Trends in Temperature Data: Micro-foundations of Their Nature

TL;DR

This paper investigates the statistical nature of global temperature trends, providing evidence that global average temperature is stationary around a non-linear trend with a structural break, influenced by individual grid changes and aggregation methods.

Contribution

It introduces a micro-founded analysis revealing that GAT is stationary around a non-linear trend with a structural break, advancing understanding of temperature data behavior.

Findings

GAT is stationary around a non-linear trend with a one-period break.

Breaks are due to individual grid changes and aggregation effects.

Simulations support the empirical findings.

Abstract

Determining whether Global Average Temperature (GAT) is an integrated process of order 1, I(1), or is a stationary process around a trend function is crucial for detection, attribution, impact and forecasting studies of climate change. In this paper, we investigate the nature of trends in GAT building on the analysis of individual temperature grids. Our 'micro-founded' evidence suggests that GAT is stationary around a non-linear deterministic trend in the form of a linear function with a one-period structural break. This break can be attributed to a combination of individual grid breaks and the standard aggregation method under acceleration in global warming. We illustrate our findings using simulations.