Presented by Tim Osborn at the Royal Meteorological Society's meeting on the History of climate science ideas & their application, March 2022

1. How do we study recent climate change?
Development of global temperature datasets
Royal Met. Soc. History of climate science ideas & their application March 2022
Tim Osborn
Climatic Research Unit
University of East Anglia

2. From Ed Hawkins (https://www.climate-lab-book.ac.uk/2015/what-have-global-temperatures-ever-done-for-us)
Based on Sutton, Suckling & Hawkins (2015) Phil. Trans. A

3. Why global temperature?
If we want to know about local climate on short timescales, then
unforced variability matters most
• Especially from natural variability in atmospheric circulation
If we want to know about forced climate change, it becomes
clearer at large spatial scales
• Global patterns and global means
But forced climate change becomes relevant even for local scales
when we want to understand or predict longer timescales
• Trends or changes on multi-decadal or longer timescales

4. Milestones towards global temperature series
Networks, compilations & standardisation of observations
• Land observations. Examples (not exhaustive):
• 1781 onwards: Meteorological Society of Mannheim, network with standards
• 1868 onwards: recommendations for Stevenson screens
• 1873: First international meteorology meeting Leipzig, predecessor to WMO
• 1900: Data sharing system for all continents (except Antarctica)
• 1927: World Weather Records
• 1948: Monthly Climatic Data for the World
• Marine observations. Examples (not exhaustive):
• ~1853: Semi-standardised naval logs (Maury, 1849; Quetelet, 1854)
• 1966: Digital archives of ship observations begin to be compiled
• 1981-1985: Preparation of COADS (Comprehensive Ocea-Atmosphere Data
Set) by US, evolved into ICOADS (International COADS)

5. Milestones towards global temperature series
Analyses and published series (not exhaustive):
• Köppen (from 1873)
• Already considering issues of quality, homogeneity, uneven data coverage
• Callendar (1938)
• Neighbour comparisons and metadata used to discard inhomogeneous series
• Revisited & improved 1961 (& other series by Willett, 1950; Mitchell, 1963)
• Budyko (1963)
• Based on hand-drawn maps: less objective but could bring in ocean data
• Hansen et al. (NASA GISS) & Jones et al. (Climatic Research Unit, CRU) early 1980s
• Gridded datasets of temperature anomalies
• 1986 CRU undertook first homogenisation effort on a global-scale network
• Jones, Wigley & Wright (1986) – forerunner to HadCRUT
• First global land & ocean homogenized global temperature series 1861-1984

7. Why use temperature anomalies?

12. Why use temperature anomalies?
Separation into time-invariant fields (geographical and annual cycle)
and time-varying anomalies has many benefits
• Simplifies spatial structures in the data which facilitates interpolation
• Reduces impact from changes in data coverage (e.g. reduces biases from a
changing observation network)
• Reduces impact of differences in measurement practice, how to calculate daily
means, instruments and exposures, microclimates
• These latter aspects can give materially different mean levels, but have much
less effect on changes over time*
• *provided they don’t change over time – then we need to deal with changing
biases over time (inhomogeneity)

13. Why use temperature anomalies?
When were the benefits of using anomalies first realised?
• Unsure! Callendar (1938) already uses them without justifying them. Just seems
an obvious way to deal with issues! Even before 1900 (e.g. Dove in the 1850s
used anomalies to reduce effect of elevation)
There are disadvantages of anomalies too:
• Series for which we don’t know the reference period temperature for that station
(location, exposure & instrument) can’t necessarily be used
• Berkeley Earth (Rohde et al. 2013) introduced a similar approach (separation into
deviations from expected values) but which doesn’t use a fixed reference period
• This opens up many possibilities – including new ways of dealing with
inhomogeneities: just split a series into sub-series that are individually
homogeneous

14. What about those inhomogeneities?
Example: “exposure bias” due to the change from older types of
exposure to Stevenson screens
• Analyses from Emily Wallis (Climatic Research Unit, UEA)

15. Stevenson-type Open Wall Intermediate Closed Unknown
Emily Wallis (CRU, UEA) Estimated exposure types 1850-1930

16. Exposure bias
example: “open”
screens
Stevenson screen minus
“open” screen
Stevenson screens tends to
read cooler Tmax and warmer
Tmin than “open” screens
There is a clear seasonal cycle
to the bias (except in Tmin)
The bias is greatest in Tmax
and DTR , but can lead to a
monthly bias in Tmean of up to
1.1°C.
Parallel Measurement Studies
Difference between the thermometer
reading in the Stevenson screen and the
“open” screen
Data sources: Adelaide Observatory Yearbooks; Detwiller, 1978; Ellis, 1891; Gaster, 1882; Gill, 1882; Greenwich
Observatory Yearbooks; Margary, 1924; Mawley, 1897; SDATS/AEMET (Brunet, pers. comms)
*Monthly data for the Southern Hemisphere studies has been shifted 6 months so the seasons align
*
Emily Wallis (CRU, UEA)

17. How many observations are needed?
Surprisingly small number of stations needed to estimate global
mean, if they are well distributed, long and reliable
Various authors in the 1980s and 1990s tried to estimate how many
(e.g. Hansen; Livezey & Chen; Jones & Briffa; Madden)
Depends on timescale and on the dominance of any forced climate
change (e.g. as few as 40 stations for seasonal, 20 annual, 10 decadal)
And we want many more than the minimum number, so overlapping
information can help with addressing issues with imperfect data!

18. Updated from Jones, Osborn, Briffa (1997) J. Geophys. Res.; based on CRUTEM4 data

19. Updated from Jones, Osborn, Briffa (1997) J. Geophys. Res.; based on CRUTEM4 data

20. Updated from Jones, Osborn, Briffa (1997) J. Geophys. Res.; based on CRUTEM4 data

21. Updated from Jones, Osborn, Briffa (1997) J. Geophys. Res.; based on CRUTEM4 data

22. Updated from Jones, Osborn, Briffa (1997) J. Geophys. Res.; based on CRUTEM4 data

23. Updated from Jones, Osborn, Briffa (1997) J. Geophys. Res.; based on CRUTEM4 data

24. Updated from Jones, Osborn, Briffa (1997) J. Geophys. Res.; based on CRUTEM4 data

25. Updated from Jones, Osborn, Briffa (1997) J. Geophys. Res.; based on CRUTEM4 data

26. What about the oceans?
Sea surface temperature (SST) observations have usually been preferred
• SST varies more slowly and with smaller diurnal cycle, so can estimate monthly
means from a small number of observations
• Day time marine air temperature measurements have traditionally been excluded
(due to heating of ship superstructures causing a warm bias that varies over time)
• Night marine air temperature (NMAT) increasingly used in recent years: NMAT
may have issues, but they tend to be different to the SST biases so there is value
in utilising both types of data

27. Before 1850, most
observations are MAT
not SST, and most
MAT are daytime
GloSAT project (led by
Liz Kent at National
Oceanography
Centre) is grasping
this nettle so we can
extend back pre-1850
MAT coverage
SST coverage
% of Marine Air Temperatures
taken during the day
Figure from Tom Cropper, National Oceanography Centre

28. What have we learned from global temperature datasets?

29. Derived from Morice et al. (2021) J. Geophys. Res.
How much has the globe warmed?
(Relevant to policy goals e.g. Paris Agreement)

30. Derived from Morice et al. (2021) J. Geophys. Res.
How much has the globe warmed?
(Relevant to policy goals e.g. Paris Agreement)

31. Degrees Celsius difference from the 1961-1990 average
HadCRUT5 Analysis annual-mean temperature anomaly maps (https://crudata.uea.ac.uk/~timo/diag/tempdiag.htm)

32. Updated from Osborn et al. (2007) Weather
Now using HadCRUT5, HadSST4 and CRUTEM5
We understand the mechanisms that determine the warming
patterns at the largest scales, including Arctic amplification,
land–ocean warming contrast and suppression of warming in the
sub-polar oceans
Arctic
amplification
Land
Ocean
Sub-polar
oceans

33. Recent developments: HadCRUT5
(improved consideration of SST biases)
(improved consideration of incomplete coverage biases)
(previous version)

34. Recent developments: HadCRUT5

35. Slower warming in the early 21st century
IPCC’s 2013 assessment was that the difference between
observed warming and climate model simulations during this
period could be explained as a combination of:
(a) unforced variability;
(b) errors in the forcing used in models;
(c) model errors.
Observational dataset error should have been considered too!

36. Key points
• Recent developments demonstrate that there is more to do than
routine updating!
• Even though we have a robust record, research underway into
extending to earlier periods and reducing biases/inhomogeneities
– Rare for improvements to dramatically alter the global-scale picture
• Milestones
– Standardised measurements
– Data sharing and building of data compilations
– Dealing with issues: using anomalies, addressing biases, incomplete and uneven
spatial coverage

37. Spare slides

38. Why global temperature?
Some definitions:
Unforced climate variability
• From the inherent variability in the atmosphere and oceans (& ice, land surface)
Forced climate change
• Caused by forcing
(radiative forcing by greenhouse gases & other forcings including natural ones)

39. From John Kennedy (https://twitter.com/micefearboggis/status/1489131404504018952)

40. Updated from Osborn & Jones (2000) Atmos. Sci. Lett.
Annual Central England
Temperature (CET)
CET after removal of
variability associated with
local circulation variations
Temperature
ºC