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

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

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

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

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

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

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)

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 1 1960 DEC 2 1960 DEC 3 1960 DEC 4 1960 DEC 5 1960 DEC 6 1960 ……. wetter3.de (20CR)

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 1 1960 DEC 2 1960 DEC 3 1960 DEC 4 1960 DEC 5 1960 DEC 6 1960 ……. wetter3.de (20CR)

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

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)

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)

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)

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

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

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

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

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

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

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)

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)

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

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

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

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

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

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

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

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

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

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

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

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

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

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 188214. 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.