Articles | Volume 16, issue 8
https://doi.org/10.5194/tc-16-3235-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-3235-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Aug 2022
Research article |
| 12 Aug 2022
Improving model-satellite comparisons of sea ice melt onset with a satellite simulator
Abigail Smith
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, Colorado, USA
now at: National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
Alexandra Jahn
Department of Atmospheric and Oceanic Sciences and Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, Colorado, USA
Clara Burgard
Max Planck Institute for Meteorology, Hamburg, Germany
now at: CNRS, IRD, Grenoble INP, IGE, Univ. Grenoble Alpes, 38000 Grenoble, France
Dirk Notz
Max Planck Institute for Meteorology, Hamburg, Germany
Center for Earth System Research and Sustainability (CEN), University of Hamburg, Hamburg, Germany
Related authors
Abigail Smith, Alexandra Jahn, and Muyin Wang
The Cryosphere, 14, 2977–2997, https://doi.org/10.5194/tc-14-2977-2020, https://doi.org/10.5194/tc-14-2977-2020, 2020
Short summary
Short summary
The annual cycle of Arctic sea ice can be used to gain more information about how climate model simulations of sea ice compare to observations. In some models, the September sea ice area agrees with observations for the wrong reasons because biases in the timing of seasonal transitions compensate for other unrealistic sea ice characteristics. This research was done to provide new process-based metrics of Arctic sea ice using satellite observations, the CESM Large Ensemble, and CMIP6 models.
Abigail Smith and Alexandra Jahn
The Cryosphere, 13, 1–20, https://doi.org/10.5194/tc-13-1-2019, https://doi.org/10.5194/tc-13-1-2019, 2019
Short summary
Short summary
Here we assessed how natural climate variations and different definitions impact the diagnosed and projected Arctic sea ice melt season length using model simulations. Irrespective of the definition or natural variability, the sea ice melt season is projected to lengthen, potentially by as much as 4–5 months by 2100 under the business as usual scenario. We also find that different definitions have a bigger impact on melt onset, while natural variations have a bigger impact on freeze onset.
Gifford H. Miller, Simon L. Pendleton, Alexandra Jahn, Yafang Zhong, John T. Andrews, Scott J. Lehman, Jason P. Briner, Jonathan H. Raberg, Helga Bueltmann, Martha Raynolds, Áslaug Geirsdóttir, and John R. Southon
Clim. Past, 19, 2341–2360, https://doi.org/10.5194/cp-19-2341-2023, https://doi.org/10.5194/cp-19-2341-2023, 2023
Short summary
Short summary
Receding Arctic ice caps reveal moss killed by earlier ice expansions; 186 moss kill dates from 71 ice caps cluster at 250–450, 850–1000 and 1240–1500 CE and continued expanding 1500–1880 CE, as recorded by regions of sparse vegetation cover, when ice caps covered > 11 000 km2 but < 100 km2 at present. The 1880 CE state approached conditions expected during the start of an ice age; climate models suggest this was only reversed by anthropogenic alterations to the planetary energy balance.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Veronique Bouchet, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Detlef Stammer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-376, https://doi.org/10.5194/essd-2023-376, 2023
Preprint under review for ESSD
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines are proposed as international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-125, https://doi.org/10.5194/gmd-2023-125, 2023
Preprint under review for GMD
Short summary
Short summary
Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Lena Nicola, Dirk Notz, and Ricarda Winkelmann
The Cryosphere, 17, 2563–2583, https://doi.org/10.5194/tc-17-2563-2023, https://doi.org/10.5194/tc-17-2563-2023, 2023
Short summary
Short summary
For future sea-level projections, approximating Antarctic precipitation increases through temperature-scaling approaches will remain important, as coupled ice-sheet simulations with regional climate models remain computationally expensive, especially on multi-centennial timescales. We here revisit the relationship between Antarctic temperature and precipitation using different scaling approaches, identifying and explaining regional differences.
Saskia Kahl, Carolin Mehlmann, and Dirk Notz
EGUsphere, https://doi.org/10.5194/egusphere-2023-982, https://doi.org/10.5194/egusphere-2023-982, 2023
Short summary
Short summary
Ice mélange is a mixture of sea ice and icebergs, which can have a strong influence on the sea-ice-ocean interaction. So far, ice mélange is not represented in climate models. We include icebergs into the most used sea-ice model by modifying the mathematical equations that describe the material law of sea ice. We show with three test cases that the modification is necessary to represent icebergs. Furthermore we suggest a numerical method to solve the ice mélange equations computational efficient.
Philipp de Vrese, Goran Georgievski, Jesus Fidel Gonzalez Rouco, Dirk Notz, Tobias Stacke, Norman Julius Steinert, Stiig Wilkenskjeld, and Victor Brovkin
The Cryosphere, 17, 2095–2118, https://doi.org/10.5194/tc-17-2095-2023, https://doi.org/10.5194/tc-17-2095-2023, 2023
Short summary
Short summary
The current generation of Earth system models exhibits large inter-model differences in the simulated climate of the Arctic and subarctic zone. We used an adapted version of the Max Planck Institute (MPI) Earth System Model to show that differences in the representation of the soil hydrology in permafrost-affected regions could help explain a large part of this inter-model spread and have pronounced impacts on important elements of Earth systems as far to the south as the tropics.
Clara Burgard, Nicolas C. Jourdain, Ronja Reese, Adrian Jenkins, and Pierre Mathiot
The Cryosphere, 16, 4931–4975, https://doi.org/10.5194/tc-16-4931-2022, https://doi.org/10.5194/tc-16-4931-2022, 2022
Short summary
Short summary
The ocean-induced melt at the base of the floating ice shelves around Antarctica is one of the largest uncertainty factors in the Antarctic contribution to future sea-level rise. We assess the performance of several existing parameterisations in simulating basal melt rates on a circum-Antarctic scale, using an ocean simulation resolving the cavities below the shelves as our reference. We find that the simple quadratic slope-independent and plume parameterisations yield the best compromise.
Andreas Wernecke, Dirk Notz, Stefan Kern, and Thomas Lavergne
EGUsphere, https://doi.org/10.5194/egusphere-2022-1189, https://doi.org/10.5194/egusphere-2022-1189, 2022
Short summary
Short summary
The total area of Arctic sea ice (SIA) is routinely monitored with the help of satellite measurements, which is an important climate indicator. Uncertainties in observations of sea-ice concentration (SIC) cancel out when averaging but the degree to which this is happening has been unclear. We calculate the uncertainty in SIA based on uncertainties in SIC and find that daily SIA estimates have uncertainties of about 300 000 km2. The 2002 to 2017 September trend is 105 000 ± 9 000 km2 per year.
Xiaoxu Shi, Dirk Notz, Jiping Liu, Hu Yang, and Gerrit Lohmann
Geosci. Model Dev., 14, 4891–4908, https://doi.org/10.5194/gmd-14-4891-2021, https://doi.org/10.5194/gmd-14-4891-2021, 2021
Short summary
Short summary
The ice–ocean heat flux is one of the key elements controlling sea ice changes. It motivates our study, which aims to examine the responses of modeled climate to three ice–ocean heat flux parameterizations, including two old approaches that assume one-way heat transport and a new one describing a double-diffusive ice–ocean heat exchange. The results show pronounced differences in the modeled sea ice, ocean, and atmosphere states for the latter as compared to the former two parameterizations.
Max Thomas, James France, Odile Crabeck, Benjamin Hall, Verena Hof, Dirk Notz, Tokoloho Rampai, Leif Riemenschneider, Oliver John Tooth, Mathilde Tranter, and Jan Kaiser
Atmos. Meas. Tech., 14, 1833–1849, https://doi.org/10.5194/amt-14-1833-2021, https://doi.org/10.5194/amt-14-1833-2021, 2021
Short summary
Short summary
We describe the Roland von Glasow Air-Sea-Ice Chamber, a laboratory facility for studying ocean–sea-ice–atmosphere interactions. We characterise the technical capabilities of our facility to help future users plan and perform experiments. We also characterise the sea ice grown in the facility, showing that the extinction of photosynthetically active radiation, the bulk salinity, and the growth rate of our artificial sea ice are within the range of natural values.
Abigail Smith, Alexandra Jahn, and Muyin Wang
The Cryosphere, 14, 2977–2997, https://doi.org/10.5194/tc-14-2977-2020, https://doi.org/10.5194/tc-14-2977-2020, 2020
Short summary
Short summary
The annual cycle of Arctic sea ice can be used to gain more information about how climate model simulations of sea ice compare to observations. In some models, the September sea ice area agrees with observations for the wrong reasons because biases in the timing of seasonal transitions compensate for other unrealistic sea ice characteristics. This research was done to provide new process-based metrics of Arctic sea ice using satellite observations, the CESM Large Ensemble, and CMIP6 models.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, and Rasmus Tonboe
The Cryosphere, 14, 2469–2493, https://doi.org/10.5194/tc-14-2469-2020, https://doi.org/10.5194/tc-14-2469-2020, 2020
Short summary
Short summary
Arctic sea-ice concentration (SIC) estimates based on satellite passive microwave observations are highly inaccurate during summer melt. We compare 10 different SIC products with independent satellite data of true SIC and melt pond fraction (MPF). All products disagree with the true SIC. Regional and inter-product differences can be large and depend on the MPF. An inadequate treatment of melting snow and melt ponds in the products’ algorithms appears to be the main explanation for our findings.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2369–2386, https://doi.org/10.5194/tc-14-2369-2020, https://doi.org/10.5194/tc-14-2369-2020, 2020
Short summary
Short summary
The high disagreement between observations of Arctic sea ice makes it difficult to evaluate climate models with observations. We investigate the possibility of translating the model state into what a satellite could observe. We find that we do not need complex information about the vertical distribution of temperature and salinity inside the ice but instead are able to assume simplified distributions to reasonably translate the simulated sea ice into satellite
language.
Clara Burgard, Dirk Notz, Leif T. Pedersen, and Rasmus T. Tonboe
The Cryosphere, 14, 2387–2407, https://doi.org/10.5194/tc-14-2387-2020, https://doi.org/10.5194/tc-14-2387-2020, 2020
Short summary
Short summary
The high disagreement between observations of Arctic sea ice inhibits the evaluation of climate models with observations. We develop a tool that translates the simulated Arctic Ocean state into what a satellite could observe from space in the form of brightness temperatures, a measure for the radiation emitted by the surface. We find that the simulated brightness temperatures compare well with the observed brightness temperatures. This tool brings a new perspective for climate model evaluation.
Stefan Kern, Thomas Lavergne, Dirk Notz, Leif Toudal Pedersen, Rasmus Tage Tonboe, Roberto Saldo, and Atle MacDonald Sørensen
The Cryosphere, 13, 3261–3307, https://doi.org/10.5194/tc-13-3261-2019, https://doi.org/10.5194/tc-13-3261-2019, 2019
Short summary
Short summary
A systematic evaluation of 10 global satellite data products of the polar sea-ice area is performed. Inter-product differences in evaluation results call for careful consideration of data product limitations when performing sea-ice area trend analyses and for further mitigation of the effects of sensor changes. We open a discussion about evaluation strategies for such data products near-0 % and near-100 % sea-ice concentration, e.g. with the aim to improve high-concentration evaluation accuracy.
Thomas Lavergne, Atle Macdonald Sørensen, Stefan Kern, Rasmus Tonboe, Dirk Notz, Signe Aaboe, Louisa Bell, Gorm Dybkjær, Steinar Eastwood, Carolina Gabarro, Georg Heygster, Mari Anne Killie, Matilde Brandt Kreiner, John Lavelle, Roberto Saldo, Stein Sandven, and Leif Toudal Pedersen
The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, https://doi.org/10.5194/tc-13-49-2019, 2019
Short summary
Short summary
The loss of polar sea ice is an iconic indicator of Earth’s climate change. Many satellite-based algorithms and resulting data exist but they differ widely in specific sea-ice conditions. This spread hinders a robust estimate of the future evolution of sea-ice cover.
In this study, we document three new climate data records of sea-ice concentration generated using satellite data available over the last 40 years. We introduce the novel algorithms, the data records, and their uncertainties.
Abigail Smith and Alexandra Jahn
The Cryosphere, 13, 1–20, https://doi.org/10.5194/tc-13-1-2019, https://doi.org/10.5194/tc-13-1-2019, 2019
Short summary
Short summary
Here we assessed how natural climate variations and different definitions impact the diagnosed and projected Arctic sea ice melt season length using model simulations. Irrespective of the definition or natural variability, the sea ice melt season is projected to lengthen, potentially by as much as 4–5 months by 2100 under the business as usual scenario. We also find that different definitions have a bigger impact on melt onset, while natural variations have a bigger impact on freeze onset.
Simon L. Pendleton, Gifford H. Miller, Robert A. Anderson, Sarah E. Crump, Yafang Zhong, Alexandra Jahn, and Áslaug Geirsdottir
Clim. Past, 13, 1527–1537, https://doi.org/10.5194/cp-13-1527-2017, https://doi.org/10.5194/cp-13-1527-2017, 2017
Short summary
Short summary
Recent warming in the high latitudes has prompted the accelerated retreat of ice caps and glaciers, especially in the Canadian Arctic. Here we use the radiocarbon age of preserved plants being exposed by shrinking ice caps that once entombed them. These ages help us to constrain the timing and magnitude of climate change on southern Baffin Island over the past ~ 2000 years. Our results show episodic cooling up until ~ 1900 CE, followed by accelerated warming through present.
Benjamin M. Sanderson, Yangyang Xu, Claudia Tebaldi, Michael Wehner, Brian O'Neill, Alexandra Jahn, Angeline G. Pendergrass, Flavio Lehner, Warren G. Strand, Lei Lin, Reto Knutti, and Jean Francois Lamarque
Earth Syst. Dynam., 8, 827–847, https://doi.org/10.5194/esd-8-827-2017, https://doi.org/10.5194/esd-8-827-2017, 2017
Short summary
Short summary
We present the results of a set of climate simulations designed to simulate futures in which the Earth's temperature is stabilized at the levels referred to in the 2015 Paris Agreement. We consider the necessary future emissions reductions and the aspects of extreme weather which differ significantly between the 2 and 1.5 °C climate in the simulations.
Sifan Gu, Zhengyu Liu, Alexandra Jahn, Johannes Rempfer, Jiaxu Zhang, and Fortunat Joos
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2017-40, https://doi.org/10.5194/gmd-2017-40, 2017
Revised manuscript not accepted
Short summary
Short summary
This paper is the documentation of the implementation of neodymium (Nd) isotopes in the ocean model of CESM. Our model can simulate both Nd concentration and Nd isotope ratio in good agreement with observations. Our Nd-enabled ocean model makes it possible for direct model-data comparison in paleoceanographic studies, which can help to resolve some uncertainties and controversies in our understanding of past ocean evolution. Therefore, our model provides a useful tool for paleoclimate studies.
Dirk Notz, Alexandra Jahn, Marika Holland, Elizabeth Hunke, François Massonnet, Julienne Stroeve, Bruno Tremblay, and Martin Vancoppenolle
Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, https://doi.org/10.5194/gmd-9-3427-2016, 2016
Short summary
Short summary
The large-scale evolution of sea ice is both an indicator and a driver of climate changes. Hence, a realistic simulation of sea ice is key for a realistic simulation of the climate system of our planet. To assess and to improve the realism of sea-ice simulations, we present here a new protocol for climate-model output that allows for an in-depth analysis of the simulated evolution of sea ice.
Sebastian Bathiany, Bregje van der Bolt, Mark S. Williamson, Timothy M. Lenton, Marten Scheffer, Egbert H. van Nes, and Dirk Notz
The Cryosphere, 10, 1631–1645, https://doi.org/10.5194/tc-10-1631-2016, https://doi.org/10.5194/tc-10-1631-2016, 2016
Short summary
Short summary
We examine if a potential "tipping point" in Arctic sea ice, causing abrupt and irreversible sea-ice loss, could be foreseen with statistical early warning signals. We assess this idea by using several models of different complexity. We find robust and consistent trends in variability that are not specific to the existence of a tipping point. While this makes an early warning impossible, it allows to estimate sea-ice variability from only short observational records or reconstructions.
A. Jahn, K. Lindsay, X. Giraud, N. Gruber, B. L. Otto-Bliesner, Z. Liu, and E. C. Brady
Geosci. Model Dev., 8, 2419–2434, https://doi.org/10.5194/gmd-8-2419-2015, https://doi.org/10.5194/gmd-8-2419-2015, 2015
Short summary
Short summary
Carbon isotopes have been added to the ocean model of the Community Earth System Model version 1 (CESM1). This paper describes the details of how the abiotic 14C tracer and the biotic 13C and 14C tracers were added to the existing ocean model of the CESM. In addition, it shows the first results of the new model features compared to observational data for the 1990s.
T. Vihma, R. Pirazzini, I. Fer, I. A. Renfrew, J. Sedlar, M. Tjernström, C. Lüpkes, T. Nygård, D. Notz, J. Weiss, D. Marsan, B. Cheng, G. Birnbaum, S. Gerland, D. Chechin, and J. C. Gascard
Atmos. Chem. Phys., 14, 9403–9450, https://doi.org/10.5194/acp-14-9403-2014, https://doi.org/10.5194/acp-14-9403-2014, 2014
S. Rysgaard, F. Wang, R. J. Galley, R. Grimm, D. Notz, M. Lemes, N.-X. Geilfus, A. Chaulk, A. A. Hare, O. Crabeck, B. G. T. Else, K. Campbell, L. L. Sørensen, J. Sievers, and T. Papakyriakou
The Cryosphere, 8, 1469–1478, https://doi.org/10.5194/tc-8-1469-2014, https://doi.org/10.5194/tc-8-1469-2014, 2014
D. Zanchettin, O. Bothe, C. Timmreck, J. Bader, A. Beitsch, H.-F. Graf, D. Notz, and J. H. Jungclaus
Earth Syst. Dynam., 5, 223–242, https://doi.org/10.5194/esd-5-223-2014, https://doi.org/10.5194/esd-5-223-2014, 2014
D. Notz
The Cryosphere, 8, 229–243, https://doi.org/10.5194/tc-8-229-2014, https://doi.org/10.5194/tc-8-229-2014, 2014
M. Vancoppenolle, D. Notz, F. Vivier, J. Tison, B. Delille, G. Carnat, J. Zhou, F. Jardon, P. Griewank, A. Lourenço, and T. Haskell
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-3209-2013, https://doi.org/10.5194/tcd-7-3209-2013, 2013
Revised manuscript not accepted
S. Tietsche, D. Notz, J. H. Jungclaus, and J. Marotzke
Ocean Sci., 9, 19–36, https://doi.org/10.5194/os-9-19-2013, https://doi.org/10.5194/os-9-19-2013, 2013
Related subject area
Discipline: Sea ice | Subject: Arctic (e.g. Greenland)
Comparing elevation and backscatter retrievals from CryoSat-2 and ICESat-2 over Arctic summer sea ice
Summer sea ice floe perimeter density in the Arctic: high-resolution optical satellite imagery and model evaluation
Patterns of wintertime Arctic sea-ice leads and their relation to winds and ocean currents
A long-term proxy for sea ice thickness in the Canadian Arctic: 1996–2020
Arctic sea ice radar freeboard retrieval from the European Remote-Sensing Satellite (ERS-2) using altimetry: toward sea ice thickness observation from 1995 to 2021
Rapid sea ice changes in the future Barents Sea
Causes and evolution of winter polynyas north of Greenland
Winter Arctic sea ice thickness from ICESat-2: upgrades to freeboard and snow loading estimates and an assessment of the first three winters of data collection
Sea ice breakup and freeze-up indicators for users of the Arctic coastal environment
Modelled variations of the inherent optical properties of summer Arctic ice and their effects on the radiation budget: A case based on ice cores from CHINARE 2008–2016
Kara and Barents sea ice thickness estimation based on CryoSat-2 radar altimeter and Sentinel-1 dual-polarized synthetic aperture radar
Contribution of warm and moist atmospheric flow to a record minimum July sea ice extent of the Arctic in 2020
Perspectives on future sea ice and navigability in the Arctic
Lasting impact of winds on Arctic sea ice through the ocean's memory
Holocene sea-ice dynamics in Petermann Fjord in relation to ice tongue stability and Nares Strait ice arch formation
Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F
Combined influence of oceanic and atmospheric circulations on Greenland sea ice concentration
Seasonal changes in sea ice kinematics and deformation in the Pacific sector of the Arctic Ocean in 2018/19
Year-round impact of winter sea ice thickness observations on seasonal forecasts
Ensemble-based estimation of sea-ice volume variations in the Baffin Bay
Sea ice drift and arch evolution in the Robeson Channel using the daily coverage of Sentinel-1 SAR data for the 2016–2017 freezing season
Brief communication: Arctic sea ice thickness internal variability and its changes under historical and anthropogenic forcing
Seasonal transition dates can reveal biases in Arctic sea ice simulations
The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) high-priority candidate mission
The MOSAiC ice floe: sediment-laden survivor from the Siberian shelf
Spectral attenuation of ocean waves in pack ice and its application in calibrating viscoelastic wave-in-ice models
New observations of the distribution, morphology and dissolution dynamics of cryogenic gypsum in the Arctic Ocean
Evaluation of Arctic sea ice drift and its dependency on near-surface wind and sea ice conditions in the coupled regional climate model HIRHAM–NAOSIM
Multidecadal Arctic sea ice thickness and volume derived from ice age
Going with the floe: tracking CESM Large Ensemble sea ice in the Arctic provides context for ship-based observations
The Arctic sea ice extent change connected to Pacific decadal variability
Impact of sea ice floe size distribution on seasonal fragmentation and melt of Arctic sea ice
Induced surface fluxes: a new framework for attributing Arctic sea ice volume balance biases to specific model errors
Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution
Benchmark seasonal prediction skill estimates based on regional indices
On the timescales and length scales of the Arctic sea ice thickness anomalies: a study based on 14 reanalyses
Past and future interannual variability in Arctic sea ice in coupled climate models
Arctic sea-ice-free season projected to extend into autumn
Definition differences and internal variability affect the simulated Arctic sea ice melt season
The potential of sea ice leads as a predictor for summer Arctic sea ice extent
Arctic climate: changes in sea ice extent outweigh changes in snow cover
Arctic Mission Benefit Analysis: impact of sea ice thickness, freeboard, and snow depth products on sea ice forecast performance
Thin Arctic sea ice in L-band observations and an ocean reanalysis
Snow depth on Arctic sea ice from historical in situ data
Impacts of a lengthening open water season on Alaskan coastal communities: deriving locally relevant indices from large-scale datasets and community observations
Thermodynamic and dynamic ice thickness contributions in the Canadian Arctic Archipelago in NEMO-LIM2 numerical simulations
Geoffrey J. Dawson and Jack C. Landy
The Cryosphere, 17, 4165–4178, https://doi.org/10.5194/tc-17-4165-2023, https://doi.org/10.5194/tc-17-4165-2023, 2023
Short summary
Short summary
In this study, we compared measurements from CryoSat-2 and ICESat-2 over Arctic summer sea ice to understand any possible biases between the two satellites. We found that there is a difference when we measure elevation over summer sea ice using CryoSat-2 and ICESat-2, and this is likely due to surface melt ponds. The differences we found were in good agreement with theoretical predictions, and this work will be valuable for summer sea ice thickness measurements from both altimeters.
Yanan Wang, Byongjun Hwang, Adam William Bateson, Yevgeny Aksenov, and Christopher Horvat
The Cryosphere, 17, 3575–3591, https://doi.org/10.5194/tc-17-3575-2023, https://doi.org/10.5194/tc-17-3575-2023, 2023
Short summary
Short summary
Sea ice is composed of small, discrete pieces of ice called floes, whose size distribution plays a critical role in the interactions between the sea ice, ocean and atmosphere. This study provides an assessment of sea ice models using new high-resolution floe size distribution observations, revealing considerable differences between them. These findings point not only to the limitations in models but also to the need for more high-resolution observations to validate and calibrate models.
Sascha Willmes, Günther Heinemann, and Frank Schnaase
The Cryosphere, 17, 3291–3308, https://doi.org/10.5194/tc-17-3291-2023, https://doi.org/10.5194/tc-17-3291-2023, 2023
Short summary
Short summary
Sea ice is an important constituent of the global climate system. We here use satellite data to identify regions in the Arctic where the sea ice breaks up in so-called leads (i.e., linear cracks) regularly during winter. This information is important because leads determine, e.g., how much heat is exchanged between the ocean and the atmosphere. We here provide first insights into the reasons for the observed patterns in sea-ice leads and their relation to ocean currents and winds.
Isolde A. Glissenaar, Jack C. Landy, David G. Babb, Geoffrey J. Dawson, and Stephen E. L. Howell
The Cryosphere, 17, 3269–3289, https://doi.org/10.5194/tc-17-3269-2023, https://doi.org/10.5194/tc-17-3269-2023, 2023
Short summary
Short summary
Observations of large-scale ice thickness have unfortunately only been available since 2003, a short record for researching trends and variability. We generated a proxy for sea ice thickness in the Canadian Arctic for 1996–2020. This is the longest available record for large-scale sea ice thickness available to date and the first record reliably covering the channels between the islands in northern Canada. The product shows that sea ice has thinned by 21 cm over the 25-year record in April.
Marion Bocquet, Sara Fleury, Fanny Piras, Eero Rinne, Heidi Sallila, Florent Garnier, and Frédérique Rémy
The Cryosphere, 17, 3013–3039, https://doi.org/10.5194/tc-17-3013-2023, https://doi.org/10.5194/tc-17-3013-2023, 2023
Short summary
Short summary
Sea ice has a large interannual variability, and studying its evolution requires long time series of observations. In this paper, we propose the first method to extend Arctic sea ice thickness time series to the ERS-2 altimeter. The developed method is based on a neural network to calibrate past missions on the current one by taking advantage of their differences during the mission-overlap periods. Data are available as monthly maps for each year during the winter period between 1995 and 2021.
Ole Rieke, Marius Årthun, and Jakob Simon Dörr
The Cryosphere, 17, 1445–1456, https://doi.org/10.5194/tc-17-1445-2023, https://doi.org/10.5194/tc-17-1445-2023, 2023
Short summary
Short summary
The Barents Sea is the region of most intense winter sea ice loss, and future projections show a continued decline towards ice-free conditions by the end of this century but with large fluctuations. Here we use climate model simulations to look at the occurrence and drivers of rapid ice change events in the Barents Sea that are much stronger than the average ice loss. A better understanding of these events will contribute to improved sea ice predictions in the Barents Sea.
Younjoo J. Lee, Wieslaw Maslowski, John J. Cassano, Jaclyn Clement Kinney, Anthony P. Craig, Samy Kamal, Robert Osinski, Mark W. Seefeldt, Julienne Stroeve, and Hailong Wang
The Cryosphere, 17, 233–253, https://doi.org/10.5194/tc-17-233-2023, https://doi.org/10.5194/tc-17-233-2023, 2023
Short summary
Short summary
During 1979–2020, four winter polynyas occurred in December 1986 and February 2011, 2017, and 2018 north of Greenland. Instead of ice melting due to the anomalous warm air intrusion, the extreme wind forcing resulted in greater ice transport offshore. Based on the two ensemble runs, representing a 1980s thicker ice vs. a 2010s thinner ice, a dominant cause of these winter polynyas stems from internal variability of atmospheric forcing rather than from the forced response to a warming climate.
Alek A. Petty, Nicole Keeney, Alex Cabaj, Paul Kushner, and Marco Bagnardi
The Cryosphere, 17, 127–156, https://doi.org/10.5194/tc-17-127-2023, https://doi.org/10.5194/tc-17-127-2023, 2023
Short summary
Short summary
We present upgrades to winter Arctic sea ice thickness estimates from NASA's ICESat-2. Our new thickness results show better agreement with independent data from ESA's CryoSat-2 compared to our first data release, as well as new, very strong comparisons with data collected by moorings in the Beaufort Sea. We analyse three winters of thickness data across the Arctic, including 50 cm thinning of the multiyear ice over this 3-year period.
John E. Walsh, Hajo Eicken, Kyle Redilla, and Mark Johnson
The Cryosphere, 16, 4617–4635, https://doi.org/10.5194/tc-16-4617-2022, https://doi.org/10.5194/tc-16-4617-2022, 2022
Short summary
Short summary
Indicators for the start and end of annual breakup and freeze-up of sea ice at various coastal locations around the Arctic are developed. Relative to broader offshore areas, some of the coastal indicators show an earlier freeze-up and later breakup, especially at locations where landfast ice is prominent. However, the trends towards earlier breakup and later freeze-up are unmistakable over the post-1979 period in synthesized metrics of the coastal breakup/freeze-up indicators.
Miao Yu, Peng Lu, Matti Leppäranta, Bin Cheng, Ruibo Lei, Bingrui Li, Qingkai Wang, and Zhijun Li
EGUsphere, https://doi.org/10.5194/egusphere-2022-552, https://doi.org/10.5194/egusphere-2022-552, 2022
Short summary
Short summary
Variations in Arctic sea ice are related not only to the macroscale properties but also to its microstructure. The Arctic ice cores in the summers of 2008 to 2016 were used to analyze variations in the ice inherent optical properties related to changes in the ice microstructure. The results reveal changing ice microstructure greatly increased the amount of solar radiation transmitted to the upper ocean even when a constant ice thickness was assumed, especially in marginal ice zones.
Juha Karvonen, Eero Rinne, Heidi Sallila, Petteri Uotila, and Marko Mäkynen
The Cryosphere, 16, 1821–1844, https://doi.org/10.5194/tc-16-1821-2022, https://doi.org/10.5194/tc-16-1821-2022, 2022
Short summary
Short summary
We propose a method to provide sea ice thickness (SIT) estimates over a test area in the Arctic utilizing radar altimeter (RA) measurement lines and C-band SAR imagery. The RA data are from CryoSat-2, and SAR imagery is from Sentinel-1. By combining them we get a SIT grid covering the whole test area instead of only narrow measurement lines from RA. This kind of SIT estimation can be extended to cover the whole Arctic (and Antarctic) for operational SIT monitoring.
Yu Liang, Haibo Bi, Haijun Huang, Ruibo Lei, Xi Liang, Bin Cheng, and Yunhe Wang
The Cryosphere, 16, 1107–1123, https://doi.org/10.5194/tc-16-1107-2022, https://doi.org/10.5194/tc-16-1107-2022, 2022
Short summary
Short summary
A record minimum July sea ice extent, since 1979, was observed in 2020. Our results reveal that an anomalously high advection of energy and water vapor prevailed during spring (April to June) 2020 over regions with noticeable sea ice retreat. The large-scale atmospheric circulation and cyclones act in concert to trigger the exceptionally warm and moist flow. The convergence of the transport changed the atmospheric characteristics and the surface energy budget, thus causing a severe sea ice melt.
Jinlei Chen, Shichang Kang, Wentao Du, Junming Guo, Min Xu, Yulan Zhang, Xinyue Zhong, Wei Zhang, and Jizu Chen
The Cryosphere, 15, 5473–5482, https://doi.org/10.5194/tc-15-5473-2021, https://doi.org/10.5194/tc-15-5473-2021, 2021
Short summary
Short summary
Sea ice is retreating with rapid warming in the Arctic. It will continue and approach the worst predicted pathway released by the IPCC. The irreversible tipping point might show around 2060 when the oldest ice will have completely disappeared. It has a huge impact on human production. Ordinary merchant ships will be able to pass the Northeast Passage and Northwest Passage by the midcentury, and the opening time will advance to the next 10 years for icebreakers with moderate ice strengthening.
Qiang Wang, Sergey Danilov, Longjiang Mu, Dmitry Sidorenko, and Claudia Wekerle
The Cryosphere, 15, 4703–4725, https://doi.org/10.5194/tc-15-4703-2021, https://doi.org/10.5194/tc-15-4703-2021, 2021
Short summary
Short summary
Using simulations, we found that changes in ocean freshwater content induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates years after the wind perturbations. The impact is through changes in sea surface height and surface geostrophic currents and the most pronounced in warm seasons. Such a lasting impact might become stronger in a warming climate and implies the importance of ocean initialization in sea ice prediction.
Henrieka Detlef, Brendan Reilly, Anne Jennings, Mads Mørk Jensen, Matt O'Regan, Marianne Glasius, Jesper Olsen, Martin Jakobsson, and Christof Pearce
The Cryosphere, 15, 4357–4380, https://doi.org/10.5194/tc-15-4357-2021, https://doi.org/10.5194/tc-15-4357-2021, 2021
Short summary
Short summary
Here we examine the Nares Strait sea ice dynamics over the last 7000 years and their implications for the late Holocene readvance of the floating part of Petermann Glacier. We propose that the historically observed sea ice dynamics are a relatively recent feature, while most of the mid-Holocene was marked by variable sea ice conditions in Nares Strait. Nonetheless, major advances of the Petermann ice tongue were preceded by a shift towards harsher sea ice conditions in Nares Strait.
Timothy Williams, Anton Korosov, Pierre Rampal, and Einar Ólason
The Cryosphere, 15, 3207–3227, https://doi.org/10.5194/tc-15-3207-2021, https://doi.org/10.5194/tc-15-3207-2021, 2021
Short summary
Short summary
neXtSIM (neXt-generation Sea Ice Model) includes a novel and extremely realistic way of modelling sea ice dynamics – i.e. how the sea ice moves and deforms in response to the drag from winds and ocean currents. It has been developed over the last few years for a variety of applications, but this paper represents its first demonstration in a forecast context. We present results for the time period from November 2018 to June 2020 and show that it agrees well with satellite observations.
Sourav Chatterjee, Roshin P. Raj, Laurent Bertino, Sebastian H. Mernild, Meethale Puthukkottu Subeesh, Nuncio Murukesh, and Muthalagu Ravichandran
The Cryosphere, 15, 1307–1319, https://doi.org/10.5194/tc-15-1307-2021, https://doi.org/10.5194/tc-15-1307-2021, 2021
Short summary
Short summary
Sea ice in the Greenland Sea (GS) is important for its climatic (fresh water), economical (shipping), and ecological contribution (light availability). The study proposes a mechanism through which sea ice concentration in GS is partly governed by the atmospheric and ocean circulation in the region. The mechanism proposed in this study can be useful for assessing the sea ice variability and its future projection in the GS.
Ruibo Lei, Mario Hoppmann, Bin Cheng, Guangyu Zuo, Dawei Gui, Qiongqiong Cai, H. Jakob Belter, and Wangxiao Yang
The Cryosphere, 15, 1321–1341, https://doi.org/10.5194/tc-15-1321-2021, https://doi.org/10.5194/tc-15-1321-2021, 2021
Short summary
Short summary
Quantification of ice deformation is useful for understanding of the role of ice dynamics in climate change. Using data of 32 buoys, we characterized spatiotemporal variations in ice kinematics and deformation in the Pacific sector of Arctic Ocean for autumn–winter 2018/19. Sea ice in the south and west has stronger mobility than in the east and north, which weakens from autumn to winter. An enhanced Arctic dipole and weakened Beaufort Gyre in winter lead to an obvious turning of ice drifting.
Beena Balan-Sarojini, Steffen Tietsche, Michael Mayer, Magdalena Balmaseda, Hao Zuo, Patricia de Rosnay, Tim Stockdale, and Frederic Vitart
The Cryosphere, 15, 325–344, https://doi.org/10.5194/tc-15-325-2021, https://doi.org/10.5194/tc-15-325-2021, 2021
Short summary
Short summary
Our study for the first time shows the impact of measured sea ice thickness (SIT) on seasonal forecasts of all the seasons. We prove that the long-term memory present in the Arctic winter SIT is helpful to improve summer sea ice forecasts. Our findings show that realistic SIT initial conditions to start a forecast are useful in (1) improving seasonal forecasts, (2) understanding errors in the forecast model, and (3) recognizing the need for continuous monitoring of world's ice-covered oceans.
Chao Min, Qinghua Yang, Longjiang Mu, Frank Kauker, and Robert Ricker
The Cryosphere, 15, 169–181, https://doi.org/10.5194/tc-15-169-2021, https://doi.org/10.5194/tc-15-169-2021, 2021
Short summary
Short summary
An ensemble of four estimates of the sea-ice volume (SIV) variations in Baffin Bay from 2011 to 2016 is generated from the locally merged satellite observations, three modeled sea ice thickness sources (CMST, NAOSIM, and PIOMAS) and NSIDC ice drift data (V4). Results show that the net increase of the ensemble mean SIV occurs from October to April with the largest SIV increase in December, and the reduction occurs from May to September with the largest SIV decline in July.
Mohammed E. Shokr, Zihan Wang, and Tingting Liu
The Cryosphere, 14, 3611–3627, https://doi.org/10.5194/tc-14-3611-2020, https://doi.org/10.5194/tc-14-3611-2020, 2020
Short summary
Short summary
This paper uses sequential daily SAR images covering the Robeson Channel to quantitatively study kinematics of individual ice floes with exploration of wind influence and the evolution of the ice arch at the entry of the channel. Results show that drift of ice floes within the Robeson Channel and the arch are both significantly influenced by wind. The study highlights the advantage of using the high-resolution daily SAR coverage in monitoring sea ice cover in narrow water passages.
Guillian Van Achter, Leandro Ponsoni, François Massonnet, Thierry Fichefet, and Vincent Legat
The Cryosphere, 14, 3479–3486, https://doi.org/10.5194/tc-14-3479-2020, https://doi.org/10.5194/tc-14-3479-2020, 2020
Short summary
Short summary
We document the spatio-temporal internal variability of Arctic sea ice thickness and its changes under anthropogenic forcing, which is key to understanding, and eventually predicting, the evolution of sea ice in response to climate change.
The patterns of sea ice thickness variability remain more or less stable during pre-industrial, historical and future periods, despite non-stationarity on short timescales. These patterns start to change once Arctic summer ice-free events occur, after 2050.
Abigail Smith, Alexandra Jahn, and Muyin Wang
The Cryosphere, 14, 2977–2997, https://doi.org/10.5194/tc-14-2977-2020, https://doi.org/10.5194/tc-14-2977-2020, 2020
Short summary
Short summary
The annual cycle of Arctic sea ice can be used to gain more information about how climate model simulations of sea ice compare to observations. In some models, the September sea ice area agrees with observations for the wrong reasons because biases in the timing of seasonal transitions compensate for other unrealistic sea ice characteristics. This research was done to provide new process-based metrics of Arctic sea ice using satellite observations, the CESM Large Ensemble, and CMIP6 models.
Michael Kern, Robert Cullen, Bruno Berruti, Jerome Bouffard, Tania Casal, Mark R. Drinkwater, Antonio Gabriele, Arnaud Lecuyot, Michael Ludwig, Rolv Midthassel, Ignacio Navas Traver, Tommaso Parrinello, Gerhard Ressler, Erik Andersson, Cristina Martin-Puig, Ole Andersen, Annett Bartsch, Sinead Farrell, Sara Fleury, Simon Gascoin, Amandine Guillot, Angelika Humbert, Eero Rinne, Andrew Shepherd, Michiel R. van den Broeke, and John Yackel
The Cryosphere, 14, 2235–2251, https://doi.org/10.5194/tc-14-2235-2020, https://doi.org/10.5194/tc-14-2235-2020, 2020
Short summary
Short summary
The Copernicus Polar Ice and Snow Topography Altimeter will provide high-resolution sea ice thickness and land ice elevation measurements and the capability to determine the properties of snow cover on ice to serve operational products and services of direct relevance to the polar regions. This paper describes the mission objectives, identifies the key contributions the CRISTAL mission will make, and presents a concept – as far as it is already defined – for the mission payload.
Thomas Krumpen, Florent Birrien, Frank Kauker, Thomas Rackow, Luisa von Albedyll, Michael Angelopoulos, H. Jakob Belter, Vladimir Bessonov, Ellen Damm, Klaus Dethloff, Jari Haapala, Christian Haas, Carolynn Harris, Stefan Hendricks, Jens Hoelemann, Mario Hoppmann, Lars Kaleschke, Michael Karcher, Nikolai Kolabutin, Ruibo Lei, Josefine Lenz, Anne Morgenstern, Marcel Nicolaus, Uwe Nixdorf, Tomash Petrovsky, Benjamin Rabe, Lasse Rabenstein, Markus Rex, Robert Ricker, Jan Rohde, Egor Shimanchuk, Suman Singha, Vasily Smolyanitsky, Vladimir Sokolov, Tim Stanton, Anna Timofeeva, Michel Tsamados, and Daniel Watkins
The Cryosphere, 14, 2173–2187, https://doi.org/10.5194/tc-14-2173-2020, https://doi.org/10.5194/tc-14-2173-2020, 2020
Short summary
Short summary
In October 2019 the research vessel Polarstern was moored to an ice floe in order to travel with it on the 1-year-long MOSAiC journey through the Arctic. Here we provide historical context of the floe's evolution and initial state for upcoming studies. We show that the ice encountered on site was exceptionally thin and was formed on the shallow Siberian shelf. The analyses presented provide the initial state for the analysis and interpretation of upcoming biogeochemical and ecological studies.
Sukun Cheng, Justin Stopa, Fabrice Ardhuin, and Hayley H. Shen
The Cryosphere, 14, 2053–2069, https://doi.org/10.5194/tc-14-2053-2020, https://doi.org/10.5194/tc-14-2053-2020, 2020
Short summary
Short summary
Wave states in ice in polar oceans are mostly studied near the ice edge. However, observations in the internal ice field, where ice morphology is very different from the ice edge, are rare. Recently derived wave data from satellite imagery are easier and cheaper than field studies and provide large coverage. This work presents a way of using these data to have a close view of some key features in the wave propagation over hundreds of kilometers and calibrate models for predicting wave decay.
Jutta E. Wollenburg, Morten Iversen, Christian Katlein, Thomas Krumpen, Marcel Nicolaus, Giulia Castellani, Ilka Peeken, and Hauke Flores
The Cryosphere, 14, 1795–1808, https://doi.org/10.5194/tc-14-1795-2020, https://doi.org/10.5194/tc-14-1795-2020, 2020
Short summary
Short summary
Based on an observed omnipresence of gypsum crystals, we concluded that their release from melting sea ice is a general feature in the Arctic Ocean. Individual gypsum crystals sank at more than 7000 m d−1, suggesting that they are an important ballast mineral. Previous observations found gypsum inside phytoplankton aggregates at 2000 m depth, supporting gypsum as an important driver for pelagic-benthic coupling in the ice-covered Arctic Ocean.
Xiaoyong Yu, Annette Rinke, Wolfgang Dorn, Gunnar Spreen, Christof Lüpkes, Hiroshi Sumata, and Vladimir M. Gryanik
The Cryosphere, 14, 1727–1746, https://doi.org/10.5194/tc-14-1727-2020, https://doi.org/10.5194/tc-14-1727-2020, 2020
Short summary
Short summary
This study presents an evaluation of Arctic sea ice drift speed for the period 2003–2014 in a state-of-the-art coupled regional model for the Arctic, called HIRHAM–NAOSIM. In particular, the dependency of the drift speed on the near-surface wind speed and sea ice conditions is presented. Effects of sea ice form drag included by an improved parameterization of the transfer coefficients for momentum and heat over sea ice are discussed.
Yinghui Liu, Jeffrey R. Key, Xuanji Wang, and Mark Tschudi
The Cryosphere, 14, 1325–1345, https://doi.org/10.5194/tc-14-1325-2020, https://doi.org/10.5194/tc-14-1325-2020, 2020
Short summary
Short summary
This study provides a consistent and accurate multi-decadal product of ice thickness and ice volume from 1984 to 2018 based on satellite-derived ice age. Sea ice volume trends from this dataset are stronger than the trends from other datasets. Changes in sea ice thickness contribute more to overall sea ice volume trends than changes in sea ice area do in all months.
Alice K. DuVivier, Patricia DeRepentigny, Marika M. Holland, Melinda Webster, Jennifer E. Kay, and Donald Perovich
The Cryosphere, 14, 1259–1271, https://doi.org/10.5194/tc-14-1259-2020, https://doi.org/10.5194/tc-14-1259-2020, 2020
Short summary
Short summary
In autumn 2019, a ship will be frozen into the Arctic sea ice for a year to study system changes. We analyze climate model data from a group of experiments and follow virtual sea ice floes throughout a year. The modeled sea ice conditions along possible tracks are highly variable. Observations that sample a wide range of sea ice conditions and represent the variety and diversity in possible conditions are necessary for improving climate model parameterizations over all types of sea ice.
Xiao-Yi Yang, Guihua Wang, and Noel Keenlyside
The Cryosphere, 14, 693–708, https://doi.org/10.5194/tc-14-693-2020, https://doi.org/10.5194/tc-14-693-2020, 2020
Short summary
Short summary
The post-2007 Arctic sea ice cover is characterized by a remarkable increase in annual cycle amplitude, which is attributed to multiyear variability in spring Bering sea ice extent. We demonstrated that changes of NPGO mode, by anomalous wind stress curl and Ekman pumping, trigger subsurface variability in the Bering basin. This accounts for the significant decadal oscillation of spring Bering sea ice after 2007. The study helps us to better understand the recent Arctic climate regime shift.
Adam W. Bateson, Daniel L. Feltham, David Schröder, Lucia Hosekova, Jeff K. Ridley, and Yevgeny Aksenov
The Cryosphere, 14, 403–428, https://doi.org/10.5194/tc-14-403-2020, https://doi.org/10.5194/tc-14-403-2020, 2020
Short summary
Short summary
The Arctic sea ice cover has been observed to be decreasing, particularly in summer. We use numerical models to gain insight into processes controlling its seasonal and decadal evolution. Sea ice is made of pieces of ice called floes. Previous models have set these floes to be the same size, which is not supported by observations. In this study we show that accounting for variable floe size reveals the importance of sea ice regions close to the open ocean in driving seasonal retreat of sea ice.
Alex West, Mat Collins, Ed Blockley, Jeff Ridley, and Alejandro Bodas-Salcedo
The Cryosphere, 13, 2001–2022, https://doi.org/10.5194/tc-13-2001-2019, https://doi.org/10.5194/tc-13-2001-2019, 2019
Short summary
Short summary
This study presents a framework for examining the causes of model errors in Arctic sea ice volume, using HadGEM2-ES as a case study. Simple models are used to estimate how much of the error in energy arriving at the ice surface is due to error in key Arctic climate variables. The method quantifies how each variable affects sea ice volume balance and shows that for HadGEM2-ES an annual mean low bias in ice thickness is likely due to errors in surface melt onset.
Caixin Wang, Robert M. Graham, Keguang Wang, Sebastian Gerland, and Mats A. Granskog
The Cryosphere, 13, 1661–1679, https://doi.org/10.5194/tc-13-1661-2019, https://doi.org/10.5194/tc-13-1661-2019, 2019
Short summary
Short summary
A warm bias and higher total precipitation and snowfall were found in ERA5 compared with ERA-Interim (ERA-I) over Arctic sea ice. The warm bias in ERA5 was larger in the cold season when 2 m air temperature was < −25 °C and smaller in the warm season than in ERA-I. Substantial anomalous Arctic rainfall in ERA-I was reduced in ERA5, particularly in summer and autumn. When using ERA5 and ERA-I to force a 1-D sea ice model, the effects on ice growth are very small (cm) during the freezing period.
John E. Walsh, J. Scott Stewart, and Florence Fetterer
The Cryosphere, 13, 1073–1088, https://doi.org/10.5194/tc-13-1073-2019, https://doi.org/10.5194/tc-13-1073-2019, 2019
Short summary
Short summary
Persistence-based statistical forecasts of a Beaufort Sea ice severity index as well as September pan-Arctic ice extent show significant statistical skill out to several seasons when the data include the trend. However, this apparent skill largely vanishes when the trends are removed from the data. This finding is consistent with the notion of a springtime “predictability barrier” that has been found in sea ice forecasts based on more sophisticated methods.
Leandro Ponsoni, François Massonnet, Thierry Fichefet, Matthieu Chevallier, and David Docquier
The Cryosphere, 13, 521–543, https://doi.org/10.5194/tc-13-521-2019, https://doi.org/10.5194/tc-13-521-2019, 2019
Short summary
Short summary
The Arctic is a main component of the Earth's climate system. It is fundamental to understand the behavior of Arctic sea ice coverage over time and in space due to many factors, e.g., shipping lanes, the travel and tourism industry, hunting and fishing activities, mineral resource extraction, and the potential impact on the weather in midlatitude regions. In this work we use observations and results from models to understand how variations in the sea ice thickness change over time and in space.
John R. Mioduszewski, Stephen Vavrus, Muyin Wang, Marika Holland, and Laura Landrum
The Cryosphere, 13, 113–124, https://doi.org/10.5194/tc-13-113-2019, https://doi.org/10.5194/tc-13-113-2019, 2019
Short summary
Short summary
Arctic sea ice is projected to thin substantially in every season by the end of the 21st century with a corresponding increase in its interannual variability as the rate of ice loss peaks. This typically occurs when the mean ice thickness falls between 0.2 and 0.6 m. The high variability in both growth and melt processes is the primary factor resulting in increased ice variability. This study emphasizes the importance of short-term variations in ice cover within the mean downward trend.
Marion Lebrun, Martin Vancoppenolle, Gurvan Madec, and François Massonnet
The Cryosphere, 13, 79–96, https://doi.org/10.5194/tc-13-79-2019, https://doi.org/10.5194/tc-13-79-2019, 2019
Short summary
Short summary
The present analysis shows that the increase in the Arctic ice-free season duration will be asymmetrical, with later autumn freeze-up contributing about twice as much as earlier spring retreat. This feature is robustly found in a hierarchy of climate models and is consistent with a simple mechanism: solar energy is absorbed more efficiently than it can be released in non-solar form and should emerge out of variability within the next few decades.
Abigail Smith and Alexandra Jahn
The Cryosphere, 13, 1–20, https://doi.org/10.5194/tc-13-1-2019, https://doi.org/10.5194/tc-13-1-2019, 2019
Short summary
Short summary
Here we assessed how natural climate variations and different definitions impact the diagnosed and projected Arctic sea ice melt season length using model simulations. Irrespective of the definition or natural variability, the sea ice melt season is projected to lengthen, potentially by as much as 4–5 months by 2100 under the business as usual scenario. We also find that different definitions have a bigger impact on melt onset, while natural variations have a bigger impact on freeze onset.
Yuanyuan Zhang, Xiao Cheng, Jiping Liu, and Fengming Hui
The Cryosphere, 12, 3747–3757, https://doi.org/10.5194/tc-12-3747-2018, https://doi.org/10.5194/tc-12-3747-2018, 2018
Aaron Letterly, Jeffrey Key, and Yinghui Liu
The Cryosphere, 12, 3373–3382, https://doi.org/10.5194/tc-12-3373-2018, https://doi.org/10.5194/tc-12-3373-2018, 2018
Short summary
Short summary
Significant reductions in Arctic sea ice and snow cover on Arctic land have led to increases in absorbed solar energy by the surface. Does one play a more important role in Arctic climate change? Using 34 years of satellite data we found that solar energy absorption increased by 10 % over the ocean, which was 3 times greater than over land. Therefore, the decreasing sea ice cover, not changes in terrestrial snow cover, has been the dominant feedback mechanism over the last few decades.
Thomas Kaminski, Frank Kauker, Leif Toudal Pedersen, Michael Voßbeck, Helmuth Haak, Laura Niederdrenk, Stefan Hendricks, Robert Ricker, Michael Karcher, Hajo Eicken, and Ola Gråbak
The Cryosphere, 12, 2569–2594, https://doi.org/10.5194/tc-12-2569-2018, https://doi.org/10.5194/tc-12-2569-2018, 2018
Short summary
Short summary
We present mathematically rigorous assessments of the observation impact (added value) of remote-sensing products and in terms of the uncertainty reduction in a 4-week forecast of sea ice volume and snow volume for three regions along the Northern Sea Route by a coupled model of the sea-ice–ocean system. We quantify the difference in impact between rawer (freeboard) and higher-level (sea ice thickness) products, and the impact of adding a snow depth product.
Steffen Tietsche, Magdalena Alonso-Balmaseda, Patricia Rosnay, Hao Zuo, Xiangshan Tian-Kunze, and Lars Kaleschke
The Cryosphere, 12, 2051–2072, https://doi.org/10.5194/tc-12-2051-2018, https://doi.org/10.5194/tc-12-2051-2018, 2018
Short summary
Short summary
We compare Arctic sea-ice thickness from L-band microwave satellite observations and an ocean–sea ice reanalysis. There is good agreement for some regions and times but systematic discrepancy in others. Errors in both the reanalysis and observational products contribute to these discrepancies. Thus, we recommend proceeding with caution when using these observations for model validation or data assimilation. At the same time we emphasise their unique value for improving sea-ice forecast models.
Elena V. Shalina and Stein Sandven
The Cryosphere, 12, 1867–1886, https://doi.org/10.5194/tc-12-1867-2018, https://doi.org/10.5194/tc-12-1867-2018, 2018
Short summary
Short summary
In this paper we analyze snow data from Soviet airborne expeditions, Sever, which operated in late winter 1959-1986, in the Arctic and made snow measurements on the ice around plane landing sites. The snow measurements were made on the multiyear ice in the central Arctic and on the first-year ice in the Eurasian seas in the areas for which snow characteristics are poorly described in the literature. The main goal of this study is to produce an improved data set of snow depth on the sea ice.
Rebecca J. Rolph, Andrew R. Mahoney, John Walsh, and Philip A. Loring
The Cryosphere, 12, 1779–1790, https://doi.org/10.5194/tc-12-1779-2018, https://doi.org/10.5194/tc-12-1779-2018, 2018
Short summary
Short summary
Using thresholds of physical climate variables developed from community observations, together with two large-scale datasets, we have produced local indices directly relevant to the impacts of a reduced sea ice cover on Alaska coastal communities. We demonstrate how community observations can inform use of large-scale datasets to derive these locally relevant indices.
Xianmin Hu, Jingfan Sun, Ting On Chan, and Paul G. Myers
The Cryosphere, 12, 1233–1247, https://doi.org/10.5194/tc-12-1233-2018, https://doi.org/10.5194/tc-12-1233-2018, 2018
Short summary
Short summary
We evaluated the sea ice thickness simulation in the Canadian Arctic Archipelago region using 1/4 and 1/12 degree NEMO LIM2 configurations. Model resolution dose not play a significant role. Relatively smaller thermodynamic contribution in the winter season is found in the thick ice covered areas, with larger contributions in the thin ice covered regions. No significant trend in winter maximum ice volume is found in the northern CAA and Baffin Bay but a decline is simulated within Parry Channel.
Cited articles
Barber, D., Fung, A., Grenfell, T., Nghiem, S., Onstott, R., Lytle, V.,
Perovich, D., and Gow, A.: The role of snow on microwave emission and
scattering over first-year sea ice, IEEE T. Geosci.
Remote, 36, 1750–1763, https://doi.org/10.1109/36.718643, 1998. a
Bliss, A. C., Miller, J. A., and Meier, W. N.: Comparison of passive
microwave-derived early melt onset records on Arctic sea ice, Remote Sens., 9, 1–23, https://doi.org/10.3390/rs9030199, 2017. a, b
Bodas-Salcedo, A., M.J., W., Bony, S., Chepfer, H., Dufresne, J.-L., Klein,
S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.:
COSP: Satellite simulation software for model assessment, B.
Am. Meteorol. Soc., 92, 1023–1043,
https://doi.org/10.1175/2011BAMS2856.1, 2011. a
Burgard, C.: Read the Docs: Arctic Ocean Observation Operator, arc3o, https://arc3o.readthedocs.io/en/latest/ (last access: October 2021), 2020. a
Burgard, C., Notz, D., Pedersen, L. T., and Tonboe, R. T.: The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output, The Cryosphere, 14, 2369–2386, https://doi.org/10.5194/tc-14-2369-2020, 2020a. a, b, c, d, e, f, g, h, i
Cavalieri, D. J., Markus, T., and Comiso, J. C.: AMSR-E/Aqua Daily L3 25 km
Brightness Temperature and Sea Ice Concentration Polar Grids, Version 3,
Subset used: 18.7V, 2003-2005, NASA National Snow and Ice Data Center
Distributed Active Archive Center [data set], Boulder, Colorado USA, https://doi.org/10.5067/AMSR-E/AE_SI25.003, 2014. a, b, c
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System Model Version 2 (CESM2),
J. Adv. Model. Earth Syst., 12, e2019MS00191, https://doi.org/10.1029/2019MS001916,
2020. a
Drobot, S. D. and Anderson, M. R.: An improved method for determining snowmelt
onset dates over Arctic sea ice using scanning multichannel microwave
radiometer and Special Sensor Microwave/Imager data, J. Geophys.
Res.-Atmos., 106, 24033–24049, https://doi.org/10.1029/2000JD000171, 2001. a, b
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S., Collins, W.,
Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P.,
Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.: IPCC
AR5 Chapter 9: Evaluation of Climate Models, Cambridge University Press,
https://doi.org/10.1017/CBO9781107415324.020, 2013. a
Griffies, S. M., Danabasoglu, G., Durack, P. J., Adcroft, A. J., Balaji, V., Böning, C. W., Chassignet, E. P., Curchitser, E., Deshayes, J., Drange, H., Fox-Kemper, B., Gleckler, P. J., Gregory, J. M., Haak, H., Hallberg, R. W., Heimbach, P., Hewitt, H. T., Holland, D. M., Ilyina, T., Jungclaus, J. H., Komuro, Y., Krasting, J. P., Large, W. G., Marsland, S. J., Masina, S., McDougall, T. J., Nurser, A. J. G., Orr, J. C., Pirani, A., Qiao, F., Stouffer, R. J., Taylor, K. E., Treguier, A. M., Tsujino, H., Uotila, P., Valdivieso, M., Wang, Q., Winton, M., and Yeager, S. G.: OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project, Geosci. Model Dev., 9, 3231–3296, https://doi.org/10.5194/gmd-9-3231-2016, 2016. a
Hart, D.: Cheyenne supercomputer, NCAR CISL Advanced Research Computing, https://doi.org/10.5065/D6RX99HX, 2021. a
Hunke, E. C., Lipscomb, W. H., Turner, A. K., Jeffery, N., and Elliott, S.:
CICE: The Los Alamos Sea Ice Model. Documentation andSoftware User's Manual.
Version 5.1, T-3 Fluid Dynamics Group, Los Alamos National Laboratory,
Technical Report, http://www.ccpo.odu.edu/~klinck/Reprints/PDF/cicedoc2015.pdf (last access: October 2021), 2015. a, b
Jahn, A., Sterling, K., Holland, M. M., Kay, J. E., Maslanik, J. A., Bitz,
C. M., Bailey, D. A., Stroeve, J., Hunke, E. C., Lipscomb, W. H., and Pollak,
D. A.: Late-twentieth-century simulation of Arctic sea ice and ocean
properties in the CCSM4, J. Climate, 25, 1431–1452,
https://doi.org/10.1175/JCLI-D-11-00201.1, 2012. a
Kay, J. E., Holland, M. M., and Jahn, A.: Inter-annual to multi-decadal Arctic
sea ice extent trends in a warming world, Geophys. Res. Lett., 38,
2–7, https://doi.org/10.1029/2011GL048008, 2011. a
Kern, S., Rösel, A., Pedersen, L. T., Ivanova, N., Saldo, R., and Tonboe, R. T.: The impact of melt ponds on summertime microwave brightness temperatures and sea-ice concentrations, The Cryosphere, 10, 2217–2239, https://doi.org/10.5194/tc-10-2217-2016, 2016. a
Kim, W., Yeager, S., and Danabasoglu, G.: Revisiting the causal connection
between the Great Salinity Anomaly of the 1970s and the shutdown of Labrador
Sea deep convection, J. Climate, 34, 1–58,
https://doi.org/10.1175/JCLI-D-20-0327.1, 2020. a
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 Reanalysis: General Specifications and Basic Characteristics,
J. Meteorol. Soc. Japan, 93, 5–48,
https://doi.org/10.2151/jmsj.2015-001, 2015. a, b
Lee, S.-M., Sohn, B.-J., and Kim, S.-J.: Differentiating between first-year and
multiyear sea ice in the Arctic using microwave-retrieved ice emissivities,
J. Geophys. Res.-Atmos., 122, 5097–5112,
https://doi.org/10.1002/2016JD026275, 2017. a
Lemmetyinen, J., Derksen, C., Rott, H., Macelloni, G., King, J., Schneebeli,
M., Wiesmann, A., Leppännen, L., Kontu, A., and Pulliainen, J.: Retrieval of
Effective Correlation Length and Snow Water Equivalent from Radar and Passive
Microwave Measurements, Remote Sens., 10, 170, https://doi.org/10.3390/rs10020170,
2018. a
Lorenz, E. N.: The Predictability of Hydrodynamic Flow, T.
New York Acad. Sci., 25, 409–432,
https://doi.org/10.1111/j.2164-0947.1963.tb01464.x, 1963. a
Massonnet, F., Vancoppenolle, M., Goosse, H., Docquier, D., Fichefet, T., and
Blanchard-Wrigglesworth, E.: Arctic sea-ice change tied to its mean state
through thermodynamic processes, Nat. Clim. Change, 8, 599–603,
https://doi.org/10.1038/s41558-018-0204-z, 2018. a
Mätzler, C.: Applications of the interaction of microwaves with the natural
snow cover, Remote Sens. Rev., 2, 259–387,
https://doi.org/10.1080/02757258709532086, 1987. a
Mätzler, C.: Relation between grain size and correlation length of snow,
J. Glaciol., 48, 461–466, https://doi.org/10.3189/172756502781831287, 2002. a
Meier, W. N., Wilcox, H., Hardman, M. A., and Stewart, J. S.: DMSP SSM/I-SSMIS
Daily Polar Gridded Brightness Temperatures, Version 5. Subset used: 19.3V,
2003–2005, NASA National Snow and Ice Data Center Distributed Active Archive
Center [data set], Boulder, Colorado USA,
https://doi.org/10.5067/QU2UYQ6T0B3P, 2019. a, b, c
Notz, D.: How well must climate models agree with observations?, Philosophical
Transactions of the Royal Society of London A: Mathematical, Phys.
Eng. Sci., 373, 20140164, https://doi.org/10.1098/rsta.2014.0164, 2015. a
Notz, D., Wettlaufer, J., and Worster, M. G.: A non-destructive method for
measuring the salinity and solid fraction of growing sea ice in situ,
J. Glaciol., 51, 159–166, https://doi.org/10.3189/172756505781829548, 2005. a
Notz, D., Jahn, A., Holland, M., Hunke, E., Massonnet, F., Stroeve, J., Tremblay, B., and Vancoppenolle, M.: The CMIP6 Sea-Ice Model Intercomparison Project (SIMIP): understanding sea ice through climate-model simulations, Geosci. Model Dev., 9, 3427–3446, https://doi.org/10.5194/gmd-9-3427-2016, 2016. a
Proksch, M., Löwe, H., and Schneebeli, M.: Density, specific surface area, and
correlation length of snow measured by high-resolution penetrometry, J. Geophys. Res.-Earth Surf., 120, 346–362,
https://doi.org/10.1002/2014JF003266, 2015. a
SIMIP-Community: Arctic Sea Ice in CMIP6, Geophys. Res. Lett., 47,
e2019GL086749, https://doi.org/10.1029/2019GL086749, 2020. a
Smith, A., Jahn, A., Burgard, C., and Notz, D.: Earliest snowmelt estimation
dates for Arctic sea ice (2003), Zenodo [data set and code], https://doi.org/10.5281/zenodo.6559861, 2022. a
Smith, D. M.: Observation of perennial Arctic sea ice melt and freeze-up using
passive microwave data, J. Geophys. Res.-Oceans, 103,
27753–27769, https://doi.org/10.1029/98JC02416, 1998. a, b
Smith, R., Jones, P., Briegleb, B., Bryan, F., Danabasoglu, G., Dennis, J., Dukowicz, J., Eden, C., Fox-Kemper, B., Gent, P., Hecht, M., Jayne, S., Jochum, M., Large, W., Lindsay, K., Maltrud, M., Norton, N., Peacock, S., Vertenstein, M., and Yeager, S.: The Parallel Ocean Program (POP) reference manual, Ocean component of
the Community Climate System Model (CCSM), Los Alamos National Laboratory
Technical Report, 1–141, https://opensky.ucar.edu/islandora/object/manuscripts:825 (last access: October 2021), 2010. a
Steele, M., Bliss, A., Peng, G., Meier, W. N., and Dickinson, S.: Arctic Sea
Ice Seasonal Change and Melt/Freeze Climate Indicators from Satellite Data,
Version 1, Data subset: 1979-03-01 to 2017-02-28, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], Boulder, Colorado USA, https://doi.org/10.5067/KINANQKEZI4T, last access: 26 August 2019,
2019.
a, b, c, d, e
Tonboe, R., Andersen, S., Toudal, L., and Heygster, G.: Sea ice emission
modelling, in: Thermal Microwave Radiation – Applications for Remote
Sensing, IET Electromagnetic Waves Series, 52, 382–400, 2006. a
Tonboe, R. T.: The simulated sea ice thermal microwave emission at window and
sounding frequencies, Tellus A, 62, 333–344,
https://doi.org/10.1111/j.1600-0870.2010.00434.x, 2010. a
Tonboe, R. T., DybkjæR, G., and Høyer, J. L.: Simulations of the snow covered
sea ice surface temperature and microwave effective temperature, Tellus A, 63, 1028–1037,
https://doi.org/10.1111/j.1600-0870.2011.00530.x, 2011. a
Tsujino, H., Urakawa, S., Nakano, H., Small, R. J., and Kim, W.: JRA-55
based surface dataset for driving ocean–sea-ice models (JRA55-do), Ocean
Model., 130, 79–139, https://doi.org/10.1016/j.ocemod.2018.07.002, 2018. a, b
Willmes, S., Nicolaus, M., and Haas, C.: The microwave emissivity variability of snow covered first-year sea ice from late winter to early summer: a model study, The Cryosphere, 8, 891–904, https://doi.org/10.5194/tc-8-891-2014, 2014. a
Short summary
The timing of Arctic sea ice melt each year is an important metric for assessing how sea ice in climate models compares to satellite observations. Here, we utilize a new tool for creating more direct comparisons between climate model projections and satellite observations of Arctic sea ice, such that the melt onset dates are defined the same way. This tool allows us to identify climate model biases more clearly and gain more information about what the satellites are observing.
The timing of Arctic sea ice melt each year is an important metric for assessing how sea ice in...
