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1. Seasonal Variations and Spatial Patterns of Arctic Cloud Changes in Association with Sea Ice Loss during 1950–2019 in ERA5

   https://doi.org/10.1175/JCLI-D-23-0117.1  

   Jenkins, Matthew T. ; Dai, Aiguo ; Deser, Clara ( January 2024 , Journal of Climate)

   The dynamic and thermodynamic mechanisms that link retreating sea ice to increased Arctic cloud amount and cloud water content are unclear. Using the fifth generation of the ECMWF Reanalysis (ERA5), the long-term changes between years 1950–79 and 1990–2019 in Arctic clouds are estimated along with their relationship to sea ice loss. A comparison of ERA5 to CERES satellite cloud fractions reveals that ERA5 simulates the seasonal cycle, variations, and changes of cloud fraction well over water surfaces during 2001–20. This suggests that ERA5 may reliably represent the cloud response to sea ice loss because melting sea ice exposes more water surfaces in the Arctic. Increases in ERA5 Arctic cloud fraction and water content are largest during October–March from ∼950 to 700 hPa over areas with significant (≥15%) sea ice loss. Further, regions with significant sea ice loss experience higher convective available potential energy (∼2–2.75 J kg⁻¹), planetary boundary layer height (∼120–200 m), and near-surface specific humidity (∼0.25–0.40 g kg⁻¹) and a greater reduction of the lower-tropospheric temperature inversion (∼3°–4°C) than regions with small (<15%) sea ice loss in autumn and winter. Areas with significant sea ice loss also show strengthened upward motion between 1000 and 700 hPa, enhanced horizontal convergence (divergence) of air, and decreased (increased) relative humidity from 1000 to 950 hPa (950–700 hPa) during the cold season. Analyses of moisture divergence, evaporation minus precipitation, and meridional moisture flux fields suggest that increased local surface water fluxes, rather than atmospheric motions, provide a key source of moisture for increased Arctic clouds over newly exposed water surfaces during October–March.

   Sea ice loss has been shown to be a primary contributor to Arctic warming. Despite the evidence linking large sea ice retreat to Arctic warming, some studies have suggested that enhanced downwelling longwave radiation associated with increased clouds and water vapor is the primary reason for Arctic amplification. However, it is unclear how sea ice loss is linked to changes in clouds and water vapor in the Arctic. Here, we investigate the relationship between Arctic sea ice loss and changes in clouds using the ERA5 dataset. Improved knowledge of the relationship between Arctic sea ice loss and changes in clouds will help further our understanding of the role of the cloud feedback in Arctic warming.

    

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2. A Lagrangian Snow‐Evolution System for Sea‐Ice Applications (SnowModel‐LG): Part I—Model Description

   https://doi.org/10.1029/2019JC015913  

   Liston, Glen E. ; Itkin, Polona ; Stroeve, Julienne ; Tschudi, Mark ; Stewart, J. Scott ; Pedersen, Stine H. ; Reinking, Adele K. ; Elder, Kelly ( October 2020 , Journal of Geophysical Research: Oceans)

   A Lagrangian snow‐evolution model (SnowModel‐LG) was used to produce daily, pan‐Arctic, snow‐on‐sea‐ice, snow property distributions on a 25 × 25‐km grid, from 1 August 1980 through 31 July 2018 (38 years). The model was forced with NASA's Modern Era Retrospective‐Analysis for Research and Applications‐Version 2 (MERRA‐2) and European Centre for Medium‐Range Weather Forecasts (ECMWF) ReAnalysis‐5th Generation (ERA5) atmospheric reanalyses, and National Snow and Ice Data Center (NSIDC) sea ice parcel concentration and trajectory data sets (approximately 61,000, 14 × 14‐km parcels). The simulations performed full surface and internal energy and mass balances within a multilayer snowpack evolution system. Processes and features accounted for included rainfall, snowfall, sublimation from static‐surfaces and blowing‐snow, snow melt, snow density evolution, snow temperature profiles, energy and mass transfers within the snowpack, superimposed ice, and ice dynamics. The simulations produced horizontal snow spatial structures that likely exist in the natural system but have not been revealed in previous studies spanning these spatial and temporal domains. Blowing‐snow sublimation made a significant contribution to the snowpack mass budget. The superimposed ice layer was minimal and decreased over the last four decades. Snow carryover to the next accumulation season was minimal and sensitive to the melt‐season atmospheric forcing (e.g., the average summer melt period was 3 weeks or 50% longer with ERA5 forcing than MERRA‐2 forcing). Observed ice dynamics controlled the ice parcel age (in days), and ice age exerted a first‐order control on snow property evolution.

    

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3. A Machine Learning Correction Model of the Winter Clear-Sky Temperature Bias over the Arctic Sea Ice in Atmospheric Reanalyses

   https://doi.org/10.1175/MWR-D-22-0130.1  

   Zampieri, Lorenzo ; Arduini, Gabriele ; Holland, Marika ; Keeley, Sarah P. E. ; Mogensen, Kristian ; Shupe, Matthew D. ; Tietsche, Steffen ( June 2023 , Monthly Weather Review)

   Atmospheric reanalyses are widely used to estimate the past atmospheric near-surface state over sea ice. They provide boundary conditions for sea ice and ocean numerical simulations and relevant information for studying polar variability and anthropogenic climate change. Previous research revealed the existence of large near-surface temperature biases (mostly warm) over the Arctic sea ice in the current generation of atmospheric reanalyses, which is linked to a poor representation of the snow over the sea ice and the stably stratified boundary layer in the forecast models used to produce the reanalyses. These errors can compromise the employment of reanalysis products in support of polar research. Here, we train a fully connected neural network that learns from remote sensing infrared temperature observations to correct the existing generation of uncoupled atmospheric reanalyses (ERA5, JRA-55) based on a set of sea ice and atmospheric predictors, which are themselves reanalysis products. The advantages of the proposed correction scheme over previous calibration attempts are the consideration of the synoptic weather and cloud state, compatibility of the predictors with the mechanism responsible for the bias, and a self-emerging seasonality and multidecadal trend consistent with the declining sea ice state in the Arctic. The correction leads on average to a 27% temperature bias reduction for ERA5 and 7% for JRA-55 if compared to independent in situ observations from the MOSAiC campaign (respectively, 32% and 10% under clear-sky conditions). These improvements can be beneficial for forced sea ice and ocean simulations, which rely on reanalyses surface fields as boundary conditions.

   This study illustrates a novel method based on machine learning for reducing the systematic surface temperature errors that characterize multiple atmospheric reanalyses in sea ice–covered regions of the Arctic under clear-sky conditions. The correction applied to the temperature field is consistent with the local weather and the sea ice and snow conditions, meaning that it responds to seasonal changes in sea ice cover as well as to its long-term decline due to global warming. The corrected reanalysis temperature can be employed to support polar research activities, and in particular to better simulate the evolution of the interacting sea ice and ocean system within numerical models.

    

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4. A Surface Radiation Balance Dataset from Siple Dome in West Antarctica for Atmospheric and Climate Model Evaluation

   https://doi.org/10.1175/JCLI-D-22-0731.1  

   Lubin, Dan ; Ghiz, Madison L. ; Castillo, Sergio ; Scott, Ryan C. ; LeBlanc, Samuel E. ; Silber, Israel ( October 2023 , Journal of Climate)

   A field campaign at Siple Dome in West Antarctica during the austral summer 2019/20 offers an opportunity to evaluate climate model performance, particularly cloud microphysical simulation. Over Antarctic ice sheets and ice shelves, clouds are a major regulator of the surface energy balance, and in the warm season their presence occasionally induces surface melt that can gradually weaken an ice shelf structure. This dataset from Siple Dome, obtained using transportable and solar-powered equipment, includes surface energy balance measurements, meteorology, and cloud remote sensing. To demonstrate how these data can be used to evaluate model performance, comparisons are made with meteorological reanalysis known to give generally good performance over Antarctica (ERA5). Surface albedo measurements show expected variability with observed cloud amount, and can be used to evaluate a model’s snowpack parameterization. One case study discussed involves a squall with northerly winds, during which ERA5 fails to produce cloud cover throughout one of the days. A second case study illustrates how shortwave spectroradiometer measurements that encompass the 1.6-μm atmospheric window reveal cloud phase transitions associated with cloud life cycle. Here, continuously precipitating mixed-phase clouds become mainly liquid water clouds from local morning through the afternoon, not reproduced by ERA5. We challenge researchers to run their various regional or global models in a manner that has the large-scale meteorology follow the conditions of this field campaign, compare cloud and radiation simulations with this Siple Dome dataset, and potentially investigate why cloud microphysical simulations or other model components might produce discrepancies with these observations.

   Antarctica is a critical region for understanding climate change and sea level rise, as the great ice sheets and the ice shelves are subject to increasing risk as global climate warms. Climate models have difficulties over Antarctica, particularly with simulation of cloud properties that regulate snow surface melting or refreezing. Atmospheric and climate-related field work has significant challenges in the Antarctic, due to the small number of research stations that can support state-of-the-art equipment. Here we present new data from a suite of transportable and solar-powered instruments that can be deployed to remote Antarctic sites, including regions where ice shelves are most at risk, and we demonstrate how key components of climate model simulations can be evaluated against these data.

    

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5. Expedition 374 Scientific Prospectus: Ross Sea West Antarctic Ice Sheet History

   https://doi.org/10.14379/iodp.sp.374.2017  

   McKay, R.M. ; De Santis, L. ; Kulhanek, D.K. ( September 2017 , Scientific prospectus)

   null (Ed.)

   Observations from the past several decades indicate that the Southern Ocean is warming significantly and that Southern Hemisphere westerly winds have migrated southward and strengthened due to increasing atmospheric CO2 concentrations and/or ozone depletion. These changes have been linked to thinning of Antarctic ice shelves and marine terminating glaciers. Results from geologic drilling on Antarctica’s continental margins show late Neogene marine-based ice sheet variability, and numerical models indicate a fundamental role for oceanic heat in controlling this variability over at least the past 20 My. Although evidence for past ice sheet variability has been observed in marginal settings, sedimentological sequences from the outer continental shelf are required to evaluate the extent of past ice sheet variability and the role of oceanic heat flux in controlling ice sheet mass balance. International Ocean Discovery Program (IODP) Expedition 374 proposes a latitudinal and depth transect of six drill sites from the outer continental shelf and rise in the eastern Ross Sea to resolve the relationship between climatic/oceanic change and West Antarctic Ice Sheet (WAIS) evolution through the Neogene and Quaternary. This location was selected because numerical ice sheet models indicate that it is highly sensitive to changes in ocean heat flux and sea level. The proposed drilling is designed for optimal data-model integration, which will enable an improved understanding of the sensitivity of Antarctic Ice Sheet mass balance during warmer-than-present climates (e.g., the early Pliocene and middle Miocene). Additionally, the proposed transect links ice-proximal records from the inner Ross Sea continental shelf (e.g., ANDRILL sites) to deepwater Southwest Pacific drilling sites/targets to obtain an ice-proximal to far-field view of Neogene climate and Antarctic cryosphere evolution. The proposed scientific objectives directly address Ocean and Climate Challenges 1 and 2 of the 2013–2023 IODP Science Plan. Drilling Neogene and Quaternary strata from the Ross Sea continental shelf-to-rise sedimentary sequence is designed to achieve five scientific objectives: 1. Evaluate the contribution of West Antarctica to far-field ice volume and sea level estimates. 2. Reconstruct ice-proximal atmospheric and oceanic temperatures to identify past polar amplification and assess its forcings/feedbacks. 3. Assess the role of oceanic forcing (e.g., sea level and temperature) on Antarctic Ice Sheet stability/instability. 4. Identify the sensitivity of the AIS to Earth’s orbital configuration under a variety of climate boundary conditions. 5. Reconstruct eastern Ross Sea bathymetry to examine relationships between seafloor geometry, ice sheet stability/instability, and global climate. To achieve these objectives, we will (1) use data and models to reconcile intervals of maximum Neogene and Quaternary Antarctic ice advance with far-field records of eustatic sea level change; (2) reconstruct past changes in oceanic and atmospheric temperatures using a multiproxy approach; (3) reconstruct Neogene and Quaternary ice margin fluctuations in datable marine continental slope and rise records and correlate these records to existing inner continental shelf records; (4) examine relationships among WAIS stability/instability, Earth’s orbital configuration, oceanic temperature and circulation, and atmospheric pCO2; and (5) constrain the timing of Ross Sea continental shelf overdeepening and assess its impact on Neogene and Quaternary ice dynamics. 

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