Estimating sea surface temperature measurement methods using characteristic differences in the diurnal cycle

Lack of reliable observational metadata represents a key barrier to understanding sea surface temperature (SST) measurement biases, a large contributor to uncertainty in the global surface record. We present a method to identify SST measurement practice by comparing the observed SST diurnal cycle from individual ships with a reference from drifting buoys under similar conditions of wind and solar radiation. Compared to existing estimates, we found a larger number of engine room-intake (ERI) reports post War World II and in the period 1960 – 1980. Differences in the inferred mixture of observations lead to a systematic warmer shift of the bias adjusted SST anomalies from 1980 compared to previous estimates, while reducing the ensemble spread. Changes in mean field differences between bucket and ERI SST anomalies in the Northern Hemisphere over the period 1955 – 1995 could be as large as 0.5 °C and are not well reproduced by current bias adjustment models

A call for new approaches to quantifying biases in observations of sea-surface temperature

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 either on physical models of the observing processes or on the assumption of an unchanging relationship between SST and a reference data set such as night marine air temperature. These approaches produce similar estimates of SST bias on the largest space and timescales, 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 of high-quality observations for validation and bias model development are likely to remain major challenges

Climatological diurnal variability in sea surface temperature characterised from drifting buoy day v1.1

Drifting-buoy sea surface temperature (SST) records have been used to characterise the diurnal variability of ocean temperature at a depth of order 20 cm. We use measurements covering the period 1986-2012 from the International Comprehensive 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 sub-daily SST variability are found by averaging across various strata of the data: in 10-degree latitudinal bands as well as globally; and stratified with respect to season, wind speed and total cloud cover. A parameterisation 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 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 sub-daily 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 parameterisations. This thorough characterisation 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. (This data set is the updated version of  <a href="http://dx.doi.org/10.6084/m9.figshare.1513832">http://dx.doi.org/10.6084/m9.figshare.1513832</a>

Diurnal variability in sea surface temperature characterised from drifting buoy day

<p>Drifting-buoy sea surface temperature (SST) records have been used to characterise the diurnal variability of ocean temperature at a depth of order 20 cm. We use measurements covering the period 1990-2009 from the International Comprehensive 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 sub-daily SST variability are found by averaging across various strata of the data: in 10-degree latitudinal bands as well as globally; and stratified with respect to season, wind speed and total cloud cover. A parameterisation 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 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 sub-daily 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 parameterisations. This thorough characterisation 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.</p