Ice Forecasting Research Articles

Interannual Variability and Scenarios Projection of Sea Ice in Bohai Sea Part I: Variation Characteristics and Interannual Hindcast

The Bohai Sea is one of the southernmost areas for sea ice formation in the northern hemisphere. Sea ice disasters in this body of water severely affect marine activities and the safety of coastal residents. In this study, we analyze the variation characteristics of the sea ice in the Bohai Sea and establish an annual regression model based on predictable mode analysis method. The results show the following: 1) From 1970 to 2018, the average ice grade is (2.6±0.8), with a maximum of 4.5 and a minimum of 1.0. Liaodong Bay (LDB) has the heaviest ice conditions in the Bohai Sea, followed by Bohai Bay (BHB) and Laizhou Bay (LZB). In-terannual variation is obvious in all three bays, but the linear decreasing trend is significant only in BHB. 2) Three modes are obtained from empirical orthogonal function analysis, namely, single polarity mode with the same sign of anomaly in all of the three bays and strong interannual variability (82.0%), the north-south dipole mode with BHB and LZB showing an opposite sign of anomalies to that in LDB and strong decadal variations (14.5%), and a linear trend mode (3.5%). Critical factors are analyzed and regression equations are established for all the principal components, and then an annual hindcast model is established by synthesizing the results of the three modes. This model provides an annual spatial prediction of the sea ice in the Bohai Sea for the first time, and meets the demand of operational sea ice forecasting.

Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble

Forecasting of river ice breakup timing is directly related to the local ice-caused flooding management. However, river ice forecasting using k-nearest neighbor (kNN) algorithms is limited. Thus, a kNN stacking ensemble learning (KSEL) method was developed and applied to forecasting breakup dates (BDs) for the Athabasca River at Fort McMurray in Canada. The kNN base models with diverse inputs and distance functions were developed and their outputs were further combined. The performance of these models was examined using the leave-one-out cross validation method based on the historical BDs and corresponding climate and river conditions in 1980–2015. The results indicated that the kNN with the Chebychev distance functions generally outperformed other kNN base models. Through the simple average methods, the ensemble kNN models using multiple-type (Mahalanobis and Chebychev) distance functions had the overall optimal performance among all models. The improved performance indicates that the kNN ensemble is a promising tool for river ice forecasting. The structure of optimal models also implies that the breakup timing is mainly linked with temperature and water flow conditions before breakup as well as during and just after freeze up.

Weakly coupled atmosphere–ocean data assimilation in the Canadian global prediction system (v1)

Abstract. A fully coupled atmosphere–ocean–ice model has been used to produce global weather forecasts at Environment and Climate Change Canada (ECCC) since November 2017. Currently, the system relies on four uncoupled data assimilation (DA) components for initializing the fully coupled global atmosphere–ocean–ice forecast model: atmosphere, ocean, sea ice and sea surface temperature (SST). The goal of the present study is to implement a weakly coupled data assimilation (WCDA) between the atmosphere and ocean components and evaluate its performance against uncoupled DA. The WCDA system uses coupled atmosphere–ocean–ice short-term forecasts as background states for the atmospheric and the ocean DA components that independently compute atmospheric and ocean analyses. This system leads to better agreement between the coupled atmosphere–ocean analyses and the coupled atmosphere–ocean–ice forecasts than between the uncoupled analyses and the coupled forecasts. The use of WCDA improves the atmospheric forecast score near the surface, but a slight increase in the atmospheric temperature bias is observed. A small positive impact from using the short-term SST forecast on the satellite radiance observation-minus-forecast statistics is noted. Ocean temperature and salinity forecasts are also improved near the surface. The next steps toward stronger DA coupling are highlighted.

Development of a new operational system for monitoring and forecasting coastal and open-ocean states around Japan

We developed a new system to monitor and forecast coastal and open-ocean states around Japan for operational use by the Japan Meteorological Agency. The system consists of an eddy-resolving analysis model based on four-dimensional variational assimilation and a high (2-km) resolution forecast model covering Japanese coastal areas that incorporates an initialization scheme with temporal and spatial filtering. Assimilation and forecast experiments were performed for 2008 to 2017, and the results were validated against various observation datasets. The assimilation results captured well the observed variability in sea surface temperature, coastal sea level, volume transport, and sea ice. Furthermore, the volume budget for the Japan Sea was significantly improved by the use of the 2-km resolution forecast model compared with the 10-km resolution analysis model. The forecast results indicate that this system has a predictive limit longer than 1 month in many areas, including in the Kuroshio current area south of Japan and the southern Japan Sea. In the forecast results of case studies, the 2017 Kuroshio large meander was well predicted, and warm water intrusions accompanying Kuroshio path variations south of Japan were also successfully reproduced. Sea ice forecasts for the Sea of Okhotsk largely captured the evolution of sea ice in late winter, but sea ice in early winter included relatively large errors. This system has high potential to meet operational requirements for monitoring and forecasting ocean phenomena at both meso- and coastal scales.

The Value of Sustained Ocean Observations for Sea Ice Predictions in the Barents Sea

ABSTRACT Dynamical prediction systems have shown potential to meet the emerging need for seasonal forecasts of regional Arctic sea ice. Observationally constrained initial conditions are a key source of skill for these predictions, but the direct influence of different observation types on prediction skill has not yet been systematically investigated. In this work, we perform a hierarchy of observing system experiments with a coupled global data assimilation and prediction system to assess the value of different classes of oceanic and atmospheric observations for seasonal sea ice predictions in the Barents Sea. We find notable skill improvements due to the inclusion of both sea surface temperature (SST) satellite observations and subsurface conductivity–temperature–depth (CTD) measurements. The SST data are found to provide the crucial source of interannual variability, whereas the CTD data primarily provide climatological and trend improvements. Analysis of the Barents Sea ocean heat budget suggests that ocean heat content anomalies in this region are driven by surface heat fluxes on seasonal time scales.

Keys to Differentiating between Small- and Large-Drop Icing Conditions in Continental Clouds

AbstractUsing observations from research aircraft flights over the Great Lakes region, synoptic and mesoscale environments that appear to drive a relationship between liquid water content, drop concentration, and drop size are investigated. In particular, conditions that fell within “small drop” and “large drop” regimes are related to cloud and stability profiles, providing insight regarding whether the clouds are tied to the local boundary layer. These findings are supported by analysis of flight data from other parts of North America and used to provide context for several icing incidents and accidents where large-drop icing was noted as a contributing factor. The relationships described for drop size discrimination in continental environments provide clues that can be applied for both human- and model-generated icing forecasts, as well as automated icing algorithms.

Grid size dependency of short-term sea ice forecast and its evaluation during extreme Arctic cyclone in August 2016

Recently, cyclones are quite active and dramatically influence the sea ice distribution in the Arctic region. Therefore, precise prediction of sea ice distribution during the cyclones is crucial for safe and efficient navigation in the Arctic Ocean. A high-resolution (about 2.5 km) ice–ocean coupled model is developed for forecasting the short-term sea ice distribution along the Arctic sea routes. Since a commercial ship navigates to avoid the sea ice as much as possible, a factor to score the forecast skill is considered to be ice edge error which is an averaged distance between forecasted and observed ice edges. First, we discuss the influence of horizontal grid resolution on short-term sea ice predictions during May to November 2015. The model grid resolution is varied from 2.5 km, 5 km, 10 km and 15 km. This grid dependency study suggests that the 2.5 km resolution model predicts the ice edge accurately compared to the satellite observations. Second, the primary experiment was run from 2 August 2016 to 7 August 2016 under the extreme cyclone in the Laptev Sea using ensemble predictions. The ensembles are constructed by using forecasted atmospheric forcing data sets from THORPEX Interactive Grand Global Ensemble (TIGGE) project and European Center for Medium-Range Weather Forecast Interim (ERA-Interim) reanalysis data. The ensemble ice edge errors are computed from 2 August 2016 to 7 August 2016. The maximum forecast skill of ensemble average ice edge error in the ice–ocean coupled model is 11.74 ± 0.54 km with the threshold of 15% ice concentration for the AMSR2 and model predicted ice edges. It can be said that the present model of 2.5 km grids satisfies the ship crew requirement of around 10 km ice edge error for 5-day forecast.

Polar Ocean Observations: A Critical Gap in the Observing System and Its Effect on Environmental Predictions From Hours to a Season.

There is a growing need for operational oceanographic predictions in both the Arctic and Antarctic polar regions. In the former, this is driven by a declining ice cover accompanied by an increase in maritime traffic and exploitation of marine resources. Oceanographic predictions in the Antarctic are also important, both to support Antarctic operations and also to help elucidate processes governing sea ice and ice shelf stability. However, a significant gap exists in the ocean observing system in polar regions, compared to most areas of the global ocean, hindering the reliability of ocean and sea ice forecasts. This gap can also be seen from the spread in ocean and sea ice reanalyses for polar regions which provide an estimate of their uncertainty. The reduced reliability of polar predictions may affect the quality of various applications including search and rescue, coupling with numerical weather and seasonal predictions, historical reconstructions (reanalysis), aquaculture and environmental management including environmental emergency response. Here, we outline the status of existing near-real time ocean observational efforts in polar regions, discuss gaps, and explore perspectives for the future. Specific recommendations include a renewed call for open access to data, especially real-time data, as a critical capability for improved sea ice and weather forecasting and other environmental prediction needs. Dedicated efforts are also needed to make use of additional observations made as part of the Year of Polar Prediction (YOPP; 2017–2019) to inform optimal observing system design. To provide a polar extension to the Argo network, it is recommended that a network of ice-borne sea ice and upper-ocean observing buoys be deployed and supported operationally in ice-covered areas together with autonomous profiling floats and gliders (potentially with ice detection capability) in seasonally ice covered seas. Finally, additional efforts to better measure and parameterize surface exchanges in polar regions are much needed to improve coupled environmental prediction.

A Spring Barrier for Regional Predictions of Summer Arctic Sea Ice

AbstractSeasonal forecast systems can skillfully predict summer Arctic sea ice up to 4 months in advance. For some regions, however, there is a springtime predictability barrier that causes forecasts initialized prior to May to be less skillful. Since this barrier has only been documented in a few general circulation models (GCMs), we evaluate GCMs participating in phase 5 of the Coupled Model Intercomparison Project. We first show sea ice volume skillfully predicts summer sea ice area (SIA) and has similar skill to a perfect model experiment. Given this result, we assess regional SIA predictability across each GCM and find a universal predictability barrier in late spring. For SIA at each summer target month in the marginal seas of the Arctic basin, a notable drop in prediction skill occurs from June to May in each GCM. This suggests summer sea ice forecasts initialized after 1 June will have substantially better prediction skill than forecasts initialized before.

An Intercomparison of Verification Scores for Evaluating the Sea Ice Edge Position in Seasonal Forecasts

AbstractWith the improving skill of sea ice forecasts, verification methods are becoming increasingly important to inform model developers and users. In this study, the Modified Hausdorff Distance (MHD), the Spatial Probability Score (SPS), and a variation of the SPS are compared in order to assess their performances for evaluating the ice edge position in the new European Centre for Medium‐Range Weather Forecasts seasonal forecasts (SEAS5). On average, the SEAS5 forecasts outperform a climatological reference during about 3 weeks using the MHD, and during about 5 weeks using the SPS. Furthermore, our results show that the MHD is more sensitive than the SPS to the presence of isolated sea ice patches. Moreover, the variation of the SPS introduced here is not seasonally dependent (contrary to the original SPS) and can be interpreted as a distance error of the ice edge position, which is a potentially relevant information for end users.

Aircraft Icing Study Using Integrated Observations and Model Data

Abstract Light (LGT) to moderate (MOD) aircraft icing (AI) is frequently reported at Cold Lake, Alberta, but forecasting AI has been a big challenge. The purpose of this study is to investigate and understand the weather conditions associated with AI based on observations in order to improve the icing forecast. To achieve this goal, Environment and Climate Change Canada in cooperation with the Department of National Defence deployed a number of ground-based instruments that include a microwave radiometer, a ceilometer, disdrometers, and conventional present weather sensors at the Cold Lake airport (CYOD). A number of pilot reports (PIREPs) of icing at Cold Lake during the 2016/17 winter period and associated observation data are examined. Most of the AI events were LGT (76%) followed by MOD (20%) and occurred during landing and takeoff at relatively warm temperatures. Two AI intensity algorithms have been tested based on an ice accumulation rate (IAR) assuming a cylindrical shape moving with airspeed υa of 60 and 89.4 m s−1, and the Canadian numerical weather prediction model forecasts. It was found that the algorithms IAR2 with υa = 89.4 m s−1 and IAR1 with υa = 60 m s−1 underestimated (overestimated) the LGT (MOD) icing events, respectively. The algorithm IAR2 with υa = 60 m s−1 appeared to be more suitable for forecasting LGT icing. Over all, the hit rate score was 0.33 for the 1200 UTC model run and 0.6 for 0000 UTC run for both algorithms, but based on the individual icing intensity scores, the IAR2 did better than IAR1 for forecasting LGT icing events.

Objective verification of global in‐flight icing forecasts using satellite observations: Verification of WAFS icing forecasts using satellite observations

The two World Area Forecast Centres (WAFC) are responsible for issuing global gridded forecasts of in‐flight icing to the aviation community. These forecasts are vital for flight planning and safety; therefore, the performance of these forecasts needs to be assessed and improvements made where necessary. New satellite instruments can detect additional information about cloud properties to then identify areas conducive to icing. At the Met Office, data from multiple geostationary satellites are combined to produce a gridded satellite icing potential product with coverage over Europe, Asia and Australasia. Recent satellite launches will give near‐global coverage of these observations. The present paper details an objective verification framework to routinely verify icing potential forecasts using the satellite‐inferred icing product as a source of truth data. To allow a fair comparison, the best method to appropriately match the forecast and truth data is investigated. This methodology includes processing the multilevel forecasts into a single field to best replicate the satellite observations and producing a time window of maximum icing potential observations to allow temporal flexing in the verification. Verification results of the mean icing forecasts issued by WAFC London are presented for the 12‐month period March 2016–February 2017 in order to illustrate how this methodology has been used.

Assessment of contemporary satellite sea ice thickness products for Arctic sea ice

Abstract. Advances in remote sensing of sea ice over the past two decades have resulted in a wide variety of satellite-derived sea ice thickness data products becoming publicly available. Selecting the most appropriate product is challenging given end user objectives range from incorporating satellite-derived thickness information in operational activities, including sea ice forecasting, routing of maritime traffic and search and rescue, to climate change analysis, longer-term modelling, prediction and future planning. Depending on the use case, selecting the most suitable satellite data product can depend on the region of interest, data latency, and whether the data are provided routinely, for example via a climate or maritime service provider. Here we examine a suite of current sea ice thickness data products, collating key details of primary interest to end users. We assess 8 years of sea ice thickness observations derived from sensors on board the CryoSat-2 (CS2), Advanced Very-High-Resolution Radiometer (AVHRR) and Soil Moisture and Ocean Salinity (SMOS) satellites. We evaluate the satellite-only observations with independent ice draft and thickness measurements obtained from the Beaufort Gyre Exploration Project (BGEP) upward looking sonar (ULS) instruments and Operation IceBridge (OIB), respectively. We find a number of key differences among data products but find that products utilizing CS2-only measurements are reliable for sea ice thickness, particularly between ∼0.5 and 4 m. Among data compared, a blended CS2-SMOS product was the most reliable for thin ice. Ice thickness distributions at the end of winter appeared realistic when compared with independent ice draft measurements, with the exception of those derived from AVHRR. There is disagreement among the products in terms of the magnitude of the mean thickness trends, especially in spring 2017. Regional comparisons reveal noticeable differences in ice thickness between products, particularly in the marginal seas in areas of considerable ship traffic.

RMetS National Meeting – Arctic prediction in a changing climate: understanding key processes and challenges

RMetS National Meeting – Arctic prediction in a changing climate: understanding key processes and challenges

New Methods and Technologies of Ice Forecasts for the Arctic Seas

Methodological approaches in the area of long- and short-range ice forecasts are discussed. Three basic modern directions of long-term ice forecast development are shown. New methods of long-range ice forecasts developed in the recent decade are presented. The ways of improving the AARI short-range ice forecast method based on the coupled ice-ocean dynamic-thermodynamic model are considered. It is shown that the numerical modeling as a specific branch of ice forecasting requires permanent development. The automation of ice forecasts can be implemented as an automated workplace or as a hardware-software complex.

A Year‐Round Subseasonal‐to‐Seasonal Sea Ice Prediction Portal

AbstractA significant barrier to understanding and quantifying current skill of Arctic sea ice forecasts is a lack of a central database to enable model evaluation and intercomparison. This study addresses this issue by introducing a central server and web portal housing multimodel ensemble forecasts. We present an overview of the portal and provide an analysis of 2018 forecast skill. Among the 16 participating models, forecasts of sea ice concentration varied widely; yet the multimodel mean generally offered skillful forecasts for up to 5 months. Models that assimilated observed concentrations with more advanced methods performed better on average than other models. Similarly, one model that incorporated satellite‐based sea ice thickness thereafter compared most favorably with thickness measured along IceBridge flight tracks. These results highlight the benefits from multimodel predictions and assimilating sea ice variables and the insights gained from near‐real‐time evaluation of operational forecasts.

Calibrated Probabilistic Forecasts of Arctic Sea Ice Concentration

Seasonal forecasts of Arctic sea ice using dynamical models are inherently uncertain and so are best communicated in terms of probabilities. Here, we describe novel statistical postprocessing methodologies intended to improve ensemble-based probabilistic forecasts of local sea ice concentration (SIC). The first of these improvements is the application of the parametric zero- and one-inflated beta (BEINF) probability distribution, suitable for doubly bounded variables such as SIC, for obtaining a smoothed forecast probability distribution. The second improvement is the introduction of a novel calibration technique, termed trend-adjusted quantile mapping (TAQM), that explicitly takes into account SIC trends and is applied using the BEINF distribution. We demonstrate these methods using a set of 10-member ensemble SIC hindcasts from the Third Generation Canadian Climate Coupled Global Climate Model (CanCM3) over the period 1981–2017. Though fitting ensemble SIC hindcasts to the BEINF distribution consistently improves probabilistic hindcast skill relative to a simpler “count based” probability approach in perfect model experiments, it does not itself correct model biases that may reduce this improvement when verifying against observations. The TAQM calibration technique is effective at removing SIC biases present in CanCM3 and improving forecast reliability. Over the recent 2000–17 period, TAQM-calibrated SIC hindcasts show improved skill relative to uncalibrated hindcasts. Compared against a climatological reference forecast adjusted for the trend, TAQM-calibrated hindcasts show widespread skill, particularly in September, even at 3–4-month lead times.

Verification of aircraft icing forecast based on the atmospheric radiometry remote method

Verification of aircraft icing forecast based on the atmospheric radiometry remote method

Ice Forecasting in the Next-Generation Great Lakes Operational Forecast System (GLOFS)

Ice Cover in the Great Lakes has significant impacts on regional weather, economy, lake ecology, and human safety. However, forecast guidance for the lakes is largely focused on the ice-free season and associated state variables (currents, water temperatures, etc.) A coupled lake-ice model is proposed with potential to provide valuable information to stakeholders and society at large about the current and near-future state of Great Lakes Ice. The model is run for three of the five Great Lakes for prior years and the modeled ice cover is compared to observations via several skill metrics. Model hindcasts of ice conditions reveal reasonable simulation of year-to-year variability of ice extent, ice season duration, and spatial distribution, though some years appear to be prone to higher error. This modeling framework will serve as the basis for NOAA’s next-generation Great Lakes Operational Forecast System (GLOFS); a set of 3-D lake circulation forecast modeling systems which provides forecast guidance out to 120 h.

Method for predicting the ice cover parameters in the waters of the Far-Eastern Seas with a long lead time

New method for long-term forecasting of mean month and mean 10-days values of the ice cover and position of the ice edge in the Far-Eastern Seas is presented. The sea ice regime is formed under influence of thermal and dynamic patterns in the atmosphere and hydrosphere, though mechanisms of its forming and evolution are not yet completely clear, so the sea ice forecasting is based mainly on statistical methods. The new method is developed for the ice parameters prediction for the period with stable ice cover. It uses a physical-statistical model with ensemble approach. The minimum lead time of this method is 7 months. The model assimilates the data on absolute topography of 500 GPa surface, atmospheric pressure at the sea level, air temperature at 850 GPa surface and at the sea surface, relative topography of 500/1000 GPa surfaces, and the South Oscillation index. Archives of these fields for the Northern Hemisphere from 1961 to 2017 are loaded. The ensemble of predictions is formed using the criterion of their maximum accuracy on independent data sets. The method is tested for the winter seasons of 2015/2016 and 2016/2017. The most accurate by 3 parameters are the forecasts for the Okhotsk Sea with the average accuracy 75–83 % that is much better than the accuracy of climatic forecasts (61–67 %). The forecast of the mean month ice cover only is satisfactory for the Japan Sea, and the forecast of the ice edge position only (65 % accuracy) exceeds the climate forecasting accuracy for the Bering Sea, while the climatic forecasting shows better results for the ice cover. The average accuracy of forecasting with new method (all parameters for all seas) exceeds 70 %, that allows to recommend the method for practical using. A prognostic product could be proposed as charts of the sea ice edge for future winter with estimations of the ice cover for each sea by months and 10-days.