Climate change heterogeneity: A new quantitative approach.

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Climate change is a spatial and temporarily non-uniform phenomenon that requires understanding its evolution to better evaluate its potential societal and economic impact. The value added of this paper lies in introducing a quantitative methodology grounded in the trend analysis of temperature distribution quantiles to analyze climate change heterogeneity (CCH). By converting these quantiles into time series objects, the methodology empowers the definition and measurement of various relevant concepts in climate change analysis (warming, warming typology, warming amplification and warming acceleration) in a straightforward and robust testable linear regression format. It also facilitates the introduction of new testable concepts like warming dominance to compare (globally or partially) the warming process experienced by different regions. Furthermore, the methodology holds the added significance of concurrently encompassing both temporal and spatial dimensions in temperature analysis, owing to the close alignment between unconditional quantiles and latitude measures. Applying our quantitative methodology for the period 1950-2019 to the Globe (2192 stations) and Spain (30 stations) as a benchmark region, we find that both experience a distributional warming process (beyond the standard average) but of very different types. While the Globe experiences a stronger warming in the lower temperatures than in the upper ones, Spain evolves from equal warming in the whole distribution toward a stronger warming in the upper quantiles (similar to the warming process experienced in the African continent). In the two cases, the warming process accelerates (non-linear behavior) over time and is asymmetrically amplified. Overall, although both the Globe and Spain suffer an equivalent warming process in the median (mean) temperature, Spain's warming dominates the Globe in the upper quantiles and is dominated in the lower tail of the global temperature distribution that corresponds to the Arctic region. Our climate change heterogeneity results open the door to the need for a non-uniform causal-effect climate analysis that goes beyond the standard causality in mean and for a more efficient design of the mitigation-adaptation policies. In particular, the heterogeneity found suggests these policies should contain a common global component and a clear local-regional idiosyncratic element. The latter is usually more straightforward to implement.