1. Analysis of automated circulation classifications
in CMIP5 GCM projections for the British Isles -
(winters 2009–2099)
Jan Stryhal & Radan Huth
Faculty of Science, Charles University, Prague
Institute of Atmospheric Physics, Academy of Sciences of the Czech Republic, Prague
EMS Dublin 2017

2. automated circulation classifications
Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
CONTENTS
INTRODUCTION
project/concepts: winter Euro-Atlantic circulation: reanalyses – historical runs – projections
automated circulation classifications
DATA & METHODS
RESULTS
reference dataset
validation of GCMs
projections of CT frequency, persistence & airflow strength
SUMMARY & CONCLUSIONS
By way of introduction = serving as ian introduction

3. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
INTRODUCTION
Large-scale atmospheric circulation has received considerable attention owing to its close link to meteorological & environmental elements at regional to local scales,
especially in winter
We know that… 1. odstravec
… f.e. we know that f.e. up to one half of the observed trends in the minimum temperature at European stations can be explained by changes in the frequency of CTs

4. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
INTRODUCTION
Large-scale atmospheric circulation has received considerable attention owing to its close link to meteorological & environmental elements at regional to local scales,
especially in winter
Cahynová & Huth (2016), IJC 36:2743–2760

5. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
INTRODUCTION
Large-scale atmospheric circulation has received considerable attention owing to its close link to meteorological & environmental elements at regional to local scales,
especially in winter
The ongoing development of climate models raises the question whether the models are able to simulate large-scale circulation & its links to local climate, and how circulation could change

6. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
INTRODUCTION
Large-scale atmospheric circulation has received considerable attention owing to its close link to meteorological & environmental elements at regional to local scales,
especially in winter
The ongoing development of climate models raises the question whether the models are able to simulate large-scale circulation & its links to local climate, and how circulation could change
Several approaches have been used to assess the circulation in models, including analyses of mean fields of SLP or GPHs, blocking, cyclone tracks and intensity, position of jet streams, teleconnections, …
Zappa et al. (2013), J Climate 26:5379–5396
Wójcik (2015), IJC 35:714–732

7. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
INTRODUCTION
…one of the most wide spread approaches having been classifications of atmospheric circulation patterns (circulation classifications)

8. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
INTRODUCTION
…one of the most wide spread approaches having been classifications of atmospheric circulation patterns (circulation classifications)
a tool that…
1. describes the entire variety of the atmospheric circulation by defining a catalogue of a few circulation types
DEC DEC DEC DEC DEC DEC …….
wetter3.de
(20CR)

9. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
INTRODUCTION
…one of the most wide spread approaches having been classifications of atmospheric circulation patterns (circulation classifications)
a tool that…
1. describes the entire variety of the atmospheric circulation by defining a catalogue of a few circulation types and
2. subsequently classifies each circulation pattern to one of these CTs
DEC DEC DEC DEC DEC DEC …….
wetter3.de
(20CR)

10. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
INTRODUCTION
simplification
requires many subjective choices (number of CTs, data, method, …) which affect the results
requires subjective choices which can have a rather dramatic effect on the results

11. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
INTRODUCTION
simplification
requires many subjective choices (number of CTs, data, method, …) which affect the results
→ relying on a single classification means that one gets only an incomplete picture of reality and, consequently, is at risk of misinterpreting the results
temperature, precipitation, trends in meteorological variables/circulation, droughts, air quality, surface ozone, wild fires, landslides, phenological phases
…in fact,
this effect was documented for a variety of variables and phenomena… (LIST)
and it is, therefore, expectable when one compares data sets (e.g., validates models)

12. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
INTRODUCTION
simplification
requires many subjective choices (number of CTs, data, method, …) which affect the results
→ relying on a single classification means that one gets only an incomplete picture of reality and, consequently, is at risk of misinterpreting the results
temperature, precipitation, trends in meteorological variables/circulation, droughts, air quality, surface ozone, wild fires, landslides, phenological phases
analyses of CTs in models have typically relied on one single classification (Demuzere et al. 2009; Perez et al. 2014; Otero et al. 2017), two at the best (Pastor and Casado 2012; Rohrer et al. 2017)
…in fact,
this effect was documented for a variety of variables and phenomena… (LIST)
and it is, therefore, expectable when one compares data sets (e.g., validates models)

13. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
INTRODUCTION
simplification
requires many subjective choices (number of CTs, data, method, …) which affect the results
→ relying on a single classification means that one gets only an incomplete picture of reality and, consequently, is at risk of misinterpreting the results
temperature, precipitation, trends in meteorological variables/circulation, droughts, air quality, surface ozone, wild fires, landslides, phenological phases
analyses of CTs in models have typically relied on one single classification (Demuzere et al. 2009; Perez et al. 2014; Otero et al. 2017), two at the best (Pastor and Casado 2012; Rohrer et al. 2017)
GOAL: use multiple classification (methods) and produce robust estimates of biases/changes + quantify the effect of the choice of the method on results
…in fact,
this effect was documented for a variety of variables and phenomena… (LIST)
and it is, therefore, expectable when one compares data sets (e.g., validates models)

14. Intercomparison of reanalyses:
Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
DATA & METHODS
Intercomparison of reanalyses:
Stryhal & Huth (2017)
J. Climate 30:7847–7861
Name
Institute
Reference
ERA-40
European Centre for Medium-Range Weather Forecasts
Uppala et al. 2005
NCEP-1
National Centers for Environmental Prediction, National Center for Atmospheric Research
Kalnay et al. 1996
JRA-55
Japan Meteorological Agency
Kobayashi et al. 2015
20CRv2
NOAA Earth System Research Laboratory, University of Colorado CIRES Climate Diagnostics Center
Compo et al. 2011
ERA-20C
Poli et al. 2016
5 reanalyses × 40 winters × 90 days
= 18,000 patterns

15. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
DATA & METHODS
Name
Institute
Reference
ERA-40
European Centre for Medium-Range Weather Forecasts
Uppala et al. 2005
NCEP-1
National Centers for Environmental Prediction, National Center for Atmospheric Research
Kalnay et al. 1996
JRA-55
Japan Meteorological Agency
Kobayashi et al. 2015
20CRv2
NOAA Earth System Research Laboratory, University of Colorado CIRES Climate Diagnostics Center
Compo et al. 2011
ERA-20C
Poli et al. 2016
5 reanalyses × 40 winters × 90 days
= 18,000 patterns
Method abbreviation
Method name
No of CTs
GWT
Grosswettertypes 10
JCT1
Jenkinson–Collison
JCT2
LND
Lund 9
PCT
T-mode PCA obliquely rotated
CKM
k-means (differing start partitions)
SAN
simulated annealing (SANDRA)
KMD
k-medoids

16. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
DATA & METHODS
Name
Institute
Reference
ERA-40
European Centre for Medium-Range Weather Forecasts
Uppala et al. 2005
NCEP-1
National Centers for Environmental Prediction, National Center for Atmospheric Research
Kalnay et al. 1996
JRA-55
Japan Meteorological Agency
Kobayashi et al. 2015
20CRv2
NOAA Earth System Research Laboratory, University of Colorado CIRES Climate Diagnostics Center
Compo et al. 2011
ERA-20C
Poli et al. 2016
5 reanalyses × 40 winters × 90 days
= 18,000 patterns
Method abbreviation
Method name
No of CTs
GWT
Grosswettertypes 10
JCT1
Jenkinson–Collison
JCT2
LND
Lund 9
PCT
T-mode PCA obliquely rotated
CKM
k-means (differing start partitions)
SAN
simulated annealing (SANDRA)
KMD
k-medoids

17. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
DATA & METHODS
Name
Institute
Reference
ERA-40
European Centre for Medium-Range Weather Forecasts
Uppala et al. 2005
NCEP-1
National Centers for Environmental Prediction, National Center for Atmospheric Research
Kalnay et al. 1996
JRA-55
Japan Meteorological Agency
Kobayashi et al. 2015
20CRv2
NOAA Earth System Research Laboratory, University of Colorado CIRES Climate Diagnostics Center
Compo et al. 2011
ERA-20C
Poli et al. 2016
5 reanalyses × 40 winters × 90 days
= 18,000 patterns
Method abbreviation
Method name
No of CTs
GWT
Grosswettertypes 10
JCT1
Jenkinson–Collison
JCT2
LND
Lund 9
PCT
T-mode PCA obliquely rotated
CKM
k-means (differing start partitions)
SAN
simulated annealing (SANDRA)
KMD
k-medoids

18. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
DATA & METHODS
Name
Institute
Reference
ERA-40
European Centre for Medium-Range Weather Forecasts
Uppala et al. 2005
NCEP-1
National Centers for Environmental Prediction, National Center for Atmospheric Research
Kalnay et al. 1996
JRA-55
Japan Meteorological Agency
Kobayashi et al. 2015
20CRv2
NOAA Earth System Research Laboratory, University of Colorado CIRES Climate Diagnostics Center
Compo et al. 2011
ERA-20C
Poli et al. 2016
5 reanalyses × 40 winters × 90 days
= 18,000 patterns
Method abbreviation
Method name
No of CTs
GWT
Grosswettertypes 10
JCT1
Jenkinson–Collison
JCT2
LND
Lund 9
PCT
T-mode PCA obliquely rotated
CKM
k-means (differing start partitions)
SAN
simulated annealing (SANDRA)
KMD
k-medoids

19. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
DATA & METHODS
Name
Institute
Reference
ERA-40
European Centre for Medium-Range Weather Forecasts
Uppala et al. 2005
NCEP-1
National Centers for Environmental Prediction, National Center for Atmospheric Research
Kalnay et al. 1996
JRA-55
Japan Meteorological Agency
Kobayashi et al. 2015
20CRv2
NOAA Earth System Research Laboratory, University of Colorado CIRES Climate Diagnostics Center
Compo et al. 2011
ERA-20C
Poli et al. 2016
5 reanalyses × 40 winters × 90 days
= 18,000 patterns
Method abbreviation
Method name
No of CTs
GWT
Grosswettertypes 10
JCT1
Jenkinson–Collison
JCT2
LND
Lund 9
PCT
T-mode PCA obliquely rotated
CKM
k-means (differing start partitions)
SAN
simulated annealing (SANDRA)
KMD
k-medoids

20. ASSIGNMENT of simulated patterns for each model and classification
Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
DATA & METHODS
Method abbreviation
Method name
No of CTs
GWT
Grosswettertypes 10
JCT1
Jenkinson–Collison
JCT2
LND
Lund 9
PCT
T-mode PCA obliquely rotated
CKM
k-means (differing start partitions)
SAN
simulated annealing (SANDRA)
KMD
k-medoids No
Model name 1
CanESM2 2
CCSM4 3
CMCC-CESM 4
CMCC-CM 5
CMCC-CMS 6
CNRM-CM5 7
EC-EARTH 8
GFDL-CM3 9
GFDL-ESM2G 10
GFDL-ESM2M 11
HadGEM2-AO 12
HadGEM2-CC 13
HadGEM2-ES 14
INM-CM4 15
IPSL-CM5A-LR 16
IPSL-CM5A-MR 17
IPSL-CM5B-LR 18
MIROC5 19
MIROC-ESM 20
MIROC-ESM-CHEM 21
MPI-ESM-LR 22
MPI-ESM-MR 23
MRI-CGCM3 24
MRI-ESM1 25
NorESM1-M
ASSIGNMENT of simulated patterns for each model and classification
VALIDATION: winter 1960–2000
PROJECTION: winter 2009 – 2099 (consecutive 30-year windows)

21. ASSIGNMENT of simulated patterns for each model and classification
Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
DATA & METHODS
Method abbreviation
Method name
No of CTs
GWT
Grosswettertypes 10
JCT1
Jenkinson–Collison
JCT2
LND
Lund 9
PCT
T-mode PCA obliquely rotated
CKM
k-means (differing start partitions)
SAN
simulated annealing (SANDRA)
KMD
k-medoids No
Model name 1
CanESM2 2
CCSM4 3
CMCC-CESM 4
CMCC-CM 5
CMCC-CMS 6
CNRM-CM5 7
EC-EARTH 8
GFDL-CM3 9
GFDL-ESM2G 10
GFDL-ESM2M 11
HadGEM2-AO 12
HadGEM2-CC 13
HadGEM2-ES 14
INM-CM4 15
IPSL-CM5A-LR 16
IPSL-CM5A-MR 17
IPSL-CM5B-LR 18
MIROC5 19
MIROC-ESM 20
MIROC-ESM-CHEM 21
MPI-ESM-LR 22
MPI-ESM-MR 23
MRI-CGCM3 24
MRI-ESM1 25
NorESM1-M
ASSIGNMENT of simulated patterns for each model and classification
VALIDATION: winter 1960–2000
PROJECTION: winter 2009 – 2099 (consecutive 30-year windows)
Separately for reanalyses and models:
relative frequency + persistence (each CT)
Separately for the two ensebles:
airflow strength + direction and shear vorticity (each CT mean map)

22. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
DATA & METHODS
At the bottom
At the top
Along the horizontal axis
On the right/left

23. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
RESULTS
1. Frequency of occurrence

24. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
RESULTS
1. Frequency of occurrence

25. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
RESULTS
1. Frequency of occurrence

26. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
RESULTS
1. Frequency of occurrence

27. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
RESULTS

28. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
RESULTS
2. Persistence
The typical persistence of CTs is about 1-3 days and the model tend to somewhat underestimate it,
But the simulations are considerably better than in the case of the fi
Altough the simulated circulation is somewhat more dynamic with the epizodes being a bit shorter on the average
TRENDS?? – význam s ohledem na extrémy xxx D07 – was apparent increase of persistence of AC CTs

29. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
RESULTS
2. Persistence
The typical persistence of CTs is about 1-3 days and the model tend to somewhat underestimate it,
But the simulations are considerably better than in the case of the fi
Altough the simulated circulation is somewhat more dynamic with the epizodes being a bit shorter on the average
TRENDS?? – význam s ohledem na extrémy xxx D07 – was apparent increase of persistence of AC CTs

30. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
RESULTS
2. Persistence
The typical persistence of CTs is about 1-3 days and the model tend to somewhat underestimate it,
But the simulations are considerably better than in the case of the fi
Altough the simulated circulation is somewhat more dynamic with the epizodes being a bit shorter on the average
TRENDS?? – význam s ohledem na extrémy xxx D07 – was apparent increase of persistence of AC CTs

31. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
RESULTS
3. Flow strength
The typical persistence of CTs is about 1-3 days and the model tend to somewhat underestimate it,
But the simulations are considerably better than in the case of the fi
Altough the simulated circulation is somewhat more dynamic with the epizodes being a bit shorter on the average
TRENDS?? – význam s ohledem na extrémy xxx D07 – was apparent increase of persistence of AC CTs

32. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
RESULTS
3. Flow strength
The typical persistence of CTs is about 1-3 days and the model tend to somewhat underestimate it,
But the simulations are considerably better than in the case of the fi
Altough the simulated circulation is somewhat more dynamic with the epizodes being a bit shorter on the average
TRENDS?? – význam s ohledem na extrémy xxx D07 – was apparent increase of persistence of AC CTs

33. Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
RESULTS
3. Flow strength
The typical persistence of CTs is about 1-3 days and the model tend to somewhat underestimate it,
But the simulations are considerably better than in the case of the fi
Altough the simulated circulation is somewhat more dynamic with the epizodes being a bit shorter on the average
TRENDS?? – význam s ohledem na extrémy xxx D07 – was apparent increase of persistence of AC CTs

34. The most apparent trends in CTs over the 21st century:
Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
SUMMARY
The most apparent trends in CTs over the 21st century:
increase in the frequency of CTs with advection from W, towards in the of the century from the whole western quadrant (10–20% relative to control)
the remaining directional CTs + AC CTs have a similar trend of the opposite sign
problem – simulation of AC CTs (-35% relative to reanalyses) – projections?
persistence is slightly underestimated and is projected to further slightly decrease
strength of airflow is simulated well and is not projected to change
vorticity is overestimated (more cyclonic) in mean maps of most CTs and is projected to increase
there is no statistically significant difference in trends projected by models that perform well and those performing poorly over the control period
robust estimates of biases/trends require a comparison of multiple classifications, interpretations of single classifications often lead to contradictory results

35. Thank you for your attention!
Analysis of automated circulation classifications in CMIP5 GCM projections for the British Isles
Thank you for your attention!
ACKNOWLEDGEMENTS
The work was funded by the Grant Agency of Charles University, project
We acknowledge the following organizations for providing their reanalysis datasets: NOAA/OAR/ESRL PSD, Boulder, Colorado, USA for NCEP/NCAR, and 20th Century Reanalysis V2, ECMWF for ERA-40, and ERA-20C, and JMA for JRA-55.
We thank all climate modelling groups for their GCM simulations, and the PCMDI for enabling access to the data.
Thanks are also due to all developers of the COST733 software.