Continental and global-scale groundwater models have been proposed recently to complete and improve the simulation of the hydrologic cycle. This development is still impeded by the resolution of these models either due to data availability and/or computational demands. One of the major challenges is determining the location of surface water bodies on the global scale based on land surface elevation data, as surface water elevation directly influences the model results. Closely connected to this problem is the comparison of simulated model results to observations of depth to groundwater. The models calculate hydraulic head so depth to groundwater is heavily influenced by how the variation in topography is reflected in one computational cell. This presentation demonstrates by means of the newly developed global groundwater model G³M that depth to groundwater observations need to be contemplated in the context of the elevation of the surface water bodies to draw proper conclusions on the model performance. The impact of uncertainty in surface water body elevation is illustrated based on multiple grid size experiments (5 arcmin, 30 arcsec, and 3 arcsec resolution) for New Zealand.

A number of insitu and passive microwave satellite sensors have observed Arctic sea ice and Greenland Ice Sheet (GrIS) mass loss trends over recent decades. Along with sea and land ice declines, above-freezing, near-surface air temperatures are observed earlier in boreal spring and later in autumn thus extending periods of melt beyond the core of summer (JJA). Little is known about whether lengthening periods of open ocean proximate to the ice sheet, for instance, demonstrably effect unseasonal GrIS melt events. Here, a new Baffin Bay sea ice advance dataset is utilized to determine dates of sea ice growth along Greenland’s west coast for the 2011-2015 period. Preceding, multi-scale ocean-atmospheric conditions, including at the Baffin-GrIS interface, are analyzed and linked to unseasonal melt events observed at a series of on-ice automatic weather stations (AWS) along the K-transect in southwest Greenland. The local marine versus synoptic influence on the above and below freezing surface air temperature events is assessed through analyses involving AWS winds, pressure, and humidity observations. These surface observations are further compared against Modele Atmospherique Regional (MAR), Regional Atmospheric Climate Model (RACMO), and ERA-Interim reanalysis fields to understand the airmass origins and (thermo)dynamic drivers of the melt events. Results suggest that the K-transect transition season melt events, primarily in the ablation zone, are strongly affected by ridging atmospheric circulation patterns that transport warm, moist air from lower latitude land-ocean areas toward west Greenland. While local conduction of oceanic surface heat appears to impact coastal air temperatures, consistent with previous studies, marine air incursions from Baffin waters onto the ice sheet are likely obstructed by barrier flows and the pressure gradient-driven katabatic regime off of central Greenland.

In 2017 Ocean Networks Canada (ONC), a research infrastructure operator, sought to redefine its core reporting metrics. We asked, “which metrics should we hold as key, essential metrics to drive our organizational priorities and decision making?” This question helped us define a collection of eleven sets of yardsticks, some inward-looking, others squarely focused on societal outcomes. Here, we introduce the individual metrics adopted, insights they are helping us glean and some of their inherent challenges. ONC’s core funding agency, the Canada Foundation for Innovation (CFI), continues to emphasize scientific output as a primary criterion. We measure this by counting peer-reviewed presentations and publications resulting from use the facility and ONC’s data archives. But this seemingly clear-cut metric has been a thorny one to define, track and grow. Training and support for post-secondary students is another core reporting metric, however this measurement is also fraught with ambiguities. Some of the easier metrics to track are those specifically related to facility operations, such as reliability and user satisfaction. But we were perplexed by the question of how to measure “optimal use” of the facility, as mandated by CFI. Optimal use is hard to define for an underwater infrastructure design like ONC’s, which can be flexibly extended with no hard limits on hardware capacity, archive volume or data access. When it comes to societal benefit, our approach has been twofold. One set of metrics examines technology transfer, grants and contracts. Another set focuses on our engagements and active collaborations with governmental, indigenous and non-governmental organizations. However, some outcomes remain challenging to measure. While it is straightforward to count up our external interactions and collaborations, how can we quantify their current and future societal impact? These and related questions will be explored.

A comprehensive, but simple-to-use software package for analysis of time-series has been developed for paleoclimate research and education. Acycle runs either in the MATLAB environment or as a stand-alone application on Windows and Macintosh OS X, and is available free of charge. Acycle provides prewhitening procedures with multiple options available to track or remove secular trends, and integrates various power spectral analysis approaches for detection and tracking of periodic signals. Acycle also provides a toolbox that evaluates astronomical signals in paleoclimate series, and estimates the most likely sedimentation rate by maximizing the correlation coefficient between power spectra of an astronomical solution and a paleoclimate series. Sedimentary noise models for sea-level variations are also included. Many of the functions are specific to cyclostratigraphy and astrochronology, and are not found in standard, statistical packages. For a demonstration, Acycle is applied to a paleoclimate series (iron content) from Core BH9/05 in the Paleogene Central Basin of Svalbard that samples the Paleocene-Eocene Thermal Maximum (PETM). Acycle detects significant astronomical forcing of the iron series and demonstrates a relatively stable sedimentation rate during and after the PETM event.