Long-term sea-level projections with two versions of a global climate model of intermediate complexity and the corresponding changes in the Earth's gravity field

Approximate estimations of future climate change can be produced by implementing numerical global climate models. In this study, versions 2.6 and 2.7 of the University of Victoria Earth System Climate Model (ESCM) were employed. Compared to other climatic projections, the novelty of this study consists in a significant extension of the projection period to the time-scale of 4200 years, and in comparisons of the results obtained with two sequential versions 2.6 and 2.7 of ESCM. Version 2.6 of ESCM couples the atmospheric, oceanic and ice processes. Version 2.7 of ESCM accounts for solar and ice-sheet forcing, as well as coupling land-vegetation-atmosphere-ocean carbon, and allows inclusion of ocean biology and dynamic vegetation modules. Our comparison exhibits essential quantitative and, moreover, qualitative differences in the parameters under consideration, which are surface air temperature, sea-ice and snow volumes, and surface pressure in a column of water averaged globally.The observed differences are attributed to the biological blocks added to ESCM version 2.7, changed numerics and explicit ice-sheet forcing. Furthermore, the non-steric sea-level change has been used to model corresponding gravity field changes (here in terms of geoid height) by evaluating Newton's volume integral and study the differences between the two software versions under consideration. In line with the model results, the estimated geoid height changes also exhibit a significant difference between the experiments' outcomes

Modelling Future Sea-level Change under Green-house Warming Scenarios with an Earth System Model of Intermediate Complexity

Recently, a lot of effort has been put into estimating possible near-future changes (say, 10-100 years) in the Earth's abiotic system, especially changes induced by human activities. One of the most studied issues is the effect of greenhouse gases on global warming and the corresponding change in sea-level around the world due to the associated de-glaciation. On a longer time-scale (>100 years), however, such climatic changes will affect the grav-ity field, location of the geocentre, and the Earth's rotation vector. In this study, the University of Victoria's (Canada) coupled Earth System Climate Model of intermediate complexity was implemented. The model was used to predict changes in global precipitation, ocean mass redistribution, and seawater salinity and temperature on timescales from hundreds to thousands years under two different greenhouse-warming scenarios. In future, the projected changes will be assimilated into an existing Synthetic Earth Gravity Model to determine the corresponding changes to the location of the geo-centre, the Earth's rotation vector, and the geoid

The use of artificial neural networks to retrieve sea-level information from remote data sources

The knowledge of near-shore sea-level variations is of great importance in applications such as ocean engineering and safe navigation. It also plays an essential role in the practical realisation of the height reference surface in geodesy. In the cases of gaps in tide-gauge records, estimates can be obtained by various methods of interpolation and/or extrapolation, which generally assume linearity of the data. Although plausible in many cases, this assumption does not provide accurate results because shallow-water oceanic processes, such as tides, are mostly of a non-linear nature. This paper employs artificial neural networks to supplement hourly tide-gauge records using observations from other distant tide gauges. A case study is presented using data from the SEAFRAME tide-gauge sta-tions at Hillarys Boat Harbour, Indian Ocean, and Esperance, Southern Ocean, for the period 1992 to 2002. The neural network methodology of sea-level supplementation demonstrates reliable results, with a fairly good overall agreement between the retrieved information and actual measurements

Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia

In the present study, the artificial intelligence meshless methodology of neural networks was used to predict hourly sea level variations for the following 24 hours, as well as for half-daily, daily, 5-daily and 10-daily mean sea levels. The methodology is site specific; therefore, as an example, the measurements from a single tide gauge at Hillarys Boat Harbour, Western Australia, for the period December 1991-December 2002 were used to train and to validate the employed neural networks. The results obtained show the feasibility of the neural sea level forecasts in terms of the correlation coefficient (0.7-0.9), root mean square error (about 10% of tidal range) and scatter index (0.1-0.2)

Artificial neural networks in wave predictions at the west coast of Portugal

In coastal and open ocean human activities, there is an increasing demand for accurate estimates of future sea state. In these activities, predictions of wave heights and periods are of particular importance. In this study, two different neural network strategies were employed to forecast significant wave heights and zero-up-crossing wave periods 3, 6, 12 and 24 h in advance. In the first approach, eight simple separate neural nets were implemented to simulate every wave parameter over each prediction interval. In the second approach, only two networks provided simultaneous forecasts of these wave parameters for the four prediction intervals. Two independent sets of measurements from a directional wave buoy moored off the Portuguese west coast were used to train and to validate the artificial neural nets. Saliency analysis of the results permitted an optimization of the networks' architectures. The optimal learning algorithm for each case was also determined. The short-term forecasts of the wave parameters verified by actual observations demonstrate the suitability of the artificial neural technique

Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture

Passive microwave remote sensing is one of the most promising techniques for soil moisture retrieval. However, the inversion of soil moisture from brightness temperature observations is not straightforward, as it is influenced by numerous factors such as surface roughness, vegetation cover, and soil texture. Moreover, the relationship between brightness temperature, soil moisture and the factors mentioned above is highly non-linear and ill-posed. Consequently, Artificial Neural Networks (ANNs) have been used to retrieve soil moisture from microwave data, but with limited success when dealing with data different to that from the training period. In this study, an ANN is tested for its ability to predict soil moisture at 1 km resolution on different dates following training at the same site for a specific date. A novel approach that utilizes information on the variability of soil moisture, in terms of its mean and standard deviation for a (sub) region of spatial dimension up to 40 km, is used to improve the current retrieval accuracy of the ANN method.A comparison between the ANN with and without the use of the variability information showed that this enhancement enables the ANN to achieve an average Root Mean Square Error (RMSE) of around 5.1% v/v when using the variability information, as compared to around 7.5% v/v without it. The accuracy of the soil moisture retrieval was further improved by the division of the target site into smaller regions down to 4 km in size, with the spatial variability of soil moisture calculated from within the smaller region used in the ANN. With the combination of an ANN architecture of a single hidden layer of 20 neurons and the dual-polarized brightness temperatures as input, the proposed use of variability and sub-region methodology achieves an average retrieval accuracy of 3.7% v/v. Although this accuracy is not the lowest as comparing to the research in this field, the main contribution is the ability of ANN in solving the problem of predicting “out-of-range” soil moisture values. However, the applicability of this method is highly dependent on the accuracy of the mean and standard deviation values within the sub-region, potentially limiting its routine application

Filling gaps in wave records with artificial neural networks

This contribution presents a neural data interpolation methodology, which was implemented to restore missing wave measurements. The methodology is based on the ability of artificial neural networks to find and reproduce non-linear dependencies within complex geophysical systems. The data were obtained from a field campaign during July 1985- ecember 1993 near Tasmania. Wave observations from a "Waverider" buoy were broadcasted as a high frequency radio signal via a quarter-wave antenna to a "Diwar" receiver. These measurements were used to train and to validate the neural nets employed. To restore missing data over time periods from 12 to 36 hours, five feed-forward, three-layered, artificial neural networks of a similar structure were implemented. The artificial neural networks' performance was estimated in terms of the bias, root mean square error, correlation coefficient, and scatter index. The methodology demonstrated reliable results with a fairly good overall agreement between the restored wave records and actual measurements

Using Artificial Neural Networks to estimate sea level in continental and island coastal environments

The knowledge of sea level variations is of great importance in geoenvironmental and ocean-engineering applications. Estimations of sea level change with different warning times are of vital importance for the population of low-lying regions and islands. This contribution describes some recent advances in the application of a meshless artificial intelligence technique (neural networks) to the tasks of sea level retrieval and forecast. This technique was employed because it has been proven to approximate the non-linear behaviour in a geophysical system. The data used were taken from several SEAFRAME stations, which provide records for the Australian Baseline Sea Level Monitoring Project. A feed-forward, three-layered, artificial neural network was implemented to retrieve and predict sea level variations with different lead times. This methodology demonstrated reliable results in terms of the correlation coefficient (0.82-0.96), root mean square error (about 10% of tidal range) and scatter index (0.1-0.2), when compared with actual observations