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A new version of sea ice motion and age products includes several significant upgrades in processing, corrects known issues with the previous version, and updates the time series through 2018, with regular updates planned for the future. First, we provide a history of these NASA products distributed at the National Snow and Ice Data Center. Then we discuss the improvements to the algorithms, provide validation results for the new (Version 4) and older versions, and intercompare the two. While Version 4 algorithm changes were significant, the impact on the products is relatively minor, particularly for more recent years. The changes in Version 4 reduce motion biases by ∼ 0.01 to 0.02 cm s−1 and error standard deviations by ∼ 0.3 cm s−1. Overall, ice speed increased in Version 4 over Version 3 by 0.5 to 2.0 cm s−1 over most of the time series. Version 4 shows a higher positive trend for the Arctic of 0.21 cm s−1 per decade compared to 0.13 cm s−1 per decade for Version 3. The new version of ice age estimates indicates more older ice than Version 3, especially earlier in the record, but similar trends toward less multiyear ice. Changes in sea ice motion and age derived from the product show a significant shift in the Arctic ice cover, from a pack with a high concentration of older ice to a sea ice cover dominated by first-year ice, which is more susceptible to summer melt. We also observe an increase in the speed of the ice over the time series ≥ 30 years, which has been shown in other studies and is anticipated with the annual decrease in sea ice extent.

Recent satellite observations yield estimates of the distribution of sea ice thickness across the entire Arctic Ocean. While these sensors were only placed in operation within the last few years, information from other sensors may assist us with estimating the distribution of sea ice thickness in the Arctic beginning in the 1980s. A previous study found that the age of sea ice is correlated to sea ice thickness from 2003 to 2006, but an extension of the temporal analysis is needed to better quantify this relationship and its variability from year to year. Estimates of the ice age/thickness relationship may allow the thickness record to be extended back to 1985, the beginning of our ice age dataset. Comparisons of ice age and thickness estimates derived from both ICESat (2004–2008) and IceBridge (2009–2015) reveal that the relationship between age and thickness differs between these two campaigns, due in part to the difference in area of coverage. Nonetheless, sea ice thickness and age exhibit a direct relationship when compared on pan-Arctic or regional spatial scales.

Sea ice is a key component of the Arctic climate system, and has impacts on global climate. Ice concentration, thickness, and volume are among the most important Arctic sea ice parameters. This study presents a new record of Arctic sea ice thickness and volume from 1984 to 2018 based on an existing satellite-derived ice age product. The relationship between ice age and ice thickness is first established for every month based on collocated ice age and ice thickness from submarine sonar data (1984–2000) and ICESat (2003–2008) and an empirical ice growth model. Based on this relationship, ice thickness is derived for the entire time period from the weekly ice age product, and the Arctic monthly sea ice volume is then calculated. The ice-age-based thickness and volume show good agreement in terms of bias and root-mean-square error with submarine, ICESat, and CryoSat-2 ice thickness, as well as ICESat and CryoSat-2 ice volume, in February–March and October–November. More detailed comparisons with independent data from Envisat for 2003 to 2010 and CryoSat-2 from CPOM, AWI, and NASA GSFC (Goddard Space Flight Center) for 2011 to 2018 show low bias in ice-age-based thickness. The ratios of the ice volume uncertainties to the mean range from 21 % to 29 %. Analysis of the derived data shows that the ice-age-based sea ice volume exhibits a decreasing trend of −411 km3 yr−1 from 1984 to 2018, stronger than the trends from other datasets. Of the factors affecting the sea ice volume trends, changes in sea ice thickness contribute more than changes in sea ice area, with a contribution of at least 80 % from changes in sea ice thickness from November to May and nearly 50 % in August and September, while less than 30 % is from changes in sea ice area in all months.

Arctic sea ice extent has continued to decline in recent years, and the fractional coverage of multi-year sea ice has decreased significantly during this period. The Beaufort Sea region has been the site of much of the loss of multi-year sea ice, and it continues to play a large role in the extinction of ice during the melt season. We present an analysis of the influence of satellite-derived ice surface temperature, ice thickness, albedo, and downwelling longwave/shortwave radiation as well as latitude and airborne snow depth estimates on the change in sea ice concentration in the Beaufort Sea from 2009 to 2016 using a Lagrangian tracking database. Results from this analysis indicate that parcels that melt during summer in the Beaufort Sea reside at lower latitudes and have lower ice thickness at the beginning of the melt season in most cases. The influence of sea ice thickness and snow depth observed by IceBridge offers less conclusive results, with some years exhibiting higher thicknesses/depths for melted parcels. Parcels that melted along IceBridge tracks do exhibit lower latitudes and ice thicknesses, however, which indicates that earlier melt and breakup of ice may contribute to a greater likelihood of extinction of parcels in the summer.

Arctic sea ice has been on the decline over the past several decades, and multi-year sea ice has decreased significantly in its areal share of the overall sea ice cover. Changes in several key variables such as radiative balances, albedo, ice surface temperature, and ice thickness have driven much of the decline, but the motion of sea ice makes studying the effects of these variables on individual parcels difficult. Previous studies have observed changes in the means of these variables and their impacts on sea ice concentration, but an accessible database of Lagrangian tracked data is not yet available for study. In order to address this, a database has been developed at the University of Colorado Boulder that performs Lagrangian tracking on individual sea ice parcels and saves coincident ancillary thermodynamic and dynamic variables for each parcel on a weekly timescale.

Continuous monitoring of the surface temperature is critical to understanding and forecasting Arctic climate change; as surface temperature integrates changes in the surface energy budget. The sea-ice surface temperature (IST) has been measured with optical and thermal infrared sensors for many years. With the IST Environmental Data Record (EDR) available from the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (NPP) and future Joint Polar Satellite System (JPSS) satellites; we can continue to monitor and investigate Arctic climate change. This work examines the quality of the VIIRS IST EDR. Validation is performed through comparisons with multiple datasets; including NASA IceBridge measurements; air temperature from Arctic drifting ice buoys; Moderate Resolution Imaging Spectroradiometer (MODIS) IST; MODIS IST simultaneous nadir overpass (SNO); and surface air temperature from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis. Results show biases of −0.34; −0.12; 0.16; −3.20; and −3.41 K compared to an aircraft-mounted downward-looking pyrometer; MODIS; MODIS SNO; drifting buoy; and NCEP/NCAR reanalysis; respectively; root-mean-square errors of 0.98; 1.02; 0.95; 4.89; and 6.94 K; and root-mean-square errors with the bias removed of 0.92; 1.01; 0.94; 3.70; and 6.04 K. Based on the IceBridge and MODIS results; the VIIRS IST uncertainty (RMSE) meets or exceeds the JPSS system requirement of 1.0 K. The product can therefore be considered useful for meteorological and climatological applications.

Arctic climate has been changing rapidly since the 1980s. This work shows distinctly different patterns of change in winter, spring, and summer for cloud fraction and surface temperature. Satellite observations over 1982–2004 have shown that the Arctic has warmed up and become cloudier in spring and summer, but cooled down and become less cloudy in winter. The annual mean surface temperature has increased at a rate of 0.34°C per decade. The decadal rates of cloud fraction trends are −3.4%, 2.3%, and 0.5% in winter, spring, and summer, respectively. Correspondingly, annually averaged surface albedo has decreased at a decadal rate of −3.2%. On the annual average, the trend of cloud forcing at the surface is −2.11 W/m2 per decade, indicating a damping effect on the surface warming by clouds. The decreasing sea ice albedo and surface warming tend to modulate cloud radiative cooling effect in spring and summer. Arctic sea ice has also declined substantially with decadal rates of −8%, −5%, and −15% in sea ice extent, thickness, and volume, respectively. Significant correlations between surface temperature anomalies and climate indices, especially the Arctic Oscillation (AO) index, exist over some areas, implying linkages between global climate change and Arctic climate change.

Since 2009, the ultra-wideband snow radar on Operation IceBridge (OIB; a NASA airborne mission to survey the polar ice covers) has acquired data in annual campaigns conducted during the Arctic and Antarctic springs. Progressive improvements in radar hardware and data processing methodologies have led to improved data quality for subsequent retrieval of snow depth. Existing retrieval algorithms differ in the way the air-snow (a-s) and snow-ice (s-i) interfaces are detected and localized in the radar returns and in how the system limitations are addressed (e.g., noise, resolution). In 2014, the Snow Thickness On Sea Ice Working Group (STOSIWG) was formed and tasked with investigating how radar data quality affects snow depth retrievals and how retrievals from the various algorithms differ. The goal is to understand the limitations of the estimates and to produce a well-documented, long-term record that can be used for understanding broader changes in the Arctic climate system. Here, we assess five retrieval algorithms by comparisons with field measurements from two groundbased campaigns, including the BRomine, Ozone, and Mercury EXperiment (BROMEX) at Barrow, Alaska; a field program by Environment and Climate Change Canada at Eureka, Nunavut; and available climatology and snowfall from ERA-Interim (ECMWF (European Centre for Medium-Range Weather Forecasts) Re-Analysis) reanalysis. The aim is to examine available algorithms and to use the assessment results to inform the development of future approaches. We present results from these assessments and highlight key considerations for the production of a long-term, calibrated geophysical record of springtime snow thickness over Arctic sea ice.