Laboratory study of initial sea-ice growth: properties of grease ice and nilas

We investigate initial sea-ice growth in an ice-tank study by freezing an NaCl solution of about 29 g kg−1 in three different setups: grease ice grew in experiments with waves and in experiments with a current and wind, while nilas formed in a quiescent experimental setup. In this paper we focus on the differences in bulk salinity, solid fraction and thickness between these two ice types. The bulk salinity of the grease-ice layer in our experiments remained almost constant until the ice began to consolidate. In contrast, the initial bulk-salinity evolution of the nilas is well described by a linear decrease of about 2.1 g kg−1 h−1 independent of air temperature. This rapid decrease can be qualitatively understood by considering a Rayleigh number that became maximum while the nilas was still less than 1 cm thick. Comparing three different methods to measure solid fraction in grease ice based on (a) salt conservation, (b) mass conservation and (c) energy conservation, we find that the method based on salt conservation does not give reliable results if the salinity of the interstitial water is approximated as being equal to the salinity of the underlying water. Instead the increase in salinity of the interstitial water during grease-ice formation must be taken into account. In our experiments, the solid fraction of grease ice was relatively constant with values of 0.25, whereas it increased to values as high as 0.50 as soon as the grease ice consolidated at its surface. In contrast, the solid fraction of the nilas increased continuously in the first hours of ice formation and reached an average value of 0.55 after 4.5 h. The spatially averaged ice thickness was twice as large in the first 24 h of ice formation in the setup with a current and wind compared to the other two setups, since the wind kept parts of the water surface ice free and therefore allowed for a higher heat loss from the water. The development of the ice thickness can be reproduced well with simple, one dimensional models that only require air temperature or ice surface temperature as input.publishedVersio

Arctic Sea Ice in CMIP6

We examine CMIP6 simulations of Arctic sea‐ice area and volume. We find that CMIP6 models produce a wide spread of mean Arctic sea‐ice area, capturing the observational estimate within the multimodel ensemble spread. The CMIP6 multimodel ensemble mean provides a more realistic estimate of the sensitivity of September Arctic sea‐ice area to a given amount of anthropogenic CO 2 emissions and to a given amount of global warming, compared with earlier CMIP experiments. Still, most CMIP6 models fail to simulate at the same time a plausible evolution of sea‐ice area and of global mean surface temperature. In the vast majority of the available CMIP6 simulations, the Arctic Ocean becomes practically sea‐ice free (sea‐ice area &amp;lt;1 &times; 10 6 &nbsp;km 2 ) in September for the first time before the Year 2050 in each of the four emission scenarios SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5 examined here.</p

Improving model-satellite comparisons of sea ice melt onset with a satellite simulator

Seasonal transitions in Arctic sea ice, such as the melt onset, have been found to be useful metrics for evaluating sea ice in climate models against observations. However, comparisons of melt onset dates between climate models and satellite observations are indirect. Satellite data products of melt onset rely on observed brightness temperatures, while climate models do not currently simulate brightness temperatures, and must therefore define melt onset with other modeled variables. Here we adapt a passive microwave sea ice satellite simulator, the Arctic Ocean Observation Operator (ARC3O), to produce simulated brightness temperatures that can be used to diagnose the timing of the earliest snowmelt in climate models, as we show here using Community Earth System Model version 2 (CESM2) ocean-ice hindcasts. By producing simulated brightness temperatures and earliest snowmelt estimation dates using CESM2 and ARC3O, we facilitate new and previously impossible comparisons between the model and satellite observations by removing the uncertainty that arises due to definition differences. Direct comparisons between the model and satellite data allow us to identify an early bias across large areas of the Arctic at the beginning of the CESM2 ocean-ice hindcast melt season, as well as improve our understanding of the physical processes underlying seasonal changes in brightness temperatures. In particular, the ARC3O allows us to show that satellite algorithm-based melt onset dates likely occur after significant snowmelt has already taken place.</p

Representation of soil hydrology in permafrost regions may explain large part of inter-model spread in simulated Arctic and subarctic climate

The current generation of Earth system models exhibits large inter-model differences in the simulated climate of the Arctic and subarctic zone, with differences in model structure and parametrizations being one of the main sources of uncertainty. One particularly challenging aspect in modelling is the representation of terrestrial processes in permafrost-affected regions, which are often governed by spatial heterogeneity far below the resolution of the models' land surface components. Here, we use the MPI Earth System model to investigate how different plausible assumptions for the representation of the permafrost hydrology modulate the land-atmosphere interactions and how the resulting feedbacks affect not only the regional and global climate, but also our ability to predict whether the high latitudes will become wetter or drier in a warmer future. Focusing on two idealized setups that induce comparatively "wet" or "dry" conditions in regions that are presently affected by permafrost, we find that the parameter settings determine the direction of the 21st-century trend in the simulated soil water content and result in substantial differences in the land-atmosphere exchange of energy and moisture. The latter leads to differences in the simulated cloud cover and thus in the planetary energy uptake. The respective effects are so pronounced that uncertainties in the representation of the Arctic hydrological cycle can help to explain a large fraction of the inter-model spread in regional surface temperatures and precipitation. Furthermore, they affect a range of components of the Earth system as far to the south as the tropics. With both setups being similarly plausible, our findings highlight the need for more observational constraints on the permafrost hydrology to reduce the inter-model spread in Arctic climate projections.publishedVersio

Antarctic Sea Ice Area in CMIP6

Fully coupled climate models have long shown a wide range of Antarctic sea ice states and evolution over the satellite era. Here, we present a high‐level evaluation of Antarctic sea ice in 40 models from the most recent phase of the Coupled Model Intercomparison Project (CMIP6). Many models capture key characteristics of the mean seasonal cycle of sea ice area (SIA), but some simulate implausible historical mean states compared to satellite observations, leading to large intermodel spread. Summer SIA is consistently biased low across the ensemble. Compared to the previous model generation (CMIP5), the intermodel spread in winter and summer SIA has reduced, and the regional distribution of sea ice concentration has improved. Over 1979–2018, many models simulate strong negative trends in SIA concurrently with stronger‐than‐observed trends in global mean surface temperature (GMST). By the end of the 21st century, models project clear differences in sea ice between forcing scenarios

Retrievals of Arctic Sea-Ice Volume and Its Trend Significantly Affected by Interannual Snow Variability

We estimate the uncertainty of satellite-retrieved Arctic sea-ice thickness, sea-ice volume, and their trends stemming from the lack of reliable snow-thickness observations. To do so, we simulate a Cryosat2-type ice-thickness retrieval in an ocean-model simulation forced by atmospheric reanalysis, pretending that only freeboard is known as model output. We then convert freeboard to sea-ice thickness using different snow climatologies and compare the resulting sea-ice thickness retrievals to each other and to the real sea-ice thickness of the reanalysis-forced simulation. We find that different snow climatologies cause significant differences in the obtained ice thickness and ice volume. In addition, we show that Arctic ice-volume trends derived from ice-thickness retrievals using any snow-depth climatology are highly unreliable because the estimated trend in ice volume can strongly be influenced by the neglected interannual variability in snow volume. ©2018. American Geophysical Union. All Rights Reserved