Global Ocean Forecasting System Research Articles

Assessing the potential impact of assimilating total surface current velocities in the Met Office’s global ocean forecasting system

Accurate prediction of ocean surface currents is important for marine safety, ship routing, tracking of pollutants and in coupled forecasting. Presently, velocity observations are not routinely assimilated in global ocean forecasting systems, largely due to the sparsity of the observation network. Several satellite missions are now being proposed with the capability to measure Total Surface Current Velocities (TSCV). If successful, these would substantially increase the coverage of ocean current observations and could improve accuracy of ocean current forecasts through data assimilation. In this paper, Observing System Simulation Experiments (OSSEs) are used to assess the impact of assimilating TSCV in the Met Office’s global ocean forecasting system. Synthetic observations are generated from a high-resolution model run for all standard observation types (sea surface temperature, profiles of temperature and salinity, sea level anomaly and sea ice concentration) as well as TSCV observations from a Sea surface KInematics Multiscale monitoring (SKIM) like satellite. The assimilation of SKIM like TSCV observations is tested over an 11 month period. Preliminary experiments assimilating idealised single TSCV observations demonstrate that ageostrophic velocity corrections are not well retained in the model. We propose a method for improving ageostrophic currents through TSCV assimilation by initialising Near Inertial Oscillations with a rotated incremental analysis update (IAU) scheme. The OSSEs show that TSCV assimilation has the potential to significantly improve the prediction of velocities, particularly in the Western Boundary Currents, Antarctic Circumpolar Current and in the near surface equatorial currents. For global surface velocity the analysis root-mean-square-errors (RMSEs) are reduced by 23% and there is a 4-day gain in forecast RMSE. There are some degradations to the subsurface in the tropics, generally in regions with complex vertical salinity structures. However, outside of the tropics, improvements are seen to velocities throughout the water column. Globally there are also improvements to temperature and sea surface height when TSCV are assimilated. The TSCV assimilation largely corrects the geostrophic ocean currents, but results using the rotated IAU method show that the energy at inertial frequencies can be improved with this method. Overall, the experiments demonstrate significant potential benefit of assimilating TSCV observations in a global ocean forecasting system.

Advanced Sea Ice Modeling for Short-Term Forecasting for Alaska’s Coasts

Abstract In Alaska’s coastal environment, accurate information of sea ice conditions is desired by operational forecasters, emergency managers, and responders. Complicated interactions among atmosphere, waves, ocean circulation, and sea ice collectively impact the ice conditions, intensity of storm surges, and flooding, making accurate predictions challenging. A collaborative work to build the Alaska Coastal Ocean Forecast System established an integrated storm surge, wave, and sea ice model system for the coasts of Alaska, where the verified model components are linked using the Earth System Modeling Framework and the National Unified Operational Prediction Capability. We present the verification of the sea ice model component based on the Los Alamos Sea Ice Model, version 6. The regional, high-resolution (3 km) configuration of the model was forced by operational atmospheric and ocean model outputs. Extensive numerical experiments were conducted from December 2018 to August 2020 to verify the model’s capability to represent detailed nearshore and offshore sea ice behavior, including landfast ice, ice thickness, and evolution of air–ice drag coefficient. Comparisons of the hindcast simulations with the observations of ice extent presented the model’s comparable performance with the Global Ocean Forecast System 3.1 (GOFS3.1). The model’s skill in reproducing landfast ice area significantly outperformed GOFS3.1. Comparison of the modeled sea ice freeboard with the Ice, Cloud, and Land Elevation Satellite-2 product showed a mean bias of −4.6 cm. Daily 5-day forecast simulations for October 2020–August 2021 presented the model’s promising performance for future implementation in the coupled model system. Significance Statement Accurate sea ice information along Alaska’s coasts is desired by the communities for preparedness of hazardous events, such as storm surges and flooding. However, such information, in particular predicted conditions, remains to be a gap. This study presents the verification of the state-of-art sea ice model for Alaska’s coasts for future use in the more comprehensive coupled model system where ocean circulation, wave, and sea ice models are integrated. The model demonstrates comparable performance with the existing operational ocean–ice coupled model product in reproducing overall sea ice extent and significantly outperformed it in reproducing landfast ice cover. Comparison with the novel satellite product presented the model’s ability to capture sea ice freeboard in the stable ice season.

Preliminary validation for an eddy-resolving Global Ocean Forecasting System –NMEFC-NEMO

This paper details the dynamical framework of the National Marine Environmental Forecasting Centre high-resolution global numerical ocean forecasting system (hereinafter referred to as NMEFC-NEMO) and provides a preliminary assessment of its simulation capability in the ocean climatic state. NMEFC-NEMO is developed based on the NEMO version 3.6 and the LIM version 3, and the Local Error Subspace Transform Kalman Filter (LESTKF) ensemble assimilation method is used. The preliminary climatic state assessment shows that the model can reproduce the temperature, salinity and current structure in the observational and reanalysis well, and the results of sea surface temperature and sea surface salinity are 0.05°C and 0.29 PSU colder and saltier, respectively, compared to WOA09. The difference of standard deviation for sea surface height is 0.027 compared to AVISO. We also find that the global mean salinity and kinetic energy of the NMEFC-NEMO are slightly larger in the upper layer relative to the other dataset, which could related to the model parameters setting or insufficient dynamic adjustment.

Evaluation of the global ocean forecast system in NMEFC with the IV-TT class4 metrics

Based on the IV-TT Class4 metrics, this paper comprehensively evaluates the forecasting performance of the National Marine Environment Forecast Center High-resolution Global Ocean Forecasting System (hereinafter referred to as NMEFC-NEMO) on sea surface temperature (SST), sea level anomaly (SLA) and temperature-salinity profiles. The RMSE of NMEFC-NEMO for SST and SLA over forecast length has the smallest error among the international operational systems, which are 0.45°C and 0.07m, respectively. The forecast accuracy of NMEFC-NEMO temperature and salt profile RMSE is in the middle among the operational systems, with the values of 0.69°C and 0.24 PSU. In the profile forecast, the error of NMEFC-NEMO is slightly larger than other systems at 50-100 m depth, which is related to the fact that NMEFC-NEMO does not assimilate ARGO temperature and salinity directly. In addition, with respect to the forecast skill scores, NMEFC-NEMO has a positive climatology skill scores for both SST and SLA, and the persistence skill scores for SST is better than that for SLA.

An Assessment of Marine Heatwaves in a Global Eddy-Resolving Ocean Forecast System: A Case Study around China

Marine heatwaves (MHWs) are becoming increasingly frequent and intense around China, impacting marine ecosystems and coastal communities. Accurate forecasting of MHWs is crucial for their management and mitigation. In this study, we assess the forecasting ability of the global eddy-resolving ocean forecast system LICOM Forecast System (LFS) for the MHW events in October 2021 around China. Our results show that the 1-day lead forecast by the LFS accounts for up to 79% of the observed MHWs, with the highest skill during the initial and decay periods. The forecasted duration and intensity of the MHW event are consistent with observations but with some deviations in specific regions of the Yellow and South China seas. A detailed analysis of the heat budget reveals that the forecasted shortwave radiation flux is a key factor in the accuracy of the forecasted MHW duration and intensity. The oceanic dynamic term also greatly contributes to the accuracy in the southern Yellow Sea. In addition, the increasing bias of the forecasted duration and intensity with lead time are mainly caused by the underestimated shortwave radiation. Our findings suggest that improving the accuracy of oceanic dynamic processes and surface radiation fluxes in the LFS could be a promising direction to enhance the forecasting ability of marine extreme events such as MHWs.

A data-assimilative model reanalysis of the U.S. Mid Atlantic Bight and Gulf of Maine: Configuration and comparison to observations and global ocean models

A 15-year reanalysis (2007–2021) of circulation in the coastal ocean and adjacent deep sea of the northeast U.S. continental shelf is described. The analysis uses the Regional Ocean Modeling System (ROMS) and four-dimensional variational (4D-Var) data assimilation (DA) of observations from in situ platforms, coastal radars, and satellites. The reanalysis downscales open boundary information from the Copernicus Marine Environmental Monitoring Service (CMEMS) global analysis. The dynamic model is forced by regional meteorological analyses, observed daily river discharges, and harmonic tides that augment the open boundary conditions.A complementary analysis of the mean seasonal cycle of regional circulation, also computed using ROMS 4D-Var but with climatological mean observations and forcing, is used to reduce biases in the CMEMS boundary data and to provide a dynamically and kinematically constrained Mean Dynamic Topography to use in conjunction with the assimilation of satellite altimeter sea level anomaly observations.The configuration of ROMS 4D-Var used is described, presenting details of the comprehensive suite of observations assembled, data pre-processing and quality control procedures, and background and observation error hypotheses. Control variables of the DA are the initial conditions, surface forcing, and boundary conditions of a sequence of non-overlapping 3-day analysis cycles.Comparisons to a non-assimilative version of the same ROMS model configuration show the added skill brought by assimilation of local observations. The improvement that downscaling with assimilation achieves over ocean state estimates from CMEMS and the U.S. Naval Research Laboratory Global Ocean Forecast System (GOFS) is demonstrated by the reduction in residuals of the DA, and by comparison to independent (unassimilated) observations. Wherever data volumes allow, skill assessments are made with the respect to anomalies from the mean seasonal cycle to emphasize performance at the ocean mesoscale.To highlight the utility of the analysis to inform studies related to coastal sea level variability and marine ecosystems, comparisons are made to unassimilated coastal sea level gauges and novel observations from sensors on fishing gear. The assimilation of coastal satellite altimetry data produces coastal sea level results that are coherent with observations across all time scales from interannual to tidal, while bias and correlation metrics show that bottom temperatures in regions of commercial fishing activity in the Mid-Atlantic Bight and the Gulf of Maine are modeled well.

Comparative analysis of the performance of the GOFS, PSY4 and AMSEAS ocean model frameworks in the Virgin Islands and Puerto Rico coastal ocean

ABSTRACT This work compares the performance of three ocean model frameworks that currently produce outputs of the ocean properties specific to the US Caribbean ocean; the Global Ocean Forecast System (GOFS), US Navy Coastal Ocean Model for the American Seas (AMSEAS) and the Daily Global Physical Bulletin (PSY4). Separate comparisons are done for the ocean properties in the open ocean and nearshore regions. For the open ocean, the model outputs are compared with the AVISO satellite altimetry data for the sea-surface height anomaly (SSHA), the OSCAR data for surface current velocities and the G1SST satellite data for sea-surface temperature (SST). For the nearshore analysis, the model outputs are compared with in-situ buoy measurements and HOBO logger data in the nearshore regions. Our analysis shows that the PSY4 produces the most realistic outputs of SSHA and surface current velocities in the open ocean, whereas all the models produce a strong correlation in terms of the seasonal variability of the surface temperature when compared to the G1SST data. The AMSEAS model, despite being a fine resolution regional model, underperforms in terms of the surface current velocity outputs in the open ocean due to the influence of the simulated submesoscale turbulence on the mesoscale variability. In the nearshore regions, none of the models produce agreeable outputs on the SSHA and current velocities. These findings provide useful insight on the applicability of the model outputs for various operations that require oceanographic data specific to the US Caribbean ocean.

Spatiotemporal variations of the thermohaline structure and cyclonic response in the northern Bay of Bengal: The evaluation of a global ocean forecasting system

Although the performance of the high-resolution global ocean forecasting system, which is based on First Institute of Oceanography surface wave-tide-circulation coupled ocean model (FIO-COM) has been demonstrated to have reasonable accuracy in global context, especially in tropical oceans and some special cases, the assessment needs to be extended in different parts of the global oceans. As such, given the significance of a regional Ocean Forecasting System (OFS) in the world's prominent freshwater dominated basin, the northern Bay of Bengal, a groundwork is crucial for the modeling community. Thus, performance of FIO-COM analysis and forecasting results is assessed by utilizing available moored buoy, satellite, and reanalysis datasets. The present study compares the thermohaline structure related variables (e.g., temperature, salinity) from FIO-COM with aforementioned datasets on a seasonal and daily basis. We focused on evaluating the FIO-COM outputs during a very severe cyclonic storm named Titli that formed in October, 2018. The root-mean-square errors (RMSEs) for sea surface temperature (SST) ranges from 0.47 to 0.71 °C, and for sea surface salinity from 0.62 to 0.83 psu; while vertical profile RMSE ranges from 0.27 to 1.0 °C for temperature, and 0.22 to 0.56 psu for salinity between FIO-COM products and observations. Daily variations in different thermohaline structure related variables at two RAMA positions are also well captured by the FIO-COM outputs. Both model and satellite data show pronounced SST cooling (approximately 2.0–2.5 °C) and increased sea surface salinity (~ 1 psu) on the right side of the cyclone track. High SST, TCHP and deep isothermal layer depth were the main oceanic triggering forces to intensify the cyclone Titli.

Tropical Cyclone Heat Potential (TCHP) from the NCMRWF NEMO based global ocean analysis and forecast system

In this study, the Nucleus European Modelling of Ocean (NEMO) based global ocean analysis and forecast system configured at National Centre for Medium Range Weather Forecasting (NCMWRF) is used to compute the upper ocean heat content up to 26° isotherm depth called as Tropical Cyclone Heat Potential (TCHP). NCMRWF has the real-time TCHP monitoring capability on daily basis. It is produced in real time using the global ocean forecast system up to 10 days using the ocean only model and up to 15 days using the coupled atmosphere-ocean model for monitoring the upper ocean and also for research purpose mainly for Tropical Cyclone (TC) study. Four pre-monsoon TCs with category above cyclonic storms in which two from Arabian Sea and two from Bay of Bengal are also examined. The Sea Surface Temperature drop is much more over the regions where the surface wind stress anomaly is strong. In both regions, various small scale warm and cold eddies are observed during TCs. TCHP anomaly is positive/negative over the region where Sea Surface Height (SSH) anomaly is positive/negative. From TCHP, it is manifest that the magnitude of TCHP anomaly is higher in BoB than AS region during TCs.

Regional Trends in Southern Ocean Eddy Kinetic Energy

AbstractPrevious model‐based studies and observation‐based studies suggest that increasing wind energy input into the Southern Ocean will primarily cause increases in eddy kinetic energy (EKE) with no significant change in the mean circulation, a result that has been named the “eddy‐saturation” hypothesis. However, due to the sparsity of the available observations, quantifying and understanding regional EKE changes in the Southern Ocean is challenging. In this study, we examine regional trends in the Southern Ocean EKE using altimetry crossover measurements and an ocean reanalysis product to quantify if undersampling by altimetry will bias EKE trend estimates and to test if previously observed EKE trends are homogenous throughout the Southern Ocean or concentrated in a few regions. To verify that the EKE computed from altimetry crossovers accurately represents yearly averaged EKE over the Southern Ocean, we first conduct a sampling experiment with the HYCOM Global Ocean Forecasting System 3.1 ocean reanalysis. We find that the crossover sampling is sufficient to represent the yearly averaged EKE when averaged over sectors of at least 30° of longitude. We find no coherent increase in EKE over the entire Southern Ocean from altimetry crossover measurements, but instead find significant EKE increase over only one region, primarily south of New Zealand and downstream of the Campbell Plateau. We conclude that the EKE change in the Southern Ocean is not as homogenous as implied by previous studies and more work is needed to understand if this is consistent with the “eddy‐saturation” hypothesis or related more to local dynamics.

A global eddy-resolving ocean forecast system in China – LICOM Forecast System (LFS)

ABSTRACT A global eddy-resolving forecast system LFS is developed based on a primitive ocean general circulation model. The system's configuration, forecast experiments, and a preliminary evaluation of the preoperational system are shown. In the preoperational stage, since the full data assimilation has not yet been set up for LFS, the initial state is obtained by nudging the ocean temperature and salinity from an ocean analysis dataset. Despite this, the LFS demonstrates a generally good performance in short-term oceanography forecasting, except for sea level anomaly (SLA). The median values of the 1-day forecast leading root mean square error (RMSE) for the sea surface temperature (SST), SLA, upper 2000 m temperature, and salinity are approximately 0.52°C, 0.10 m, 0.57°C and 0.13 psu, respectively. Although there are slight warm biases in the forecasted SST, the forecasts of temperature and salinity in the thermocline by the LFS are comparable with the results of operational oceanography systems under the framework of the Intercomparison and Validation Task Team. However, the forecast SLA has a relatively large RMSE related to the absence of direct observational constraints in the initial state. Further investigations are needed to improve the performance of LFS.

Analyzing the impact of CryoSat-2 ice thickness initialization on seasonal Arctic Sea Ice prediction

AbstractTwin 5-month seasonal forecast experiments are performed to predict the September 2018 mean and minimum ice extent using the fully coupled Navy Earth System Prediction Capability (ESPC). In the control run, ensemble forecasts are initialized from the operational US Navy Global Ocean Forecasting System (GOFS) 3.1 but do not assimilate ice thickness data. Another set of forecasts are initialized from the same GOFS 3.1 fields but with sea ice thickness derived from CryoSat-2 (CS2). The Navy ESPC ensemble mean September 2018 minimum sea ice extent initialized with GOFS 3.1 ice thickness was over-predicted by 0.68 M km2 (5.27 M km2) vs the ensemble forecasts initialized with CS2 ice thickness that had an error of 0.40 M km2 (4.99 M km2), a 43% reduction in error. The September mean integrated ice edge error shows a 18% improvement for the Pan-Arctic with the CS2 data vs the control forecasts. Comparison against upward looking sonar ice thickness in the Beaufort Sea reveals a lower bias and RMSE with the CS2 forecasts at all three moorings. Ice concentration at these locations is also improved, but neither set of forecasts show ice free conditions as observed at moorings A and D.

Bluelink ocean forecasting Australia: 15 years of operational ocean service delivery with societal, economic and environmental benefits

ABSTRACTThe operational Australian Bluelink ocean forecast system is used to transform physical oceanographic observations into coherent analyses and predictions. These analyses and predictions form the basis for information services about the marine environment and its ecosystem, and can provide boundary data for weather predictions. Bluelink information services are available to marine industries (e.g. commercial fishing, aquaculture, shipping, oil and gas, renewable energy), government agencies (e.g. search and rescue, defence, coastal management, environmental protection), and other stakeholders (e.g. recreation, water sports, artisanal and sport fishing) who depend on timely and accurate information about the marine environment. This review highlights the last 15 years of Bluelink achievements delivering mesoscale (eddy-resolving) to sub-mesoscale and short- to medium-range (days to weeks) ocean forecasts and reanalyses. Key achievements include the development of a global ocean forecasting and reanalysis system, a relocatable ocean-atmosphere model and a littoral zone analysis and forecasting capability. Beyond the traditional short-term forecasting of physical ocean properties (temperature, salinity, surface height, currents, waves), marine activities such as water quality and habitat management as well as climate monitoring increasingly rely on operational oceanographic data and products. These are areas of active research of the Bluelink team in collaboration with national and international partners.

High resolution modeling of western Alaskan tides and storm surge under varying sea ice conditions

A depth-integrated high resolution (down to 25–50 m) tide and storm surge model has been developed for the Alaska region with a focus on the coasts of western Alaska. The model uses the ADCIRC basin-to-channel scale unstructured grid circulation code forced with Climate Forecast System version 2 reanalysis meteorology. The tidal solution has been validated at 121 shelf and nearshore stations, with lower overall errors than a data assimilated tidal model (FES2012). The ADCIRC model shows considerable skill at simulating tide and coastal surge, and resulting overland flooding on the Yukon–Kuskokwim Delta, during both summer and winter storms. The root-mean-square-error of the model does not exceed 20 cm at any station, outperforming the Global Ocean Forecasting System (GOFS 3.1), a 1/12∘ data assimilated ocean general circulation model which is coupled to sea ice physics. The ADCIRC model incorporates the effect of sea ice through parameterizations of the wind drag coefficient, modifying the air–sea momentum transfer under ice coverage. Three large winter storms with distinctly different ice coverages along the coasts of western Alaska were chosen to exhibit the variable effect of sea ice on the resulting storm surge. Under forming coastal ice coverage, local increases in water levels due to ice is seen in coastal areas, and under predominantly dense pack ice, an increase in momentum transfer in the marginal sea ice at the shelf break leads to an increase in sea levels across the entire Bering Sea, both commensurate with observations. Under the most variable ice fields, results are mixed, in part due to uncertainties in the air–sea–ice drag parameterization such as related to the assumption that sea ice drift and wind velocities are always well correlated. Including a description of sea ice drift and current speeds explicitly in the air–sea–ice drag formulation and considering wave–surge–ice interaction should improve the description of momentum transferred to the water column and resulting simulation of surge.

Baroclinic Coupling Improves Depth‐Integrated Modeling of Coastal Sea Level Variations Around Puerto Rico and the U.S. Virgin Islands

AbstractThis study applies a baroclinic‐coupled depth‐integrated modeling system to the North Atlantic Ocean, where an unstructured mesh is used to focus resolution down to ∼30 m along the coasts of Puerto Rico and the U.S. Virgin Islands. Ocean baroclinicity is incorporated through one‐way coupling from operational data‐assimilated Global Ocean Forecasting System 3.1 temperature and salinity fields at just 12% additional computational time. The main objectives are to provide a comprehensive analysis of observed and modeled coastal sea levels (spanning from seasonal to supertidal variations) in Puerto Rico and the U.S. Virgin Islands during 2017 and to evaluate the associated model performance with and without baroclinic coupling at 14 National Oceanic and Atmospheric Administration/National Ocean Service tide gauges deployed in the region. It is found that baroclinic coupling increases modeled energy across the entire frequency spectrum, which is more commensurate with observations. In particular, density‐driven effects such as the seasonal cycle and sea level setdown due to trailing cold wakes from passing hurricanes are largely reproduced. Supertidal shelf‐resonant seiching at one to two cycles per hour is observed and modeled at a number of locations, where excitation of these modes is often promoted by the baroclinic coupling. Baroclinicity improves the yearlong model skill at every tide gauge, where the mean total skill is increased from 87% to 93% accuracy (54% to 85% for the nontidal residual). In September 2017 during Hurricanes Irma and Maria, baroclinicity increases model skill at 10 out of 14 tide gauges even when the barotropic mode is adjusted to have no mean offset from the observations.

Assimilating satellite sea‐surface salinity data from SMOS, Aquarius and SMAP into a global ocean forecasting system

Measuring Sea‐Surface Salinity (SSS) from space is a relatively recent technique that relies on L‐band radiometry that has evolved to a point where useful information is provided every few days. The impact of assimilating satellite SSS data is investigated using the global FOAM ocean forecasting system. This system assimilates daily satellite SSS products from the ESA Soil Moisture and Ocean Salinity (SMOS), NASA Aquarius and Soil Moisture Active Passive (SMAP) missions equatorward of 40°N/S, in addition to other observing systems. The data are assimilated using a 3D‐Var scheme that includes an observation bias correction scheme to estimate spatially and temporally varying bias estimates for each SSS satellite.The SSS assimilation is tested over 2 years and 3 months covering the 2015/2016 El Niño with assessment focussed on the tropical regions, particularly in the Pacific. Consistent reductions in root‐mean‐square errors are found in all tropical regions from each of the satellite SSS datasets. The largest improvements of up to about 8% are found in the tropical Pacific from assimilating both the SMOS and SMAP datasets together. The largest impact is in the intertropical convergence zone (ITCZ) in the central Pacific during 2015 and early 2016 where the surface salinity is reduced by about 0.03 pss on average, correcting for too little precipitation. A smaller magnitude, large‐scale reduction in the SSS is also seen in the tropical Pacific that increases the modelled surface stratification and leads to reduced vertical mixing. Changes to the SSS in the ITCZ lead to changes in the meridional gradients of SSS that affects the sea surface height and surface currents, both of which are improved in the SMOS assimilation experiment compared to externally produced observation‐based datasets. The results suggest that assimilation of SMOS and SMAP satellite data is now of an appropriate quality for operational implementation.

Evaluation of an operational ocean model configuration at 1/12° spatial resolution for the Indonesian seas (NEMO2.3/INDO12) – Part 2: Biogeochemistry

Abstract. In the framework of the INDESO (Infrastructure Development of Space Oceanography) project, an operational ocean forecasting system was developed to monitor the state of the Indonesian seas in terms of circulation, biogeochemistry and fisheries. This forecasting system combines a suite of numerical models connecting physical and biogeochemical variables to population dynamics of large marine predators (tunas). The physical–biogeochemical coupled component (the INDO12BIO configuration) covers a large region extending from the western Pacific Ocean to the eastern Indian Ocean at 1/12° horizontal resolution. The NEMO-OPA (Nucleus for European Model of the Ocean) physical ocean model and the PISCES (Pelagic Interactions Scheme for Carbon and Ecosystem Studies) biogeochemical model are running simultaneously ("online" coupling), at the same resolution. The operational global ocean forecasting system (1/4°) operated by Mercator Océan provides the physical forcing, while climatological open boundary conditions are prescribed for the biogeochemistry. This paper describes the skill assessment of the INDO12BIO configuration. Model skill is assessed by evaluating a reference hindcast simulation covering the last 8 years (2007–2014). Model results are compared to satellite, climatological and in situ observations. Diagnostics are performed on nutrients, oxygen, chlorophyll a, net primary production and mesozooplankton. The model reproduces large-scale distributions of nutrients, oxygen, chlorophyll a, net primary production and mesozooplankton biomasses. Modelled vertical distributions of nutrients and oxygen are comparable to in situ data sets although gradients are slightly smoothed. The model simulates realistic biogeochemical characteristics of North Pacific tropical waters entering in the archipelago. Hydrodynamic transformation of water masses across the Indonesian archipelago allows for conserving nitrate and oxygen vertical distribution close to observations, in the Banda Sea and at the exit of the archipelago. While the model overestimates the mean surface chlorophyll a, the seasonal cycle is in phase with satellite estimations, with higher chlorophyll a concentrations in the southern part of the archipelago during the SE monsoon and in the northern part during the NW monsoon. The time series of chlorophyll a anomalies suggests that meteorological and ocean physical processes that drive the interannual variability of biogeochemical properties in the Indonesian region are reproduced by the model.

Modeling of 137Cs as a Tracer in a Regional Model for the Western Pacific, after the Fukushima–Daiichi Nuclear Power Plant Accident of March 2011

Abstract In this study, results are presented from the first operational ocean tracer dispersion model operated by the National Oceanic and Atmospheric Administration/National Weather Service/National Centers for Environmental Prediction (NOAA/NWS/NCEP). This study addresses the dispersion of radionuclide contaminants after the Fukushima–Daiichi nuclear accident that was triggered by the 11 March 2011 earthquake and tsunami. The tracer capabilities of the Hybrid Coordinate Ocean Model (HYCOM) were used in a regional domain for the northwestern Pacific, with nesting lateral boundary conditions using daily nowcast–forecast fields from the global operational Real-Time Ocean Forecast System (RTOFS-Global), a ° HYCOM global forecast from NCEP, based on data-assimilative ° HYCOM Global Ocean Forecast System (GOFS) analyses from the Naval Research Laboratory/Naval Oceanographic Office (NRL/NAVOCEANO). This regional model, RTOFS Episodic Tracers for a region of the North West Pacific (RTOFS-ET_WPA), was in operation until the beginning of 2014, when the simulated 137Cs concentration was very close to the background level in the Pacific before the accident, which was about 2 Becquerel m−3 [Bq; 1 Becquerel = 1 (nuclear decay) s−1].

Evaluation of an operational ocean model configuration at 1/12° spatial resolution for the Indonesian seas (NEMO2.3/INDO12) – Part 1: Ocean physics

Abstract. INDO12 is a 1/12° regional version of the NEMO physical ocean model covering the whole Indonesian EEZ (Exclusive Economic Zone). It has been developed and is now running every week in the framework of the INDESO (Infrastructure Development of Space Oceanography) project implemented by the Indonesian Ministry of Marine Affairs and Fisheries. The initial hydrographic conditions as well as open-boundary conditions are derived from the operational global ocean forecasting system at 1/4° operated by Mercator Océan. Atmospheric forcing fields (3-hourly ECMWF (European Centre for Medium-Range Weather Forecast) analyses) are used to force the regional model. INDO12 is also forced by tidal currents and elevations, and by the inverse barometer effect. The turbulent mixing induced by internal tides is taken into account through a specific parameterisation. In this study we evaluate the model skill through comparisons with various data sets including outputs of the parent model, climatologies, in situ temperature and salinity measurements, and satellite data. The biogeochemical model results assessment is presented in a companion paper (Gutknecht et al., 2015). The simulated and altimeter-derived Eddy Kinetic Energy fields display similar patterns and confirm that tides are a dominant forcing in the area. The volume transport of the Indonesian throughflow (ITF) is in good agreement with the INSTANT estimates while the transport through Luzon Strait is, on average, westward but probably too weak. Compared to satellite data, surface salinity and temperature fields display marked biases in the South China Sea. Significant water mass transformation occurs along the main routes of the ITF and compares well with observations. Vertical mixing is able to modify the South and North Pacific subtropical water-salinity maximum as seen in T–S diagrams. In spite of a few weaknesses, INDO12 proves to be able to provide a very realistic simulation of the ocean circulation and water mass transformation through the Indonesian Archipelago. Work is ongoing to reduce or eliminate the remaining problems in the second INDO12 version.

Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems

Abstract. This study presents the improvement in ice edge error within the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, the ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system (Arctic Cap Nowcast/Forecast System – ACNFS) went into operations with a horizontal resolution of ~ 3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea ice mask produced by the National Ice Center (NIC) has been developed, resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone. This paper describes the technique used to create the blended sea ice concentration product and the significant improvements in ice edge forecasting in both of the Navy's sea ice forecasting systems.