Four Decades of Air Temperature Data over Iran Reveal Linear and Nonlinear Warming

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 is considerable research on air temperature change across the globe, most of it has been on linear trends and time series analysis of nonlinear trends has not received enough attention. Here, we analyze 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-year time period (1978–2017). We applied a polynomial fitting scheme (Polytrend) to both monthly and annual air temperature data to detect trends and classify them into linear and nonlinear (quadratic and cubic) categories. The highest magnitude of increasing trends were observed in the annual Tmin (0.47 °C per decade) and the lowest magnitude was for the annual Tmax (0.4°C per decade). Across the country, increasing trends (x̄ = 37.2%) had higher spatial coverage than the decreasing trends (x̄ = 3.2%). Warming trends in Tmean (65.3%) and Tmin (73.1%) were mainly observed in humid climate zone while warming trends in Tmax were in semi-arid (43.9%) and arid (34.1%) climates. Linear change with a positive trend was predominant in all Tmean (56.7%), Tmax (67.8%) and Tmin (71.2%) and for both monthly and annual datasets. Further, the linear trends had the highest warming rate in annual Tmin (0.83°C per decade) and Tmean (0.46°C per decade) whereas the nonlinear trends had the highest warming rate in annual Tmax (0.52°C per decade). The linear trend type was predominant in humid climate zones whereas the nonlinear trends (quadratic and cubic) were mainly observed in the arid climate zones