STSM-ES Final report

Transcription

1 STSM-ES Final report Testing the wind direction frequency during opposite phases of the 11-year solar cycle over the past two-centuries in central European Hana Kapolková 1 and Benjamin A. Laken 2, 3 1. Charles University in Prague, Albertov 6, Praha 2, Czech Republic 2. Instituto de Astrofísica de Canarias, Via Lactea s/n, La Laguna, Tenerife, Spain 3. Department of Astrophysics, Faculty of Physics, Universidad de La Laguna, La Laguna, Tenerife, Spain Abstract Numerous paleoclimatic studies have suggested the existence of a persistent link between regional climate variability and solar activity. Despite this evidence, clear detections of such a link are lacking in current climatological data sets. Several studies by Huth et al. (2006, 2008) have indicated that Hess-Brezowsky (HB) measurements of synoptic types over central Europe, collected over a long-timescale (>100yr), may provide verification of a solar climate link in observational data sets. These data reportedly show indications that changes in the wind direction occur with differing levels of solar activity. In order to further explore this notion we have designed a stringent hypothesis test, and applied it to the HB data. We have used Monte Carlo methods to define probability density functions of the HB data which represent the variability expected under non-deterministic (random) cases, against which we examine HB-data corresponding to solar maximum and minimum periods. We find no clear indication of a solar cycle influence on changes in the frequency of wind direction over central Europe. This is only a first step however; we plan to investigate additional variables and restrictions to examine variants of the hypothesis test described here. 2. Purpose of Study 2.1. Motivation Evidence of linkages between solar activity and the Earth s climate have been identified in paleoclimatic records, such as: the dust concentration in Greenland ice cores (Ram et al. 1997); the occurrence of ice rafting events in the North Atlantic (Bond et al. 1997); variations in the intensity of the Asian monsoon (Neff et al. 2001; Wang et al. 2005); and historical records of river and lake fluctuations (Hodel et al. 2001; Holzhauser et al. 2005; Stager et al. 2005). Such findings indicate that variations in solar activity may influence regional climate variability, rather than a direct correspondence between solar activity and global temperature. Present observations however, have been less clear regarding the robust identification of a solar influence on climate. While some studies have reported positive results, others have identified unclear or confusing findings. Furthermore, although paleoclimatic evidences presents a good indication that a solar terrestrial link is occurring, it does not provide the necessary information to identify a mechanism capable of linking small changes in solar activity to variations in Earth s climate, thereby understanding it and predicting its potential influence on past and future climate change. In this work, we have examined a long-term dataset of weather conditions recorded in central Germany, known as Hess-Brezowsky (HB) synoptic data. These data provide information regarding the dominant wind direction (with a resolution of 45 or eight compass points) and the synoptic condition (e.g. cyclone/anticyclone) at a daily resolution for approximately 130 years. Specifically, we establish the average wind direction during composites of the peak solar maximum/solar minimum years of the 11-year solar cycle (as identified by NOAA), and use a robust Monte Carlo methods to test the probability of achieving these samples by chance. These results provide a good opportunity to test if solar activity is statistically linked to a dynamic regional influence on climate over central Europe during the past two centuries. 1

2 2.2. Background and hypothesis test Huth et al. (2006, 2008), hereafter H0608, have already performed an analysis of the HB data in relation to solar activity. They examined changes in individual synoptic classifications of which there are thirty during wintertime, with solar activity divided into high, moderate and low phases according to monthly Wolf sunspot number. H0608 reported that northeast and northwest flows showed a weak increase (two-tailed p<0.05) under moderate solar activity relative to other solar conditions. They also found some indications that cyclonic conditions became more frequent under moderate solar activity (and by extension anticylonic conditions less frequent). Huth et al. (2008) concluded that these results suggest that reduced zonal flow occurs during solar minimum. In this work, we will expand upon the studies of H0608: previously, the individual HB types during winter conditions were examined. Defining the samples in this way effectively created numerous parallel hypothesis tests one for each synoptic type. As the total population size and distribution of each HB-type may vary markedly, dividing the samples creates difficulties in accurately gauge the significance of results using traditional statistical tests (such as a Student s T-test). Consequently, H0608 employed a series of Monte Carlo tests, randomizing their data and comparing their samples to these random distributions. We also utilize such a testing procedure in this work, however we have classified the synoptic conditions only by the direction of flow and only into two categories (solar maximum and solar minimum): this has been designed to minimize the assumptions of the hypothesis testing, and to maximize the sample size. Our alternate H 1 (and null, H 0 ) hypothesis is as follows: there is (not) a statistically significant difference in the frequency (counts/month) of the dominant wind direction of synoptic types in the HB data, during composites of solar maximum/minimum years, in relation to random samples of identical size. We note that in that the solar maximum and minimum samples are independent, and thus the hypothesis tests of these samples are also independent: i.e. if one shows a positive result and the other does not, it has no bearing on the validity of the positive result. 3. Description of work carried out during the study 3.1. Overview of the synoptic data The HB-data indicate the prevailing direction of wind flow, and type of weather system (persistent for at least three-days) over an observation site in central Germany based on surface level pressure and wind measurements: these data may be taken as an indication of conditions over central Europe. We have grouped the twenty-nine individual HBclassifications (ignoring one unclassified type) by their prevailing direction of flow, creating eight categories of 45 separation (i.e. N, NE, E, SE, S, SW, W, NW). Additionally, the monthly frequency (counts/month) for which each flow-direction occurs over the span of the data set (from January 1881 December 2000) was calculated. For presentation, these data have been convolved into four categories (N, E, S, W) and are plotted in Figure 1. We find the most frequently occurring flow type to be westerly winds. This finding is expected, as it is well known that westerly dominate the middle latitudes of the Northern Hemisphere, arising from patterns of global atmospheric circulation. In Figure 2 the same data are binned and averaged by calendar month to create a seasonal climatology. The mean monthly flow (counts/month) is shown with ±1 standard error range. The data again clearly show the Westerly flow to be the most frequent, having approximately double the number of days per month compared to the other wind directions in most months. We observe from the seasonal climatology that Westerly and Northerly flows demonstrate anti-phased behavior, with Westerly (Northerly) flow reaching a maximum (minimum) centered on December, and a minimum (maximum) centered on the month of May. In contrast, the frequency of Easterly and Southerly wind is generally far lower (ranging from ~2 2

3 to 4 counts/month) seasonally, and appears to undergo no drastic changes. A slight exception to this is for a stepped behavior in the Easterly flow, which appears to be slightly more frequent between January to May, than for the rest of the year Creating composites at times of solar maximum and minimum We proceeded by constructing two composite samples, reflecting anti-phased conditions of the 11-year solar cycle, with one sample centering around times of solar maximum (Smax) while the other centers on solar minimum (Smin) conditions. We used the NGDC NOAA sunspot number-based identification of the month of solar maximum and minimum for each solar cycle (accessible from From these data we took the 12 solar maximum and minimum months over solar cycles We then composited (separately) the 12 key months, adding a further 11 months in time to each key month: i.e. the solar maximum and minimum composite samples are both comprised of 12 key months (+11 months), giving a total sample size (n) of 144. We have used the key month +11 as a sampling basis because considering a full calendar year (starting from the key month) allows us to factor in the potential of a lagged response, and it also allows us to discount the influence of seasonal bias on our samples. From our eight (45 ) wind frequency time series we have identified the simple mean (σ) and ±1SEM error of the Smax and Smin composites. These will be compared to Monte Carlogenerated distributions explained in the following section Monte Carlo-based significance testing Our hypothesis test requires us to construct a probability density function (PDF) of composite means, where the composites are created in an identical sample procedure to our Smax/Smin samples: i.e. a single composite mean of our Monte Carlo-generated data is made up of n = 144 months, comprised of 12 random 12-months of data. This procedure is repeated 10,000 times to generate one PDF: these procedures are detailed in Laken and Čalogović (2013). We generated a unique PDF for each of the eight wind frequency time series we have calculated: an example PDF for the Westerly HB-types is shown in Figure 3. The PDFs are then converted into a cumulative density function (CDF) from which we are able to identify key probability (p) values a CDF of the Westerly HB-types is shown in Figure 4 with p- values at the p0.05, p0.5, and p0.95 intervals marked. We have calculated similar CDFs for all eight wind directions in the HB-data, identifying the key probability values in each instance. These probability values are compared with the means identified from the Smax and Smin samples to test if the frequency of any wind direction is statistically unusual. This statistical test therefore examines if the frequency (counts/month) of flow from any of the eight directions is significantly distinct from completely random (non-deterministic) samples. By non-deterministic samples, we specifically mean samples where we are sure there is no causal influence that may influence the average frequency; they are purely random. Therefore, if the Smax/Smin composite means lie significantly outside the distribution range identified by the Monte Carlo simulation, we can be confident that a causal (deterministic) factor is influencing the samples in this instance, the phase of the solar cycle. 4. Description of main results obtained 4.1. Analysis The results of our analysis are presented in Figure 5: mean (and ±1SEM) frequency is plotted for each of the eight wind directions. In addition we have over-plot the Monte Carlocalculated confidence intervals at the p0.1, p0.05, and p0.01 two-tailed intervals. We find that no statistically significant (p<0.05) variation in the frequency of wind direction occurs during either the solar maximum or solar minimum composite samples. While we do observe 3

4 differences between the solar samples most prominently for Northern and Northwesterly flows these differences are all within normal ranges according to the Monte Carlo results Conclusions from the analysis Based on the analysis presented in Figure 5 we reject H 1, and accept H 0. Thus, we conclude that no statistically significant changes in the wind direction (of at least 45 ) detectable at different phases of the solar cycle from the HB-data. 5. Future collaborations with host institute The results presented here are only a preliminary investigation. We are continuing to examine variations to the hypothesis test presented in this report, adding further variables such as seasonality as Huth et al. (2008) has suggested may be important and distinguishing between cyclone/anticyclone conditions. Furthermore, we hope to extend the analysis to investigate the properties of the HB data in relation to both further solar variations (short and long-term) and additionally to variations in regional climate, indicated by ENSO, and the NAO. We will also investigate differing lag times, and the sensitivity of the results to changes in the Monte Carlo approach. We foresee that this work will continue to develop over the course of H. Kapolková s PhD, and form an integral part of her thesis. 6. Projected outcomes We aim to present this material at upcoming conferences, and we are currently preparing abstracts for submission. We foresee submitting an abstract to the upcoming TOSCA session at the European Geophysical Union meeting: CL5.12 on "Solar Influence on the Middle Atmosphere and Dynamical Coupling to the Troposphere and the Ocean" at the EGU (Vienna, 28 April-2 May 2014). We also aim to publish these immediate results in a peerreviewed journal; currently a draft manuscript is in production intended for Geophysical Research Letters. 7. Other comments The STSM has given H. Kapalková the chance to learn a wide array of invaluable skills and practices which will form an integral part of her PhD. We are exceptionally grateful to TOSCA for providing the opportunity for this collaboration to occur. We would also like to thank Jasa Calogovic (Hvar Observatory) for advice and comments while developing this work. References Bond, Gerard, William Showers, Maziet Cheseby, Rusty Lotti, Peter Almasi, Paul Priore, Heidi Cullen, Irka Hajdas, and Georges Bonani. "A pervasive millennial-scale cycle in North Atlantic Holocene and glacial climates." science278, no (1997): Hodell, David A., Mark Brenner, Jason H. Curtis, and Thomas Guilderson. "Solar forcing of drought frequency in the Maya lowlands." Science 292, no (2001): Holzhauser, Hanspeter, Michel Magny, and Heinz J. Zumbuühl. "Glacier and lake-level variations in west-central Europe over the last 3500 years." The Holocene 15, no. 6 (2005): Huth, Radan, Lucie Pokorná, Josef Bochníček, and Pavel Hejda. "Solar cycle effects on modes of low frequency circulation variability." Journal of Geophysical Research: Atmospheres ( ) 111, no. D22 (2006). 4

5 Huth, R., J. Kysely, J. Bochnicek, and P. Hejda. "Solar activity affects the occurrence of synoptic types over Europe." In Annales geophysicae: atmospheres, hydrospheres and space sciences, vol. 26, no. 7, p (2008). Laken, Benjamin A., and Jaša Čalogović. "Composite analysis with Monte Carlo methods: an example with cosmic rays and clouds." Journal of Space Weather and Space Climate 3 A29. (2013). Neff, U., S. J. Burns, A. Mangini, M. Mudelsee, D. Fleitmann, and A. Matter. "Strong coherence between solar variability and the monsoon in Oman between 9 and 6 kyr ago." Nature 411, no (2001): Stager, J. Curt, David Ryves, Brian F. Cumming, L. David Meeker, and Juerg Beer. "Solar variability and the levels of Lake Victoria, East Africa, during the last millenium." Journal of Paleolimnology 33, no. 2 (2005): Ram, Michael, Michael Stolz, and Gerson Koenig. "Eleven year cycle of dust concentration variability observed in the dust profile of the GISP2 ice core from Central Greenland: Possible solar cycle connection." Geophysical Research Letters 24, no. 19 (1997): Wang, Yongjin, Hai Cheng, R. Lawrence Edwards, Yaoqi He, Xinggong Kong, Zhisheng An, Jiangying Wu, Megan J. Kelly, Carolyn A. Dykoski, and Xiangdong Li. "The Holocene Asian monsoon: links to solar changes and North Atlantic climate." Science 308, no (2005):

6 Figures Figure 1. Frequency (counts/month) of prevailing (>3 day persistent) surface-level wind direction, from January 1881 December The 29 assigned synoptic types of the HB data have been classified by wind direction in to Northerly (red line), Easterly (magenta line), Southerly (blue line), and Westerly (green line). 6

7 Figure 2. Seasonal climatology of HB types categorized by wind direction. A simple mean and ±1 standard error range are presented for four primary wind directions: Northerly (red line), Easterly (magenta line), Southerly (blue line), and Westerly (green line). Figure 3. An example of the Monte Carlo generated probability density function (PDF) from the Westerly flow time series. Stepped histogram shows the mean frequency (counts/month) for composite samples of n = 144 (where each composite samples is made up of 12 random 12 months groups). A normal distribution is over-plotted (red line). 7

8 Figure 4. A cumulative density function (CDF) of Monte Carlo-generated composite means from the Westerly flow time series (red dashed line). Percentiles at the 0.05, 0.50, and 0.95 (two-tailed) intervals are marked (dashed black lines). 8

9 Figure 5. Solar max (green line) and solar minimum (blue line) composite means and ±1SEM range (vertical error bars) for each of the eight wind directions of the HB data. These are plotted as: (a) frequency (counts/month), and also as (b) normalized frequency (counts/month), where each of the data have been normalized against the 50 th percentile (median) value of the relevant CDF. The normalization was done for plotting purposes only, and does not alter the significance of the data. The means are found to be almost entirely p<0.10 across both the solar maximum and minimum samples, indicating there is no significant difference detected in wind direction over the solar cycle in Europe from the HB data. 9