Statistical Analysis of Global Surface Temperature and Sea Level Using Cointegration Methods

Schmith, Torben; Johansen, Søren; Thejll, Peter

doi:10.1175/jcli-d-11-00598.1

Cited by 41 publications

(51 citation statements)

References 57 publications

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“…Their results also confirm the hypothesis underlying the DSM approach [16]. As stated in [17], the sea surface air temperatures will adjust to the average temperatures of the upper ocean due to the larger heat capacity of oceans relative to the atmosphere. As a result of this difference in heat capacities, SLR will directly affect the SST.…”

Section: Introductionsupporting

confidence: 71%

“…This underlying hypothesis is distinctly different from all other empirical models that are used in the literature and explained in more detail in [16]. Later on, a vector-autoregressive (VAR) model that employs the same mathematical form of the discrete DSM approach was also developed in [17] that uses a stochastic cointegration method to describe the relationship between SST and SLR. This model has the same structure as the DSM model used in [16].…”

Section: Introductionmentioning

confidence: 97%

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

Aral

Guan

2016

Water

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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.

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Section: Introductionsupporting

confidence: 71%

Section: Introductionmentioning

confidence: 97%

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

Aral

Guan

2016

Water

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“…The results of VAR and MC-DSM also confirmed the hypothesis underlying the DSM approach [26,27]. In their studies [30,31], the authors stated that: "the SSTs will adjust to the average temperatures of the upper ocean due to the larger heat capacity of oceans relative to the atmosphere. As a result of this difference in heat capacities, SLR will directly affect the SST.…”

Section: Introductionmentioning

confidence: 54%

“…This hypothesis is distinctly different from the other empirical and unidirectional models that are referenced above. Later on, the mathematical form of DSM was also used in a vector-autoregressive (VAR) model in an independent study [30], where the stochastic cointegration method is employed to describe the relationship between SST and SLR, and the DSM model was also employed in a Monte Carlo analysis (MC-DSM) of SLR designed to improve the predictive capability of DSM [31]. The results of VAR and MC-DSM also confirmed the hypothesis underlying the DSM approach [26,27].…”

Section: Introductionmentioning

confidence: 72%

Spatial Variation of Sea Level Rise at Atlantic and Mediterranean Coastline of Europe

Aral

Chang

2017

Water

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The inundation impact of sea level rise (SLR) is critical, since coastal regions of Europe house important critical infrastructures and large population centers. According to International Panel on Climate Change (IPCC) studies, the analysis of the SLR problem is complicated. Beyond the reported complexities involved in the analysis of this phenomenon, the expected spatial variability of SLR in oceans further complicates this analysis. Spatial variability of SLR in oceans is both observed and also expected, according to IPCC studies. Estimation of spatial variation of SLR in oceans is necessary to identify the level of potential threats that may impact different coastline regions. Identification of geographic patterns of SLR based on local coastal data has been reported in the literature. Unfortunately, these estimates cannot be used in predictive analysis over a century. Thus, the solution of this problem using mathematical models is the other alternative that can be employed. Modeling solutions to this problem is currently in its infancy, and further studies in this field are needed. In this study, a methodology developed by the authors is used to estimate the SLR for the Atlantic and the Mediterranean coastline of Europe that also includes the other oceans. This effort utilizes the dynamic system model (DSM) with spatial analysis capability (S-DSM) to predict the regional sea level change. Results obtained provide consistent assessment of spatial variability of SLR pattern in oceans as well as the temperature changes over the 21st century. This approach may also be used in other coastal regions to aid management decision in a timely manner.

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“…The linear regression approach with trending time series potentially invalidates standard statistical inference (Schmith et al, 2007(Schmith et al, , 2012Granger and Newbold, 1974). This is caused by the properties of the residual process (error t ), which may be statistically indistinguishable from a random walk.…”

Section: Introductionmentioning

confidence: 99%

The statistical relation of sea-level and temperature revisited

Grassi

Hillebrand

Ventosa‐Santaulària

2013

Dynamics of Atmospheres and Oceans

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b s t r a c tWe propose a semi-empirical model for the relation between global mean surface temperature and global sea-levels. In contradistinction to earlier approaches to this problem, the model allows for valid statistical inference and joint estimation of trend components and interaction term of temperature and sea-level. Estimation of the model on the data set used in Rahmstorf (2007) yields a proportionality coefficient of 4.6 mm/year per• C at a one-sided significance level of 7.6 percent or higher. Long-term simulations of the model result in a two-sided 90-percent confidence interval for the sea-level rise in the year 2100 of [15 cm, 150 cm] above the 1990 level. This is a wider margin of error than was reported in the previous literature, and it reflects the substantial uncertainty in relating two trending time series.

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Statistical Analysis of Global Surface Temperature and Sea Level Using Cointegration Methods

Cited by 41 publications

References 57 publications

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

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

Spatial Variation of Sea Level Rise at Atlantic and Mediterranean Coastline of Europe

The statistical relation of sea-level and temperature revisited

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