Links between circulationindices/types and precipitation inCentral Europe in RCMs               Eva Plavcová, Jan Kyselý, ...
Motivation Regional climate models (RCMs) – driven by GCMs, re- analysis Tool to simulate climate at the regional and loca...
Data Daily precipitation amounts (PREC), daily mean sea level pressure 40-yr period (1961–2000), summer (JJA) and winter (...
Methods Links investigated for PREC in 3 regions in the Czech Republic:    CL – Central lowland (Polabí, 18 gridpoints) – ...
Atmospheric circulation Represented by 3 circulation indices (Jenkinson & Collison 1977) derived from gridded MSLP by mean...
Atmospheric circulation   Predominate westerly flow (especially in winter)   Relative frequency < 0.5%:      4 types in wi...
Seasonal mean PREC in RCMs•   Mean seasonal precipitation amount:                [mm/season]           DJF                ...
Mean precipitation – OBS Mean observed PREC on days falling into each index bin and circulation type To ensure representat...
Mean precipitation – RCMs      RCMs are able to capture some basic features:        links more pronounced for highlands (e...
Probability of wet day - OBS Wet day = when PREC > 0.3 mm Links are better pronounced than for mean PREC Relationships are...
Probability of wet day - RCMs RCMs are generally able to capture the links   more dry days for southerly flow, low STR and...
Probability of wet day - RCMs The link to VORT:    in winter, RCMs overestimate probability of wet days for both strong AC...
Probability of heavy rainy day - OBS Heavy rain = mean area-averaged PREC amount over all grids in a region > 90% quantile...
Probability of heavy rainy day - DJFWinter:   very low probability for anticyclonic types – all RCMs   simulate less heavy...
Probability of heavy rainy day - JJASummer:  high probability for strongly C typesHighest probability of heavy rainy day  ...
Types with extreme PREC - DJF Extreme PREC = in at least one grid over the region, grid box PREC higher than the 99% quant...
Types with extreme PREC - JJA OBS   largest probability for C and CNE types in all   regions (U and A are the most frequen...
Summary   RCMs overestimate mean seasonal precipitation amount in winter, and some models have biases   in summer, too   T...
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Eva Plavcová, Jan Kyselý, Petr Štěpánek: Links between circulation indices/types and precipitation in Central Europe in RCMs

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  • Motivace pro vsechny jasna
  • Most widely used tools for simulating climate e.g. with respect to dependence of errors on atmospheric Such studies focusing on links of surface climate characteristics to atmospheric circulation and/or other variables may bring insight into physical processes affecting model’s performance or reveal potential common deficiencies if an ensemble of climate model simulations is examined.
  • Central Europe Dobre porovnani kdyz rizene ERA-40
  • CL region represents the main agricultural area main mountain ranges with windward precipitation effects
  • purely
  • Nejvice zapadni proudeni, v zime AC, v lete U a AC caused
  • in contrast to
  • defined as day
  • Eva Plavcová, Jan Kyselý, Petr Štěpánek: Links between circulation indices/types and precipitation in Central Europe in RCMs

    1. 1. Links between circulationindices/types and precipitation inCentral Europe in RCMs Eva Plavcová, Jan Kyselý, Petr Štěpánek e-mail: plavcova@ufa.cas.cz kysely@ufa.cas.cz
    2. 2. Motivation Regional climate models (RCMs) – driven by GCMs, re- analysis Tool to simulate climate at the regional and local scale and future climate change scenarios Biases in their simulations of recent climate – important to identify sources of these errors and to address them in further developing the models Particularly useful to identify links between individual variables and their biases (dependence of errors on atmospheric circulation, soil moisture characteristics, cloud amount, etc.) May bring insight into physical processes affecting model’s performance or reveal potential common deficiencies
    3. 3. Data Daily precipitation amounts (PREC), daily mean sea level pressure 40-yr period (1961–2000), summer (JJA) and winter (DJF) seasons Observations (OBS) PREC - taken from the gridded dataset (GriSt) interpolated from a high-density station network operated by the CHMI Pressure data from the ERA-40 re-analysis Models 5 RCMs (~25km) from the ENSEMBLES project, runs driven by the ERA-40 re-analysis (‘perfect’ boundary condition, comparison) Institution RCM Reference DMI (Danish Meteorological Institute) HIRHAM Christensen et al. (1996) KNMI (Royal Netherlands Meteorological Institute) RACMO Lenderink et al. (2003) ICTP (Abdus Salam International Centre for RegCM Giorgi et al. (2004) Theoretical Physics) MPI (Max-Planck Institute) REMO Jacob (2001) SMHI (Swedish Meteorological and Hydrological Kjellström et al. (2005), RCA Institute) Samuelsson et al. (2011)
    4. 4. Methods Links investigated for PREC in 3 regions in the Czech Republic: CL – Central lowland (Polabí, 18 gridpoints) – 257 m a.s.l. SH – Southern highland (Šumava, 7 gridpoints) – 790 m a.s.l. EH – Eastern highland (Jeseníky, 7 gridpoints) – 597 m a.s.l. Precipitation amounts area-averaged in most cases
    5. 5. Atmospheric circulation Represented by 3 circulation indices (Jenkinson & Collison 1977) derived from gridded MSLP by means of equations given in Plavcová & Kyselý 2011 flow direction (DIR) flow strength (STR) flow vorticity (VORT) The circulation indices are used to produce a simple classification of 27 circulation types (Jones et al 1993): STR and VORT < 6: unclassified U |VORT| ≥ 2∙STR & VORT>0: strongly cyclonic C |VORT| ≥ 2∙STR & VORT<0: strongly anticyclonic AC Directional types: |VORT| < 1∙STR, DIR is divided into 8 quadrants: N, NE, E, SE, S, SW, W, NW Hybrid types (combination of directional and non-directional): CN, CNE, CE, CSE, CS, CSW, CW, CNW AN, ANE, AE, ASE, AS, ASW, AW, ANW
    6. 6. Atmospheric circulation Predominate westerly flow (especially in winter) Relative frequency < 0.5%: 4 types in winter (CS, CSW, CSW, ANE) 3 types in summer (CS, CSW, ANE)RCMs driven by the ERA-40 re-analysis Overestimation of STR More strong cyclonic flow at the expense of anticyclonic flow
    7. 7. Seasonal mean PREC in RCMs• Mean seasonal precipitation amount:  [mm/season]   DJF     JJA   Differences larger than ±10% are   CL SH EH CL SH EH highlighted by colour observation 103 181 167 213 284 287 HIRHAM 144 328 244 178 385 311 RACMO 130 208 187 150 289 220 RegCM 187 270 212 231 290 274 REMO 139 137 124 235 235 210 RCA 134 246 189 233 425 361• Biases due to biases in circulation/links between circulation and PREC• In winter: all RCMs (except REMO in highlands) simulate more precipitation• In summer: RCA overestimates PREC RACMO underestimates PREC in CL and EH HIRHAM enhances differences between lowland and highlands RegCM vs. REMO: (un)realistic orography x PREC in highlands (RegCM – the ‘best’ model in summer, REMO does not capture differences for highlands and lowlands)
    8. 8. Mean precipitation – OBS Mean observed PREC on days falling into each index bin and circulation type To ensure representativeness, means are only calculated for bins with samples of at least 15 days throughout the period, grey lines represent relative frequencies of circulation type/index bin Links are more pronounced for highlands (SH, EH) In summer, strong links between VORT and PREC: large mean precipitation amounts during cyclonic flow days Stronger flows – larger PREC amounts in both seasons Generally larger mean PREC for westerly (Atlantic influence) and northeasterly flows (cyclones over central and eastern Europe, Mediterranean cyclones) in comparison to southerly flow; in CL, these links are weaker
    9. 9. Mean precipitation – RCMs RCMs are able to capture some basic features: links more pronounced for highlands (except REMO) larger mean precipitation amounts during cyclonic flow days than anticyclonic in both seasons stronger flows – larger PREC amounts in both seasons RCMs have difficulties to capture some features: unrealistic links to DIR in summer – larger PREC for SW flow, for northerly flow large PREC only in EH (x CL and SH) CN in EH – to some extend reproduced in some RCMs CSW - only in EH x in all/no regionsJJA
    10. 10. Probability of wet day - OBS Wet day = when PREC > 0.3 mm Links are better pronounced than for mean PREC Relationships are analogous in both (all) seasons The link to DIR is larger in winter and for highlands (northerly flow – more wet days) – orography particularly important in inducing PREC for flow from the NE quadrant The link to VORT is very strong in summer, when the probability of wet days is almost equal to 1 for strongly cyclonic days Generally, more wet days are for westerly flow, higher STR and cyclonic flow more dry days: southerly flow, low STR and anticyclonic flow
    11. 11. Probability of wet day - RCMs RCMs are generally able to capture the links more dry days for southerly flow, low STR and anticyclonic flow All RCMs overestimate probability of wet days in winter, the overestimation is largest for CL Some RCMs do not capture the lower probability of wet days for NE flow in CL in comparison to highlands
    12. 12. Probability of wet day - RCMs The link to VORT: in winter, RCMs overestimate probability of wet days for both strong AC and C flow (except AC in REMO) in winter, all RCMs except RCA overestimate the differences between probability of wet days for strong AC and C flow days (REMO most) in summer, RegCM and RCA simulate more wet days among strong AC days, and that is why the differences between strong AC and C days are much smaller than in the observed data Probability of wet days for 2% of days with strongest anticyclonic/cyclonic flow, and their differences; averaged over 3 regions DJF PROB DJF     JJA     wet AC C C-AC AC C C-AC         0.22          0.85          0.63          0.18          0.92          0.74   observed            0.31             0.96             0.65             0.18             0.93             0.76   HIRHAM            0.27             0.97             0.70             0.09             0.88             0.79   RACMO            0.28             0.99             0.71             0.42             0.92             0.50   RegCM            0.12          0.95          0.84          0.12          0.90                         0.77   REMO            0.38          0.96          0.58          0.52          0.97                         0.45   JJA RCA                  
    13. 13. Probability of heavy rainy day - OBS Heavy rain = mean area-averaged PREC amount over all grids in a region > 90% quantile in given season The probability is high especially for strong flow days strong cyclonic flow westerly (winter) and northeasterly (summer) flow
    14. 14. Probability of heavy rainy day - DJFWinter: very low probability for anticyclonic types – all RCMs simulate less heavy rainy days for AC types higher probability for westerly types RCMs reproduce these linksHighest probability of heavy rainy day in SH – CW type – RegCM, REMO HIRHAM – W, CNW; RACMO – W, RCA – CNW in EH – CN, W RACMO and RCA – CSW, CSE in CL – W, CE, CS HIRHAM – W; RACMO – W; RegCM – C, CW; REMO – CSW, W; RCA – W, SW
    15. 15. Probability of heavy rainy day - JJASummer: high probability for strongly C typesHighest probability of heavy rainy day in CL – C, SW HIRHAM – CS, C; RACMO – CW (0.5); RegCM – CSW; REMO – CW, W; RCA – CW, CSW in SH – C, CS type – HIRHAM RACMO and RegCM – CSW; REMO – C; RCA - CNW in EH – CN, CNE, CSW HIRHAM – CN; RACMO – CW, CN, CSW; RegCM and REMO – CSW; RCA – CSW, CN
    16. 16. Types with extreme PREC - DJF Extreme PREC = in at least one grid over the region, grid box PREC higher than the 99% quantile of PREC distribution in that grid Barplot – total number of events, crosses – relative frequency for given circulation type OBS largest probability for CSW and W types in CL and SH, while for CS type in EH in SH, almost all events occur during westerly types never for CW, CNW, AN, ANE, AE, ASE RCMs simulate extreme PREC for CW and CNW types
    17. 17. Types with extreme PREC - JJA OBS largest probability for C and CNE types in all regions (U and A are the most frequent circulation types in summer) in CL, CS ~30%, SW in EH also CN never for AN, ANE, AE, ASE RCMs are relatively able to capture the types during which extreme precipitation events occur/do not occur
    18. 18. Summary RCMs overestimate mean seasonal precipitation amount in winter, and some models have biases in summer, too There are quite strong links in the observed data between atmospheric circulation and mean PREC, probability of wet days and days with heavy and extreme precipitation RCMs are generally able to capture most of the links However, RCMs often do not capture the differences of the links for the regions (highland x lowland) – RCM outputs more useful/realistic at a larger scale than several gridboxes If the links between large-scale atmospheric circulation and precipitation are realistically reproduced in a climate model, biases in simulation of total precipitation amounts could be attributed to biases of large-scale circulation itself • (e.g. HIRHAM simulates more strong flow days + strong flow days are linked to higher PREC => HIRHAM overestimate mean PREC)References: Jenkinson AF, Collison FP (1977) An initial climatology of gales over the North Sea, Synoptic Climatology Branch Memorandum No. 62, Meteorological Office, Bracknell, United Kingdom Jones PD, Hulme M, Briffa KR (1993) A comparison of Lamb circulation types with an objective classification scheme. International Journal of Climatology 13: 655–663 Plavcová E, Kyselý J (2011) Evaluation of daily temperatures in Central Europe and their links to large-scale circulation in an ensemble of regional climate models. Tellus 63A: 763–781

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