This paper describes the new global Navy Earth System Prediction Capability (Navy-ESPC) coupled atmosphere-ocean-sea ice prediction system developed at the Naval Research Laboratory (NRL) for operational forecasting for timescales of days to the subseasonal. Navy-ESPC will become operational in late 2020, and this system will be the first time the NRL operational partner, Fleet Numerical Meteorology and Oceanography Center, will provide global coupled atmosphere-ocean-sea ice forecasts, with atmospheric forecasts extending to 16 days, and ocean and sea ice ensemble forecasts. Two configurations of the system are validated: (1) a low-resolution 16-member ensemble system and (2) a highresolution deterministic system. A unique aspect of the Navy-ESPC is that the global ocean model is eddy resolving at 1/12° in the ensemble and at 1/25° in the deterministic configurations. The component models are current Navy operational systems: NAVy Global Environmental Model (NAVGEM) for the atmosphere; HYbrid Coordinate Ocean Model (HYCOM) for the ocean; and Community Ice CodE (CICE) for the sea ice. Physics updates to improve the simulation of equatorial phenomena, particularly the Madden Julian Oscillation (MJO) were introduced into NAVGEM. The low resolution ensemble configuration and highresolution deterministic configuration are evaluated based on analyses and forecasts from January 2017 to January 2018. Navy-ESPC ensemble forecast skill for large-scale atmospheric phenomena, such as the Madden-Julian Oscillation (MJO), North Atlantic Oscillation (NAO), Antarctic Oscillation (AAO), and other indices, is comparable to that of other numerical weather prediction (NWP) centers. Ensemble forecasts of ocean sea surface temperatures perform better than climatology in the tropics and mid-latitudes out to 60 days. In addition, the Navy-ESPC Pan-Arctic and Pan-Antarctic sea ice extent predictions perform better than climatology out to about 45 days, although the skill is dependent on season.

High-fidelity analyses and forecasts of integrated vapor transport (VT) are central to the study of earth’s hydrological cycle as well as high-impact phenomena such as monsoons and atmospheric rivers. The impact of the in-line Analysis Correction-based Additive Inflation (ACAI) on IVT biases and forecast errors is examined within the Navy Earth System Prediction Capability (Navy ESPC) global coupled system. The ACAI technique uses atmospheric analysis corrections from the data assimilation system to approximate model bias and as a representation of stochastic model error to simultaneously reduce systematic and random errors and improve ensemble performance. ACAI reduces the global average magnitude of the 7-day and 14-day IVT bias by 16-17% during Northern Hemisphere summer, reaching 70% reductions in some tropical regions. The global average IVT bias reduction is similar to the bias reduction for low-level wind speed bias and considerably smaller than the bias reduction in total precipitable water. The localized regions where ACAI increases IVT bias occur where the control IVT biases change sign and structure with increasing forecast lead time, such as the south Asian monsoon region. Substituting analyzed wind or moisture fields for the forecast fields when calculating the forecast IVT confirms that, on average, wind errors dominate the IVT error calculation in the tropics, although wind and moisture error contributions are comparable in the extratropics. The existence of regions where using either analyzed winds or analyzed moisture increases IVT bias or mean absolute error reveal areas with compensating errors.

El Niño–Southern Oscillation (ENSO) can have global impacts, affecting daily temperature and precipitation, and extreme weather, such as hurricanes and tornadoes. Because of its importance, scientists strive to understand the processes that govern ENSO and develop models to predict its evolution and changes in variability. Here long‐short‐term‐memory models (LSTMs) were compared to linear regression models (LR) to explore the benefits of simple, deep neural networks in predicting ENSO, in addition to quantifying the relative importance of the sources of ENSO's predictability. The models use central Pacific sea surface temperatures (SST), equatorial Pacific warm water volumes, and western Pacific zonal winds as predictors, individually and in combinations, on monthly and daily resolutions, from 1‐ to 11‐month leads. By using these predictors, many characteristic time scales are encompassed—from days‐to‐weeks in the atmosphere, to months‐to‐seasons in the coupled system, and interseasonal‐to‐interannual in the subsurface ocean. Results show, with monthly input, predictions from LSTM were like predictions from LR. However, with daily SST at longer leads, LSTM exhibited some advantage over LR in terms of the correlation coefficient. This suggests that daily SST may contain some nonlinear element that improves LSTM predictability compared to LR. In addition, this suggests that more information, such as gridded data and additional variables, would likely improve predictability using LSTM, but results would be more difficult to interpret. Overall, LSTM may be appealing because once the computationally expensive training of LSTM is complete, the predictions employing the trained model can be relatively cheap to perform thereafter.

Tropical cyclones (TCs) tend to cool sea surface temperature (SST) via enhanced vertical mixing and evaporative fluxes. This cooling is substantially reduced in the subtropics, especially in the northeastern Pacific where the occurrence of TCs can warm the ocean surface. Here we investigate the cause of this anomalous warming by analyzing the local oceanic features and TC‐induced anomalies of SST, surface fluxes, and cloud fraction using satellite and in situ data. We find that TCs tend to suppress low clouds at the margins of the tropical ocean warm pool, enhancing shortwave radiative surface fluxes within the first week following storm passage, which, combined with spatial variations in ocean thermal structure, can produce a ~1°C near‐surface warming in the northeastern Pacific. These findings, supported by high‐resolution Earth system model simulations, point to potential connections between TCs, ocean temperature, and low cloud distributions that can influence tropical surface heat budgets.

Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June – September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (<6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multi-model means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multi-model means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.