To improve the detection range and signal to noise ratio in the remote sensing of atmospheric processes using long-range scanning Mie lidar, simultaneously obtained analog (A) and photon-counting (P) ...data traces were merged using a novel gluing method based on the modification of the A trace so that it follows Poisson statistics. In comparison with the traditional method based on the minimization of the distance between A and P, the advantage of this new approach is an improved uniformity of the merged data trace as well as a reduced influence of atmospheric conditions on the obtained transfer coefficients. The atmospheric extinction was subsequently retrieved using the two- and multi-angle method. The offset of the laser beam direction was calibrated against a self-leveling laser, as an offset in elevation angle was found to produce considerable error in the atmospheric extinction profiles obtained under the assumption of horizontal atmospheric homogeneity.
► Characteristics of analog and PC data trace were evaluated by variance calculation. ► PC data trace is Poissonian distributed, while analog data trace is linear shifted. ► Novel gluing method by modifying analog data to follow Poissonian was investigated. ► Lidar elevation angle was horizontally calibrated against a self-leveling laser. ► Small angular offset was found to have a visible impact on atmospheric extinction.
A measuring campaign was performed over the neighboring towns of Nova Gorica in Slovenia and Gorizia in Italy on 24 and 25 May 2010, to investigate the concentration and distribution of urban ...aerosols. Tracking of two-dimensional spatial and temporal aerosol distributions was performed using scanning elastic LIDAR, operating at 1064 nm. In addition, PM10 concentrations of particles, NOx concentrations and meteorological data were continuously monitored within the LIDAR scanning region. Based on the data we collected, we investigated the flow dynamics and the aerosol concentrations within the lower troposphere and found an evidence for daily aerosol cycles. We observed a number of cases with spatially localized increased LIDAR returns, which are associated with the presence of point sources of particulate matter. Daily aerosol concentration cycles were also clearly visible with a peak in aerosol concentration during the morning rush hours and daily plateau at around 17:00 Central European Time. We also found that horizontal atmospheric extinction at the height of 200 m, averaged in limited region with a radius of 300 m directly above the ground-based measuring site, was linearly correlated to the PM10 concentration with a correlation coefficient of 0.84. When considering the average of the horizontal atmospheric extinction over the entire scanning region, a strong dependence on traffic conditions (concentration of NOx) in the vicinity of the ground-based measuring site was observed.
Optical properties of the atmospheric boundary layer (ABL) above the land–sea transition interface were measured using a scanning Mie lidar located 30
km away from the Adriatic coast. Based on the ...two-dimensional range-height-indicator scans, detailed information on the ABL was obtained, including parameters such as atmospheric optical depth, aerosol extinction coefficient and the height of the ABL. The presented case study indicates that the height of the ABL in the land–sea transition zone and the adjacent mountainous region was changing rapidly due to highly variable atmospheric conditions.
The eruption of the Eyjafjallajökull volcano starting on 14 April 2010 resulted in the spreading of volcanic ash over most parts of Europe. In Slovenia, the presence of volcanic ash was monitored ...using ground-based in-situ measurements, lidar-based remote sensing and airborne in-situ measurements. Volcanic origin of the detected aerosols was confirmed by subsequent spectral and chemical analysis of the collected samples. The initial arrival of volcanic ash to Slovenia was first detected through the analysis of precipitation, which occurred on 17 April 2010 at 01:00 UTC and confirmed by satellite-based remote sensing. At this time, the presence of low clouds and occasional precipitation prevented ash monitoring using lidar-based remote sensing. The second arrival of volcanic ash on 20 April 2010 was detected by both lidar-based remote sensing and airborne in-situ measurements, revealing two or more elevated atmospheric aerosol layers. The ash was not seen in satellite images due to lower concentrations. The identification of aerosol samples from ground-based and airborne in-situ measurements based on energy-dispersive X-ray spectroscopy confirmed that a fraction of particles were volcanic ash from the Eyjafjallajökull eruption. To explain the history of the air masses bringing volcanic ash to Slovenia, we analyzed airflow trajectories using ECMWF and HYSPLIT models.
Sea level (SLH) variability at the Adriatic coast was investigated for the period 1872–2001 using monthly average values of observations at 13 tide gauge stations. Linear trends and seasonal cycles ...were investigated first and removed afterwards from the data. Empirical Orthogonal Functions (EOF) analysis was used further on remaining anomalies (SLA) to extract the regional intermonthly variability of SLH. It was found that the leading EOF and its principal component (PC) explain a major part of SLA variability (92%). The correlation between the reconstructed SLA, based on leading EOF and its PC, and overlapping observed SLA values for selected tide gauge stations is between 0.93 and 0.99. Actual SLH values at tide gauge stations can be reconstructed and some gaps in the data can be filled in on the basis of estimated SLA values if reasonable estimates of long-term trends and seasonal cycles are also available. A strong, seasonally dependent relationship between SLA at the Adriatic coast and atmospheric forcing, represented by sea level pressure (SLP) fields, was also found. Comparing the time series of leading PC and gridded SLP data for the period 1948–2001, the highest correlation coefficients (r) of –0.92 in winter, –0.84 in spring, –0.66 in summer, and –0.91 in autumn were estimated for a SLP grid point located in northern Italy. The SLP variability on this grid point contains information about the isostatic response of the sea level at the Adriatic coast, but can also be treated as a sort of teleconnection index representing the large-scale SLP variability across central and southern Europe. To some extent the large-scale SLP variability that affects the SLA at the Adriatic coast can be related to the North Atlantic Oscillation (NAO), because significant correlations were found between the NAO index and the first PC of SLA (rwinter=–0.56, rspring=–0.45, rsummer=–0.48, and rautumn=–0.43) for the period 1872–2001. The use of partial least-squares (PLS) regression between large-scale SLP fields and SLA only slightly improved the description of the SLA dependence on SLP forcing in comparison to the single grid point approach. A strong relationship between atmospheric pressure and the sea level could represent an additional possibility for filling in the gaps in the tide gauge data. Keywords. Oceanography: general (Climate and interannual variability) – Oceanography: physical (Air-sea interactions; sea level variations)
Phenology models are useful tools for various sectors of human activity, particularly for all environmental studies. An historical, 46-year phenological record, and meteorological data set for period ...1955–2000 were used to analyse the abilities of the statistical models to predict flowering and leaf unfolding time for a set of wild and cultivated plants in Slovenia. With single
phenological model, we have predicted timing of phenophase for particular plant on the base of previous phenological data of the same plant or on the base of previous phenological data of the other plants. The most frequently included independent variables in such models were common silver birch, dandelion and horse-chestnut. It was stated that these plants may be used as phenological indicators in given conditions. Correlation analysis and linear multiple regression were applied to establish the relationship among phenological development and climatic variables (temperature, rainfall, North Atlantic Oscillation, day length). Different thresholds temperatures have been selected for eight different locations with the smallest standard deviation in growing degree day's method to calculate thermal time. Various plant species responded differently to the same climatic factors and were best fitted to certain geographic region. The timings of spring phenophases strongly correlated with temperature of the precedent months, on the other hand rainfall and North Atlantic Oscillation explained smaller part of phenological variability. Photothermal time significantly improved results of
phenoclimatic models when taken into account instead of thermal time. The validity of the results was tested with cross validation method and using independent data set for the year 2000, respectively. Considering the high year-to-year variability of phenological events, the models presented provide satisfactory estimations of the leaf unfolding and flowering dates. Formal equations presented in this study could be powerfully extended and applied to other sites and plants, provided that a sufficiently long time series of phenological and meteorological data were available.
An N-PLS regression technique was tested as an empirical downscaling method. Average monthly near-ground air temperature (t), specific humidity (q), and sea-level pressure (p) fields across Central ...and Western Europe were used as predictors for average monthly air temperature (T), dew temperature (D), and precipitation amount (P) at 4 locations in Slovenia. The empirical downscaling models (EM) were developed by means of available predictand data from the ARSO archive and predictor data from the NCEP/NCAR reanalysis project, for the period 1951-2002, separately for single months. Using the combination of t and p as predictors, the EM for T explained from 73% to 95% of predictand variability, for D from 74% to 97% of predictand variability, and for P from 31% to 76% of predictand variability. The use of q as an additional predictor did not improve the quality of the EM considerably. Developed EM using p and t as predictors were applied to the results of 5 general circulation models (GCM): CSIRO/Mk2, CCC/CGCM2, UKMO/HadCM3, DOE-NCAR/PCM, and MPI-DMI/ECHAM4-OPYC3. Only the simulations based on SRES A2 and B2 emission scenarios were considered in our calculations. Available mean monthly values of predictors for the period 1951-2100 were used. All the projections of GCM results indicate an increase in T and D and decrease in P in the 21st-century at all 4 locations. The expected range of changes in T, D, and P is wide due to the different response of GCM to identical changes in the atmospheric composition, and represents a source of uncertainty in empirical downscaling results. Another important source of uncertainty in empirical downscaling studies, especially when temperature dependent predictors are used, is the problem of extrapolation. By using the proper mathematical approach for EM development we only reduce a part of the uncertainty related to the quality of empirical models that also strongly depend on the quality of input data and predictor selection. The N-PLS regression seems to be a suitable choice of mathematical method, as the feature selection from a large number of predictor time series is not predictand independent. Finally, any climate change and impact studies for the future are affected by many other uncertainties that we have to be fully aware of, while interpreting their results. PUBLICATION ABSTRACT
Uncertainties related to the climate impact studies are reviewed on the case of onion thrips in Slovenia. Paper illustrates cumulative uncertainty introduced by future emission scenarios, general ...circulation models, downscaling procedures, weather generators and impact (degree-day) models as common tools in climate impact studies. The result of cumulative uncertainty is a wide range of expected warming in Slovenia in the 21st century. An expected increase of air temperature, relative to 1990, is between 1.5 and 7
°C till the end of present century. Due to heterogeneity of the present climate conditions in Slovenia, quite uniform air temperature increase on the entire area will have different impact on the change of cumulative degree-days (DD) and related number of generations of onion thrips per year (
N
gen) in different regions. An example of Ljubljana and Bilje shows that larger increase of DD and related
N
gen is expected for regions with warmer climate. As a result, more damage will probably be caused in areas that are already strongly exposed to the problem of onion thrips. Harmfulness of the pest in the areas, where it is not a serious threat at present, will also increase. However, we have to be fully aware of a large amount of uncertainty related to studies of climate change impact while interpreting their results.
The relation of sea surface height (SSH) in the Mediterranean Sea with numerous atmospheric variables on time scales from month to few years were investigated in order to construct simple linear ...statistical models for predicting SSH. Monthly SSH data for the period from January 1993 to December 2001 from the Topex/Poseidon altimetry are used as the dependent variable in models. Numerous large scale atmospheric indices (LSI) as well as gridded fields of various meteorological variables above the Mediterranean region are used as predictors. The annual cycle derived from nine year SSH climatology explains 40% of SSH variance. The statistical models were built on the anomalies from the annual cycle, separated to the following seasons: winter (December–March), spring (April–May), summer (June–September) and autumn (October–November). The results were tested with cross-validation where the quality of the models were judged on the basis of the explained variance of SSH. Among all seasons, SSH was the best predicted in the winter. The most successful LSI, Mediterranean Oscillation Index, is able to explain 46% of winter variance of SSH anomaly from annual cycle (SSHA). The best model, which uses sea level pressure (SLP), northward and eastward components of wind speed at 10 meters above sea level, is able to explain on average 55% of SSHA variance with the values up to 80% in Ionian, Tyrrhenian and eastern part of Algero-Provençal Sea. SSH is the worse predictable in the summer, when the root mean square of SSH is not well above the threshold of its measurements. The SLP and temperature at 2 meters in the atmosphere (T2M) as combined predictors are able to explain 20% of SSHA variance, but only in the Adriatic and Tyrrhenian Seas noticeable part of SSHA could be explained. In spring explained variance of SSHA reaches 38% (when SLP and T2M are used as predictors), while in autumn 28% of SSHA can be explained with SLP as a predictor.
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