Historical Ocean Surface Temperatures: Adjustment, Characterisation and Evaluation (HOSTACE)

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. We will use 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. We can then adjust for the expected biases according to method, and indicate how uncertain our adjustment may be.

The next step will be to combine the scattered observations into maps of monthly average SST over the whole ocean. We must also calculate our degree of uncertainty in these monthly maps. There are few observations in the 19thC, so a global SST map requires sophisticated gap-filling methods. The final step is to compare our 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, we will separate these different effects. For example, we will all use identical inputs, and isolate the effects of different gap-filling methods. This will also test our 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. We will use the new SST history to reassess explanations of phases of climate warming during in the 20th C.

There is intense public interest in climate change and the level of certainty we have in those changes. Our research will provide new clarity in the understanding of biases and uncertainty that exist in historical observations of sea surface temperature (SST). All the results of our research will be made available to the public, including estimates of bias and uncertainty for every observation in the archive. We will make our results available through partnership with the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) Value-Added Database project (IVAD). This will ensure that our results are integrated with the original data archives. Such transparency will help to build confidence in the bias adjustments we apply and hence in the climate change signals seen in the adjusted SST observations. Anyone will be able to download the adjustments and explore their characteristics of our bias adjustments themselves. Further public engagement is through our partnership with the oldWeather citizen science digitisation activity and through material specifically developed for use in secondary schools.

One important outcome from the proposed work will be a comparison of SST datasets from different providers, including ourselves, the National Climate Data Center and Lamont Doherty Earth Observatory in the US and the Met Office in the UK. The comparison will be designed to tease out the causes of the differences seen between the datasets and also to provide some verification of the uncertainty estimates. Such comparisons have been shown to be important in reconciling and understanding climate datasets, for example different datasets of upper-air temperatures are now much better understood following such comparisons. To make the information derived from the dataset comparison more relevant to users of SST datasets we will, with our project partners, produce advice on the strengths and weaknesses of the datasets for different applications. Direct users of our research outside the academic community will include fisheries research, and planning for commercial and military marine operations.

Better estimates of SST and its uncertainty will feed through to better climate predictions. One of our aims is to improve the regional accuracy of historical SST. Improved regional prediction of climate change will have wide societal and economic impact. Benefits will be felt by policy makers who will have improved information for decision making, by everyone affected by climate change and mitigation policies, by engineers designing structures resilient to a changing environment and many others. Our main point of engagement with policy makers themselves is through the UK Department of Energy and Climate Change (DECC) and we will also continue to contribute directly to the Marine Climate Change Impacts Partnership (MCCIP) Annual Reports which transfer high quality evidence on marine climate change impacts to the UK and devolved governments, their agencies and industry.

The early-career researchers and PhD studentships engaged by this project will gain skills in analysis techniques that will be widely applicable beyond the immediate area of their research. Such skills will include statistical and physical analysis, the quantification of uncertainty in observations and analyses, the management of large datasets and the dissemination of complex information.