Modulation of wet‐season rainfall over Iran by the Madden–Julian Oscillation, Indian Ocean Dipole and El Niño–Southern Oscillation

Pourasghar, Farnaz; Oliver, Eric C. J.; Holbrook, Neil J.

doi:10.1002/joc.6057

Cited by 14 publications

(13 citation statements)

References 35 publications

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“…The results of the ENSO influence are somewhat consistent with the report of Karabörk and Kahya (2009) about the significant ENSO contribution to Turkey's precipitation and rivers streamflows, but contradict with other regional studies (e.g., Cullen et al 2002;Pourasghar et al 2019). Cullen et al (2002) pointed to the strong influence of the NAO to six regional rivers discharge, but we have obtained only weak SRCs between the precipitations and NAO, as well with the NDVIs.…”

Section: ) Possible Predictors Of Ndvicontrasting

confidence: 61%

“…Cullen et al (2002) pointed to the strong influence of the NAO to six regional rivers discharge, but we have obtained only weak SRCs between the precipitations and NAO, as well with the NDVIs. More recently, Pourasghar et al (2019) indicated that rainfalls in Iran (October-May) are more affected by the IOD than by ENSO. Indeed, we have obtained, respectively, a moderate SRC of 0.7 and 0.55 between March and April NDVI and December month of IOD index, named the dipole mode index (DMI), but these values are almost the same as between NDVIs and ENSO links.…”

Section: ) Possible Predictors Of Ndvimentioning

confidence: 99%

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Tigris Basin Landscapes: Sensitivity of Vegetation Index NDVI to Climate Variability Derived from Observational and Reanalysis Data

Alhumaima

Abdullaev

2020

Earth Interactions

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The primary aim of this work is to study the response of the normalized difference vegetation index (NDVI) of landscapes in the Lower Tigris Basin to current global and regional climate variability presented, respectively, by the global circulation indices and monthly temperatures and precipitation extracted from five observational/reanalysis datasets. The second task is to find the dataset that best reflects the regional vegetation and climate conditions. Comparison of the Köppen-Trewartha bioclimatic landscapes with the positions of botanical districts, land cover types, and streamflow estimates led to the conclusion that only two datasets correctly describe regional climatic zones. Therefore, searching for the NDVI response to regional climate variability requires the use of normalized analogues of temperatures and precipitations, as well as the Spearman Rank Correlation. We found that March/April NDVI, as proxies of the maximum biological productivity of the regional landscapes, are strongly correlated with October- March precipitation derived from three datasets and January-March temperatures derived from one dataset. We discovered the significant impact of autumn-winter El-Niño-Southern Oscillation and winter Indian Oceanic Dipole states on regional weather (e.g. all recent five severe droughts occurred during strong La Niña events). However, the strength of this impact on the vegetation was clearly linked to the zonal landscape type. By selecting pairs of the temperature/precipitation time-series that best correlated with NDVI at a given landscape, we have built a synthetic climate dataset. The landscape approach presented in this work can be used to validate the viability of any dataset when assessing the impacts of climate change and variability on weather-dependent components of the Earth's surface.

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Section: ) Possible Predictors Of Ndvicontrasting

confidence: 61%

Section: ) Possible Predictors Of Ndvimentioning

confidence: 99%

Tigris Basin Landscapes: Sensitivity of Vegetation Index NDVI to Climate Variability Derived from Observational and Reanalysis Data

Alhumaima

Abdullaev

2020

Earth Interactions

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“…Some investigators have listed the prevailing years of the positive or negative IOD phase (Meyers et al ., 2007; Lestari et al ., 2018; Pourasghar et al ., 2019). We used these provided lists and particularly the list obtained by the Australian Bureau of Meteorology (http://www.bom.gov.au/climate/iod/) to re‐generate Figure 3a,b for those days of the SWPP or SWWD that concur with the IOD negative phase (Figure S1a,b, Supporting Information).…”

Section: Resultsmentioning

confidence: 99%

“…The above‐mentioned discussion shows some discrepancies with the findings of Pourasghar et al . (2019). They reported that “the negative (positive) IOD strengthens (suppresses) the MJO‐rainfall relationship in the wet and dry MJO phases.” However, our study shows that, for the Nov–Apr period, the prevalence of MJO phase 1 suppresses the evolution of the IOD negative phase and subsequently, diminishes the effect of this negative phase on precipitation variability over southwestern parts of Iran.…”

Section: Resultsmentioning

confidence: 99%

“…Heavy precipitation events over the central plateau of Iran are attributed to the persistence of a deep trough over the eastern Mediterranean Sea (Rousta et al ., 2016). While the fall and early winter precipitation in Iran have significant relationships with the Indian Ocean Dipole (IOD) and ENSO, the inter‐annual variation in late winter and spring precipitation is more strongly influenced by modes of variability over the Mediterranean Sea (Pourasghar et al ., 2012; Pourasghar et al ., 2019). In most parts of Iran, rainy months start from early November and end in late April.…”

Section: Introductionmentioning

confidence: 99%

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Introducing new climate indices for identifying wet/dry spells within an Madden‐Julian Oscillation phase

Nazemosadat

Shahgholian

Ghaedamini

et al. 2020

Intl Journal of Climatology

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Daily values of precipitation in southwestern parts of Iran along with the corresponding Madden-Julian oscillation (MJO) phase are analysed to introduce some auxiliary indicators that can identify the wet and dry spells within a particular MJO phase. As a case study, the identification procedure focuses on phase 1 during the rainy season (Nov-Apr). The study shows that this phase contains two opposite spells, referred to as spells with pervasive precipitation (SWPP) and spells with widespread dryness (SWWD). Although the precipitation characteristics are essentially different between SWPP and SWWD, their associated MJO indices are found statistically identical. Therefore, the MJOprecipitation relationship fails to identify the wet/dry spells within an MJO phase. To resolve this deficiency, other climate indicators, which behave differently between these two spells, are introduced. The westward moisture flux over equatorial parts of the Indian Ocean and the northeastward moisture flux over the North African tropics are up to seven times stronger in the SWPP compared to SWWD. The dominance of a strong cyclonic (anticyclonic) circulation over the eastern side of the Mediterranean Sea and the negative (positive) west-east temperature gradient over southern parts of Iran are introduced as SWPP (SWWD) indicators. The study reveals that the SWPP (SWWD) coincides with a strong (weak) convection dipole that resembles the positive Indian Ocean dipole. The intensified (weakened) wind speed in the subtropical jet stream and persistence of the jet-exit-regions above Iran (Egypt and Saudi Arabia) are other SWPP (SWWD) indicators. Negative (positive) anomalies of sea surface temperature over the North Atlantic Ocean, the Black, Mediterranean, and Caspian Seas, as well as the Persian Gulf, signify the prevalence of the SWPP (SWWD). To the best of our knowledge, this is the first time that auxiliary indices are utilized to improve the MJO-precipitation relationship in southwestern Iran.

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Influence of the MJO on daily surface air temperature over Iran

Pourasghar

Oliver

Holbrook

2021

Intl Journal of Climatology

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The influence of the Madden-Julian Oscillation (MJO) on surface air temperature over Iran is examined using daily data from meteorological stations from 1979-2015. Composites of daily surface air temperature anomalies are positive in MJO phases 1 and 8 and negative in MJO phases 3-4 with broader region positive tendencies also in MJO phases 2 and 7, and negative tendencies in MJO phases 5-6. This variability is associated with horizontal temperature advection, whereby the southerly (northward) winds act to heat and the northerly (southward) winds cool Iran, in association with the MJO. Further, we find that daily minimum surface air temperatures respond more strongly to the MJO than do daily maximum surface temperatures.These signals correspond to cloudy and humid conditions.

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Modulation of wet‐season rainfall over Iran by the Madden–Julian Oscillation, Indian Ocean Dipole and El Niño–Southern Oscillation

Cited by 14 publications

References 35 publications

Tigris Basin Landscapes: Sensitivity of Vegetation Index NDVI to Climate Variability Derived from Observational and Reanalysis Data

Tigris Basin Landscapes: Sensitivity of Vegetation Index NDVI to Climate Variability Derived from Observational and Reanalysis Data

Introducing new climate indices for identifying wet/dry spells within an Madden‐Julian Oscillation phase

Influence of the MJO on daily surface air temperature over Iran

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