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A coherent picture of global surface temperature change since the late nineteenth century ennerges from a statistical reconstruction of an integrated collection of historical temperature observations over the land and ocean.T he most widely recognized measure of observed climate change is the century-scale trend in globally averaged surface temperature. The global average is a simple theoretical concept, but its computation in practice is far from trivial. The complexity stems mainly from the idiosyncrasies of historical weather observations, most of which were collected for operational purposes, such as aviation and agriculture, rather than climate change detection. In particular, certain practices that are of little operational significance such as relocating a station or changing its instrumentation, may profoundly impact the integrity of the climate record (Aguilar et al.

The remotely sensed sea surface temperature (SST) estimated from the 4-km-resolution Pathfinder SST algorithm is compared to a SST locally measured by the Marine Atmospheric Emitted Radiance Interferometer (MAERI) during five oceanographic cruises in the Atlantic and Pacific Oceans, in conditions ranging from Arctic to equatorial. The Pathfinder SST is a product of the satellite-based Advanced Very High Resolution Radiometer, while the MAERI is an infrared radiometric interferometer with continuous onboard calibration that can provide highly accurate (better than 0.05°C) in situ skin temperatures during extended shipboard deployments. Matchups, which are collocated (within 4 km) and coincident (±40 min during the day; ±120 min during the night) data, from these two different sources under cloud-free conditions are compared. The average difference between the MAERI and Pathfinder SSTs is found to be 0.07 ±0.31°C from 219 matchups during the low-and midlatitude cruises; inclusion of 80 more matchups from the Arctic comparisons produces an average global difference of 0.14 ±0.36°C. The MAERI-Pathfinder differences compare favorably with the average midlatitude differences between the MAERI skin SST and other bulk SST estimates commonly available for these cruises such as the research vessels' thermosalinograph SST (0.12 ±0.17°C) and the weekly National Centers for Environmental Prediction optimally interpolated SST analysis (0.41 ±0.58°C). While not representative of all possible oceanic and atmospheric regimes, the accuracy of the Pathfinder SST estimates under the conditions sampled by the five cruises is found to be at least twice as good as previously demonstrated.

The National Oceanic and Atmospheric Administration’s (NOAA) Big Data Partnership (BDP) was established in April 2015 through cooperative research agreements between NOAA and selected commercial and academic partners. The BDP is investigating how the value inherent in NOAA’s data may be leveraged to broaden their utilization through modern cloud infrastructures and advanced “big data” techniques. NOAA’s Next Generation Weather Radar (NEXRAD) data were identified as an ideal candidate for such collaborative efforts. NEXRAD Level II data are valuable yet challenging to utilize in their entirety, and recent advances in weather radar science can be applied to both the archived and real-time data streams. NOAA’s National Centers for Environmental Information (NCEI) transferred the complete NEXRAD Level II historical archive, originating in 1991, through North Carolina State University’s Cooperative Institute for Climate and Satellites (CICS-NC) to interested BDP collaborators. Amazon Web Services (AWS) has received and made freely available the complete archived Level II data through its AWS platform. AWS then partnered with Unidata/University Corporation for Atmospheric Research (UCAR) to establish a real-time NEXRAD feed, thereby providing on-demand dissemination of both archived and current data seamlessly through the same access mechanism by October 2015. To organize, verify, and utilize the NEXRAD data on its platform, AWS further partnered with the Climate Corporation. This collective effort among federal government, private industry, and academia has already realized a number of new and novel applications that employ NOAA’s NEXRAD data, at no net cost to the U.S. taxpayer. The volume of accessed NEXRAD data, including this new AWS platform service, has increased by 130%, while the amount of data delivered by NOAA/NCEI has decreased by 50%.

[1] Using ship-of-opportunity platform Explorer of the Seas, five years of full-depth velocity data have been collected across the Florida Straits at 26°N. Between May 2001 and May 2006 the mean transport of the Florida Current was 31.0 ± 4.0 Sv. This compares to a mean transport of 32.4 ± 3.2 Sv inferred from cable voltages at 27°N over the same period, implying an average 1.4 Sv transport into the Straits through the Northwest Providence Channel. The climatological core of the Florida Current is 170 cms À1 and is positioned at 79.8°W, about 10 km east of the shelf break. The largest variability in velocity occurs over the shelf and shelf break and is likely related to shelf waves. A secondary maximum occurs across much of the Straits over the top 100 m of the water column and may be associated with wind events. The annual cycle of Florida Current transports has a range of 4.7 Sv, with a maximum in May-June-July and a minimum in January. The difference between the summer and winter current structure appears as a first baroclinic mode with zero crossing at 150 m. The maximum difference is about 15 cms À1at the surface and is centered just offshore of the mean current core. On interannual timescales, low-pass filtered Explorer and cable transports show similar downward trends between 2002 and 2005, but diverge over the last year or so of the record.

This paper presents a stewardship maturity assessment model in the form of a matrix for digital environmental datasets. Nine key components are identified based on requirements imposed on digital environmental data and information that are cared for and disseminated by U.S. Federal agencies by U.S. law, i.e., Information Quality Act of 2001, agencies' guidance, expert bodies' recommendations, and users. These components include: preservability, accessibility, usability, production sustainability, data quality assurance, data quality control/monitoring, data quality assessment, transparency/traceability, and data integrity. A five-level progressive maturity scale is then defined for each component associated with measurable practices applied to individual datasets, representing Ad Hoc, Minimal, Intermediate, Advanced, and Optimal stages. The rationale for each key component and its maturity levels is described. This maturity model, leveraging community best practices and standards, provides a unified framework for assessing scientific data stewardship. It can be used to create a stewardship maturity scoreboard of dataset(s) and a roadmap for scientific data stewardship improvement or to provide data quality and usability information to users, stakeholders, and decision makers.

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