Satellite Sea Surface Temperature Observations Research Articles

Ensemble 4DVAR (En4DVar) data assimilation in a coastal ocean circulation model. Part II: Implementation offshore Oregon–Washington, USA

The ensemble four-dimensional variational (En4DVar) data assimilation (DA) system introduced in Part I (Pasmans and Kurapov, 2019) is tested in the coastal waters offshore Oregon and Washington, U.S. West coast, during the spring and summer of 2011. The background error covariance B is derived from the forecast ensemble. Satellite sea-surface temperature (SST), sea-surface height (SSH), and daily-averaged radial surface currents from high-frequency radars (HFRs) are assimilated. The performance of the En4DVar system is compared with a “traditional” 4DVAR system using a static B. It is found that the presence of the Columbia River plume has a profound impact on the ensemble B. Near the plume front the SST–SSS covariance can be up to a factor 20 larger in magnitude than in the static B. This introduces large spatial and temporal variability in the ensemble B. The En4DVar system is more successful than the 4DVAR with the static B preserving the temperature–salinity properties when compared to glider data. The En4DVar system also produces more accurate forecasts and analyses for temperature in the subsurface below 30 m at a buoy location on the continental shelf. In comparisons with other surface and subsurface observations En4DVar shows consistent, albeit not significant, improvement over traditional 4DVAR. Large surface temperature–salinity covariances in combination with the episodic occurrence of large-scale errors in the SST observations lead to erroneous freshening in the centre of the model domain. Adding constraints on the surface salinity corrections based on the prior model reduces this effect.

Spatio-Temporal Variations of Water Vapor Budget over the Tibetan Plateau in Summer and Its Relationship with the Indo-Pacific Warm Pool

The water vapor budget (WVB) over the Tibetan Plateau (TP) is closely related to the large-scale atmospheric moisture transportation of the surrounding mainland and oceans, especially for the Indo-Pacific warm pool (IPWP). However, the procession linkage between the WVBs over the TP and its inner basins and IPWP has not been sufficiently elucidated. In this study, the relationship between the summer WVB over the TP and the IPWP was quantitatively investigated using reanalysis datasets and satellite-observed sea surface temperature (SST). The results show that: (1) the mean total summer vapor budget (WVBt) over the TP in the period of 1979–2018 was 72.5 × 106 kg s−1. Additionally, for the 13 basins within the TP, the summer WVB has decreased from southeast to northwest; the Yarlung Zangbo River Basin had the highest WVB (33.7%), followed by the Upper Yangtze River Basin, Ganges River Basin and Qiangtang Plateau. (2) For the past several decades, the WVBt over the TP has experienced an increasing trend (3.81 × 106 kg s−1 decade−1), although the southern boundary budget (WVBs) contributed the most and is most closely related with the WVBt, while the eastern boundary budget (WVBe) experienced a decreasing trend (4.21 × 106 kg s−1 decade−1) which was almost equal to the interdecadal variations of the WVBt. (3) For the IPWP, we defined a new warm pool index of surface latent heat flux (WPI-slhf), and found that an increasing WPI-slhf would cause an anticyclone anomaly in the equatorial western Indian Ocean (near 70° E), resulting in the increased advent of water vapor to the TP. (4) On the interdecadal scale, the correlation coefficients of the variation of the summer WVBt over the TP with the WPI-slhf and Indian Ocean Dipole (IOD) signal were 0.86 and 0.85, respectively (significant at the 0.05% level). Therefore, the warming and the increasing slhf of the IPWP would significantly contribute to the increasing WVB of the TP in recent decades.

Accurate Evaluation of Sea Surface Temperature Cooling Induced by Typhoons Based on Satellite Remote Sensing Observations

We introduce a novel method to accurately evaluate the satellite-observed sea surface temperature (SST) cooling induced by typhoons with complex tracks, which is widely used but only roughly calculated in previous studies. This method first records the typhoon forcing period and the SST response grid by grid, then evaluates the SST cooling in each grid by choosing the maximum decrease in SST within this time period. This grid-based flexible forcing date method can accurately evaluate typhoon-induced SST cooling and its corresponding date in each grid, as indicated by applying the method to the irregular track of Typhoon Lupit (2009) and three sequential typhoons in 2016 (Malakas, Megi, and Chaba). The method was used to accurately calculate the impact of Typhoon Megi by removing the influence of the other two typhoons. The SST cooling events induced by all typhoons in the northwest Pacific from 2004 to 2018 were extracted well using this method. Our findings provide new insights for accurately calculating the response of the ocean using multi-satellite remote sensing and simulation data, including the sea surface salinity, sea surface height, mixed layer depth, and the heat content of the upper levels of the ocean.

DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations

Abstract. A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.

Seasonal and Interannual Variability of the Indo-Pacific Warm Pool and its Associated Climate Factors Based on Remote Sensing

With satellite observed Sea Surface Temperature (SST) accumulated for multiple decades, multi-time scale variabilities of the Indo-Pacific Warm Pool are examined and contrasted in this study by separating it into the Indian Ocean sector and the Pacific Ocean sector. Surface size, zonal center, meridional center, maximum SST and mean SST as the practical warm pool properties are chosen to investigate the warm pool variations for the period 1982–2018. On the seasonal time scale, the oscillation of the Indian Warm Pool is found much more vigorous than the Pacific Warm Pool on size and intensity, yet the interannual variabilities of the Indian Warm Pool and the Pacific Warm Pool are comparable. The Indian Warm Pool has weak interannual variations (3–5 years) and the Pacific Warm Pool has mighty interdecadal variations. The size, zonal movement and mean SST of the Indian Ocean Warm Pool (IW) are more accurate to depict the seasonal variability, and for the Pacific Ocean Warm Pool (PW), the size, zonal and meridional movements and maximum SST are more suitable. On the interannual scale, except for the meridional movements, all the other properties of the same basin have similar interannual variation signals. Following the correlation analysis, it turns out that the Indian Ocean basin-wide index (IOBW) is the most important contributor to the variabilities of both sectors. Lead-lag correlation results show that variation of the Pacific Ocean Warm Pool leads the IOBW and variation of the Indian Ocean Warm Pool is synchronous with the IOBW. This indicates that both sectors of the Indo-Pacific Warm Pool are significantly correlated with basin-wide warming or cooling.

Temporal evolution of temperatures in the Red Sea and the Gulf of Aden based on in situ observations (1958–2017)

Abstract. The Red Sea holds one of the most diverse marine ecosystems in the world, although fragile and vulnerable to ocean warming. Several studies have analysed the spatio-temporal evolution of temperature in the Red Sea using satellite data, thus focusing only on the surface layer and covering the last ∼30 years. To better understand the long-term variability and trends of temperature in the whole water column, we produce a 3-D gridded temperature product (TEMPERSEA) for the period 1958–2017, based on a large number of in situ observations, covering the Red Sea and the Gulf of Aden. After a specific quality control, a mapping algorithm based on optimal interpolation have been applied to homogenize the data. Also, an estimate of the uncertainties of the product has been generated. The calibration of the algorithm and the uncertainty computation has been done through sensitivity experiments based on synthetic data from a realistic numerical simulation. TEMPERSEA has been compared to satellite observations of sea surface temperature for the period 1981–2017, showing good agreement especially in those periods when a reasonable number of observations were available. Also, very good agreement has been found between air temperatures and reconstructed sea temperatures in the upper 100 m for the whole period 1958–2017, enhancing confidence in the quality of the product. The product has been used to characterize the spatio-temporal variability of the temperature field in the Red Sea and the Gulf of Aden at different timescales (seasonal, interannual and multidecadal). Clear differences have been found between the two regions suggesting that the Red Sea variability is mainly driven by air–sea interactions, while in the Gulf of Aden the lateral advection of water plays a relevant role. Regarding long-term evolution, our results show only positive trends above 40 m depth, with maximum trends of 0.045 + 0.016 ∘C decade−1 at 15 m, and the largest negative trends at 125 m (-0.072+0.011 ∘C decade−1). Multidecadal variations have a strong impact on the trend computation and restricting them to the last 30–40 years of data can bias high the trend estimates.

Sensitivity Analysis of Bias in Satellite Sea Surface Temperature Measurements

The satellite sea surface temperature (SST) measurement is based on the detection of ocean radiation using microwave or infrared wavelengths within the electromagnetic spectrum. The radiance of individual wavelengths can be converted into brightness temperatures for using in SST determination. The calibration and validation of the determined SST data require reference measurements from in-situ observations. These in-situ observations are from various platforms such as ships, drifters, floats and mooring buoys and require a high measurement accuracy. This paper presents an investigation about the possibility of using a glider as in-situ platform. A glider is a type of autonomous underwater vehicle (AUV) which can log oceanographic data over a period of up to one year by following predetermined routes. In contrast to buoys, a glider allows a targeted investigation of regional anomalies in SST circulations. To assess the quality of SST observations from a glider, logged data from a glider mission in the Atlantic Ocean from 2018 to 2019 and corresponding satellite SST data were used. The influence of variables (e.g. measurement depth, latitude, view zenith angle, local solar time) of the bias between satellite and glider SST data was investigated using sensitivity analysis. A new and efficient distribution-based method for global sensitivity analyzes, called PAWN, was used successfully. Interested readers will find information about its operation principle and the usage for passive observations where only “given-data” are available.

New high-resolution sea surface temperature forecasts for coral reef management on the Great Barrier Reef

Great Barrier Reef (GBR) marine park managers rely on seasonal forecasts of sea surface temperature (SST) to better inform and coordinate their management responses to mass coral bleaching events. The Bureau of Meteorology’s new seasonal forecast model ACCESS-S1 is well placed for integration in marine park managers’ risk management systems, with model benefits including high ocean resolution and probabilistic forecasts from a 99 member ensemble. The SST forecast skill was assessed for the GBR region against satellite SST observations over the model hindcast period 1990–2012. ACCESS-S1 was most successful in forecasting larger warm anomalies in the GBR associated with climate drivers that persisted over many months (e.g. ENSO events). The model consistently performed better than persistence reference forecasts over the critical summer period. The model was less successful in forecasting short-term events driven by regional weather patterns, with a reduction in skill between pre-monsoon and post-monsoon onset. Forecasts in the northern GBR often exhibited the highest skill. The model was successfully able to predict SST anomalies associated with the peak of the East Australian Current. The ability of the model to discriminate between two dichotomous events (whether or not a threshold is exceeded) ranged from excellent at lead time 0 (first month forecast) to reasonable at lead times 1 and 2. Increasing the ensemble size using time-lagged ensemble members showed improvement in probabilistic skill for warm anomaly events. Model reliability showed good ability in matching the observed frequency for warm anomaly events, although slightly overconfident. The results demonstrate that ACCESS-S1 can provide skilful SST forecasts in support of coral reef management activities on sub-seasonal to seasonal timescales. Seasonal SST forecasts from ACCESS-S1 are currently available at the Bureau of Meteorology’s website for the GBR and greater Coral Sea region.

Intraseasonal responses of sea surface and deep oceanic temperature anomalies in the northern Indian Ocean–western Pacific to the 30–60-day boreal summer atmospheric intraseasonal oscillation

The 30–60-day boreal summer atmospheric intraseasonal oscillation (BSISO) is the predominant intraseasonal variability of the Asian summer monsoon, and has a strong dynamical and thermodynamical impact on the underlying oceans in the northern Indian Ocean–western Pacific. This study uses composite analysis of satellite-observed sea surface temperature (SST) data, as well as atmospheric and oceanic reanalysis datasets for the period 1998–2013, to identify the intraseasonal behavior of SST and deep ocean temperatures during the life cycle of the BSISO. With the BSISO as an organized entity evolving both eastward and northward from the equatorial Indian Ocean, the BSISO-induced SST anomalies propagate coherently into several basins, including the Arabian Sea, Bay of Bengal, South China Sea, and western Pacific, characterized by the alternate occurrence of basin-wide positive and negative SST anomalies, which primarily respond to the active and suppressed convection phases of the BSISO due to anomalous southwesterlies and northeasterlies together with the resultant surface heating anomalies. Significant BSISO-induced oceanic temperature anomalies occur in the equatorial Indian Ocean and western Pacific, with the largest-amplitude anomalous signals arising around the thermocline and extending downward into the deep ocean to below 1000 m or more, which are associated with the equatorial eastward-propagating oceanic Kelvin waves forced by anomalous surface wind stress. However, the local and remote effects of such equatorial oceanic Kelvin waves on the SST and deep oceanic temperature anomalies, especially north of the equator, differ from different basins through the consequential coastal Kelvin waves and reflected Rossby waves.

Unusual coastal ocean cooling in the northern South China Sea by a katabatic cold jet associated with Typhoon Mujigea (2015)

This study deals with a unusual cooling event after Typhoon Mujigea passed over the northern South China Sea (SCS) in October 2015. We analyze the satellite sea surface temperature (SST) time series from October 3 to 18, 2015 and find that the cooling process in the coastal ocean had two different stages. The first stage occurred immediately after typhoon passage on October 3, and reached a maximum SST drop of −2°C on October 7 as the usual cold wake after typhoon. The second stage or the unusual extended cooling event occurred after 7 d of the typhoon passage, and lasted for 5 d from October 10 to 15. The maximum SST cooling was −4°C and occurred after 12 d of typhoon passage. The mechanism analysis results indicate that after landing and moving northwestward to the Yunnan-Guizhou Plateau (YGP), Typhoon Mujigea (2015) met the westerly wind front on October 5. The low-pressure and positive-vorticity disturbances to the front triggered meridional air flow and low-pressure trough, thus induced a katabatic cold jet downward from the Qinghai-Tibet Plateau (QTP) passing through the YGP to the northwestern SCS. The second cooling reached the maximum SST drop 4 d later after the maximum air temperature drop of −9°C on October 11. The simultaneous air temperature and SST observations at three coastal stations reveal that it is this katabatic cold jet intrusion to lead the unusual SST cooling event.

Mean and Eddy‐Driven Heat Advection in the Ocean Region Adjacent to the Greenland‐Scotland Ridge Derived From Satellite Altimetry

AbstractAlong‐track altimeter and sea surface temperature satellite observations and ARGO in situ measurements of temperature and salinity are used to investigate the heat transport by mean currents and eddies in the ocean region adjacent to the Greenland‐Scotland Ridge from 2003 to 2008. Our results show that the heat advection by the mean flow in the surface layer is zonally asymmetric with a higher magnitude in the western part of the region. This asymmetry is associated with an excessive mean heat advection in an area adjacent to the Denmark Strait. The advection of heat is high and positive south of the strait and low and negative north of it. We suggest that this heat advection impacts the local budgets of heat and potential energy of the mean flow in the surface layer. The mesoscale eddies are identified and their characteristics, including radius, sea level anomaly, lifetime, and paths of propagation, are assessed by using along‐track altimeter data. About 70% of the eddies are observed in the eastern part of the studied region. The eddy‐induced heat transport by warm mesoscale eddies in the Norwegian Sea was found to be about 2.5 times larger than the mean advection by the Norwegian Atlantic Current. We suggest, therefore, that the eddy‐induced transport is a dominant factor in the heat budget of this region.

Interannual Variability of the Exchange Flow Through the Strait of Bab‐al‐Mandeb

AbstractThe interannual variability of the exchange flow between the Red Sea and the Gulf of Aden through the Bab‐al‐Mandeb strait is examined based on a high‐resolution, nonhydrostatic hindcast model simulation covering a 19‐year period (1995–2013), using the MITgcm (MIT general circulation model). The model is validated against moored profiles and along‐strait cruise observations collected during the period from June 1995 to November 1996 and 19‐year sea surface temperature satellite observations. The model well reproduces the properties of the water masses at the strait over a wide range of spatiotemporal scales, including the typical two‐ and three‐layer seasonal patterns and the related intraseasonal‐to‐interannual cycles. The seasonality of the exchange flow is predominately determined by the time‐varying surface winds, with a higher correlation over the Gulf of Aden, reflecting the importance of local Gulf of Aden processes for the exchanges at the strait. The alternation of the two seasonal patterns is driven by a combination of the buoyancy‐driven mean circulation with the wind‐induced transport. The onset/offset of the two patterns is estimated to take place one‐to‐two weeks after the respective monsoon‐driven wind reversal. Model results indicate that the onset dates and durations of both patterns exhibit a considerable interannual variability. Additionally, the duration of the summer (winter) exchange pattern presents a significant increasing (decreasing) trend of ~1.45 day/year (~1.22 day/year) over the 19‐year period. Significant interannual variabilities and trends are observed in terms of the total volume of water, salt mass, and stored heat of the exchanges. Budget analysis of these trends suggests that the duration of the two exchange patterns is more important in determining the interannual variability and the related trends than the intensity of the exchange, or the variations in mean salinity or temperature of the exchanged water masses.

The Interannual Variability of Sea Surface Temperature in the Red Sea From 35 Years of Satellite and In Situ Observations

AbstractFalling between the dynamics of the seasonal cycle and expressions of longer‐term trends, the interannual variability of sea surface temperature (SST) in the Red Sea has not received enough attention. With multiple decades of satellite SST observations with spatial resolution capable of resolving patterns of variability within the Red Sea, the time has come for a description and diagnosis of the observed interannual variability of SST in this important semienclosed sea. While interannual variability of SST occurs throughout the Red Sea in both summer and winter, the greatest variability is found in the northern Red Sea during winter. Objective analysis reveals two dominant statistical modes of interannual variability of Red Sea SST: a whole‐sea mode described by general warm or cool anomalies throughout the Red Sea (∼60% of total variance), and a meridional gradient mode of opposing SST anomalies in the northern and southern Red Sea (∼20% of total variance). The warm phase of the whole‐sea mode corresponds to warm surface air temperature anomalies extending across the broader region and local surface wind anomalies opposite to the mean low‐level circulation. The whole‐sea mode is found to be a regional response to remote forcing by the East Atlantic/Western Russia (EAWR) pattern. The gradient mode, on the other hand, is a manifestation of superimposing (and statistically independent) remote impacts of El Niño‐Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) whereby ENSO drives SST anomalies in the southern Red Sea and the NAO drives SST anomalies in the northern Red Sea.

Development of a conceptual warning system for toxic levels of Alexandrium fundyense in the Bay of Fundy based on remote sensing data

Harmful algal blooms (HABs) present a potential danger for human health and commercial activities, especially in coastal regions. Observing systems increasingly rely on remote sensors to monitor and possibly predict the locations and intensity of such blooms. Here we present a novel approach for detecting HABs of Alexandrium fundyense in the Bay of Fundy, Canada. A. fundyense is considered toxic for individuals who consume shellfish when cell abundances adjacent to shellfish harvesting areas are as low as 200 cells·L−1, making it difficult to use direct remote sensing techniques to assess the threat in the early stages of the development of the bloom. Using in situ A. fundyense cell abundance measurements, together with satellite observations of sea-surface temperature and the occurrence of diatom-dominated phytoplankton populations, a warning system was developed based on three levels of alerts: green (low abundance of A. fundyense), orange (possible threat of A. fundyense) and red (high probability of A. fundyense concentrations that would result in shellfish toxicity above safe levels for human consumption). Combined information on diatom phenology and variations in sea-surface temperature are key to the timing of A. fundyense blooms: our data reveal that the termination of the diatom spring bloom, associated with the warming of the water, can trigger an increase in A. fundyense cell abundance. The objective criteria for a HAB warning system was developed and tested in the Bay of Fundy using two different datasets: one to develop the algorithm (data collected between 1998 and 2007) and one to assess its performance (data collected in 2011). The warning system is based on the cautionary principle that a false negative (warning not issued when it should have been) is far more serious than a false positive (warning issued when it should not have been). The overall success of the algorithm when tested on the validation dataset is about 70% using a threshold of 150 A. fundyense cells·L−1, with a low occurrence of false negative red alerts (<8%). The satellite-data-based warning can be used to optimize an in situ monitoring system, which can be designed to be more intensive when the warning status is orange or red. This study demonstrates that combined satellite information on phytoplankton phenology and sea-surface temperature can help predict low abundances of toxic A. fundyense cells. It also highlights the importance of an integrated approach combining satellite and in situ observations to monitor HABs.

Impact of Langmuir Turbulence on Upper Ocean Response to Hurricane Edouard: Model and Observations

AbstractTropical cyclone intensity is strongly affected by the air‐sea heat flux beneath the storm. When strong storm winds enhance upper ocean turbulent mixing and entrainment of colder water from below the thermocline, the resulting sea surface temperature cooling may reduce the heat flux to the storm and weaken the storm. Recent studies suggest that this upper ocean turbulence is strongly affected by different sea states (Langmuir turbulence), which are highly complex and variable in tropical cyclone conditions. In this study, the upper ocean response under Hurricane Edouard (2014) is investigated using a coupled ocean‐wave model with and without an explicit sea state dependent Langmuir turbulence parameterization. The results are compared with in situ observations of sea surface temperature and mixed layer depth from AXBTs, as well as satellite sea surface temperature observations. Overall, the model results of mixed layer deepening and sea surface temperature cooling under and behind the storm are consistent with observations. The model results show that the effects of sea state dependent Langmuir turbulence can be significant, particularly on the mixed layer depth evolution. Although available observations are not sufficient to confirm such effects, some observed trends suggest that the sea state dependent parameterization might be more accurate than the traditional (sea state independent) parameterization.

The Effect of ENSO on the Variability of Chlorophyll-a and Sea Surface Temperature in the Maluku Sea

The Maluku Sea features strong Chlorophyll-a (Chl-a) bloom and sea surface temperature (SST) cooling during the Southeast Monsoon season. Using high-resolution Chl-a and SST satellite observations over a long time window, this study aims to investigate the effect of El Nino Southern Oscillation (ENSO) on the seasonal variability of Chl-a and SST in the Maluku Sea. During the El Nino and La Nina, the maximum Chl-a concentration (greater than 0.5 mg/m3) and minimum SST (less than 27.5 °C) are observed in August. This finding is in line with prior investigation. Moreover, we reveal that the ENSO does not alter the seasonal variability of Chl-a and SST in the region, but the magnitudes of summertime Chl-a bloom and SST cooling are enhanced (reduced) by about 0.1 mg/m3 and 0.5 °C during the El Nino (La Nina). This is caused by an increase (decrease) in the sea level pressure gradients, which possibly enhanced (reduced) wind-driven mixing in the Maluku Sea.

Inter comparison of Tropical Indian Ocean features in different ocean reanalysis products

This study makes an inter comparison of ocean state of the Tropical Indian Ocean (TIO) in different ocean reanalyses such as global ocean data assimilation system (GODAS), ensemble coupled data assimilation (ECDA), ocean reanalysis system 4 (ORAS4) and simple ocean data assimilation (SODA) with reference to the in-situ buoy observations, satellite observed sea surface temperature (SST), EN4 analysis and ocean surface current analysis real time (OSCAR). Analysis of mean state of SST and sea surface salinity (SSS) reveals that ORAS4 is better comparable with satellite observations as well as EN4 analysis, and is followed by SODA, ECDA and GODAS. The surface circulation in ORAS4 is closer to OSCAR compared to the other reanalyses. However mixed layer depth (MLD) is better simulated by SODA, followed by ECDA, ORAS4 and GODAS. Seasonal evolution of error indicates that the highest deviation in SST and MLD over the TIO exists during spring and summer in GODAS. Statistical analysis with concurrent data of EN4 for the period of 1980–2010 supports that the difference and standard deviation (variability strength) ratio for SSS and MLD is mostly greater than one. In general the strength of variability is overestimated by all the reanalyses. Further comparison with in-situ buoy observations supports that MLD errors over the equatorial Indian Ocean (EIO) and the Bay of Bengal are higher than with EN4 analysis. Overall ORAS4 displays higher correlation and lower error among all reanalyses with respect to both EN4 analysis and buoy observations. Major issues in the reanalyses are the underestimation of upper ocean stability in the TIO, underestimation of surface current in the EIO, overestimation of vertical shear of current and improper variability in different oceanic variables. To improve the skill of reanalyses over the TIO, salinity vertical structure and upper ocean circulation need to be better represented in reanalyses.

A balanced Kalman filter ocean data assimilation system with application to the South Australian Sea

In this paper, an Ensemble Kalman Filter (EnKF) based regional ocean data assimilation system has been developed and applied to the South Australian Sea. This system consists of the data assimilation algorithm provided by the NCAR Data Assimilation Research Testbed (DART) and the Regional Ocean Modelling System (ROMS). We describe the first implementation of the physical balance operator (temperature-salinity, hydrostatic and geostrophic balance) to DART, to reduce the spurious waves which may be introduced during the data assimilation process. The effect of the balance operator is validated in both an idealised shallow water model and the ROMS model real case study. In the shallow water model, the geostrophic balance operator eliminates spurious ageostrophic waves and produces a better sea surface height (SSH) and velocity analysis and forecast. Its impact increases as the sea surface height and wind stress increase. In the real case, satellite-observed sea surface temperature (SST) and SSH are assimilated in the South Australian Sea with 50 ensembles using the Ensemble Adjustment Kalman Filter (EAKF). Assimilating SSH and SST enhances the estimation of SSH and SST in the entire domain, respectively. Assimilation with the balance operator produces a more realistic simulation of surface currents and subsurface temperature profile. The best improvement is obtained when only SSH is assimilated with the balance operator. A case study with a storm suggests that the benefit of the balance operator is of particular importance under high wind stress conditions. Implementing the balance operator could be a general benefit to ocean data assimilation systems.

Beyond peak summer temperatures, branching corals in the Gulf of Aqaba are resilient to thermal stress but sensitive to high light

Despite rapidly rising sea surface temperatures and recurrent positive temperature anomalies, corals in the Gulf of Aqaba (GoA) rarely experience thermal bleaching. Elsewhere, mass coral bleaching has been observed in corals when the water temperature exceeds 1–2 °C above the local maximum monthly mean (MMM). This threshold value or “bleaching rule” has been used to create predictive models of bleaching from satellite sea surface temperature observations, namely the “degree heating week” index. This study aimed to characterize the physiological changes of dominant reef building corals from the GoA in response to a temperature and light stress gradient. Coral collection and experiments began after a period of 14 consecutive days above MMM in the field. Stylophora pistillata showed negligible changes in symbiont and host physiology parameters after accumulating up to 9.4 degree heating weeks during peak summer temperatures, for which the index predicts widespread bleaching and some mortality. This result demonstrates acute thermal tolerance in S. pistillata from the GoA and deviation from the bleaching rule. In a second experiment after 4 weeks at 4 °C above peak summer temperatures, S. pistillata and Acropora eurystoma in the high-light treatment visibly paled and suffered greater midday and afternoon photoinhibition compared to corals under low-light conditions (35% of high-light treatment). However, light, not temperature (alone or in synergy with light), was the dominant factor in causing paling and the effective quantum yield of corals at 4 °C above ambient was indistinguishable from those in the ambient control. This result highlights the exceptional, atypical thermal tolerance of dominant GoA branching corals. Concomitantly, it validates the efficacy of protecting GoA reefs from local stressors if they are to serve as a coral refuge in the face of global sea temperature rise.

Effects of super typhoons on cyclonic ocean eddies in the western North Pacific: A satellite data‐based evaluation between 2000 and 2008

AbstractA composite time series of the merged satellite altimeters sea surface height anomaly (SSHA) data and satellite‐observed sea surface temperature (SST) data were used to identify eddies in the Western North Pacific Ocean (WNPO), where there were numbers of intense typhoons. This study systematically investigated 15 super typhoons during the period of 2000‐2008 in the WNPO to study their impacts on the pre‐typhoon ocean features, e.g., the cyclonic ocean eddy (COE) feature (closed contours of SSHA &lt; −6 cm) and neutral condition (SSHA between −6 and 6 cm). Two new COEs are generated by two super typhoons, and 18 pre‐existing COEs are intensified by 13 super typhoons. 5 of the 13 super typhoons each influenced two pre‐exisiting COEs. Although the typhoon‐induced maximum cooling centers had a right bias along the tracks due to wind conditions, pre‐existing COEs also play a significant role in determining the strength and location of large SST cooling. Three possible factors (maximum wind speed, typhoon translation speed and the typhoon forcing time, Tf) are employed to explain the interactions. Above all, the changes of the COE geometric and physical parameters (e.g., effective radius, area, SST, SSHA, and eddy kinetic energy) were mostly related to the typhoon forcing time, Tf. This is because Tf is a parameter that is a combination of the typhoon's translation speed, intensity and size. Although the typhoons may significantly impact COEs, such samples were not commonly observed. Thus, the impact of typhoon on the strength of COEs is generally inefficient.