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Most students living in disadvantaged areas do not trust that their school education will have much bearing on their future and become disaffected from school as early as in lower secondary school.We here report the results of teaching Geosciences through several practical activities carried out in suburban schools characterized by a high dropout rate. The lectures were given in the framework of the STEM project Next Land, which has the overarching goal of instilling interest towards scientific topics into young students and promoting the role of scientific education for sustainable development.We propose a sequence of four laboratory activities on the subject of natural calamities (e.g. earthquakes and tsunamis), which are based on the use of both manual and IT skills.The didactical experimentation has been conducted on 21 seventh-grade classes (age ~12) involving about 350 students from the area of Naples (Southern Italy). The final aim of the teaching is to test the potential of the Geosciences in generating attitudes and behaviours of solidarity and responsible global citizenship.All the proposed activities try to make students aware that they are part of a connected global system, therefore through these lectures we attempt to help these young students to put in perspective their local reality in a larger one.

The area of Naples, in southern Italy, is densely populated and seriously affected by a variety of environmental hazards related to meteorological conditions. The city is characterized by a complex topography therefore the meteorological risks sensibly vary throughout its territory. In the past decade, heat waves have been increasingly frequent: the production of heat maps would help the comprehension of the differential risks in town and would allow more accurate information on the related health risks to be available to the population. At present, maps of heat wave vulnerability show the spatial distribution of single factors, whereas vulnerability is governed by several competing or additive factors, so ambiguities about the identification of the most vulnerable areas currently remain. In the attempt to overcome this limit and identify areas more vulnerable to the effects of heat waves not only confined in Naples city centre, but also in suburban areas, we will apply unsupervised machine learning algorithms to integrate information from different factors and provide detailed heat wave vulnerability maps.

© Copyright 2022 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development. The results discussed in this article were supported by the project APPLICATE (727862), funded by the European Union's Horizon 2020 research and innovation programme. PO was additionally supported by the Spanish fellowship RYC-2017-22772. Peer Reviewed Article signat per 29 autors/es: Pablo Ortega (1), Edward W. Blockley (2), Morten Køltzow (3), François Massonnet (4), Irina Sandu (5), Gunilla Svensson (6), Juan C. Acosta Navarro (1), Gabriele Arduini (5), Lauriane Batté (7), Eric Bazile (7), Matthieu Chevallier (8), Rubén Cruz-García (1), Jonathan J. Day (5), Thierry Fichefet (4), Daniela Flocco (9), Mukesh Gupta (4), Kerstin Hartung (6,10), Ed Hawkins (9), Claudia Hinrichs (11), Linus Magnusson (5), Eduardo Moreno-Chamarro (1), Sergio Pérez-Montero (1), Leandro Ponsoni (4), Tido Semmler (11), Doug Smith (2), Jean Sterlin (4), Michael Tjernström (6), Ilona Välisuo (7,12), and Thomas Jung (11,13) // (1) Barcelona Supercomputing Center, Barcelona, Spain | (2) Met Office, Exeter, UK | (3) Norwegian Meteorological Institute, Oslo, Norway | (4) Université catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Louvain-la-Neuve, Belgium | (5) European Centre for Medium-Range Weather Forecasts, Reading, UK | (6) Department of Meteorology, Stockholm University, Stockholm, Sweden | (7) CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France | (8) Météo-France, Toulouse, France | (9) National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, UK. | (10) Now at: Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany | (11) Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany | (12) Now at: Meteorology Unit, Finnish Meteorological Institute, Helsinki, Finland | (13) Department of Physics and Electrical Engineering, University of Bremen, Bremen, Germany

Numerical systems used for weather and climate predictions have substantially improved over past decades. We argue that despite a continued need for further addressing remaining limitations of their key components, numerical prediction systems have reached a sufficient level of maturity to examine and critically assess the suitability of Earth's current observing systems – remote and in situ, for prediction purposes; and that they can provide evidence-based support for the deployment of future observational networks. We illustrate this point by presenting recent, co-ordinated international efforts focused on Arctic observing systems, led in the framework of the Year of Polar Prediction and the H2020 project APPLICATE. The Arctic, one of the world's most rapidly changing regions, is relatively poorly covered in terms of in situ data but richly covered in terms of satellite data. In this study, we demonstrate that existing state-of-the-art datasets and targeted sensitivity experiments produced with numerical prediction systems can inform us of the added value of existing or even hypothetical Arctic observations, in the context of predictions from hourly to interannual time-scales. Furthermore, we argue that these datasets and experiments can also inform us how the uptake of Arctic observations in numerical prediction systems can be enhanced to maximise predictive skill. Based on these efforts we suggest that (a) conventional in situ observations in the Arctic play a particularly important role in initializing numerical weather forecasts during the winter season, (b) observations from satellite microwave sounders play a particularly important role during the summer season, and their enhanced usage over snow and sea ice is expected to further improve their impact on predictive skill in the Arctic region and beyond, (c) the deployment of a small number of in situ sea-ice thickness monitoring devices at strategic sampling sites in the Arctic could be sufficient to monitor most of the large-scale sea-ice volume variability, and (d) sea-ice thickness observations can improve the simulation of both the sea ice and near-surface air temperatures on seasonal time-scales in the Arctic and beyond. This study was supported by the APPLICATE project (727862), which was funded by the European Union’s Horizon 2020 research and innovation programme. It was also supported by the Norwegian Research Council project no. 280573 “Advanced models and weather prediction in the Arctic: enhanced capacity from observations and polar process representations (ALERTNESS)”. Peer Reviewed Article signat per 23 autors/es: Irina Sandu (1), François Massonnet (2), Guillian van Achter (2), Juan C. Acosta Navarro (3), Gabriele Arduini (1), Peter Bauer (1), Ed Blockley (4), Niels Bormann (1), Matthieu Chevallier (5), Jonathan Day (1), Mohamed Dahoui (1), Thierry Fichefet (2), Daniela Flocco (6), Thomas Jung (7), Ed Hawkins (6), Stephane Laroche (8), Heather Lawrence (1,4), Jorn Kristianssen (9), Eduardo Moreno-Chamarro (3), Pablo Ortega (3), Emmanuel Poan (8), Leandro Ponsoni (2), Roger Randriamampianina (9) // (1) European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK (2) Université Catholique de Louvain, Brussels, Belgium (3) Barcelona Supercomputing Centre, Barcelona, Spain (4) Met Office, Exeter, UK (5) Meteo-France, Toulouse, France (6) University of Reading, Reading, UK (7) Alfred Wegener Institute, Bremerhaven, Germany (8) Environment and Climate Change, Gatineau, Quebec Canada (9) Met Norway, Oslo, Norway

Assimilation of sea ice concentration satellite products has successfully been used to initialize sea ice models and coupled NWP systems. Sea-ice thickness observations, being much less mature, are typically not assimilated. However, many studies suggest that initialization of winter sea-ice thickness could lead to improved prediction of Arctic summer sea ice. We have examined the potential for sea ice thickness observations to improve forecast skill on timescales from days to a year ahead in two state-of-the-art coupled GCMs.Here we examine the influence of Arctic sea-ice thickness observations on the potential predictability of the sea-ice and atmospheric circulation using idealised ‘data denial’ experiments. We perform paired sets of ensembles with the HadGEM3 and EC-Earth GCMs using different initial conditions retrieved from present-day control runs.One set of ensembles start with complete information about the sea-ice conditions and is treated as “truth”, and one set has degraded sea ice information. We investigate how the pairs of ensembles, all started in January, predict the subsequent evolution of the sea-ice state, sea level pressure and circulation within the Arctic with the aim of quantifying the value of sea-ice observations for improving predictions.We show that accurate initialization of sea ice thickness improves the model prediction skill during the first month of simulation and that several sea ice state and atmospheric variables present a re-emergence of skill in September. Prediction skill of several oceanic variables is also observed. The two models present a good agreement in terms of the regions where they show either a skill gain or loss.

In this work, we make use of an inter-model comparison and of a perfect model approach, in which model outputs are used as true reference states, to assess the impact that denying sea ice information has on the prediction of atmospheric processes, both over the Arctic and at mid-latitude regions. To do so, two long-term control runs (longer than 250 years) were generated with two state-of-the-art General Circulation Models (GCM), namely EC-Earth and HadGEM. From these two reference states, we have identified three different years in which the Arctic sea ice volume (SIV) was (i) maximum, (ii) minimum and (iii) a representative case for the mean state. By departing from each of these three dates (not necessarily the same for the two models), we generated a set of experiments in which the control runs are restarted both from original and climatological sea ice conditions. Here, climatological sea ice conditions are estimated as the time-average of sea ice parameters from the respective long-term control runs. The experiments are 1-year long and all of them start in January when ice is still thin, snow depth is small, air-ocean temperatures contrast the most and, therefore, the heat conductive flux in sea ice (at the surface) is nearly maximum. To robustly separate the response to degrading the initial sea ice state from background internal variability, each of the two counterfactual experiments (reference and climatological) consists of 50 ensembles members. Threstatedese ensembles are generated by adding small random perturbations to the sea surface temperature (EC-Earth) or to the air temperature (HadGEM) fields. Preliminary results reinforce the importance of having the right sea ice state for improving the (sub-)seasonal prediction of atmospheric parameters (e.g., 2m-temperature and geopotential) and circulation (e.g., Westerlies and Jet Stream) not only over the Arctic, but also at mid-latitude regions.

Arctic sea ice extent has steadily declined in the past 30 years. Aside from the global impact on climate change, regional information on the sea ice presence and on its impact on oceanic and atmospheric patterns has witnessed a growing interest. There is a growing need for seasonal-to-decadal timescale climate forecasts to help inform local communities and industry stakeholders.Here we examine the influence of sea-ice thickness observations on the predictability of the sea-ice and atmospheric circulation. We perform paired sets of ensembles with the HadGEM3 GCM starting from different initial conditions in a present-day control run. One set of ensembles start with complete information about the sea-ice conditions, and one set have degraded information. We investigate how the pairs of ensembles predict the subsequent evolution of the sea-ice, sea level pressure and circulation within the Arctic and beyond with the aim of quantifying the value of sea-ice observations for improving predictions.

CH was supported by the NOAA Climate and Global Change Postdoctoral Fellowship Program, sponsored in part through cooperative agreement number NA16NWS4620043, Years 2017–2021, with the National Oceanic and Atmospheric Administration, U.S. Department of Commerce. K.M.G. acknowledges support from the the Applied and Computational Analysis Program and the Arctic and Global Prediction Program at the US Office of Naval Research through grants N00014-13-10291, N00014-15-1-2455, N00014-18-1-2041, and N00014-18-1-2552, as well as support from the Division of Mathematical Sciences and the Division of Polar Programs at the U.S. National Science Foundation through Grants DMS-0940249, DMS-1413454, and DMS-1715680. LR was supported by Marsden contract VUW1408 and the Deep South National Science Challenge. Sea ice plays a critical role in the climate system through its albedo, which constrains light transmission into the upper ocean. In spring and summer, light transmission through sea ice is influenced by its iconic blue melt ponds, which significantly reduce surface albedo. We show that the geometry of surface melt ponds plays an important role in the partitioning of instantaneous solar radiation under sea ice by modeling the three-dimensional light field under ponded sea ice. We find that aggregate properties of the instantaneous sub-ice light field, such as the enhancement of available solar energy under bare ice regions, can be described using a new parameter closely related to pond fractal geometry. We then explore the influence of pond geometry on the ecological and thermodynamic sea-ice processes that depend on solar radiation. Publisher PDF

In this study, we demonstrate skillful spring forecasts of detrended September Arctic sea ice extent using passive microwave observations of sea ice concentration (SIC) and melt onset (MO). We compare these to forecasts produced using data from a sophisticated melt pond model, and find similar to higher skill values, where the forecast skill is calculated relative to linear trend persistence. The MO forecasts shows the highest skill in March–May, while the SIC forecasts produce the highest skill in June–August, especially when the forecasts are evaluated over recent years (since 2008). The high MO forecast skill in early spring appears to be driven primarily by the presence and timing of open water anomalies, while the high SIC forecast skill appears to be driven by both open water and surface melt processes. Spatial maps of detrended anomalies highlight the drivers of the different forecasts, and enable us to understand regions of predictive importance. Correctly capturing sea ice state anomalies, along with changes in open water coverage appear to be key processes in skillfully forecasting summer Arctic sea ice.

Recent thinning and ponding of Arctic sea ice may have led to frequent, extensive phytoplankton blooms under sea ice., In July 2011, the observation of a massive phytoplankton bloom underneath a sea ice–covered region of the Chukchi Sea shifted the scientific consensus that regions of the Arctic Ocean covered by sea ice were inhospitable to photosynthetic life. Although the impact of widespread phytoplankton blooms under sea ice on Arctic Ocean ecology and carbon fixation is potentially marked, the prevalence of these events in the modern Arctic and in the recent past is, to date, unknown. We investigate the timing, frequency, and evolution of these events over the past 30 years. Although sea ice strongly attenuates solar radiation, it has thinned significantly over the past 30 years. The thinner summertime Arctic sea ice is increasingly covered in melt ponds, which permit more light penetration than bare or snow-covered ice. Our model results indicate that the recent thinning of Arctic sea ice is the main cause of a marked increase in the prevalence of light conditions conducive to sub-ice blooms. We find that as little as 20 years ago, the conditions required for sub-ice blooms may have been uncommon, but their frequency has increased to the point that nearly 30% of the ice-covered Arctic Ocean in July permits sub-ice blooms. Recent climate change may have markedly altered the ecology of the Arctic Ocean.

Antarctic sea ice is constantly exported from the shore by strong near surface winds that open leads and large polynyas in the pack ice. The latter, known as wind-driven polynyas, are responsible for significant water mass modification due to the high salt flux into the ocean associated with enhanced ice growth. In this article, we focus on the wind-driven Terra Nova Bay (TNB) polynya, in the western Ross Sea. Brine rejected during sea ice formation processes that occur in the TNB polynya densifies the water column leading to the formation of the most characteristic water mass of the Ross Sea, the High Salinity Shelf Water (HSSW). This water mass, in turn, takes part in the formation of Antarctic Bottom Water (AABW), the densest water mass of the world ocean, which plays a major role in the global meridional overturning circulation, thus affecting the global climate system. A simple coupled sea ice–ocean model has been developed to simulate the seasonal cycle of sea ice formation and export within a polynya. The sea ice model accounts for both thermal and mechanical ice processes. The oceanic circulation is described by a one-and-a-half layer, reduced gravity model. The domain resolution is 1 km × 1 km, which is sufficient to represent the salient features of the coastline geometry, notably the Drygalski Ice Tongue. The model is forced by a combination of Era Interim reanalysis and in-situ data from automatic weather stations, and also by a climatological oceanic dataset developed from in situ hydrographic observations. The sensitivity of the polynya to the atmospheric forcing is well reproduced by the model when atmospheric in situ measurements are combined with reanalysis data. Merging the two datasets allows us to capture in detail the strength and the spatial distribution of the katabatic winds that often drive the opening of the polynya. The model resolves fairly accurately the sea ice drift and sea ice production rates in the TNB polynya, leading to realistic polynya extent estimates. The model-derived polynya extent has been validated by comparing the modelled sea ice concentration against MODIS high resolution satellite images, confirming that the model is able to reproduce reasonably well the TNB polynya evolution in terms of both shape and extent.

Melt ponds forming over the sea ice cover in the Arctic profoundly impact the surface albedo inducing a positive feedback leading to further melting. Here we examine the processes involved in melt pond refreezing and their impact on basal sea ice growth. When ponds freeze, the ice that forms on them insulates the pond trapping it between the sea ice and the ice lid. Trapped melt ponds delay basal sea ice growth in Autumn: ice thickens only after (1) the pond water has been fully frozen and (2) a temperature gradient is established that will conduct heat away from the ocean. Sea ice thickening in the areas where ponds are present is mainly due to the pond's water refreezing. Pan-Arctic simulations with a stand-alone sea ice model and studies with a high-resolution one-dimensional, three-layer refreezing model are used to study the impact on sea ice growth of trapped melt ponds. Basal sea ice growth may be inhibited by up to two months. We estimate an inhibited basal growth of up to 228 km3, which represents 25 % of the basal sea ice growth estimated by PIOMAS during the months of September and October. The brine not released due to the inhibited basal growth during this period could have implications for the ocean properties and circulation. The impact of trapped melt ponds has not been accounted for so far in any climate model.

The presence of melt ponds on the surface of Arctic sea ice significantly reduces its albedo,\ud inducing a positive feedback leading to sea ice thinning. While the role of melt ponds in enhancing the\ud summer melt of sea ice is well known, their impact on suppressing winter freezing of sea ice has, hitherto,\ud received less attention. Melt ponds freeze by forming an ice lid at the upper surface, which insulates\ud them from the atmosphere and traps pond water between the underlying sea ice and the ice lid. The\ud pond water is a store of latent heat, which is released during refreezing. Until a pond freezes completely,\ud there can be minimal ice growth at the base of the underlying sea ice. In this work, we present a model of\ud the refreezing of a melt pond that includes the heat and salt balances in the ice lid, trapped pond, and\ud underlying sea ice. The model uses a two-stream radiation model to account for radiative scattering at\ud phase boundaries. Simulations and related sensitivity studies suggest that trapped pond water may survive\ud for over a month. We focus on the role that pond salinity has on delaying the refreezing process and\ud retarding basal sea ice growth. We estimate that for a typical sea ice pond coverage in autumn, excluding\ud the impact of trapped ponds in models overestimates ice growth by up to 265 million km3, an overestimate\ud of 26%.

Introducing a parameterization of the interactions between wind-driven snow depth changes and melt\ud pond evolution allows us to improve large scale models. In this paper we have implemented an explicit\ud melt pond scheme and, for the first time, a wind dependant snow redistribution model and new snow\ud thermophysics into a coupled ocean–sea ice model.\ud The comparison of long-term mean statistics of melt pond fractions against observations demonstrates\ud realistic melt pond cover on average over Arctic sea ice, but a clear underestimation of the pond coverage\ud on the multi-year ice (MYI) of the western Arctic Ocean. The latter shortcoming originates from the concealing\ud effect of persistent snow on forming ponds, impeding their growth. Analyzing a second simulation\ud with intensified snow drift enables the identification of two distinct modes of sensitivity in the\ud melt pond formation process. First, the larger proportion of wind-transported snow that is lost in leads\ud directly curtails the late spring snow volume on sea ice and facilitates the early development of melt\ud ponds on MYI. In contrast, a combination of higher air temperatures and thinner snow prior to the onset\ud of melting sometimes make the snow cover switch to a regime where it melts entirely and rapidly. In the\ud latter situation, seemingly more frequent on first-year ice (FYI), a smaller snow volume directly relates to\ud a reduced melt pond cover.\ud Notwithstanding, changes in snow and water accumulation on seasonal sea ice is naturally limited,\ud which lessens the impacts of wind-blown snow redistribution on FYI, as compared to those on MYI. At\ud the basin scale, the overall increased melt pond cover results in decreased ice volume via the ice-albedo\ud feedback in summer, which is experienced almost exclusively by MYI.

We present a modelling study of processes controlling the summer melt of the Arctic sea ice cover. We perform a sensitivity study and focus our interest on the thermodynamics at the ice–atmosphere and ice–ocean interfaces. We use the Los Alamos community sea ice model CICE, and additionally implement and test three new parametrization schemes: (i) a prognostic mixed layer; (ii) a three equation boundary condition for the salt and heat flux at the ice–ocean interface; and (iii) a new lateral melt parametrization. Recent additions to the CICE model are also tested, including explicit melt ponds, a form drag parametrization and a halodynamic brine drainage scheme. The various sea ice parametrizations tested in this sensitivity study introduce a wide spread in the simulated sea ice characteristics. For each simulation, the total melt is decomposed into its surface, bottom and lateral melt components to assess the processes driving melt and how this varies regionally and temporally. Because this study quantifies the relative importance of several processes in driving the summer melt of sea ice, this work can serve as a guide for future research priorities.

Over Arctic sea ice, pressure ridges and floe and melt pond edges all introduce discrete obstructions to the flow of air or water past the ice and are a source of form drag. In current climate models form drag is only accounted for by tuning the air–ice and ice–ocean drag coefficients, that is, by effectively altering the roughness length in a surface drag parameterization. The existing approach of the skin drag parameter tuning is poorly constrained by observations and fails to describe correctly the physics associated with the air–ice and ocean–ice drag. Here, the authors combine recent theoretical developments to deduce the total neutral form drag coefficients from properties of the ice cover such as ice concentration, vertical extent and area of the ridges, freeboard and floe draft, and the size of floes and melt ponds. The drag coefficients are incorporated into the Los Alamos Sea Ice Model (CICE) and show the influence of the new drag parameterization on the motion and state of the ice cover, with the most noticeable being a depletion of sea ice over the west boundary of the Arctic Ocean and over the Beaufort Sea. The new parameterization allows the drag coefficients to be coupled to the sea ice state and therefore to evolve spatially and temporally. It is found that the range of values predicted for the drag coefficients agree with the range of values measured in several regions of the Arctic. Finally, the implications of the new form drag formulation for the spinup or spindown of the Arctic Ocean are discussed.

Prediction of seasonal Arctic sea-ice extent is of increased interest as the region opens up due to climate change. This work uses spring melt-pond area to forecast the Arctic sea-ice minimum in September. This proves accurate, as increasing melt-ponds reduce surface albedo, allowing more melt to occur, creating a positive feedback mechanism. The area of Arctic September sea ice has diminished from about 7 million km2 in the 1990s to less than 5 million km2 in five of the past seven years, with a record minimum of 3.6 million km2 in 2012 (ref. 1). The strength of this decrease is greater than expected by the scientific community, the reasons for this are not fully understood, and its simulation is an on-going challenge for existing climate models2,3. With growing Arctic marine activity there is an urgent demand for forecasting Arctic summer sea ice4. Previous attempts at seasonal forecasts of ice extent were of limited skill5,6,7,8,9. However, here we show that the Arctic sea-ice minimum can be accurately forecasted from melt-pond area in spring. We find a strong correlation between the spring pond fraction and September sea-ice extent. This is explained by a positive feedback mechanism: more ponds reduce the albedo; a lower albedo causes more melting; more melting increases pond fraction. Our results help explain the acceleration of Arctic sea-ice decrease during the past decade. The inclusion of our new melt-pond model10 promises to improve the skill of future forecast and climate models in Arctic regions and beyond.

[1] The extent and thickness of the Arctic sea ice cover has decreased dramatically in the past few decades with minima in sea ice extent in September 2007 and 2011 and climate models did not predict this decline. One of the processes poorly represented in sea ice models is the formation and evolution of melt ponds. Melt ponds form on Arctic sea ice during the melting season and their presence affects the heat and mass balances of the ice cover, mainly by decreasing the value of the surface albedo by up to 20%. We have developed a melt pond model suitable for forecasting the presence of melt ponds based on sea ice conditions. This model has been incorporated into the Los Alamos CICE sea ice model, the sea ice component of several IPCC climate models. Simulations for the period 1990 to 2007 are in good agreement with observed ice concentration. In comparison to simulations without ponds, the September ice volume is nearly 40% lower. Sensitivity studies within the range of uncertainty reveal that, of the parameters pertinent to the present melt pond parameterization and for our prescribed atmospheric and oceanic forcing, variations of optical properties and the amount of snowfall have the strongest impact on sea ice extent and volume. We conclude that melt ponds will play an increasingly important role in the melting of the Arctic ice cover and their incorporation in the sea ice component of Global Circulation Models is essential for accurate future sea ice forecasts.

The extent and thickness of the Arctic sea ice cover has decreased dramatically in the past few decades with minima in sea ice extent in September 2005 and 2007. These minima have not been predicted in the IPCC AR4 report, suggesting that the sea ice component of climate models should more realistically represent the processes controlling the sea ice mass balance. One of the processes poorly represented in sea ice models is the formation and evolution of melt ponds. Melt ponds accumulate on the surface of sea ice from snow and sea ice melt and their presence reduces the albedo of the ice cover, leading to further melt. Toward the end of the melt season, melt ponds cover up to 50% of the sea ice surface. We have developed a melt pond evolution theory. Here, we have incorporated this melt pond theory into the Los Alamos CICE sea ice model, which has required us to include the refreezing of melt ponds. We present results showing that the presence, or otherwise, of a representation of melt ponds has a significant effect on the predicted sea ice thickness and extent. We also present a sensitivity study to uncertainty in the sea ice permeability, number of thickness categories in the model representation, meltwater redistribution scheme, and pond albedo. We conclude with a recommendation that our melt pond scheme is included in sea ice models, and the number of thickness categories should be increased and concentrated at lower thicknesses. Copyright 2010 by the American Geophysical Union.

During the Northern Hemisphere summer, absorbed solar radiation melts snow and the upper surface of Arctic sea ice to generate meltwater that accumulates in ponds. The melt ponds reduce the albedo of the sea ice cover during the melting season, with a significant impact on the heat and mass budget of the sea ice and the upper ocean. We have developed a model, designed to be suitable for inclusion into a global circulation model (GCM), which simulates the formation and evolution of the melt pond cover. In order to be compatible with existing GCM sea ice models, our melt pond model builds upon the existing theory of the evolution of the sea ice thickness distribution. Since this theory does not describe the topography of the ice cover, which is crucial to determining the location, extent, and depth of individual ponds, we have needed to introduce some assumptions. We describe our model, present calculations and a sensitivity analysis, and discuss our results. Copyright 2007 by the American Geophysical Union.

We present a process study on the dynamics and variability of the Terra Nova Bay polynya in the western sector of the Ross Sea. The air-sea heat exchange is known to be particularly large in polynyas during the winter, when differences between air and sea temperatures are large. We apply a 1-D model (Pease, 1987; Van Woert, 1999a, 1999b), which is modified in the latent heat parameterisation in order to account for time-dependent relative humidity and cloud coverage. Furthermore, the Ice Collection Depth is correlated linearly with a variable wind speed. The model is forced with two different meteorological data sets: the operational analysis of the European Center for Medium Range Weather Forecasts atmospheric data set and the meteorological parameters measured by an Automatic Weather Station located on the coast of Terra Nova Bay. The results are compared in terms of polynya extension, ice, and High Salinity Shelf Water production. According to the two different wind velocities, the results obtained from the different data sets clearly differ. Qualitatively, however, the results are in good agreement. © 2002 Blackwell Verlag, Berlin.

During summer (2 December 1999–23 January 2000) an Ocean Surface Current Radar (OSCR-II) was used to provide surface current measurements within the Terra Nova Bay polynya, one of the most important coastal polynyas of the Ross Sea. This represents an important step towards a continuous monitoring of the area. Useful information is now available as a basis for future work in this field, although the two radar sites, necessary to calculate the total current vector, did not work together throughout the whole period of the experiment as one of the units was damaged. The results demonstrate the feasibility of this kind of measurement and suggest that very important dynamical characteristics of the polynya could be deduced from long term deployment of such a system.