Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook 2008-2013

Abstract Since 2008, the Study of Environmental Arctic Change Sea Ice Outlook has solicited predictions of September sea-ice extent from the Arctic research community. Individuals and teams employ a variety of modeling, statistical, and heuristic approaches to make these predictions. Viewed as monthly ensembles each with one or two dozen individual predictions, they display a bimodal pattern of success. In years when observed ice extent is near its trend, the median predictions tend to be accurate. In years when the observed extent is anomalous, the median and most individual predictions are less accurate. Statistical analysis suggests that year-to-year variability, rather than methods, dominate the variation in ensemble prediction success. Furthermore, ensemble predictions do not improve as the season evolves. We consider the role of initial ice, atmosphere and ocean conditions, and summer storms and weather in contributing to the challenge of sea-ice prediction. Key Points Analysis of Sea Ice Outlook contributions 2008-2013 shows bimodal success Years when observations depart from trend are hard to predict despite preconditioning Yearly conditions dominate variations in ensemble prediction success

Sea ice variability and atmosphere forcing in the Arctic North Atlantic

Digitisation of this thesis was sponsored by Arcadia Fund, a charitable fund of Lisbet Rausing and Peter Baldwin

Predicting September sea ice: Ensemble skill of the SEARCH Sea Ice Outlook 2008–2013

Since 2008, the Study of Environmental Arctic Change Sea Ice Outlook has solicited predictions of September sea-ice extent from the Arctic research community. Individuals and teams employ a variety of modeling, statistical, and heuristic approaches to make these predictions. Viewed as monthly ensembles each with one or two dozen individual predictions, they display a bimodal pattern of success. In years when observed ice extent is near its trend, the median predictions tend to be accurate. In years when the observed extent is anomalous, the median and most individual predictions are less accurate. Statistical analysis suggests that year-to-year variability, rather than methods, dominate the variation in ensemble prediction success. Furthermore, ensemble predictions do not improve as the season evolves. We consider the role of initial ice, atmosphere and ocean conditions, and summer storms and weather in contributing to the challenge of sea-ice prediction

A mechanism for the Arctic sea ice spring predictability barrier

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

A mechanism for the Arctic sea ice spring predictability barrier

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

Skill metrics for evaluation and comparison of sea ice models

© The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Geophysical Research: Oceans 120 (2015): 5910–5931, doi:10.1002/2015JC010989.Five quantitative methodologies (metrics) that may be used to assess the skill of sea ice models against a control field are analyzed. The methodologies are Absolute Deviation, Root-Mean-Square Deviation, Mean Displacement, Hausdorff Distance, and Modified Hausdorff Distance. The methodologies are employed to quantify similarity between spatial distribution of the simulated and control scalar fields providing measures of model performance. To analyze their response to dissimilarities in two-dimensional fields (contours), the metrics undergo sensitivity tests (scale, rotation, translation, and noise). Furthermore, in order to assess their ability to quantify resemblance of three-dimensional fields, the metrics are subjected to sensitivity tests where tested fields have continuous random spatial patterns inside the contours. The Modified Hausdorff Distance approach demonstrates the best response to tested differences, with the other methods limited by weak responses to scale and translation. Both Hausdorff Distance and Modified Hausdorff Distance metrics are robust to noise, as opposed to the other methods. The metrics are then employed in realistic cases that validate sea ice concentration fields from numerical models and sea ice mean outlook against control data and observations. The Modified Hausdorff Distance method again exhibits high skill in quantifying similarity between both two-dimensional (ice contour) and three-dimensional (ice concentration) sea ice fields. The study demonstrates that the Modified Hausdorff Distance is a mathematically tractable and efficient method for model skill assessment and comparison providing effective and objective evaluation of both two-dimensional and three-dimensional sea ice characteristics across data sets.U.S. National Science Foundation (NSF) Grant Number: PLR-0804017, NASA JPL OVWST, Bureau of Ocean Energy Management (BOEM), FSU Grant Number: M12PC00003, NSF Grant Numbers: projects PLR-0804010 , PLR-1313614 , PLR-1203720, BP/The Gulf of Mexico Research Initiative Grant Number: SA12-12, GoMRI-008, DoD High Performance Computing Modernization Progra

Sea Ice Prediction Has Easy and Difficult Years

Arctic sea ice follows an annual cycle, reaching its low point in September each year. The extent of sea ice remaining at this low point has been trending downwards for decades as the Arctic warms. Around the long-term downward trend, however, there is significant variation in the minimum extent from one year to the next. Accurate forecasts of yearly conditions would have great value to Arctic residents, shipping companies, and other stakeholders and are the subject of much current research. Since 2008 the Sea Ice Outlook (SIO) (http://www.arcus.org/search-program/seaiceoutlook) organized by the Study of Environmental Arctic Change (SEARCH) (http://www.arcus.org/search-program) has invited predictions of the September Arctic sea ice minimum extent, which are contributed from the Arctic research community. Individual predictions, based on a variety of approaches, are solicited in three cycles each year in early June, July, and August. (SEARCH 2013)

On the predictability of sea ice

Thesis (Ph.D.)--University of Washington, 2013We investigate the persistence and predictability of sea ice in numerical models and observations. We first use the 3rd generation Community Climate System Model (CCSM3) General Circulation Model (GCM) to investigate the inherent persistence of sea-ice area and thickness. We find that sea-ice area anomalies have a seasonal decay timescale, exhibiting an initial decorrelation similar to a first order auto-regressive (AR1, or red noise) process. Beyond this initial loss of memory, there is a re-emergence of memory at certain times of the year. There are two distinct modes of re-emergence in the model, one driven by the seasonal coupling of area and thickness anomalies in the summer, the other by the persistence of upper ocean temperature anomalies that originate from ice anomalies in the melt season and then influence ice anomalies in the growth season. Comparison with satellite observations where available indicate these processes appear in nature. We then use the 4th generation CCSM (CCSM4) to investigate the partition of Arctic sea-ice predictability into its initial-value and boundary forced components under present day forcing conditions. We find that initial-value predictability lasts for 1-2 years for sea-ice area, and 3-4 years for sea-ice volume. Forced predictability arises after just 4-5 years for both area and volume. Initial-value predictability of sea-ice area during the summer hinges on the coupling between thickness and area anomalies during that season. We find that the loss of initial-value predictability with time is not uniform --- there is a rapid loss of predictability of sea-ice volume during the late spring early summer associated with snow melt and albedo feedbacks. At the same time, loss of predictability is not uniform across different regions. Given the usefulness of ice thickness as a predictor of summer sea-ice area, we obtain a hindcast of September sea-ice area initializing the GCM on May 1with an estimate of observed sea-ice thickness anomalies. We run the GCM in a slab-ocean model configuration and obtain predictability that is lower than expected from the perfect model fully coupled GCM. We next make use of models submitted to the CMIP5 archive to investigate the spatial and temporal characteristics of ice thickness anomalies, together with the CCSM3, CCSM4 and two forced ice-ocean models, PIOMAS and CCSM4 in ice-ocean mode. We find that there is a wide spread in the characteristics of ice thickness anomalies across models, partially explained by biases in mean thickness. Additionally, forced ice-ocean models show reduced ice-thickness variability. These results have significant implications for the initialization of fully-coupled GCMs from forced GCM output. Finally we investigate the initial-value predictability of Antarctic sea ice in the CCSM3. We find that Antarctic sea-ice anomaly persistence is comparable to that of Arctic sea-ice anomalies. High values of initial-value predictability of sea-ice area can last for up to two years, and tend to advect eastward in time. We also find memory re-emergence that is driven by upper ocean heat anomalies from the melt to the growth season. Unlike the Arctic, we do not find evidence for an ice-thickness driven mechanism of memory re-emergenc

Wave-in-ice Statistics from IS-2 (10/2018-09/2019)

Contains netcdf files with statistics from collected IS-2 data over the period from October 2018 to September 2019, which are used in Horvat, Blanchard-Wrigglesworth, and Petty (2020): Observing waves in sea ice from IS-2. Each month and beam (i.e. 2018-gt1l) contains relevant geographically collated information about wave affected fraction, dynamic topography, and other variables. A list of contents is given as README.txt with instructions for how to visualize. These netcdf files were compiled with the help of Lettie Roach ([email protected])

Data to accompany the article "Two-way teleconnections between the Southern Ocean and the tropical Pacific via a dynamic feedback"

Despite substantial global mean warming, surface cooling has occurred in both the tropical eastern Pacific and Southern Ocean over the past 40 years, influencing both regional climates and estimates of Earth’s climate sensitivity to rising greenhouse gases. Using a paleo-reconstruction dataset of the past 2000 years, we find that sea-surface temperatures (SSTs) over the tropical eastern Pacific and part of the Southern Ocean near the southeast Pacific closely co-vary with each other on decadal to multidecadal timescales. While a tropical influence on the extratropics has been extensively studied in the literature, here we demonstrate that the teleconnection works in the other direction as well, suggesting that the observed tropical eastern Pacific cooling may be connected to the observed Southern Ocean cooling. Using a slab-ocean model, we find that the tropical Pacific SST response to an imposed Southern Ocean surface heat flux forcing is sensitive to the longitudinal location of that forcing, suggesting an atmospheric pathway associated with regional dynamics rather than reflecting a zonal-mean energetic constraint. The transient response shows that an imposed Southern Ocean cooling first propagates into the tropics by mean-wind advection. Once tropical Pacific SSTs are perturbed, they then drive remote changes to the sea-level pressure and surface winds in the extratropics which further enhance both Southern Ocean and tropical cooling. These results suggest a mutually interactive teleconnection between the Southern Ocean and tropical Pacific through atmospheric circulations, and highlight potential impacts on the tropics from the extratropical climate changes over the instrumental record and in the future