مقایسه مدل‌های اقلیمی CMIP6 و روش‌های تصحیح اریبی نگاشت چندکی در شبیه‌سازی بارش

نوع مقاله : مقاله پژوهشی

نویسندگان

گروه مهندسی و مدیریت آب، دانشکده کشاورزی، دانشگاه تربیت مدرس، تهران، ایران

چکیده

به دلیل محدودیت‌های ذاتی در مدل‌های اقلیمی، خروجی آن‌ها نسبت به مقادیر مشاهداتی دارای اریب قابل توجهی است که می‌تواند منجر به ارائه پیش‌نگری‌های اقلیمی غیرقابل اعتماد گردد. در مطالعۀ حاضر عملکرد 10 مدل اقلیمی از مجموعه مدل‌های CMIP6 در شبیه‌سازی بارش دوره‌های واسنجی (2005-1986) و صحت‌سنجی (2014-2006) برای محدودۀ مطالعاتی رفسنجان مورد ارزیابی قرار گرفت. به منظور اصلاح بارش شبیه‌سازی شده، روش‌های مختلف تصحیح اریبی نگاشت چندکی در این دو دوره اعمال شده و ارزیابی عملکرد مدل‌های مختلف، روش‌ها و رویکردهای تصحیح اریبی نگاشت چندکی با استفاده از معیارهای آماری NSE، PBIAS، MAE و KGE و دیاگرام تیلور انجام شد. در نهایت، بارش شبیه‌سازی شده از مدل منتخب برای دورۀ پیش‌نگری تحت سناریوهای SSP1-2.6، SSP2-4.5 و SSP3-7.0 استخراج و این مقادیر با استفاده از روش مناسب تصحیح اریبی اصلاح شدند. نتایج نشان داد که مدل MPI-ESM1-2-LR قابلیت بالایی در شبیه‌سازی بارش در دوره‌های واسنجی و صحت‌سنجی نسبت به سایر مدل‌های اقلیمی دارد. نتایج ارزیابی عملکرد روش‌های تصحیح اریبی نگاشت چندکی نیز عملکرد بهتر روش‌ bernlnorm را نسبت به سایر روش‌ها در اصلاح بارش شبیه‌سازی شده در هر دو دوره توسط مدل‌های اقلیمی نشان داد. همچنین، ماحصل ارزیابی رویکردهای نگاشت چندکی NTP، PT و DDT در این دوره‌ها حاکی از توانمندی بالای رویکردهای NTP و PT نسبت به رویکرد DDT بود. مطالعه حاضر می‌تواند به بهبود اعتبار پیش‌نگری‌های اقلیمی آینده با استفاده از مدل‌های اقلیمی CMIP6 کمک کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Comparison of CMIP6 climate models and quantile mapping bias correction methods in the simulation of precipitation

نویسندگان [English]

  • Nima Nemati Shishehgaran
  • Fariba Babaeian
  • Hojjat Mianabadi
Department of Water Engineering and Management, Faculty of Agriculture, Tarbiat Modarres University, Tehran, Iran
چکیده [English]

Due to inherent limitations of global climate models, their outputs are significantly biased in comparison to observed values which could provide unreliable climate projections. This study evaluates the performance of 10 global climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) for simulating precipitation in the Rafsanjan study area over calibration (1986-2005) and validation (2006-2014) period. For correcting simulated precipitation, various quantile mapping-based bias correction methods applied in these two periods. Evaluating the performance of various climate models and quantile mapping-based bias correction methods and approaches is carried out through multiple statistical metrics including NSE, PBIAS, MAE, and KGE as well as Taylor's diagram. Finally, simulated precipitation of selected model extracted for projection period under SSP1-2.6, SSP2-4.5 and SSP3-7.0 scenarios and corrected by suitable bias correction method. Results showed that the MPI-ESM1-2-LR model has better performance in simulating precipitation over calibration and validation periods compared to other climate models. The results of evaluating the performance of quantile mapping-based bias correction methods in both periods also showed that bernlnorm method performs better than others for the correction of simulated precipitation by climate models. In addition, the evaluation results of quantile mapping approaches including NTP, PT, and DDT in these periods demonstrated that NTP and PT have an acceptable performance compared to the DDT approach. Present study can help to improve the credibility of future climate projections using CMIP6 climate models.

کلیدواژه‌ها [English]

  • Calibration
  • Climate change
  • Projection Period
  • Rafsanjan study area
  • Validation

Comparison of CMIP6 Climate Models and Quantile Mapping Bias Correction Methods in the Simulation of Precipitation

 

EXTENDED ABSTRACT

Introduction

Globally, rising temperature due to continuous greenhouse gas emissions (GHG) have caused climate change. Accordingly, many important sectors to human existence including water resources, agriculture, health, energy, and the environment have been affected. Due to the consequences of climate change, there have been numerous studies on climate change projections and impacts in which General Circulation Models (GCMs) have been popularly used to analyze the simulated historical and future changes in climatic variables. Due to inherent limitations of global climate models, their outputs are significantly biased in comparison to observed values which could provide unreliable climate projections. To fix different problems with GCM output, researchers use various methods to correct bias. This study evaluates the performance of 10 global climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) for simulating precipitation in the Rafsanjan study area over a historical reference period. Various quantile mapping-based bias correction methods and approaches are also used and evaluated to correct simulated precipitation by climate models.

Methods

Rafsanjan study area has hot summers and cold and dry winters due to its location on the edge of the desert. This area is located near the desert, so it does not get much rain and does not have a river all year round. It mostly depends on underground water for its water supply. The region's geographical conditions, along with the negative impacts of climate, can put water resources at risk in this area. Evaluating the performance of various climate models and quantile mapping-based bias correction methods and approaches are carried out through multiple statistical metrics including NSE, PBIAS, MAE, and KGE as well as Taylor's diagram in calibration (1986-2005) and validation (2006-2014) periods. The procedure used in this study was as follows:

Downloading simulated precipitation data for the Rafsanjan study area from the Copernicus Climate Change Service website

Comparing selected climate models, various quantile mapping-based bias correction methods and approaches for simulated precipitation values in calibration and validation periods

Evaluating the performance of each model and bias correction methods and approaches, using selected evaluation metrics.

Projecting future precipitation by selected model and bias correction method

Results and Discussion

To check how accurate climate models are, GCMs-simulated Precipitation data are calibrated against the corresponding observed data from 1986 to 2005 (calibration period) and then validated from 2006 to 2014 (validation period). According to the evaluation metrics used, MPI-ESM1-2-LR performed the best in estimating precipitation in the Rafsanjan study area. Unlike MPI-ESM1-2-LR, GFDL-ESM4 performs poorly compared to other models and is not recommended for simulating precipitation in the mentioned study area. Simply put, the bernlnorm method was found to be better at correcting simulated precipitation values compared to other methods when tested in both periods. On the other hand, the bernexp performs poorly compared to other methods in terms of correcting simulated values due to evaluating criteria. In general, with the exception of the bernexp method, all the other methods show acceptable abilities in correcting biased data according to the evaluation criteria. After evaluating each bias correction method separately, the results of comparing the NTP, PT, and DDT approaches indicate that NTP and PT demonstrate greater capability compared to the DDT approach. Specifically, the NTP approach demonstrated satisfactory performance compared to both PT and DDT approaches. Finally, MPI-ESM1-2-LR model and the bernlnorm bias correction method were used to estimate future precipitation changes (2024-2043) in the study area of Rafsanjan.

  1. Ahmed, K., Sachindra, D. A., Shahid, S., Demirel, M. C., & Chung, E. S. (2019). Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics. Hydrology and Earth System Sciences, 23(11), 4803–4824. https://doi.org/10.5194/hess-23-4803-2019

    Ajaaj, A. A., Mishra, A. K., & Khan, A. A. (2016). Comparison of BIAS correction techniques for GPCC rainfall data in semi-arid climate. Stochastic Environmental Research and Risk Assessment, 30(6), 1659–1675. https://doi.org/10.1007/s00477-015-1155-9

    Babaeian, F., Bagheri, A., & Rafieian, M. (2016). Vulnerability Analysis of Water Resources Systems to Water Scarcity Based on a Water Accounting Framework (Case Study: Rafsanjan Study Area). Iran-Water Resources Research, 12(1), 1–17. (In Persian)

    Baker, N. C., & Huang, H. P. (2014). A comparative study of precipitation and evaporation between CMIP3 and CMIP5 climate model ensembles in semiarid regions. Journal of Climate, 27(10), 3731–3749. https://doi.org/10.1175/JCLI-D-13-00398.1

    Block, P. J., Souza Filho, F. A., Sun, L., & Kwon, H. H. (2009). A streamflow forecasting framework using multiple climate and hydrological models. Journal of the American Water Resources Association, 45(4), 828–843. https://doi.org/10.1111/j.1752-1688.2009.00327.x

    Cannon, A. J. (2008). Probabilistic multisite precipitation downscaling by an expanded Bernoulli-gamma density network. Journal of Hydrometeorology, 9(6), 1284–1300. https://doi.org/10.1175/2008JHM960.1

    Charlton, M. B., & Arnell, N. W. (2011). Adapting to climate change impacts on water resources in England-An assessment of draft Water Resources Management Plans. Global Environmental Change, 21(1), 238–248. https://doi.org/10.1016/j.gloenvcha.2010.07.012

    Chen, J., Brissette, F. P., Chaumont, D., & Braun, M. (2013). Finding appropriate bias correction methods in downscaling precipitation for hydrologic impact studies over North America. Water Resources Research, 49(7), 4187–4205. https://doi.org/10.1002/wrcr.20331

    Chen, J., Brissette, F. P., Zhang, X. J., Chen, H., Guo, S., & Zhao, Y. (2019). Bias correcting climate model multi-member ensembles to assess climate change impacts on hydrology. Climatic Change, 153(3), 361–377. https://doi.org/10.1007/s10584-019-02393-x

    1. N. Moriasi, J. G. Arnold, M. W. Van Liew, R. L. Bingner, R. D. Harmel, & T. L. Veith. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153

    Déqué, M., Rowell, D. P., Lüthi, D., Giorgi, F., Christensen, J. H., Rockel, B., Jacob, D., Kjellström, E., De Castro, M., & Van Den Hurk, B. (2007). An intercomparison of regional climate simulations for Europe: Assessing uncertainties in model projections. Climatic Change, 81(SUPPL. 1), 53–70. https://doi.org/10.1007/s10584-006-9228-x

    Diallo, I., Sylla, M. B., Giorgi, F., Gaye, A. T., & Camara, M. (2012). Multimodel GCM-RCM ensemble-based projections of temperature and precipitation over West Africa for the Early 21st Century. International Journal of Geophysics, 2012. https://doi.org/10.1155/2012/972896

    Dore, M. H. I. (2005). Climate change and changes in global precipitation patterns: What do we know? Environment International, 31(8), 1167–1181. https://doi.org/10.1016/j.envint.2005.03.004

    Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., & Liebert, J. (2012). HESS Opinions “should we apply bias correction to global and regional climate model data?” Hydrology and Earth System Sciences, 16(9), 3391–3404. https://doi.org/10.5194/hess-16-3391-2012

    Enayati, M., Bozorg-Haddad, O., Bazrafshan, J., Hejabi, S., & Chu, X. (2021). Bias correction capabilities of quantile mapping methods for rainfall and temperature variables. Journal of Water and Climate Change, 12(2), 401–419. https://doi.org/10.2166/wcc.2020.261

    Estrela, T., Pérez-Martin, M. A., & Vargas, E. (2012). Impacts du changement climatique sur les ressources en eau en Espagne. Hydrological Sciences Journal, 57(6), 1154–1167. https://doi.org/10.1080/02626667.2012.702213

    Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., & Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016

    Fang, G. H., Yang, J., Chen, Y. N., & Zammit, C. (2015). Comparing bias correction methods in downscaling meteorological variables for a hydrologic impact study in an arid area in China. Hydrology and Earth System Sciences, 19(6), 2547–2559. https://doi.org/10.5194/hess-19-2547-2015

    Franchini, M., & Mannucci, P. M. (2015). Impact on human health of climate changes. European Journal of Internal Medicine, 26(1), 1–5. https://doi.org/10.1016/j.ejim.2014.12.008

    Ghafouri Fard, S., Bagheri, A., & Shajari, S. (2015). Stakeholders Assessment in Water Sector (Case Study: Rafsanjan Area). Iran-Water Resources Research, 11(2), 16–28. (In Persian)

    Ggalami, V., Saghafian, B., Raziei, T. (2022). Investigating the Effect of Bias Correction on Quality Improvement of NEX-GDDP Downscaled Precipitation Data. Iran-Water Resources Research, 18(1), 68-83. (In Persian)

    Gudmundsson, L., Bremnes, J. B., Haugen, J. E., & Engen-Skaugen, T. (2012). Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – A comparison of methods. Hydrology and Earth System Sciences, 16(9), 3383–3390. https://doi.org/10.5194/hess-16-3383-2012

    Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1–2), 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003

    Hamed, M. M., Nashwan, M. S., Shahid, S., Ismail, T. bin, Wang, X. jun, Dewan, A., & Asaduzzaman, M. (2022). Inconsistency in historical simulations and future projections of temperature and rainfall: A comparison of CMIP5 and CMIP6 models over Southeast Asia. Atmospheric Research, 265, 1–38. https://doi.org/10.1016/j.atmosres.2021.105927

    He, X., Chaney, N. W., Schleiss, M., & Sheffield, J. (2016). Spatial downscaling of precipitation using adaptable random forests. Water Resources Research, 52(10), 8217–8237. https://doi.org/10.1002/2016WR019034

    Hong, J., Javan, K., Shin, Y., & Park, J. S. (2021). Future projections and uncertainty assessment of precipitation extremes in iran from the cmip6 ensemble. Atmosphere, 12(8), 1–16. https://doi.org/10.3390/ATMOS12081052

    Im, E. S., Ahn, J. B., & Jo, S. R. (2015). Regional climate projection over South Korea simulated by the HadGEM2-AO and WRF model chain under RCP emission scenarios. Climate Research, 63(3), 249–266. https://doi.org/10.3354/cr01292

    Jakob Themeßl, M., Gobiet, A., & Leuprecht, A. (2011). Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. International Journal of Climatology, 31(10), 1530–1544. https://doi.org/10.1002/joc.2168

    Karimi, V., Karami, E., & Keshavarz, M. (2018). Climate change and agriculture: Impacts and adaptive responses in Iran. Journal of Integrative Agriculture, 17(1), 1–15. https://doi.org/10.1016/S2095-3119(17)61794-5

    Kim, K. B., Kwon, H. H., & Han, D. (2015). Bias correction methods for regional climate model simulations considering the distributional parametric uncertainty underlying the observations. Journal of Hydrology, 530, 568–579. https://doi.org/10.1016/j.jhydrol.2015.10.015

    Li, H., Sheffield, J., & Wood, E. F. (2010). Bias correction of monthly precipitation and temperature fields from Intergovernmental Panel on Climate Change AR4 models using equidistant quantile matching. Journal of Geophysical Research Atmospheres, 115(10). https://doi.org/10.1029/2009JD012882

    Li, Y., Jiang, Y., Lei, X., Tian, F., Duan, H., & Lu, H. (2018). Comparison of precipitation and streamflow correcting for ensemble streamflow forecasts. Water (Switzerland), 10(2), 1–17. https://doi.org/10.3390/w10020177

    Malhi, G. S., Kaur, M., & Kaushik, P. (2021). Impact of climate change on agriculture and its mitigation strategies: A review. Sustainability (Switzerland), 13(3), 1–21. https://doi.org/10.3390/su13031318

    Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themel, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., & Thiele-Eich, I. (2010). Precipitation downscaling under climate change: Recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48(3), 1–34. https://doi.org/10.1029/2009RG000314

    Mendez, M., Maathuis, B., Hein-Griggs, D., & Alvarado-Gamboa, L. F. (2020). Performance evaluation of bias correction methods for climate change monthly precipitation projections over Costa Rica. Water (Switzerland), 12(2). https://doi.org/10.3390/w12020482

    Muerth, M. J., Gauvin St-Denis, B., Ricard, S., Velázquez, J. A., Schmid, J., Minville, M., Caya, D., Chaumont, D., Ludwig, R., & Turcotte, R. (2013). On the need for bias correction in regional climate scenarios to assess climate change impacts on river runoff. Hydrology and Earth System Sciences, 17(3), 1189–1204. https://doi.org/10.5194/hess-17-1189-2013

    N’Tcha M’Po, Y. (2016). Comparison of Daily Precipitation Bias Correction Methods Based on Four Regional Climate Model Outputs in Ouémé Basin, Benin. Hydrology, 4(6), 58. https://doi.org/10.11648/j.hyd.20160406.11

    Piani, C., Weedon, G. P., Best, M., Gomes, S. M., Viterbo, P., Hagemann, S., & Haerter, J. O. (2010). Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. Journal of Hydrology, 395(3–4), 199–215. https://doi.org/10.1016/j.jhydrol.2010.10.024

    Pour, S. H., Shahid, S., Chung, E. S., & Wang, X. J. (2018). Model output statistics downscaling using support vector machine for the projection of spatial and temporal changes in rainfall of Bangladesh. Atmospheric Research, 213(June), 149–162. https://doi.org/10.1016/j.atmosres.2018.06.006

    Radcliffe, D. E., & Mukundan, R. (2017). PRISM vs. CFSR Precipitation Data Effects on Calibration and Validation of SWAT Models. Journal of the American Water Resources Association, 53(1), 89–100. https://doi.org/10.1111/1752-1688.12484

    Salman, S. A., Nashwan, M. S., Ismail, T., & Shahid, S. (2020). Selection of CMIP5 general circulation model outputs of precipitation for peninsular Malaysia. Hydrology Research, 51(4), 781–798. https://doi.org/10.2166/NH.2020.154

    Song, Y. H., Chung, E. S., & Shiru, M. S. (2020). Uncertainty analysis of monthly precipitation in GCMs using multiple bias correction methods under different RCPs. Sustainability (Switzerland), 12(18). https://doi.org/10.3390/su12187508

    Su, X., Shao, W., Liu, J., & Jiang, Y. (2020). Multi-site statistical downscaling method using GCM-based monthly data for daily precipitation generation. Water (Switzerland), 12(3). https://doi.org/10.3390/w12030904

    Taylor, K. E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research: Atmospheres, 106(D7), 7183–7192.

    Teutschbein, C., & Seibert, J. (2010). Regional climate models for hydrological impact studies at the catchment scale: A review of recent modeling strategies. Geography Compass, 4(7), 834–860. https://doi.org/10.1111/j.1749-8198.2010.00357.x

    Teutschbein, C., & Seibert, J. (2012). Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology, 456457, 12–29. https://doi.org/10.1016/j.jhydrol.2012.05.052

    van Ruijven, B. J., De Cian, E., & Sue Wing, I. (2019). Amplification of future energy demand growth due to climate change. Nature Communications, 10(1), 1–12. https://doi.org/10.1038/s41467-019-10399-3

    Watts, N., Adger, W. N., & Agnolucci, P. (2015). Health and climate change: Policy responses to protect public health. In Environnement, Risques et Sante (Vol. 14, Issue 6, pp. 466–468). John Libbey Eurotext. https://doi.org/10.1016/S0140-6736(15)60854-6

    Yang, X., Yu, X., Wang, Y., Liu, Y., Zhang, M., Ren, L., ... & Jiang, S. (2018). Estimating the response of hydrological regimes to future projections of precipitation and temperature over the upper Yangtze River. Atmospheric Research, 230, 104627.

    Yazdandoost, F., Moradian, S., Izadi, A., & Aghakouchak, A. (2021). Evaluation of CMIP6 precipitation simulations across different climatic zones: Uncertainty and model intercomparison. Atmospheric Research, 250(November), 105369. https://doi.org/10.1016/j.atmosres.2020.105369

    Zarrin, A., & Dadashi-Roudbari, A. (2021). Projection of future extreme precipitation in Iran based on CMIP6 multi-model ensemble. Theoretical and Applied Climatology, 144(1–2), 643–660. https://doi.org/10.1007/s00704-021-03568-2.