Precipitation and temperature of the southwest Caspian Sea region during the last 55 years: their trends and teleconnections with large‐scale atmospheric phenomena

Molavi-Arabshahi, Mahboubeh; Arpe, Klaus; Leroy, Suzanne A.G.

doi:10.1002/joc.4483

Cited by 63 publications

(33 citation statements)

References 42 publications

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“…This is also the case for the Caspian Hyrcanian Forests ecoregion in the early fall. The increase of rainfall on the north slopes of the mountain ranges of the Alborz [62] can increase dead fine fuels' moisture content while the effect of rainfall on dead fine fuel moisture content is quite a complex process [63]. For example, less rain could be absorbed by dead fine fuel when initial moisture content in fuel is high [64].…”

Section: Mapping Leaf Moisture and Dry Matter Content (Mci) Using Etmmentioning

confidence: 99%

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

2016

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Vegetation moisture and dry matter content are important indicators in predicting the behavior of fire and it is widely used in fire spread models. In this study, leaf fuel moisture content such as Live Fuel Moisture Content (LFMC), Leaf Relative Water Content (RWC), Dead Fuel Moisture Content (DFMC), and Leaf Dry Matter Content (LDMC) (hereinafter known as moisture content indices (MCI)) were calculated in the field for different forest species at 32 sites in a temperate humid forest (Zaringol forest) located in northeastern Iran. These data and several relevant vegetation-biophysical indices and atmospheric variables calculated using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data with moderate spatial resolution (30 m) were used to estimate MCI of the Zaringol forest using Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) methods. The prediction of MCI using ANN showed that ETM+ predicted MCI slightly better (Mean Absolute Percentage Error (MAPE) of 6%-12%)) than MLR (MAPE between 8% and 17%). Once satisfactory results in estimating MCI were obtained by using ANN from ETM+ data, these data were then upscaled to estimate MCI using MODIS data for daily monitoring of leaf water and leaf dry matter content at 500 m spatial resolution. For MODIS derived LFMC, LDMC, RWC, and DLMC, the ANN produced a MAPE between 11% and 29% for the indices compared to MLR which produced an MAPE of 14%-33%. In conclusion, we suggest that upscaling is necessary for solving the scale discrepancy problems between the indicators and low spatial resolution MODIS data. The scaling up of MCI could be used for pre-fire alert system and thereby can detect fire prone areas in near real time for fire-fighting operations.

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Section: Mapping Leaf Moisture and Dry Matter Content (Mci) Using Etmmentioning

confidence: 99%

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

2016

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“…This can be linked to a poleward shift in the storm tracks which is accompanied by a poleward shift in midlatitude precipitation (Yin, 2005;Meehl et al, 2007;Chang et al, 2012). Moreover, a change in the frequency and magnitude of winter blocking events during NAO may also affect precipitation over the southwestern CS (Molavi-Arabshahi et al, 2015).…”

Section: Projected Changes In the Nao Signal And Influence On Wintementioning

confidence: 99%

“…Atmospheric circulation features influencing the CS catchment area include the mid-latitude westerly winds and, at upper tropospheric levels, the polar and subtropical jet streams (Rodionov, 1994;Molavi-Arabshahi et al, 2015). Several air masses affect the regional climate, that is, cold air from the Arctic, moist temperate air masses from the Atlantic Ocean, warm subtropical air masses arising from the Black Sea, and dry continental air masses from the east.…”

Section: Introductionmentioning

confidence: 99%

Past and future impact of the winter North Atlantic Oscillation in the Caspian Sea catchment area

Nandini-Weiss

Prange

Arpe

et al. 2019

Intl Journal of Climatology

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The Caspian Sea level (CSL) has undergone variations of more than 3 m during the past century with important implications for the life of coastal people, economy and the ecosystem. The origin of these variations as well as future changes in the Caspian water budget are still a matter of debate. Here, the major modes of North Atlantic winter climate variability and atmospheric teleconnections that have a potential effect on the hydroclimate of the Caspian catchment region are examined. The skill of the Community Earth System Model (CESM1.2.2) regarding the simulation of the modern climatology in the Caspian region and the major North Atlantic modes are analysed using different atmospheric grid resolutions and setups of the atmospheric component, the Community Atmosphere Model (CAM4 and CAM5). CESM1.2.2 with CAM5 atmosphere physics and 1° atmospheric grid resolution shows reasonable skill in simulating the regional Caspian basin climatology and the winter North Atlantic Oscillation (NAO). Using this model version, a weakly positive (r = .2) statistically significant (p < .05) correlation between the catchment winter water budget (precipitation minus evaporation, P‐E, integrated over the catchment area) and the NAO is found for the historical period 1850–2000. Climate projections of the 21st century under the Representative Concentration Pathways RCP4.5 and RCP8.5 show that the NAO remains the leading mode of winter variability with a dominant influence on the climate in the Caspian catchment region. Under the RCP4.5 scenario the correlation between the winter NAO and winter P‐E over the Caspian catchment region increases (r = .5, p < .05). For RCP8.5, however, this correlation disappears due to a north–south dipole pattern with a positive P‐E anomaly over the northern and a negative anomaly over the southern parts of the Caspian catchment region, cancelling out an effect on the total Caspian water budget. Nevertheless, due to increasing annual evaporation over the Caspian Sea in the warming climate, the model predicts an additional CSL decrease of about 9 and 18 m between 2020 and 2100 for the RCP4.5 and RCP8.5 scenarios, respectively. Even though the model tends to overestimate the total evaporation due to a too large Caspian Sea surface area, these values are larger than previous projections of CSL decline.

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“…Molavi‐Arabshahi et al . () analysed the impacts of SO and NAO on precipitation and temperature of southwestern coast of Caspian Sea and found a weak connection between the precipitation and NAO variability.…”

Section: Introductionmentioning

confidence: 99%

Hydrological drought associations with extreme phases of the North Atlantic and Arctic Oscillations over Turkey and northern Iran

Vazifehkhah

Kahya

2018

Intl Journal of Climatology

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Impacts of the North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) on hydroclimatic variables have been previously studied in various viewpoints. This study adds a new perspective by focusing on the influences of winter NAO and AO extreme phases on hydrological drought using a standardized streamflow index (SSFI) over Turkey and northern Iran. Moreover, the physical mechanisms associated with the extreme phases of NAO over different atmospheric conditions were investigated. The results concerning short-term droughts across Turkey revealed that wet conditions dominate particularly in the winter and following spring during the negative NAO and AO extreme phases while the NAO and AO impacts in Iran are only significant during simultaneous winter. Furthermore, the outputs of SSFI for the positive extreme phases of NAO and AO considerably differed in Turkey and Iran so that multiple drought events were detected in Turkey for all timescales as generally opposed to Iran. Western and eastern Turkey suffered from drought in various magnitudes during the positive extreme phases of NAO while fewer droughts were observed in Iran around the Caspian Sea during the negative NAO and AO extreme phases. Results of correlation analysis depicts that negative cycles are controlling wetness (drought) in Turkey (Iran) while there is a weak correlation between positive NAO cycles and SSFIs in the study area. In addition, our study showed that negative NAO and AO extreme phases could affect the hydrological drought stronger but in a shorter period compared with a longer period regarding the positive NAO and AO phases.

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Precipitation and temperature of the southwest Caspian Sea region during the last 55 years: their trends and teleconnections with large‐scale atmospheric phenomena

Cited by 63 publications

References 42 publications

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

Estimating and Up-Scaling Fuel Moisture and Leaf Dry Matter Content of a Temperate Humid Forest Using Multi Resolution Remote Sensing Data

Past and future impact of the winter North Atlantic Oscillation in the Caspian Sea catchment area

Hydrological drought associations with extreme phases of the North Atlantic and Arctic Oscillations over Turkey and northern Iran

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