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Strategies for the Future

OVERARCHING STRATEGY


Key Strategy: A Deliberately Integrative Approach for Sustained and Coordinated Collaboration among User, Modeling and Observation Communities

A deliberately integrative approach is needed to facilitate coordinated and sustained discussions and collaborations among the user, modeling, and observation communities to inform effective research activities and to set realistic expectations for predictions. Such an approach would need to take full advantage of existing infrastructure and draw from comparable efforts in other fields.


The need for sustained and facilitated communications among the many stakeholder communities is both a challenge and a significant opportunity. End-user needs are important drivers for science and research efforts and for more accurate, timely and useful sea ice forecasts. For example, regularly scheduled, iterative, and sustained discussions among end users, modelers, field scientists, and the remote sensing community could help the science communities understand user tolerances for the accuracy of particular ice characteristics. They could also help to determine the most practical approaches to obtaining the highest priority products.

Some user requirements may be beyond existing capabilities because of inherent limitations in predictability or owing to technical or practical limitations. Thus, it is incumbent upon the research element of the stakeholder community, including modelers and observers, to understand the basis of these requests, to explore alternatives, and to properly manage expectations. Increased collaboration can help overcome the challenge of communicating to the public about what can reasonably be expected from seasonal to decadal sea ice prediction.

Within the framework of organized stakeholder communication, true collaborations between the modeling and observation communities are essential. Given the reality of limited resources, these collaborative efforts are more likely to identify resource needs and shared resources. The need for integrative, sustained conversations calls for routine and frequent engagement. Although workshops and topical meetings have their place within the process, this strategy calls for a longer-term, continuous interaction. The



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3 Strategies for the Future OVERARCHING STRATEGY practical approaches to obtaining the highest priority products. Key Strategy: A Deliberately Integrative Some user requirements may be beyond Approach for Sustained and Coordinated existing capabilities because of inherent Collaboration among User, Modeling and Observation Communities limitations in predictability or owing to technical or practical limitations. Thus, it is A deliberately integrative approach is incumbent upon the research element of the needed to facilitate coordinated and sustained discussions and collaborations stakeholder community, including modelers among the user, modeling, and observation and observers, to understand the basis of communities to inform effective research these requests, to explore alternatives, and to activities and to set realistic expectations for predictions. Such an approach would need properly manage expectations. Increased to take full advantage of existing collaboration can help overcome the infrastructure and draw from comparable challenge of communicating to the public efforts in other fields. about what can reasonably be expected from The need for sustained and facilitated seasonal to decadal sea ice prediction. communications among the many Within the framework of organized stakeholder communities is both a challenge stakeholder communication, true and a significant opportunity. End-user collaborations between the modeling and needs are important drivers for science and observation communities are essential. research efforts and for more accurate, Given the reality of limited resources, these timely and useful sea ice forecasts. For collaborative efforts are more likely to example, regularly scheduled, iterative, and identify resource needs and shared sustained discussions among end users, resources. The need for integrative, modelers, field scientists, and the remote sustained conversations calls for routine and sensing community could help the science frequent engagement. Although workshops communities understand user tolerances for and topical meetings have their place within the accuracy of particular ice characteristics. the process, this strategy calls for a longer- They could also help to determine the most term, continuous interaction. The 27

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28 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies BOX 3.1 Examples of Sustained Conversations: ArcticNet, Alaska Ocean Observing System (AOOS), and Alaska Center for Climate Assessment and Policy (ACCAP) An example of a large-scale program that facilitates sustained communication across the research and stakeholder communities is ArcticNet,a a network of Canadian institutions that aims to bring together scientists and representatives from academia, government, industry, international agencies, and northern communities. The main objective of ArcticNet is to utilize information from these sources to help Canada prepare for and adapt to climate change and increased activity in the Arctic. Through research collaborations and partnerships with corporations operating in the Arctic, reliable scientific data can be made available to a wide variety of stakeholders. Research results are shared with the community in the form of scientific publications and a data catalogue and through various media outlets. This integrated and cross- disciplinary approach to Arctic research provides unique opportunities for education, collaboration, and entrainment of the next generation of polar researchers. The Alaska Ocean Observing System (AOOSb) is another example of a network intended to address both national and regional needs for researchers and stakeholders on coastal and ocean issues. In coordinating federal, state, local, and private needs, AOOS identifies gaps in data, helps fill those gaps when appropriate, and increases the usefulness of existing data. AOOS demonstrates the integration and collaboration necessary to enable a variety of users to obtain information and to make decisions about the marine environment in the Alaska region. mechanisms to support and facilitate these Looking more specifically at the issue of sustained conversations would need to be sea ice prediction, one example of an initial deliberately identified and implemented, as framework for capturing stakeholder needs opposed to relying on self-organization. This in terms of variables utilized in sea ice would require close and effective science is provided in Table 3.1. Other engagement among public, private, and frameworks can be used to organize user- academic institutions. Participants in these science connections with the goal of conversations could serve a role akin to that advancing the utility of sea ice predictions. of a diplomat, seeking and communicating For this process to be successful, it is ideas and suggestions that reflect a broad important for communities to learn each viewpoint. There are excellent examples of others' language and to be aware of usage efforts underway, on both national and local differences. For instance, the terms sea ice levels, to develop and facilitate interactions "memory" and "persistence" have specific among relevant research communities and meanings within the sea ice research users (Box 3.1). community that may not be appreciated by

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Strategies for the Future 29 Information is made available through various data management and information products (including a website and data portal), workshops and reports, and newsletters. AOOS also makes educational resources available for teachers and interested community members. The AOOS network includes mariners, fishermen and subsistence users, search and rescue operations, scientists, coastal security operations, resource managers, and educators. A regional-scale example of a forum through which stakeholders work directly with scientists is the Alaska Center for Climate Assessment and Policy (ACCAPc). The goal is to enable Alaskans to be able to respond to climate changes by targeting products (e.g., National Oceanic and Atmospheric Administration [NOAA] forecasts) to address specific user needs. Using tools such as webinars, video conferences, web-based guides and maps, and social media, ACCAP reaches out to encourage dialogue between scientists and end users. These resources help convey valuable information on climate change science as well as information on uncertainty and risk management. Strategies and plans to adapt to climate change are developed in coordination with stakeholders, agencies, industries, and citizens to ensure that partnerships are built and information needs are met. The ACCAP program is part of the NOAA Regional Integrated Sciences and Assessments (RISAd) program, which supports research on complex, interdisciplinary issues that are addressed at the regional level. a www.arcticnet.ulaval.ca/index.php b www.aoos.org/ c http://ine.uaf.edu/accap/ d www.climate.noaa.gov/cpo_pa/risa/ others. Even commonly used terms such as including designated funding. For example, "multiyear" sea ice may have different an NRC report noted that maintaining some meanings that need to be clearly networks developed and cultivated during communicated among the relevant IPY has been difficult and many of its valued stakeholders. components--such as the international IPY These ongoing efforts and suggested website, its publication database, and framework offer important building blocks educational and outreach efforts--have to advance the strategy envisioned here. struggled to find alternative resources (NRC, However, in the committee's view this 2012a). The characteristics of these activity will likely not be effectively sustained conversations suggest leadership facilitated without a dedicated and deliberate from a high-level, inter-governmental office, effort backed by sufficient resources, agency, or consortium.

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TABLE 3.1 Example of an initial framework that could be useful in determining key stakeholder needs and prioritizing variables to be measured by the research community to better meet those needs. Stakeholder Ice edge Ice %FY/ Mean FY Mean MY Ice Ice Floe % % Snow Melt/ Fast ice Ice Ice Local Reg- Pan- Activity location concentration MY thickness thickness growth melt size Ridged Leads depth freeze break- velocity character ional Arctic rate rate ice mean onset up date width Infraructure siting Commercial fishing Marine Tourism Shipping National Security Indigenous use Science campaigns Science applications Marine Mammals Offshore resource extraction Coastal operations NOTE: The top row lists sea ice variables and measurements. Appropriate cells could be filled with check marks or indications of priority. If such a table is completed by various stakeholders, a particular variable (e.g., floe size on seasonal timescales) for which a large number of stakeholders indicate a need may be given higher priority than a variable needed by fewer stakeholders. This table also shows the breadth of stakeholders and their needs. It can be used for both seasonal and decadal timescales. SOURCE: Generated from discussions at the workshop.

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Strategies for the Future 31 STRATEGIES TO IMPROVE SEA ICE various sea routes or specific coastal PREDICTIVE CAPABILITIES: locations. Various comparison approaches SEASONAL TO DECADAL TIMESCALES need to be considered, such as hindcast and so-called "perfect-model" studies. In a hindcast model study, a While recognizing that there are retrospective assessment of past years, both limitations in current modeling and initial conditions and validation data are observational techniques, the committee needed. An evaluation of this kind was offers possible strategies to significantly recently performed on forecasts of the El enhance our understanding and predictions Nio-Southern Oscillation phenomenon of Arctic sea ice cover over seasonal to (Barnston et al., 2012; Box 3.2). In the case decadal timescales. Implementation of these of sea ice prediction experiments, proposed strategies will require iterative initialization will be limited by the accuracy interaction between model development and of key predictor variables (e.g., ice observational input, balanced by a sustained thickness), but such limitations will be dialogue with end users. common to all three approaches. Perfect- model studies can also provide useful Key Strategy: Evaluation of Existing insights to predictability. These studies treat Seasonal Prediction Methods simulation output from a coupled numerical model reference experiment as the "truth" A coordinated and detailed comparison of (i.e., equivalent to observations). The the different approaches used to generate seasonal sea ice forecasts could establish different prediction methods discussed baseline expectations for predictive skill and above can then be applied to forecast the identify priority needs, setting the stage for conditions from this reference simulation. advances in predictive capability. This can be performed for both past and future model-simulated conditions, allowing As previously discussed, several methods for information on how predictability are used to obtain seasonal forecasts of characteristics may change with changes in Arctic sea ice, including (1) statistical the climate state. Such studies have the methods, (2) ice-ocean models driven by advantage of a complete knowledge of initial prescribed atmospheric forcing, and (3) fully and time-varying conditions and provide the coupled atmosphere-ocean-ice models. A ability to address the implications for coordinated comparison of these prediction possible nonstationarity in statistical methods will serve to inform both the relationships for future climate states. It is science and the needs of stakeholders. It is important to remember that the results from important that the evaluations be based on these studies need to be considered within regional metrics and not be limited to ice the context of the imperfect coupled model coverage. For instance, other candidate being used. metrics are dates of ice retreat and closure of

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32 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies BOX 3.2 COMPARATIVE EVALUATION OF SEASONAL FORECASTING METHODOLOGIES FOR ENSO Given that the El Nio-Southern Oscillation (ENSO) affects climate and weather events (e.g., drought, flooding, and tropical storms) on a global scale, understanding and improving forecasts are critical to both the scientific community and the public (McPhaden et al., 2006). Real-time ENSO prediction capability during the 1990s was assessed to be somewhat useful (Barnston et al., 1994), with dynamical and statistical models showing comparable skills. Although there has been progress in the prediction of ENSO during the last decades (Randall et al., 2007), numerous issues regarding its dynamics, impacts, and predictability remain uncertain. In the last two to three decades, the ability to predict warm and cold episodes of ENSO at short and intermediate lead times has gradually improved due to: Improved observing and analysis/assimilation systems, Improved physical parameterizations, Higher spatial resolution, and Better understanding of the tropical oceanic and atmospheric processes underlying the ENSO phenomenon (e.g., Guilyardi et al. 2009). Findings from a more recent study on ENSO predictions suggest that additional advances in capabilities are likely with the expected implementation of better physics, numeric and assimilation schemes, finer resolution, and larger ensemble sizes (Barnston et al., 2012). The study evaluated real-time model predictions of ENSO conditions during the 2002-2011 period and compared them with skill levels documented in studies from the 1990s. The skills of 2002-2011 models is slightly better than that of earlier decades, with the recent decade's dynamical ENSO prediction models outperforming their statistical counterparts to a slight but statistically significant extent. The greater power of dynamical models is largely attributable to the subset of dynamical models with the most advanced, high-resolution, fully coupled ocean- atmosphere prediction systems using sophisticated data assimilation systems and large ensembles (Barnston et al., 2012).

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Strategies for the Future 33 Key Strategy: Process-Based Studies Amundrud et al., 2006; Barber et al., 2012). Targeted at the Increasingly Prevalent Moreover, as the sea ice cover becomes First-Year Ice Cover dominated by the weaker and less stable first-year sea ice, it may be more susceptible Questions surrounding the impact of the trend toward an increasingly seasonal to extreme events that could have impacts Arctic sea ice cover could be addressed with lasting from seasonal to decadal timescales. the development of a highly coordinated and Observations of these processes and integrated process-based study, analogous their interactions are needed to determine to the Surface Heat Budget of the Arctic which aspects of existing predictive models Ocean (SHEBA) project, focused on require further development and to help understanding oceanic, atmospheric, and terrestrial contributions to seasonal sea ice determine requirements for sustained predictions. observations necessary to verify model realism as the ice cover continues to evolve. The fundamental properties of the ice Conversely, model experiments can identify cover are changing as the Arctic transitions which process parameterizations are the toward a seasonally ice-free state, resulting greatest sources of uncertainty (or error) in in a significant reduction in the amount of climate model simulations. Systematic multiyear ice compared with first-year ice. sensitivity experiments, performed with an In the face of this significant transition, ensemble of different models, would initiate there is the need to identify and understand an end-to-end process study in which the whether and how key parameters (i.e., first- needs of models guide field experiments, order effects) influence predictability. A which in turn feed back (via improvements likely outcome is the need for improved in process formulations or parameter model formulations of the dynamic and estimates) to models used for sea ice thermodynamic processes governing the prediction. behavior of a sea ice cover composed of An end-to-end process study in the largely first-year ice. seasonal ice zone, guided by past work, Previous work done on the fundamental historical data and the output from properties of first-year sea ice (e.g., Weeks sensitivity studies using current models, and Ackley, 1982; Timco and Weeks, 2010) would enhance process understanding and can inform the design of process studies that simulation capability. Although process will advance our understanding of first-year understanding is especially important for ice in a predictive context. The challenge lies predicting the evolution of initial conditions in developing a thorough understanding of over seasonal and interannual timescales, it the fundamental properties of first-year sea also has potential for multiyear timescales, ice act together on a large, pan-Arctic scale based on the apparent multiyear timescales to affect air-ice and ocean-ice heat transfer of the ocean inflow anomalies, at least in the (ice thermodynamics) and ice pack mobility Atlantic sector (Polyakov et al., 2010). The (ice dynamics, e.g., Melling et al., 2005; nature of a process-based study of

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34 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies BOX 3.3 Examples of SHEBA-Like Initiatives There are a few initiatives under way that could be built upon and possibly integrated to conduct a SHEBA-like project in seasonal ice: Office of Naval Research 5-year Emerging Dynamics of the Marginal Ice Zone Department Research Initiativea (ONR MIZ-DRI). The 5-year ONR MIZ-DRI was initiated in 2011 and includes plans for a field project in 2014. The observational dataset that is generated from an integrated suite of platforms can be used to evaluate the skill of numerical forecast models. Ocean Observations to Improve Sea Ice Forecasting. This project was initiated during the Arctic Observing Coordination Workshop, held March 20-22, 2012, in Anchorage, Alaskab. In its very early planning stages, the project is designed to provide the necessary ocean observations to improve sea ice forecasting on daily, seasonal, interannual and decadal timescales. Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MoSAIC).c:. The concept of MoSAIC is to establish an international, multiyear, manned, drifting observatory in the central Arctic sea ice pack to obtain observations of atmosphere, sea ice, and ocean processes that will compose a testbed for process, regional, and global model evaluation and development. atmosphere-sea ice-ocean coupling in the designed to facilitate interactions between seasonal ice zone will almost certainly observation and modeling research require a set of observations over a full communities, through the planning, annual cycle. The process study is also implementation, and analysis phases of the inherently interdisciplinary because of the project. Now that it has been over a decade broad science scope encompassing surface- since the project formally concluded, it is to-satellite observations and models apparent that one of the major successes of extending across the atmosphere, ocean, SHEBA was the interdisciplinary teamwork seafloor, and land. that brought together a diverse group of The year-long Surface Heat Budget of researchers, each bringing their own the Arctic Ocean (SHEBA) project (Perovich particular expertise, to work on the common et al., 1999), conducted from 1997 to 1998 in goals of the program (Perovich et al., 2003). multiyear ice, serves as an excellent model SHEBA continues to motivate cross- for a study of this kind. SHEBA was cutting collaborations that advance our

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Strategies for the Future 35 Year of Polar Predictiond (YOPP): This is a joint effort from the World Weather Research Progamme and the World Climate Research Programme. The YOPP is tentatively scheduled for 2017-2018 and will include an intensive observation and modeling period to provide data for model development, data denial experiments, and predictability and diagnostic studies. Other examples of successful studies of seasonal ice in the past 15 years include: International North Water Polynya Study (NOW): This program was initiated to study the North Water polynya and the mechanisms associated with its formation and biological production. Canadian Arctic Shelf Exchange Study (CASES): This was an international effort under Canadian leadership to understand the biogeochemical and ecological consequences of sea ice variability and change on the Mackenzie Shelf. Circumpolar Flaw Lead (CFL) Study: The objective of this project was to examine how physical changes affect biological processes within the flaw lead. a www.onr.navy.mil/en/Science-Technology/Departments/Code-32/All-Programs/Atmosphere-Research- 322/Arctic-Global-Prediction/Marginal-Ice-Zone-DRI.aspx b www.arcus.org/search/meetings/2012/coordination-workshop c www.esrl.noaa.gov/psd/events/2012/mosaic/ d www.wmo.int/wwrp understanding of the processes governing fishing communities, commercial shippers, sea ice thermodynamics. This significant and marine tourism operators). Some and enduring outcome suggests that the examples of SHEBA-like initiatives can be investment in a large, focused effort, if found in Box 3.3. effectively coordinated and implemented, A comprehensive process study in the can have a greater impact than a more seasonal ice zone also offers the opportunity diffuse approach. If followed, a key addition to identify, develop, and test instruments to the approach used during SHEBA would and observational platforms that can be the increased involvement of stakeholders effectively and efficiently support both outside of the sea ice research community, seasonal and decadal prediction capabilities. including a greater emphasis on the Arctic Measurements of sea ice motion, ice marine ecosystem, atmospheric chemistry, thickness, and snow cover/depth are made the coastal terrestrial system, and, more from a variety of sensors--including in-situ, generally, end users (e.g., indigenous airborne, and satellite instruments--each populations, natural resource industries, having different capabilities. Existing surface

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36 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies networks (e.g., buoys and weather stations) wind forcing, and enhanced mixing has been will need to be sustained and extended with proposed by Shimada et al. (2006), while aircraft campaigns and new instruments. processes of double diffusion and eddy Continuous satellite observations (e.g., mixing have been suggested as mechanisms multispectral sensor, synthetic aperture by which Atlantic water heat may move radar, passive microwave radiometer, active upward in the water column north of microwave scatterometer, and altimeter) Eurasia (Polyakov et al., 2011). over decadal timescales are critical to assess Compounding the uncertainty about the the role of sea ice change in the global role of ocean processes is the varying ability climate system. of global and regional ocean models to To enhance predictive capability, the reproduce the vertical structure of process study needs to have a focus on temperature and salinity in the upper layers improving predictions on seasonal to of the Arctic Ocean. Inadequacies in process decadal scales. It also needs to involve formulation as well as vertical resolution are stakeholders and modeling and likely sources of errors in the upper Arctic observational communities in its planning Ocean in global and regional models. and implementation. If designed in this way, Moreover, the CMIP5 decadal predictability results from the process study can play a experiments, which are targeting seasonal to central role in selecting and optimizing a decadal predictability inherent in ocean suite of observations that can best meet the initializations, have yet to address the role of broad requirements for more robust, the Arctic Ocean in interannual-to-decadal sustained circumpolar and regional prediction. This is largely because there are observations to meet different demands, few data to initialize the Arctic Ocean in including initial conditions for models, decadal-scale hindcast experiments. improved process understanding, model validation, and long-term prediction. Key Strategy: Model Sensitivity Studies to Influxes of oceanic heat to the Arctic Determine Key, First-Order Observational from the Pacific Ocean and the Atlantic Needs Ocean have likely contributed to the recent There is a particular need at this time for a loss of Arctic sea ice (e.g., Shimada et al, coordinated effort to design and implement 2006; Walsh et al., 2011). In both regions, a set of model sensitivity studies that will the warmer water subducts and circulates provide quantitative metrics to assess the below the fresh surface layer of the Arctic impact of various observation types, Ocean. The rates, locations, and processes by locations, and densities on seasonal sea ice forecasts. which these heat sources reach the overlying sea ice cover and affect sea ice anomaly Observations play a critical role in evolution are poorly understood, in large seasonal and decadal sea ice prediction. part because of a lack of in situ observations. There are broad requirements for more A mechanism involving reductions of ice robust sustained circumpolar and regional concentration, increased responsiveness to

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Strategies for the Future 37 observations to meet different demands, for Buoy Program improve atmospheric instance, initial and boundary conditions for reanalyses. However, impacts on models, improved process understanding, atmospheric reanalyses are not equivalent to model validation, and long-term prediction. impacts on seasonal sea ice predictions (and Sea ice model predictive capabilities have sea ice simulations in general). Model- evolved and will continue to evolve. That derived predictions of sea ice for timescales said, to the extent that particular processes of several seasons are almost certainly (e.g., ocean heat fluxes and cloud-radiative affected by the initialization of ice thickness interactions) are found to exert high (and corresponding distributions of ice leverage on the models' simulation of the concentration), but the atmospheric Arctic system and sea ice prediction in initialization probably has little effect. There particular, the diagnostic model is large uncertainty, however, about the intercomparison can point to priorities for importance of initializations of other observations and/or process studies targeted variables, such as snow on sea ice, ocean at improved model simulations and temperature and salinity profiles below the predictions of sea ice. This should build on ice, and ocean current distributions. previous work that has identified areas of The modeling tools to conduct these systematic model bias (e.g., Sorteberg et al., data-model synthesis experiments exist. For 2007) and the importance of various instance, coupled atmosphere-ice-ocean simulated feedbacks (e.g., Winton, 2006; Kay forecast models, such as the National et al., 2012). Centers for Environmental Prediction's The modeling infrastructure, especially (NCEP's) Coupled Forecast System, as it pertains to data assimilation, has routinely assimilate observational data in advanced sufficiently that one can now producing seasonal forecasts of the envision a series of sensitivity studies atmosphere, ocean, and sea ice out to a designed to strategically inform research range of a year. Ice-ocean models have been investments related to observational needs developed and used to produce forecasts for for sea ice modeling and prediction. the SEARCH Sea Ice Outlook 1 (Figure 3.1), At the seasonal timescale, observing as well as for ice-ocean model system experiments (OSEs--testing impacts intercomparisons. In addition, data of actual observations) and observing system assimilation systems have been developed simulation experiments (OSSEs--testing for reanalyses of the Arctic atmosphere and sensitivities to hypothetical or simulated ocean (Bromwich et al., 2010; Panteleev et observations) can be directed at al., 2011). systematically investigating the effects of It is noteworthy that whereas all of the specific observations on prediction forecasts represented in Figure 3.1 projected capabilities. Among the limited studies to a 2012 September minimum ice extent that date, Inoue et al. (2009) have shown that the assimilation of sea level pressure 1 http://www.arcus.org/search/seaiceoutlook/index. measurements from the International Arctic php.

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38 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies FIGURE 3.1 July 2012 Pan-Arctic Sea Ice Outlook. Ice-ocean models have been developed and are one method used to produce forecasts for the SEARCH Sea Ice Outlook. This figure shows 21 community estimates of the expected minimum of sea ice for 2012. The projected Arctic sea ice extent median value for September 2012 was 4.6 million square kilometers. However, on September 16, 2012, Arctic sea ice reached a minimum extent of 3.41 million square kilometers, the lowest seasonal minimum extent on satellite record. SOURCE: Adapted from ARCUS and SEARCH.

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Strategies for the Future 39 was well below the long-term (1979-2007) address impacts of measurement errors as average, they all also overestimated it. This well as varying distributions of result suggests that although there is a skill measurements. in the seasonal forecasts relative to These types of experiments may be climatology (albeit a changing climatology), regarded as prerequisites for the design of an there remains the need for a concerted effort Arctic observing network (NRC, 2006), and to improve sea ice predictions--the need seasonal sea ice prediction can provide a that motivated the present report. compelling focus for such experiments. In the case of seasonal predictions, the Further, many of the variables listed above existing capabilities in data-model synthesis (e.g., ice thickness, snow on sea ice, and have yet to be exploited in the Arctic under-ice ocean profiles) are observational because prediction capabilities at these challenges in their own right. The logistics timescales are relatively new and because the and expenses involved in obtaining these Arctic observational and modeling measurements adds to the urgency of OSEs communities have tended to be distinct. and OSSEs to justify, for sea ice prediction Therefore, as a first step in designing OSEs and for other applications, future and OSSEs, there is a particularly urgent investments in the observations. need for a coordinated effort by these communities to design a set of experiments Key Strategy: Enhanced Numerical Model that will provide quantitative metrics of the Capabilities impact of various observation types, Enhancement of model-based predictive locations, and densities on seasonal sea ice capabilities will require coordinated forecasts, as well as the accuracy and experiments to (a) identify which variables temporal resolution that are required. and processes are critical to simulating a Possible observational variables for realistic ice cover, (b) investigate the source inclusion in these experiments are ice of climate model drift, and (c) guide thickness distributions, ice extent and decisions regarding high-priority model development needs and the expansion of concentration, snow on sea ice, and the models to include additional capabilities upper-ocean profiles of temperature, salinity and variables of interest to stakeholders. and current velocities. The latter category of observations includes ocean measurements Model intercomparison projects (MIPs), not only from under the ice, but from the such as for the Arctic (AMIP2), the Arctic surrounding open ocean. In addition to Ocean (AOMIP3), and sea ice (SIMIP4), have these state variables, consideration should be played an important role in identifying given to measurements focused on the exchange of energy between the air-ice and 2 www-pcmdi.llnl.gov/projects/amip/index.php/ ice-ocean boundaries, which drive ice 3 www.whoi.edu/page.do?pid=29836 dynamics and thermodynamics (e.g. 4 http://gaim.unh.edu/Structure/Future/MIPs/SIMIP. radiation, sensible heat, moisture, and html momentum) The OSEs and OSSEs would

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40 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies BOX 3.4 HURRICANE FORECAST IMPROVEMENT PROGRAM The NOAA-led Hurricane Forecast Improvement Program (HFIP) is a 10-year program started in 2007 with the overarching goal of improving hurricane forecast skill, with an emphasis on hurricane intensity and structure.a Hurricane forecasts have improved significantly in the last 10 years, but rapid intensification remains a significant challenge. The specific goals of HFIP are to: 1) improve the accuracy of hurricane intensity and track forecasts and 2) increase the forecast confidence of customers and decision makers, especially those in the emergency management community (NOAA, 2010). To help facilitate these goals, the National Oceanic and Atmospheric Association has partnered with other federal agencies, the National Center for Atmospheric Research, universities, and the Naval Research Laboratory (NRL). These collaborations help address the challenges associated with transitioning new forecasting research and technology into operations. Furthermore, there has been an effort within HFIP to ensure open access to the data involved (NOAA, 2010). a www.hfip.org/ differences among models and, in some greater control achieved in these approaches cases, the component(s) of the model that is by prescribed (e.g., observationally- or the source of those differences. To date, reanalysis-derived) lateral and/or surface however, MIP efforts have had limited forcing of the Arctic. Interestingly, one success in pinpointing the variables or outcome of these studies, along with the processes that are the root cause of identification of factors that influence sea ice simulation errors, and any conclusions have prediction skill, may be to realize often not efficiently fed back to the model simplifications that can be applied to developers to improve the models. coupled models. This result would allow for A new strategy for model models with reduced complexity to be used intercomparison is needed that will identify for seasonal-scale sea ice prediction. specific, key processes of importance to sea At the decadal timescale, where ice prediction; incorporate lessons learned predictions are largely influenced by forcing, from model sensitivity studies; and model sensitivity studies explore and collaborate closely with model developers to quantify the impact of a range of parameters identify approaches to resolve unrealistic or representations of physical processes on model behavior. Regional models and ice- predicted model outputs. These experiments ocean coupled systems will likely be an show how a particular scenario may be essential part of the strategy, given the affected by multiple parameters. Performing

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Strategies for the Future 41 targeted sensitivity studies with new Additionally, various mechanisms to "de- parameterizations can reveal weaknesses of drift" predictions need to be assessed in other parameterizations. These simulations retrospective studies to determine their make it possible to analyze the sensitivity of utility in realizing useful predictive skill. simulation results to some of the decisions Related to the call for targeted model made in model development. The weather sensitivity studies, there is a need for community may be able to contribute enhanced capabilities in numerical models lessons learned for conducting sensitivity to provide useful information on key studies (Box 3.4). variables of interest. For example, many As the Arctic ice cover transitions to a climate models do not distinguish between predominantly thinner state, the decadal land-fast ice and other sea ice, yet the model sensitivities among variables affecting behavior of land-fast ice is of keen interest to ice growth, melt, and movement may change a variety of stakeholders. The date of spring relative to those of the past. Targeted breakup is a particularly influential event for sensitivity studies would help identify which coastal infrastructure and operations, but variables and processes are critical to most models lack sufficient resolution and simulating a realistic ice cover in the new the specific processes that govern evolution state, including the mean climatology, of land-fast ice. Many of these requirements simulated variability of and response to call for predictions that have sufficient changes in external forcing, and where spatial detail to resolve the highly varying ice uncertainties inherent in model characteristics that occur near the coastline. parameterizations have the largest influence The nature of seasonal sea ice prediction on simulated sea ice processes. Through demands accuracy within a few hundred collaborative efforts with the model kilometers of shore and within the marginal development community, this should feed ice zone. In addition, forcing from tides and back into improvements in the physics of ocean waves may play an important role in the models. sea ice evolution on seasonal to decadal A particularly important issue to address timescales. These factors are not typically via the model sensitivity studies and considered in large-scale numerical models. intercomparison activities is climate model Model enhancements that incorporate these drift from an observationally-based and other relevant processes would allow for initialized state, which contaminates investigations of their role in sea ice predictions on seasonal to decadal timescale. prediction and ultimately result in better This drift results from systematic biases in predictive skill and more useful information coupled climate simulations. Improvements for stakeholders. in model simulations are required to address One way in which model capabilities can this issue. Research on data assimilation be enhanced is by finer resolution. Recent methods and alternative methods of model studies have shown that models with higher initialization, for example, by using anomaly horizontal and vertical resolution are able to fields, will need to be considered. more realistically simulate certain processes

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42 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies in the atmosphere (e.g. Byrkjedal et al., 2008; No single organization or agency has Girard and Bekcic, 2005) and ocean (e.g., adequate resources to systematically Fieg-Rudiger et al., 2010) Higher resolution undertake the task of robust field information may also be necessary to meet observations, data synthesis, and certain stakeholder needs. However simply environmental modeling. Collaborative increasing model resolution is not a efforts and data sharing are therefore panacea, because enhanced computational essential. Moreover, data continuity is a and storage costs need to be considered in fundamental imperative so that long-term light of the relevant benefits for sea ice Arctic sea ice trends can be ascertained and prediction. Moreover, model provided to stakeholders for reliable parameterizations that have been developed planning. Sharing data also enables for coarse resolutions may not be ideal for researchers to communicate and collaborate considerably finer spatial-scales (e.g., more effectively. Lipscomb et al., 2007; Girard et al., 2009) Given the vast amounts of disparate data and may need to be revisited, requiring on Arctic sea ice, background information, further model developments. Nevertheless, model results, observational date, etc. can be with computational resources increasing and difficult to find for the numerous likely benefits in terms of simulation quality, stakeholders who use these data, particularly increased resolution in global and regional for new users. The committee acknowledges models together with regionally refined and that a more centralized framework could adaptive model grids need to be explored in improve information management (Parsons the context of benefits for sea ice predictive et al., 2011). Although there are numerous capability. data repositories for climate-relevant data, they tend to be scattered and inconsistently cross-linked. Rich measurement datasets are KNOWLEDGE MANAGEMENT often reduced to their basic parameters with a loss of important information. Also, data Key Strategy: Improved Information and transfer and data transformation at data Data Management centers add additional layers of complexity and data latency. Given the vast amounts of disparate data on In the committee's opinion, the main Arctic sea ice and the numerous stakeholders who use these data, there is a purpose of a centralized information hub is need for a coordinated and centralized to serve as a primary launching pad for information hub for Arctic datasets that searches aimed at gaining access to this wide facilitates timely access to observational array of information. The intention is not to and modeling results and encourages recreate existing and diverse resources, but sustained communication among to facilitate the ease of their retrieval. A stakeholders. central information hub would unify the various databases, providing a seamless and consistent system for information and data

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Strategies for the Future 43 BOX 3.5 DATA SHARING: TWO CASE STUDIES Some progress has been made in data management. Case 1: As of 2011 Shell, ConocoPhillips and Statoil have signed an agreement with the National Oceanic and Atmospheric Association to formally share scientific information that the companies have acquired individually and jointly in the Arctic region.a This collaboration leverages the complementary strengths of NOAA's scientific expertise and the industry's significant offshore resources. Scientific datasets for the Arctic region are shared, including weather and ocean observations, biological information, and sea ice and seafloor mapping studies. NOAA's ability to monitor climate change and provide useful products and services that inform energy exploration activities in the Arctic will likely be improved through the sharing of high-quality data. The integration of these data could also provide a greater national capacity to effectively manage and respond to environmental disasters in an area where limited personnel and facilities exist. Data and information are shared with the public through NOAA's existing outlets. Quality control on all data provided is conducted by NOAA before it is incorporated into its products and services. Case 2: Since 2011, all National Science Foundation proposals must include a supplementary "data management plan," which is subject to peer review. The primary goal of this new data- sharing policy is to "assure that products of research help NSF achieve its mission to promote the progress of science and engineering."b The plan should outline the types of data to be produced, the standards to be used, the policies for data access and reuse, and the plans for archiving. a http://www-static.shell.com/static/usa/downloads/alaska/alaska_arcticmou082311.pdf b www.nsf.gov/geo/geo-data-policies/index.jsp discovery and access. Recent Web standards data, etc. The centralized hub could also provide distributed databases that appear serve as an integrating resource, providing uniform and singular to the user. Therefore access to information on the various it is not necessary to create new archives, but elements of the Arctic sea ice system (e.g., rather to leverage existing infrastructure. A ocean, atmosphere, and sea ice.) key characteristic of the central information A separate, but related issue is long-term hub and the individual components that lie data storage limitations. In the climate behind it is the timeliness of the available modeling community, the push toward resources. This is particularly critical for high-resolution and complex models applications related to seasonal sea ice coupled with diverse stakeholder needs has predictions, which require real-time data resulted in a rapid and increasing demand access for model output, in situ for data storage, analysis and distribution observations, satellite and aircraft survey (NRC, 2012a). Thus many climate modeling

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44 Seasonal to Decadal Predictions of Arctic Sea Ice: Challenges and Strategies groups are increasingly limited by the cost of value that could be derived from it. The long-term data storage. major challenge lies in its initiation. There is Consistent data storage protocols need no shortage of candidate organizations well to be adopted that preserve ancillary data suited to facilitate the design and and original sample rates along with implementation of a central information interoperability standards for data hub, though effective data management interchange. Indexes need to be based on requires resources. Because permanence is a agreed metadata vocabularies (e.g., Marine critical characteristic, there would be an Metadata Interoperability Project5) for expectation of long-term and stable support search and reference efficiency. Data density for this important activity. Given the broad and timely accessibility of information to and critical nature of these needs, which and from nonfederal sources are some reaches beyond the issue of sea ice obstacles to be resolved, although there has predictability, it may be most appropriately been slight progress (Box 3.5). addressed by a high-level, cross-cutting It is hard to dispute the widespread entity. desire for a central information hub and the 5 https://marinemetadata.org/