Fine resolution satellite sea surface temperatures capture the conditions experienced by corals at monthly but not daily time scales.

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Jaelyn T Bos, Malin L Pinsky This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5314629/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jan, 2025 Read the published version in Coral Reefs → Version 1 posted 8 You are reading this latest preprint version Abstract Water temperature is a strong driver of growth, survival, and local adaptation in corals, but our knowledge of the temperatures experienced by corals on reefs worldwide remains limited. While in situ temperature loggers can provide high quality data, they are relatively expensive to place and retrieve. Alternatively, remotely sensed sea surface temperature data is globally available but may be a biased representation of the temperatures experienced by corals. Here, we compared data from 314 temperature loggers on coral reefs to the ~1 km 2 resolution remotely sensed Multi-scale Ultra-high Resolution Sea Surface Temperature (MUR) product from NASA. We found good agreement (Pearson’s r = 0.95) between maximum monthly mean temperatures calculated from remote and in situ data, with 84% of temperatures within 0.5 °C of each other. However, remotely sensed temperature did not effectively capture sub-diel temperature fluctuations and the highest peak temperatures that may be most dangerous for corals. Predictions of in situ temperatures were significantly but weakly improved by a consideration of reef geomorphology. Ultimately, we found that remotely sensed temperatures can accurately represent the monthly conditions experienced by most corals but should be used with caution at finer temporal scales. Remote sensing temperature microclimates climate geomorphology coral reef Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Climate varies on multiple scales. While broad climate patterns define biomes, spatial temperature variation also occurs within regions and even habitats. “Microclimates” exist across scales from centimeters to meters in ecosystems as diverse as forests, mountains, and the intertidal zone, and they can complicate our understanding of climate change impacts by serving to either buffer or exacerbate the effects of rising regional temperatures on individual organisms (Seabra et al. 2011; Verrall and Pickering 2020; Zellweger et al. 2020). Microclimates also exist on coral reefs, and a growing body of research suggests that microclimate variation plays a key role in determining thermal tolerance in corals (Barshis et al. 2013; Palumbi et al. 2014; Schoepf et al. 2015). However, accurately mapping in situ temperatures on coral reefs remains challenging. Here we assess the utility of a globally-available remote sensing product for predicting geographically fine-scale temperatures on coral reefs. Coral reefs occur in more than one hundred countries and territories in every tropical ocean basin (UNEP-WCMC et al. 2018). This broad range naturally leads to substantial differences in temperature between reefs, with some regions such as the Red Sea and the Persian Gulf exhibiting summer temperatures multiple degrees warmer than those found elsewhere in the world (Howells et al. 2012). However, temperature differences also exist at finer spatial scales. In situ temperatures on coral reefs depend not only on regional climate patterns but also on within-reef processes including tidal cycling and wave action (MacKellar 2013; Bachman 2022). These fine-scale processes affect both long-term temperature averages and sub-diel temperature variability (Reid et al. 2020). Sites separated by as little as 200 meters can have daily temperature ranges that differ substantially, from less than 1 °C to as great as 5 °C (Davis et al. 2011). Ultimately, the temperature regime experienced by an individual coral results from the combination of regional processes, within-reef water circulation, depth (Cyronak et al. 2020), and atmospheric conditions, including clouds (Leahy et al. 2013). As a result, regional temperature averages may differ substantially from the temperature actually experienced by an individual coral animal in its microclimate (Thomas et al. 2022). Microclimate variations in temperature have clear effects on growth, survival, and local genetic adaptation in corals. Differences in thermal tolerance between populations of conspecific corals inhabiting different parts of the same reefs have been documented in Porites in Florida (Kenkel 2015), Pocillopora in Australia (Marhoefer et al. 2021), and Acropora in American Samoa (Palumbi et al. 2014), among others. Furthermore, physiological differences between corals in different microclimates have in some cases been explicitly linked to genetic differences in coral animals (Palumbi et al. 2014; Thomas et al. 2022) or symbionts (Hoadley et al. 2019). Multiple studies suggest that natural variation in heat tolerance between coral populations may be a key factor contributing to climate resilience of coral reefs (Kleypas et al. 2016; McManus et al. 2021), emphasizing the importance of accurately assessing temperature landscapes on reefs. Studies make use of multiple temperature metrics to delineate microclimates, though a subset of metrics are particularly common. First, maximum monthly mean (MMM) temperatures are commonly used as baselines in coral bleaching studies and are closely related to local heat tolerance in coral assemblages (Kayanne 2017). MMM temperatures are equal to mean water temperatures during the hottest month of the year, usually calculated across a timespan of several years (Liu et al. 2014). Heat acclimation and local climate adaptation in corals can also respond to peak daily temperatures (Thomas et al. 2018; Marhoefer et al. 2021). Third, heat adaptation in corals can relate to daily temperature ranges, with corals that experience wider ranges also having more protection against bleaching (Kenkel et al. 2015; Safaie et al. 2018). This latter observation suggests that the difference between local daily maximum and minimum temperatures may have significant effects on coral health. Measuring local temperatures on coral reefs usually involves the placement of in situ temperature loggers, a process that can be expensive and time consuming (Colin and Johnston 2020). Consequently, many coral reefs, particularly in remote areas, lack in situ temperature measurements entirely. By contrast, remotely sensed data is often globally available and free to researchers. For example, global remotely-sensed sea surface temperature data from NOAA has long been used to both explain and predict coral bleaching (Gleeson and Strong 1995). However, it remains unclear to what extent remotely sensed products are accurate, precise, and unbiased predictors of in situ temperatures on coral reefs because of differences between the ocean surface observed by satellites and the more complex conditions experienced by corals. Comparison of maximum monthly mean temperatures calculated from in situ loggers and NOAA’s 5 km 2 Coral Reef Watch remote sea surface temperature product in American Samoa revealed a greater than 0.5 °C difference between the two metrics (Klepac and Barshis 2022). Similarly, a comparison of the NOAA product with in situ loggers in Belize noted mean offsets of greater than 0.5 °C that varied seasonally, despite high linear correlations between remote and in situ temperatures (Castillo et al. 2012). A comparison between the NOAA product and in situ data using 17 temperature loggers in Kiritimati also found strong linear correlations in long term averages but spatially localized multi-degree differences between the remote and in situ calculations during cold upwelling events (Claar et al. 2019). These local examples suggest possible discrepancies between in situ and remotely sensed temperatures more broadly; however, comparison of in situ and remotely sensed temperatures across reefs in multiple geographic regions or with more modern and higher resolution SST products is lacking. Here, we compared in situ temperature records on coral reefs to the 0.01 degree resolution Multi-scale Ultra-high Resolution Sea Surface Temperature (MUR) dataset from NASA. We examined three temperature metrics linked to local adaptation in corals: maximum monthly mean temperature (MMM), maximum monthly 99th percentile temperature (peak annual temperature), and mean daily temperature range (DTR). We found that remotely sensed MMMs closely correlated with those calculated in situ , but remotely sensed data missed the highest annual temperatures and failed to predict daily temperature ranges. Materials and Methods We tested the accuracy and bias of remotely sensed temperature on coral reefs by comparing it to an assembled dataset of in situ temperature loggers. We calculated each temperature metric separately from both the remote and in situ data. We also assessed how bias differed across latitude, depth, and reef geomorphology. Data sources We obtained remotely sensed sea surface temperature from the Multi-scale Ultra-high Resolution (MUR) Sea Surface Temperature dataset (NASA/JPL 2015). MUR is a daily sea surface temperature product at a spatial resolution of 0.01 degrees by 0.01 degrees, or approximately one kilometer by one kilometer, available beginning in 2002. The dataset is a processed product that includes data from MODIS satellites and from Advanced Very High Resolution Radiometer (AVHRR) and microwave sensors carried by a variety of satellites, all of which has been calibrated to in situ data from the NOAA buoy network (Chin et al. 2017). The daily remotely sensed SST product is designed to represent “bulk” temperatures in the mixed layer of the ocean; roughly 10 meters deep to just below the “skin” at the sea-air interface. The product uses only remote sensed data taken at night (Chin et al. 2017; Koutantou et al. 2023). We used in situ temperature logger records on coral reefs from a variety of sources. In situ temperatures are typically recorded using battery-powered submerged temperature loggers that can be left on reefs to record temperatures at set time intervals for several months or years and later recovered (Colin and Johnston 2020). Here, we use temperature logger data from Australia, several Pacific islands, Puerto Rico, the Virgin Islands, the South China Sea, Florida, and Panama, for an incomplete but broad representation of temperatures on global coral reefs over the last twenty years (Fig 1). After filtering, we used data from 314 loggers (Table 1). Table 1. Sources of in situ temperature logger data. Region Institution Loggers in unfiltered dataset Loggers kept post- filtering Depth range (meters) Reference(s) Hawai’i, U.S. Pacific Territories, Puerto Rico, U.S. Virgin Islands, Florida U.S. National Oceanographic and Atmospheric Administration (NOAA) 412 104 0.6 - 25.0 (Coral Reef Ecosystem Program, Pacific Islands Fisheries Science Center, 2017) (Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, 2020) (Manzello et al 2018; 2020) Hawai’i U.S. Geological Survey (USGS) 32 14 0.3 - 14.6 (Grossman and Marrack 2019) Australia (inc. Great Barrier Reef, Western Australia, and Coral Sea) Australian Institute of Marine Science 306 113 0.1 - 19.7 (Australian Institute of Marine Science 2017) Palau Stanford University 90 36 No depth data (Palumbi 2021) New Caledonia ReefTEMPS 94 35 1.0 - 60.0 (Varillon et al. 2019) Panama Smithsonian Tropical Research Institute 18 7 1.0 - 18.3 (Physical Monitoring Program of the Smithsonian Tropical Research Institute) Dongsha Atoll, South China Sea Woods Hole Oceanographic Institution 5 3 1.0 - 5.0 (Cohen 2013) Kiritimati, Northern Line Islands Georgia Institute of Technology 2 1 No depth data (Cobb and Gates 2016) Phoenix Islands Woods Hole Oceanographic Institution 4 1 8.0 (Fox and Cohen 2022) We also obtained additional environmental data for the location of each temperature logger. We downloaded reef geomorphology data from the Allen Coral Atlas (Allen Coral Atlas 2022), which uses “geomorphic classes” to qualitatively characterize reef zones by their physical form, such as lagoons, reef crests, and reef flats, and maps these features on a 10 m 2 scale on reefs up to 15 meters deep. We also used the global coastlines dataset from OpenStreetMap (OpenStreetMap contributors 2024) to calculate distance between loggers and the shore. Logger data preprocessing All temperature logger records included metadata with latitude and longitude, and many but not all metadata records included the logger depth (Table 1). We matched logger locations to Allen Coral Atlas geomorphic data and discarded the 54% of logger points (513 removed of 955) that fell outside the area for which the Allen Coral Atlas defined geomorphic classes. We trimmed the temporal extent of the logger data to match that of the sea surface temperatures (June 2002 to January 2020) where necessary. We filtered the frequency of each logger’s measurement to one data point every 30 minutes to equalize temporal resolution between loggers. For each logger, we discarded data from any day with fewer than 24 timepoints and any month with fewer than 15 days of data. Next, we discarded data from loggers with fewer than 12 continuous months of data, leaving us with 9.9 million total logged hours of data across 314 loggers. We calculated MMM temperature, peak annual temperature, and DTR for each logger. MMM temperatures were calculated by taking the mean temperature of each calendar month (i.e. January, February, etc.) across the entire logged timeframe and selecting the hottest mean month. For peak annual temperature, we used a similar process but calculated the 99th percentile temperature of each calendar month rather than the mean, then took the maximum of those. This gave us a proxy for maximum annual temperature, while filtering out the highest temperatures that may be caused by instrument error or other anomalies. Finally, for DTR we calculated the daily minimum and maximum temperatures for each logger across the entire logged timeframe, subtracted the minimum from the maximum, and took the mean across all daily differences. We processed remotely sensed SST for each logger point by trimming the MUR data to the same temporal extent as the logger data. We then used the temporally trimmed data to calculate MMM, peak annual temperature, and mean weekly temperature variation. We calculated the peak annual temperature by taking the highest temperature across the entire time frame, since the MUR SST has a one-day temporal resolution. We used hottest daily temperature instead of 99th percentile temperature due to the greater quality control and coarser spatial precision of the remotely sensed data compared to the in situ data. Mean weekly temperature variation was calculated by taking the maximum and minimum temperatures of each calendar week (Sunday - Saturday), subtracting the minimum from the maximum, and taking the mean of all of the differences. We used mean weekly temperature range in lieu of mean daily temperature range since remote SST is not available at sub-daily timescales. We chose this timescale because one week should encompass most of a tidal cycle while minimizing seasonal variation that would be associated with longer timescales. We used the Allen Coral Atlas to define the reef “geomorphic class” at each logger point. Finally, we calculated distance to shore for each logger point using the OpenStreetMap coastlines and the “great circle” distance function from Python’s Xarray-Spatial library. All data preprocessing and analysis was performed in Python 3.8.3. Scripts are available at https://github.com/pinskylab/logger_remote_temps. Statistics Prior to processing, we normalized the “distance to shore” data by taking the natural logarithm of one plus the distance. To understand linear relationships between variables, we calculated Pearson’s correlation coefficient ( r ). For factor-level relationships between geomorphic classes and temperatures, we calculated H statistics and P values using Kruskal-Wallis tests in the scipy.stats library version 1.11.4. To test our ability to predict in situ MMM and peak temperatures, we fit linear models to predict each in situ temperature metric from all of the remotely sensed variables, including distance to shore, an interaction term of remote MMM and distance to shore, and all geomorphic classes. We selected models by stepwise addition based on cross-validated R 2 score, performed with default parameters in scikit-learn library version 1.0.2. Here, we define “accuracy” of each model as the mean absolute difference between the predicted value and actual in situ value of each temperature metric. We define “bias” as the difference in means between predicted values and actual in situ values. Therefore, a measure may be unbiased but inaccurate if predicted values are far from the actual values but evenly split between positive and negative differences. Results For the commonly used MMMs, we found a close linear relationship between in situ and remote temperatures (Pearson’s r = 0.95, linear regression slope of 1.001 ± 0.001, n = 314, Fig. 2a). Remotely sensed MMMs averaged 0.04 °C cooler than in situ , with a standard deviation of the difference of 0.47 °C ( n = 314 loggers). The maximum absolute difference was for a logger in the southern Great Barrier Reef, which had a MMM 2.56 °C warmer than the remotely sensed MMM. Across all 314 comparisons of MMM, 96% of the remotely sensed values were within 1 °C of the in situ values, and 84% were within 0.5 °C (n = 314, Fig. 2b). We tested whether the bias of remotely sensed MMMs differed across gradients of latitude, depth, distance to shore, or geomorphology. There was no clear relationship with either latitude (see Supplemental Fig. 1a) or distance to shore (Supplemental Fig. 1b). For the loggers with associated depth measurements, deeper loggers exhibited colder MMMs relative to the remotely sensed MMMs (Fig. 3a). However, the linear correlation with depth relationship was weak (Pearson’s r = -0.402 ± 0.052, n = 277). There was a weak relationship between bias and geomorphic class, and both terrestrial reef flats and outer reef flats each had in situ MMMs that were 0.27 °C hotter than remote MMMs on average (Fig. 3b). A Kruskal-Wallis test of bias vs. geomorphic class was statistically significant (n = 314, H = 19.8, p = 0.02). We tested whether latitude, distance from shore, and geomorphic class together helped to explain additional variation in in situ temperatures. The model selection routine identified a linear model that included distance to shore, an interaction term of remote MMM and distance to shore, and three geomorphic classes (outer reef flat, shallow lagoon, and an aggregate of all other geomorphic classes). The model suggested that in situ MMMs were colder relative to remotely sensed MMM at sites further from shore, though less so on outer reef flats, and warmer in shallow lagoons. However, the improvement in cross-validated R 2 was negligible (only 0.004, see Supplemental Table 1 and Supplemental Text). After comparing in situ and remote MMMs, we assessed the similarity of peak annual temperatures measured in situ and remotely. The mean difference between in situ and remote peak annual temperatures was 0.23°C, with the in situ temperatures hotter than the remote temperatures. These metrics showed weaker linear correlation than did remote and in situ MMMs, especially at the highest temperatures (Pearson’s r = 0.83, Fig. 4a). In addition, this fit was not consistent across the full range of temperatures. In situ peak annual temperatures below 31 °C (70% of logged temperatures, n=223) correlated closely with the remote peak annual temperatures (r = 0.88), but above this temperature, higher in situ temperatures were only weakly associated with higher remotely sensed temperatures (r =0.21, n= 91). Similarly to MMMs, we evaluated potential sources of bias in the remotely sensed peak annual temperatures, including latitude, depth, distance to shore, and geomorphology (see Supplemental Fig. 2). We found no clear bias by latitude or distance to shore. The difference between in situ and remotely sensed peak annual temperatures showed a negative but nonlinear relationship with depth, with in situ peak annual temperatures up to 4 °C hotter than remote sensing for depths 20 m (Fig. 5a). The bias was moderately correlated with the natural log of depth (Pearson’s r = -0.56). Geomorphic class was a stronger predictor of bias, with inner reef flats consistently hotter than remotely sensed maxima (Kruskal-Wallis of in situ vs. remote differences across geomorphic classes, n = 314, H = 58.7, p=2.4x10 -9 , Fig. 5b). Statistical models predicting peak temperatures from remotely sensed variables performed less well than those predicting MMMs (see Supplemental Table 2 and Supplemental Text). Finally, we examined the relationship between daily temperature range and each of the remotely sensed temperature metrics (MMM, maximum annual temperature, and weekly temperature range), and found no strong relationship with any of them (all Pearson’s r < 0.1). There were notable relationships between daily temperature range and both geomorphic class (Kruskal-Wallis H= 71.67, p = 7x10 -12 , n =314) and the natural logarithm of depth (Pearson’s R = -0.51, n =277). Inner reef flats had the highest mean DTR (1.4°C), and both outer reef flats and shallow lagoons also had mean DTRs above 1 °C. All other geomorphic classes had mean DTRs between 0.5 and 1 °C (see Supplemental Fig. 3). Furthermore, daily temperature range was correlated with the difference between in situ and remote maximum temperatures, suggesting that some of the sites with high daily temperature range may be the same as the sites with high maximum temperatures missed by the remotely sensed data. However, these relationships gave us little ability to predict in situ daily temperature ranges from remotely sensed data (see Supplemental Table 3 and Supplemental Text). Discussion This study evaluated how closely temperature metrics relevant to coral thermal adaptation calculated from the remotely sensed 1 km 2 NASA-MUR dataset resembled those calculated from in situ temperature loggers on coral reefs. Overall, we found that remotely sensed sea surface temperature was in many cases a good proxy for in situ temperatures on coral reefs, though the accuracy and bias depended on both the specific temperature metric and within-reef geomorphic features. In particular, we found remotely sensed temperatures to be effective proxies for MMMs calculated in situ . In particular, the <0.1 °C difference in means for remote and in situ data suggests that the subsurface corrections applied by NASA for buoy depth are appropriate and effective in coral reef environments. This is especially notable since the 1 km 2 resolution MUR data, while more spatially precise than many other remotely sensed SST products, is still coarser grained than many of the oceanographic processes known to contribute to variation in reef temperatures and coral bleaching. Water circulation on reefs depends on fine-scale patterns including internal waves, turbulence, and tides, all of which can result in temperature variation on fine spatial scales (Herdman et al. 2015; Reid et al. 2020; Davis et al. 2021). For example, a 2008 study on Mo’orea by Lenihan et al found differences in current speed and corresponding differences in coral bleaching at spatial scales as fine as meters and even centimeters (Lenihan et al. 2008). Most MMM temperatures calculated from SST were within 0.5 °C of those calculated in situ and nearly all were within 1 °C, similar to results collected by Claar on Kiritimati (2019). Since bleaching predictions are often calculated from SSTs that are more than 1 °C above MMM temperature (Liu et al. 2014), accurately measuring baseline temperatures to within one degree is helpful for a meaningful understanding of heat adaptation in corals. The close agreement between remote and in situ temperatures is in contrast to the Klepac (2022) study, which found a greater than 0.5 °C offset between in situ and remote temperature. However, the latter study used daily temperature averages rather than maximum monthly means, and the 5 km x 5 km NOAA Coral Reef Watch sea surface temperature in lieu of the finer-scale NASA-MUR dataset used here. The highly linear relationship between remote and in situ MMMs found here suggests comparable accuracy between the highest and lowest extrema of the temperature range, as well as the median temperatures. Moreover, this relationship proved unbiased by latitude, indicating that remotely sensed MMMs may be equally appropriate for tropical and subtropical reefs. Remotely sensed sea surface temperature proved less effective for measuring peak annual temperatures than maximum monthly means and unsuitable for estimating daily temperature range. Peak annual temperatures measured in situ agreed well with those measured remotely for cooler locations, showing a linear relationship similar to that seen between in situ and remote MMMs. However, for the 30% of logger points with the highest in situ peak temperatures (>31 °C), the remote peak annual temperature substantially underestimated the in situ peak temperature. The magnitude of this underestimate increased with increasing in situ temperature. These sites where in situ peak annual temperatures were hotter than remote peak annual temperatures also had disproportionately high daily temperature ranges, suggesting that these hottest peak temperatures may occur as part of high frequency temperature fluctuations. Since NASA-MUR is a daily product, it has an inherently limited ability to detect sub-diel variation, a well-established issue with remote sea surface temperature products (Leichter 2006). We refer to the sites with high peak annual temperatures underestimated by remote sensing and high daily temperature ranges as “highly variable sites”. Consistent with past research on heat fluxes in coral reefs, these highly variable sites were disproportionately shallow (Leichter et al. 2006). High frequency temperature variability strongly depends on reef geomorphology at scales as fine as meters, even where average temperatures are similar between sites. These differences in temperature variability appear to correspond to differences in water retention time, depth, and turbulence (Guadayol et al. 2014). These findings are consistent with our data, as we saw strong relationships between geomorphic class and both peak annual temperatures and daily temperature range. However, the literature also shows that the relationship between temperature and specific geomorphic classes differs by location. For example, studies in the Red Sea show the highest levels of high frequency temperature variation on wave-protected reef flats (Davis et al. 2011), whereas studies of Dongsha Atoll in the South China show the opposite pattern, with wave-exposed reef crests exhibiting more temperature variation than the protected reef flat (Reid et al. 2020). This inconsistency hampers our ability to use remotely sensed geomorphic classes to predict either peak temperatures or daily temperature range across different reefs. Any interpretation of these results must also consider known issues in these datasets. The existence of a logger in New Caledonia at greater than 50 meters depth within areas defined as “reef” by the Allen Coral Atlas reveals inaccuracies in the Allen Coral Atlas dataset, including its delineation of geomorphic classes. This logger in particular seems to have been placed at the deep end of a steep drop off and most likely does not represent a living coral area. Similarly, the lack of accurate, easily accessible depth data for most reef areas (and the lack of depth metadata for 37 of the loggers) means that many of our analyses included no direct measurement of the most important determinants of in situ water temperatures. We compensated for this omission by including geomorphic classes, which the Allen Coral Atlas defines partially by depth, and by post-hoc analysis of depth effects using loggers with defined depths. Increased availability of fine-scale bathymetric maps for coral reefs worldwide would enable researchers to better incorporate depth into future temperature predictions. The in-situ temperature loggers used in this study were assembled from various other studies and long-term monitoring projects, none of which were specifically designed for comparison with remote temperatures, leading to multiple potential issues. First, different loggers covered substantially different timeframes, some of which were as short as one year. While we compensated for this issue by temporally trimming remote temperatures to match the in situ dataset at each point, longer and more consistent timeframes would give us a better sense for how medium-term climate fluctuations affect concordance between in situ and remote data (e.g., El Niño cycles). This is particularly true for the calculation of maximum monthly means, which are typically defined across decades-long historical timeframes. Similarly, the spatial distribution of loggers varied substantially by site, with some loggers in Palau spaced closer than 2.5 meters and other loggers, such as the one in the Phoenix Islands Protected Area, totally isolated. Additional research comparing in situ and remotely sensed temperature products, especially using dense networks of temperature loggers in geomorphically complex reef areas, could improve our understanding of temperature variation at fine spatial scales. For example, recent work in Australia (Brown et al. 2023) and Hawai’i (Gorospe and Karl 2011) both use dense networks of temperature loggers on coral reefs to link fine-scale differences in coral bleaching to temperature fluctuations caused by local geomorphology and meter-scale hydrodynamic processes. Further studies such as these are especially important for improving our ability to predict peak daily temperatures and related metrics such as sub-diel temperature variability. Relatedly, additional research into which temperature metrics best predict thermal tolerance in corals, and whether that differs by factors such as geographic region or coral species, will allow us to refine the use of remotely sensed temperatures. MMM remains commonly used in coral studies for good reason: it is empirically linked to coral thermal tolerance and allows for effective calculation of heat stress above baseline, as with degree heating weeks. For studies relying on MMMs or related DHWs, remotely sensed temperatures may well be adequate substitutes for those measured in situ. Still, recent research suggests that both peak temperatures and sub-diel variability can play key roles in determining coral heat tolerance (Bay and Palumbi 2014; Thomas et al. 2018). The large (>1 °C) differences between remote and in situ temperature maxima for inner reef flats in particular indicate that remote data should still be used cautiously or not at all to approximate maximum temperature in shallow nearshore environments. Overall, the NASA MUR dataset is a useful dataset for the study of fine-scale climates on coral reefs, albeit with some important limitations. The 25-fold improvement in spatial precision as compared to the more commonly used NOAA Coral Reef Watch dataset means that the NASA-MUR is an important tool for the study of microclimate adaptation in corals and could be more widely used by coral reef scientists for this purpose. Additionally, the availability of the entire dataset on Amazon Web Services in a cloud-computing format (Zarr) reduces the required computational and memory overhead for analysis, a clear benefit for scientists and practitioners. Finally, the further development of remotely sensed temperature products open new avenues for comparison with in situ data. For example, the Ecostress 70 meter by 70 meter satellite temperature product is now intermittently available in select coastal locations and provides a dramatic improvement in spatial resolution over the NASA-MUR product (Weidberg 2021). The broad availability of an ever-improving suite of remotely sensed ocean temperature products has incredible potential to facilitate a previously arduous and important aspect of coral reef science. Declarations Acknowledgements This study was made possible by funding from a Rutgers School of Environmental and Biological Sciences Fellowship (JTB), a National Defense Science and Engineering Graduate Fellowship (JTB), and the Paul M. Angell Foundation (MLP and JTB). The authors gratefully thank the researchers and institutions that have generously made their data publicly available, including the Allen Coral Atlas, NASA, AIMS, NOAA, USGS, the ReefTEMPS network, the Smithsonian Tropical Research Institute, the Biological and Chemical Oceanography Data Management Office at Woods Hole Oceanographic Institution, Dr. Anne L. Cohen at Woods Hole Oceanographic Institution, Dr. Stephen Palumbi at Stanford University, and Dr. Kim Cobb at the Georgia Institute of Technology. The authors declare no competing interests. References Allen Coral Atlas (2022) Imagery, maps and monitoring of the world’s tropical coral reefs. allencoralatlas.org/atlas. Accessed 1 November 2022. Australian Institute of Marine Science (2017) AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program). 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Science 368:772–775. doi.org/10.1126/science.aba6880 Additional Declarations No competing interests reported. Supplementary Files SupplementalmaterialBosPinskyremotereeftemps.pdf Cite Share Download PDF Status: Published Journal Publication published 16 Jan, 2025 Read the published version in Coral Reefs → Version 1 posted Editorial decision: Accepted 23 Dec, 2024 Reviews received at journal 05 Dec, 2024 Reviewers agreed at journal 30 Nov, 2024 Reviewers agreed at journal 28 Nov, 2024 Reviewers invited by journal 28 Nov, 2024 Editor assigned by journal 25 Nov, 2024 Submission checks completed at journal 24 Oct, 2024 First submitted to journal 22 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5314629","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369811924,"identity":"d306cf5a-816e-407d-b39c-9ad160e21cc7","order_by":0,"name":"Jaelyn T Bos","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApUlEQVRIiWNgGAWjYBACxhlA4gOUI0G0FrA24rWAlDHzkKSFeXbz4c+2bXb2/A3MB2/zENYAdNScY2nSuW3JiTMOsCVbE6dlRo4Zc27bgQQDBh4zaWK1GH+2bDtgb8DA/41oLQbSjG0HGDcw8LARqQXoF8mec0C/HGYztpxDjBZDYIh9+FEGDLH25oc33hClpQHGYiZGOQjIE6twFIyCUTAKRjAAALgzK7ZKQauTAAAAAElFTkSuQmCC","orcid":"","institution":"University of California, Santa Cruz","correspondingAuthor":true,"prefix":"","firstName":"Jaelyn","middleName":"T","lastName":"Bos","suffix":""},{"id":369811925,"identity":"1d2ddf3c-6f19-419a-b0f0-39fec713cea5","order_by":1,"name":"Malin L Pinsky","email":"","orcid":"","institution":"University of California, Santa Cruz","correspondingAuthor":false,"prefix":"","firstName":"Malin","middleName":"L","lastName":"Pinsky","suffix":""}],"badges":[],"createdAt":"2024-10-23 00:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5314629/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5314629/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00338-024-02611-8","type":"published","date":"2025-01-16T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":68274101,"identity":"164f2350-9c74-4e9a-9268-1106815e12dc","added_by":"auto","created_at":"2024-11-05 14:23:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2346976,"visible":true,"origin":"","legend":"\u003cp\u003eTemperature loggers retained for analysis by location (a) and date ranges (b). The retained logger records span the Florida Keys, central Caribbean, both coasts of Panama, multiple Hawaiian Islands, the South China Sea, both the east and west coasts of Australia, and scattered islands throughout the Pacific. The earliest logger records begin in 2003 and the last end in 2019, though the majority of the individual loggers span much less time.\u003c/p\u003e","description":"","filename":"Figure1ab.png","url":"https://assets-eu.researchsquare.com/files/rs-5314629/v1/17f93b3d06bc8925b767e6e6.png"},{"id":68275225,"identity":"ad6693df-cd04-4a04-96a7-507952460f49","added_by":"auto","created_at":"2024-11-05 14:31:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1688002,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum monthly mean (MMM) temperatures from remote sensing were closely correlated with those from \u003cem\u003ein situ\u003c/em\u003e temperature loggers. The dashed line shows a hypothetical 1:1 relationship between \u003cem\u003ein situ \u003c/em\u003eand remotely sensed temperatures (a). Most \u003cem\u003ein situ\u003c/em\u003e MMMs were within one degree of remotely sensed MMM (b). Values to the right indicate \u003cem\u003ein situ\u003c/em\u003e MMMs that were hotter than remotely sensed values.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5314629/v1/6c59de4285a8c189a97a6bb7.png"},{"id":68274104,"identity":"91879cab-257f-4462-9908-84e11f21de0f","added_by":"auto","created_at":"2024-11-05 14:23:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3658803,"visible":true,"origin":"","legend":"\u003cp\u003eThe difference between \u003cem\u003ein situ\u003c/em\u003e and remotely sensed MMMs varied with depth (a) and geomorphic class (b).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5314629/v1/d0441b3ef54251ab1527acb0.png"},{"id":68276090,"identity":"4b0e7bf9-d083-4c42-a694-78b15a79c7c3","added_by":"auto","created_at":"2024-11-05 14:39:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1893883,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Peak annual temperature from remotely sensed sea surface temperatures systematically underestimated the hottest \u003cem\u003ein situ\u003c/em\u003e peak annual temperatures. (b) Histogram of \u003cem\u003ein situ \u003c/em\u003eminus remotely sensed peak temperatures.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5314629/v1/bbe3112e4966295dc8718f82.png"},{"id":68274100,"identity":"a5af871f-e04a-4c9b-ab9a-1ac6e0452707","added_by":"auto","created_at":"2024-11-05 14:23:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3864398,"visible":true,"origin":"","legend":"\u003cp\u003eThe difference between in situ and remotely sensed peak temperatures varied with depth (a) and geomorphic class (b).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5314629/v1/26122c601ee88521c885b4bb.png"},{"id":68274099,"identity":"3b093591-87d0-4073-9d82-f1bf42233c32","added_by":"auto","created_at":"2024-11-05 14:23:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":373752,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5314629/v1/6e3997e0-98c4-4d41-917b-7f74ff0f3e64.pdf"},{"id":68274103,"identity":"bcf0edab-ccfb-4b56-aafb-c053be12f78a","added_by":"auto","created_at":"2024-11-05 14:23:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":295606,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalmaterialBosPinskyremotereeftemps.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5314629/v1/4713a897cd23fe1808651614.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fine resolution satellite sea surface temperatures capture the conditions experienced by corals at monthly but not daily time scales.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClimate varies on multiple scales. While broad climate patterns define biomes, spatial temperature variation also occurs within regions and even habitats. “Microclimates” exist across scales from centimeters to meters in ecosystems as diverse as forests, mountains, and the intertidal zone, and they can complicate our understanding of climate change impacts by serving to either buffer or exacerbate the effects of rising regional temperatures on individual organisms (Seabra et al. 2011; Verrall and Pickering 2020; Zellweger et al. 2020). Microclimates also exist on coral reefs, and a growing body of research suggests that microclimate variation plays a key role in determining thermal tolerance in corals (Barshis et al. 2013; Palumbi et al. 2014; Schoepf et al. 2015). However, accurately mapping \u003cem\u003ein situ\u003c/em\u003e temperatures on coral reefs remains challenging. Here we assess the utility of a globally-available remote sensing product for predicting geographically fine-scale temperatures on coral reefs.\u003c/p\u003e\n\u003cp\u003eCoral reefs occur in more than one hundred countries and territories in every tropical ocean basin (UNEP-WCMC et al. 2018). This broad range naturally leads to substantial differences in temperature between reefs, with some regions such as the Red Sea and the Persian Gulf exhibiting summer temperatures multiple degrees warmer than those found elsewhere in the world (Howells et al. 2012). However, temperature differences also exist at finer spatial scales. \u003cem\u003eIn situ \u003c/em\u003etemperatures on coral reefs depend not only on regional climate patterns but also on within-reef processes including tidal cycling and wave action (MacKellar 2013; Bachman 2022). These fine-scale processes affect both long-term temperature averages and sub-diel temperature variability (Reid et al. 2020). Sites separated by as little as 200 meters can have daily temperature ranges that differ substantially, from less than 1 °C to as great as 5 °C (Davis et al. 2011). Ultimately, the temperature regime experienced by an individual coral results from the combination of regional processes, within-reef water circulation, depth (Cyronak et al. 2020), and atmospheric conditions, including clouds (Leahy et al. 2013). As a result, regional temperature averages may differ substantially from the temperature actually experienced by an individual coral animal in its microclimate (Thomas et al. 2022).\u003c/p\u003e\n\u003cp\u003eMicroclimate variations in temperature have clear effects on growth, survival, and local genetic adaptation in corals. Differences in thermal tolerance between populations of conspecific corals inhabiting different parts of the same reefs have been documented in \u003cem\u003ePorites \u003c/em\u003ein Florida (Kenkel 2015), \u003cem\u003ePocillopora \u003c/em\u003ein Australia (Marhoefer et al. 2021), and \u003cem\u003eAcropora \u003c/em\u003ein American Samoa (Palumbi et al. 2014), among others. Furthermore, physiological differences between corals in different microclimates have in some cases been explicitly linked to genetic differences in coral animals (Palumbi et al. 2014; Thomas et al. 2022) or symbionts (Hoadley et al. 2019). Multiple studies suggest that natural variation in heat tolerance between coral populations may be a key factor contributing to climate resilience of coral reefs (Kleypas et al. 2016; McManus et al. 2021), emphasizing the importance of accurately assessing temperature landscapes on reefs.\u003c/p\u003e\n\u003cp\u003eStudies make use of multiple temperature metrics to delineate microclimates, though a subset of metrics are particularly common. First, maximum monthly mean (MMM) temperatures are commonly used as baselines in coral bleaching studies and are closely related to local heat tolerance in coral assemblages (Kayanne 2017). MMM temperatures are equal to mean water temperatures during the hottest month of the year, usually calculated across a timespan of several years (Liu et al. 2014). Heat acclimation and local climate adaptation in corals can also respond to peak daily temperatures (Thomas et al. 2018; Marhoefer et al. 2021). Third, heat adaptation in corals can relate to daily temperature ranges, with corals that experience wider ranges also having more protection against bleaching (Kenkel et al. 2015; Safaie et al. 2018). This latter observation suggests that the difference between local daily maximum and minimum temperatures may have significant effects on coral health.\u003c/p\u003e\n\u003cp\u003eMeasuring local temperatures on coral reefs usually involves the placement of \u003cem\u003ein situ \u003c/em\u003etemperature loggers, a process that can be expensive and time consuming (Colin and Johnston 2020). Consequently, many coral reefs, particularly in remote areas, lack \u003cem\u003ein situ \u003c/em\u003etemperature measurements entirely. By contrast, remotely sensed data is often globally available and free to researchers. For example, global remotely-sensed sea surface temperature data from NOAA has long been used to both explain and predict coral bleaching (Gleeson and Strong 1995). However, it remains unclear to what extent remotely sensed products are accurate, precise, and unbiased predictors of \u003cem\u003ein situ \u003c/em\u003etemperatures on coral reefs because of differences between the ocean surface observed by satellites and the more complex conditions experienced by corals. Comparison of maximum monthly mean temperatures calculated from \u003cem\u003ein situ \u003c/em\u003eloggers and NOAA’s 5 km\u003csup\u003e2 \u003c/sup\u003eCoral Reef Watch remote sea surface temperature product in American Samoa revealed a greater than 0.5 °C difference between the two metrics (Klepac and Barshis 2022). Similarly, a comparison of the NOAA product with \u003cem\u003ein situ \u003c/em\u003eloggers in Belize noted mean offsets of greater than 0.5 °C that varied seasonally, despite high linear correlations between remote and \u003cem\u003ein situ \u003c/em\u003etemperatures (Castillo et al. 2012). A comparison between the NOAA product and \u003cem\u003ein situ \u003c/em\u003edata using 17 temperature loggers in Kiritimati also found strong linear correlations in long term averages but spatially localized multi-degree differences between the remote and \u003cem\u003ein situ \u003c/em\u003ecalculations during cold upwelling events (Claar et al. 2019). These local examples suggest possible discrepancies between \u003cem\u003ein situ \u003c/em\u003eand remotely sensed temperatures more broadly; however, comparison of \u003cem\u003ein situ \u003c/em\u003eand remotely sensed temperatures across reefs in multiple geographic regions or with more modern and higher resolution SST products is lacking.\u003c/p\u003e\n\u003cp\u003eHere, we compared \u003cem\u003ein situ \u003c/em\u003etemperature records on coral reefs to the 0.01 degree resolution \u003cem\u003eMulti-scale Ultra-high Resolution Sea Surface Temperature \u003c/em\u003e(MUR) dataset from NASA. We examined three temperature metrics linked to local adaptation in corals: maximum monthly mean temperature (MMM), maximum monthly 99th percentile temperature (peak annual temperature), and mean daily temperature range (DTR). We found that remotely sensed MMMs closely correlated with those calculated \u003cem\u003ein situ\u003c/em\u003e, but remotely sensed data missed the highest annual temperatures and failed to predict daily temperature ranges. \u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eWe tested the accuracy and bias of remotely sensed temperature on coral reefs by comparing it to an assembled dataset of \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003etemperature loggers. We calculated each temperature metric separately from both the remote and \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003edata. We also assessed how bias differed across latitude, depth, and reef geomorphology.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eData sources\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained remotely sensed sea surface temperature from the \u003cem\u003eMulti-scale Ultra-high Resolution (MUR) Sea Surface Temperature\u003c/em\u003e dataset (NASA/JPL 2015). MUR is a daily sea surface temperature product at a spatial resolution of 0.01 degrees by 0.01 degrees, or approximately one kilometer by one kilometer, available beginning in 2002. The dataset is a processed product that includes data from MODIS satellites and from Advanced Very High Resolution Radiometer (AVHRR) and microwave sensors carried by a variety of satellites, all of which has been calibrated to \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003edata from the NOAA buoy network (Chin et al. 2017). The daily remotely sensed SST product is designed to represent \u0026ldquo;bulk\u0026rdquo; temperatures in the mixed layer of the ocean; roughly 10 meters deep to just below the \u0026ldquo;skin\u0026rdquo; at the sea-air interface. The product uses only remote sensed data taken at night (Chin et al. 2017; Koutantou et al. 2023).\u003c/p\u003e\n\u003cp\u003eWe used \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003etemperature logger records on coral reefs from a variety of sources. \u003cem\u003eIn situ\u0026nbsp;\u003c/em\u003etemperatures are typically recorded using battery-powered submerged temperature loggers that can be left on reefs to record temperatures at set time intervals for several months or years and later recovered (Colin and Johnston 2020). Here, we use temperature logger data from Australia, several Pacific islands, Puerto Rico, the Virgin Islands, the South China Sea, Florida, and Panama, for an incomplete but broad representation of temperatures on global coral reefs over the last twenty years (Fig 1). After filtering, we used data from 314 loggers (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Sources of \u003cem\u003ein situ\u003c/em\u003e temperature logger data.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"623\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6067%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.175%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInstitution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6437%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLoggers in unfiltered dataset\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLoggers kept post- filtering\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepth range (meters)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference(s)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6067%;\"\u003e\n \u003cp\u003eHawai\u0026rsquo;i, U.S. Pacific Territories, Puerto Rico, U.S. Virgin Islands, Florida\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.175%;\"\u003e\n \u003cp\u003eU.S. National Oceanographic and Atmospheric Administration (NOAA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6437%;\"\u003e\n \u003cp\u003e412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e0.6 - 25.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e(Coral Reef Ecosystem Program, Pacific Islands Fisheries Science Center, 2017)\u003c/p\u003e\n \u003cp\u003e(Ecosystem Sciences Division, Pacific Islands Fisheries Science Center, 2020)\u003c/p\u003e\n \u003cp\u003e(Manzello et al 2018; 2020)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6067%;\"\u003e\n \u003cp\u003eHawai\u0026rsquo;i\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.175%;\"\u003e\n \u003cp\u003eU.S. Geological Survey (USGS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6437%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e0.3 - 14.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e(Grossman and Marrack 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6067%;\"\u003e\n \u003cp\u003eAustralia (inc. Great Barrier Reef, Western Australia, and Coral Sea)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.175%;\"\u003e\n \u003cp\u003eAustralian Institute of Marine Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6437%;\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e0.1 - 19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e(Australian Institute of Marine Science 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6067%;\"\u003e\n \u003cp\u003ePalau\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.175%;\"\u003e\n \u003cp\u003eStanford University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6437%;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003eNo depth data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e(Palumbi 2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6067%;\"\u003e\n \u003cp\u003eNew Caledonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.175%;\"\u003e\n \u003cp\u003eReefTEMPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6437%;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e1.0 - 60.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e(Varillon et al. 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6067%;\"\u003e\n \u003cp\u003ePanama\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.175%;\"\u003e\n \u003cp\u003eSmithsonian Tropical Research Institute\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6437%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e1.0 - 18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e(Physical Monitoring Program of the Smithsonian Tropical Research Institute)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6067%;\"\u003e\n \u003cp\u003eDongsha Atoll, South China Sea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.175%;\"\u003e\n \u003cp\u003eWoods Hole Oceanographic Institution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6437%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e1.0 - 5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e(Cohen 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6067%;\"\u003e\n \u003cp\u003eKiritimati, Northern Line Islands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.175%;\"\u003e\n \u003cp\u003eGeorgia Institute of Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6437%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003eNo depth data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e(Cobb and Gates 2016)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 14.6067%;\"\u003e\n \u003cp\u003ePhoenix Islands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.175%;\"\u003e\n \u003cp\u003eWoods Hole Oceanographic Institution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.6437%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10.5939%;\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.3868%;\"\u003e\n \u003cp\u003e(Fox and Cohen 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWe also obtained additional environmental data for the location of each temperature logger. We downloaded reef geomorphology data from the Allen Coral Atlas (Allen Coral Atlas 2022), which uses \u0026ldquo;geomorphic classes\u0026rdquo; to qualitatively characterize reef zones by their physical form, such as lagoons, reef crests, and reef flats, and maps these features on a 10 m\u003csup\u003e2\u003c/sup\u003e scale on reefs up to 15 meters deep. We also used the global coastlines dataset from OpenStreetMap (OpenStreetMap contributors 2024) to calculate distance between loggers and the shore.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eLogger data preprocessing\u003c/u\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll temperature logger records included metadata with latitude and longitude, and many but not all metadata records included the logger depth (Table 1). We matched logger locations to Allen Coral Atlas geomorphic data and discarded the 54% of logger points (513 removed of 955) that fell outside the area for which the Allen Coral Atlas defined geomorphic classes. We trimmed the temporal extent of the logger data to match that of the sea surface temperatures (June 2002 to January 2020) where necessary. We filtered the frequency of each logger\u0026rsquo;s measurement to one data point every 30 minutes to equalize temporal resolution between loggers. For each logger, we discarded data from any day with fewer than 24 timepoints and any month with fewer than 15 days of data. Next, we discarded data from loggers with fewer than 12 continuous months of data, leaving us with 9.9 million total logged hours of data across 314 loggers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe calculated MMM temperature, peak annual temperature, and DTR for each logger. MMM temperatures were calculated by taking the mean temperature of each calendar month (i.e. January, February, etc.) across the entire logged timeframe and selecting the hottest mean month. For peak annual temperature, we used a similar process but calculated the 99th percentile temperature of each calendar month rather than the mean, then took the maximum of those. This gave us a proxy for maximum annual temperature, while filtering out the highest temperatures that may be caused by instrument error or other anomalies. Finally, for DTR we calculated the daily minimum and maximum temperatures for each logger across the entire logged timeframe, subtracted the minimum from the maximum, and took the mean across all daily differences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe processed remotely sensed SST for each logger point by trimming the MUR data to the same temporal extent as the logger data. We then used the temporally trimmed data to calculate MMM, peak annual temperature, and mean weekly temperature variation. We calculated the peak annual temperature by taking the highest temperature across the entire time frame, since the MUR SST has a one-day temporal resolution. We used hottest daily temperature instead of 99th percentile temperature due to the greater quality control and coarser spatial precision of the remotely sensed data compared to the \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003edata. Mean weekly temperature variation was calculated by taking the maximum and minimum temperatures of each calendar week (Sunday - Saturday), subtracting the minimum from the maximum, and taking the mean of all of the differences. We used mean weekly temperature range in lieu of mean daily temperature range since remote SST is not available at sub-daily timescales. We chose this timescale because one week should encompass most of a tidal cycle while minimizing seasonal variation that would be associated with longer timescales.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used the Allen Coral Atlas to define the reef \u0026ldquo;geomorphic class\u0026rdquo; at each logger point. Finally, we calculated distance to shore for each logger point using the OpenStreetMap coastlines and the \u0026ldquo;great circle\u0026rdquo; distance function from Python\u0026rsquo;s Xarray-Spatial library. All data preprocessing and analysis was performed in Python 3.8.3. Scripts are available at https://github.com/pinskylab/logger_remote_temps.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eStatistics\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003ePrior to processing, we normalized the \u0026ldquo;distance to shore\u0026rdquo; data by taking the natural logarithm of one plus the distance. To understand linear relationships between variables, we calculated Pearson\u0026rsquo;s correlation coefficient (\u003cem\u003er\u003c/em\u003e). For factor-level relationships between geomorphic classes and temperatures, we calculated H statistics and P values using Kruskal-Wallis tests in the scipy.stats library version 1.11.4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo test our ability to predict \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eMMM and peak temperatures, we fit linear models to predict each \u003cem\u003ein situ\u003c/em\u003e temperature metric from all of the remotely sensed variables, including distance to shore, an interaction term of remote MMM and distance to shore, and all geomorphic classes. We selected models by stepwise addition based on cross-validated R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003escore, performed with default parameters in scikit-learn library version 1.0.2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we define \u0026ldquo;accuracy\u0026rdquo; of each model as the mean absolute difference between the predicted value and actual \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003evalue of each temperature metric. We define \u0026ldquo;bias\u0026rdquo; as the difference in means between predicted values and actual \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003evalues. Therefore, a measure may be unbiased but inaccurate if predicted values are far from the actual values but evenly split between positive and negative differences.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFor the commonly used MMMs, we found a close linear relationship between \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eand remote temperatures (Pearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e = 0.95, linear regression slope of 1.001 \u0026plusmn; 0.001, n = 314, Fig. 2a). Remotely sensed MMMs averaged 0.04 \u0026deg;C cooler than \u003cem\u003ein situ\u003c/em\u003e, with a standard deviation of the difference of 0.47 \u0026deg;C (\u003cem\u003en\u003c/em\u003e = 314 loggers). The maximum absolute difference was for a logger in the southern Great Barrier Reef, which had a MMM 2.56 \u0026deg;C warmer than the remotely sensed MMM. Across all 314 comparisons of MMM, 96% of the remotely sensed values were within 1 \u0026deg;C of the \u003cem\u003ein situ\u003c/em\u003e values, and 84% were within 0.5 \u0026deg;C (n = 314, Fig. 2b).\u003c/p\u003e\n\u003cp\u003eWe tested whether the bias of remotely sensed MMMs differed across gradients of latitude, depth, distance to shore, or geomorphology. There was no clear relationship with either latitude (see Supplemental Fig. 1a) or distance to shore (Supplemental Fig. 1b). For the loggers with associated depth measurements, deeper loggers exhibited colder MMMs relative to the remotely sensed MMMs (Fig. 3a). However, the linear correlation with depth relationship was weak (Pearson\u0026rsquo;s r = -0.402 \u0026plusmn; 0.052, n = 277). There was a weak relationship between bias and geomorphic class, and both terrestrial reef flats and outer reef flats each had \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eMMMs that were 0.27 \u0026deg;C hotter than remote MMMs on average (Fig. 3b). A Kruskal-Wallis test of bias vs. geomorphic class was statistically significant (n = 314, H = 19.8, p = 0.02).\u003c/p\u003e\n\u003cp\u003eWe tested whether latitude, distance from shore, and geomorphic class together helped to explain additional variation in \u003cem\u003ein situ\u003c/em\u003e temperatures. The model selection routine identified a linear model that included distance to shore, an interaction term of remote MMM and distance to shore, and three geomorphic classes (outer reef flat, shallow lagoon, and an aggregate of all other geomorphic classes). The model suggested that \u003cem\u003ein situ\u003c/em\u003e MMMs were colder relative to remotely sensed MMM at sites further from shore, though less so on outer reef flats, and warmer in shallow lagoons. However, the improvement in cross-validated R\u003csup\u003e2\u0026nbsp;\u003c/sup\u003ewas negligible (only 0.004, see Supplemental Table 1 and Supplemental Text).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter comparing \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eand remote MMMs, we assessed the similarity of peak annual temperatures measured \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eand remotely. The mean difference between \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eand remote peak annual temperatures was 0.23\u0026deg;C, with the \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003etemperatures hotter than the remote temperatures. These metrics showed weaker linear correlation than did remote and \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eMMMs, especially at the highest temperatures (Pearson\u0026rsquo;s r = 0.83, Fig. 4a). In addition, this fit was not consistent across the full range of temperatures. \u003cem\u003eIn situ\u0026nbsp;\u003c/em\u003epeak annual temperatures below 31 \u0026deg;C (70% of logged temperatures, n=223) correlated closely with the remote peak annual temperatures (r = 0.88), but above this temperature, higher \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003etemperatures were only weakly associated with higher remotely sensed temperatures (r =0.21, n= 91).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly to MMMs, we evaluated potential sources of bias in the remotely sensed peak annual temperatures, including latitude, depth, distance to shore, and geomorphology (see Supplemental Fig. 2). We found no clear bias by latitude or distance to shore. The difference between \u003cem\u003ein situ\u003c/em\u003e and remotely sensed peak annual temperatures showed a negative but nonlinear relationship with depth, with \u003cem\u003ein situ\u003c/em\u003e peak annual temperatures up to 4 \u0026deg;C hotter than remote sensing for depths \u0026lt;5 m, and closer to 1 \u0026deg;C cooler for depths \u0026gt;20 m (Fig. 5a). The bias was moderately correlated with the natural log of depth (Pearson\u0026rsquo;s r = -0.56). Geomorphic class was a stronger predictor of bias, with inner reef flats consistently hotter than remotely sensed maxima (Kruskal-Wallis of \u003cem\u003ein situ\u003c/em\u003e vs. remote differences across geomorphic classes, n = 314, H = 58.7, p=2.4x10\u003csup\u003e-9\u003c/sup\u003e, Fig. 5b). Statistical models predicting peak temperatures from remotely sensed variables performed less well than those predicting MMMs (see Supplemental Table 2 and Supplemental Text).\u003c/p\u003e\n\u003cp\u003eFinally, we examined the relationship between daily temperature range and each of the remotely sensed temperature metrics (MMM, maximum annual temperature, and weekly temperature range), and found no strong relationship with any of them (all Pearson\u0026rsquo;s r \u0026lt; 0.1). There were notable relationships between daily temperature range and both geomorphic class (Kruskal-Wallis H= 71.67, p = 7x10\u003csup\u003e-12\u003c/sup\u003e, n =314) and the natural logarithm of depth (Pearson\u0026rsquo;s R = -0.51, n =277). Inner reef flats had the highest mean DTR (1.4\u0026deg;C), and both outer reef flats and shallow lagoons also had mean DTRs above 1 \u0026deg;C. All other geomorphic classes had mean DTRs between 0.5 and 1 \u0026deg;C (see Supplemental Fig. 3). Furthermore, daily temperature range was correlated with the difference between \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eand remote maximum temperatures, suggesting that some of the sites with high daily temperature range may be the same as the sites with high maximum temperatures missed by the remotely sensed data. However, these relationships gave us little ability to predict \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003edaily temperature ranges from remotely sensed data (see Supplemental Table 3 and Supplemental Text).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated how closely temperature metrics relevant to coral thermal adaptation calculated from the remotely sensed 1 km\u003csup\u003e2\u003c/sup\u003e NASA-MUR dataset resembled those calculated from \u003cem\u003ein situ\u003c/em\u003e temperature loggers on coral reefs. Overall, we found that remotely sensed sea surface temperature was in many cases a good proxy for in situ temperatures on coral reefs, though the accuracy and bias depended on both the specific temperature metric and within-reef geomorphic features.\u003c/p\u003e\n\u003cp\u003eIn particular, we found remotely sensed temperatures to be effective proxies for MMMs calculated\u003cem\u003e\u0026nbsp;in situ\u003c/em\u003e. In particular, the \u0026lt;0.1 \u0026deg;C difference in means for remote and \u003cem\u003ein situ\u003c/em\u003e data suggests that the subsurface corrections applied by NASA for buoy depth are appropriate and effective in coral reef environments. This is especially notable since the 1 km\u003csup\u003e2\u0026nbsp;\u003c/sup\u003eresolution MUR data, while more spatially precise than many other remotely sensed SST products, is still coarser grained than many of the oceanographic processes known to contribute to variation in reef temperatures and coral bleaching. Water circulation on reefs depends on fine-scale patterns including internal waves, turbulence, and tides, all of which can result in temperature variation on fine spatial scales (Herdman et al. 2015; Reid et al. 2020; Davis et al. 2021). For example, a 2008 study on Mo\u0026rsquo;orea by Lenihan \u003cem\u003eet al\u003c/em\u003e found differences in current speed and corresponding differences in coral bleaching at spatial scales as fine as meters and even centimeters (Lenihan et al. 2008).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMost MMM temperatures calculated from SST were within 0.5 \u0026deg;C of those calculated \u003cem\u003ein situ\u003c/em\u003e and nearly all were within 1 \u0026deg;C, similar to results collected by Claar on Kiritimati (2019). Since bleaching predictions are often calculated from SSTs that are more than 1 \u0026deg;C above MMM temperature (Liu et al. 2014), accurately measuring baseline temperatures to within one degree is helpful for a meaningful understanding of heat adaptation in corals. The close agreement between remote and \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003etemperatures is in contrast to the Klepac (2022) study, which found a greater than 0.5 \u0026deg;C offset between \u003cem\u003ein situ\u003c/em\u003e and remote temperature. However, the latter study used daily temperature averages rather than maximum monthly means, and the 5 km x 5 km NOAA Coral Reef Watch sea surface temperature in lieu of the finer-scale NASA-MUR dataset used here. The highly linear relationship between remote and \u003cem\u003ein situ\u003c/em\u003e MMMs found here suggests comparable accuracy between the highest and lowest extrema of the temperature range, as well as the median temperatures. Moreover, this relationship proved unbiased by latitude, indicating that remotely sensed MMMs may be equally appropriate for tropical and subtropical reefs.\u003c/p\u003e\n\u003cp\u003eRemotely sensed sea surface temperature proved less effective for measuring peak annual temperatures than maximum monthly means and unsuitable for estimating daily temperature range. Peak annual temperatures measured \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eagreed well with those measured remotely for cooler locations, showing a linear relationship similar to that seen between \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eand remote MMMs. However, for the 30% of logger points with the highest \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003epeak temperatures (\u0026gt;31 \u0026deg;C), the remote peak annual temperature substantially underestimated the \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003epeak temperature. The magnitude of this underestimate increased with increasing \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003etemperature. These sites where \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003epeak annual temperatures were hotter than remote peak annual temperatures also had disproportionately high daily temperature ranges, suggesting that these hottest peak temperatures may occur as part of high frequency temperature fluctuations. Since NASA-MUR is a daily product, it has an inherently limited ability to detect sub-diel variation, a well-established issue with remote sea surface temperature products (Leichter 2006). We refer to the sites with high peak annual temperatures underestimated by remote sensing and high daily temperature ranges as \u0026ldquo;highly variable sites\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConsistent with past research on heat fluxes in coral reefs, these highly variable sites were disproportionately shallow (Leichter et al. 2006). High frequency temperature variability strongly depends on reef geomorphology at scales as fine as meters, even where average temperatures are similar between sites. These differences in temperature variability appear to correspond to differences in water retention time, depth, and turbulence (Guadayol et al. 2014). These findings are consistent with our data, as we saw strong relationships between geomorphic class and both peak annual temperatures and daily temperature range. However, the literature also shows that the relationship between temperature and specific geomorphic classes differs by location. For example, studies in the Red Sea show the highest levels of high frequency temperature variation on wave-protected reef flats (Davis et al. 2011), whereas studies of Dongsha Atoll in the South China show the opposite pattern, with wave-exposed reef crests exhibiting more temperature variation than the protected reef flat (Reid et al. 2020). This inconsistency hampers our ability to use remotely sensed geomorphic classes to predict either peak temperatures or daily temperature range across different reefs.\u003c/p\u003e\n\u003cp\u003eAny interpretation of these results must also consider known issues in these datasets. The existence of a logger in New Caledonia at greater than 50 meters depth within areas defined as \u0026ldquo;reef\u0026rdquo; by the Allen Coral Atlas reveals inaccuracies in the Allen Coral Atlas dataset, including its delineation of geomorphic classes. This logger in particular seems to have been placed at the deep end of a steep drop off and most likely does not represent a living coral area. Similarly, the lack of accurate, easily accessible depth data for most reef areas (and the lack of depth metadata for 37 of the loggers) means that many of our analyses included no direct measurement of the most important determinants of \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003ewater temperatures. We compensated for this omission by including geomorphic classes, which the Allen Coral Atlas defines partially by depth, and by post-hoc analysis of depth effects using loggers with defined depths. Increased availability of fine-scale bathymetric maps for coral reefs worldwide would enable researchers to better incorporate depth into future temperature predictions.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The \u003cem\u003ein-situ\u0026nbsp;\u003c/em\u003etemperature loggers used in this study were assembled from various other studies and long-term monitoring projects, none of which were specifically designed for comparison with remote temperatures, leading to multiple potential issues. First, different loggers covered substantially different timeframes, some of which were as short as one year. While we compensated for this issue by temporally trimming remote temperatures to match the \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003edataset at each point, longer and more consistent timeframes would give us a better sense for how medium-term climate fluctuations affect concordance between \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eand remote data (e.g., El Ni\u0026ntilde;o cycles). This is particularly true for the calculation of maximum monthly means, which are typically defined across decades-long historical timeframes. Similarly, the spatial distribution of loggers varied substantially by site, with some loggers in Palau spaced closer than 2.5 meters and other loggers, such as the one in the Phoenix Islands Protected Area, totally isolated. Additional research comparing \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003eand remotely sensed temperature products, especially using dense networks of temperature loggers in geomorphically complex reef areas, could improve our understanding of temperature variation at fine spatial scales. For example, recent work in Australia (Brown et al. 2023) and Hawai\u0026rsquo;i (Gorospe and Karl 2011) both use dense networks of temperature loggers on coral reefs to link fine-scale differences in coral bleaching to temperature fluctuations caused by local geomorphology and meter-scale hydrodynamic processes. Further studies such as these are especially important for improving our ability to predict peak daily temperatures and related metrics such as sub-diel temperature variability.\u003c/p\u003e\n\u003cp\u003eRelatedly, additional research into which temperature metrics best predict thermal tolerance in corals, and whether that differs by factors such as geographic region or coral species, will allow us to refine the use of remotely sensed temperatures. MMM remains commonly used in coral studies for good reason: it is empirically linked to coral thermal tolerance and allows for effective calculation of heat stress above baseline, as with degree heating weeks. For studies relying on MMMs or related DHWs, remotely sensed temperatures may well be adequate substitutes for those measured \u003cem\u003ein situ.\u0026nbsp;\u003c/em\u003eStill, recent research suggests that both peak temperatures and sub-diel variability can play key roles in determining coral heat tolerance (Bay and Palumbi 2014; Thomas et al. 2018). The large (\u0026gt;1 \u0026deg;C) differences between remote and \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003etemperature maxima for inner reef flats in particular indicate that remote data should still be used cautiously or not at all to approximate maximum temperature in shallow nearshore environments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, the NASA MUR dataset is a useful dataset for the study of fine-scale climates on coral reefs, albeit with some important limitations. The 25-fold improvement in spatial precision as compared to the more commonly used NOAA Coral Reef Watch dataset means that the NASA-MUR is an important tool for the study of microclimate adaptation in corals and could be more widely used by coral reef scientists for this purpose. Additionally, the availability of the entire dataset on Amazon Web Services in a cloud-computing format (Zarr) reduces the required computational and memory overhead for analysis, a clear benefit for scientists and practitioners. Finally, the further development of remotely sensed temperature products open new avenues for comparison with \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003edata. For example, the Ecostress 70 meter by 70 meter satellite temperature product is now intermittently available in select coastal locations and provides a dramatic improvement in spatial resolution over the NASA-MUR product (Weidberg 2021). The broad availability of an ever-improving suite of remotely sensed ocean temperature products has incredible potential to facilitate a previously arduous and important aspect of coral reef science.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was made possible by funding from a Rutgers School of Environmental and Biological Sciences Fellowship (JTB), a National Defense Science and Engineering Graduate Fellowship (JTB), and the Paul M. Angell Foundation (MLP and JTB). The authors gratefully thank the researchers and institutions that have generously made their data publicly available, including the Allen Coral Atlas, NASA, AIMS, NOAA, USGS, the ReefTEMPS network, the Smithsonian Tropical Research Institute, the Biological and Chemical Oceanography Data Management Office at Woods Hole Oceanographic Institution, Dr. Anne L. Cohen at Woods Hole Oceanographic Institution, Dr. Stephen Palumbi at Stanford University, and Dr. Kim Cobb at the Georgia Institute of Technology.\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen Coral Atlas (2022) Imagery, maps and monitoring of the world\u0026rsquo;s tropical coral reefs. allencoralatlas.org/atlas. Accessed 1 November 2022.\u003c/li\u003e\n\u003cli\u003eAustralian Institute of Marine Science (2017) AIMS Sea Water Temperature Observing System (AIMS Temperature Logger Program). Accessed 1 November 2022. doi.org/10.25845/5b4eb0f9bb848\u003c/li\u003e\n\u003cli\u003eBarshis DJ, Ladner JT, Oliver TA, Seneca FO, Traylor-Knowles N, Palumbi SR (2013) Genomic basis for coral resilience to climate change. Proc Natl Acad Sci 110:1387\u0026ndash;1392. doi.org/10.1073/pnas.1210224110\u003c/li\u003e\n\u003cli\u003eBay RA, Palumbi SR (2014) Multilocus Adaptation Associated with Heat Resistance in Reef-Building Corals. Curr Biol 24:2952\u0026ndash;2956. doi.org/10.1016/j.cub.2014.10.044\u003c/li\u003e\n\u003cli\u003eBrown KT, Eyal G, Dove SG, Barott KL (2023) Fine-scale heterogeneity reveals disproportionate thermal stress and coral mortality in thermally variable reef habitats during a marine heatwave. Coral Reefs 42:131\u0026ndash;142. doi.org/10.1007/s00338-022-02328-6.\u003c/li\u003e\n\u003cli\u003eCastillo KD, Ries JB, Weiss JM, Lima FP (2012) Decline of forereef corals in response to recent warming linked to history of thermal exposure. Nat Clim Change 2:756\u0026ndash;760. doi.org/10.1038/nclimate1577\u003c/li\u003e\n\u003cli\u003eChin TM, Vazquez-Cuervo J, Armstrong EM (2017) A multi-scale high-resolution analysis of global sea surface temperature. 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J Mar Sci Eng 8:680. doi.org/10.3390/jmse8090680\u003c/li\u003e\n\u003cli\u003eCoral Reef Ecosystem Program; Pacific Islands Fisheries Science Center (2017) National Coral Reef Monitoring Program: Water Temperature Data from Subsurface Temperature Recorders (STRs) deployed at coral reef sites in American Samoa from 2012-03-21 to 2015-03-25 (NCEI Accession 0162246). NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0162246. Accessed March 2021.\u003c/li\u003e\n\u003cli\u003eCoral Reef Ecosystem Program; Pacific Islands Fisheries Science Center (2017) National Coral Reef Monitoring Program: Water Temperature Data from Subsurface Temperature Recorders (STRs) deployed at coral reef sites in the Hawaiian Archipelago from 2008-09-20 to 2013-09-14 (NCEI Accession 0162219). NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0162219. Accessed March 2021.\u003c/li\u003e\n\u003cli\u003eCoral Reef Ecosystem Program; Pacific Islands Fisheries Science Center (2017) National Coral Reef Monitoring Program: Water Temperature Data from Subsurface Temperature Recorders (STRs) deployed at coral reef sites in the Hawaiian Archipelago from 2013-07-13 to 2016-09-28 (NCEI Accession 0162216). NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0162216. Accessed March 2021.\u003c/li\u003e\n\u003cli\u003eCoral Reef Ecosystem Program; Pacific Islands Fisheries Science Center (2017) Water temperature data from Subsurface Temperature Recorders (STRs) deployed at coral reef sites in Batangas, Philippines from 2012-03-13 to 2015-05-28 (NCEI Accession 0163746). NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0163746. 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Accessed March 2021.\u003c/li\u003e\n\u003cli\u003eCoral Reef Ecosystem Program; Pacific Islands Fisheries Science Center (2017) National Coral Reef Monitoring Program: Water Temperature Data from Subsurface Temperature Recorders (STRs) deployed at coral reef sites at Wake Island from 2011-03-22 to 2014-03-19 (NCEI Accession 0162218). NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0162218. Accessed March 2021.\u003c/li\u003e\n\u003cli\u003eCyronak T, Takeshita Y, Courtney TA, DeCarlo EH, Eyre BD, Kline DI, Martz T, Page H, Price NN, Smith J, Stoltenberg L, Tresguerres M, Andersson AJ (2020) Diel temperature and pH variability scale with depth across diverse coral reef habitats. Limnol Oceanogr Lett 5:193\u0026ndash;203. doi.org/\u003c/li\u003e\n\u003cli\u003eDavis KA, Lentz SJ, Pineda J, Farrar JT, Starczak VR, Churchill JH (2011) Observations of the thermal environment on Red Sea platform reefs: a heat budget analysis. Coral Reefs 30:25\u0026ndash;36. doi.org/10.1007/s00338-011-0740-8\u003c/li\u003e\n\u003cli\u003eDavis KA, Pawlak G, Monismith SG (2021) Turbulence and Coral Reefs. Annu Rev Mar Sci 13:343\u0026ndash;373. doi.org/10.1146/annurev-marine-042120-071823\u003c/li\u003e\n\u003cli\u003eEcosystem Sciences Division, Pacific Islands Fisheries Science Center (2020) National Coral Reef Monitoring Program: Water temperature data from subsurface temperature recorders (STRs) deployed at coral reef sites in the Hawaiian Archipelago from 2013-07-14 to 2019-09-05 (NCEI Accession 0210383). NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0210383. Accessed March 2021.\u003c/li\u003e\n\u003cli\u003eEcosystem Sciences Division, Pacific Islands Fisheries Science Center (2020) National Coral Reef Monitoring Program: Water temperature data from subsurface temperature recorders (STRs) deployed at coral reef sites across the Marianas Archipelago with time-series spanning 2011-04-20 to 2017-06-20 (NCEI Accession 0176112).NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0176112. Accessed March 2021.\u003c/li\u003e\n\u003cli\u003eEcosystem Sciences Division, Pacific Islands Fisheries Science Center (2020) National Coral Reef Monitoring Program: Water temperature data from subsurface temperature recorders (STRs) deployed at coral reef sites in the Pacific Remote Islands Marine National Monument from 2014-03-16 to 2017-04-22 (NCEI Accession 0176111). NOAA National Centers for Environmental Information. Dataset. https://www.ncei.noaa.gov/archive/accession/0176111. 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Science 368:772\u0026ndash;775. doi.org/10.1126/science.aba6880\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"coral-reefs","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"core","sideBox":"Learn more about [Coral Reefs](http://link.springer.com/journal/338)","snPcode":"338","submissionUrl":"https://submission.nature.com/new-submission/338/3","title":"Coral Reefs","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Remote sensing, temperature, microclimates, climate, geomorphology, coral reef","lastPublishedDoi":"10.21203/rs.3.rs-5314629/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5314629/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWater temperature is a strong driver of growth, survival, and local adaptation in corals, but our knowledge of the temperatures experienced by corals on reefs worldwide remains limited. While \u003cem\u003ein situ \u003c/em\u003etemperature loggers can provide high quality data, they are relatively expensive to place and retrieve. Alternatively, remotely sensed sea surface temperature data is globally available but may be a biased representation of the temperatures experienced by corals. Here, we compared data from 314 temperature loggers on coral reefs to the ~1 km\u003csup\u003e2 \u003c/sup\u003eresolution remotely sensed Multi-scale Ultra-high Resolution Sea Surface Temperature\u003cem\u003e \u003c/em\u003e(MUR) product from NASA. We found good agreement (Pearson’s \u003cem\u003er\u003c/em\u003e = 0.95) between maximum monthly mean temperatures calculated from remote and \u003cem\u003ein situ \u003c/em\u003edata, with 84% of temperatures within 0.5 °C of each other. However, remotely sensed temperature did not effectively capture sub-diel temperature fluctuations and the highest peak temperatures that may be most dangerous for corals. Predictions of \u003cem\u003ein situ \u003c/em\u003etemperatures were significantly but weakly improved by a consideration of reef geomorphology. Ultimately, we found that remotely sensed temperatures can accurately represent the monthly conditions experienced by most corals but should be used with caution at finer temporal scales.\u003c/p\u003e","manuscriptTitle":"Fine resolution satellite sea surface temperatures capture the conditions experienced by corals at monthly but not daily time scales.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-05 14:22:55","doi":"10.21203/rs.3.rs-5314629/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2024-12-23T23:20:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-05T14:44:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165191988568821924474220477192311181124","date":"2024-11-30T15:29:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305970307992353581842848016449217899382","date":"2024-11-28T14:58:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-28T14:34:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-11-26T00:49:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-24T06:15:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Coral Reefs","date":"2024-10-23T00:02:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"coral-reefs","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"core","sideBox":"Learn more about [Coral Reefs](http://link.springer.com/journal/338)","snPcode":"338","submissionUrl":"https://submission.nature.com/new-submission/338/3","title":"Coral Reefs","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"37bd33ca-867d-4d4b-80ab-878ec6211b66","owner":[],"postedDate":"November 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-01-20T16:00:37+00:00","versionOfRecord":{"articleIdentity":"rs-5314629","link":"https://doi.org/10.1007/s00338-024-02611-8","journal":{"identity":"coral-reefs","isVorOnly":false,"title":"Coral Reefs"},"publishedOn":"2025-01-16 15:56:54","publishedOnDateReadable":"January 16th, 2025"},"versionCreatedAt":"2024-11-05 14:22:55","video":"","vorDoi":"10.1007/s00338-024-02611-8","vorDoiUrl":"https://doi.org/10.1007/s00338-024-02611-8","workflowStages":[]},"version":"v1","identity":"rs-5314629","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5314629","identity":"rs-5314629","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}