Objective determination of feature resolution in two sea surface temperature analyses

Considerable effort is presently being devoted to producing high-resolution sea surface temperature (SST) analyses with a goal of spatial grid resolutions as low as 1 km. Because grid resolution is not the same as feature resolution, a method is needed to objectively determine the resolution capability and accuracy of SST analysis products. Ocean model SST fields are used in this study as simulated “true” SST data and subsampled based on actual infrared and microwave satellite data coverage. The subsampled data are used to simulate sampling errors due to missing data. Two different SST analyses are considered and run using both the full and the subsampled model SST fields, with and without additional noise. The results are compared as a function of spatial scales of variability using wavenumber auto- and cross-spectral analysis. The spectral variance at high wavenumbers (smallest wavelengths) is shown to be attenuated relative to the true SST because of smoothing that is inherent to both analysis procedures. Comparisons of the two analyses (both having grid sizes of roughly ) show important differences. One analysis tends to reproduce small-scale features more accurately when the high-resolution data coverage is good but produces more spurious small-scale noise when the high-resolution data coverage is poor. Analysis procedures can thus generate small-scale features with and without data, but the small-scale features in an SST analysis may be just noise when high-resolution data are sparse. Users must therefore be skeptical of high-resolution SST products, especially in regions where high-resolution (~5 km) infrared satellite data are limited because of cloud cover.

Sea surface temperature climate change initiative: alternative image classification algorithms for sea-ice affected oceans

We present a Bayesian image classification scheme for discriminating cloud, clear and sea-ice observations at high latitudes to improve identification of areas of clear-sky over ice-free ocean for SST retrieval. We validate the image classification against a manually classified dataset using Advanced Along Track Scanning Radiometer (AATSR) data. A three way classification scheme using a near-infrared textural feature improves classifier accuracy by 9.9 % over the nadir only version of the cloud clearing used in the ATSR Reprocessing for Climate (ARC) project in high latitude regions. The three way classification gives similar numbers of cloud and ice scenes misclassified as clear but significantly more clear-sky cases are correctly identified (89.9 % compared with 65 % for ARC). We also demonstrate the poetential of a Bayesian image classifier including information from the 0.6 micron channel to be used in sea-ice extent and ice surface temperature retrieval with 77.7 % of ice scenes correctly identified and an overall classifier accuracy of 96 %.

Dynamical and observational constraints on tropical Pacific sea surface temperatures at the last glacial maximum

Asynchronously coupled atmosphere and ocean general circulation model simulations are used to examine the consequences of changes in the west/east sea-surface temperature (SST) gradient across the equatorial Pacific at the last glacial maximum (LGM). Simulations forced by the CLIMAP SST for the LGM, where the west/east SST gradient across the Pacific is reduced compared to present, produce a reduction in the strength of the trade winds and a decrease in the west/east slope of the equatorial thermocline that is incompatible with thermocline depths newly inferred from foraminiferal assemblages. Stronger-than-present trade winds, and a more realistic simulation of the thermocline slope, are produced when eastern Pacific SSTs are 2°C cooler than western Pacific SSTs. Our study highlights the importance of spatial heterogeneity in tropical SSTs in determining key features of the glacial climate.

A case study of sea breeze blocking regulated by sea surface temperature along the English south coast

The sensitivity of sea breeze structure to sea surface temperature (SST) and coastal orography is investigated in convection-permitting Met Office Unified Model simulations of a case study along the south coast of England. Changes in SST of 1 K are shown to significantly modify the structure of the sea breeze immediately offshore. On the day of the case study, the sea breeze was partially blocked by coastal orography, particularly within Lyme Bay. The extent to which the flow is blocked depends strongly on the static stability of the marine boundary layer. In experiments with colder SST, the marine boundary layer is more stable, and the degree of blocking is more pronounced. Although a colder SST would also imply a larger land–sea temperature contrast and hence a stronger onshore wind – an effect which alone would discourage blocking – the increased static stability exerts a dominant control over whether blocking takes place. The implications of prescribing fixed SST from climatology in numerical weather prediction model forecasts of the sea breeze are discussed.

Sea surface temperature datasets for climate applications from Phase 1 of the European Space Agency Climate Change Initiative (SST CCI)

Sea surface temperature (SST) datasets have been generated from satellite observations for the period 1991–2010, intended for use in climate science applications. Attributes of the datasets specifically relevant to climate applications are: first, independence from in situ observations; second, effort to ensure homogeneity and stability through the time-series; third, context-specific uncertainty estimates attached to each SST value; and, fourth, provision of estimates of both skin SST (the fundamental measure- ment, relevant to air-sea fluxes) and SST at standard depth and local time (partly model mediated, enabling comparison with his- torical in situ datasets). These attributes in part reflect requirements solicited from climate data users prior to and during the project. Datasets consisting of SSTs on satellite swaths are derived from the Along-Track Scanning Radiometers (ATSRs) and Advanced Very High Resolution Radiometers (AVHRRs). These are then used as sole SST inputs to a daily, spatially complete, analysis SST product, with a latitude-longitude resolution of 0.05°C and good discrimination of ocean surface thermal features. A product user guide is available, linking to reports describing the datasets’ algorithmic basis, validation results, format, uncer- tainty information and experimental use in trial climate applications. Future versions of the datasets will span at least 1982–2015, better addressing the need in many climate applications for stable records of global SST that are at least 30 years in length.

Thermal remote sensing of sea surface temperature

Sea surface temperature has been an important application of remote sensing from space for three decades. This chapter first describes well-established methods that have delivered valuable routine observations of sea surface temperature for meteorology and oceanography. Increasingly demanding requirements, often related to climate science, have highlighted some limitations of these ap-proaches. Practitioners have had to revisit techniques of estimation, of characterising uncertainty, and of validating observations—and even to reconsider the meaning(s) of “sea surface temperature”. The current understanding of these issues is reviewed, drawing attention to ongoing questions. Lastly, the prospect for thermal remote sens-ing of sea surface temperature over coming years is discussed.

Implication of methodological uncertainties for mid-Holocene sea surface temperature reconstructions

We present and examine a multi-sensor global compilation of mid-Holocene (MH) sea surface temperatures (SST), based on Mg/Ca and alkenone palaeothermometry and reconstructions obtained using planktonic foraminifera and organic-walled dinoflagellate cyst census counts. We assess the uncertainties originating from using different methodologies and evaluate the potential of MH SST reconstructions as a benchmark for climate-model simulations. The comparison between different analytical approaches (time frame, baseline climate) shows the choice of time window for the MH has a negligible effect on the reconstructed SST pattern, but the choice of baseline climate affects both the magnitude and spatial pattern of the reconstructed SSTs. Comparison of the SST reconstructions made using different sensors shows significant discrepancies at a regional scale, with uncertainties often exceeding the reconstructed SST anomaly. Apparent patterns in SST may largely be a reflection of the use of different sensors in different regions. Overall, the uncertainties associated with the SST reconstructions are generally larger than the MH anomalies. Thus, the SST data currently available cannot serve as a target for benchmarking model simulations. Further evaluations of potential subsurface and/or seasonal artifacts that may contribute to obscure the MH SST reconstructions are urgently needed to provide reliable benchmarks for model evaluations.

Sensitivity of terrestrial precipitation trends to the structural evolution of sea surface temperatures

Pronounced intermodel differences in the projected response of land surface precipitation (LSP) to future anthropogenic forcing remain in the Coupled Model Intercomparison Project Phase 5 model integrations. A large fraction of the intermodel spread in projected LSP trends is demonstrated here to be associated with systematic differences in simulated sea surface temperature (SST) trends, especially the representation of changes in (i) the interhemispheric SST gradient and (ii) the tropical Pacific SSTs. By contrast, intermodel differences in global mean SST, representative of differing global climate sensitivities, exert limited systematic influence on LSP patterns. These results highlight the importance to regional terrestrial precipitation changes of properly simulating the spatial distribution of large-scale, remote changes as reflected in the SST response to increasing greenhouse gases. Moreover, they provide guidance regarding which region-specific precipitation projections may be potentially better constrained for use in climate change impact assessments.

Communicating uncertainties in sea surface temperature

A project on sea surface temperature is generating new climate data records from satellite observations. The data are independent of in situ observations and are harmonious across satellite sensors to maximize stability and have realistic, context-sensitive uncertainty estimates at all spatial and temporal scales. The project, part of the European Space Agency Climate Change Initiative (SST CCI), now seeks to establish a useful method for communicating uncertainty in sea surface temperatures. This goal was the impetus for a workshop held in November 2014 in Exeter in the United Kingdom, summarised in this article.

Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures

The level of agreement between climate model simulations and observed surface temperature change is a topic of scientific and policy concern. While the Earth system continues to accumulate energy due to anthropogenic and other radiative forcings, estimates of recent surface temperature evolution fall at the lower end of climate model projections. Global mean temperatures from climate model simulations are typically calculated using surface air temperatures, while the corresponding observations are based on a blend of air and sea surface temperatures. This work quantifies a systematic bias in model-observation comparisons arising from differential warming rates between sea surface temperatures and surface air temperatures over oceans. A further bias arises from the treatment of temperatures in regions where the sea ice boundary has changed. Applying the methodology of the HadCRUT4 record to climate model temperature fields accounts for 38% of the discrepancy in trend between models and observations over the period 1975–2014.

Response of El Niño sea surface temperature variability to greenhouse warming

The destructive environmental and socio-economic impacts of the El Niño/Southern Oscillation1, 2 (ENSO) demand an improved understanding of how ENSO will change under future greenhouse warming. Robust projected changes in certain aspects of ENSO have been recently established3, 4, 5. However, there is as yet no consensus on the change in the magnitude of the associated sea surface temperature (SST) variability6, 7, 8, commonly used to represent ENSO amplitude1, 6, despite its strong effects on marine ecosystems and rainfall worldwide1, 2, 3, 4, 9. Here we show that the response of ENSO SST amplitude is time-varying, with an increasing trend in ENSO amplitude before 2040, followed by a decreasing trend thereafter. We attribute the previous lack of consensus to an expectation that the trend in ENSO amplitude over the entire twenty-first century is unidirectional, and to unrealistic model dynamics of tropical Pacific SST variability. We examine these complex processes across 22 models in the Coupled Model Intercomparison Project phase 5 (CMIP5) database10, forced under historical and greenhouse warming conditions. The nine most realistic models identified show a strong consensus on the time-varying response and reveal that the non-unidirectional behaviour is linked to a longitudinal difference in the surface warming rate across the Indo-Pacific basin. Our results carry important implications for climate projections and climate adaptation pathways.

Independent uncertainty estimates for coefficient based sea surface temperature retrieval from the Along-Trck Scanning Radiometer instruments

We establish a methodology for calculating uncertainties in sea surface temperature estimates from coefficient based satellite retrievals. The uncertainty estimates are derived independently of in-situ data. This enables validation of both the retrieved SSTs and their uncertainty estimate using in-situ data records. The total uncertainty budget is comprised of a number of components, arising from uncorrelated (eg. noise), locally systematic (eg. atmospheric), large scale systematic and sampling effects (for gridded products). The importance of distinguishing these components arises in propagating uncertainty across spatio-temporal scales. We apply the method to SST data retrieved from the Advanced Along Track Scanning Radiometer (AATSR) and validate the results for two different SST retrieval algorithms, both at a per pixel level and for gridded data. We find good agreement between our estimated uncertainties and validation data. This approach to calculating uncertainties in SST retrievals has a wider application to data from other instruments and retrieval of other geophysical variables.

Sampling uncertainty in gridded sea surface temperature products and Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) data

Sea surface temperature (SST) data are often provided as gridded products, typically at resolutions of order 0.05 degrees from satellite observations to reduce data volume at the request of data users and facilitate comparison against other products or models. Sampling uncertainty is introduced in gridded products where the full surface area of the ocean within a grid cell cannot be fully observed because of cloud cover. In this paper we parameterise uncertainties in SST as a function of the percentage of clear-sky pixels available and the SST variability in that subsample. This parameterisation is developed from Advanced Along Track Scanning Radiometer (AATSR) data, but is applicable to all gridded L3U SST products at resolutions of 0.05-0.1 degrees, irrespective of instrument and retrieval algorithm, provided that instrument noise propagated into the SST is accounted for. We also calculate the sampling uncertainty of ~0.04 K in Global Area Coverage (GAC) Advanced Very High Resolution Radiometer (AVHRR) products, using related methods.

The teleconnection of the tropical Atlantic to Indo-Pacific sea surface temperatures on inter-annual to centennial time scales: a review of recent findings

In this paper, the teleconnections from the tropical Atlantic to the Indo-Pacific region from inter-annual to centennial time scales will be reviewed. Identified teleconnections and hypotheses on mechanisms at work are reviewed and further explored in a century-long pacemaker coupled ocean-atmosphere simulation ensemble. There is a substantial impact of the tropical Atlantic on the Pacific region at inter-annual time scales. An Atlantic Niño (Niña) event leads to rising (sinking) motion in the Atlantic region, which is compensated by sinking (rising) motion in the central-western Pacific. The sinking (rising) motion in the central-western Pacific induces easterly (westerly) surface wind anomalies just to the west, which alter the thermocline. These perturbations propagate eastward as upwelling (downwelling) Kelvin-waves, where they increase the probability for a La Niña (El Niño) event. Moreover, tropical North Atlantic sea surface temperature anomalies are also able to lead La Niña/El Niño development. At multidecadal time scales, a positive (negative) Atlantic Multidecadal Oscillation leads to a cooling (warming) of the eastern Pacific and a warming (cooling) of the western Pacific and Indian Ocean regions. The physical mechanism for this impact is similar to that at inter-annual time scales. At centennial time scales, the Atlantic warming induces a substantial reduction of the eastern Pacific warming even under CO2 increase and to a strong subsurface cooling.