Abstract
This study proposes a nonlinear cointegrating regression model based on the well-known energy balance climate model. Specifically, I investigate the nonlinear cointegrating regression of the mean of temperature anomaly distributions on total radiative forcing using estimated spatial distributions of temperature anomalies for the Globe, Northern Hemisphere, and Southern Hemisphere. Further, I provide two types of nonlinear response functions that map the total radiative forcing level to mean temperature anomalies. The proposed statistical model provides a climatological implication that spatially heterogenous warming effects play a significant role in identifying nonlinear climate sensitivity. Cointegration and specification tests are provided that support the existence of nonlinear effects of total radiative forcing.
Highlights
Over the past few decades, the observed global mean surface temperature has increased
The resulting climate sensitivity, D1, of the nonlinear cointegration model is provided as the value 0.380 for the Globe, indicating that the global mean temperature anomaly increases by 0.380 ◦ C when TRF increases by
I proposed the nonlinear cointegration model based on the well-known
Summary
Over the past few decades, the observed global mean surface temperature has increased. Using Dynamic Ordinary Least Squares estimation, they concluded that the increase in global mean temperature can be associated with the change in radiative forcing variables Such a linear cointegration analysis has been investigated by Pretis (2020). They reasoned that the seemingly structural break in the global mean temperature anomaly trend, as argued by many authors, are more likely inherited from unit-root type persistency (stochastic trend), than from higher order persistency associated with deterministic trends Based on their analysis, global temperature anomaly distribution and radiative forcing variables can share common stochastic trends and their linear combination can produce a stationary process. The empirical results and interpretation of the results are presented in Section 5, while Section 6 concludes
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