The validation and error analysis of remote sensing data are important for their application. Currently, due to the relative scarcity of in-situ observations in the ocean, the accumulation of collocations between remote sensing and in-situ data is slow. This study proposes an engineering trick to address this issue: using the output of numerical models as a "bridge" to connect remote sensing data with in-situ data, thereby expanding the spatiotemporal window of collocation and improving collocation efficiency. The basic idea of this method is that numerical models based on differential equations can partially simulate the local spatiotemporal variations of the parameter near in-situ data. These variations can be used to compensate for the representation errors caused by the spatiotemporal differences between remote sensing and in-situ observations. Therefore, this method can enlarge the spatiotemporal window for collocation when the error limit is given. This collocation process considers the dynamic processes through numerical models and is thus named "dynamic collocation." This study demonstrates through several simple experiments that this dynamic method is superior to traditional "static" windows and has application potential. In addition to improving data collocation efficiency in the open ocean, dynamic collocation can also address the issue of the difficulty of direct comparison between satellite and buoy data in coastal areas due to significant spatial gradients in sea waves. Besides data comparison, dynamic collocation can provide a larger sample size for the model training of narrow swath sensors' empirical algorithms. For example, we applied this method to SWIM data from CFOSAT and proposed a high-precision empirical retrieval algorithm for wave mean periods.

How to cite: Jiang, H.: Dynamic collocation between satellite and in-situ measurements in wave remote sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3336, https://doi.org/10.5194/egusphere-egu24-3336, 2024.

In this study, low-cost software-defined GPS and SBAS Reflectometry (GPS&SBAS-R) systems have been built and proposed to measure ocean-surface wave parameters on board the research vessel New Ocean Researcher 1 (R/V NOR-1) and other ground-based coastal stations of Taiwan. A power-law ocean wave spectrum model has been used and applied with the Small Perturbation Method (SPM) approach to solve the electromagnetic wave scattering problem from rough ocean surface and compare with experimental seaborne GPS&SBAS-R observations. Meanwhile, the intensity scintillations of high-sampling GPS&SBAS-R signal acquisition data are thought to be caused by the moving rough surfaces of the targeted oceans. We found that each derived scintillation power spectrum is a Fresnel filtering result on sea/ocean-surface elevation fluctuations and depends on the First Fresnel Zone (FFZ) value and the ocean-surface wave velocity. The determined ocean-surface wave parameters, e.g. wave velocity and spectral index, have been compared and validated against nearby buoy measurements.

How to cite: Tsai, L.-C., Hwa, C., Su, S.-Y., and Lv, J.-X.: Ocean-surface wave measurements using scintillation theories on seaborne and coastal software-defined GPS and SBAS reflectometry observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5059, https://doi.org/10.5194/egusphere-egu24-5059, 2024.

Synthetic aperture radar (SAR) offers the possibility to observe the sea surface current with very high spatial resolution thanks to techniques such as Doppler centroid analysis and Along-Track Interferometry. These observations are relevant in coastal areas and shelf seas. SAR has been routinely providing valuable information on sea surface winds and waves for decades. However, the Doppler signature of the surface (aka Wind-wave Artefact Surface Velocity - WASV) is strongly affected by the waves and needs to be corrected accurately. In this study, we assess how strongly and how far convergent and divergent current fields impact the wave fields, hence the WASV. This is a numerical study, combining a numerical wave model (Simulating WAves Nearshore - SWAN) with a semi-empirical wave Doppler electromagnetic simulator (Yurovsky et al., 2019 - KaDOP).

In this study, the simulation of the effect of the wave-current interaction on the significant wave height is carried out using the SWAN model. Simulations are carried out for two different wind speeds 5 m/s and 10 m/s to represent different wave height regimes and two current profiles, convergent and divergent. The domain grid is one dimensional from x=0 to x=200 km with a spacing of 1 km. The depth is set to a constant value of 1000 m. The direction of propagation of the waves is perpendicular to the current front. Two scenarios are simulated waves propagating along the current and waves propagating against the current. The wind is set uniform over the whole fetch. The Doppler model KaDOP is used to estimate wave Doppler velocity (U_D). This model takes as inputs the incidence angle, wind speed, relative wind direction, significant wave height (H_s) and peak radial frequency (Ω_p) for wind sea and swell. The latter two quantities are affected by the wave-current interaction which affects the estimated wave Doppler. 

In summary, a combination of two different front widths (1 km and 3 km) and two wind speeds (5 m/s and 10 m/s) resulted in eight simulations. First,  it is shown that the spectral density increases (decreases) due to the convergent (divergent) current. The modulation is more important at the intermediate waves between the peak and around 0.6 Hz while it is negligible at the lowest and highest frequencies. As expected, the variation magnitude of H_s and U_D increases with increasing magnitude of the current divergence and wind speed. It is also noted that the convergent current, when the waves propagate in a direction opposing the current, yields larger variations ∆Hs and ∆U_D. Only in cases when the current front is 1 km wide, i.e. the divergence ≈ 0.19 10⁻³, ∆U_D exceeds 0.1 m/s, but this limit is only exceeded locally (over a few pixels). 

How to cite: Elyouncha, A., Martin, A., and Gommenginger, C.: Simulation of the wave-current interaction effect on the SAR-derived radial velocity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16367, https://doi.org/10.5194/egusphere-egu24-16367, 2024.

    This study delves into the impact of wave age on GNSS-R L1 observations, with a specific emphasis on Delay-Doppler Map (DDM) and Normalized Bistatic Radar Cross Section (NBRCS) signal strength, as well as its consequent effects on L2 wind speed inversion. Conventionally, DDMs have been simplified in wind speed inversion algorithms to solely represent sea surface roughness influenced by wind speed. However, this research underscores the significant, yet often overlooked, role of sea surface roughness induced by wave characteristics in the reflection of microwave signals. Wave age, denoting the delay of waves under wind influence and the proportion of swell to wind waves, is identified as a crucial factor affecting sea surface roughness and, thereby, the scattering properties of ocean surface signals.

    Recognizing the intricate physical correlation between wave age and sea surface wind speed, along with the multi-variable dependency inherent in GNSS-R observation theory, the study employed machine learning techniques to assess the extent of wave age's influence throughout the observation to wind speed inversion process. For this purpose, the Resnet18 deep convolutional neural network was chosen for its adept handling of the complex features present in DDM data, which can be considered as images. This choice was anchored in Resnet18’s robust feature extraction abilities and its proven track record in tasks requiring high-accuracy image classification.

    This study utilized CyGNSS L1 data along with corresponding ECMWF wind speed and sea surface parameter data for specific time and location. To conduct a comparative analysis, two methodologies were used: a traditional geophysical model function (GMF) developed by our self and machine learning. Preliminary testing indicated a marked enhancement in the accuracy of wind speed predictions when incorporating wave age into both GMF and machine learning approaches. The root mean square error notably decreased from approximately 1.8-2 meters per second to about 1.1 meters per second. The study also found a link between wave age and NBRCS intensity distribution, noting that larger inverse wave ages correlate with more signal scattering and weaker signal strength, underlining the vital impact of wave age on NBRCS intensity distribution.

How to cite: Chen, M., Liou, Y., Yang, K., and Chien, H.: Assessing the Impact of Wave Age on GNSS-R L1 products and L2 Wind Speed Inversion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8952, https://doi.org/10.5194/egusphere-egu24-8952, 2024.

    This study focuses on the spatiotemporal analysis of High Frequency Radar (HFR) signal intensity under various environmental conditions, utilizing data from four LERA MKIII systems along the northern coast of Taiwan. This investigation spans from 2023 to 2024. The radar systems are strategically positioned at different locations: Shalun station operates at 24.4 MHz with a boresight angle of 0 degrees, Beigang station at 26.77 MHz with a boresight of 345 degrees, Chaojing station at 27.75 MHz with a boresight of 0 degrees, and Zhongjiao Bay station at 31.75 MHz with a boresight of 55 degrees. The coordinates for these stations are respectively 121.24°E, 25.11°N (Shalun); 121.16°E, 25.08°N (Beigang); 121.80°E, 25.14°N (Chaojing); and 121.63°E, 25.24°N (Zhongjiao Bay). Each HFR system consists of a linear array of 16 receiving antennas and utilizes the beamforming method to process signals within a boresight angle range of ±60 degrees.
    This study's methodology employs time series analysis techniques to scrutinize High Frequency Radar (HFR) data, correlating it with various environmental observations. By charting the Signal-to-Noise Ratio (SNR) of the radar signals across different range cells along the radar's boresight azimuth over time, we identify temporal and spatial trends. Notably, periodic fluctuations in the radar signal time series have been observed. These fluctuations seem intricately linked to tidal cycles, exhibiting a significant correlation with the angular disparity between tidal flow direction and the radar's boresight azimuth. Additionally, a decrease in radar signal strength coupled with an increase in noise levels was noticed under conditions of elevated winds and waves. The research aims to precisely quantify the interplay between radar signals and the collective influence of tides, waves, and wind speeds. This thorough analysis seeks to deepen our understanding of how environmental elements impact radar performance, thereby contributing to a more nuanced comprehension of coastal ocean dynamics.

How to cite: Cheng, A., Chang, H. M., Chien, H., and Yu, H. Y.: Exploring Environmental Impacts on High Frequency Radar Signal Variability in Taiwan's Northern Coastal Waters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15081, https://doi.org/10.5194/egusphere-egu24-15081, 2024.

Using absolute dynamic topography and satellite altimeter eddy tracking data, the intrusion of the Kuroshio caused by the impingement of mesoscale cyclone eddies east of Taiwan into the northern South China Sea (NSCS) through the Luzon Strait was studied. Between 1993 and 2021, a total of 12 such cases were identified. Six of them occurred when the Kuroshio upstream (east of Luzon) strengthened and the Kuroshio downstream (east of Taiwan) weakened. A composite analysis of all cases shows that the average time from when a mesoscale cyclonic eddy impinges the Kuroshio east of Taiwan to when the NSCS reaches maximum negative vorticity is about 28 days. When the Kuroshio upstream strengthens and downstream weakens, the negative vorticity of the NSCS west of the Luzon Strait increases by 45% compared with normal conditions. The duration of the interaction between the Kuroshio and the mesoscale cyclonic eddy east of Taiwan is 28 days, compared with the normal 41 days.

How to cite: Tseng, Y.-H. and Ho, C.-R.: On the connection between eddy impingement east of Taiwan and Kuroshio intrusion into South China Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4534, https://doi.org/10.5194/egusphere-egu24-4534, 2024.

The Gulf Stream (GS) transports a massive amount of heat northward to high latitudes and releases sensible and latent heat to the atmosphere, playing an important role in the North Atlantic and European climate change. The change trends of the GS transport and pathway are still uncertain to date. Our analyses of altimeter observations from 1993 to 2016 indicate that the linear trends in surface maximum speed, transport and latitudinal location of the GS are significant east of 61ºW at the 95% level while they are small and not significant between 72ºW and 61ºW. The weakening trend of the GS during the period from 1993 to 2016 is accompanied with a southward-shifting path, which is associated with the decline of the North Atlantic Oscillation (NAO) and possibly reduction in the Atlantic meridional overturning circulation (AMOC). 

How to cite: Zhang, W., Chai, F., Xue, H., and Oey, L.: Remote sensing linear trends of the Gulf Stream from 1993 to 2016, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4592, https://doi.org/10.5194/egusphere-egu24-4592, 2024.

Direct in situ measurements of ocean currents are still quite limited and, due to its small magnitude, measurements of the vertical velocity remain one of the biggest challenges in oceanography. Vertical velocities are therefore generally inferred indirectly, and a common approach to diagnose them is to use the quasi-geostrophic omega equation. In the framework of the European Space Agency World Ocean Circulation project, a new high-resolution (1/10°) data-driven dataset of 3D ocean currents, including the vertical component, has been developed: the WOC-NATL3D dataset. The product domain extends over a wide portion of the North Atlantic Ocean from the surface down to 1500 m depth, and the dataset covers the period between 2010 and 2019. This entire domain holds immense importance for fishery activities and is identified as a key area within international conventions for the conservation of fishing resources, such as tuna and tuna-like fishes under ICCAT (International Commission for the Conservation of Atlantic Tunas). To generate this product, a diabatic quasi-geostrophic diagnostic model is applied to data-driven 3D temperature and salinity fields obtained through a deep learning technique, along with ERA5 fluxes and empirical estimates of the horizontal Ekman currents based on input provided by the European Copernicus Marine Service. The assessment of WOC-NATL3D currents is performed by direct validation of the total horizontal velocities with independent drifter estimates at various depths (0, 15 and 1000 m) and by comparing them with existing reanalyses that are obtained through the assimilation of observations into ocean general circulation numerical models. Our estimates of the ageostrophic components of the flow improve the total horizontal velocity reconstruction, being more accurate and closer-to-observations than model reanalyses in the upper layers, also providing an indirect proof of the reliability of the resulting vertical velocities.

How to cite: asdar, S., Ciani, D., and Buongiorno Nardelli, B.: 3D reconstruction of horizontal and vertical quasi-geostrophiccurrents in the North Atlantic Ocean, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18280, https://doi.org/10.5194/egusphere-egu24-18280, 2024.

Sea Surface Salinity (SSS) is an increasingly-used Essential Ocean and Climate Variable. The SMOS, Aquarius, and SMAP satellite missions all provide SSS measurements, with very different instrumental features leading to specific measurement characteristics. The Climate Change Initiative Salinity project (CCI+SSS) aims to produce a SSS Climate Data Record (CDR) that addresses well-established user needs based on those satellite measurements. To generate a homogeneous CDR, instrumental differences are carefully adjusted based on in-depth analysis of the measurements themselves, together with some limited use of independent reference data [Boutin et al., 2021]. An optimal interpolation in the time domain without temporal relaxation to reference data or spatial smoothing is applied. This allows preserving the original datasets variability. SSS CCI fields are well-suited for monitoring weekly to interannual signals, at spatial scales ranging from 50 km to the basin scale.

In this poster, we review recent advances of the CCI+SSS phase 2 project and the performances of the last (version 4) CCI+SSS product which covers the 2010-2022 period.

With respect to global CCI+SSS v3 dataset, CCI+SSS v4 dataset includes the following updates. According to several users recommendations, global fields are now on a rectangular 0.25°grid, and polar fields on EASE polar grid are also delivered. The ice filtering has been refined (it was too strong in CCI v3). A correction for contamination by radio frequency interferences has been developed and applied around Samoa island, Barbados island and in the Gulf of Guinea. Latitudinal-seasonal corrections have been applied on SMOS, Aquarius and SMAP SSS. SSS changes related to SMOS direct models updates (wind, dielectric constant, rain) have also been taken into account. This leads to significant improvements at high latitudes, allowing to monitor the interannual SSS variability in the Barents Sea, or the spatio-temporal evolution of a fresh event west of Greenland in Fall 2021. In the tropics, we show that the RFI contamination correction allows to restore the interannual SSS variability related to ENSO which was completely masked by RFI contamination around the island of Samoa. We also illustrate how the CCI+SSS fields have been used to assess model results, at global scale with or without data assimilation (GLORYS model), and in the Amazone plume (NEMO-PISCES biogeochemical model).

References

Boutin, J., et al. (2021), Satellite-Based Sea Surface Salinity Designed for Ocean and Climate Studies, Journal of Geophysical Research: Oceans, 126(11), e2021JC017676, doi:https://doi.org/10.1029/2021JC017676.

Gévaudan et al. (2022). Influence of the Amazon-Orinoco discharge interannual variability on the western tropical Atlantic salinity and temperature. Journal of Geophysical Research: Oceans, 127, e2022JC018495. https://doi.org/10.1029/2022JC018495

How to cite: Boutin, J. and Thouvenin-Masson, C. and the CCI+SSS consortium: Recent advances from the ESA CCI+Sea Surface Salinity project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11240, https://doi.org/10.5194/egusphere-egu24-11240, 2024.

The concentration of particulate organic nitrogen (PON) in seawater plays a central role in ocean biogeochemistry. Limited availability of PON data obtained directly from in situ sampling methods hinders our ability to better characterize the spatio-temporal variability of PON within the global ocean. Tight relationships have recently been developed between in situ measurements of seawater inherent optical properties (IOPs) and PON. Knowing that IOPs can now be estimated from ocean color remote-sensing, these relationships could then be used to assess PON from semi-analytical algorithms applied to satellite ocean color observations. The present study aims at evaluating which IOPs, as estimated from space, can be used as the best proxy for the remote sensing retrieval of PON. The different considered IOPs are the absorption coefficients of total particulate matter, a_p(λ), phytoplankton, a_ph(λ), and non-algal particles, a_d(λ), as well as the particulate backscattering coefficient, b_bp(l). IOPs have been derived from satellite ocean remote-sensing reflectance, R_rs(l), using different available inverse methods. The validation of the algorithms is based on matchup between an extensive dataset of 156 concurrent measurements of in situ PON and satellite-derived particulate IOPs. Our results show that reasonably strong PON vs satellite-derived IOPs relationships hold across a range of diverse oceanic and coastal environments. a_ph(443) shows the best ability to serve as a PON proxy over a broad range of PON from open ocean oligotrophic to coastal waters (MdAPD of 25.39 %). b_bp(555) can also be considered as a good proxy of PON in open ocean environments (MdAPD of 22.03 %). Comparison with in situ time series over ten years shows also the good performance of the algorithm to reproduce the seasonal variation of PON. The application of this algorithm to Moderate-Resolution Imaging Spectroradiometer (MODIS; 2002-present) observations provides global PON distributions pattern which agree with in situ PON expected geographical distribution. High PON concentrations are observed in turbid shelf and coastal regions as well as in upwelling areas; while low PON are observed in oligotrophic regions. The presented relationships demonstrate a promising means to assess long-term trends and/or budget of PON in specific areas of the ocean or at the global oceanic scale.

How to cite: Fumenia, A., Loisel, H., Jorge, D., Bretagnon, M., Demaria, J., and Mangin, A.: New ocean color algorithms for estimating the concentration of particulate organic nitrogen from remote sensing in oceanic surface waters, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15346, https://doi.org/10.5194/egusphere-egu24-15346, 2024.

Marine Heat Waves (MHWs) are defined as discrete periods of anomalously warm water temperature at a given location. MHWs can have a huge impact on marine ecosystems, already under stress because of the effects of a warming ocean under climate change and high anthropogenic pressure. This work will assess the spatio-temporal evolution of MHWs in the southern North Sea, with an emphasis on the 2022 events. Studying the impact of MHWs on coastal marine ecosystems is currently hampered by the resolution mismatch between traditional satellite data (typically 1 km spatial resolution for SST and CHL) and species habitat/substrate. In the southern North Sea, a multitude of shallow sandbanks, sand, mud and coarser sediment substrats are for instance present, offering a multitude of habitats to different species. With ocean dynamics, and hence water mass and temperature distribution being impacted by the presence of these sandbanks, fine spatial resolution data are required for accurate analysis of the consequences of MHWs and cold spells on the ecosystem. The Thermal InfraRed Sensor (TIRS) sensor onboard the Landsat constellation provides SST at a spatial resolution of 30 m with an accuracy of 0.1 to 0.2K, and can allow the study of the evolution of small-scale dynamics in coastal regions, including the development of MHWs. However, Landsat data have a very low revisit time (7-9 days), not optimal to study specific MHW events, which can evolve on a daily basis. This work will assess the synergy between Landsat data and daily, low-spatial resolution SST data to analyse the evolution of MHWs at coastal regions. DINEOF (Data Interpolating Empirical Orthogonal Functions) will be used to merge these tow data sources and provide high spatial and temporal resolution SST data. This work is a first attempt at linking MHW variability and their consequences on marine ecosystems at very fine spatio-temporal scales, and is part of the North-Heat project. We aim at providing key insights for our comprehension of MHWs in the southern North Sea, a region where marine ecosystems are already under high anthropogenic pressure.

How to cite: Alvera-Azcárate, A., Mohamed, B., Barth, A., Massant, J., and Van der Zande, D.: High spatial resolution Sea Surface Temperature data for the study of Marine Heat Waves in the souther North Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16471, https://doi.org/10.5194/egusphere-egu24-16471, 2024.

Sea Surface Nitrate (SSN) plays an important role in assessing new production and phytoplankton growth in the ocean, yet it has been challenging to estimate SSN from satellites due to its complex and varying relationship with different environmental proxies. The different SSN trends in the northwest Pacific reported in previous studies call for more detailed research to examine the interannual variabilities in SSN. We addressed this problem by developing a stacking-random-forest (SRF) based algorithm for Moderate Resolution Imaging Spectroradiometer (MODIS). It allows estimating SSN from daily sea surface temperature (SST) and Chlorophyll-a concentration (Chl) at a spatial resolution of 4 km. For SSN ranging between 0.0005 and 25.88 μmol/kg, the model had a root mean square difference of 1.34 μmol/kg (5.3%) and coefficient of determination of 0.92. Further independent validation and sensitivity tests demonstrated the validity of the algorithm in retrieving SSN. Using this novel data record, for the first time, we investigated the SSN interannual variabilities and trends from MODIS. Overall, the SSN showed a weak decreasing trend of -0.01±0.007 μmol kg-1 yr-1 (p < 0.05) in the northwest Pacific in 2002-2020, associated with an increasing trend in SST. The interannual variabilities of SSN were significantly correlated with the environmental proxies (SST, Chl) and the climate indices (Pacific Decadal Oscillation and North Pacific Gyre Oscillation). The SSN trends can be further restricted with more data available.

How to cite: Chen, S., Wang, Y., and Chai, F.: Remote estimates of sea surface nitrate from ocean color in the northwest Pacific , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21436, https://doi.org/10.5194/egusphere-egu24-21436, 2024.

A pioneering exploration into oceanographic phenomena in Macaronesia and the west North Atlantic Ocean is presented utilizing animal telemetry data collected from four captive-born Loggerhead sea turtles. Noting the cosmopolitan nature and migratory behaviors of sea turtles, this analysis assesses the potential of sea turtles to offer valuable insights into oceanic environmental variables. 

The turtles' interactions with upwelling sites, eddies, and ocean currents are revealed through the integration of georeferenced data (location and time) and oceanographic products from the Marine Copernicus Service. The oceanographic data were interpolated to the turtles’ location in time to obtain remotely-sensed Sea Surface Temperature (SST), chlorophyll-a (chl-a) concentrations and altimeter-derived ocean currents along the routes they performed. 

The results showcase the versatility of these animals as ocean gliders if they were instrumented with temperature and chl-a sensors, providing valuable measurements mostly about the atmosphere-ocean interaction, given their known diving behavior usually ranges from the surface down to 200 m. One of the turtles stayed for over two years near Banc d’Arguin. The simulation of its performance (if it were instrumented with oceanographic sensors) resembled that of a mooring in a key region of high productivity, capturing the seasonal cycle of SST and the chl-a bloom. A second turtle crossed the North Atlantic Subtropical Gyre as if it were an ocean glider leaving the Canary Islands and reaching the Gulf Stream in about 4 months. The path followed by this turtle reveals likely corridors used by sea turtles in cross-basin migrations which would be of high interest to be used for long-term and recurrent monitoring of the upper open ocean interior. Lastly, the third and fourth turtle navigated along the shelf of the Northwest African upwelling area, demonstrating their capability to sample not only open ocean but also coastal areas; the latter being particularly important given the relevance of counting with in situ SST measurements for validation of satellite data. 

Jointly, the four turtles of study and their associated navigational routes encourage further study of the potential of instrumenting Loggerhead sea turtles in Macaronesia as a complementary tool to more traditional approaches for measuring environmental variables.

How to cite: Ferro, F. L., Laguéns-Expósito, O., Aguiar-González, B., Varo-Cruz, N., Liria-Loza, A., Usategui-Martín, A., and Manchín, F.: Exploring Oceanographic Frontiers: Insights from Animal Telemetry Data of four Captive-Born Loggerhead Sea Turtles released in the Macaronesia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13362, https://doi.org/10.5194/egusphere-egu24-13362, 2024.

Coral reefs are extremely vulnerable to climate-driven warming of the ocean, which threatenstheir survival. Coral responses to rising temperatures are currently studied and predictedusing sea surface temperature (SST) from multiple sources. Despite the importance ofharmonizing complementary data from different sources, there is no clear understanding ofthe consistency or lack of it among the main datasets used and the predictions made usingthem. Understanding the consistency among the different SST data applied to coral reefsmay facilitate monitoring and understanding global warming's impact on coral reefs. Fourtypes of SST data across North-Western and South-Western Australia are compared toassess their differences and ability to predict historical coral bleaching events. Four decadesof coral bleaching indicators, Degree Heating Week (DHW) and Degree Heating Month (DHM)were calculated based on satellite-derived SST, global climate models (GCM), and coral corederived proxies. Both DHW and DHM were inconsistent among datasets and did notaccurately predict moderate and severe bleaching events. Despite high DHWs and DHMs,some reefs did not experience bleaching, suggesting site-specific coral adaptation. SST datafrom different sources had better consistency for frequency and were consistent with coralcore derived proxies of SST, highlighting the importance of coral cores in understanding pastthermal stress. By exploring the differences and similarities among data sources, this studyhighlights the need to compare thermal stress indicators from different datasets for a betterunderstanding and a more robust prediction of coral response to thermal stress.

How to cite: Neo, V. H. F., Maina, J. M., Zinke, J., Fung, T., Merchant, C., Zawada, K., and Krawczyk, H.:  Inconsistencies in Ocean Temperature Monitoring for Coral Reef Applications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13356, https://doi.org/10.5194/egusphere-egu24-13356, 2024.