Abstract
Methods are proposed to estimate the monthly relative humidity and wet bulb temperature based on observations from a dynamical downscaling coupled general circulation model with a regional climate model (RCM) for a quantitative assessment of climate change impacts. The water vapor pressure estimation model developed was a regression model with a monthly saturated water vapor pressure that used minimum air temperature as a variable. The monthly minimum air temperature correction model for RCM bias was developed by stepwise multiple regression analysis using the difference in monthly minimum air temperatures between observations and RCM output as a dependent variable and geographic factors as independent variables. The wet bulb temperature was estimated using the estimated water vapor pressure, air temperature, and atmospheric pressure at ground level both corrected for RCM bias. Root mean square errors of the data decreased considerably in August.
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Arakawa A, Schubert WH (1974) Interaction of a cumulus cloud ensemble with the large-scale environment, part I. J Atmos Sci 31:674–701
Fowler HJ, Kilsby CG (2007) Using regional climate model data to simulate historical and future river flows in northwest England. Clim Chang 80:337–367
Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27:1547–1578
Hashimoto A (1976) Infection of rice blast disease and water drop—development of dewfall meter and its utilization. Plant Protection 30(7):264–268
Hara M, Yoshikane T, Kawase H, Kimura F (2008) Estimation of the impact of global warming on snow depth in Japan by the pseudo-global-warming method. Hydrological Research Letters 2:61–64
Hirai G, Okumura T, Takeuchi S, Tanaka O, Chujo H (1998) Studies on the effect of the relative humidity of the atmosphere on the growth and physiology of rice plants: effects of ambient humidity in the dark period on the growth and the translocation of 3C-labelled photosynthetic products. Japanese Journal of Crop Science 67(2):216–220
Iizumi T, Hayashi Y, Kimura F (2007) Influence on rice production in Japan from cool and hot summers after global warming. J Agric Meteorol 63(1):11–23
Iizumi T, Nishimori M, Yokozawa M (2008) Combined equations for estimating global solar radiation: projection of radiation field over Japan under global warming conditions by statistical downscaling. J Agric Meteorol 64(1):9–23
Iizumi T, Yokozawa M, Nishimori M (2011) Probabilistic evaluation of climate change impacts on paddy rice productivity in Japan. Clim Chang 107:391–415. doi:10.1007/s10584-010-9990-7
IPCC (2001) Special report on emission scenarios. Cambridge University Press, UK
Kimura F, Kitoh A (2007) Downscaling by pseudo global warming method. Report of Research Projection on Impact of Climate Changes on Agricultural Production System in Arid Areas, Kyoto, pp 43–46
Loveland TR, Reed BC, Brown JF, Ohlen DO, Zhu J, Yang L, Merchant JW (2000) Development of a global land cover characteristics database and IGBP Discover from 1-km AVHRR data. Int J Remote Sensing 21:1303–1330
Louis JF (1979) A parametric model of vertical eddy fluxes in the atmosphere. Bound-Layer Meteor 17:187–202
Mitchell JFB, Johns TC, Eagles M, Ingram WJ, Davis RA (1999) Towards the construction of climate change scenarios. Clim Chang 41:547–581
Murray FW (1967) On the computation of saturation vapor pressure. J Appl Meteorol 6:203–204
Nakajima T, Tsukumoto M, Tsushima Y, Numaguti A, Kimura T (2000) Modeling of the radiative process in an atmospheric general circulation model. Appl Opt 39:4869–7878
Pielke RA, Cotton WR, Walko RL, Tremback CJ, Lyons WA, Grasso LD, Nicholls ME, Moran MD, Wesley TJ, Copeland JH (1992) A comprehensive meteorological modeling system—RAMS. Meteorol Atmos Phys 49:69–91
Reynolds RW, Rayner NA, Smith TM, Stokes DC, Wang W (2002) An improved in situ and satellite SST analysis for climate. J Climate 15:1609–1625
Sato T, Kimura F, Kitoh A (2007) Projection of global warming onto regional precipitation over Mongolia using a regional climate model. J Hydrol 333:144–154
Seino H (1993) An estimating of distribution of meteorological elements using GIS and AMeDAS data. J Agric Meteor 48(4):379–383
Shiga H (2003) Estimation of vapor pressure using minimum temperature at AMeDAS weather observation point. Bull Hokkaido Prefectural Agricultural Experiment Stations 84:99–100
Toda K, Nakai F, Ieki H, Fujioka K, Watanabe H, Iuchi H, Terada F (2002) Effect of “effective temperature” on milk yield of Holstein cows in hot and humid environments. Nihon Chikusan Gakkaiho 73(1):63–70
Tremback CJ, Kessler R (1985) A surface temperature and moisture parameterization for use in mesoscale numerical models. Preprint: Seventh Conference on Numerical Weather Prediction, Montreal, QC, Canada, American Meteorological Society, pp 355–358
Ueyama H, Adachi S, Kimura F (2010) Compilation method for 1 km grid data of monthly mean air temperature for quantitative assessments of climate change impacts. Theor Appl Climatol 101:421–431
Umetsu T, Kimura K, Nakano K, Hasegawa S, Matsuda H, Oota H, Haga S, Takeda M, Yajima M (1993) Classification of suitable areas for paddy rice cultivars using mesh climatic data 1. Estimation of the safety cropping seasons and suitable areas. Bull Yamagata Agric Exp Stn 27:1–21
Walko RL, Cotton WR, Meyers MP, Harington JY (1995) New RAMS cloud microphysics parameterization. Part 1: The single-moment scheme. Atmos Res 38:29–62
Wood AW, Leung LR, Sridhar V, Lettenmaier DP (2004) Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs. Clim Chang 62:189–216
Yamazaki M, Murakami H, Nakashima K, Abe H, Sugiura T, Yokozawa M, Kurihara M (2006) Impact of global warming on broiler meat production estimated from changes of the mean ambient temperature. Nihon Chikusan Gakkaiho 77(2):231–235
Yoshikane T, Kimura F (2003) Formation mechanism of the simulated SPCZ and Baiu front using a regional climate model. J Atmos Sci 60:2612–2632
Yukimoto S, Noda A, Kitoh A, Sugi M, Kitamura Y, Hosaka M, Shibata K, Maeda S, Uchiyama T (2001) The new Meteorological Research Institute coupled GCM (MRI-CGCM2): model climate and variability. Papers in Meteorological and Geophysics 51(2):47–88
Acknowledgments
I am grateful for the valuable comments and discussions on this study with Dr. Kimura F. and Dr. S. A. Adachi, Japan Agency for Marine-Earth Science and Technology. I also acknowledge members of the Graduate School of Life and Environmental Sciences, University of Tsukuba, for instructions on the PGW method. MRI-CGCM2 data were downloaded from the PCMDI. Data from the AMeDAS meteorological stations and data from five meteorological observatories were provided by the Agriculture, Forestry and Fisheries Research Information Center, Ministry of Agriculture, Forestry and Fisheries (MAFF), Japan. The software for multiple regression analysis, SAS, was provided by the Computer Center for Agriculture, Forestry and Fisheries Research, MAFF, Japan. The 250-m resolution digital elevation model was published by the Geospatial Information Authority of Japan.
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Ueyama, H. Estimation methods for monthly humidity from dynamical downscaling data for quantitative assessments of climate change impacts. Theor Appl Climatol 109, 15–26 (2012). https://doi.org/10.1007/s00704-011-0558-x
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DOI: https://doi.org/10.1007/s00704-011-0558-x