Spatial and temporal variability of precipitation over the Mediterranean Basin based on 32‐year satellite Global Precipitation Climatology Project data. Part‐<scp>II</scp>: inter‐annual variability and trends

Hatzianastassiou, N.; Papadimas, C. D.; Lolis, C. J.; Bartzokas, A.; Levizzani, Vincenzo; Pnevmatikos, John D.; Katsoulis, B. D.

doi:10.1002/joc.4665

Cited by 10 publications

(7 citation statements)

References 54 publications

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“…The time series of mean annual values of DA's frequency over the Mediterranean Basin (Figure 12b) indicate that dust covers from 2.02% up to 7.07% of the study area. The observed inter-annual variability is attributed to changes in the produced dust (from sources that are mainly located in North Africa) as well as changes in dust transport [117] and deposition (e.g., precipitation [119]) mechanisms. For example, the high frequencies of dust observed in 2008 are associated with a high number of active dust sources and strong cyclonic circulation over North Africa in this year [109].…”

Section: Mediterranean Basinmentioning

confidence: 99%

A Global Climatology of Dust Aerosols Based on Satellite Data: Spatial, Seasonal and Inter-Annual Patterns over the Period 2005–2019

et al. 2021

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A satellite-based algorithm is developed and used to determine the presence of dust aerosols on a global scale. The algorithm uses as input aerosol optical properties from the MOderate Resolution Imaging Spectroradiometer (MODIS)-Aqua Collection 6.1 and Ozone Monitoring Instrument (OMI)-Aura version v003 (OMAER-UV) datasets and identifies the existence of dust aerosols in the atmosphere by applying specific thresholds, which ensure the coarse size and the absorptivity of dust aerosols, on the input optical properties. The utilized aerosol optical properties are the multiwavelength aerosol optical depth (AOD), the Aerosol Absorption Index (AI) and the Ångström Exponent (a). The algorithm operates on a daily basis and at 1° × 1° latitude-longitude spatial resolution for the period 2005–2019 and computes the absolute and relative frequency of the occurrence of dust. The monthly and annual mean frequencies are calculated on a pixel level for each year of the study period, enabling the study of the seasonal as well as the inter-annual variation of dust aerosols’ occurrence all over the globe. Temporal averaging is also applied to the annual values in order to estimate the 15-year climatological mean values. Apart from temporal, a spatial averaging is also applied for the entire globe as well as for specific regions of interest, namely great global deserts and areas of desert dust export. According to the algorithm results, the highest frequencies of dust occurrence (up to 160 days/year) are primarily observed over the western part of North Africa (Sahara), and over the broader area of Bodélé, and secondarily over the Asian Taklamakan desert (140 days/year). For most of the study regions, the maximum frequencies appear in boreal spring and/or summer and the minimum ones in winter or autumn. A clear seasonality of global dust is revealed, with the lowest frequencies in November–December and the highest ones in June. Finally, an increasing trend of global dust frequency of occurrence from 2005 to 2019, equal to 56.2%, is also found. Such an increasing trend is observed over all study regions except for North Middle East, where a slight decreasing trend (−2.4%) is found.

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Section: Mediterranean Basinmentioning

confidence: 99%

A Global Climatology of Dust Aerosols Based on Satellite Data: Spatial, Seasonal and Inter-Annual Patterns over the Period 2005–2019

et al. 2021

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“…Climate analysis for the MR as a whole is difficult due to the spatial complexity of this region in addition to the dynamical and physical atmospheric processes complexities observed in this region (Lionello et al, 2012;Michaelides et al, 2018). The climate across the MR exhibits intricate spatial and temporal characteristics associated with high inter-annual and seasonal variability (Hatzianastassiou et al, 2016;Deitch et al, 2017) which relates to the interaction between topography and large-scale atmospheric circulation (Nojarov, 2017;Taibi et al, 2017) causing spatial variation in hydrologic patterns (Cid et al, 2017). The annual precipitation characteristics over the MR reveal a strong temporal irregularity and have a wide frequency of heavy rainfall events (Dayan et al, 2015;Polade et al, 2017) especially the southern parts (IPCC, 2013;Guiot and Kaniewski, 2015;García-Barrón et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal variability of daily precipitation concentration and its relationship to teleconnection patterns over the Mediterranean during 1975–2015

Mathbout

López-Bustins

Royé

et al. 2019

Intl Journal of Climatology

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This study has addressed the spatiotemporal distribution of the daily rainfall concentration and its relation to the teleconnection patterns across the Mediterranean (MR). Daily concentration index (CI) and the ordered n index ( nor) are used at annual time scale to reveal the statistical structure of precipitation across the MR based on 233 daily rainfall series for the period 1975–2015. Eight teleconnection patterns, North Atlantic Oscillation (NAO), Mediterranean Oscillation (MO), Western Mediterranean Oscillation (WeMO), Upper‐Level Mediterranean Oscillation index (ULMO), East Atlantic (EA) pattern, East Atlantic/West Russia (EATL/WRUS) pattern, Scandinavia (SCAND) pattern and Southern Oscillation (SO) at annual time scale are selected. The spatiotemporal patterns in precipitation concentration indices, annual precipitation and their teleconnections with previous large‐scale circulations are investigated. Results show a strong connection between the CI and the nor (r = 0.70, p < .05) which present the same relative areas of high and low concentration. The annual values range from 0.57 to 0.70 for CI and 0.49 to 0.71 for nor index which show a high daily precipitation concentration across the MR. Trend analysis demonstrated mostly significant increasing trends for both indices. This increase is mainly found in south France, northern coastlands of the Iberian Peninsula (IP), Greece and Tunisia. An inverse relationship between the number of rainy days and concentration indices is evident. Both of WeMO and MO can play an important role in modulating rainfall in the northwest Mediterranean. The positive EATL/WRUS phase is mainly connected with positive precipitation mean anomalies in the eastern Mediterranean and vice versa in the west. The high daily precipitation concentration values over south France, northeast Spain, Croatia and Tunisia are linked to the low values of WeMO and high values of EA. These results could pave the way for new possibilities regarding the projection of precipitation concentration and precipitation irregularity in downscaling techniques.

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“…Station rain gauge series are used most frequently, followed by gridded data (González‐Hidalgo et al ., 2011; Wang et al ., 2014; Jones et al ., 2016; Iqbal et al ., 2019). By contrast, for data sparse land regions and overseas and oceans satellite data are frequently used (e.g., Hatzianastassiou et al ., 2016; Zhang et al ., 2017; Qin et al ., 2019). However, it should be noted that rain gauge data are generally more accurate (Tong et al ., 2014), therefore their use is preferred where they are available with sufficient temporal coverage and spatial density.…”

Section: Introductionmentioning

confidence: 99%

Precipitation trends in the island of Ireland using a dense, homogenized, observational dataset

Domonkos¹,

Coll

Guijarro

et al. 2020

Intl Journal of Climatology

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A dense monthly precipitation dataset of Ireland and Northern Ireland was homogenized with several modern homogenization methods. The efficiency of these homogenizations was tested by examining the similarity of homogenization results both in the real data homogenization and in the homogenization of a simulated dataset. The analysis of homogenization results shows that the real dataset is characterized by a large number of, but mostly small, nonclimatic biases, and a moderate reduction of such biases can be achieved with homogenization. Finally, a combination of the ACMANT and Climatol homogenization results was applied to improve the data accuracy before the trend calculations. These two methods were selected for their proven high accuracy, missing data tolerance and ability to complete time series via the infilling of missing values before the trend calculations. Metadata were used within the Climatol method. To facilitate this analysis the study area was split into smaller climatic regions by using the Ward clustering method. Five climatic zones consistent with the known spatial patterns of precipitation in Ireland were established. Linear regression fitting and the Mann-Kendall test were applied. Low frequency fluctuations were also examined by applying a Gaussian filter. The results show that the precipitation amount generally increases in the study area, particularly in the northwestern region. The most significant increasing trends for the whole study period (1941-2010) are found for late winter and spring precipitation, as well as for the annual totals. In the period from the early 1970s the increase of precipitation is general in all seasons of the year except in winter, but the statistical significance of this increase is weak.

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Spatial and temporal variability of precipitation over the Mediterranean Basin based on 32‐year satellite Global Precipitation Climatology Project data. Part‐II: inter‐annual variability and trends

Cited by 10 publications

References 54 publications

A Global Climatology of Dust Aerosols Based on Satellite Data: Spatial, Seasonal and Inter-Annual Patterns over the Period 2005–2019

A Global Climatology of Dust Aerosols Based on Satellite Data: Spatial, Seasonal and Inter-Annual Patterns over the Period 2005–2019

Spatiotemporal variability of daily precipitation concentration and its relationship to teleconnection patterns over the Mediterranean during 1975–2015

Precipitation trends in the island of Ireland using a dense, homogenized, observational dataset

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