# Emergent constraint on equilibrium climate sensitivity from global temperature variability

## Overview

This recipe reproduces the emergent constraint proposed by Cox et al. (2018) for the equilibrium climate sensitivity (ECS) using global temperature variability. The latter is defined by a metric which can be calculated from the global temperature variance (in time) $$\sigma_T$$ and the one-year-lag autocorrelation of the global temperature $$\alpha_{1T}$$ by

$\psi = \frac{\sigma_T}{\sqrt{-\ln(\alpha_{1T})}}$

Using the simple Hasselmann model they show that this quantity is linearly correlated with the ECS. Since it only depends on the temporal evolution of the global surface temperature, there is lots of observational data available which allows the construction of an emergent relationship. This method predicts an ECS range of 2.2K to 3.4K (66% confidence limit).

## Available recipes and diagnostics

Recipes are stored in recipes/

• recipe_cox18nature.yml

Diagnostics are stored in diag_scripts/

• emergent_constraints/cox18nature.py

• climate_metrics/ecs.py

• climate_metrics/psi.py

## User settings in recipe

• Preprocessor

• area_statistics (operation: mean): Calculate global mean.

• Script emergent_constraints/cox18nature.py

See here.

• Script climate_metrics/ecs.py

See here.

• Script climate_metrics/psi.py

• output_attributes, dict, optional: Write additional attributes to all output netcdf files.

• lag, int, optional (default: 1): Lag (in years) for the autocorrelation function.

• window_length, int, optional (default: 55): Number of years used for the moving window average.

## Variables

• tas (atmos, monthly, longitude, latitude, time)

• tasa (atmos, monthly, longitude, latitude, time)

## References

• Cox, Peter M., Chris Huntingford, and Mark S. Williamson. “Emergent constraint on equilibrium climate sensitivity from global temperature variability.” Nature 553.7688 (2018): 319.