A new approach to downscaling and project of the climatic components, with emphasis on the temperature parameter (Case Study: Golestan province)



Output of global climatic scenarios resulted from large-scale predictions (Usually 125 to 500 km network) of GSM models are not appropriate for important applications. Because small-scale spatial variability due to factors such as land cover, topography, etc., has a significant impact on climatic variables of desired area and some processes such as runoff are sensitive to this variability. Therefore, to explore the values of climatic components and to achieve a clear picture of future changes in climate in different regions of the earth, Large-scale output of general circulation models of the atmosphere, are downscaled. In this regard, there are various statistical and dynamical methods for downscaling, each with their own strengths and weaknesses. This study tries to using a new statistical approach predict and downsize maximum monthly amount of components and minimum temperature at weather stations in Golestan province in the context of changes in climate caused by human activity. So therefore as much as possible, it tries to provide a clear picture of future climate change for the studied area.
In this study, in order to climatic prediction of the output of general circulation model of climate, HadcM3 with two scenarios A and B were used. It should be noted that
Atmospheric general circulation model outputs of HadcM3, include26 components of the general circulation of the atmosphere which have been provided for all past and future
decades with a resolution of 3.75 × 2.5 ° for the entire globe. 26-fold output of HadcM3 model includes sea level pressure, power flow, and the orbital velocity of the wind,
meridional velocity, volubility, wind direction, divergence, high pressure, relative humidity, Specific humidity and temperature elevation of 2 meters above the ground which are considered for three levels of SLP, 850 and 500 hPa heights. It is noticeable that in the later stages, the 26 component will be used as predictors.
Then it is noteworthy that 26 predictor variables in a space of 3.75 × 2.5 °, In order to predict the temperatures and the location could not be a suitable solution for prediction and downscaling. For example, in the synoptic view, detection of temperature changes in an area is not only dependent on the changes of pressure patterns in that specific pixel
located in that station, but the temperature changes of the desired station is dependent on high and low pressure or heat waves and cold flaps that are part of this system whose dimensions are sometimes more than one pixel. Based on abovementioned facts, as it was described, 2 predicting component of a pixel cannot provide proper results, to do this, following successive tests of this output, it was concluded that the best range for downscaling of climatic parameters of Golestan province includes a range of 15 workspaces (i.e. 15 pixels) that each pixel has a total of 26 variables, including 390 Total predictor variable.
Based on the results of this study it was shown that in general, more areas of the province based on scenario B, in comparison with scenario A, will have an increase in temperature. While, in a comparison of different months in a year, t was concluded that May, August, September and Feb respectively, for the factors of maximum temperature based on scenario A and B, and minimum temperature based on those two scenarios have experienced the maximum temperature increase for future years and on the other hand, the maximum area of the province, in terms of temperature decrease considering two factors of minimum and maximum temperatures based on scenario A belongs to December and for two factors of maximum and minimum temperature based on scenario B belongs to September. Also, map output for temperature changes of future decades of Golestan, confirms this fact that the maximum of increase and decrease in temperature in different areas of province, based on different months in a year, do not follow a specific pattern, so that in each month, a different spatial patterns of temperature change can be seen. Thus it seems that for risk management in order to reduce the harmful effects of temperature changes in different areas, different models and scenarios should be defined separately for each month.
Nevertheless, these inevitable uncertainties in climatic predictions result from different factors such as Uncertainty of the values of meteorological observations, the output of general circulation models of the atmosphere and the uncertainty arising from the use of stem downscaling methods. What is important in this context is to be aware of these uncertainties, as well as to make efforts to reduce them as much as possible and to consider them in regional planning which all these cases have been taken into in this study.


Main Subjects

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