@article{Nab2024-ru,

title = {Optimising interannual sea ice thickness variability retrieved 

 from CryoSat‐2},

author = {Carmen Nab and Robbie Mallett and Connor Nelson and Julienne Stroeve and Michel Tsamados},

year  = {2024},

date = {2024-11-01},

journal = {Geophys. Res. Lett.},

volume = {51},

number = {21},

publisher = {American Geophysical Union (AGU)},

abstract = {AbstractSatellite radar altimeters like CryoSat‐2 estimate sea 

 ice thickness by measuring the return‐time of transmitted radar 

 pulses, reflected from the sea ice and ocean surface, to measure 

 the radar freeboard. Converting freeboard to thickness requires 

 an assumption regarding the fractional depth of the snowpack 

 from which the radar waves backscatter . We derive sea ice 

 thickness from CryoSat‐2 radar freeboard data with incremental 

 values for , for the 2010–2021 winter periods. By comparing 

 these to sea ice thickness estimates derived from upward‐looking 

 sonar moorings, we find that values between 35%–80% result in 

 the best representation of interannual variability observed over 

 first‐year ice, reduced to 55% over multi‐year ice. The 

 underestimating bias in retrievals caused by optimizing this 

 metric can be removed by reducing the waveform retracking 

 threshold to 20%–50%. Our results pave the way for a new 

 generation of `partial penetration' sea ice thickness products 

 from radar altimeters.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Le_Merle2024-dt,

title = {ALBATROSS: Advancing Southern Ocean tide modelling with high 

 resolution and enhanced bathymetry},

author = {Eva Le Merle and Carole Belot and Ergane Fouchet and Mathilde Cancet and Ole Baltazar Andersen and Florent Lyard and Geir Moholdt and Michel Tsamados and Mahmoud El Hajj and Josephine Maton and Jérôme Benveniste and Marco Restano},

year  = {2024},

date = {2024-10-01},

journal = {Polar Sci.},

number = {101124},

pages = {101124},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Blockley2024-gd,

title = {The sea ice component of GC5: coupling SI3 to HadGEM3 using conductive fluxes},

author = {Ed Blockley and Emma Fiedler and Jeff Ridley and Luke Roberts and Alex West and Dan Copsey and Daniel Feltham and Tim Graham and David Livings and Clement Rousset and David Schroeder and Martin Vancoppenolle},

year  = {2024},

date = {2024-09-01},

urldate = {2024-09-01},

journal = {Geosci. Model Dev.},

volume = {17},

number = {17},

pages = {6799–6817},

publisher = {Copernicus GmbH},

abstract = {Abstract. We present an overview of the UK's Global Sea Ice 

 model configuration version 9 (GSI9), the sea ice component of 

 the latest Met Office Global Coupled model, GC5. The GC5 

 configuration will, amongst other uses, form the physical basis 

 for the HadGEM3 (Hadley Centre Global Environment Model version 

 3) climate model and UKESM2 (UK Earth System Model version 2) 

 Earth system model that will provide the Met Office Hadley 

 Centre/UK model contributions to CMIP7 (Coupled Model 

 Intercomparison Project Phase 7). Although UK ocean model 

 configurations have been developed for many years around the 

 NEMO (Nucleus for European Modelling of the Ocean) ocean 

 modelling framework, the GSI9 configuration is the first UK sea 

 ice model configuration to use the new native NEMO sea ice 

 model, SI3 (Sea Ice modelling Integrated Initiative). This 

 replaces the CICE (Community Ice CodE) model used in previous 

 configuration versions. In this paper we document the physical 

 and technical options used within the GSI9 sea ice 

 configuration. We provide details of the implementation of SI3 

 into the Met Office coupled model and the adaptations required 

 to work with our ``conductivity coupling'' approach and provide 

 a thorough description of the GC5 coupling methodology. A brief 

 evaluation of sea ice simulated by the GC5 model is included, 

 with results compared to observational references and a previous 

 Global Coupled model version (GC3.1) used for CMIP6, to 

 demonstrate the scientific credibility of the results.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Zhou2024-kg,

title = {Quantifying the influence of snow over sea ice morphology on 

 L-band passive microwave satellite observations in the Southern 

 Ocean},

author = {Lu Zhou and Julienne Stroeve and Vishnu Nandan and Rosemary Willatt and Shiming Xu and Weixin Zhu and Sahra Kacimi and Stefanie Arndt and Zifan Yang},

year  = {2024},

date = {2024-09-01},

journal = {Cryosphere},

volume = {18},

number = {9},

pages = {4399–4434},

publisher = {Copernicus GmbH},

abstract = {Abstract. Antarctic snow on sea ice can contain slush, snow ice, 

 and stratified layers, complicating satellite retrieval 

 processes for snow depth, ice thickness, and sea ice 

 concentration. The presence of moist and brine-wetted snow 

 alters microwave snow emissions and modifies the energy and mass 

 balance of sea ice. This study assesses the impact of 

 brine-wetted snow and slush layers on L-band surface brightness 

 temperatures (TBs) by synergizing a snow stratigraphy model 

 (SNOWPACK) driven by atmospheric reanalysis data and the 

 RAdiative transfer model Developed for Ice and Snow in the 

 L-band (RADIS-L) v1.0 The updated RADIS-L v1.1 further 

 introduces parameterizations for brine-wetted snow and slush 

 layers over Antarctic sea ice. Our findings highlight the 

 importance of including both brine-wetted snow and slush layers 

 in order to accurately simulate L-band brightness temperatures, 

 laying the groundwork for improved satellite retrievals of snow 

 depth and ice thickness using satellite sensors such as Soil 

 Moisture and Ocean Salinity (SMOS) and Soil Moisture Active 

 Passive (SMAP). However, biases in modelled and observed L-band 

 brightness temperatures persist, which we attribute to 

 small-scale sea ice heterogeneity and snow stratigraphy. Given 

 the scarcity of comprehensive in situ snow and ice data in the 

 Southern Ocean, ramping up observational initiatives is 

 imperative to not only provide satellite validation datasets but 

 also improve process-level understanding that can scale up to 

 improving the precision of satellite snow and ice thickness 

 retrievals.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Saddier2024-pf,

title = {Breaking of a floating particle raft by water waves},

author = {Louis Saddier and Ambre Palotai and Mathéo Aksil and Michel Tsamados and Michael Berhanu},

year  = {2024},

date = {2024-09-01},

journal = {Phys. Rev. Fluids},

volume = {9},

number = {9},

publisher = {American Physical Society (APS)},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Au2024-gn,

title = {ARISGAN: Extreme super-resolution of arctic surface imagery 

 using generative adversarial networks},

author = {Christian Au and Michel Tsamados and Petru Manescu and So Takao},

year  = {2024},

date = {2024-08-01},

journal = {Front. Remote Sens.},

volume = {5},

publisher = {Frontiers Media SA},

abstract = {Introduction: This research explores the application of 

 generative artificial intelligence, specifically the novel 

 ARISGAN framework, for generating high-resolution synthetic 

 satellite imagery in the challenging arctic environment. 

 Realistic and high-resolution surface imagery in the Arctic is 

 crucial for applications ranging from satellite retrieval 

 systems to the wellbeing and safety of Inuit populations relying 

 on detailed surface observations.Methods: The ARISGAN framework 

 was designed by combining dense block, multireceptive field, and 

 Pix2Pix architecture. This innovative combination aims to 

 address the need for high-quality imagery and improve upon 

 existing state-of-the-art models. Various tasks and metrics were 

 employed to evaluate the performance of ARISGAN, with particular 

 attention to land-based and sea ice-based imagery.Results: The 

 results demonstrate that the ARISGAN framework surpasses 

 existing state-of-the-art models across diverse tasks and 

 metrics. Specifically, land-based imagery super-resolution 

 exhibits superior metrics compared to sea ice-based imagery when 

 evaluated across multiple models. These findings confirm the 

 ARISGAN framework's effectiveness in generating perceptually 

 valid high-resolution arctic surface imagery.Discussion: This 

 study contributes to the advancement of Earth Observation in 

 polar regions by introducing a framework that combines advanced 

 image processing techniques with a well-designed architecture. 

 The ARISGAN framework's ability to outperform existing models 

 underscores its potential. Identified limitations include 

 challenges in temporal synchronicity, multi-spectral image 

 analysis, preprocessing, and quality metrics. The discussion 

 also highlights potential avenues for future research, 

 encouraging further refinement of the ARISGAN framework to 

 enhance the quality and availability of high-resolution 

 satellite imagery in the Arctic.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Gregory2024-bi,

title = {Scalable interpolation of satellite altimetry data with 

 probabilistic machine learning},

author = {William Gregory and Ronald MacEachern and So Takao and Isobel R Lawrence and Carmen Nab and Marc Peter Deisenroth and Michel Tsamados},

year  = {2024},

date = {2024-08-01},

journal = {Nat. Commun.},

volume = {15},

number = {1},

pages = {7453},

publisher = {Springer Science and Business Media LLC},

abstract = {We present GPSat; an open-source Python programming library for 

 performing efficient interpolation of non-stationary satellite 

 altimetry data, using scalable Gaussian process techniques. We 

 use GPSat to generate complete maps of daily 50 km-gridded 

 Arctic sea ice radar freeboard, and find that, relative to a 

 previous interpolation scheme, GPSat offers a 504 $times$ 

 computational speedup, with less than 4 mm difference on the 

 derived freeboards on average. We then demonstrate the 

 scalability of GPSat through freeboard interpolation at 5 km 

 resolution, and Sea-Level Anomalies (SLA) at the resolution of 

 the altimeter footprint. Interpolated 5 km radar freeboards show 

 strong agreement with airborne data (linear correlation of 

 0.66). Footprint-level SLA interpolation also shows improvements 

 in predictive skill over linear regression. In this work, we 

 suggest that GPSat could overcome the computational bottlenecks 

 faced in many altimetry-based interpolation routines, and hence 

 advance critical understanding of ocean and sea ice variability 

 over short spatio-temporal scales.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Carter2024-oz,

title = {Bayesian hierarchical model for bias-correcting climate models},

author = {Jeremy Carter and Erick A Chacón-Montalván and Amber Leeson},

year  = {2024},

date = {2024-07-01},

journal = {Geosci. Model Dev.},

volume = {17},

number = {14},

pages = {5733–5757},

publisher = {Copernicus GmbH},

abstract = {Abstract. Climate models, derived from process understanding, 

 are essential tools in the study of climate change and its 

 wide-ranging impacts. Hindcast and future simulations provide 

 comprehensive spatiotemporal estimates of climatology that are 

 frequently employed within the environmental sciences community, 

 although the output can be afflicted with bias that impedes 

 direct interpretation. Post-processing bias correction 

 approaches utilise observational data to address this challenge, 

 although they are typically criticised for not being physically 

 justified and not considering uncertainty in the correction. 

 This paper proposes a novel Bayesian bias correction framework 

 that robustly propagates uncertainty and models underlying 

 spatial covariance patterns. Shared latent Gaussian processes 

 are assumed between the in situ observations and climate model 

 output, with the aim of partially preserving the covariance 

 structure from the climate model after bias correction, which is 

 based on well-established physical laws. Results demonstrate 

 added value in modelling shared generating processes under 

 several simulated scenarios, with the most value added for the 

 case of sparse in situ observations and smooth underlying bias. 

 Additionally, the propagation of uncertainty to a simulated 

 final bias-corrected time series is illustrated, which is of key 

 importance to a range of stakeholders, such as climate 

 scientists engaged in impact studies, decision-makers trying to 

 understand the likelihood of particular scenarios and 

 individuals involved in climate change adaption strategies where 

 accurate risk assessment is required for optimal resource 

 allocation. This paper focuses on one-dimensional simulated 

 examples for clarity, although the code implementation is 

 developed to also work on multi-dimensional input data, 

 encouraging follow-on real-world application studies that will 

 further validate performance and remaining limitations. The 

 Bayesian framework supports uncertainty propagation under model 

 adaptations required for specific applications, providing a 

 flexible approach that increases the scope of data assimilation 

 tasks more generally.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Aylmer2024-xe,

title = {Impact of ocean heat transport on sea ice captured by a simple 

 energy balance model},

author = {Jake R Aylmer and David Ferreira and Daniel L Feltham},

year  = {2024},

date = {2024-07-01},

journal = {Commun. Earth Environ.},

volume = {5},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractFuture projections of Arctic and Antarctic sea ice 

 suffer from uncertainties largely associated with inter-model 

 spread. Ocean heat transport has been hypothesised as a source 

 of this uncertainty, based on correlations with sea ice extent 

 across climate models. However, a physical explanation of what 

 sets the sea ice sensitivity to ocean heat transport remains to 

 be uncovered. Here, we derive a simple equation using an 

 idealised energy-balance model that captures the emergent 

 relationship between ocean heat transport and sea ice in climate 

 models. Inter-model spread of Arctic sea ice loss depends 

 strongly on the spread in ocean heat transport, with a 

 sensitivity set by compensation of atmospheric heat transport 

 and radiative feedbacks. Southern Ocean heat transport exhibits 

 a comparatively weak relationship with Antarctic sea ice and 

 plays a passive role secondary to atmospheric heat transport. 

 Our results suggest that addressing ocean model biases will 

 substantially reduce uncertainty in projections of Arctic sea 

 ice.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Chen2024-jh,

title = {Co-located OLCI optical imagery and SAR altimetry from 

 Sentinel-3 for enhanced Arctic spring sea ice surface 

 classification},

author = {Weibin Chen and Michel Tsamados and Rosemary Willatt and So Takao and David Brockley and Claude Rijke-Thomas and Alistair Francis and Thomas Johnson and Jack Landy and Isobel R Lawrence and Sanggyun Lee and Dorsa Nasrollahi Shirazi and Wenxuan Liu and Connor Nelson and Julienne C Stroeve and Len Hirata and Marc Peter Deisenroth},

year  = {2024},

date = {2024-07-01},

journal = {Front. Remote Sens.},

volume = {5},

publisher = {Frontiers Media SA},

abstract = {The Sentinel-3A and Sentinel-3B satellites, launched in February 

 2016 and April 2018 respectively, build on the legacy of 

 CryoSat-2 by providing high-resolution Ku-band radar altimetry 

 data over the polar regions up to 81° North. The combination of 

 synthetic aperture radar (SAR) mode altimetry (SRAL instrument) 

 from Sentinel-3A and Sentinel-3B, and the Ocean and Land Colour 

 Instrument (OLCI) imaging spectrometer, results in the creation 

 of the first satellite platform that offers coincident optical 

 imagery and SAR radar altimetry. We utilise this synergy between 

 altimetry and imagery to demonstrate a novel application of deep 

 learning to distinguish sea ice from leads in spring. We use 

 SRAL classified leads as training input for pan-Arctic lead 

 detection from OLCI imagery. This surface classification is an 

 important step for estimating sea ice thickness and to predict 

 future sea ice changes in the Arctic and Antarctic regions. We 

 propose the use of Vision Transformers (ViT), an approach 

 adapting the popular deep learning algorithm Transformer, for 

 this task. Their effectiveness, in terms of both quantitative 

 metric including accuracy and qualitative metric including model 

 roll-out, on several entire OLCI images is demonstrated and we 

 show improved skill compared to previous machine learning and 

 empirical approaches. We show the potential for this method to 

 provide lead fraction retrievals at improved accuracy and 

 spatial resolution for sunlit periods before melt onset.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Bushuk2024-rg,

title = {Predicting September Arctic sea ice: A multimodel seasonal skill 

 comparison},

author = {Mitchell Bushuk and Sahara Ali and David A Bailey and Qing Bao and Lauriane Batté and Uma S Bhatt and Edward Blanchard-Wrigglesworth and Ed Blockley and Gavin Cawley and Junhwa Chi and François Counillon and Philippe Goulet Coulombe and Richard I Cullather and Francis X Diebold and Arlan Dirkson and Eleftheria Exarchou and Maximilian Göbel and William Gregory and Virginie Guemas and Lawrence Hamilton and Bian He and Sean Horvath and Monica Ionita and Jennifer E Kay and Eliot Kim and Noriaki Kimura and Dmitri Kondrashov and Zachary M Labe and Woosung Lee and Younjoo J Lee and Cuihua Li and Xuewei Li and Yongcheng Lin and Yanyun Liu and Wieslaw Maslowski and Franc cois Massonnet and Walter N Meier and William J Merryfield and Hannah Myint and Juan C Acosta Navarro and Alek Petty and Fangli Qiao and David Schröder and Axel Schweiger and Qi Shu and Michael Sigmond and Michael Steele and Julienne Stroeve and Nico Sun and Steffen Tietsche and Michel Tsamados and Keguang Wang and Jianwu Wang and Wanqiu Wang and Yiguo Wang and Yun Wang and James Williams and Qinghua Yang and Xiaojun Yuan and Jinlun Zhang and Yongfei Zhang},

year  = {2024},

date = {2024-07-01},

journal = {Bull. Am. Meteorol. Soc.},

volume = {105},

number = {7},

pages = {E1170–E1203},

publisher = {American Meteorological Society},

abstract = {Abstract This study quantifies the state of the art in the 

 rapidly growing field of seasonal Arctic sea ice prediction. A 

 novel multimodel dataset of retrospective seasonal predictions 

 of September Arctic sea ice is created and analyzed, consisting 

 of community contributions from 17 statistical models and 17 

 dynamical models. Prediction skill is compared over the period 

 2001–20 for predictions of pan-Arctic sea ice extent (SIE), 

 regional SIE, and local sea ice concentration (SIC) initialized 

 on 1 June, 1 July, 1 August, and 1 September. This diverse set 

 of statistical and dynamical models can individually predict 

 linearly detrended pan-Arctic SIE anomalies with skill, and a 

 multimodel median prediction has correlation coefficients of 

 0.79, 0.86, 0.92, and 0.99 at these respective initialization 

 times. Regional SIE predictions have similar skill to pan-Arctic 

 predictions in the Alaskan and Siberian regions, whereas 

 regional skill is lower in the Canadian, Atlantic, and central 

 Arctic sectors. The skill of dynamical and statistical models is 

 generally comparable for pan-Arctic SIE, whereas dynamical 

 models outperform their statistical counterparts for regional 

 and local predictions. The prediction systems are found to 

 provide the most value added relative to basic reference 

 forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE 

 prediction errors do not show clear trends over time, suggesting 

 that there has been minimal change in inherent sea ice 

 predictability over the satellite era. Overall, this study 

 demonstrates that there are bright prospects for skillful 

 operational predictions of September sea ice at least 3 months 

 in advance.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Huang2024-kg,

title = {A study of sea ice topography in the Weddell and Ross seas using 

 dual-polarimetric TanDEM-X imagery},

author = {Lanqing Huang and Irena Hajnsek},

year  = {2024},

date = {2024-07-01},

journal = {Cryosphere},

volume = {18},

number = {7},

pages = {3117–3140},

publisher = {Copernicus GmbH},

abstract = {Abstract. The total freeboard, which is the ice layer above 

 water level and includes the snow thickness, is needed to 

 retrieve the ice thickness and ice surface topography. 

 Single-pass interferometric synthetic aperture radar (InSAR) 

 allows for the generation of digital elevation models (DEMs) 

 over the drifting sea ice. However, accurate sea ice DEMs (i.e., 

 the total freeboard) derived from InSAR are impeded due to 

 variation in the penetration of the radar signals into the snow 

 and ice layers. This research introduces a novel methodology for 

 retrieving sea ice DEMs using dual-polarization interferometric 

 SAR images, considering the variation in radar penetration bias 

 across multiple ice types. The accuracy of the method is 

 verified through photogrammetric measurements, demonstrating 

 that the derived DEM has a root-mean-square error of 0.26 m over 

 a 200 km $times$ 19 km area. The method is further applied to 

 broader regions in the Weddell Sea and the Ross Sea, offering 

 new insights into the regional variations of the sea ice 

 topography in the Antarctic. We also characterize the 

 non-Gaussian statistical behavior of the total freeboard using 

 log-normal and exponential-normal distributions. The results 

 suggest that the exponential-normal distribution is superior in 

 the thicker-sea-ice region (average total freeboard > 0.5 m), 

 whereas the two distributions exhibit similar performance in the 

 thinner-ice region (average total freeboard},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Stroeve2024-vs,

title = {Ice-free period too long for Southern and Western Hudson Bay 

 polar bear populations if global warming exceeds 1.6 to 2.6 °C},

author = {Julienne Stroeve and Alex Crawford and Steve Ferguson and Ian Stirling and Louise Archer and Geoffrey York and David Babb and Robbie Mallett},

year  = {2024},

date = {2024-06-01},

journal = {Commun. Earth Environ.},

volume = {5},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractHudson Bay has warmed over 1 °C in the last 30 years. 

 Coincident with this warming, seasonal patterns have shifted, 

 with the spring sea ice melting earlier and the fall freeze-up 

 occurring later, leading to a month longer of ice-free 

 conditions. This extended ice-free period presents a significant 

 challenge for polar bears, as it restricts their hunting 

 opportunities for seals and their ability to accumulate the 

 necessary body weight for successful reproduction. Drawing on 

 the latest insights from CMIP6, our updated projections of the 

 ice-free period indicate a more spatially detailed and alarming 

 outlook for polar bear survival. Limiting global warming to 2 °C 

 above pre-industrial levels may prevent the ice-free period from 

 exceeding 183 days in both western and southern Hudson Bay, 

 providing some optimism for adult polar bear survival. However, 

 with longer ice-free periods already substantially impacting 

 recruitment, extirpation for polar bears in this region may 

 already be inevitable.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Li2024-si,

title = {GLAMOUR: GLobAl building MOrphology dataset for URban 

 hydroclimate modelling},

author = {Ruidong Li and Ting Sun and Saman Ghaffarian and Michel Tsamados and Guangheng Ni},

year  = {2024},

date = {2024-06-01},

journal = {Sci. Data},

volume = {11},

number = {1},

pages = {618},

publisher = {Springer Science and Business Media LLC},

abstract = {Understanding building morphology is crucial for accurately 

 simulating interactions between urban structures and 

 hydroclimate dynamics. Despite significant efforts to generate 

 detailed global building morphology datasets, there is a lack of 

 practical solutions using publicly accessible resources. In this 

 work, we present GLAMOUR, a dataset derived from open-source 

 Sentinel imagery that captures the average building height and 

 footprint at a resolution of 0.0009° across urbanized areas 

 worldwide. Validated in 18 cities, GLAMOUR exhibits superior 

 accuracy with median root mean square errors of 7.5 m and 0.14 

 for building height and footprint estimations, indicating better 

 overall performance against existing published datasets. The 

 GLAMOUR dataset provides essential morphological information of 

 3D building structures and can be integrated with other datasets 

 and tools for a wide range of applications including 3D building 

 model generation and urban morphometric parameter derivation. 

 These extended applications enable refined hydroclimate 

 simulation and hazard assessment on a broader scale and offer 

 valuable insights for researchers and policymakers in building 

 sustainable and resilient urban environments prepared for future 

 climate adaptation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Shakesby2024-me,

title = {Was there a low-altitude Younger Dryas Stadial glacier in 

 south-east Wales? Re-interpretation of landforms and 

 palaeo-climatic inferences},

author = {Richard A Shakesby and Stephen L Cornford and John F Hiemstra},

year  = {2024},

date = {2024-06-01},

journal = {Proc. Geol. Assoc.},

volume = {135},

number = {3},

pages = {301–309},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Diamond2024-eb,

title = {CMIP6 models rarely simulate Antarctic winter sea‐ice 

 anomalies as large as observed in 2023},

author = {Rachel Diamond and Louise C Sime and Caroline R Holmes and David Schroeder},

year  = {2024},

date = {2024-05-01},

journal = {Geophys. Res. Lett.},

volume = {51},

number = {10},

publisher = {American Geophysical Union (AGU)},

abstract = {AbstractIn 2023, Antarctic sea‐ice extent (SIE) reached record 

 lows, with winter SIE falling to 2.5Mkm2 below the satellite era 

 average. With this multi‐model study, we investigate the 

 occurrence of anomalies of this magnitude in latest‐generation 

 global climate models. When these anomalies occur, SIE takes 

 decades to recover: this indicates that SIE may transition to a 

 new, lower, state over the next few decades. Under internal 

 variability alone, models are extremely unlikely to simulate 

 these anomalies, with return period >1000 years for most models. 

 The only models with return period},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Stroeve2024-lq,

title = {Mapping potential timing of ice algal blooms from satellite},

author = {J C Stroeve and G Veyssiere and C Nab and B Light and D Perovich and J Laliberté and K Campbell and J Landy and R Mallett and A Barrett and G E Liston and A Haddon and J Wilkinson},

year  = {2024},

date = {2024-04-01},

journal = {Geophys. Res. Lett.},

volume = {51},

number = {8},

publisher = {American Geophysical Union (AGU)},

abstract = {AbstractAs Arctic sea ice and its overlying snow cover thin, 

 more light penetrates into the ice and upper ocean, shifting the 

 phenology of algal growth within the bottom of sea ice, with 

 cascading impacts on higher trophic levels of the Arctic marine 

 ecosystem. While field data or autonomous observatories provide 

 direct measurements of the coupled sea ice‐algal system, they 

 are limited in space and time. Satellite observations of key sea 

 ice variables that control the amount of light penetrating 

 through sea ice offer the possibility to map the under‐ice light 

 field across the entire Arctic basin. This study provides the 

 first satellite‐based estimates of potential sea ice‐associated 

 algal bloom onset dates since the launch of CryoSat‐2 and 

 explores how a changing snowpack may have shifted bloom onset 

 timings over the last four decades.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Alevropoulos-Borrill2024-ju,

title = {Sustained ocean cooling insufficient to reverse sea level rise 

 from Antarctica},

author = {Alanna Alevropoulos-Borrill and Nicholas R Golledge and Stephen L Cornford and Daniel P Lowry and Mario Krapp},

year  = {2024},

date = {2024-04-01},

journal = {Commun. Earth Environ.},

volume = {5},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {AbstractGlobal mean sea level has risen at an accelerating rate 

 in the last decade and will continue to rise for centuries. The 

 Amundsen Sea Embayment in West Antarctica is a critical region 

 for present and future ice loss, however most studies consider 

 only a worst-case future for the region. Here we use ice sheet 

 model sensitivity experiments to investigate the centennial 

 scale implications of short-term periods of enhanced ocean 

 driven sub-ice shelf melting on ice loss and assess what future 

 reduction in melting is necessary to mitigate ice stream retreat 

 and offset global sea level rise. Our findings reveal that 

 restoring elevated melt rates to present-day levels within 100 

 years causes rates of ice discharge to immediately decline, 

 thereby limiting the overall sea level contribution from the 

 region. However, while ice stream re-advance and slowed ice 

 discharge is possible with reduced basal melting, a centennial 

 scale increase in accumulation must occur to offset the 

 extensive ice loss.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Surawy-Stepney2024-or,

title = {The effect of landfast sea ice buttressing on ice dynamic 

 speedup in the Larsen B embayment, Antarctica},

author = {Trystan Surawy-Stepney and Anna E Hogg and Stephen L Cornford and Benjamin J Wallis and Benjamin J Davison and Heather L Selley and Ross A W Slater and Elise K Lie and Livia Jakob and Andrew Ridout and Noel Gourmelen and Bryony I D Freer and Sally F Wilson and Andrew Shepherd},

year  = {2024},

date = {2024-03-01},

journal = {Cryosphere},

volume = {18},

number = {3},

pages = {977–993},

publisher = {Copernicus GmbH},

abstract = {Abstract. We observe the evacuation of 11-year-old landfast sea 

 ice in the Larsen B embayment on the East Antarctic Peninsula in 

 January 2022, which was in part triggered by warm atmospheric 

 conditions and strong offshore winds. This evacuation of sea ice 

 was closely followed by major changes in the calving behaviour 

 and dynamics of a subset of the ocean-terminating glaciers in 

 the region. We show using satellite measurements that, following 

 a decade of gradual slow-down, Hektoria, Green, and Crane 

 glaciers sped up by approximately 20 %–50 % between February 

 and the end of 2022, each increasing in speed by more than 100 m 

 a−1. Circumstantially, this is attributable to their transition 

 into tidewater glaciers following the loss of their ice shelves 

 after the landfast sea ice evacuation. However, a question 

 remains as to whether the landfast sea ice could have influenced 

 the dynamics of these glaciers, or the stability of their ice 

 shelves, through a buttressing effect akin to that of confined 

 ice shelves on grounded ice streams. We show, with a series of 

 diagnostic modelling experiments, that direct landfast sea ice 

 buttressing had a negligible impact on the dynamics of the 

 grounded ice streams. Furthermore, we suggest that the loss of 

 landfast sea ice buttressing could have impacted the dynamics of 

 the rheologically weak ice shelves, in turn diminishing their 

 stability over time; however, the accompanying shifts in the 

 distributions of resistive stress within the ice shelves would 

 have been minor. This indicates that this loss of buttressing by 

 landfast sea ice is likely to have been a secondary process in 

 the ice shelf disaggregation compared to, for example, increased 

 ocean swell or the drivers of the initial landfast sea ice 

 disintegration.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Huang2024-ak,

title = {Multipeak retracking of radar altimetry waveforms over ice 

 sheets},

author = {Qi Huang and Malcolm McMillan and Alan Muir and Joe Phillips and Thomas Slater},

year  = {2024},

date = {2024-03-01},

journal = {Remote Sens. Environ.},

volume = {303},

number = {114020},

pages = {114020},

publisher = {Elsevier BV},

keywords = {},

pubstate = {published},

tppubtype = {article}

}