The tenth IMSC, Beijing, China, 2007 - International Meetings on ...

as Homogen Data, Gap, Index, Statistic and Return Period for calculating extreme indices ,
their trends and the return period of climatic extreme indices. <strong>The</strong> geostatistical methods such
as weighting with inverse distances and the Kriging methods are used for interpolations and
data production. In ECIS, the trends is calculated on the basis of daily series of temperature
and precipitation observations from 16 synoptic stations in Iran in the period 1951-2005.<strong>The</strong>
indices are selected from the list of climate change indices recommended by CLIVAR.Results
indicate that the symmetric warming in the tails of most indices is seen over most regions of
Iran except in the northwest region. Also, results show that the frequency of warm (cold)
extreme indices is increased (decreased) and the precipitation extreme indices vary
depending upon geographic location. Finally, the observed changes in indices of climate
extremes show that a large proportion of Iran was increasingly affected by a significant change
in climate extremes and the frequency of extreme events is increased.
Keywords: Trend, Extreme Event, Climate Index, Frequency.
Trend analysis of agrometeorological and meteorological indices of climate change in several
climatic regions of Iran
Speaker: Vahid Varshavian
Vahid Varshavian
University of Tehran, Iran
vahid.varshavian@gmail.com
Nozar Ghahreman
College of Soil and Water Engineering, University of Tehran, Karaj,Iran
To detect the possible occurrence of climate change it is necessary to define a set of
agrometeorological indices, derived from the climatic data that are capable of indicating the
consequences of climate change on crop production. In this study, maximum and minimum
daily air temperatures (Tmax and Tmin), mean daily air temperature (Tmean) and total daily
rainfall data in a 40 years period (1961-2004) of five synoptic stations, namely Tehran, Tabriz,
Zahedan and Bushehr were collected. <strong>The</strong>se stations represent different climates of Iran
based on Koppen climatic classification. <strong>The</strong> collected data were used to calculate extreme
temperature and rainfall values including the magnitudes of the lower (1st, 5th, 10th) and
upper (90th, 95th, 99th) percentile threshold values for each year and number of days below
the lower threshold values and above the upper threshold values, date of first autumn frost, AF
(day), date of last spring frost, SF (day) and length of frost free period LFF (day). Besides,
number of frost days, rainy days, snowy days and annual rainfall amounts were derived using
the monthly data obtained from the Islamic Republic of Iran Meteorological Organization
(IRIMO). In case of Bushehr station, based on its climate, (Desert climate) frost dates and
numbers of snowy days were not included in the analysis. All time series have been checked
for normality with the Kolmogorov-Smirnov test. Time trends for all variables were analysed
using parametric and nonparametric techniques (Least squares linear regression, Pearson,
Spearman and Kendall's τ-significance test).
25