On the Estimation of Climate Normals and Anomalies

TL;DR

This paper introduces a novel real-time filtering method using local trigonometric regression and seasonal kernels to improve climate normal and anomaly estimation amid climate change, demonstrated on sea surface temperature and wind data.

Contribution

It proposes a regularized local trigonometric regression filter with seasonal kernels to better estimate climate normals in nonstationary conditions caused by climate change.

Findings

Enhanced estimation accuracy of climate normals.

Effective handling of nonstationary climate data.

Improved detection of climate anomalies.

Abstract

The quantification of the interannual component of variability in climatological time series is essential for the assessment and prediction of the El Ni\~{n}o - Southern Oscillation phenomenon. This is achieved by estimating the deviation of a climate variable (e.g., temperature, pressure, precipitation, or wind strength) from its normal conditions, defined by its baseline level and seasonal patterns. Climate normals are currently estimated by simple arithmetic averages calculated over the most recent 30-year period ending in a year divisible by 10. The suitability of the standard methodology has been questioned in the context of a changing climate, characterized by nonstationary conditions. The literature has focused on the choice of the bandwidth and the ability to account for trends induced by climate change. The paper contributes to the literature by proposing a regularized real time filter based on local trigonometric regression, optimizing the estimation bias-variance trade-off in the presence of climate change, and by introducing a class of seasonal kernels enhancing the localization of the estimates of climate normals. Application to sea surface temperature series in the Ni\~{n}o 3.4 region and zonal and trade winds strength in the equatorial and tropical Pacific region, illustrates the relevance of our proposal.