Global Ocean Data Assimilation System Research Articles

Intercomparison of tropical Indian Ocean circulation in ocean reanalysis and evaluation in CMIP6 climate models

In the present study, we assess the Tropical Indian Ocean (TIO) circulation features from the available ocean reanalysis products and the latest version of Coupled Model Intercomparison Project (CMIP6) climate model simulations. We considered the following reanalysis products; Ocean Reanalysis System 5 (ORAS5), Estimating the Circulation and Climate of the Ocean (ECCO), Global Ocean Data Assimilation System (GODAS), Ensemble Coupled Data Assimilation (ECDA), the Bluelink Reanalysis (BRAN) and Simple Ocean Data Assimilation (SODA) and compared them against in-situ observations and the satellite-derived Ocean Surface Current Analyses Real-time (OSCAR). The reanalysis products underestimate the strength and location of the Wyrtki Jets. The BRAN reanalysis performed well compared to the other products in representing the TIO surface zonal currents, followed by ORAS5. The vertical extension of subsurface zonal currents in the equatorial Indian Ocean and their seasonal maxima are well captured in ORAS5. Thus, our analysis suggests that the ORAS5 is a qualitative product to estimate the TIO circulation. We further evaluated the TIO current patterns simulated by CMIP6 models with in-situ data/ ORAS5. The majority of the models show discrepancies in simulating equatorial and south equatorial current systems with a mean bias of 0.1cms−1 and 0.2cms−1, respectively. NorESM2-MM, NorESM2-LM, CanESM5, CESM2-WACCM-FV2, and E3SM-1-ECA models showed a superior skill in reproducing the TIO circulation compared to the rest of the models. Our analysis highlights the importance of assessing various reanalysis products and coupled climate models in representing the circulation of the TIO and, consequently, their role in depicting regional weather and climate.

On the Estimation of the Interannual Variability of the Ocean Surface Temperature in the Area of the Peruvian Upwelling

The interannual variability of the ocean surface temperature in the area of the Peruvian upwelling for the period 1980–2022 is considered according to the satellite archive GODAS (Global Ocean Data Assimilation System) using the methods of multivariate statistical analysis. Local foci of significant trends, for average annual Sea Surface Temperature (SST) values, were identified near the Peruvian offshore. Four regions (clusters) were obtained, which describe the variability of SST in front off Peru, which could be used to pretend to develop a prognostic oceanographic model. Furthermore, coincidences of temperature fluctuations were found between the first cluster and the region N3+4

Multi-scale spatial features and temporal attention mechanisms: advancing the accuracy of ENSO prediction

The exponential progression in oceanic observational technology has fostered the accumulation of substantial time series data pivotal for predictions in ocean meteorology. Foremost among the phenomena observed is El Niño-Southern Oscillation (ENSO), a critical determinant in the interplay of global ocean atmosphere interactions, with its severe manifestations inducing extreme meteorological conditions. Therefore, precisely predicting ENSO events carries immense gravitas. Historically, predictions hinged primarily on dynamic models and statistical approaches; however, the intricate and multifaceted spatiotemporal dynamics of ENSO events have often impeded the accuracy of these traditional methodologies. A notable lacuna in contemporary research is the insufficient exploration of long-term dependencies within oceanic data and the suboptimal integration of spatial information derived from spatiotemporal data. To address these limitations, this study introduces a forward-thinking ENSO prediction framework synergizing multiscale spatial features with temporal attention mechanisms. This innovation facilitates a more profound exploration of temporal and spatial domains, enhancing the retention of extensive-period data while optimizing the use of spatial information. Preliminary analyses executed on the global ocean data assimilation system dataset attest to the superior efficacy of the proposed method, underscoring a substantial improvement over established methods including SA-convolutional long short-term memory, particularly in facilitating long-term predictions.The source code and datasets are provided. The code is available at https://github.com/tse1998/ENSO-prediction.

Role of Improved Ocean Initial State in the Seasonal Prediction of Indian Summer Monsoon: A Case Study

This case study has made an effort to show the impact of improved ocean initial conditions (ICs) in a coupled forecast system (CFSv2) simulation on the seasonal prediction of Indian summer monsoon rainfall (ISMR). CFSv2 is used as an operational dynamical model for the seasonal prediction of ISMR. Here, we show an improved ISMR skill by initializing the ocean component of CFSv2 using new improved ocean ICs based on Global Ocean Data Assimilation System (GODAS) analysis. This new analysis is better than the NCEP GODAS, which uses the earlier-generation ocean model MOM4p0d and assimilates observed temperature and synthetic salinity using the 3DVar assimilation scheme. However, the new, improved GODAS analysis uses the MOM4p1 ocean model and assimilates observed salinity instead of synthetic salinity. We performed twin sets of nearly identical model experiments differing only in their ICs, with one set using NCEP ICs and the other using the new ICs (NIC). The NIC experiment consistently shows better El Niño–Southern Oscillation prediction skill than the NCEP IC experiment. This advancement leads to improvement in the ISMR skill. We found that the substantial improvements in both oceanic and atmospheric variables in a coupled feedback system contributed to the improved ISMR skills. The enhanced ISMR skill score of the NIC experiment might be the result of improved teleconnections, better depiction of large-scale monsoon circulations, and reduced model drift.

ТЕНДЕНЦИИ СОВРЕМЕННЫХ МЕЖГОДОВЫХ ИЗМЕНЕНИЙ ТЕРМИЧЕСКИХ ХАРАКТЕРИСТИК ВЕРХНЕГО 1000-МЕТРОВОГО СЛОЯ ЯПОНСКОГО МОРЯ В ПЕРВЫЕ ДЕСЯТИЛЕТИЯ XXI ВЕКА

Based on the NOAA climate datasets and Global Ocean Data Assimilation System (GODAS), trends and regional features of interannual changes in water temperature on the surface and in the upper 1000-meter layer of the Japan Sea were analyzed. Their possible causal relationships with variations in atmospheric pressure, climatic indices, and thermal characteristics in the adjacent area of the Kuroshio energetically active zone were determined. The results of the study made it possible to describe a three-dimensional structure of the anomalies of temperature and heat content in various layers of the sea water column and to quantify a rate of warming in different parts of the study area under conditions of the current phase of global warming.

Evaluation of a global ocean reanalysis generated by a global ocean data assimilation system based on a four-dimensional variational (4DVAR) method

Japan Meteorological Agency (JMA) started to use a new global ocean data assimilation system for the operational seasonal predictions in February 2022. The system is composed of two subsystems with non-eddy-permitting (lower) and eddy-permitting (higher) resolutions. The lower-resolution subsystem adopts a four-dimensional variational (4DVAR) method to optimize the temperature and salinity fields, and the data-assimilated fields are downscaled into the higher-resolution subsystem using incremental analysis updates. The impact of introducing the 4DVAR method in the new ocean data assimilation system is investigated through the comparison of a regular reanalysis run of the system using the 4DVAR method with another run using a three-dimensional variational (3DVAR) method. A comparison of the temperature fields before the downscaling between the two reanalysis runs indicates that the 4DVAR method can more effectively reduce the misfits between the model field and assimilated observation data. However, the increase of the temperature root mean square difference (RMSD) relative to independent Argo float data, along with the larger variance, for the run with the 4DVAR method reveals that the 4DVAR method adjusts the temperature field more significantly but the adjustments are inconsistent with the independent data due to insufficient model physics and resolution. The increase of the RMSD is mitigated after the assimilated fields are downscaled into the higher-resolution subsystem. The 4DVAR method reduces the bias and RMSD of temperature relative to the independent data along the thermocline, as well as near the surface, in the equatorial vertical section, which is expected to affect the prediction of El Niño-Southern Oscillation (ENSO).

Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System Version 3 (JMA/MRI–CPS3)

A new operational seasonal forecast system, Japan Meteorological Agency (JMA)/Meteorological Research Institute (MRI) Coupled Prediction System (CPS) version 3 (JMA/MRI–CPS3), has been developed. This system represents a major upgrade of the former system, CPS2. CPS3 comprises atmosphere, land, ocean, and sea ice forecast models and the necessary initialization systems for these models. For historical reforecasts, the atmospheric reanalysis dataset JRA-3Q provides initial conditions for the atmosphere and the external forcings for land, ocean, and sea ice analysis. In the operational forecast, JMA's operational atmospheric analysis is used in conjunction with JRA-3Q to initialize the system in near-real time. The land surface model is initialized using an uncoupled free simulation, forced by the atmospheric analysis. The ocean and sea ice models are initialized with the global ocean data assimilation system MOVE-G3, which incorporates a newly developed four-dimensional variational method for temperature, salinity, and sea surface height and a three-dimensional method for sea ice concentration. Compared with the previous system, the CPS3 forecast model components have approximately 2-4 times higher resolution: the atmosphere and land models are configured with ∼ 55 km horizontal resolution, with 100 vertical atmosphere layers; and the ocean and sea ice models have a resolution of 0.25° × 0.25°, with 60 vertical ocean layers. The physical processes of the atmosphere are greatly refined in CPS3 relative to CPS2, resulting in improved representation of sub-seasonal to seasonal scale variability, including the eastward propagation of the Madden–Julian Oscillation, winter blocking highs in the North Atlantic, and coupled atmosphere–ocean variability during El Niño–Southern Oscillation events. Our historical reforecast experiment for 1991-2020 suggests that CPS3 has greater forecast skill than CPS2. The usability of the model output has been improved in CPS3 by reorganizing the operation schedule to provide daily updates of five-member ensemble forecasts.

Coral‐Based Sea Surface Salinity Reconstructions and the Role of Observational Uncertainties in Inferred Variability and Trends

AbstractClimate observations in much of the tropical oceans are scarce during most of the 20th century, so paleoclimate proxies are needed to understand the full range of natural climate variability. Past proxy studies have focused primarily on sea surface temperatures, but there are comparatively few salinity reconstructions. Such reconstructions can extend our understanding of hydroclimate across the tropical oceans, including variability in precipitation, evaporation, and ocean circulation. Here we compile a network of salinity‐sensitive coral δ18O records, then apply a reduced‐space method based on empirical orthogonal function analysis to reconstruct annual tropical salinity anomalies over the 20th century. A comparison of surface salinity data sets, including reanalyzes (SODA2/3, Ocean ReAnalysis System 5 (ORAS5), Global Ocean Data Assimilation System) and objective analyses (Institute of Atmospheric Physics (IAP), EN4, Delcroix), show large discrepancies in the spatial structure, temporal evolution, and importance of the leading modes of variability. Two salinity data sets, IAP and ORAS5, are retained for climate reconstruction. Our coral‐based salinity reconstructions reveal significant long‐term trends over the 20th century, which are likely associated with hydrological cycle intensification and possibly a weakening of the Walker Circulation. These reconstructions also capture the spatial and temporal patterns of salinity anomalies associated with the El Niño‐Southern Oscillation, Interdecadal Pacific Oscillation, and Atlantic Multidecadal Oscillation. Ultimately, this approach can enhance our understanding of tropical hydroclimate prior to the observational era.

Contribution of a constellation of two wide-swath altimetry missions to global ocean analysis and forecasting

Abstract. Swath altimetry is likely to revolutionize our ability to monitor and forecast ocean dynamics. To meet the requirements of the EU Copernicus Marine Service, a constellation of two wide-swath altimeters is envisioned for the long-term (post-2030) evolution of the Copernicus Sentinel 3 topography mission. A series of observing system simulation experiments (OSSEs) is carried out to quantify the expected performances. The OSSEs use a state-of-the-art high-resolution (1/12∘) global ocean data assimilation system similar to the one used operationally by the Copernicus Marine Service. Flying a constellation of two wide-swath altimeters will provide a major improvement of our capabilities to monitor and forecast the oceans. Compared to the present situation with three nadir altimeters flying simultaneously, the sea surface height (SSH) analysis and 7 d forecast error are globally reduced by about 50 % in the OSSEs. With two wide-swath altimeters, the quality of SSH 7 d forecasts is equivalent to the quality of SSH analysis errors from three nadir altimeters. Our understanding of ocean currents is also greatly improved (30 % improvements at the surface and 50 % at 300 m depth). The resolution capabilities will be drastically improved and will be closer to 100 km wavelength compared to about 250 km today. Flying a constellation of two wide-swath altimeters thus looks to be a very promising solution for the long-term evolution of the Sentinel 3 constellation and the Copernicus Marine Service.

Quantifying upwelling in tropical shallow waters: A novel method using a temperature stratification index

AbstractUpwelling has profound effects on nearshore tropical ecosystems, but our ability to study these patterns and processes depends on quantifying upwelling dynamics in a repeatable and rigorous manner. Previous methods often lack the ability to identify individual cold‐pulse events within temperature time series data or require several user‐defined parameters, and therefore previous hydrographic knowledge of the study site. When unavailable, parameters are chosen arbitrarily or from previous studies potentially conducted under different environmental contexts. Previous methods also require the user to manually separate upwelling‐induced cold‐pulse events from those caused by other physical mechanisms like surface downwelling. Here, we present a novel method that uses a temperature stratification index (TSI) to detect upwelling‐induced cold‐water intrusions in tropical waters. We define a cold pulse as a continuous period having an abnormally low TSI, with this criterion based on a climatological threshold of the temperature profile at the study site calculated from the National Centers for Environmental Prediction's Global Ocean Data Assimilation System (GODAS) reanalysis product. Our TSI method is therefore automatically tuned for the study site in question, removing biases associated with user‐defined input parameters. The method also automatically determines the directional origin of the cold‐water mass to isolate upwelling‐induced cooling and can achieve overall cold‐pulse detection rates 10–14.2% higher than previous methods. Our new TSI method can easily be adapted to detect a range of physical processes in shallow waters, including intrusion of water masses through upwelling, downwelling, and horizontal advection.

Oil spill trajectory prediction assessment using multi model ocean currents - case study of MT Dawn Oil spill 2017

Abstract 684276 An assessment was carried out to judge the performance of the modeled ocean currents in oil spill trajectory prediction. Ocean circulation is the key factor in determining the drift pattern of the spilled marine oil pollutant. General National Oceanic and Atmospheric Administration Operational Modeling Environment (GNOME), an oil spill trajectory model, in diagnostic mode was set for simulating drift pattern of Heavy Fuel Oil (HFO). On 28 January 2017, 0345 hrs, Indian Standard Time (IST), approximately 196.4 MT of HFO was spilled due to vessel collision. The oil spill model was set and run during 28-Jan-2017 to 05-Feb-2017 with 196 tons of HFO. Wind velocity fields were obtained from European Centre for Medium-Range Weather Forecasts (ECMWF). The modeled ocean currents were obtained from High resolution Operational Ocean Forecasting and reanalysis System (HOOFS) with two model set ups such as Indian ocean (IO) and Bay Of Bengal (BOB). Ocean current pattern were also obtained from Hybrid Co-ordinate Ocean Model (HYCOM) and Global Ocean Data Assimilation System (GODAS) based Modular Ocean Model (GM4P1). The oil drift patterns were simulated individually for the spillage due to MT Dawn vessel, by forcing GNOME with the above said wind and ocean currents. Radar data obtained for 29-Jan-2017, from Sentinel -1A was processed for detecting oil slicks. The respective drift patterns obtained were compared individually with the oil slick signatures of Sentinel -1A on 29-Jan-2017. It was found that the drift pattern obtained while using the ocean currents of HOOFS_BOB was in better agreement with that of the observed slicks. Unlike other oil drift patterns, offshore spread of the slicks are well captured while using the ocean currents of HOOFS_BOB. This paper illustrates the method of oil spill trajectory prediction using various ocean currents and validating the simulated drift with the ground truth. It also emphasize the need of using various modeled ocean currents in assessing the performance of oil spill trajectory model.

Global Ocean Data Assimilation and Prediction System in KMA: Description and Assessment

Global Ocean Data Assimilation and Prediction System in KMA: Description and Assessment

Roles of Wind Stress and Subsurface Cold Water in the Second-Year Cooling of the 2017/18 La Niña Event

After the strong 2015/16 El Nino event, cold conditions prevailed in the tropical Pacific with the second-year cooling of the 2017/18 La Nina event. Many coupled models failed to predict the cold SST anomalies (SSTAs) in 2017. By using the ERA5 and GODAS (Global Ocean Data Assimilation System) products, atmospheric and oceanic factors were examined that could have been responsible for the second-year cooling, including surface wind and the subsurface thermal state. A time sequence is described to demonstrate how the cold SSTAs were produced in the central-eastern equatorial Pacific in late 2017. Since July 2017, easterly anomalies strengthened in the central Pacific; in the meantime, wind stress divergence anomalies emerged in the far eastern region, which strengthened during the following months and propagated westward, contributing to the development of the second-year cooling in 2017. At the subsurface, weak negative temperature anomalies were accompanied by upwelling in the eastern equatorial Pacific, which provided the cold water source for the sea surface. Thereafter, both the cold anomalies and upwelling were enhanced and extended westward in the centraleastern equatorial Pacific. These changes were associated with the seasonally weakened EUC (the Equatorial Undercurrent) and strengthened SEC (the South Equatorial Current), which favored more cold waters being accumulated in the central-equatorial Pacific. Then, the subsurface cold waters stretched upward with the convergence of the horizontal currents and eventually outcropped to the surface. The subsurface-induced SSTAs acted to induce local coupled air-sea interactions, which generated atmospheric-oceanic anomalies developing and evolving into the second-year cooling in the fall of 2017.

Coupled Ocean–Atmosphere Summer Intraseasonal Oscillation over the Bay of Bengal

This study shows the active role of subsurface ocean temperature in the evolution of coupled intra-seasonal oscillation (ISO) in the Bay of Bengal (BoB) using multi-satellite observations and ocean analysis product. Satellite-derived humidity profiles obtained from the atmospheric infrared sounder (AIRS) show that intense rains over the BoB are associated with the moistening (drying) in the lower and mid-troposphere during the active (break) phase of summer intraseasonal oscillation (ISO). Anomalous moistening in the mid-troposphere up to 500 hPa ahead of the maximum precipitation band over north BoB gives a precursor signal for the northward movement of the rain band. During the active (break) phase, the upper-tropospheric positive (negative) temperature anomaly ahead of the maximum rain band also sets a precondition by heating the mid- to upper troposphere. Daily subsurface temperature from Global Ocean Data Assimilation System (GODAS) analysis show that during the active phase, tropospheric moistening (drying) coincides with the subsurface warm (cold) temperature up to 200-m depth. The upper ocean warms uniformly by ~ 1 °C during the active phase as compared to the break phase in the entire BoB. The presence of a thin warm layer below the maximum rain band creates an environment conducive to sustaining the active phase on the ISO time scale. A positive sea surface temperature (SST) anomaly along with upper ocean warming ahead of a rain band in the north BoB in association with lower and mid-tropospheric moistening sets a precondition for the northward movement of the rain band. The anomalous warming (cooling) in the thermocline is associated with deeper (shallower) thermocline depth [23° isotherms (D23)] and coincides with the mixed-layer warming.

Improved ocean analysis for the Indian Ocean

ABSTRACTThe National Centers for Environmental Prediction (NCEP) and the Indian National Centre for Ocean Information Services (INCOIS) produce global ocean analyses based on the Global Ocean Data Assimilation System (GODAS). This system uses a state of the art ocean general circulation model named moduler ocean model (MOM) and the 3D-Variational (3DVar) data assimilation technique. In this study we have evaluated the INCOIS-GODAS operational analysis products with an upgrade of the physical model from MOM4p0d to MOM4p1. Two experiments were performed with same atmospheric forcing fields:(i) using MOM4p0d (GODAS_p0), and (ii) using MOM4p1 (GODAS_p1). Observed temperature and salinity profiles were assimilated in both experiments. Validation with independent observations show improvement of sea surface temperature(SST), sea surface salinity (SSS) and surface currents in the new analysis GODAS_p1 as compared to the old analysis GODAS_p0.

South Indian Ocean Rossby Waves

ABSTRACTSouth Indian Ocean Rossby waves (SIO-RW) are identified in the Global Ocean Data Assimilation System (GODAS) 1.5–7 yr filtered sea surface height (SSH) time series. There is a persistent three-year oscillation in the 5°–15°S latitude band from 55° to 85°E. Field correlations show little coupling at 90°E, but as the SIO-RW undulates westward at approximately 0.19 m s−1 across the mid-basin, a northwest–southeast axis of warm sea surface temperatures (SSTs) and deep convection forms. Many teleconnections in earlier work are confirmed: interannual pulses of zonal wind in the eastern basin trigger the SIO-RW via anticyclonic wind stress curl. New insights derive from an understanding of links with the upper troposphere. As the SIO-RWs move westward with the onset of an El Niño in the Pacific, increased convection over the north Indian Ocean corresponds to reduced evaporation and SST warming. Mid-tropospheric heating T′ > 2°C over the northwest Indian Ocean accelerates the southern sub-tropical jet to greater than 10 m s−1 over the southeast Indian Ocean, reinforcing the anticyclonic vorticity. The downstream acceleration of the jet generates upper-level divergence and moist convection over the western basin, anchoring an atmospheric Rossby wave in a northwest–southeast alignment underpinned by differential propagation of the SIO-RW. As the ocean Rossby wave reaches Africa, the coupling fades and transitions. What distinguishes Indian Ocean from Pacific Ocean Rossby waves are their southern latitude and higher frequency. The tropical mid-tropospheric heating that accelerates the southern sub-tropical jet shifts westward in tandem with the SIO-RW.

An assessment on oil spill trajectory prediction: Case study on oil spill off Ennore Port

A Liquefied Petroleum Gas (LPG) tanker and a chemical tanker collided two nautical miles off Ennore port on 28 January, 2017. Around 196.4 metric tons (MT) of Heavy Furnace Oil (HFO) was spilled and drifted towards the shore. Oil spill drift advisory and prediction was made by Indian National Centre for Ocean Information Services (INCOIS) using General National Oceanic and Atmospheric Administration (NOAA) Operational Modeling Environment (GNOME), an oil spill trajectory model. The trajectory model was forced with analysed and forecasted ocean currents from Global Ocean Data Assimilation System (GODAS) based on Modular Ocean Model 4p1 (GM4p1). It was found that spread of HFO obtained from oil spill trajectory model GNOME, has matched well with the observed spread from Sentinel-1A satellite dataset. However, the spread of the HFO was underestimated by the trajectory model, when forced with forecasted GM4p1 currents. Additional ground truth observation from Indian Coast Guard also corroborates this finding.

Impact of Aquarius Sea-Surface Salinity Assimilation in Improving the Ocean Analysis Over Indian Ocean

ABSTRACTThe distribution of ocean salinity controls the density field and thereby plays a major role in influencing the ocean dynamics. It has been a challenging task to understand the variability of salinity structure in the regions of large fresh water discharge and high precipitation such as Bay of Bengal (BoB). Recent advancement in satellite technology has made possible the measurement of sea surface salinity (SSS). Aquarius is the satellite which measured the global SSS for the period 2011 to 2015. In the present study, we assimilated Aquarius SSS in the Global Ocean Data Assimilation System based on 3DVAR technique. The assimilation of Aquarius SSS resulted in reduced biases in salinity not only at the surface, but also in the vertical distribution of salinity and better captured the temporal variations of salinity structure in sensitive regions, such as the Bay of Bengal. In addition, the assimilation of SSS showed marginal improvement in ocean thermal structure over data sparse regions of Indian Ocean. It is also shown that the assimilation of Aquarius SSS has improved the stratification in the upper Ocean which is the key factor in the observed improvement in ocean analysis.

Implications of ocean color in the upper water thermal structure at NINO3.4 region: a sensitivity study for optical algorithms and ocean color variabilities

Chlorophyll a (Chl-a) has been the most commonly used biomass metric in biological oceanographic processes. Although limited to two-dimensional surfaces, remote-sensing tools have been successfully providing the most recent state of marine phytoplankton biomass to better understand bottom-up processes initiating daily marine material cycles. In this exercise, ocean color products with various time-scales, derived from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), were used to investigate how their bio-optical properties affect the upper-ocean thermal structure in a global ocean modeling framework. This study used a ¼-degree Hybrid Coordinate Ocean Model forced by hourly atmospheric fluxes from the Climate Forecast System Reanalysis at National Oceanic Atmospheric Administration. Three numerical experiments were prepared by combining two ocean color products – downwelling diffuse attenuation coefficients (KdPAR) and chlorophyll a (Chl-a) – and two shortwave radiant flux algorithms. These three runs are: (1) KparCLM, based on a 13-year long-term climatological KdPAR derived from SeaWiFS; (2) ChlaCLM, based on a 13-year long-term Chl-a derived from SeaWiFS; and (3) ChlaID, which uses the inter-annual time-series of monthly-mean SeaWiFS Chl-a product. The KparCLM experiment uses a Jerlov-like two-band scheme; whereas, both ChlaCLM and ChlaID use a two-band scheme that considers inherent (absorption (a) and backscattering (bb) coefficients) and apparent optical properties (downwelling attenuation coefficient (Kd) and solar zenith angle (θ, varying 0–60°)). It is found that algorithmic differences in optical parameterizations have a bigger impact on the simulated temperatures in the upper-100 m of the eastern equatorial Pacific, NINO3.4 region, than other parts of the ocean. Overall, the KdPAR-based approach estimated relatively low surface temperatures compared to those estimated from the chlorophyll-based method. In specific, this cold bias, pronounced in the upper 20–30 m, is speculated to be due to optical characteristics of the algorithm and KdPAR products, or due to nonlinear hydrodynamical processes involving displacement of mixed-layer depth. Comparisons between each experiment against Global Ocean Data Assimilation System (GODAS; Behringer and Xue 2004) analyses find that KparCLM-based simulations have lower mean differences and variabilities with higher cross-correlation coefficients compared to ChlaCLM- and ChlaID-based experiments.

Scaling wind stirring effects in an oceanic bulk mixed layer model with application to an OGCM of the tropical Pacific

The mixed layer depth (MLD) plays an important role in the climate system through its influences on sea surface temperature (SST). The Kraus–Turner–Niiler (hereafter referred to as KTN) bulk mixed layer (ML) model is designed for describing the MLD and has been adopted widely by many ocean modeling. However, large biases exist in the MLD simulation using the original KTN model in the tropical Pacific. This is partly due to the uncertainties in representing wind stirring effect in the model, which is scaled by a parameter (m0). Traditionally, m0 is taken as a constant uniformly in space. In this study, the m0 is estimated as spatially and seasonally varying through its inverse calculation from a balance equation describing the turbulent kinetic energy (TKE) budget of the ML. It is illustrated that the m0 is spatially varying over the tropical Pacific. The derived m0 fields are then embedded into an ocean general circulation model (OGCM). Compared with the observations and the global ocean data assimilation system (GODAS) analyses, the MLD simulations in the OGCM with varying m0 are substantially improved in the tropical Pacific Ocean on seasonal and interannual time scales. Additionally, the Pacific subtropical cells (STCs) become intensified, accompanied with the strengthening of upwelling in the eastern equatorial Pacific; thus, more realistic simulations are obtained in spatially and seasonally varying m0 case compared with the constant m0 case. As the related cooling effect from the upwelling is enhanced, the simulated SST is slightly cooled down in the eastern equatorial Pacific. Further applications and implications are also discussed.