Climate models are the main tools available for investigating the response of the Earth's climate to various external and internal changes (‘forcings’, such as changes to solar radiation or volcanic eruptions), for making climate predictions on seasonal time scales, and for making projections of future climate. For this reason it is important to evaluate the performance of these models.
Model evaluation determines how well climate models represent historical climate and forms an integral part of the confidence building exercise for climate change projections. The assumption is that the better models perform over the historical (observed) period, the more confidence can be assigned to their projected changes.
The direct approach to model evaluation is to compare climate model output with observations and analyse the resulting difference. Where possible, averages over the same time period in both models and observations are compared.
There are several generic model evaluation approaches (See the IPCC, Chapter 9, 2013):
The evaluation of model simulations of historical climate is of direct relevance to detection and attribution studies since these rely on model-derived patterns of climate response to external forcing, and on the ability of models to simulate decadal and longer-timescale internal variability.
Confidence in climate model projections is based on physical understanding of the climate system and its representation in climate models, and on a demonstration of how well models represent a wide range of processes and climate characteristics on various spatial and temporal scales. A climate model’s credibility is increased if the model is able to simulate past variations in climate.
Page updated 17th December 2020