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A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

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Probabilistic Evaluation of Drought in CMIP6 Simulations.

Simon Michael Papalexiou^1,2,3, Chandra Rupa Rajulapati^2,4, Konstantinos M Andreadis⁵

¹Department of Civil Engineering University of Calgary Calgary AB Canada.

Earth'S Future

|November 25, 2021

Summary

Climate models are crucial for drought prediction. This study found that while many models show good drought characteristics, their low precipitation values have biases, emphasizing the need for probabilistic evaluation.

Keywords:

CMIP6 Hellinger distance climate change droughts precipitation reliability of climate models

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Published on: April 17, 2015

Area of Science:

Climate Science
Environmental Science
Meteorology

Background:

Droughts pose significant social and ecological risks, necessitating effective adaptation and mitigation strategies.
Climate models are essential tools for understanding and projecting drought dynamics.

Purpose of the Study:

To evaluate the performance of 285 Climate Model Intercomparison Project Phase 6 (CMIP6) historical simulations in reproducing observed drought duration and severity.
To develop and apply a novel probabilistic framework for assessing climate model accuracy in drought characterization.

Main Methods:

Utilized 285 CMIP6 simulations from 17 climate models and three observational datasets.
Employed the Standardized Precipitation Index (SPI) to quantify drought characteristics.
Applied summary statistics beyond mean and standard deviation, and a Hellinger distance-based probabilistic framework to compare simulations and observations.

Main Results:

Many simulations exhibited low error () in reproducing observed drought summary statistics.
The null hypothesis that simulations and observations follow the same distribution could not be rejected for over of the grids using the Hellinger distance framework.
No single model consistently outperformed others globally; tropical regions showed higher variance in drought statistics among simulations.
Models accurately captured dry spell characteristics but showed biases in simulating low precipitation values.

Conclusions:

Probabilistic evaluation is essential for identifying climate model weaknesses and selecting suitable models for impact assessments.
Despite good performance in SPI metrics, models may still exhibit biases in simulating extreme low precipitation events.
A comprehensive assessment is needed to understand the reliability of climate models for drought-related projections.