New indices for assessing changes in seasons and in timing characteristics of air temperature

Abstract

Previous studies examining climate change and changes in the timing of seasons have used a fixed temperature threshold for season onset. In this study, the timing of seasons was determined using non-fixed threshold methods. Twelve new timing indices were defined to account for shifts in seasons and season onset day, thermal centroid day, and length. The Mann-Kendall test, Theil-Sen's slope estimator, sequential Mann-Kendall test, and least square linear regression were used to assess trends. The timing indices were examined using data from two meteorological stations in Iran with 50 years of records. Spatio-temporal variations in each index over 30 years (1987-2016) were then determined for Khuzestan province in southwestern Iran. Trend analysis for several indices indicated that the timing of seasons had probably changed in the south and west of the study area, while mountainous regions showed non-significant trends. Based on the hottest and coldest 90-day periods (summer and winter, respectively), during the three decades studied, spring lengthened by 5-10 days/decade in the plain region of Khuzestan province and autumn shortened by about 5-8 days/decade. The centroid of winter occurred earlier, by 2-5 days/decade, in the plains area, while the thermal centroid of summer did not change significantly. Overall, the difference between the thermal centroid of winter and summer (C win-sum) in the plains area significantly decreased, by 6-8 days/decade, in the 30-year period.

Investigating changes in rainfall seasonality across South Africa: 1987–2016

“…Some disagreement with literature regarding seasonality changes highlights the necessity of using consistent methods for cross-study and spatial comparability. As rainfall seasonality is a location specific concept, this highlights the necessity of quantifying rainfall seasonality using appropriate methods for the location/region of interest (Hekmatzadeh et al, 2020;Roffe et al, 2020). Importantly, the multi-method approach adopted here allows for a comprehensive and complete understanding of rainfall seasonality changes using appropriate methods for southern Africa (Feng et al, 2013;Thackeray and Fitchett, 2016;Roffe et al, 2019Roffe et al, , 2020.…”

South African winter rainfall zone shifts: A comparison of seasonality metrics for Cape Town from 1841–1899 and 1933–2020

“…Trends were calculated for 1841-1899, 1933-2020 and 1841-2020 using the non-parametric Mann-Kendall (MK) trend test with the Sen's slope (ss) estimator, calculating the trend magnitude (Mahlalela et al 2019;Hekmatzadeh et al 2020). Considering parametric test assumptions, this test, and all the non-parametric tests discussed below, were applied as they are robust to outliers and have no data distribution assumptions (Hekmatzadeh et al 2020).…”

“…Accounting for the data gap, the non-parametric sequential Mann-Kendall test (SQMKT) was applied only to records spanning 1841-1899 and 1933-2020 to explore the evolution of trends, to determine the start of statistically signi cant trends if present, and to determine whether change points exist, following George and Athira (2020) and Hekmatzadeh et al (2020). Using this method, two indicators, termed U(t) and U′(t), are de ned based on ranks of the prograde, forward time series and retrograde, backward time series, respectively (Sneyers 1991).…”

“…Using this method, two indicators, termed U(t) and U′(t), are de ned based on ranks of the prograde, forward time series and retrograde, backward time series, respectively (Sneyers 1991). Abrupt change points, representing shifts in data distribution properties including the mean and variance (Beaulieu et al 2012), are identi ed by plotting U(t) and U′(t), where the intersection of the two curves indicates a potential abrupt change point location (Sneyers 1991); changes in the trend direction are also evident from U(t) line direction changes (Hekmatzadeh et al 2020).…”

“…Abrupt change points are considered statistically signi cant when Z > 1.96 (or <-1.96), and the same applies for trends, however, trends are only statistically signi cant when this occurs for U(t) values (Sneyers 1991;Hekmatzadeh et al 2020). Where statistically signi cant change points exist, trends were additionally calculated, using the abovementioned trend test, for periods before and after the change point (Ndebele et al 2020).…”