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Today’s landscape of computational science is evolving rapidly, with a need for new, flexible, and responsive supercomputing platforms for addressing the growing areas of artificial intelligence (AI), data analytics (DA) and convergent collaborative research. To support this community, we designed and deployed the Bridges-2 platform. Building on our highly successful Bridges supercomputer, which was a high-performance computing resource supporting new communities and complex workflows, Bridges-2 supports traditional and nontraditional research communities and applications; integrates new technologies for converged, scalable high-performance computing (HPC), AI, and data analytics; prioritizes researcher productivity and ease of use; and provides an extensible architecture for interoperation with complementary data intensive projects, campuses, and clouds. In this report, we describe Bridges-2’s hardware and configuration, user environments, and systems support and present the results of the successful Early User Program. 

Substantial marine, terrestrial, and atmospheric changes have occurred over the Greenland region during the last century. Several studies have documented record‐levels of Greenland Ice Sheet (GrIS) summer melt extent during the 2000s and 2010s, but relatively little work has been carried out to assess regional climatic changes in other seasons. Here, we focus on the less studied cold‐season (i.e., autumn and winter) climate, tracing the long‐term (1873–2013) variability of Greenland's air temperatures through analyses of coastal observations and model‐derived outlet glacier series and their linkages with North Atlantic sea ice, sea surface temperature (SST), and atmospheric circulation indices. Through a statistical framework, large amounts of west and south Greenland temperature variance (up tor² ~ 50%) can be explained by the seasonally‐contemporaneous combination of the Greenland Blocking Index (GBI) and the North Atlantic Oscillation (NAO; hereafter the combination of GBI and NAO is termed GBI). Lagged and concomitant regional sea‐ice concentration (SIC) and the Atlantic Multidecadal Oscillation (AMO) seasonal indices account for small amounts of residual air temperature variance (r² < ~10%) relative to the GBI. The correlations between GBI and cold‐season temperatures are predominantly positive and statistically‐significant through time, while regional SIC conditions emerge as a significant covariate from the mid‐20th century through the conclusion of the study period. The inclusion of the cold‐season Pacific Decadal Oscillation (PDO) in multivariate analyses bolsters the air temperature variance explained by the North Atlantic regional predictors, suggesting the remote, background climate state is important to long‐term Greenland temperature variability. These findings imply that large‐scale tropospheric circulation has a strong control on surface temperature over Greenland through dynamic and thermodynamic impacts and stress the importance of understanding the evolving two‐way linkages between the North Atlantic marine and atmospheric environment in order to more accurately predict Greenland seasonal climate variability and change through the 21st century.

 

We investigate factors influencing European winter (DJFM) air temperatures for the period 1979–2015 with the focus on changes during the recent period of rapid Arctic warming (1998–2015). We employ meteorological reanalyses analysed with a combination of correlation analysis, two pattern clustering techniques, and back‐trajectory airmass identification. In all five selected European regions, severe cold winter events lasting at least 4 days are significantly correlated with warm Arctic episodes. Relationships during opposite conditions of warm Europe/cold Arctic are also significant. Correlations have become consistently stronger since 1998. Large‐scale pattern analysis reveals that cold spells are associated with the negative phase of the North Atlantic Oscillation (NAO‐) and the positive phase of the Scandinavian (SCA+) pattern, which in turn are correlated with the divergence of dry‐static energy transport. Warm European extremes are associated with opposite phases of these patterns and the convergence of latent heat transport. Airmass trajectory analysis is consistent with these findings, as airmasses associated with extreme cold events typically originate over continents, while warm events tend to occur with prevailing maritime airmasses. Despite Arctic‐wide warming, significant cooling has occurred in northeastern Europe owing to a decrease in adiabatic subsidence heating in airmasses arriving from the southeast, along with increased occurrence of circulation patterns favouring low temperature advection. These dynamic effects dominated over the increased mean temperature of most circulation patterns. Lagged correlation analysis reveals that SCA‐ and NAO+ are typically preceded by cold Arctic anomalies during the previous 2–3 months, which may aid seasonal forecasting.