Jae N. Lee

Analysis of the warmest Arctic winter, 2015–2016

December through February 2015-2016 defines the warmest winter season over the Arctic in the observational record. Positive 2 m temperature anomalies were focused over regions of reduced sea ice cover in the Kara and Barents Seas, and southwestern Alaska. A third region is found over the ice-covered central Arctic Ocean. The period is marked by a strong synoptic pattern which produced melting temperatures in close proximity to the North Pole in late December, and anomalous high pressure near the Taymyr Peninsula. Atmospheric teleconnections from the Atlantic contributed to warming over Eurasian high-latitude land surfaces, and El Nino-related teleconnections explain warming over southwestern Alaska and British Columbia, while warm anomalies over the central Arctic are associated with physical processes including the presence of enhanced atmospheric water vapor and an increased downwelling longwave radiative flux. Preconditioning of sea ice conditions by warm temperatures affected the ensuing spring extent.

Atmospheric summer teleconnections and Greenland Ice Sheet surface mass variations: insights from MERRA-2

The relationship between leading atmospheric teleconnection patterns and Greenland Ice Sheet (GrIS) temperature, precipitation, and surface mass balance (SMB) are investigated for the last 36 summers (1979–2014) based on Modern-Era Retrospective analysis for Research and Applications version 2 reanalyses. The results indicate that the negative phase of both the North Atlantic Oscillation (NAO) and Arctic Oscillation, associated with warm and dry conditions for the GrIS, lead to SMB decreases within 0–1 months. Furthermore, the positive phase of the East Atlantic (EA) pattern often lags the negative NAO, reflecting a dynamical linkage between these modes that acts to further enhance the warm and dry conditions over the GrIS, leading to a favorable environment for enhanced surface mass loss. The development of a strong negative NAO in combination with a strong positive EA in recent years leads to significantly larger GrIS warming compared to when the negative NAO occurs in combination with a negative or weak positive EA (0.69 K versus 0.13 K anomaly). During 2009 and 2011, weakened (as compared to conditions during the severe surface melt cases of 2010 and 2012) local high pressure blocking produced colder northerly flow over the GrIS inhibiting warming despite the occurrence of a strong negative NAO, reflecting an important role for the EA during those years. In particular, the EA acts with the NAO to enhance warming in 2010 and 2012, and weaken high pressure blocking in 2009 and 2011. In general, high pressure blocking primarily impacts the western areas of the GrIS via advective temperature increases, while changes in net surface radiative fluxes account for both western and eastern GrIS temperature changes.

Using remotely sensed data from AIRS to estimate the vapor flux on the Greenland ice sheet: Comparisons with observations and a regional climate model

Mass loss from the Greenland ice sheet (GrIS) in recent years has been dominated by runoff from surface melt. It is currently being studied extensively, while little interest has been given to the smallest component of surface mass balance (SMB): the vapor flux. Although poorly understood, it is not negligible and could potentially play a larger role in SMB in a warming climate where temperature, relative humidity, and precipitation changes remain uncertain. Here we present an innovative approach to estimate the vapor flux using the Atmospheric Infrared Sounder (AIRS) version 6 data and a modified vapor flux model (BMF13) over the GrIS between 2003 and 2014. One modification to the BMF13 model includes a new Multiangle Imaging SpectroRadiometer surface aerodynamic roughness product, which likely produces more accurate estimates of the drag coefficient on the ice sheet. When comparing AIRS data with GC-Net and Programme for Monitoring of the Greenland Ice Sheet automatic weather station observations of skin temperature, near-surface air temperature, and humidity, they agree within 2 K, 2.68 K, and 0.34 g kg−1. Largest differences occur in the ablation zone where there is significant subgrid heterogeneity. Overall, the average vapor flux from the GrIS between 2003 and 2014 was found to be 14.6 ± 3.6 Gt yr−1. No statistically significant trends were found during the data record. This data set is compared to the Regional Atmospheric Climate Model (RACMO2.3) vapor flux, and BMF13 produced smaller vapor fluxes in the summer (~0.05 Gt d−1) and slightly more deposition in the winter (~9.4 × 10−3 Gt d−1). Annually, differences between BMF13 and RACMO2.3 were only 30 ± 15%.

Intercomparison of Surface Temperatures from AIRS, MERRA, and MERRA-2, with NOAA and GC-Net Weather Stations at Summit, Greenland

The surface skin and air temperatures reported by the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU-A), the Modern-Era Retrospective analysis for Research and Applications (MERRA), and MERRA-2 at Summit, Greenland are compared with near surface air temperatures measured at National Oceanic and Atmospheric Administration (NOAA) and Greenland Climate Network (GC-Net) weather stations. The AIRS/AMSU-A Surface Skin Temperature (TS) is best correlated with the NOAA 2 m air temperature (T2M) but tends to be colder than the station measurements. The difference may be the result of the frequent near surface temperature inversions in the region. The AIRS/AMSU-A Surface Air Temperature (SAT) is also correlated with the NOAA T2M but has a warm bias during the cold season and a larger standard error than the surface temperature. The extrapolation of the temperature profile to calculate the AIRS SAT may not be valid for the strongest inversions. The GC-Net temperature sensors are not held at fixed heights throughout the year; however, they are typically closer to the surface than the NOAA station sensors. Comparing the lapse rates at the 2 stations shows that it is larger closer to the surface. The difference between the AIRS/AMSU-A SAT and TS is sensitive to near surface inversions and tends to measure stronger inversions than both stations. The AIRS/AMSU-A may be sampling a thicker layer than either station. The MERRA-2 surface and near surface temperatures show improvements over MERRA but little sensitivity to near surface temperature inversions.