# Forty Years of Air Temperature Change over Iran Reveals Linear and Nonlinear Warming

## 伊朗地区40年气温变化趋势：线性和非线性变暖

• Spatiotemporal analysis of long-term changes in air temperature is of prime importance for climate change research and the development of effective mitigation and adaptation strategies. Although there are considerable studies on air temperature change across the globe, most of them have been on linear trends and time series analysis of nonlinear trends have not received enough attention. Here, spatiotemporal patterns of monthly and annual mean (Tmean), maximum (Tmax), and minimum (Tmin) air temperature at 47 synoptic stations across climate zones in Iran for a 40-yr period (1978–2017) are analyzed. A polynomial fitting scheme (Polytrend) is used to both monthly and annual air temperature data to detect trends and classify them into linear and nonlinear (quadratic and cubic) categories. The significant (non-significant) trends in Tmean, Tmax, and Tmin across all climate zones are 41.1% (58.9%), 34.1% (65.9%), and 46% (54%), respectively. The highest magnitude of increasing trends is observed in the annual Tmin (0.47°C decade−1) and the lowest magnitude is for the annual Tmax (0.4°C decade−1). Across the country, increasing trends ($\bar {x}$ = 37.2%) have higher spatial coverage than the decreasing trends ($\bar {x}$ = 3.2%). Warming trends in Tmean (65.3%) and Tmin (73.1%) are mainly observed in humid climate zone while warming trends in Tmax are in semi-arid (43.9%) and arid (34.1%) climates. Linear change with a positive trend is predominant in all Tmean (56.7%), Tmax (67.8%), and Tmin (71.2%) and for both monthly and annual data. Further, the linear trends have the highest warming rate in annual Tmin (0.83°C decade−1) and Tmean (0.46°C decade−1) whereas the nonlinear trends have the highest warming rate in annual Tmax (0.52°C decade−1). The linear trend type is predominant across the country especially in humid climate zones whereas the nonlinear trends (quadratic and cubic) are mainly observed in the arid climate zones. This study highlights nonlinear changes and spatiotemporal trends in air temperature in Iran and contributes to a growing body of climate change literature that is necessary for the development of effective mitigation and adaptation strategies in the Middle East.
气温长期变化的时空分析对气候变化研究和制定有效的减缓和适应战略至关重要。虽然对全球范围气温变化的研究相当多，但大多是线性趋势的研究，而对非线性趋势的时间序列分析还没有得到足够的重视。本研究对伊朗不同气候带47个气象站近40年(1978–2017年)的月、年平均气温（Tmean）、最高气温（Tmax）和最低气温（Tmin）的时空变化特征进行了分析。采用多项式拟合方案（Polytrend）对月度和年度气温数据进行趋势检测，并将其划分为线性和非线性（二次及三次方）类别。各气候带TmeanTmaxTmin的显著（不显著）趋势分别为41.1%（58.9%）、34.1%（65.9%）和46%（54%）。年Tmin（0.47°C decade−1）的增加幅度最大，年Tmax（0.4°C decade−1）的增加幅度最小。伊朗区域内，增加趋势（$\bar {x}$ = 37.2%）比减少趋势（$\bar {x}$ = 3.2%）有较高的空间覆盖范围。Tmean（65.3%）和Tmin（73.1%）的增温趋势主要集中在湿润气候区，而Tmax的增温趋势主要集中在半干旱（43.9%）和干旱（34.1%）气候区。所有Tmean（56.7%）、Tmax（67.8%）和Tmin（71.2%）以及月度和年度数据集均以正趋势的线性变化为主。此外，线性趋势变暖速率最高在年Tmin（0.83°C decade−1）和Tmean（0.46°C decade−1），而非线性趋势变暖速率最高在年Tmax（0.52°C decade−1）。伊朗的气温变化以线性趋势为主，特别是在湿润气候区，而非线性趋势（二次及三次方）主要分布在干旱区。本研究强调了伊朗气温的非线性变化及其时空分布，有助于更好地认识中东地区的气候变化和制定有效的缓解和适应战略。
• Fig. 1.  Synoptic station distribution in the different climate zones of Iran (Khalili, 1992; Rahimi et al., 2013).

Fig. 2.  Flowchart of our methodology for analyzing trends in air temperature using Polytrend (Jamali et al., 2014; shown in the dashed box to the right).

Fig. 3.  Distributions of (a) significant and non-significant trends, (b) significant increasing and decreasing trends, and (c) type of trends detected in monthly and annual Tmean time series in different climate zones over Iran.

Fig. 4.  Typical examples of Tmean time series with a detected trend type of (a, b) cubic, (c, d) cubic concealed, (e, f) quadratic, (g, h) quadratic concealed, (i, j) linear, and (k) no-trend. The dashed lines in the concealed trends denote non-significant linear fits.

Fig. 5.  Temporal distributions of (a) the significant and non-significant trends, (b) significant increasing and decreasing trends, (c) linear, quadratic, and cubic trends in monthly and annual time series of Tmean.

Fig. 6.  Distribution of significant trend types and trend magnitude (slope) in annual time series of Tmean.

Fig. 7.  As in Fig. 3, but for Tmax.

Fig. 8.  As in Fig. 5, but for Tmax.

Fig. 9.  As in Fig. 6, but for Tmax.

Fig. 10.  As in Fig. 3, but for Tmin.

Fig. 11.  As in Fig. 5, but for Tmin.

Fig. 12.  As in Fig. 6, but for Tmin.

###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## Forty Years of Air Temperature Change over Iran Reveals Linear and Nonlinear Warming

• 1. Faculty of Natural Resources, University of Tehran, 77871-31587, Karaj, Iran
• 2. Department of Technology and Society, Lund University, SE-22100, Lund, Sweden
• 3. Centre for Environmental and Climate Science, Lund University, SE-22362, Lund, Sweden

Abstract: Spatiotemporal analysis of long-term changes in air temperature is of prime importance for climate change research and the development of effective mitigation and adaptation strategies. Although there are considerable studies on air temperature change across the globe, most of them have been on linear trends and time series analysis of nonlinear trends have not received enough attention. Here, spatiotemporal patterns of monthly and annual mean (Tmean), maximum (Tmax), and minimum (Tmin) air temperature at 47 synoptic stations across climate zones in Iran for a 40-yr period (1978–2017) are analyzed. A polynomial fitting scheme (Polytrend) is used to both monthly and annual air temperature data to detect trends and classify them into linear and nonlinear (quadratic and cubic) categories. The significant (non-significant) trends in Tmean, Tmax, and Tmin across all climate zones are 41.1% (58.9%), 34.1% (65.9%), and 46% (54%), respectively. The highest magnitude of increasing trends is observed in the annual Tmin (0.47°C decade−1) and the lowest magnitude is for the annual Tmax (0.4°C decade−1). Across the country, increasing trends ($\bar {x}$ = 37.2%) have higher spatial coverage than the decreasing trends ($\bar {x}$ = 3.2%). Warming trends in Tmean (65.3%) and Tmin (73.1%) are mainly observed in humid climate zone while warming trends in Tmax are in semi-arid (43.9%) and arid (34.1%) climates. Linear change with a positive trend is predominant in all Tmean (56.7%), Tmax (67.8%), and Tmin (71.2%) and for both monthly and annual data. Further, the linear trends have the highest warming rate in annual Tmin (0.83°C decade−1) and Tmean (0.46°C decade−1) whereas the nonlinear trends have the highest warming rate in annual Tmax (0.52°C decade−1). The linear trend type is predominant across the country especially in humid climate zones whereas the nonlinear trends (quadratic and cubic) are mainly observed in the arid climate zones. This study highlights nonlinear changes and spatiotemporal trends in air temperature in Iran and contributes to a growing body of climate change literature that is necessary for the development of effective mitigation and adaptation strategies in the Middle East.

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