Multi‐model assessment of global temperature variability on different time scales. (16th July 2019)

• Record Type: Journal Article
• Authors: Yu, Haipeng; Wei, Yun; Zhang, Qiang; Liu, Xiaoyue; Huang, Jianping; Feng, Taichen; Zhang, Meng
• Is Part Of: International journal of climatology. Volume 40:Number 1(2020)
• Page Start: 273
• Page End: 291
• Publication Date: 2019-07-16
• Subjects: coupled models -- different time scales -- surface air temperature; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.6209
• Languages: English
• ISSNs: 0899-8418

Abstract

Abstract: This study explores the near‐surface air temperature (TAS) prediction skills at different time scales via coupled climate models that were involved in the Coupled Model Intercomparison Project phase 5 (CMIP5). The simulation skills of the global mean TAS are first assessed between the observations and models; then, the temporal variability is separated into three parts (the linear trend, decadal variability and inter‐annual variability), and each part is compared with the observations. It is found that the global mean TAS anomaly and the decadal variability are well captured by the model, while the inter‐annual variability is poorly presented. In all the three parts of different time scales, there are larger differences among the 18 models, and the ensemble mean of CMIP5 is the closest to the observations. Besides, the TAS decadal variability is better presented for global ocean than for global land. In the assessment of climate oscillation, it is found that the models can well reproduce the TAS decadal variation patterns correlated with the Pacific Decadal Oscillation (PDO) and the inter‐annual variation patterns correlated with El Niño–Southern Oscillation (ENSO) but poor for the decadal variation patterns related to the Atlantic Multidecadal Oscillation (AMO). This study provides a reference assessing the simulation skills of climate models and an indicator evaluating the advantage of CMIP6 in comparison with CMIP5. Abstract : This study explores the prediction skills of global air temperature at different time scales via coupled climate models. The temporal variability of global air temperature is separated into three parts of the linear trend, decadal variability and inter‐annual variability, and each part is compared with the observations. The models can well reproduce the decadal variation patterns correlated with PDO and the inter‐annual variation patterns correlated with ENSO but poor for the decadal variation patterns related to AMO. In the figure, Taylor diagram for TAS (a) anomalies, (b) trend, (c) inter‐annual and (d) decadal time series. Obs, EM and 1‐18 represents Gistemp, CMIP5‐EM and individual models, respectively. The distance of any point from the origin indicates standard deviation of time series, and the distance of any point from the Obs reference point indicates the centred root‐mean‐square (RMS) difference between each model or ensemble and observation. Correlation between each model or ensemble mean and observation is given by the azimuthal coordinate.