scholarly journals An Improved Estimate of the Coupled Arctic Energy Budget

2019 ◽  
Vol 32 (22) ◽  
pp. 7915-7934 ◽  
Author(s):  
Michael Mayer ◽  
Steffen Tietsche ◽  
Leopold Haimberger ◽  
Takamasa Tsubouchi ◽  
Johannes Mayer ◽  
...  
Keyword(s):  
Sea Ice ◽  
Energy Budget ◽  
Annual Cycle ◽  
Heat Content ◽  
Diagnostic Techniques ◽  
The Arctic ◽  
Heat Accumulation ◽  
Ocean Reanalysis ◽  
Sea Ice Thickness ◽  
The Mean

Abstract This study combines state-of-the-art reanalyses such as the fifth-generation European Re-Analysis (ERA5) and the Ocean Reanalysis System 5 (ORAS5) with novel observational products to present an updated estimate of the coupled atmosphere–ocean–sea ice Arctic energy budget, including flux and storage terms covering 2001–17. Observational products provide independent estimates of crucial budget terms, including oceanic heat transport from unique mooring-derived data, radiative fluxes from satellites, and sea ice volume from merged satellite data. Results show that the time averages of independent estimates of radiative, atmospheric, and oceanic energy fluxes into the Arctic Ocean domain are remarkably consistent in the sense that their sum closely matches the observed rate of regional long-term oceanic heat accumulation of ~1 W m−2. Atmospheric and oceanic heat transports are found to be stronger compared to earlier assessments (~100 and ~16 W m−2, respectively). Data inconsistencies are larger when considering the mean annual cycle of the coupled energy budget, with RMS values of the monthly budget residual between 7 and 15 W m−2, depending on the employed datasets. This nevertheless represents an average reduction of ~72% of the residual compared to earlier work and demonstrates the progress made in data quality and diagnostic techniques. Finally, the budget residual is eliminated using a variational approach to provide a best estimate of the mean annual cycle. The largest remaining sources of uncertainty are ocean heat content and latent heat associated with sea ice melt and freeze, which both suffer from the lack of observational constraints. More ocean in situ observations and reliable sea ice thickness observations and their routinely assimilation into reanalyses are needed to further reduce uncertainty.

The Annual Cycle of the Energy Budget. Part I: Global Mean and Land–Ocean Exchanges

2008 ◽  
Vol 21 (10) ◽  
pp. 2297-2312 ◽  
Author(s):  
John T. Fasullo ◽  
Kevin E. Trenberth
Keyword(s):  
Energy Budget ◽  
Annual Cycle ◽  
Radiant Energy ◽  
Heat Content ◽  
Ocean Heat Content ◽  
Radiation Budget ◽  
Global Ocean ◽  
Net Radiation ◽  
Community Land Model ◽  
The Mean

Abstract The mean and annual cycle of energy flowing into the climate system and its storage, release, and transport in the atmosphere, ocean, and land surface are estimated with recent observations. An emphasis is placed on establishing internally consistent quantitative estimates with discussion and assessment of uncertainty. At the top of the atmosphere (TOA), adjusted radiances from the Earth Radiation Budget Experiment (ERBE) and Clouds and the Earth’s Radiant Energy System (CERES) are used, while in the atmosphere the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis and 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40) estimates are used. The net upward surface flux (FS) over ocean is derived as the residual of the TOA and atmospheric energy budgets, and is compared with direct calculations of ocean heat content (OE) and its tendency (δOE/δt) from several ocean temperature datasets. Over land, FS from a stand-alone simulation of the Community Land Model forced by observed fields is used. A depiction of the full energy budget based on ERBE fluxes from 1985 to 1989 and CERES fluxes from 2000 to 2004 is constructed that matches estimates of the global, global ocean, and global land imbalances. In addition, the annual cycle of the energy budget during both periods is examined and compared with ocean heat content changes. The near balance between the net TOA radiation (RT) and FS over ocean and thus with OE, and between RT and atmospheric total energy divergence over land, are documented both in the mean and for the annual cycle. However, there is an annual mean transport of energy by the atmosphere from ocean to land regions of 2.2 ± 0.1 PW (1 PW = 1015 W) primarily in the northern winter when the transport exceeds 5 PW. The global albedo is dominated by a semiannual cycle over the oceans, but combines with the large annual cycle in solar insolation to produce a peak in absorbed solar and net radiation in February, somewhat after the perihelion, and with the net radiation 4.3 PW higher than the annual mean, as it is enhanced by the annual cycle of outgoing longwave radiation that is dominated by land regions. In situ estimates of the annual variation of OE are found to be unrealistically large. Challenges in diagnosing the interannual variability in the energy budget and its relationship to climate change are identified in the context of the episodic and inconsistent nature of the observations.

scholarly journals Seasonal prediction and predictability of regional Antarctic sea ice

2021 ◽  
pp. 1-68
Author(s):  
Mitchell Bushuk ◽  
Michael Winton ◽  
F. Alexander Haumann ◽  
Thomas Delworth ◽  
Feiyu Lu ◽  
...  
Keyword(s):  
Sea Ice ◽  
Heat Content ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
The Arctic ◽  
Ice Thickness ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Antarctic Sea Ice

AbstractCompared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen and Bellingshausen, Indian, and West Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently-developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal timescales.

scholarly journals Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends

2014 ◽  
Vol 8 (2) ◽  
pp. 705-720 ◽  
Author(s):  
M. Zygmuntowska ◽  
P. Rampal ◽  
N. Ivanova ◽  
L. H. Smedsrud
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Minor Importance ◽  
Sea Ice Thickness ◽  
Total Uncertainty ◽  
Ice Volume ◽  
The Mean

Abstract. Sea ice volume has decreased in the last decades, evoked by changes in sea ice area and thickness. Estimates of sea ice area and thickness rely on a number of geophysical parameters which introduce large uncertainties. To quantify these uncertainties we use freeboard retrievals from ICESat and investigate different assumptions about snow depth, sea ice density and area. We find that uncertainties in ice area are of minor importance for the estimates of sea ice volume during the cold season in the Arctic basin. The choice of mean ice density used when converting sea ice freeboard into thickness mainly influences the resulting mean sea ice thickness, while snow depth on top of the ice is the main driver for the year-to-year variability, particularly in late winter. The absolute uncertainty in the mean sea ice thickness is 0.28 m in February/March and 0.21 m in October/November. The uncertainty in snow depth contributes up to 70% of the total uncertainty and the ice density 30–35%, with higher values in October/November. We find large uncertainties in the total sea ice volume and trend. The mean total sea ice volume is 10 120 ± 1280 km3 in October/November and 13 250 ± 1860 km3 in February/March for the time period 2005–2007. Based on these uncertainties we obtain trends in sea ice volume of −1450 ± 530 km3 a−1 in October/November and −880 ± 260 km3 a−1 in February/March over the ICESat period (2003–2008). Our results indicate that, taking into account the uncertainties, the decline in sea ice volume in the Arctic between the ICESat (2003–2008) and CryoSat-2 (2010–2012) periods may have been less dramatic than reported in previous studies. However, more work and validation is required to quantify these changes and analyse possible unresolved biases in the freeboard retrievals.

Improving sea-ice cover and SST forecasts by sea-ice thickness initialization

2020 ◽  
Author(s):  
Steffen Tietsche ◽  
Beena Balan Sarojini ◽  
Michael Mayer ◽  
Hao Zuo ◽  
Frederic Vitart ◽  
...  
Keyword(s):  
Sea Ice ◽  
Daily Basis ◽  
The Arctic ◽  
Ice Thickness ◽  
Ocean Reanalysis ◽  
Data Set ◽  
Sea Ice Thickness ◽  
Arctic Ice ◽  
The Impact ◽  
Melting Season

<p>A substantial amount of subseasonal-to-seasonal sea-ice variability is potentially predictable, but improved model biases and initialization techniques are needed to realize this potential. Forecasts for other Earth System components can be expected to benefit from improved sea-ice forecasts as well, because the presence of sea ice drastically alters exchanges of heat and momentum between the atmosphere and the ocean. Here, we present the impact of initializing subseasonal forecasts with observed sea-ice thickness. The newly developed sea-ice thickness data set CS2SMOS that we use is derived from radar altimetry and L-band radiance satellite observations. It allows for the first time a spatially complete view of pan-Arctic ice thickness on a near-daily basis during the freezing season. The ingestion of this data into the ECMWF ocean reanalysis system improves subseasonal forecasts of the Arctic ice edge during the melting season by up to 10%. Sea-surface temperature forecasts at high latitudes are also significantly improved during the melting season, because an improved prediction of ice-free date allows an improved forecast of the amount of seasonal warming. These results illustrate the potential for improving subseasonal-to-seasonal predictions by initializing the sea-ice thickness.</p>

scholarly journals Uncertainties in Arctic sea ice thickness and volume: new estimates and implications for trends

2013 ◽  
Vol 7 (5) ◽  
pp. 5051-5095 ◽  
Author(s):  
M. Zygmuntowska ◽  
P. Rampal ◽  
N. Ivanova ◽  
L. H. Smedsrud
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
Minor Importance ◽  
Sea Ice Thickness ◽  
Total Uncertainty ◽  
Ice Volume ◽  
The Mean

Abstract. Sea ice volume has been found to decrease in the last decades, evoked by changes in sea ice area and thickness. Estimates of sea ice area and thickness rely on a number of geophysical parameters which introduce large uncertainties. To quantify these uncertainties we use freeboard retrievals from ICESat and investigate different assumptions on snow depth, sea ice density and area. We find that uncertainties in ice area are of minor importance for the estimates of sea ice volume during the cold season in the Arctic basin. The choice of mean ice density used when converting sea ice freeboard into thickness mainly influences the resulting mean sea ice thickness, while snow depth on top of the ice is the main driver for the year-to-year variability, particularly in late winter. The absolute uncertainty in the mean sea ice thickness is 0.28 m in February/March and 0.21 m in October/November. The uncertainty in snow depth contributes up to 70% of the total uncertainty and the ice density 30–35%, with higher values in October/November. We find large uncertainties in the total sea ice volume and trend. The mean total sea ice volume is 10 120 ± 1278 km3 in October/November and 13 254 ± 1858 km3 in February/March for the time period 2005–2007. Based on these uncertainties we obtain trends in sea ice volume of −1445 ± 531 km^3 a−1 in October/November and −875 ± 257 km3 a−1 in February/March over the ICESat period (2003–2008). Our results indicate that, taking into account the uncertainties, the decline in sea ice volume in the Arctic between the ICESat (2003–2008) and CryoSat-2 (2010–2012) periods may have been less dramatic than reported in previous studies.

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

The implications of selected processing methods on satellite altimetry derived sea ice thickness state and trends in the seasonal ice zone

2021 ◽  
Author(s):  
Isolde Glissenaar ◽  
Jack Landy ◽  
Alek Petty ◽  
Nathan Kurtz ◽  
Julienne Stroeve
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Satellite Altimetry ◽  
Region Of Interest ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Baffin Bay ◽  
The Impact

<p>The ice cover of the Arctic Ocean is increasingly becoming dominated by seasonal sea ice. It is important to focus on the processing of altimetry ice thickness data in thinner seasonal ice regions to understand seasonal sea ice behaviour better. This study focusses on Baffin Bay as a region of interest to study seasonal ice behaviour.</p><p>We aim to reconcile the spring sea ice thickness derived from multiple satellite altimetry sensors and sea ice charts in Baffin Bay and produce a robust long-term record (2003-2020) for analysing trends in sea ice thickness. We investigate the impact of choosing different snow depth products (the Warren climatology, a passive microwave snow depth product and modelled snow depth from reanalysis data) and snow redistribution methods (a sigmoidal function and an empirical piecewise function) to retrieve sea ice thickness from satellite altimetry sea ice freeboard data.</p><p>The choice of snow depth product and redistribution method results in an uncertainty envelope around the March mean sea ice thickness in Baffin Bay of 10%. Moreover, the sea ice thickness trend ranges from -15 cm/dec to 20 cm/dec depending on the applied snow depth product and redistribution method. Previous studies have shown a possible long-term asymmetrical trend in sea ice thinning in Baffin Bay. The present study shows that whether a significant long-term asymmetrical trend was found depends on the choice of snow depth product and redistribution method. The satellite altimetry sea ice thickness results with different snow depth products and snow redistribution methods show that different processing techniques can lead to different results and can influence conclusions on total and spatial sea ice thickness trends. Further processing work on the historic radar altimetry record is needed to create reliable sea ice thickness products in the marginal ice zone.</p>

scholarly journals Near Real Time Arctic sea ice thickness and volume from CryoSat-2

2016 ◽  
Author(s):  
R. L. Tilling ◽  
A. Ridout ◽  
A. Shepherd
Keyword(s):  
Sea Ice ◽  
Real Time ◽  
Arctic Sea Ice ◽  
Arctic Climate ◽  
The Arctic ◽  
Ice Thickness ◽  
Satellite Orbits ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Arctic Sea Ice Thickness

Abstract. Timely observations of sea ice thickness help us to understand Arctic climate, and can support maritime activities in the Polar Regions. Although it is possible to calculate Arctic sea ice thickness using measurements acquired by CryoSat-2, the latency of the final release dataset is typically one month, due to the time required to determine precise satellite orbits. We use a new fast delivery CryoSat-2 dataset based on preliminary orbits to compute Arctic sea ice thickness in near real time (NRT), and analyse this data for one sea ice growth season from October 2014 to April 2015. We show that this NRT sea ice thickness product is of comparable accuracy to that produced using the final release CryoSat-2 data, with an average thickness difference of 5 cm, demonstrating that the satellite orbit is not a critical factor in determining sea ice freeboard. In addition, the CryoSat-2 fast delivery product also provides measurements of Arctic sea ice thickness within three days of acquisition by the satellite, and a measurement is delivered, on average, within 10, 7 and 6 km of each location in the Arctic every 2, 14 and 28 days respectively. The CryoSat-2 NRT sea ice thickness dataset provides an additional constraint for seasonal predictions of Arctic climate change, and will allow industries such as tourism and transport to navigate the polar oceans with safety and care.

scholarly journals Satellite-based sea ice thickness changes in the Laptev Sea from 2002 to 2017: comparison to mooring observations

2020 ◽  
Vol 14 (7) ◽  
pp. 2189-2203
Author(s):  
H. Jakob Belter ◽  
Thomas Krumpen ◽  
Stefan Hendricks ◽  
Jens Hoelemann ◽  
Markus A. Janout ◽  
...  
Keyword(s):  
Sea Ice ◽  
Source Region ◽  
European Space Agency ◽  
The Arctic ◽  
Ice Thickness ◽  
Laptev Sea ◽  
Validation Data ◽  
Data Set ◽  
Sea Ice Thickness ◽  
The Laptev Sea

Abstract. The gridded sea ice thickness (SIT) climate data record (CDR) produced by the European Space Agency (ESA) Sea Ice Climate Change Initiative Phase 2 (CCI-2) is the longest available, Arctic-wide SIT record covering the period from 2002 to 2017. SIT data are based on radar altimetry measurements of sea ice freeboard from the Environmental Satellite (ENVISAT) and CryoSat-2 (CS2). The CCI-2 SIT has previously been validated with in situ observations from drilling, airborne remote sensing, electromagnetic (EM) measurements and upward-looking sonars (ULSs) from multiple ice-covered regions of the Arctic. Here we present the Laptev Sea CCI-2 SIT record from 2002 to 2017 and use newly acquired ULS and upward-looking acoustic Doppler current profiler (ADCP) sea ice draft (VAL) data for validation of the gridded CCI-2 and additional satellite SIT products. The ULS and ADCP time series provide the first long-term satellite SIT validation data set from this important source region of sea ice in the Transpolar Drift. The comparison of VAL sea ice draft data with gridded monthly mean and orbit trajectory CCI-2 data, as well as merged CryoSat-2–SMOS (CS2SMOS) sea ice draft, shows that the agreement between the satellite and VAL draft data strongly depends on the thickness of the sampled ice. Rather than providing mean sea ice draft, the considered satellite products provide modal sea ice draft in the Laptev Sea. Ice drafts thinner than 0.7 m are overestimated, while drafts thicker than approximately 1.3 m are increasingly underestimated by all satellite products investigated for this study. The tendency of the satellite SIT products to better agree with modal sea ice draft and underestimate thicker ice needs to be considered for all past and future investigations into SIT changes in this important region. The performance of the CCI-2 SIT CDR is considered stable over time; however, observed trends in gridded CCI-2 SIT are strongly influenced by the uncertainties of ENVISAT and CS2 and the comparably short investigation period.

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