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Seasonal Hydrodynamic Forecasts Using the INM-CM5 Model for Estimating the Beginning of Birch Pollen Dispersion

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Abstract

Experimental seasonal forecasts using the INM-CM5 climate model have been applied as the input data for the temperature–time phenological model of birch pollination. A test method has been developed within the framework of the joint model for the seasonal forecasting of the time of the beginning of birch pollen dispersion in the European territory of Russia. Verification of this method on seasonal retrospective forecasts of the INM-CM5 model (in 1991–2019) has shown an adequate reproduction of the days of the beginning of birch pollen season simulated for the same period based on ERA5 reanalysis. The mean systematic errors are ±2 days, and the spatial correlation coefficients exceed +0.84. The forecasts of the date of pollen dispersion beginning in 2022 simulated from the experimental operational seasonal forecasts using the INM-CM5 model with a monthly lead time and with a zero lead time are also evaluated. It is shown that the errors in forecasting the onset of pollen dispersion are ±5–10 days, with smaller errors of forecasts with a 1-month lead time. The results allow us to conclude that the seasonal forecast of the surface temperature based on the INM-CM5 model can be used as input information for the temperature–time phenological model for the operational forecast of the start time of birch pollen dispersion in the European territory of Russia.

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REFERENCES

  1. Andersen, T.B., A model to predict the beginning of the pollen season, Grana, 1991, vol. 30, pp. 269–275.

    Article  Google Scholar 

  2. Bastl, K., Kmenta, M., Pessi, A.M., Prank, M., Saarto, A., Sofiev, M., Bergmann, K.C., Buters, J.T.M., Thibaudon, M., Jager, S., and Berger, U., First comparison of symptom data with allergen content (Bet v 1 and Phl p 5 measurements) and pollen data from four European regions during 2009–2011, Sci. Total Environ., 2016, vols. 548–549, pp. 229–235.

    Article  Google Scholar 

  3. Bogova, A.V., Il’ina, N.I., and Luss, L.V., Trends in the study of the epidemiology of allergic diseases in Russia over the past 10 years, Ross. Allergol. Zh., 2008, no. 6, pp. 3–14.

  4. Cannell, M.G.R. and Smith, R.I., Thermal time, chill days and prediction of budburst in picea-sitchensis, J. Appl. Ecol., 1983, no. 20, pp. 951–963.

  5. Carton, J.A., Chepurin, G.A., and Chen, L., SODA3: A new ocean climate reanalysis, J. Clim., 2018, vol. 31, no. 17, pp. 6967–6983.

    Article  Google Scholar 

  6. D’Amato, G., Cecchi, L., Bonini, S., Nunes, C., Liccardi, G., Popov, T., and Cauwenberge, P. Van., Allergenic pollen and pollen allergy in Europe, Allergy, 2007, vol. 9, no. 62, pp. 976–990.

    Article  Google Scholar 

  7. Dorota, M., Prediction of the birch pollen season characteristics in Cracow, Poland using an 18-year data series, Aerobiologia, 2013, vol. 29, pp. 31–44.

    Article  Google Scholar 

  8. Emelina, S.V., Nabokova, E.V., and Rubinshtein, K.G., Comparison of phenological models for the beginning of birch dusting for numerical prediction of allergen transfer, Gidrometeorol. Issled. Prognozy, 2019, no. 3, pp. 151–160.

  9. Eyring, V., Bony, S., Meehl, G.A., et al., Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 2016, vol. 9, no. 5, pp. 1937–1958.

    Article  Google Scholar 

  10. Fu, Y., Campioli, M., Deckmyn, G., and Janssens, I., The impact of winter and spring temperatures on temperate tree budburst dates: Results from an experimental climate manipulation, PLoS ONE, 2012, vol. 7, no. 10, E47324.

    Article  Google Scholar 

  11. Haahtela, T., Valovirta, E., Saarinen, K., et al., The Finnish allergy program 2008–2018: Society-wide proactive program for change of management to mitigate allergy burden, J. Allergy Clin. Immunol., 2021, vol. 148, pp. 319–326.

    Article  Google Scholar 

  12. Helbig, N., Vogel, B., Vogel, H., and Fiedler, F., Numerical modeling of pollen dispersion on the regional scale, Aerobiologia (Bologna), 2004, vol. 3, pp. 3–19.

    Article  Google Scholar 

  13. Hersbach, H., Bell, B., Berrisford, P., et al., The ERA5 global reanalysis, Q. J. R. Meteorol. Soc., 2020, vol. 146, pp. 1999–2049.

    Article  Google Scholar 

  14. Huynen, M., Menne, B., Behrendt, H., et al., Phenology and Human Health: Allergic Disorders, World Health Organization, 2003.

    Google Scholar 

  15. Khaitov, R.M. and Il’ina, N.I., Allergologiya i klinicheskaya immunologiya. Klinicheskie rekomendatsii (Allergology and Clinical Immunology. Clinical Guidelines), Moscow: GEOTAR-Media, 2019.

  16. Khan, V.M., Structural–statistical approach for improving long-term meteorological forecasts, Doctoral (Geogr.) Dissertation, Moscow, 2012.

  17. Kim, Y.H., Min, S.K., Zhang, X., et al., Evaluation of the CMIP6 multi-model ensemble for climate extreme indices, Weather Clim. Extremes, 2020, vol. 29, p. 100269.

    Article  Google Scholar 

  18. Klimek, L., Bachert, C., Pfaar, O., et al., ARIA guideline 2019: Treatment of allergic rhinitis in the German health system, Allergo J. Int., 2019, vol. 28, no. 7, pp. 255–276.

    Article  Google Scholar 

  19. Koivikko, A., Kupas, R., Makinen, Y., and Pohjola, A., Pollen seasons: Forecasts of the most important allergenic plants in Finland, Allergy, 1986, vol. 41, no. 4, pp. 233–242.

    Article  Google Scholar 

  20. Kozulina, I.E., Kurbacheva, O.M., and Il’ina, N.I., Allergy today. Analysis of new epidemiological data, Ross. Allergol. Zh., 2014, no. 3, pp. 3–10.

  21. Kukkonen, J., Olsson, T., Schultz, D.M., et al., A review of operational, regional-scale, chemical weather forecasting models in Europe, Atmos. Chem. Phys., 2012, vol. 12, pp. 1–87.

    Article  Google Scholar 

  22. Laadi, M., Regional variations in the pollen season of Betula in Burgundy: Two models for predicting the start of the pollination, Aerobiologia, 2001, vol. 17, pp. 247–254.

    Article  Google Scholar 

  23. Latałowa M., Miętus, M., and Uruska, A., Pollen seasonal variations in the atmospheric Betula pollen count in Gdańsk (southern Baltic coast) in relation to meteorological parameters, Aerobiologia, 2022, vol. 18, pp. 33–43.

    Article  Google Scholar 

  24. Linkosalo, T., Ranta, H., Oksanen, A., Siljamo, P., Luomajoki, A., Kukkonen, J., et al., A double-threshold temperature sum model for predicting the flowering duration and relative intensity of Betula pendula and B. pubescens, Agric. For. Meteorol., 2010, vol. 150, pp. 1579–1584.

    Article  Google Scholar 

  25. Mahura, A., Baklanov, A., and Korsholm, U., Parameterization of the birch pollen diurnal cycle, Aerobiologia (Bologna), 2009, vol. 25, pp. 203–208.

    Article  Google Scholar 

  26. Mirvis, V.M. and Meleshko, V.P., Current state and prospects for the development of meteorological monthly and seasonal forecasts, Tr. Gl. Geofiz. Obs. im. A.I. Voeikova, 2008, no. 558, pp. 3–40.

  27. Myszkowska, D., Jenner, B., Stępalska, D., and Czarnobilska, E., The pollen dynamics and the relationship among some pollen season characteristics (start, end, annual total, pollen season phases) in Kraków: Poland, 1991–2008, Aerobiologia, 2011, vol. 27, no. 3, pp. 229–238.

    Article  Google Scholar 

  28. Namazova-Baranova, L.S., Allergiya u detei - ot teorii k praktike (Child Allergy: From Theory to Practice), Moscow: Soyuz pediatrov Rossii, 2011.

  29. Newnham, R., Sparks, T., Skjøth, C., Head, K., Adams-Groom, B., and Smith, M., Pollen season and climate: Is the timing of birch pollen release in the UK approaching its limit?, Int. J. Biometeorol., 2013, vol. 57, pp. 391–400.

    Article  Google Scholar 

  30. Norris-Hill, J., A method to forecast the start of the Betula, Platanus and Quercus pollen seasons in North London, Aerobiologia, 1998, vol. 14, pp. 165–170.

    Article  Google Scholar 

  31. Nosova, M.B., Severova, E.E., and Volkova, O.A., Long-term studies of modern palynological spectra in the middle zone of European Russia, Byull. Mosk. O-va Ispytatelei Prirody. Otdel biologii, 2015, vol. 120, no. 6, pp. 42–50.

  32. Pauling, A., Rotach, M.W., Gehrig, R., and Clot, B., A method to derive vegetation distribution maps for pollen dispersion models using birch as an example, Int. J. Biometeorol., 2012, vol. 56, pp. 949–958.

    Article  Google Scholar 

  33. Porteous, T., Wyke, S., Smith, S., Bond, C., Francis, J., Lee, A.J., Lowrie, R., Scotland, G., Sheikh, A., Thomas, M., and Smith, L., “Help for Hay fever,” a goal-focused intervention for people with intermittent allergic rhinitis, delivered in Scottish community pharmacies: Study protocol a pilot cluster randomized controlled trial, Trials, 2013, vol. 15, no. 14, p. 217.

    Article  Google Scholar 

  34. Ritenberga, O., Sofiev, M., Siljamo, P., Saarto, A., Dahl, A., Ekebom, A., Sauliene, I., Shalaboda, V., Severova, E., Hoebeke, L., and Ramfjord, H., A statistical model for predicting the inter-annual variability of birch pollen abundance in Northern and North-Eastern Europe, Sci. Total Environ., 2018, vol. 615, pp. 228–239.

    Article  Google Scholar 

  35. Rodriguez-Rajo, F.J., Frenguelli, G., and Jato, M.V., Effect of air temperature on forecasting the start of the Betula pollen season at two contrasting sites in the south of Europe (1995–2001), Int. J. Biometeorol., 2003, vol. 47, pp. 117–125.

    Article  Google Scholar 

  36. Siljamo, P., Sofiev, M., Filatova, E., Grewling, L., Jäger, S., Khoreva, E., Linkosalo, T., Ortega Jimenez, S., Rant-a, H., Rantio-Lehtimäki, A., Svetlov, A., Veriankaite, L., Yakovleva, E., and Kukkonen, J., A numerical model of birch pollen emission and dispersion in the atmosphere. Model evaluation and sensitivity analysis, Int. J. Biometeorol., 2012, vol. 57, pp. 125–136.

    Article  Google Scholar 

  37. Simpson, D., Benedictow, A., Berge, H., Bergström, R., Emberson, L.D., Fagerli, H., Flechard, C.R., Hayman, G.D., Gauss, M., Jonson, J.E., Jenkin, M.E., Nyíri, A., Richter, C., Semeena, V.S., Tsyro, S., et al., The EMEP MSC-W chemical transport model: Technical description, Atmos. Chem. Phys., 2012, vol. 12, pp. 7825–7865.

    Article  Google Scholar 

  38. Sofiev, M., Siljamo, P., and Ranta, H., Towards numerical forecasting of long-range air transport of birch pollen: Theoretical considerations and a feasibility study, Int. J. Biometeorol., 2006, vol. 50, pp. 392–402.

    Article  Google Scholar 

  39. Sofiev, M., Siljamo, P., Ranta, H., and Linkosalo, T., A numerical model of birch pollen emission and dispersion in the atmosphere, description of the emission module, Int. J. Biometeorol., 2012, vol. 57, pp. 45–58.

    Article  Google Scholar 

  40. Stach, A., Emberlin, J., Adams-Groom, B., Smith, M., and Myszkowska, D., Factors that determine the severity of Betula spp. pollen seasons in Poland (Poznań and Cracow) and the United Kingdom (Worcester and London), Int. J. Biometeorol., 2008, vol. 52, no. 4, pp. 311–321.

    Article  Google Scholar 

  41. Stepanov, V.N., Resnyanskii, Yu.D., Strukov, B.S., and Zelen’ko, A.A., Large-scale ocean circulation and sea ice characteristics derived from numerical experiments with the NEMO model, Russ. Meteorol. Hydrol., 2019, vol. 44, no. 1, pp. 33–44.

    Article  Google Scholar 

  42. Tarasevich, M.A. and Volodin, E.M., Influence of various parameters of INM RAS climate model on the results of extreme precipitation simulation, in International Young Scientists School and Conference on Computational Information Technologies for Environmental Sciences, IOP Conference Series: Earth and Environmental Science, 2019, vol. 386, p. 012012.

  43. Tarasevich, M.A. and Volodin, E.M., The influence of autumn Eurasian snow cover on the atmospheric dynamics anomalies during the next winter in INMCM5 model data, Supercomput. Front. Innov., 2021, vol. 8, no. 4, pp. 24–39.

    Google Scholar 

  44. Tolstykh, M.A., Geleyn, J.-F., Volodin, E.M., Bogoslovskii, N.N., Vilfand, R.M., Kiktev, D.B., Kr-asyuk, T.V., Kostrykin, S.V., Mizyak, V.G., Fadeev, R.Yu., Shashkin, V.V., Shlyaeva, A.V., Ezau, I.N., and Yurova, A.Yu., Development of the multiscale version of the SL-AV global atmosphere model, Russ. Meteorol. Hydrol., 2015, vol. 40, no. 6, pp. 374–382.

    Article  Google Scholar 

  45. Vargin, P.N. and Volodin, E.M., Analysis of the reproduction of dynamic processes in the stratosphere using the climate model of the Institute of Numerical Mathematics, Russian Academy of Sciences, Izv., Atmos. Ocean. Phys., 2016, vol. 52, no. 1, pp. 1–15.

    Article  Google Scholar 

  46. Vargin, P.N., Kostrykin, S.V., and Volodin, E.M., Analysis of simulation of stratosphere-troposphere dynamical coupling with the INM-CM5 climate model, Russ. Meteorol. Hydrol., 2018, vol. 43, no. 11, pp. 780–786.

    Article  Google Scholar 

  47. Vil’fand, R.M., Zaripov, R.B., Kiktev, D.B., Kruglova, E.N., Kryzhov, V.N., Kulikova, I.A., Tishchenko, V.A., Tolstykh, M.A., and Khan, V.M., Long-term meteorological forecasts in the Hydrometeorological Center of Russia, Gidrometeorol. Issled. Prognozy, 2019, no. 4, pp. 12–36.

  48. Vishneva, E.A., Namazova-Baranova, L.S., and Alekseeva, A.A., Modern principles of the therapy of child allergic rhinitis, Pediatr. Farmakol., 2014, vol. 11, no. 1, pp. 6–14.

    Article  Google Scholar 

  49. Vogel, H., Pauling, A., and Vogel, B., Numerical simulation of birch pollen dispersion with an operational weather forecast system, Int. J. Biometeorol., 2008, vol. 52, pp. 805–814.

    Article  Google Scholar 

  50. Volodin, E.M. and Gritsun, A.S., Simulation of observed climate changes in 1850–2014 with climate model INM-CM5, Earth Syst. Dyn., 2018, vol. 9, no. 4, pp. 1235–1242.

    Article  Google Scholar 

  51. Volodin, E.M., Mortikov, E.V., Kostrykin, S.V., Galin, V.Ya., Lykosov, V.N., Gritsun, A.S., Diansky, N.A., Gusev, A.V., and Yakovlev, N.G., Simulation of modern climate with the new version of the INM RAS climate model, Izv., Atmos. Ocean. Phys., 2017a, vol. 53, no. 2, pp. 142–155.

    Article  Google Scholar 

  52. Volodin, E.M., Mortikov, E.V., Kostrykin, S.V., et al., Simulation of the present-day climate with the climate model INMCM5, Clim. Dyn., 2017b, vol. 49, pp. 3715–3734.

    Article  Google Scholar 

  53. Vorobyeva, V. and Volodin, E., Evaluation of the INM RAS climate model skill in climate indices and stratospheric anomalies on seasonal timescale, Tellus A: Dyn. Meteorol. Oceanogr., 2021a, vol. 73, no. 1, pp. 1–12.

    Article  Google Scholar 

  54. Vorobyeva, V.V. and Volodin, E.M., Experimental studies of seasonal weather predictability based on the INM RAS climate model, Math. Models Comput. Simul., 2021b, vol. 13, no. 4, pp. 571–578.

    Article  Google Scholar 

  55. Zink, K., Pauling, A., Rotach, M.W., Vogel, H., Kaufmann, P., and Clot, B., EMPOL 1.0: A new parameterization of pollen emission in numerical weather prediction models, Geosci. Model Dev., 2013, vol. 6, pp. 1961–1975.

    Article  Google Scholar 

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Funding

This work was supported by the Ministry of Education and Science of the Russian Federation, agreement no. 075-15-2021-577 with the Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences (development and evaluation of a method for predicting the timing of birch pollen season) and the Russian Science Foundation, no. 20-17-00190 (calculation of retrospective and operational seasonal forecasts using the INM-CM5 model).

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Authors and Affiliations

  1. Hydrometeorological Center of Russia, 123242, Moscow, Russia

    S. V. Emelina, V. M. Khan, V. V. Vorobyeva, M. A. Tarasevich & E. M. Volodin

  2. Institute of Numerical Mathematics, Russian Academy of Sciences, 119991, Moscow, Russia

    S. V. Emelina, V. M. Khan, V. V. Vorobyeva, M. A. Tarasevich & E. M. Volodin

  3. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, 119017, Moscow, Russia

    S. V. Emelina, V. M. Khan, V. A. Semenov, V. V. Vorobyeva & E. M. Volodin

  4. Institute of Geography, Russian Academy of Sciences, 119017, Moscow, Russia

    V. A. Semenov

  5. Moscow Institute of Physics and Technology, 141701, Dolgoprudny, Moscow oblast, Russia

    M. A. Tarasevich

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  1. S. V. Emelina
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  2. V. M. Khan
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  4. V. V. Vorobyeva
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Emelina, S.V., Khan, V.M., Semenov, V.A. et al. Seasonal Hydrodynamic Forecasts Using the INM-CM5 Model for Estimating the Beginning of Birch Pollen Dispersion. Izv. Atmos. Ocean. Phys. 59, 351–359 (2023). https://doi.org/10.1134/S0001433823040059

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