Evaluation of AMIP Models from CMIP6 in Simulating Winter Surface Air Temperature Trends over Eurasia During 1998–2012 Based on Dynamical Adjustment
CLIMATE DYNAMICS(2023)
Nanjing University
Abstract
The relationship between winter cooling in Eurasia and Arctic amplification during the period 1998–2012 under global warming has received increasing attention in recent years. This relationship is controversial and is often studied using model simulations. However, the process of evaluating these models is challenging as a result of the different internal variability among models and between the models and observations. We applied a dynamical adjustment method based on constructed circulation analogs to the model simulations and observations to remove the effects of the internal variability of the atmosphere and then evaluated the performance of the models in simulating the winter surface air temperature (SAT) trends over Eurasia from 1998 to 2012 based on 11 models of the Atmospheric Model Intercomparison Project (AMIP) from phase 6 of the Coupled Model Intercomparison Project. Our results show that the overall performance of all the model ensemble simulations was poor, but was much improved after applying dynamical adjustment, with the median values of the 11 AMIP ensemble simulations fairly close to the observed winter SAT trends averaged over Eurasia. When considering both the model-simulated SAT trends averaged over Eurasia and the skill scores of the trend pattern, the HadGEM3-GC31-LL simulation gave the best performance among the models with multiple runs. This method allows a more objective evaluation of the performance of models and provides an alternative way to evaluate the ability of models to simulate the “warm Arctic and cold Eurasia” trend pattern. The cold Eurasia, especially central Eurasia, in the observations is found to be mainly induced by the contribution from the internal variability of the atmosphere.
MoreTranslated text
Key words
Warm Arctic and cold Eurasia,Hiatus,Dynamical adjustment,Constructed circulation analogs,Model evaluation method,Atmospheric internal variability
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined