Tentative Agenda

The Fundamental Role of Sea Ice Predictions to Minimize Climatic Impacts on Arctic Communities

Gisele Arruda, Oxford Brookes University, gisele.arruda-2015 [at] brookes.ac.uk

The indigenous populations of the Arctic region possess an immense knowledge of their environments, based on centuries of living close to nature. Living in and from the richness and variety of complex ecosystems, they have an in-depth understanding of the properties of plants and animals, the functioning of ecosystems and the techniques for using and managing them in a sustainable way. However, when the subject is sea ice prediction and ice retreat there are potential hazards triggering the exposure of communities to extreme climatic events dependent on specific geographical patterns that, consequently, restrict their economic, social and cultural activities. Traditional knowledge can benefit immensely from western science research and communication to minimise the exposure of Arctic local communities to climatic adversities. For this reason, it is fundamental that the sea ice predictions research teams design and provide effective communication strategies to translate relevant data of research findings to address local social needs and priorities. In this sense, it is essential to make Arctic science accessible to indigenous communities by improving as much as possible cross-community communication, multi-cultural dialogue and the communities’ understanding of useful data about ice, ocean and atmospheric processes. Cross-cultural understanding is fundamental to investigate and transplant concepts from the indigenous world to the western culture and vice-versa in order to contribute effectively to more consistent patterns of adaptation.

Break

Spring Melt Ponds Predict Regional September Ice in Coupled Climate Simulations for Different Climatic Scenarios

David Schroeder, CPOM, Department of Meteorology, University of Reading, D.Schroeder [at] reading.ac.uk

Stand-alone sea ice simulations with a physical based melt pond model reveal a strong correlation between the simulated spring pond fraction and the observed as well as simulated September sea ice extent for the period 1979 to 2015. This is explained by a positive feedback mechanism: more ponds reduce the albedo; a lower albedo causes more melting; more melting increases pond fraction. We implemented the Los Alamos sea ice model CICE 5 including our physical based melt pond model into the latest version of the Hadley Centre coupled climate model, HadGEM3. The model surface shortwave radiation scheme has been adjusted to account for pond fraction and depth. We performed three 55-year HadGEM3 simulations with constant external forcing for the years 1985, 2010 and 2035. In all three simulations we find a strong correlation between the April/May pond fraction and the September sea ice conditions. We demonstrate that spring melt ponds are an important driver for summer ice melt and enable us to make skilful predictions for the consequent September sea ice in most Arctic regions for current and future climate conditions.

Connections Between the Spring Pacific-Arctic Dipole and Summer Sea Ice in the Beaufort-Chukchi Seas

Minghong Zhang, Bedford Institute of Oceanography, Minghong.zhang [at] dfo-mpo.gc.ca
William perrie, Bedford Institute of Oceanography, William.Perrie [at] dfo-mpo.gc.ca
Zhenxia Long, Bedford Institute of Oceanography, zhenxia.long [at] dfo-mpo.gc.ca

We identified an atmospheric circulation dipole anomaly in the Pacific-Arctic sector and we showed that it is related to the following September sea ice in the Beaufort-Chukchi Sea, using sea ice observations and model-generated data from PIOMAS (Pan-Arctic Ice-Ocean Modeling and Assimilation System), and the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis. The dipole anomaly is the second leading EOF mode of spring (April-June) sea level pressure (SLP) in the Pacific-Arctic (600N-900N, 1200E-1200W) and it accounts for 21% of the variance. This dipole anomaly, which we denote as the Pacific-Arctic Dipole, has a positive anomaly in the Beaufort Sea and a negative anomaly extending from East Siberia to Northwest America, and it exhibits co-variance with the Beaufort High and the Aleutian Low. The dipole mode also reflects the re-distribution of cyclone activities in the Pacific-Arctic sector, with fewer cyclones in the Beaufort Sea and central Arctic and more cyclones in the subpolar Pacific. We also define a cyclone activity dipole index using the difference between the cyclone system density in the Arctic (700N-900N, 900E-600W) and that in the subpolar Pacific (500N-600N, 1500E-2100W), which is highly correlated with the time series of the Pacific-Arctic Dipole. We found that the spring Pacific-Arctic Dipole accounts for about 20% of the interannual variance of the following summer SIC in the Beaufort-Chukchi Sea. A positive Pacific-Arctic Dipole has an enhanced Beaufort high and the resulting intensified eastern winds in the Beaufort Sea lead to enhanced ice advection and weakened sea ice thickness. Ice is advected into warm Alaskan coastal waters which results in extensive melting. Moreover, less cyclone activity leads to less middle level cloud cover and less warm air advection in the Beaufort Sea and central Arctic, which causes a net surface heat flux gain, resulting in further reductions in sea ice.

Lunch (provided)

Water Mass Transformation Under Southern Ocean Sea Ice

Ryan Abernathey, Lamont–Doherty Earth Observatory, Columbia University, abernethygillisr [at] inac-ainc.gc.ca

This study quantifies the role of Antarctic sea ice in the transformations of water masses within the Southern Ocean State Estimate (SOSE). Winds drive a strong export of sea ice away from the continent towards the open ocean. The resulting freshwater fluxes at the ocean surface dominate the Southern Ocean freshwater budget (compared with direct precipitation and glacial melt), and these strong fluxes have a major impact on density, stratification, and circulation. Using Walin’s water mass transformation framework, we isolate the contributions of brine rejection, ice melt, and snow interception on the modification of seawater density. Together with direct atmospheric precipitation - evaporation, glacial melt, surface heat flux, and interior mixing, these processes provide the thermodynamic transformations necessary to sustain the meridional overturning circulation. The transformation analysis reveals that sea-ice freshwater fluxes are the main contributor to the transformation of upwelling Upper Circumpolar Deep Water, pushing it primarily towards lighter Antarctic Intermediate and Subantarctic Mode Water but also partly toward denser classes. We also examine the seasonal cycle in transformation, revealing a subtle interplay between brine rejection and upper ocean mixing. Overall these results indicate a tight coupling between Antarctic sea ice and the upper branch of the Southern Ocean overturning circulation.

Understanding Physical Processes and Evaluating Parameterizations During the 2015 Freeze-Up Season Using a Coupled Sea Ice-Ocean-Atmosphere Forecast Model 

Amy Solomon, CIRES/University of Colorado and PSD/ESRL/NOAA, amy.solomon [at] noaa.gov
Janet Intrieri, PSD/ESRL/NOAA, Janet.Intrieri [at] noaa.gov
Ola Persson, CIRES/University of Colorado and PSD/ESRL/NOAA, Ola.Persson [at] noaa.gov
Mimi Hughes, CIRES/University of Colorado and PSD/ESRL/NOAA, Mimi.Hughes [at] noaa.gov
Christopher Cox, CIRES/University of Colorado and PSD/ESRL/NOAA, christopher.j.cox [at] noaa.gov

Improved sea ice forecasting must be based on improved model representation of coupled system processes that impact the sea ice thermodynamic and dynamic state.  Pertinent coupled system processes remain uncertain and include surface energy fluxes, clouds, precipitation, boundary layer structure, momentum transfer and sea-ice dynamics, interactions between large-scale circulation and local processes, and others.

We will present results, comparisons, process-oriented diagnostics, and parameterization assessment from sea ice forecasts using a version of the Regional Arctic System Model (RASM) adapted for short-term Arctic sea ice forecasting. Specifically, cloud, atmosphere, and ocean observations for studies of atmospheric predictability, air-ocean turbulent fluxes, and sea ice conditions collected in the marginal ice zone from ship-based campaign and coastal Arctic land stations will be used. We will also outline future model improvements based on a comparison of observations that include replacing the mixed-layer ocean model with a multi-layer upper ocean model to allow for observed mixed layer variability, such as storing heat below the surface layer that is transferred up in large storms and horizontal ocean advection.

RASM is a limited-area, fully coupled ice-ocean-atmosphere-land model. It includes the Weather Research and Forecasting (WRF) atmospheric model, the LANL Parallel Ocean Program (POP) and Community Ice Model Version 5 (CICE5) and the NCAR Community Land Model (CLMv4.5) configured for the pan-Arctic region. These components are coupled using a regionalized version of the CESM flux coupler (CPL7), which includes modifications important for resolving the sea ice pack’s inertial response to transient (i.e. weather) events.

In order to optimize the model for short-term forecasts the dynamic level ocean model has been replaced with a mixed–layer ocean model, the horizontal domain is limited to the Arctic, and all components are run with 10km horizontal resolution. This model is run with a bulk double-moment cloud microphysics scheme for droplets and frozen hydrometeors that allows both size and number of hydrometeors to vary in response to environmental conditions (Morrison et al. 2009).

Daily 5-day forecasts with RASM-ESRL were run for the 2015 freeze-up season, initialized with GFS atmosphere and AMSR2 sea ice analyses and forced by 3-hourly GFS forecasts at the lateral boundaries. The forecasts were delivered daily and used for guidance on the UNOLS research vessel Sikuliaq during the SeaState campaign. These daily forecasts have been validated with observations of surface fluxes and vertical profiles of cloud ice and liquid at land sites, and with observations of surface fluxes and sea ice characteristics from recent ship campaign and ice mass balance buoys. These relatively short forecasts are currently being used to validate and improve simulations of synoptic evolution, atmospheric boundary-layer structure and surface energy fluxes over sea ice and the adjacent ocean.

Increase in the Frequency and Extent of Sub-Ice Phytoplankton Blooms in the Arctic Ocean

Christopher Horvat, Harvard University, USA, bndnchrs [at] gmail.com
Sarah Iams, Harvard University, USA, siams [at] seas.harvard.edu
David Rees Jones, Oxford University, United Kingdom, David.ReesJones [at] physics.ox.ac.uk
David Schroeder, CPOM, University of Reading, United Kingdom, d.schroeder [at] reading.ac.uk
Daniela Flocco, CPOM, University of Reading, United Kingdom, d.flocco [at] reading.ac.uk
Daniel Feltham, CPOM, University of Reading, United Kingdom, d.l.feltham [at] reading.ac.uk

Phytoplankton are a fundamental component of the Arctic ecosystem and carbon cycle. Through their growth and decay, they form the foundation of the oceanic food web and are a sink for atmospheric CO2. Phytoplankton populations undergo periods of exponential growth, known as ``blooms", which occur seasonally in many of the world's oceans. In 2011, a phytoplankton bloom was observed underneath a region of the Arctic fully covered by sea ice, unexpected as sea ice is typically understood to transmit little solar radiation to the ocean below. To investigate the likelihood and location of sub-ice blooms, we develop a critical-depth model for regions of the ice-covered Arctic ocean that incorporates the transmission of solar radiation through regions of sea ice that are covered by melt ponds. We find that favorable conditions for sub-ice blooms exist over a large portion of the modern Arctic. The development of bloom-permitting regions of the ice-covered Arctic, which comprise greater than 30\% of the latitudes above 70N in July has occurred only in the recent two, coinciding with the thinning of Arctic sea ice and an increase in melt pond coverage. Our results demonstrate that these biological events may be both possible and likely in regions previously considered off-limits to photosynthetic activity. Projections of a thinner Arctic sea ice cover in a warming world suggests that the likelihood and extent of sub-ice phytoplankton blooms will increase in the future.

Adjourn for day

Short Term Forecasting of Sea Ice Drift and Ice Edge Position Using a Coupled Ice Ocean Model

Axel Schweiger, University of Washington/Applied Physics Laboratory/Polar Science Center, axel [at] apl.uw.edu
Zhang Schweiger, University of Washington/Applied Physics Lab/Polar Science Center, zhang [at] apl.uw.edu

Sea ice forecasts for the Arctic are investigated. Sea ice forecasts are generated for 6 hours to 9 days using the Marginal Ice Zone Modelling and Assimilation System (MIZMAS) driven with 6 hourly forecasts of atmospheric forcing variables from the NOAA Climate Forecast System (CFSv2). Forecast sea ice drift speed is compared to observations from drifting buoys and other observation platforms. Forecast buoy positions are compared with observed positions at 24 hours to 9 days from the initial forecast. The initial thickness fields are compared to aircraft remote sensing data from Operation IceBridge (OIB). Forecast Ice concentrations and ice edge positions are compared to passive microwave products. Forecast skill is assessed relative to reference forecasts based on climatology or persistence. RMS errors for ice speed are found in the order of 5 km/day for 24 h to 48 h using the sea ice model vs. 12 km/day using climatology. Following adjustments in the sea ice model to remove systematic biases in direction and speed, predicted buoy position RMS errors are 6.5 km for 24 hour forecasts and 15 km after 72 hours. Using the forecast model increases the probability of tracking a target drifting in sea ice with a 10x10 km sized image to 95% vs. 50% using climatology. Initial ice thickness computed by MIZMAS compares well with OIB data. Skill assessment of ice edge position forecasts faces challenges due to observational uncertainty and shows room for improvement. The results are generated in the context of planning and scheduling the acquisition of high resolution images which need to follow buoys or research platforms for scientific research but additional applications such as navigation in the Arctic waters may benefit from this accuracy assessment. Ideas for future improvement of short term sea ice forecasts are explored. Implications for intra-seasonal forecasts are considered.

Prediction of Arctic Sea Ice Modes on Seasonal to Interannual Time Scales (And Arctic Research Activities at Barcelona Supercomputing Center)

Neven-Stjepan Fuckar, Barcelona Supercomputing Center, Barcelona, Spain, neven.fuckar [at] bsc.es
Virginie Guemas, Barcelona Supercomputing Center, Barcelona, Spain, virginie.guemas [at] bsc.es
Nathaniel Johnson, Cooperative Institute for Climate Science, Princeton University, Princeton, NJ, USA, nathaniel.johnson [at] noaa.gov
Francisco Doblas-Reyes, Barcelona Supercomputing Center, Barcelona, Spain, francisco.doblas-reyes [at] bsc.es

Sea ice thickness (SIT) has a potential to contain substantial climate memory and predictability in the northern hemisphere (NH) sea ice system. We use 5-member NH SIT, reconstructed with an ocean-sea-ice general circulation model (NEMOv3.3 with LIM2) with a simple data assimilation routine, to determine NH SIT modes of variability disentangled from the long-term climate change. Specifically, we apply the K-means cluster analysis - one of nonhierarchical clustering methods that partition data into modes or clusters based on their distances in the physical – to determine optimal number of NH SIT clusters (K=3) and their historical variability. To examine prediction skill of NH SIT clusters in EC-Earth2.3, a state-of-the-art coupled climate forecast system, we use 5-member ocean and sea ice initial conditions (IC) from the same ocean-sea-ice historical reconstruction and atmospheric IC from ERA-Interim reanalysis. We focus on May 1st and Nov 1st start dates from 1979 to 2010. Common skill metrics of probability forecast, such as rank probability skill score (RPSS) and ROC (relative operating characteristics - hit rate versus false alarm rate) show that our dynamical model predominately perform better than the 1st order Marko chain forecast (that beats climatological forecast) over the first forecast year. On average May 1st start dates initially have lower skill than Nov 1st start dates, but their skill is degraded at slower rate than skill of forecast started on Nov 1st. Besides this study the full spectrum of Arctic research activities at the Earth Sciences Department at the Barcelona Supercomputing Center will be also presented.

Assessment of CFSv2 Operational Seasonal Forecast in the Arctic

Muyin Wang, University of Washington, muyin.wang [at] noaa.gov
Qiong Yang, University of Washington, qiong.wang [at] noaa.gov
James Overland, Pacific Marine Environmental Laboratory, james.e.overland [at] noaa.gov

In order to meet the urgent need of sub-seasonal to seasonal forecast NCEP started their 9-month seasonal forecasts from 2012 using Climate Forecast System version 2 (CFSv2). In this study, we examined the predictability of Arctic sea ice and atmospheric forcing fields produced by CFSv2. What we found is that the extra large sea ice cover at the end of summer are actually caused by the relative large cold temperature biases in the same region. The predicted atmospheric forcing fields are evaluated against several reanalysis products including the European Center for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim), NASA the Modern Era Retrospective-analysis for Research and Applications, version 2 (MERRA2), NCE-CFSR, and NCEP/NCAR-Reanaysis (R1). While there are discrepancies among the reanalysis products, we do find that some are better performed than the others on selected fields. The variables we investigated are those regional or stand-alone ocean-ice models will take as their atmospheric forcings. The quality of these variables is therefore has direct impact on the regional or stand-alone ocean-ice model simulation results. Further analyses reveal that biases in turbulent heat fluxes are believed to be the main factor leading to the sea ice forecast biases. The forecast bias in the surface short wave and long wave radiative forcing can mainly be attributed to the cloud cover uncertainty in the Arctic.

Assessment of 2015 Sea Ice Forecasts at the NCEP Climate Prediction Center and a Look Forward

Thomas Collow, INNOVIM, LLC/ NOAA/NWS/NCEP Climate Prediction Center, thomas.collow [at] noaa.gov
Wanqiu Wang, NOAA/NWS/NCEP Climate Prediction Center, wanqiu.wang [at] noaa.gov
Arun Kumar, NOAA/NWS/NCEP Climate Prediction Center, arun.kumar [at] noaa.gov

From March through October 2015, the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center provided monthly sea ice outlooks to the National Weather Service Alaska Region. The outlooks were based on output from an experimental set-up of the Climate Forecast System Version 2 (CFSv2) model, which ingests initial sea ice thickness data from the Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). All other initialization data come from the Climate Forecast System Reanalysis (CFSR). Twenty ensemble model runs were integrated from the 8th through the 12th of each month and several products were delivered including sea ice concentration, sea ice concentration standard deviation, sea ice probability, first projected sea ice melt date, and first projected sea ice freeze date (starting in June). Here we present an overall verification of different products against available observations.

Results show stronger skill in the prediction of sea ice extent and sea ice concentration than its operational counterpart and illustrates the importance of using an improved forecast system as sea ice prediction continues to be a crucial factor for a variety of Arctic interests. Predictions of ice melt date and ice freeze date, while not as widely used, also proved valuable. Exact prediction of these variables is near impossible due to atmospheric processes that cannot be predicted months in advance. However, it is shown that through modeled trends and standard deviations, that it is indeed plausible to issue a meaningful forecast of first ice freeze date and ice melt date given a spread is quantified. Further refinements to the experimental model will continue and plans are to resume monthly sea ice outlooks beginning in March 2016.

Assessing the Impact of Sea Ice Data Assimilation and Thickness Initialisation in the Met Office Seasonal Prediction System

Ed Blockley, Met Office Hadley Centre, ed.blockley [at] metoffice.gov.uk
K. Andrew Peterson, Met Office Hadley Centre, drew.peterson [at] metoffice.gov.uk

Seasonal predictions at the Met Office are made using the GloSea5 coupled forecasting system which has contributed September sea ice predictions to SIPN since its implementation in 2013. The ocean and sea ice components of GloSea5 are initialised using analysis fields from the FOAM ocean-sea ice analysis and forecast system. Both GloSea and FOAM are run daily at the Met Office and use the Nucleus for European Modelling of the Ocean (NEMO) model coupled to the Los Alamos sea ice model (CICE).

Data assimilation in the FOAM analysis system is performed using the NEMOVAR 3D-Var First Guess at Appropriate Time (FGAT) scheme. Satellite and in-situ observations of temperature, salinity, sea level anomaly and sea ice concentration are assimilated by FOAM each day. Sea ice assimilation is performed using SSMI/S sea ice concentration data obtained from the EUMETSAT OSI-SAF. At present assimilation of sea ice is unbalanced from the rest of the ocean-sea ice system and is performed in a separate NEMOVAR minimisation. In this talk a brief overview of the challenges associated with assimilating sea ice concentration in a multi-category sea ice model will be given and the various assumptions made will be discussed. Details will be provided of planned sensitivity studies intended to increase our awareness of how these assumptions affect the predictability of sea ice on short-seasonal time scales.

Although the FOAM analyses used to initialise GloSea benefit from assimilation of sea ice concentration data, other aspects of the sea ice - particularly thickness - is unconstrained by observations. Work is under way at the Met Office to implement assimilation of sea ice thickness data derived from satellite altimetry (i.e., CryoSat-2) within the FOAM-GloSea system. A key goal of this involves assessing the potential impact of sea ice thickness initialisation for the GloSea forecasts. Details of planned work designed to achieve these goals will be presented which includes exploring the link between winter thickness biases/errors and the evolution of concentration forecast errors in GloSea. This assessment can include participation/involvement from other modelling groups within SIPN and we would be interested in exploring potential collaboration in this regard.

Naval Research Laboratory Participation in the Sea Ice Outlook

E. Joseph Metzger, Naval Research Laboratory, joe.metzger [at] nrlssc.navy.mil
Pamela Posey, Naval Research Laboratory, pam.posey [at] nrlssc.navy.mil
Alan Wallcraft, Naval Research Laboratory, alan.wallcraft [at] nrlssc.navy.mil
Michael Phelps, Jacobs Technology, michael.phelps.ctr [at] nrlssc.navy.mil

For several years the Naval Research Laboratory has participated in the Sea Ice Outlook (SIO) to predict the Arctic September minimum sea ice extent. In earlier years these forecasts used the regional Arctic Cap Nowcast/Forecast System, but have more recently used the Global Ocean Forecast System (GOFS) 3.1, which is comprised of the two-way coupled Community Ice CodE (CICE) and the HYbrid Coordinate Ocean Model (HYCOM). The horizontal resolution is ~3.5 km near the North Pole and the system runs daily in pre-operational mode at the Naval Oceanographic Office assimilating satellite ice concentration data. Using ice and ocean initial conditions from this data-assimilative system at the appropriate start date, the ice/ocean models are integrated in forecast mode through October 1st. Atmospheric forcing is from the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR) for the years 2005-2014. This ensemble of 10 members gives an indication of how sea ice can respond to variable atmospheric conditions during summer and the projected sea ice extent for September is the average across all ensemble members. Each year’s forecast ice concentration bias is pre-determined by comparing a non-assimilative GOFS forecast with a data-assimilative GOFS hindcast. Monthly ice concentration error is translated into a monthly heat flux offset that is added to the net surface heat flux during the final SIO forecasts. This methodology has proven effective in reducing GOFS forecast ice concentration biases.

Break

Regional Forecast of the Minimum Sea Ice Extent: a Lagrangian Approach

Bruno Tremblay, McGill University, bruno.tremblay [at] mcgill.ca
Charles Brunette, McGill University, charles.brunette [at] mcgill.ca
James Williams , McGill University , james.williams [at] mail.mcgill.ca
Robert Newton, Lamont Doherty Earth Observatory, bnewton [at] ldeo.columbia.edu
Stephanie Pfirman, Barnard College, spfirman [at] barnard.edu

In this contribution we extend the pan-Arctic analysis presented in Williams et al. (2015) to produce a regional forecast of sea-ice conditions for each of the Arctic peripheral seas including the Beaufort, Chukchi, East Siberian, Laptev and Kara seas. The model builds on the finding that the mean large scale sea ice circulation the previous winter determines where the following summer minimum sea ice extent. We show that, in particular, the mid to late winter coastal sea-ice divergence plays a major role. When ice divergence occurs late in the winter, new ice forms but it does not have the time to grow to sufficient thickness to survive the following summer melt. To produce a regional forecast, we use a Lagrangian sea-ice model to backtrack an imaginary line defining the boundary of a given sea, starting from the beginning of the melt season in June and ending in November of the previous year. Results show that the position of the ice edge in each of the peripheral seas of the Arctic is well correlated with the previous winter’s divergence. The maximum correlation is obtained when the synthetic ice edge is backtracked from May to February. Note that, unlike the previous pan-Arctic study of Williams and colleagues in which the net ice divergence could be correlated with the winter mean Arctic Oscillation index, this regional analysis requires sea ice drifts in order to calculate the winter mean ice divergence along the coast. This finding supports the need for continuous production of real-time satellite-derived sea-ice velocity vectors, which can now be used for observation-based regional forecasting.

Discussion

Discussion about emerging research themes; prediction and predictability needs of observing/theoretical/technical systems.

Predicting Arctic Sea Ice Anomalies with Kernel Ensemble Analog Forecasting

Darin Comeau, Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, NYU, comeau [at] cims.nyu.edu
Zhizhen Zhao, Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, NYU, jzhao [at] cims.nyu.edu
Dimitris Giannakis, Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, NYU, dimitris [at] cims.nyu.edu
Andrew Majda, Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, NYU, jonjon [at] cims.nyu.edu

Motivated by Arctic intra-annual variability phenomena such as SST \& sea ice re-emergence, we use a prediction approach for sea ice anomalies based on analog forecasting. Traditional analog forecasting relies on identifying a single analog in a historical record, usually by minimizing Euclidean distance, and forming a forecast from the analog's historical trajectory. We use an ensemble of analogs for our forecasts, where the ensemble weights are determined by a dynamics-adapted kernel, which take into account nonlinear geometry on the underlying data manifold. We apply this method for forecasting pan Arctic and regional sea ice concentration anomalies from CCSM4 model data, and in many cases find improvement over the persistence forecast, notably a 2 month increase in predicting September sea ice extent.

Arctic Sea Ice Predictability at the Intra-Seasonal Time Scale in a Stochastic Model

Xiaojun Yuan, Lamont-Doherty Earth Observatory of Columbia University, xyuan [at] ldeo.columbia.edu
Cuihua Li, Lamont-Doherty Earth Observatory of Columbia University, cli [at] ldeo.columbia.edu
Lei Wang, Lamont-Doherty Earth Observatory of Columbia University, lwang.cu [at] gmail.com

This study examines the predictability of Arctic sea ice concentration at the intra-seasonal time scale through developing a stochastic model using weekly time series of sea ice concentration (SIC), oceanic and atmospheric variables from 1979 to 2012. The first order selection of predictors was achieved through anomaly correlations (AC) between SIC and oceanic and atmospheric variables. Sea surface temperature (SST), surface air temperature (SAT), 300mb geopotential height (GH300) and downward long-wave radiation (DLR) stands out than other variables. A linear Markov model was then developed in a multi-variate space of SIC, SST, SAT, GH300 and DLR. Model variables were further selected through prediction skill measured by AC and root-mean-squared error (RMSE) between predictions and observations. The SIC and SST combination is sufficient to capture predictable variability in such a model. Due to different ocean-sea ice coupled relationships in the Pacific and Atlantic sectors of the Arctic, the final model was built in two regions' multi-variate spaces for capturing regional co-variability. Weekly predictions of SIC were made at grid points up to 12-week lead-time, initialized by each week of the time series. The model skill was evaluated by the cross-validated model experiments. Because SIC has relatively high persistence, the model usually cannot beat persistence in first two-week predictions in most study areas but posses predictable skill for longer lead time predictions. The model can out-performs persistence for the predictions with lead-time longer than 2-week in the Chukchi Sea and East Siberian Sea but only beats persistence with lead time longer than 6-week in the Baffin Bay and Bering Sea. The model has predictive skill (AC>0.6) with RMSE less than 20% for predictions up to 6-week lead-time in the most areas in the Arctic Basin during the warm season (May to September).

Break

Lessons from a Multi-Model SIO Experiment

Edward Blanchard-Wrigglesworth, University of Washington, ed [at] atmos.washington.edu
Cecilia Bitz, University of Washington, bitz [at] uw.edu
Richard Cullather, National Aeronautics and Space Administration (NASA), richard.cullather [at] nasa.gov
Wanqiu Wang, National Centers for Environmental Prediction (NOAA), Wanqiu.Wang [at] noaa.gov
Jinlun Zhang, University of Washington, Applied Physics Laboratory (APL), zhang [at] apl.washington.edu
Francois Massonnet, Institut Català de Ciències del Clima and Université Catholique de Louvain (IC3&UCL), francois.massonnet [at] uclouvain.be
Neven Fuckar, Institut Català de Ciències del Clima (IC3), neven.fuckar [at] ic3.cat
Pamela Posey, Naval Research Laboratory (NRL), posey [at] nrlssc.navy.mil
Matthieu Chevalier, National Centre for Meteorological Research, matthieu.chevallier [at] meteo.fr
Alan Wallcraft, Naval Research Laboratory (NRL), alan.wallcraft [at] nrlssc.navy.mil

In the Sea Ice Outlook prediction system, we find that dynamical models not only show poor skill in forecasting observed September sea ice extent but are also equally unsuccessful at predicting each other, indicating a large divergence in model physics and/or initial conditions. Motivated by this, we have performed a modeling experiment with SIO models that include both fully coupled global models, and global/regional ice-ocean models. We run two sets of simulations — a control set initialized with a climatological May 1 sea ice thickness and an experiment set initialized with May 1 2015 sea ice thickness. This allows us to investigate both model uncertainty and error growth, and the importance of post-processing to forecast uncertainty. We find that without post-processing, model uncertainty is the dominant source of forecast uncertainty, while with post-processing, overall forecast uncertainty is greatly reduced and error growth dominates forecast uncertainty. This result suggests that uniform bias correction across SIO models’ forecasts will greatly reduce the forecast spread. We also investigate the spatial patterns of forecast uncertainty and find that forecast uncertainty grows at a faster pace along coastal areas compared to the central Arctic basin. Potential ways of offering forecast information based on probabilistic metrics are also explored.

Discussion

Discussion about emerging research themes, major accomplishments, recommendations for the Sea Ice Outlook and prediction programs to advance polar prediction and predictability, and special journal issue possibility.

Meeting Adjourns

Arrange ride or hotel shuttle on your own.