Changes in Arid Climate over North China Detected by the Koppen Climate Classification

Kim, Hyungjin; Wang, Bin; Ding, Qinghua; Chung, Il-Ung

doi:10.2151/jmsj.86.981

Cited by 24 publications

(11 citation statements)

References 30 publications

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“…The CCS of Köppen has been used in various European countries (De Castro et al, 2007), in China (Kim et al, 2008), in Brazil (Alvares et al, 2014) and worldwide (Peel et al, 2007); it has been used in studies of agrometeorology (Rolim;Aparecido, 2015), geography (Petersen;Sack;Gabler, 2012) and climatology (Rohli;Vega, 2012).…”

Section: Introductionmentioning

confidence: 99%

Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil

et al. 2016

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Climate is the set of average atmospheric conditions that characterizes a region. It directly influences the majority of human activities, especially agriculture. Climate classification systems (CCSs) are important tools in the study of agriculture, enabling knowledge of the climatic characteristics of a region. Thus, we aimed to perform the climatic characterization of the State of Paraná using the methods proposed by Köppen and Geiger (1928), modified by Trewartha (1954) (KT), Thornthwaite (1948) (TH) and Camargo (1991) and modified by Maluf (2000) (CM), using data from the European Center for Medium-Range Weather Forecast (ECMWF) model. The results of spatial interpolation (virtual stations) were performed using the Kriging method in spherical shape with one neighbour and resolution of 0.25°. The CCSs displayed the ability to separate the warm and dry from cold and wet regions. The most predominant climates were Cfa (temperate humid with hot summers), C 1 rA'a' (sub-humid with little water deficiency, megathermal) and ST-UMi (humid subtropical with dry winter), according to KT, TH and CM, respectively. CM is an intermediate CCS between KT and TH.

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Section: Introductionmentioning

confidence: 99%

Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil

et al. 2016

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“…For detecting climate change signals, the widely used methods are Mann Kendall test (Mondal and Mujumdar 2012;Dreapela and Drapelova 2011;Yue and Wang 2004), t test (Yue and Pilon 2004;Kim et al 2008) and fingerprint analysis (Hegerl et al 2003;Hasselmann 1997;Ribes et al 2009). …”

Section: Detection Of Climate Change Signalsmentioning

confidence: 99%

Detection and Attribution of Climate Change Signals in Precipitation in the Chaliyar River Basin, Kerala, India

Chithra

Thampi

2015

Aquatic Procedia

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Scientific studies have yielded evidence to support the common perception that climatic variables and associated natural resources and human systems are being affected by external forcings. Detection and attribution (D&A) of climate change provides a formal tool to decipher the complex causes of climate change. This work aims to statistically detect such climatic change signals, if any, in the monthly precipitation data of the Chaliyar river basin, Kerala, India and to evaluate the factors contributing to it. Data employed for the study includes monthly mean precipitation observations, National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data sets and results of several climate model runs based on natural internal variability and anthropogenic effects. Precipitation data from the GCMs were statistically downscaled to river basin scale using ANN based models and the potential predictors were identified by correlation coefficient analysis.Five widely used General Circulation Models were chosen based on availability of the predictor data in this study. GCM projections for three SRES scenarios, A1B, A2 and B1 were downscaled and the t test at 99% confidence interval was performed on the downscaled monthly precipitation data. Results show that there is consistency in the observed data and anthropogenically forced data. Using a standard fingerprint approach, it was noticed that the observed trends in precipitation over the second half of the 20th century lie outside the range expected from natural internal climate variability alone at 95% confidence level for most of the GCMs. To determine the reason for this climate change signal, attribution study was performed based on correlation coefficient analysis and this yielded a positive relationship between the downscaled A2 scenario precipitation and the observed precipitation. The detection and attribution analysis can be further extended to consider the effects of non climatic localized influences like land use and land cover changes, urbanization etc.

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“…Different classification techniques are used to classify the climate regimes in Asia, neighbouring Pakistan. Köppen classification is used to detect changes in arid climates in China (Kim et al, ). The findings of this study indicate that there is increase in semi‐arid areas during 1981−2000.…”

Section: Introductionmentioning

confidence: 99%

Classification of precipitation regimes in Pakistan using wet and dry spells

Nabeel

Athar

2017

Intl Journal of Climatology

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Wet (dry) spells can cause extreme climatic conditions, such as floods (droughts) which can adversely influence the natural resources. A spatio‐temporal analysis of observed wet and dry spells is carried out for vulnerable and data sparse region of Pakistan (24°–38°N and 61°–78°E). Observed monthly precipitation data sets from 46 weather station are used for a length of consecutive 32 years (1976−2007). Additionally, after bias adjustment, the Asian Precipitation Highly‐Resolved Observational Data Integration Towards Evaluation (APHRODITE) precipitation data set, abbreviated as APH, is utilized to corroborate the findings. For both data sets, a threshold of 1 mm is used to define a wet spell. Decadal variability of precipitation for observed and APH data sets indicates that there is gradual decrease in wet spell length for arid and humid regimes, as compared to semi‐arid regimes where there is no change in wet spell length. Monthly dry and wet slope difference (SD), on a log–log plot between number of spells and spell length, is used to classify precipitation regimes in Pakistan, for the first time. A weather station is categorized as humid if SD is less than −2.38 in units of number of spells per length of spells. If SD lies between −2.37 and −0.51, then the weather station is classified as semi‐arid and if SD is greater than −0.51, then it is classified as arid. Thus, according to SD classification, 66% of area in Pakistan is arid, whereas 30% (4%) is semi‐arid (humid). Comparison of precipitation regimes based upon observed and APH data sets with other climate classification schemes that involve both precipitation and temperature is presented.

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Changes in Arid Climate over North China Detected by the Koppen Climate Classification

Cited by 24 publications

References 30 publications

Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil

Köppen, Thornthwaite and Camargo climate classifications for climatic zoning in the State of Paraná, Brazil

Detection and Attribution of Climate Change Signals in Precipitation in the Chaliyar River Basin, Kerala, India

Classification of precipitation regimes in Pakistan using wet and dry spells

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