Ice Model Research Articles

Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models

Further analysis of multilevel Stein variational gradient descent with an application to the Bayesian inference of glacier ice models

Suitability of the CICE sea ice model for seasonal prediction and positive impact of CryoSat-2 ice thickness initialization

Abstract. The Los Alamos Community Ice CodE (CICE) sea ice model is being tested in standalone mode to identify biases that limit its suitability for seasonal prediction, where it is driven by atmospheric forcings from the NCEP Climate Forecast System Reanalysis (CFSR) and a built-in mixed-layer ocean model in CICE. The initial conditions for the sea ice and mixed-layer ocean are also from CFSR in the control experiments. The simulated sea ice extent agrees well with observations during the warm season at all lead times up to 12 months, in both the Arctic and the Antarctic. This suggests that CICE is able to provide useful sea ice edge information for seasonal prediction. However, the model's initial conditions have ice that is too thick in the Beaufort Sea, resulting in excessive ice extent in the Arctic at 6-month lead forecasts and errors in ice volume at all lead times when compared to available observations. To address this limitation, additional CS2_IC experiments were conducted, where the Arctic ice thickness was initialized using CryoSat-2 satellite observations while keeping all other initial fields the same as in the control experiments. This reduced the positive bias in the ice thickness in the initial conditions, leading to improvements in both the simulated ice edge and the ice thickness at the seasonal timescale. This indicates that CICE has the potential to improve its seasonal forecast skill and provide more accurate predictions of sea ice extent and thickness when initialized with a more realistic sea ice thickness. This study highlights that the suitability of CICE for seasonal prediction depends on various factors, including initial conditions such as sea ice thickness, in addition to sea ice coverage, as well as oceanic and atmospheric conditions.

Sensitivities of the West Greenland Current to Greenland Ice Sheet Meltwater in a Mesoscale Ocean/Sea Ice Model

Abstract Meltwater from the Greenland Ice Sheet can alter the continental shelf/slope circulation and cross-shelf freshwater fluxes and limit deep convection in adjacent basins through surface freshening. We explore the impacts on the West Greenland Current and eastern Labrador Sea with different vertical distributions of the meltwater forcing. In this study, we present the results from global coupled ocean/sea ice simulations, forced with atmospheric reanalysis, that are mesoscale eddy-active (∼2–3-km horizontal spacing) and eddy-permitting (∼6–7-km horizontal spacing) in the study region. We compare the West Greenland Current in mesoscale eddy-active and eddy-permitting without meltwater to highlight the role of small-scale features. The mesoscale eddy-active configuration is then used to assess the change in the eastern Labrador Sea when meltwater is added to the surface or vertically distributed to account for mixing within fjords. In both simulations with meltwater, the West Greenland and West Greenland Coastal Currents are faster than in the simulation with no meltwater; their mean surface speeds are the highest in the vertical distribution case. In the latter case, there is enhanced baroclinic conversion at the shelf break compared to the simulation with no meltwater. When meltwater is vertically distributed, there is an increase in baroclinic conversion at the shelf break associated with increased eddy kinetic energy. In addition, in the eastern Labrador Sea, the salinity is lower and the meltwater volume is greater when meltwater is vertically distributed. Therefore, the West Greenland Current is sensitive to how meltwater is added to the ocean with implications for the freshening of the Labrador Sea. Significance Statement Our goal is to understand how the flux of freshwater across the West Greenland continental slope into the Labrador Sea is modified by meltwater from the Greenland Ice Sheet. We compare the simulations of the ocean that capture key dynamics along the West Greenland continental slope that have no meltwater, meltwater added to the ocean surface, and meltwater distributed vertically to represent the mixing within fjords. When meltwater is added, the currents along the continental slope are faster, with the greatest increase when meltwater is vertically distributed. In that case, there is enhanced freshening of the Labrador Sea because modified density gradients generate more eddies. Proper representation of the vertical structure of meltwater is important for projecting the impact of freshwater on the subpolar North Atlantic.

Mechanism analysis on tensile fracture properties of heterogeneous BFLAC at cryogenic temperature: Experimental investigation and mesoscale simulation

This study aims to investigate the tensile failure behaviours and corresponding fracture mechanisms of basalt fiber reinforced lightweight-aggregate concrete (BFLAC) at various temperatures via a comprehensive cryogenic tests and mesoscale simulations, with a special focus on the quantitative effects of cryogenic temperature and fiber volume fraction. Firstly, Macro- and micro-scale split-tensile tests of BFLAC with fiber volume fractions of 0.0–0.3 % at 20∼-90 °C were conducted. Secondly, a two-steps sequentially thermo-mechanical coupled meso-scale analysis approach with explicit modelling of fibers and pore ice was developed to simulate the corresponding direct-tensile failures of BFLAC with more fiber volume fractions. The results show that as the temperature falls from 20 °C to −90 °C, the dominant action mechanism of basalt fibers changes from Mode-1 (pull-out of fibers) to Mode-2 (rupture of fibers) due to the ice formation and the interactions between meso-components. Tensile strengths of BFLAC present a significant low-temperature enhancing effect, with a maximum increase of 90 % for direct-tensile strength while 104 % for split-tensile strength. Besides, as the temperature drops, although a larger proportion of fibers are in a low bridging stress state and a smaller proportion reach yield stress for rupture, the average fiber stress increases and the utilization degree of fibers with more stresses transferred improves, which results in that the fiber reinforcement effect is strengthened. Finally, based on experimental and numerical results, the quantitative relationships between split-tensile and direct-tensile strengths at different cryogenic temperatures were given. The present research results can better understand the cryogenic mechanical properties of BFLAC, which have important reference value for its extensive promotions and applications in engineering structures exposed to extreme low-temperature environments.

The Impact of an Antarctic Circumpolar Current Meander on Air‐Sea Interaction and Water Subduction

AbstractStanding meanders along the Antarctic Circumpolar Current (ACC) have been shown to be regions of elevated eddy variability, meridional heat transport, and vertical exchange. In this study, we investigate the influence of a standing meander south of Australia on air‐sea heat fluxes, upper ocean structure, and subduction in the 1/10° ACCESS‐OM2 ocean‐sea ice model forced by the JRA55 atmospheric reanalysis. We track the model's Subantarctic and Polar Fronts based on their jet and water mass structure, and produce composites of thermodynamical and dynamical properties of the meander in relaxed and flexed states. The standing meander induces trough‐to‐crest variations in surface heat flux, mixed layer depth (MLD), wind stress curl, vertical velocity, and subduction. At the crests, the ocean loses heat and the mixed layer is deeper; at the troughs, the ocean gains heat and the mixed layer is shallower. Wind stress curl, vertical velocity, and subduction change sign on entering and exiting crests and troughs. Vertical velocity due to the curvature of the meander is an order of magnitude larger than Ekman vertical velocity. The poleward excursion of Polar Front meander crests extends subduction to Antarctic Intermediate Water density classes. Finally, flexing of the meander enhances both air‐sea exchange and vertical velocity. The results show that standing meanders of the ACC influence the distribution and magnitude of air‐sea fluxes of heat and momentum and exchange between the surface and interior ocean.

Binding Energies of N-bearing Species on Interstellar Water Ice Mantles by Quantum Chemical Calculations

Of the about 300 gas-phase molecular species so far detected in the interstellar medium (ISM), mostly via observations of their rotational lines, around 40% contain nitrogen (N) atoms. Likewise, of the less than a dozen interstellar molecules, firmly or likely detected in the solid-state water-dominated icy matrix by means of infrared observations, two bear N. A crucial parameter that regulates whether a species is in the gas or adsorbed on the icy phase is their binding energy (BE) toward the icy grain. Therefore, an accurate quantification of the BE is of paramount importance to properly model the ISM chemistry through numerical models. However, very few BEs are available in the literature, either determined experimentally or theoretically. In the present study, we calculate the BEs of 21 among the most abundant interstellar N-bearing species. We adopted two structural water ice models, representing a crystalline and an amorphous surface, using a reliable cost-effective procedure based on the density functional theory. While on the crystalline surface model only one BE per species is obtained due to the high symmetry of the unit cell, on the amorphous model from 5 to 10 BEs are obtained, due to its richer surface morphological variety. Most of our computed BEs agree with available experimental and other computational values. Finally, we discuss how the newly computed BEs can help estimate which N-bearing species can be frozen at the water snow line and, therefore, incorporated in water-rich ice planetesimals.

Assessing the benefits of approximately exact step sizes for Picard and Newton solver in simulating ice flow (FEniCS-full-Stokes v.1.3.2)

Abstract. Solving the momentum balance is the computationally expensive part of simulating the evolution of ice sheets. The momentum balance is described by the nonlinear full-Stokes equations, which are solved iteratively. We use the Picard iteration and Newton's method combined with Armijo step sizes and approximately exact step sizes, respectively, to solve these equations. The Picard iteration uses either no step size control or the approximately exact step sizes. We compare the variants of Newton's method and the Picard iteration in benchmark experiments, called ISMIP-HOM experiments A, B, E1, and E2. The ISMIP-HOM experiments consist of a more realistic domain and are designed to test the quality of ice models. For an even more realistic test case, we simulate the experiments E1 and E2 with a time-dependent surface. We obtain that approximately exact step sizes greatly reduce the necessary number of iterations for the Picard iteration and Newton's method with nearly no increase in the computation time for each iteration.

Nuclear quantum effects in phase transition between Ice VII and Ice X.

The theoretical modeling of high-pressure ice remains challenging owing to the complexity in accurately reflecting its properties attributable to nuclear quantum effects. To explore the nuclear quantum effects of the phase transition between Ice VII and Ice X, we introduce an approach based on abinitio path-integral molecular dynamics. The results indicate that quantum effects facilitate the phase transition, with the observed isotope effects consistent with the experimental outcomes. We demonstrate that quantum effects manifest differently across ice phases: In Ice VII, quantum effects reduce the pressure through the centralization of protons. In contrast, in Ice X, quantum effects increase the pressure owing to the increased kinetic energy of zero-point vibration.

Bias correction of Arctic sea ice thickness products based on factor selection and machine learning methods

With the accelerated melting of Arctic sea ice in the context of climate change, accurate sea ice thickness observations are needed for long-term climate simulation, short-term prediction, and shipping navigation. Limited by their special geographical location, in-situ data of Arctic sea ice thickness is scarce, and the quality of observation products from existing satellites and models is uneven. In this situation, we selected the key meteorological and marine environmental factors that affect the thickness of sea ice based on the thermodynamic law of sea ice, the traditional method of causal analysis, and the data mining ability of machine learning. Then, we constructed bias correction models combining these factors with geographic and temporal information, integrating random forest, extreme gradient boosting tree, and generalised regression neural network to correct the sea ice thickness data. The segmented regression models of thick and thin ice were constructed according to the characteristics of sea ice thickness products. The results show that the proposed bias correction model of sea ice thickness can effectively improve the quality of existing products and reduce the bias from the in-situ data.

Physicomathematical Model of Icing of the Rotating Sphere in a Coaxial Supercooled Gas-Drop Flow

Physicomathematical Model of Icing of the Rotating Sphere in a Coaxial Supercooled Gas-Drop Flow

Prediction model for transmission line icing based on data assimilation and model integration

With the increase of extreme weather events such as cold waves, power transmission line icing has become more and more severe, affecting the safe and stable operation of power systems. Thus, icing prediction has become crucial for power grids. In this study, we collect multi-source data including the historical observations of meteorological elements and transmission line icing in Sichuan during 2017–2019, and develop an artificial intelligence (artificial intelligence)-based integrated model to achieve icing thickness prediction according to meteorological elements. Using the Weather Research and Forecasting model and the three-dimensional variational data assimilation method, we analyze the weather conditions in Sichuan of China during the 2020 winter, and obtain the high-precision meteorological element fields that are related to icing prediction. The forecasted meteorological elements are then combined with the AI-based integrated model to predict icing conditions, assisting in the warning of transmission line icing. The results indicate that the AI-based integrated model displays superior performance on the accurate prediction of icing thickness in the test set, with only two samples having prediction errors of more than 3 mm. Data assimilation can effectively improve the forecast accuracy of meteorological elements near icing observation stations and thus enhance the accuracy of icing thickness prediction. In particular, icing thickness prediction is remarkably improved at Gaoqiao, Laolinkou and Erlangshan stations.

Modelling the effect of cascade reservoir regulation on ice-jam flooding

In regulated rivers, dams and their reservoirs are the main means to change the flow conditions, and strategic reservoir operations can be an important measure for mitigating ice-jam flooding (IJF). Previous studies have shown the effect of single reservoir operation on IJF probabilities, but little is known about the effect of cascade reservoir operation on IJF occurrences and severity. This study presents a probabilistic modelling framework that couples machine learning (LSTM, Long Short-Term Memory) and river ice (RIVICE) models to assess the effects of regulated and naturalized flows on IJF backwater levels. The trained LTSM shows good performance in both validation (RMSE = 398.88, R = 0.86 and NSE = 0.67) and testing (RMSE = 366.57, R = 0.84 and NSE = 0.71) periods and the calibrated RIVICE can well simulate backwater levels caused by three IJFs from 2008 to 2010 with total volumes of incoming ice for 0.4 million m3, 0.55 million m3 and 0.32 million m3. The completed framework was used to assess IJF hazard along the Sanhuhekou bend, a reach of the upper Yellow River, whose hydraulic regime is impacted by eight upstream reservoirs. The effect of naturalized and regulated flows on IJF are compared from two aspects. Firstly, comparing the median of naturalized and regulated IJF backwater level ensembles, the results show that regulation increases the IJF backwater levels. Secondly, comparing the extreme values of naturalized and regulated IJF backwater level ensembles, results show that regulation can increase the hazard of IJF inundation when annual exceedance probability (AEP) is less severe than 1:100 and regulation can decrease the hazard of IJF when AEP is more severe than 1:125.

The Impact of Interactive Ocean Dynamics on Atlantic Sea Surface Temperature Variability

Abstract The impact of interactive ocean dynamics on internal variations of Atlantic sea surface temperature (SST) is investigated by comparing preindustrial control simulations of a fully coupled atmosphere–ocean–ice model to the same atmosphere–ice model with the ocean replaced by a motionless slab layer (henceforth slab ocean model). Differences in SST variability between the two models are diagnosed by an optimization technique that finds components whose variance differs as much as possible. This technique reveals that Atlantic SST variability differs significantly between the two models. The two components with the most extreme enhancement of SST variance in the slab ocean model resemble the tripole SST pattern associated with the North Atlantic Oscillation (NAO) and the Atlantic multidecadal variability (AMV) pattern. This result supports previous claims that ocean dynamics are not necessary for the AMV, although ocean dynamics lead to slight increases in the memory of both the AMV and the NAO tripole. The component with the most extreme enhancement of SST variance in the fully coupled model resembles the Atlantic Niño pattern, confirming the ability of our technique to isolate physical modes known to require ocean dynamics. The second component with more variance in the fully coupled model is a mode of subpolar SST variability. Both the reemergence of SST anomalies and changes in ocean heat transport lead to increased SST variance and memory in the subpolar Atlantic. Despite large differences in the mean and variability of SST, atmospheric variability is quite similar between the two models, confirming that most atmospheric variability is generated by internal atmospheric dynamics.

Modeling Climate Characteristics of Qinghai Lake Ice in 1979–2017 by a Quasi-Steady Model

Lakes on the Qinghai Tibet Plateau (QTP) are widely distributed spatially, and they are mostly seasonally frozen. Due to global warming, the thickness and phenology of the lake ice has been changing, which profoundly affects the regional climate evolution. There are a few studies about lake ice in alpine regions, but the understanding of climatological characteristics of lake ice on the QTP is still limited. Based on a field experiment in the winter of 2022, the thermal conductivity of Qinghai Lake ice was determined as 1.64 W·m−1·°C−1. Airborne radar ice thickness data, meteorological observations, and remote sensing images were used to evaluate a quasi-steady ice model (Leppäranta model) performance of the lake. This is an analytic model of lake ice thickness and phenology. The long-term (1979–2017) ice history of the lake was simulated. The results showed that the modeled mean ice thickness was 0.35 m with a trend of −0.002 m·a−1, and the average freeze-up start (FUS) and break-up end (BUE) were 30 December and 5 April, respectively, which are close to the field and satellite observations. The simulated trend of the maximum ice thickness from 1979 to 2017 (0.004 m·a−1) was slightly higher than the observed result (0.003 m·a−1). The simulated trend was 0.20 d·a−1 for the FUS, −0.34 d·a−1 for the BUE, and −0.54 d·a−1 for the ice duration (ID). Correlation and detrending analysis were adopted for the contribution of meteorological factors. In the winters of 1979–2017, downward longwave radiation and air temperature were the two main factors that had the best correlation with lake ice thickness. In a detrending analysis, air temperature, downward longwave radiation, and solar radiation contributed the most to the average thickness variability, with contributions of 42%, 49%, and −48%, respectively, and to the maximum thickness variability, with contributions of 41%, 45%, and −48%, respectively. If the six meteorological factors (air temperature, downward longwave radiation, solar radiation, wind speed, pressure, and specific humidity) are detrending, ice thickness variability will increase 83% on average and 87% at maximum. Specific humidity, wind, and air pressure had a poor correlation with ice thickness. The findings in this study give insights into the long-term evolutionary trajectory of Qinghai Lake ice cover and serve as a point of reference for investigating other lakes in the QTP during cold seasons.

Selection of second step micro-morphology for anti-icing surfaces based on icing time

Superhydrophobicity are associated with surface two-step micro/nanostructure together morphology. However, it is uncertain for Superhydrophobic surface (SHS) whether having anti-icing ability if without detailed knowledge of the surface morphology. For this reason, we proposed a three-dimensional (3-D) icing model to analyze anti-icing properties and determine the second step morphology suitable for the anti-icing. Followed by it, representative samples (hydrophobic or hydrophilic, metal or nonmetal, organic or inorganic) were selected for freezing measurement to confirm theoretical results. Ranked order is a sine wave/cone frustum/prism/cylinder/cuboid/paraboloid, and a semi-sine-wave model by the anti-icing time from high to low. The anti-icing property still depends on the surface micro-scale and micro-morphology to a great extent as hydrophobicity does. Effect of morphology on anti–icing will be significantly reduced even neglected, if the solid–liquid contact fraction being small enough. Meanwhile we also found that both the time for drop in temperature and the icing time are directly related to the solid fraction, whether the freezing singularity appears depends on freezing velocity or hydrophilicity. The predictions of the model largely agree with the experimental observations, also opening up possibilities for rational design of anti-icing SHS by selecting suitable morphology in micro scales.

Arctic Sea ice leads detected using sentinel-1B SAR image and their responses to atmosphere circulation and sea ice dynamics

Arctic sea ice leads are linear fractures in pack ice, which provide narrow windows for the enhanced exchange of mass, energy, and momentum between the atmosphere and ocean. However, the parameterisations of the sub-grid (< 1 km) lead distribution and its associated dynamic processes are still challenging for numerical sea ice modelling. This study explores the dynamics governing lead formation in the Arctic Ocean using multiple data sources including Sentinel-1 synthetic aperture radar (SAR) images, buoy array, and atmospheric reanalysis. Random forest (RF) models trained based on the polarisation and texture features of SAR images were compared, and an optimal RF model sensitive to narrow leads was selected with implementation of screening based on lead geometry. As a case study, the lead distribution along the trajectory of buoy array in the central Arctic Ocean during the freezing period of 2018–2019 was obtained. An enhanced lead opening was observed as the sea ice motion swerved from clockwise circulation following the Beaufort Gyre (BG) to meridional advection when assembled into the Transpolar Drift (TPD). Synoptically, active lead-opening events associated with ice divergence were driven by episodic cyclones. We noticed a dramatic change in the backscatter signal over the leads caused by the growth of thin ice and formation of frost flowers. The identification and the evolution of narrow leads during the freezing season given in this study are conducive to increasing our understanding of the response mechanism of sea ice to atmospheric dynamic processes and supporting the numerical simulation of leads.

Dust storms from the Taklamakan Desert significantly darken snow surface on surrounding mountains

Abstract. The Taklamakan Desert (TD) is a major source of mineral dust emissions into the atmosphere. These dust particles have the ability to darken the surface of snow on the surrounding high mountains after deposition, significantly impacting the regional radiation balance. However, previous field measurements have been unable to capture the effects of severe dust storms accurately, and their representation on regional scales has been inadequate. In this study, we propose a modified remote-sensing approach that combines data from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and simulations from the Snow, Ice, and Aerosol Radiative (SNICAR) model. This approach allows us to detect and analyze the substantial snow darkening resulting from dust storm deposition. We focus on three typical dust events originating from the Taklamakan Desert and observe significant snow darkening over an area of ∼ 2160, ∼ 610, and ∼ 640 km2 in the Tien Shan, Kunlun, and Qilian mountains, respectively. Our findings reveal that the impact of dust storms extends beyond the local high mountains, reaching mountains located approximately 1000 km away from the source. Furthermore, we observe that dust storms not only darken the snowpack during the spring but also in the summer and autumn seasons, leading to increased absorption of solar radiation. Specifically, the snow albedo reduction (radiative forcing) triggered by severe dust deposition is up to 0.028–0.079 (11–31.5 W m−2), 0.088–0.136 (31–49 W m−2), and 0.092–0.153 (22–38 W m−2) across the Tien Shan, Kunlun, and Qilian mountains, respectively. This further contributes to the aging of the snow, as evidenced by the growth of snow grain size. Comparatively, the impact of persistent but relatively slow dust deposition over several months during non-event periods is significantly lower than that of individual dust events. This highlights the necessity of giving more attention to the influence of extreme events on the regional radiation balance. This study provides a deeper understanding of how a single dust event can affect the extensive snowpack and demonstrates the potential of employing satellite remote sensing to monitor large-scale snow darkening.

Advanced Sea Ice Modeling for Short-Term Forecasting for Alaska’s Coasts

Abstract In Alaska’s coastal environment, accurate information of sea ice conditions is desired by operational forecasters, emergency managers, and responders. Complicated interactions among atmosphere, waves, ocean circulation, and sea ice collectively impact the ice conditions, intensity of storm surges, and flooding, making accurate predictions challenging. A collaborative work to build the Alaska Coastal Ocean Forecast System established an integrated storm surge, wave, and sea ice model system for the coasts of Alaska, where the verified model components are linked using the Earth System Modeling Framework and the National Unified Operational Prediction Capability. We present the verification of the sea ice model component based on the Los Alamos Sea Ice Model, version 6. The regional, high-resolution (3 km) configuration of the model was forced by operational atmospheric and ocean model outputs. Extensive numerical experiments were conducted from December 2018 to August 2020 to verify the model’s capability to represent detailed nearshore and offshore sea ice behavior, including landfast ice, ice thickness, and evolution of air–ice drag coefficient. Comparisons of the hindcast simulations with the observations of ice extent presented the model’s comparable performance with the Global Ocean Forecast System 3.1 (GOFS3.1). The model’s skill in reproducing landfast ice area significantly outperformed GOFS3.1. Comparison of the modeled sea ice freeboard with the Ice, Cloud, and Land Elevation Satellite-2 product showed a mean bias of −4.6 cm. Daily 5-day forecast simulations for October 2020–August 2021 presented the model’s promising performance for future implementation in the coupled model system. Significance Statement Accurate sea ice information along Alaska’s coasts is desired by the communities for preparedness of hazardous events, such as storm surges and flooding. However, such information, in particular predicted conditions, remains to be a gap. This study presents the verification of the state-of-art sea ice model for Alaska’s coasts for future use in the more comprehensive coupled model system where ocean circulation, wave, and sea ice models are integrated. The model demonstrates comparable performance with the existing operational ocean–ice coupled model product in reproducing overall sea ice extent and significantly outperformed it in reproducing landfast ice cover. Comparison with the novel satellite product presented the model’s ability to capture sea ice freeboard in the stable ice season.

Eastern Boundary Upwelling Systems in Ocean–Sea Ice Simulations Forced by CORE and JRA55-do: Mean State and Variability at the Surface

Abstract In this paper we summarize improvements in climate model simulation of eastern boundary upwelling systems (EBUS) when changing the forcing dataset from the Coordinated Ocean-Ice Reference Experiments (CORE; ∼2° winds) to the higher-resolution Japanese 55-year Atmospheric Reanalysis for driving ocean–sea ice models (JRA55-do, ∼0.5°) and also due to refining ocean grid spacing from 1° to 0.1°. The focus is on sea surface temperature (SST), a key variable for climate studies, and which is typically too warm in climate model representation of EBUS. The change in forcing leads to a better-defined atmospheric low-level coastal jet, leading to more equatorward ocean flow and coastal upwelling, both in turn acting to reduce SST over the upwelling regions off the west coast of North America, Peru, and Chile. The refinement of ocean resolution then leads to narrower and stronger alongshore ocean flow and coastal upwelling, and the emergence of strong across-shore temperature gradients not seen with the coarse ocean model. Off northwest Africa the SST bias mainly improves with ocean resolution but not with forcing, while in the Benguela, JRA55-do with high-resolution ocean leads to lower SST but a substantial bias relative to observations remains. Reasons for the Benguela bias are discussed in the context of companion regional ocean model simulations. Finally, we address to what extent improvements in mean state lead to changes to the monthly to interannual variability. It is found that large-scale SST variability in EBUS on monthly and longer time scales is largely governed by teleconnections from climate modes and less sensitive to model resolution and forcing than the mean state.

Investigating ANU and ICE-6G Ice Model Relative Sea Level Misfits to Determine Viscosity Constraints since the Last Glacial Maximum

Abstract Glacial isostatic adjustment (GIA) incorporates glacial rebound and gravitational potential to calculate sea level rise. However, many ice models struggle to accurately represent these effects in relative sea level (RSL) predictions. Two widely used ice models that incorporate GIA effects, ANU and ICE-6G, require further analysis to gauge their validity. In this thesis, we compare the model predictions of RSL of the ANU and ICE-6G models against six distinct RSL field datasets using a semi-analytic GIA model. To predict RSL, a GIA model requires an ice model and models of the Earth’s mantle viscosity, density, and elastic structure. While mantle density and elastic structures are reliably constructed from seismology studies, mantle viscosity is not well constrained. We developed 864 simulated mantle viscosity models to run with the ANU and ICE-6G ice models to obtain the predicted RSL data. We analyzed the Root Mean Square (RMS) misfits of the simulated test RSL outputs against the observed RSL field data. We showed that RMS demonstrates error quality of field data is critically important to the GIA modeling process. We also showed the strengths and weaknesses of each ice model through further RMS analysis. Lay Summary Sea level change is not only a critical aspect of our climate’s response, but also a novel gateway to understanding the structure of the Earth’s mantle. This understanding is made possible by studying sea level changes over long timescales, such as since the Last Glacial Maximum. The Earth’s crust responds to increased (or decreased) weight of ice on its surface by deforming (or rebounding) and reaching a new equilibrium state over thousands of years. The response time and altered geometryof the crust give information about mantle viscosity. Observations and calculations of sea level over time geospatially constrain historical ice models, creating an opportunity to determine the most appropriate viscosity. This research identified two widely used ice models (ANU and ICE-6G) to reconstruct present day sea level, combined with varying values for the Earth’s upper and lower mantle. Findings were then compared with observations to identify minimum misfits—calculations that are closest to observation values. By understanding the factors that influence these discrepancies, the researchers hope to improve the accuracy of future models and predictions of sea level changes. For the full text and references, please visit https://scholar.colorado.edu/concern/undergraduate_honors_theses/z603qz79c.