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Entrainment rate (λ) in convective parameterizations remains a sensitive parameter with much uncertainty in model simulations. This study estimates λ using carbon monoxide (CO) measurements jointly from the Microwave Limb Sounder and Tropospheric Emission Spectrometer onboard the Aura satellite, associated with deep convective cases identified by CloudSat and CALIPSO observations. CO is treated as a conserved quantity over convective transport time scales, and a simple entraining‐plume model is used to derive entrainment rates. The relationships of the observational estimates of λ as a function of convective height, environmental relative humidity, and convective available potential energy (CAPE) derived from Atmospheric Infrared Sounder data are compared with those from Goddard Earth Observing System Model (GEOS‐5) simulations. Bulk statistics of λ show that the values of λ are predominately below 20%/km for deep convection and the occurrence frequency of any λ decreases with increasing λ. Composite λ values are generally lower in the tropics compared to northern midlatitudes in both observations and the GEOS‐5 model. A decrease of λ with increasing convective height is found in both observations and model simulations. We also find that λ tends to decrease with increasing CAPE in the observation‐based λs and plume‐based GEOS‐5 λs, although the model given λs have a nonmonotonic relation with CAPE. The observed λs have a weak relation with lower‐to‐middle tropospheric relative humidity, while both GEOS‐5 plume‐based and given λ increase with increasing relative humidity. Estimated and given entrainment rates are predominately less than 20%/km as frequency decreases with increasing entrainment rateEntrainment rate decreases with increasing cloud top height (CTH) or level of maximum detrainment (LMD)Plume estimates of entrainment rate decrease with increasing convective available potential energy (CAPE)

Subseasonal‐to‐seasonal (S2S) forecasts of atmospheric rivers (ARs) are in high demand in the water supply management and flood control communities. This study focuses on a new metric, the accumulated water vapor transport associated with ARs, which is closely related to the winter precipitation over the western U.S., and provides a multi‐model S2S prediction skill assessment. The prediction skill is evaluated at lead time 1–4 weeks in four dynamical model hindcast data sets from National Centers for Environmental Prediction (NCEP), European Center for Medium‐Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), and Global Modeling and Assimilation Office (GMAO) at National Aeronautics and Space Administration. Three reanalysis data sets are used to evaluate the uncertainty of prediction skills related to the choice of references. The AR‐related water vapor transport is underestimated in ECMWF and ECCC over most of the investigated region, while its maximum has a southeastward shift in NCEP and GMAO at lead time 3–4 weeks. The root mean square error, anomaly correlation coefficient, and Brier skill score are calculated to quantify the prediction skill in both deterministic and probabilistic sense. At week‐3 lead time, the models have significant skill near the lower latitudes (<40°N) of the eastern North Pacific, extending northeastward to the California coastal area. Models have higher skills in forecasting no and strong AR cases than weak cases. The Madden–Julian Oscillation can modulate the prediction skill at week‐3 lead over central and Southern California, but with large uncertainties across models. An atmospheric river (AR) is a long and narrow corridor of strong horizontal water vapor transport, which plays a key role in transporting water vapor from the tropics and subtropics to feed precipitation over the U.S. West Coast. Therefore, subseasonal‐to‐seasonal (S2S) forecasts of ARs are in high demand in water supply management and flood control communities. This study provides an S2S prediction skill assessment for the weekly accumulated water vapor transport from ARs at 1–4 weeks lead times in four dynamical model hindcast data sets. Three reanalysis data sets are used as a reference to evaluate the model hindcasts and test the impacts of different reference data sets on the evaluation results. Several metrics are used to quantify the prediction skills. The results show that the models have some useful skills in predicting the weekly water vapor transport related to ARs at week‐3 lead over the lower latitudes of the eastern North Pacific, extending northeastward to the California coastal area. Models have higher skills in forecasting no and strong AR weeks than weak AR weeks. The Madden–Julian Oscillation has some impacts on the prediction skill at week‐3 lead over central and Southern California, but the impacts vary across models. Dynamical models are skillful in forecasting weekly water vapor transport by atmospheric rivers (ARs) at week‐3 lead over the western U.SModels have higher skill in forecasting no and strong AR‐related water vapor transport cases than weak cases at weeks 1–4 lead timeMadden‐Julian Oscillation can modulate the forecast skill of water vapor transport by ARs at week 3 over central and Southern California Dynamical models are skillful in forecasting weekly water vapor transport by atmospheric rivers (ARs) at week‐3 lead over the western U.S Models have higher skill in forecasting no and strong AR‐related water vapor transport cases than weak cases at weeks 1–4 lead time Madden‐Julian Oscillation can modulate the forecast skill of water vapor transport by ARs at week 3 over central and Southern California

The prediction skill of the Madden‐Julian Oscillation (MJO) in Version 2 of NASA's Global Earth Observing System Subseasonal to Seasonal (GEOS‐S2S) forecast system is investigated for winter and summer focusing on moistening‐related processes crucial for eastward propagating MJO activity. It is found that the annual bivariate correlation of the Real‐time Multivariate MJO time series between prediction and observation is ∼0.70, ∼0.57, and ∼0.50 at 20‐, 25‐, and 30‐day forecast leads. Correlation at long‐leads (>30 days) is noticeably higher for boreal summer initial conditions (June‐September [JJAS]), with correlations remaining above 0.5 at 35–40 days leads. Correlations are lower for boreal winter initial conditions from January through March (JFM), dropping to ∼0.5 at 25‐day lead, still comparable to the skills in the other reliable S2S forecast systems. The predicted eastward MJO propagation across the Indo‐western Pacific sector is well captured in JJAS, but is slower than observed in JFM. Investigations of the moisture field and advection and moisture sink, moist static energy (MSE) budget, and tropical circulation/pressure responses to the MJO convective heating reveal that, in JFM, those responses and moistening processes, especially the vertical MSE advection, are underestimated over and to the east of the Maritime Continent when the MJO anomaly approaches from the west. In contrast, those processes are well represented in JJAS, although moistening is overestimated due to large surface evaporation. This study suggests that improvement of the moistening tendency over that region in the boreal winter could contribute to further increases in MJO prediction skill of the GEOS‐S2S system. The Madden‐Julian Oscillation (MJO) is the dominant tropical atmospheric variability at the 30–60‐day time scale. It has major impacts on regional precipitation in the tropics and development of extratropical weather/climate systems through large‐scale teleconnections. It is found that, for boreal summer initial conditions (June‐September), Version2 of NASA's Global Earth Observing System Subseasonal to Seasonal (GEOS‐S2S) forecast model has the prediction skill of the MJO at equatorial region with anomaly correlation remaining above 0.5 at 35–40‐day forecast leads, which is greater than the skills in the other S2S forecast models. However, the correlation is relatively low for boreal winter initial conditions (January‐March) dropping to 0.5 at ∼25‐day lead. The investigation reveals that the GEOS‐S2S captures the eastward MJO propagation speed and intensity over the Maritime Continent and western Pacific more realistically in summer. The moisture‐related key atmospheric factors show that, in winter, the moistening processes that are crucial for maintenance and eastward propagation of the MJO anomalies are underestimated, while they are well represented in boreal summer. This study suggests that improvement of the vertical moistening process ahead of the MJO convection in the boreal winter can contribute to further increases in MJO prediction skill of the GEOS‐S2S system. Madden‐Julian Oscillation (MJO) prediction skill in NASA's Global Earth Observing System (GEOS) Sub‐seasonal to Seasonal forecast system is greater than the other forecast models in summerThe forecasts represent well the circulation and moistening process ahead of MJO convection, favoring eastward MJO propagationImprovement of vertical moist static energy advection ahead of the MJO anomaly is needed for further increase in forecast skill in winter Madden‐Julian Oscillation (MJO) prediction skill in NASA's Global Earth Observing System (GEOS) Sub‐seasonal to Seasonal forecast system is greater than the other forecast models in summer The forecasts represent well the circulation and moistening process ahead of MJO convection, favoring eastward MJO propagation Improvement of vertical moist static energy advection ahead of the MJO anomaly is needed for further increase in forecast skill in winter

We applied the Goddard Earth Observing System for subseasonal to seasonal climate prediction to assess the impact of inclusion biomass burning (BB) aerosols over South America (SA) during the austral winter. We also evaluated the model sensitivity to the BB emissions prescription using no emissions, monthly climatological, and daily emissions. Each hindcast consisted of four members running from June to November of each year between 2000 and 2015. Our results indicated that interactive BB aerosols improve the seasonal climate prediction performance over SA. More realistic daily based emissions significantly further improve the performance in comparison with the climatological ones. Therefore, improvements in the BB emissions representation are urged to represent the aerosol impacts on seasonal climate prediction performance adequately. Vegetation fires severely affect tropical forest and savannah‐type biomes in South America (SA) during winter in Southern Hemisphere. Biomass burning (BB) aerosols are important agents changing energy budget and clouds. This study focused on assessing whether including aerosol‐radiation‐cloud interaction in a climate model, particularly the contribution of BB aerosols, can provide additional information for improving seasonal climate predictions. This study has two primary outcomes. First, that including BB aerosols does improve the model's ability to predicted precipitation and near‐surface temperature in SA. Second, it proved it is indeed essential to improve BB emissions representation to further elevate seasonal climate prediction performance. This study documents the impacts of biomass burning aerosols on South America seasonal climate predictions in a coupled modeling systemUse of interactive biomass burning aerosols improves seasonal prediction performance for the austral winter over South AmericaPrescribing daily emission estimates provides better performance in comparison with prescribing monthly climatological mean emissions