Droughts are phenomena that affect large areas. Remote sensing data covering large territories can be used to assess the impact and extent of droughts. Drought effect on vegetation was determined using the normalized difference vegetation index (NDVI) and Vegetation Condition Index (VCI) in the eastern Baltic Sea region located between 53–60° N and 20–30° E. The effect of precipitation deficit on vegetation in arable land and broadleaved and coniferous forest was analysed using the Standardized Precipitation Index (SPI) calculated for 1- to 9-month timescales. Vegetation has strong seasonality in the analysed area. The beginning and the end of the vegetation season depends on the distance from the Baltic Sea, which affects temperature and precipitation patterns. The vegetation season in the southeastern part of the region is 5–6 weeks longer than in the northwestern part. The early spring air temperature, snowmelt water storage in the soil and precipitation have the largest influence on the NDVI values in the first half of the active growing season. Precipitation deficit in the first part of the vegetation season only has a significant impact on the vegetation on arable land. The vegetation in the forests is less sensitive to the moisture deficit. Correlation between VCI and the same month SPI1 is usually negative in the study area. It means that wetter conditions lead to lower VCI values, while the correlation is usually positive between the VCI and the SPI of the previous month. With a longer SPI scale the correlation gradually shifts towards the positive coefficients. The positive correlation between 3- and 6-month SPI and VCI was observed on the arable land and in both types of forests in the second half of vegetation season. The precipitation deficit is only one of the vegetation condition drivers and NDVI cannot be used universally to identify droughts, but it may be applied to better assess the effect of droughts on vegetation in the eastern Baltic Sea region.

The climate continentality or oceanity is one of the main characteristics of the local climatic conditions, which varies with global and regional climate change. This paper analyzes indexes of continentality and oceanity, as well as their variations in the middle and high latitudes of the Northern Hemisphere in the period 1950–2015. Climatology and changes in continentality and oceanity are examined using Conrad’s Continentality Index (CCI) and Kerner’s Oceanity Index (KOI). The impact of Northern Hemisphere teleconnection patterns on continentality/oceanity conditions was also evaluated. According to CCI, continentality is more significant in Northeast Siberia and lower along the Pacific coast of North America as well as in coastal areas in the northern part of the Atlantic Ocean. However, according to KOI, areas of high continentality do not precisely correspond with those of low oceanity, appearing to the south and west of those identified by CCI. The spatial patterns of changes in continentality thus seem to be different. According to CCI, a statistically significant increase in continentality has only been found in Northeast Siberia. In contrast, in the western part of North America and the majority of Asia, continentality has weakened. According to KOI, the climate has become increasingly continental in Northern Europe and the majority of North America and East Asia. Oceanity has increased in the Canadian Arctic Archipelago and in some parts of the Mediterranean region. Changes in continentality were primarily related to the increased temperature of the coldest month as a consequence of changes in atmospheric circulation: the positive phase of North Atlantic Oscillation (NAO) and East Atlantic (EA) patterns has dominated in winter in recent decades. Trends in oceanity may be connected with the diminishing extent of seasonal sea ice and an associated increase in sea surface temperature.

The article examines the dynamics of the Lithuanian climate in the 18^th–20^th centuries. Evaluation of air temperature, precipitation amount and the intensity of cyclonic circulation during the observation period was made. Seasonal trends of different climatological indices were determined. To predict climate change trends in Lithuania in the 21^st century, the outputs of Global Climate Model (GCM) experiments from five modelling centres were used. The results of investigation show that current climate change tendencies in Lithuania will remain in the future: against the background of air temperature rise the continentality of the Lithuanian climate will decrease.

Straipsnyje pateikiami prognostiniai klimato rodiklių (vidutinės oro temperatūros ir kritulių kiekio) pokyčiai Lietuvos teritorijoje XXI a. Pasinaudota 24 klimato modelių išvesties duomenimis. Oro temperatūros ir kritulių kiekio reikšmės atskirais mėnesiais analizuotos Lietuvos teritorijoje 1861–2100 m. Sudaryti 2016–2035 ir 2081–2100 m. klimato rodiklių scenarijai, palyginti su 1986–2005 m. bazinio laikotarpio vidutinėmis reikšmėmis. Prognostiniai dydžiai pateikiami pagal skirtingus RCP scenarijus 5 gardelėms (jų dydis – 2,5° × 2,5°), dengiančioms Lietuvos teritoriją. Taip pat tyrime buvo panaudoti 16 Lietuvos meteorologijos stočių kritulių kiekio ir oro temperatūros duomenys. Tikimasi, jog XXI a. oro temperatūra Lietuvos teritorijoje didės visais metų laikais, o teritoriniai pokyčių skirtumai bus nedideli (iki 0,5 °C). Iki 2035 m. vidutinė metinė temperatūra padidės 1,1–1,4 °C, o iki 2100 m. – 1,5–5,1 °C. Labiausiai turėtų padidėti šaltojo metų laikotarpio temperatūra. Vidutinis metinis kritulių kiekis iki 2035 m. turėtų išaugti 1,6–4,0 %, o iki 2100 m. – nuo 3,7 iki 13,5 %. Kritulių kiekio augimas prognozuojamas spalio–birželio mėn., o liepą–rugsėjį galimas kritulių kiekio mažėjimas. Šiaurinėje šalies dalyje tikėtini didesni kritulių kiekio pasikeitimai.

The objective of this study was to develop the best methodology for determining agricultural droughts in Lithuania. The currently used assessment methods do not always accurately reflect drought conditions in the country, especially in the first half of the growing season. For this purpose, the relevance of the currently used Hydrothermal Coefficient (HTC) and five drought indices widely used in other countries were reassessed. It was found that the methodologies applied in Lithuania and other countries are not completely suitable under current conditions. A new agricultural drought identification methodology using the Temperature–Precipitation Index (TPI) is proposed as a result of this study. Analysis of long-term changes (1961–2019) in reoccurrence of droughts was carried out. It was determined that the largest number of droughts in Lithuania was recorded in the last decade of the 20th century and in the first decade of the 21st century. Despite the fact that there is a positive tendency in reoccurrence of droughts, the changes are not statistically significant.