APPLICATION IN CLIMATOLOGY 2: LONG-TERM TRENDS IN PERSISTENCE Radan HUTH, Monika CAHYNOVÁ, Jan KYSELÝ Radan HUTH, Monika CAHYNOVÁ, Jan KYSELÝ

Hess&Brezowsky groups of types dashed: lifetime (persistence) smoothed DJF

Hess&Brezowsky groups of types dashed: lifetime (persistence) smoothed JJA

Hess&Brezowsky: groups of types with cyclonic / anticyclonic character over central Europe dashed: lifetime (persistence) smoothed

Hess&Brezowsky: all types lifetime (persistence)

Application in climatology 3: Links between circulation changes and climatic trends in Europe

Outline we want to assess the magnitude of climatic trends over Europe in that can be linked to changing frequency of circulation types (as opposed to changing climatic properties of circulation types) data –29 stations from the ECA&D project, daily Tmax, Tmin, precipitation –8 objective catalogues from cat.1.2 (CKMEANS, GWT, Litynski, LUND, P27, PETISCO, SANDRA, TPCA), each in 3 variants with 9, 18, 27 CTs –all COST733 domains except for D03 – lack of stations methods –seasonal climatic trends from station data –proportion of climatic trends linked to circulation changes we want to assess the magnitude of climatic trends over Europe in that can be linked to changing frequency of circulation types (as opposed to changing climatic properties of circulation types) data –29 stations from the ECA&D project, daily Tmax, Tmin, precipitation –8 objective catalogues from cat.1.2 (CKMEANS, GWT, Litynski, LUND, P27, PETISCO, SANDRA, TPCA), each in 3 variants with 9, 18, 27 CTs –all COST733 domains except for D03 – lack of stations methods –seasonal climatic trends from station data –proportion of climatic trends linked to circulation changes

Stations

Trends in the frequency of CTs Percentage of days occupied by CTs with trends in the seasonal frequency significant at the 95% level in

Trends in the frequency of CTs Magnitude of significant trends in frequency of CTs in GWTC10 (days per season in )

Results – seasonal climatic trends trend significant at the 95% level

Ratio of “hypothetical” (circulation-induced) and observed long-term seasonal trends. The “hypothetical” trend is calculated from a daily series, constructed by assigning the long-term monthly mean of the given variable under the specific circulation type to each day. See e.g. Huth (2001). Ratio of “hypothetical” (circulation-induced) and observed long-term seasonal trends. The “hypothetical” trend is calculated from a daily series, constructed by assigning the long-term monthly mean of the given variable under the specific circulation type to each day. See e.g. Huth (2001). Method to attribute climatic trends to changes in frequency of circulation types

Ratio of circulation-induced (“hypothetical”) and observed trends at stations where the observed trend is significant at the 95% level Results of 24 classifications on D00 and small domains

Averages of individual stations where observed trends are significant at the 95% level Ratio of circulation-induced (“hypothetical”) and observed trends Comparison of individual classifications

Conclusions Significant trends in the frequency of CTs occur mostly in winter in domains 00 and 04 through 11, and also in summer in the Mediterranean. Climatic trends can be only partly explained by the changing frequency of CTs, the link being the strongest in winter. In the other seasons, within-type climatic trends are responsible for a major part of the observed trends. Classifications in the small domains are usually more tightly connected with climatic trends than those in D00, except for the northernmost stations. There are large differences between results obtained with individual classifications – therefore all studies using just a limited number of them should be taken with a grain of salt. Significant trends in the frequency of CTs occur mostly in winter in domains 00 and 04 through 11, and also in summer in the Mediterranean. Climatic trends can be only partly explained by the changing frequency of CTs, the link being the strongest in winter. In the other seasons, within-type climatic trends are responsible for a major part of the observed trends. Classifications in the small domains are usually more tightly connected with climatic trends than those in D00, except for the northernmost stations. There are large differences between results obtained with individual classifications – therefore all studies using just a limited number of them should be taken with a grain of salt.

Application in climatology 4: Analysis of climate model outputs

How to compare circulation types between two climates? Isn’t it nonsense? We have just one climate... Comparisons between –real climate and simulated present climate  model validation –simulated present and perturbed (typically future) climate  climate change response –real climate in two distinct periods (e.g., current vs. little ice age) Isn’t it nonsense? We have just one climate... Comparisons between –real climate and simulated present climate  model validation –simulated present and perturbed (typically future) climate  climate change response –real climate in two distinct periods (e.g., current vs. little ice age)

OBSCTR “INSTRINSIC” TYPES

OBSERVED TYPES projected onto CONTROL OBSOBS  CTR BUT: isn’t it an artefact of the projection?

projection in the opposite “direction”: CONTROL TYPES projected onto OBSERVED CTR CTR  OBS

How to compare circulation types between two climates? (at least) four possible approaches 1. Find circulation types in each climate separately + you may get truly dominant types in both datasets (if you are lucky...) –no clear structure in data  types are to a certain extent random  comparison may be misleading (at least) four possible approaches 1. Find circulation types in each climate separately + you may get truly dominant types in both datasets (if you are lucky...) –no clear structure in data  types are to a certain extent random  comparison may be misleading

How to compare circulation types between two climates? 2. Use types defined a priori, independently of the datasets objectivized catalogues types defined on a short(er) period + easy and fair comparison –may not reflect real structure in either dataset 2. Use types defined a priori, independently of the datasets objectivized catalogues types defined on a short(er) period + easy and fair comparison –may not reflect real structure in either dataset

How to compare circulation types between two climates? 3. Concatenation of two datasets, “joint” classification performed simultaneously for the two climates (typically used with SOMs) + good compromise: types are likely to be close to ‘real’ types in both datasets 3. Concatenation of two datasets, “joint” classification performed simultaneously for the two climates (typically used with SOMs) + good compromise: types are likely to be close to ‘real’ types in both datasets

How to compare circulation types between two climates? 4. Projection from one climate to the other and vice versa + wrong conclusions are eliminated 4. Projection from one climate to the other and vice versa + wrong conclusions are eliminated

Where to find it? References Huth R. et al., 2008: Classifications of atmospheric circulation patterns: Recent advances and applications. Ann. N. Y. Acad. Sci., 1146, ad 1) (heat waves) Kyselý J., Huth R., 2008: Adv. Geosci., 14, ad 2) (trends in persistence) Kyselý J., Huth R., 2006: Theor. Appl. Climatol., 85, Cahynová M., Huth R., 2009: Tellus A, 61, ad 3) (climate change vs. circulation) Huth R., 2001: Int. J. Climatol., 21, Cahynová M., Huth R., 2009: Theor. Appl. Climatol., 96, ad 4) (analysis of GCM outputs) Huth R., 1997: J. Climate, 10, Huth R., 2000: Theor. Appl. Climatol., 67, Huth R. et al., 2008: Classifications of atmospheric circulation patterns: Recent advances and applications. Ann. N. Y. Acad. Sci., 1146, ad 1) (heat waves) Kyselý J., Huth R., 2008: Adv. Geosci., 14, ad 2) (trends in persistence) Kyselý J., Huth R., 2006: Theor. Appl. Climatol., 85, Cahynová M., Huth R., 2009: Tellus A, 61, ad 3) (climate change vs. circulation) Huth R., 2001: Int. J. Climatol., 21, Cahynová M., Huth R., 2009: Theor. Appl. Climatol., 96, ad 4) (analysis of GCM outputs) Huth R., 1997: J. Climate, 10, Huth R., 2000: Theor. Appl. Climatol., 67, 1-18.