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Seasonal and annual segregation of liquid water and ice clouds in Iran and their relation to geographic components and precipitation

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Abstract

Efficient and proper understanding of the state of the clouds regarding different seasons of the year will have profound effects on different economic and environmental sectors. The purpose of this study is to determine the hourly dissociation of ice and liquid clouds in Iran. To this end, cloud optical thickness (COT) data, as well as optical depth of clouds in two phases of liquid and ice were obtained and processed from 31 synoptic meteorological stations (1960–2015), MODIS data from Terra satellite during the years 2001 to 2011, and they were processed then. Next, using the RegCM4 model, the cloud fraction (clt) was simulated to accurately identify the cloud cover situation in Iran. The results showed that the maximum annual mean abundance of liquid and ice clouds was 18.95 days for the time 15:00 and 3.99 days for the time 06:00, respectively. Climatic zones of the Caspian and Persian Gulf coasts at 15 o’clock had the highest decreasing trend of liquid clouds. Ice clouds in all parts of Iran’s climate, with the exception of the eastern plateau, also declined. From south to north and east to west of Iran, the occurrence of ice and liquid clouds is increasing. Therefore, the spatio-temporal distribution of liquid and ice clouds in the country was also dependent on spatial components and latitude had the greatest impact. From the satellite and modeled data, the RegCM4 model has been able to detect the Monsoon phenomenon in southeastern Iran during the summer. CLT simulation in Iran has also shown that cloud cover in Iran fluctuates between 28 and 65% on average, with 81.5% of Iranian stations having a significant change in the amount of annual cloud cover. Correlation of liquid and ice clouds with precipitation showed that liquid clouds in summer and ice clouds in spring had higher correlation with precipitation in Iran. Northern coasts of Iran due to greater ascent mechanisms such as coastal compressors, north latitude atmospheric circulation systems, and maximum winds in the north and west of Iran due to the location of western systems entry and sufficient thermal gradient, had maximum ice clouds in the last half century. Also, south of Iran, despite having extended and great water-bodies, is less cloudy due to descending air in Hadley’s circulation (Hadley cell) of air.

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References

  1. Ahmadi M, Dadashiroudbari A, Ahmadi H (2018) Spatiotemporal variations of total cloud cover and cloud optical thickness in Iran. J Earth Space Phys 44(4):145–164

  2. Alijani B (2008) Effect of the Zagros Mountains on the spatial distribution of precipitation. J Mt Sci 5(3):218–231

  3. Alijani B, Harman JR (1985) Synoptic climatology of precipitation in Iran. Ann Assoc Am Geogr 75(3):404–416

  4. Attada R, Dasari HP, Parekh A, Chowdary JS, Langodan S, Knio O, Hoteit I (2019) The role of the Indian Summer Monsoon variability on Arabian Peninsula summer climate. Clim Dyn 52(5–6):3389–3404

  5. Banta RM (1990) the role of mountain flows in making clouds. Chapter 9 of atmospheric processes over complex terrain. American Meteorological Society, 23:229–283

  6. Barnes ML, Miura T, Giambelluca TW (2016) An assessment of diurnal and seasonal cloud cover changes over the Hawaiian Islands using Terra and Aqua MODIS. J Clim 29(1):77–90

  7. Bretherton CS, McCaa JR, Grenier H (2004) A new parameterization for shallow cumulus convection and its application to marine subtropical cloud-topped boundary layers. Part I: description and 1D results. Mon Weather Rev 132(4):864–882

  8. Duhan D, Pandey A (2013) Statistical analysis of long term spatial and temporal trends of precipitation during 1901–2002 at Madhya Pradesh, India. Atmos Res 122:136–149

  9. Eliasson S, Buehler SA, Milz M, Eriksson P, John VO (2010) Assessing modelled spatial distributions of ice water path using satellite data. Atmospheric Chemistry and Physics Discussions, 10(5):12185–12224

  10. Evans JP, Smith RB, Oglesby RJ (2004) Middle East climate simulation and dominant precipitation processes. Int J Climatol 24(13):1671–1694

  11. Fallah-Ghalhari G, Shakeri F, Dadashi-Roudbari A (2019) Impacts of climate changes on the maximum and minimum temperature in Iran. Theoretical and Applied Climatology, 138(3-4):1539–1562

  12. Filipiak J, Miȩtus M (2009) Spatial and temporal variability of cloudiness in Poland, 1971–2000. Int J Climatol 29(9):1294–1311

  13. Ghasemifar E, Farajzadeh M, Perry MC, Rahimi YG, Bidokhti AA (2018) Analysis of spatiotemporal variations of cloud fraction based on geographic characteristics over Iran. Theoretical and applied climatology, 134(3-4):1429–1445

  14. Giorgi F, Coppola E, Solmon F, Mariotti L, Sylla MB, Bi X et al (2012) RegCM4: model description and preliminary tests over multiple CORDEX domains. Clim Res 52:7–29

  15. Hartmann DL, Larson K (2002) An important constraint on tropical cloud-climate feedback. Geophys Res Lett 29(20):12–11

  16. Holz RE, Platnick S, Meyer K, Vaughan M, Heidinger A, Yang P, Nagle F (2015) Resolving ice cloud optical thickness biases between CALIOP and MODIS using infrared retrievals. Atmos Chem Phys Discuss 15(20)

  17. Huang J, Minnis P, Lin B, Yi Y, Fan TF, Sun-Mack S, Ayers JK (2006) Determination of ice water path in ice-over-water cloud systems using combined MODIS and AMSR-E measurements. Geophys Res Lett 33(21)

  18. IPCC. (2007). Summary for policymakers of climate change 2007: the physical science basis. In contribution of working group, I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press: Cambridge, UK

  19. IPCC (2014) In: Core Writing Team, Pachauri RK, Meyer LA (eds) Climate change 2014: synthesis report. The contribution of working groups I, II and III to the fifth assessment report of the intergovernmental panel on climate change. IPCC, Geneva 151 pp

  20. Jones TA, Stensrud DJ (2015) Assimilating cloud water path as a function of model cloud microphysics in an idealized simulation. Mon Weather Rev 143(6):2052–2081

  21. Kendall M (1975) Rank correlation methods, 4th edn. Charles Griffin, London

  22. King MD, Platnick S, Menzel WP, Ackerman SA, Hubanks PA (2013a) Spatial and temporal distribution of clouds observed by MODIS onboard the Terra and Aqua satellites. IEEE Trans Geosci Remote Sens 51(7):3826–3852

  23. King MD, Platnick S, Menzel WP, Ackerman SA, Hubanks PA (2013b) Spatial and temporal distribution of clouds observed by MODIS onboard the Terra and Aqua satellites. IEEE Trans Geosci Remote Sens 51(7):3826–3852

  24. Leinonen J, Lebsock MD, Stephens GL, Suzuki K (2016) Improved retrieval of cloud liquid water from CloudSat and MODIS. J Appl Meteorol Climatol 55(8):1831–1844

  25. Li J, Carlson BE, Dubovik O, Lacis AA (2014) Recent trends in aerosol optical properties derived from AERONET measurements. Atmos Chem Phys 14(22):12271–12289

  26. Lin H, Wu Z (2012) Indian summer monsoon influence on the climate in the North Atlantic–European region. Clim Dyn 39(1–2):303–311

  27. Mann, H. B. (1945). Nonparametric tests against trend. Econometrica: Journal of the Econometric Society, 245–259

  28. Masoudian A (2012) Iranian Climate, 1th edn. Sharia Toos Publishing, Mashhad. (In Persian)

  29. Myers L, Sirois MJ (2004) Spearman correlation coefficients, differences between. Encyclopedia of statistical sciences, 12:1–2

  30. Norris JR, Evan AT (2015) Empirical removal of artifacts from the ISCCP and PATMOS-x satellite cloud records. J Atmos Ocean Technol 32(4):691–702

  31. Norris JR, Allen RJ, Evan AT, Zelinka MD, O’Dell CW, Klein SA (2016) Evidence for climate change in the satellite cloud record. Nature 536(7614):72–75

  32. Pal JS, Small EE, Eltahir EA (2000) Simulation of regional-scale water and energy budgets: representation of subgrid cloud and precipitation processes within RegCM. J Geophys Res-Atmos 105(D24):29579–29594

  33. Pincus R, Platnick S, Ackerman SA, Hemler RS, Patrick Hofmann RJ (2012) Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators. J Clim 25(13):4699–4720

  34. Platnick S, Meyer KG, King MD, Wind G, Amarasinghe N, Marchant B et al (2017) The MODIS cloud optical and microphysical products: collection 6 updates and examples from Terra and Aqua. IEEE Trans Geosci Remote Sens 55(1):502–525

  35. Sato T, Kimura F, Hasegawa AS (2007) Vegetation and topographic control of cloud activity over arid/semiarid Asia. J Geophys Res-Atmos 112(D24)

  36. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389

  37. Sherwood SC, Alexander MJ, Brown AR, McFarlane NA, Gerber EP, Feingold G, Scaife AA, Grabowski WW (2013) Climate processes: clouds, aerosols and dynamics. 1th edn. Springer, Dordrecht

  38. Simmons A (2006) ERA-interim: new ECMWF reanalysis products from 1989 onwards. ECMWF Newslett 110:25–36

  39. Stubenrauch CJ, Rossow WB, Kinne S, Ackerman S, Cesana G, Chepfer H, Di Girolamo L, Getzewich B, Guignard A, Heidinger A, Maddux BC (2013) Assessment of global cloud datasets from satellites: Project and database initiated by the GEWEX radiation panel. Bulletin of the American Meteorological Society, 94(7):1031–1049

  40. Tiedtke MICHAEL (1989) A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon Weather Rev 117(8):1779–1800

  41. Wibig J (2008) Cloudiness variations in Łódź in the second half of the 20th century. Int J Climatol 28(4):479–491

  42. Wild M, Ohmura A, Gilgen H, Rosenfeld D (2004) On the consistency of trends in radiation and temperature records and implications for the global hydrological cycle. Geophys Res Lett 31(11)

  43. Wilson MF, Henderson-Sellers A, Dickinson RE, Kennedy PJ (1987). Sensitivity of the Biosphere–Atmosphere Transfer Scheme (BATS) to the inclusion of variable soil characteristics. Journal of climate and applied meteorology, 26(3):341–362.

  44. WMO (2011) Manual on Codes, Regional Codes and National Coding Practices, 2th edn, Chairperson, Geneva

  45. Yadav RK (2016) On the relationship between Iran surface temperature and northwest India summer monsoon rainfall. Int J Climatol 36(13):4425–4438

  46. You Q, Jiao Y, Lin H, Min J, Kang S, Ren G, Meng X (2014) Comparison of NCEP/NCAR and ERA-40 total cloud cover with surface observations over the Tibetan Plateau. Int J Climatol 34(8):2529–2537

  47. Zarrin A, Ghaemi H, Azadi M, Mofidi A, Mirzaei E (2011) The effect of the Zagros Mountains on the formation and maintenance of the Iran Anticyclone using RegCM4. Meteorog Atmos Phys 112(3–4):91–100

  48. Zelinka MD, Hartmann DL (2010) Why is longwave cloud feedback positive? J Geophys Res-Atmos 115(D16)

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Acknowledgments

The authors thank the Islamic Republic of Iran Meteorological Organization (IRIMO) for providing timely precipitation and long-term cloud data in Iran. We also acknowledge the MODIS mission scientists and associated NASA personnel for the production of the data used in this research effort.

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Correspondence to Mahmoud Ahmadi.

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Ahmadi, M., Dadashi-Roudbari, A., Akbari-Azirani, T. et al. Seasonal and annual segregation of liquid water and ice clouds in Iran and their relation to geographic components and precipitation. Theor Appl Climatol (2020). https://doi.org/10.1007/s00704-020-03131-5

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