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Part of the book series: Springer Polar Sciences ((SPPS))

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Abstract

Forecasting the likely future prevalence of climate sensitive infectous diseases (CSIs) in the Arctic requires prediction of how environmental conditions, both aquatic and on the land, will change under a changing climate, together with knowledge of how these changes relate to the environmental conditionals for viability of CSI host organisms. This requires the use of land surface and hydro-climatic models that have been tested against past data and can be driven by climate projections provided by Global Circulation Models for a range of climate scenarios (Representative Concentration Pathways). Uncertainties in the climate projections combine with uncertainties in the environmental models, and this combined uncertainty propagates through into subsequent CSI occurrence modelling. This chapter will describe the available environmental models, together with the data needed to drive and test them, and how we can address the uncertainty within these models, in the context of Arctic CSI prediction.

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References

  • Asokan, S. M., Rogberg, P., Bring, A., et al. (2016). Climate model performance and change projection for freshwater fluxes: Comparison for irrigated areas in central and South Asia. Journal of Hydrology: Regional Studies, 5, 48–65. https://doi.org/10.1016/j.ejrh.2015.11.017.

    Article  Google Scholar 

  • Barnett, et al. (2005). Potential impacts of a warming climate on water availability in snow-dominated regions. Nature, 438, 303–309.

    Article  Google Scholar 

  • Bintanja, & Andry. (2014). Towards a rain dominated Arctic. Nature Climate Change, 7, 263–267.

    Article  Google Scholar 

  • Bintanja, R., & Selten, F. M. (2014). Future increases in Arctic precipitation linked to local evaporation and sea-ice retreat. Nature, 509, 479. https://doi.org/10.1038/nature13259.

    Article  Google Scholar 

  • Bring, A., & Destouni, G. (2014). Arctic climate and water change: Model and observation relevance for assessment and adaptation. Surveys in Geophysics, 35, 853–877. https://doi.org/10.1007/s10712-013-9267-6.

    Article  Google Scholar 

  • Bring, A., Asokan, S. M., Jaramillo, F., et al. (2015). Implications of freshwater flux data from the CMIP5 multimodel output across a set of Northern Hemisphere drainage basins. Earth’s Future, 3, 206–217. https://doi.org/10.1002/2014ef000296.

    Article  Google Scholar 

  • Bring, A., Shiklomanov, A., & Lammers, R. B. (2017). Pan-Arctic river discharge: Prioritizing monitoring of future climate change hot spots. Earth’s Future, 5, 72–92. https://doi.org/10.1002/2016ef000434.

    Article  Google Scholar 

  • Bring, A., Goldenberg, R., Kalantari, Z., et al. (2019). Contrasting hydroclimatic model-data agreements over the Nordic-Arctic region. Earth’s Future, 7(12), 1270–1282. https://doi.org/10.1029/2019EF001296.

    Article  Google Scholar 

  • Brown et al. (2017). Arctic terrestrial snow cover. In: Snow, Water, Ice and Permafrost in the Arctic (SWIPA) (pp. 25–64).

    Google Scholar 

  • Comyn-Platt, E., Hayman, G., Huntingford, C., et al. (2018). Carbon budgets for 1.5 and 2°C targets lowered by natural wetland and permafrost feedbacks. Nature Geoscience, 11, 568–573. https://doi.org/10.1038/s41561-018-0174-9.

    Article  Google Scholar 

  • Daley, K., Castleden, H., Jamieson, R., et al. (2014). Municipal water quantities and health in Nunavut households: An exploratory case study in coral harbour, Nunavut, Canada. International Journal of Circumpolar Health, 73, 1–10. https://doi.org/10.3402/ijch.v73.23843.

    Article  Google Scholar 

  • Derksen, & Brown. (2012). Spring snow cover extent reductions in the 2008-2012 period exceeding climate model projections. Geophysical Research Letters, 39, L19504.

    Article  Google Scholar 

  • Druel, A., Peylin, P., Krinner, G., et al. (2017). Towards a more detailed representation of high-latitude vegetation in the global land surface model ORCHIDEE (ORC-HLVEGv1.0). Geoscientific Model Development, 10, 4693–4722. https://doi.org/10.5194/gmd-10-4693-2017.

    Article  Google Scholar 

  • Dyurgerov, M., Bring, A., & Destouni, G. (2010). Integrated assessment of changes in freshwater inflow to the Arctic Ocean. Journal of Geophysical Research, 115, D12116.

    Article  Google Scholar 

  • Fauchald, et al. (2017). Arctic greening from warming promotes declines in caribou populations. Science Advances, 3, e16013652017.

    Article  Google Scholar 

  • Groß, E., MÃ¥rd, J., Kalantari, Z., et al. (2018). Links between Nordic and Arctic hydroclimate and vegetation changes as possible landscape-scale nature based solutions. Land Degradation & Development, 29, 3663–3673. https://doi.org/10.1002/ldr.3115.

    Article  Google Scholar 

  • Guimberteau, M., Zhu, D., Maignan, F., et al. (2018). ORCHIDEE-MICT (v8.4.1): A land surface model for the high latitudes: Model description and validation. Geoscientific Model Development, 11, 121–163. https://doi.org/10.5194/gmd-11-121-2018.

    Article  Google Scholar 

  • Hellmann, et al. (2008). Five potential consequences of climate change for invasive species. Conservation Biology, 22, 534–543.

    Article  Google Scholar 

  • Hickler, T., Vohland, K., Feehan, J., et al. (2012). Projecting the future distribution of European potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model. Global Ecology & Biogeography, 21, 50–63.

    Article  Google Scholar 

  • Hoberg, E. P., & Brooks, D. R. (2015). Evolution in action: Climate change, biodiversity dynamics and emerging infectious disease. Philosphical Transactions of the Royal Society of London B Biological Sciences, 370, 20130553. https://doi.org/10.1098/rstb.2013.0553.

    Article  Google Scholar 

  • IPCC. (2019). Summary for policymakers. In: H.-O. Pörtner, D.C. Roberts, V. Masson-Delmotte, P. Zhai, M. Tignor, E. Poloczanska, K. Mintenbeck, M. Nicolai, A. Okem, J. Petzold, B. Rama, & N. Weyer (Eds.), IPCC special report on the ocean and cryosphere in a changing climate. In press.

    Google Scholar 

  • Karlsson, J. M., Bring, A., Peterson, G. D., et al. (2011). Opportunities and limitations to detect climate-related regime shifts in inland Arctic ecosystems through eco-hydrological monitoring. Environmental Research Letters, 6, 014015. https://doi.org/10.1088/1748-9326/6/1/014015.

    Article  Google Scholar 

  • Karlsson, J. M., Lyon, S. W., & Destouni, G. (2012). Thermokarst lake, hydrological flow and water balance indicators of permafrost change in Western Siberia. Journal of Hydrology, 464–465, 459–466.

    Article  Google Scholar 

  • Langlois, et al. (2017). Detection of rain-on-snow (ROS) events and ice layer formation using passive microwave radiometry: A context for Peary caribou habitat in the Canadian Arctic. Remote Sensing of Environment, 189, 84–95.

    Article  Google Scholar 

  • Lawrence, D., Fisher, R., Koven, C., et al. (2019). The community Land model version 5: Description of new features, benchmarking, and impact of forcing uncertainty. Journal of Advances in Modeling Earth Systems. https://doi.org/10.1029/2018MS001583.

  • Leibovici, D. G. (2010). Spatio-temporal multiway decompositions using principal tensor analysis on k-modes: The R package PTAk. Journal of Statistical Software, 34(10), 1–34. https://doi.org/10.18637/jss.v034.i10.

    Article  Google Scholar 

  • Leibovici, D. G., & Claramunt, C. (2019). On integrating size and shape distributions into a spatio-temporal information entropy framework. Entropy, 21, 1112. https://doi.org/10.3390/e21111112.

    Article  Google Scholar 

  • Leibovici, D. G., Quegan, S., Comyn-Platt, E. et al (2019). Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis. Biogeosciences (accepted). https://doi.org/10.5194/bg-2019-252.

  • Ma, Y., Bring, A., Kalantari, Z., & Destouni, G. (2019). Potential for hydroclimatically driven shifts in infectious disease outbreaks: The case of tularemia in high-latitude regions. International Journal of Environmental Research and Public Health, 16, 3717. https://www.mdpi.com/1660-4601/16/19/3717.

    Article  Google Scholar 

  • Miles, V. V., & Esau, I. (2016). Spatial heterogeneity of greening and browning between and within bioclimatic zones in northern West Siberia. Environmental Research Letters, 11, 115002. https://doi.org/10.1088/1748-9326/11/11/115002.

    Article  Google Scholar 

  • Mizukami, N., Clark, M. P., Gutmann, E. D., et al. (2016). Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: Statistically downscaled forcing data and hydrologic models. Journal of Hydrometeorology, 17, 73–98. https://doi.org/10.1175/jhm-d-14-0187.1.

    Article  Google Scholar 

  • Myers-Smith, I., Forbes, B. C., Wilmking, M., et al. (2011). Shrub expansion in tundra ecosystems: Dynamics, impacts and research priorities. Environmental Research Letters, 6, 045509.

    Article  Google Scholar 

  • Pulliainen, J., Aurela, M., Laurila, T., et al. (2017). Early snowmelt significantly enhances boreal springtime carbon uptake. PNAS, 114(42), 11081–11086. https://doi.org/10.1073/pnas.1707889114.

    Article  Google Scholar 

  • Revich, B., Tokarevich, N., & Parkinson, A. J. (2012). Climate change and zoonotic infections in the Russian Arctic. International Journal of Circumpolar Health, 71, 18792–18792. https://doi.org/10.3402/ijch.v71i0.18792.

    Article  Google Scholar 

  • Romanovsky, V. E., Smith, S. L., & Christiansen, H. H. (2010). Permafrost thermal state in the polar northern hemisphere during the international polar year 2007–2009: A synthesis. Permafrost and Periglacial Processes, 21, 106–116. https://doi.org/10.1002/ppp.689.

    Article  Google Scholar 

  • Rydén, P., Björk, R., Schäfer, M. L., et al. (2012). Outbreaks of tularemia in a boreal forest region depends on mosquito prevalence. The Journal of Infectious Diseases, 205, 297–304. https://doi.org/10.1093/infdis/jir732.

    Article  Google Scholar 

  • Sauvala, et al. (2019). Microbial contamination of moose (Alces alces) and white-tailed deer (Odocoileus virginianus) carcasses harvested by hunters. Food Microbiology, 78, 82–88.

    Article  Google Scholar 

  • Schuur, E. G., Mcguire, A. D., Schädel, C., et al. (2015). Climate change and the permafrost carbon feedback. Nature, 520, 171. https://doi.org/10.1038/nature14338.

    Article  Google Scholar 

  • Seifollahi-Aghmiuni, S., Kalantari, Z., Land, M., et al. (2019). Change drivers and impacts in Arctic wetland landscapes – Literature review and gap analysis. Water, 11, 722.

    Article  Google Scholar 

  • Selroos, J. -O., Cheng, H., Vidstrand, P. et al. (2019). Permafrost thaw with thermokarst wetland-lake and societal-health risks: Dependence on local soil conditions under large-scale warming. Water 11, 574. https://www.mdpi.com/2073-4441/11/3/574, https://www.mdpi.com/2073-4441/11/4/722

  • Stroeve, et al. (2012). The Arctic’s rapidly shrinking sea ice cover: A research synthesis. Climatic Change, 110, 1005–1027. https://doi.org/10.1007/s10584-011-0101-1.

    Article  Google Scholar 

  • Sturm, M., Goldstein, M. A., & Parr, C. (2017). Water and life from snow: A trillion dollar science question. Water Resources Research, 53. https://doi.org/10.1002/2017WR020840.

  • Sun, S., Sun, G., Cohen, E., et al. (2016). Projecting water yield and ecosystem productivity across the United States by linking an ecohydrological model to WRF dynamically downscaled climate data. Hydrology and Earth System Sciences, 20, 935–952. https://doi.org/10.5194/hess-20-935-2016.

    Article  Google Scholar 

  • Takala, M., Pulliainen, J., Metsämäki, S., et al. (2009). Detection of snow melt using spaceborne microwave radiometer data in Eurasia from 1979-2007. IEEE Transactions on Geoscience and Remote Sensing, 47, 2996–3007.

    Article  Google Scholar 

  • Waits, A., Emelyanova, A., Oksanen, A., et al. (2018). Human infectious diseases and the changing climate in the Arctic. Environment International, 121, 703–713. https://doi.org/10.1016/j.envint.2018.09.042.

    Article  Google Scholar 

  • Wrona, F. J., Johansson, M., Culp, J. M., et al. (2016). Transitions in Arctic ecosystems: Ecological implications of a changing hydrological regime. Journal of Geophysical Research: Biogeosciences, 121, 650–674. https://doi.org/10.1002/2015jg003133.

    Article  Google Scholar 

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Correspondence to Shaun Quegan .

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Destouni, G., Kalantari, Z., Quegan, S., Leibovici, D., Lemmetyinen, J., Ikonen, J. (2021). Modeling Climate Sensitive Infectious Diseases in the Arctic. In: Nord, D.C. (eds) Nordic Perspectives on the Responsible Development of the Arctic: Pathways to Action. Springer Polar Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-52324-4_5

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