Projected Changes in Temperature and Precipitation over Kashafrood Basin Based on Statistical and Dynamical Downscaling Methods

Ataei, Hooshmand; Kouhi, Mansoureh; Modirian, Rahele; Bazrafshan, Bahare

doi:10.22111/jneh.2021.37827.1777

Projected Changes in Temperature and Precipitation over Kashafrood Basin Based on Statistical and Dynamical Downscaling Methods

Document Type : Research Article

Authors

¹ Associate Prof. Payam Noor University, Iran.

² Assistant Prof., Natural Disasters and Climate Change Research Group, Climatological Research Institute, Iran.

³ M.Sc. in Physics, Member of Modeling Research Group, Climatological Research Institute, Iran.

⁴ M.Sc. in Climatology, Climatological Research Institute, Iran.

10.22111/jneh.2021.37827.1777

Abstract

It is well-known that climate is changing continuously under the intricate influences of natural and artificial factors at global and regional scales. The global Coupled Model Intercomparison Project (CMIP) already provides multi model data resources in order to improve the scientific research for investigating the vulnerability of climate change and future climate risk at regional or local scales and then developing the corresponding adaptation strategies. Global climate models (GCMs) have proven to be unable to resolve the details of regional climate change features because of the limitation of their coarse resolution. To bridge these gaps, downscaling methods, that is, statistical and dynamical downscaling, are multi method ways to get fine resolution projections of GCMs.Since the provision of robust climate information with a multimodel, multimethod, and multiscale (M⁵S) method can assist decision-making responding to climate change in agriculutral and water sectors, this study aims to provide the climate change scenarios of temperature and precipitation over Kashafrood Basin (KB) using three downsclaing methods. In this study the CanESM model outputs have been downscaled using two statistical downscaling methods (BCSD and SDSM) and one regional climate model (RegCM) during the period of 1984-2005 and the near future period (2021-2050) under RCP4.5. Results show that the mean temperature is projected to increase in the Kashafrood basin throughout all seasons. Precipitation changes exhibit a larger variability. By the end of the near future, an annual precipitation decrease by 4% and 9% are projected under RCP4.5 based on SDSM and RegCM model respectively in Mashad station, while an increase of over 24% is projected using BCSD downscaling method which is statistically significant.

Keywords

Main Subjects

Climate-change-new-findings

References

References (in Persian)

Kouhi, M., Shirmohammadi Aliakbarkhani, Z., Mohamadian , A., Habibi Nokhandan, M. (2020). Study of Spatial and Temporal Characteristics of ETo and Temperature in Khorasan Razavi Province Using CRU TS Dataset and Their Future Projections Based on CMIP5 Climate Models, Iranian Journal of Remote Sencing & GIS, 12(45), pp 55-72. [In Persian]

Modirian, R., Karimian, M., Bazrafshan, B., Babaeian, I., Halabian, A., Falamarzi, Y. (2020). Climate Change Over Khorasan Razavi using Dynamic Postprocessing Method, 6th International Climate Change Conference, Tehran, Iran, https://civilica.com/doc/1002841. [In Persian]

Sobhani, B., Eslahi, M., Babeian, I. (2017). Comparison of statistical downscaling in climate change models to simulate climate elements in Northwest Iran, Physical Geography Research Quarterly, 49(2), pp 301-325.[In Persian]

References (in English)

Akhter, M. S., Shamseldin, A. Y., & Melville, B. W. (2019). Comparison of dynamical and statistical rainfall downscaling of CMIP5 ensembles at a small urban catchment scale. Stochastic Environmental Research and Risk Assessment, 33(4), pp 989-1012.

Chu, J. T., Xia, J., Xu, C. Y., Singh, V. P. (2010). Statistical downscaling of daily mean temperature, pan evaporation and precipitation for climate change scenarios in Haihe River. China. Theoretical and Applied Climatology, 99, pp 149–161.

Dibike, Y. B., Coulibaly, P. (2006). Temporal neural networks for downscaling climate variability and extremes. Neural Networks, 19, pp 135–144.

Giorgi F, Marinucci MR, Bates G. (1993). Development of a second generation regional climate model (RegCM2). I.Boundary layer and radiative transfer processes. Mon Weather Rev, 121, pp 2794–2813.

Harpham, C., & Wilby, R. L. (2005). Multi-site downscaling of heavy daily precipitation occurrence and amounts. Journal of Hydrology, 312(1-4), pp 235-255.

Hessami, M., Gachon, P., Ouarda, T.B.M.J., St-Hilaire A. (2008). Automated regression-based statistical downscaling tool, Environmental Modelling and Software, 23, pp 813–834.

Hewitt, C., S. Mason, and D. Walland, (2012). The Global Framework for Climate Services. Nat. Climate Change, 2, pp 831–832.

Intergovernmental Panel on Climate Change. (2007). Summary for policymakers. In Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden, and C.E. Hanson, Eds. Cambridge University Press, pp 7-22. https:// pubs.giss.nasa.gov/ docs/ 2007/ 2007_IPCC_ip01000b.pdf.

Intergovernmental Panel on Climate Change. (2010). Meeting Report, IPCC Expert Meeting on Assessing and Combining Multi Model Climate Projections, National Center for Atmospheric Research, Boulder, Colorado. USA. https:// www.ipcc.ch / publication/ ipcc expert meeting on assessing and combining multi model climate projections/.

Intergovernmental Panel on Climate Change. (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press, Cambridge. . http://www.ipcc.ch/report/ar5/wg1/.

Karamouz, M., Fallahi, M., Nazif, S., Rahimi Farahani, M. (2009). Long lead rainfall prediction using statistical downscaling and artificial neural network modeling. Scientia Iranica, 16(2), pp 165-172.

Khan, M. S., Coulibaly, P., Dibike, Y. (2006). Uncertainty analysis of statistical downscaling methods. Journal of Hydrology, 319, pp 357-382.

Motovilov, Y.G., Gottschalk, L., Engeland, K., and Rodhe, A. (1999). Validation of a distributed hydrological model against spatial observations. Agricultural and Forest Meteorology. 98-99,pp 257-277.

Ning, L., Bradley, R. S. (2015). NAO and PNA influences on winter temperature and precipitation over the eastern United States in CMIP5 GCMs. Climate Dyn., 46, pp 1257–1276.

Pal, J., Small, E. & Eltahir, E. (2000): Simulation of regional-scale water and energy budgets: Representation of subgrid cloud and precipitation processes within regcm, Journal of Geophysical Research-Atmospheres 105, pp 29579-29594.

Rahimi, J., Malekian, A., & Khalili, A. (2019). Climate change impacts in Iran: assessing our current knowledge. Theoretical and Applied Climatology, 135(1-2), pp 545-564.

Semenov, M. (2008). Simulation of extreme weather events by a stochastic weather generator. Climate Research, 35, pp 203–212.

Semenov, M. A., Brooks, R. J. 1999. Spatial interpolation of the LARS-WG stochastic weather generator in Great Britain. Climate Research, 11, 137-148.

Souvignet, M., Heinrich, J. (2010). Statistical downscaling in the arid central Andes: uncertainty analysis of multi-model simulated temperature and precipitation. Theoretical and Applied Climatology, 106, pp 229–244.

Taylor, K.E., Stouffer, R.J., Meehl, G.A. (2012). An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society. 93,pp 485–498.

Tiwari, P. R., Kar, S. C., Mohanty, U. C., Dey, S., Sinha, P., Shekhar, M. S., & Sokhi, R. S. (2019). Comparison of statistical and dynamical downscaling methods for seasonal‐scale winter precipitation predictions over north India. International Journal of Climatology, 39(3), pp 1504-1516.

Werner, A. T., Cannon, A. J. (2016). Hydrologic extremes—An intercomparison of multiple gridded statistical downscaling methods. Hydrol. Earth Syst. Sci., 20, pp 1483–1508.

Wetterhall, F., Halldin, S., Xu, C. Y. (2009). Seasonality properties of four statistical downscaling methods in central Sweden. Theoretical and Applied Climatology, 87, pp 123-137.

Wilby, R. L., Dawson, C. W., Barrow, E. M. (2001). A decision support tool for the assessment of regional climate change impacts. Journal of Environmental Modeling and Software, 17, pp 147-159.

Wilby, R. L., Hay, L.E, Leavesley, G. H. (1999). A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River Basin, Colorado. Journal of Hydrology, 225, pp 67–91.

Wilby, R. L., Tomlinson, O. J., Dawson, C. W. (2003). Multi-site simulation of precipitation by conditional resampling. Climate Research, 23, pp 183–194.

Wilson, S., Hassell, D., Hein, D., Morrell, C., Tucker, S., Jones, R., Taylor, R. (2011). Installing and Using the Hadley Centre Regional Climate Modelling System, PRECIS (Version 1.9.3). Met Office Hadley Centre: Exeter, UK. https://www.metoffice.gov.uk/research/applied/international/precis.

Wood, A.W., Leung, L.R., Sridhar, V., Lettenmaier, D.P. (2004). Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Climatic Change 62 (1), pp189–216.

Zhang, L., Xu, Y., Meng, C., Li, X., Liu, H., & Wang, C. (2020). Comparison of Statistical and Dynamic Downscaling Techniques in Generating High-Resolution Temperatures in China from CMIP5 GCMs. Journal of Applied Meteorology and Climatology, 59(2), pp 207-235.