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

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

<p>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.</p><p>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.</p>

scholarly journals Extended Range Arctic Sea Ice Forecast with Convolutional Long-Short Term Memory Networks

2020 ◽  
Author(s):  
Yang Liu ◽  
Laurens Bogaardt ◽  
Jisk Attema ◽  
Wilco Hazeleger
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
Climate Models ◽  
Short Term Memory ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Short Term ◽  
Term Memory ◽  
Arctic Sea ◽  
Long Short Term Memory

<p>Operational Arctic sea ice forecasts are of crucial importance to commercial and scientific activities in the Arctic region. Currently, numerical climate models, including General Circulation Models (GCMs) and regional climate models, are widely used to generate the Arctic sea ice predictions at weather time-scales. However, these numerical climate models require near real-time input of weather conditions to assure the quality of the predictions and these are hard to obtain and the simulations are computationally expensive. In this study, we propose a deep learning approach to forecasts of sea ice in the Barents sea at weather time scales. To work with such spatial-temporal sequence problems, Convolutional Long Short Term Memory Networks (ConvLSTM) are useful.  ConvLSTM are LSTM (Long-Short Term Memory) networks with convolutional cells embedded in the LSTM cells. This approach is unsupervised learning and it can make use of enormous amounts of historical records of weather and climate. With input fields from atmospheric (ERA-Interim) and oceanic (ORAS4) reanalysis data sets, we demonstrate that the ConvLSTM is able to learn the variability of the Arctic sea ice within historical records and effectively predict regional sea ice concentration patterns at weekly to monthly time scales. Based on the known sources of predictability, sensitivity tests with different climate fields were also performed. The influences of different predictors on the quality of predictions are evaluated. This method outperforms predictions with climatology and persistence and is promising to act as a fast and cost-efficient operational sea ice forecast system in the future.</p>

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

2015 ◽  
Vol 28 (16) ◽  
pp. 6335-6350 ◽  
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.

scholarly journals Extended Range Arctic Sea Ice Forecast with Convolutional Long-Short Term Memory Networks

2021 ◽  
Author(s):  
Yang Liu ◽  
Laurens Bogaardt ◽  
Jisk Attema ◽  
Wilco Hazeleger
Keyword(s):  
Sea Ice ◽  
Time Scales ◽  
Short Term Memory ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Learning Approach ◽  
Short Term ◽  
Term Memory ◽  
Arctic Sea ◽  
Long Short Term Memory

AbstractOperational Arctic sea ice forecasts are of crucial importance to science and to society in the Arctic region. Currently, statistical and numerical climate models are widely used to generate the Arctic sea ice forecasts at weather time-scales. Numerical models require near real-time input of relevant environmental conditions consistent with the model equations and they are computationally expensive. In this study, we propose a deep learning approach, namely Convolutional Long Short Term Memory Networks (ConvLSTM), to forecast sea ice in the Barents Sea at weather to sub-seasonal time scales. This is an unsupervised learning approach. It makes use of historical records and it exploits the covariances between different variables, including spatial and temporal relations. With input fields from reanalysis data, we demonstrate that ConvLSTM is able to learn the variability of the Arctic sea ice and can forecast regional sea ice concentration skillfully at weekly to monthly time scales. It preserves the physical consistency between predictors and predictands, and generally outperforms forecasts with climatology, persistence and a statistical model. Based on the known sources of predictability, sensitivity tests with different climate fields as input for learning were performed. The impact of different predictors on the quality of the forecasts are evaluated and we demonstrate that the surface energy budget components have a large impact on the predictability of sea ice at weather time scales. This method is promising to enhance operational Arctic sea ice forecasting in the near future.

scholarly journals Observation-based selection of climate models projects Arctic ice-free summers around 2035

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David Docquier ◽  
Torben Koenigk
Keyword(s):  
Model Selection ◽  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Arctic Sea ◽  
Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

scholarly journals Low-frequency bursts of horizontally polarized waves in the Arctic sea-ice cover

2011 ◽  
Vol 57 (202) ◽  
pp. 231-237 ◽  
Author(s):  
David Marsan ◽  
Jérôme Weiss ◽  
Jean-Philippe Métaxian ◽  
Jacques Grangeon ◽  
Pierre-François Roux ◽  
...  
Keyword(s):  
Sea Ice ◽  
Regional Scale ◽  
Low Frequency ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Strong Activity ◽  
Temporal Sampling ◽  
Polarized Waves ◽  
Arctic Sea

AbstractWe report the detection of bursts of low-frequency waves, typically f = 0.025 Hz, on horizontal channels of broadband seismometers deployed on the Arctic sea-ice cover during the DAMOCLES (Developing Arctic Modeling and Observing Capabilities for Long-term Environmental Studies) experiment in spring 2007. These bursts have amplitudes well above the ambient ice swell and a lower frequency content. Their typical duration is of the order of minutes. They occur at irregular times, with periods of relative quietness alternating with periods of strong activity. A significant correlation between the rate of burst occurrences and the ice-cover deformation at the ∼400 km scale centered on the seismic network suggests that these bursts are caused by remote, episodic deformation involving shearing across regional-scale leads. This observation opens the possibility of complementing satellite measurements of ice-cover deformation, by providing a much more precise temporal sampling, hence a better characterization of the processes involved during these deformation events.

scholarly journals What Do Global Climate Models Tell Us about Future Arctic Sea Ice Coverage Changes?

Climate ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 15 ◽  
Author(s):  
Ge Peng ◽  
Jessica L. Matthews ◽  
Muyin Wang ◽  
Russell Vose ◽  
Liqiang Sun
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Adaptation ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Climate Projections ◽  
Emission Scenarios ◽  
Ice Coverage ◽  
Arctic Sea

The prospect of an ice-free Arctic in our near future due to the rapid and accelerated Arctic sea ice decline has brought about the urgent need for reliable projections of the first ice-free Arctic summer year (FIASY). Together with up-to-date observations and characterizations of Arctic ice state, they are essential to business strategic planning, climate adaptation, and risk mitigation. In this study, the monthly Arctic sea ice extents from 12 global climate models are utilized to obtain projected FIASYs and their dependency on different emission scenarios, as well as to examine the nature of the ice retreat projections. The average value of model-projected FIASYs is 2054/2042, with a spread of 74/42 years for the medium/high emission scenarios, respectively. The earliest FIASY is projected to occur in year 2023, which may not be realistic, for both scenarios. The sensitivity of individual climate models to scenarios in projecting FIASYs is very model-dependent. The nature of model-projected Arctic sea ice coverage changes is shown to be primarily linear. FIASY values predicted by six commonly used statistical models that were curve-fitted with the first 30 years of climate projections (2006–2035), on other hand, show a preferred range of 2030–2040, with a distinct peak at 2034 for both scenarios, which is more comparable with those from previous studies.

scholarly journals Seasonal to interannual Arctic sea ice predictability in current global climate models

2014 ◽  
Vol 41 (3) ◽  
pp. 1035-1043 ◽  
Author(s):  
S. Tietsche ◽  
J. J. Day ◽  
V. Guemas ◽  
W. J. Hurlin ◽  
S. P. E. Keeley ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
Arctic Sea

scholarly journals Arctic sea ice area changes in CMIP3 and CMIP5 climate models’ ensembles

2017 ◽  
Vol 57 (1) ◽  
pp. 77-107 ◽  
Author(s):  
V. A. Semenov ◽  
T. Martin ◽  
L. K. Behrens ◽  
M. Latif ◽  
E. S. Astafieva
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Arctic Sea Ice ◽  
Cmip5 Climate Models ◽  
Arctic Sea

scholarly journals Achieving realistic Arctic-midlatitude teleconnections in a climate model through stochastic process representation

2021 ◽  
Author(s):  
Kristian Strommen ◽  
Stephan Juricke
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
Stochastic Perturbations ◽  
Model Studies ◽  
Process Representation ◽  
Arctic Sea ◽  
Ocean Atmosphere ◽  
Winter Circulation

Abstract. The extent to which interannual variability in Arctic sea ice influences the midlatitude circulation has been extensively debated. While observational data supports the existence of a teleconnection between November sea ice in the Barents-Kara region and the subsequent winter circulation, climate models do not consistently reproduce such a link, with only very weak inter-model consensus. We show, using the EC-Earth3 climate model, that while a deterministic ensemble of coupled simulations shows no evidence of such a teleconnection, the inclusion of stochastic parameterizations to the ocean and sea ice component of EC-Earth3 results in the emergence of a robust teleconnection comparable in magnitude to that observed. We show that this can be accounted for entirely by an improved ice-ocean-atmosphere coupling due to the stochastic perturbations. In particular, the inconsistent signal in existing climate model studies may be due to model biases in surface coupling, with stochastic parameterizations being one possible remedy.

scholarly journals Modeled Arctic sea ice evolution through 2300 in CMIP5 extended RCPs

2014 ◽  
Vol 8 (1) ◽  
pp. 1383-1406 ◽  
Author(s):  
P. J. Hezel ◽  
T. Fichefet ◽  
F. Massonnet
Keyword(s):  
Sea Ice ◽  
Radiative Forcing ◽  
Climate Models ◽  
Global Climate ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Sea Ice Extent ◽  
Arctic Sea ◽  
Arctic Sea Ice Extent

Abstract. Almost all global climate models and Earth system models that participated in the Coupled Model Intercomparison Project 5 (CMIP5) show strong declines in Arctic sea ice extent and volume under the highest forcing scenario of the Radiative Concentration Pathways (RCPs) through 2100, including a transition from perennial to seasonal ice cover. Extended RCP simulations through 2300 were completed for a~subset of models, and here we examine the time evolution of Arctic sea ice in these simulations. In RCP2.6, the summer Arctic sea ice extent increases compared to its minimum following the peak radiative forcing in 2044 in all 9 models. RCP4.5 demonstrates continued summer Arctic sea ice decline due to continued warming on longer time scales. These two scenarios imply that summer sea ice extent could begin to recover if and when radiative forcing from greenhouse gas concentrations were to decrease. In RCP8.5 the Arctic Ocean reaches annually ice-free conditions in 7 of 9 models. The ensemble of simulations completed under the extended RCPs provide insight into the global temperature increase at which sea ice disappears in the Arctic and reversibility of declines in seasonal sea ice extent.

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