As a result of climate change, sea level is changing all over the world at unprecedented rates. Sea-level change can have significant impacts on coastal communities, infrastructure and global economy, as most of the major cities are located near to or at the coast. Rising sea levels can lead to, for instance, more severe and more frequent flooding, increasing coastal erosion and salt water intrusion. In addition, sea-level change can also influence coastal ecosystems, by altering the habitats of many plant and animals species. Therefore, it is crucial that we understand what is causing sea-level change and at what rate sea levels are changing.

Global mean sea level has been rising at a rate of about 3.4 millimetres per year over the last 30 years. Regionally, however, sea level can be changing at a much higher or lower rate. That is because local processes, such as ocean dynamics and gravitational effects associated with continental ice mass changes, cause regional deviations from the global average. But what is causing sea level to change at a specific location? Is sea level changing because the oceans are warming, and thus expanding? Or because the ice from glaciers and ice sheets are melting? The attribution of sea-level change to these and other drivers can be done using a sea-level budget approach. Sea-level budget studies can be used to constrain missing or poorly known contributions and to validate climate models. While the global mean sea-level budget is considered closed within uncertainties, closing the budget on a regional to local scale is still challenging.

In this thesis, I focused on the question: Can we close the regional sea-level budget in the satellite altimetry era on a sub-basin scale consistently for the entire world? For this, we need not only high quality observations of sea-level change and each component, but also of the uncertainties within each process. Therefore, in Chapter 2 and 3, I explored the main drivers of regional sea-level change, focusing on the uncertainty characterization of each component. I then looked at which spatial scale is optimal for analysing the regional sea-level budget, and compared the sum of the drivers with the total observed change in these regions in Chapter 4.

Ice, a pervasive element across the Solar System, holds immense importance in understanding the response of the Earth to ongoing climate change as well as the dynamics of planetary bodies. This dissertation investigates ice fractures on terrestrial and planetary ice bodies, focusing on their impact on the melting of ice shelves in Antarctica and their dynamics on Europa, one of Jupiter’s moons.

The urgency to understand the behavior of terrestrial ice shelves under environmental forcing is driven by the ongoing climate crisis. Antarctica is experiencing a rapid loss of mass, primarily due to increasing ocean-induced melting at the base of its ice shelves in response to global warming. The release of glacier meltwater into the world’s oceans contributes to arising the global sea level. However, the rate and magnitude of sea-level rise are highly uncertain and the potential ice mass-loss from Antarctica could significantly accelerate sea-level rise throughout this century due to the instability of its ice shelves. Thus, accurately projecting Antarctica’s contribution to global sea level necessitates a better understanding of the processes behind the loss of its ice shelves.

In this dissertation, I examine the thinning of Antarctic ice shelves caused by enhanced melting at their base due to warming oceans. Intrusion of ocean heat beneath the ice shelves indeed plays a crucial role in projecting their future. Through idealized ocean modeling using the Massachussetts Institute of Technology general circulation model (MITgcm), I simulate ocean dynamics under the ice, investigating the impact of fractures and ice front retreat on the sub-shelf ocean circulation. Results indicate that fractures may act as barriers, inhibiting the intrusion of warm water towards the inland sections of the ice shelves, and thereby reducing basal melt. Furthermore, I examine the impact of the separation of iceberg A-68 from the Larsen C ice shelf in July 2017 on the sub-shelf ocean dynamics. This specific retreat event leads to the redistribution of heat under the ice, resulting in enhanced melting in specific sections of the ice shelf, suggesting future destabilisation of Larsen C. These findings highlight the importance of considering updated ice-shelf coastlines to accurately project ocean circulation and its implications for ice shelf stability.

Furthermore, this dissertation explores the dynamics of specific lineament features observed on the surface of Europa, which are identified as ice fractures. Although limited observations restrict our understanding of ice fracturing events on this moon, insights from studying terrestrial ice sheets provide valuable knowledge. By extend ing an existing terrestrial-based numerical model of fracture propagation on ice shelves, I show that some lineaments on the surface of Europa exhibit a behavior that is similar to ice fractures on Antarctic ice shelves. The model depicts the evolution of these lineament features as bursts of fracture propagation events interspersed with periods of inactivity, which is a typical behavior of fractures on terrestrial ice shelves.

Overall, this dissertation shows the potential for synergy between Earth and planetary science. By leveraging advances in our understanding of physical processes on Earth, terrestrial-based models and theories contribute to expanding our knowledge of physics on other celestial bodies. This interdisciplinary approach, supported and validated by remote sensing and in-situ missions, is fundamental in order to advance our understanding of ice fractures, their interaction with the surrounding environment and their dynamics throughout the Solar System. On Earth, a better understanding of the dynamics of Antarctic ice shelves is imperative to correctly project Antarctica’s contribution to global sea level.

Machine learning is becoming an increasingly important tool for climate scientists, but hampered by lacking uncertainty quantification. Here, a machine learning approach for detecting patterns indicating a changing climate is combined with probabilistic modelling to retrieve uncertainty values. We train neural networks on climate model simulations of temperature and precipitation under historical and future scenarios. We find that the resulting so-called Bayesian neural network (BNN) has similar predictive strength to an Artificial neural network (ANN), with a post-year 2000 mean absolute error of 9.00 years for temperature, but over-fits less. The BNN is able to recognise temperature change starting in 1994, which is 14 years later than the ANN. Our analysis shows that uncertainties in found climate patterns are much higher than the patterns themselves, reducing their value for further use. This work demonstrates that BNNs are a suitable tool for quantifying uncertainties of patterns indicating a changing climate.

The subsurface of Mars is impossible to measure directly, yet it has been the subject of many studies. An understanding of the subsurface of Mars would yield large amounts of information on the history of the planet. Two of the tools available to indirectly interact with the Martian subsurface, in particular the lithosphere, are the gravity and topography signals of the planet. These two datasets can be combined using a variety of geological theories in order to investigate the subsurface. In this study, an isostatic model (Airy-type) and two flexural isostatic models (an infinite plate model and a thin shell model) will be the methods of choice. A distinction is made between isotropic or global models, which use one set of physical parameters for the entire planet, and anisotropic or multi-region models which allow for regional variation in physical parameters. The goal of this study is to investigate the performance of the novel thin shell model as compared to the older infinite plate model.

To investigate this, the theory behind each model is explained, after which the models are validated using results from literature. Several regions of interest are defined, mostly among large geological formations or gravity anomalies. Two parameters are chiefly investigated: the average thickness of the lithosphere and the lithospheric elastic thickness, which is a measure of the strength of the lithosphere. Each model is run globally for a variety of these two parameters, and the best fitting parameters are identified. After this, the planet is split into different regions with their own physical parameters. The first study is a dichotomy study which splits the planet into a northern and southern hemisphere, aimed at characterizing the disparity between the Martian north and south. After this, each region is assigned its best fitting physical parameters and the regions are combined into a 'global' regional model. A best fitting multi-region model is obtained via manual observation of the results and adjustment of the inputs until a visual best fit is achieved.

The results are then discussed. A key takeaway is that better methods of judging the performance of models without human visual inspection of their results is necessary in order to realize the full potential of the flexural isostasy models presented in this study. The lack of suitable methods leads to a manifold of best fitting solutions for many of the problems modelled in this study, hindering firm conclusions about the subsurface of Mars. Having said this, global average lithospheric values of about 200 km combined with very low effective lithospheric elastic thickness values of 0 to 40 km are the best fits found in this study. Literature values are typically lower, but this can partially be explained by differences between the flexural isostasy models in this study and the models from literature. Regionally there are large variations, with some features (Hellas basin, Alba mons) being isostatically compensated, others being supported by locally strong lithospheres (much of the Tharsis region), and others resting on buried mass anomalies that cannot be explained with the models in this study (Isidis planitia, Argyre basin). In a dichotomy study, the best fitting values were found for a northern lithosphere zero to ten kilometers thinner than the southern lithosphere. In general, the thin shell model is more sensitive to nonzero lithospheric elastic thickness values, providing very strong lithospheres at low elastic thicknesses. This is due to its aggressive flexural response function's filtering of higher spherical harmonic degree signals. The thin shell models yields higher residuals in the global analyses, but lower residuals in the multi-region studies. At the same time, 80% of the error in all models can be attributed to spherical harmonic degrees between 1 and 10. These signals are likely not caused by flexural isostasy, and require models incorporating more physics (mantle plumes, mass anomalies, etc) to be explained.

Long-term sea level change and its spatial and temporal variability measured with tide gauge stations along the Dutch coast have been studied. This study investigates how time series length and modeling choices influence the adoption of a quadratic over a linear sea level model. We apply linear and quadratic models to corrected tide gauges, for differing model start years. Longer models show more consistent results between stations, with less inter-station variability and smaller uncertainties. This improvement of consistency diminishes when using time series longer than 40 years. We find indications of a break-point in trends in the period 1978-1998. Quadratic models result in minor but relevant acceleration for longer time series, but do not perform sufficiently for time series shorter than 20 years. Comparing model quality between linear and quadratic models generally indicate better performance of quadratic models, but results are not conclusive to justify model adoption. A station mean is less conclusive for quadratic models than for linear models and sensitive to choice of stations and model length. Keywords: mean sea level variability, sea level change, sea level acceleration, tide gauge records, Dutch coast

Predictions of the morphology of coastal areas are used to make decisions on coastal defence and nature conservation. To predict this morphology, simulations made by morphological models are used. To base decisions on this morphology predictions, we want the uncertainties in these predictions to be small. The goal of this research is to get a better insight in the uncertainties in a morphological model. By having a better understanding of these uncertainties, the decisions made using modelled predictions are stronger substantiated. The specific area focused on in this research is the Frisian Inlet, which is located between Ameland and Schiermonnikoog in the Wadden Sea. To achieve the goal of this research, data assimilation is used. Data assimilation combines data with prior knowledge of a model to find the distribution of probabilities of estimates of a true state. In this research, the used data are bed level measurements and the estimates are simulations for the bed level height made by Delft3D. Data assimilation methods make use of a distribution of model outcomes which should cover all possible outcomes. So, to set up data assimilation successfully, we want to use a parameter that induces a significant change in the bathymetry outcomes of Delft3D. Therefore, a sensitivity study is performed which considers the following six parameters: current related roughness, wave related roughness, wave-related suspended load sediment transport factor, wave-related bedload sediment transport factor, the transverse bed slope and the tidal amplitude. For each parameter, ten values are chosen to simulate. Which of the parameters induces the most change in bathymetry is assessed, using the difference between simulation result and the observation and the mean squared error skill score. The transverse bed slope shows the most variation and is further used in the data assimilation method, which is a particle filter. Hundred uniformly distributed values of the transverse bed slope, between 0.5-100, are used to create different Delft3D simulations that give a bed level prediction. This initial distribution is used for three different periods: 1970-1975, 1975-1979 and 1979-1982. These epochs are defined by the availability of bed level measurements. After one iteration of the particle filter, a new distribution of the parameter values is found. This is used in the next iteration in the same epoch. In each epoch, three iterations are made. The methodology as described in this thesis leads to a convergence of the initial distribution. In epoch 1 and 3, the distribution focuses on values of the transverse bed slope in the high range of the initial distribution. The resulting distribution of epoch 2 focuses on values around a transverse bed slope value of 70. This study shows that it is possible to get a better understanding of the distribution of \gls{alfa} values that leads to probable bedlevel predictions by applying a particle filter. The application of this method brings the simulations closer to each other, but the simulations did not get closer to the observations. The contribution to our understanding of data assimilation for morphodynamic models is that it can be used as a calibration tool for a specific parameter.

In Southeast Alaska extreme uplift rates are primarily caused by Glacial Isostatic Adjustment (GIA), as a result of ice load changes from the Little Ice Age to the present combined with a low viscosity asthenosphere. Current GIA models adopt a one-dimensional (1D) stratified Earth structure. The three-dimensional (3D) structure of the Earth is complex in this region due to the proximity of a subduction zone and the transition from a continental to oceanic plate. A simplified 1D Earth model may not be an accurate representation in this region and therefore affect the GIA predictions. In this study a numerical model for 3D GIA is constructed for Southeast Alaska. We present an assessment on the impact of lateral variations in the upper mantle rheology on the vertical GIA component. We test two different approaches to obtain a 3D viscosity structure in the upper mantle. For the first approach, viscosities are inferred from lateral variations in temperature through flow laws for olivine. The water and grain size are varied to find a viscosity structure that best fits the GPS data. We find a best-fit viscosity structure with wet rheology (400 wt ppm H₂O) and grain size 8 mm. However, this model does not perform better (in terms of Χ²) than a radially symmetric model, because predicted uplift rates are much lower than the observed values. For the second approach, we infer a viscosity distribution obtained directly from shear wave anomalies through scaling relationships. Our 3D model reduces the residuals between 1.0-2.7 mm/yr. We find that the effects of lateral variations in viscosity (up to 0.4 log units or, equivalently, a factor of 2.5) are in principle detectable by the GPS network and provide a better overall fit than a radially symmetric 1D viscosity model.

As models are becoming more dominant for the prediction of sea level rise, a review of how this method type is operating is needed. Hence, for this thesis the research objective was to what degree of accurateness is the VM6_C model estimating the relative sea-level during the late Holocene of Sardinia and Crete? To support this question, the following three sub-questions were formulated: 1. What is the influence of components of the simplified sea-level equation in Sardinia and Crete? 2. What are the current tectonic trends for these locations? 3. To what extend can the archaeological sites at these locations be used for validating the model? The analysis for answering these questions, were divided into two case studies with three different acquisition methods: paleo data (1), GPS stations (2) and the VM6_C current rate of change (3). The accurateness was reviewed by comparing the computed trends and relative sea level heights of both these methods and case studies. This resulted in a rate of change of RSL by paleo data by Crete of 0.6173 mm/yr, whilst Sardinia had 1.297 [mm/yr]. In comparison the difference between the paleo and model data was respectively 0.45 [mm/yr] for Crete and for Sardinia is 1.03 [mm/yr]. The tectonic movement of both islands was concluded to be both subsiding at a millimetre magnitude. Therefore, the conclusion was drawn that the sea level was rising relative to the structures, yet the island was subsiding over time. Finally, the accurateness was reviewed by comparing the average difference between the historic sea level of the paleo data and the VM6_C model. Henceforth the variation was respectively -0.4302 [m] for Sardinia and -1.550 [m]. Thus, according to the results, the model has a higher accurateness relative to the paleo data when measuring at tectonic stable locations, however the trend can be better estimated at tectonic active locations. Which can be caused by the temporal resolution of paleo sites.

The gravity field changes associated with the earthquake are analysed using the GRACE (Gravity Recovery and Climate Experiment) data. GRACE data can track the temporal variations in the gravity field and therefore information on mass redistribution can be achieved. There have been many studies already carried out using the GRACE data to analyse the coseismic and postseismic effects of the earthquakes. The previous studies mainly concentrated on the separation of the earthquake signals from various other signals and noises to understand the internal mass redistribution. In this work two recent past earthquakes have been considered. Sumatra-Andaman earthquake that occurred on 26th December 2004 with a magnitude of Mw 9.1. The other major earthquake that has been taken into account is the off coast Northern Sumatra earthquake (also called as Indian Ocean earthquake) which occurred on 11th April 2012 with a magnitude of Mw 8.6. A new initiative has been taken to separate the long term postseismic term (2004 earthquake) from the coseismic term and the effects of the 2012 earthquake (both the postseismic and coseismic effect). This decoupling process was done using the GRACE monthly solutions of spherical harmonics. Gravity disturbances were calculated from GRACE monthly solutions to understand the internal mass redistribution.

River width is an important indication for water storage, with the development of remote sensing techniques, estimating river width in a large area becomes possible. In this report, fully-focused SAR altimetry data with an advantage of high spatial resolution is applied to compute river width for the first time. Methodology developed based on morphologic characters of rivers and data features is validated in two different areas, the Netherlands and Vietnam, with good statistical results.

The C2,1 and S2,1 spherical harmonic coefficients of glacial isostatic adjustment (GIA) models are dominated by true polar wander (TPW), the secular drift of the position of the Earth’s rotational axis with respect to the Earth’s surface. The Earth’s rotational state and its interior are closely linked. The deformation of the body induced by a surface ice load depends on the local viscosity, while the adjustment of the equatorial bulge to the position perpendicular to the rotational axis depends on the average global viscosity of the body. Therefore, lateral viscosity variations could play a role in GIA-induced TPW. Here our goal is to extend an existing GIA model based on a finite element method (FEM) with a simulation for TPW (Hu et al., JGR 2017).

The model is based on FE software ABAQUS coupled to the solution of the Laplace equation. The perturbed gravitational potential is a function of the radial displacements. Therefore, an iterative process is required to solve for the displacements in the body. For the TPW algorithm we assume that during the process of TPW the equatorial bulge readjustment is fast enough such that the equatorial bulge is always nearly perpendicular to the rotational axis. In that case the linearized Liouville equation can be used by using coordinate transformations for each time step. This method allows for large-angle TPW and takes into account non-stationary surface loads with respect to the rotational axis. In the spin-up phase the flattening of the Earth is simulated by applying a centrifugal force for a long-enough duration.

We find that when the perturbed gravitational potential in the first time step is not fully converged, it affects the perturbed gravitational potential in future time steps and thus TPW. Furthermore, when a surface ice load is applied to the model, TPW is triggered. The centrifugal potential changes based on the new position of the rotational axis, and this also affects the perturbed gravitational potential for the following time steps. As a result, the perturbed gravitational potential and centrifugal potential cannot simultaneously for all time steps be iterated for using our TPW approach. Therefore, to be able to study the effect of lateral viscosity variations on GIA-induced TPW in the numerical model, the iteration between radial displacements and centrifugal potential needs to occur per time step.

In the present research, the activation parameterization method introduced by Nenes and Seinfeld (2003) was compared and evaluated to a remote sensing-based method by Rusli, Donovan & Russchenberg (2017) for determining the cloud drop number concentration. Both methods have fundamentally different approaches for indirectly determining the cloud droplet number concentration. The parameterization method is based on the Köhler Theory, in which the activation process of particles contained in a rising parcel is modelled for predicting the number concentrations of cloud droplets. The remote sensing method, on the other hand, applies theories about particle-light interactions. Since the remote sensing method determines the cloud droplet concentrations in a more direct manner than the parameterization method, it is regarded here as the reference. An agreement was found between the two models, with a relative error of cloud droplet number concentrations between 41.1% and 78.0%, which lead to errors of the cloud’s scattering intensity in the range of 13% and 26%. Despite some discrepancies between the obtained droplet concentrations, the parameterization model shows similar trends to the remote sensing observations. It was found that the updraft velocity that is needed as input variable for the parameterization model has the largest influence on the model’s prediction of droplets concentrations, and that it is likely to be an important cause for the seen discrepancies. Furthermore, the present research shows how assumptions were made on the size distribution input variable used in the parameterization model, which were not available from observations.

In this report a first assessment is made of the impact of hurricanes on Saint Martin. Two bays at the east coast of Saint Martin, Baie Orientale and Baie de L'Embouchure, are studied under the conditions of hurricane Irma and Maria, which both have passed over in September 2017. During these hurricanes the bays were exposed to large wind speeds up to 40 [m/s]. Also the bays were subject to runoff due to large rainfall. The runoff has been flowing into the bays as fresh water, but also as high saline waters. This due to the salt ponds which are located in the region.

The model has been run in Delft3D and as result large wind-setup driven circulations were obtained. These circulations caused a drop of waterlevel during Irma and an increase under Maria. The saline and fresh waters discharged during the hurricanes were mostly kept onshore and is distributed over the two basins.

Shallow bays in the Caribbean, like Baie Orientale and Baie de L'Embouchure in Saint Martin, are often sheltered by coral reefs and covered by seagrass meadows. They provide valuable services as tourism and coastal protection. The ecosystems are linked through biological, chemical and physical processes. But they are under pressure due to sea-level rise. The response of one of the ecosystems to climate change could impact the other ecosystem.

In order to predict the impact of sea-level rise on the biogeomorphology in Baie Orientale and Baie de L'Embouchure, the hydrodynamic model Delft3D Flexible Mesh is applied. The effect of seagrass meadows and coral reefs on both flow and waves are captured with this model. In this way, the long term change in average hydrodynamic conditions due to sea-level rise is determined depending on the response of the ecosystems.

A wave-driven circulation is found in both bays with flows of 0.5 m/s over the reefs and currents of 0.2 m/s inside the bays. The hydrodynamic conditions are mainly determined by the reef height. Depending on the response of coral reefs to climate change and the amount of sea-level rise, the wave height inside the bays and the wave-induced currents increase. Under the worst-case scenario, where coral reefs degrade and seagrass meadows die, flow velocities increase by more than 100% in Baie de L'Embouchure and by 200% in Baie Orientale under a sea-level rise of 0.87 m. The significant wave height rises to 300% in Baie Orientale and doubles in Baie de L'Embouchure. But this increase of hydrodynamic stresses is not expected to lead to devastating damage to coral reefs and seagrass meadows. Instead, the response of coral reefs will be determined by changing water temperatures and ocean acidification. A shift in seagrass occurrence due to the changed hydrodynamics is expected.

The long term impact of sea-level rise on the biogeomorphology of Baie de L'Embouchure and Baie Orientale seems to be limited. The ability to mitigate the impact of sea-level rise is shown and the resilience of the ecosystems proved, which is very promising for other shallow Caribbean bays that are threatened by sea-level rise.

Satellite radar altimetry is often considered to be the most succesful spaceborne remote sensing technique ever. Satellite radar altimeters were designed for static geodetic and ocean dynamics applications. The goal of the geodetic mission phases, which have a dense ground-track spacing, is primarily to acquire information about the marine gravity field. This enables the estimation of mean dynamic topography (geographical sea surface height patterns due to ocean currents) and deep-ocean bathymetry. The primary goal of the oceanographic mission phases is to gain information about time-varying currents and ocean dynamics. TOPEX/Poseidon is the first altimetry mission to reveal sea surface height variations related to ocean dynamics as the El Niño Southern Oscillation (ENSO). During the mission it became clear that secular changes in sea level could also be monitored. Already in 1995, Nerem (1995) computed a Global Mean Sea Level (GMSL) time series from the TOPEX/Poseidon data. Currently, the GMSL record spans 26 years, in which TOPEX/Poseidon time series is extended with the Jason-1&2&3 observations. The estimated secular trend of GMSL over the altimetry era is approximately 3 mm yr−1. The succes of the TOPEX/Poseidon mission spawned the Argo project with the deployment of the first floats in the year 2000. One argued that Argo would support the future Jason missions in separating changes into the two components (density and mass) of sea level. The Argo project aims to estimate temperature and salinity over a depth of 2000 meter using floats, which enable the estimation of density or steric sea level changes. By subtracting the steric signal from the absolute sea level measured by Jason (steric-corrected altimetry), the second component of sea level changes, mass, is estimated. The launch of the Gravity Recovery And Climate Experiment (GRACE) satellites in 2002 made it possible to independently validate oceanic mass variations. If the sum of the mass and steric components equals total sea level within the uncertainties, the sea level is said to be closed. Besides these two oceanic components, ocean bottom deformation or Vertical Land Motion (VLM) also affects the sea level observed by altimeters. Over the open ocean VLM signals are generally small after a correction for Glacial Isostatic Adjustment (GIA), but near large mass variations they might become significant. Additionally, tide-gauge records are affected by VLM changes, because they are connected to land. Therefore they measure sea level relative to the sea floor, while the satellite altimeters observe the absolute variations. To bring tide gauges in the same reference frame as the altimeters, corrections for VLM have to be applied, which is usually done with nearby Global Navigation Satellite System (GNSS) data...

Sometime late in the fall of 2007, the NetLander mission will land four probes on the surface of Mars containing geodetic and seismic experiments, thereby establishing the first network of geophysics stations on the surface of a terrestrial body other than our own Earth. This mission will yield a tremendous amount of information pertaining the internal structure and orientation of Mars. Thanks to such missions, the data available for the terrestrial bodies will increase and with it our understanding of the rotational instabilities.
Although this was not always the case, it is now known that forces and deformations due to the non-rigid characteristics of the Earth constantly perturb the motion of the planet to various degrees. The fact that our planet, and all realistic bodies for that matter,is not wholly solid, that it has oceans, an atmosphere and a visco-elastic crust, mantle and core, means that the actual position of the rotational axis and rotation rate of the Earth vary from the idealized rigid body motion on virtually every time scale. The Earth constantly reshapes itself to cope with the ever changing loads and other geo-dynamic forces that act upon it. This deformation in turn leads to shifts in the position of the rotation axis with respect to the Earth’s surface, or polar motion, and to a change in rotation rate, also known as a change in length-of-day. This reshaping of a body due to geo-dynamic forces is dependent on the rheology of that body, since material properties such as rigidity and viscosity determine how a body deforms and flows under certain stresses. Although their regularities in the rotation of the Earth complicate astronomical research, for the geophysicist they are a gift. The rotational perturbations must have sources and thus provide information on the internal structure of the Earth and the geophysical processes acting on and within it. The main objective of this thesis is to examine the influences of some of the parameters that determine the polar motion of a terrestrial body, without adhering to the constraints put on them by the application to the Earth. For instance, the influence of the absolute size of a body as defined by its radius has never been examined since the radius of the Earth is known very accurately. This leads to more general and more widely applicable results as the driving parameters are examined in wide ranges.

To this end, a linearized formulation of the polar motion was used in conjunction with the Normal Mode technique, which uses the Laplace domain to calculate the elastic equivalence of the visco-elastic problem in the time domain.