Redefining the climate regions of Romania through objective methods

<p>The south-eastern part of the European continent is known as a region where the types of climate are hard to be delimited, being indicated by Trewartha since 1961 among the so-called Earth's Climate Problem regions of the world. This is given especially by its position at the merges of arid and cold climate of the temperate zone in Europe. Taking to account this aspect, it is not surprisingly that after almost 100 years of climate classification attempts, there is still no agreement regarding the climate type of Romania and its corresponding subdivisions. Even if a weak majority of the Romanian climatologists plead for a temperate continental climate, some others consider that Romania has a typically temperate transitional climate specific for central Europe. However, most of previous regionalizations are highly subjective with no proper quantitative assessment of climate conditions. </p><p>In our study a climate regionalization of Romania’s territory is proposed, based on an objective approach. For this purpose, 9 monthly climate parameters extracted from interpolation gridded data sets (ERA-5 land and ROCADA) were used.</p><p>The regionalization was performed by mixing two objective methods. Firstly, all the 108 input variables were reduced at 8 major factors using factor analysis. Secondly, those factors were used in a k-means clustering method and a new scheme of climate regionalization of Romanian territory was obtained. Through this, we succeed to delimitate 8 different climate subtypes within Romania's territory which we aggregated firstly in 2 major zonal climate types: (i) temperate transitional climate (TTC) from maritime to continental type, extended in the north-east part of Romania and (ii) temperate orographically sheltered climate (TOSC) with 2 major subtypes. The first sub-type of TOSC is extended within the Carpathian mountain arch (an extension of pannonian climate) and the second one covers the romanian part of the region between Carpathian and Balkan Mountain (lower danubian climate). Besides these two zonal types the major landforms of Romania impose specific climate conditions: (iii) the Carpathian mountains and sub-mountains area have their own climate features (CMSC) with 3 climate subtypes (precarpathian, eastern Carpathian and alpine climates), while the (iv) Black Sea shapes the main climate conditions of the south-eastern side of the country especially along the coast with 2 climate subtypes (ponto-deltaic and western pontic type). The main features of these climate types/subtypes are presented in detailed in the study.</p><p>In the meantime, the proposed climate regionalization covers partially the neighbor countries in an attempt to homogenize the different national perspectives on the climate types along the states boundaries in central and south-eastern Europe.</p>

Climate Regionalization of Asphalt Pavement Based on the K-Means Clustering Algorithm

The climate regionalization of asphalt pavement plays an active role in ensuring the good performance and service life of asphalt pavement. In order to better adapt to the climate characteristics of a region, this study developed a multi-index method of climate regionalization of asphalt pavement. First, meteorological data from the research region were statistically analyzed and the major climate variables were identified. Then, a principal component analysis (PCA) was used to eliminate any correlation between the major climate variables. Three principal components were extracted by the PCA as cluster factors, namely, the temperature factor, precipitation factor, and radiation factor. The research region was divided into the following four asphalt pavement climate zones via the K-means clustering algorithm. Those zones are affected by the climate comprehensively: an inland zone with high temperatures, little rainfall, and radiation, a coastal zone with high temperatures, and a rainy mountainous zone. The results of the climate regionalization were compared with the results of on-site investigations. The pavement degradation in each climatic zone was related to the climate characteristics of the region. Probabilistic neural network (PNN) and support vector machine (SVM) climate regionalization predictive models were established with MATLAB. The clustering factors were used as the input data to identify the climate zones, and the identification accuracy rate was determined to be over 90%. The climate regionalization of pavement can provide reference and guidance for the selection of reasonable technical measures, parameters, and building materials in highway projects with similar climatic conditions.

Synoptic Circulation Patterns and Climate Regionalization of East Africa

<p><strong>Abstract</strong>. Climate regionalization is crucial for climate studies, especially in the case of heterogeneous regions like East Africa. This paper focuses on categorizing Ethiopia into homogeneous climatic sub-regions by applying a classification of circulation patterns on precipitation. The sub-regions obtained will be applied on the verification of WRF-NOAHMP seasonal simulations performed over the Horn of Africa. We analyzed the occurrence of each circulation type per month and per year over the whole country. Then, trend analysis of temperature and precipitation over the respective sub-regions were performed. Principal Component Analysis (PCA) were applied to group daily mean Sea Level Pressure (SLP) into Circulation Types (CTs). Then, PCA coupled with k-means clustering employed to regionalize precipitation fields (distributed spatially) following CTs into homogeneous climatic sub-regions. Observational data were obtained from the National Center for Environmental Prediction (NCEP) reanalysis, Climate Hazards Group Infrared Precipitation with Stations (CHIRPS version 2), and National Meteorology Agency (NMA) of Ethiopia (gauge 1st and 2nd classes). Five principal components, which explain 98% of the total variance, were maintained using the Scree test technique. Ten CTs were obtained using positive and negative phases of each principal component scores following the extreme score values (> 2 and < −2) procedure. From ten CTs, we found that three (CT1, CT3, and CT8) were characterized by low pressure over the southwest corner of the domain, which consequently brings rainfall over the Ethiopian highlands. The number of days classified under different CTs shows different trends. CTs seasonal distribution agreed with the regional seasons. Long-term monthly mean rainfall ranges from 0-600 mm over the region. Ethiopia is clustered into four homogeneous sub-regions based on the spatial distribution of precipitation following CTs. Rainfall from CHIRPS and gauge did not have any specific trend over the sub-regions, however high standardized anomalies were observed compared to the long term mean. The temperature showed a 2 °C change for the past three decades. There was a negligible difference in the shape, size, and location of regions using data from different sources. The final decision on the optimal number of homogeneous climatic sub-regions depends upon the research objective, geographical domain size, and topographic features of the domain. This study provides an assessment and decision pathway.</p><p> </p><p><strong>Keywords: </strong>climatology, regionalization, Ethiopia, precipitation, k-means, circulation types</p>

THORNTHWAITE’S CLIMATE REGIONALIZATION FOR THE STATE OF TOCANTINS, BRAZIL

Tocantins State faces a large-scale agricultural expansion. Thus, climate studies are essential for a better understanding of climate variability supporting agricultural and environmental planning. In this context, this study applies the climatic classification of Thornthwaite and develops a climate regionalization through geostatistical techniques, assessing the performance of the interpolators ordinary kriging (OK) and cokriging (CK). Data from 26 weather stations located in Tocantins State and surroundings were used. The variables of interest to climate regionalization, obtained by the climatic water balance, were mapped by geostatistical techniques. The results of cross-validation showed that ordinary kriging and cokriging performed well. The spherical and exponential semivariogram models obtained the best fit in 40% of the analyzes each, and the gaussian in 20%. The climatic classification of Thornthwaite applied to Tocantins State showed the presence of humid (B1), moist subhumid (C2), and dry subhumid (C1) climates. There were found three climatic regions: B1A’wa’: Humid, megathermal, with moderate winter water deficiency, and a temperature efficiency regime normal to megathermal , occurring in the western region of the state; C2A’wa’: Moist subhumid, megathermal, with moderate winter water deficiency, and a temperature efficiency regime normal to megathermal , occurring in the central region and extending from the north to the south of the state; and C1A’w2a’: Dry subhumid, megathermal, with large summer water surplus, and a temperature efficiency regime normal to megathermal , in the east and northeast of the state.

Climate regionalization and trends based on daily temperature and precipitation extremes in the south of the Pampas (Argentina)

The south of Pampas (36° 32’-40° 44’ S; 63° 24’-60° 30’ W), as most of Argentina, is a semiarid region. Its economy is based on rain-fed agriculture and livestock. Traditionally, the climate has been studied considering the analyses of monthly and annual climate parameters, but there is evidence that in this type of areas, the short-term climatic events have a substantial impact on the climate. Therefore, this study aimed at developing a climate regionalization from the analysis of daily temperature and precipitation extremes in the south of the Pampas for the period 1970-2017. Subsequently, it focuses on analyzing both trends and breakpoints of these events in the different sub-climates. To do so, we applied a Cluster-based Principal Component Analyses with a Ward hierarchical supervised method to generate a climate regionalization considering 29 daily extreme climatic indices and the elevation. We identify four sub-regions, and we analyzed trends during 1970-2017, and in the two-time series defined by applying breakpoints. Both minimum and maximum temperatures and precipitation had structural changes in the last 15 years, exposing the region to warming and dryness trends. The maximum temperature increases 0.5ºC, while precipitation decreases 30 mm. The short-term climate variability allows us to identify areas climatically more detailed and to conclude that the south of the Pampas is characterized by its high dependency on short-term climatic events.