An example of principal component analysis application on climate change assessment

Tadić, Lidija; Bonacci, Ognjen; Brleković, Tamara

doi:10.1007/s00704-019-02887-9

Cited by 23 publications

(14 citation statements)

References 27 publications

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“…Statistically significant increasing or decreasing trends cannot be seen in the months of the year. Global and regional precipitation patterns showed a mix of increasing and decreasing trends, and most were statistically insignificant, e.g., [16,33,37].…”

Section: Discussionmentioning

confidence: 99%

Analyses of Climate Variations at Four Meteorological Stations on Remote Islands in the Croatian Part of the Adriatic Sea

et al. 2020

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The Mediterranean region is one of the regions in the world that is most vulnerable to the impact of imminent climate change. In particular, climate change has an adverse effect on both the ecosystem and socioeconomic system, influencing water availability for both human and environmental purposes. The most endangered water resources are along the coasts and on islands since they have relatively small volumes and are intensively exploited. We analyzed the time series of air temperature and precipitation measured at four meteorological stations (Komiža, Palagruža, Lastovo, and Biševo) located on small islands in the Croatian part of the Adriatic Sea in this study. The investigated time series extends from the 1950s to the present, being contemporaneous for approximately 50 years. Despite possessing discontinuity, they can be considered as representative for assessing climate change and variability in the scattered environment of the Croatian islands. The results showed increasing trends in the annual air temperature, while the annual cumulative precipitation did not show significant variations. In addition, the analyses of the monthly air temperature showed that statistically significant increasing trends occurred from April to August, suggesting a more severe impact during these months. These results are in accordance with regional and local studies and climate models. Although the climate variability during the analyzed period can be considered as moderate, the impact on water resources could be severe due to the combined effect of the increase in air temperature during warm periods and the intensive exploitation for tourism purposes.

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Section: Discussionmentioning

confidence: 99%

Analyses of Climate Variations at Four Meteorological Stations on Remote Islands in the Croatian Part of the Adriatic Sea

et al. 2020

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“…These variables may have causal relationships with each other, and the significant correlation between some of these factors demonstrates these relationships (see Table S 2 online). The principal component analysis (PCA) is a technique for reducing the number of parameters to extract the most important factors in the analysis of meteorological data 74 , 75 . Thus, PCA was conducted to determine the contributions of climate change and human activities to wind speed changes using Social Sciences software (Version 24.0).…”

Section: Resultsmentioning

confidence: 99%

Inter- and intra-annual wind speed variabilities in wide valley regions of the middle reaches of the Yarlung Tsangpo River, China

Ben

Mei

Chen

et al. 2020

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Wind speed and variability are the most critical climatic factors affecting sand/dust storms, which have not been sufficiently studied in the middle reaches of the Yarlung Tsangpo River (MYR). In this study, wind speed variability was investigated using the moving average over shifting horizon method (MASH), combined with the modified Mann–Kendall test and Sen’s slope based on data from the Tsetang, Lhasa, and Nyêmo meteorological stations during 1960–2015. The results indicated that annual wind speeds for the MYR wide valley regions declined significantly at decadal rates of − 0.216 m/s and underwent three stages from 1960 to 2015: an increasing trend from 1960 to 1975 (0.44 m/s per decade), a weakening until 2006 (− 0.46 m/s per decade), and a remarkable subsequent recovery (1.05 m/s per decade). Different variability trends were observed for the three stations: wind speed decreased significantly during all months at the Tsetang and Nyêmo stations, particularly in the spring, while for Lhasa, a non-significant wind speed increase was detected in summer, and the highest decline occurred in winter. The MASH method resulted in the effective visualization of different patterns, making seasonal process analysis and trend detection easier. In addition, the possible main causes for wind speed change were also discussed. The wind speed change in the study region was strongly associated with the large-scale atmospheric patterns, and the surface pressure gradient variability between the mid and low latitudes may have been a primary driving force. Positive/negative phases of the Pacific Decadal Oscillation (PDO) corresponded well with wind speed decreases/increases and were regarded as an indicator of wind speed variations. The effects of human activities associated with surface roughness change in the MYR were minor compared with the climatic changes.

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“…Cueto & Casenave, 1999;Estrada-Peña & Venzal, 2007;Jarema et al, 2009;Loarie et al, 2008;Pinto et al, 2011;Short & Trembanis, 2004;Silverberg et al, 2013;Sousa et al, 2007;Voigt et al, 2003). It is also common for climatologists to use PCA to analyse climate data (Ehrendorfer, 1987;Huth & Pokorná, 2005;Tadić et al, 2019). However, this method assumes not only linearity but also stationarity in the climate data, an assumption that we tested in this paper.…”

Section: Discussionmentioning

confidence: 99%

Taking climate change into account: Non‐stationarity in climate drivers of ecological response

et al. 2021

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1. Changes in the global climate system are creating increasingly non-analogue climate conditions with expectations of non-stationarity among climate drivers. Decoupling among climate drivers complicates the assessment of ecological response to the changing climate as characteristics that could be once treated as a suite of conditions now need to be treated as potentially independent with possible synergistic effects. 2. Ecologists commonly use ordination techniques (often principal component analysis; PCA) on large climate and environmental datasets to reduce a range of variables to a few axes that are uncorrelated with each other and often explain large proportion of the variation in the original data. However, non-stationarity, with correlations among variables changing over time, can affect this approach. Here, we use a 37-year climate dataset from the Niwot Ridge Long Term Ecological Research site (Colorado, USA) to present the use of both moving window principal component analysis and moving window correlation analysis to determine nonstationarity in climate data. 3. Relationships among climate variables and between input variables and PCA axes changed over time; this obscured interpretation of relationships between PCA axes and an ecological response (plant biomass), suggesting that one-time PCA for environmental variables may lead to inappropriate inferences. 4. Synthesis. Care must be taken in analysing climate-ecological relationships when predictor variables exhibit non-stationarity. We present a conceptual decisionmaking tree to help ecologists consider when to use PCA and extract axis scores or use alternative approaches for incorporating non-stationarity into subsequent analysis, including testing variables individually to aid in interpretation, break point analyses and averaging PC scores.

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An example of principal component analysis application on climate change assessment

Cited by 23 publications

References 27 publications

Analyses of Climate Variations at Four Meteorological Stations on Remote Islands in the Croatian Part of the Adriatic Sea

Analyses of Climate Variations at Four Meteorological Stations on Remote Islands in the Croatian Part of the Adriatic Sea

Inter- and intra-annual wind speed variabilities in wide valley regions of the middle reaches of the Yarlung Tsangpo River, China

Taking climate change into account: Non‐stationarity in climate drivers of ecological response

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