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At least 325 records · Page 18
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NASA NTRS2007

Thin Sea-Ice Thickness as Inferred from Passive Microwave and In Situ Observations

Since microwave radiometric signals from sea-ice strongly reflect physical conditions of a layer near the ice surface, a relationship of brightness temperature with thickness is possible especially during the early stages of ice growth. Sea ice is most saline during formation stage and as the salinity decreases with time while at the same time the thickness of the sea ice increases, a corresponding change in the dielectric properties and hence the brightness temperature may occur. This study examines the extent to which the relationships of thickness with brightness temperature (and with emissivity) hold for thin sea-ice, approximately less than 0.2 -0.3 m, using near concurrent measurements of sea-ice thickness in the Sea of Okhotsk from a ship and passive microwave brightness temperature data from an over-flying aircraft. The results show that the brightness temperature and emissivity increase with ice thickness for the frequency range of 10-37 GHz. The relationship is more pronounced at lower frequencies and at the horizontal polarization. We also established an empirical relationship between ice thickness and salinity in the layer near the ice surface from a field experiment, which qualitatively support the idea that changes in the near-surface brine characteristics contribute to the observed thickness-brightness temperature/emissivity relationship. Our results suggest that for thin ice, passive microwave radiometric signals contain, ice thickness information which can be utilized in polar process studies.

Naoki, Kazuhiro↗
NASA NTRS2008

Arctic Sea Ice Variability and Trends, 1979-2006

Analysis of Arctic sea ice extents derived from satellite passive-microwave data for the 28 years, 1979-2006 yields an overall negative trend of -45,100 +/- 4,600 km2/yr (-3.7 +/- 0.4%/decade) in the yearly averages, with negative ice-extent trends also occurring for each of the four seasons and each of the 12 months. For the yearly averages the largest decreases occur in the Kara and Barents Seas and the Arctic Ocean, with linear least squares slopes of -10,600 +/- 2,800 km2/yr (-7.4 +/- 2.0%/decade) and -10,100 +/- 2,200 km2/yr (-1.5 +/- 0.3%/decade), respectively, followed by Baffin Bay/Labrador Sea, with a slope of -8,000 +/- 2,000 km2/yr) -9.0 +/- 2.3%/decade), the Greenland Sea, with a slope of -7,000 +/- 1,400 km2/yr (-9.3 +/- 1.9%/decade), and Hudson Bay, with a slope of -4,500 +/- 900 km2/yr (-5.3 +/- 1.1%/decade). These are all statistically significant decreases at a 99% confidence level. The Seas of Okhotsk and Japan also have a statistically significant ice decrease, although at a 95% confidence level, and the three remaining regions, the Bering Sea, Canadian Archipelago, and Gulf of St. Lawrence, have negative slopes that are not statistically significant. The 28-year trends in ice areas for the Northern Hemisphere total are also statistically significant and negative in each season, each month, and for the yearly averages.

Parkinson, Claire L.↗
NASA NTRS2010

Influence of Arctic Sea Ice Extent on Polar Cloud Fraction and Vertical Structure and Implications for Regional Climate

Recent satellite lidar measurements of cloud properties spanning a period of 5 years are used to examine a possible connection between Arctic sea ice amount and polar cloud fraction and vertical distribution. We find an anticorrelation between sea ice extent and cloud fraction with maximum cloudiness occurring over areas with little or no sea ice. We also find that over ice!free regions, there is greater low cloud frequency and average optical depth. Most of the optical depth increase is due to the presence of geometrically thicker clouds over water. In addition, our analysis indicates that over the last 5 years, October and March average polar cloud fraction has increased by about 7% and 10%, respectively, as year average sea ice extent has decreased by 5% 7%. The observed cloud changes are likely due to a number of effects including, but not limited to, the observed decrease in sea ice extent and thickness. Increasing cloud amount and changes in vertical distribution and optical properties have the potential to affect the radiative balance of the Arctic region by decreasing both the upwelling terrestrial longwave radiation and the downward shortwave solar radiation. Because longwave radiation dominates in the long polar winter, the overall effect of increasing low cloud cover is likely a warming of the Arctic and thus a positive climate feedback, possibly accelerating the melting of Arctic sea ice.

Palm, Stephen P.↗
NASA NTRS2010

The Influence of Arctic Sea Ice Extent on Polar Cloud Fraction and Vertical Structure and Implications for Regional Climate

Recent satellite lidar measurements of cloud properties spanning a period of five years are used to examine a possible connection between Arctic sea ice amount and polar cloud fraction and vertical distribution. We find an anti-correlation between sea ice extent and cloud fraction with maximum cloudiness occurring over areas with little or no sea ice. We also find that over ice free regions, there is greater low cloud frequency and average optical depth. Most of the optical depth increase is due to the presence of geometrically thicker clouds over water. In addition, our analysis indicates that over the last 5 years, October and March average polar cloud fraction has increased by about 7 and 10 percent, respectively, as year average sea ice extent has decreased by 5 to 7 percent. The observed cloud changes are likely due to a number of effects including, but not limited to, the observed decrease in sea ice extent and thickness. Increasing cloud amount and changes in vertical distribution and optical properties have the potential to affect the radiative balance of the Arctic region by decreasing both the upwelling terrestrial longwave radiation and the downward shortwave solar radiation. Since longwave radiation dominates in the long polar winter, the overall effect of increasing low cloud cover is likely a warming of the Arctic and thus a positive climate feedback, possibly accelerating the melting of Arctic sea ice.

Palm, Stephen P.↗
NASA NTRS2015

Sea Ice Outlook for September 2015 June Report - NASA Global Modeling and Assimilation Office

The recent decline in perennial sea ice cover in Arctic Ocean is a topic of enormous scientific interest and has relevance to a broad variety of scientific disciplines and human endeavors including biological and physical oceanography, atmospheric circulation, high latitude ecology, the sustainability of indigenous communities, commerce, and resource exploration. A credible seasonal prediction of sea ice extent would be of substantial use to many of the stakeholders in these fields and may also reveal details on the physical processes that result in the current trends in the ice cover. Forecasts are challenging due in part to limitations in the polar observing network, the large variability in the climate system, and an incomplete knowledge of the significant processes. Nevertheless it is a useful to understand the current capabilities of high latitude seasonal forecasting and identify areas where such forecasts may be improved. Since 2008 the Arctic Research Consortium of the United States (ARCUS) has conducted a seasonal forecasting contest in which the average Arctic sea ice extent for the month of September (the month of the annual extent minimum) is predicted from available forecasts in early June, July, and August. The competition is known as the Sea Ice Outlook (SIO) but recently came under the auspices of the Sea Ice Prediction Network (SIPN), and multi-agency funded project to evaluate the SIO. The forecasts are submitted based on modeling, statistical, and heuristic methods. Forecasts of Arctic sea ice extent from the GMAO are derived from seasonal prediction system of the NASA Goddard Earth Observing System model, version 5 (GEOS 5) coupled atmosphere and ocean general circulation model (AOGCM). The projections are made in order to understand the relative skill of the forecasting system and to determine the effects of future improvements to the system. This years prediction is for a September average Arctic ice extent of 5.030.41 million km2.

SIPN↗
NASA NTRS2015

Evaluating Coastal Landscape Response to Sea-Level Rise in the Northeastern United States - Approach and Methods

The U.S. Geological Survey is examining effects of future sea-level rise on the coastal landscape from Maine to Virginia by producing spatially explicit, probabilistic predictions using sea-level projections, vertical land movement rates (due to isostacy), elevation data, and land-cover data. Sea-level-rise scenarios used as model inputs are generated by using multiple sources of information, including Coupled Model Intercomparison Project Phase 5 models following representative concentration pathways 4.5 and 8.5 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. A Bayesian network is used to develop a predictive coastal response model that integrates the sea-level, elevation, and land-cover data with assigned probabilities that account for interactions with coastal geomorphology as well as the corresponding ecological and societal systems it supports. The effects of sea-level rise are presented as (1) level of landscape submergence and (2) coastal response type characterized as either static (that is, inundation) or dynamic (that is, landform or landscape change). Results are produced at a spatial scale of 30 meters for four decades (the 2020s, 2030s, 2050s, and 2080s). The probabilistic predictions can be applied to landscape management decisions based on sea-level-rise effects as well as on assessments of the prediction uncertainty and need for improved data or fundamental understanding. This report describes the methods used to produce predictions, including information on input datasets; the modeling approach; model outputs; data-quality-control procedures; and information on how to access the data and metadata online.

terrain↗
NASA NTRS2015

Characterization of Aerosol Episodes in the Greater Mediterranean Sea Area from Satellite Observations (2000-2007)

An algorithm able to identify and characterize episodes of different aerosol types above sea surfaces of the greater Mediterranean basin (GMB), including the Black Sea and the Atlantic Ocean off the coasts of Iberia and northwest Africa, is presented in this study. Based on this algorithm, five types of intense (strong and extreme) aerosol episodes in the GMB are identified and characterized using daily aerosol optical properties from satellite measurements, namely MODIS-Terra, Earth Probe (EP)-TOMS and OMIAura. These aerosol episodes are: (i) biomass-burning/urban-industrial (BU), (ii) desert dust (DD), (iii) dust/sea-salt (DSS), (iv) mixed (MX) and (v) undetermined (UN). The identification and characterization is made with our algorithm using a variety of aerosol properties, namely aerosol optical depth (AOD), Angstrom exponent (a), fine fraction (FF), effective radius (reff) and Aerosol Index (AI). During the study period (2000e2007), the most frequent aerosol episodes are DD, observed primarily in the western and central Mediterranean Sea, and off the northern African coasts, 7 times/year for strong episodes and 4 times/year for extreme ones, on average. The DD episodes yield 40% of all types of strong aerosol episodes in the study region, while they account for 71.5% of all extreme episodes. The frequency of occurrence of strong episodes exhibits specific geographical patterns, for example the BU are mostly observed along the coasts of southern Europe and off the Atlantic coasts of Portugal, the MX episodes off the Spanish Mediterranean coast and over the Adriatic and northern Aegean Sea, while the DSS ones over the western and central Mediterranean Sea. On the other hand, the extreme episodes for all but DD aerosol display more patchy spatial patterns. The strong episodes exhibit AOD at 550 nm as high as 1.6 in the southernmost parts of central and eastern Mediterranean Sea, which rise up to 5 for the extreme, mainly DD and DSS, episodes. Although more than 90% of all aerosol episodes last 1 day, there are few cases, mainly extreme DD episodes, which last up to 4 days. Independently of their type, the Mediterranean aerosol episodes occur more frequently in spring (strong and extreme episodes) and summer (strong episodes) and most rarely during winter. A significant year by year variability of Mediterranean aerosol episodes has been identified, more in terms of their frequency than intensity. An analysis of 5-day back trajectories for the most extreme episodes provides confidence on the obtained results of the algorithm, based on the revealed origin and track of air masses causing the episodes. The 25 and 6% of all strong and extreme episodes, respectively, are MX, thus highlighting the co-existence of different aerosol types in the greater Mediterranean. The intensity of both MX and DSS episodes exhibits similar patterns to those of DD strong ones, indicating that desert dust is a determinant factor for the intensity of aerosol episodes in the Mediterranean, including DSS and MX episodes.

Atmospheric Chemistry↗
NASA NTRS2015

The Influence of Sea Ice on Arctic Low Cloud Properties and Radiative Effects

The Arctic is one of the most climatically sensitive regions of the Earth. Climate models robustly project the Arctic to warm 2-3 times faster than the global mean surface temperature, termed polar warming amplification (PWA), but also display the widest range of surface temperature projections in this region. The response of the Arctic to increased CO2 modulates the response in tropical and extra-tropical regions through teleconnections in the atmospheric circulation. An increased frequency of extreme precipitation events in the northern mid-latitudes, for example, has been linked to the change in the background equator-to-pole temperature gradient implied by PWA. Understanding the Arctic climate system is therefore important for predicting global climate change. The ice albedo feedback is the primary mechanism driving PWA, however cloud and dynamical feedbacks significantly contribute. These feedback mechanisms, however, do not operate independently. How do clouds respond to variations in sea ice? This critical question is addressed by combining sea ice, cloud, and radiation observations from satellites, including CERES, CloudSAT, CALIPSO, MODIS, and microwave radiometers, to investigate sea ice-cloud interactions at the interannual timescale in the Arctic. Cloud characteristics are strongly tied to the atmospheric dynamic and thermodynamic state. Therefore, the sensitivity of Arctic cloud characteristics, vertical distribution and optical properties, to sea ice anomalies is computed within atmospheric dynamic and thermodynamic regimes. Results indicate that the cloud response to changes in sea ice concentration differs significantly between atmospheric state regimes. This suggests that (1) the atmospheric dynamic and thermodynamic characteristics and (2) the characteristics of the marginal ice zone are important for determining the seasonal forcing by cloud on sea ice variability.

Taylor, Patrick C.↗
NASA NTRS2017

US Navy Submarine Sea Trial of NASA developed Multi-Gas Monitor

During a successful 2 year technology demonstration of the tunable diode laser spectroscopy (TDLS) based Multi-Gas Monitor (MGM) on the International Space Station (ISS), we began discussing with the US Navy the possibility of conducting a sea trial of an MGM on a submarine. The sea trial would also include a gas chromatography/differential mobility spectrometer based Air Quality Monitor (AQM), which is used operationally on ISS for volatile organic compound analysis. AQM preparation and results will be the subject of a separate paper. The Navy's interest in testing NASA equipment in general relates to their ongoing search for better air monitoring technology. NASA's goal is studying submarines as closed environment analogs to spacecraft. MGM's core technology was developed by Vista Photonics Inc. using Small Business Innovation Research (SBIR) grants and expanded for various applications using NASA program funding. The MGM measures oxygen, carbon dioxide, ammonia and water vapor in ambient air, displays concentrations with temperature and pressure, and stores 30 second moving averages. The sea trial involves collocating the instrument with the Central Atmosphere Monitoring System (CAMS Mk II) of the submarine, connecting it to rack power prior to departure, and letting it run during the entire 90 day patrol. All data is stored within MGM, with no connection to the vessel data bus. Crew intervention is limited to checking MGM periodically to see that it is working and power cycling if necessary. After the trial is over, the unit with its data will be retrieved. Post sea trial calibration check and data analysis are planned and results will be compared with both CAMS Mk II data and results from MGM's ISS technology demonstration. Since the sea trial itself has been delayed, this paper describes the preparation of MGM for the sea trial and also provides a summary of the latest data from the ISS MGM technology demonstration.

Mudgett, Paul D.↗
NASA NTRS2016

Does a Relationship Between Arctic Low Clouds and Sea Ice Matter?

Arctic low clouds strongly affect the Arctic surface energy budget. Through this impact Arctic low clouds influence important aspects of the Arctic climate system, namely surface and atmospheric temperature, sea ice extent and thickness, and atmospheric circulation. Arctic clouds are in turn influenced by these elements of the Arctic climate system, and these interactions create the potential for Arctic cloud-climate feedbacks. To further our understanding of potential Arctic cloudclimate feedbacks, the goal of this paper is to quantify the influence of atmospheric state on the surface cloud radiative effect (CRE) and its covariation with sea ice concentration (SIC). We build on previous research using instantaneous, active remote sensing satellite footprint data from the NASA A-Train. First, the results indicate significant differences in the surface CRE when stratified by atmospheric state. Second, there is a weak covariation between CRE and SIC for most atmospheric conditions. Third, the results show statistically significant differences in the average surface CRE under different SIC values in fall indicating a 3-5 W m(exp -2) larger LW CRE in 0% versus 100% SIC footprints. Because systematic changes on the order of 1 W m(exp -2) are sufficient to explain the observed long-term reductions in sea ice extent, our results indicate a potentially significant amplifying sea ice-cloud feedback, under certain meteorological conditions, that could delay the fall freeze-up and influence the variability in sea ice extent and volume. Lastly, a small change in the frequency of occurrence of atmosphere states may yield a larger Arctic cloud feedback than any cloud response to sea ice.

Taylor, Patrick C.↗
NASA NTRS2015

Connections Between the TTL and Sea Surface Temperatures: Interannual Variability and Trends

Comprehensive chemistry climate models and satellite data are used to investigate the forcing of variability in the tropical lower stratosphere and upper troposphere. As this region is the origination region for air parcels which enter the stratosphere, it is important to understand variability in this region on timescales ranging from the seasonal to decadal. The warming trend in the tropical upper troposphere over the past 30 years is strongest near the Indo Pacific warm pool, while the warming trend in the western and central Pacific is much weaker. In the lower stratosphere, these trends are reversed: the historical cooling trend is strongest over the Indo Pacific warm pool and is weakest in the western and central Pacific. These zonal variations are stronger than the zonal mean response in boreal winter. Targeted experiments with a chemistry climate model are used to demonstrate that sea surface temperature trends are driving the zonal asymmetry in upper tropospheric and lower stratospheric tropical temperature trends. The anomalous circulation set up by the changing SSTs has led to zonal structure in the ozone and water vapor trends near the tropopause, and subsequently to less water vapor entering the stratosphere. Projected future sea surface temperatures appear to drive a temperature and water vapor response whose zonal structure is similar to the historical response. In the lower stratosphere, the changes in water vapor and temperature due to projected future sea surface temperatures is of similar strength to, though slightly weaker than, that due directly to projected future CO2, ozone, and methane. Finally, targeted experiments with a chemistry climate model are used to demonstrate that seasonality and the location of the peak warming of sea surface temperatures dictate the response of stratospheric water vapor to El Nino. In spring, El Nino events in which sea surface temperature anomalies peak in the eastern Pacific lead to a warming at the tropopause above the warm pool region, and subsequently to more stratospheric water vapor (consistent with previous work). However, in fall and in early winter, and also during El Nino events in which the sea surface temperature anomaly is found mainly in the central Pacific, the response is qualitatively different: temperature changes in the warm pool region are nonuniform and less water vapor enters the stratosphere. The difference in water vapor in the lower stratosphere between the two variants of El Nino approaches 0.3 ppmv, while the difference between the winter and spring responses exceeds 0.5 ppmv.

Tropical Tropopause Layer↗
NASA NTRS2016

Satellite Sea Ice Measurements in the Arctic Ocean

Satellite observations sustained since 1979 have been the primary source of information to reveal Arctic Ocean sea ice extent is diminishing rapidly. Sea ice is also thinning based on historical surface and near-surface records and more recent satellite retrievals. There is a new “normal” environment in the Arctic environment with substantial socio-economic impacts. A key question is: What are the spatial and temporal characteristics of the sea ice thickness distribution throughout the annual cycle, and what is the evolving inter-annual trend? Arctic Ocean sea ice conditions have been determined with passive microwave radiometer, active scatterometer and other satellite instrument measurements recorded by a continuing series of satellites. An important feature of the continuous time series measurements has been the overlap of each new satellite dataset with ongoing measurements so as to provide adequate time intervals for calibration and validation. Satellite instrument diversity has provided both coarse spatial resolution measurements over the entire Arctic Ocean for long-period time series and limited-duration fine spatial resolution data over selected regions for navigation and other applications. Satellite observations of Arctic Ocean sea ice will continue to increase in importance because predictability of sea ice is poor and societal interest is great. Unfortunately, however the sustainability of some critical elements of the current Arctic Ocean satellite measurement suite beyond 2020 remains uncertain, e.g., after the CryoSat-2 and ICESat-2 missions have concluded.

Halpern, David↗
NASA NTRS2018

On the Increasing Importance of Air-Sea Exchanges in a Thawing Arctic: A Review

Forty years ago, climate scientists predicted the Arctic to be on of Earth’s most sensitive climate regions and thus extremely vulnerable to increased CO2. The rapid and unprecedented changes observed in the Arctic confirm this prediction, which has consequences that ripple through the global climate system. Especially significant, sea ice loss is altering the exchange of mass, energy, and momentum between the atmosphere and Arctic Ocean. A thick, extensive, and multiyear ice cover has historically limited such exchanges, however, the summertime Arctic Ocean is expected to be nearly ice‐free within 15 years increasing the potential for air‐sea exchange. Changes in surface turbulent fluxes can alter the Arctic surface energy budget, sea ice, clouds, boundary layer temperature and humidity, and atmospheric and oceanic circulations. This paper reviews current knowledge of surface turbulent fluxes across the Arctic Ocean and the known effects on climate. We conclude that Arctic air‐sea energy exchanges are becoming an increasingly consequential factor driving Arctic climate. Arctic Ocean surface turbulent energy exchanges are not smooth and steady but rather irregular and episodic, considering this nature of air‐sea energy exchanges is essential for improving Arctic climate projections. New field data focusing on the episodic nature of air‐sea exchange will accelerate our understanding of Arctic climate change.

Taylor, Patrick C.↗
NASA NTRS2020

Clouds Damp the Impacts of Polar Sea Ice Loss

The amount of solar energy absorbed by the Earth is believed to strongly depend on clouds, due to their ability to scatter and reflect part of solar energy back to space. Here, we investigate this relationship using satellite data and 32 climate models, showing that this relationship holds everywhere except over polar seas, where an increased reflection by clouds corresponds to an increase in absorbed solar radiation at the surface. We resolve this paradox by quantifying a strong correlation between clouds and sea-ice extent. An increase in absorbed solar radiation, when sea ice retreats, leads to an increase in cloud cover/thickness and associated reflection to space. This interplay between clouds and sea ice reduces by half the increase of net radiation at the surface that follows the sea-ice retreat, therefore damping the impact of polar sea ice loss. We further highlight how this process is mis-represented in some climate models.

Ramdane Alkama↗
NASA NTRS2022

Assimilation of Sea Ice Thickness Derived from Cryosat-2 Along-Track Freeboard Measurements into the Met Office's Forecast Ocean Assimilation Model (FOAM)

The feasibility of assimilating sea ice thickness (SIT) observations derived from CryoSat-2 along-track measurements of sea ice freeboard is successfully demonstrated using a 3D-Var assimilation scheme, NEMOVAR, within the Met Office's global, coupled ocean–sea-ice model, Forecast Ocean Assimilation Model (FOAM). The CryoSat-2 Arctic freeboard measurements are produced by the Centre for Polar Observation and Modelling (CPOM) and are converted to SIT within FOAM using modelled snow depth. This is the first time along-track observations of SIT have been used in this way, with other centres assimilating gridded and temporally averaged observations. The assimilation leads to improvements in the SIT analysis and forecast fields generated by FOAM, particularly in the Canadian Arctic. Arctic-wide observation-minus-background assimilation statistics for 2015–2017 show improvements of 0.75 m mean difference and 0.41 m root-mean-square difference (RMSD) in the freeze-up period and 0.46 m mean difference and 0.33 m RMSD in the ice break-up period. Validation of the SIT analysis against independent springtime in situ SIT observations from NASA Operation IceBridge (OIB) shows improvement in the SIT analysis of 0.61 m mean difference (0.42 m RMSD) compared to a control without SIT assimilation. Similar improvements are seen in the FOAM 5 d SIT forecast. Validation of the SIT assimilation with independent Beaufort Gyre Exploration Project (BGEP) sea ice draft observations does not show an improvement, since the assimilated CryoSat-2 observations compare similarly to the model without assimilation in this region. Comparison with airborne electromagnetic induction (Air-EM) combined measurements of SIT and snow depth shows poorer results for the assimilation compared to the control, despite covering similar locations to the OIB and BGEP datasets. This may be evidence of sampling uncertainty in the matchups with the Air-EM validation dataset, owing to the limited number of observations available over the time period of interest. This may also be evidence of noise in the SIT analysis or uncertainties in the modelled snow depth, in the assimilated SIT observations, or in the data used for validation. The SIT analysis could be improved by upgrading the observation uncertainties used in the assimilation. Despite the lack of CryoSat-2 SIT observations available for assimilation over the summer due to the detrimental effect of melt ponds on retrievals, it is shown that the model is able to retain improvements to the SIT field throughout the summer months due to prior, wintertime SIT assimilation. This also results in regional improvements to the July modelled sea ice concentration (SIC) of 5 % RMSD in the European sector, due to slower melt of the thicker sea ice.

Emma K. Fiedler↗
NASA NTRS2022

Trends in Europe Storm Surge Extremes Match the Rate of Sea-level Rise

Coastal communities across the world are already feeling the disastrous impacts of climate change through variations in extreme sea levels1. These variations reflect the combined effect of sea-level rise and changes in storm surge activity. Understanding the relative importance of these two factors in altering the likelihood of extreme events is crucial to the success of coastal adaptation measures. Existing analyses of tide gauge records agree that sea-level rise has been a considerable driver of trends in sea-level extremes since at least 1960. However, the contribution from changes in storminess remains unclear, owing to the difficulty of inferring this contribution from sparse data and the consequent inconclusive results that have accumulated in the literature. Here we analyse tide gauge observations using spatial Bayesian methods to show that, contrary to current thought, trends in surge extremes and sea-level rise both made comparable contributions to the overall change in extreme sea levels in Europe since 1960 . We determine that the trend pattern of surge extremes reflects the contributions from a dominant north–south dipole associated with internal climate variability and a single-sign positive pattern related to anthropogenic forcing. Our results demonstrate that both external and internal influences can considerably affect the likelihood of surge extremes over periods as long as 60 years, suggesting that the current coastal planning practice of assuming stationary surge extremes might be inadequate.

Francisco M Calafat↗
NASA NTRSDate not supplied

Decadal Changes in the Antarctic Sea Ice Response to the Changing ENSO in the Last Four Decades

Variation of the sea ice fraction (SIF) over the Ross/Amundsen/Bellingshausen Sea (RAB) is investigated focusing on the decadal difference in time-lagged response to ENSO between the late 20th century (1980-2000, L20) and the early 21st century (2001-2021, E21). Moving correlation of the ENSO index against the SIF and sea surface temperature (SST), respectively, over the RAB region indicates that they are more strongly linked in the L20 than in the E21, with a time lag of up to two seasons. Time-lagged regressions onto the mature phase of El Niño reveal that the SIF decrease and SST increase over the RAB is relatively weaker in the E21 and most pronounced at a shorter time-lag of 0-4 months. In contrast, the SIF melt in the L20 continues to grow and reaches its peak at approximately two season lag (5-7 month). Near-surface wind, upper-level circulation and geopotential height, and wave activity in response to the ENSO in the L20 explain that they create a zonally oriented system of high- and low-pressure areas with a two-season lag, enhancing the poleward meridional flow that plays a key role in sea ice melt in the Ross and Amundsen Sea during El Niño. However, in the E21, this pattern is no longer active at the same lag, and the circulation determines the region of maximum sea ice melt east of the pattern observed in the L20. This study suggests that stronger ENSO on average and higher frequency of eastern Pacific ENSO occurrence in the L20 are associated with this decadal change in the lagged response of the RAB SIF to ENSO.

Young-Kwon Lim↗
NASA NTRS2026

Changes in Arctic and Antarctic Sea-Ice Properties and Processes

Although sea ice covers ~9% of the world’s oceans, its presence, and absence, vastly alter the energetic balance of Earth’s climate. Because of geographic differences, Arctic and Antarctic sea ice have different dominant properties and processes, which produce different responses to natural variability and anthropogenic change. In this Review, we survey changes in sea-ice properties and processes of the Arctic and Antarctic environments. In the Arctic, the melt season has lengthened by ~7.4 days decade −1 (1979-2024). There has been a corresponding decrease in winter sea-ice thickness (1.6 m mean total over 1980-2023), summer surface albedo (0.03 decade −1 over 1979-2020), and spring snow depth (2.5 cm decade −1 over 1954-2024). In comparison, corresponding changes in the Antarctic are small overall due to contrasting regional and temporal trends. From a dynamic perspective, sea-ice motion in both hemispheres has increased, with 0.63 cm s −1 decade −1 in the Arctic (1978-2024) and 0.69 cm s −1 decade −1 in the Antarctic (1982-2024), whereas trends in polynya occurrence differ by region. Future research should prioritize sea-ice observations that can inform how the climate system responds to natural variability and anthropogenic change, be standardized to increase uptake by the broader scientific community, and target process-oriented interactions to support model development.

Melinda A Webster↗