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Stochastic simulation of daily air temperature and precipitation from monthly normals in North America north of Mexico

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Abstract

A simple, stochastic daily temperature and precipitation generator (TEMPGEN) was developed to generate inputs for the study of the effects of climate change on models driven by daily weather information when climate data are available as monthly summaries. The model uses as input only 11 sets of monthly normal statistics from individual weather stations. It needs no calibration, and was parameterized and validated for use in Canada and the continental United States. Monthly normals needed are: mean and standard deviation of daily minimum and maximum temperature, first and second order autoregressive terms for daily deviations of minimum and maximum temperatures from their daily means, correlation of deviations of daily minimum and maximum temperatures, total precipitation, and the interannual variance of total precipitation. The statistical properties and distributions of daily temperature and precipitation data produced by this generator compared quite favorably with observations from 708 stations throughout North America (north of Mexico). The algorithm generates realistic seasonal patterns, variability and extremes of temperature, precipitation, frost-free periods and hot spells. However, it predicts less accurately the daily probability of precipitation, extreme precipitation events and the duration of extreme droughts.

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References

  • Bruhn JA (1980) A stochastic model for the simulation of daily weather. Prot Ecol 2:199–208

    Google Scholar 

  • Bürger G (1997) On the disaggregation of climatological means and anomalies. Clim Res 8:183–194

    Google Scholar 

  • Chin EH (1977) Modelling daily precipitation occurrence process with Markov chain. Water Resour Res 13:949–956

    Google Scholar 

  • Dubrovský M (1997) Creating daily weather series with use of the weather generator. Environmetrics 8:409–424

    Article  Google Scholar 

  • Dubrovský M, Buchtele J, Žalud Z (2004) High-frequency and low-frequency variability in stochastic daily weather generator and its effect on agricultural and hydrologic modelling. Clim Change 63:145–179

    Article  Google Scholar 

  • Fowler HJ, Kilsby CG, O’Connell PE, Burton A (2005) A weather-type conditioned multi-site stochastic rainfall model for the generation of scenarios of climatic variability and change. J Hydrol 308:50–66

    Article  Google Scholar 

  • Friend AD (1998) Parameterisation of a global daily weather generator for terrestrial ecosystem modelling. Ecol Model 109:121–140

    Article  Google Scholar 

  • Comparaison de la régression spatiale et du krigeage avec dérive pour interpoler les extrants de modèles de simulation de développement d’insectes au Québec en fonction de l’échelle, de la topographie et de l’influence maritime. M.Sc. Thesis, Faculté de foresterie et géomatique, Université Laval, Québec, Québec, Canada

  • Gray DR (2004) The gypsy moth life stage model: landscape-wide estimates of gypsy moth establishment using a multi-generational phenology model. Ecol Model 176:155–171

    Article  Google Scholar 

  • Hansen JW, Ines AVM (2005) Stochastic disaggregation of monthly rainfall data for crop simulation studies. Agric For Meteorol 131:233–246

    Article  Google Scholar 

  • Hansen JW, Mavromatis T (2001) Correcting low-frequency variability bias in stochastic weather generators. Agric For Meteorol 109:297–310

    Article  Google Scholar 

  • Hastings NAJ, Peacock JB (1975) Statistical distributions: A handbook for students and practitioners. Butterworth, London

    Google Scholar 

  • Hutchinson MF (1991) The application of thin-plate smoothing splines to continent-wide data assimilation. In: Jasper JD (ed) Data assimilation systems. BMRC Res Rep No. 27, Melbourne, pp 104–113

    Google Scholar 

  • Hutchinson MF (1995a) Stochastic space-time weather models from ground-based data. Agric For Meteorol 73:237–264

    Article  Google Scholar 

  • Hutchinson MF (1995b) Interpolating mean rainfall using thin-plate smoothing splines. Int J GIS 9:385–403

    Google Scholar 

  • Johnson GL, Hanson CL, Hardegree SP, Ballard EB (1996) Stochastic weather simulation: overview and analysis of two commonly used models. J Appl Meteorol 35:1878–1896

    Article  Google Scholar 

  • Jones PG, Thornton PK (2000) MarkSim: software to generate daily weather data for Latin America and Africa. Agron J 92:445–453

    Article  Google Scholar 

  • Katz RW (1996) The use of stochastic models to generate climate change scenarios. Clim Change 32:237–255

    Article  Google Scholar 

  • Kimball JS, Thornton PE, White MA, Running SW (1997) Simulating forest productivity and surface-atmosphere carbon exchange in the BOREAS study region. Tree Physiol 17:589–599

    PubMed  CAS  Google Scholar 

  • Lasch P, Lindner M, Erhard M, Suckow F, Wenzel A (2002) Regional impact assessment on forest structure and functions under climate change - the Brandenburg case study. For Ecol Manag 162:73–86

    Article  Google Scholar 

  • Latif M, Barnett TP (1994) Causes of decadal climate variability over the North Pacific and North America. Science 266:634–637

    Article  CAS  PubMed  Google Scholar 

  • Logan JA, Régnière J, Powell JA (2003) Assessing the impacts of global warming on forest pest dynamics. Front Ecol Environ 1:130–137

    Article  Google Scholar 

  • Mavromatis T, Hansen JW (2001) Interannual variability characteristics and simulated crop response of four stochastic weather generators. Agric For Meteorol 109:283–296

    Article  Google Scholar 

  • Mearns LO, Rosenzweig C, Goldberg R (1997) Mean and variance change in climate scenarios: methods, agricultural applications, and measures of uncertainty. Clim Change 35:367–396

    Article  Google Scholar 

  • Oelschlägel B (1995) A method for downscaling global climate model calculations by a statistical weather generator. Ecol Model 82:199–204

    Article  Google Scholar 

  • Philander SGH (1990) El Niño, La Niña and the Southern Oscillation. Academic, San Diego CA

    Google Scholar 

  • Prentice IC, Sykes MT, Cramer W (1993) A simulation model for the transient effects of climate change on forest landscapes. Ecol Model 65:51–70

    Article  Google Scholar 

  • Price DT, McKenney DW, Nalder IA, Hutchinson MF, Kesteven JL (2000) A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate data. Agric For Meteorol 101:81–94

    Article  Google Scholar 

  • Qian B, Hayhoe H, Gameda S (2005) Evaluation of the stochastic weather generators LARS-WG and AAFC-WG for climate change impact studies. Clim Res 29:3–21

    Google Scholar 

  • Racsko P, Szeidl L, Semonov M (1991) A serial approach to local stochastic weather models. Ecol Model 57:27–41

    Article  Google Scholar 

  • Régnière J (1996) A generalized approach to landscape-wide seasonal forecasting with temperature-driven simulation models. Environ Entomol 25:869–881

    Google Scholar 

  • Régnière J, Bolstad P (1994) Statistical simulation of daily air temperature patterns in eastern North America to forecast seasonal events in insect pest management. Environ Entomol 23:1368–1380

    Google Scholar 

  • Régnière J, Nealis V (2002) Modelling seasonality of gypsy moth, Lymantria dispar (Lepidoptera: Lymantriidae), to evaluate probability of its persistence in novel environments. Can Entomol 134:805–824

    Article  Google Scholar 

  • Richardon CW (1981) Stochastic simulation of daily precipitation, temperature and solar radiation. Water Resour Res 17:182–190

    Article  Google Scholar 

  • Richardson CW, Wright DA (1984) WGEN: A model for generating daily weather variables. United States Department of Agriculture, Washington, ARS–8

  • Semenov MA, Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios. Clim Change 35:397–414

    Article  Google Scholar 

  • Semenov MA, Brooks RJ, Barrow EM, Richardson CW (1998) Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates. Clim Res 10:95–107

    Google Scholar 

  • Skiles JW, Richardson CW (1998) A stochastic weather generation model for Alaska. Ecol Model 110:211–232

    Article  Google Scholar 

  • Soltani A, Hoogenboom G (2003) Minimum data requirements for parameter estimation of stochastic weather generators. Clim Res 25:109–119

    Google Scholar 

  • Wallis TWR, Griffiths JF (1995) An assessment of the weather generator (WXGEN) used in the erosion /productivity impact calculator (EPIC). Agric For Meteorol 73:115–133

    Article  Google Scholar 

  • Watanabe M, Nitta T (1999) Decadal changes in the atmospheric circulation and associated surface climate variations in the Northern Hemisphere winter. J Clim 12:494–510

    Article  Google Scholar 

  • Wilks DS (1999) Simultaneous stochastic simulation of daily precipitation, temperature and solar radiation at multiple sites in complex terrain. Agric For Meteorol 96:85–101

    Article  Google Scholar 

  • Yu B (2003) An assessment of uncalibrated CLIGEN in Australia. Agric For Meteorol 119:131–148

    Article  Google Scholar 

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Acknowledgements

Thanks to M. Lindner for ideas in the development of this model. Thanks also to D. Houle, D.W. McKenney and two anonymous reviewers for very helpful comments on an earlier draft. This work was funded in part by members of SERG-International, including Forest Protection Limited, the Quebec SOPFIM, the Province of Ontario, and the USDA Forest Service.

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  1. Natural Resources Canada, Canadian Forest Service, Laurentian Forestry Centre, 1055 PEPS Street, P.O. Box 10380, Stn. Sainte-Foy, Quebec, QC, G1V 4C7, Canada

    Jacques Régnière & Rémi St-Amant

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  1. Jacques Régnière
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  2. Rémi St-Amant
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Correspondence to Jacques Régnière.

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Régnière, J., St-Amant, R. Stochastic simulation of daily air temperature and precipitation from monthly normals in North America north of Mexico. Int J Biometeorol 51, 415–430 (2007). https://doi.org/10.1007/s00484-006-0078-z

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  • DOI: https://doi.org/10.1007/s00484-006-0078-z

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