SPATIO‐TEMPORAL VARIATIONS OF SEVEN WEATHER VARIABLES IN IRAN: APPLICATION OF CRU TS AND GPCC DATA SETS†

Abstract

AbstractIran's climate‐sensitive agriculture and water resources are vulnerable to climate change and investigation of climatic trends helps in preparing adaptation strategies. Weather stations are sparsely distributed and access to complete weather data is limited. In such situations, gridded global/regional data sets are promising alternatives. Here, monthly time series of seven weather variables (i.e. monthly averages or monthly totals of daily values) were obtained from the Climatic Research Unit TS V4.01 and Global Precipitation Climatology Centre V7 gridded data sets in 675 grid cells covering the country and analysed over the periods 1957–1986 and 1987–2016 at annual, seasonal and monthly scales. Over the two periods and at a national scale, mean temperature has increased by 0.004 (P = 0.717) and 0.04 °C yr−1 (P = 0.000), while the diurnal temperature range has not significantly changed (P > 0.6). Annual total precipitation experienced an insignificant increase (0.81 mm yr−1; P = 0.666) over the first period but declined by 2.12 mm yr−1 (P = 0.041) over the second. Potential evapotranspiration (PET) has increased by 0.32 (P = 0.398) and 1.43 mm yr−1 (P = 0.015), respectively. Since 1987, significant increasing trends in temperature were detected all over the country. While significant increasing trends in annual precipitation were detected in the central regions and south‐west over the first period, decreasing trends prevailed during 1987–2016 in the south, southwest and east with winter being the largest contributor to annual trends. Over the last three decades, annual PET has increased mostly in the north‐west and south‐east while significant increasing trends were detected in 89% of grid cells, except in a few cells in the north‐east. Cloud cover, vapour pressure and frequency of frost days were also analysed. These results are crucial for policy‐makers, researchers and engineers in the country and internationally who usually base their decisions and designs on outdated data sparsely distributed in space. © 2020 John Wiley & Sons, Ltd.