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Very large (~2000 members) initial-condition ensemble simulations have been performed to advance understanding of mean climate and extreme weather responses to projected Arctic sea-ice loss under 2 °C global warming above preindustrial levels. These simulations better sample internal atmospheric variability and extremes for each model compared to those from the Polar Amplification Model Intercomparison Project (PAMIP). The mean climate response is mostly consistent with that from the PAMIP multi-model ensemble, including tropospheric warming, reduced midlatitude westerlies and storm track activity, an equatorward shift of the eddy-driven jet and increased mid-to-high latitude blocking. Two resolutions of the same model exhibit significant differences in the stratospheric circulation response; however, these differences only weakly modulate the tropospheric response. The response of temperature and precipitation extremes largely follows the seasonal-mean response. Sub-sampling confirms that large ensembles (e.g. ≥400) are needed to robustly estimate the seasonal-mean large-scale circulation response, and very large ensembles (e.g. ≥1000) for regional climate and extremes. [ABSTRACT FROM AUTHOR]

The Canadian Earth System Model version 5.0 (CanESM5.0), the most recent major version of the global climate model developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) at Environment and Climate Change Canada (ECCC), has been used extensively in climate research and for providing future climate projections in the context of climate services. Previous studies have shown that CanESM5.0 performs well compared to other models and have revealed several model biases. To address these biases, the CCCma has recently initiated the "Analysis for Development" (A4D) activity, a coordinated analysis activity in support of CanESM development. Here we describe the goals and organization of this effort and introduce two variants ("p1" and "p2") of a new CanESM version, CanESM5.1, which features important improvements as a result of the A4D activity. These improvements include the elimination of spurious stratospheric temperature spikes and an improved simulation of tropospheric dust. Other climate aspects of the p1 variant of CanESM5.1 are similar to those of CanESM5.0, while the p2 variant of CanESM5.1 features reduced equilibrium climate sensitivity and improved El Niño–Southern Oscillation (ENSO) variability as a result of intentional tuning of the atmospheric component. The A4D activity has also led to the improved understanding of other notable CanESM5.0 and CanESM5.1 biases, including the overestimation of North Atlantic sea ice, a cold bias over sea ice, biases in the stratospheric circulation and a cold bias over the Himalayas. It provides a potential framework for the broader climate community to contribute to CanESM development, which will facilitate further model improvements and ultimately lead to improved climate change information. [ABSTRACT FROM AUTHOR]

Our previous study revealed the link between Barents–Kara sea ice and rainfall in eastern China. This study continues evaluating the performance of multiple models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating this linkage. Most CMIP6 models can simulate Arctic sea ice coverage in the present climate system, although the sea ice extent in the edge areas show some biases. Only a few models can roughly reproduce the observed rainfall dipole pattern associated with Arctic sea ice variability. The linkage between Arctic sea ice variability in winter and eastern China rainfall in early summer is performed through a long memory of the sea ice, the stratospheric variability as the mediator, and downward propagation of stratospheric signals. Very few CMIP6 models can exhibit a realistic interannual relationship between the Arctic sea ice and China rainfall. The selected high-skill models with a more realistic linkage between sea ice and China rainfall present a clear downward impact of the stratospheric circulation anomalies associated with sea ice variability. The reversal of the Northern Hemisphere Annular Mode (NAM) from the negative phase in early winter to the positive phase in spring in the high-skill models and observations denotes the important role of the stratosphere as a mediator to bridge the Arctic sea ice and China rainfall. The long memory of the Arctic sea ice with the stratosphere as the mediator has a deep implication on the seasonal forecasts of East Asian countries. [ABSTRACT FROM AUTHOR]