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1. Tracking Multiyear Sea‐Ice Variation in the Arctic Ocean Over Decades With Microseism

   https://doi.org/10.1029/2024GL111159  

   Chen, Jui‐Chun_Freya; Park, Sunyoung; MacAyeal, Douglas_R (January 2025, Geophysical Research Letters)

   Abstract We construct a linear model of microseism power as a function of sea‐ice concentration and ocean‐wave activity with a seismic station located on northern Ellesmere Island. The influence of wind‐ice‐ocean interactions on microseism has been taken into account. We find the increase in microseism power over the last 32 years reflects the long‐term loss of sea ice and increasing ocean‐wave activity in the Arctic Ocean likely associated with climate change. We further assess model performance to determine a representative region over which sea‐ice concentration and ocean‐wave activity most directly influence the microseism power. The seismological methods developed here suggest that there is the potential to augment or refine observations of sea‐ice conditions obtained from satellites and fromin‐situobservations. Seismological methods may thus help determine properties such as sea‐ice thickness, which are less amenable to conventional observations, under a changing climate, particularly in remote areas like the High Arctic. 

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2. Evaluation of Arctic warming in mid-Pliocene climate simulations

   https://doi.org/10.5194/cp-16-2325-2020  

   de Nooijer, Wesley; Zhang, Qiong; Li, Qiang; Zhang, Qiang; Li, Xiangyu; Zhang, Zhongshi; Guo, Chuncheng; Nisancioglu, Kerim H.; Haywood, Alan M.; Tindall, Julia C.; et al (January 2020, Climate of the Past)

   null (Ed.)

   Abstract. Palaeoclimate simulations improve our understanding ofthe climate, inform us about the performance of climate models in adifferent climate scenario, and help to identify robust features of theclimate system. Here, we analyse Arctic warming in an ensemble of 16simulations of the mid-Pliocene Warm Period (mPWP), derived from thePliocene Model Intercomparison Project Phase 2 (PlioMIP2). The PlioMIP2 ensemble simulates Arctic (60–90∘ N) annual meansurface air temperature (SAT) increases of 3.7 to 11.6 ∘Ccompared to the pre-industrial period, with a multi-model mean (MMM) increase of7.2 ∘C. The Arctic warming amplification ratio relative to globalSAT anomalies in the ensemble ranges from 1.8 to 3.1 (MMM is 2.3). Sea iceextent anomalies range from −3.0 to -10.4×106 km2, with a MMManomaly of -5.6×106 km2, which constitutes a decrease of 53 %compared to the pre-industrial period. The majority (11 out of 16) of models simulatesummer sea-ice-free conditions (≤1×106 km2) in their mPWPsimulation. The ensemble tends to underestimate SAT in the Arctic whencompared to available reconstructions, although the degree of underestimationvaries strongly between the simulations. The simulations with the highestArctic SAT anomalies tend to match the proxy dataset in its current formbetter. The ensemble shows some agreement with reconstructions of sea ice,particularly with regard to seasonal sea ice. Large uncertainties limit theconfidence that can be placed in the findings and the compatibility of thedifferent proxy datasets. We show that while reducing uncertainties in thereconstructions could decrease the SAT data–model discord substantially,further improvements are likely to be found in enhanced boundary conditionsor model physics. Lastly, we compare the Arctic warming in the mPWP toprojections of future Arctic warming and find that the PlioMIP2 ensemblesimulates greater Arctic amplification than CMIP5 future climate simulationsand an increase instead of a decrease in Atlantic Meridional OverturningCirculation (AMOC) strength compared topre-industrial period. The results highlight the importance of slow feedbacks inequilibrium climate simulations, and that caution must be taken when usingsimulations of the mPWP as an analogue for future climate change. 

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3. The impact of floating ice on sea level estimates across multiple glacial cycles

   Gasson, E. (December 2018, AGU Fall Meeting)

   Unlike grounded ice sheets, floating ice shelves have minimal direct impact on sea level. However, depending on the method, proxy sea level reconstructions may be affected by the existence of floating ice. Unless ice shelves intercept the seafloor, their former presence is very difficult to detect. The recent discovery of ice erosional features in deep water depths at various locations throughout the Arctic Basin may indicate that extensive ice shelves existed in the Arctic Ocean during glacial maxima. Although dating these features is difficult, the current constraints suggest that a thick ice shelf existed during the penultimate glacial maximum. However, this does not rule out that ice shelves also existed during other glacial stages. Here we use ice sheet and oxygen isotope modeling to explore what impact a former Arctic ice shelf would have on our interpretations of past ice volumes during glacial maxima. We suggest that a former Arctic ice shelf may help to resolve some existing discrepancies between different proxy sea level reconstructions. 

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4. Do Multi‐Model Ensembles Improve Reconstruction Skill in Paleoclimate Data Assimilation?

   https://doi.org/10.1029/2020EA001467  

   Parsons, Luke A.; Amrhein, Daniel E.; Sanchez, Sara C.; Tardif, Robert; Brennan, M. Kathleen; Hakim, Gregory J. (April 2021, Earth and Space Science)

   Abstract Reconstructing past climates remains a difficult task because pre‐instrumental observational networks are composed of geographically sparse and noisy paleoclimate proxy records that require statistical techniques to inform complete climate fields. Traditionally, instrumental or climate model statistical relationships are used to spread information from proxy measurements to other locations and to other climate variables. Here ensembles drawn from single climate models and from combinations of multiple climate models are used to reconstruct temperature variability over the last millennium in idealized experiments. We find that reconstructions derived from multi‐model ensembles produce lower error than reconstructions from single‐model ensembles when reconstructing independent model and instrumental data. Specifically, we find the largest decreases in error over regions far from proxy locations that are often associated with large uncertainties in model physics, such as mid‐ and high‐latitude ocean and sea‐ice regions. Furthermore, we find that multi‐model ensemble reconstructions outperform single‐model reconstructions that use covariance localization. We propose that multi‐model ensembles could be used to improve paleoclimate reconstructions in time periods beyond the last millennium and for climate variables other than air temperature, such as drought metrics or sea ice variables. 

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5. Future trends of arctic surface wind speeds and their relationship with sea ice in CMIP5 climate model simulations

   https://doi.org/10.1007/s00382-021-06071-6  

   Vavrus, Stephen J.; Alkama, Ramdane (December 2021, Climate Dynamics)

   Recent climate change in the Arctic has been rapid and dramatic, leading to numerous physical and societal consequences. Many studies have investigated these ongoing and projected future changes across a range of climatic variables, but surprisingly little attention has been paid to wind speed, despite its known importance for sea ice motion, ocean wave heights, and coastal erosion. Here we analyzed future trends in Arctic surface wind speed and its relationship with sea ice cover among CMIP5 global climate models. There is a strong anticorrelation between climatological sea ice concentration and wind speed in the early 21st-century reference climate, and the vast majority of models simulate widespread future strengthening of surface winds over the Arctic Ocean (annual multi-model mean trend of up to 0.8 m s−1 or 13%). Nearly all models produce an inverse relationship between projected changes in sea ice cover and wind speed, such that grid cells with virtually total ice loss almost always experience stronger winds. Consistent with the largest regional ice losses during autumn and winter, the greatest increases in future wind speeds are expected during these two seasons, with localized strengthening up to 23%. As in other studies, stronger surface winds cannot be attributed to tighter pressure gradients but rather to some combination of weakened atmospheric stability and reduced surface roughness as the surface warms and melts. The intermodel spread of wind speed changes, as expressed by the two most contrasting model results, appears to stem from differences in the treatment of surface roughness. 

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