Tag Archives: ocean temperatures

It was hot in June and its getting hotter

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Decorative image of a summer flowerLast month was the first June on record when the daily average global 2-metre surface temperature exceeded 1.5 °C above pre-industrial levels [1] and last week, on July 6th, the daily global air temperature hit a record high of 17.23 °C [2]  In 2020 it was estimated that the world’s remaining carbon budget was about 500 gigatonnes CO2 if there was to be a 50% chance of limiting global warming to 1.5 °C. It is now estimated that the remaining budget is about 250 gigatonnes CO2, i.e., it has halved in three years, as a result of continued pollution and temperatures rising faster than expected [3].  At the current rate of emissions, this budget will be exhausted in about six years.  Hence, it seems very likely that global temperatures will rise by more than 1.5 °C and perhaps by as much as 4 °C this century.  The last time the Earth was that hot was about 15 million years ago during the Miocene when sea levels were 40 m higher [4].  It is time to get much more serious about reducing carbon emissions instead of just talking about it.  Current targets for reducing emissions are so far in the future that they are beyond the horizon – effectively out of sight and out of mind.  We need to be costing everything in terms of carbon emissions and making decisions that reduce emissions now.

[1] Climate graphic of the week: first days of June bring record heat, FT June 17, 2023.

[2] Global temperature hits record high, FT Weekend 8th July 2023 based on data from NOAA.

[3] Forster PM, Smith CJ, Walsh T, Lamb WF, Lamboll R, Hauser M, Ribes A, Rosen D, Gillett N, Palmer MD, Rogelj J. Indicators of Global Climate Change 2022: annual update of large-scale indicators of the state of the climate system and human influence. Earth System Science Data. 15(6):2295-327, 2023.

[4] Foster GL, Rohling EJ. Relationship between sea level and climate forcing by CO2 on geological timescales. Proceedings of the National Academy of Sciences. 110(4):1209-14, 2013.

From strain measurements to assessing El Niño events

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Figure 11 from RSOS 201086One of the exciting aspects of leading a university research group is that you can never be quite sure where the research is going next.  We published a nice example of this unpredictability last week in Royal Society Open Science in a paper called ‘Transformation of measurement uncertainties into low-dimensional feature space‘ [1].  While the title is an accurate description of the contents, it does not give much away and certainly does not reveal that we proposed a new method for assessing the occurrence of El Niño events.  For some time we have been working with massive datasets of measurements from arrays of sensors and representing them by fitting polynomials in a process known as image decomposition [see ‘Recognising strain‘ on October 28th, 2015]. The relatively small number of coefficients from these polynomials can be collated into a feature vector which facilitates comparison with other datasets [see for example, ‘Out of the valley of death into a hype cycle‘ on February 24th, 2021].  Our recent paper provides a solution to the issue of representing the measurement uncertainty in the same space as the feature vector which is roughly what we set out to do.  We demonstrated our new method for representing the measurement uncertainty by calibrating and validating a computational model of a simple beam in bending using data from an earlier study in a EU-funded project called VANESSA [2] — so no surprises there.  However, then my co-author and PhD student, Antonis Alexiadis went looking for other interesting datasets with which to demonstrate the new method.  He found a set of spatially-varying uncertainties associated with a metamodel of soil moisture in a river basin in China [3] and global oceanographic temperature fields collected monthly over 11 years from 2002 to 2012 [4].  We used the latter set of data to develop a new technique for assessing the occurrence of El-Niño events in the Pacific Ocean.  Our technique is based on global ocean dynamics rather than on the small region in the Pacific Ocean which is usually used and has the added advantages of providing a confidence level on the assessment as well as enabling straightforward comparisons of predictions and measurements.  The comparison of predictions and measurements is a recurring theme in our current research but I did not expect it to lead into ocean dynamics.

Image is Figure 11 from [1] showing convex hulls fitted to the cloud of points representing the uncertainty intervals for the ocean temperature measurements for each month in 2002 using only the three most significant principal components . The lack of overlap between hulls can be interpreted as implying a significant difference in the temperature between months.

References:

[1] Alexiadis, A. and Ferson, S. and  Patterson, E.A., , 2021. Transformation of measurement uncertainties into low-dimensional feature vector space. Royal Society Open Science, 8(3): 201086.

[2] Lampeas G, Pasialis V, Lin X, Patterson EA. 2015.  On the validation of solid mechanics models using optical measurements and data decomposition. Simulation Modelling Practice and Theory 52, 92-107.

[3] Kang J, Jin R, Li X, Zhang Y. 2017, Block Kriging with measurement errors: a case study of the spatial prediction of soil moisture in the middle reaches of Heihe River Basin. IEEE Geoscience and Remote Sensing Letters, 14, 87-91.

[4] Gaillard F, Reynaud T, Thierry V, Kolodziejczyk N, von Schuckmann K. 2016. In situ-based reanalysis of the global ocean temperature and salinity with ISAS: variability of the heat content and steric height. J. Climate. 29, 1305-1323.