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Ignatius rigor thesis

Polar Science Center Phone: Applied Physics Laboratory Fax: University of Washington Email: ignatius apl. Work Experience. University of Washington , Seattle Washington. University of Washington , Seattle , Washington. Advisor: John M. Professional Activities. Coordinator of the International Arctic Buoy Programme. Reviewer for various science journals. Rigor, I. Richter-Menge, J. Rigor , State of the Arctic. Diane M.

USA , Perovich, B. Elder, K. Claffey, I. Rigor , M. Serreze, M. Rigor , The cryosphere and climate change: perspectives on the Arctic's shrinking sea ice cover. Eicken , H. Gradinger , A. Graves, A. Mahoney, I. Melling, Sediment transport by sea ice in the Chukchi and Beaufort Seas: Increasing importance due to changing ice conditions? Richter-Menge, C. Dissertation, Univ. USA , pp. Pfirman, S. Haxby , R. Colony, and I. Steel, M. Durack , Nathan P. Durack Human-induced changes to the global ocean water masses and their time of emergence, Nature Climate Change , accepted.

Gleckler, Robert Ferraro, Karl E. Slingo, Eric Rignot, John T. Reager, Maria Z. Hakuba, Paul J. Taylor, Paul J. Cook, Celine J. Bonfils, Paul J. Durack , Jason E. Smerdon and A. Park Williams Twentieth-century hydroclimate changes consistent with human influence, Nature , , pp Durack Mechanisms causing east Australian spring rainfall differences between three strong El Nino events, Climate Dynamics , 53 , pp Taylor, Martin Juckes, Bryan N. Lawrence, Paul J. Wentz and Cheng-Zhi Zou Human influence on the seasonal cycle of tropospheric temperature, Science , , 11 pp.

Justin Small, Who M. Kim, Stephen G. Bamber, Mats Bentsen, Claus W. Boning, Alexandra Bozec, Eric P. Durack , Stephen M. Santer, Thomas J. Phillips, Karl E. Taylor, Matthias Cuntz, Mark D. Zelinka, Kate Marvel, Benjamin I. Cook, Ivana Cvijanovic and Paul J.

Gleckler, Karl E. Durack , Karl E. Accessible online Gleckler, Peter J. Durack , Ronald J. Stouffer, Gregory C. Johnson and Chris E. Forest Industrial-era global ocean heat uptake doubles in recent decades, Nature Climate Change , 6 4 , pp Adcroft, V. Durack , Peter J. Gleckler, Jonathan M. Krasting, William Large, Simon J.

Marsland, Simona Masina, Trevor J. McDougall, James C. Orr, Anna Pirani, Ronald J. Stouffer, Karl E. Wijffels, Paul J. Durack , John A. Church, Nathaniel, L. Bindoff and Simon J. Marsland Simulating the role of surface forcing on observed multidecadal upper ocean salinity changes, Journal of Climate , 25 19 , pp Allan, William J. Ingram, Debbie Polson, Kevin E. Trenberth, Robin S. Chadwick, Phillip A. Durack and co-authors Challenges in quantifying changes in the global water cycle, Bulletin of the American Meteorological Society , 96 7 , pp Yin, S.

Bates, E. Behrens, M. Bentsen, D. Bi, A. Biastoch, C. Bozec, C. Cassou, E. Chassignet, G. Danabasoglu, S. Danilov, C. Domingues, H. Drange, P. Accessible online Pierce, David W. Gleckler, Tim P. Barnett, Benjamin D. Santer, Paul J. Durack The fingerprint of human-induced changes in the ocean's salinity and temperature fields, Geophysical Research Letters , 39 21 , GL Muir, Jaclyn N.

Brown, Steven J. Phipps, Paul J. Durack , Didier Monselesan, Susan E. Jones, Paul J. Durack , Warrick Dawes and Peter Hairsine Climate change impact on water and salt balances: an assessment of the impact of climate change on catchment salt and water balances in the Murray-Darling Basin, Australia, Climatic Change , , pp Jones, Ainsley Jolley, Benjamin L. Preston, Matthew Clarke, Paul J. Durack , Sardar M. Islam, Penny H. Whetton Climate change and the new world economy: Implications for the nature and timing of policy responses, Global Environmental Change , 18 3 , pp Accessible online.

I United Nations. Durack , Guiseppe M. Manzella, Kazuaki Tadokoro, Raymond W. World Ocean Assessment I Accessible online Durack, Paul J. Wijffels and Tim P. Boyer Long-term salinity changes and implications for the global water cycle Chapter Siedler, Gerold, Stephen M. Griffies, John Gould and John A. Church Eds. Also available through ResearchGate. Bindoff, N. Stott, K. AchutaRao, M. Allen, N. Gillett, D. Gutzler, K.

Hansingo, G. Hegerl, Y. Hu, S. Jain I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari, X. Qin and G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P. Midgley eds.

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Examples of term paper outlines High-latitude ocean and sea ice surface fluxes: challenges for climate research. Figure 10 shows the 7-day forecast skill evaluated against 3DVar ice analyses from weekly forecasts over Part III: hydrography and fluxes. For polar regions this requires the inclusion of a channel around 6. A Math. Impact of assimilating temperature and salinity measurements by animal-borne sensors on FOAM ocean model fields.
Sample essay with transition words Pearsall, G. Suppiah, I. Your thesis Ignatius Rigor Thesis Onli is delivered to you ready to submit for faculty review. Buoy clusters sampling various elements of the atmosphere, sea ice and upper ocean have proven particularly valuable. Bathols and Paul J.
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Write top essays A new estimation of mean sea level in the Arctic Ocean from satellite altimetry. Earth Syst. 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. Ice-borne observing systems that have proven their utility in the Arctic should now be adapted and tested in the Antarctic marginal ice zone. DOI:
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At a 6-h lead time, there are equivalent errors in the ocean and atmosphere, less than 0. Due to the initialization of the ocean mixed-layer with satellite SSTs, errors in the ocean grow slowly up to 5-day lead times. This is an indication that the model is unable to maintain the observed boundary layer stratification and rapidly evolves into a less-stable state.

Process studies are currently underway to identify if this bias is due to cloud processes or boundary layer parameterizations. Note, the vertical scale is model levels. The approach can be used to inform the design of observing networks or space missions. QND evaluates a set of observations network in terms of its constraint on a target quantity, i.

This evaluation is performed in a modeling system that is capable of simulating counterparts of the observations and of the target quantity from a set of unknowns in the system control variables. For a detailed description of the formalism we refer to Kaminski and Rayner Briefly, it proceeds in two steps: In the first step, the observational information is used to infer the uncertainty in the posterior control vector, C x , that is consistent with the observational uncertainty, C d , and the prior uncertainty of the control vector, C x 0 [ Equation 1 ].

The first step, an inversion step, is formalized by. The approach represents a network through observational locations and times and the observational uncertainty, but does not require real observations. Historically, QND was first applied by Hardt and Scherbaum to optimize the locations of a set of seismic sensors. Rayner et al. For the physical sea ice-ocean system in the Arctic domain, Kaminski et al.

Target quantities were day to five-month forecasts of snow and ice volumes over areas relevant for maritime traffic along the Alaskan coast and offshore resource exploration Chukchi Sea. The system exploits the fact that model sensitivities at observational times and locations as well as the target quantities can be pre-computed, so that the actual assessment of a data set requires only matrix multiplications and inversions [ Equation 1 and Equation 2 ]. This means the assessment can be performed so fast that the ArcMBA system could be used as an interactive tool to assist decision makers, for example, in a meeting.

Kaminski et al. These real products were complemented by two hypothetical monthly laser freeboard products 2 and 20 cm accuracy, respectively , as well as two hypothetical monthly snow depth products 2 and 15 cm accuracy, respectively. Target quantities are 4-week forecasts of snow and ice volumes over three target regions along the Northern Sea Route.

As an example, Figure 13 shows the posterior uncertainty in snow and ice volumes over the three target regions, when the CryoSat-2 SIT product Ricker et al. Comparison of the top and middle panels shows the added value of the hypothetical snow product, not only for the snow volume forecast but also for the sea ice volume SIV forecast. SIV is sensitive not only to initial SIT but also to initial ice concentration and snow depth, which are both constrained by the snow depth product.

SIV is also sensitive to some of the process parameters that are constrained by snow depth, notably the ice strength, see Kaminski et al. Comparison of the middle and top panels shows the added value of a higher accuracy in the snow depth product. Increasing the accuracy from 15 to 2 cm results in a reduction in uncertainty of the SIV forecast for the East Siberian Sea target region from to 63 to 24 km 3. Evaluation of CryoSat-2 sea ice thickness product alone top , in combination with 15 cm accuracy snow depth product middle , and in combination with 2 cm accuracy snow depth product bottom.

Prior gray, no observations and posterior orange, sea ice thickness and snow depth products uncertainties in sea ice volume SIV and snow volume SNV predictions for three regions along the Northeast Passage in km 3. In the setup used here, the model can simulate a range of sea ice-ocean variables in addition to those considered in the present study e.

Switching to a more comprehensive model configuration would enable the investigation of yet further variables. The extension of ArcMBA by a terrestrial biosphere component is planned, which will allow joint assessment of ocean and land observations. Various international efforts contribute to coordinate and support the vast and complex polar observing networks. These networks are maintained by a collection of national and international efforts and scientific projects.

While these efforts aim to address a broad range of societal needs, in this section we describe several particular initiatives that work to address specific gaps in the observing system important for environmental prediction. This requires collaborative efforts among many institutions to extend, improve and unify existing systems, which in many cases are designed and developed for specific scientific disciplines.

INTAROS focuses on the in situ part of the observing systems, which represent the largest component of the integrated observing system. Satellite Earth Observation programs provide the most developed and operational components of the system, which are run by space agencies and satellite monitoring services such as Copernicus The satellite systems provide data for near real-time monitoring as well as for long-term climate observations.

Validation of the satellite-derived variables is an important part of the operational services. In situ observations play an important role for this validation, but there is very limited access to such data from the Arctic Ocean.

Furthermore, most of the ocean observations are only available in delayed mode, because they are provided by underwater moorings and seafloor observatories. Some platforms that operate at the surface can transmit data in near real-time and can therefore contribute to operational monitoring of sea ice and ocean variables. INTAROS is multidisciplinary, implying that the observing systems encompass atmospheric, marine and terrestrial systems in the different regions of the Arctic.

Marine observing systems are also divided into physical and biogeochemical components of the ocean surface including sea ice , the water column and the seafloor. INTAROS contributes to all these components in collaboration with other observing programs and projects, by deploying new sensors and platforms to enhance the observing capacity in different Arctic regions.

Data can be transmitted to land in near realtime and is available for operational monitoring. Ship of Opportunity is promising method to collect oceanographic data in Arctic waters, since there is a growing number of ships operating in the Arctic, especially tourist ships. Arctic observing systems will benefit greatly from collaboration with local communities Johnson et al.

CBMs can be supplementary to scientific observations when indigenous and local people collect scientifically relevant data and made them available via websites Community-based monitoring can also provide valuable data that cannot be obtained from normal research and monitoring programs. In the circumpolar Arctic region there are a number of observing programs addressing sea ice, oceanographic data and observations of marine mammals and fish which are very important for the communities.

Observations of the ocean are usually made for specific purposes. In order to save costs and improve marine knowledge, the European Union is now moving to a new paradigm where data are collected once and then used for many purposes. This paradigm is being implemented as part of the European Marine Observation and Data Network EMODnet consisting of a variety of organizations working together to assemble marine data and products, and to facilitate the dissemination of these resources to both public and private users.

EMODnet is currently in its third development phase with the target to be fully deployed by The project — aimed to identify problems and knowledge gaps and was organized in 10 challenges: wind farm sitting, marine protected areas, oil platform leak, climate change, coasts, fisheries management, fisheries impact, river input, bathymetry and alien species. Within the project, each dataset found in total was assessed on its spatial and temporal coverage, its accessibility and cost to access, the responsiveness and the temporal and vertical resolution.

Also, each assessment report using a dataset in total was assessed on its adequacy for the project. For the oil spill challenge an oil accident was simulated. This demonstrated the necessity for rapid acquisition and inspection of ocean current and wind data in order to provide a reliable response capacity.

The German research icebreaker Polarstern will be frozen into the pack ice and over-winter in the Transpolar Drift to obtain measurements over a complete annual cycle. The MOSAiC sea ice platform provides an opportunity for greatly enhanced deployment of autonomous instrumentation and coordinated intensive field studies from research vessels, manned and unmanned aircraft, and distributed surface stations.

MOSAiC observations have been designed specifically to characterize the important coupled processes within the atmosphere-ice-ocean system that impact sea ice melting and freezing. These processes include heat, moisture, and momentum fluxes in the atmosphere and ocean, water vapor, clouds and aerosols, biogeochemical cycles in the ocean and ice, and many others. The MOSAiC central observatory will be a manned, icebreaker ship-based ice camp with comprehensive instrumentation to measure coupled processes within the atmosphere, ice, and ocean.

This intensive observatory will be embedded within a constellation of distributed measurements made by buoys, ice-tethered profilers, remote meteorology stations, underwater drifters, unmanned aerial systems, aircraft, additional ships, and satellites. These distributed observations will provide critical information on the spatial context and variability of key parameters, and allow for limited measurements in environments with sea ice of differing age, thickness, and concentration.

Results will provide a quantitative baseline for use in decisions regarding how to configure a sustained Arctic observing system appropriate for the needs of environmental prediction. The need for improved environmental predictions i. Additional observations gathered through field programs will also be used to improve our understanding of key polar processes relevant for improving prediction skill.

Results will provide a quantitative baseline for use in decisions regarding how to configure a sustained Arctic observing system appropriate for the needs of Environmental Prediction. The scarcity of observations, the unique balance of physical processes, the key importance of sea ice, and the rapidly evolving climate of the Arctic, and the uncertainties in Antarctic sea ice trends and carbon uptake lead to a number of scientific challenges for observations in the context of a polar prediction system.

Addressing these challenges motivates the following recommendations:. The presence of a seasonal ice cover limits the availability of real-time in situ data in polar regions to assist operational requirements.

While several technologies have been developed e. It is therefore recommended that a network of ice-borne measurement systems be deployed and supported operationally in ice-covered areas. These platforms represent well-proven technologies for year-round data collection and near-real time data transmission via satellites. Antarctic measurements are also needed, in particular, to evaluate changes that could be harbingers of continental ice melt.

Recent studies also highlight the importance of measuring and understanding the intra-hemispheric ocean interactions on numerical weather prediction to climate time-scales Foppert et al. Ice-borne observing systems that have proven their utility in the Arctic should now be adapted and tested in the Antarctic marginal ice zone.

Conditions are changing rapidly with the loss of summer sea ice extent in the Arctic and changing ice-cover patterns in Antarctic marginal seas. Phenomena long considered negligible in the Arctic and Antarctic may be becoming important e. The observing system needs to be reevaluated with this in mind.

The retreat of the Arctic ice cover increases the area where open-water or seasonally ice free observing systems e. Ships of opportunity present a promising method to collect oceanographic data in polar waters, since there is a growing number of ships operating in these regions.

Moreover, community-based monitoring can also provide valuable data that cannot be obtained from normal research and monitoring programs. These atypical observing methods should be encouraged and enhanced to provide a low-cost expansion to the in situ observing system. In situ observations are routinely used for the calibration of remote sensing products over much of the globe, with fewer such calibrations made in polar regions due to lack of in situ observations. Studies have shown the large benefits that such calibrations can have e.

The availability of near-real time in situ measurements could be used to improve the quality of satellite products and thus on downstream environment predictions that assimilate them. Additional efforts involving multi-platform calibration are needed to improve the quality of remote sensing products.

The increasing maturity of satellite sea-ice thickness winter-time products merging several sensors e. Polar surface properties are often dominated by various forms of ice that vary rapidly on small spatial scales. Neither is currently able to address the need for high spatial and temporal resolution observations of sea ice deformation over large regions.

Observations providing information regarding ice deformation and redistribution during ridging are also lacking. There is a need for high-resolution km-scale remotely sensed snow and ice property data for both the Arctic and Southern Ocean with sufficient temporal resolution to address these relevant features. The value of polar observations for multi-range environmental prediction emerged during the last decade from a variety of impact studies. The importance of SIT initialization for seasonal forecasting, the significance of sub-surface initialization in ice-covered areas, are non-exhaustive examples that call for coordinated efforts including QND, OSEs and Observing System Simulation Studies to enhance the Arctic and Southern Ocean observing networks.

To date, few data withholding experiments OSEs or observation design experiments OSSEs have been undertaken for polar regions using real-time prediction systems. Performing such experiments using additional observations available during YOPP or other periods of additional observational coverage such as IPY is suggested to provide valuable information to guide the design of a sustainable real-time observing system for polar regions suitable for environmental prediction. In particular, multisystem exercises shall be encouraged to gain robustness in the observation impact assessments.

Polar environmental prediction using coupled atmosphere-ice-ocean models is strongly sensitive to errors in fluxes across the surface interface and thus requires collocated information about the state of the atmosphere, sea ice and ocean, to be used for improving interface fluxes i. Direct flux covariance measurements, in particular, would be immensely valuable in constraining bulk parameterizations used to represent fluxes in models.

Open access to data, especially real-time data, is a critical capability for improved sea-ice and weather forecasting and other environmental prediction needs. The optimal observing system will no doubt include a suite of different instrument types, since no single platform can be optimized for the full range of observing needs i.

This means that real-time dissemination of in situ observations in polar regions to global data assembly centers must be prioritized in order to make the observational efforts suitable for environmental prediction applications. International collaboration will continue to be key for facilitating deployment of polar ocean instrument systems, including the fielding of drifting and anchored buoys, floats and gliders and free, rapid dissemination of the resulting data.

The relative remoteness and harsh environmental conditions over polar regions will always hinder efforts to provide adequate observations for polar prediction. Over recent years, we have seen improvements in observing technology and capabilities that create new possibilities for how to construct and maintain the polar ocean observing system. The technologies make an adequate polar ocean observing system feasible, but the question remains, is it worth the cost?

YOPP aims to help address this question by coordinating international observing activities and fostering efforts to assess the impact of additional observations on environmental prediction skill, including impacts on downstream users and products. Following the YOPP core period — , there will be a consolidation phase to assess these impacts and develop recommendations toward sustained polar observation. This effort will culminate in a YOPP Final Summit planned for summer , providing an ideal opportunity for funding and implementation agencies to benefit from this community effort.

We would like to acknowledge the valuable suggestions made by both reviewers that has improved the quality of this manuscript. Leymarie and C. The publication fee is provided by ECCC. Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

National Center for Biotechnology Information , U. Front Mar Sci. Author manuscript; available in PMC Sep Gregory C. Julia Crout 8 Perspecta, Inc. Sarah T. Belinda Kater 16 Arcadis Nederland B. Joseph Metzger.

Steffen M. Michael Phelps 8 Perspecta, Inc. Pamela Posey 8 Perspecta, Inc. Ole Martin Smedstad 8 Perspecta, Inc. Author information Copyright and License information Disclaimer. Navy Coupled Ice-Ocean Models. Smith Gregory. Copyright notice. See other articles in PMC that cite the published article.

Abstract There is a growing need for operational oceanographic predictions in both the Arctic and Antarctic polar regions. Keywords: polar observations, operational oceanography, ocean data assimilation, ocean modeling, forecasting, sea ice, air-sea-ice fluxes, YOPP. Overview of Current Observing System Profiling Floats Argo floats are the backbone of the global ocean observing system developed over the last two decades with more than units currently in operation e.

Ice-Borne Observing Systems Many types of ice-borne ocean measurement systems have been developed and fielded in the last 2—3 decades. Open in a separate window. Sea Ice Concentration Satellite-based sea ice observations are required for assimilation to provide accurate polar environmental forecasts. Sea Surface Height Satellite observations of sea level are required to constrain surface geostrophic currents in ocean forecasting systems, and several teams try to tackle the issue Prandi et al.

Sea-Ice Drift Sea ice drift data are now obtained all year round both in the Arctic and Antarctic by pattern cross-correlation of scatterometers and passive microwave images. Impacts of Arctic and Antarctic Observations in U. Navy Coupled Ice-Ocean Models The ability to forecast sea ice conditions is of crucial importance for maritime operational planning Greenert, Sensitivity of Sea Ice Forecasting Skill to Ocean Mixing Around Antarctica The rapid evolution of the sea ice cover can have important impacts on coupled environmental predictions through a variety of processes Smith et al.

Year of Polar Prediction The need for improved environmental predictions i. Addressing these challenges motivates the following recommendations: The presence of a seasonal ice cover limits the availability of real-time in situ data in polar regions to assist operational requirements. OUTLOOK The relative remoteness and harsh environmental conditions over polar regions will always hinder efforts to provide adequate observations for polar prediction. Footnotes Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Oceanogr 82 , 81— The Argo Program: observing the global ocean with profiling floats. Oceanography 22 , 34— Education is impossible without Ignatius Rigor Thesis Onli writing college homework papers. Ignatius Rigor Thesis Onli I wanted some cheap assignment writing help — but I didn't expect you to be that good! Rigor studies sea ice, and how it interacts with the atmosphere and ignatius rigor thesis ocean. I had looked into many tutoring services, but they weren't affordable and did not understand my custom-written needs.

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The intrinsic turbulence of the atmosphere limits predictive skill to timescales of days to weeks Mariotti et al. This provides a mechanism by which the predictability of atmosphere may be extended allowing skillful seasonal predictions. A particular limitation in this regard is with respect to the significant uncertainty in the atmosphere-ocean boundary layer, made even more egregious when considering sea ice. Boundary layer dynamics involve vertical scales unresolved by coupled forecasting systems, which must be parameterized e.

The exchanges between components are parameterized by so-called bulk formulae, which estimate air-sea-ice exchanges based on near-surface large-scale properties. Currently, the uncertainties in bulk formulae are a primary bottleneck to seasonal prediction Penny and Hamill, In other words, even if we had a perfect ocean model with perfect initial conditions, the information retained in the monthly ocean prediction would be degraded when propagated to the atmosphere due to uncertainty in estimating the true exchanges Vecchi et al.

Therefore, a priority in the coming decade must be to gather in situ estimates of air-sea-ice exchanges in the context of large-scale properties informing how to minimize errors in the bulk formulae parameterizations. We recommend further research to determine how best to represent these exchanges in a coupled forecasting system, with a focus on determining what aspects of boundary layer physics need to be resolved and what can be skillfully parameterized.

For parameterized physics, we require that process studies be carried out to determine parameterizations and parameterization coefficients, including identifying the observations that should be sustained to validate estimated fluxes. Addressing these areas are primary goals to enable weekly-to-seasonal skillful predictions in the polar regions.

Understanding how assimilating more observations will impact modeled analyses and short-term forecasts is of fundamental importance as new coupled models are being developed. Forecast experiments using a high-resolution fully coupled regional model show that these water masses impact sea ice evolution on synoptic time scales through upper-ocean mixing and heat flux at the ice—ocean interface.

It is expected these effects will increase as sea ice continues to decline and surface heat flux processes increase. In addition, assimilating real-time ocean observations in the initial forecast conditions allows for the identification of biases in the coupled system, which are difficult to isolate when the ocean is allowed to drift away from the observed state.

Some studies e. Additional focused research in this area would allow us to explore coupled model data assimilation issues, better understand physical processes, and assess model performance in comparison to non-coupled atmospheric model frameworks. In the next sections, we review a few results from operational centers. The ability to forecast sea ice conditions is of crucial importance for maritime operational planning Greenert, The current U.

The precursor to GOFS 3. Assimilation of observational data is performed to reduce errors in model forecasts that can result from many factors including non-linear processes that are not deterministic responses to atmospheric forcing, poorly parameterized physical processes, limitations in numerical algorithms, and limitations in model resolution. Polar observational data assimilation is an essential part of GOFS forecasts.

The impact of data assimilation in the U. Navy models is significant. One measure that the U. Navy uses to determine the accuracy of the modeled sea ice edge defined in Hebert et al. GOFS was run for 1 year without data assimilation and compared to the current GOFS system with ocean and sea ice data assimilation, including sea ice concentration.

Ice edge error for individual regions km. Each region contains three numbers. First number is ice edge error without assimilation. Second bold number is error with assimilation. Third number is percent improvement with assimilation. As model resolution increases, so does the need for high-resolution observations. This result points to the need for higher resolution sea ice concentration observations to use in model applications. In an earlier study using ACNFS to examine the impacts of sea ice concentration observations in ship routing and planning in boreal winter January—March , assimilating satellite ice concentration observations reduced the projected track an ice breaker would take to a ship near the sea ice edge by an average of km versus not assimilating sea ice concentration observations.

This improved the time for planning operations by 12 h and reduced the distance a ship needs to prepare to encounter ice by km. SIT observations are also important. Currently, pan-Arctic SIT observations on a daily basis are not available. Only limited satellite tracks per day are available that are aggregated on a monthly basis.

In a recent study by Allard et al. It showed a reduction in SIT bias by 0. The impact of this one-time re-initialization was significant and work is underway to assimilate daily satellite track SIT observations on a daily basis. The rapid evolution of the sea ice cover can have important impacts on coupled environmental predictions through a variety of processes Smith et al. These include the formation of leads and coastal polynyas, as well as changes in the ice cover along the marginal ice zone MIZ.

In these regions, the rapid formation, melt and advection of the sea ice cover can modify atmosphere-ocean fluxes on relatively short timescales. Interestingly, small-scale ocean variability has a role to play here as the timing and intensity of changes will be sensitive to the surface ocean mixing layer depth, water mass properties and mesoscale ocean circulation e. As an illustration of the sensitivity of sea ice evolution to ocean mixing, an evaluation of the skill of two sets of sea ice forecasting experiments is shown in Figure The second set of experiments is identical to the first with the parameterization for surface wave breaking deactivated.

Figure 10 shows the 7-day forecast skill evaluated against 3DVar ice analyses from weekly forecasts over The verification method used here Lemieux et al. Sensitivity of sea ice forecasting skill to ocean mixing around Antarctica. Warmer colors indicate larger root-mean squared error maximum of 0. Panels a and b show the forecast skill for experiments without and with additional ocean mixing respectively.

Adapted from Smith et al. From Figure 10 it can be seen that a small modification to the ocean vertical mixing can have a first order impact on the ice forecast errors. Interestingly, while the surface wave breaking parameterization degrades ice forecast skill, it does lead to an improvement in water mass properties over ice-free waters as evaluated against Argo profiles; not shown. This is perhaps not surprising given that the mixing regime in polar regions is quite different from that at lower latitudes.

This highlights the need for an expanded under-ice ocean monitoring program to be able to adequately model vertical mixing and constrain water mass properties and mixed layer depths. There have been special observing periods of the Arctic and Antarctic in the past, in particular the successive International Polar Years IPYs , the latest of which took place in — with a gradual ramping up of ocean observing systems in the preceding years.

Looking back at the impact of a recent IPY in a period with similar low-ice-coverage conditions in the Arctic, expanded sea ice in the Antarctic, and similar satellite coverage as today can provide another measure of the expected impact of the YOPP Special Observing Periods.

It assimilates both satellite and in situ observations using an Ensemble Kalman Filter Sakov et al. It assimilates the same types of ocean and sea ice observations: along-track sea-level anomalies from altimeters, sea surface temperatures, sea ice concentrations and drift and in situ temperature and salinity profiles.

Ocean models have well-known limitations in simulating the advection of Atlantic Waters into the Arctic Ilicak et al. Even though the quantitative impact on the TOPAZ4 system is dependent on the practical setup of the model and its assimilation scheme, the qualitative behavior may apply to other forecasting systems based on similar types of models and data assimilation schemes and indicates that a density of ITP profiles at least equal to that of the IPY should be sustained continuously to constrain efficiently the Atlantic Water properties in the Arctic.

Time series of TOPAZ4 data assimilation diagnostics across the year reanalysis for all temperature profiles in the depths — m in the whole Arctic. The blue line is the bias, the green line is the related standard deviation Root Mean Square , the red line is the ensemble spread, and the gray line the number of temperature observations, increasing during the IPY. The other vertical lines and the bottom bars indicate changes of the other observation data sources and modifications of the data assimilation system.

NOAA ESRL has provided experimental, daily, day forecasts of Arctic weather and sea ice evolution to stakeholders during freeze-up seasons since and daily forecasts year-round starting on February 14, CAFS produces high-resolution 10 km regional coupled-model Arctic forecasts using global 0. The current configuration of the model includes the POP2 dynamical ocean model adapted from Maslowski et al. Real-time CAFS products are made available to the community Figures and animations from the day forecasts are provided for sea ice, atmosphere, and ocean variability, as well as, an archive of model output for users to download.

National Ice Center, and by the U. In order to identify whether using these satellite products in the initial conditions increases the skill of the day forecasts, a series of day hindcasts were performed for the time period of the ONR SeaState DRI, Oct.

The hindcasts are setup exactly like the real-time forecasts except the lateral boundary conditions are the GFS analyses instead of the GFS forecasts, in order to identify potential model biases. Intensive measurements were taken of the ocean, surface, and atmospheric state during the SeaState campaign. This provides for a comprehensive observational database for model validation. At a 6-h lead time, there are equivalent errors in the ocean and atmosphere, less than 0.

Due to the initialization of the ocean mixed-layer with satellite SSTs, errors in the ocean grow slowly up to 5-day lead times. This is an indication that the model is unable to maintain the observed boundary layer stratification and rapidly evolves into a less-stable state. Process studies are currently underway to identify if this bias is due to cloud processes or boundary layer parameterizations. Note, the vertical scale is model levels.

The approach can be used to inform the design of observing networks or space missions. QND evaluates a set of observations network in terms of its constraint on a target quantity, i. This evaluation is performed in a modeling system that is capable of simulating counterparts of the observations and of the target quantity from a set of unknowns in the system control variables. For a detailed description of the formalism we refer to Kaminski and Rayner Briefly, it proceeds in two steps: In the first step, the observational information is used to infer the uncertainty in the posterior control vector, C x , that is consistent with the observational uncertainty, C d , and the prior uncertainty of the control vector, C x 0 [ Equation 1 ].

The first step, an inversion step, is formalized by. The approach represents a network through observational locations and times and the observational uncertainty, but does not require real observations. Historically, QND was first applied by Hardt and Scherbaum to optimize the locations of a set of seismic sensors. Rayner et al. For the physical sea ice-ocean system in the Arctic domain, Kaminski et al.

Target quantities were day to five-month forecasts of snow and ice volumes over areas relevant for maritime traffic along the Alaskan coast and offshore resource exploration Chukchi Sea. The system exploits the fact that model sensitivities at observational times and locations as well as the target quantities can be pre-computed, so that the actual assessment of a data set requires only matrix multiplications and inversions [ Equation 1 and Equation 2 ]. This means the assessment can be performed so fast that the ArcMBA system could be used as an interactive tool to assist decision makers, for example, in a meeting.

Kaminski et al. These real products were complemented by two hypothetical monthly laser freeboard products 2 and 20 cm accuracy, respectively , as well as two hypothetical monthly snow depth products 2 and 15 cm accuracy, respectively. Target quantities are 4-week forecasts of snow and ice volumes over three target regions along the Northern Sea Route. As an example, Figure 13 shows the posterior uncertainty in snow and ice volumes over the three target regions, when the CryoSat-2 SIT product Ricker et al.

Comparison of the top and middle panels shows the added value of the hypothetical snow product, not only for the snow volume forecast but also for the sea ice volume SIV forecast. SIV is sensitive not only to initial SIT but also to initial ice concentration and snow depth, which are both constrained by the snow depth product. SIV is also sensitive to some of the process parameters that are constrained by snow depth, notably the ice strength, see Kaminski et al.

Comparison of the middle and top panels shows the added value of a higher accuracy in the snow depth product. Increasing the accuracy from 15 to 2 cm results in a reduction in uncertainty of the SIV forecast for the East Siberian Sea target region from to 63 to 24 km 3. Evaluation of CryoSat-2 sea ice thickness product alone top , in combination with 15 cm accuracy snow depth product middle , and in combination with 2 cm accuracy snow depth product bottom.

Prior gray, no observations and posterior orange, sea ice thickness and snow depth products uncertainties in sea ice volume SIV and snow volume SNV predictions for three regions along the Northeast Passage in km 3. In the setup used here, the model can simulate a range of sea ice-ocean variables in addition to those considered in the present study e. Switching to a more comprehensive model configuration would enable the investigation of yet further variables.

The extension of ArcMBA by a terrestrial biosphere component is planned, which will allow joint assessment of ocean and land observations. Various international efforts contribute to coordinate and support the vast and complex polar observing networks. These networks are maintained by a collection of national and international efforts and scientific projects.

While these efforts aim to address a broad range of societal needs, in this section we describe several particular initiatives that work to address specific gaps in the observing system important for environmental prediction. This requires collaborative efforts among many institutions to extend, improve and unify existing systems, which in many cases are designed and developed for specific scientific disciplines.

INTAROS focuses on the in situ part of the observing systems, which represent the largest component of the integrated observing system. Satellite Earth Observation programs provide the most developed and operational components of the system, which are run by space agencies and satellite monitoring services such as Copernicus The satellite systems provide data for near real-time monitoring as well as for long-term climate observations. Validation of the satellite-derived variables is an important part of the operational services.

In situ observations play an important role for this validation, but there is very limited access to such data from the Arctic Ocean. Furthermore, most of the ocean observations are only available in delayed mode, because they are provided by underwater moorings and seafloor observatories. Some platforms that operate at the surface can transmit data in near real-time and can therefore contribute to operational monitoring of sea ice and ocean variables.

INTAROS is multidisciplinary, implying that the observing systems encompass atmospheric, marine and terrestrial systems in the different regions of the Arctic. Marine observing systems are also divided into physical and biogeochemical components of the ocean surface including sea ice , the water column and the seafloor. INTAROS contributes to all these components in collaboration with other observing programs and projects, by deploying new sensors and platforms to enhance the observing capacity in different Arctic regions.

Data can be transmitted to land in near realtime and is available for operational monitoring. Ship of Opportunity is promising method to collect oceanographic data in Arctic waters, since there is a growing number of ships operating in the Arctic, especially tourist ships. Arctic observing systems will benefit greatly from collaboration with local communities Johnson et al.

CBMs can be supplementary to scientific observations when indigenous and local people collect scientifically relevant data and made them available via websites Community-based monitoring can also provide valuable data that cannot be obtained from normal research and monitoring programs.

In the circumpolar Arctic region there are a number of observing programs addressing sea ice, oceanographic data and observations of marine mammals and fish which are very important for the communities. Observations of the ocean are usually made for specific purposes. In order to save costs and improve marine knowledge, the European Union is now moving to a new paradigm where data are collected once and then used for many purposes. This paradigm is being implemented as part of the European Marine Observation and Data Network EMODnet consisting of a variety of organizations working together to assemble marine data and products, and to facilitate the dissemination of these resources to both public and private users.

EMODnet is currently in its third development phase with the target to be fully deployed by The project — aimed to identify problems and knowledge gaps and was organized in 10 challenges: wind farm sitting, marine protected areas, oil platform leak, climate change, coasts, fisheries management, fisheries impact, river input, bathymetry and alien species.

Within the project, each dataset found in total was assessed on its spatial and temporal coverage, its accessibility and cost to access, the responsiveness and the temporal and vertical resolution. Also, each assessment report using a dataset in total was assessed on its adequacy for the project. For the oil spill challenge an oil accident was simulated.

This demonstrated the necessity for rapid acquisition and inspection of ocean current and wind data in order to provide a reliable response capacity. The German research icebreaker Polarstern will be frozen into the pack ice and over-winter in the Transpolar Drift to obtain measurements over a complete annual cycle. The MOSAiC sea ice platform provides an opportunity for greatly enhanced deployment of autonomous instrumentation and coordinated intensive field studies from research vessels, manned and unmanned aircraft, and distributed surface stations.

MOSAiC observations have been designed specifically to characterize the important coupled processes within the atmosphere-ice-ocean system that impact sea ice melting and freezing. These processes include heat, moisture, and momentum fluxes in the atmosphere and ocean, water vapor, clouds and aerosols, biogeochemical cycles in the ocean and ice, and many others. The MOSAiC central observatory will be a manned, icebreaker ship-based ice camp with comprehensive instrumentation to measure coupled processes within the atmosphere, ice, and ocean.

This intensive observatory will be embedded within a constellation of distributed measurements made by buoys, ice-tethered profilers, remote meteorology stations, underwater drifters, unmanned aerial systems, aircraft, additional ships, and satellites. These distributed observations will provide critical information on the spatial context and variability of key parameters, and allow for limited measurements in environments with sea ice of differing age, thickness, and concentration.

Results will provide a quantitative baseline for use in decisions regarding how to configure a sustained Arctic observing system appropriate for the needs of environmental prediction. The need for improved environmental predictions i. Additional observations gathered through field programs will also be used to improve our understanding of key polar processes relevant for improving prediction skill. Results will provide a quantitative baseline for use in decisions regarding how to configure a sustained Arctic observing system appropriate for the needs of Environmental Prediction.

The scarcity of observations, the unique balance of physical processes, the key importance of sea ice, and the rapidly evolving climate of the Arctic, and the uncertainties in Antarctic sea ice trends and carbon uptake lead to a number of scientific challenges for observations in the context of a polar prediction system.

Addressing these challenges motivates the following recommendations:. The presence of a seasonal ice cover limits the availability of real-time in situ data in polar regions to assist operational requirements. While several technologies have been developed e. It is therefore recommended that a network of ice-borne measurement systems be deployed and supported operationally in ice-covered areas.

These platforms represent well-proven technologies for year-round data collection and near-real time data transmission via satellites. Antarctic measurements are also needed, in particular, to evaluate changes that could be harbingers of continental ice melt. Recent studies also highlight the importance of measuring and understanding the intra-hemispheric ocean interactions on numerical weather prediction to climate time-scales Foppert et al.

Ice-borne observing systems that have proven their utility in the Arctic should now be adapted and tested in the Antarctic marginal ice zone. Conditions are changing rapidly with the loss of summer sea ice extent in the Arctic and changing ice-cover patterns in Antarctic marginal seas. Phenomena long considered negligible in the Arctic and Antarctic may be becoming important e.

The observing system needs to be reevaluated with this in mind. The retreat of the Arctic ice cover increases the area where open-water or seasonally ice free observing systems e. Ships of opportunity present a promising method to collect oceanographic data in polar waters, since there is a growing number of ships operating in these regions.

Moreover, community-based monitoring can also provide valuable data that cannot be obtained from normal research and monitoring programs. These atypical observing methods should be encouraged and enhanced to provide a low-cost expansion to the in situ observing system. In situ observations are routinely used for the calibration of remote sensing products over much of the globe, with fewer such calibrations made in polar regions due to lack of in situ observations.

Studies have shown the large benefits that such calibrations can have e. The availability of near-real time in situ measurements could be used to improve the quality of satellite products and thus on downstream environment predictions that assimilate them. Additional efforts involving multi-platform calibration are needed to improve the quality of remote sensing products. The increasing maturity of satellite sea-ice thickness winter-time products merging several sensors e.

Polar surface properties are often dominated by various forms of ice that vary rapidly on small spatial scales. Neither is currently able to address the need for high spatial and temporal resolution observations of sea ice deformation over large regions. Observations providing information regarding ice deformation and redistribution during ridging are also lacking.

There is a need for high-resolution km-scale remotely sensed snow and ice property data for both the Arctic and Southern Ocean with sufficient temporal resolution to address these relevant features. The value of polar observations for multi-range environmental prediction emerged during the last decade from a variety of impact studies. The importance of SIT initialization for seasonal forecasting, the significance of sub-surface initialization in ice-covered areas, are non-exhaustive examples that call for coordinated efforts including QND, OSEs and Observing System Simulation Studies to enhance the Arctic and Southern Ocean observing networks.

To date, few data withholding experiments OSEs or observation design experiments OSSEs have been undertaken for polar regions using real-time prediction systems. Performing such experiments using additional observations available during YOPP or other periods of additional observational coverage such as IPY is suggested to provide valuable information to guide the design of a sustainable real-time observing system for polar regions suitable for environmental prediction.

In particular, multisystem exercises shall be encouraged to gain robustness in the observation impact assessments. Polar environmental prediction using coupled atmosphere-ice-ocean models is strongly sensitive to errors in fluxes across the surface interface and thus requires collocated information about the state of the atmosphere, sea ice and ocean, to be used for improving interface fluxes i.

Direct flux covariance measurements, in particular, would be immensely valuable in constraining bulk parameterizations used to represent fluxes in models. Open access to data, especially real-time data, is a critical capability for improved sea-ice and weather forecasting and other environmental prediction needs. The optimal observing system will no doubt include a suite of different instrument types, since no single platform can be optimized for the full range of observing needs i.

This means that real-time dissemination of in situ observations in polar regions to global data assembly centers must be prioritized in order to make the observational efforts suitable for environmental prediction applications. International collaboration will continue to be key for facilitating deployment of polar ocean instrument systems, including the fielding of drifting and anchored buoys, floats and gliders and free, rapid dissemination of the resulting data.

The relative remoteness and harsh environmental conditions over polar regions will always hinder efforts to provide adequate observations for polar prediction. Over recent years, we have seen improvements in observing technology and capabilities that create new possibilities for how to construct and maintain the polar ocean observing system.

The technologies make an adequate polar ocean observing system feasible, but the question remains, is it worth the cost? YOPP aims to help address this question by coordinating international observing activities and fostering efforts to assess the impact of additional observations on environmental prediction skill, including impacts on downstream users and products.

Following the YOPP core period — , there will be a consolidation phase to assess these impacts and develop recommendations toward sustained polar observation. This effort will culminate in a YOPP Final Summit planned for summer , providing an ideal opportunity for funding and implementation agencies to benefit from this community effort.

We would like to acknowledge the valuable suggestions made by both reviewers that has improved the quality of this manuscript. Leymarie and C. The publication fee is provided by ECCC. Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. National Center for Biotechnology Information , U.

Front Mar Sci. Author manuscript; available in PMC Sep Gregory C. Julia Crout 8 Perspecta, Inc. Sarah T. Belinda Kater 16 Arcadis Nederland B. Joseph Metzger. Steffen M. Michael Phelps 8 Perspecta, Inc. Pamela Posey 8 Perspecta, Inc. Ole Martin Smedstad 8 Perspecta, Inc. Author information Copyright and License information Disclaimer. Navy Coupled Ice-Ocean Models. Smith Gregory. Copyright notice. See other articles in PMC that cite the published article.

Abstract There is a growing need for operational oceanographic predictions in both the Arctic and Antarctic polar regions. Keywords: polar observations, operational oceanography, ocean data assimilation, ocean modeling, forecasting, sea ice, air-sea-ice fluxes, YOPP. Overview of Current Observing System Profiling Floats Argo floats are the backbone of the global ocean observing system developed over the last two decades with more than units currently in operation e. Ice-Borne Observing Systems Many types of ice-borne ocean measurement systems have been developed and fielded in the last 2—3 decades.

Open in a separate window. Sea Ice Concentration Satellite-based sea ice observations are required for assimilation to provide accurate polar environmental forecasts. Sea Surface Height Satellite observations of sea level are required to constrain surface geostrophic currents in ocean forecasting systems, and several teams try to tackle the issue Prandi et al.

Sea-Ice Drift Sea ice drift data are now obtained all year round both in the Arctic and Antarctic by pattern cross-correlation of scatterometers and passive microwave images. Impacts of Arctic and Antarctic Observations in U. Navy Coupled Ice-Ocean Models The ability to forecast sea ice conditions is of crucial importance for maritime operational planning Greenert, Sensitivity of Sea Ice Forecasting Skill to Ocean Mixing Around Antarctica The rapid evolution of the sea ice cover can have important impacts on coupled environmental predictions through a variety of processes Smith et al.

Year of Polar Prediction The need for improved environmental predictions i. Addressing these challenges motivates the following recommendations: The presence of a seasonal ice cover limits the availability of real-time in situ data in polar regions to assist operational requirements. OUTLOOK The relative remoteness and harsh environmental conditions over polar regions will always hinder efforts to provide adequate observations for polar prediction.

Footnotes Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The relation between sea ice thickness and freeboard in the Arctic.

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Overland, J. Perlwitz, R. Sebbari, X. Qin and G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P. Midgley eds. Accessible online Flato, G. Marotzke, B. Abiodun, P. Braconnot, S. C Chou, W. Collins, P. Cox, F. Driouech, S. Emori, V. Eyring, C. Forest, P. Gleckler, E. Guilyardi, C. Jakob, V. Kattsov, C. Reason, M. Rummukainen and contributing authors , Evaluation of Climate Models Chapter 9. Accessible online Kirtman, B.

Power, A. Adedoyin, G. Boer, R. Bojariu, I. Camilloni, F. Doblas-Reyes, A. Fiore, M. Kimoto, G. Meehl, M. Prather, A. Sarr, C. Schar, R. Sutton, G. Vecchi, H. Accessible online Rhein, M. Rintoul, S. Aoki, E. Campos, D. Chambers, R. Feely, S. Gulev, G.

Johnson, S. Josey, A. Kostianoy, C. Mauritzen, D. Roemmich, L. Talley, F. Wang and contributing authors , Observations: Ocean Chapter 3. Reports: Collins, D. Sen Gupta, S. Power, K. Braganza, J. Brown, J. Brown, W. Cai, J. Church, R. Colman, A. Dowdy, P.

Durack , D. Jones, M. Kuchinke, Y. Kuleshov, A. Lorrey, C. Lucas, S. McGee, K. McInnes, A. Moise, L. Muir, B. Murphy, S. Phipps, I. Smith, B. Tilbrook, N. Accessible online Jones, Roger N. Durack Melbourne Water climate change study technical report.

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The Cassiciacum Thesis, by Fr. Nicolás E. Despósito

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