Gridded Sea Surface Temperature Research Articles

Two centuries of southwest Iceland annually-resolved marine temperature reconstructed from Arctica islandica shells

Iceland's exposure to major ocean current pathways of the central North Atlantic makes it a useful location for developing long-term proxy records of past marine climate. Such records provide more detailed understanding of the full range of past variability which is necessary to improve predictions of future changes. We constructed a 225-year (1791–2015 CE) master shell growth chronology from 29 shells of Arctica islandica collected at 100 m water depth in southwest Iceland (Faxaflói). The growth chronology provides a robust age model for shell oxygen isotope (δ18Oshell) data produced at annual resolution for 251 years (1765–2015 CE). The temperature reconstruction derived from δ18Oshell shows coherence with May–October local surface temperature records and sea surface temperatures in the North Atlantic region, suggesting it is a useful proxy indicator of water temperature variability at 100 m depth within Faxaflói. Field correlations between the shell-based records and gridded sea surface temperature data reveal strong positive correlations between the 1-year lagged shell growth and temperatures within the subpolar gyre post-1972, suggesting a delayed influence of subpolar gyre dynamics on ecological indicators in southwest Iceland in recent decades. However, the shell growth chronology and δ18Oshell record generally show relatively weak and insignificant correlations with larger region climate indices including the Atlantic Multidecadal Variability, North Atlantic Oscillation, and East Atlantic pattern. Therefore the interannual variations in the newly produced shell-based records appear to reflect more local to regional dynamics around southwest Iceland than large-scale modes of climate variability.

Temperature amplification and marine heatwave alteration in shallow coastal bays

Shallow coastal ecosystems are threatened by marine heatwaves, but few long-term records exist to quantify these heatwaves. Here, 40-year records of measured water temperature were constructed for a site in a system of shallow bays with documented heatwave impacts and a nearby ocean site; available gridded sea-surface temperature datasets in the region were also examined. Water temperatures at both sites increased significantly though bay temperatures were consistently 3-4°C hotter in summer and colder in winter and were more variable overall, differences not captured in high-resolution gridded sea-surface temperature datasets. There was considerable overlap in heatwave events at the coastal bay and ocean sites. Annual heatwave exposure was similar and significantly increased at both sites while annual heatwave intensity was significantly higher at the bay site owing to the high variance of the daily temperature anomaly there. Event frequency at both sites increased at a rate of about 1 event/decade. Future simulations indicate all heatwave metrics increase, as do days above 28°C, a heat stress threshold for seagrass. Ocean temperatures on the U.S. mid-Atlantic margin have rarely exceeded this threshold, while summer bay temperatures commonly do, allowing ocean exchange with coastal bays to provide thermal relief to bay ecosystems. This will have changed by 2100, creating a thermal environment that threatens seagrass communities in these systems. Documenting such change requires development of long-term water temperature records in more shallow coastal systems.

Understanding Differences in Sea Surface Temperature Intercomparisons

Abstract Our study shows that the intercomparison among sea surface temperature (SST) products is influenced by the choice of SST reference, and the interpolation of SST products. The influence of reference SST depends on whether the reference SSTs are averaged to a grid or in pointwise in situ locations, including buoy or Argo observations, and filtered by first-guess or climatology quality control (QC) algorithms. The influence of the interpolation depends on whether SST products are in their original grids or preprocessed into common coarse grids. The impacts of these factors are demonstrated in our assessments of eight widely used SST products (DOISST, MUR25, MGDSST, GAMSSA, OSTIA, GPB, CCI, CMC) relative to buoy observations: (i) when the reference SSTs are averaged onto 0.25° × 0.25° grid boxes, the magnitude of biases is lower in DOISST and MGDSST (<0.03°C), and magnitude of root-mean-square differences (RMSDs) is lower in DOISST (0.38°C) and OSTIA (0.43°C); (ii) when the same reference SSTs are evaluated at pointwise in situ locations, the standard deviations (SDs) are smaller in DOISST (0.38°C) and OSTIA (0.39°C) on 0.25° × 0.25° grids; but the SDs become smaller in OSTIA (0.34°C) and CMC (0.37°C) on products’ original grids, showing the advantage of those high-resolution analyses for resolving finer-scale SSTs; (iii) when a loose QC algorithm is applied to the reference buoy observations, SDs increase; and vice versa; however, the relative performance of products remains the same; and (iv) when the drifting-buoy or Argo observations are used as the reference, the magnitude of RMSDs and SDs become smaller, potentially due to changes in observing intervals. These results suggest that high-resolution SST analyses may take advantage in intercomparisons. Significance Statement Intercomparisons of gridded SST products be affected by how the products are compared with in situ observations: whether the products are in coarse (0.25°) or original (0.05°–0.10°) grids, whether the in situ SSTs are in their reported locations or gridded and how they are quality controlled, and whether the biases of satellite SSTs are corrected by localized matchups or large-scale patterns. By taking all these factors into account, our analyses indicate that the NOAA DOISST is among the best SST products for the long period (1981–present) and relatively coarse (0.25°) resolution that it was designed for.

Uncertainty in Atlantic Multidecadal Oscillation derived from different observed datasets and their possible causes

As a leading mode of sea surface temperature (SST) variability over the North Atlantic in both observations and model simulations, the Atlantic Multidecadal Oscillation (AMO) can have a substantial influence on regional and global climate. By using Low-Frequency Component Analysis, we explore the uncertainties of the resulting AMO indices and the corresponding spatial patterns derived from three observational SST datasets. We found that the known coherent spatial pattern of the AMO at the basin scale over the North Atlantic appears in two out of the three datasets. Further analysis indicates that both the warming trend and the different techniques used to construct these observed gridded SSTs contribute to the AMO’s spatial coherence over the North Atlantic, especially during periods of sparse data sampling. The SST in the Extended Reconstructed SST dataset version 5 (ERSSTv5), changes from being systematically below the other datasets during the dense sampling periods on either side of the Second World War (WWII), to systematically above the other datasets during WWII, thereby introducing an artificial 10–20-year variability that affects the AMO’s spatial coherence. This coherence in the AMO’s spatial pattern is also affected by bias adjustment in ERSSTv5 at relative cool (i.e., non-summer) seasons, and by the heterogeneous North Atlantic warming pattern. The different AMO patterns can induce the different effects of wind, surface heat fluxes, and then drive ocean circulation and its heat transport convergence, especially for some seasons. For AMO indices, both the different detrending methods and different observational data result in uncertainty for the period 1935–1950. Such SST uncertainty is important to detect the relative role of the atmosphere and ocean in shaping the AMO.

A new global surface temperature reconstruction for the Last Glacial Maximum

Abstract. We present a new reconstruction of surface air temperature and sea surface temperature for the Last Glacial Maximum. The method blends model fields and sparse proxy-based point estimates through a data assimilation approach. Our reconstruction updates that of Annan and Hargreaves (2013), using the full range of general circulation model (GCM) simulations which contributed to three generations of the PMIP database, three major compilations of gridded sea surface temperature (SST) and surface air temperature (SAT) estimates from proxy data, and an improved methodology based on an ensemble Kalman filter. Our reconstruction has a global annual mean surface air temperature anomaly of -4.5±0.9 ∘C relative to the pre-industrial climate. This is slightly colder than the previous estimate of Annan and Hargreaves (2013), with an upwards revision on the uncertainty due to different methodological assumptions. It is, however, substantially less cold than the recent reconstruction of Tierney et al. (2020). We show that the main reason for this discrepancy is in the choice of prior. We recommend the use of the multi-model ensemble of opportunity as potentially offering a credible prior, but it is important that the range of models included in the PMIP ensembles represent the main sources of uncertainty as realistically and comprehensively as practicable if they are to be used in this way.

An online ensemble coupled data assimilation capability for the Community Earth System Model: system design and evaluation

Abstract. The Community Earth System Model (CESM) developed by the National Center for Atmospheric Research (NCAR) has been used worldwide for climate studies. This study extends the efforts of CESM development to include an online (i.e., in-core) ensemble coupled data assimilation system (CESM-ECDA) to enhance CESM's capability for climate predictability studies and prediction applications. The CESM-ECDA system consists of an online atmospheric data assimilation (ADA) component implemented in both the finite-volume and spectral-element dynamical cores and an online ocean data assimilation (ODA) component. In ADA, surface pressures (Ps) are assimilated, while in ODA, gridded sea surface temperature (SST) and ocean temperature and salinity profiles at real Argo locations are assimilated. The system has been evaluated within a perfect twin experiment framework, showing significantly reduced errors of the model atmosphere and ocean states through “observation” constraints by ADA and ODA. The weakly coupled data assimilation (CDA) in which both the online ADA and ODA are conducted during the coupled model integration shows smaller errors of air–sea fluxes than the single ADA and ODA, facilitating the future utilization of cross-covariance between the atmosphere and ocean at the air–sea interface. A 3-year CDA reanalysis experiment is also implemented by assimilating Ps, SST and ocean temperature and salinity profiles from the real world spanning the period 1978 to 1980 using 12 ensemble members. The success of the online CESM-ECDA system is the first step to implementing a high-resolution long-term climate reanalysis once the algorithm efficiency is much improved.

An ensemble reconstruction of global monthly sea surface temperature and sea ice concentration 1000\u20131849

This paper describes a global monthly gridded Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) dataset for the period 1000–1849, which can be used as boundary conditions for atmospheric model simulations. The reconstruction is based on existing coarse-resolution annual temperature ensemble reconstructions, which are then augmented with intra-annual and sub-grid scale variability. The intra-annual component of HadISST.2.0 and oceanic indices estimated from the reconstructed annual mean are used to develop grid-based linear regressions in a monthly stratified approach. Similarly, we reconstruct SIC using analog resampling of HadISST.2.0 SIC (1941–2000), for both hemispheres. Analogs are pooled in four seasons, comprising of 3-months each. The best analogs are selected based on the correlation between each member of the reconstructed SST and its target. For the period 1780 to 1849, We assimilate historical observations of SST and night-time marine air temperature from the ICOADS dataset into our reconstruction using an offline Ensemble Kalman Filter approach. The resulting dataset is physically consistent with information from models, proxies, and observations.

A new global gridded sea surface temperature data product based on multisource data

Abstract. Sea surface temperature (SST) is an important geophysical parameter that is essential for studying global climate change. Although sea surface temperature can currently be obtained through a variety of sensors (MODIS, AVHRR, AMSR-E, AMSR2, WindSat, in situ sensors), the temperature values obtained by different sensors come from different ocean depths and different observation times, so different temperature products lack consistency. In addition, different thermal infrared temperature products have many invalid values due to the influence of clouds, and passive microwave temperature products have very low resolutions. These factors greatly limit the applications of ocean temperature products in practice. To overcome these shortcomings, this paper first took MODIS SST products as a reference benchmark and constructed a temperature depth and observation time correction model to correct the influences of the different sampling depths and observation times obtained by different sensors. Then, we built a reconstructed spatial model to overcome the effects of clouds, rainfall, and land interference that makes full use of the complementarities and advantages of SST data from different sensors. We applied these two models to generate a unique global 0.041∘ gridded monthly SST product covering the years 2002–2019. In this dataset, approximately 25 % of the invalid pixels in the original MODIS monthly images were effectively removed, and the accuracies of these reconstructed pixels were improved by more than 0.65 ∘C compared to the accuracies of the original pixels. The accuracy assessments indicate that the reconstructed dataset exhibits significant improvements and can be used for mesoscale ocean phenomenon analyses. The product will be of great use in research related to global change, disaster prevention, and mitigation and is available at https://doi.org/10.5281/zenodo.4419804 (Cao et al., 2021a).

An Arctic Gridded Sea Surface Temperature Product Constructed from Spaceborne Radiometer Data

Sun, W.; Zhang, J.; Meng, J.; Li, Y., and Cao, K., 2020. An arctic gridded sea surface temperature product constructed from spaceborne radiometer data. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 280-286. Coconut Creek (Florida), ISSN 0749-0208.A sea surface temperature (SST) record with a grid size of 0.1° for the Arctic region is presented by fusing satellite-derived SST products from three passive-microwave (WindSat, AMSR-E and ASMR2) and four infrared (AVHRR, MODIS onboard Terra and Aqua, and VIIRS) radiometers (RMs). In this study, optimum interpolation (OI) is used to construct the dataset. The fused SST data of Arctic covering the regime north of 60°N span from 2010 to 2018, available with the format of NetcDF. The accuracy of the gridded Arctic SST product was assessed with the drifting buoy measurements. Comparisons with the measurements from Argo buoys show that the Root Mean Squared Errors (RMSE) of the fusion SST data obtained in this study is relatively stable with values ranging from 0.54 to 0.75 °C. This gridded SST data set is characterized with a high spatiotemporal resolution and an assured accuracy.

Sea Surface Temperatures Derived From COCTS Onboard the HY-1C Satellite

As one of the key payloads onboard the HY-1C satellite, the Chinese ocean color and temperature scanner (COCTS) was designed for global sea surface temperature (SST) and ocean color detection. In this study, a nonlinear SST algorithm (NLSST) is applied to derive SSTs from the brightness temperatures (BTs) of the two COCTS thermal infrared bands at 10.8 and 12.0 μm. The SST retrieval results from the multichannel SST algorithm (MCSST) are used as the first guess for the input of the NLSST algorithm. The coefficients of both the MCSST and NLSST algorithms are regressed from the matchup datasets of the COCTS BT observations and the measurements from the in situ quality monitor system (iQuam), which were collected from September 10 to December 31, 2018. The retrieval SSTs from January 1, 2019 to March 31, 2020 are evaluated by the in situ measurements from iQuam with root mean square errors of 0.84 °C for daytime and 0.97 °C for nighttime and robust standard deviations (RSDs) of 0.73 °C for daytime and 0.72 °C for nighttime. Daily gridded retrieval SSTs from September 10, 2018 to March 31, 2020 are compared with SSTs from moderate-resolution imaging spectroradiometer (MODIS) onboard Terra satellite. The cross comparison RSDs are 0.56 ± 0.05 °C for daytime and 0.67 ± 0.05 °C for nighttime.

Harmonization of Space-Borne Infra-Red Sensors Measuring Sea Surface Temperature

Sea surface temperature (SST) is observed by a constellation of sensors, and SST retrievals are commonly combined into gridded SST analyses and climate data records (CDRs). Differential biases between SSTs from different sensors cause errors in such products, including feature artefacts. We introduce a new method for reducing differential biases across the SST constellation, by reconciling the brightness temperature (BT) calibration and SST retrieval parameters between sensors. We use the Advanced Along-Track Scanning Radiometer (AATSR) and the Sea and Land Surface Temperature Radiometer (SLSTR) as reference sensors, and the Advanced Very High Resolution Radiometer (AVHRR) of the MetOp-A mission to bridge the gap between these references. Observations across a range of AVHRR zenith angles are matched with dual-view three-channel skin SST retrievals from the AATSR and SLSTR. These skin SSTs act as the harmonization reference for AVHRR retrievals by optimal estimation (OE). Parameters for the harmonized AVHRR OE are iteratively determined, including BT bias corrections and observation error covariance matrices as functions of water-vapor path. The OE SSTs obtained from AVHRR are shown to be closely consistent with the reference sensor SSTs. Independent validation against drifting buoy SSTs shows that the AVHRR OE retrieval is stable across the reference-sensor gap. We discuss that this method is suitable to improve consistency across the whole constellation of SST sensors. The approach will help stabilize and reduce errors in future SST CDRs, as well as having application to other domains of remote sensing.

Assessment and Improvement of Global Gridded Sea Surface Temperature Datasets in the Yellow Sea Using In Situ Ocean Buoy and Research Vessel Observations

The sea surface temperature (SST) is essential data for the ocean and atmospheric prediction systems and climate change studies. Five global gridded sea surface temperature products were evaluated with independent in situ SST data of the Yellow Sea (YS) from 2010 to 2013 and the sources of SST error were identified. On average, SST from the gridded optimally interpolated level 4 (L4) datasets had a root mean square difference (RMSD) of less than 1 °C compared to the in situ observation data of the YS. However, the RMSD was relatively high (2.3 °C) in the shallow coastal region in June and July and this RMSD was mostly attributed to the large warm bias (>2 °C). The level 3 (L3) SST data were frequently missing in early summer because of frequent sea fog formation and a strong (>1.2 °C/12 km) spatial temperature gradient across the tidal mixing front in the eastern YS. The missing data were optimally interpolated from the SST observation in offshore warm water and warm biased SST climatology in the region. To fundamentally improve the accuracy of the L4 gridded SST data, it is necessary to increase the number of SST observation data in the tidally well mixed region. As an interim solution to the warm bias in the gridded SST datasets in the eastern YS, the SST climatology for the optimal interpolation can be improved based on long-term in situ observation data. To reduce the warm bias in the gridded SST products, two bias correction methods were suggested and compared. Bias correction methods using a simple analytical function and using climatological observation data reduced the RMSD by 19–29% and 37–49%, respectively, in June.

Testing the validity of regional detail in global analyses of sea surface temperature – the case of Chinese coastal waters

Abstract. We have designed a method for testing the quality of multidecadal analyses of sea surface temperature (SST) in regional seas by using a set of high-quality local SST observations. In recognizing that local data may reflect local effects, we focus on the dominant empirical orthogonal functions (EOFs) of the local data and of the localized data of the gridded SST analyses. We examine the patterns, variability, and trends of the principal components. This method is applied to examine three different SST analyses, i.e., HadISST1, ERSST, and COBE SST. They have been assessed using a newly constructed high-quality dataset of SST at 26 coastal stations along the Chinese coast in 1960–2015, which underwent careful examination with respect to quality and a number of corrections for inhomogeneities. The three gridded analyses perform generally well from 1960 to 2015, in particular since 1980. However, for the pre-satellite period prior to the 1980s, the analyses differ among each other and show some inconsistencies with the local data, such as artificial break points, periods of bias, and differences in trends. We conclude that gridded SST analyses need improvement in the pre-satellite period (prior to the 1980s) by reexamining in detail archives of local quality-controlled SST data in many data-sparse regions of the world.

An enigmatic decoupling between heat stress and coral bleaching on the Great Barrier Reef.

Ocean warming threatens the functioning of coral reef ecosystems by inducing mass coral bleaching and mortality events. The link between temperature and coral bleaching is now well-established based on observations that mass bleaching events usually occur when seawater temperatures are anomalously high. However, times of high heat stress but without coral bleaching are equally important because they can inform an understanding of factors that regulate temperature-induced bleaching. Here, we investigate the absence of mass coral bleaching on the Great Barrier Reef (GBR) during austral summer 2004. Using four gridded sea surface temperature data products, validated with in situ temperature loggers, we demonstrate that the summer of 2004 was among the warmest summers of the satellite era (1982–2017) on the GBR. At least half of the GBR experienced temperatures that were high enough to initiate bleaching in other years, yet mass bleaching was not reported during 2004. The absence of bleaching is not fully explained by wind speed or cloud cover. Rather, 2004 is clearly differentiated from bleaching years by the slow speed of the East Australian Current (EAC) offshore of the GBR. An anomalously slow EAC during summer 2004 may have dampened the upwelling of nutrient-rich waters onto the GBR shelf, potentially mitigating bleaching due to the lower susceptibility of corals to heat stress in low-nutrient conditions. Although other factors such as irradiance or acclimatization may have played a role in the absence of mass bleaching, 2004 remains a key case study for demonstrating the dynamic nature of coral responses to marine heatwaves.

Assessing the impact of Argo floats temperature measurements on the numerical weather prediction forecast skill

The forecast skill of numerical weather prediction (NWP) models relies, among other factors such as the prediction itself and the assimilation scheme, on the accuracy of the observations utilized in the assimilation systems for the production of initial and boundary conditions. One of the most crucial parameters in weather forecasting is the sea surface temperature (SST). In the majority of NWP models, the initial and lower boundary conditions involve gridded (SST) analyses which consist of data obtained by buoys, ships and satellites. The main aim of this study is to integrate Argo temperature measurements in gridded SST analyses and to assess their impact on the forecast skill of a limited area atmospheric model. Argo floats are “state-of-the-art” oceanographic instruments producing high-quality temperature profiles for the ice-free ocean. In this study, Argo temperatures are incorporated into gridded SST fields without applying any smoothing method in order to directly assess the impact of Argo temperatures on numerical weather prediction. Their impact is assessed under intense weather cyclonic conditions at the Mediterranean Sea by performing two sensitivity simulations either incorporating or not Argo temperatures into gridded SST fields used in the generation of the initial and lower boundary conditions. The results indicate that the inclusion of Argo-measured near-surface temperatures in the lower boundary condition modifies the surface heat fluxes, thus affecting mean sea level pressure and precipitation. In particular, an overall improvement of the precipitation forecast skill up to 3% has been demonstrated. Moreover, the incorporation of Argo temperatures affects the simulated track and intensity of the cyclone over the Balkan Peninsula.

Inter-Annual Variability and Trends of Sea Level and Sea Surface Temperature in the Mediterranean Sea over the Last 25 Years

Sea level and sea surface temperature inter-annual variability and trends in the Mediterranean Sea were investigated during the period 1993–2017. These were carried out using gridded absolute dynamic topography from satellite altimetry, tide gauge (TG) time series from 25 stations and gridded sea surface temperature (SST) from advanced very-high-resolution radiometer (AVHRR) data. The coastal TG data were used to verify the satellite derived sea level. Moreover, the contributions of atmospheric pressure and North Atlantic Oscillation (NAO) to sea level changes were also examined. The results revealed that the Mediterranean Sea exhibits inter-annual spatiotemporal coherent variability in both sea level and SST. The spatial variability in sea level is more significant over the Adriatic and Aegean Seas, most of the Levantine basin, and along the Tunisian shelf. Marked spatial variability in SST occurs over the central part of the Mediterranean Sea with maximum amplitude in the Tyrrhenian Sea. The highest temporal variability of sea level and SST was found in 2010 and 2003, respectively. The inter-annual variability of sea level and SST accounts for about 32% and 3% of the total variance of sea level and SST, respectively. An analysis of sea level anomaly reveled large negative values during the extended winter of 2011–2012, which may be attributed to the strong positive phase of NAO index. Satellite altimetry indicated a significant positive sea level trend of 2.7 ± 0.41 mm/year together with a significant warming of 0.036 ± 0.003 °C/year over the whole Mediterranean Sea for the period 1993–2017.

A new global gridded sea surface temperature product constructed from infrared and microwave radiometer data using the optimum interpolation method

A new 0.1° gridded daily sea surface temperature (SST) data product is presented covering the years 2003–2015. It is created by fusing satellite SST data retrievals from four microwave (WindSat, AMSR-E, ASMR2 and HY-2A RM) and two infrared (MODIS and AVHRR) radiometers (RMs) based on the optimum interpolation (OI) method. The effect of including HY-2A RM SST data in the fusion product is studied, and the accuracy of the new SST product is determined by various comparisons with moored and drifting buoy measurements. An evaluation using global tropical moored buoy measurements shows that the root mean square error (RMSE) of the new gridded SST product is generally less than 0.5°C. A comparison with US National Data Buoy Center meteorological and oceanographic moored buoy observations shows that the RMSE of the new product is generally less than 0.8°C. A comparison with measurements from drifting buoys shows an RMSE of 0.52–0.69°C. Furthermore, the consistency of the new gridded SST dataset and the Remote Sensing Systems microwave-infrared SST dataset is evaluated, and the result shows that no significant inconsistency exists between these two products.

Assimilation of Different SST Datasets to a Coastal Ocean Modeling System in the Yellow and East China Sea

Kwon, K.; Choi, B.-J., and Lee, S.-H., 2018. Assimilation of Different SST Datasets to a Coastal Ocean Modeling System in the Yellow and East China Sea. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 1041–1045. Coconut Creek (Florida), ISSN 0749-0208.Global gridded gap-free sea surface temperature (SST) datasets were quantitatively evaluated using independent insitu datasets in the Yellow and East China Sea from September 2011 to February 2012. The assimilation effect of the four different SST datasets on a regional ocean modeling system was examined. The SST from the gridded SST datasets was compared with two ocean buoy SST data and bi-monthly routine hydrographic observation data in the Yellow Sea (YS). The root-mean-square differences (RMSDs) of the OISST, MGDSST, OSTIA, and MWIR SST datasets relative to the in-situ SST were 0.64, 0.53, 0.62, and 0.91°C, respectively. The RMSDs of SST were higher than 1°C for all datasets in the southeastern corner of the YS. Ocean circulation was simulated with a regional ocean model, and the SST data from the four SST datasets were assimilated. Assimilation of the SST to the ocean model improved the ocean current as well as temperature distribution. The daily mean simulated temperature was compared to the observed SST and vertical profiles of the temperature at the ocean buoys and hydrographic observation stations. After the assimilation of OISST, MGDSST, OSTIA and MWIR datasets, RMSDs of the simulated SST were 0.78, 0.92, 0.66 and 0.86°C, respectively, at the buoy stations. The RMSDs of the simulated subsurface temperature were 1.60, 1.67, 1.56 and 1.70°C, respectively, at the hydrographic observation stations. The simulated SSTs from the model with assimilation of the OSTIA and OISST datasets had relatively smaller RMSDs.

Spatio-Temporal Interpolation of Cloudy SST Fields Using Conditional Analog Data Assimilation

The ever increasing geophysical data streams pouring from earth observation satellite missions and numerical simulations along with the development of dedicated big data infrastructure advocate for truly exploiting the potential of these datasets, through novel data-driven strategies, to deliver enhanced satellite-derived gapfilled geophysical products from partial satellite observations. We here demonstrate the relevance of the analog data assimilation (AnDA) for an application to the reconstruction of cloud-free level-4 gridded Sea Surface Temperature (SST). We propose novel AnDA models which exploit auxiliary variables such as sea surface currents and significantly reduce the computational complexity of AnDA. Numerical experiments benchmark the proposed models with respect to state-of-the-art interpolation techniques such as optimal interpolation and EOF-based schemes. We report relative improvement up to 40%/50% in terms of RMSE and also show a good parallelization performance, which supports the feasibility of an upscaling on a global scale.

A Call for New Approaches to Quantifying Biases in Observations of Sea Surface Temperature

Abstract Global surface temperature changes are a fundamental expression of climate change. Recent, much-debated variations in the observed rate of surface temperature change have highlighted the importance of uncertainty in adjustments applied to sea surface temperature (SST) measurements. These adjustments are applied to compensate for systematic biases and changes in observing protocol. Better quantification of the adjustments and their uncertainties would increase confidence in estimated surface temperature change and provide higher-quality gridded SST fields for use in many applications. Bias adjustments have been based on either physical models of the observing processes or the assumption of an unchanging relationship between SST and a reference dataset, such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and time scales, but regional differences can exceed the estimated uncertainty. We describe challenges to improving our understanding of SST biases. Overcoming these will require clarification of past observational methods, improved modeling of biases associated with each observing method, and the development of statistical bias estimates that are less sensitive to the absence of metadata regarding the observing method. New approaches are required that embed bias models, specific to each type of observation, within a robust statistical framework. Mobile platforms and rapid changes in observation type require biases to be assessed for individual historic and present-day platforms (i.e., ships or buoys) or groups of platforms. Lack of observational metadata and high-quality observations for validation and bias model development are likely to remain major challenges.