Arctic sea ice seasonal-to-decadal variability and long-term change

AbstractVariability of the sea-ice cover (extent) in the Northern Hemisphere (Arctic and subpolar regions) associated with the Arctic Oscillation (AO) is investigated using historical data from 1901 to 1997. A principal-component analysis (empirical orthogonal functions (EOFs)) was applied to sea-ice area (SIA) anomalies for the period 1953−95. The leading EOF mode for the SI A anomaly shows an in-phase fluctuation in response to the AO and is called the Arctic sea-ice oscillation (ASIO). Arctic sea ice experiences seasonal variations that differ in timing and magnitude. Four types of seasonal variation are identified in the Arctic sea ice, and are superimposed on long-term interannual to decadal variability. Consistent with the total Arctic SIA anomaly eight regional SIA anomalies have shown significant in-phase decrease (downward trend) since 1970, possibly part of a very long-term (century) cycle. Thus, it is recommended that SIA anomalies in the sensitive seasons be used to better capture interannual, interdecadal and longer (century) variability. Major decadal and interdecadal time-scales of SIA anomalies are found at 12−14 and 17−20 years. In the Sea of Okhotsk, a century time-scale is evident. The reduction rate (negative trend) of the total Arctic sea-ice cover in the last three decades is −4.5% per decade, with the summer rate being the highest (-10.2% per decade). The contribution to this total reduction varies from region to region, with sea-ice cover in the Greenland and Norwegian Seas experiencing the highest reduction rate of −20.2 % per decade.

Download Full-text

Sources of multi-decadal variability in Arctic sea ice extent

Environmental Research Letters ◽

10.1088/1748-9326/7/3/034011 ◽

2012 ◽

Vol 7 (3) ◽

pp. 034011 ◽

Cited By ~ 75

Author(s):

J J Day ◽

J C Hargreaves ◽

J D Annan ◽

A Abe-Ouchi

Keyword(s):

Sea Ice ◽

Decadal Variability ◽

Arctic Sea Ice ◽

Sea Ice Extent ◽

Arctic Sea ◽

Arctic Sea Ice Extent

Download Full-text

Tropical Decadal Variability and the Rate of Arctic Sea Ice Decrease

Geophysical Research Letters ◽

10.1029/2018gl079989 ◽

2018 ◽

Vol 45 (20) ◽

Cited By ~ 14

Author(s):

Gerald A. Meehl ◽

Christine T. Y. Chung ◽

Julie M. Arblaster ◽

Marika M. Holland ◽

Cecilia M. Bitz

Keyword(s):

Sea Ice ◽

Decadal Variability ◽

Arctic Sea Ice ◽

Arctic Sea

Download Full-text

Reassessing Sea Ice Drift and Its Relationship to Long‐Term Arctic Sea Ice Loss in Coupled Climate Models

Journal of Geophysical Research Oceans ◽

10.1029/2017jc013697 ◽

2018 ◽

Vol 123 (6) ◽

pp. 4338-4359 ◽

Cited By ~ 6

Author(s):

Neil F. Tandon ◽

Paul J. Kushner ◽

David Docquier ◽

Justin J. Wettstein ◽

Camille Li

Keyword(s):

Sea Ice ◽

Climate Models ◽

Arctic Sea Ice ◽

Coupled Climate Models ◽

Ice Drift ◽

Arctic Sea ◽

Sea Ice Drift ◽

Arctic Sea Ice Loss

Download Full-text

Interannual and decadal variability of Arctic summer sea ice associated with atmospheric teleconnection patterns during 1850-2017

Journal of Climate ◽

10.1175/jcli-d-20-0330.1 ◽

2021 ◽

pp. 1-89

Author(s):

Qiongqiong Cai ◽

Dmitry Beletsky ◽

Jia Wang ◽

Ruibo Lei

Keyword(s):

Sea Ice ◽

Decadal Variability ◽

Arctic Sea Ice ◽

Total Variance ◽

The Arctic ◽

Teleconnection Patterns ◽

Arctic Sea ◽

Mode 2 ◽

Interannual And Decadal Variability ◽

Arctic Summer

AbstractThe interannual and decadal variability of summer Arctic sea ice is analyzed, using the longest reconstruction (1850-2017) of Arctic sea ice extent available, and its relationship with the dominant internal variabilities of the climate system is further investigated quantitatively. The leading empirical orthogonal function (EOF) mode of summer Arctic sea ice variability captures an in-phase fluctuation over the Arctic Basin. The second mode characterizes a sea ice dipolar pattern with out-of-phase variability between the Pacific Arctic and the Atlantic Arctic. Summer sea ice variability is impacted by the major internal climate patterns: the Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), Pacific Decadal Oscillation (PDO) and Dipole Anomaly (DA), with descending order of importance based on the multiple regression analyses. The internal climate variability of the five teleconnection patterns accounts for up to 46% of the total variance in sea ice mode 1 (thermodynamical effect), and up to 30% of the total variance in mode 2 (dynamical effect). Furthermore, the variability of sea ice mode 1 decreased from 46% during 1953-2017 to 28% during 1979-2017, while the variability of mode 2 increased from 11% during 1953-2017 to 30% during 1979-2017. The increasingly greater reduction of Arctic summer sea ice during the recent four decades was enhanced with the positive ice/ocean albedo feedback loop being accelerated by the Arctic amplification, contributed in part by the atmospheric thermodynamical forcing from -AO, +NAO, +DA, +AMO, and –PDO and by the dynamical transpolar sea ice advection and outflow driven by +DA- and +AMO-derived strong anomalous meridional winds. Further analysis, using multiple large ensembles of climate simulations and single-forcing ensembles, indicates that the mode 1 of summer sea ice, dominated by the multidecadal oscillation, is partially a forced response to anthropogenic warming.

Download Full-text

Inter-comparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

10.5194/tc-2017-103 ◽

2017 ◽

Cited By ~ 1

Author(s):

Ron Kwok ◽

Nathan T. Kurtz ◽

Ludovic Brucker ◽

Alvaro Ivanoff ◽

Thomas Newman ◽

...

Keyword(s):

Sea Ice ◽

Data Quality ◽

Snow Depth ◽

Radar Data ◽

Arctic Sea Ice ◽

The Arctic ◽

Arctic Sea ◽

Retrieval Algorithms ◽

Snow Thickness

Abstract. Since 2009, the ultra-wideband snow-radar on Operation IceBridge 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 and snow-ice 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 affect 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 ground-based campaigns, including the BRomine Ozone Mercury EXperiment (BROMEX) at Barrow, Alaska and a field program by Environment and Climate Change Canada (ECCC) at Eureka, Nunavut, available climatology and snowfall from ERA-Interim 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.

Download Full-text

Differences between Arctic Interannual and Decadal Variability across Climate States

Journal of Climate ◽

10.1175/jcli-d-18-0672.1 ◽

2019 ◽

Vol 32 (18) ◽

pp. 6035-6050 ◽

Cited By ~ 4

Author(s):

Jesse Reusen ◽

Eveline van der Linden ◽

Richard Bintanja

Keyword(s):

Sea Ice ◽

Time Scales ◽

Decadal Variability ◽

Ice Cover ◽

Arctic Sea Ice ◽

Atmospheric Heat Transport ◽

Arctic Sea ◽

Arctic Temperature ◽

Atmospheric Heat ◽

Oceanic Transport

ABSTRACTLong-term climate variations have the potential to amplify or dampen (human-induced) trends in temperature. Understanding natural climate variability is therefore of vital importance, especially since the variability itself may change with a changing climate. Here, we quantify the magnitude and other characteristics of interannual to decadal variability in Arctic temperature and their dependence on the climate state. Moreover, we identify the processes responsible for the state dependency of the variations, using five quasi-equilibrium climate simulations of a state-of-the-art global climate model with 0.25, 0.5, 1, 2, and 4 times present-day atmospheric CO2 forcing. The natural fluctuations in Arctic temperature, including their dependence on the state of the climate, are linked to anomalous atmospheric and oceanic heat transports toward the Arctic. Model results suggest that atmospheric heat transport leads (and also controls) Arctic temperature variations on interannual time scales, whereas oceanic transport is found to govern the fluctuations on decadal time scales. This time-scale transition of atmospheric to oceanic dominance for Arctic temperature variations is most obvious when there is interannual to decadal variability in Arctic sea ice cover. In warm climates (without Arctic sea ice cover), there is no correlation between oceanic transport and surface air temperature on any time scale. In cold climates (with full Arctic sea ice cover), interaction between ocean and atmosphere is limited, leaving poleward atmospheric heat transport to be the primary driver on all time scales (interannual and decadal).

Download Full-text

Intercomparison of snow depth retrievals over Arctic sea ice from radar data acquired by Operation IceBridge

The Cryosphere ◽

10.5194/tc-11-2571-2017 ◽

2017 ◽

Vol 11 (6) ◽

pp. 2571-2593 ◽

Cited By ~ 27

Author(s):

Ron Kwok ◽

Nathan T. Kurtz ◽

Ludovic Brucker ◽

Alvaro Ivanoff ◽

Thomas Newman ◽

...

Keyword(s):

Sea Ice ◽

Data Quality ◽

Snow Depth ◽

Radar Data ◽

Arctic Sea Ice ◽

The Arctic ◽

Arctic Sea ◽

Retrieval Algorithms ◽

Snow Thickness

Abstract. 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 ground-based 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 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.

Download Full-text

Modulation of Arctic Sea Ice Loss by Atmospheric Teleconnections from Atlantic Multidecadal Variability

Journal of Climate ◽

10.1175/jcli-d-18-0307.1 ◽

2019 ◽

Vol 32 (5) ◽

pp. 1419-1441 ◽

Cited By ~ 10

Author(s):

Frederic S. Castruccio ◽

Yohan Ruprich-Robert ◽

Stephen G. Yeager ◽

Gokhan Danabasoglu ◽

Rym Msadek ◽

...

Keyword(s):

Sea Ice ◽

Climate Models ◽

Arctic Sea Ice ◽

The Arctic ◽

Atlantic Multidecadal Variability ◽

Sea Level Pressure ◽

Atmospheric Teleconnections ◽

Arctic Sea ◽

Long Term Trend

Abstract Observed September Arctic sea ice has declined sharply over the satellite era. While most climate models forced by observed external forcing simulate a decline, few show trends matching the observations, suggesting either model deficiencies or significant contributions from internal variability. Using a set of perturbed climate model experiments, we provide evidence that atmospheric teleconnections associated with the Atlantic multidecadal variability (AMV) can drive low-frequency Arctic sea ice fluctuations. Even without AMV-related changes in ocean heat transport, AMV-like surface temperature anomalies lead to adjustments in atmospheric circulation patterns that produce similar Arctic sea ice changes in three different climate models. Positive AMV anomalies induce a decrease in the frequency of winter polar anticyclones, which is reflected both in the sea level pressure as a weakening of the Beaufort Sea high and in the surface temperature as warm anomalies in response to increased low-cloud cover. Positive AMV anomalies are also shown to favor an increased prevalence of an Arctic dipole–like sea level pressure pattern in late winter/early spring. The resulting anomalous winds drive anomalous ice motions (dynamic effect). Combined with the reduced winter sea ice formation (thermodynamic effect), the Arctic sea ice becomes thinner, younger, and more prone to melt in summer. Following a phase shift to positive AMV, the resulting atmospheric teleconnections can lead to a decadal ice thinning trend in the Arctic Ocean on the order of 8%–16% of the reconstructed long-term trend, and a decadal trend (decline) in September Arctic sea ice area of up to 21% of the observed long-term trend.

Download Full-text

Trends, noise and re-entrant long-term persistence in Arctic sea ice

Proceedings of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rspa.2011.0728 ◽

2012 ◽

Vol 468 (2144) ◽

pp. 2416-2432 ◽

Cited By ~ 18

Author(s):

S. Agarwal ◽

W. Moon ◽

J. S. Wettlaufer

Keyword(s):

Sea Ice ◽

Time Scales ◽

Seasonal Cycle ◽

Arctic Sea Ice ◽

Fluctuation Analysis ◽

Fractal Properties ◽

Arctic Sea ◽

Original Record ◽

Detrended Fluctuation

We examine the long-term correlations and multi-fractal properties of daily satellite retrievals of Arctic sea ice albedo and extent, for periods of approximately 23 years and 32 years, respectively. The approach harnesses a recent development called multi-fractal temporally weighted detrended fluctuation analysis, which exploits the intuition that points closer in time are more likely to be related than distant points. In both datasets, we extract multiple crossover times, as characterized by generalized Hurst exponents, ranging from synoptic to decadal. The method goes beyond treatments that assume a single decay scale process, such as a first-order autoregression, which cannot be justifiably fitted to these observations. Importantly, the strength of the seasonal cycle ‘masks’ long-term correlations on time scales beyond seasonal. When removing the seasonal cycle from the original record, the ice extent data exhibit white noise behaviour from seasonal to bi-seasonal time scales, whereas the clear fingerprints of the short (weather) and long (approx. 7 and 9 year) time scales remain, the latter associated with the recent decay in the ice cover. Therefore, long-term persistence is re-entrant beyond the seasonal scale and it is not possible to distinguish whether a given ice extent minimum/maximum will be followed by a minimum/maximum that is larger or smaller in magnitude.

Download Full-text