A Spring Barrier for Regional Predictions of Summer Arctic Sea Ice

Bonan, David B.; Bushuk, Mitchell; Winton, Michael

doi:10.1029/2019gl082947

Cited by 31 publications

(29 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interestingly, both forecast values and anomaly persistence outputs show a decrease in skill as lead times decrease from winter to spring with the least accurate model forecasts occurring in March and the least accurate anomaly persistence forecasts occurring in April for NSE and RMSE and May for MAE. This touches on the idea of a “spring predictability barrier” found by Bonan et al (2019) in which forecasts initialized prior to May are less skillful than forecasts initialized after May. However, here we have demonstrated that winter geopotential height and SSTs add predictive power at longer lead times.…”

Section: Resultssupporting

confidence: 77%

A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover

Horvath

Stroeve

Rajagopalan

et al. 2020

Earth and Space Science

View full text Add to dashboard Cite

This study introduces a Bayesian logistic regression framework that is capable of providing skillful probabilistic forecasts of Arctic sea ice cover, along with quantifying the attendant uncertainties. The presence or absence of ice (absence defined as ice concentration below 15%) is modeled using a categorical regression model, with atmospheric, oceanic, and sea ice covariates at 1-to 7-month lead times. The model parameters are estimated in a Bayesian framework, thus enabling the posterior predictive probabilities of the minimum sea ice cover and parametric uncertainty quantification. The model is fitted and validated to September minimum sea ice cover data from 1980 through 2018. Results show overall skillful forecasts of the minimum sea ice cover at all lead times, with higher skills at shorter lead times, along with a direct measure of forecast uncertainty to aide in assessing the reliability. Plain Language Summary Every summer, sea ice in the Arctic undergoes melt and retreat, allowing access to otherwise difficult to reach areas. This has sparked growing interest in short-and longterm forecasting of summer sea ice to assist in planning and preparation of logistically intensive Arctic expeditions. Currently, forecasts more than 3 months in advance tend to be less skillful than forecasts made less than 3 months in advance. This study presents a novel approach to seasonal probabilistic forecasts of the minimum September sea ice cover through regression analysis, relating minimum summer sea ice to winter and spring sea ice, atmospheric, and oceanic conditions. We use "skill scores" to evaluate how well our forecasts perform in a variety of circumstances. We find that this method is able to skillfully predict up to 7 months early the probability that sea ice will be present across the entire Arctic Ocean at the summer minimum. This means that stakeholders interested in access to the Arctic Ocean during summer can have reliable long-term forecasts to aide in planning and preparation. The changing Arctic climate and high variability of sea ice has sparked growing interest in modeling and predicting summer sea ice conditions. Following the then record low September sea ice extent in 2007,

show abstract

Section: Resultssupporting

confidence: 77%

A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover

Horvath

Stroeve

Rajagopalan

et al. 2020

Earth and Space Science

View full text Add to dashboard Cite

show abstract

“…On this note, we have tested increasing the present methodology to 4-month lead time (May SIC data); however, the resultant detrended prediction skill is very poor for both the pan-Arctic and regional cases. This may be in part related to the spring predictability barrier that has been observed in model studies (Bushuk et al 2017;Bonan et al 2019); however, we have not investigated this in any detail as of yet. Irrespective of this, the methodology presented here can be advanced in order FIG.…”

Section: Discussionmentioning

confidence: 94%

Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes

Gregory

Stroeve

Sollich

2020

Weather and Forecasting

View full text Add to dashboard Cite

Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.

show abstract

“…Bonan et al. (2019) extended these initial results to a broader suite of fully coupled global climate models (GCMs), showing that a spring predictability barrier for regional Arctic sea ice area (SIA) was a robust feature across nearly all GCMs participating in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). They showed some intermodel spread in the timing of the predictability barrier but found that most GCMs had a common barrier date between 1 May and 1 June.…”

Section: Introductionmentioning

confidence: 99%

A Mechanism for the Arctic Sea Ice Spring Predictability Barrier

Bushuk

Winton

Bonan

et al. 2020

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

The decline of Arctic sea ice extent has created a pressing need for accurate seasonal predictions of regional summer sea ice. Recent work has shown evidence for an Arctic sea ice spring predictability barrier, which may impose a sharp limit on regional forecasts initialized prior to spring. However, the physical mechanism for this barrier has remained elusive. In this work, we perform a daily sea ice mass (SIM) budget analysis in large ensemble experiments from two global climate models to investigate the mechanisms that underpin the spring predictability barrier. We find that predictability is limited in winter months by synoptically driven SIM export and negative feedbacks from sea ice growth. The spring barrier results from a sharp increase in predictability at melt onset, when ice‐albedo feedbacks act to enhance and persist the preexisting export‐generated mass anomaly. These results imply that ice thickness observations collected after melt onset are particularly critical for summer Arctic sea ice predictions.

show abstract

A Spring Barrier for Regional Predictions of Summer Arctic Sea Ice

Cited by 31 publications

References 72 publications

A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover

A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover

Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes

A Mechanism for the Arctic Sea Ice Spring Predictability Barrier

Contact Info

Product

Resources

About