پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شماره‌ها | 6,225 |
| تعداد مقالات | 67,685 |
| تعداد مشاهده مقاله | 114,999,671 |
| تعداد دریافت فایل اصل مقاله | 89,676,288 |
رویکردی متفاوت در ریز مقیاس نمایی و پیش یابی اقلیمی مولفه دما (مطالعه موردی استان گلستان) | ||
| فیزیک زمین و فضا | ||
| مقاله 17، دوره 42، شماره 1، خرداد 1395، صفحه 197-212 اصل مقاله (2.12 M) | ||
| شناسه دیجیتال (DOI): 10.22059/jesphys.2016.54495 | ||
| نویسندگان | ||
| غلامرضا روشن* ؛ عبدالعظیم قانقرمه | ||
| استادیار اقلیم‌شناسی، گروه جغرافیا، دانشگاه گلستان، گرگان، ایران | ||
| چکیده | ||
| پیش یابی تغییرپذیری زمانی- مکانی متغیرهای اقلیمی در مقیاس محلی و منطقه ای جهت برنامه ریزی های آتی در سراسر جهان، ضرورتی اجتناب ناپذیر است. لذا پژوهش حاضر سعی دارد با استفاده از یک رویکرد جدید، به پیش یابی و سپس ریزمقیاس نمایی مقادیر مولفه دمایی ایستگاههای هواشناسی استان گلستان برای دوره آماری 1391 تا 1450 بپردازد. در روش پیشنهادی جهت پیش یابی و ریزمقیاس نمایی مولفه دما، از پیش بینی کننده 26 مولفه مدل گردش عمومی جو HadCM3 برای 15 پیکسل که ابعاد هر پیکسل به میزان 3.75×2.5 درجه می باشد استفاده گردید، که بر این اساس تعداد پیش گوکننده ها به 390 مولفه بسط داده شد. در ادامه این تحقیق جهت عملکرد مدل پیشنهادی از 5 ایستگاه شاهد با توجه به شرایط متفاوت توپوگرافی و اقلیمی در سطح استان استفاده شد که نتایج موید اعتبار و تطبیق بالای داده های شبیه سازی شده در مقایسه با داده های مشاهداتی سالهای 1350 تا 1390 می باشد. در نهایت خروجیها نشان داده است که با توجه به تغییرات اقلیمی دهه های آتی، افزایش دما برای اکثر ماههای سال انکارناپذیر بوده بگونه ای که در مقایسه بین ماههای مختلف سال نیز این نتیجه استنتاج شد که، پهنه های دمایی استان در ماههای اردیبهشت، مرداد، شهریور و بهمن بر اساس سناریوها و مولفه های مختلف دمایی، بیشینه مساحت را از لحاظ افزایش دما در مقایسه با سایر ماههای سال تجربه خواهند نمود. | ||
| کلیدواژه‌ها | ||
| ریز مقیاس نمایی آماری؛ شبیه سازی؛ تغییر اقلیم؛ مدلهای گردش عمومی جو | ||
| عنوان مقاله [English] | ||
| A new approach to downscaling and project of the climatic components, with emphasis on the temperature parameter (Case Study: Golestan province) | ||
| نویسندگان [English] | ||
| Gholamreza Roshan؛ Abdolazim Ghangherme | ||
| چکیده [English] | ||
| Summery 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. | ||
| کلیدواژه‌ها [English] | ||
| Statistical Downscaling, simulation, climate change, general circulation model | ||
| مراجع | ||
|
آذرانفر، آ.، ابریشمچی، ا. و تجریشی، م.، 1386، ارزیابی اثرات تغییر اقلیم بر بارش و دما در حوضۀ آبریز زاینده‌رود با استفاده از خروجی مدلهای چرخش عمومی، دومین همایش ملی منابع آب ایران، اصفهان. آشفته، پ. و مساح بوانی، ع.، 1389، تأثیر تغییر اقلیم بر دبی‌های حداکثر مطالعه موردی، حوضۀ آیدوغموش، آذربایجان شرقی، م. علوم و فنون کشاورزی و منابع طبیعی، علوم آب و خاک، 14(53)، 25-39 شمسی‌پور، ع.، 1392، مدل‌سازی آب و هوایی نظریه و روش، انتشارات دانشگاه تهران، ص 297. نصرتی، ک.، زهتابیان، غ. ر.، مرادی، ا. و شهبازی، ا.، 1386، ارزیابیروششبیه‌سازیتصادفیبرایتولیدداده‌های هواشناسی، پژوهش‌های جغرافیای طبیعی، 62، 1-9. Bürger, G., Murdock, T. Q., Werner, A. T., Sobie, S. R. and Cannon, A. J. 2012, Downscaling extremes - an intercomparison of multiple statistical methods for present climate, Journal of Climate, 25, 4366-4388.
Cannon, A. J. 2012, Regression-guided clustering: a semisupervised method for circulation-to-environment synoptic classification, Journal of Apply Meteorology and Climatology, 51, 185-190.
Dibike, Y. B. and Coulibaly, P., 2006, Temporal neural networks for downscaling climate variability and extremes, Neural Networks, 19, 135-144
Farajzadeh, M., Oji, R., Cannon, A. J., Ghavidel, Y. and Massah Bavani, A. R., 2014, An evaluation of single-site statistical downscaling techniques in terms of indices of climate extremes for the Midwest of Iran, Theoretical and Applied Climatology, 10.1007/s00704-014-1157-4.
Frost, A. J., Charles, S. P. and Timbal, B., 2011, A comparison of multi-site daily rainfall downscaling techniques under Australian conditions, Journal of Hydrology, 408, 1-18.
Furrer, E. M. and Katz, R. W., 2007, Generalized linear modeling approach to stochastic weather generators, Climate Research, 34, 129-144.
Ghanghermeh, A., Roshan, G. R., Orosa, J., Calvo-Rolle, L. and Costa Ángel, M., 2013, New climatic indicators for improving Urban Sprawl: a case study of Tehran city, Entropy, 15, 999-1013.
Roshan, G. R., Ghanghermeh, A., Nasrabadi, T. and Bahari Meimandi, J., 2013, Effect of global warming on intensity and frequency curves of precipitation, case study of Northwestern Iran, Water Resour Manage, doi: 10.1007/s11269-013-0258-7.
Roshan, G. R., Ghanghermeh, A. and Orosa, J., 2013, Thermal comfort and forecast of energy consumption in Northwest Iran, Arab J Geosci, doi: 10.1007/s12517-013-0973-7.
Michelangeli, P. A., Vrac, M. and Loukos, H., 2009, Probabilistic downscaling approaches: application to wind cumulative distribution functions, Geophys Res Lett, 36, L11708, doi: 10.1029/2009GL038401.
Moradi, I. and Nosrati, K., 2002, Evaluation of stoshastic simulation methods for generating meteorological data, Procceding of 3th International Iran and Russia Conference Agricalture and Natural Resources, Moscow, 246-251.
Soltani, A., Latifi, N. and Nasiri, M., 2000, Evaluation of WGEN for generation long term weather data for crop simulations, Agric. For. Meteorol., 102: 1-12.
Yarnal, B., 1993, Synoptic climatology in environmental analysis, Wiley Publisher. 256pp.
Wilby, R. L., Dawson, C. W. and Barrow, E. M., 2002, A decision support tool for the assessment of regional climate Impacts, Environmental Modeling & Software, http://dx.doi.org/10.1016/S1364-8152 (01)00060-3.
Wilby, R. L. and Dawson, C. W., 2012, The statistical downscaling model: insights from one decade of application, Int J Climatol., doi: 10.1002/joc.3544.
Wood, A., Maurer, E., Kumar, A. and Lettenmaier, D. P., 2002, Long-range experimental hydrologic forecasting for the eastern United States, J. Geophys. Res., 107. 4429, doi: 10.1029/2001JD000659.
| ||
|
آمار تعداد مشاهده مقاله: 2,297 تعداد دریافت فایل اصل مقاله: 1,042 |
||