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Assessment of surface air warming in northeast China, with emphasis on the impacts of urbanization

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Abstract

Based on homogenized land surface air temperature (SAT) data (derived from China Homogenized Historical Temperature (CHHT) 1.0), the warming trends over Northeast China are detected in this paper, and the impacts of urban heat islands (UHIs) evaluated. Results show that this region is undergoing rapid warming: the trends of annual mean minimum temperature (MMIT), mean temperature (MT), and mean maximum temperature (MMAT) are 0.40 C decade−1, 0.32 C decade−1, and 0.23 C decade−1, respectively. Regional average temperature series built with these networks including and excluding “typical urban stations” are compared for the periods of 1954–2005. Although impacts of UHIs on the absolute annual and seasonal temperature are identified, UHI contributions to the long-term trends are less than 10% of the regional total warming during the period. The large warming trend during the period is due to a regime shift in around 1988, which accounted for about 51% of the regional warming.

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Acknowledgements

This paper has been supported by the National Natural Science Foundation of China (grant 40605021), National Key Technology R&D Program (2007BAC29B01-01) and the Ministry of Science and Technology of China through the based platforms special project of scientific data sharing system (grant 2005DKA31700-01). The corresponding author would like to thank David Parker for many constructive suggestions during the corresponding author's visit to the Met Office Hadley Centre. Phil Jones has been supported by the U.S. Department of Energy (grant DE-FG02-98ER62601).

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Correspondence to Qingxiang Li.

Appendix

Appendix

1.1 Methodologies of sequential regime shift detection method

Let x1, x2, ..., xi, be a time-series with new data arriving regularly. When a new observation arrives, a check is performed to determine if it represents a statistically significant deviation from the mean value of the current regime. If it does, that year is marked as a potential change point c, and subsequent observations are used to confirm or reject this hypothesis. The hypothesis is tested using the regime shift index (RSI), which is calculated for each c:

$$ {\text{RS}}{{\text{I}}_c} = \sum\limits_{i = c}^{c + m} {\frac{x_i^*}{{l{\sigma_l}}}}, $$

where m = 0,1…, l − 1 (i.e., number of years since the start of a new regime), l being the cut-off length of the regimes to be tested, and σ 1 is the average standard deviation for all 1-year intervals in the time series. RSI represents a cumulative sum of normalized deviations \( x_i^* \)) from the hypothetical mean level for the new regime (\( \overline {{x_{\text{new}}}} \)), for which the difference, diff, from the mean level for the current regime (\( \overline {{x_{\text{cur}}}} \)) is statistically significant according to a Student's t test:

$$ {\text{diff}} = \overline {{x_{\text{new}}}} - \overline {{x_{\text{cur}}}} = t\sqrt {2\sigma_l^2/l}, $$

where t is the value of the t distribution with 2 l − 2 degrees of freedom at the given probability level p. If, at any time from the start of the new regime, RSI becomes negative, the test fails and a zero value is assigned. If RSI remains positive throughout l − 1, then c is declared to be the time of a regime shift at the level ≤p. The search for the next regime shift starts with c + 1 to ensure that its timing is detected correctly even if the actual duration of the new regime is <l year.

In a previous version of the program, Rodionov (2004) used a running window of a fixed size equal to 2l (i.e. [c − l, c + l], centered at c). In this case, the average value for the current regime \( \overline {{x_{\text{cur}}}} \) is calculated for the period (c − l, c). If a transition from one regime to another is gradual, the program might not detect it because \( \overline {{x_{\text{cur}}}} \) is also changing as the window slides along the time axis, so the difference between the new arriving observations and \( \overline {{x_{\text{cur}}}} \) may not be statistically significant to become a potential change point and trigger the calculation of RSI. In the new version used here, \( \overline {{x_{\text{cur}}}} \) is calculated for the period from the previous regime shift to the point immediately before the current point in time. As a result, a stepwise function of regimes is produced in almost all cases, whereas the previous version of the program could detect abrupt regime shifts only. To improve the performance at the beginning of the time series, the testing for a regime shift starts not from x l + 1, as in the previous version, but from x 2. The average value \( \overline {{x_{\text{cur}}}} \) is still calculated for the entire initial period [1, l], but if a regime shift occurred prior to i = l, it is detected.

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Li, Q., Li, W., Si, P. et al. Assessment of surface air warming in northeast China, with emphasis on the impacts of urbanization. Theor Appl Climatol 99, 469–478 (2010). https://doi.org/10.1007/s00704-009-0155-4

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