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

2012 ◽  
Vol 468 (2144) ◽  
pp. 2416-2432 ◽  
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.

Sub-seasonal prediction of Arctic sea ice concentration using time series forecasting technique

2020 ◽  
Author(s):  
Baek-Min Kim ◽  
Ha-Rim Kim ◽  
Yong-Sang Choi ◽  
Yejin Lee ◽  
Gun-Hwan Yang
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Time Scales ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Ncep Cfs

<div> <div> <div> <p>Recently, many studies have highlighted the importance of the ability to predict the Arctic sea ice concentration in the sub-seasonal time scales. Notably, the Arctic sea ice concentration has a potential for skillful predictions through their long-term trend memory. Based on the long-term memory of Arctic sea ice concentration, we evaluate the predictability of Arctic sea ice concentration (SIC) by applying a time-series analysis technique of the Prophet model on sub-seasonal time scales. A Prophet is a recently introduced method as a statistical approach inspired by the nature of time series forecasted at Facebook and has not been applied to the prediction of Arctic SIC before. Sub-seasonal prediction skills of Arctic SIC in the Prophet model were compared with the NCEP Climate Forecast System Reforecast (CFS-Reforecast) model as a dynamical approach and verified with the satellite observation during wintertime from 2000 to 2018 for 1 to 8 weeks lead times. The result shows that the Prophet model exhibits much better skill than the NCEP CFS-Reforecast model in the climatology prediction except for the 1 to 3 weeks lead times, as the Prophet model has mainly the ability to capture the long-term trend. In the anomaly prediction, however, the NCEP CFS-Reforecast model is superior to the Prophet model in the prediction of sub-seasonal time scales, as the NCEP CFS-Reforecast captures more effectively the sub-seasonal transition of the underlying dynamical system. Therefore, even if the Prophet model has shown a useful skill in predicting the climatological Arctic SIC, there is still a need to improve the accuracy and robustness of the predictions in an anomalous Arctic SIC. Further, we suggest that the bias correction method is needed to improve the forecast skill of Arctic SIC using the time-series analysis technique, and it will be critical to advance the field of the Arctic SIC forecasting on the sub-seasonal time scales.</p> </div> </div> </div>

scholarly journals Predictability of Arctic sea ice on weather time scales

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
M. Mohammadi-Aragh ◽  
H. F. Goessling ◽  
M. Losch ◽  
N. Hutter ◽  
T. Jung
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
Arctic Sea Ice ◽  
Arctic Sea

scholarly journals Evaluation of the representation of Arctic sea ice in the U.K. Hadley Centre GCM

1997 ◽  
Vol 25 ◽  
pp. 423-428
Author(s):  
Douglas M. Smith ◽  
Claire Cooper ◽  
Duncan J. Wingham ◽  
Seymour W. Laxon
Keyword(s):  
Sea Ice ◽  
Sea Of Okhotsk ◽  
Seasonal Cycle ◽  
Arctic Sea Ice ◽  
Surface Energy Budget ◽  
Sea Surface ◽  
Hudson Bay ◽  
The Sea Of Okhotsk ◽  
Arctic Sea ◽  
Hadley Centre

The amount of Arctic sea ice predicted by the Hadley Centre Global Cilimate Model (GCM) is evaluated using 15 years of passive-microwave data. While the Hadley model reproduces the seasonal cycle reasonably well, it underestimates the total area of sea ice by more than 3 × 106km2for most of the year. In the winter months, most of the underestimate in ice area results from the prediction of far too little ice in Hudson Bay and the Sea of Okhotsk, leading to an excess of up to 0.2 PW heat input to the atmosphere from Hudson Bay alone. The surface-energy budget of Hudson Bay is investigated using a mixture of surface observations (POLES), satellite data (ATSR, SSM/I and ISCCP) and output from the Goddard Data Assimilation Office analysis. Flux adjustments of the order of 200 Wm−2, resulting from anomalously high sea-surface temperatures in the Levitus (1982) climatology, are found to be the cause of the model’s underestimation of sea ice in both Hudson Bay and the Sea of Okhotsk. The fact that flux adjustments based on an inaccurate climatology will produce errors, even if the model physics is correct, underlines the need both for improved climatologies and for models accurate enough not to require flux adjustment.

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

2018 ◽  
Vol 123 (6) ◽  
pp. 4338-4359 ◽  
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

Scaling behavior of earthquakes’ inter-events time series

Open Physics ◽  
2009 ◽  
Vol 7 (3) ◽  
Author(s):  
Shahriar Shadkhoo ◽  
Fakhteh Ghanbarnejad ◽  
Gholam Jafari ◽  
Mohammad Tabar
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Hurst Exponent ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Data Set ◽  
Scaling Properties ◽  
Independent Events ◽  
Detrended Fluctuation

AbstractIn this paper, we investigate the statistical and scaling properties of the California earthquakes’ inter-events over a period of the recent 40 years. To detect long-term correlations behavior, we apply detrended fluctuation analysis (DFA), which can systematically detect and overcome nonstationarities in the data set at all time scales. We calculate for various earthquakes with magnitudes larger than a given M. The results indicate that the Hurst exponent decreases with increasing M; characterized by a Hurst exponent, which is given by, H = 0:34 + 1:53/M, indicating that for events with very large magnitudes M, the Hurst exponent decreases to 0:50, which is for independent events.

scholarly journals Can we reconstruct Arctic sea ice back to 1900 with a hybrid approach?

2008 ◽  
Vol 4 (4) ◽  
pp. 955-979 ◽  
Author(s):  
S. Brönnimann ◽  
T. Lehmann ◽  
T. Griesser ◽  
T. Ewen ◽  
A. N. Grant ◽  
...  
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
Historical Data ◽  
Arctic Sea Ice ◽  
Coupled Processes ◽  
The Arctic ◽  
Statistical Reconstruction ◽  
Historical Climatology ◽  
Arctic Sea ◽  
The Future

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.

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

2017 ◽  
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.

scholarly journals Differences between Arctic Interannual and Decadal Variability across Climate States

2019 ◽  
Vol 32 (18) ◽  
pp. 6035-6050 ◽  
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).

scholarly journals Trends and Persistence of Dry–Wet Conditions in Northeast Brazil

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1134
Author(s):  
Antonio Samuel Alves da Silva ◽  
Moacyr Cunha Filho ◽  
Rômulo Simões Cezar Menezes ◽  
Tatijana Stosic ◽  
Borko Stosic
Keyword(s):  
Time Scales ◽  
Water Resource Management ◽  
Standardized Precipitation Index ◽  
Kendall Test ◽  
Spatially Explicit ◽  
Fluctuation Analysis ◽  
Short Term ◽  
Agricultural Planning ◽  
Detrended Fluctuation

We analyze trend and persistence in Standardized Precipitation Index (SPI) time series derived from monthly rainfall data at 133 gauging stations in Pernambuco state, Brazil, using a suite of complementary methods to address the spatially explicit tendencies, and persistence. SPI was calculated for 1-, 3-, 6-, and 12-month time scales from 1950 to 2012. We use Mann–Kendall test and Sen’s slope to determine sign and magnitude of the trend, and detrended fluctuation analysis (DFA) method to quantify long-term correlations. For all time scales significant negative trends are obtained in the Sertão (deep inland) region, while significant positive trends are found in the Agreste (intermediate inland), and Zona da Mata (coastal) regions. The values of DFA exponents show different scaling behavior for different time scales. For short-term conditions described by SPI-1 the DFA exponent is close to 0.5 indicating weak persistency and low predictability, while for medium-term conditions (SPI-3 and SPI-6) DFA exponents are greater than 0.5 and increase with time scale indicating stronger persistency and higher predictability. For SPI-12 that describes long-term precipitation patterns, the values of DFA exponents for inland regions are around 1, indicating strong persistency, while in the shoreline the value of the DFA exponent is between 1.0 and 1.5, indicating anti-persistent fractional Brownian motion. These results should be useful for agricultural planning and water resource management in the region.

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

2017 ◽  
Vol 11 (6) ◽  
pp. 2571-2593 ◽  
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.

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