The Evaluations of NEX-GDDP and Marksim Downscaled Data Sets Over Lali Region, Southwest Iran

نوع مقاله : مقاله پژوهشی

نویسندگان

1 Ph.D. Student, Department of Mineral Geology and Hydrogeology, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran

2 Professor, Department of Mineral Geology and Hydrogeology, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran

3 Associate Professor, Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran

4 Assistant Professor, Department of Mineral Geology and Hydrogeology, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran

چکیده

Downscaling of climatic variables is a difficult problem in the climate change impact studies. However, some climatic data sets exist that have been universally downscaled. These data sets introduce climatic data even in regions with scarce observations. In this study, NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) and Markov simulation (Marksim) downscaled data sets were evaluated over Lali region, southwest Iran by comparing the monthly RMSE, average and variance differences between the observation data and General Circulation Models' (GCMs') outputs during the time period 2010-2016. The NEX-GDDP data set contains 21 GCMs under two Representative Concentration Pathways (RCPs), i.e. RCP4.5 and RCP8.5, from 1951 to 2099, and the Marksim data set includes 17 GCMs under all RCPs from 2010 to 2095. Results acknowledged the ability of both data sets in projecting the climatic variables in the study area. Finally, NorESM1-M and GFDL-CM3 depicted the best operation for precipitation and temperature, respectively.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

The Evaluations of NEX-GDDP and Marksim Downscaled Data Sets Over Lali Region, Southwest Iran

نویسندگان [English]

  • Nejat Zeydalinejad 1
  • Hamid Reza Nassery 2
  • Ali Reza Shakiba 3
  • Farshad Alijani 4
1 Ph.D. Student, Department of Mineral Geology and Hydrogeology, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
2 Professor, Department of Mineral Geology and Hydrogeology, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
3 Associate Professor, Department of Remote Sensing and GIS, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
4 Assistant Professor, Department of Mineral Geology and Hydrogeology, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran
چکیده [English]

Downscaling of climatic variables is a difficult problem in the climate change impact studies. However, some climatic data sets exist that have been universally downscaled. These data sets introduce climatic data even in regions with scarce observations. In this study, NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) and Markov simulation (Marksim) downscaled data sets were evaluated over Lali region, southwest Iran by comparing the monthly RMSE, average and variance differences between the observation data and General Circulation Models' (GCMs') outputs during the time period 2010-2016. The NEX-GDDP data set contains 21 GCMs under two Representative Concentration Pathways (RCPs), i.e. RCP4.5 and RCP8.5, from 1951 to 2099, and the Marksim data set includes 17 GCMs under all RCPs from 2010 to 2095. Results acknowledged the ability of both data sets in projecting the climatic variables in the study area. Finally, NorESM1-M and GFDL-CM3 depicted the best operation for precipitation and temperature, respectively.

کلیدواژه‌ها [English]

  • NEX-GDDP
  • Marksim
  • GCM
  • Lali region
  • RCP

مراجع

Bao, Y. and Wen, X., 2017, Projection of China’s near- and long-term climate in a new high-resolution daily downscaled data set NEX-GDDP, J. Meteorol. Res., 31(1), 236-249.
Bharati, L., Gurung, P., Jayakody, P., Smakhtin, V. and Bhattarai, U., 2014, The projected impact of climate change on water availability and development in the Koshi Basin, Nepal, Mt. Res. Dev., 34(2), 118-130.
Chaturvedi, R. K., Joshi, J., Jayaraman, M., Bala, G. and Ravindranath, N., 2012, Multi-model climate change projections for India under representative concentration pathways, Curr. Sci., 103(7), 791-802.
Chen, H. P., Sun, J. Q. and Li, H. X., 2017, Future changes in precipitation extremes over China using the NEX-GDDP high-resolution daily downscaled data-set, Atmos. Ocean. Sci. Lett., 10(6), 403-410.
Chen, H., Sun, J. and Chen, X., 2014, Projection and uncertainty analysis of global precipitation‐related extremes using CMIP5 models, Int. J. Climatol., 34(8), 2730-2748.
Chou, S. C., Lyra, A., Mourão, C., Dereczynski, C., Pilotto, I., Gomes, J., Bustamante, J., Tavares, P., Silva, A., Rodrigues, D., Campos, D., Chagas, D., Sueiro, G., Siqueira, G., Nobre, P. and Marengo, J., 2014, Assessment of climate change over South America under RCP 4.5 and 8.5 downscaling scenarios, Am. J. of Clim. Change, 3(5), 512-525.
Chylek, P., Li, J., Dubey, M., Wang, M. and Lesins, G., 2011, Observed and model simulated 20th century Arctic temperature variability: Canadian earth system model CanESM2, Atmos. Chem. Phys., 8, 22893-22907.
Daksiya, V., Mandapaka, P. and Lo, E. Y., 2017, A Comparative Frequency Analysis of Maximum Daily Rainfall for a SE Asian Region under Current and Future Climate Conditions, Adv. Meteorol., 2, 1-16.
De Trincheria, J., Craufurd, P., Harris, D., Mannke, F., Nyamangara, J., Rao, K. and Leal Filho, W., 2015, Adapting agriculture to climate change by developing promising strategies using analogue locations in eastern and southern Africa: a systematic approach to develop practical solutions, in: Leal Filho, W., Esilaba, A., Rao, K. and Sridhar, G. (Eds.), Adapting African Agriculture to Climate Change, Climate Change Management, Springer, Cham, pp. 1-23.
Evans, J. P., 2009, 21st century climate change in the Middle East, Clim. Change, 92(3-4), 417-432.
Funk, C., Michaelsen, J. and Marshall, M., 2010, Mapping recent decadal climate variations in Eastern Africa and the Sahel, in: Wardlow, B. D., Anderson, M. C. and Verdin, J. P. (Eds.), Remote Sensing of Drought: Innovative Monitoring Approaches, CRC Press, pp. 331-358.
Geng, S., Auburn, J., Brandstetter, E. and Li, B., 1988, A Program to Simulate Meteorological Variables: Documentation for SIMMETEO, University of California, Agricultural Experiment Station, Davis.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. and Jarvis, A. J. I., 2005, Very high resolution interpolated climate surfaces for global land areas, Int. J. Climatol., 25(15), 1965-1978.
https://cds.nccs.nasa.gov/nex-gddp/
http://gismap.ciat.cgiar.org/MarksimGCM/docs/FAQ.html
http://gisweb.ciat.cgiar.org/MarkSimGCM
http://gisweb.ciat.cgiar.org/MarkSimGCM/docs/doc.html
Hutchinson, M. F., 1997, ANUSPLIN Version 3.2 User's Guide, The Australian National University, Centre for Resource and Environmental Studies, Canberra, Australia.
IPCC, 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, in: Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S. K., Boschung, J. et al. (Eds.), Cambridge University Press, Cambridge, UK and New York, USA.
IPCC, 2014, Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth assessment report of the intergovernmental panel on climate change, in: Core Writing Team, Pachauri, R. K. and Meyer, L. A. (Eds.), IPCC, Geneva, Switzerland.
Iran Meteorological Organization, 2018, Climate data including daily precipitation, temperature and solar radiation for Lali synoptic station for 2007-2016 in Excel Format.
Jones, P. G., 2013, MarkSim Standalone for DSSAT Users. International Center for Tropical Agriculture (CIAT), Cali, Colombia.
Jones, P. G. and Thornton, P. K., 2013, Generating downscaled weather data from a suite of climate models for agricultural modelling applications, Agr. Sys., 114, 1-5.
Kahimba, F., Tumbo, S., Mpeta, E., Yonah, I., Timiza, W. and Mbungu, W., 2014, Accuracy of Giovanni and Marksim software packages for generating daily rainfall data in selected bimodal climatic areas in Tanzania, Tanz. J. Agr. Sci., 13(1), 12-25.
Kim, H. M., Webster, P. J. and Curry, J. A., 2012, Evaluation of short‐term climate change prediction in multi‐model CMIP5 decadal hindcasts, Geophys. Res. Lett., 39(10), 1-7.  
Knutti, R., Masson, D. and Gettelman, A., 2013, Climate model genealogy: Generation CMIP5 and how we got there, Geophys. Res. Lett., 40(6), 1194-1199.
Kundzewicz, Z. W., Mata, L. J., Arnell N. W., Doll, P., Kabat, P., Jimenez, B., Miller, K. A., Oki, T., Sen, Z. and Shiklomanov, I. A., 2007, Freshwater resources and their management, in: Parry, M. L., Canziani, O. F., Palutikof, J. P., van der Linden, P. J. and Hanson, C. E. (Eds.), Climate Change 2007: Impacts, Adaptation and Vulnerability, Contribution of Working Group 11 to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, USA, pp. 173-210.
Ma, C., Pan, S., Wang, G., Liao, Y. and Xu, Y. P., 2016, Changes in precipitation and temperature in Xiangjiang River Basin, China, Theor. appl. climatol., 123(3-4), 859-871.
Masson, D. and Knutti, R., 2011, Spatial-scale dependence of climate model performance in the CMIP3 ensemble, J. Climate, 24(11), 2680-2692.
Maurer, E. P. and Hidalgo, H. G., 2008, Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods, Hydrol. Earth. Syst. Sci., 12, 551-563.
Mavromatis, T. and Hansen, J. W., 2001, Interannual variability characteristics and simulated crop response of four stochastic weather generators, Agr. Forest Meteorol., 109(4), 283-296.
McSweeney, C., Jones, R., Lee, R. W. and Rowell, D., 2015, Selecting CMIP5 GCMs for downscaling over multiple regions, Clim. Dynam., 44(11-12), 3237-3260.
McSweeney, C. F., Jones, R. G. and Booth, B. B., 2012, Selecting ensemble members to provide regional climate change information, J. Climate, 25(20), 7100-7121.
Meinshausen, M., Smith, S. J., Calvin, K., Daniel, J. S., Kainuma, M., Lamarque, J. F., Matsumoto, K., Montzka, S. A., Raper, S. C. B., Riahi, K., Thomson, A., Velders, G. J. M. and  van Vuuren D. P. P., 2011, The RCP greenhouse gas concentrations and their extensions from 1765 to 2300, Clim. Change, 109, 213-241.
Miao, C., Duan, Q., Sun, Q., Huang, Y., Kong, D., Yang, T., Ye, A., Di, Z. and Gong, W., 2014, Assessment of CMIP5 climate models and projected temperature changes over Northern Eurasia, Environ. Res. Lett., 9(5), 1-12.
Mohanty, M., Sinha, N. K., Hati, K., Chaudhary, R. and Patra, A., 2015, Crop Growth Simulation Modelling and Climate Change, Scientific Publishers, India.
Mzirai, O., Tumbo, S., Bwana, T., Hatibu, N., Rwehumbiza, F. and Gowing, J., 2005, Evaluation of simulator of missing weather data (SMWD) required in simulation of agro hydrological modeling n the catchment and basin level: case of the PARCHED-THIRST and Marksim Model, Proceedings of the International Water Management Institute Conference Papers, Santiago, Chile.
Nassery, H. and Salami, H., 2016, Identifying vulnerable areas of aquifer under future climate change (case study: Hamadan aquifer, West Iran), Arab. J. Geosci., 9(8), 1-16.
Nyunt, C. T., Koike, T. and Yamamoto, A., 2016, Statistical bias correction for climate change impact on the basin scale precipitation in Sri Lanka, Philippines, Japan and Tunisia, Hydrol. Earth Syst. Sci. Discuss. doi: 10.5194/hess-2016-14
Overland, J. E., Wang, M., Bond, N. A., Walsh, J. E., Kattsov, V. M. and Chapman, W. L. 2011, Considerations in the selection of global climate models for regional climate projections: The Arctic as a case study, J. Climate, 24(6), 1583-1597.
Rao, M. S., Swathi, P., Rao, C. A. R., Rao, K., Raju, B., Srinivas, K., Manimanjari, D. and Maheswari, M., 2015, Model and scenario variations in predicted number of generations of Spodoptera litura Fab. On peanut during future climate change scenario, PloS one, 10(2), 1-12.
Samadi, S., Sagareswar, G. and Tajiki, M., 2010, Comparison of general circulation models: methodology for selecting the best GCM in Kermanshah Synoptic Station, Iran, Int. J. Global Warm., 2(4), 347-365.
Santer, B. D., Bonfils, C., Painter, J. F., Zelinka, M. D., Mears, C., Solomon, S., Schmidt, G. A., Fyfe, J. C., Cole, J. N. S., Nazarenko, L., Taylor, K. E. and Wentz, F. J., 2014, Volcanic contribution to decadal changes in tropospheric temperature, Nat. Geosci., 7(3), 185-189.
Semenov, M. A. and Stratonovitch, P., 2015, Adapting wheat ideotypes for climate change: accounting for uncertainties in CMIP5 climate projections, Climate Res., 65, 123-139.
Sheffield, J., Goteti, G. and Wood, E. F., 2006, Development of a 50-year high-resolution global data set of meteorological forcings for land surface modeling, J. Climate, 19(13), 3088-3111.
Solomon, S., Daniel, J. S., Neely, R. R., Vernier, J. P., Dutton, E. G. and Thomason, L. W., 2011, The persistently variable background stratospheric aerosol layer and global climate change, Science, 333(6044), 866-870.  
Taylor, K. E., Stouffer, R. J. and Meehl, G. A., 2012, An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93(4), 485-498.
Thrasher, B., Maurer, E. P., McKellar, C. and Duffy, P. B., 2012, Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping, Hydrol. Earth Syst. Sci., 16, 3309-3314.
Thrasher, B. and Nemani, R., 2015, Nasa earth exchange global daily downscaled projections (nex-gddp), NASA, Washington, D.C., USA.
Trotochaud, J., Flanagan, D. C. and Engel, B. A., 2016, A simple technique for obtaining future climate data inputs for natural resource models, Appl. Eng. Agric., 32(3), 371-381.
Ul Hasson, S., Pascale, S., Lucarini, V. and Böhner, J., 2016, Seasonal cycle of precipitation over major river basins in South and Southeast Asia: a review of the CMIP5 climate models data for present climate and future climate projections, Atmos. Res., 180, 42-63.
Washington, R., Harrison, M., Conway, D., Black, E., Challinor, A., Grimes, D., Jones, R., Morse, A., Kay, G. and Todd, M., 2006, African climate change: taking the shorter route, B. Am. Meteorol. Soc., 87(10), 1355-1366.
Wilby, R. L., 2007, Decadal Forecasting Techniques for Adaptation and Development Planning: A Briefing Document on Available Methods, Constraints, Risks and Opportunities, UK Department for International Development, London.
Wilby, R. L., Charles, S., Zorita, E., Timbal, B., Whetton, P. and Mearns, L., 2004, Guidelines for Use of Climate Scenarios Developed from Statistical Downscaling Methods, Supporting Material of the Intergovernmental Panel on Climate Change.
Wilby, R. L., Troni, J., Biot, Y., Tedd, L., Hewitson, B. C., Smith, D. M. and Sutton, R. T., 2009, A review of climate risk information for adaptation and development planning, Int. J. Climatol., 29(9), 1193-1215.
Wilby, R. L. and Wigley, T., 1997, Downscaling general circulation model output: a review of methods and limitations, Prog. Phys. Geog., 21(4), 530-548.
Wood, A. W., Leung, L. R., Sridhar, V. and Lettenmaier, D. J., 2004, Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs, Clim. Change, 62(1-3), 189-216.  
Yoo, C. and Cho, E., 2018, Comparison of GCM precipitation predictions with their RMSEs and pattern correlation coefficients, Water, 10(1), 1-17.
Zhao, L., Xu, J. and Powell J. A., 2013, Discrepancies of surface temperature trends in the CMIP5 simulations and observations on the global and regional scales, Clim. Past, 6, 6161-6178.
Zubler, E. M., Fischer, A. M., Fröb, F. and Liniger, M. A., 2016, Climate change signals of CMIP5 general circulation models over the Alps–impact of model selection, Int. J. Climatol., 36(8), 3088-3104.
فیزیک زمین و فضا
دوره 46، شماره 4
بهمن 1399
صفحه 213-230

فایل ها

هم رسانی

ارجاع به این مقاله

آمار