The El Niño-Southern Oscillation (ENSO) phenomenon – the dominant source of climate variability on seasonal to multi-year timescales – is predictable a few seasons in advance. Forecast skill at longer multi-year timescales has been found in a few models and forecast systems, but the robustness of this predictability across models has not been firmly established owing to the cost of running dynamical model predictions at longer lead times. In this study, we use a massive collection of multi-model hindcasts performed using model analogs to show that multi-year ENSO predictability is robust across models and arises predominantly due to skillful prediction of multi-year La Niña events following strong El Niño events.

This study isolates the influence of sea ice mean state on pre-industrial climate and transient 1850-2100 climate change within a fully coupled global model: The Community Earth System Model version 2 (CESM2). The CESM2 sea ice model physics is modified to increase surface albedo, reduce surface sea ice melt, and increase Arctic sea ice thickness and late summer cover. Importantly, increased Arctic sea ice in the modified model reduces a present-day late-summer ice cover bias. Of interest to coupled model development, this bias reduction is realized without degrading the global simulation including top-of-atmosphere energy imbalance, surface temperature, surface precipitation, and major modes of climate variability. The influence of these sea ice physics changes on transient 1850-2100 climate change is compared within a large initial condition ensemble framework. Despite similar global warming, the modified model with thicker Arctic sea ice than CESM2 has a delayed and more realistic transition to a seasonally ice free Arctic Ocean. Differences in transient climate change between the modified model and CESM2 are challenging to detect due to large internally generated climate variability. In particular, two common sea ice benchmarks - sea ice sensitivity and sea ice trends - are of limited value for comparing models with similar global warming. More broadly, these results show the importance of a reasonable Arctic sea ice mean state when simulating the transition to an ice-free Arctic Ocean in a warming world. Additionally, this work highlights the importance of large initial condition ensembles for credible model-to-model and observation-model comparisons.

Despite global warming, SSTs in the Southern Ocean (SO) have cooled in recent decades largely as a result of internal variability. The global impact of this cooling is assessed by nudging evolving SO SST anomalies to observations in an ensemble of coupled climate model simulations under historical radiative forcing, and comparing against a control ensemble. The most significant remote response to observed SO cooling is found in the tropical South Atlantic, where increased clouds and strengthened trade winds cool the sea surface, partially offsetting the radiatively-forced warming trend. The SO ensemble produces a more realistic tropical South Atlantic SST trend, and exhibits a higher pattern correlation with observed SST trends in the greater Atlantic basin, compared to the control ensemble. SO cooling also produces a significant increase in Antarctic sea ice, but not enough to offset radiatively-induced ice loss; thus, the SO ensemble remains biased in its sea ice trends.

Studies have long reported the existence of pronounced diurnal and semi-diurnal variations in near-surface winds and divergence over the tropical oceans. Diurnal cycles of convective precipitation and cloudiness in the tropics are also well recognized from in-situ and satellite observations. However, the linkages between diurnal variations in tropospheric circulation, cloudiness and precipitation over the tropical oceans remain to be fully documented and understood. Recently, global storm-resolving models, which do not require convective parameterizations, have created an unprecedented opportunity to investigate the full three-dimensional structure of the diurnal cycle over the tropical oceans. In this study, we used one such model – the Model for Prediction Across Scales (MPAS) – for two main purposes: first, to evaluate the model’s representation of semi-diurnal and diurnal variations in near-surface winds, precipitation, and cloudiness over the tropical oceans; and second, to extend the analyses to provide a full three-dimensional picture of the daily variations in tropospheric circulation and their linkage with the hydrological cycle. A 40-day MPAS simulation (the same as used for DYAMOND-1 global storm-resolving models inter-comparison project) was utilized in this study to examine the large-scale geographical patterns and vertical structures of mean daily variations of zonal and meridional wind components, wind divergence, vertical velocity, cloudiness, water vapor mixing ratio and precipitation. The model shows generally good agreement with the previously reported observational results for near-surface winds and divergence. In particular, MPAS exhibits a pronounced large-scale diurnal cycle in the local Hadley Circulation over the Tropical Pacific Ocean, with lower tropospheric divergence (convergence) relative to the daily mean, maximizing around 1000 (2200) LT. The amplitude of the diurnal variation in near-surface wind divergence at the equator is approximately 0.8×10-6 s-1, or approximately 44% of the daily mean. The vertical structure of this diurnal circulation, along with its signature in vertical velocity and its association with water vapor, cloudiness and precipitation, will be presented.