A global climatology of the diurnal variations in sea-surface temperature and implications for MSU temperature trends

A global climatology of diurnal variations in sea-surface temperature based on in situ drifting-buoy data has been created. The diurnal warming signal derived from these data correlates well with estimates from a version of the Stuart-Menteth (2004) model, which parametrises the diurnal cycle based on incoming short-wave radiation, wind speed and time of day, that has been modified to accept monthly inputs. An estimate is also made of the bias in estimates of tropospheric temperature derived from the Microwave Sounding Unit instruments that is due to the drift in local equator crossing time of the satellite orbits. In the tropics, this contribution is approximately 13% of the observed trend in tropospheric temperatures

Climatological diurnal variability in sea surface temperature characterized from drifting buoy data

Drifting buoy sea-surface temperature (SST) records have been used to characterize the diurnal variability of ocean tem- perature at a depth of order 20 cm. We use measurements covering the period 1986–2012 from the International Com- prehensive Ocean-Atmosphere Data Set (ICOADS) version 2.5, which is a collection of marine surface observations that includes individual SST records from drifting buoys. Appropriately transformed, this dataset is well suited for estimation of the diurnal cycle, since many drifting buoys have high temporal coverage (many reports per day), and are globally distributed. For each drifter for each day, we compute the local-time daily SST variation relative to the local-time daily mean SST. Climatological estimates of subdaily SST variability are found by averaging across various strata of the data: in 10° latitudinal bands as well as globally; and stratified with respect to season, wind speed and cloud cover. A parame- terization of the diurnal variability is fitted as a function of the variables used to stratify the data, and the coefficients for this fit are also provided with the data. Results are consistent with expectations based on the previous work: the diurnal temperature cycle peaks in early afternoon (circa 2 pm local time); there is an increase in amplitude and a decrease in seasonality towards the equator. Generally, the ocean at this depth cools on windy days and warms on calm days, so that a component of subdaily variability is the SST tendency on slower timescales. By not ‘closing’ the diurnal cycle when stratified by environmental conditions, this dataset differs from previously published diurnal-cycle parameter- izations. This thorough characterization of the SST diurnal cycle will assist in interpreting SST observations made at different local times of day for climatological purposes, and in testing and constraining models of the diurnal-cycle and air-sea interaction at high temporal resolution

Comparison of SST diurnal variation models over the Tropical Warm Pool region

Four sea surface temperature (SST) diurnal variation (DV) models have been compared against Multi-functional Transport Satellite - 1R (MTSAT-1R) SST measurements over the Tropical Warm Pool region (TWP, 90°E-170°E, 25°S-15°N) for four months from January to April 2010. The four models include one empirical model formulated by Chelle Gentemann (hereafter CG03), one physical model proposed by Zeng and Beljaars in 2005 (ZB05) and its updated version (ZB+T), and one air-sea coupled model (the Met Office Unified Model Global Coupled configuration 2, GC2) with ZB05 warm layer scheme added on top of the standard configuration. The sensitivity of the v3 MTSAT-1R data to the “true” changes in SST is first investigated using drifting buoys and is estimated to be 0.60 ± 0.05. This being significantly different from 1, the models are validated against MTSAT-1R data and the same data scaled by the inverse of the sensitivity (representing an estimate of the true variability). Results indicate that all models are able to capture the general DV patterns but with differing accuracies and features. Specifically, CG03 and ZB+T underestimate strong (> 2 K) DV events’ amplitudes especially if we assume that sensitivity-scaled MTSAT-1R variability is most realistic. ZB05 can effectively capture the DV cycles under most DV and wind conditions, as well as the DV spatial distribution. GC2 tends to overestimate small-moderate (< 2 K) DV events but can reasonably predict large DV events. 1-3 hr lags in warming start and peak times are found in GC2

Optimal estimation of sea surface temperature from AMSR-E

The Optimal Estimation (OE) technique is developed within the European Space Agency Climate Change Initiative (ESA-CCI) to retrieve subskin Sea Surface Temperature (SST) from AQUA’s Advanced Microwave Scanning Radiometer—Earth Observing System (AMSR-E). A comprehensive matchup database with drifting buoy observations is used to develop and test the OE setup. It is shown that it is essential to update the first guess atmospheric and oceanic state variables and to perform several iterations to reach an optimal retrieval. The optimal number of iterations is typically three to four in the current setup. In addition, updating the forward model, using a multivariate regression model is shown to improve the capability of the forward model to reproduce the observations. The average sensitivity of the OE retrieval is 0.5 and shows a latitudinal dependency with smaller sensitivity for cold waters and larger sensitivity for warmer waters. The OE SSTs are evaluated against drifting buoy measurements during 2010. The results show an average difference of 0.02 K with a standard deviation of 0.47 K when considering the 64% matchups, where the simulated and observed brightness temperatures are most consistent. The corresponding mean uncertainty is estimated to 0.48 K including the in situ and sampling uncertainties. An independent validation against Argo observations from 2009 to 2011 shows an average difference of 0.01 K, a standard deviation of 0.50 K and a mean uncertainty of 0.47 K, when considering the best 62% of retrievals. The satellite versus in situ discrepancies are highest in the dynamic oceanic regions due to the large satellite footprint size and the associated sampling effects. Uncertainty estimates are available for all retrievals and have been validated to be accurate. They can thus be used to obtain very good retrieval results. In general, the results from the OE retrieval are very encouraging and demonstrate that passive microwave observations provide a valuable alternative to infrared satellite observations for retrieving SST

Cloud-fraction-dependent bias in satellite liquid water path retrievals of shallow, non-precipitating marine clouds

This study compares Wentz microwave liquid water path retrievals with MODIS and MISR optical estimates in shallow, non-precipitating marine clouds. In overcast conditions, the microwave and optical estimates are comparable; however, as cloud fraction decreases microwave retrievals strongly and increasingly overestimate optical ones. This positive microwave bias cannot be explained neither by the elimination of negative values in the operational Wentz dataset, nor by the somewhat reduced sensitivity of MODIS cloud detection to small clouds

A perspective on the importance of oceanic fronts in promoting aggregation of visitors to seamounts

Recent evidence has demonstrated that not all seamounts are areas where productivity, biomass and biodiversity of marine life thrive. Therefore, understanding the drivers and mechanisms underlying seamount productivity is a major challenge in today's seamount research. Incorporating oceanographic data in future analyses has been suggested to be of paramount importance to unveil many of the seamount ecology paradigms. Persistent hydrographic features, such as oceanic fronts, have been recognized to enhance biological activity and to drive marine animal distributions and migration patterns. However, the importance of oceanic fronts in driving aggregations of visiting animals on seamounts has not been understood yet. Here, we analysed a data set of seamounts in the Pacific Ocean alongside satellite-derived maps of strong, persistent and frequently occurring oceanographic features, to evaluate if oceanic fronts promote aggregation of visitors on seamounts. Our analyses suggest that seamounts with a higher front frequency were more likely to aggregate tuna catch than average seamounts. However, it appears that fronts may be driving factors for aggregation only if present above a certain threshold. These results highlight the importance of environmental conditions in general, and oceanic fronts in particular, in promoting seamount productivity. We therefore argue that a thorough examination of the oceanographic conditions promoting seamount productivity at various temporal and spatial scales is warranted in future seamount research agendas

Observational needs of sea surface temperature

Sea surface temperature (SST) is a fundamental physical variable for understanding, quantifying and predicting complex interactions between the ocean and the atmosphere. Such processes determine how heat from the sun is redistributed across the global oceans, directly impacting large- and small-scale weather and climate patterns. The provision of daily maps of global SST for operational systems, climate modeling and the broader scientific community is now a mature and sustained service coordinated by the Group for High Resolution Sea Surface Temperature (GHRSST) and the CEOS SST Virtual Constellation (CEOS SST-VC). Data streams are shared, indexed, processed, quality controlled, analyzed, and documented within a Regional/Global Task Sharing (R/GTS) framework, which is implemented internationally in a distributed manner. Products rely on a combination of low-Earth orbit infrared and microwave satellite imagery, geostationary orbit infrared satellite imagery, and in situ data from moored and drifting buoys, Argo floats, and a suite of independent, fully characterized and traceable in situ measurements for product validation (Fiducial Reference Measurements, FRM). Research and development continues to tackle problems such as instrument calibration, algorithm development, diurnal variability, derivation of high-quality skin and depth temperatures, and areas of specific interest such as the high latitudes and coastal areas. In this white paper, we review progress versus the challenges we set out 10 years ago in a previous paper, highlight remaining and new research and development challenges for the next 10 years (such as the need for sustained continuity of passive microwave SST using a 6.9 GHz channel), and conclude with needs to achieve an integrated global high-resolution SST observing system, with focus on satellite observations exploited in conjunction with in situ SSTs. The paper directly relates to the theme of Data Information Systems and also contributes to Ocean Observing Governance and Ocean Technology and Networks within the OceanObs2019 objectives. Applications of SST contribute to all the seven societal benefits, covering Discovery; Ecosystem Health & Biodiversity; Climate Variability & Change; Water, Food, & Energy Security; Pollution & Human Health; Hazards and Maritime Safety; and the Blue Economy