Predictability of Arctic sea ice on weather time scales

Abstract. The variability and trend of Arctic sea ice since the mid 1970s is well documented and linked to rising temperatures. However, much less is known for the first half of the 20th century, when the Arctic also underwent a period of strong warming. For studying this period in atmospheric models, gridded sea ice data are needed as boundary conditions. Current data sets (e.g., HadISST) provide a historical climatology, but may not be suitable when interannual-to-decadal variability is important, as they are interpolated and relaxed towards a (historical) climatology to fill in gaps, particularly in winter. Regional historical sea ice information exhibits considerable variability on interannnual-to-decadal scales, but is only available for summer and not in gridded form. Combining the advantages of both types of information could be used to constrain model simulations in a more realistic way. Here we discuss the feasibility of reconstructing year-round gridded Arctic sea ice from 1900 to 1953 from historical information and a coupled climate model. We decompose sea ice variability into centennial (due to climate forcings), decadal (coupled processes in the ocean-sea ice system) and interannual time scales (atmospheric circulation). The three time scales are represented by a historical climatology from HadISST (centennial), a closest analogue approach using the coupled control run of the CCSM-3.0 model (decadal), and a statistical reconstruction based on high-pass filtered data (interannual variability), respectively. Results show that differences in the model climatology, the length of the control run, and inconsistent historical data strongly limit the quality of the product. However, with more realistic and longer simulations becoming available in the future as well as with improved historical data, useful reconstructions are possible. We suggest that hybrid approaches, using both statistical reconstruction methods and numerical models, may find wider applications in the future.

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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).

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Assessment of the Regional Arctic System Model Intra-Annual Ensemble Predictions of Arctic Sea Ice

10.5194/egusphere-egu2020-12849 ◽

2020 ◽

Author(s):

Wieslaw Maslowski ◽

Younjoo Lee ◽

Anthony Craig ◽

Mark Seefeldt ◽

Robert Osinski ◽

...

Keyword(s):

Sea Ice ◽

Time Scales ◽

Weather Prediction ◽

Arctic Sea Ice ◽

System Model ◽

The Arctic ◽

Ensemble Prediction ◽

Ensemble Forecasts ◽

The Past ◽

Arctic Sea

The Regional Arctic System Model (RASM) has been developed and used to investigate the past to present evolution of the Arctic climate system and to address increasing demands for Arctic forecasts beyond synoptic time scales. RASM is a fully coupled ice-ocean-atmosphere-land hydrology model configured over the pan-Arctic domain with horizontal resolution of 50 km or 25 km for the atmosphere and land and 9.3 km or 2.4 km for the ocean and sea ice components. As a regional model, RASM requires boundary conditions along its lateral boundaries and in the upper atmosphere, which for simulations of the past to present are derived from global atmospheric reanalyses, such as the National Center for Environmental Predictions (NCEP) Coupled Forecast System version 2 and Reanalysis (CFSv2/CFSR). This dynamical downscaling approach allows comparison of RASM results with observations, in place and time, to diagnose and reduce model biases. This in turn allows a unique capability not available in global weather prediction and Earth system models to produce realistic and physically consistent initial conditions for prediction without data assimilation.More recently, we have developed a new capability for an intra-annual (up to 6 months) ensemble prediction of the Arctic sea ice and climate using RASM forced with the routinely produced (every 6 hours) NCEP CFSv2 global 9-month forecasts. RASM intra-annual ensemble forecasts have been initialized on the 1st of each month starting in 2019 with forcing for each ensemble member derived from CSFv2 forecasts, 24-hr apart from the month preceding the initial forecast date. &#160;Several key processes and feedbacks will be discussed with regard to their impact on model physics, the representation of initial state and ensemble prediction skill of Arctic sea ice variability at time scales from synoptic to decadal. The skill of RASM ensemble forecasts will be assessed against available satellite observations with reference to reanalysis as well as hindcast data using several metrics, including the standard deviation, root mean square difference, Taylor diagrams and integrated ice-edge error.

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Revisiting the Linkages between the Variability of Atmospheric Circulations and Arctic Melt-Season Sea Ice Cover at Multiple Time Scales

10.5194/egusphere-egu2020-373 ◽

2020 ◽

Author(s):

Lejiang Yu ◽

Sharon Zhong

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Time Scales ◽

Arctic Sea Ice ◽

The Arctic ◽

Multiple Time Scales ◽

Multiple Time ◽

Atmospheric Circulations ◽

The Pacific ◽

Arctic Sea

The sharp decline of Arctic sea ice in recent decades has captured the attention of the climate science community. A majority of climate analyses performed to date have used monthly or seasonal data. Here, however, we analyze daily sea ice data for 1979&#8211;2016 using the self-organizing map (SOM) method to further examine and quantify the contributions of atmospheric circulation changes to the melt-season Arctic sea ice variability. Our results reveal two main variability modes: the Pacific sector mode and the Barents and Kara Seas mode, which together explain about two-thirds of the melt-season Arctic sea ice variability and more than 40% of its trend for the study period. The change in the frequencies of the two modes appears to be associated with the phase shift of the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO). The PDO and AMO trigger anomalous atmospheric circulations, in particular, the Greenland high and the North Atlantic Oscillation and anomalous warm and cold air advections into the Arctic Ocean. The changes in surface air temperature, lower-atmosphere moisture, and downwelling longwave radiation associated with the advection are consistent with the melt-season sea ice anomalies observed in various regions of the Arctic Ocean. These results help better understand the predictability of Arctic sea ice on multiple (synoptic, intraseasonal, and interannual) time scales.

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Possible Link Between Arctic Sea Ice and January PM10 Concentrations in South Korea

Atmosphere ◽

10.3390/atmos10100619 ◽

2019 ◽

Vol 10 (10) ◽

pp. 619 ◽

Cited By ~ 5

Author(s):

Jeong-Hun Kim ◽

Maeng-Ki Kim ◽

Chang-Hoi Ho ◽

Rokjin J. Park ◽

Minjoong J. Kim ◽

...

Keyword(s):

High Pressure ◽

South Korea ◽

Sea Ice ◽

Time Scales ◽

Korean Peninsula ◽

Barents Sea ◽

Arctic Sea Ice ◽

The Barents Sea ◽

Arctic Sea ◽

Annual Time

In this study, we investigated the possible teleconnection between PM10 concentrations in South Korea and Arctic Sea ice concentrations at inter-annual time scales using observed PM10 data from South Korea, NCEP R2 data, and NOAA Sea Ice Concentration (SIC) data from 2001 to 2018. From the empirical orthogonal function (EOF) analysis, we found that the first mode (TC1) was a large-scale mode for PM10 in South Korea and explained about 27.4% of the total variability. Interestingly, the TC1 is more dominantly influenced by the horizontal ventilation effect than the vertical atmospheric stability effect. The pollution potential index (PPI), which is defined by the weighted average of the two ventilation effects, is highly correlated with the TC1 of PM10 at a correlation coefficient of 0.75, indicating that the PPI is a good measure for PM10 in South Korea at inter-annual time scales. Regression maps show that the decrease of SIC over the Barents Sea is significantly correlated with weakening of high pressure over the Ural mountain range region, the anomalous high pressure at 500 hPa over the Korean peninsula, and the weakening of the Siberian High and Aleutian low. Moreover, these patterns are similar to the correlation pattern with the PPI, suggesting that the variability of SIC over the Barents Sea may play an important role in modulating the variability of PM10 in South Korea through teleconnection from the Barents Sea to the Korean peninsula via Eurasia.

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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.

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Arctic Energy Budget in Relation to Sea Ice Variability on Monthly-to-Annual Time Scales

Journal of Climate ◽

10.1175/jcli-d-15-0002.1 ◽

2015 ◽

Vol 28 (16) ◽

pp. 6335-6350 ◽

Cited By ~ 5

Author(s):

F. Krikken ◽

W. Hazeleger

Keyword(s):

Sea Ice ◽

Time Scales ◽

Energy Budget ◽

Climate Models ◽

Arctic Sea Ice ◽

The Arctic ◽

Albedo Feedback ◽

Arctic Sea ◽

Cloud Ice

Abstract The large decrease in Arctic sea ice in recent years has triggered a strong interest in Arctic sea ice predictions on seasonal-to-decadal time scales. Hence, it is important to understand physical processes that provide enhanced predictability beyond persistence of sea ice anomalies. This study analyzes the natural variability of Arctic sea ice from an energy budget perspective, using 15 climate models from phase 5 of CMIP (CMIP5), and compares these results to reanalysis data. The authors quantify the persistence of sea ice anomalies and the cross correlation with the surface and top-of-atmosphere energy budget components. The Arctic energy balance components primarily indicate the important role of the seasonal ice–albedo feedback, through which sea ice anomalies in the melt season reemerge in the growth season. This is a robust anomaly reemergence mechanism among all 15 climate models. The role of the ocean lies mainly in storing heat content anomalies in spring and releasing them in autumn. Ocean heat flux variations play only a minor role. Confirming a previous (observational) study, the authors demonstrate that there is no direct atmospheric response of clouds to spring sea ice anomalies, but a delayed response is evident in autumn. Hence, there is no cloud–ice feedback in late spring and summer, but there is a cloud–ice feedback in autumn, which strengthens the ice–albedo feedback. Anomalies in insolation are positively correlated with sea ice variability. This is primarily a result of reduced multiple reflection of insolation due to an albedo decrease. This effect counteracts the ice-albedo effect up to 50%. ERA-Interim and Ocean Reanalysis System 4 (ORAS4) confirm the main findings from the climate models.

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Memory of Arctic sea ice in model simulations, observations, and reanalyses

10.5194/egusphere-egu2020-8003 ◽

2020 ◽

Author(s):

Céline Gieße ◽

Dirk Notz ◽

Johanna Baehr

Keyword(s):

Sea Ice ◽

Time Scales ◽

Climate Models ◽

Short Term Memory ◽

Regional Scale ◽

Reanalysis Data ◽

Arctic Sea Ice ◽

Mean Values ◽

Specific Memory ◽

Arctic Sea

The strong decline of Arctic sea ice in recent years has raised growing interest in seasonal-to-interannual predictions of Arctic sea ice. Previous studies have revealed a large predictability gap between potential and operational forecast skill of Arctic sea ice, which could indicate a strong potential for improvement of operational sea ice predictions or hint at a systematic overestimation of sea ice memory in current climate models.Here, we assess and compare memory of Arctic sea ice in terms of lagged correlations of sea ice area anomalies on seasonal to interannual time scales in a large model ensemble (MPI Grand Ensemble) as well as several reanalysis and observational products. While the different datasets show good agreement for short-term memory on time scales of a few months on which persistence is the dominant source of memory, we find substantial differences between model and observational memory behaviour on longer time scales. In particular, we find that memory from the summer sea ice minimum into the following year is significantly overestimated in the model, as lagged correlation values in all observational datasets are outside the range of model variability. Reanalysis data show correlation values that lie in between observational and model mean values, underpinning the hybrid nature of reanalyses combining observations and model behaviour. Extending the analysis of sea ice memory to a regional scale provides further information on the spatial origin of specific memory features in the different datasets and helps in understanding differences between model and real-world behaviour on a physical process level.

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Effects of Northern Hemisphere Atmospheric Blocking on Arctic Sea Ice Decline in Winter at Weekly Time Scales

Atmosphere ◽

10.3390/atmos9090331 ◽

2018 ◽

Vol 9 (9) ◽

pp. 331 ◽

Cited By ~ 5

Author(s):

Yao Yao ◽

Dehai Luo ◽

Linhao Zhong

Keyword(s):

Life Cycle ◽

Sea Ice ◽

Time Scales ◽

Bering Sea ◽

Arctic Sea Ice ◽

Okhotsk Sea ◽

Atmospheric Blocking ◽

Baffin Bay ◽

Sea Ice Decline ◽

Arctic Sea

In this study, the effects of the Northern Hemisphere atmospheric blocking circulation on Arctic sea ice decline at weekly time scales are examined by defining four key regions based on observational data analysis. Given the regression analysis, the frequently occurring atmospheric patterns related to the sea ice decline in four key sea regions (Baffin Bay, Barents-Kara Seas, Okhotsk Sea and Bering Sea) are found to be Greenland blocking (GB), Ural blocking (UB), western Pacific blocking (PB-W) and eastern Pacific blocking (PB-E), respectively. The results show that the regional blocking frequency is higher (lower) in lower (higher) sea ice winters for each key region. Moreover, composite analysis indicates that blocking evolution is usually accompanied by significant sea ice decline at weekly time scales during the blocking life cycle for each key region. In addition, the intensified surface downward infrared radiation (IR) anomaly and the precipitable water for the entire atmosphere (PWA) in each key region are found to make significant contributions to the positive surface air temperature (SAT) anomaly, which is beneficial for the reduction in sea ice. The approximate quantitative analysis of different surface energy fluxes induced by blocking is also applied. Further analysis shows that the blocking event and the associated changes in SAT and radiation anomalies for each key region lead the sea ice decline by approximately 3~6 days. This result indicates that regional blocking can contribute to regional sea ice decline at weekly time scales through surface warming associated with enhanced water vapor and associated IR variations. Further quantitative estimates indicate that regional blocking can reduce regional sea ice cover (SIC) by 49.6%, 49.4%, 52.2% and 49.5% for Baffin Bay, Barents-Kara Seas, Okhotsk Sea and Bering Sea, respectively, during the blocking life cycle. Finally, a physical process diagrammatic sketch is given to illustrate how blocking affects SIC decline.

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Revisiting the Linkages between the Variability of Atmospheric Circulations and Arctic Melt-Season Sea Ice Cover at Multiple Time Scales

Journal of Climate ◽

10.1175/jcli-d-18-0301.1 ◽

2019 ◽

Vol 32 (5) ◽

pp. 1461-1482 ◽

Cited By ~ 5

Author(s):

Lejiang Yu ◽

Shiyuan Zhong ◽

Mingyu Zhou ◽

Donald H. Lenschow ◽

Bo Sun

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Time Scales ◽

Arctic Sea Ice ◽

The Arctic ◽

Multiple Time Scales ◽

Atmospheric Circulations ◽

The Pacific ◽

Arctic Sea ◽

The Arctic Ocean

Abstract The sharp decline of Arctic sea ice in recent decades has captured the attention of the climate science community. A majority of climate analyses performed to date have used monthly or seasonal data. Here, however, we analyze daily sea ice data for 1979–2016 using the self-organizing map (SOM) method to further examine and quantify the contributions of atmospheric circulation changes to the melt-season Arctic sea ice variability. Our results reveal two main variability modes: the Pacific sector mode and the Barents and Kara Seas mode, which together explain about two-thirds of the melt-season Arctic sea ice variability and more than 40% of its trend for the study period. The change in the frequencies of the two modes appears to be associated with the phase shift of the Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO). The PDO and AMO trigger anomalous atmospheric circulations, in particular, the Greenland high and the North Atlantic Oscillation and anomalous warm and cold air advections into the Arctic Ocean. The changes in surface air temperature, lower-atmosphere moisture, and downwelling longwave radiation associated with the advection are consistent with the melt-season sea ice anomalies observed in various regions of the Arctic Ocean. These results help better understand the predictability of Arctic sea ice on multiple (synoptic, intraseasonal, and interannual) time scales.

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