Month: May 2019

Investigating the use of early satellite data to test historical reconstructions of sea surface temperature

Thomas Hall1 Comment

Email: t.hall@pgr.reading.ac.uk

Observations of sea surface temperature (SST) form one of the key components of the climate record. There are a number of different in-situ based reconstructions of SST extending back over 150 years, but they are not truly independent of each other because the observations they are based on are largely the same (Berry et al., 2018). Datasets of SST retrieved from satellite radiometers exist for the 1980s onwards, providing an independent record of SST. Before this, SST reconstructions are based on sparse, ship-based measurements.

There were meteorological measurements being made from satellites in the 1960s and 70s, however, some of which can potentially be used to retrieve SST. My PhD focuses on investigating if we can retrieve SST from one of these early satellite instruments, to test the reliability of the SST reconstructions before the 1980s. This instrument is the Infrared Interferometer Spectrometer (IRIS), which made measurements of atmospheric emission spectra on-board the Nimbus 4 satellite from April 1970 to January 1971. IRIS had over 800 thermal infrared (IR) channels, covering the 400-1600 cm-1 spectral region. Figure 1 shows an example of two typical IRIS radiance spectra, with the main spectral absorption features labelled as well as the atmospheric window regions, which are the main spectral regions used for SST retrieval.

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Figure 1: Example of two typical IRIS radiance spectra; the main spectral absorption features are labelled as well as the atmospheric window regions.

Before using the IRIS data to retrieve SST, it was necessary to apply a series of quality assurance tests to filter out bad data. A few months into my PhD, work by Bantges et al. (2016) revealed evidence for a wavelength dependent cold bias of up to 2K in the data. A large part of my PhD was spent trying to quantify this bias. This was done by comparing clear-sky IRIS spectra with spectra simulated with a radiative transfer model. Unfortunately, this meant that the SSTs eventually retrieved from IRIS are not totally independent of the SST reconstructions as the simulations are based on reanalysis data forced by the HadISST2 reconstruction. Figure 2 compares our estimate of the IRIS spectral bias with globally averaged spectral differences between IRIS, the Interferometric Monitor for Greenhouse Gases (IMG) and the Infrared Atmospheric Sounding Instrument (IASI) from Bantges et al. (2016). This shows close agreement between our bias estimate and the IRIS-IMG and IRIS-IASI differences outside of the ozone spectral region, which is not relevant for SST retrieval.

It cannot just be assumed that the bias is the same for each IRIS measurement. Comparison of IRIS (bias-corrected using our initial bias estimate) with window channel data from the Temperature-Humidity Infrared Radiometer (THIR), also on-board Nimbus 4, reveals that the relative IRIS-THIR bias varies with window brightness temperature and orbit angle. The THIR, however, may have biases of its own, so these biases cannot be attributed to IRIS.

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Figure 2: Area-weighted global mean brightness temperature difference averaged over AMJ between IRIS (1970), IMG (1997) and IASI (2012) (black and blue lines) from Bantges et al. (2016), compared with our IRIS bias estimate, also area-weighted and averaged over AMJ (red line). The ozone absorption band is not used for SST retrieval, so is shaded grey.

The technique of optimal estimation was applied to retrieve SST from IRIS. This uses the observation-simulation differences together with information about the sensitivity of the simulations to the state of the atmosphere and ocean to estimate the SST. IR satellite retrievals of SST are usually performed in clear-sky conditions only. However, the low spatial resolution of IRIS means that very few cases are fully clear-sky. For this reason, we had to adapt the retrieval method to be tolerant of some cloud. This involves retrieving SST simultaneously with cloud fraction (CF). The retrieval method was then tested on partly cloudy (≤0.2 CF) IASI spectra made ‘IRIS-like’ by spatial averaging, spectral smoothing and simulating IRIS-like errors. The retrieved IRIS-like SSTs were validated against quality-controlled drifting buoy SSTs. This revealed latitudinal biases in the retrieved SSTs for the partly cloudy cases, not present in the SSTs for clear-sky cases.

SSTs were then retrieved for all IRIS cases with an expected CF ≤ 0.2. Figure 3 shows the difference between the gridded, monthly average IRIS SSTs and two of the SST reconstructions (HadSST3 and HadISST2) for July 1970. There are large, spatially correlated differences between the IRIS SSTs and reconstructions. We expect a latitudinal bias in the IRIS SSTs and some level of remaining bias in the IRIS spectra is likely, contributing to further SST bias. It is therefore likely that the differences in Figure 3 are mainly due to bias in the IRIS SSTs rather than the reconstructions.

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Figure 3: Gridded IRIS-HadSST3 (left) and IRIS-HadISST2 (right) SST for July 1970. HadISST2 is a globally complete, interpolated dataset whereas HadSST3 is not globally complete.

Despite being unable to retrieve bias-free SST estimates from IRIS, my work has contributed towards better understanding the characteristics of IRIS. This ties in with a current project aiming to recover and assess the quality of data from a number of different historic satellite sensors, including IRIS, for assimilation in the next generation of climate reanalyses.

References

Bantges, R., H. Brindley, X. H. Chen, X. L. Huang, J. Harries, J. Murray (2016), On the detection of robust multi-decadal changes in the Earth’s Outgoing Longwave Radiation spectrum. J. Climate, 29, 4939-4947. https://doi.org/10.1175/JCLI-D-15-0713.1

Berry, D. I., G. K. Corlett, O. Embury, C. J. Merchant (2018), Stability assessment of the (A)ATSR Sea Surface Temperature climate dataset from the European Space Agency Climate Change Initiative. Remote Sens., 10, 126. https://doi.org/10.3390/rs10010126

EGU 2019

Bethan Harris1 Comment

From 7th-12th April, I had the exciting opportunity to attend the European Geosciences Union (EGU) General Assembly in Vienna. This was a much larger conference than any I had attended previously, with 16,273 scientists in attendance and 683 scientific sessions, which made for a whirlwind experience. I was staying with other PhD students from the department, so many evenings were spent comparing schedules and pointing out interesting courses to make sure none of us missed anything useful!

As part of the Tropical Meteorology and Tropical Cyclones session, I gave an oral presentation about my PhD work, which investigates the use of Available Potential Energy theory to study the processes involved in tropical cyclone intensification. The session included many excellent talks on different aspects of tropical meteorology, and it was great to speak with scientists whose interests are similar to mine about possible avenues for combining our work.

PhD students from the department present their research at EGU

One of the major advantages of attending such a large conference was the opportunity to learn more about areas of geoscience research that I wouldn’t normally encounter. I made a specific effort to attend a few sessions on topics that I am not familiar with, including wildfires (#FIREMIP), landslides and exoplanets. It was fascinating to see the work that goes on in different fields and I hope that being exposed to different methods and perspectives will help me to become a more creative researcher.

EGU is such a huge event that the scientific sessions are only part of the story. There were Great Debates on topics ranging from science in policy to the prioritisation of mental wellbeing for Early Career Scientists, two artists-in-residence creating pieces inspired by the science of the conference, and an extremely entertaining Poetry Slam event, which two of the Reading Meteorology PhD students were brave enough to participate in (or possibly just desperate enough for a ticket to the conveners’ party).

poetry

So now it’s the end of EGU
I caught the train – and I flew
We both did a talk
Learnt the German for fork
“Eine Gabel bitte” – thank you

– Sally Woodhouse & Kaja Milczewska

EGU was a great experience and after the conference I was able to take some time to explore Vienna, see some historic landmarks, and unwind from an enjoyably exhausting week of science. Although to begin my break from geoscience I did go straight to the Globe Museum, so perhaps I need to work on my relaxation techniques.

#traintoEGU – Sally Woodhouse

Aviation currently contributes over 2% of the annual global CO2 emissions which, if classed as a country, would make it one of the top ten emitters. A return flight to Vienna from London adds about 0.2 metric tonnes of CO2 to your carbon footprint (the UK annual mean per person is 6.5 metric tonnes).

An important part of science is sharing our research and one of the best ways to do that is at conferences, so we can’t just stop going! But there is another way … the train (0.04 metric tonnes CO2)! And if I’m spending all that time why not have a little adventure.

With the help of the man in seat 61 (check it out if you’re getting the train anywhere it’s so helpful!) we decided to go via Zurich. We had a night’s stop in Zurich, a morning there exploring and then an afternoon train through the stunning Arlberg Pass and beautiful views of Alpine Austria. Honestly the views made the 5am start the day before and sprint for the Eurostar all worth it. It was breath-taking for the whole 8 hour journey.

 

traintoEGU_Paris_Zurich

For the return trip we took the speedy route through Germany and Belgium – this is actually doable in a day but I decided to have an overnight in Brussels. I spent a lovely day wandering around the main sites and even managed a visit to the European Parliament!

It might take a bit longer but it was a wonderful adventure and I’d definitely recommend it to everyone traveling to EGU in future – maybe I’ll see you on the train.

traintoEGU_Brussels