Investigation of climate change effects on early autumn chilling and late spring chilling in Iran using SDSM

Increasing the greenhouse gases change the global climate and lead to global warming. Global warming makes change in dates of early and late chilling. These dates are very important in agricultural meteorology. Recognizing these changes can help us managing and scheduling for future.
General Circulation Models (GCMs) indicate that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Unfortunately, GCMs are restricted in their usefulness for local impact studies by. Their coarse spatial resolution (typically of the order 50,000 km2) and inability to resolve important sub–grid scale features such as clouds and topography. Statistical downscaling methodologies have several practical advantages over dynamical downscaling approaches. In situations where low–cost, rapid assessments of localized climate change impacts are required, statistical downscaling (currently) represents the more promising option. In this manual we describe a software package, and accompanying statistical downscaling methodology, that enables the construction of climate change scenarios for individual sites at daily time–scales, using grid resolution GCM output. Stochastic downscaling approaches typically involve modifying the parameters of conventional weather generators such as WGEN or LARS–WG. The WGEN model simulates precipitation occurrence using two–state, first order Markov chains: precipitation amounts on wet days using a gamma distribution; temperature and radiation components using first–order trivariate autoregression that is conditional on precipitation occurrence. Climate change scenarios are generated stochastically using revised parameter sets scaled in direct proportion to the corresponding parameter changes in a GCM. The main advantage of the technique is that it can exactly reproduce many observed climate statistics and has been widely used, particularly for agricultural impact assessment. Furthermore, stochastic weather generators enable the efficient production of large ensembles of scenarios for risk analysis. The key disadvantages relate to the arbitrary manner in which precipitation parameters are adjusted for future climate conditions, and to the unanticipated effects that these changes may have on secondary variables such as temperature.
Regression: Regression–based downscaling methods rely on empirical relationships between local scale predictands and regional scale predictor(s). Individual downscaling schemes differ according to the choice of mathematical transfer function, predictor variables or statistical fitting procedure. To date, linear and non–linear regression, artificial neural networks, canonical correlation and principal components analyses have all been used to derive predictor–predictand relationships. The main strength of regression downscaling is the relative ease of application, coupled with their use of observable trans–scale relationships. The main weakness of regression–based methods is that the models often explain only a fraction of the observed climate variability (especially in precipitation series). In common with weather typing methods, regression methods also assume validity of the model parameters under future climate conditions, and regression–based downscaling is highly sensitive to the choice of predictor variables and statistical transfer function (see below). Furthermore, downscaling future extreme events using regression methods is problematic since these phenomena, by definition, tend to lie at the limits or beyond the range of the calibration data set.
One of the methods to study of future climate is using the general circulation models, but these models have low temporal and spatial resolution and they can’t show local changes in climate of a region. One of the methods to downscale the output of these models is using SDSM model. This research was tried using this method to study future climate of 12 synoptic stations in Iran. First we took daily minimum temperature, maximum temperature and precipitation of these stations from 1961 to 2005. These data are inputs of SDSM and the outputs of GCMs were downscaled with these observed data. Future climate was predicted for these stations. Then the dates of early and late chilling were extracted of predicted temperature for future climate in these stations.
The results show that the date of late spring chilling increases and the date of first autumn chilling decreases in all station but on Rasht station the both parameters for all scenarios decrease and in Gorgan station just for a2 scenario the dates of late chilling decrease.
This research shows that in the major of stations growing season decreases. So we recommend that farmers and gardeners change their crops or they cultivate early crops. In the rest of stations they should cultivate late crops or alternatively.