Abstract. This paper investigates the impact of the parallel computational uncertainty due to the round-off error on climate simulations using the Community Climate System Model Version 3 (CCSM3). A series of sensitivity experiments have been conducted and the analyses are focused on the Global and Nino3.4 average sea surface temperatures (SST). For the monthly time series, it is shown that the amplitude of the deviation induced by the parallel computational uncertainty is the same order as that of the climate system change. However, the ensemble mean method can reduce the influence and the ensemble member number of 15 is enough to ignore the uncertainty. For climatology, the influence can be ignored when the climatological mean is calculated by using more than 30-yr simulations. It is also found that the parallel computational uncertainty has no distinguishable effect on power spectrum analysis of climate variability such as ENSO. Finally, it is suggested that the influence of the parallel computational uncertainty on Coupled General Climate Models (CGCMs) can be a quality standard or a metric for developing CGCMs.

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As the most significant interannual variability in the climate system, El Niño-Southern Oscillation (ENSO) has critical effects on global weather and climate patterns. To simulate and predict ENSO, coupled general circulation models (CGCMs) have become a key tool. However, the accurate simulation of ENSO is still a challenge for CGCMs. The performance of El Niño simulations conducted through FIO-ESM v1.0 is examined based on the outputs of the Coupled Model Intercomparsion Project phase 5 (CMIP5) historical experiments. The results show that FIO-ESM v1.0 suffers from similar common problems to other CMIP5 models, including an eastward shift in the central locations of El Niño, adopting a regular period of roughly 3 years, addressing excessively high amplitude, spurious eastward propagation of El Niño events, and Aborted El Niño events. El Niño composite and mixed layer heat budget analyses indicate that these simulation biases are mainly associated with the mean state biases, including a warm Sea Surface Temperature (SST) bias for the central-eastern Pacific, a cold SST bias for the western and central Pacific, seasonal cycles of SST of the equatorial eastern Pacific, and weaker trade winds. Weaker SST-cloud-shortwave radiation feedback in La Niña events than in El Niño events is what creates spurious ENSO amplitude symmetry in the model. We suggest that the improvement of El Niño simulations may be realized by focusing on the mean state and SST-cloud-shortwave radiation feedback in the tropical region. Specifically, further incremental improvements in the mean state of the tropical Pacific should constitute the first step to realizing more accurate ENSO simulation.

This paper investigates the impact of the parallel computational uncertainty on climate simulations using the Community Climate System Model Version 3 (CCSM3). A series of sensitivity experiments have been conducted and the analyses are focused on the Global and Nino3.4 sea surface temperatures. It is shown that the amplitude of the deviation induced by the parallel computational uncertainty is the same order as that of the climate system change. However, the ensemble mean method can reduce the influence and the ensemble member number of 15 is enough to ignore simulated errors. For climatology, the influence can be ignored when the climatological mean is calculated by using more than 30-yr simulations. It is also found that the parallel computational uncertainty has no effect on the simulated periods of climate variability such as ENSO. Finally, it is suggested that the influence of the parallel computational uncertainty on Coupled General Climate Models (CGCMs) can be a quality standard or a metric for developing CGCMs