Seasonal Ice Research Articles

A Chlorophyll Biomass Time-Series for the Distributed Biological Observatory in the Context of Seasonal Sea Ice Declines in the Pacific Arctic Region

Declines in seasonal sea ice in polar regions have stimulated projections of how primary production has shifted in response to greater light penetration over a longer open water season. Despite the limitations of remotely sensed observations in an often cloudy environment, remote sensing data provide strong indications that surface chlorophyll biomass has increased (since 2000) as sea ice has declined in the Pacific Arctic region. We present here shipboard measurements of chlorophyll-a that have been made annually in July since 2000 from the Distributed Biological Observatory (DBO) stations in the Bering Strait region. This time series as well as shipboard observations made in other months since the late 1980s implicate complexities that intrude on a simple expectation that, as open water periods increase, the production and biomass of phytoplankton will increase predictably. These shipboard observations indicate that there have not been sharp increases in chlorophyll-a, for either maxima observed in the water column or integrated over the whole water column, at the DBO stations over a time-series extending for as long as 20 years coinciding with seasonal sea ice declines. On the other hand, biomass may be increasing in other months: we provide a shipboard confirmation of a fall bloom in October as wind mixing introduced nutrients back into the upper water column. The productive DBO stations may be at a high enough production already that additional enhancements in chlorophyll-a biomass should not be expected, but our time-series record does not exclude the possibility that additional enhanced production may be present in other areas outside the DBO station grid. These findings may also reflect limitations imposed by nutrient cycling and water column structure. The increasing freshwater component of waters flowing through the Bering Strait is likely associated with increased stratification that limits the potential change in biological production associated with decreases in seasonal sea ice persistence.

Seasonal mesozooplankton patterns and timing of life history events in high-arctic fjord environments

Seasonal patterns in mesozooplankton composition, vertical distribution, and timing of reproduction are challenging to study in the open sea due to ocean currents and mix of populations of different origins. Sill fjords, on the other hand, with restricted water exchange, are ideal locations for studying taxa- and community-specific adaptations to the prevailing environment. Here, we present re-occurring patterns in the mesozooplankton community structure in Billefjorden, Svalbard, a high Arctic sill fjord with extensive seasonal ice cover, based on monthly sampling from 2011 to 2013. The zooplankton community composition confirmed the Arctic character of this fjord. Predominantly herbivorous taxa, such as Calanus glacialis and Pseudocalanus spp., showed strong seasonal variation in abundance and depth distribution, with population minima in spring being compensated by a rapid population recovery during summer. Omnivorous taxa, such as Microcalanus spp. and copepods of the family Aetideidae, largely remained at depth throughout the year and had an extended or year-round reproductive period. Deep-dwelling omnivorous/carnivorous species peaked in abundance in winter–spring when herbivorous populations were severely depleted. Taxa with seasonally limited occurrences, i.e., meroplankton, peaked in spring and summer at the surface, but were largely absent for the rest of the year. The different life histories, with contrasting feeding modes, depth preferences, and timing of reproduction lead to reduced interspecies competition and allow for a rather high and stable abundance of mesozooplankton year-round despite the short primary production window at high latitudes.

Content in fatty acids and carotenoids in phytoplankton blooms during the seasonal sea ice retreat in Hudson Bay complex, Canada

The Hudson Bay complex (HBC) is home to numerous indigenous communities that traditionally have relied heavily on its marine resources. The nutritional quality and stocks of the entire HBC food web depend in large part on the phytoplankton production of bioactive molecules (long chain polyunsaturated fatty acids and carotenoids) and their transfer through trophic levels. The purpose of this study was thus to determine which molecules were produced during spring phytoplankton blooms, as well as the environmental factors driving this production. We investigated 21 stations in 5 sub-regions of the HBC. At the time of sampling, the sub-regions studied had different environmental settings (e.g., ice cover, nutrients, seawater salinity and temperature) conditioning their bloom stages. Pre- and post-bloom stages were associated with relatively low concentrations of bioactive molecules (either fatty acids or carotenoids). In contrast, the highest concentrations of bioactive molecules (dominated by eicosapentaenoic acid and fucoxanthin) were associated with the diatom bloom that typically occurs at the ice edge when silicates remain available. Interestingly, the large riverine inputs in eastern Hudson Bay led to a change in protist composition (larger contribution of Dinophyceae), resulting in lower while more diverse content of bioactive molecules, whether fatty acids (e.g., α-linolenic acid) or carotenoids (e.g., peridinin). As greater stratification of the HBC is expected in the future, we suggest that a mixotrophic/heterotrophic flagellate-based food web would become more prevalent, resulting in a smaller supply of bioactive molecules for the food web.

Arctic sea ice albedo: Spectral composition, spatial heterogeneity, and temporal evolution observed during the MOSAiC drift

The magnitude, spectral composition, and variability of the Arctic sea ice surface albedo are key to understanding and numerically simulating Earth’s shortwave energy budget. Spectral and broadband albedos of Arctic sea ice were spatially and temporally sampled by on-ice observers along individual survey lines throughout the sunlit season (April–September, 2020) during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The seasonal evolution of albedo for the MOSAiC year was constructed from spatially averaged broadband albedo values for each line. Specific locations were identified as representative of individual ice surface types, including accumulated dry snow, melting snow, bare and melting ice, melting and refreezing ponded ice, and sediment-laden ice. The area-averaged seasonal progression of total albedo recorded during MOSAiC showed remarkable similarity to that recorded 22 years prior on multiyear sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) expedition. In accord with these and other previous field efforts, the spectral albedo of relatively thick, snow-free, melting sea ice shows invariance across location, decade, and ice type. In particular, the albedo of snow-free, melting seasonal ice was indistinguishable from that of snow-free, melting second-year ice, suggesting that the highly scattering surface layer that forms on sea ice during the summer is robust and stabilizing. In contrast, the albedo of ponded ice was observed to be highly variable at visible wavelengths. Notable temporal changes in albedo were documented during melt and freeze onset, formation and deepening of melt ponds, and during melt evolution of sediment-laden ice. While model simulations show considerable agreement with the observed seasonal albedo progression, disparities suggest the need to improve how the albedo of both ponded ice and thin, melting ice are simulated.

Modelling trivariate distribution of directional ocean data in the Barents Sea seasonal ice zone

The Arctic Ocean has proved to possess a wealth of natural oil and gas resources. Although the ice cover complicates marine industrial activities and transportation, exploratory drilling and processing facilities operation is possible in the region where the sea ice is only present for a portion of the year. This study focuses on the statistical characteristics of directional wave climate in the seasonal ice zone of the Barents Sea. The joint distributions of significant wave height, mean wave period, and mean wave direction were constructed using a mixture trivariate distribution model. The total ocean data were classified into four groups based on the relative weights of the energy content of wind wave and swell fields. The joint distribution for each wave component was constructed by the vine copula model, where the marginal distributions can be well described by the mixture univariate distribution models, the bivariate distributions of angular-linear variables were fitted by the Johnson-Wehrly model adequately, and the bivariate distribution of the linear-linear variable can be accurately modelled by a product of two Gaussian copulas. The results suggested that the mixture trivariate distribution for the total sea state constructed by a linear combination of the four submodels is capable of providing reasonable description for the directional wave data. To generate trivariate samples for model evaluation, an improved pseudorandom number generation algorithm was applied. Furthermore, the environmental contours associated with return periods of 1-, 5-, 20-, and 100-year in different directions were calculated and used to determine the extreme tensions of a semi-submersible platform model based on the global maxima approach.

A 41-year (1979–2019) passive-microwave-derived lake ice phenology data record of the Northern Hemisphere

Abstract. Seasonal ice cover is one of the important attributes of lakes in middle- and high-latitude regions. The annual freeze-up and breakup dates as well as the duration of ice cover (i.e., lake ice phenology) are sensitive to the weather and climate; hence, they can be used as an indicator of climate variability and change. In addition to optical, active microwave, and raw passive microwave data that can provide daily observations, the Calibrated Enhanced-Resolution Brightness Temperature (CETB) dataset available from the National Snow and Ice Data Center (NSIDC) provides an alternate source of passive microwave brightness temperature (TB) measurements for the determination of lake ice phenology on a 3.125 km grid. This study used Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave/Imager (SSM/I), and Special Sensor Microwave Imager/Sounder (SSMIS) data from the CETB dataset to extract the ice phenology for 56 lakes across the Northern Hemisphere from 1979 to 2019. According to the differences in TB between lake ice and open water, a threshold algorithm based on the moving t test method was applied to determine the lake ice status for grids located at least 6.25 km away from the lake shore, and the ice phenology dates for each lake were then extracted. When ice phenology could be extracted from more than one satellite over overlapping periods, results from the satellite offering the largest number of observations were prioritized. The lake ice phenology results showed strong agreement with an existing product derived from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) data (2002 to 2015), with mean absolute errors of ice dates ranging from 2 to 4 d. Compared with near-shore in situ observations, the lake ice results, while different in terms of spatial coverage, still showed overall consistency. The produced lake ice record also displayed significant consistency when compared to a historical record of annual maximum ice cover of the Laurentian Great Lakes of North America. From 1979 to 2019, the average complete freezing duration and ice cover duration for lakes forming a complete ice cover on an annual basis were 153 and 161 d, respectively. The lake ice phenology dataset – a new climate data record (CDR) – will provide valuable information to the user community about the changing ice cover of lakes over the last 4 decades. The dataset is available at https://doi.org/10.1594/PANGAEA.937904 (Cai et al., 2021).

Seasonality and timing of sea ice mass balance and heat fluxes in the Arctic transpolar drift during 2019–2020

Sea ice growth and decay are critical processes in the Arctic climate system, but comprehensive observations are very sparse. We analyzed data from 23 sea ice mass balance buoys (IMBs) deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020 to investigate the seasonality and timing of sea ice thermodynamic mass balance in the Arctic Transpolar Drift. The data reveal four stages of the ice season: (I) onset of ice basal freezing, mid-October to November; (II) rapid ice growth, December–March; (III) slow ice growth, April–May; and (IV) melting, June onward. Ice basal growth ranged from 0.64 to 1.38 m at a rate of 0.004–0.006 m d–1, depending mainly on initial ice thickness. Compared to a buoy deployed close to the MOSAiC setup site in September 2012, total ice growth was about twice as high, due to the relatively thin initial ice thickness at the MOSAiC sites. Ice growth from the top, caused by surface flooding and subsequent snow-ice formation, was observed at two sites and likely linked to dynamic processes. Snow reached a maximum depth of 0.25 ± 0.08 m by May 2, 2020, and had melted completely by June 25, 2020. The relatively early onset of ice basal melt on June 7 (±10 d), 2019, can be partly attributed to the unusually rapid advection of the MOSAiC floes towards Fram Strait. The oceanic heat flux, calculated based on the heat balance at the ice bottom, was 2.8 ± 1.1 W m–2 in December–April, and increased gradually from May onward, reaching 10.0 ± 2.6 W m–2 by mid-June 2020. Subsequently, under-ice melt ponds formed at most sites in connection with increasing ice permeability. Our analysis provides crucial information on the Arctic sea ice mass balance for future studies related to MOSAiC and beyond.

Phytoplankton bloom stages estimated from chlorophyll pigment proportions suggest delayed summer production in low sea ice years in the northern Bering Sea.

Decreased sea ice cover in the northern Bering Sea has altered annual phytoplankton phenology owing to an expansion of open water duration and its impact on ocean stratification. Limitations of satellite remote sensing such as the inability to detect bloom activity throughout the water column, under ice, and in cloudy conditions dictate the need for shipboard based measurements to provide more information on bloom dynamics. In this study, we adapted remote sensing land cover classification techniques to provide a new means to determine bloom stage from shipboard samples. Specifically, we used multiyear satellite time series of chlorophyll a to determine whether in-situ blooms were actively growing or mature (i.e., past-peak) at the time of field sampling. Field observations of chlorophyll a and pheophytin (degraded and oxidized chlorophyll products) were used to calculate pheophytin proportions, i.e., (Pheophytin/(Chlorophyll a + Pheophytin)) and empirically determine whether the bloom was growing or mature based on remotely sensed bloom stages. Data collected at 13 north Bering Sea stations each July from 2013–2019 supported a pheophytin proportion of 28% as the best empirical threshold to distinguish a growing vs. mature bloom stage. One outcome was that low vs. high sea ice years resulted in significantly different pheophytin proportions in July; in years with low winter-to-spring ice, more blooms with growing status were observed, compared to later stage, more mature blooms following springs with abundant seasonal sea ice. The detection of growing blooms in July following low ice years suggests that changes in the timing of the spring bloom triggers cascading effects on mid-summer production.

Simulation of Arctic sea ice within the DeepMIP Eocene ensemble: Thresholds, seasonality and factors controlling sea ice development

The early Eocene greenhouse climate maintained by high atmospheric CO2 concentrations serves as a testbed for future climate changes dominated by increasing CO2 forcing. In particular, the early Eocene Arctic region is important in the context of future CO2 driven climate warming in the northern polar region and associated shrinking Arctic sea ice. Here, we present early Eocene Arctic sea ice simulations carried out by six coupled climate models within the framework of the Deep-Time Model Intercomparison Project (DeepMIP). We find differences in sea ice responses to CO2 changes across the ensemble and compare the results with available proxy-based sea ice reconstructions from the Arctic Ocean. Most of the models simulate seasonal sea ice presence at high CO2 levels (≥ 840 ppmv = 3× pre-industrial (PI) level of 280 ppmv). However, the threshold when sea ice permanently disappears from the ocean varies considerably between the models (from <840 ppmv to >1680 ppmv). Based on a one-dimensional energy balance model analysis we find that the greenhouse effect likely caused by increased atmospheric water vapor concentration plays an important role in the inter-model spread in Arctic winter surface temperature changes in response to a CO2 rise from 1× to 3× the PI level. Furthermore, differences in simulated surface salinity in the Arctic Ocean play an important role in the control of local sea ice formation. These differences result from different implementations of river run-off between the models, but also from differences in the exchange of waters between a brackish Arctic and a more saline North Atlantic Ocean that are controlled by the width of the gateway between both basins. As there is no geological evidence for Arctic sea ice in the early Eocene, its presence in most of the simulations with 3× PI CO2 level indicates either a higher CO2 level and/or an overly weak polar sensitivity in these models.

Decadal community structure shifts with cold pool variability in the eastern Bering Sea shelf.

A characteristic feature of the eastern Bering Sea (EBS) is a subsurface layer linked to seasonal sea ice (SSI) and defined by bottom temperatures less than 2 °C, which is termed the cold pool. Cold pool variability is directly tied to regional zooplankton and fish dynamics. Multifrequency (200 and 460 kHz) acoustic backscatter data were collected remotely using upward looking echosounders along the EBS shelf from 2008 and 2018 and used as a proxy of biological abundance. Acoustic data were coupled with bottom temperature and regional SSI data from the cold (2006-2013) and warm (2014-2018) regimes to assess the relationship between biological scattering communities and cold pool variation. Acoustic backscatter was 2 orders of magnitude greater during the cold regime than during the warm regime, with multifrequency analysis indicating a shift in the warm regime frequency-dependent scattering communities. Cold pool proxy SSI was a stronger predictor for biological scattering than bottom temperature in the cold regime, while warm regime bottom temperature and SSI were equal in predictive power and resulted in improved predictive model performance. Results suggest coupled cold pool and frequency-dependent scattering dynamics are a potential regime shift indicator and may be useful for management practices in surrounding Arctic ecosystems.

Mechanisms of Regional Arctic Sea Ice Predictability in Two Dynamical Seasonal Forecast Systems

Abstract Research over the past decade has demonstrated that dynamical forecast systems can skillfully predict pan-Arctic sea ice extent (SIE) on the seasonal time scale; however, there have been fewer assessments of prediction skill on user-relevant spatial scales. In this work, we evaluate regional Arctic SIE predictions made with the Forecast-Oriented Low Ocean Resolution (FLOR) and Seamless System for Prediction and Earth System Research (SPEAR_MED) dynamical seasonal forecast systems developed at the NOAA/Geophysical Fluid Dynamics Laboratory. Compared to FLOR, we find that the recently developed SPEAR_MED system displays improved skill in predicting regional detrended SIE anomalies, partially owing to improvements in sea ice concentration (SIC) and thickness (SIT) initial conditions. In both systems, winter SIE is skillfully predicted up to 11 months in advance, whereas summer minimum SIE predictions are limited by the Arctic spring predictability barrier, with typical skill horizons of roughly 4 months. We construct a parsimonious set of simple statistical prediction models to investigate the mechanisms of sea ice predictability in these systems. Three distinct predictability regimes are identified: a summer regime dominated by SIE and SIT anomaly persistence; a winter regime dominated by SIE and upper-ocean heat content (uOHC) anomaly persistence; and a combined regime in the Chukchi Sea, characterized by a trade-off between uOHC-based and SIT-based predictability that occurs as the sea ice edge position evolves seasonally. The combination of regional SIE, SIT, and uOHC predictors is able to reproduce the SIE skill of the dynamical models in nearly all regions, suggesting that these statistical predictors provide a stringent skill benchmark for assessing seasonal sea ice prediction systems.

Phytoplankton and ice-algal communities in the seasonal ice zone during January (Southern Ocean, Indian sector)

Phytoplankton and ice-algal communities in the seasonal ice zone during January (Southern Ocean, Indian sector)

Validation of turbulent heat transfer models against eddy covariance flux measurements over a seasonally ice-covered lake

Abstract. In this study we analyzed turbulent heat fluxes over a seasonal ice cover on a boreal lake located in southern Finland. Eddy covariance (EC) flux measurements of sensible (H) and latent heat (LE) from four ice-on seasons between 2014 and 2019 are compared to three different bulk transfer models: one with a constant transfer coefficient and two with stability-adjusted transfer coefficients: the Lake Heat Flux Analyzer and SEA-ICE. All three models correlate well with the EC results in general while typically underestimating the magnitude and the standard deviation of the flux in comparison to the EC observations. Differences between the models are small, with the constant transfer coefficient model performing slightly better than the stability-adjusted models. Small difference in temperature and humidity between surface and air results in low correlation between models and EC. During melting periods (surface temperature T0&gt;0 ∘C), the model performance for LE decreases when compared to the freezing periods (T0&lt;0 ∘C), while the opposite is true for H. At low wind speed, EC shows relatively high fluxes (±20 W m−2) for H and LE due to non-local effects that the bulk models are not able to reproduce. The complex topography of the lake surroundings creates local violations of the Monin–Obukhov similarity theory, which helps explain this counterintuitive result. Finally, the uncertainty in the estimation of the surface temperature and humidity affects the bulk heat fluxes, especially when the differences between surface and air values are small.

Observations of Upwelling and Downwelling Around Antarctica Mediated by Sea Ice

We infer circumpolar maps of stress imparted to the ocean by the wind, mediated by sea-ice, in and around the Seasonal Ice Zone (SIZ) of Antarctica. In the open ocean we compute the wind stress using surface winds from daily atmospheric reanalyses and applying bulk formulae. In the presence of sea ice, the stress imparted to the underlying ocean is computed from satellite observations of daily ice concentration and drift velocity assuming, first, that the ocean geostrophic currents beneath are negligible, and then including surface geostrophic ocean currents inferred from satellite altimetry. In this way maps of surface ocean stress in the SIZ are obtained. The maps are discussed and interpreted, and their importance in setting the circulation emphasised. Just as in parallel observational studies in the Arctic, we find that ocean currents significantly modify the stress field, the sense of the surface ageostrophic flow and thus pathways of exchange across the SIZ. Maps of Ekman pumping reveal broad patterns of upwelling within the SIZ enhanced near the sea ice edge, which are offset by strong narrow downwelling regions adjacent to the Antarctic continent.

The Effect of Ocean Salinity on Climate and Its Implications for Earth's Habitability

The influence of atmospheric composition on the climates of present‐day and early Earth has been studied extensively, but the role of ocean composition has received less attention. We use the ROCKE‐3D ocean‐atmosphere general circulation model to investigate the response of Earth's present‐day and Archean climate system to low versus high ocean salinity. We find that saltier oceans yield warmer climates in large part due to changes in ocean dynamics. Increasing ocean salinity from 20 to 50 g/kg results in a 71% reduction in sea ice cover in our present‐day Earth scenario. This same salinity change also halves the pCO2 threshold at which Snowball glaciation occurs in our Archean scenarios. In combination with higher levels of greenhouse gases such as CO2 and CH4, a saltier ocean may allow for a warm Archean Earth with only seasonal ice at the poles despite receiving ∼20% less energy from the Sun.

Climate Change Fosters Competing Effects of Dynamics and Thermodynamics in Seasonal Predictability of Arctic Sea Ice

Abstract The fast decline of Arctic sea ice necessitates a stronger focus on understanding the Arctic sea ice predictability and developing advanced forecast methods for all seasons and for pan-Arctic and regional scales. In this study, the operational forecasting system combining an advanced eddy-permitting ocean–sea ice ensemble reanalysis ORAS5 and state-of-the-art seasonal model-based forecasting system SEAS5 is used to investigate effects of sea ice dynamics and thermodynamics on seasonal (growth-to-melt) Arctic sea ice predictability in 1993–2020. We demonstrate that thermodynamics (growth/melt) dominates the seasonal evolution of mean sea ice thickness at pan-Arctic and regional scales. The thermodynamics also dominates the seasonal predictability of sea ice thickness at pan-Arctic scale; however, at regional scales, the predictability is dominated by dynamics (advection), although the contribution from ice growth/melt remains perceptible. We show competing influences of sea ice dynamics and thermodynamics on the temporal change of ice thickness predictability from 1993–2006 to 2007–20. Over these decades, there was increasing predictability due to growth/melt, attributed to increased winter ocean heat flux in both Eurasian and Amerasian basins, and decreasing predictability due to advection. Our results demonstrate an increasing impact of advection on seasonal sea ice predictability as the region of interest becomes smaller, implying that correct modeling of sea ice drift is crucial for developing reliable regional sea ice predictions. This study delivers important information about sea ice predictability in the “new Arctic” conditions. It increases awareness regarding sea ice state and implementation of sea ice forecasts for various scientific and practical needs that depend on accurate seasonal sea ice forecasts.

A New Norm for Seasonal Sea Ice Advance Predictability in the Chukchi Sea: Rising Influence of Ocean Heat Advection

Abstract Predictability of seasonal sea ice advance in the Chukchi Sea has been investigated in the context of ocean heat transport from the Bering Strait; however, the underlying physical processes have yet to be fully clarified. Using the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS) reanalysis product (1979–2016), we examined seasonal predictability of sea ice advance in early winter (November–December) and its source using canonical correlation analysis. It was found that 2-month leading (September–October) surface heat flux and ocean heat advection is the major predictor for interannual variability of sea ice advance. Surface heat flux is related to the atmospheric cooling process, which has influenced sea ice area in the southeastern Chukchi Sea particularly in the 1980s and 1990s. Anomalous surface heat flux is induced by strong northeasterly winds related to the east Pacific/North Pacific teleconnection pattern. Ocean heat advection, which is related to fluctuation of volume transport in the Bering Strait, leads to decrease in the sea ice area in the northwestern Chukchi Sea. Diagnostic analysis revealed that interannual variability of the Bering Strait volume transport is governed by arrested topographic waves (ATWs) forced by southeasterly wind stress along the shelf of the East Siberian Sea. The contribution of ocean heat flux to sea ice advance has increased since the 2000s; therefore, it is suggested that the major factor influencing interannual variability of sea ice advance in early winter has shifted from atmospheric cooling to ocean heat advection processes. Significance Statement Predictability of sea ice advance in the marginal Arctic seas in early winter is a crucial issue regarding future projections of the midlatitude winter climate and marine ecosystem. This study examined seasonal predictability of sea ice advance in the Chukchi Sea in early winter using a statistical technique and historical model simulation data. We identified that atmospheric cooling and ocean heat transport are the two main predictors of sea ice advance, and that the impact of the latter has become amplified since the 2000s. Our new finding suggests that the precise information on wind-driven ocean currents and temperatures is crucial for the skillful prediction of interannual variability of sea ice advance under present and future climatic regimes.

Representation of Lake–Atmosphere Interactions and Lake-Effect Snowfall in the Laurentian Great Lakes Basin among HighResMIP Global Climate Models

Abstract Credible modeling, tools, and guidance, regarding the changing Laurentian Great Lakes and the climatic impacts, are needed by local decision-makers to inform their management and planning. The present study addresses this need through a model evaluation study of the representation of lake–atmosphere interactions and resulting lake-effect snowfall in the Great Lakes region. Analysis focuses on an extensive ensemble of 74 historical simulations generated by 23 high-resolution global climate models (GCMs) from the High-Resolution Model Intercomparison Project (HighResMIP). The model assessment addresses the modeling treatment of the Great Lakes, the spatial distribution and seasonality of climatological snowfall, the seasonal cycle of lake-surface temperatures and overlake turbulent fluxes, and the lake-effect ratio between upwind and downwind precipitation. A deeper understanding of model performance and biases is achieved by partitioning results between HighResMIP GCMs that are 1) coupled to 1D lake models versus GCMs that exclude lake models, 2) between prescribed-ocean model configurations versus fully coupled configurations, and 3) between deep Lake Superior versus relatively shallow Lake Erie. While the HighResMIP GCMs represent the Great Lakes by a spectrum of approaches that include land grid cells, ocean grid cells (with lake surface temperature and ice cover boundary conditions provided by the Met Office Hadley Center Sea Ice and Sea Surface Temperature Dataset), and 1D lake models, the current investigation demonstrates that none of these rudimentary approaches adequately represent the complex nature of seasonal lake temperature and ice cover evolution and its impact on lake–atmosphere interactions and lake-effect precipitation in the Great Lakes region. Significance Statement The purpose of this study is to evaluate the capability of high-resolution global climate models to simulate lake–atmosphere interactions and lake-effect snowfall in the Great Lakes region, given the critical influence of the lakes on regional climate and vast societal and environmental impacts of lake-effect snowfall. It is determined that the models inadequately represent lake temperatures and ice cover, often leading to insufficient annual snowfall in the lake-effect zones. More advanced, three-dimensional lake models need to be coupled to climate models to support greater credibility in regional lake and climate simulations and future climate projections.

InSight Pressure Data Recalibration, and Its Application to the Study of Long-Term Pressure Changes on Mars.

Observations of the South Polar Residual Cap suggest a possible erosion of the cap, leading to an increase of the global mass of the atmosphere. We test this assumption by making the first comparison between Viking 1 and InSight surface pressure data, which were recorded 40 years apart. Such a comparison also allows us to determine changes in the dynamics of the seasonal ice caps between these two periods. To do so, we first had to recalibrate the InSight pressure data because of their unexpected sensitivity to the sensor temperature. Then, we had to design a procedure to compare distant pressure measurements. We propose two surface pressure interpolation methods at the local and global scale to do the comparison. The comparison of Viking and InSight seasonal surface pressure variations does not show changes larger than ±8 Pa in the CO2 cycle. Such conclusions are supported by an analysis of Mars Science Laboratory (MSL) pressure data. Further comparisons with images of the south seasonal cap taken by the Viking 2 orbiter and MARCI camera do not display significant changes in the dynamics of this cap over a 40 year period. Only a possible larger extension of the North Cap after the global storm of MY 34 is observed, but the physical mechanisms behind this anomaly are not well determined. Finally, the first comparison of MSL and InSight pressure data suggests a pressure deficit at Gale crater during southern summer, possibly resulting from a large presence of dust suspended within the crater.

Calculating the heat loss coefficients for performance modelling of seasonal ice thermal storage

This paper details the use of piece-wise linear regression and non-linear optimisation to determine the heat transfer properties of two ice thermal stores of different volumes (85 m3 and 11 m3). The available energy of each ice storage was determined by the fraction of ice stored in the vessel. The heat loss coefficient was determined using an optimisation algorithm. Using this approach it was possible to determine the heat loss coefficients occurring at different layers of the storage. Validation of the approach yielded a relative mean error of 5.4% and 3.8% for the 85 m3 and 11 m3 storage respectively. This approach is dependent on the measurement of temperature at different segments and the quantity of ice in the storage. It is believed that this approach is scalable and could contribute to a performance database that could provide inputs to energy modelling studies.