Earlier Melt Onset Research Articles

Inter-comparison of melt pond products from optical satellite imagery

Given the importance that melt ponds have on the energy balance of summer sea ice, there have been several efforts to develop pan-Arctic datasets using satellite data. Here we intercompare three melt pond data sets that rely on multi-frequency optical satellite data. Early in the melt season, the three data sets have similar spatial patterns in melt pond fraction, but this agreement weakens as the melt season progresses despite relatively high interannual correlations in pond fractions between the data products. Most of the data sets do not exhibit trends towards increased melt pond fractions from 2002 to 2011 despite overall Arctic warming and earlier melt onset. Further comparisons are made against higher resolution optical data to assess relative accuracy. These comparisons reveal the challenges in retrieving melt ponds from coarse resolution satellite data, and the need to better discriminate between leads, small open water areas and melt ponds. Finally, we assess melt pond data sets as a function of ice type and how well they correlate with surface albedo. As expected, melt pond fractions are negatively correlated with surface albedo, though the strength of the correlation varies across products and regions. Overall, first-year ice has larger melt pond fractions than multi-year ice.

Disentangling Dynamic from Thermodynamic Summer Ice Area Loss from Observations (1979–2021): A Potential Mechanism for a “First-Time” Ice-Free Arctic

Abstract In recent decades, the Arctic minimum sea ice extent has transitioned from a predominantly thick multiyear ice cover to a thinner seasonal ice cover. We partition the total (observed) Arctic summer area loss into thermodynamic and dynamic (convergence, ridging, and export) sea ice area loss during the satellite era from 1979 to 2021 using a Lagrangian sea ice tracking model driven by satellite-derived sea ice velocities. Results show that the thermodynamic signal dominates the total summer ice area loss and the dynamic signal remains small (∼20%) even in 2007 when dynamic loss was largest. Sea ice loss by compaction (within pack ice convergence) dominates the dynamic area loss, even in years when the export is largest. Results from a simple (Ekman) free-drift sea ice model, supported by results from the Lagrangian model, suggest that nonlinear effects between dynamic and thermodynamic area loss can be important for large negative anomalies in sea ice extent, in accord with previous modeling studies. A detailed analysis of two all-time record minimum years (2007 and 2012)—one with a semipermanent high in the southern Beaufort Sea and the other with a short-lived but extreme storm in the Pacific sector of the Arctic in late summer—shows that compaction by Ekman convergence together with large thermodynamic melt in the marginal ice zone dominated the sea ice area loss in 2007 whereas, in 2012, it was dominated by Ekman divergence amplified by sea–ice albedo feedback—together with an early melt onset. We argue that Ekman divergence from more intense summer storms when the sun is high above the horizon is a more likely mechanism for a “first-time” ice-free Arctic.

Arctic Sea Ice Melt Onset in the Laptev Sea and East Siberian Sea in Association with the Arctic Oscillation and Barents Oscillation

Abstract Arctic summer sea ice has been declining in recent decades. In this study, we investigate the beginning of the Arctic melting season, i.e., sea ice melt onset (MO), in the Laptev Sea (LS) and East Siberian Sea (ESS) along the Northern Sea route. Three leading modes are identified by EOF decomposition, which we call the LE-mode, L-mode, and E-mode. In positive phases these modes exhibit earlier MO in the two seas, a seesaw-like structure in the southwest–northeast direction with earlier MO in the LS, or in the southeast–northwest direction with earlier MO in the ESS. The LE-mode, L-mode, and E-mode are closely related to the Arctic Oscillation (AO) in April, the Barents Oscillation (BO) in April, and the AO in May, respectively. When the AO in April is positive, a low pressure anomaly northwest of the LS and ESS brings warm, moist air masses from the lower latitudes toward the LS and ESS and causes earlier MO, corresponding to the positive LE-mode. When the BO in April is negative, a cyclonic anomaly around the Barents Sea tends to warm and moisten the LS and cause earlier MO there, corresponding to the positive L-mode. When AO in May is positive, a low pressure anomaly northeast of the LS and ESS brings more warm, moist air toward the ESS and causes earlier MO there, corresponding to the positive E-mode. In the 1980s, the negative LE-mode was prominent whereas in the early 1990s the positive LE-mode was dominant. Since the mid-1990s, the L-mode and E-mode have appeared more frequently.

Climate prediction of the seasonal sea-ice early melt onset in the Bering Sea

Based on the impact of large-scale circulation anomalies on sea-ice melting, this paper develops a statistical forecasting model for the seasonal sea-ice early melt onset (EMO) in the Bering Sea using the interannual increment prediction method. The prediction model considers three physically meaningful predictors: the January Beaufort High (P1-H500), the November sea-level pressure (P2-SLP) over eastern Siberia, and the November snow cover over the eastern European Plain (P3-Snowc). P1-H500 can influence the sea surface temperature (SST) anomaly in the Bering Sea through ocean–atmosphere interactions, and this SST anomaly can persist from January to March. Subsequently, it affects the EMO in the Bering Sea. P2-SLP exhibits a close association with the east part of the midlatitude North Pacific SST in November. The colder midlatitude North Pacific SST anomalies, which persist from November until January and February of the following year, will be accompanied by warmer SST anomalies in the Bering Sea, which result in a decreased sea-ice extent and a later-than-usual EMO. The Arctic dipole anomaly in January is one of the ways in which P3-Snowc affects the EMO in the following year. The predicted EMO shows good agreement with the observed EMO in the cross-validation test for 1981–2022, with a temporal correlation coefficient of 0.45, exceeding the 99% confidence level. The prediction accuracy of the prediction model for positive and negative abnormal years of EMO is 60% and 41%, respectively.摘要基于大尺度环流异常对海冰消融的影响过程, 本文采用年际增量预测方法研制了白令海季节性海冰早期消融开始日期(EMO)的统计预测模型. 预测模型选取了3个具有明确物理意义的预测因子: 1月波弗特高压, 前期11月东西伯利亚地区海平面气压, 以及11月东欧平原积雪覆盖率. 1月波弗特高压可以通过海气相互作用影响白令海地区海温异常, 该海温异常能够从1月持续到3月, 进而影响白令海EMO. 11月东西伯利亚地区海平面气压与11月至次年2月北太平洋中纬度东部海温密切相关. 伴随着北太平洋中纬度东部冷海温异常的出现, 白令海地区会出现暖海温异常, 进而导致白令海海冰范围减少, EMO较晚. 1月北极偶极子异常是11月东欧平原积雪覆盖率影响次年白令海EMO的桥梁之一. 1981−2022年的交叉检验结果表明: 统计模型对白令海EMO具有较好的预测能力, 预测与观测的EMO之间时间相关系数达到了0.45, 超过了99%的置信水平. 统计模型对白令海EMO正常年份和异常年份的预测准确率分别为60%和41%.

Observing the evolution of summer melt on multiyear sea ice with ICESat-2 and Sentinel-2

Abstract. We investigate sea ice conditions during the 2020 melt season, when warm air temperature anomalies in spring led to early melt onset, an extended melt season, and the second-lowest September minimum Arctic ice extent observed. We focus on the region of the most persistent ice cover and examine melt pond depth retrieved from Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) using two distinct algorithms in concert with a time series of melt pond fraction and ice concentration derived from Sentinel-2 imagery to obtain insights about the melting ice surface in three dimensions. We find the melt pond fraction derived from Sentinel-2 in the study region increased rapidly in June, with the mean melt pond fraction peaking at 16 % ± 6 % on 24 June 2020, followed by a slow decrease to 8 % ± 6 % by 3 July, and remained below 10 % for the remainder of the season through 15 September. Sea ice concentration was consistently high (>95 %) at the beginning of the melt season until 4 July, and as floes disintegrated, it decreased to a minimum of 70 % on 30 July and then became more variable, ranging from 75 % to 90 % for the remainder of the melt season. Pond depth increased steadily from a median depth of 0.40 m ± 0.17 m in early June and peaked at 0.97 m ± 0.51 m on 16 July, even as melt pond fraction had already started to decrease. Our results demonstrate that by combining high-resolution passive and active remote sensing we now have the ability to track evolving melt conditions and observe changes in the sea ice cover throughout the summer season.

Improved algorithm for determining the freeze onset of Arctic sea ice using AMSR-E/2 data

Over the past several decades, Arctic sea ice has experienced a significantly longer summer melt season, with an earlier melt onset and delayed freeze onset. The early melt onset/melt onset date (EMO/MO) and early freeze onset/freeze onset date (EFO/FO) products developed using the passive microwave (PMW) algorithm derived from Special Sensor Microwave/Imager (SSM/I) passive microwave data are widely used in the study of the variability in Arctic sea ice freezing melting. Here, we apply the PMW algorithm to the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E/2) dataset at a higher spatial resolution and improve the algorithm. We first limit the start and end dates according to the sea ice concentration (SIC). Then, we exclude false freezing signals by identifying high variability in SIC; finally, we consider changes in the distribution of sea ice types (first-year ice and multiyear ice) over time to obtain an improved algorithm. The early freeze onset (EFO) and FO derived from the AMSR data are then compared with results derived from the surface air temperature and sea skin temperature (SAT & SKT) and SSM/I products. The spatial distribution of the new EFO results shows greater consistency with SAT & SKT than with the results of SSM/I, with a mean error, absolute error, and root mean square error for the SAT & SKT comparison of 3.7 days, 10.4 days, and 16.0 days, respectively, for the AMSR2 period. The improved FO and EFO correct the small values caused by misidentified summer melt signals and the large values caused by sea ice dynamic processes after the melt season. There is a constant change in ice type over 20%–30% of the ice cover, and the improved FO and EFO correct for anomalies due to misidentified ice types. The improved results correlate better with the interannual variability in EFO derived from SAT & SKT in subregions, especially in ice marginal regions. The Arctic sea ice EFO north of 70°N was delayed at a rate of 6.4 day/dec from 2003 to 2021, with remarkable trends found in the Laptev (14.6 day/dec) and Kara (14.1 day/dec) Seas.

Global Snowmelt Onset Reflects Climate Variability: Insights from Spaceborne Radiometer Observations

Abstract Snowmelt is a critical component in the cryosphere and has a direct impact on Earth’s energy and water budget. Here, a 40-yr integrated melt onset (MO) dataset over sea ice, ice sheets, and terrestrial snow is compiled from spaceborne microwave radiometers and ERA5, allowing an overall assessment of the cryosphere. Results suggest that MO in both hemispheres shows latitudinal and vertical zonalities. The global cryosphere presented a trend toward earlier MO (−2 days decade−1) with hotpots distributed at the Northern Hemisphere high latitudes where the warming rate is much higher than that at lower latitudes. Overall, variations in MO showed a similar pattern to that in near-surface temperature. The advance of MO has been slowing down since the 1990s and no significant trend was observed during the so-called warming hiatus period (1998–2012). Regionally, climatic linkage analyses suggest the local MO variations were associated with different climate indices. MO in the pan-Arctic region is related with the Arctic Oscillation and North Atlantic Oscillation, while that in the pan-Antarctic region is associated with El Niño–Southern Oscillation and the southern annular mode. Occasionally, abnormal MO occurs as a result of extreme weather conditions. In February 2018, abnormal early melt events that occurred in the Arctic Ocean are found to be linked with the warm southerly flow due to sudden stratospheric warming. These findings suggest the satellite-based MO allows examining the dynamics and extremes in the climate system, both regionally and globally.

Contribution of warm and moist atmospheric flow to a record minimum July sea ice extent of the Arctic in 2020

Abstract. The satellite observations unveiled that the July sea ice extent of the Arctic shrank to the lowest value, since 1979, in 2020 with a major ice retreat in the Eurasian shelf seas including Kara, Laptev, and East Siberian seas. Based on the ERA-5 reanalysis products, we explored the impacts of warm and moist air-mass transport on this extreme event. The results revealed that anomalously high energy and moisture converged into these regions in the spring months (April to June) of 2020, leading to a burst of high moisture content and warming within the atmospheric column. The convergence is accompanied by local enhanced downward longwave surface radiation and turbulent fluxes, which is favorable for initiating an early melt onset in the region with severe ice loss. Once the melt begins, solar radiation plays a decisive role in leading to further sea ice depletion due to ice–albedo positive feedback. The typical trajectories of the synoptic cyclones that occurred on the Eurasian side in spring 2020 agree well with the path of atmospheric flow. Assessments suggest that variations in characteristics of the spring cyclones are conducive to the severe melt of sea ice. We argue that large-scale atmospheric circulation and synoptic cyclones acted in concert to trigger the exceptional poleward transport of total energy and moisture from April to June to cause this record minimum of sea ice extent in the following July.

An Updated Assessment of the Changing Arctic Sea Ice Cover

Sea ice is an essential component of the Arctic climate system. The Arctic sea ice cover has undergone substantial changes in the past 40+ years, including decline in areal extent in all months (strongest during summer), thinning, loss of multiyear ice cover, earlier melt onset and ice retreat, and later freeze-up and ice advance. In the past 10 years, these trends have been further reinforced, though the trends (not statistically significant at p <0.05) in some parameters (e.g., extent) over the past decade are more moderate. Since 2011, observing capabilities have improved significantly, including collection of the first basin-wide routine observations of sea ice freeboard and thickness by radar and laser altimeters (except during summer). In addition, data from a year-long field campaign during 2019–2020 promises to yield a bounty of in situ data that will vastly improve understanding of small-scale processes and the interactions between sea ice, the ocean, and the atmosphere, as well as provide valuable validation data for satellite missions. Sea ice impacts within the Arctic are clear and are already affecting humans as well as flora and fauna. Impacts outside of the Arctic, while garnering much attention, remain unclear. The future of Arctic sea ice is dependent on future CO2 emissions, but a seasonally ice-free Arctic Ocean is likely in the coming decades. However, year-to-year variability causes considerable uncertainty on exactly when this will happen. The variability is also a challenge for seasonal prediction.

Long time-series measurements of high-frequency acoustic scattering from sea ice

Understanding relatively recent rapid changes in Arctic sea ice calls for extensive and continuous observations of the ice cover. Utilizing datasets obtained with 38, 70, and 200 kHz upward-looking transducers in the northeast Chukchi Sea, seasonal and year-to year changes in acoustic scattering were measured. Two complete annual cycles of scattering returns from the winters of 2017–2018 and 2018–2019 were analyzed. The initial effort in this research is constrained to the stable growth phase of sea ice in winter through the early melt onset the following spring. We also restricted our analysis to individual, minimally deformed ice floes to limit the variability caused by large-scale roughness. Results from this two-year analysis will be discussed along with their implications for future measurements and acoustic remote sensing of sea ice properties.

Precipitation influence on and response to early and late Arctic sea ice melt onset during melt season

AbstractThe region containing portions of the East Siberian Sea and Laptev Sea (73°–84°N, 90°–155°E) is the area of focus (AOF) for this study. The impacts of precipitation, latent heat (LH) and sensible heat (SH) fluxes on sea ice melt onset in the AOF are investigated. Four early melting years (1990, 2012, 2003, and 1991) and four late melting years (1982, 1983, 1984, and 1996) are compared to better identify the different responses to melt onset timing. A consistency check is performed between multiple Arctic precipitation products (including NASA MERRA‐2, ECMWF ERA‐Interim [ERA‐I], and ECMWF ERA5 reanalyses as well as GPCP V2.3 observations) since there is not yet a high‐quality ground‐truth Arctic precipitation data product. MERRA‐2 has the greatest monthly average precipitation, snowfall, evaporation, and net LH flux. ERA‐I suggests that liquid precipitation starts earlier in the year than MERRA‐2 and ERA5, while GPCP shows different seasonal precipitation variations from the reanalyses. MERRA‐2 has the clearest and most amplified seasonal trends for the parameters used in this study, so the daily time series and anomalies of MERRA‐2 variables before and after the first major melt event are investigated. ERA5 is used to check these results because ERA‐I and ERA5 display similar seasonal trends. According to MERRA‐2, during early melt years, surface SH flux loss and precipitation are above average in the days before and after the first major melt event. During late melt years, surface SH flux loss and precipitation are below average in the month leading up to the first major melt event.

Arctic sea ice melt onset favored by an atmospheric pressure pattern reminiscent of the North American-Eurasian Arctic pattern

The timing of melt onset in the Arctic plays a key role in the evolution of sea ice throughout Spring, Summer and Autumn. A major catalyst of early melt onset is increased downwelling longwave radiation, associated with increased levels of moisture in the atmosphere. Determining the atmospheric moisture pathways that are tied to increased downwelling longwave radiation and melt onset is therefore of keen interest. We employed Self Organizing Maps (SOM) on the daily sea level pressure for the period 1979–2018 over the Arctic during the melt season (April–July) and identified distinct circulation patterns. Melt onset dates were mapped on to these SOM patterns. The dominant moisture transport to much of the Arctic is enabled by a broad low pressure region stretching over Siberia and a high pressure over northern North America and Greenland. This configuration, which is reminiscent of the North American-Eurasian Arctic dipole pattern, funnels moisture from lower latitudes and through the Bering and Chukchi Seas. Other leading patterns are variations of this which transport moisture from North America and the Atlantic to the Central Arctic and Canadian Arctic Archipelago. Our analysis further indicates that most of the early and late melt onset timings in the Arctic are strongly related to the strong and weak emergence of these preferred circulation patterns, respectively.

Measurements of high-frequency acoustic scattering from sea ice over one season in the Chukchi Sea

Understanding recent and rapid changes in Arctic sea ice calls for extensive and continuous observations of the ice cover. In-situ stations are spatially limited and satellite remote sensing techniques miss key variables of internal sea-ice structure. Utilizing a dataset obtained with 38, 70, and 200 kHz upward-looking transducers in the northeast Chukchi Sea, we take a first look at how acoustic scattering changes in response to environmental forcing. A complete annual cycle of narrow band scattering returns from the winter of 2017–2018 are investigated with comparisons to the overlying sea ice cover. The initial effort in this research is constrained to the stable growth phase of sea ice in winter through the early melt onset the following spring. Individual, minimally deformed ice floes are identified and tracked when located over the deployment sites. Additionally, atmospheric conditions relevant to changes in the internal properties of sea ice are cross-compared to scattering returns. Results from this preliminary analysis will guide pending Arctic fieldwork and the future development of inverse methods for acoustic remote sensing of sea ice.

Subseasonal atmospheric regimes and ocean background forcing of Pacific Arctic sea ice melt onset

One observed fingerprint of Pacific Arctic environmental change, induced by climate warming and amplified local feedbacks, is a shift toward earlier onset of sea ice melt. Shorter freeze periods impact the melt season energy balance with cascading effects on ecological productivity and human presence in the region. Through this study, a non-linear technique, self-organizing maps, is utilized to investigate the subseasonal role of regional pressure patterns and associated lower-tropospheric wind regimes on melt onset in the Beaufort and Chukchi Seas. Focus is directed on the frequency and duration (≥ 3 consecutive days) of offshore, onshore, and zonal/weak flow that tend to precede anomalous (late and early) and average times of melt. Background North Pacific climate forcing ascribed from the Pacific Decadal Oscillation (PDO) phase and Bering Strait oceanic heat flux measurements provide a surface thermal context to the composite wind fields. In early melt onset years, onshore (northerly) winds occur approximately 1–3 fewer days with offsetting increases in zonal and offshore flow in the Beaufort and Chukchi Seas. During these cases, the Beaufort High pattern tends to set-up more frequently around the southeastern Beaufort Sea region, yielding winds of a southerly and/or easterly nature that are enhanced by cyclone activity to the south or downstream. Chukchi Sea weather analyses, in particular, suggest that interacting, precursor mechanisms involving warm air advection off snow-free Arctic lands and from southerly latitudes coupled with a slightly positive PDO state and anomalous, poleward oceanic heat transfer condition the seasonal ice pack for increasingly early melt.

A survey of the atmospheric physical processes key to the onset of Arctic sea ice melt in spring

September sea ice concentration (SIC) is found to be most sensitive to the early melt onset over the East Siberian Sea and Laptev Sea (73°–84°N, 90°–155°) in the Arctic, a region defined here as the area of focus (AOF). The areal initial melt date for a given year is marked when sea ice melting extends beyond 10% of the AOF size. With this definition, four early melting years (1990, 2012, 2003, 1991) and four late melting years (1996, 1984, 1983, 1982) were selected. The impacts and feedbacks of atmospheric physical and dynamical variables on the Arctic SIC variations were investigated for the selected early and late melting years based on the NASA MERRA-2 reanalysis. The sea ice melting tends to happen in a shorter period of time with larger magnitude in late melting years, while the melting lasts longer and tends to be more temporally smooth in early melting years. The first major melting event in each year has been further investigated and compared. In the early melting years, the positive Arctic Oscillation (AO) phase is dominant during springtime, which is accompanied by intensified atmospheric transient eddy activities in the Arctic and enhanced moisture flux convergence in the AOF and consequently enhanced northward transport of moist and warm air. As a result, positive anomalies of air temperature, precipitable water vapor (PWV) and/or cloud fraction and cloud water path were found over the AOF, increasing downward longwave radiative flux at the surface. The associated warming effect further contributes to the initial melt of sea ice. In contrast, the late melt onset is usually linked to the negative AO phase in spring accompanied with negative anomalies of PWV and downward longwave flux at the surface. The increased downward shortwave radiation during middle to late June plays a more important role in triggering the melting, aided further by the stronger than normal cloud warming effects.

Modulation of Sea Ice Melt Onset and Retreat in the Laptev Sea by the Timing of Snow Retreat in the West Siberian Plain

AbstractRecent years have seen growing interest in improving seasonal predictions of Arctic sea ice conditions, including the timing of ice melt onset and retreat, especially on the regional scale. This paper investigates potential links between regional sea ice melt onset and retreat in the southern Laptev Sea and retreat of terrestrial snow cover. Past studies have shown that variability of snow extent over Eurasia can substantially impact regional atmospheric circulation patterns over the North Pacific and Arctic Oceans. It is shown here that for the Laptev Sea, earlier melt onset and retreat of sea ice are encouraged by earlier retreat of snow cover over the West Siberian Plain. Earlier snow retreat in spring encourages greater ridging (e.g., at 500 hPa) over the East Siberian Sea through the summer. This results in more frequently southerly flow of warm, moist air over the Laptev Sea. This relationship could provide modest improvements to predictive skill for sea ice melt onset and retreat in the southern Laptev Sea at lead times of approximately 50 and 90 days, respectively. The detrended time series of snow retreat in the West Siberian Plain explains 26 and 29% of the detrended variance of the timing of sea ice melt onset and retreat in southern Laptev Sea, respectively.

The Arctic sea ice cover of 2016: a year of record-low highs and higher-than-expected lows

Abstract. The Arctic sea ice cover of 2016 was highly noteworthy, as it featured record low monthly sea ice extents at the start of the year but a summer (September) extent that was higher than expected by most seasonal forecasts. Here we explore the 2016 Arctic sea ice state in terms of its monthly sea ice cover, placing this in the context of the sea ice conditions observed since 2000. We demonstrate the sensitivity of monthly Arctic sea ice extent and area estimates, in terms of their magnitude and annual rankings, to the ice concentration input data (using two widely used datasets) and to the averaging methodology used to convert concentration to extent (daily or monthly extent calculations). We use estimates of sea ice area over sea ice extent to analyse the relative “compactness” of the Arctic sea ice cover, highlighting anomalously low compactness in the summer of 2016 which contributed to the higher-than-expected September ice extent. Two cyclones that entered the Arctic Ocean during August appear to have driven this low-concentration/compactness ice cover but were not sufficient to cause more widespread melt-out and a new record-low September ice extent. We use concentration budgets to explore the regions and processes (thermodynamics/dynamics) contributing to the monthly 2016 extent/area estimates highlighting, amongst other things, rapid ice intensification across the central eastern Arctic through September. Two different products show significant early melt onset across the Arctic Ocean in 2016, including record-early melt onset in the North Atlantic sector of the Arctic. Our results also show record-late 2016 freeze-up in the central Arctic, North Atlantic and the Alaskan Arctic sector in particular, associated with strong sea surface temperature anomalies that appeared shortly after the 2016 minimum (October onwards). We explore the implications of this low summer ice compactness for seasonal forecasting, suggesting that sea ice area could be a more reliable metric to forecast in this more seasonal, “New Arctic”, sea ice regime.

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.

Atmospheric conditions in the central Arctic Ocean through the melt seasons of 2012 and 2013: Impact on surface conditions and solar energy deposition into the ice‐ocean system

AbstractSpectral Radiation Buoys and ice mass balance buoys were deployed on first‐year ice near the North Pole in April 2012 and 2013, collecting in‐band (350–800 nm) solar radiation and ice and snow mass balance data over the complete summer melt seasons. With complementary European ERA‐Interim reanalysis, National Centers for Environmental Prediction (NCEP) Climate forecast system version 2 (CFSv2) analysis and satellite passive microwave data, we examine the evolution of atmospheric and surface melt conditions in the two differing melt seasons. Prevailing atmospheric conditions contributed to a longer and more continuous melt season in summer 2012 than in 2013, which was corroborated by in situ observations. ERA‐Interim reanalysis data showed that longwave radiation likely played a key role in delaying the snowmelt onset in 2013. The earlier melt onset in 2012 reduced the albedo, providing a positive ice‐albedo feedback at a time when solar insolation was high. Due to earlier melt onset and later freeze‐up in 2012, more solar heat was deposited into the ice‐ocean system than in 2013. Summer 2013 was characterized by later melt onset, intermittent freezing events and an earlier fall freeze‐up, resulting in considerably fewer effective days of surface melt and a higher average albedo. Calculations for idealized seasonal albedo evolution show that moving the melt onset just 1 week earlier in mid‐June increases the total absorbed solar radiation by nearly 14% for the summer season. Therefore, the earlier melt onset may have been one of the most important factors driving the more dramatic melt season in 2012 than 2013, though atmospheric circulation patterns, e.g., cyclone in early August 2012, likely contributed as well.

Separability of sea ice types from wide swath C- and L-band synthetic aperture radar imagery acquired during the melt season

Abstract Differentiating between first-year ice (FYI) and multi-year ice (MYI) in C-band synthetic aperture radar (SAR) imagery during spring–summer melt, when wet snow and melt ponds mask the underlying ice, is difficult. It has been suggested that the use of L-band SAR may alleviate this concern given increased penetration depths at longer wavelengths; however, this has not been thoroughly assessed. Here the separability of FYI and MYI is compared using horizontally polarized (HH) C-band (RADARSAT-2) and L-band (ALOS/PALSAR) ScanSAR images acquired over landfast sea ice in the Canadian Arctic Archipelago in the spring and summer of 2009. L-band provided enhanced contrast between FYI and MYI during early melt onset and during the drainage phase of advanced melt, while C-band was found to provide enhanced contrast when the wet snowpack was transitioning from the pendular regime to the funicular regime. At the time of the pendular–funicular transition, the backscatter signatures of FYI and MYI reversed at both C- and L-band. This behavior is well established at C-band, but has not been reported previously at L-band. The L-band imagery also provided improved definition of floe boundaries and ridges throughout the melt season. Finally, the L-band data had reduced speckle (equivalent number of looks ~ 12), relative to the C-band data (~ 9 equivalent looks). These results indicate that L-band SAR data acquired during the melt season could be used to enhance operational and scientific sea ice information products that have traditionally been derived from single-frequency C-band SAR data.