واکاوی اثر سیگنال‌های اقلیمی بر بارش دوره‌های خشک و تر (مطالعه موردی: بخشی از حوزه آبخیز خلیج‌فارس و دریای عمان)

در این مقاله به بررسی تاثیر شاخص های اقلیمی بر میزان بارش دو دوره خشک و تر در حوزه آبخیز خلیج‌فارس و دریای عمان با استفاده از تجزیه‌وتحلیل عاملی پرداخته شده است. لذا داده های دو دوره یادشده در 22 ایستگاه باران‌سنجی در حوزه آبخیز خلیج‌فارس و دریای عمان و 13 سیگنال اقلیمی در طی دوره آماری 2014-1970 مورد بررسی قرار گرفت. با توجه به نتایج تحلیل عاملی داده ها با استفاده از نرم‌افزار past از بین 40 سیگنال اقلیمی، مشخص شد در دوره خشک در حوزه آبخیز خلیج‌فارس و دریای عمان، سه مولفه اصلی در مجموع 96/6 درصد از واریانس داده‌های اولیه را تبیین می‌کنند که برای مولفه اول، دوم و سوم به‌ترتیب، 83/1، 9/1 و 2/6 درصد می‌باشند و در دوره تر، سه مولفه 97/8 درصد از واریانس داده‌های اولیه را تبیین میکنند که مولفه اول، دوم و سوم، به‌ترتیب 91/1، 4/3 و 2/4 درصد از واریانس داده‌های اولیه را تبیین میکنند. مشخص شد که مهم‌ترین عوامل موثر بر تغییرات بارش در دوره خشک بر روی اقیانوس اطلس و پس از آن قطب (با واریانس 92/2 درصد) واقع شده است و بخش کمی از تغییرپذیری بارش (با واریانس 2/6 درصد) در دوره خشک مربوط به نوسانات چند دهه‌ای اقیانوس اطلس شمالی است؛ به‌طوری‌که در این دوره بالاترین همبستگی بین شاخص nao و ao (به‌ترتیب 100 و 95 درصد) با ایستگاه‌های مورد مطالعه وجود دارد. در دوره تر مهم‌ترین کانون تغییرات از سوی قطب شمال و تغییرات دمای اقیانوس آرام جنوبی (با واریانس 95/4 درصد) واقع شده و پدیده نوسانات جنوبی تنها تغییرات کمی (2/4 درصد از واریانس) را بر عهده دارد. به‌طوری‌که در دوره تر، 85 و 65 درصد ایستگاه‌ها به‌ترتیب بالاترین همبستگی معنی‌‌دار را با شاخص ao و nino1+2 دارند.

Impact of climatic signals on the wet and dry season precipitation (case study: Persian Gulf and Oman Sea watersheds)

Recently, the occurrences of extreme events such as droughts have been on the rise almost worldwide. Several researchers speculated the chance of an increase in meteorological extreme conditions in relation to local climate change. Rainfall is the final response to complex global atmospheric phenomena and longterm prediction of rainfall remains a challenge for years to come. An accurate longterm rainfall prediction is necessary for water resources management, food production and maintaining flood risks. Several largescale climate phenomena affect the occurrence of rainfall around the world; of these large scale climate modes El Nino Southern Oscillation (ENSO) and Multivariate ENSO Index (MEI) are well known. Many studies have tried to establish the relationship between these climate modes for daily, monthly and seasonal rainfall occurrence around the world but the majority of these studies have not considered the effect of lagged climate modes on future monthly rainfall predictions.Interannual to multidecadal natural local climate variability is afflicted by the El Niño/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). ENSO phenomenon on the tropical Pacific and also PDO are quite important because of their enormous impacts on hydrometeorological disasters like droughts and floods. The El NiñoSouthern Oscillation (ENSO) is strongly linked to the interannual to interseasonal modifications of Sea Surface Temperature (SST) over the Pacific Ocean equators. On the other hand, the Decadal Pacific Oscillation (PDO) is related to near decadal fluctuations of the Pacific SSTs in the northeastern parts of the ocean. The influence of these oscillations on the global climate is generally more obvious when the ENSO or PDO is in its extreme condition. For such circumstances, the SST deviance over a perdefined ocean waters are highly positive or negative (positive or negative period, respectively).Identifying factors impacting the fluctuations in rainfall and forecasting seasonal trends over several months before any significant role in the planning and development of water resources, are among the significant factors impacting different areas of climate signals. Principal component analysis (PCA) was used in this study to explore the impact of climatic indices on the amount of wet and dry season’s precipitation variability in the Persian Gulf and Oman Sea watershed. PCA is used to reduce the dimensionality of spatially distributed time series of precipitation and to interpret spatial patterns, from a statistical viewpoint, through the distribution of significant eigenvectors that explain an important portion of the series variability. PCA can summarize the prevailing variability in a number of dependent factors into fewer principal components.Wet and dry season data were explored from 22 rain gauge stations in the Persian Gulf and Oman Sea watershed in which 40 climatic indices are analyzed in the period of 19702014.  PCA analysis is performed using PAST software for the area with 40 synoptic stations. Result showed that there are three variables that determine more than 96.6% of variance consists of NAO, AO and AMO in the dry season, while AO, Nino1+2 and SOI determine more than 97.8% of variance in the wet season. These climate indices can be attributed to precipitation changes over the Persian Gulf and Oman Sea in the dry and wet season, respectively.The study also shows correlation between SPI of 23 rainfall gauges of the Persian Gulf and Oman Sea watershed and climatic signals. These are significant in large number of weather stations. Numbers of stations with significant correlations are 100% and 95% with NAO and AO in the dry season, whereas 85% and 65% have significant correlations with AO and Nino1+2 in wet season. The linkage between climatic signals and precipitation presented in this paper can be used as one of the important components for wet and dry season precipitation prediction over Iran southern stations.