Abstract
Prediction of global temperatures and sea level rise (SLR) is important for sustainable development planning of coastal regions of the world and the health and safety of communities living in these regions. In this study, climate change effects on sea level rise is investigated using a dynamic system model (DSM) with time lag on historical input data. A time-invariant (TI-DSM) and time-variant dynamic system model (TV-DSM) with time lag is developed to predict global temperatures and SLR in the 21st century. The proposed model is an extension of the DSM developed by the authors. The proposed model includes the effect of temperature and sea level states of several previous years on the current temperature and sea level over stationary and also moving scale time periods. The optimal time lag period used in the model is determined by minimizing a synthetic performance index comprised of the root mean square error and coefficient of determination which is a measure for the reliability of the predictions. Historical records of global temperature and sea level from 1880 to 2001 are used to calibrate the model. The optimal time lag is determined to be eight years, based on the performance measures. The calibrated model was then used to predict the global temperature and sea levels in the 21st century using a fixed time lag period and moving scale time lag periods. To evaluate the adverse effect of greenhouse gas emissions on SLR, the proposed model was also uncoupled to project the SLR based on global temperatures that are obtained from the Intergovernmental Panel on Climate Change (IPCC) emission scenarios. The projected SLR estimates for the 21st century are presented comparatively with the predictions made in previous studies.
Highlights
Analysis of environmental and human impact of global warming is an important challenge which is studied in the literature, considering several different problems and approaches
ĤCI (k ) = Ĥ (k) ± tα/2,N −4n σH e p where TCI (k) and ĤCI (k) represent the 100 (1 − α) confidence intervals of global sea surface temperatures (SST) and sea level rise (SLR) at the kth year, t denotes the t-distribution, tα,m is the value of t for m degree of freedom and α confidence level, N is the number of historical data used for system recognition, n is the time lag, σT and σH are the standard deviations for global SST and SLR, which are estimated in the model calibration, ep is the term accounting for the error propagation in dynamic prediction, given by v u k u ε p = t1 +
In comparison with the empirical or semi-empirical models used in previous studies, this model accounts for the impact of global SST and sea level status from several previous years on the current accounts for the impact of global SST and sea level status from several previous years on the current
Summary
Analysis of environmental and human impact of global warming is an important challenge which is studied in the literature, considering several different problems and approaches. To address the time lag effect between temperature increase and SLR, a time lag coefficient was introduced to the earlier semi-empirical models as well [9,12] This approach overlooks the impact of a series of temperature and sea level status over the previous years on the current temperature and sea level. We have noticed that, if the impact of SST and sea level status of a series of previous years are considered, the results obtained would be more accurate based on the evaluation of the performance measures that are discussed in this paper Using this approach, the warming trend and SLR that is predicted with the DSM analysis would be different. The results obtained for this case are presented comparatively with earlier results which can be used in inundation studies of coastal regions [19]
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