Ocean Variability Research Articles

Significant Inverse Influence of Tropical Indian Ocean SST on SIF of Indian Vegetation during the Summer Monsoon Onset Phase

Sea surface temperature (SST) substantially influences the land climate conditions through the co-variability of multiple climate variables, which in turn affect the structural and functional characteristics of terrestrial vegetation. Our study explored the varying responses of vegetation photosynthesis in India to the SST variations in the tropical Indian Ocean during the summer monsoon. To characterise the terrestrial photosynthetic activity, we used solar-induced chlorophyll fluorescence (SIF). Our results demonstrated a significant negative SST-SIF relationship during the onset phase of the summer monsoon: the SIF anomalies in the northern and central Indian regions decrease when strong warm SST anomalies persist in the tropical Indian Ocean. Further, SIF anomalies increase with cold anomalies of SST. However, the negative SST anomalies in the tropical Indian Ocean are less impactful on SIF anomalies relative to the positive SST anomalies. The observed statistically significant SST–SIF link is feasible through atmospheric teleconnections. During monsoon onset, positive SST anomalies in the tropical Indian Ocean favour weakened monsoon flow, decreasing moisture transport from the ocean to the Indian mainland. The resultant water deficiency, along with the high air temperature, created a stress condition and reduced the photosynthetic rate, thus demonstrating negative SIF anomalies across India. Conversely, negative SST anomalies strengthened monsoon winds in the onset period and increased moisture availability across India. Negative air temperature anomalies also dampen water stress conditions and increased photosynthetic activity, resulting in positive SIF anomalies. The identified SST-SIF relationship would be beneficial to generate a simple framework that aids in the detection of the probable impact on vegetation growth across India associated with the rapidly varying climate conditions in the Indian Ocean.

Environmental variability and krill abundance in the central California current: Implications for ecosystem monitoring

Krill are a direct conduit between primary productivity and recreationally and commercially important higher trophic level species globally. Determining how krill abundance varies with temporal environmental variation is key to understanding their function in coastal-pelagic food webs, as well as applications in fisheries management. We used nine years (2012–19 and 2021) of late spring/early summer hydroacoustic-trawl survey data in the California Current Ecosystem (CCE), coupled with new target strength models of two krill species (Euphausia pacifica and Thysanoessa spinifera), to investigate how adult krill biomass varied during a decade of unusual ocean climate variability. We estimate a mean biomass of 1.75–2.0 million metric tons on the central and northern California continental shelf. Overall, relative krill biomass was ~30% lower during 2015 and 2016, corresponding to a major warming event, and ~30% higher in 2013 and 2018, years of exceptionally strong upwelling. Variation in biomass was related to the prior year’s environmental conditions derived from our seasonal Multivariate Ocean Climate Index (MOCI), and E. pacifica and T. spinifera showed similar covariation during the study period. Biomass co-varied at different spatial scales and across sampling devices, suggesting that multiple indicators of abundance (and dispersion) are available and should be applied in ecosystem monitoring and modeling of krill and krill-dependent predators in the California Current ecosystem.

Ocean data assimilation for the initialization of seasonal prediction with the Community Earth System Model

Seasonal prediction highly depends on its initialization scheme. Currently, the operational utilization of advanced data assimilation methods in the oceanic initialization is still relatively less and its benefit is far from being fully unveiled. In this study, the Community Earth System Model (CESM) is configured with an Ensemble Adjustment Kalman Filter (EAKF) to initialize the seasonal prediction. The performance of the established prediction system is assessed by comparing it with a benchmark prediction system, which is based on the same model but with a nudging scheme. Results show that the assimilation of only oceanic observations can not only effectively constrain the oceanic variables, but also improves the atmospheric variables through coupling processes, which helps to produce accurate and compatible initial conditions. With the initial conditions, the current prediction system is better than the benchmark prediction system and can produce skillful predictions for sea surface temperature, ocean heat content, air temperature at 2 m, precipitation and major climate variabilities including El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). The current prediction system also shows better ensemble consistency and less prediction drifts, which could contribute to the improvement of prediction skill.

Possible influence of the interdecadal variation of the extratropical southern Indian Ocean SST on East Asian summer monsoon precipitation

Observations indicate that East Asian summer precipitation exhibits significant interdecadal variability, with an alternating reverse phase distribution of rainfall from south to north, which is different from the consistent increase or decrease of precipitation in the whole region. The East Asian summer monsoon mainly affects the spatial pattern of meridional tripole precipitation on the interdecadal timescale. Moreover, the interdecadal sea surface temperature variability in the extratropical southern Indian Ocean is the most significant in the boreal summer. When there is anomalous warming in the extratropical southern Indian Ocean, the Somali cross-equatorial flow weakens and then alternating positive and negative waves propagate to central East Asia. In contrast, the New Guinea cross-equatorial flow experiences interdecadal enhancement with cyclonic circulation anomalies over the Northwest Pacific and anticyclonic circulation anomalies over the Philippines, which leads to a weakening of the South China Sea monsoon trough. On one hand, the cyclonic-anticyclonic coupling over the Philippines and Northwest Pacific results in an anomalous eastward transport of water vapor and reduced water vapor input over southern East Asia. On the other hand, the coupling strengthens the water vapor transport from the mid-latitude Northwest Pacific to central East Asia. These circulation anomalies favor the development of a meridional tripole pattern, i.e. a “negative – positive – negative” pattern of East Asian summer monsoon precipitation anomalies on the interdecadal timescale, which is specifically manifested in the abnormal increase of precipitation in central East Asia and the decrease of precipitation in northern and southern East Asia.

What Causes the Arabian Gulf Significant Summer Sea Surface Temperature Warming Trend?

The present study investigated the significant sea surface temperature (SST) warming trend during the summer season over the Arabian Gulf (AG) and its links with the large-scale atmospheric driver, namely, the Atlantic multidecadal oscillation (AMO), from 1900 to 2021. The link between the AMO and the AGs oceanic circulations has received little scientific attention. It has been found that there is a significant spatial positive trend, with a maximum of up to 0.6 °C per decade over the far northern end, while the time series trend shows a significant shift after 1995, with an average value of about 0.36 °C per decade. The spatial trend in the AG is eight times and four times higher than the global value from 1980 to 2005 using HadISST and OISST, respectively. The AMOs significant role in the AGs SST significant warming trend has been confirmed by the spatial and temporal correlation coefficient, which is above 0.50 and 0.48, respectively, with statistical significance at the 99% level. The underlying mechanisms that explained the AMO-related AGs SST decadal variability can be explained as follows: when the AMO is in a positive phase, the surface northwesterly wind weakens, leading to (1) less advection of the relatively cold air masses from Southern Europe to the AG and surrounding areas, (2) less evaporation, and thus less surface cooling (3); thus, this enhances the water masses stratification and decreases the upwelling process, and vice versa occurs for the negative phase. For the air temperature, the positive AMO phase coincides with the occurrence of warm air masses covering all of the Arabian Peninsula, North Africa, and Southern Europe. These processes prove that the AMO is a possible candidate for the AGs SST decadal variability, hence enabling a better evaluation of future climate scenarios for this important region. Our results provide initial insights into the AMO-driven spatiotemporal variability in the SST over the AG and prove that the relation is nonstationary over time. Further analyses are required to explore whether the impacts of the AMO are extended to other oceanic variables such as evaporation rate, heat transport, etc.

A global gridded ocean salinity dataset with 0.5° horizontal resolution since 1960 for the upper 2000 m

A gridded salinity dataset with high resolution is essential for investigating global ocean salinity variability and understanding its role in climate and the ocean ecosystem. In this study, a new version of the Institute of Atmospheric Physics gridded salinity dataset with a higher resolution (0.5° by 0.5°) is provided by using a revised ensemble optimal interpolation scheme with a dynamic ensemble. The performance of this dataset is evaluated using “subsample test” and the high-resolution satellite-based data. Compared with the previous 1° by 1° resolution IAP product, the new dataset is more capable of representing regional salinity changes with the meso-scale and small-scale signals (i.e., in the coastal and boundary currents regions), meanwhile, maintains the large-scale structure and variability. Therefore, the new dataset complements the previous data product. Besides, the new dataset is compared with in situ observations and several international salinity products for the salinity multiscale variabilities and patterns. The comparison shows the smaller magnitude of mean difference and Root-mean-square deviation (RMSD) in basin scale for the new dataset, some differences in strength and fine structure of the “fresh gets fresher, salty gets saltier” surface and subsurface salinity pattern amplification trends from 1980 to 2017, a broad similarity for the salinity changes associated with El Niño-Southern Oscillation (ENSO) and a consistent salinity dipole mode in the tropical Indian Ocean (S-IOD). These results support the future use of gridded salinity data.

The Ocean Carbon and Acidification Data System

The Ocean Carbon and Acidification Data System (OCADS) is a data management system at the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI). It manages a wide range of ocean carbon and acidification data, including chemical, physical, and biological observations collected from research vessels, ships of opportunity, and uncrewed platforms, as well as laboratory experiment results, and model outputs. Additionally, OCADS serves as a repository for related Global Ocean Observing System (GOOS) biogeochemistry Essential Ocean Variables (EOVs), e.g., oxygen, nutrients, transient tracers, and stable isotopes. OCADS endeavors to be one of the world’s leading providers of ocean carbon and acidification data, information, products, and services. To provide the best data management services to the ocean carbon and acidification research community, OCADS prioritizes adopting a customer-centric approach and gathering knowledge and expertise from the research community to improve its data management practices. OCADS aims to make all ocean carbon and acidification data accessible via a single portal, and welcomes submissions from around the world: https://www.ncei.noaa.gov/products/ocean-carbon-acidification-data-system/.

Using satellite observations of ocean variables to improve estimates of water mass (trans)formation

For the first time, an accurate and complete picture of Mixed Layer (ML) water mass dynamics can be inferred at high spatio-temporal resolution via the material derivative derived from Sea Surface Salinity/Temperature (SSS/T) and Currents (SSC). The product between this satellite derived material derivative and in-situ derived Mixed Layer Depth (MLD) provides a satellite based kinematic approach to the water mass (trans)formation framework (WMT/F) above ML. We compare this approach to the standard thermodynamic approach based on air-sea fluxes provided by satellites, an ocean state estimate and in-situ observations. Southern Hemisphere surface density flux and water mass (trans)formation framework (WMT/F) were analysed in geographic and potential density space for the year 2014. Surface density flux differences between the satellite derived thermodynamic and kinematic approaches and ECCO (an ocean state estimate) underline: 1) air-sea heat fluxes dominate variability in the thermodynamic approach; and 2) fine scale structures from the satellite derived kinematic approach are most likely geophysical and not artefacts from noise in SSS/T or SSC—as suggested by a series of smoothing experiments. Additionally, ECCO revealed surface density flux integrated over ML are positively biased as compared to similar estimates assuming that surface conditions are homogeneous over ML—in part owing to the e-folding nature of shortwave solar radiation. Major differences between the satellite derived kinematic and thermodynamic approaches are associated to: 1) lateral mixing and mesoscale dynamics in the kinematic framework; 2) vertical excursions of, and vertical velocities through the ML base; and 3) interactions between ML horizontal velocities and ML base spatial gradients.

Spatial-temporal transformer network for multi-year ENSO prediction

The El Niño-Southern Oscillation (ENSO) is a quasi-periodic climate type that occurs near the equatorial Pacific Ocean. Extreme periods of this climate type can cause terrible weather and climate anomalies on a global scale. Therefore, it is critical to accurately, quickly, and effectively predict the occurrence of ENSO events. Most existing research methods rely on the powerful data-fitting capability of deep learning which does not fully consider the spatio-temporal evolution of ENSO and its quasi-periodic character, resulting in neural networks with complex structures but a poor prediction. Moreover, due to the large magnitude of ocean climate variability over long intervals, they also ignored nearby prediction results when predicting the Niño 3.4 index for the next month, which led to large errors. To solve these problem, we propose a spatio-temporal transformer network to model the inherent characteristics of the sea surface temperature anomaly map and heat content anomaly map along with the changes in space and time by designing an effective attention mechanism, and innovatively incorporate temporal index into the feature learning procedure to model the influence of seasonal variation on the prediction of the ENSO phenomenon. More importantly, to better conduct long-term prediction, we propose an effective recurrent prediction strategy using previous prediction as prior knowledge to enhance the reliability of long-term prediction. Extensive experimental results show that our model can provide an 18-month valid ENSO prediction, which validates the effectiveness of our method.

Diel variations in planktonic ciliate community structure in the northern South China Sea and tropical Western Pacific

Though diel variations are geographically widespread phenomena among phytoplankton and zooplankton, knowledge is limited regarding diel variations in planktonic ciliate (microzooplankton) community structure. In this study, we analyzed diel variations in community structure of planktonic ciliates in the northern South China Sea (nSCS) and tropical Western Pacific (tWP). Hydrological characteristics during day and night were slightly different over both the nSCS and tWP, while ciliate average abundance at night was clearly higher than in the day in the upper 200 m. In both the nSCS and tWP, abundance proportions of large size-fraction (> 30 μm) aloricate ciliates at night were higher than in the day. While for tintinnids, abundance proportion of large lorica oral diameter at night were lower than in the day. The relationship between environmental factors and ciliate abundance pointed out that depth and temperature were main factors influencing aloricate ciliate and tintinnid in both day and night. For some dominant tintinnid species, chlorophyll a was another important factor influencing their diel vertical distribution. Our results provide fundamental data for better understanding the mechanisms of planktonic ciliate community diel variation in the tropical Western Pacific Ocean.

Contemporary oceanic radiocarbon response to ocean circulation changes

Radiocarbon (14C) is a valuable tracer of ocean circulation, owing to its natural decay over thousands of years and to its perturbation by nuclear weapons testing in the 1950s and 1960s. Previous studies have used 14C to evaluate models or to investigate past climate change. However, the relationship between ocean 14C and ocean circulation changes over the past few decades has not been explored. Here we use an Ocean-Sea-ice model (NEMO) forced with transient or fixed atmospheric reanalysis (JRA-55-do) and atmospheric 14C and CO2 boundary conditions to investigate the effect of ocean circulation trends and variability on 14C. We find that 14C/C (∆14C) variability is generally anti-correlated with potential density variability. The areas where the largest variability occurs varies by depth: in upwelling regions at the surface, at the edges of the subtropical gyres at 300 m depth, and in Antarctic Intermediate Water and North Atlantic Deep Water at 1000 m depth. We find that trends in the Atlantic Meridional Overturning Circulation may influence trends in ∆14C in the North Atlantic. In the high-variability regions the simulated variations are larger than typical ocean ∆14C measurement uncertainty of 2–5‰ suggesting that ∆14C data could provide a useful tracer of circulation changes.

Chasing inter-species communication: what marine mammals are telling us about our oceans

Abstract I describe my path through a series of opportunities that provided stepping stones from childhood years in the landlocked US Midwest to a 45-year-long career focused on cetacean behaviour and ecology. My early interest in the ocean and dolphins led me to switch from majoring in journalism to biology during my undergraduate years. While pursuing a master’s degree focused on bioacoustics, I was employed as a contract scientist with the US Navy’s marine mammal laboratory. During 20 years there, my work ranged from dolphin calling behaviour to marine mammal distribution in Alaskan waters, culminating in a Ph.D. dissertation on cetacean habitats in the Alaskan Arctic. Subsequently, I enjoyed a 20-year career with the US NOAA National Marine Fisheries Service. There, I developed and advanced the idea that marine mammals can act as sentinels of ocean variability. To interpret the messages that marine mammals convey about the ocean, we must broaden science discourse to include Indigenous Knowledge and lessons from the experiences of people whose livelihoods depend on the sea. My advice to students and young professionals is to follow your passion while seeking the perspectives of colleagues from a variety of disciplines and people from all cultures and backgrounds. Coupled with a healthy dose of luck, this approach worked for me.

Maximum Northern Hemisphere warming rates before and after 1880 during the Common Era

We find that maximal decadal Northern Hemisphere warming increases as rapidly before as after the industrial revolution (0.86 °C decade−1 before 1880 and 0.60–0.68 °C decade−1 after 1880). However, whereas the number of decadal periods with large increase and decrease rates were about equal before 1880 (267 vs. 273), after 1880 there are more periods with high increase rates (35) than with high decrease rates (9). The same patterns hold for bi-decadal rates. However, for time windows greater than about 20 years, the slope in global warming with time becomes greater after 1880. After 1971, there is only one short 11 year period with negative slopes. This reflects the higher frequency of positive slopes during the industrial period caused by the contribution of greenhouse gases (GHG). Maximum temperature changes for detrended series were associated with the beginning and end of extreme warm or cold sub periods. They occurred throughout all of the Common Era. Because the detrended temperature series showed sign of a pacemaker mechanism (regular cycle periods) we suggest that ocean variabilities were a dominating mechanism for multidecadal temperature variability during the Common Era.

Enhanced Asian warming increases Arctic amplification

The Arctic has been experiencing prominent warming amplification. However, despite anthropogenic emissions and oceanic variability, whether Arctic amplification has a connection with land in the lower latitudes remains unknown. Here, we newly identify enhanced Asian warming as a factor underlying Arctic amplification. The simulations demonstrate that enhanced Asian warming contributes 22% of the wintertime amplified warming over the Barents–Kara Seas (BKS). We demonstrate that Asian warming remotely affects the Arctic by affecting poleward atmospheric heat and moisture transport. The external anomalous heat and moisture further trigger local feedbacks concerning sea ice-albedo feedback and changes in longwave radiation and evaporation, thus facilitating BKS warming amplification. The capacitor effect of the Arctic Ocean further modulates the seasonality of BKS warming via turbulent heat flux exchange between the atmosphere and ocean. Moreover, anomalous Rossby wave trains are responsible for the anomalous atmospheric circulations favoring the atmospheric heat and moisture transport into BKS. Our findings illuminate a new factor from remote lower latitudes affecting Arctic climate change.

Key Role of Subdaily Wind Variability for Tropical Surface Wind Stress

Abstract High-frequency observations of surface winds over the open ocean are available only at limited locations. However, these observations are essential for assessing atmospheric influences on the ocean, validating reanalysis products, and building parameterization schemes. By analyzing high-frequency measurements from the Global Tropical Moored Buoy Array, the effects of subdaily winds on the mean surface wind stress magnitude are systematically examined. Subdaily winds account for 12.4% of the total stress magnitude on average. The contribution is enhanced over the intertropical convergence zone and reaches a maximum (28.5%) in the equatorial western Pacific. The magnitude of the contribution is primarily determined by the kinetic energy of subdaily winds. Compared to the buoy observations, the ERA5 and MERRA2 subdaily winds underestimate this contribution by 51% and 63% due to underestimations of subdaily kinetic energy, leading to 7% and 8% underestimations in the total stress magnitude, respectively. Two new gustiness parameterization schemes related to precipitation are developed to account for the effect of subdaily winds, explaining ∼80% of the contribution from subdaily winds. Considering the importance of wind stress for ocean–atmosphere interactions, the inclusion of these parameterization schemes in climate models is expected to substantially improve simulations of large-scale climate variability. Significance Statement Surface wind stress drives upper-ocean circulation, which is critical for the redistribution of mass, momentum, and energy in the ocean. Moreover, it is one of the key factors controlling oceanic turbulent mixing and therefore has significant impacts on the distribution of temperature, salinity, and associated ocean variability. Using high-resolution buoy observations, this study highlights the importance of subdaily winds for integrated wind stress estimates. In addition, it finds that current state-of-the-art and widely used reanalysis products largely underestimate the effect of subdaily winds. Two new parameterization schemes are developed, leading to a better representation of the effect of subdaily winds. Including the proposed parameterization schemes in climate models is expected to substantially improve their simulations of large-scale climate variability.

Simulation of spatiotemporal interannual variability of oceanic subsurface temperature off East Africa

The oceanic subsurface variability off East Africa in the tropical western Indian Ocean plays a crucial role in ocean dynamics and living resources as well as weather and climate variability. A regional ocean model is applied to understand the oceanic subsurface interannual variability off East Africa. The region with the highest sea surface temperature (SST) variability in the offshore region lies adjacent to strong subsurface temperature variations located between 30 and 130 m corresponding with strong variations in the thermocline depth. The weakest SST variations in the Tanzanian shelf waters lie over the subsurface waters with the smallest temperature variations in the upper 200 m with weak variations in the thermocline depth. Such signals are associated with induced forcings from the Indian Ocean Dipole (IOD) and El Niño-Southern Oscillation (ENSO) in both regions with different intensity and peaking times. The IOD-induced forcings are weaker, evolving in October and November-December in the region with the weakest and strongest SST variations, respectively. However, relatively stronger ENSO-induced forcings occur in both regions with stronger signals in the region with the strongest SST variations throughout the years except in August, and it peaks in January. The ENSO-induced forcings occur in January to May peaking in March and April in the region with the weakest SST. Consequently, anomalous Rossby waves as well as local Ekman downwelling and upwelling associated with both large-scale modes occur in the region leading to the subsurface temperature variations.

Sea ice and productivity changes over the last glacial cycle in the Adélie Land region, East Antarctica, based on diatom assemblage variability

Abstract. Although diatoms can provide important palaeoenvironmental information about seasonal sea ice extent productivity, sea surface temperature, and ocean circulation variability, there are still relatively few studies analysing the last glacial cycle near the Antarctic continent. This study examines diatom assemblages over the last glacial cycle from core TAN1302-44, offshore Adélie Land, East Antarctica. Two distinct diatom assemblages were identified using principal component analysis (PC 1–PC 2). The PC 1 assemblage is characterised by Thalassiosira lentiginosa, Actinocyclus actinochilus, Eucampia antarctica, Azpeitia tabularis and Asteromphalus hyalinus and is associated with the interglacial, sedimentary Facies 1, suggesting that the MIS 5e and Holocene interglacials were characterised by seasonal sea ice environments with similar ocean temperature and circulation. The PC 2 assemblage is characterised by Fragilariopsis obliquecostata, Asteromphalus parvulus and Thalassiosira tumida and is associated with the glacial Facies 2. The variability of PC 2 indicates that, during the MIS 4–2 glacial and the last glaciation, there was an increase in the length of the sea ice season compared with that of the interglacial period, yet there was still no permanent sea ice cover. The initial increase of PC 2 at the start of the glaciation stage and then the gradual increase throughout late MIS 4–2 suggest that sea ice cover steadily increased, reaching a maximum towards the end of MIS 2. The increase in sea ice during glaciation and MIS 4–2 glacial is further supported by the increase in the Eucampia index (terminal/intercalary valve ratio), an additional proxy for sea ice, which coincides with increases in PC 2. Aside from the statistical results, the increase in the relative abundance of Thalassiothrix antarctica at 40 and 270 cm suggests that, during the last two deglacials, there was a period of enhanced nutrient delivery, which is inferred to reflect an increase in upwelling of Circumpolar Deep Water. Interestingly, the diatom data suggest that, during the last deglacial, the onset of increased Circumpolar Deep Water occurred after the loss of a prolonged sea ice season (decrease in PC 2) but before the ice sheet started to retreat (increase in IRD). Together, these results suggest the changes in sea ice season potentially influenced the ocean's thermohaline circulation and were important factors in driving the climate transitions. The results contribute to our understanding of the sea ice extent and ocean circulation changes proximal to East Antarctica over the last glacial cycle.

Multi-temporal variability forecast of particulate organic carbon in the Indonesian seas.

The current global condition characterized by high levels of CO2 is altering the carbon cycle and elemental biogeochemistry, resulting in subsequent global warming, climate change, ocean acidification, and the indirect response of deoxygenation. The features of Indonesia's coastal ecosystems and continental shelf waters also contribute to spatio-temporal ocean carbon variability. For instance, the level of particulate organic carbon (POC) will change annually, and thus, over a decadal period, ocean dynamics may affect the temporal variability of POC. Motivated by such conditions, future forecasting is needed to envision the productivity of Indonesian seas by predicting vital parameters such as POC. This research aimed to forecast the temporal variability of POC in Indonesian waters. The Seasonal Autoregressive Integrated Moving Average (SARIMA) forecasting model was used by considering the lowest value of the Akaike information criterion (AIC) and the mean absolute percentage error/MAPE (threshold < 10%). Using the highest correlation coefficient (threshold: 0.75), we obtained the best fit for forecasting POC temporal variability. Hindcast POC data (2002-2020/2021) was used to train the forecasting model. The result shows that forecasting of POC temporal variability can be conducted up to 2030. The validity of prediction is ensured for less than 5years forward after 2020 with correlation coefficients of 0.65 and 0.83 for seasonal and monthly POC, respectively. The hindcast and forecast estimates of POC in the Indonesian seas show a decreasing trend. The present study emphasizes the different forecasting results obtained using the different approaches of annual versus inter-annual variability. A sustained research effort is still required to assess POC forecasting for its potential benefits in marine system monitoring and assessment.

Impact of assimilating repeated subsurface temperature transects on state estimates of a western boundary current

Western Boundary Currents and the eddies they shed are high priorities for numerical estimation and forecasting due to their economic, ecological and dynamical importance. However, the rapid evolution, complex dynamics and baroclinic structure that is typical of eddies and the relatively sparse sampling in western boundary currents leads to significant challenges in understanding the 3-dimensional structure of these boundary currents and mesoscale eddies. Here, we use Observing System Simulation Experiments (OSSEs) to explore the impact of assimilating synthetic subsurface temperature observations at a range of temporal resolutions, to emulate expendable bathythermograph transects with different repeat frequencies (weekly to quarterly). We explore the improvement in the representation of mesoscale eddies and subsurface conditions in a dynamic western boundary current system, the East Australian Current, with a data-assimilating regional ocean model. A characterisation of the spatial and temporal ocean variability spectrum demonstrates the potential for undersampling and aliasing by a lower sampling frequency. We find that assimilating subsurface temperature data with at least a weekly repeat time best improves subsurface representation of this dynamic, eddy-rich region. However, systemic biases introduced by the data assimilation system hinder the ability of the model to produce more accurate subsurface representation with fortnightly or monthly sampling. Removal of this bias may improve subsurface representation in eddy-rich regions with fortnightly or even less frequent observations. These results highlight the value of both increased subsurface observation density in regions of dynamic oceanography as well as continued development of data assimilation techniques in order to optimise the impact of existing observations.

Probabilistic sea level rise flood projections using a localized ocean reference surface

Projecting sea level rise (SLR) impacts requires defining ocean surface variability as a source of uncertainty. We analyze ocean surface height data from a Regional Ocean Modeling System reanalysis to produce an ocean reference surface (ORS) as a proxy for the local mean higher high water. This method allows incorporation of ocean surface level uncertainty into bathtub modeling and generation of probability-based projections of SLR-induced flooding. For demonstration, we model the NOAA Intermediate, Intermediate-high and High regional SLR scenarios at three locations on the island of Oʻahu, Hawai’i. We compare 80% probability-based flood projections generated using our approach to those generated using the Tidal Constituents and Residual Interpolation (TCARI) method. TCARI is the predecessor of VDatum, the standard method used by NOAA available only for the continental U.S., Puerto Rico, and U.S. Virgin Islands. For validation, ORS pixel values representing the Honolulu tide gauge location are compared to tide gauge observations. The more realistic distribution of daily higher high water provided by ORS improves projections of SLR-induced flooding for locations where VDatum is not available. We highlight the importance of uncertainty and user-defined probability in identifying locations of flooding and pathways for additional sources of flooding.