University of East Anglia

Transcription

1 Figure : Differences (hpa) in NCEP minus UKMO MSLP in 99: annual (top panel), January (middle panel) and July (bottom panel).

2 Figure : Comparison between the first two PCs of NCEP and observed MSLP (left) and associated time series (eigenamplitudes) (right). Variance for each component is shown in the top left hand of each panel.

3 Figure : Location of stations with daily temperature and precipitation records.

4 Figure : Time series of correlation between NCEP and observed temperature for Central and Eastern England and for Italy.

5 Figure : Time series of correlation between NCEP and observed precipitation for Central and Eastern England and for Italy.

6 Figure : Long term variability of correlations between NCEP and observed precipitation rates for Eastern and Central England and Italy.

7 DJF - - MAM - - JJA - - SON Figure 7: Seasonal NAO indices calculated as the difference of normalised MSLP at Ponta Delgada and Reykjavik using NCEP Reanalysis data (dashed lines) and observed data (solid lines). DJF - - MAM - - JJA - - SON Figure 8: Seasonal NAO indices calculated as the difference of normalised MSLP at Gibraltar and Reykjavik using NCEP Reanalysis data (dashed lines) and observed data (solid lines).

8 Figure 9: Time series of NAO from NCEP and observations along with the difference (NCEP observations). 7

9 Figure : Correlations for the winter season between surface climate (temperature and precipitation) and the NAO (the difference of normalised pressures at Gibraltar and Reykjavik). Correlations are shown for temperature (left) and precipitation (right) for two observed periods and for NCEP-derived pressure and surface variables. 8

10 Figure : Correlation between winter (DJF) NAO and observed temperature and precipitation. The longitude of the observations is expressed as a mean of the values between the bands W to W, W to E and E to E. The length of the correlation period is the years centred on the middle value. 9

11 Figure : Correlation between the derived variables (time series) of the PCA of MSLP and precipitation for the NCEP analyses (left) and the UKMO data (right). 7

12 Figure : Correlation between the derived variables (time series) of the PCA of MSLP and temperature for the NCEP analyses (left) and the UKMO data (right). 7

13 Figure : MSLP composites for high precipitation events Central England. 7

14 Figure : MSLP composites for high precipitation events Eastern England. 7

15 Figure : MSLP composites for high precipitation events Italy. 7

16 Figure 7: hpa height composites for high precipitation events Central England. 7

17 Figure 8: hpa height composites for high precipitation events Eastern England. 7

18 Figure 9: hpa height composites for high precipitation events Italy. 77

19 .. Proportion of patterns classified correctly training data test data Number of principal components.. Proportion of patterns classified correctly training data test data 8 8 Number of principal components.. Proportion of patterns classified correctly..... training data test data 8 8 Number of principal components Figure : Graph of the mean proportion of correctly classified patterns against the number of principal components for generalised linear models trained to reproduce the Lamb index on the basis of surface level pressure (upper panel), 7 hpa geopotential height (middle panel) and hpa geopotential height (lower panel), averaged over cross-validation trials. 78

20 .7. Proportion of patterns classified correctly Month Figure : Bar chart showing the proportion of correctly classified patterns according to the month of the year for a generalized linear model reproducing the Lamb catalogue operating on principal components of the surface level pressure data, averaged over cross-validation trials. 79

21 .9.8 Proportion of patterns classified correctly A ANE AE ASE AS ASW AW ANW AN NE E SE S SW W NW N C CNE CE CSE CS CSW CW CNW CN U Prior class probability Figure : Scatter plot of the proportion of correctly classified Lamb weather types against prior class probability for an ensemble of generalized linear models operating on surface level pressure. 8

22 .8. Proportion of patterns correctly classified k.. Proportion of patterns correctly classified k. Proportion of patterns correctly classified k Figure : Graph of the leave-one-out generalization error against k for k-nearest neighbour classifiers trained to reproduce the Lamb index on the basis of surface level pressure (upper panel), 7 hpa geopotential height (middle panel) and hpa geopotential height (lower panel). 8

23 .. Proportion of patterns classified correctly..... training data test data Number of principal components.. Proportion of patterns classified correctly..... training data test data 8 8 Number of principal components.. Proportion of patterns classified correctly..... training data test data 8 8 Number of principal components Figure : Graph of the mean proportion of correctly classified patterns against the number of principal components for generalised linear models trained to reproduce the Grosswetterlagen index on the basis of surface level pressure (upper panel), 7 hpa geopotential height (middle panel) and hpa geopotential height (lower panel), averaged over crossvalidation trials. 8

24 .7. Proportion of patterns classified correctly Month Figure : Bar chart showing the proportion of correctly classified Grosswetterlagen patterns according to the month of the year for a generalized linear model operating on principal components of the surface level pressure data, averaged over cross-validation trials. 8

25 .8.7 Proportion of patterns classified correctly Wa Wz Ws Ww SWa SWz NWa NWz HM BM TM Na Nz HNa HNz HB TrM NEa NEz HFa HFz HNFa HNFz SEa SEz Sa Sz TB TrW U Prior class probability Figure : Scatter plot of the proportion of correctly classified Grosswetterlagen patterns against prior class probability for an ensemble of generalized linear models operating on surface level pressure. 8

26 .. Proportion of patterns correctly classified k.7. Proportion of patterns correctly classified k.7.7 Proportion of patterns correctly classified k Figure 7: Graph of the leave-one-out generalization error against k for k-nearest neighbour classifiers trained to reproduce the Grosswetterlagen index on the basis of surface level pressure (upper panel), 7 hpa geopotential height (middle panel) and hpa geopotential height (lower panel). 8

27 N Gijon (Asturias) Presaras (La Coruna) Sondica Aeropuerto (Vizcaya) Ginzo de Limia (Orense) Monzon de Campos (Palencia) Monteagudo (Navarra) Cardedeu (Barcelona) Salto de Bolarque (Madrid) Alcuescar (Caceres) Munera (Albacete) Valencia (Valencia) 8 Pozoblanzo (Cordoba) Niebla El Guijo (Huelva) Bobadilla Estacion (Malaga) Murcia/Alcantarilla (Murcia) W 8 E Figure 8: Location of the Spanish precipitation stations. Province names are given in brackets after the station name. 8

28 Number of days Cyclonic Number of days N Number of days Number of days Number of days Number of days Number of days S O N D J F M A M J J A Hybrid C S O N D J F M A M J J A Unclassified C S O N D J F M A M J J A Anticyclonic S O N D J F M A M J J A Hybrid A S O N D J F M A M J J A Unclassified A S O N D J F M A M J J A Number of days Number of days Number of days Number of days Number of days Number of days S O N D J F M A M J J A NE S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A E SE S O N D J F M A M J J A S O N D J F M A M J J A S SW W S O N D J F M A M J J A Number of days NW S O N D J F M A M J J A Figure 9: Mean monthly frequencies of the circulation types over southeast Spain for the period calculated using NCEP Reanalysis data (solid line) and NMC data (dashed line). 87

29 Winter C Winter HYC Winter UC Winter A Winter HYA Winter UA Winter S Winter SW Winter W Figure : Winter SLP anomalies (hpa) for nine circulation types calculated from NCAR CDROM observed data, Negative anomalies are indicated by dashed lines and positive anomalies by solid lines. 88

30 Winter Spring N N W 8 E Winter W 8 E Spring N -. N W 8 E Winter W 8 E Spring N N W 8 E 8 -. W 8 E -... Figure : Correlations between the NAO index and mean winter and spring precipitation at Spanish stations over the periods 98997; and Circles indicate significant station correlations. Diamonds indicate non-significant correlations. 89

31 - - - Hue Mal Mur Val Nav Bar Viz Ast LaC Ore Pal Mad Alb Cor Cac Mean precipitation - Winter Wet day amount - Winter Number of rain day s - Winter Hue Mal Mur Val Nav Bar Viz Ast LaC Ore Pal Mad Alb Cor Cac Figure : Winter trends in mean precipitation (mm per year), wet day amount (mm per year) and number of rain days (days per year) for Spanish stations. Circles indicate significant trends. 9

32 - - - Hue Mal Mur Val Nav Bar Viz Ast LaC Ore Pal Mad Alb Cor Cac Mean precipitation - Summer Wet day amount - Summer Number of rain day s - Summer Hue Mal Mur Val Nav Bar Viz Ast LaC Ore Pal Mad Alb Cor Cac Figure : Summer trends in mean precipitation (mm per year), wet day amount (mm per year) and number of rain days (days per year) for Spanish stations. Circles indicate significant trends. 9

33 Contribution trend Contribution trend Contribution trend Contribution trend Contribution trend Niebla El Guijo (Huelva) Amount quantile (%) Winter Spring Autumn Bobadilla Estacion (Malaga) Amount quantile (%) 8 Amount quantile (%) Winter Spring Autumn Murcia/Alcantarilla (Murcia) Amount quantile (%) 8 Amount quantile (%) Winter Spring Autumn Valencia (Valencia) Amount quantile (%) 8 Amount quantile (%) Winter Spring Autumn Monteagudo (Navarra) Amount quantile (%) 8 Amount quantile (%) Winter Spring Autumn 8 Amount quantile (%) 8 Amount quantile (%) 8 Amount quantile (%) 8 Amount quantile (%) 8 Amount quantile (%) 8 Amount quantile (%) Figure : Trends in the contribution (tenths) to total winter, spring and autumn precipitation from % precipitation amount quantiles for five Spanish stations. Circles indicate significant trends. 9

34 Winter precipitation quantiles PC - 7% N W 8 E Spring precipitation quantiles PC - % N W 8 E Autumn precipitation quantiles PC - 7% N W 8 E Figure : Loadings (left hand panel) and scores (right hand panel) for PC % precipitation amount quantiles. 9

35 Winter: Northwestern group Winter: Northern group 8 N 8 N W E W E Winter: Northeastern group 8 N 8 N Winter: Central group W E W E Winter: Southeastern group Winter: Southern group 8 N 8 8 N W E W E 8 N Winter: Southwestern group W E Figure : Winter SLP anomaly composites (hpa) for precipitation quantile constructed for seven groups of stations. 9

36 Winter: Northwestern group Winter: Northern group 8 N N W E W E Winter: Northeastern group 8 N W E - Winter: Central group 8 N W E Winter: Southeastern group Winter: Southern group 8 N N W E W E 8 N Winter: Southwestern group W E Figure 7: Winter SLP anomaly composites (hpa) for precipitation quantiles 9 and constructed for seven groups of stations. 9

37 Niebla El Guijo (Huelva) Dry day s Quantile Quantiles 9/ 8 8 c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw Bobadilla Estacion (Malaga) Dry day s Quantile Quantiles 9/ 8 8 c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw Murcia/Alcantarilla (Murcia) Dry day s Quantile Quantiles 9/ 8 8 c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw Valencia (Valencia) Dry day s Quantile Quantiles 9/ 8 8 c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw Monteagudo (Navarra) Dry day s Quantile Quantiles 9/ 8 8 c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw Figure 8: Winter q:ct ratios for five Spanish stations and three precipitation categories (see text for explanation). The horizontal line indicates a ratio of one. 9

38 Niebla El Guijo (Huelva) Dry day s Quantile Quantiles 9/ c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw Bobadilla Estacion (Malaga) Dry day s Quantile Quantiles 9/ c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw Murcia/Alcantarilla (Murcia) Dry day s Quantile Quantiles 9/ c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw Valencia (Valencia) Dry day s Quantile Quantiles 9/ c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw Monteagudo (Navarra) Dry day s Quantile Quantiles 9/ c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw c hc uc a ha ua n ne e se s sw w nw Figure 9: Winter CT:q percentages for five Spanish stations and three precipitation categories (see text for explanation). The horizontal line indicates the expected occurrence if events were evenly distributed across the CTs. 97

39 Winter precipitation PC - 7% N W 8 E Winter precipitation PC - % -. N W 8 E Winter precipitation PC - 7% N W 8 E Winter precipitation PC - %. N W 8 E Figure : First four PCs of winter precipitation. The loadings are shown in the left-hand panel and scores in the right-hand panel. 98

40 SLP composite : DJF precipitation PC + SLP composite: DJF precipitation PC 8 N N W E W E SLP composite: DJF precipitation PC + SLP composite: DJF precipitation PC 8 N N W E W E SLP composite: DJF precipitation PC + SLP composite: DJF precipitation PC 8 N 8 N W E W E SLP composite: DJF precipitation PC + SLP composite: DJF precipitation PC 8 N 8 N W E W E Figure : SLP anomaly composites (hpa) for high positive (> +) and high negative (< ) loadings for the first four PCs of winter precipitation. 99

41 8 N z composite : DJF precipitation PC + 8 N z composite: DJF precipitation PC W E W E z composite: DJF precipitation PC + z composite: DJF precipitation PC 8 N 8 N W E W E z composite: DJF precipitation PC + z composite: DJF precipitation PC 8 N - 8 N W E W E z composite: DJF precipitation PC + z composite: DJF precipitation PC 8 N 8 N - - W E W E Figure : hpa anomaly composites (hpa) for high positive (> +) and high negative (< ) loadings for the first four PCs of winter precipitation.