Global Sea Surface Temperature and Sea Level Rise Estimation with Optimal Historical Time Lag Data

• Main Authors: Mustafa M. Aral, Jiabao Guan
• Format: Article
• Language: English
• Published: MDPI AG 2016-11-01
• Series: Water
• Subjects: sea level rise; temperature change; dynamic system model; climate change
• Online Access: http://www.mdpi.com/2073-4441/8/11/519
• ISSN: 2073-4441

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.