Background

Year‐to‐year global temperature variability can be large compared to the long‐term progression of global warming, and such year‐to‐year variability has been shown to have considerable environmental and societal effects. Thus, approximate foreknowledge of yearly global temperature deviations should be of value for anticipating some climate impacts. Below is a forecast from a relatively simple, empirical method for predicting year‐to‐year global temperature progression (full study: Brown and Caldeira, 2020). The method uses information on the global spatial patterns of surface air temperature to predict global average temperature several years in advance. We found that this method performs favorably compared to predictions from much more computationally expensive Global Climate Models.

Part of this work was conducted in coordination with Climateai. See a research poster presented at the American Geophysical Union’s fall meeting and a research talk at the American Meteorological Society’s annual meeting for potential future applications of this research.

Operational forecast

Below are hind-casts and a real-time forecast of global temperature over the next several years, modified from the method published in Brown and Caldeira (2020).

Below is the final validation of Brown and Caldeira (2020)’s initial four-year forecast (2020, 2021, 2022, 2023).

We calculate the chance of exceeding (on an annual basis) the Paris Accord-articulated limit of +1.5C above preindustrial temperatures but it should be noted that this value does not represent a particularly meaningful geophysical threshold.

The above is a statistical forecast of global-mean annual-mean temperature. Dynamical models can help us anticipate local temperature deviations away from normal conditions over the next several months and are essential for anticipating climate impacts at higher spatiotemporal resolutions.