1. © Crown copyright Met Office Atmospheric Blocking and Mean Biases in Climate Models Adam Scaife, Tim Hinton, Tim Woollings, Jeff Knight, Srah Keeley, Gill Martin and Malcolm Roberts Reading University December 2010

2. © Crown copyright Met Office Atmospheric Blocking Pelly and Hoskins (2003): Blocking index B is the difference between the average potential temperature in the N box and the average potential temperature in the S box. B > 0 implies blocking Tibaldi and Molteni (1990): similar index based on GPH at 500hPa A signature of atmospheric wave breaking

3. © Crown copyright Met Office Blocking Maintenance Maintenance of blocks by potential vorticity flux from small scale eddies (Shutts 1983, 1986, 1987) SELF – Synoptic Eddy and Low Frequency flow interaction (Lau 1988) General rule for transient eddy feedback on low frequency variability (Kug et al 2009) These suggest upscale feedback and a behaviour in this case that is opposite to that in L.F. Richardson’s famous rhyme: Big whirls have little whirls that feed on their velocity, and little whirls have lesser whirls and so on to viscosity Motivates some key questions: Does this imply that blocking frequency in climate models is very sensitive to resolution? Does this mean that regional climate change signals could be very wrong? (e.g. Palmer et al 2008)

4. © Crown copyright Met Office Blocking Errors “recent studies have found that GCMs tend to simulate the location of NH blocking more accurately than frequency or duration” (IPCC, AR4, WG1 Chapter 8) Climate model blocking frequencies (D’Andrea et al. 1997) Almost all models underestimate blocking in almost all regions Concentrate on frequency

5. © Crown copyright Met Office Mean or variability errors? If this procedure corrects errors in blocking frequency and the variability term is realistic then the error lies in the mean M = modelO = obs Overbar = climate mean Prime = time varying part Swap model mean for observed mean:

6. © Crown copyright Met Office Atmospheric Blocking Lack of blocking in both Atlantic and Pacific Same error in Summer and Winter Peak deficit > 0.15 day-1 Mean values ~0.25 day -1 Winter Summer

7. © Crown copyright Met Office Other Models “recent studies have found that GCMs tend to simulate the location of NH blocking more accurately than frequency or duration” (IPCC, AR4, WG1 Chapter 8)

8. © Crown copyright Met Office Blocking Errors and Resolution Sensitivity to Horizontal Resolution in JMA/MRI AGCM (Matsueda et al 2009) Sensitivity to Vertical Resolution in HadAM3 (Scaife and Knight 2009)

9. © Crown copyright Met Office Mean versus variability Underestimated blocking Balanced by overestimated ‘anti-blocking’ or ‘mobile’ days! => width (variability) is relatively well modelled => error is in mean climate and not in variability So can our model simulate the blocking process after all?

10. © Crown copyright Met Office Bias corrected errors in our model Error removed in both Atlantic and Pacific Error removed in Summer and Winter Winter Summer Winter bias corrected Summer bias corrected

11. © Crown copyright Met Office Bias corrected errors in IPCC models Greatly reduced Smaller amount remaining

12. © Crown copyright Met Office Bias corrected errors in IPCC models Greatly reduced Smaller amount remaining

13. © Crown copyright Met Office Mean State Errors Sensitivity to Horizontal Resolution in JMA/MRI AGCM (Matsueda et al 2009) Pacific mean errors are larger in high resolution model Atlantic mean errors are smaller in high resolution Model Has the right sense to explain the blocking result

14. © Crown copyright Met Office New HadGEM3 model: See Scaife et al 2010: Atmospheric Blocking and Mean Biases in Climate Models, J.Clim., in press New model has small atmospheric mean biases. This leads to a good representation of Atlantic blocking. Old Model New Model Old Model – bias removed Observed Blocking

15. © Crown copyright Met Office Here’s the cause: North Atlantic Gulf Stream Cold bias is creating the mean climate error in the atmosphere N96L85O(1)N216L85O(0.25)

16. © Crown copyright Met Office An example blocking event:

17. © Crown copyright Met Office An example blocking event:

18. © Crown copyright Met Office An example blocking event:

19. © Crown copyright Met Office Summary Atmospheric blocking is still underestimated in current climate models Most of the deficit is directly attributable to a westerly bias in mean climate This westerly bias is removed in latest HadGEM3 models It stems from the ubiquitous cold bias in the N Atlantic This also explains why it occurs in so many models Latest HadGEM3 models also show good Atlantic blocking frequency Is this all circular: low resol’n -> no blocking -> mean bias? No :- N96 A runs show blocking, ocean only runs show no Atlantic bias Blocking duration also increases accordingly Wave-breaking is alive and well in the model

20. © Crown copyright Met Office Revisiting high resolution blocking results: Swap high and low resolution mean states TL95 looks like TL959! Mio Matsueda