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At least 19 records

Geoid undulations and gravity anomalies over the Aral Sea, the Black Sea and the Caspian Sea from a combined GEOS-3/SEASAT/GEOSAT altimeter data set

Satellite-based altimetric data taken by GOES-3, SEASAT, and GEOSAT over the Aral Sea, the Black Sea, and the Caspian Sea are analyzed and a least squares collocation technique is used to predict the geoid undulations on a 0.25x0.25 deg. grid and to transform these geoid undulations to free air gravity anomalies. Rapp's 180x180 geopotential model is used as the reference surface for the collocation procedure. The result of geoid to gravity transformation is, however, sensitive to the information content of the reference geopotential model used. For example, considerable detailed surface gravity data were incorporated into the reference model over the Black Sea, resulting in a reference model with significant information content at short wavelengths. Thus, estimation of short wavelength gravity anomalies from gridded geoid heights is generally reliable over regions such as the Black Sea, using the conventional collocation technique with local empirical covariance functions. Over regions such as the Caspian Sea, where detailed surface data are generally not incorporated into the reference model, unconventional techniques are needed to obtain reliable gravity anomalies. Based on the predicted gravity anomalies over these inland seas, speculative tectonic structures are identified and geophysical processes are inferred.

Au, Andrew Y.

Continuous assimilation of simulated Geosat altimetric sea level into an eddy-resolving numerical ocean model. I - Sea level differences. II - Referenced sea level differences

The optimal interpolation method of Lorenc (1981) was used to conduct continuous assimilation of altimetric sea level differences from the simulated Geosat exact repeat mission (ERM) into a three-layer quasi-geostrophic eddy-resolving numerical ocean box model that simulates the statistics of mesoscale eddy activity in the western North Pacific. Assimilation was conducted continuously as the Geosat tracks appeared in simulated real time/space, with each track repeating every 17 days, but occurring at different times and locations within the 17-day period, as would have occurred in a realistic nowcast situation. This interpolation method was also used to conduct the assimilation of referenced altimetric sea level differences into the same model, performing the referencing of altimetric sea sevel differences by using the simulated sea level. The results of this dynamical interpolation procedure are compared with those of a statistical (i.e., optimum) interpolation procedure.

White, Warren B.

Are You In or Out: the Influence of Sea Ice Drift on Sea Ice Survivability

The state of Arctic sea ice influences aspects of many global systems including ecosystems, economies, geopolitics, and climate. And, it is in severe decline. Numerous studies have considered the factors that influence Arctic sea ice decline using monthly mean or gridded data. However, these methods struggle to cleanly separate the influences on sea ice melt because the sea ice cover is always moving; thus, different sea ice floes and sea ice regimes constitute the monthly average sea ice state within the Eulerian frame. This study uses a LaGrangian sea ice parcel tracking satellite database to investigate the factors that influence sea ice parcel survivability. The concept of sea ice survivability is defined as the likelihood that a sea ice parcel will last through the summer melt season. Survivability can be stratified in many ways to analyze how it changes as a function of sea ice regime, region, and sea ice characteristics. How is the survivability of first year (FY) and multi-year (MY) ice classes influenced by sea ice motion? This is the central question addressed in this presentation. Specifically, this analysis determines sea ice survivability as a function of distance traveled by sea ice parcels and compares the survivability statistics for sea ice parcels that stay in their starting region versus those that leave their starting region. The preliminary results indicate that the influence of sea ice movement on survivability depends strongly on which region the sea ice resides in at the beginning of the growth season. Lastly, we find substantial inter-annual variability in the regional distribution of sea ice survivability that seems to be strongly linked to sea ice parcel drift between regions of the Arctic Ocean. Moreover, we use these results to discuss the potential contribution of changes in sea ice motion on the observed slowdown in Arctic sea ice extent loss.

Patrick C Taylor

The Satellite Passive-Microwave Record of Sea Ice in the Ross Sea Since Late 1978

Satellites have provided us with a remarkable ability to monitor many aspects of the globe day-in and day-out and sea ice is one of numerous variables that by now have quite substantial satellite records. Passive-microwave data have been particularly valuable in sea ice monitoring, with a record that extends back to August 1987 on daily basis (for most of the period), to November 1970 on a less complete basis (again for most of the period), and to December 1972 on a less complete basis. For the period since November 1970, Ross Sea sea ice imagery is available at spatial resolution of approximately 25 km. This allows good depictions of the seasonal advance and retreat of the ice cover each year, along with its marked interannual variability. The Ross Sea ice extent typically reaches a minimum of approximately 0.7 x 10(exp 6) square kilometers in February, rising to a maximum of approximately 4.0 x 10(exp 6) square kilometers in September, with much variability among years for both those numbers. The Ross Sea images show clearly the day-by-day activity greatly from year to year. Animations of the data help to highlight the dynamic nature of the Ross Sea ice cover. The satellite data also allow calculation of trends in the ice cover over the period of the satellite record. Using linear least-squares fits, the Ross Sea ice extent increased at an average rate of 12,600 plus or minus 1,800 square kilometers per year between November 1978 and December 2007, with every month exhibiting increased ice extent and the rates of increase ranging from a low of 7,500 plus or minus 5,000 square kilometers per year for the February ice extents to a high of 20,300 plus or minus 6,100 kilometers per year for the October ice extents. On a yearly average basis, for 1979-2007 the Ross Sea ice extent increased at a rate of 4.8 plus or minus 1.6 % per decade. Placing the Ross Sea in the context of the Southern Ocean as a whole, over the November 1978-December 2007 period the Ross Sea had the highest rate of increase in sea ice coverage of any of five standard divisions of the Southern Ocean, although the Weddell Sea, Indian Ocean, and Western Pacific Ocean all also had sea ice increases, while only the Bellingshausen/Smundsen Seas experienced overall sea ice decreases. Overall, the Southern Ocean sea ice cover increased at an average rate of 10,800 plus or minus 2,500 square kilometers per year between November 1978 and December 2007, with every month showing positive values although with some of these values not being statistically significant. The sea ice increase since November 1978 was preceded by a sharp decrease in Southern Ocean ice coverage in the 1970's and is in marked contrast to the decrease in Arctic sea ice coverage that has occurred both in the period since November 1978 and since earlier in the 1970's. On a yearly average bases, for 1979-2007 the Southern Ocean sea ice extent increased at a rate of 1.0 plus or minus 0.4% per decade, whereas the Arctic ice extent decreased at the much greater rate of 4.0 plus or minus 0.4 percent per decade (closer to the % per decade rate of increase in the Ross Sea). Considerable research is ongoing to explain the differences.

Parkinson, Claire L.

Sea Ice Variability in the Sea of Okhotsk from Passive Microwave Satellite Observations

The Sea of Okhotsk, located between 50 and 60 N, is bounded by the Kamchatka Peninsula, Siberia, Sakhalin Island, and the Kuril Island chain and is the largest midlatitude seasonal sea ice zone in the Northern Hemisphere. The winter sea ice cover begins to form in November and expands to cover most of the sea by March. Over the following three months, the ice retreats with only small ice-covered areas remaining by the beginning of June. The sea is ice free or nearly ice free on average for six months of the year, from June through November. The recent compilation of a consistent, long-term record of Northern Hemisphere sea ice extents based on passive microwave satellite observations from the Nimbus 7 Scanning Multichannel Microwave Radiometer and from four Defense Meteorological Satellite Program Special Sensor Microwave Imagers provides the basis for assessing long-term sea ice extent variability in the Sea of Okhotsk. Analysis of this 20-year data record (1979-1998) shows that based on yearly averages the overall extent of the Sea of Okhotsk ice cover is decreasing at the rate of -8.1+/-2.1x10(exp 3) sq km/yr (-17.2%/decade), in contrast to the rate of decrease of -33.3+/-0.7x10(exp 3) sq km/yr (-2.7%/decade) for the Northern Hemisphere as a whole. There is large regional sea ice extent variability of the Arctic ice cover. Two of the nine Arctic regions analyzed, the Bering Sea and the Gulf of St. Lawrence, show increases of 0.8+/-1.4xl0(exp 3) sq km/yr (2.7%/decade) and 1.2+/-0.5xl0(exp 3) sq km/yr (17.1%/decade), respectively. Interestingly, the Sea of Okhotsk and the Gulf of St. Lawrence show about equal percentage changes, but of opposite sign. The Sea of Okhotsk exhibits its greatest percent decrease (-24.3%/decade) during spring (April-June). The year of maximum winter sea ice extent for the Sea of Okhotsk was 1979, whereas the minimum winter sea ice extent occurred in 1984.

Cavalieri, Donald J.

A multi-model CMIP6-PMIP4 study of Arctic sea ice at 127 ka: sea ice data compilation and model differences

The Last Interglacial period (LIG) is a period with increased summer insolation at high northern latitudes, which results in strong changes in the terrestrial and marine cryosphere. Understanding the mechanisms for this response via climate modelling and comparing the models' representation of climate reconstructions is one of the objectives set up by the Paleoclimate Modelling Intercomparison Project for its contribution to the sixth phase of the Coupled Model Intercomparison Project. Here we analyse the results from 16 climate models in terms of Arctic sea ice. The multi-model mean reduction in minimum sea ice area from the pre industrial period (PI) to the LIG reaches 50 % (multi-model mean LIG area is 3.20×10^6 sq.km, compared to 6.46×10^6 sq.km for the PI). On the other hand, there is little change for the maximum sea ice area (which is 15–16×10^6 sq.km for both the PI and the LIG. To evaluate the model results we synthesise LIG sea ice data from marine cores collected in the Arctic Ocean, Nordic Seas and northern North Atlantic. The reconstructions for the northern North Atlantic show year-round ice-free conditions, and most models yield results in agreement with these reconstructions. Model–data disagreement appear for the sites in the Nordic Seas close to Greenland and at the edge of the Arctic Ocean. The northernmost site with good chronology, for which a sea ice concentration larger than 75 % is reconstructed even in summer, discriminates those models which simulate too little sea ice. However, the remaining models appear to simulate too much sea ice over the two sites south of the northernmost one, for which the reconstructed sea ice cover is seasonal. Hence models either underestimate or overestimate sea ice cover for the LIG, and their bias does not appear to be related to their bias for the pre-industrial period. Drivers for the inter-model differences are different phasing of the up and down short-wave anomalies over the Arctic Ocean, which are associated with differences in model albedo; possible cloud property differences, in terms of optical depth; and LIG ocean circulation changes which occur for some, but not all, LIG simulations. Finally, we note that inter-comparisons between the LIG simulations and simulations for future climate with moderate (1 %/yr) CO2 increase show a relationship between LIG sea ice and sea ice simulated under CO2 increase around the years of doubling CO2. The LIG may therefore yield insight into likely 21st century Arctic sea ice changes using these LIG simulations.

Arctic sea ice

Variability and Trends in Sea Ice Extent and Ice Production in the Ross Sea

Salt release during sea ice formation in the Ross Sea coastal regions is regarded as a primary forcing for the regional generation of Antarctic Bottom Water. Passive microwave data from November 1978 through 2008 are used to examine the detailed seasonal and interannual characteristics of the sea ice cover of the Ross Sea and the adjacent Bellingshausen and Amundsen seas. For this period the sea ice extent in the Ross Sea shows the greatest increase of all the Antarctic seas. Variability in the ice cover in these regions is linked to changes in the Southern Annular Mode and secondarily to the Antarctic Circumpolar Wave. Over the Ross Sea shelf, analysis of sea ice drift data from 1992 to 2008 yields a positive rate of increase in the net ice export of about 30,000 sq km/yr. For a characteristic ice thickness of 0.6 m, this yields a volume transport of about 20 cu km/yr, which is almost identical, within error bars, to our estimate of the trend in ice production. The increase in brine rejection in the Ross Shelf Polynya associated with the estimated increase with the ice production, however, is not consistent with the reported Ross Sea salinity decrease. The locally generated sea ice enhancement of Ross Sea salinity may be offset by an increase of relatively low salinity of the water advected into the region from the Amundsen Sea, a consequence of increased precipitation and regional glacial ice melt.

Comiso, Josefino

The role of sea ice in 2 x CO2 climate model sensitivity. Part 1: The total influence of sea ice thickness and extent

As a first step in investigating the effects of sea ice changes on the climate sensitivity to doubled atmospheric CO2, the authors use a standard simple sea ice model while varying the sea ice distributions and thicknesses in the control run. Thinner ice amplifies the atmospheric temperature senstivity in these experiments by about 15% (to a warming of 4.8 C), because it is easier for the thinner ice to be removed as the climate warms. Thus, its impact on sensitivity is similar to that of greater sea ice extent in the control run, which provides more opportunity for sea ice reduction. An experiment with sea ice not allowed to change between the control and doubled CO2 simulations illustrates that the total effect of sea ice on surface air temperature changes, including cloud cover and water vapor feedbacks that arise in response to sea ice variations, amounts to 37% of the temperature sensitivity to the CO2 doubling, accounting for 1.56 C of the 4.17 C global warming. This is about four times larger than the sea ice impact when no feedbacks are allowed. The different experiments produce a range of results for southern high latitudes with the hydrologic budget over Antarctica implying sea level increases of varying magnitude or no change. These results highlight the importance of properly constraining the sea ice response to climate perturbations, necessitating the use of more realistic sea ice and ocean models.

Rind, D.

Turbulent Heat Flux, Downward Longwave Radiation and Large-Scale Atmospheric Circulation Associated with the Wintertime Barents-Kara Sea Extreme Sea Ice Loss Events

We investigate wintertime extreme sea ice loss events on synoptic to subseasonal time scales over the Barents-Kara Sea, where the largest sea ice variability is located. Consistent with previous studies, extreme sea ice loss events are associated with moisture intrusions over the Barents-Kara Sea, which are driven by the large-scale atmospheric circulation. In addition to the role of downward longwave radiation associated with moisture intrusions, which is emphasized by previous studies, our analysis shows strong turbulent heat fluxes are associated with extreme sea ice melting events, with both turbulent sensible and latent heat fluxes contributing, though turbulent sensible heat fluxes dominate. Our analysis also shows that these events are connected to tropical convective anomalies. A dipole pattern of convective anomalies with enhanced convection over the Maritime Continent and suppressed convection over the central to eastern Pacific is consistently detected about 6 to 10 days prior to extreme sea ice loss events. This pattern is associated with either the Madden-Julian Oscillation (MJO) or El Niño–Southern Oscillation (ENSO). Composites show that extreme sea ice loss events are connected to tropical convection via Rossby wave propagation in the midlatitudes. However, tropical convective anomalies alone are not sufficient to trigger extreme sea ice loss events, suggesting that extratropical variability likely modulates the connection between tropical convection and extreme sea ice loss events.

Turbulent heat flux

C-band radar backscatter of sea ice in the Weddell Sea, Antarctica during the austral winter of 1992

A C-band ship-based scatterometer was used to measure the backscatter coefficient of sea ice in the Weddell Sea during June and July 1992. These are the first microwave scatterometer data ever to be collected in the Antarctic sea ice cover during the austral winter. The instrument was a frequency-modulated continuous-wave (FM-CW) radar altimeter modified by the University of Kansas Radar Systems and Remote Sensing Laboratory to perform backscatter measurements. Measurements were taken as part of a Jet Propulsion Laboratory experiment aboard the German ice research vessel F.S. Polarstern. Backscatter measurements were performed at incidence angles ranging from 17 to 65 degrees with VV and HV polarization as the Polarstern travelled from east to west across the central Weddell Sea. Backscatter measurements were made of several different types of ice sea including pancake, dark nilas, white nilas, grey, first-year and second-year ice. Periodic external calibrations were performed with the aid of a Luneberg Lens to enable absolute values of backscatter to be derived from the data. At each radar measurement location, in-situ measurements were made of snow and sea ice. Physical and chemical analyses of ice core and snow samples, together with high magnification photography of snow crystallography provide important information with which to develop physical models of the scattering systems. Meteorological information and oceanographic conditions were also recorded throughout the experiment. Many of the stations were chosen to coincide with periods of near-simultaneous or coincident imaging by the ERS-1 satellite Synthetic Aperture Radar (SAR). This enabled spaceborne imaging by the C-band SAR of areas of sea ice in which backscatter measurements were taken. This provides a valuable tool for interpretation of satellite SAR imagery from Antarctic sea ice in terms of the physical properties of the sea ice and snow. Preliminary results of the backscatter from the various ice types and their relation to the physical properties of sea ice are presented.

Hosseinmostafa, R.

ICESat Observations of Seasonal and Interannual Variations of Sea-Ice Freeboard and Estimated Thickness in the Weddell Sea, Antarctica (2003-2009)

Sea-ice freeboard heights for 17 ICESat campaign periods from 2003 to 2009 are derived from ICESat data. Freeboard is combined with snow depth from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) data and nominal densities of snow, water and sea ice, to estimate sea-ice thickness. Sea-ice freeboard and thickness distributions show clear seasonal variations that reflect the yearly cycle of growth and decay of the Weddell Sea (Antarctica) pack ice. During October-November, sea ice grows to its seasonal maximum both in area and thickness; the mean freeboards are 0.33-0.41 m and the mean thicknesses are 2.10-2.59 m. During February-March, thinner sea ice melts away and the sea-ice pack is mainly distributed in the west Weddell Sea; the mean freeboards are 0.35-0.46 m and the mean thicknesses are 1.48-1.94 m. During May-June, the mean freeboards and thicknesses are 0.26-0.29 m and 1.32-1.37 m, respectively. The 6 year trends in sea-ice extent and volume are (0.023+/-0.051) x 10(exp 6)sq km/a (0.45%/a) and (0.007+/-1.0.092) x 10(exp 3)cu km/a (0.08%/a); however, the large standard deviations indicate that these positive trends are not statistically significant.

Yi, Donghui

Turbulent Heat Flux, Downward Longwave Radiation, and Large-Scale Atmospheric Circulation Associated with Wintertime Barents–Kara Sea Extreme Sea Ice Loss Events

We investigate wintertime extreme sea ice loss events on synoptic to subseasonal time scales over the Barents–Kara Sea, where the largest sea ice variability is located. Consistent with previous studies, extreme sea ice loss events are associated with moisture intrusions over the Barents–Kara Sea, which are driven by the large-scale atmospheric circulation. In addition to the role of downward longwave radiation associated with moisture intrusions, which is emphasized by previous studies, our analysis shows that strong turbulent heat fluxes are associated with extreme sea ice melting events, with both turbulent sensible and latent heat fluxes contributing, although turbulent sensible heat fluxes dominate. Our analysis also shows that these events are connected to tropical convective anomalies. A dipole pattern of convective anomalies with enhanced convection over the Maritime Continent and suppressed convection over the central to eastern Pacific is consistently detected about 6–10 days prior to extreme sea ice loss events. This pattern is associated with either the Madden–Julian oscillation (MJO) or El Nino–Southern Oscillation (ENSO). Composites show that extreme sea ice loss events are connected to tropical convection via Rossby wave propagation in the midlatitudes. However, tropical convective anomalies alone are not sufficient to trigger extreme sea ice loss events, suggesting that extratropical variability likely modulates the connection between tropical convection and extreme sea ice loss events.

Arctic

Weekly Mapping of Sea Ice Freeboard in the Ross Sea from ICESat-2

NASA’s ICESat-2 has been providing sea ice freeboard measurements across the polar regions since October 2018. In spite of the outstanding spatial resolution and precision of ICESat-2, the spatial sparsity of the data can be a critical issue for sea ice monitoring. This study employs a geostatistical approach (i.e., ordinary kriging) to characterize the spatial autocorrelation of the ICESat-2 freeboard measurements (ATL10) to estimate weekly freeboard variations in 2019 for the entire Ross Sea area, including where ICESat-2 tracks are not directly available. Three variogram models (exponential, Gaussian, and spherical) are compared in this study. According to the cross-validation results, the kriging-estimated freeboards show correlation coefficients of 0.56–0.57, root mean square error (RMSE) of ~0.12 m, and mean absolute error (MAE) of ~0.07 m with the actual ATL10 freeboard measurements. In addition, the estimated errors of the kriging interpolation are low in autumn and high in winter to spring, and low in southern regions and high in northern regions of the Ross Sea. The effective ranges of the variograms are 5–10 km and the results from the three variogram models do not show significant differences with each other. The southwest (SW) sector of the Ross Sea shows low and consistent freeboard over the entire year because of the frequent opening of wide polynya areas generating new ice in this sector. However, the southeast (SE) sector shows large variations in freeboard, which demonstrates the advection of thick multiyear ice from the Amundsen Sea into the Ross Sea. Thus, this kriging-based interpolation of ICESat-2 freeboard can be used in the future to estimate accurate sea ice production over the Ross Sea by incorporating other remote sensing data.

Satellite altimeter

Modulation of the Seasonal Cycle of the Antarctic Sea Ice Extent By Sea Ice Processes and Feedbacks With the Ocean and the Atmosphere

The seasonal cycle of the Antarctic sea ice extent is strongly asymmetric, with a relatively slow increase after the summer minimum followed by a more rapid decrease after the winter maximum. This cycle is intimately linked to the seasonal cycle of the insolation received at the top of the atmosphere, but sea ice processes as well as the exchanges with the atmosphere and ocean may also play a role. To quantify these contributions, a series of idealized sensitivity experiments have been performed with an eddy-permitting (∘) NEMO-LIM3 (Nucleus for European Modelling of the Ocean–Louvain-la-Neuve sea ice model version 3) Southern Ocean configuration, including a representation of ice shelf cavities, in which the model was either driven by an atmospheric reanalysis or coupled to the COSMO-CLM2 regional atmospheric model. In those experiments, sea ice thermodynamics and dynamics as well as the exchanges with the ocean and atmosphere are strongly perturbed. This perturbation is achieved by modifying snow and ice thermal conductivities, the vertical mixing in the ocean top layers, the effect of freshwater uptake and release upon sea ice growth and melt, ice dynamics, and surface albedo. We find that the evolution of sea ice extent during the ice advance season is largely independent of the direct effect of the perturbation and appears thus mainly controlled by initial state in summer and subsequent insolation changes. In contrast, the melting rate varies strongly between the experiments during the retreat, in particular if the surface albedo or sea ice transport are modified, demonstrating a strong contribution of those elements to the evolution of ice coverage through spring and summer. As with the advance phase, the retreat is also influenced by conditions at the beginning of the melt season in September. Atmospheric feedbacks enhance the model winter ice extent response to any of the perturbed processes, and the enhancement is strongest when the albedo is modified. The response of sea ice volume and extent to changes in entrainment of subsurface warm waters to the ocean surface is also greatly amplified by the coupling with the atmosphere.

regional atmosphere ocean sea ice model

Comparison of TOPEX sea surface heights and tide gauge sea levels

TOPEX sea surface height data from the first 300 days of the mission are compared to sea level data from 71 tide gauges. The initial comparison uses sea surface height data processed according to standard procedures as defined in the users handbook. It is found that the median correlations for island and for coastal tide gauges are 0.53 and 0.42, respectively. The analogous root mean square (RMS) differences between the two data sets are 7.9 and 10.4 cm. The comparisons improve significantly when a 60-day harmonic is fit to the differences and removed. This period captures aliased M(sub 2) and S(sub 2) tidal energy that is not removed by the tide model. Making this correction and smoothing the sea surface height data over 25-km along-track segments results in median correlations of 0.58 and 0.46 for the islands and coastal stations, and median RMS differences of 5.8 and 7.7 cm, respectively. Removing once per revolution signals from the sea surface heights results in degraded comparisons with the sea levels. It is also found that a number of stations have poor comparisons due to propagating signals that introduce temporal lags between the altimeter and tide gauge time series. A final comparison is made by eliminating stations where this propagation effect is large, discarding two stations that are suspected to have problems with the sea level data, smoothing over 10-day intervals, and restricting attention to islands gauges. This results in a set of 552 data pairs that have a correlation of 0.66 and a RMS difference of 4.3 cm. The conclusion is that on timescales longer than about 10 days the RMS sea surface height errors are less than or of the order of several centimeters.

Mitchum, Gary T.

An Observationally-Based Determination of the Arctic Sea Ice-Cloud Feedback Since 2000: Isolating the Arctic Cloud Response to Sea Ice Loss

Arctic sea ice responds to and drives Arctic climate change. The interactions between Arctic sea ice and clouds represent a mechanism through which sea ice can drive climate change. We composite active remote sensing satellite cloud properties for ice-free, marginal ice zone (MIZ), and ice-covered surfaces during MIZ crossing events to investigate the influence of the transition from an ice-covered to an ice-free surface on low-level clouds. We demonstrate that the event-based methodology controls for large-scale meteorological factors and isolates the sea ice effect on clouds. We find larger cloud fraction and total water content below ~1.5 km over ice-free relative to ice-covered surfaces during non-summer months, indicating a low-level cloud sensitivity to Arctic sea ice decline. During summer, results show larger cloud fraction and water content over ice-free surfaces, however the differences are statistically indistinguishable. Evidence is provided that atmospheric thermodynamic profile differences cause the cloud property differences, namely that ice-free footprints are warmer, moister, have more positive surface turbulent fluxes and are less stable than their ice-covered counterparts. Ice-free and ice-covered surface cloud property differences scale with surface temperature differences such that cloud property differences are only found in the presence of a surface temperature difference. We conclude that surface temperature differences modulate the cloud response to sea ice loss through influences on surface turbulent fluxes and lower tropospheric stability. The results imply a positive non-summer sea ice-cloud feedback and that up to a 0.02 cloud fraction and 0.05 g m 3 total water content increase in fall are due to the observed sea ice decline.

Patrick C Taylor

Effects of Increasing the Category Resolution of the Sea Ice Thickness Distribution in a Coupled Climate Model on Arctic and Antarctic Sea Ice Mean State

Many modern sea ice models used in global climate models represent the subgrid-scale heterogeneity in sea ice thickness with an ice thickness distribution (ITD), which improves model realism by representing the significant impact of the high spatial heterogeneity of sea ice thickness on thermodynamic and dynamic processes. Most models default to five thickness categories. However, little has been done to explore the effects of the resolution of this distribution (number of categories) on sea-ice feedbacks in a coupled model framework and resulting representation of the sea ice mean state. Here, we explore this using sensitivity experiments in CESM2 with the standard 5 ice thickness categories and 15 ice thickness categories. Increasing the resolution of the ITD in a run with preindustrial climate forcing results in substantially thicker Arctic sea ice year-round. Analyses show that this is a result of the ITD influence on ice strength. With 15 ITD categories, weaker ice occurs for the same average thickness, resulting in a higher fraction of ridged sea ice. In contrast, the higher resolution of thin ice categories results in enhanced heat conduction and bottom growth and leads to only somewhat increased winter Antarctic sea ice volume. The spatial resolution of the ICESat-2 satellite mission provides a new opportunity to compare model outputs with observations of seasonal evolution of the ITD in the Arctic (ICESat-2; 2018–2021). Comparisons highlight significant differences from the ITD modeled with both runs over this period, likely pointing to underlying issues contributing to the representation of average thickness.

Madison M. Smith

Large Scale Variability of Phytoplankton Blooms in the Arctic and Peripheral Seas: Relationships with Sea Ice, Temperature, Clouds, and Wind

Spatially detailed satellite data of mean color, sea ice concentration, surface temperature, clouds, and wind have been analyzed to quantify and study the large scale regional and temporal variability of phytoplankton blooms in the Arctic and peripheral seas from 1998 to 2002. In the Arctic basin, phytoplankton chlorophyll displays a large symmetry with the Eastern Arctic having about fivefold higher concentrations than those of the Western Arctic. Large monthly and yearly variability is also observed in the peripheral seas with the largest blooms occurring in the Bering Sea, Sea of Okhotsk, and the Barents Sea during spring. There is large interannual and seasonal variability in biomass with average chlorophyll concentrations in 2002 and 2001 being higher than earlier years in spring and summer. The seasonality in the latitudinal distribution of blooms is also very different such that the North Atlantic is usually most expansive in spring while the North Pacific is more extensive in autumn. Environmental factors that influence phytoplankton growth were examined, and results show relatively high negative correlation with sea ice retreat and strong positive correlation with temperature in early spring. Plankton growth, as indicated by biomass accumulation, in the Arctic and subarctic increases up to a threshold surface temperature of about 276-277 degree K (3-4 degree C) beyond which the concentrations start to decrease suggesting an optimal temperature or nutrient depletion. The correlation with clouds is significant in some areas but negligible in other areas, while the correlations with wind speed and its components are generally weak. The effects of clouds and winds are less predictable with weekly climatologies because of unknown effects of averaging variable and intermittent physical forcing (e.g. over storm event scales with mixing and upwelling of nutrients) and the time scales of acclimation by the phytoplankton.

Comiso, Josefino C.