More Accurate Climate Trend Attribution by Using Cointegrating Vector Time Series Models

نویسندگان

چکیده

Adapting to human-induced climate change is becoming an increasingly important aspect of sustainable development. To be able do this effectively, it know how much human influence has contributed observed trends. Climate detection and attribution (D&A) studies achieve by estimating scaling factors usually obtained performing a least squares regression the trending variable on equivalent simulated model. This study proposed instead estimate using econometric approach dynamically modelling time series as cointegrating Vector Auto-Regressive (VAR) process. It shown that 2nd-order VAR(2) model theoretically justified if variables can represented one-box AR(1) response common integrated forcing. The expressed Error-Correction Model (VECM) then fitted data obtain cointegration relationship, stationary linear combination two variables, from which factor easily obtained. Estimates are critically compared those Ordinary Least Squares (OLS) Total (TLS) for annual Global Mean Surface Temperature (GMST) simple stochastic carbon–climate system historical simulations 16 models in Coupled Intercomparison Project 5 (CMIP5) experiment. Results toy show slope estimates OLS negatively biased, TLS less biased but have high variance, unbiased lower variance provide most accurate with smallest mean squared error. Similar behaviour noted CMIP5 data. Hypothesis tests fits found strong evidence relationship observations all simulations.

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ژورنال

عنوان ژورنال: Sustainability

سال: 2023

ISSN: ['2071-1050']

DOI: https://doi.org/10.3390/su151612142