The correlation between three teleconnections and leptospirosis incidence in the Kandy District, Sri Lanka, 2004–2019

Background Leptospirosis is a bacterial zoonosis. Leptospirosis incidence (LI) in Sri Lanka is high. Infected animals excrete leptospires into the environment via their urine. Survival of leptospires in the environment until they enter into a person and several other factors that influence leptospirosis transmission are dependent upon local weather. Past studies show that rainfall and other weather parameters are correlated with the LI in the Kandy district, Sri Lanka. El Niño Southern Oscillation (ENSO), ENSO Modoki, and the Indian Ocean Dipole (IOD) are teleconnections known to be modulating rainfall in Sri Lanka. There is a severe dearth of published studies on the correlations between indices of these teleconnections and LI. Methods We acquired the counts of leptospirosis cases notified and midyear estimated population data of the Kandy district from 2004 to 2019, respectively, from weekly epidemiology reports of the Ministry of Health and Department of Census and Statistics of Sri Lanka. We estimated weekly and monthly LI of Kandy. We obtained weekly and monthly teleconnection indices data for the same period from the National Oceanic and Atmospheric Administration (NOAA) of the USA and Japan Agency for Marine-Earth Science and Technology (JAMSTEC). We performed wavelet time series analysis to determine correlations with lag periods between teleconnection indices and LI time series. Then, we did time-lagged detrended cross-correlation analysis (DCCA) to verify wavelet analysis results and to find the magnitudes of the correlations detected. Results Wavelet analysis displayed indices of ENSO, IOD, and ENSO Modoki were correlated with the LI of Kandy with 1.9–11.5-month lags. Indices of ENSO showed two correlation patterns with Kandy LI. Time-lagged DCCA results show all indices of the three teleconnections studied were significantly correlated with the LI of Kandy with 2–5-month lag periods. Conclusions Results of the two analysis methods generally agree indicating that ENSO and IOD modulate LI in Kandy by modulating local rainfall and probably other weather parameters. We recommend further studies about the ENSO Modoki and LI correlation in Sri Lanka. Monitoring for extreme teleconnection events and enhancing preventive measures during lag periods can blunt LI peaks that may follow.

Identification of atmospheric and oceanic teleconnection patterns in a 20-year global data set of the atmospheric water vapour column measured from satellites in the visible spectral range

We used a global long-term (1995–2015) data set of total column water vapour (TCWV) derived from satellite observations to quantify to which extent the temporal patterns of various teleconnections can be identified in this data set. To our knowledge, such a comprehensive global TCWV data set was rarely used for teleconnection studies. One important property of the TCWV data set is that it is purely based on observational data. We developed a new empirical method to decide whether a teleconnection index is significantly detected in the global data set. We compared our new method to well-established hypothesis tests and found good agreement with the results of our approach. Based on our empirical method more than 40 teleconnection indices were significantly detected in the global TCWV data set derived from satellite observations. In addition to the satellite data we also applied our method to other global data sets derived from ERA-Interim. One important finding is that the spatial patterns obtained for the ERA TCWV data are very similar to the observational TCWV data set indicating a high consistency between the satellite and ERA data. Moreover, similar results are also found for two selections of ERA data (either all data or mainly clear-sky data). This finding indicates that the clear-sky bias of the satellite data set is negligible for the results of this study. However, for some indices, also systematic differences in the spatial patterns between the satellite and model data set were found probably indicating possible shortcomings in the model data. For most “traditional” teleconnection data sets (surface temperature, surface pressure, geopotential heights and meridional winds at different altitudes) a smaller number of significant teleconnection indices was found than for the TCWV data sets, while for zonal winds at different altitudes, the number of significant teleconnection indices (up to > 50) was higher. The strongest teleconnection signals were found in the data sets of tropospheric geopotential heights and surface pressure. In all global data sets, no “other indices” (solar variability, stratospheric AOD or hurricane frequency) were significantly detected. Since many teleconnection indices are strongly correlated, we also applied our method to a set of orthogonalised indices, which represent the dominant independent temporal teleconnection patterns. The number of significantly detected orthogonalised indices (20) was found to be much smaller than for the original indices (42). Based on the orthogonalised indices we derived the global spatial distribution of the cumulative effect of teleconnections. The strongest effect on the TCWV is found in the tropics and high latitudes.

Enhancement of seasonal hydrological forecasting with “pattern-based” large-scale climatology

<p>The hydrological forecasting on seasonal (up to 7 months ahead) timescales is needed for decision-making in the hydropower sector. Being one of the vital influencing factors on hydro-production, a lot of development in dynamical forecasting at seasonal timescales has been done recently. However, the forecast bias still remains in different variables and consequently the skill of corresponding streamflow forecasts varies from month to month.</p><p>This study aims to explore the potential for “pattern-based” seasonal hydrological forecasts that make use of hydrological weather regimes and teleconnection indices to improve forecast skill. The work is built on the hypothesis that hydrological weather regimes and teleconnection indices can be used to select analogue years (setting an ensemble) from a record of historical precipitation and temperature data with which to force a hydrological model to generate tailored seasonal forecasts of reservoir inflows. The hydrological weather regimes have been classified based on the concept of fuzzy sets using the anomalies of daily mean sea level pressure from reanalysis data (i.e., ERA-Interim). Precipitation records, measured in the Umeälven river basin during 1981-2016 are used as local observations to optimize each fuzzy rule that describes a type of “average” variability of local climate in terms of the frequency and magnitude of precipitation events. The teleconnection indices are compiled from the Climate Prediction Center, which describe global atmospheric variability. The methodology has been applied to 84 sub-catchments across seven of the most important hydropower producing river systems in Northern Sweden. However, the performance for the Umeälven river system is of particular interest here.</p><p>Comparing to the traditional Ensemble Streamflow Prediction (ESP) method, the “pattern-based” seasonal hydrological forecasting shows a marked improvement, which is likely due to the weighted analogue-ESP approach as well as the selected analogues using the large-scale climate information described by hydrological weather regimes and teleconnection indices. The general performance of the two different approaches for selecting the analogues are similar; however, occasionally there are large differences in both the best analysis lead times and the spread of skill across the sub-catchments suggesting that those results are achieved using analogues based on different physical processes.</p>

Statistical associations of teleconnection indices and space weather with spring weather pattern in the Eastern Baltic region

<p><span>The studies of associations between solar inputs and climate are mostly designed for winter or cold period; </span><span>whereas</span> <span>the knowledge about these associations during spring </span>on a day-to-day time scale are very scarce. Therefore, the aim of this study is to detect the response of spring air temperature (T), relative humidity (RH), and atmospheric pressure (ATP) to variation in teleconnection indices and space weather variables on the day-to-day timescale during the period of 1998–2017 in six cities of Eastern part of the Baltic region. We created<span> a multivariate linear regression model for weather variables including month, the linear and seasonal trend, different teleconnection patterns, </span>El Niño–Southern Oscillation (<span>ENSO), the Quasi-biennial Oscillation (QBO) phase, the presence of Sudden Stratospheric Warming (SSW), and space weather variables.</span></p><p>T<span>he multivariate models for </span>the mean daily weather variables showed a positive association between T and the daily Arctic oscillation (AO), monthly Scandinavian pattern (SCA) indices, solar proton events (SPEs) with a lag of 1-9 days, and solar wind dynamic pressure (P) with a lag of 1-2 days and negative association between T and East Atlantic/West Russia (EA/WR) index. <span>The linear and seasonal trends, the presence of SSW during March, and changes in AO, EA/WR, and SCA indices explained about 73% of the variation in mean daily T in the investigated region in spring. </span>The presence of the daily mean proton flux of > 10 MeV and energy over 10 pfu with a lag of 1-9 days and higher P with a lag of 1-2 <span>days </span>were also related to higher mean T. The mean RH positively correlated with a long-term and short-term variation in galactic cosmic rays (GCR) and solar wind speed (SWS) with a lag of 0-6 days and negatively correlated with EAWR and NINO3.4 indices. <span>The seasonal variation, the presence of SSW during March, the QBO phase, and the changes in the EA/WRI and ENSO explained over 38% of variation in the daily mean RH in spring.</span></p><p>The mean <span>ATP was negatively associated with both long-term and short-term changes in GCR</span> <span>and positively associated with </span>the North Atlantic oscillation (NAO), EA/WR, and SCA indices, B<sub>y</sub> component of interplanetary magnetic field <span>with a lag of 2 days, P, days of </span><span>Stream Interaction Regions (SIRs)</span><span>, and SWS with a lag of 4-6 days. These space weather variables had stronger effect on spring ATP </span>in the eastern part of the Baltic region<span> as compared to stratospheric events and teleconnection patterns. </span>Results of the present study show the significant short-term effects of SSW, SPEs, SIRs, and solar wind variables on spring weather pattern in the Eastern part of the Baltic region.</p>