excluding the major application areas. The availability of relevant and similar type sensor data from missions planned by the other countries will complement and expand the availability of satellite missions to be available for future studies of the Arctic Ocean. The study is undertaken within the framework of the Arktalas Hoavva (Arctic Ocean in the Sami language) project funded by the European Space Agency (ESA) with the aim to advance the use of satellite measurements in synergy with in-situ data and computer models to characterize and quantify the processes driving changes in the Arctic sea ice cover and in the Arctic Ocean. In so doing, knowledge gaps must be removed to advance our quantitative understanding of sea ice, ocean and atmosphere interactive processes and mutual feedback at various temporal and spatial scales.
Further efforts to improve synergies within a comprehensive Arctic Observing Network are, therefore, recommended  [21]. Based on this the Arktalas Hoavva study project has adopted a stepwise multi-modal analyses framework approach to address the four major Arctic Scientific Challenges listed above, benefitting from multiscale resolution satellite observations together with complementary in-situ data, computer model simulations, data assimilation, analyses, and integrated visualization tools. In the high-latitude seas and the Arctic Ocean, global warming and Arctic Amplification are considered to occur across a range of environmental state variables with complex interactions and feedback mechanisms at regional to global scales. Central among these are changes in the radiation balance, changes in ocean-sea ice-atmosphere momentum, heat and gas exchanges, reduction in the sea ice extent and thickness, and changes in the bio-optical properties in the upper ocean.
Cassianides et al. [13]  have developed a new method to detect ocean eddies based on the response in the sea ice drift and vorticity fields retrieved from SAR images, which is particularly promising. Thanks to the innovative use of data from ICESat-2, Sentinel-2, Sentinel-1, and Sentinel-3 Fully-Focused SAR altimetry and the Chinese-French Oceanographic Satellite Surface Waves Investigation and Monitoring instrument (CFOSAT-SWIM), wave patterns have been clearly detected in the sea ice and ocean of the MIZ by Collard et al. [14]. This clearly advocates for a synergetic approach, building co-located datasets to achieve a better quantitative understanding of the propagation and interactions of waves and sea ice. As thinning sea ice and growing areas of open water within the Arctic Ocean will also be more effectively exposed to extreme events, it is important to better understand, characterize and predict the impact of extreme storms on sea ice formation and break-up.
Finally, the impact of increased temperatures in the Arctic-manifested through the Arctic Amplification-on the basin scale atmospheric and ocean circulation was explored by Regan et al. [12]  with a focus on the Beaufort gyre and its evolution over the period 1990-2014. Using altimetry-based Dynamic Ocean Topography datasets  [24, 25]  together with a high-resolution eddy-resolving model, they concluded that the accumulation of freshwater due to changes in the wind forcing and sea ice conditions led to the spin-up of the Beaufort Gyre. Clearly, the changes taking place in the Arctic today may have profound impacts on the general circulation of the Arctic and its interaction with other ocean basins and the global thermohaline circulation  [26].
Efficient and timely exploitation of the growing and vast amounts of data is, therefore, urgently needed. (X) (X) X (X) (X) Sea Ice Type X X (X) (X) Sea Ice Albedo X Sea Ice Surface Temperature X X Sea Ice Freeboard X X Sea Ice Thickness Thinner than 50 cm Thicker than 50 cm Thicker than 50 cm Sea Ice Drift (X) X X X Snow Depth on Sea Ice First year ice X X Sea Ice Leads X X X X X Melt Ponds on Sea Ice X (X) X (X) Waves in Sea Ice (X) X (X) The environmental variables are predominantly retrieved from imaging sensors with varying swath widths, except for the altimetry nadir profiling sensing method. The largest observation gaps in the Polar regions are related to radar and laser altimetry. However, thanks to the launch of CryoSat-2 in its near Polar orbit in 2010, the altimeter-based observation gap has been significantly reduced from south of 82 • N to 89 • N.
The Sankey diagrams displayed in Figures  2  and 3 , for a selected number of missions, link the past, present and future approved satellite missions with the observed ocean and sea-ice-related parameters and quantities, respectively. The figures provide another synthetic view of the remote sensing observational continuity and complementarity, although not all relevant missions are included in order to maintain the readability of the diagrams. Despite their fundamental importance to examine the main Arctic Ocean scientific challenges, satellite observations still have limitations, justifying the need for complementary information sources. These limitations are further addressed below. First, the compilation of satellite observations to build homogenous, cross-calibrated and cross-validated, long and continuous data records of climate quality, across multiple satellite missions, is difficult.
Sensors and platforms degrade with time, sometimes stop functioning, and need to be substituted with new ones, some with different technical specifications. The reconciliation of data sets is very challenging, as Esau et al. [17]  show in an extensive literature discussion. As environmental changes in the Arctic are known to be relatively fast (e. g. ,  [8] ), and the observational records are rather short (in the climatic sense), even a single year with lower-quality data can cause significant deviations and uncertainty for long-term climate records. Literature is, therefore, very inconclusive as to what degree satellite sensor calibration and inter-calibration properly apply to climate-quality long-term data records in the Polar regions. The lack of Fiducial Reference Measurement (FRM)  [27]  in-situ data to accurately calibrate and validate the satellite observations in the Arctic Ocean is also a major limitation in the generation and validation of homogeneous datasets and for the evaluation of new processing strategies.
Several initiatives have been launched by ESA to define FRM strategies for satellite observations, but these are currently limited to a few types of observations (FRM4SOC for Ocean Colour, FRM4STS for Surface Temperature, FRM4Alt and St3TART for radar altimetry topography of the ocean, inland waters, sea ice and land ice). Each study is designed to develop and apply rigorous metrology approaches to ensure that satellite data are traceable to S. I units with a full uncertainty budget. More generally, comparing satellite observations with in-situ data in the Arctic Ocean, for instance, to better understand local dynamical processes and interactions, is highly challenging as the limited amount of in-situ data is usually available in given locations for practical logistic reasons. As such, the dynamical marginal ice zones are not properly sampled with in-situ data.
In other cases, in-situ observations may exist but are not publicly available, or only distributed at low temporal resolutions (typically monthly) that are not suited to study high-frequency processes and interactions. The heterogeneity, in time and space, of ocean and ice processes also imposes challenges in terms of sampling strategies for satellite data validation and the use of multiple data sets in synergy, since the acquisition times and locations introduce uncertainties due to natural geophysical variability. Despite their fundamental importance to examine the main Arctic Ocean scientific challenges, satellite observations still have limitations, justifying the need for complementary information sources. These limitations are further addressed below. First, the compilation of satellite observations to build homogenous, cross-calibrated and cross-validated, long and continuous data records of climate quality, across multiple satellite missions, is difficult.
Sensors and platforms degrade with time, sometimes stop functioning, and need to be substituted with new ones, some with different technical specifications. The reconciliation of data sets is very challenging, as Esau et al. [17]  show in an extensive literature discussion. As environmental changes in the Arctic are known to be relatively fast (e. g. ,  [8] ), and the observational records are rather short (in the climatic sense), even a single year with lower-quality data can cause significant deviations and uncertainty for long-term climate records. Literature is, therefore, very inconclusive as to what degree satellite sensor calibration and inter-calibration properly apply to climatequality long-term data records in the Polar regions. The lack of Fiducial Reference Measurement (FRM)  [27]  in-situ data to accurately calibrate and validate the satellite observations in the Arctic Ocean is also a major Second, many long-term optical (visible to thermal infrared) satellite data products are fragmented due to cloud cover, haze, and daylight variability.
Therefore, a strong bias towards observations in clear sky conditions is observed, with both seasonal and geographical variations, as winter darkness is an additional challenge in the Polar regions. Interpolation and gap closure methods are thus questionable as they rely on an unjustified assumption that the statistics under a clear sky and overcast are the same. This is not the case in the Arctic boundary layer, but detailed studies of this problem from a climate perspective are lacking. In turn, climate datasets such as the ESA Climate Change Initiative (CCI)  [28]  may still encounter limitations such as scarcity and low resolutions, in addition to a lack of sufficiently long time series of consistent and high-quality measurements. In addition, other types of limitations are more specific to individual missions, instrument type, cross-calibration between similar sensors at different missions, and retrieved variables.
The seasonal sun cycle at polar latitudes, with winter darkness and 24-h summer sunlight, limits the data availability during most of the year. Regarding satellite radar altimetry, the current processing of conventional Low-Resolution Mode (LRM) altimeters such as ERS-1/2, ENVISAT Ku-band and SARAL Ka-band radars was developed for open water surfaces. In the presence of sea ice, these algorithms break down, meaning that the accuracy of the sea surface height (SSH) observations and the sea ice freeboard estimation can be strongly impacted in large areas of the Arctic Ocean and adjacent seas  [24, 25]. The Ku-band SAR altimeters onboard the CryoSat-2 and Sentinel-3 missions have proven to be extremely valuable in sea ice-covered regions, thanks to higher along-track resolution (typical processing delivering an along-track resolution of about 300 m), which enables to better detect sea ice leads from specular reflection signals in the radar echo return.
However, brightness temperatures must provide data at high radiometric fidelity. Additional observations from spectroradiometers such as Sentinel-3 OLCI (visible spectrum) and SLSTR (shortwave and thermal infrared) or MODIS onboard the NASA Aqua mission and AVHRR onboard past NOAA satellite missions, are also very useful. Merged sea ice thickness products can then be generated, such as the weekly CS2SMOS products  [34]  that are delivered at 25 km resolution. SAR imagery from ENVISAT, RadarSat-2 or Sentinel-1 can also be used to infer sea ice thickness, considering the ratio of backscattering at different polarizations (e. g. ,  [35, 36] ); however, this approach still has strong limitations as it is based on strong empirical assumptions on the relationship between sea ice thickness and the polarimetric response, and only works for some sea ice types.
New approaches based on machine learning trained with in-situ sea ice thickness data and applied to Sentinel-1 SAR data show promising results  [37]. Optical and SAR images have also been recently used to train a neural network algorithm and produce an unprecedented and promising record of 10 years of Summer sea ice thickness based on CryoSat-2 altimetry, but still with some underestimates in regions of thick multi-year ice  [38]. Vertical in-water profiles of temperature, salinity, oxygen, etc. , obtained from gliders, ARGO floats, fixed moorings and research shipborne expeditions provide a vital set of measurements that are necessary to complement satellite measurements in the open ocean. In ice-covered waters, icebreakers of polar-class vessels are required. With the notable exception of ocean color radiometry, satellite observations measure signals expressed at the very surface of the water column.
The continuity of currently operational radar and infrared radiometer missions is already ensured, allowing the creation of long-term overlapping of high qualitative datasets. Sentinel-1 C and D will sustain C-band SAR measurements up to 2034, while Sentinel-3 C and D SRAL, SLSTR and OLCI instruments are planned to fly up to 2035, providing valuable ice and ocean measurements up to 82 • N and thus covering the regions with most of the human activities in the Arctic Ocean. Concept studies are also ongoing to prepare the future Sentinel Next Generation missions (beyond 2032), in particular, the Sentinel-3 NG Topography mission. Several approaches (i/a constellation of a dozen of nadir radar altimeters, ii/a constellation of two swath radar altimeters, or iii/a constellation of one swath altimeter and several nadir altimeters) have been evaluated, that should, whatever the selected configuration, provide unprecedented coverage of the global ocean for a topography mission.
These design elements have a long unbroken heritage from ATSR-1 first flown on ERS-1 in the 1990s out to 2032 in the form of Sentinel-3C and Sentinel-3D. After this, the Sentinel-3 Next Generation Optical mission (S3NG-O) will include an advanced SLSTR instrument (ASLSTR) that will guarantee the future of SLSTR measurements while enhancing performance in the 2032+ timeframe. As for the sea ice freeboard parameter, sea surface height observation in the Arctic Ocean highly relies on CryoSat-2 and Sentinel-3. A CryoSat-2 EOL in 2024 as it is planned today, before the CRISTAL launch expected end of 2027, will strongly limit the Arctic coverage with high-quality radar altimeter data (SAR and SARin modes), as the orbits of the Sentinel-3 and SWOT missions, which should be still operated during the CryoSat-2/CRISTAL gap, are limited to 81.
Reprocessing existing SAR data for retrieval of the Doppler shift is, therefore, highly recommended. Moreover, HARMONY will bring new bistatic observations in this field in 2028. A few years later, the proposed Earth Explorer mission SEASTAR could allow pan-Arctic surface current and sea ice drift measurement at 1 km resolution. Additionally, a future decision for the SKIM-like mission could complement those observations up to 82 • N at a mean repeat cycle of 4 days. Despite the various missions measuring ocean surface wind, the observations will be limited to 82 • N after CFOSAT EOL and before HARMONY, CRISTAL, CIMR and ROSE-L provide a complementary and multi-sensor complete coverage of the Arctic Ocean. In addition, it should be noted that none of these missions will retrieve near-surface wind speed and direction in the presence of sea ice, which will remain as an observational gap when addressing the challenge of better understanding the air/sea ice/ocean interactions, Arctic weather and ocean predictability in the high latitude and Arctic Ocean.
Finally, sea surface salinity was not measured before the SMOS mission in 2009. Today both the SMOS and SMAP missions provide measurements of salinity derived from L-band microwave radiometers. It is, therefore, highly desirable to sustain salinity measurement from SMOS and SMAP until CIMR-A is launched in 2029, to ensure continuity and overlap of measurements and stability of the climate data record of sea surface salinity.
Conclusions Comprehensive and quantitative understanding of the Arctic Amplification, sea ice break-up event, gyre spin-up, eddy generation and decay and dynamics in the MIZ are all limited due to observational gaps and limited process understanding. Exploitation and full use of the multi-sensor satellite measurements are, therefore, urgently needed although still challenging due to the lack of high-quality validation. In the future, new products and estimates of variables are expected to gradually emerge from the existing observations due to novel strategies and approaches including data-driven co-variability analyses, machine learning and Artificial Intelligence. Blended with data from new satellite missions this is, therefore, anticipated to accelerate our quantitative understanding and knowledge, allowing for significant advances in re-analyses and provision of long time series resolving sea ice and ocean parameters as well as major interactive processes and mutual feedbacks in the Arctic Ocean.
Satellite observations are fundamental in the monitoring of the Arctic due to the harsh climate, accessibility, and the difficulty to gather relevant and quantitative in-situ measurements above, at, and below the ocean surface. Therefore, the quality, continuity and novelty of satellite acquisitions are of prime importance. As such it is also highly urgent to secure proper validation and uncertainty assessments, requiring also continued dedicated ground truth measurements of essential ocean and ice variables, process studies and under-ice observations within the Arctic Ocean, and particularly in the marginal ice zone. This will require regular access to icebreaker facilities and the utilization of new technological instrumentation built for long-term in-water and surface observations, with efficient energy demands and capabilities for data retrieval also in year-round and seasonally ice-covered waters.
Some proposed but not yet selected missions such as SEASTAR will significantly contribute to filling some of these gaps for the ocean and sea ice aspects, as will mission concepts such as SKIM, building on the success of CFOSAT. Because of the difficulty to observe the Arctic Ocean with the spatial-temporal resolution that would be needed, either from satellites or in-situ instruments, most studies rely today on the synergy between different types of satellite observations, in-situ measurements and numerical modeling, including re-analyses. Advances expected to emerge from the development of a Digital Arctic Twin, collocated and combined multi-modal data (in-situ and satellite) and model-driven physical constrained analyses, as well as the use of artificial intelligence, will strengthen the ability to deliver more reliable estimates, and hence predictions, of sea ice deformation, break-up, leads formation, new ice formation, sea ice freeboard height, sea ice volume and mean sea level.
Study sample Analyses were conducted using data from 46 studies (16 prospective cohorts, 14 population-based case-control studies, and 16 non-population based studies) participating in the BCAC. We excluded participants if they were genotypically male, of non-European descent, or had a breast tumor of unknown invasiveness or in-situ breast cancer. Women with prevalent breast cancer at the time of recruitment or with unknown reference age (defined as age at diagnosis for cases and age at interview for controls) were also excluded from the analyses. Further, studies with fewer than 150 cases and 150 controls for the risk factor under evaluation were excluded from those analyses. Each participating study obtained informed consent from the participants and was approved by their local ethics committee.
Risk factor data Risk factor data from individual studies was checked for quality using a multi-step harmonization process based on a common data dictionary. Time-dependent risk factor variables were derived with respect to the reference date defined as date of diagnosis for cases and date of interview for controls. Analyses were conducted with the Background Breast cancer is a complex disease involving interplay between lifestyle/environmental and genetic risk factors. Risk factors such as parity, breastfeeding, age at menarche, age at first full-term pregnancy, body mass index (BMI), height, mammographic density, exogenous hormonal use, and alcohol consumption are wellestablished  [1] [2] [3] [4] [5] [6] [7]. Through continued collaborative efforts such as the Collaborative Oncological Geneenvironment Study (COGS) and the OncoArray project  [8] , more than 200 common single nucleotide polymorphisms (SNPs) associated with risk of breast cancer have been identified  [9] [10] [11].
Genetic data All samples were genotyped either using the iCOGS  [20, 21]  or OncoArray  [9, 10, 22]. Briefly, iCOGS is a customized iSelect SNP genotyping array, consisting of ~ 211,000 SNPs  [20, 21] , whereas OncoArray includes ~ 533,000 SNPs of which nearly 260,000 were selected as a GWAS backbone (Illumina HumanCore)  [22]. Detailed information is provided elsewhere  [9, 10, [20] [21] [22]. Data were imputed to the 1000 Genomes Reference Panel (phase 3 version 5). Overall, 28,176 cases and 32,209 controls of European ancestry who were genotyped by the iCOGS array, and 44,109 cases and 48,145 controls who were genotyped using the OncoArray array were included in this analysis. Genetic variants with imputation quality score < 0. 5 in iCOGS or < 0. 8 in OncoArray, or with minor allele frequency < 0.
Although this is the largest breast cancer dataset available to date with more than 60,000 cases and 70,000 controls, the study is underpowered to detect weak interactions. Also, this study included only women of European ancestry and the findings may not be generalizable to women of other ancestries. Using LDSC regression, we estimated the overall heritability due to G×E for each of the risk factors. The estimated frailty scale heritability (≤ 0. 015) can be compared with corresponding heritability for the SNP main effects (for which heritability is about 0. 47) or the overall heritability based on the familial risk (~ 1. 4)  [28, 41]. The implication is that G×E interactions make very little contribution to the heritability of breast cancer, at least for the known risk factors and common genetic variants that can be evaluated using genome-wide arrays, and hence do not make an important contribution to risk prediction at the population level.