The paper analyzes air quality changes in Ukraine during a wildfire event in April 2020 and a dust storm episode during the 16th of April 2020. The wildfire event contained two episodes of active fires and huge pollutants' emission: 4-14 April and 16-21 April, respectively. Using the Sentinel-5P data of CO and NO 2 column number density and ground-based measurements, there was estimated air quality deterioration. Advection of polluted air masses and analysis of affected territories were made in combination with a Web-based HYSPLIT model. Satellite data described air quality changes better than in-situ measurements. Data intercomparison showed better coincidence in regions that were not affected by wildfire emissions. The paper described the dust storm event based on absorbing aerosol index (AAI) data that occurred between two wildfire episodes.

Abstract. Biomass burning is one of the biggest sources of atmospheric black carbon (BC), which negatively impacts human health and contributes to climate forcing. In this work, we explore the horizontal and vertical variability of BC concentrations over Ukraine during wildfires in August 2010. Using the Enviro-HIRLAM modelling framework, the BC atmospheric transport was modelled for coarse, accumulation, and Aitken mode aerosol particles emitted by the wildfire. Elevated pollution levels were observed within the boundary layer. The influence of the BC emissions from the wildfire was identified up to 550 hPa level for the coarse and accumulation modes and at distances of about 2000 km from the fire areas. BC was mainly transported in the lowest 3 km layer and mainly deposited at night and in the morning hours due to the formation of strong surface temperature inversions. As modelling is the only available source of BC data in Ukraine, our results were compared with ground-level measurements of dust, which showed an increase in concentration of up to 73 % during wildfires in comparison to average values. The BC contribution was found to be 10 %–20 % of the total aerosol mass near the wildfires in the lowest 2 km layer. At a distance, BC contribution exceeded 10 % only in urban areas. In the areas with a high BC content represented by both accumulation and coarse modes, downwelling surface long-wave radiation increased up to 20 W m−2, and 2 m air temperature increased by 1–4 ∘C during the midday hours. The findings of this case study can help to understand the behaviour of BC distribution and possible direct aerosol effects during anticyclonic conditions, which are often observed in mid-latitudes in the summer and lead to wildfire occurrences.

<p>The last decade proved to be the warmest in Ukraine for the whole period of instrumental weather observations. Recent and projected future warming will cause changes in the duration of climatic seasons in Ukraine with corresponding shifts in dates of their start and end.</p> <p>As specified by the Expert Team on Climate Change Detection and Indices, climatic seasons are determined as periods from the first day after the start of a year to the first date after 1 July when at least 6 consecutive days mean daily temperature (t) exceeds (drops under) different thresholds. We analysed four climatic periods: warm period (t>0<sup>o</sup>C), growing season (t>5<sup>o</sup>C), active vegetation (t>10<sup>o</sup>C), and summer season (t>15<sup>o</sup>C).</p> <p>To assess these projected changes bias-adjusted daily data of EuroCORDEX were used from 34 regional climate models for RCP4.5 and RCP8.5 scenarios for 3 future periods: near-term 2021-2040, mid-term 2041-2060 and far-term 2081-2100. Data of ensemble mean for two scenarios firstly were compared with E-Obs v20.0e results in the base period 1991-2010 and showed different biases for different climatic seasons, but very similar behaviour for both scenarios and both variables (length and start of climatic seasons). The least biases (< 0.5 days) were obtained for growing season, while biases reached -10 days for length of warm season and were within 1-3 days for other two seasons.</p> <p>In general by the end of the century, under the RCP4.5 scenario in Ukraine, all analysed climatic season lengths may be the same as in the middle of the century under the RCP8.5 scenario.</p> <p>By the end of the century, for the RCP8.5 scenario, the changes in the climatic seasons range from 40 to almost 70 days, increasing from east to west. As a result, in the coldest in Ukraine region winter is projected to last only from 10 to 30 days, and the vegetation will last throughout the year not only at the southern coast of the Black Sea but also the steppe part of the Crimea and some southern parts of Odesa region. There are almost no differences between scenarios for growing season length, start and end days in the near-term. The area with the longest growing season (from 240 to 260 days) will extend almost 200 km to the north.  </p> <p>Under the RCP8.5 scenario, the length of active vegetation at the end of the century can range from 200 to 240 days, and in the Crimea and the south of Odesa region - 240-285 days, and summer length can vary from 140 days in the north to over half-year in the Crimea and southern Odesa. At the end of the 21<sup>st</sup> century, projected summer in Polissya will be as in the Crimea now - 140-160 days. Such climatic conditions were not observed in Ukraine previously. Increasing the length of the growing season and the period of active vegetation will strengthen the agro-climatic potential of Ukraine and contribute to obtaining higher yields of crops if corresponding measures in providing enough water supply will be implemented.</p>

No abstract

<p>Climate change is one of the major challenges for future development in every country including Ukraine where actual warming already has impacted many sectors, population, and ecosystems. Recently, the International Initiative of Coordinated Downscaling Experiment for Europe (Euro-CORDEX) has provided RCM data for 0.1<sup>o</sup> grid. This detailed RCM projection dataset is an excellent basis for estimation of exposure and vulnerability to climate change of different objects and for updating projections for a new National Communication of Ukraine to UNFCCC as well as for Strategy of Ecological Safety and Adaptation to Climate Change in Ukraine.</p><p>The study is focused on the estimation of the essential and special climatic characteristics and their changes in the near future (2021-2040) as well as to the middle (2041-2050) and end (2081-2100) of the century over the base period 1991-2010 for three scenarios: RCP2.6, RCP4.5, and RCP8.5. We used bias-adjusted RCM data for daily maximum, mean, and minimum temperature and precipitation provided via ESGF web-portal. We applied a multi-model ensemble approach with further bias-correction by delta-method for multi-year monthly values of the essential characteristics as well as calculated climatic indices using a gridded observational dataset of E-Obs v.20.0e. Ensembles for RCP4.5 and RCP8.5 consisted of 34 RCMs while for RCP2.6 only data of 3 RCMs were available. That is why RCP2.6 is only indicative, while the other two scenarios results have a high confidence level and quartiles and percentiles of the ensemble range are estimated.</p><p>More consistent temporally and spatially results were obtained for temperature projections. Increases relative to the baseline were in the range of 0.5-1.5ºC for all the RCPs with a bit higher warming in the North of the country in 2021-2040. In 2041-2060, the increases were 1.0-2.0ºC under RCP2.6 and 1.5-2.5ºC under RCP8.5, with RCP4.5 in between. By the end of the century 2081-2100 the differences between scenarios became much pronounced: from 1-2ºC for RCP2.6 to 4-6ºC for RCP8.5.</p><p>Precipitation changes are much complex with high variability across the seasons and the territory. In winter precipitation tends to increase relative to the baseline in most of the country for all the RCPs. In early spring (March) there is a relative decline in the near-future period, especially in RCP2.6 and RCP8.5 but not in RCP4.5. In later periods the decline becomes less and in the higher RCPs, there is a relative increase. Later spring rainfall changes show a decline in RCP2.6 but an increase for the other RCPs. The summer months show a relative decline with all the higher RCPs getting drier over time. In the fall relative changes are mixed, with declines in some months and increases in others.</p><p>Based on these two essential climatic characteristics other important indices were calculated and analyzed: length of vegetation season, tropical nights, summer days, water deficit, aridity/humidity index, etc.</p><p>Obtained projections of climatic characteristics were(will be) used for further agriculture, forest, and human health impact assessments, that will be the basis for the development of adaptation measures to climate change in the frames of the National Adaptation Plan of Ukraine.  </p>