Solicited presentation by Tim Osborn at EGU General Assembly, Vienna, 2016

1. Tim Osborn
Climatic Research Unit, School of Environmental Sciences, UEA
Twitter: @TimOsbornClim
EGU, 19 April 2016
Interdecadal variability in surface
climate during the instrumental period

2. Ed Hawkins, Climate Lab Book blog
– Updated by Ed Hawkins

3. 2006‐2012 ref
1986‐2005 ref

4. 2006‐2012 ref
1986‐2005 ref
– Expert judgement: reduce near‐term CMIP5 changes by 10% to 40%
depending on interpretation of variability/slowdown
• 10% relative to 2006‐2012 baseline (implies slowdown is not unforced variability)
• 40% relative to 1986‐2005 baseline (is that compatible with long‐term projections?)

5. – Observations falling within multi‐model spread does not
“validate” the entire multi‐model spread

6. – Due to unforced variability & uncertainty in forcings, ranges of
projections from groups of models with high & low climate
response do not fully separate for another few decades

10. HadCRUT4 global temperature; Cowtan & Way (2014) v2 temperature fields

27. AMO

28. A “forced” signal was first removed from the global‐mean temperature
(multi‐model‐mean of low TCR CMIP5 models,
scaled to fit the observed temperature, then subtracted)

29. Correlation between the slopes of piecewise continuous trends fitted to global‐mean
temperature and fitted to zonal‐mean temperature, for a range of trend lengths
HadCRUT4 global temperature & HadCRUT4 zonal‐mean temperatures

30. Correlation between the slopes of piecewise continuous trends fitted to global‐mean
temperature and fitted to zonal‐mean temperature, for a range of trend lengths
HadCRUT4 global temperature & HadCRUT4 zonal‐mean temperatures
ENSO
AMO

31. Correlation between the slopes of piecewise continuous trends fitted to global‐mean
temperature and fitted to zonal‐mean sea level pressure, for a range of trend lengths
HadCRUT4 global temperature & HadSLP2(r) zonal‐mean SLP

32. Correlation between the slopes of piecewise continuous trends fitted to global‐mean
temperature and fitted to zonal‐mean sea level pressure, for a range of trend lengths
HadCRUT4 global temperature & HadSLP2(r) zonal‐mean SLP

33. Correlation between the slopes of piecewise continuous trends fitted to global‐mean
temperature and fitted to zonal‐mean sea level pressure, for a range of trend lengths
HadCRUT4 global temperature & HadSLP2(r) zonal‐mean SLP
Stronger westward flow

34. Correlation between the slopes of piecewise continuous trends fitted to global‐mean
temperature & fitted to 15S‐15N mean sea level pressure, for a range of trend lengths
HadCRUT4 global temperature & HadSLP2(r) mean of SLP between 15S and 15N for each longitude

35. Correlation between the slopes of piecewise continuous trends fitted to global‐mean
temperature & fitted to 15S‐15N mean sea level pressure, for a range of trend lengths
HadCRUT4 global temperature & HadSLP2(r) mean of SLP between 15S and 15N for each longitude
El Nino
La Nina?

36. Summary
Motivation:
• Constraining future projections using observations
– Influenced by what part of observing warming is forced and what is
unforced variability
– Important for baselines and understanding near‐term & long‐term IPCC
projections
Periods of warming & cooling (or faster & slower warming):
• Have distinct spatial structures that depend on timescale
– ENSO then Pacific (Inter‐)Decadal Variability then Atlantic Multidecadal
Variability
– Relative prominence of PDV and AMV depend on whether an
estimated forced signal is removed, to leave an estimate of unforced
variability (PDO and AMO)
This work was supported by the Natural Environment Research Council
[NERC, grant number NE/N006348/1, part of the SMURPHS project]