The HOSTACE project was funded by the Natural Environment Research Council (NERC) with the grant reference - NE/J02306X/2 - led by Professor Christopher Merchant (University of Reading).

The surface temperature of the land and sea is the main measure of "global warming". Measurements of sea surface temperature (SST) have been made for more than 200 years, first on sailing ships, now on a mixture of ships and buoys (drifting and moored). Technology has changed dramatically over this period, raising serious questions about whether technology changes over time give a misleading impression of how the temperature has changed - and therefore how climate has changed.

People first measured the temperature of a seawater sample hauled up in a wooden bucket. Buckets are now made of insulating rubber. Most direct SST measurements are now sent via satellites from drifting buoys. Many other measurement methods have also been used. Different methods don't yield precisely the same SST values, and because global warming is a gradual change, these subtle discrepancies (or "biases") could distort our picture about the timing and magnitude of global warming. So, we must be sure that we understand how the different methods used to measure SST have affected the observations.

These biases in SST have been a known problem for years, so why do we believe we can solve it? One reason is that recently many more observations have been retrieved from historical sources. Many ships' logbooks containing weather observations have been digitised. This has nearly doubled the number of observations before World War 2. Another reason is new, stable observations of SST from sensors on satellites orbiting Earth. Most satellite sensors give a detailed picture of patterns in SST and are tuned to drifting buoy SSTs to give reasonable accuracy. But compared to the subtle trends of global warming, they are not stable enough from year to year and across large distances. New high-quality SST measurements from a reworking of the SST measurements of a particular series of sensors are accurate and stable enough. Even better, they do not rely on ship or buoy SST observations, so we can use them as an independent point of reference.

A major challenge is that the biases in SST made on ships are different for different measurement methods and we don't always know what methods were used. But we do know how we expect the biases for each method to vary with factors like the amount of heating by the Sun and wind speed. The project used these variations of the biases for each ship or buoy to assign measurement methods to observations (or, where it is not clear cut, the likelihood that the method is one or another type). E.g., we might be 80% confident that a particular ship used a canvas bucket to sample the water, but allow a 20% chance that a wooden bucket was used. The project then adjusted for the expected biases according to method, and indicated how uncertain the adjustment may be.

The next step was to combine the scattered observations into maps of monthly average SST over the whole ocean. They also calculated the degree of uncertainty in these monthly maps. There were few observations in the 19thC, so a global SST map required sophisticated gap-filling methods. The final step was to compare the projects maps of SST with those produced by other scientists. Normally when such comparisons are made it is hard to understand the source of differences between the datasets. Was it due to different input data? Or different bias adjustments? Or the way the gaps were filled? Collaborating with other dataset producers, the project separated these different effects. For example, they all used identical inputs, and isolated the effects of different gap-filling methods. This also tested the uncertainty estimates - if important factors affecting the SST biases have been missed, then estimates of uncertainty may be too small to explain the differences between the SST maps produced by different groups.

Such problems can mislead us in interpreting climate changes. The project used the new SST history to reassess explanations of phases of climate warming during the 20thC.

Historical sea surface temperature (SST) is a crucial parameter for climate change science but the high degree of present uncertainty in our knowledge of it is underestimated. This projects aims were to radically revise understanding of historical SST by quantifying the bias and uncertainty of SST observations throughout the instrumental record and to produce a new bias-adjusted gridded SST analysis quantified with credible estimates of uncertainty. In this way, use of recently recovered historical data and new climate-quality satellite data allowed us to improve our understanding of the evolution of centennial climate change. As an example, the project used the new historical SSTs to attribute the causes of the early 20thC warming. This is one of many areas where the results will advance understanding of climate, leading to increased confidence in predictions of future change.

Specific objectives of this project were:

O1: Quantification, correction and validation of in situ SST bias for the entire observational record (from 1850 or earlier, to present)
O2: Assessment of measurement, sampling and residual bias uncertainty in the in situ SST record
O3: Development and publication of a new long-term bias-adjusted SST gridded analysis with estimates of measurement, sampling and residual bias uncertainty along with their correlations
O4: Quantification of methodological uncertainty in gridded analyses of SST through an international SST dataset comparison exercise
O5: Application of new SST to elucidate drivers of early 20thC climate change

The objectives directly addressed challenges in the Climate System theme of the NERC strategy:

- Increase knowledge of natural climate variability, and diagnose and improve its representation in climate models
- Provide accurate observations of the global climate system for long-term monitoring of climate to quantify changes and to test and evaluate climate models

The project argued that available uncertainty estimates in SST analyses are too small, leading to over-confidence in the estimates of trends and variability, and problems in understanding 20thC climate variability with climate models. Objectives 1 and 2 address problems with accurately estimating the uncertainty. Objective 3 applied the knowledge gained from 1 and 2 to develop a new SST analysis, including uncertainty estimates, for use in: assessment of climate change and our confidence in those changes; understanding the variability of the SST on long time-scales and validation of climate models. Objective 4 sought to address the uncertainty in our assessments and SST analyses due to methodological choices ("structural uncertainty"). Objective 5 applied the new SST analysis in a study to attribute the cause of early 20thC climatic change.