On the Application of Machine Learning Techniques to Regression Problems in Sea Level Studies

Long sea level records with high temporal resolution are of paramount importance for future coastal protection and adaptation plans. Here we discuss the application of machine learning techniques to some regression problems commonly encountered when analyzing such time series. The performance of artificial neural networks is compared with that of multiple linear regression models on sea level data from the Swedish coast. The neural networks are found to be superior when local sea level forcing is used together with remote sea level forcing and meteorological forcing, whereas the linear models and the neural networks show similar performance when local sea level forcing is excluded. The overall performance of the machine learning algorithms is good, often surpassing that of the much more computationally costly numerical ocean models used at our institute

A Novel Machine Learning Based Bias Correction Method and Its Application to Sea Level in an Ensemble of Downscaled Climate Projections

A new machine learning based bias correction method is presented and applied to sea level in a regional climate model. The bias corrections derived using this method depend on the state of the model it corrects. This contrasts with conventional bias correction methods that operate on distributions of output variables. The dependence on model states allows for better performance on classical skill scores, but it also limits the applicability of the method to models that can perform hindcasts. A very large dataset of corrected hourly sea levels from many different emission scenarios is created. In total the dataset contains over 2600 model years and exists for seven different tide-gauge stations on the Swedish Baltic Sea coast. The prevalence of significant trends in yearly sea level maximum is found to be independent of emission scenario, suggesting that anthropogenic climate change is no significant driver of storm surge variability in the area. Lastly, the dataset is used to estimate return levels for very long return periods, and the block length used in the return level computation is found to affect the result at some stations. This suggests that the commonly used annual maximum approach is not always applicable for determining return levels for sea level

An update on the thermosteric sea level rise commitment to global warming

The equilibrium thermosteric sea level rise caused by global warming is evaluated in several coupled climate models. The thermosteric sea level rise is found to be well approximated as a linear function of the mean ocean temperature increase in the models. However, the mean ocean temperature increase as a function of the mean surface temperature increase differs between the models. Our models can be divided into two branches; models with an Atlantic meridional overturning circulation that increases with warming have large mean ocean temperature increases and vice versa. These two different branches give estimates of the equilibrium thermosteric sea level rise per degree of surface warming that are respectively 98% and 21% larger than the estimate given in the IPCC Fifth Assessment Report. Our estimates of the equilibrium thermosteric sea level rise are also used to infer an equilibrium sea level sensitivity, a parameter akin to the often used equilibrium climate sensitivity metric

Nonlinear Interactions and Some Other Aspects of Probabilistic Sea Level Projections

Probabilistic sea level projections are frequently used to characterise the uncertainty in future sea level rise. Here, it is investigated how different modelling assumptions and process estimates affect such projections using two process-based models that add up the sea level contributions from different processes such as thermosteric expansion and ice sheet melt. A method is applied to estimate the direct contributions from the different processes as well as that of nonlinear interactions between the processes to the projections. In general, the nonlinear interaction terms are found to be small compared to the direct contributions from the processes, and only a few interaction terms give significant contributions to the projections. Apart from the process estimates, probabilistic models often also incorporate some expert judgements that inflate the uncertainty compared with that derived from climate and ice-sheet models, and the effects of some such judgements are also evaluated and found to have a considerable influence on the projections. Lastly, sea level projections are most often given contingent on representative concentration pathways for atmospheric greenhouse gases. Here, we generalize this approach by also providing projections for a probabilistic baseline scenario

Should Swedish sea level planners worry more about mean sea level rise or sea level extremes?

Current coastal spatial planning in Sweden uses simple methods to account for how flood risks increase owing to sea level rise. Those methods, however, fail to account for several important aspects of sea level rise, such as: projection uncertainty, emission scenario uncertainty and time dependence. Here, enhanced methods that account for these uncertainties are applied at several locations along the coast. The relative importance of mean sea level rise and extreme events for flood risk is explored for different timeframes. A general conclusion for all locations is that, extreme events dominate the flood risk for planning periods lasting a few decades. For longer planning periods, lasting toward the end of the century, the flood risk is instead dominated by the risk of high sea level rise. It is argued that these findings are important for assessments of future flood risk, and that they should be reflected in coastal spatial planning

[Stammbuch Hieronymus Magnus Pius Linck] / H. M. P. Linck

[STAMMBUCH HIERONYMUS MAGNUS PIUS LINCK] / H. M. P. LINCK [Stammbuch Hieronymus Magnus Pius Linck] / H. M. P. Linck (1) Einband (1) Besitzvermerk (8) Einträge Bl. 10 - 59 (11) Einträge Bl. 60 - 99 (49) Einträge Bl. 100 - 183 (86