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Globally, coral bleaching has been responsible for a significant decline in both coral cover and diversity over the past two decades. During the summer of 2010-11, anomalous large-scale ocean warming induced unprecedented levels of coral bleaching accompanied by substantial storminess across more than 12° of latitude and 1200 kilometers of coastline in Western Australia (WA). Extreme La-Niña conditions caused extensive warming of waters and drove considerable storminess and cyclonic activity across WA from October 2010 to May 2011. Satellite-derived sea surface temperature measurements recorded anomalies of up to 5°C above long-term averages. Benthic surveys quantified the extent of bleaching at 10 locations across four regions from tropical to temperate waters. Bleaching was recorded in all locations across regions and ranged between 17% (±5.5) in the temperate Perth region, to 95% (±3.5) in the Exmouth Gulf of the tropical Ningaloo region. Coincident with high levels of bleaching, three cyclones passed in close proximity to study locations around the time of peak temperatures. Follow-up surveys revealed spatial heterogeneity in coral cover change with four of ten locations recording significant loss of coral cover. Relative decreases ranged between 22%-83.9% of total coral cover, with the greatest losses in the Exmouth Gulf. The anomalous thermal stress of 2010-11 induced mass bleaching of corals along central and southern WA coral reefs. Significant coral bleaching was observed at multiple locations across the tropical-temperate divide spanning more than 1200 km of coastline. Resultant spatially patchy loss of coral cover under widespread and high levels of bleaching and cyclonic activity, suggests a degree of resilience for WA coral communities. However, the spatial extent of bleaching casts some doubt over hypotheses suggesting that future impacts to coral reefs under forecast warming regimes may in part be mitigated by southern thermal refugia.

The International Comprehensive Ocean–Atmosphere Dataset (ICOADS) is a cornerstone for estimating changes in sea surface temperatures (SST) over the instrumental era. Interest in determining SST changes to within 0.1°C makes detecting systematic offsets within ICOADS important. Previous studies have corrected for offsets among engine room intake, buoy, and wooden and canvas bucket measurements, as well as noted discrepancies among various other groupings of data. In this study, a systematic examination of differences in collocated bucket SST measurements from ICOADS3.0 is undertaken using a linear-mixed-effect model according to nations and more-resolved groupings. Six nations and a grouping for which nation metadata are missing, referred to as “deck 156,” together contribute 91% of all bucket measurements and have systematic offsets among one another of as much as 0.22°C. Measurements from the Netherlands and deck 156 are colder than the global average by −0.10° and −0.13°C, respectively, both at p < 0.01, whereas Russian measurements are offset warm by 0.10°C at p °0.1. Furthermore, of the 31 nations whose measurements are present in more than one grouping of data (i.e., deck), 14 contain decks that show significant offsets at p < 0.1, including all major collecting nations. Results are found to be robust to assumptions regarding the independence and distribution of errors as well as to influences from the diurnal cycle and spatially heterogeneous noise variance. Correction for systematic offsets among these groupings should improve the accuracy of estimated SSTs and their trends.

Ocean currents play a key role in Earth's climate – they impact almost any process taking place in the ocean and are of major importance for navigation and human activities at sea. Nevertheless, their observation and forecasting are still difficult. First, no observing system is able to provide direct measurements of global ocean currents on synoptic scales. Consequently, it has been necessary to use sea surface height and sea surface temperature measurements and refer to dynamical frameworks to derive the velocity field. Second, the assimilation of the velocity field into numerical models of ocean circulation is difficult mainly due to lack of data. Recent experiments that assimilate coastal-based radar data have shown that ocean currents will contribute to increasing the forecast skill of surface currents, but require application in multidata assimilation approaches to better identify the thermohaline structure of the ocean. In this paper we review the current knowledge in these fields and provide a global and systematic view of the technologies to retrieve ocean velocities in the upper ocean and the available approaches to assimilate this information into ocean models.

Mesoscale dynamics of the Agulhas Current system determine the exchange between the Indian and Atlantic oceans, thereby influencing the global overturning circulation. Using a series of ocean model experiments compared to observations, we show that the representation of mesoscale eddies in the Agulhas ring path improves with increasing resolution of submesoscale flows. Simulated submesoscale dynamics are validated with time‐mean horizontal‐wavenumber spectra from satellite sea surface temperature measurements and mesoscale dynamics with spectra from sea surface height. While the Agulhas ring path in a nonsubmesoscale‐resolving (1/20)° configuration is associated with too less power spectral densities on all scales and too steep spectral slopes, the representation of the mesoscale dynamics improves when the diffusion and the dissipation of the model are reduced and some small‐scale features are resolved. Realistic power spectral densities over all scales are achieved when additionally the horizontal resolution is increased to (1/60)° and a larger portion of the submesoscale spectrum is resolved. Results of an eddy detection algorithm applied to the model outputs as well as to a gridded sea surface height satellite product show that in particular strong cyclones are much better represented when submesoscale flows are resolved by the model. The validation of the submesoscale dynamics with sea surface temperature spectra provides guidance for the choice of advection schemes and explicit diffusion and dissipation as well as for further subgrid‐scale parameterizations. For the Agulhas ring path, the use of upstream biased advection schemes without explicit diffusion and dissipation is found to be associated with realistically simulated submesoscales. Key Points Submesoscale‐permitting ocean models can be validated with wavenumber spectra from satellite observations Simulated mesoscale Agulhas eddies strengthen with the increasing resolution of submesoscale dynamics Simulated mesoscale dynamics in the Agulhas ring path converge to observations in a (1/60)° ocean model

The mixed layer heat balance in the Southern Ocean is examined by combining remotely sensed measurements and in situ observations from 1 June 2002 to 31 May 2006, coinciding with the period during which Advanced Microwave Scanning Radiometer-Earth Observing System (EOS) (AMSR-E) sea surface temperature measurements are available. Temperature/salinity profiles from Argo floats are used to derive the mixed layer depth. All terms in the heat budget are estimated directly from available data. The domain-averaged terms of oceanic heat advection, entrainment, diffusion, and air–sea flux are largely consistent with the evolution of the mixed layer temperature. The mixed layer temperature undergoes a strong seasonal cycle, which is largely attributed to the air–sea heat fluxes. Entrainment plays a secondary role. Oceanic advection also experiences a seasonal cycle, although it is relatively weak. Most of the seasonal variations in the advection term come from the Ekman advection, in contrast with western boundary current regions where geostrophic advection controls the total advection. Substantial imbalances exist in the regional heat budgets, especially near the northern boundary of the Antarctic Circumpolar Current. The biggest contributor to the surface heat budget error is thought to be the air–sea heat fluxes, because only limited Southern Hemisphere data are available for the reanalysis products, and hence these fluxes have large uncertainties. In particular, the lack of in situ measurements during winter is of fundamental concern. Sensitivity tests suggest that a proper representation of the mixed layer depth is important to close the budget. Salinity influences the stratification in the Southern Ocean; temperature alone provides an imperfect estimate of mixed layer depth and, because of this, also an imperfect estimate of the temperature of water entrained into the mixed layer from below.

Assimilation systems absorb both satellite measurements and Argo observations. This assimilation is essential to diagnose and evaluate the contribution from each type of data to the reconstructed analysis, allowing for better configuration of assimilation parameters. To achieve this, two comparative reconstruction schemes were designed under the optimal interpolation framework. Using a static scheme, an in situ -only field of ocean temperature was derived by correcting climatology with only Argo profiles. Through a dynamic scheme, a synthetic field was first derived from only satellite sea surface height and sea surface temperature measurements through vertical projection, and then a combined field was reconstructed by correcting the synthetic field with in situ profiles. For both schemes, a diagnostic iterative method was performed to optimize the background and observation error covariance statics. The root mean square difference (RMSD) of the in situ -only field, synthetic field and combined field were analyzed toward assimilated observations and independent observations, respectively. The rationale behind the distribution of RMSD was discussed using the following diagnostics: (1) The synthetic field has a smaller RMSD within the global mixed layer and extratropical deep waters, as in the Northwest Pacific Ocean; this is controlled by the explained variance of the vertical surface-underwater regression that reflects the ocean upper mixing and interior baroclinicity. (2) The in situ -only field has a smaller RMSD in the tropical upper layer and at midlatitudes; this is determined by the actual noise-to-signal ratio of ocean temperature. (3) The satellite observations make a more significant contribution to the analysis toward independent observations in the extratropics; this is determined by both the geographical feature of the synthetic field RMSD (smaller at depth in the extratropics) and that of the covariance correlation scales (smaller in the extratropics).

Obtaining stable and accurate satellite radiances for climate change research requires extremely high standards for satellite calibration. Many satellite sensors do not currently meet the accuracy criteria, especially heritage sensors such as the Advanced Very High Resolution Radiometer (AVHRR), which shows scene temperature–dependent trends and biases of up to 0.5 K. Recently, however, a detailed study of the AVHRR/3 prelaunch data showed significant problems with both the calibration algorithm and the prelaunch data and indicated that the inherent accuracy of the AVHRR may actually be quite high. A new approach has been suggested that fixed many of the issues with the current (operational) calibration, but has not yet been applied to the in-orbit case. In this paper the behavior of the AVHRR in orbit is examined and compared to the operational AVHRR radiances from the Meteorological Operation (MetOp)-A with those based on the new calibration to radiances derived from the Infrared Atmosphere Sounding Instrument (IASI). It is shown that the current AVHRR calibration does indeed introduce large (0.5 K) biases, but these biases are remarkably stable. It is further shown that, with some modification related to differences between the prelaunch test environment and the in-orbit environment, a physically based AVHRR calibration can match IASI to better than ~0.05 K, which is an order of magnitude better than what is currently available. Finally, it is shown that, while the new calibration is capable of providing accurate and stable radiances for the nadir view, off-nadir biases of up to 1.5 K still exist at the largest zenith angles and at the coldest scene temperatures (~210 K). For surface temperature determination, however, the scan angle bias is very small (<0.02 K), implying that the new AVHRR calibration will provide a significant improvement to, for example, sea surface temperature measurements, one of the Global Climate Observing System (GCOS)-designated essential climate variables.

An ocean data assimilation system was developed for the Pacific–Indian oceans with the aim of assimilating altimetry data, sea surface temperature, and in situ measurements from Argo (Array for Real-time Geostrophic Oceanography), XBT (expendable bathythermographs), CTD (conductivity temperature depth), and TAO (Tropical Atmosphere Ocean). The altimetry data assimilation requires the addition of the mean dynamic topography to the altimetric sea level anomaly to match the model sea surface height. The mean dynamic topography is usually computed from the model long-term mean sea surface height, and is also available from gravimetric satellite data. In this study, the impact of different mean dynamic topographies on the sea level anomaly assimilation is examined. Results show that impacts of the mean dynamic topography cannot be neglected. The mean dynamic topography from the model long-term mean sea surface height without assimilating in situ observations results in worsened subsurface temperature and salinity estimates. Even if all available observations including in situ measurements, sea surface temperature measurements, and altimetry data are assimilated, the estimates are still not improved. This proves the significant impact of the MDT (mean dynamic topography) on the analysis system, as the other types of observations do not compensate for the shortcoming due to the altimetry data assimilation. The gravimeter-based mean dynamic topography results in a good estimate compared with that of the experiment without assimilation. The mean dynamic topography computed from the model long-term mean sea surface height after assimilating in situ observations presents better results.

A new comprehensive record of long-term Icelandic sea surface temperature measurements, which have been updated and filled in with reference to air temperature records, is presented. The new SST series reveal important features of the variability of climate in Iceland and the northern North Atlantic. This study documents site histories and possible resulting inconsistencies and biases, for example, changes in observing sites and instruments. A new 119-yr continuous time series for north Iceland SST is presented, which should prove particularly useful for investigating air–sea ice interactions around northern Iceland. As this is the only part of the country to be regularly engulfed by winter and/or spring sea ice, it is therefore highly sensitive to climatic change. The coastal series correlate well overall with independent Hadley Centre Sea Ice and SST dataset version 1 (HadISST1) series from the adjacent open ocean (meanr= 0.59), although correlations are generally higher in summer than winter and for south and east Iceland compared with the west and north. The seasonal temperature range is generally twice as large at the coastal sites because of differential effects of radiation, melting, mixing, and advection of warmer or colder air or water masses, as well as spatial resolution differences and smoothing in HadISST1. The long-term climatological averages and graphs for the 10 SST stations and/or their composites reveal decadal variations and trends that are generally similar to Icelandic air temperature records: a cold latenineteenth- century, rapid warming around the 1920s, an overall warm peak circa 1940, cooling until an “icy” period circa 1970, followed by warming. Regional differences between sites include relatively greater (lesser) long-term variations for the eastern and southern (western and northern) Icelandic coasts, suggesting greater variability and influence of ocean current advection in the southeast. Moreover, Vestmannaeyjar SST data reveal that the late-nineteenth-century cold period in the ocean was not confined to the cold currents off north and east Iceland but also affected the south coast markedly. The Stykkishólmur, Iceland, SST record is relatively noisy and shows very little decadal variation, which may largely be due to fjord ice in cold winters suppressing low temperatures. It is anticipated that researchers may find these Icelandic SST series of practical use as a historic measure of air–sea–climate interactions around Iceland.

MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature measurements in combination with in situ air temperature records from 119 meteorological stations are used to reconstruct a monthly near-surface air temperature product over the Antarctic Ice Sheet (AIS) by means of a neural network model. The product is generated on a regular grid of 0.05° × 0.05°, spanning from 2001 to 2018. Comparison with independent in situ air temperature measurements shows low uncertainty, with a mean bias of 0.09°C, a mean absolute error of 2.23°C, and a correlation coefficient of 97%. Furthermore, the performance of the reconstruction is better than ERA5 (the fifth-generation ECMWF reanalysis model) against in situ measurements. For the 2001–18 period, the MODIS-based near-surface air temperature product yields annual warming in the East Antarctica, but cooling in the Antarctic Peninsula and West Antarctica. However, they are not statistically significant. This product can also be used to investigate the impact of the Southern Hemisphere annual mode (SAM) on year-to-year variability of air temperature. The enhanced positive phase of SAM in recent decades in austral summer has a cooling effect on East and West Antarctica. In addition, the dataset has the potential application for climate model validation and data assimilation due to the independence of the input of a numerical weather prediction model.