scholarly journals STATISTICAL CHARACTERISTICS OF DAILY AMOUNTS OF ATMOSPHERIC PRECIPITATION ON THE TERRITORY OF THE ODESSA REGION IN THE CONDITIONS OF GLOBAL CLIMATE CHANGE

2021 ◽  
Vol 26 (1(38)) ◽  
pp. 67-80
Author(s):  
O. M. Prokofiev ◽  
L. D. Goncharova
Keyword(s):  
Global Climate ◽  
Regional Climate ◽  
Atmospheric Precipitation ◽  
Practical Interest ◽  
Warm Season ◽  
Statistical Characteristics ◽  
Daily Maximum ◽  
Precipitation Regime ◽  
Maximum Rainfall ◽  
Minimum Number

Problem Statement and Purpose. Rational use of nature, solution of natural and environmental problems, planning and location of various sectors of the economy are based on climatological information. As empirical data accumulate, some values, as well as the probabilistic characteristics of climate-relatednatural resources, need constant refinement due to the fact that hydrometeorological phenomena are extremely variable in time and space. Of great practical interest is information on daily and maximum rainfall and therefore there is a need for their comprehensive analysis, study and forecasting. Data & Methods. The initial information for estimating the precipitation regime is the term data for 00, 06, 12 and 18 hours at nine stations of Odessa region in the period 2010–2015, to which a number of general scientific and statistical methods were applied. Results. Some indicators of the precipitation regime, which are widely used in scientific and practical developments, are analyzed. The total number of days with precipitation and their recurrence are determined. For the territory of the region it is 34.4%. The most days with precipitation were observed in January, and the least – in August, except for Art. Calm, at which the minimum number of days with precipitation was recorded in November. The frequency of precipitation of different gradations was studied and significant precipitations were analyzed: 10, 20, 30 mm and more per day. The region is dominated by precipitation up to 10.0 mm, the frequency of which ranges from 86% (station Rozdilna) to 91.4% (station Odessa). The maximum frequency of days with precipitation of 30 mm or more per day falls on the warm season (April-October). The fields of daily maximum of 1% and 5% probability are heterogeneous and at some stations of Odessa region the values of this indicator exceed the climatic norm (up to 10–12 mm), at others – less than the norm. The obtained results are a certain contribution to the study of both theoretical and practical aspects of the study of daily amounts and daily maximum precipitation, which are indicators of the regional climate. They can be used to make a climatological forecast, which is necessary for a more complete development of climatic resources of Odessa region.

scholarly journals The potential effects of climate change on air quality across the conterminous U.S. at 2030 under three Representative Concentration Pathways (RCPs)

2018 ◽  
Author(s):  
Christopher G. Nolte ◽  
Tanya L. Spero ◽  
Jared H. Bowden ◽  
Megan S. Mallard ◽  
Patrick D. Dolwick
Keyword(s):  
Climate Change ◽  
Air Quality ◽  
Global Climate ◽  
Regional Climate ◽  
The United States ◽  
Historical Period ◽  
Daily Maximum ◽  
Good Representation ◽  
Representative Concentration Pathways ◽  
Concentration Changes

Abstract. The potential impacts of climate change on regional ozone (O3) and fine particulate (PM2.5) air quality in the United States are investigated by downscaling Community Earth System Model (CESM) global climate simulations with the Weather Research and Forecasting (WRF) model, then using the downscaled meteorological fields with the Community Multiscale Air Quality (CMAQ) model. Regional climate and air quality change between 2000 and 2030 under three Representative Concentration Pathways (RCPs) is simulated using 11-year time slices from CESM. The regional climate fields represent historical daily maximum and daily minimum temperatures well, with mean biases less than 2 K for most regions of the U.S. and most seasons of the year and good representation of the variability. Precipitation in the central and eastern U.S. is well simulated for the historical period, with seasonal and annual biases generally less than 25 %, and positive biases in the western U.S. throughout the year and in part of the eastern U.S. during summer. Maximum daily 8-h ozone (MDA8 O3) is projected to increase during summer and autumn in the central and eastern U.S. The increase in summer mean MDA8 O3 is largest under RCP8.5, exceeding 4 ppb in some locations, with smaller seasonal mean increases of up to 2 ppb simulated during autumn and changes during spring generally less than 1 ppb. Increases are magnified at the upper end of the O3 distribution, particularly where projected increases in temperature are greater. Annual average PM2.5 concentration changes range from −1.0 to 1.0 μg m−3. Organic PM2.5 concentrations increase during summer and autumn due to increased biogenic emissions. Decreases in aerosol nitrate occur during winter, accompanied by lesser decreases in ammonium and sulfate, due to warmer temperatures causing increased partitioning to the gas phase. Among meteorological factors examined to account for modeled changes in pollution, temperature and isoprene emissions are found to have the largest changes and the greatest impact on O3 concentrations.

scholarly journals The Impact of Incongruous Lake Temperatures on Regional Climate Extremes Downscaled from the CMIP5 Archive Using the WRF Model

2016 ◽  
Vol 29 (2) ◽  
pp. 839-853 ◽  
Author(s):  
Tanya L. Spero ◽  
Christopher G. Nolte ◽  
Jared H. Bowden ◽  
Megan S. Mallard ◽  
Jerold A. Herwehe
Keyword(s):  
Global Climate ◽  
Regional Climate ◽  
Wrf Model ◽  
Climate Extremes ◽  
Daily Maximum ◽  
Sea Level Pressure ◽  
Thermally Induced ◽  
Monthly Average ◽  
Southern Florida ◽  
The Impact

Abstract The impact of incongruous lake temperatures is demonstrated using the Weather Research and Forecasting (WRF) Model to downscale global climate fields. Unrealistic lake temperatures prescribed by the default WRF configuration cause obvious biases near the lakes and also affect predicted extremes hundreds of kilometers from the lakes, especially during winter. Using these default temperatures for the Great Lakes in winter creates a thermally induced wave in the modeled monthly average sea level pressure field, which reaches southern Florida. Differences of more than 0.5 K in monthly average daily maximum 2-m temperature occur along that wave during winter. Noteworthy changes to temperature variability, precipitation, and mesoscale circulation also occur when the default method is used for downscaling. Consequently, improperly setting lake temperatures for downscaling could result in misinterpreting changes in regional climate and adversely affect applications reliant on downscaled data, even in areas remote from the lakes.

scholarly journals Analysis of the observed and modelled budburst dates of grapevine in Croatia

2021 ◽  
Author(s):  
Branimir Omazić ◽  
Maja Telišman Prtenjak ◽  
Ivan Prša ◽  
Marko Karoglan
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climatic Conditions ◽  
Global Climate Models ◽  
Statistical Characteristics ◽  
Base Temperature ◽  
Spring Frost

<p>Since changes in temperatures and precipitation significantly affect the biosphere, viticulture as an important economic branch in the moderate latitudes (e.g., mainly between 35°N and 55°N) is strongly influenced by climate change. The most commonly analysed/modelled phenological phases of grapevines are budburst (beginning of grapevine seasonal growth), flowering (crucial for the reproductive cycle) and veraison (initiation of the ripening). Resent studies indicate that budburst is greatly regulated by temperature. Due to climate change and temperature increase, budburst dates show trends in earlier occurrences at several available stations throughout Croatia which increases the vulnerability of the grapevine to the spring frost.</p><p>The aim of this study is to determine trends and changes in budburst date, their statistical characteristics at available stations in period 1961-2020 in Croatia. We focus on four grapevine varieties, two white (Graševina and Chardonnay) and two red (Merlot and Plavac Mali) and performance of statistical models (GDD, Riou’s model and BRIN model) that predict budburst dates in the present climate. For this purpose an effect of the dormancy period and base temperature on the simulated budburst date have been explored. The study is further extended to future climatic conditions using statistical and numerical climate models. Therefore, a daily output from three CORDEX Regional Climate Models’ (RCMs) simulations (CLMcom-CCLM4-8-17, SMHI-RCA4, CNRM-ALADIN5.3) for Croatian domain are used. All RCMs are forced by Global Climate Models (GCMs) with a moderate (RCP4.5) and a high-end (RCP8.5) green-house gass (GHG) scenario(s) and all the simulations have horizontal grid spacing of 0.11°. Results indicate further earlier appearance of budburst regardless of varieties.</p>

scholarly journals Detection of future changes in seasonality in extreme short-term rainfall in selected stations of Slovakia

2017 ◽  
Vol 47 (2) ◽  
pp. 133-148 ◽  
Author(s):  
Marianna Vasilaki ◽  
Silvia Kohnová ◽  
Martin Hanel ◽  
Ján Szolgay ◽  
Kamila Hlavčová ◽  
...  
Keyword(s):  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Mountainous Areas ◽  
Short Term ◽  
Maximum Rainfall ◽  
Rainfall Events ◽  
Daily Events ◽  
Future Events ◽  
Projected Changes

AbstractThis paper analyses the projected changes in short-term rainfall events during the warm season (April–October) in an ensemble of 30 regional climate model (RCM) simulations. The seasonality analysis was done for the Hurbanovo, Bratislava, Oravska Lesna, and Myjava stations in Slovakia. The characteristics of maximum rainfall events were analysed for two scenario periods, one past and one future (1960–2000 and 2070–2100) and compared to the characteristics of the actual observed events. The main findings from the analysis show that short-term events of 60 minutes appear to have stronger seasonality than daily events that show a rather high variability. The seasonality concentration index calculated for the 60 min events averages to 0.77, while that of daily events averaged to 0.65. The differences between the dates of the occurrence of past and future events are not significant in the lowland areas, while in the mountainous areas the future events have been found to occur earlier than past ones.

Regional Climate Scenarios for use in Nordic Water Resources Studies

2003 ◽  
Vol 34 (5) ◽  
pp. 399-412 ◽  
Author(s):  
M. Rummukainen ◽  
J. Räisänen ◽  
D. Bjørge ◽  
J.H. Christensen ◽  
O.B. Christensen ◽  
...  
Keyword(s):  
Water Resources ◽  
Climate Models ◽  
Global Climate ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Climate Scenarios ◽  
Soil Frost ◽  
Nordic Region ◽  
Regional Climate Projections

According to global climate projections, a substantial global climate change will occur during the next decades, under the assumption of continuous anthropogenic climate forcing. Global models, although fundamental in simulating the response of the climate system to anthropogenic forcing are typically geographically too coarse to well represent many regional or local features. In the Nordic region, climate studies are conducted in each of the Nordic countries to prepare regional climate projections with more detail than in global ones. Results so far indicate larger temperature changes in the Nordic region than in the global mean, regional increases and decreases in net precipitation, longer growing season, shorter snow season etc. These in turn affect runoff, snowpack, groundwater, soil frost and moisture, and thus hydropower production potential, flooding risks etc. Regional climate models do not yet fully incorporate hydrology. Water resources studies are carried out off-line using hydrological models. This requires archived meteorological output from climate models. This paper discusses Nordic regional climate scenarios for use in regional water resources studies. Potential end-users of water resources scenarios are the hydropower industry, dam safety instances and planners of other lasting infrastructure exposed to precipitation, river flows and flooding.

scholarly journals Projecting Health Impacts of Future Temperature: A Comparison of Quantile-Mapping Bias-Correction Methods

2021 ◽  
Vol 18 (4) ◽  
pp. 1992
Author(s):  
Weijia Qian ◽  
Howard H. Chang
Keyword(s):  
Bias Correction ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Warm Season ◽  
Climate Science ◽  
Regional Climate Models ◽  
Health Impact ◽  
Quantile Mapping ◽  
Ed Visits

Health impact assessments of future environmental exposures are routinely conducted to quantify population burdens associated with the changing climate. It is well-recognized that simulations from climate models need to be bias-corrected against observations to estimate future exposures. Quantile mapping (QM) is a technique that has gained popularity in climate science because of its focus on bias-correcting the entire exposure distribution. Even though improved bias-correction at the extreme tails of exposure may be particularly important for estimating health burdens, the application of QM in health impact projection has been limited. In this paper we describe and apply five QM methods to estimate excess emergency department (ED) visits due to projected changes in warm-season minimum temperature in Atlanta, USA. We utilized temperature projections from an ensemble of regional climate models in the North American-Coordinated Regional Climate Downscaling Experiment (NA-CORDEX). Across QM methods, we estimated consistent increase in ED visits across climate model ensemble under RCP 8.5 during the period 2050 to 2099. We found that QM methods can significantly reduce between-model variation in health impact projections (50–70% decreases in between-model standard deviation). Particularly, the quantile delta mapping approach had the largest reduction and is recommended also because of its ability to preserve model-projected absolute temporal changes in quantiles.

scholarly journals Techniques for analyses of trends in GRUAN data

2015 ◽  
Vol 8 (4) ◽  
pp. 1673-1684 ◽  
Author(s):  
G. E. Bodeker ◽  
S. Kremser
Keyword(s):  
Measurement Uncertainty ◽  
Trend Analysis ◽  
Global Climate ◽  
Regional Climate ◽  
Added Value ◽  
Least Squares Regression ◽  
Target Uncertainty ◽  
Reference Quality ◽  
Measurement Uncertainties

Abstract. The Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) provides reference quality RS92 radiosonde measurements of temperature, pressure and humidity. A key attribute of reference quality measurements, and hence GRUAN data, is that each datum has a well characterized and traceable estimate of the measurement uncertainty. The long-term homogeneity of the measurement records, and their well characterized uncertainties, make these data suitable for reliably detecting changes in global and regional climate on decadal time scales. Considerable effort is invested in GRUAN operations to (i) describe and analyse all sources of measurement uncertainty to the extent possible, (ii) quantify and synthesize the contribution of each source of uncertainty to the total measurement uncertainty, and (iii) verify that the evaluated net uncertainty is within the required target uncertainty. However, if the climate science community is not sufficiently well informed on how to capitalize on this added value, the significant investment in estimating meaningful measurement uncertainties is largely wasted. This paper presents and discusses the techniques that will need to be employed to reliably quantify long-term trends in GRUAN data records. A pedagogical approach is taken whereby numerical recipes for key parts of the trend analysis process are explored. The paper discusses the construction of linear least squares regression models for trend analysis, boot-strapping approaches to determine uncertainties in trends, dealing with the combined effects of autocorrelation in the data and measurement uncertainties in calculating the uncertainty on trends, best practice for determining seasonality in trends, how to deal with co-linear basis functions, and interpreting derived trends. Synthetic data sets are used to demonstrate these concepts which are then applied to a first analysis of temperature trends in RS92 radiosonde upper air soundings at the GRUAN site at Lindenberg, Germany (52.21° N, 14.12° E).

scholarly journals Statistical Characteristics of Unsteady Storms in Radar Observations for the Beijing–Tianjin Region

2015 ◽  
Vol 54 (1) ◽  
pp. 106-116 ◽  
Author(s):  
Yu Wang ◽  
Hong-Qing Wang ◽  
Lei Han ◽  
Yin-Jing Lin ◽  
Yan Zhang
Keyword(s):  
Warm Season ◽  
Environmental Parameters ◽  
Statistical Characteristics ◽  
Movement Speed ◽  
Movement Direction ◽  
Upper Troposphere ◽  
Maximum Value ◽  
Dynamic Factors ◽  
The Mean ◽  
Wind Profiles

AbstractThis study was designed to provide basic information for the improvement of storm nowcasting. According to the mean direction deviation of storm movement, storms were classified into three types: 1) steady storms (S storms, extrapolated efficiently), 2) unsteady storms (U storms, extrapolated poorly), and 3) transitional storms (T storms). The U storms do not fit the linear extrapolation processes because of their unsteady movements. A 6-yr warm-season radar observation dataset was used to highlight and analyze the differences between U storms and S storms. The analysis included geometric features, dynamic factors, and environmental parameters. The results showed that storms with the following characteristics changed movement direction most easily in the Beijing–Tianjin region: 1) smaller storm area, 2) lower thickness (echo-top height minus base height), 3) lower movement speed, 4) weaker updrafts and the maximum value located in the mid- and upper troposphere, 5) storm-relative vertical wind profiles dominated by directional shear instead of speed shear, 6) lower relative humidity in the mid- and upper troposphere, and 7) higher surface evaporation and ground roughness.

scholarly journals Joint Occurrence of Daily Temperature and Precipitation Extreme Events over Canada

2014 ◽  
Vol 53 (9) ◽  
pp. 2148-2162 ◽  
Author(s):  
Bárbara Tencer ◽  
Andrew Weaver ◽  
Francis Zwiers
Keyword(s):  
Climate Models ◽  
Regional Climate ◽  
Heavy Precipitation ◽  
Maximum Temperature ◽  
Daily Minimum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Negative Consequences ◽  
Temperature And Precipitation ◽  
Joint Occurrence

AbstractThe occurrence of individual extremes such as temperature and precipitation extremes can have a great impact on the environment. Agriculture, energy demands, and human health, among other activities, can be affected by extremely high or low temperatures and by extremely dry or wet conditions. The simultaneous or proximate occurrence of both types of extremes could lead to even more profound consequences, however. For example, a dry period can have more negative consequences on agriculture if it is concomitant with or followed by a period of extremely high temperatures. This study analyzes the joint occurrence of very wet conditions and high/low temperature events at stations in Canada. More than one-half of the stations showed a significant positive relationship at the daily time scale between warm nights (daily minimum temperature greater than the 90th percentile) or warm days (daily maximum temperature above the 90th percentile) and heavy-precipitation events (daily precipitation exceeding the 75th percentile), with the greater frequencies found for the east and southwest coasts during autumn and winter. Cold days (daily maximum temperature below the 10th percentile) occur together with intense precipitation more frequently during spring and summer. Simulations by regional climate models show good agreement with observations in the seasonal and spatial variability of the joint distribution, especially when an ensemble of simulations was used.

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