Jeffrey Miller

Changes in Arctic melt season and implications for sea ice loss

The Arctic-wide melt season has lengthened at a rate of 5 days decade−1 from 1979 to 2013, dominated by later autumn freezeup within the Kara, Laptev, East Siberian, Chukchi, and Beaufort seas between 6 and 11 days decade−1. While melt onset trends are generally smaller, the timing of melt onset has a large influence on the total amount of solar energy absorbed during summer. The additional heat stored in the upper ocean of approximately 752 MJ m−2 during the last decade increases sea surface temperatures by 0.5 to 1.5 °C and largely explains the observed delays in autumn freezeup within the Arctic Oceans adjacent seas. Cumulative anomalies in total absorbed solar radiation from May through September for the most recent pentad locally exceed 300–400 MJ m−2 in the Beaufort, Chukchi, and East Siberian seas. This extra solar energy is equivalent to melting 0.97 to 1.3 m of ice during the summer.

Comparison of ICESat Data With Airborne Laser Altimeter Measurements Over Arctic Sea Ice

Surface elevation and roughness measurements from NASAs Ice, Cloud, and land Elevation Satellite (ICESat) are compared with high-resolution airborne laser altimeter measurements over the Arctic sea ice north of Alaska, which were taken during the March 2006 EOS Aqua Advanced Microwave Scanning Radiometer sea ice validation campaign. The comparison of the elevation measurements shows that they agree quite well with correlations of around 0.9 for individual shots and a bias of less than 2 cm. The differences are found to decrease quite rapidly when applying running means. The comparison of the roughness measurements show that there are significant differences between the two data sets, with ICESat generally having higher values. The roughness values are only moderately correlated on an individual-shot basis, but applying running means to the data significantly improves the correlations to as high as 0.9. For the conversion of the elevation measurements into snow-ice freeboard, ocean surface elevation estimates are made with the high-resolution laser altimeter data, as well as several methods using lower resolution ICESat data. Under optimum conditions, i.e., when leads that are larger than the ICESat footprint are present, the ICESat- and Airborne Topographic Mapper-derived freeboards are found to agree to within 2 cm. For other areas, ICESat tends to underestimate the freeboard by up to 9 cm.

Recent changes in the Arctic melt season

Abstract Melt-season duration, melt-onset and freeze-up dates are derived from Satellite passive microwave data and analyzed from 1979 to 2005 over Arctic Sea ice. Results indicate a Shift towards a longer melt Season, particularly north of Alaska and Siberia, corresponding to large retreats of Sea ice observed in these regions. Although there is large interannual and regional variability in the length of the melt Season, the Arctic is experiencing an overall lengthening of the melt Season at a rate of about 2 weeks decade–1. In fact, all regions in the Arctic (except for the central Arctic) have Statistically Significant (at the 99% level or higher) longer melt Seasons by >1 week decade–1. The central Arctic Shows a Statistically Significant trend (at the 98% level) of 5.4 days decade–1. In 2005 the Arctic experienced its longest melt Season, corresponding with the least amount of Sea ice Since 1979 and the warmest temperatures Since the 1880s. Overall, the length of the melt Season is inversely correlated with the lack of Sea ice Seen in September north of Alaska and Siberia, with a mean correlation of –0.8.

A Comparison of Snow Depth on Sea Ice Retrievals Using Airborne Altimeters and an AMSR-E Simulator

A comparison of snow depths on sea ice was made using airborne altimeters and an Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) simulator. The data were collected during the March 2006 National Aeronautics and Space Administration (NASA) Arctic field campaign utilizing the NASA P-3B aircraft. The campaign consisted of an initial series of coordinated surface and aircraft measurements over Elson Lagoon, Alaska and adjacent seas followed by a series of large-scale (100 km × 50 km) coordinated aircraft and AMSR-E snow depth measurements over portions of the Chukchi and Beaufort seas. This paper focuses on the latter part of the campaign. The P-3B aircraft carried the University of Colorado Polarimetric Scanning Radiometer (PSR-A), the NASA Wallops Airborne Topographic Mapper (ATM) lidar altimeter, and the University of Kansas Delay-Doppler (D2P) radar altimeter. The PSR-A was used as an AMSR-E simulator, whereas the ATM and D2P altimeters were used in combination to provide an independent estimate of snow depth. Results of a comparison between the altimeter-derived snow depths and the equivalent AMSR-E snow depths using PSR-A brightness temperatures calibrated relative to AMSR-E are presented. Data collected over a frozen coastal polynya were used to intercalibrate the ATM and D2P altimeters before estimating an altimeter snow depth. Results show that the mean difference between the PSR and altimeter snow depths is -2.4 cm (PSR minus altimeter) with a standard deviation of 7.7 cm. The RMS difference is 8.0 cm. The overall correlation between the two snow depth data sets is 0.59.

Skillful spring forecasts of September Arctic sea ice extent using passive microwave sea ice observations

In this study, we demonstrate skillful spring forecasts of detrended September Arctic sea ice extent using passive microwave observations of sea ice concentration (SIC) and melt onset (MO). We compare these to forecasts produced using data from a sophisticated melt pond model, and find similar to higher skill values, where the forecast skill is calculated relative to linear trend persistence. The MO forecasts shows the highest skill in March–May, while the SIC forecasts produce the highest skill in June–August, especially when the forecasts are evaluated over recent years (since 2008). The high MO forecast skill in early spring appears to be driven primarily by the presence and timing of open water anomalies, while the high SIC forecast skill appears to be driven by both open water and surface melt processes. Spatial maps of detrended anomalies highlight the drivers of the different forecasts, and enable us to understand regions of predictive importance. Correctly capturing sea ice state anomalies, along with changes in open water coverage appear to be key processes in skillfully forecasting summer Arctic sea ice.

Comparison of Passive Microwave-Derived Early Melt Onset Records on Arctic Sea Ice

Two long records of melt onset (MO) on Arctic sea ice from passive microwave brightness temperatures (Tbs) obtained by a series of satellite-borne instruments are compared. The Passive Microwave (PMW) method and Advanced Horizontal Range Algorithm (AHRA) detect the increase in emissivity that occurs when liquid water develops around snow grains at the onset of early melting on sea ice. The timing of MO on Arctic sea ice influences the amount of solar radiation absorbed by the ice–ocean system throughout the melt season by reducing surface albedos in the early spring. This work presents a thorough comparison of these two methods for the time series of MO dates from 1979 through 2012. The methods are first compared using the published data as a baseline comparison of the publically available data products. A second comparison is performed on adjusted MO dates we produced to remove known differences in inter-sensor calibration of Tbs and masking techniques used to develop the original MO date products. These adjustments result in a more consistent set of input Tbs for the algorithms. Tests of significance indicate that the trends in the time series of annual mean MO dates for the PMW and AHRA are statistically different for the majority of the Arctic Ocean including the Laptev, E. Siberian, Chukchi, Beaufort, and central Arctic regions with mean differences as large as 38.3 days in the Barents Sea. Trend agreement improves for our more consistent MO dates for nearly all regions. Mean differences remain large, primarily due to differing sensitivity of in-algorithm thresholds and larger uncertainties in thin-ice regions.

Anomalous blocking over Greenland preceded the 2013 extreme early melt of local sea ice

ABSTRACT The Arctic marine environment is undergoing a transition from thick multi-year to first-year sea-ice cover with coincident lengthening of the melt season. Such changes are evident in the Baffin Bay-Davis Strait-Labrador Sea (BDL) region where melt onset has occurred ~8 days decade−1 earlier from 1979 to 2015. A series of anomalously early events has occurred since the mid-1990s, overlapping a period of increased upper-air ridging across Greenland and the northwestern North Atlantic. We investigate an extreme early melt event observed in spring 2013. (~6σ below the 1981–2010 melt climatology), with respect to preceding sub-seasonal mid-tropospheric circulation conditions as described by a daily Greenland Blocking Index (GBI). The 40-days prior to the 2013 BDL melt onset are characterized by a persistent, strong 500 hPa anticyclone over the region (GBI >+1 on >75% of days). This circulation pattern advected warm air from northeastern Canada and the northwestern Atlantic poleward onto the thin, first-year sea ice and caused melt ~50 days earlier than normal. The episodic increase in the ridging atmospheric pattern near western Greenland as in 2013, exemplified by large positive GBI values, is an important recent process impacting the atmospheric circulation over a North Atlantic cryosphere undergoing accelerated regional climate change.

Operational Implementation of Sea Ice Concentration Estimates From the AMSR2 Sensor

An operation implementation of a passive microwave sea ice concentration algorithm to support NOAAs operational mission is presented. The NASA team 2 algorithm, previously developed for the NASA advanced microwave scanning radiometer for the Earth observing system (AMSR-E) product suite, is adapted for operational use with the JAXA AMSR2 sensor through several enhancements. First, the algorithm is modified to process individual swaths and provide concentration from the most recent swaths instead of a 24-hour average. A latency (time since observation) field and a 24-hour concentration range (maximum–minimum) are included to provide indications of data timeliness and variability. Concentration from the Bootstrap algorithm is a secondary field to provide complementary sea ice information. A quality flag is implemented to provide information on interpolation, filtering, and other quality control steps. The AMSR2 concentration fields are compared with a different AMSR2 passive microwave product, and then validated via comparison with sea ice concentration from the Suomi visible and infrared imaging radiometer suite. This validation indicates the AMSR2 concentrations have a bias of 3.9% and an RMSE of 11.0% in the Arctic, and a bias of 4.45% and RMSE of 8.8% in the Antarctic. In most cases, the NOAA operational requirements for accuracy are met. However, in low-concentration regimes, such as during melt and near the ice edge, errors are higher because of the limitations of passive microwave sensors and the algorithm retrieval.