Testing for climate warming in Sweden during 1850–1999, using wavelets analysis

Almasri, Abdullah; Locking, Håkan; Shukur, Ghazi

doi:10.1080/02664760701835011

Cited by 19 publications

(10 citation statements)

References 11 publications

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“…Among a variety of techniques available for analyzing variability, wavelet analysis has emerged in the last decade as a useful statistical tool for this purpose (e.g. Almasri et al 2008). The wavelet method can detrend time series according to time and scale simultaneously.…”

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confidence: 99%

Projected changes of temperature and precipitation in Texas from downscaled global climate models

Jiang¹,

Yang²

2012

Clim. Res.

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Climate change projections, in particular precipitation and temperature under different IPCC future emissions scenarios in Texas, were based on statistically downscaled multi-model ensembles. A comparison of downscaled model results with observations and reanalysis data for the present-day climate shows that all models simulate monthly variations in surface air temperature well (correlation coefficient: 0.98), while precipitation correlation coefficients vary widely across different models (from 0.79 to 0.92). We performed a detailed analysis for the Texas region with an emphasis on 5 sub-regions. Our probability analysis shows an overall increase in surface air temperature towards the end of the 21st century of 4.8, 3.6, and 2.2°C for A2, A1B, and B1 emission scenarios, respectively, relative to the mean of 1971−2000. Surface air temperatures in northwestern Texas increase more under various scenarios, while they are projected to increase steadily in southeastern Texas in response to the large thermal capacity of the Gulf of Mexico. The trends in precipitation are not as clear as those in temperature, suggesting more complicated mechanisms. Precipitation and surface air temperature changes are negatively correlated on an annual basis. This indicates that, as surface air temperature increases in Texas, most regions are projected to become drier. Precipitation changes correlate negatively with surface air temperature changes in summer, while no correlation appears between them for the winter season. KEY WORDS: Downscaling · Emissions scenarios · Precipitation · Temperature · Future climate change Resale or republication not permitted without written consent of the publisherClim Res 53: [229][230][231][232][233][234][235][236][237][238][239][240][241][242][243][244] 2012 the total amount of water in the atmosphere under a warming climate (Wentz et al. 2007), while mid-latitude regions, are projected to have less precipitation (IPCC 2007).The present analysis focuses on Texas, which is home to 4 of the top 10 fastest growing cities in the USA, and also a very productive agricultural state. Increases in climate variability could make adaptation by farmers more difficult. A warmer climate and in creased evaporation may increase the need for irrigation. As the state population is projected to double by 2060, demand for water is projected to increase by 27% (www.twdb. state.tx.us/wrpi/data/ proj/ 2012popproj. asp).Projected impacts of climate change in Texas have previously been assessed based on global climate model simulations and downscaled regional climate information. By use of a scaling method on global climate model simulations, Loaiciga et al. (2000) demonstrated the vulnerability of the Edwards aquifer, an important water resource for Texas, to climate change. Using geographic information systems, vegetation models, and general circulation models, Cameron & Scheel (2001) re vealed that climate change could have a large impact on regional distributions of vegetation and rodents. Based on simulation...

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confidence: 99%

Projected changes of temperature and precipitation in Texas from downscaled global climate models

Jiang¹,

Yang²

2012

Clim. Res.

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“…For testing H 0 : Trend = 0 against H 1 : Trend ̸ = 0, under normality assumption of scaling coefficients, Almasri, Locking and Shukur (2008) proposed the test statistic:…”

Section: Wavelet Methodology For Estimation and Testing Of Trendmentioning

confidence: 99%

“…For the latter approach, Howlader and Chaubey (2009) have proposed a noise reduction technique that exploits the dependency between the wavelet transform coefficients by using a locally-adaptive joint statistical model. A test statistic to test the significance of trend in a time-series data by using wavelet decomposition on a scale by scale basis is proposed by Almasri, Locking and Shukur (2008).…”

Section: Introductionmentioning

confidence: 99%

Wavelet Frequency Domain Approach for Statistical Modeling of Rainfall Time-Series Data

Ghosh

Paul

Prajneshu

2010

Journal of Statistical Theory and Practice

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The powerful methodology of "Wavelet analysis in frequency domain" for analyzing time-series data is studied. As an illustration, Indian monsoon rainfall time-series data from 1879-2006 is considered. The entire data analysis is carried out using SPLUS WAVELET TOOLKIT software package. The discrete wavelet transform (DWT) and multiresolution analysis (MRA) of the data are computed to analyze the behaviour of trend present in the time-series data in terms of different times and scales. By using bootstrap method, size and power of the test for testing significance of trend in the data is computed. It is found that the size of the test for Daubechies wavelet is more than that for Haar wavelet. In respect of both Daubechies and Haar wavelet filters, it is found that the test for presence of trend is unbiased. Also, power of the test for both Daubechies (D4) and Haar wavelets, at level 5 is less than the one at level 6. Further, Haar wavelet at level 6 has generally performed better than Daubechies (D4) wavelet at level 6 in terms of power of the test. Using the former wavelet, a declining trend in the data under consideration is revealed.

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“…Almasria et al [36] applied WA to the empirical study of Swedish temperature data from 1850 to 1999. Kisi [37] predicted monthly runoff using wavelet regression instead of ANN.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Decomposition-Reconfiguration Models for Long-Term Solar Radiation Prediction Only Using Historical Radiation Records

Wang

Qiu

2018

Energies

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Solar radiation prediction is significant for solar energy utilization. This paper presents hybrid methods following the decomposition-prediction-reconfiguration paradigm using only historical radiation records with different combination of decomposition methods, Ensemble Empirical Mode Decomposition (EEMD) and Wavelet Analysis (WA), and the reconfiguration methods, regression model (RE) and Artificial Neural Network (ANN). The application in west China indicates that these hybrid decomposition-reconfiguration models perform well for monthly prediction, while the comparisons of the daily prediction show that the hybrid EEMD-RE model has a higher degree of fitting and a better prediction effect in long-term prediction of solar radiation intensity, which verifies (1) decomposition of original solar radiation data results in components with regular characteristics; (2) the relationship between the original solar radiation sequence and the derived intrinsic mode functions (IMFs) is linear; and (3) EEMD has strong adaptivity for non-linear and non-stationary series. The proposed hybrid decomposition-reconfiguration models have great application prospect for monthly long-term prediction of solar radiation intensity, especially in the areas where complex climate data is difficult to obtain, and the EEMD-RE model is recommended for the daily long-term prediction.

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Testing for climate warming in Sweden during 1850–1999, using wavelets analysis

Cited by 19 publications

References 11 publications

Projected changes of temperature and precipitation in Texas from downscaled global climate models

Projected changes of temperature and precipitation in Texas from downscaled global climate models

Wavelet Frequency Domain Approach for Statistical Modeling of Rainfall Time-Series Data

Hybrid Decomposition-Reconfiguration Models for Long-Term Solar Radiation Prediction Only Using Historical Radiation Records

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