Estimation of future climate change in cold weather areas with the LARS-WG model under CMIP5 scenarios

  • Jian Sha
  • Xue Li
  • Zhong-Liang WangEmail author
Original Paper


Global warming has considerably challenged the natural environment and livelihood conditions. Understanding potential future changes in critical climatic variables, such as temperature and precipitation, is important for regional agricultural and water resource management. This study proposes a new approach to the application of the Long Ashton Research Station Weather Generator (LARS-WG) in Coupled Model Intercomparison Project Phase 5 (CMIP5) emission scenarios and aims to test its applicability in cold areas and to evaluate the response of temperature and precipitation, in amount and form, under future warmer climate trends. Three stations in northeastern China are set as case sites, and 50 years of daily weather observations are used for model calibration and validation. Future synthetic time-series of daily precipitation and daily maximum and minimum temperatures is generated by the calibrated LARS-WG based on three Representative Concentration Pathway (RCP) scenarios with various radiative forcing levels of 14 general circulation models (GCMs) outputs for the periods 2041–2060 (2050s) and 2061–2080 (2070s). The results show that the CMIP5 scenarios can be successfully used in a LARS-WG model and that the model performs well in cold weather conditions to repeat the current status of the case sites; the model is able to provide downscaling analysis for future daily weather generation via updating calibrated model parameters based on various GCM outputs. A generally warming and wetting conversion would last into the future for the study sites, but there is great inconsistency among different GCMs. An ensemble approach is adopted with mean values of multi-GCMs to avoid the uncertainty associated with using a single GCM, based on which the changes in the form of precipitation are further estimated. As a result of the decrease in freezing conditions, although annual precipitation will continue to increase in the future, there will be relatively less annual snowfall, which will be primarily focused in deep winter. Such changes in snow cover conditions will potentially disturb the original rules of local overwintering agriculture. In addition, more intense and earlier snowmelt discharge and more rainfall in summer will latently impact the watershed hydrologic process. The influences of climate change are significant, and related projects for agricultural and water resource management should be of great concern in local decision-making.



The authors acknowledge the developer of the LARS-WG model for access to software and license agreement. The data used for model application were provided by the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (, and the China Meteorological Data Service Center (CMDC) (

Funding information

The study is financially supported by the innovation team training plan of the Tianjin Education Committee (TD13-5073), the National Natural Science Foundation of China (No. 41372373), the Opening Fund of Tianjin Key Laboratory of Water Resources and Environment (117-YF11700102), and the Science & Technology Development Fund of Tianjin Education Commission for Higher Education (2018KJ160).

Supplementary material

704_2019_2781_MOESM1_ESM.rar (3.8 mb)
ESM 1 (RAR 3905 kb)


  1. Ahmadzadeh Araji H, Wayayok A, Massah Bavani A, Amiri E, Abdullah AF, Daneshian J, Teh CBS (2018) Impacts of climate change on soybean production under different treatments of field experiments considering the uncertainty of general circulation models. Agric Water Manag 205:63–71. CrossRefGoogle Scholar
  2. Amin MZM, Islam T, Ishak AM (2014) Downscaling and projection of precipitation from general circulation model predictors in an equatorial climate region by the automated regression-based statistical method. Theor Appl Climatol 118:347–364. CrossRefGoogle Scholar
  3. Bannayan M, Paymard P, Ashraf B (2016) Vulnerability of maize production under future climate change: possible adaptation strategies. J Sci Food Agric 96:4465–4474. CrossRefGoogle Scholar
  4. Chen H, Guo J, Zhang Z, Xu C-Y (2013) Prediction of temperature and precipitation in Sudan and South Sudan by using LARS-WG in future. Theor Appl Climatol 113:363–375. CrossRefGoogle Scholar
  5. Conway D, van Garderen EA, Deryng D, Dorling S, Krueger T, Landman W, Lankford B, Lebek K, Osborn T, Ringler C, Thurlow J, Zhu T, Dalin C (2015) Climate and southern Africa’s water-energy-food nexus. Nat Clim Chang 5:837–846. CrossRefGoogle Scholar
  6. Dumont B, Basso B, Bodson B, Destain JP, Destain MF (2016) Assessing and modeling economic and environmental impact of wheat nitrogen management in Belgium. Environ Model Softw 79:184–196. CrossRefGoogle Scholar
  7. Fenta Mekonnen D, Disse M (2018) Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques. Hydrol Earth Syst Sci 22:2391–2408. CrossRefGoogle Scholar
  8. Fezzi C, Harwood AR, Lovett AA, Bateman IJ (2015) The environmental impact of climate change adaptation on land use and water quality. Nat Clim Chang 5:255–260. CrossRefGoogle Scholar
  9. Gao X, Shi Y, Giorgi F (2011) A high resolution simulation of climate change over China. Sci China Earth Sci 54:462–472. CrossRefGoogle Scholar
  10. Giorgi F, Lionello P (2008) Climate change projections for the Mediterranean region. Glob Planet Chang 63:90–104. CrossRefGoogle Scholar
  11. Grafton RQ, Pittock J, Davis R, Williams J, Fu G, Warburton M, Udall B, McKenzie R, Yu X, Che N, Connell D, Jiang Q, Kompas T, Lynch A, Norris R, Possingham H, Quiggin J (2013) Global insights into water resources, climate change and governance. Nat Clim Chang 3:315–321. CrossRefGoogle Scholar
  12. Hassan Z, Shamsudin S, Harun S (2014) Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature. Theor Appl Climatol 116:243–257. CrossRefGoogle Scholar
  13. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978. CrossRefGoogle Scholar
  14. Hussain M, Yusof KW, Mustafa MRU, Mahmood R, Jia S (2018) Evaluation of CMIP5 models for projection of future precipitation change in Bornean tropical rainforests. Theor Appl Climatol 134:423–440. CrossRefGoogle Scholar
  15. Kim HK, Parajuli PB, Filip To SD (2013) Assessing impacts of bioenergy crops and climate change on hydrometeorology in the Yazoo River Basin, Mississippi. Agric For Meteorol 169:61–73. CrossRefGoogle Scholar
  16. Kumar D, Arya DS, Murumkar AR, Rahman MM (2014) Impact of climate change on rainfall in northwestern Bangladesh using multi-GCM ensembles. Int J Climatol 34:1395–1404. CrossRefGoogle Scholar
  17. Ma C, Pan S, Wang G, Liao Y, Xu Y-P (2016) Changes in precipitation and temperature in Xiangjiang River Basin. China Theor Applied Climatol 123:859–871. CrossRefGoogle Scholar
  18. Mahat V, Anderson A (2013) Impacts of climate and catastrophic forest changes on streamflow and water balance in a mountainous headwater stream in southern Alberta. Hydrol Earth Syst Sci 17:4941–4956. CrossRefGoogle Scholar
  19. McNutt M (2013) Climate change impacts. Science 341:435–435. CrossRefGoogle Scholar
  20. Naderi M, Raeisi E (2016) Climate change in a region with altitude differences and with precipitation from various sources, South-Central Iran. Theor Appl Climatol 124:529–540. CrossRefGoogle Scholar
  21. Pervez MS, Henebry GM (2014) Projections of the Ganges–Brahmaputra precipitation—downscaled from GCM predictors. J Hydrol 517:120–134. CrossRefGoogle Scholar
  22. Qin XS, Lu Y (2014) Study of climate change impact on flood frequencies: a combined weather generator and hydrological modeling approach. J Hydrometeorol 15:1205–1219. CrossRefGoogle Scholar
  23. Reddy KS, Kumar M, Maruthi V, Umesha B, Vijayalaxmi RC (2014) Climate change analysis in southern Telangana region, Andhra Pradesh using LARS-WG model. Curr Sci 107:54–62Google Scholar
  24. Sanchez E, Gallardo C, Gaertner MA, Arribas A, Castro M (2004) Future climate extreme events in the Mediterranean simulated by a regional climate model: a first approach. Glob Planet Chang 44:163–180. CrossRefGoogle Scholar
  25. Sarkar J, Chicholikar JR, Rathore LS (2015) Predicting future changes in temperature and precipitation in arid climate of Kutch, Gujarat: analyses based on LARS-WG model. Curr Sci 109:2084–2093CrossRefGoogle Scholar
  26. Semenov MA, Barrow EM (1997) Use of a stochastic weather generator in the development of climate change scenarios. Clim Chang 35:397–414. CrossRefGoogle Scholar
  27. Semenov MA, Stratonovitch P (2015) Adapting wheat ideotypes for climate change: accounting for uncertainties in CMIP5 climate projections. Clim Res 65:123–139CrossRefGoogle Scholar
  28. Solow AR (2015) Extreme weather, made by us? Science 349:1444–1445. CrossRefGoogle Scholar
  29. Stevens B, Giorgetta M, Esch M, Mauritsen T, Crueger T, Rast S, Salzmann M, Schmidt H, Bader J, Block K, Brokopf R, Fast I, Kinne S, Kornblueh L, Lohmann U, Pincus R, Reichler T, Roeckner E (2013) Atmospheric component of the MPI-M earth system model: ECHAM6. J Adv Model Earth Syst 5:146–172. CrossRefGoogle Scholar
  30. Tripathi S, Srinivas VV, Nanjundiah RS (2006) Downscaling of precipitation for climate change scenarios: a support vector machine approach. J Hydrol 330:621–640. CrossRefGoogle Scholar
  31. Vallam P, Qin XS (2018) Projecting future precipitation and temperature at sites with diverse climate through multiple statistical downscaling schemes. Theor Appl Climatol 134:669–688. CrossRefGoogle Scholar
  32. Wilby RL, Dawson CW, Barrow EM (2002) SDSM—a decision support tool for the assessment of regional climate change impacts. Environ Model Softw 17:147–159CrossRefGoogle Scholar
  33. Zarghami M, Abdi A, Babaeian I, Hassanzadeh Y, Kanani R (2011) Impacts of climate change on runoffs in East Azerbaijan, Iran. Glob Planet Chang 78:137–146. CrossRefGoogle Scholar
  34. Zhang XC (2005) Spatial downscaling of global climate model output for site-specific assessment of crop production and soil erosion. Agric For Meteorol 135:215–229. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Tianjin Key Laboratory of Water Resources and EnvironmentTianjin Normal UniversityTianjinChina

Personalised recommendations