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Atmospheric rivers (ARs), responsible for extreme weather conditions, are mid-latitude systems that can cause significant damage to coastal areas. While forecasting ARs beyond two weeks remains a challenge, past research suggests potential benefits may come from properly accounting for the changes in sea surface temperature (SST) through air–sea interactions. In this paper, we investigate the impact of ARs on SST over the North Pacific by analyzing 25 years of ocean reanalysis data using an SST budget equation. We show that in the region of strong ocean modification, ocean dynamics can offset over 100% of the anomalous SST warming that would otherwise arise from atmospheric forcing. Among all ocean processes, ageostrophic advection and vertical mixing (diffusion and entrainment) are the most important factors in modifying the SST tendency response. The SST tendency response to ARs varies spatially. For example, in coastal California, the driver of enhanced SST warming is the reduction in ageostrophic advection due to anomalous southerly winds. Moreover, there is a large region where the SST shows a warming response to ARs due to the overall reduction in the total clouds and subsequent increase in total incoming shortwave radiation. Strong air-sea interactions during atmospheric rivers often lead to modest upper ocean heat changes. The authors show that interior ocean dynamics are compensating for these air-sea exchanges. These findings can help improve subseasonal forecasts. [ABSTRACT FROM AUTHOR]

Ocean surface rain layers (RLs) form when relatively colder, fresher, less dense rain water stably stratifies the upper ocean. RLs cool sea surface temperature (SST) by confining surface evaporative cooling to a thin near‐surface layer, and generate sharp SST gradients between the cool RL and the surrounding ocean. In this study, ocean‐atmosphere coupled simulations of the November 2011 Madden‐Julian Oscillation (MJO) event are conducted with and without RLs to evaluate two pathways for RLs to influence the atmosphere. The first, termed the "SST gradient effect," arises from the hydrostatic adjustment of the boundary layer to RL‐enhanced SST gradients. The second, termed the "SST effect," arises from RL‐induced SST reductions impeding the development of deep atmospheric convection. RLs are found to sharpen SST gradients throughout the MJO suppressed and suppressed‐to‐enhanced convection transition phases, but their effect on convection is only detected during the MJO suppressed phase when RL‐induced SST gradients enhance low‐level convergence/divergence and broaden the atmospheric vertical velocity probability distribution below 5 km. The SST effect is more evident than the SST gradient effect during the MJO transition phase, as RLs reduce domain average SST by 0.03 K and narrow vertical velocity distribution, thus delaying onset of deep convection. A delayed SST effect is also identified, wherein frequent RLs during the MJO transition phase isolate accumulated subsurface ocean heat from the atmosphere. The arrival of strong winds at the onset of the MJO active phase erodes RLs and releases subsurface ocean heat to the atmosphere, supporting the development of deep convection. Plain Language Summary: Rain water is less dense than near‐surface ocean water. For this reason, rain water can float on the ocean surface following rain events and form a "rain layer" in the upper ocean. Rain layers that form in the tropical Indian Ocean reduce sea surface temperature (SST) and shield the subsurface ocean below the rain layer from the atmosphere, thus altering heat exchange between the ocean and atmosphere. In this study, we conduct model experiments over the tropical Indian Ocean to investigate rain layer feedbacks to the atmosphere. We identify two potential rain layer feedback mechanisms, one in which rain‐enhancement of SST gradients contributes to the formation of clouds and precipitation in the atmosphere, and a second in which rain‐driven SST reduction suppresses the development of clouds and precipitation. Our results indicate that rain‐driven SST reduction is the dominant immediate feedback, as clouds and precipitation are reduced in the presence of rain layers. A delayed rain layer feedback is also identified, wherein rain layers insulate subsurface ocean heat from the atmosphere. When rain layers are destroyed by wind‐driven mixing, the stored ocean heat is released to the atmosphere, and supports the development of clouds and precipitation. Key Points: Cold and statically stable freshwater rain layers (RLs) warm the subsurface ocean by reducing nocturnal vertical mixingRL‐induced surface cooling and sub‐surface warming first delay, but then enhance the development of deep convection within the Madden‐Julian Oscillation (MJO)RL regulation of convective intensity may thus play a role in setting MJO period and propagation speed [ABSTRACT FROM AUTHOR]

In this work, we integrated the WAVEWATCH III model into the regional coupled model SKRIPS (Scripps–KAUST Regional Integrated Prediction System). The WAVEWATCH III model is implemented with flexibility, meaning the coupled system can run with or without the wave component. In our implementations, we considered the effect of Stokes drift, Langmuir turbulence, sea surface roughness, and wave-induced momentum fluxes. To demonstrate the impact of coupling we performed a case study using a series of coupled and uncoupled simulations of Tropical Cyclone Mekunu, which occurred in the Arabian Sea in May 2018. We examined the model skill in these simulations and further investigated the impact of Langmuir turbulence in the coupled system. Because of the chaotic nature of the atmosphere, we ran an ensemble of 20 members for each coupled and uncoupled experiment. We found that the characteristics of the tropical cyclone are not significantly different due to the effect of surface waves when using different parameterizations, but the coupled models better capture the minimum pressure and maximum wind speed compared with the benchmark stand-alone Weather Research and Forecasting (WRF) model. Moreover, in the region of the cold wake, when Langmuir turbulence is considered in the coupled system, the sea surface temperature is about 0.5 ∘ C colder, and the mixed layer is about 20 m deeper. This indicates the ocean model is sensitive to the parameterization of Langmuir turbulence in the coupled simulations. [ABSTRACT FROM AUTHOR]

In this work, we integrated the WaveWatch III model into the regional coupled model SKRIPS (Scripps-KAUST Regional Integrated Prediction System). TheWaveWatch III model is implemented with flexibility, meaning the coupled system can run with or without the wave component. To demonstrate the impact of coupling we performed a case study using a series of coupled and uncoupled simulations of tropical cyclone Mekunu, which occurred in the Arabian Sea in May 2018. We examined the skill of the coupled model against the stand-alone WRF model and further investigated the impact of Langmuir turbulence in the coupled system. We found that the coupled model better captures the minimum pressure and maximum wind speed compared with the stand-alone WRF model. The characteristics of the tropical cyclone do not change significantly when using different options to parameterize the influence of waves on the ocean and the atmosphere. However, in the region of the cold wake, when Langmuir turbulence is considered in the coupled system, the sea surface temperature is about 0.5◦C colder and the mixed layer is about 20 meters deeper. This indicates the ocean model is sensitive to the parameterization of Langmuir turbulence in the coupled simulations. [ABSTRACT FROM AUTHOR]

Regional and temporal patterns of air–sea carbon exchange are strongly linked to the surface ocean partial pressure of carbon dioxide (pCO2), which varies with sea surface temperature (SST), salinity, dissolved inorganic carbon (DIC), and alkalinity. It is well‐known that temperature controls the pCO2 seasonal cycle in the subtropics, whereas DIC dominates at high latitudes. The balance of mechanisms governing the boundary between these regimes, however, are not well characterized due to lack of year‐round pCO2 data. Here, we use autonomous biogeochemical float measurements from the South Pacific to investigate the processes that control meridional variations in pCO2 seasonality. We find that the transition between pCO2 regimes is linked to the poleward decrease in SST seasonal cycle amplitude, which is closely associated with the northern boundary of deep winter mixed layers. Processes that determine the annual SST range are, therefore, central to the response of oceanic carbon uptake to anthropogenic forcing. Plain Language Summary: Air–sea carbon fluxes, which regulate the global climate system, vary seasonally and geographically, in large part due to variability in the surface ocean partial pressure of carbon dioxide (pCO2). Surface pCO2 variability can be separated into components due to changes in temperature and biology. The relative importance of these components varies regionally. In the subtropics, the pCO2 seasonal cycle is controlled by temperature, while the non‐thermal component dominates in polar regions. In this study, we examine what controls the location of the boundary between these two pCO2 regimes. To do this, we use measurements from autonomous biogeochemical floats, which sample year round in the South Pacific, a region with limited historical measurements. We find that the transition between pCO2 regimes is most closely related to the decrease in annual surface temperature range toward the south, where winter mixing is deep. This result can help diagnose biases in modeled air–sea carbon fluxes and indicate whether locations of oceanic carbon uptake will change in the future. Key Points: Biogeochemical float data confirm that the leading driver of pCO2 seasonality shifts with latitude in the southern subtropical gyreThe boundary between pCO2 regimes is primarily set by the poleward decrease in sea surface temperature (SST) seasonal cycle amplitudeThe reduction in SST seasonal cycle amplitude, in turn, is linked to the northern boundary of deep winter mixed layers [ABSTRACT FROM AUTHOR]

The 2010–11 North Pacific Acoustic Laboratory (NPAL) Philippine Sea experiment measured travel times between six acoustic transceiver moorings in a 660-km diameter ocean acoustic tomography array in the northern Philippine Sea (NPS). The travel-time series compare favorably with travel times computed for a yearlong series of state estimates produced for this region using the Massachusetts Institute of Technology General Circulation Model–Estimating the Circulation and Climate of the Ocean four-dimensional variational (MITgcm-ECCO 4DVAR) assimilation system constrained by satellite sea surface height and sea surface temperature observations and by Argo temperature and salinity profiles. Fluctuations in the computed travel times largely match the fluctuations in the measurements caused by the intense mesoscale eddy field in the NPS, providing a powerful test of the observations and state estimates. The computed travel times tend to be shorter than the measured travel times, however, reflecting a warm bias in the state estimates. After processing the travel times to remove tidal signals and extract the low-frequency variability, the differences between the measured and computed travel times were used in addition to SSH, SST, and Argo temperature and salinity observations to further constrain the model and generate improved state estimates. The assimilation of the travel times reduced the misfit between the measured and computed travel times, while not increasing the misfits with the other assimilated observations. The state estimates that used the travel times are more consistent with temperature measurements from an independent oceanographic mooring than the state estimates that did not incorporate the travel times. [ABSTRACT FROM AUTHOR]

Satellite remote sensing and numerical models are widely used to estimate large-scale variations in ocean carbon export, but the relationship between export efficiency (e-ratio) of sinking organic carbon out of the surface ocean and its drivers remains poorly understood, especially in the Southern Ocean. Here, we assess the effects of temperature and primary productivity on e-ratio by combining particulate organic carbon export flux from in situ measurements during 1997–2013, environmental parameters from satellite products, and outputs from ocean biogeochemical models in the Southern Ocean. Results show that "High Productivity Low E-ratio" (HPLE) is a common phenomenon in the Subantarctic Zone and the Polar Frontal Zone, but not the Antarctic Zone. The e-ratio shows little dependence on temperature below 6 °C. Our results support the hypothesis that the HPLE phenomenon is due to the large contribution of non-sinking organic carbon. Both temperature and ballast minerals play less important roles in controlling e-ratio than ecosystem structure at low temperatures. These findings suggest that non-sinking organic carbon, ecosystem structure, and region-specific parameterizations of e-ratio are key factors to quantify the carbon export in the Southern Ocean. [ABSTRACT FROM AUTHOR]

A data-constrained state estimate of the southern California Current System (CCS) is presented and compared with withheld California Cooperative Oceanic Fisheries Investigations (CalCOFI) data and assimilated glider data over 2007–17. The objective of this comparison is to assess the ability of the California State Estimate (CASE) to reproduce the key physical features of the CCS mean state, annual cycles, and interannual variability along the three sections of the California Underwater Glider Network (CUGN). The assessment focuses on several oceanic metrics deemed most important for characterizing physical variability in the CCS: 50-m potential temperature, 80-m salinity, and 26 kg m−3 isopycnal depth and salinity. In the time mean, the CASE reproduces large-scale thermohaline and circulation structures, including observed temperature gradients, shoaling isopycnals, and the locations and magnitudes of the equatorward California Current and poleward California Undercurrent. With respect to the annual cycle, the CASE captures the phase and, to a lesser extent, the magnitude of upper-ocean warming and stratification from late summer to early fall and of isopycnal heave during springtime upwelling. The CASE also realistically captures near-surface diapycnal mixing during upwelling season and the semiannual cycle of the California Undercurrent. In terms of interannual variability, the most pronounced signals are the persistent warming and downwelling anomalies of 2014–16 and a positive isopycnal salinity anomaly that peaked with the 2015–16 El Niño. [ABSTRACT FROM AUTHOR]

The ability to construct nitrate maps in the Southern Ocean (SO) from sparse observations is important for marine biogeochemistry research, as it offers a geographical estimate of biological productivity. The goal of this study is to infer the skill of constructed SO nitrate maps using varying data sampling strategies. The mapping method uses multivariate empirical orthogonal functions (MEOFs) constructed from nitrate, salinity, and potential temperature (N-S-T) fields from a biogeochemical general circulation model simulation Synthetic N-S-T datasets are created by sampling modeled N-S-T fields in specific regions, determined either by random selection or by selecting regions over a certain threshold of nitrate temporal variances. The first 500 MEOF modes, determined by their capability to reconstruct the original N-S-T fields, are projected onto these synthetic N-S-T data to construct time-varying nitrate maps. Normalized root-mean-square errors (NRMSEs) are calculated between the constructed nitrate maps and the original modeled fields for different sampling strategies. The sampling strategy according to nitrate variances is shown to yield maps with lower NRMSEs than mapping adopting random sampling. A k-means cluster method that considers the N-S-T combined variances to identify key regions to insert data is most effective in reducing the mapping errors. These findings are further quantified by a series of mapping error analyses that also address the significance of data sampling density. The results provide a sampling framework to prioritize the deployment of biogeochemical Argo floats for constructing nitrate maps. [ABSTRACT FROM AUTHOR]

Argo float trajectories are simulated in the southwest Pacific basin (25°-45°S, 170°E-165°W) using velocity fields from a 1/12° Southern Ocean model and a Lagrangian particle tracking model programmed to represent the vertical motions of profiling Argo floats. The system is applied to simulate both core Argo floats (typically parked at 1000-m depth and profiling to 2000-m depth) and Deep Argo floats (parked 500 m above the seafloor). The goal is to estimate probability density functions (PDFs) predicting future float positions. Differences are expected in the trajectory statistics, largely because of limitations in the temporal and spatial resolution of the model fields and uncertainties associated with a random walk component included in the particle advection scheme to represent this unresolved variability. Nonetheless, the core Argo float displacements over ~ 100-day time intervals are mostly consistent with the derived PDFs, particularly in regions with stable midlayer flows. For the Deep Argo floats, which are released into the open ocean and parked near the bottom, the simulations predict an average total displacement of less than 50 km within 100 days, in good agreement with the Deep Argo floats deployed as part of a pilot study. The study explores both the representativeness and the predictability of float displacements, with an aim to contribute to planning for the float observing system. [ABSTRACT FROM AUTHOR]

Subantarctic Mode Water (SAMW) is examined using the data-assimilating, eddy-permitting Southern Ocean State Estimate, for 2005 and 2006. Surface formation due to air-sea buoyancy flux is estimated using Walin analysis, and diapycnal mixing is diagnosed as the difference between surface formation and transport across 30°S, accounting for volume change with time. Water in the density range 26.5 < σe < 27.1 kg m-3 that includes SAMW is exported northward in all three ocean sectors, with a net transport of (18.2,17.1) Sv (1 Sv = 106m3s-1;for years 2005,2006); air-sea buoyancy fluxes form (13.2,6.8) Sv, diapycnal mixing removes (-14.5, -12.6) Sv, and there is a volume loss of (-19.3, -22.9) Sv mostly occurring in the strongest SAMW formation locations. The most vigorous SAMW formation is in the Indian Ocean by air-sea buoyancy flux (9.4,10.9) Sv, where it is partially destroyed by diapycnal mixing (-6.6, -3.1) Sv. There is strong export to the Pacific, where SAMW is destroyed both by air-sea buoyancy flux (-1.1, -4.6) Sv and diapycnal mixing (-5.6, -8.4) Sv. In the South Atlantic, SAMW is formed by air-sea buoyancy flux (5.0, 0.5) Sv and is destroyed by diapycnal mixing (-2.3, -1.1) Sv. Peaks in air-sea flux formation occur at the Southeast Indian and Southeast Pacific SAMWs (SEISAMWs, SEPSAMWs) densities. Formation over the broad SAMW circumpolar outcrop windows is largely from denser water, driven by differential freshwater gain, augmented or decreased by heating or cooling. In the SEISAMW and SEPSAMW source regions, however, formation is from lighter water, driven by differential heat loss. [ABSTRACT FROM AUTHOR]

We investigate the impact of ocean data assimilation using the Ensemble Adjustment Kalman Filter (EAKF) from the Data Assimilation Research Testbed (DART) on the oceanic and atmospheric states of the Red Sea. Our study extends the ocean data assimilation experiment performed by Sanikommu et al. (2020) by utilizing the SKRIPS model coupling the MITgcm ocean model and the Weather Research and Forecasting (WRF) atmosphere model. Using a 50-member ensemble, we assimilate satellite-derived sea surface temperature and height and in situ temperature and salinity profiles every three days for one year, starting January 01 2011. Atmospheric data are not assimilated in the experiments. To improve the ensemble realism, perturbations are added to the WRF model using several physics options and the stochastic kinetic energy backscatter (SKEB) scheme. Compared with the control experiments using uncoupled MITgcm with ECMWF ensemble forcing, the EAKF ensemble mean oceanic states from the coupled model are better or insignificantly worse (root-mean-square errors are 23% to −1.3% smaller), especially when the atmospheric model uncertainties are accounted for with stochastic perturbations. We hypothesize that the ensemble spreads of the air–sea fluxes are better represented in the downscaled WRF ensembles when uncertainties are well accounted for, leading to improved representation of the ensemble oceanic states from the new experiments with the coupled model. This indicates the ocean model assimilation will be improved with coupled models and may relax the need for operational centers to provide atmospheric ensembles to drive ocean forecasts. Although the feedback from ocean to atmosphere is included in this two-way regional coupled configuration, we find no significant effect of ocean data assimilation on the ensemble mean latent heat flux and 10-m wind speed over the Red Sea. This suggests that the improved skill using the coupled model is not from the two-way coupling, but from downscaling the ensemble atmospheric forcings (one-way coupled) to drive the ocean model. • We implement an ocean ensemble DA system using the coupled model SKRIPS for the Red Sea. • The diversity of the atmospheric forcing is important for the ensemble spread in the ocean model. • An ensemble generated by SKRIPS performs as well or better than ocean only model ensemble. [ABSTRACT FROM AUTHOR]