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Global sea level provides an important indicator of the state of the warming climate, but changes in regional sea level are most relevant for coastal communities around the world. With improvements to the sea-level observing system, the knowledge of regional sea-level change has advanced dramatically in recent years. Satellite measurements coupled with in situ observations have allowed for comprehensive study and improved understanding of the diverse set of drivers that lead to variations in sea level in space and time. Despite the advances, gaps in the understanding of contemporary sea-level change remain and inhibit the ability to predict how the relevant processes may lead to future change. These gaps arise in part due to the complexity of the linkages between the drivers of sea-level change. Here we review the individual processes which lead to sea-level change and then describe how they combine and vary regionally. The intent of the paper is to provide an overview of the current state of understanding of the processes that cause regional sea-level change and to identify and discuss limitations and uncertainty in our understanding of these processes. Areas where the lack of understanding or gaps in knowledge inhibit the ability to provide the needed information for comprehensive planning efforts are of particular focus. Finally, a goal of this paper is to highlight the role of the expanded sea-level observation network-particularly as related to satellite observations-in the improved scientific understanding of the contributors to regional sea-level change.

During the Summer of 2015, the University of Washington eScience Institute ran an interdisciplinary summer internship program focused on urban informatics, civic engagement, and data-intensive social science. Borrowing elements from the successful Data Science for Social Good (DSSG) programs at the University of Chicago and Georgia Tech, and building on our own previous consulting and "incubation" programs for data-intensive projects in physical, life, and social sciences, we brought together teams of students (graduate, undergraduate, and high school), data scientists, project leads and stakeholders from the University of Washington and local NGOs to design, develop, and deploy new solutions to high-impact problems in the Seattle Metro Area. In this paper, we describe the inaugural offering of the eScience DSSG and reflect on the process of organizing and structuring the program. The DSSG attracted 144 graduate and undergraduate student applicants from over 10 different fields of study. The final DSSG fellow cohort included 16 students accepted from this pool of applicants. In addition, we included six high school students who joined us from a separate program designed to expose young people to research activities and an undergraduate student who had already started working on one of the projects through another summer research program. We solicited project proposals from research professionals across academic, non-profit, and government institutions. Ultimately, 4 projects were chosen out of 11 submitted proposals: two addressing transportation access for people with limited mobility, one identifying factors affecting whether homeless families find permanent housing, and one deriving new metrics of community well-being from social media data and other relevant data sources. All datasets were sourced from Seattle businesses, foundations, and agencies, with the exception of social media. The teams worked in a shared studio space designed in part for this purpose, and participated in tutorials on relevant tools and technologies, such as GitHub, Python, R, Amazon Web Services, and SQL, as well as topical presentations and discussions related to social good and multi-stakeholder collaborations. We found that striking a balance between training and software "flow time" is essential, and that determining the right balance between structured and unstructured activities is delicate. The diversity in software and disciplinary experience among participants was initially challenging for tutorial organization and scoping projects. A mix of advanced and introductory material meant that some participants were either lost or bored at any given time. But in the end, this diversity actually helped to improve the scope of the projects. For example, GIS experts added mapping components, software engineering experts designed APIs, and domain experts sanity-checked findings. Overall, the enormous interest implied by the number and diversity of the applicants to our program suggests that similar programs could be operated in many other cities. We intend this paper to facilitate reuse and optimization of the key components of our program.

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the ~198 000 glaciers in its latest version, 3.2. Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as 726 800 ± 34 000 km2. The uncertainty, about ±5%, is derived from careful single-glacier and basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliably is an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise.