dataesr/scientific-paragraphs-categorization huggingface dataset:
1 J/m 3 ;  Wang et al. , 2006) , at the majority of monitoring wells. The 2010 M w 7. 1 Darfield and the 2011 M w 6. 3 Christchurch earthquake both induced tidal behavior changes in two  Note. In 308 instances, monitoring wells lacked water-level data before and/or after the earthquake of interest; therefore, a water-level change could not be deduced. In 866 cases no earthquake-induced water-level change was observed, while a response was observed in 203 cases. 10. 1029/2018WR022784 Water Resources Research separate monitoring wells, probably related to a decrease in permeability. A simultaneous increase in water level was observed at these wells. Furthermore, the 2010 M w 7. 1 Darfield earthquake-induced postseismic persistent water-level increases in the near field (see the supporting information).
Acknowledgments This study was funded by the  Royal Society of New Zealand Marsden Fund  ( 2012-GNS-003 ). Hydrological data were provided by  Shaun Thomsen  of  Environment Canterbury , and seismic data were provided by GeoNet. Valuable feedback was provided by  Helen Rutter ,  Chi-Yuen Wang ,  Xin Liao ,  Zeb Etheridge , and  Sung Soo Koh. The data supporting the conclusions of this study can be found in the supporting information.
One recent success in the area of addressing diplomatic obstacles was the workshop on enhancing ocean observations and research, and the free exchange of data, to foster services for the safety of life and property hosted by WMO in February 2019. The workshop recognized the importance of Observing System Simulation Experiments (OSSEs) and sensitivity analyses to be used to investigate the importance of data collected within nation's exclusive economic zones. Despite the successes since OceanObs'09, a number of the recommendations saw little progress in the past decade. There is still a need to advance instrument technology for autonomous sampling, particularly for cloud cover, cloud type, and sea state. Adequate resources have not been available to coordinate research vessel cruise data, to develop consolidated marine datasets, standardize data quality evaluation across multiple ship measurement programs, or conduct systematic comparisons of different sensor systems typically deployed on ships.
Historically, ship observations have been the primary source of many of these variables (Table  1 ) and underpin many of the climate datasets used (e. g. ,  Kent et al. , 2019). More recently, ship observations are used indirectly through assimilation into model forecasts, hindcasts, reanalyses, and resulting databases (e. g. ,  Pilar et al. , 2008; Geyer and Rockel, 2013; von Schuckmann et al. , 2018)  to support climate studies. Models provide a self-consistent and spatiotemporally complete representation of the atmosphere and oceans beyond what can be sampled directly. Ship observations, alongside other ocean, land-based, and satellite observations, are now regularly assimilated into global and regional climate analyses that depict the four-dimensional evolution of our environment. Atmospheric reanalyses (e.
g. ,  Gelaro et al. , 2017) , ocean reanalyses (e. g. ,  Storto et al. , 2016) , coupled reanalyses (e. g. ,  Saha et al. , 2010) , and ocean/sea-ice state estimates assimilate observations from various sources into domain models (ocean, land, atmosphere, and ice) used for a range of climate applications. New approaches combining components via coupling are emerging  (Laloyaux et al. , 2018) , while traditional approaches continue to mature. Multivariate climate reanalysis products form the backbone of climate services, describing the past and present states of the climate. The first use of ship data to describe the steady ocean circulation using different hydrographic datasets and inverse box applications was introduced in the 1970s  (Wunsch, 1978). Since then, a great variety of integrated ship data products have been developed, demonstrating the essential role of ship data in producing ocean state estimates, including, but not limited to, circulation, ocean energetics, air-sea exchange, property fluxes, dynamical balances, and ventilation and mixing.
For many applications, such gridded information forms the tip of the iceberg of Earth's environmental digital history, alongside geological and biological datasets  (Keim, 2011). Development of datasets to support climate studies began in 1963 when the international exchange of delayed-mode marine climatological data was put in place through establishment of the WMO Marine Climatological Summaries Scheme. The JCOMM Expert Team on Marine Climatology updated the summaries scheme to facilitate timely exchange, access, and longterm preservation of marine climate data  (Pinardi et al. , 2019). This new Marine Climate Data System (MCDS), endorsed in 2012 by JCOMM, provides a JCOMM-wide unified approach to data management and higher-quality climate observations. The MCDS is now in the implementation phase and encompasses a data flow structure with defined roles and tasks to be applied to all data types across JCOMM.
In 2016, JCOMM confirmed the establishment of the first MCDS Center for Marine Meteorological and Oceanographic Climate Data. Since then, additional centers have been established and the MCDS will be operational by 2020. Ship data, as in the International Comprehensive Ocean-Atmosphere Data Set (ICOADS;  Freeman et al. , 2017) , are among the most important contributions to reanalyses  (Buizza et al. , 2018)  and for climate studies. The surface marine observational record is almost entirely based on ship observations prior to the 1980s  (Freeman et al. , 2017)  with records for some parameters dating back to approximately 1750 (Table  1 ). Efforts to extend and enhance the climate data record via data rescue  (Brönnimann et al. , 2018)  are often motivated by the need to improve reanalyses (e.
, 2010 (Baker et al. , , 2013)). Improved model flux estimates require improved data on the atmospheric concentrations of these components to test and validate models. A step change in observations of nutrients and contaminants over and into the ocean, ideally using commercial ships, is required if we are to provide reliable estimates of atmospheric deposition at appropriate scales to quantify the impact of these atmospheric fluxes. Building knowledge of atmospheric composition over the oceans requires a significantly greater number of direct observations. Several Global Atmosphere Watch (GAW) stations (e. g. , Mauna Loa, Mace Head, Cape Verde, Cape Grim) 2  are able to sample air that arrives from the open ocean, but these observatories are located on land and routinely measure terrestrial signals.
Increasing satellite communications bandwidth continues to reduce the latency and to increase the volume of real-time VOS data transmissions. The recent introduction of table-driven codes (the Binary Universal Form for the Representation of meteorological data or BUFR) to replace traditional alphanumeric codes (i. e. , FM-13 SHIP Code) will help to increase the availability of high-resolution data and more detailed metadata in the coming years. However, the transition to BUFR is ongoing, and challenges remain for real-time data systems relating to varying ship-to-shore data exchange formats, the shore-side conversion to BUFR, and ensuring that all required data and metadata make it from real-time observations into the long-term climate archives  (Kent et al. , 2019). Meteorological observations on research vessels include both the standard weather observations made to support the VOS Scheme and also dedicated air-sea interaction experiments that directly measure turbulent exchanges (i.
Although capability to make comprehensive flux measurements on other types of ships, moored buoys, or autonomous surface vessels is developing rapidly  (Cronin et al. , 2019; Swart et al. , 2019) , research vessels will be the primary platform used to evaluate data from these emerging technologies. Research vessel observations are geolocated via the highprecision navigational systems typically deployed to support research vessel science operations. Research vessels often carry both standard weather instruments of the type supplied to vessels by NMHS to contribute to the VOS Scheme and sophisticated automatic weather stations, which are frequently custom designed and built by individual research vessel operators. Although research vessels may transmit a subset of their observations in real-time via the GTS, most observations are recorded and used in delayed mode, typically as part of an end of cruise dataset.
There are no international standards for meteorological parameters to be sampled by research vessels, but since 2005, the U. S. -funded Shipboard Automated Meteorological and Oceanographic System (SAMOS) initiative  (Smith et al. , 2018)  has provided guidance to the research vessel community. SAMOS has developed standard data formats for research vessel ship-to-shore data exchange, distribution, and archival along with training materials for marine technicians on research vessels with regard to sensor selection, siting and exposure, data processing, and quality evaluation. Most research vessels, selected commercial vessels (e. g. ,  Alory et al. , 2015; Gaillard et al. , 2015) , and more recently racing yachts (e. g. ,  Kramp et al. , 2010)   3  are equipped with flow-through sea water systems that measure sea temperature and salinity [using a thermosalinograph (TSG)], fluorescence, transmissivity, and other biogeochemical properties (e.
Metadata Almost as important as the measurements themselves are the associated metadata that provide supporting information about host vessels, instrumentation, and the data. A sophisticated metadata catalog allows for detailed and standardized quality monitoring of individual vessels and instruments, as well as the health of the network of VOS, research vessels, and complementary private and autonomous vessels. WIGOS has developed metadata requirements (WMO, 2017) that are designed to make the observations (e. g. , Table  1 ) useful to downstream users. Mapping community-developed metadata schemas (e. g. , VOS, SAMOS, and GOSUD) to identify overlaps and gaps with respect to these requirements is an ongoing challenge. Information on the ship's characteristics and meteorological instrument type and siting is also important for climate studies.
, 2017). To account for these changes, metadata are required, such as the height or depth of observation and the measurement method. Metadata for ships participating in the VOS Scheme are recorded in the WMO Publication No. 47 (Pub 47;  Kent et al. , 2007). This framework was initially developed by the WMO but is presently managed by a task team of the JCOMM Ship Observation Team. In an effort to modernize and sustain the VOS metadata record and transition from Pub 47 to the WIGOS standard, a new VOS metadata database is being implemented and managed by the JCOMM Observations Programme Support Centre (JCOMMOPS). While Pub47 is an extensive metadata schema, there are only five mandatory metadata fields required to register a ship within the VOS Scheme: recruiting country, callsign, VOS recruitment date, vessel type, and general observing practice.
The present challenge is to map all the prior Pub47 metadata fields into the new WIGOS schema and define new metadata fields to support a broader user community while also working with VOS operators to collect and provide a larger number of mandatory metadata fields. Another metadata challenge arose from an increased sensitivity of vessel owners and operators to the location and identification of their vessels (IDs) being available in near real time. There have been a number of different schemes [callsign masking, use of generic callsigns (e. g. , SHIP), callsign encryption, etc. ] implemented to accommodate the vessel operators and maintain their contribution to the VOS Scheme. However, these all come with limitations, for both the operational and climate communities. The loss of IDs makes quality control and blacklisting of low-quality data difficult for the operational users.
The loss of callsigns makes associating the observations with vessel metadata (e. g. , Pub 47) impossible, resulting in lower-quality climate monitoring products. Some attempts have been made to overcome this by expanding the range of metadata in the VOS real-time formats to include those required to fully understand and use the observations, but reporting of the additional metadata has been limited to date. Parallel to these developments, advancements in Automated Information Systems (AIS) technology largely negate attempts to "hide" ships from unwanted interest. In light of this, JCOMMOPS is implementing a new approach for ship metadata that separates the "ship" metadata from the "observing station" metadata so that no detectable reference to the ship is included with the observation. A master list of all participating ships will be retained by JCOMMOPS to re-associate the ship with the instruments for authorized users.
At present, not all countries operating VOS contribute their metadata to the international database. With the global decline in the number of manually observing VOS and the increasing number of automated systems, a fully representative metadata database containing records for all international VOS would improve the capabilities of operational monitoring and therefore maximize data availability for climate and research purposes. Discovery metadata are required to enable users to find and retrieve the observations. This includes information such as the spatial and temporal coverage of the observations, information on the reported parameters, licensing and usage rights, and how to access and download the data. Historically, this information has been provided through project web pages, but there is an increasing requirement for the metadata to be machine readable and interoperable, driven by, inter alia, the following: the volume and distributed nature of the observations; ease of use and the increasing standardization of data delivery; and the development of web services.
This is usually achieved through the publication of discovery metadata records for observation collections based on international standards (e. g. , the ISO 19115 standard). Once published through online data catalogs, such as those provided by the IODE Ocean Data Portal 9  and the ERDDAP software 10  , the observation collections are easily locatable and retrievable through a single or limited number of locations. In addition to providing access to observational collections, systems are being developed to provide bespoke access to individual observations (e. g. , through the Copernicus Climate Data Store 11  , systems based on the OGC Web Feature Service interface).
, 2019). The quality of the adjustment for bias and the estimation of uncertainty in observations are dependent on the availability of observational metadata (providing information on instruments, observing protocols, etc. ), on information on data management practices (e. g. , data precision and any conversions or calibrations applied), and, in some cases, on knowledge of the ambient environmental conditions  (Kent et al. , 2019). An alternative to the provision of uncertainty estimates is an end-to-end quality assessment, which is much more challenging than quality control completed following data collection as described above. The GAW atmospheric composition monitoring program is one example of a quality assessment approach that ensures all its observing sites are comparable. This is essential to produce globally consistent datasets.
GAW mandates the use of a primary standard for most parameters to define a measurement scale. The scale is maintained by a single institution and propagated to all other members of the program in a traceable manner. Clear data quality objectives have been articulated, and these are supported by measurement guidelines developed by the community of researchers. Detailed logbooks and metadata are required to be maintained, along with a commitment to submit data to the World Data Centers in a timely manner. Audits and inter-comparison programs are routine. Participating institutions are encouraged to deliver measurements with uncertainty within agreed limits. The atmospheric laboratories on the research vessel Investigator have opted to adhere to the quality assessment guidelines of the GAW program, making it the first mobile GAW station.
Better coordination between the observational programs is essential for unified requests (i. e. , not from multiple individual researchers or operational groups) to be made to the vessel owner/operators to ensure that requests do not adversely impact the core mission of the vessels. The WOC is one option to address these coordination needs. Despite the diverse observing capabilities of research vessels, they are an underutilized component of the observing network. A concerted effort is needed to coordinate marine observations from research vessels. Firstly, we recommend all research vessels to be recruited into the VOS Scheme, have their metadata documented by JCOMMOPS, and be provided with tools to submit data via the GTS. Ensuring research vessel data acquisition systems are compatible with existing (e.
g. , TurboWin) or new VOS reporting software will increase research vessel capabilities to provide real-time observations. The authors further recommend engaging the research vessel operator community in a dialogue to (a) determine which research vessels routinely operate in high-priority ocean regions, (b) support installation of multiple sensor packages on research vessels in these regions, and (c) maximize data collection by ensuring that all sensor packages on each research vessel are operating on every voyage. The Arctic Research Icebreakers Consortium 13  program provides an example of how countries are working to coordinate research vessels for data collection, and interaction with the commercial fleets via the WOC. Additional ships are needed to collect observations to estimate air-sea fluxes by installing sensors and undertaking further automated approaches to collect the full suite of observations required.
This is particularly true in regions where we currently have few observations and large uncertainties (e. g. , the Southern Ocean,  Swart et al. , 2019; Western Boundary Currents, Bentamy et al. , 2017 ) and yet where there is growing ship activity via research, tourist, or other ships. We recommend enhancing current capability for direct flux measurements from research vessels and ensuring that high temporal resolution underway observations from all research vessels and a subset of commercial ships are routinely collected and managed in a SAMOS-type data system, including real-time transmission if needs are identified. These enhancements will help identify biases and improve data quality. Direct flux measurements will always be sparse, so meeting required accuracy targets for regional and global fluxes  (Cronin et al.
Examples include the Community Collaborative Rain, Hail and Snow Network 14  , and Weather Underground's Personal Weather Station Network 15. Several user communities (e. g. , satellite product developers and numerical modelers) acknowledge the value of observations from thirdparty platforms, provided that the platform metadata are sufficient to allow these users to conduct quality evaluation necessary for their application. In some cases, this may result in rejection of these records based on comparison with model first guess fields or neighboring ship, mooring, or other observations with a known quality. The authors recommend establishing a pilot project, possibly in partnership with JCOMM, to develop a web portal to support third-party data collection. Such a portal would support registration of the vessel, collecting minimum vessel and instrumental metadata, and submission of their observations.
For example, the National Centers for Environmental Information currently combines two GTS data streams to create monthly updates of ICOADS  (Freeman et al. , 2017). Creating such interim products supports climate assessment and monitoring. Identifying duplicate and near-duplicate reports can be difficult, especially if ship identifiers have been masked. Data derived from the GTS are periodically combined with data from delayed-mode data systems in major ICOADS updates, again with challenges for identifying duplicate reports received from different data management systems. For the climate record, it is sometimes the case that only a real-time observation exists, and there is no corresponding delayed-mode source. We recommend preserving all metadata included with BUFR or earlier fixed character-based messages.
With the current transition to BUFR, more metadata can be made available alongside the observed data, which should eventually remove the need for the VOS delayed-mode data system. While metadata fields available in BUFR are much more expansive than those in past formats, encoding of metadata into the BUFR records may not be complete, depending on the network owner responsible for providing this information; so a central metadata repository, such as that provided by JCOMMOPS, is still required to maintain historical vessel/platform metadata and lineage. GTS collections at different NMHS may vary in their contents (JCOMM, 2010), and in some cases, data are circulating on the GTS with a low level of metadata (a situation that will significantly improve when the BUFR format is fully adopted). To facilitate capturing the full range of observations on the GTS, the authors recommend establishing a real-time GTS data assembly center within the JCOMM MCDS to harvest and manage all ship observations circulating on the GTS.
This center could accept data received from the GTS by multiple NMHS, conduct duplicate evaluation, and provide these observations to downstream users and product developers. The result would be a consolidated source for real-time GTS data with agreed quality control that could be aggregated to climate datasets such as ICOADS or the World Ocean Database, providing full provenance from instruments to archives/products as intended by the MCDS  (Pinardi et al. , 2019). Aggregating the data in the MCDS would provide a more complete collection and assure permanent preservation in international archives and repositories, guaranteeing their existence for future applications. Beyond the real-time data transmitted via the GTS, there presently is no global repository dedicated to all data types (Table  1 ) collected onboard vessels.
Most research vessel observations are neither available in near real time for operational applications nor processed in delayed mode to be submitted to global climate archives. There are examples of good stewardship of EOVs and ECVs, including the Rolling Deck to Repository, SAMOS, GOSUD, SOCAT, GLODAP, PANGAEA Data Publisher 18  , and the Australian Ocean Data Network, which provide quality assurance and dissemination and securely archive observations from research vessels (e. g. ,  Gaillard et al. , 2015). Much of the data are archived by different national data centers, and only some of these data are included in global marine climate databases (e. g. , ICOADS and World Ocean Database). There are counter-examples where national research vessel operators do not always continuously log or manage data from underway systems.
Within the VOS Scheme, a dedicated "ZZ" category exists for recruited VOS ships that have no particular affiliation with a national VOS fleet. There are currently over 300 such ships registered within the VOS metadata database. This allows for the ad hoc recruitment of certain vessels where, for example, their voyage route looks to be beneficial (e. g. , within a data sparse area), the voyage is only for a dedicated period of time, or there is limited resource to manage the data as part of a national fleet. Other benefits can be obtained by building relationships with private operators (e. g. , yachts and racing teams) of vessels equipped with atmospheric or oceanic sensors, which may not be of the highest quality, but are occupying routes/regions not typically occupied by vessels recruited to VOS or research vessels (e.