The simulation of internal climate variability is key to understanding climate change. Internal fluctuations can fully obscure or amplify the underlying climate-change signal in many fields over years to decades. The Pacific Decadal Oscillation (PDO) is an important mechanism that dominates interdecadal climate variations. Here, the capacity of 36 Coupled Model Intercomparison Project Phase 6 (CMIP6) models for reproducing the PDO during the period 1901-2014 was evaluated across spatial pattern, amplitude, periodicity and phase aspects. The results suggested that approximately 71% of the models were capable of capturing the spatial pattern of the PDO. However, most of the other models underestimated the percentage of the variance explained and the pattern amplitude. CMIP6 models can capture the periodicity over shorter periods (<20 yr), while most CMIP6 models underestimate the periodicity of the PDO on the interdecadal scale (60 yr periods). In particular, the probability distribution functions (PDFs) of the raw PDO index showed that most CMIP6 models could capture the unimodal probability distribution. Some of the models were able to reproduce the bimodal probability distribution of the PDO index filtered by the ensemble empirical mode decomposition method. Moreover, the findings also showed that the multimodel ensemble (MME) for 36 CMIP6 models could effectively capture the spatial pattern and periodicity over a shorter period (<20 yr) of the PDO. Moreover, the MME exhibited higher fidelity to the observation in the PDF of the PDO index, although the positive area was larger than the observation. These findings are crucial because they may help detect simulation gaps and provide valuable references for the design and applicability of future models worldwide.
Low clouds are crucial to the earth climate system because they directly participate in the energy and water cycles and the radiation balance of the earth system (Wood, 2012), and have a significant role in stabilizing climate (Goldblatt et al., 2021). Low clouds are regarded as the most significant source of uncertainties in affecting cloud-climate feedback and climate sensitivities of climate models, and predicting future climate change (Bony & Dufresne, 2005;Hartmann et al., 1992;Stephens, 2005;Xu et al., 2010). Despite advancements in both observations and modeling in recent years, significant uncertainties remain in our knowledge of low clouds. For example, significant underestimation of coastal low-level clouds in general circulation models (GCMs) is a well-known problem (Cheng & Xu, 2011Donner et al., 2011;Schmidt et al., 2006). Therefore, it is necessary to improve the low-cloud simulation to understand climate change and cloud-climate feedback.One of the great challenges in accurate simulation of low clouds is providing a physically robust representation of sub-grid processes in GCMs (Bony & Dufresne, 2005;Randall et al., 2007;Zhu & Zuidema, 2009). Low clouds depend on small-scale turbulent physical processes and their interaction with radiation. The intensity of the temperature inversion, specific humidity, and latent heat flux in the atmospheric boundary layer plays a leading role in the variation of low-cloud fraction (Myers & Norris, 2015, 2016. Because turbulence has characteristic sizes that are much smaller than grid boxes used in large-scale models, it is not resolved and must therefore be parameterized in most GCMs.
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