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Dr. Walter Meier
National Snow and Ice Data Center (NSIDC)

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Journal article
Published: 29 July 2021 in Remote Sensing
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An effective blended Sea-Ice Concentration (SIC) product has been developed that utilizes ice concentrations from passive microwave and visible/infrared satellite instruments, specifically the Advanced Microwave Scanning Radiometer-2 (AMSR2) and the Visible Infrared Imaging Radiometer Suite (VIIRS). The blending takes advantage of the all-sky capability of the AMSR2 sensor and the high spatial resolution of VIIRS, though it utilizes only the clear sky characteristics of VIIRS. After both VIIRS and AMSR2 images are remapped to a 1 km EASE-Grid version 2, a Best Linear Unbiased Estimator (BLUE) method is used to combine the AMSR2 and VIIRS SIC for a blended product at 1 km resolution under clear-sky conditions. Under cloudy-sky conditions the AMSR2 SIC with bias correction is used. For validation, high spatial resolution Landsat data are collocated with VIIRS and AMSR2 from 1 February 2017 to 31 October 2019. Bias, standard deviation, and root mean squared errors are calculated for the SICs of VIIRS, AMSR2, and the blended field. The blended SIC outperforms the individual VIIRS and AMSR2 SICs. The higher spatial resolution VIIRS data provide beneficial information to improve upon AMSR2 SIC under clear-sky conditions, especially during the summer melt season, as the AMSR2 SIC has a consistent negative bias near and above the melting point.

ACS Style

Richard Dworak; Yinghui Liu; Jeffrey Key; Walter Meier. A Blended Sea Ice Concentration Product from AMSR2 and VIIRS. Remote Sensing 2021, 13, 2982 .

AMA Style

Richard Dworak, Yinghui Liu, Jeffrey Key, Walter Meier. A Blended Sea Ice Concentration Product from AMSR2 and VIIRS. Remote Sensing. 2021; 13 (15):2982.

Chicago/Turabian Style

Richard Dworak; Yinghui Liu; Jeffrey Key; Walter Meier. 2021. "A Blended Sea Ice Concentration Product from AMSR2 and VIIRS." Remote Sensing 13, no. 15: 2982.

Journal article
Published: 08 August 2020 in Remote Sensing
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A new enhanced resolution gridded passive microwave brightness temperature (TB) product is used to estimate sea ice concentration and motion. The effective resolution of the TBs is found to be roughly twice that of the standard 25 km resolution, though the gridded resolution of the distributed product is higher. Enhanced resolution sea ice concentrations from the Bootstrap algorithm show more detail in the sea ice, including relatively small open water regions within the ice pack. Sea ice motion estimates from the enhanced resolution TBs using a maximum cross-correlation method show a smoother motion circulation pattern; in comparison to buoys, RMS errors are 15–20% lower than motion estimates from the standard resolution fields and the magnitude of the bias is smaller as well. The enhanced resolution product includes other potentially beneficial characteristics, including twice-daily grids based on local time of day and a complete timeseries of data from nearly all multi-channel passive microwave radiometers since 1978. These enhanced resolution TBs are potential new source for long-term records of sea ice concentration, motion, age, melt, as well as salinity and ocean-atmosphere fluxes.

ACS Style

Walter Meier; J. Stewart. Assessing the Potential of Enhanced Resolution Gridded Passive Microwave Brightness Temperatures for Retrieval of Sea Ice Parameters. Remote Sensing 2020, 12, 2552 .

AMA Style

Walter Meier, J. Stewart. Assessing the Potential of Enhanced Resolution Gridded Passive Microwave Brightness Temperatures for Retrieval of Sea Ice Parameters. Remote Sensing. 2020; 12 (16):2552.

Chicago/Turabian Style

Walter Meier; J. Stewart. 2020. "Assessing the Potential of Enhanced Resolution Gridded Passive Microwave Brightness Temperatures for Retrieval of Sea Ice Parameters." Remote Sensing 12, no. 16: 2552.

Journal article
Published: 09 July 2020 in Remote Sensing
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Gridded passive microwave brightness temperatures (TB) from special sensor microwave imager and sounder (SSMIS) instruments on three different satellite platforms are compared in different years to investigate the consistency between the sensors over time. The orbits of the three platforms have drifted over their years of operation, resulting in changing relative observing times that could cause biases in TB estimates and near-real-time sea ice concentrations derived from the NASA Team algorithm that are produced at the National Snow and Ice Data Center. Comparisons of TB histograms and concentrations show that there are small mean differences between sensors, but variability within an individual sensor is much greater. There are some indications of small changes due to orbital drift, but these are not consistent across different frequencies. Further, the overall effect of the drift, while not definitive, is small compared to the intra- and interannual variability in individual sensors. These results suggest that, for near-real-time use, the differences in the sensors are not critical. However, for long-term time series, even the small biases should be corrected for. The strong day-to-day, seasonal, and interannual variability in TB distributions indicate that time-varying algorithm coefficients in the NASA team algorithm would lead to improved, more consistent sea ice concentration estimates.

ACS Style

Walter Meier; J. Stewart. Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals. Remote Sensing 2020, 12, 2197 .

AMA Style

Walter Meier, J. Stewart. Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals. Remote Sensing. 2020; 12 (14):2197.

Chicago/Turabian Style

Walter Meier; J. Stewart. 2020. "Assessment of the Stability of Passive Microwave Brightness Temperatures for NASA Team Sea Ice Concentration Retrievals." Remote Sensing 12, no. 14: 2197.

Letter
Published: 16 May 2020 in Remote Sensing
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This work assesses the AMSR2 (the Advanced Microwave Scanning Radiometer 2) ice extent and ice edge in the Arctic using the ice extent products of NOAA’s Interactive Multisensor Snow and Ice Mapping System (IMS) from the period of July 2015 to July 2019. Daily values and monthly means of four statistical scores (hit rate, false alarm ratio, false alarm rate, and Hanssen-Kuiper Skill Score) over the Arctic Ocean show distinct annual cycles. IMS ice edges often extend further south compared to those from AMSR2, with up to 100 km differences over the Beaufort, Chukchi, and East Siberian Seas in August and September.

ACS Style

Yinghui Liu; Sean Helfrich; Walter N. Meier; Richard Dworak. Assessment of AMSR2 Ice Extent and Ice Edge in the Arctic Using IMS. Remote Sensing 2020, 12, 1582 .

AMA Style

Yinghui Liu, Sean Helfrich, Walter N. Meier, Richard Dworak. Assessment of AMSR2 Ice Extent and Ice Edge in the Arctic Using IMS. Remote Sensing. 2020; 12 (10):1582.

Chicago/Turabian Style

Yinghui Liu; Sean Helfrich; Walter N. Meier; Richard Dworak. 2020. "Assessment of AMSR2 Ice Extent and Ice Edge in the Arctic Using IMS." Remote Sensing 12, no. 10: 1582.

Research article
Published: 07 May 2020 in The Cryosphere
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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.

ACS Style

Mark A. Tschudi; Walter N. Meier; J. Scott Stewart. An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC). The Cryosphere 2020, 14, 1519 -1536.

AMA Style

Mark A. Tschudi, Walter N. Meier, J. Scott Stewart. An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC). The Cryosphere. 2020; 14 (5):1519-1536.

Chicago/Turabian Style

Mark A. Tschudi; Walter N. Meier; J. Scott Stewart. 2020. "An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC)." The Cryosphere 14, no. 5: 1519-1536.

Journal article
Published: 03 March 2020 in Remote Sensing
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Arctic sea ice extent has been utilized to monitor sea ice changes since the late 1970s using remotely sensed sea ice data derived from passive microwave (PM) sensors. A 15% sea ice concentration threshold value has been used traditionally when computing sea ice extent (SIE), although other threshold values have been employed. Does the rapid depletion of Arctic sea ice potentially alter the basic characteristics of Arctic ice extent? In this paper, we explore whether and how the statistical characteristics of Arctic sea ice have changed during the satellite data record period of 1979–2017 and examine the sensitivity of sea ice extents and their decadal trends to sea ice concentration threshold values. Threshold choice can affect the timing of annual SIE minimums: a threshold choice as low as 30% can change the timing to August instead of September. Threshold choice impacts the value of annual SIE minimums: in particular, changing the threshold from 15% to 35% can change the annual SIE by more than 10% in magnitude. Monthly SIE data distributions are seasonally dependent. Although little impact was seen for threshold choice on data distributions during annual minimum times (August and September), there is a strong impact in May. Threshold choices were not found to impact the choice of optimal statistical models characterizing annual minimum SIE time series. However, the first ice-free Arctic summer year (FIASY) estimates are impacted; higher threshold values produce earlier FIASY estimates and, more notably, FIASY estimates amongst all considered models are more consistent. This analysis suggests that some of the threshold choice impacts to SIE trends may actually be the result of biased data due to surface melt. Given that the rapid Arctic sea ice depletion appears to have statistically changed SIE characteristics, particularly in the summer months, a more extensive investigation to verify surface melt impacts on this data set is warranted.

ACS Style

Jessica L. Matthews; Ge Peng; Walter N. Meier; Otis Brown. Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections. Remote Sensing 2020, 12, 807 .

AMA Style

Jessica L. Matthews, Ge Peng, Walter N. Meier, Otis Brown. Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections. Remote Sensing. 2020; 12 (5):807.

Chicago/Turabian Style

Jessica L. Matthews; Ge Peng; Walter N. Meier; Otis Brown. 2020. "Sensitivity of Arctic Sea Ice Extent to Sea Ice Concentration Threshold Choice and Its Implication to Ice Coverage Decadal Trends and Statistical Projections." Remote Sensing 12, no. 5: 807.

Article
Published: 29 January 2020
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Arctic sea ice coverage has changed considerably over the last few decades. Sea ice extent record minimums have been observed in recent years, the distribution of sea ice age now heavily favors younger ice, and sea ice is thinning. To investigate the response of the ice pack to climate forcing during summertime melt, we have developed a technique to track individual Arctic sea ice parcels along with associated properties as these parcels advect through the Arctic Ocean. This sea ice parcel tracking method utilizes our sea ice motion dataset, archived at NASA’s National Snow and Ice Data Center. Tracked sea ice parcel locations coincide with other environmental products that influence sea ice growth/decay. We have recently tracked variables such as ice surface temperature, albedo, ice concentration, and ice thickness for hundreds of sea ice parcels, defined as occupying one EASE-grid cell location. These parcels can be tracked through a melt season, to determine the influence of these properties on sea ice melt, along with determining what fraction of the parcels survive or don’t survive summer melt. This analysis can be applied to determine the impact of these and other properties on ice melt, in terms of their relative importance. Here, we focus on tracking recent sea ice freeboard, produced from NASA’s ICESat-2 observations. We look at the evolution of ice thickness for individual parcels through the recent ICESat-2 record, to determine the rate of ice growth over part or all of the winter, depending on product availability (as of this writing, freeboard is available through Dec 27, 2018). We determine how sea ice growth varies for hundreds of parcels, and determine how growth is affected by location and time.

ACS Style

Mark TschudiiD; Walter MeieriD; J StewartiD. Tracking ICESat-2 Arctic Sea Ice Freeboard. 2020, 1 .

AMA Style

Mark TschudiiD, Walter MeieriD, J StewartiD. Tracking ICESat-2 Arctic Sea Ice Freeboard. . 2020; ():1.

Chicago/Turabian Style

Mark TschudiiD; Walter MeieriD; J StewartiD. 2020. "Tracking ICESat-2 Arctic Sea Ice Freeboard." , no. : 1.

Data descriptor
Published: 10 August 2019 in Data
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The climate normal, that is, the latest three full-decade average, of Arctic sea ice parameters is useful for baselining the sea ice state. A baseline ice state on both regional and local scales is important for monitoring how the current regional and local states depart from their normal to understand the vulnerability of marine and sea ice-based ecosystems to the changing climate conditions. Combined with up-to-date observations and reliable projections, normals are essential to business strategic planning, climate adaptation and risk mitigation. In this paper, monthly and annual climate normals of sea ice parameters (concentration, area, and extent) of the whole Arctic Ocean and 15 regional divisions are derived for the period of 1981–2010 using monthly satellite sea ice concentration estimates from a climate data record (CDR) produced by NOAA and the National Snow and Ice Data Center (NSIDC). Basic descriptions and characteristics of the normals are provided. Empirical Orthogonal Function (EOF) analysis has been utilized to describe spatial modes of sea ice concentration variability and how the corresponding principal components change over time. To provide users with basic information on data product accuracy and uncertainty, the climate normal values of Arctic sea ice extents (SIE) are compared with that of other products, including a product from NSIDC and two products from the Copernicus Climate Change Service (C3S). The SIE differences between different products are in the range of 2.3–4.5% of the CDR SIE mean. Additionally, data uncertainty estimates are represented by using the range (the difference between the maximum and minimum), standard deviation, 10th and 90th percentiles, and the first, second, and third quartile distribution of all monthly values, a distinct feature of these sea ice normal products.

ACS Style

Ge Peng; Anthony Arguez; Walter N. Meier; Freja Vamborg; Jake Crouch; Philip Jones. Sea Ice Climate Normals for Seasonal Ice Monitoring of Arctic and Sub-Regions. Data 2019, 4, 122 .

AMA Style

Ge Peng, Anthony Arguez, Walter N. Meier, Freja Vamborg, Jake Crouch, Philip Jones. Sea Ice Climate Normals for Seasonal Ice Monitoring of Arctic and Sub-Regions. Data. 2019; 4 (3):122.

Chicago/Turabian Style

Ge Peng; Anthony Arguez; Walter N. Meier; Freja Vamborg; Jake Crouch; Philip Jones. 2019. "Sea Ice Climate Normals for Seasonal Ice Monitoring of Arctic and Sub-Regions." Data 4, no. 3: 122.

Letter
Published: 01 April 2019 in Environmental Research Letters
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Key observational indicators of climate change in the Arctic, most spanning a 47 year period (1971–2017) demonstrate fundamental changes among nine key elements of the Arctic system. We find that, coherent with increasing air temperature, there is an intensification of the hydrological cycle, evident from increases in humidity, precipitation, river discharge, glacier equilibrium line altitude and land ice wastage. Downward trends continue in sea ice thickness (and extent) and spring snow cover extent and duration, while near-surface permafrost continues to warm. Several of the climate indicators exhibit a significant statistical correlation with air temperature or precipitation, reinforcing the notion that increasing air temperatures and precipitation are drivers of major changes in various components of the Arctic system. To progress beyond a presentation of the Arctic physical climate changes, we find a correspondence between air temperature and biophysical indicators such as tundra biomass and identify numerous biophysical disruptions with cascading effects throughout the trophic levels. These include: increased delivery of organic matter and nutrients to Arctic near‐coastal zones; condensed flowering and pollination plant species periods; timing mismatch between plant flowering and pollinators; increased plant vulnerability to insect disturbance; increased shrub biomass; increased ignition of wildfires; increased growing season CO2 uptake, with counterbalancing increases in shoulder season and winter CO2 emissions; increased carbon cycling, regulated by local hydrology and permafrost thaw; conversion between terrestrial and aquatic ecosystems; and shifting animal distribution and demographics. The Arctic biophysical system is now clearly trending away from its 20th Century state and into an unprecedented state, with implications not only within but beyond the Arctic. The indicator time series of this study are freely downloadable at AMAP.no.

ACS Style

Jason E Box; William T Colgan; Torben Røjle Christensen; Niels Martin Schmidt; Magnus Lund; Frans-Jan W Parmentier; Ross Brown; Uma S Bhatt; Eugénie S Euskirchen; Vladimir E Romanovsky; John Walsh; James E Overland; Muyin Wang; Robert W Corell; Walter N Meier; Bert Wouters; Sebastian Mernild; Johanna Mård; Janet Pawlak; Morten Skovgård Olsen. Key indicators of Arctic climate change: 1971–2017. Environmental Research Letters 2019, 14, 045010 .

AMA Style

Jason E Box, William T Colgan, Torben Røjle Christensen, Niels Martin Schmidt, Magnus Lund, Frans-Jan W Parmentier, Ross Brown, Uma S Bhatt, Eugénie S Euskirchen, Vladimir E Romanovsky, John Walsh, James E Overland, Muyin Wang, Robert W Corell, Walter N Meier, Bert Wouters, Sebastian Mernild, Johanna Mård, Janet Pawlak, Morten Skovgård Olsen. Key indicators of Arctic climate change: 1971–2017. Environmental Research Letters. 2019; 14 (4):045010.

Chicago/Turabian Style

Jason E Box; William T Colgan; Torben Røjle Christensen; Niels Martin Schmidt; Magnus Lund; Frans-Jan W Parmentier; Ross Brown; Uma S Bhatt; Eugénie S Euskirchen; Vladimir E Romanovsky; John Walsh; James E Overland; Muyin Wang; Robert W Corell; Walter N Meier; Bert Wouters; Sebastian Mernild; Johanna Mård; Janet Pawlak; Morten Skovgård Olsen. 2019. "Key indicators of Arctic climate change: 1971–2017." Environmental Research Letters 14, no. 4: 045010.

Preprint content
Published: 28 February 2019 in The Cryosphere Discussions
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A new version of the sea ice motion and age products distributed at the National Snow and Ice Data Center's NASA Snow and Ice Distributed Active Archive Center has been developed. The new version, 4.0, 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. Here, we provide a history of the product development, discuss the improvements to the algorithms that create these products, and compare the Version 4 products to the previous version. While Version 4 algorithm changes were significant, the impact on the products is relatively minor, particularly for more recent years. Trends in motion and age are not substantially different between the versions. 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 in recent years, which is anticipated with the annual decrease in sea ice extent.

ACS Style

Mark A. Tschudi; Walter N. Meier; J. Scott Stewart. An enhancement to sea ice motion and age products. The Cryosphere Discussions 2019, 2019, 1 -29.

AMA Style

Mark A. Tschudi, Walter N. Meier, J. Scott Stewart. An enhancement to sea ice motion and age products. The Cryosphere Discussions. 2019; 2019 ():1-29.

Chicago/Turabian Style

Mark A. Tschudi; Walter N. Meier; J. Scott Stewart. 2019. "An enhancement to sea ice motion and age products." The Cryosphere Discussions 2019, no. : 1-29.

Accepted manuscript
Published: 22 February 2019 in Environmental Research Letters
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While Arctic sea ice is changing, new observation methods are developed and process understanding improves, whereas gaps in observations and understanding evolve. Some previous gaps are filled, while others remain, or come up new. Knowing about the status of observation and knowledge gaps is important for interpreting observation and research results, interpretation and use of key climate indicators, and for research and observation planning. This paper deals with identifying some of the important current gaps connected to Arctic sea ice and related climate indicators, including their role and functions in the sea ice and climate systems. Subtopics that are discussed here include Arctic sea-ice extent, concentration, and thickness, sea-ice thermodynamics, age and dynamic processes, and biological implications of changing sea ice. Among crucial gaps are few in situ observations during the winter season, limited observational data on snow and ice thickness from the Arctic Basin, and wide gaps in biological rate measurements in or under sea ice. There is a need to develop or improve analyses and products of remote sensing, especially for new sensors and technology such as remotely operated vehicles. Potential gaps in observations are inevitably associated with interruptions in long-term observational time series due to sensor failure or cuts in observation programmes.

ACS Style

Sebastian Gerland; David Barber; Walter N Meier; Christopher J. Mundy; Marika Holland; Stefan Kern; Zhijun Li; Christine Michel; Donald K Perovich; Takeshi Tamura. Essential gaps and uncertainties in the understanding of the roles and functions of Arctic sea ice. Environmental Research Letters 2019, 14, 043002 .

AMA Style

Sebastian Gerland, David Barber, Walter N Meier, Christopher J. Mundy, Marika Holland, Stefan Kern, Zhijun Li, Christine Michel, Donald K Perovich, Takeshi Tamura. Essential gaps and uncertainties in the understanding of the roles and functions of Arctic sea ice. Environmental Research Letters. 2019; 14 (4):043002.

Chicago/Turabian Style

Sebastian Gerland; David Barber; Walter N Meier; Christopher J. Mundy; Marika Holland; Stefan Kern; Zhijun Li; Christine Michel; Donald K Perovich; Takeshi Tamura. 2019. "Essential gaps and uncertainties in the understanding of the roles and functions of Arctic sea ice." Environmental Research Letters 14, no. 4: 043002.

Accepted manuscript
Published: 02 January 2019 in Environmental Research Letters
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The seasonal evolution of Arctic sea ice can be described by the timing of key dates of sea ice concentration (SIC) change during its annual retreat and advance cycle. Here, we use SICs from a satellite passive microwave climate data record to identify the sea ice dates of opening (DOO), retreat (DOR), advance (DOA), and closing (DOC) and the periods of time between these events. Regional variability in these key dates, periods, and sea ice melt onset and freeze-up dates for 12 Arctic regions during the melt seasons of 1979 – 2016 is investigated. We find statistically significant positive trends in the length of the melt season (outer ice-free period) for most of the eastern Arctic, the Bering Sea, and Hudson and Baffin Bays with trends as large as 11.9 days decade-1 observed in the Kara Sea. Trends in the DOR and DOA contribute to statistically significant increases in the length of the open water period for all regions within the Arctic Ocean ranging from 3.9 to 13.8 days decade-1. The length of the ice retreat period (DOR - DOO) ranges from 17.1 days in the Sea of Okhotsk to 41 days in the Greenland Sea. The length of the ice advance period (DOC - DOA) is generally much shorter and ranges from 17.9 days to 25.3 days in the Sea of Okhotsk and Greenland Sea, respectively. Additionally, we derive the extent of the seasonal ice zone (SIZ) and find statistically significant negative trends (SIZ is shrinking) in the Sea of Okhotsk, Baffin Bay, Greenland Sea, and Barents Sea regions, which are geographically open to the oceans and influenced by reduced winter sea ice extent. Within regions of the Arctic Ocean, statistically significant positive trends indicate that the extent of the SIZ is expanding as Arctic summer sea ice declines.

ACS Style

Angela C Bliss; Michael Steele; Ge Peng; Walter N Meier; Suzanne Dickinson. Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record. Environmental Research Letters 2019, 14, 045003 .

AMA Style

Angela C Bliss, Michael Steele, Ge Peng, Walter N Meier, Suzanne Dickinson. Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record. Environmental Research Letters. 2019; 14 (4):045003.

Chicago/Turabian Style

Angela C Bliss; Michael Steele; Ge Peng; Walter N Meier; Suzanne Dickinson. 2019. "Regional variability of Arctic sea ice seasonal change climate indicators from a passive microwave climate data record." Environmental Research Letters 14, no. 4: 045003.

Accepted manuscript
Published: 30 November 2018 in Environmental Research Letters
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Uncertainties in sea ice extent (total area covered by sea ice with concentration >15%) derived from passive microwave sensors are assessed in two ways. Absolute uncertainty (accuracy) is evaluated based the comparison of extent between several products. There are clear biases between extent from the different products that are on the order of 500,000 to 1x106 km2 depending on season and hemisphere. These biases are due to differences in algorithm sensitivity to ice edge conditions and the spatial resolution of different sensors. Relative uncertainty is assessed by examining extents from the NSIDC Sea Ice Index product. The largest source of uncertainty, ~100,000 km2, is between near-real-time and final products due different input source data and different processing and quality control. For consistent processing, the uncertainty is assessed using different input source data and by varying concentration algorithm parameters. This yields a relative uncertainty of 30,000 – 70,000 km2. The Arctic minimum extent uncertainty is ~40,000 km2. Uncertainties in comparing with earlier parts of the record may be higher due to sensor transitions. For the first time, this study provides a quantitative estimates of sea ice extent uncertainty.

ACS Style

Walter N Meier; J Scott Stewart. Assessing uncertainties in sea ice extent climate indicators. Environmental Research Letters 2018, 14, 035005 .

AMA Style

Walter N Meier, J Scott Stewart. Assessing uncertainties in sea ice extent climate indicators. Environmental Research Letters. 2018; 14 (3):035005.

Chicago/Turabian Style

Walter N Meier; J Scott Stewart. 2018. "Assessing uncertainties in sea ice extent climate indicators." Environmental Research Letters 14, no. 3: 035005.

Journal article
Published: 15 October 2018 in Eos
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Barry, a giant in climate and cryospheric sciences, pioneered the archival of computer data and traveled the world to share his vision with others.

ACS Style

Ronald L. S. Weaver; Walt Meier; Florence Fetterer. Roger G. Barry (1935–2018). Eos 2018, 99, 1 .

AMA Style

Ronald L. S. Weaver, Walt Meier, Florence Fetterer. Roger G. Barry (1935–2018). Eos. 2018; 99 ():1.

Chicago/Turabian Style

Ronald L. S. Weaver; Walt Meier; Florence Fetterer. 2018. "Roger G. Barry (1935–2018)." Eos 99, no. : 1.

Journal article
Published: 21 August 2018 in Remote Sensing
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Information on the timing of Arctic snow and ice melt onset, sea ice opening, retreat, advance, and closing, can be beneficial to a variety of stakeholders. Sea ice modelers can use information on the evolution of the ice cover through the rest of the summer to improve their seasonal sea ice forecasts. The length of the open water season (as derived from retreat/advance dates) is important for human activities and for wildlife. Long-term averages and variability of these dates as climate indicators are beneficial to business strategic planning and climate monitoring. In this study, basic characteristics of temporal means and variability of Arctic sea ice climate indicators derived from a satellite-based climate data record from March 1979 to February 2017 melt and freeze seasons are described. Our results show that, over the Arctic region, anomalies of snow and ice melt onset, ice opening and retreat dates are getting earlier in the year at a rate of more than 5 days per decade, while that of ice advance and closing dates are getting later at a rate of more than 5 days per decade. These significant trends resulted in significant upward trends for anomalies of inner and outer ice-free periods at a rate of nearly 12 days per decade. Small but significant downward trends of seasonal ice loss and gain period anomalies were also observed at a rate of −1.48 and −0.53 days per decade, respectively. Our analyses also demonstrated that the means of these indicators and their trends are sensitive to valid data masks and regional averaging methods.

ACS Style

Ge Peng; Michael Steele; Angela C. Bliss; Walter N. Meier; Suzanne Dickinson. Temporal Means and Variability of Arctic Sea Ice Melt and Freeze Season Climate Indicators Using a Satellite Climate Data Record. Remote Sensing 2018, 10, 1328 .

AMA Style

Ge Peng, Michael Steele, Angela C. Bliss, Walter N. Meier, Suzanne Dickinson. Temporal Means and Variability of Arctic Sea Ice Melt and Freeze Season Climate Indicators Using a Satellite Climate Data Record. Remote Sensing. 2018; 10 (9):1328.

Chicago/Turabian Style

Ge Peng; Michael Steele; Angela C. Bliss; Walter N. Meier; Suzanne Dickinson. 2018. "Temporal Means and Variability of Arctic Sea Ice Melt and Freeze Season Climate Indicators Using a Satellite Climate Data Record." Remote Sensing 10, no. 9: 1328.

Review
Published: 28 May 2018 in Annals of the New York Academy of Sciences
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As assessed over the period of satellite observations, October 1978 to present, there are downward linear trends in Arctic sea ice extent for all months, largest at the end of the melt season in September. The ice cover is also thinning. Downward trends in extent and thickness have been accompanied by pronounced interannual and multiyear variability, forced by both the atmosphere and ocean. As the ice thins, its response to atmospheric and oceanic forcing may be changing. In support of a busier Arctic, there is a growing need to predict ice conditions on a variety of time and space scales. A major challenge to providing seasonal scale predictions is the 7–10 days limit of numerical weather prediction. While a seasonally ice‐free Arctic Ocean is likely well within this century, there is much uncertainty in the timing. This reflects differences in climate model structure, the unknown evolution of anthropogenic forcing, and natural climate variability. In sharp contrast to the Arctic, Antarctic sea ice extent, while highly variable, has increased slightly over the period of satellite observations. The reasons for this different behavior remain to be resolved, but responses to changing atmospheric circulation patterns appear to play a strong role.

ACS Style

Mark C. Serreze; Walter N. Meier. The Arctic's sea ice cover: trends, variability, predictability, and comparisons to the Antarctic. Annals of the New York Academy of Sciences 2018, 1436, 36 -53.

AMA Style

Mark C. Serreze, Walter N. Meier. The Arctic's sea ice cover: trends, variability, predictability, and comparisons to the Antarctic. Annals of the New York Academy of Sciences. 2018; 1436 (1):36-53.

Chicago/Turabian Style

Mark C. Serreze; Walter N. Meier. 2018. "The Arctic's sea ice cover: trends, variability, predictability, and comparisons to the Antarctic." Annals of the New York Academy of Sciences 1436, no. 1: 36-53.

Journal article
Published: 06 February 2018 in The Cryosphere
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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.

ACS Style

Alek A. Petty; Julienne C. Stroeve; Paul R. Holland; Linette N. Boisvert; Angela C. Bliss; Noriaki Kimura; Walter N. Meier. The Arctic sea ice cover of 2016: a year of record-low highs and higher-than-expected lows. The Cryosphere 2018, 12, 433 -452.

AMA Style

Alek A. Petty, Julienne C. Stroeve, Paul R. Holland, Linette N. Boisvert, Angela C. Bliss, Noriaki Kimura, Walter N. Meier. The Arctic sea ice cover of 2016: a year of record-low highs and higher-than-expected lows. The Cryosphere. 2018; 12 (2):433-452.

Chicago/Turabian Style

Alek A. Petty; Julienne C. Stroeve; Paul R. Holland; Linette N. Boisvert; Angela C. Bliss; Noriaki Kimura; Walter N. Meier. 2018. "The Arctic sea ice cover of 2016: a year of record-low highs and higher-than-expected lows." The Cryosphere 12, no. 2: 433-452.

Preprint content
Published: 09 October 2017
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2016 was an interesting year in the Arctic, with record low sea ice at the start of the year, but a summer (September) Arctic sea ice 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 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 methodology used to convert concentration to extent (daily or monthly extent calculations). We use estimates of sea ice area 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.

ACS Style

Alek A. Petty; Julienne C. Stroeve; Paul R. Holland; Linette N. Boisvert; Angela C. Bliss; Noriaki Kimura; Walter N. Meier. The Arctic sea ice cover of 2016: A year of record low highs and higher than expected lows. 2017, 2017, 1 -33.

AMA Style

Alek A. Petty, Julienne C. Stroeve, Paul R. Holland, Linette N. Boisvert, Angela C. Bliss, Noriaki Kimura, Walter N. Meier. The Arctic sea ice cover of 2016: A year of record low highs and higher than expected lows. . 2017; 2017 ():1-33.

Chicago/Turabian Style

Alek A. Petty; Julienne C. Stroeve; Paul R. Holland; Linette N. Boisvert; Angela C. Bliss; Noriaki Kimura; Walter N. Meier. 2017. "The Arctic sea ice cover of 2016: A year of record low highs and higher than expected lows." 2017, no. : 1-33.

Preprint content
Published: 09 October 2017
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ACS Style

Alek A. Petty; Julienne C. Stroeve; Paul R. Holland; Linette N. Boisvert; Angela C. Bliss; Noriaki Kimura; Walter N. Meier. Supplementary material to "The Arctic sea ice cover of 2016: A year of record low highs and higher than expected lows". 2017, 1 .

AMA Style

Alek A. Petty, Julienne C. Stroeve, Paul R. Holland, Linette N. Boisvert, Angela C. Bliss, Noriaki Kimura, Walter N. Meier. Supplementary material to "The Arctic sea ice cover of 2016: A year of record low highs and higher than expected lows". . 2017; ():1.

Chicago/Turabian Style

Alek A. Petty; Julienne C. Stroeve; Paul R. Holland; Linette N. Boisvert; Angela C. Bliss; Noriaki Kimura; Walter N. Meier. 2017. "Supplementary material to "The Arctic sea ice cover of 2016: A year of record low highs and higher than expected lows"." , no. : 1.

Article
Published: 30 August 2017 in Journal of Geophysical Research: Oceans
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Variability and trend studies of sea ice in the Arctic have been conducted using products derived from the same raw passive microwave data but by different groups using different algorithms. This study provides consistency assessment of four of the leading products, namely, Goddard Bootstrap (SB2), Goddard NASA Team (NT1), EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI-SAF 1.2), and Hadley HadISST 2.2 data in evaluating variability and trends in the Arctic sea ice cover. All four provide generally similar ice patterns but significant disagreements in ice concentration distributions especially in the marginal ice zone and adjacent regions in winter and meltponded areas in summer. The discrepancies are primarily due to different ways the four techniques account for occurrences of new ice and meltponding. However, results show that the different products generally provide consistent and similar representation of the state of the Arctic sea ice cover. Hadley and NT1 data usually provide the highest and lowest monthly ice extents, respectively. The Hadley data also show the lowest trends in ice extent and ice area at −3.88%/decade and −4.37%/decade, respectively, compared to an average of −4.36%/decade and −4.57%/decade for all four. Trend maps also show similar spatial distribution for all four with the largest negative trends occurring at the Kara/Barents Sea and Beaufort Sea regions, where sea ice has been retreating the fastest. The good agreement of the trends especially with updated data provides strong confidence in the quantification of the rate of decline in the Arctic sea ice cover.

ACS Style

Josefino C. Comiso; Walter N. Meier; Robert Gersten. Variability and trends in the Arctic Sea ice cover: Results from different techniques. Journal of Geophysical Research: Oceans 2017, 122, 6883 -6900.

AMA Style

Josefino C. Comiso, Walter N. Meier, Robert Gersten. Variability and trends in the Arctic Sea ice cover: Results from different techniques. Journal of Geophysical Research: Oceans. 2017; 122 (8):6883-6900.

Chicago/Turabian Style

Josefino C. Comiso; Walter N. Meier; Robert Gersten. 2017. "Variability and trends in the Arctic Sea ice cover: Results from different techniques." Journal of Geophysical Research: Oceans 122, no. 8: 6883-6900.