Ubern report 9/02/2006
Download 0.97 Mb.
|
- Navigate this page:
- The influence of the AMO on summer heatwaves (Della-Marta et al. 2006)
- Multiple lagged predictors of heat waves (Della-Marta et al. 2006)
- Discussion and Summary
Figure 4. Lagged SST (up to three months) influence on the Bern tx90 series calculated over all months. Shown is the p-value of the Omega statistic Granger causality test. Della-Marta et al. (2006) show that a similar pattern of both SST patterns shown in Figs. 3 and 4 is found in a CCA using SST to predict extreme summer temperatures at 54 locations in western Europe. The atmospheric pattern associated with these SSTs consists of higher SLP over the western European domain and lower SLP west of the Iberian Peninsula in a pattern which resembles a large scale Rossby wave pattern. Some researchers believe that this blocking activity is the result of tropical interactions with the summer mid-latitude climate however the correlations are weak and are not discussed further here (see Cassou et. al. 2005; Della-Marta et. al. 2006 and references therein). The influence of the AMO on summer heatwaves (Della-Marta et al. 2006) Many studies suggested that potential predictability of climate can be found in the decadal and longer cycles of SSTs (e.g. Rodwell et al., 1999). Here we find evidence supporting the analysis of Sutton and Hodson (2005) that the occurrence of warmer than average summer temperatures over Europe are related to long term changes in the Atlantic Multidecadal Oscillation (AMO) (Enfield et al., 2001). The AMO is believed to be caused by the North-Atlantic thermohaline circulation (Knight et al., 2005). The correlation between the LOESS smoothed (Cleveland and Devlin, 1988) first PC of summer heat wave variability (HWPC1) score series and the AMO (as defined by Sutton and Hodson, 2005) shown in figure 5a is 0.8. The significance is hard to determine since the effective number of degrees of freedom is around 2. Figure 5b, shows the loading pattern associated with HWPC1. According to this PC the western European domain is under the influence of anomalously high (periods with positive scores) or low (periods with negative scores) frequency of HWs. High occurrence of HWs between 1880-1905, 1925-1950 and 1990-2003 periods is coincident with anomalously high SSTs in the region defined by the AMO. Although this analysis does not provide a causal link between the long term variations in North Atlantic SSTs, it is interesting to compare them since for the first time such a long-term analysis of European HWs has been performed. It is tempting to extrapolate from figure 5a that there is a phase lag between the AMO series and the HWPC1 since the peaks (troughs) of the HWPC1 series tend to lag the AMO peaks (troughs) by approximately five years. Could the atmosphere retain and react to an ocean forcing from five years earlier? It is certainly an interesting question that could only be answered by many forced and unforced complex model simulations. a) b)
 
Figure 5: A timeseries plot a), showing the smoothed Atlantic Multidecadal Oscillation (AMO, dashed line) index and the smoothed first PC of JJA HWs (HWPC1) from 1880 to 2003 and b) the loading patterns associated with the raw (unsmoothed) first PC. In b), the size of the crosses ('+') and the open circles ('o') denote the magnitude of the positive and negative PCA loadings according to the legend on the left side of the figure. The correlation between the two timeseries is 0.8, however it is hard to determine the statistical significance due to the limited number of degrees of freedom. The first PC of the HW index explains 37% of HW variability. The two timeseries have been smoothed using a LOESS smoother with a period set approximately to 25 years. Multiple lagged predictors of heat waves (Della-Marta et al. 2006) In this section we explore the use of a CCA model with lagged SSTs and lagged Mediterranean precipitation as predictors of summer HWs. We used DJF North Atlantic SSTs and JFMAM land based precipitation from the Mediterranean region (area 70°W - 50°E, 42°N - 46°N) as predictors of JJA HWs. The first CCA (Fig. 6a) shows a classic tripole pattern with anomalously cool SSTs east of Newfoundland, warm SSTs over most of central northern Atlantic and cool SSTs in the tropical north Atlantic. This is also associated with a dry northern Mediterranean region over the extended season JFMAM (Fig. 6b) and results in anomalously more HWs over most of the domain, especially in central western Europe and over the Iberian Peninsula. This predictive model has an overall hindcast correlation skill score (Fig. 6e) of 0.28 and in some cases reaches up to 0.5 indicating that between 10 and 25% of HW variance can be explained using the combination of these predictors. a) 
b) 
c) 
d) 
e) 
Figure 6: The first multiple predictor CCA between DJF averaged SSTAT, JFMAM PRECME and the JJA HW index which explains approximately 10.1% of JJA HW variability. The SSTNA canonical pattern a), b) the PRECWE canonical pattern, c) the HW canonical pattern and d) the canonical score series and e) the hindcast (1982-2003) Spearman rank correlation skill score. In c) and e) the size of the crosses ('+') and open circles ('o') show the canonical loadings expressed as a correlation coefficient and the Spearman rank correlation skill score respectively for each station. In d) the solid and dashed lines are the multiple predictor and HW canonical score series respectively with a canonical correlation of 0.56 (adapted from Della-Marta et al. 2006). Discussion and Summary VAR models show that extreme temperatures in different locations of Europe show different areas of potential predictability from north Atlantic SSTs. We have not investigated the use of these models in a strict cross-validated and hindcast framework, however the preliminary results indicate that different regions play important roles to explain the occurrence of extreme temperature events. Whether these areas represent true predictability and not only occur by chance is the goal of future research in this area. A strict caveat of this work is that Granger causality is not true causality, it is only based on statistical principles and therefore all the limitations associated with statistical analysis apply when drawing conclusions of the path of causality. On the interannual timescale we have shown that winter North Atlantic SSTs and the extended season, JFMAM Mediterranean precipitation can be used to predict up to 15-25% of summer HW variability (Vautard et al., 2006; Colman, 1997), however other important predictors we have not explored such as the Eurasian snow cover extent (Qian and Saunders, 2003). A preceding dry winter and spring Mediterranean initiates a regional soil moisture feedback process (Schär et al., 1999) that is capable of amplifying the affects anomalous large scale circulation patterns such as those discussed earlier. The influence of winter and spring SSTs on European summer temperature has been discussed by Colman and Davey (1999). In this study it was suggested that the warm SSTs noticeable in JJA close to the European coast and extending in Azores region (same region as shown in Figure 3) are likely to be the result of air-to-sea interaction and the westward advection of latent heat. The excess latent heat was suggested to be gained by a sea-to-air interaction from previous (winter and spring) North Atlantic SSTs. We believe another plausible explanation for higher JJA SSTs associated with a higher number of HWs is due simply to the presumed increased insolation as a result of anomalously high pressure over the same region(s) also suggested by Xoplaki et al. 2003. Della-Marta et al. (2006) shows in detail that north Atlantic SLP, SSTs and western Europe precipitation are not collinear, but increase the skill of the CCA model and hence represent a complex chain of regional and large-scale feedback processes. We have not investigated this complex chain of causality since it is beyond the scope of this paper. One way to tackle this problem would be to perform sensitivity experiments with a Regional Climate Model (RCM). Predictability at the decadal and interdecadal timescales appears to be modulated by the AMO. With a forecast of multidecadal weakening of the AMO in the next 50 years (Knight et al., 2005) the increase in HWs expected from anthropogenic influences (Schär et al., 2004; Stott et al., 2004) could be partially offset making the summer climate of Europe less extreme than it otherwise would be. References Cassou C, Terray L, Phillips AS (2005) Tropical Atlantic Influence on European Heat Waves. Journal of Climate 18(15):2805-2811 Cleveland WS, Devlin SJ (1988) Locally-Weighted Fitting: An Approach to Fitting Analysis by Local Fitting. Journal of the American Statistical Association 83:596-610 Colman A (1997) Prediction of summer central England temperature from preceding North Atlantic winter sea surface temperature. International Journal of Climatology 17(12):1285-1300 Colman A, Davey M (1999) Prediction of summer temperature, rainfall and pressure in Europe from preceding winter North Atlantic ocean temperature. International Journal of Climatology 19(5):513-536 Della-Marta PM, Luterbacher J, von Weissenfluh H, Xoplaki E, Brunet M, Wanner H (2006) Summer heat waves over western Europe 1880-2003, their change and relationship to large scale forcings. Climate Dynamics, submitted. Enfield DB, Mestas-Nunez AM, Trimble PJ (2001) The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental US. Geophysical Research Letters 28(10):2077-2080
variability and its connection to the large-scale atmospheric circulation and SSTs. Climate Dynamics 20(7- 8):723-739. Directory: projects projects -> H1N1 Pandemic 21-393 Fall 2009 projects -> Terminal Decision Support Tool Systems Engineering Graduate Capstone Course Aiman Al Gingihy Danielle Murray Sara Ataya projects -> Rajinder Sachar Committee projects -> Cape Lookout National Seashore Historic Resource Study By projects -> Cape Lookout National Seashore Historic Resource Study By projects -> Revolutionizing Climate Modeling – Project Athena: a multi-Institutional, International Collaboration projects -> What is a Hurricane? projects -> General Information projects -> Press Information projects -> 1 Introduction 3 2 Designing an Embedded System 4 Download 0.97 Mb. Share with your friends:
|