Statistical Variability and Persistence Change in Daily Air Temperature Time Series from High Latitude Arctic Stations

Suteanu, Cristian

doi:10.1007/s00024-014-0878-8

Cited by 5 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One way around this issue is to use the inter‐annual variability of the SAT to define the degree of AA. Note that here we use the term variability to refer to the standard deviation of anomalies regardless of their temporal structure (Suteanu, ). There is a larger inter‐annual variability in the SAT in the Arctic than in the globe as a whole (Legate and Willmott, ; Jones and Moberg, ).…”

Section: Introductionmentioning

confidence: 99%

Arctic amplification metrics

Davy¹,

Chen²,

Hanna

2018

Intl Journal of Climatology

View full text Add to dashboard Cite

One of the defining features of both recent and historical cases of global climate change is Arctic amplification (AA). This is the more rapid change in the surface air temperature (SAT) in the Arctic compared to some wider reference region, such as the Northern Hemisphere (NH) mean. Many different metrics have been developed to quantify the degree of AA based on SAT anomalies, trends and variability. The use of different metrics, as well as the choice of data set to use, can affect conclusions about the magnitude and temporal variability of AA. Here we review the established metrics of AA to see how well they agree upon the temporal signature of AA, such as the multi-decadal variability, and assess the consistency in these metrics across different commonly used data sets which cover both the early and late 20th century warming in the Arctic. We find the NOAA 20th century reanalysis most closely matches the observations when using metrics based upon SAT trends (A 2 ), variability (A 3 ) and regression (A 4 ) of the SAT anomalies, and the ERA 20th century reanalysis is closest to the observations in the SAT anomalies (A 1 ) and variability of SAT anomalies (A 3 ). However, there are large seasonal differences in the consistency between data sets. Moreover, the largest differences between the century-long reanalysis products and observations are during the early warming period, likely due to the sparseness of the observations in the Arctic at that time. In the modern warming period, the high density of observations strongly constrains all the reanalysis products, whether they include satellite observations or only surface observations. Thus, all the reanalysis and observation products produce very similar magnitudes and temporal variability in the degree of AA during the recent warming period.

show abstract

Section: Introductionmentioning

confidence: 99%

Arctic amplification metrics

Davy¹,

Chen²,

Hanna

2018

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…(); Rypdal et al . (), Suteanu (). However, the temporal resolution of the available data leads us to base the present study on mean monthly temperatures.…”

Section: Methodsmentioning

confidence: 99%

Persistence of observed air temperatures in Iceland

Degenhardt

Ólafsson

2018

Intl Journal of Climatology

View full text Add to dashboard Cite

A large data set from 40 weather stations in Iceland is explored for persistence in monthly mean temperatures. There are great seasonal and regional variations in the persistence. Extremely high values of correlation (r > 0.8) of temperatures with subsequent months are found. These values are higher than reported elsewhere in the scientific literature. The highest values are found in coastal regions in the summer, while in the early winter there is overall little correlation. In general, there are two distinct maxima in the temperature correlations, one in late winter/early spring and one in the summer. In most seasons, there is greater correlation at the coast than inland. The high correlations are linked to snow melt, persistence in sea surface temperatures, weak winds and strong static stability. Remarkably low correlations reveal a negative feedback process: A warm May leads to less snow in inland regions, which favours a cold sea breeze in coastal regions in June. In regions of high correlation, the persistence is indeed useful for subseasonal forecasting of mean temperatures in late winter/spring and summer.

show abstract

“…The most commonly used wavelet functions are the Morlet wavelet [36], the Haar wavelet [37], and the Mexican hat [38]. The Morlet wavelet has already been validated in geophysical applications among several "mother" wavelets [39,40], and it was therefore adopted in this study.…”

Section: Wavelet Analysismentioning

confidence: 99%

Statistical Downscaling and Projection of Future Air Temperature Changes in Yunnan Province, China

Jiaxu

Chen

et al. 2017

Advances in Meteorology

View full text Add to dashboard Cite

The SDSM was employed for downscaling of daily mean temperature of 32 meteorological stations (1954–2014) and future scenarios were generated up to 2100. The data were daily NCEP/NCAR reanalysis data and the daily mean climate model outputs for the RCP2.6, RCP4.5, and RCP8.5 scenarios from the MRI of Japan. Periodic features were obtained by wavelet analysis. The results showed the following. (1) The pattern of change and the numerical values of the air temperature could be reasonably simulated, with the averageR2between observed and generated data being 0.963 for calibration and 0.964 for validation. (2) All scenarios projected increases of different degrees of temperature in all seasons, except for spring in the 2020s. Annually, the most remarkable changes in the 2020s, 2050s, and 2080s were 0.27, 1.00, and 1.84°C, respectively. Seven dominant periods appeared under RCP4.5 and RCP8.5 from 1954 to 2100; however, an additional period appeared under RCP2.6. (3) In future periods, especially the 2020s, decreases in temperature were significantly located in the center of Yunnan under all three scenarios, whereas there were distinct increases in northwest and southeast Yunnan in most future periods. Besides, the RCP8.5 scenario showed the greatest increase in the 2080s.

show abstract

Statistical Variability and Persistence Change in Daily Air Temperature Time Series from High Latitude Arctic Stations

Cited by 5 publications

References 47 publications

Arctic amplification metrics

Arctic amplification metrics

Persistence of observed air temperatures in Iceland

Statistical Downscaling and Projection of Future Air Temperature Changes in Yunnan Province, China

Contact Info

Product

Resources

About