Seasonal Hydrodynamic Forecasts of INM-CM5 Model for Estimation of the Start of the Birch Pollen Season

S. V. Emelina; Емелина С. В.; V. M. Khan; Хан В. М.; V. A. Semenov; Семенов В. А.; V. V. Vorobyeva; Воробьева В. В.; M. A. Tarasevich; Тарасевич М. А.; E. M. Volodin; Володин Е. М.

doi:10.31857/S0002351523040053

Seasonal Hydrodynamic Forecasts of INM-CM5 Model for Estimation of the Start of the Birch Pollen Season

Authors: Emelina S.V.¹^,2^,3, Khan V.M.¹^,2^,3, Semenov V.A.³^,4, Vorobyeva V.V.¹^,2^,3, Tarasevich M.A.¹^,2^,5, Volodin E.M.¹^,2^,3
Affiliations:
1. Hydrometeorological Center of Russia
2. Institute of Numerical Mathematics RAS
3. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences
4. Institute of Geography, Russian Academy of Sciences
5. Moscow Institute of Physics and Technology
Issue: Vol 59, No 4 (2023)
Pages: 407-416
Section: Articles
URL: https://journals.rcsi.science/0002-3515/article/view/136921
DOI: https://doi.org/10.31857/S0002351523040053
EDN: https://elibrary.ru/YNCBRI
ID: 136921

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Abstract

The experimental seasonal forecasts of the INM-CM5 climate model were used as input data for the temperature-time phenological model of birch dusting. Within the framework of the joint model, a test technology was developed for seasonal forecasting of the timing of the beginning of birch dusting in the European territory of Russia. Verification of this technology on seasonal retrospective forecasts of the INM-CM5 model (1991–2019) showed an adequate reproduction of the birch dusting start dates calculated for the same period according to the ERA5 reanalysis. The mean systematic errors are ±2 days, and the spatial correlation coefficients are above +0.84. The forecasts of the date of dusting start in 2022, calculated from the experimental operational seasonal forecasts of 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 beginning of dusting are ±5–10 days, and the forecasts with a one-month lead-time have fewer errors. The obtained results allow us to conclude that the seasonal forecast of the surface temperature of the INM-CM5 model can be used as input information for the temperature-time phenological model for the operational forecast of the timing of the start of birch dusting in the European territory of Russia.

Keywords

seasonal forecast, start of the pollen season, phenological model, birch pollen, INM-CM5 model

References

Богова А.В., Ильина Н.И., Лусс Л.В. Тенденции в изучении эпидемиологии аллергических заболеваний в России за последние 10 лет // Российский аллергологический журн. 2008. № 6. С. 3–14.
Варгин П.Н., Володин Е.М. Анализ воспроизведения динамических процессов в стратосфере климатической моделью ИВМ РАН // Изв. РАН. Физика атмосферы и океана. 2016. Т. 52. № 1. С. 3.
Варгин П.Н., Кострыкин С.В., Володин Е.М. Анализ воспроизведения динамического взаимодействия тропосферы и стратосферы в расчетах климатической модели ИВМ РАН // Метеорология и гидрология. 2018. № 11. С. 34–39.
Вильфанд Р.М., Зарипов Р.Б., Киктев Д.Б., Круглова Е.Н., Крыжов В.Н., Куликова И.А., Тищенко В.А., Толстых М.А., Хан В.М. Долгосрочные метеорологические прогнозы в Гидрометцентре России // Гидрометеорологические исследования и прогнозы. 2019. № 4(374). С. 12–36.
Вишнева Е.А., Намазова-Баранова Л.С., Алексеева А.А. Современные принципы терапии аллергического ринита у детей // Педиатрическая фармакология. 2014. Т. 11. № 1. С. 6–14.
Володин Е.М., Мортиков Е.В., Кострыкин С.В., Галин В.Я., Лыкосов В.Н., Грицун А.С., Дианский Н.А., Гусев А.В., Яковлев Н.Г. Воспроизведение современного климата в новой версии климатической модели ИВМ РАН // Известия РАН. Физика атмосферы и океана. 2017. V. 53. № 2. P. 164–178
Емелина С.В., Набокова Е.В., Рубинштейн К.Г. Сравнение фенологических моделей определения начала пыления березы для численного прогнозирования переноса аллергенов // Гидрометеорологические исследования и прогнозы. 2019. № 3(373). С. 151–160.
Козулина И.Е., Курбачева О.М., Ильина Н.И. Аллергия сегодня. Анализ новых эпидемиологических данных // Российский аллергологический журн. 2014. № 3. С. 3–10.
Мирвис В.М., Мелешко В.П. Современное состояние и перспективы развития метеорологических прогнозов на месяц и сезон // Труды ГГО. 2008. Вып. 558. С. 3–40.
Намазова-Баранова Л.С. Аллергия у детей – от теории к практике. М.: Союз педиатров России, 2011. 668 с.
Носова М.Б., Северова Е.Э., Волкова О.А. Многолетние исследования современных палинологических спектров в средней полосе европейской части России // Бюллетень московского общества испытателей природы. Отдел биологии. 2015. Т. 120. № 6. С. 42–50.
Степанов В.Н., Реснянский Ю.Д., Струков Б.С., Зеленько А.А. Крупномасштабная циркуляция океана и характеристики ледового покрова по данным численных экспериментов с использованием модели NEMO // Метеорология и гидрология. 2019. № 1. С. 50–66.
Толстых М.А., Желен Ж.Ф., Володин Е.М., Богословский Н.Н., Вильфанд Р.М., Киктев Д.Б., Красюк Т.В., Кострыкин С.В., Мизяк В.Г., Фадеев Р.Ю., Шашкин В.В., Шляева А.В., Эзау И.Н., Юрова А.Ю. Разработка многомасштабной версии глобальной модели атмосферы ПЛАВ. // Метеорология и гидрология. 2015. № 6. С. 25–35.
Хаитов Р.М., Ильина Н.И. Аллергология и клиническая иммунология. Клинические рекомендации. М.: ГЭОТАР – Медиа, 2019. 352 с.
Хан В.М. Усовершенствование долгосрочных метеорологических прогнозов на основе структурно-статистического подхода. Диссертация на соискание ученой степени д. г. н. Москва, 2012. 305 с.
Andersen T.B. A model to predict the beginning of the pollen season // Grana. 1991. V. 30. P. 269–275.
Bastl K., Kmenta M., Pessi A.M., Prank M., Saarto A., Sofiev M., Bergmann K.C., Buters J.T.M., Thibaudon M., Jäger S., 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. V. 548–549. P. 229–235.
Cannell M.G.R., Smith R.I. Thermal Time, Chill Days and Prediction of Budburst in Picea-Sitchensis // J. Applied Ecology. 1983. № 20. P. 951–963.
Carton J.A., Chepurin G.A., Chen L. SODA3: A New Ocean Climate Reanalysis // J. Clim. 2018. V. 31. № 17. P. 6967–6983.
D’Amato G., Cecchi L., Bonini S., Nunes C., Liccardi G., Popov T., Cauwenberge P. Van. Allergenic pollen and pollen allergy in Europe // Allergy. 2007. V. 9(62). P. 976–990.
Dorota M. Prediction of the birch pollen season characteristics in Cracow, Poland using an 18-year data series // Aerobiologia. 2013. V. 29. P. 31–44.
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. V. 9. № 5. P. 1937–1958.
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. V. 148. P. 319–326.
Helbig N., Vogel B., Vogel H., Fiedler F. Numerical modeling of pollen dispersion on the regional scale // Aerobiologia (Bologna). 2004. V. 3. P. 3–19.
Hersbach H., Bell B., Berrisford P. et al. The ERA5 global reanalysis // Quarterly J. Royal Meteorological Society. 2020. V. 146. P. 1999–2049.
Huynen M., Menne B., Behrendt H. et al. Phenology and Human Health: Allergic Disorders. 2003. World Health Organisation.
Fu Y., Campioli M., Deckmyn G., Janssens I. The Impact of Winter and Spring Temperatures on Temperate Tree Budburst Dates: Results from an Experimental Climate Manipulation // PLoS ONE. 2012. V. 7(10). E. 47324.
Kim Y.H., Min S.K., Zhang X. et al. Evaluation of the CMIP6 multi-model ensemble for climate extreme indices // Weather and Climate Extremes. 2020. V. 29. 100 269
Klimek L., Bachert C., Pfaar O. ARIA guideline 2019: treatment of allergic rhinitis in the German health system // Allergo J. Int. 2019. V. 28(7). P. 255–276.
Koivikko A., Kupas R., Makinen Y., Pohjola A. Pollen seasons: forecasts of the most important allergenic plants in Finland // Allergy. 1986. V. 41(4). P. 233–242.
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. V. 12. P. 1–87.
Laadi M. Regional variations in the pollen season of Betula in Burgundy: Two models for predicting the start of the pollination // Aerobiologia. 2001b. V. 17. P. 247–254.
Latałowa M., Miętus M., Uruska A. Pollen seasonal variations in the atmospheric Betula pollen count in Gdańsk (southern Baltic coast) in relation to meteorological parameters // Aerobiologia. 2022. V. 18. P. 33–43.
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 // Agricultural and Forest Meteorology. 2010. V. 150. P. 1579–1584.
Mahura A., Baklanov A., Korsholm U. Parameterization of the birch pollen diurnal cycle // Aerobiologia (Bologna). 2009. V. 25. P. 203–208.
Myszkowska D., Jenner B., Stępalska D., 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. V. 27(3). P. 229–238.
Newnham R., Sparks T., Skjøth C., Head K., Adams-Groom B., Smith M. Pollen season and climate: is the timing of birch pollen release in the UK approaching its limit? // Int. J. Biometeorol. 2013. V. 57. P. 391–400.
Norris-Hill J. A method to forecast the start of the Betula, Platanus and Quercus pollen seasons in North London // Aerobiologia. 1998. V. 14. P. 165–170.
Pauling A., Rotach M.W., Gehrig R., Clot B. A method to derive vegetation distribution maps for pollen dispersion models using birch as an example // Int. J. Biometeorol. 2012. V. 56. P. 949–958.
Porteous T., Wyke S., Smith S., Bond C., Francis J., Lee AJ., Lowrie R., Scotland G., Sheikh A., Thomas M., 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. V. 15. № 14. P. 217.
Ritenberga O., Sofiev M., Siljamo P., Saarto A., Dahl A., Ekebom A., Sauliene I., Shalaboda V., Severova E., Hoebeke L., Ramfjord H. A statistical model for predicting the inter-annual variability of birch pollen abundance in Northern and North-Eastern Europe // Science of the Total Environment. 2018. V. 615. P. 228–239.
Rodriguez-Rajo F.J., Frenguelli G., 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) // International J. Biometeorology. 2003. V. 47. P. 117–125.
Siljamo P., Sofiev M., Filatova E., Grewling L., Jäger S., Khoreva E., Linkosalo T., Ortega Jimenez S., Ranta H., Rantio-Lehtimäki A., Svetlov A., Veriankaite L., Yakovleva E., Kukkonen J. A numerical model of birch pollen emission and dispersion in the atmosphere. Model evaluation and sensitivity analysi // Int. J. Biometeorol. 2012. V. 57. P. 125–136.
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., Tuovinen J.-P., Valdebenito Á., Wind P. The EMEP MSC-W chemical transport model – technical description // Atmos. Chem. Phys. 2012. V. 12. P. 7825–7865.
Sofiev M., Siljamo P., Ranta H. Towards numerical forecasting of long-range air transport of birch pollen: theoretical considerations and a feasibility study // Int. J. Biometeorol. 2006. V. 50. P. 392.
Sofiev M., Siljamo P., Ranta H., Linkosalo T. A numerical model of birch pollen emission and dispersion in the atmosphere, Description of the emission module // Int. J. Biometeorol. 2012(a). V. 57. P. 45–58.
Stach A., Emberlin J., Adams-Groom B., Smith M., Myszkowska D. Factors that determine the severity of Betula spp. pollen seasons in Poland (Poznań and Cracow) and the United Kingdom (Worcester and London) // International J. Biometeorology. 2008. V. 52(4). P. 311–321.
Tarasevich M.A., Volodin E.M. Influence of various parameters of INM RAS climate modelon the results of extreme precipitation simulation // International Young Scientists Schooland Conference on Computational Information Technologies for Environmental Sciences, May 27–June 6, 2019. IOP Conference Series: Earth and Environmental Science. V. 386. P. 012012.
Tarasevich M.A., Volodin E.M. The Influence of Autumn Eurasian Snow Cover on the Atmospheric Dynamics Anomalies during the Next Winter in INMCM5 Model Data // Supercomputing Frontiers and Innovations. 2021. V. 8(4). P. 24–39.
Vogel H., Pauling A., Vogel B. Numerical simulation of birch pollen dispersion with an operational weather forecast system // Int. J. Biometeorol. 2008. V. 52. P. 805–814.
Volodin E.M., Mortikov E.V., Kostrykin S.V. et al. Simulation of the present-day climate with the climate model INMCM5 // Clim. Dyn. 2017. V. 49. P. 3715–3734.
Volodin E.M., Gritsun A.S. Simulation of observed climate changes in 1850–2014 with climate model INM-CM5 // Earth System Dynamics. 2018. V. 9(4). P. 1235–1242.
Vorobyeva V., Volodin E. Evaluation of the INM RAS climate model skill in climate indicesand stratospheric anomalies on seasonal timescale // Tellus A: Dynamic Meteorology and Oceanography. 2021. V. 73(1). P. 1–12. (a)
Vorobyeva V.V., Volodin E.M. Experimental Studies of Seasonal Weather Predictability Based on the INM RAS Climate Model // Mathematical Models and Computer Simulations. 2021. V. 13(4). P. 571–578. (b)
Zink K., Pauling A., Rotach M.W., Vogel H., Kaufmann P., Clot B. EMPOL 1.0: a new parameterization of pollen emission in numerical weather prediction models // Geosci. Model Dev. 2013. V. 6. P. 1961–1975.