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Abstract This study investigates the seasonal prediction capabilities of three models, all developed by the National Science Foundation (NSF) National Center for Atmospheric Research (NCAR) and implemented by the University of Miami, within the North American Multimodel Ensemble (NMME) framework. All three models, Community Climate System Model, version 3 (CCSM3), CCSM4, and Community Earth System Model, version 1 (CESM1), are initialized using the Climate Forecast System Reanalysis (CFSR) and have a common period of 1991–2018. The models’ performance in predicting key climate variables including surface temperature, precipitation, and El Niño–Southern Oscillation (ENSO) teleconnections is assessed. The models’ prediction skill is assessed using the sign test, a robust nonparametric method for comparing forecast errors. CCSM4 succeeded CCSM3 in 2014, bringing a much more accurate representation of global temperature trends and improved prediction of precipitation extremes and 2-m temperature over land. CESM1, introduced in 2023, shows further improvement relative to CCSM4 in the prediction of sea surface temperature in the tropical Pacific and precipitation extremes over land. The improvement in precipitation prediction skill is encouraging, as this field has seen little improvement over the life of the NMME. The modeled similarity to observed ENSO teleconnection patterns of 2-m temperature is somewhat less in CESM1 than in CCSM4, although precipitation teleconnection patterns are similar. CCSM4 and CESM1 show stronger surface temperature trends in the tropical Pacific and Southern Ocean than observed trends over the same period, a common problem for current state-of-the-art climate models with implications for prediction and for climate projections. Significance StatementThis study documents the improvements in seasonal climate prediction across three generations of coupled models developed by the National Science Foundation National Center for Atmospheric Research (NCAR) and implemented within the North American Multimodel Ensemble (NMME) by the University of Miami. Model upgrades are an important aspect of the NMME and have contributed to incremental increases in forecast skill. A thorough and ongoing assessment of individual models is critical to our understanding of the NMME system’s evolution and to future model improvements. 

Key Points Subseasonal monsoon variability is linked to rainfall signals over U.S. Great Plains and its associated dynamical drivers A cause‐and‐effect algorithm verified a pathway from regional monsoon rainfall to Great Plains rainfall, which takes approximately 2 weeks Weekly East Asian monsoon rainfall is causally linked to Rossby wave excitation and active Great Plains convection about 1 week later 

Abstract We investigate the predictability of the sign of daily southeastern U.S. (SEUS) precipitation anomalies associated with simultaneous predictors of large-scale climate variability using machine learning models. Models using index-based climate predictors and gridded fields of large-scale circulation as predictors are utilized. Logistic regression (LR) and fully connected neural networks using indices of climate phenomena as predictors produce neither accurate nor reliable predictions, indicating that the indices themselves are not good predictors. Using gridded fields as predictors, an LR and convolutional neural network (CNN) are more accurate than the index-based models. However, only the CNN can produce reliable predictions that can be used to identify forecasts of opportunity. Using explainable machine learning we identify which variables and grid points of the input fields are most relevant for confident and correct predictions in the CNN. Our results show that the local circulation is most important as represented by maximum relevance of 850-hPa geopotential heights and zonal winds to making skillful, high-probability predictions. Corresponding composite anomalies identify connections with El Niño–Southern Oscillation during winter and the Atlantic multidecadal oscillation and North Atlantic subtropical high during summer. 

Abstract High-frequency precipitation variance is calculated in 12 different free-running (non-data-assimilative) coupled high resolution atmosphere–ocean model simulations, an assimilative coupled atmosphere–ocean weather forecast model, and an assimilative reanalysis. The results are compared with results from satellite estimates of precipitation and rain gauge observations. An analysis of irregular sub-daily fluctuations, which was applied by Covey et al. (Geophys Res Lett 45:12514–12522, 2018.https://doi.org/10.1029/2018GL078926) to satellite products and low-resolution climate models, is applied here to rain gauges and higher-resolution models. In contrast to lower-resolution climate simulations, which Covey et al. (2018) found to be lacking with respect to variance in irregular sub-daily fluctuations, the highest-resolution simulations examined here display an irregular sub-daily fluctuation variance that lies closer to that found in satellite products. Most of the simulations used here cannot be analyzed via the Covey et al. (2018) technique, because they do not output precipitation at sub-daily intervals. Thus the remainder of the paper focuses on frequency power spectral density of precipitation and on cumulative distribution functions over time scales (2–100 days) that are still relatively “high-frequency” in the context of climate modeling. Refined atmospheric or oceanic model grid spacing is generally found to increase high-frequency precipitation variance in simulations, approaching the values derived from observations. Mesoscale-eddy-rich ocean simulations significantly increase precipitation variance only when the atmosphere grid spacing is sufficiently fine (< 0.5°). Despite the improvements noted above, all of the simulations examined here suffer from the “drizzle effect”, in which precipitation is not temporally intermittent to the extent found in observations. 

Abstract Cloud radiative feedbacks are disabled via “cloud-locking” in the Community Earth System Model, version 1.2 (CESM1.2), to result in a shift in El Niño–Southern Oscillation (ENSO) periodicity from 2–7 years to decadal time scales. We hypothesize that cloud radiative feedbacks may impact the periodicity in three ways: by 1) modulating heat flux locally into the equatorial Pacific subsurface through negative shortwave cloud feedback on sea surface temperature anomalies (SSTA), 2) damping the persistence of subtropical southeast Pacific SSTA such that the South Pacific meridional mode impacts the duration of ENSO events, or 3) controlling the meridional width of off-equatorial westerly winds, which impacts the periodicity of ENSO by initiating longer Rossby waves. The result of cloud-locking in CESM1.2 contrasts that of another study, which found that cloud-locking in a different global climate model led to decreased ENSO magnitude across all time scales due to a lack of positive longwave feedback on the anomalous Walker circulation. CESM1.2 contains this positive longwave feedback on the anomalous Walker circulation, but either its influence on the surface is decoupled from ocean dynamics or the feedback is only active on interannual time scales. The roles of cloud radiative feedbacks in ENSO in other global climate models are additionally considered. In particular, it is shown that one cannot predict the role of cloud radiative feedbacks in ENSO through a multimodel diagnostic analysis. Instead, they must be directly altered. 

Abstract Ocean variability is a dominant source of remote rainfall predictability, but in many cases the physical mechanisms driving this predictability are not fully understood. This study examines how ocean mesoscales (i.e., the Gulf Stream SST front) affect decadal Southeast US (SEUS) rainfall, arguing that the local imprint of large‐scale teleconnections is sensitive to resolved mesoscale features. Based on global coupled model experiments with eddying and eddy‐parameterizing ocean, we find that a resolved Gulf Stream improves localized rainfall and remote circulation response in the SEUS. The eddying model generally improves the air‐sea interactions in the Gulf Stream and the North Atlantic Subtropical High that modulate SEUS rainfall over decadal timescales. The eddy‐parameterizing simulation fails to capture the sharp SST gradient associated with the Gulf Stream and overestimates the role of tropical Pacific SST anomalies in the SEUS rainfall.