Larger Carpathian region climate according to Köppen, Feddema and the Worldwide Bioclimatic Classification System methods

Abstract

The Köppen method, the Worldwide Bioclimatic Classification System (WBCS), and Feddema's descriptive climate classification method are used to analyse the Larger Carpathian Region (LCR) climate. Minimum, maximum and average temperature, precipitation and latitude and elevation data as input data are taken from the CarpatClim dataset. It refers to the period 1961-2010 with a 1-day temporal resolution and a spatial resolution of 0.1 × 0.1. The methods are compared in terms of their capability to represent climate heterogeneity and thermal and moisture characteristics on the annual and seasonal time scales. The region's climate is analysed separately for lowland, hill, mountain and high mountain regions using the results of the Köppen method as reference. The WBCS gave the highest spatial climate heterogeneity, followed by Feddema's method, and then the Köppen method in all three region types. However, the WBCS has the largest data input requirement. The rarest climate type according to the Köppen method is ET. This climate type is registered according to the Feddema and WBCS methods by three different climate types. The most frequent climate type according to the Köppen method is Cfb. This climate type is represented by 19 and 14 climate types according to the Feddema and the WBCS methods, respectively. The most frequent climate type according to Feddema's method is cool and dry with extreme seasonality of T and according to WBCS it is temperate continental steppic supratemperate sub-humid. The coldest climate is produced by Feddema, the warmest by the WBCS. The Feddema method treats seasonality in the most comprehensive way. The Feddema and the WBCS methods reproduced the large climate heterogeneity of the LCR. The results can also be used as basic information in the PannEx project, as well as in future environmental and regional climate change-related investigations for estimating thermal and moisture stresses.

Towards better characterization of global warming impacts in the environment through climate classifications with improved global models

“…Overall, most biases are related to deficiencies in modelled precipitation, as argued by several authors (Phillips and Bonfils, 2015;Zhang et al, 2017b;Tapiador et al, 2019a). According to the results in Figure 4, the spatial distribution, amount of precipitation and representation of the seasonal cycle of precipitation were the main contributors to degrading model performance.…”

“…Applications of CMIP models to environmental studies began in the early stages of the project. Some common research topics have been: landscape analysis (Lu et al, 2020;Guan et al, 2021;Zhou et al, 2021), climate zonation (Rubel and Kottek, 2010;Kriticos et al, 2012;Tapiador et al, 2019a), changes in environmental conditions (Belda et al, 2016;Akinsanola et al, 2020a;Dong et al, 2021), and adaptation to climate change (Läderach et al, 2017;Chen et al, 2020;Mondal et al, 2021).…”

“…It consists in a dimensional reduction of data that captures the most salient climate features, particularly those useful for human life. In a sense, climate classifications simplify the complex interactions of the climate system and serve as a single, aggregated parameter for environmental applications (Chang, 1955;Tapiador et al, 2019a;Daru, 2021). Climate classifications play another key role that they help assess landscape changes in the context of a climate emergency.…”

Projected changes in Feddema climate characteristics in the Larger Carpathian Region by the end of the 21st century

“…The future projection of these processes is a required part of the adaptation to the changes, hence treatment of seasonality is an important feature of Feddema's method to assist in this assessment process. The method obtains more information about seasonality than the most widely used Köppen (1936) scheme (Szabó et al ., 2021). The Feddema‐discrete method shows six seasonality types during the reference period, and six and five seasonality types by the end of the century according to the RCP4.5 and RCP8.5 scenarios, respectively.…”

“…Changes to climate variables associated with anthropogenic global warming, such as temperature or precipitation, can result in geographical shifts of climatic zones (Jylhä et al, 2010). The investigation of global or regional changes in climate using climate classification schemes can provide additional benefits, because they can be understood not only by the scientific community but also by the general public (Szab o et al, 2019). According to Rohli and Vega (2018), climate classification approaches may be exact or empirical.…”