Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Lựa chọn và điều chỉnh tổ hợp mô hình RCM: một nghiên cứu trường hợp để đánh giá sự khó chịu về nhiệt độ cho thành phố Prato
Tóm tắt
Trong nghiên cứu này, chúng tôi đề xuất một phương pháp để lựa chọn một tập hợp con các mô hình khí hậu khu vực có kỹ năng hợp lý trong việc mô phỏng khí hậu trong quá khứ và thể hiện tốt các thay đổi trong điều kiện khí hậu trung bình và cực đoan. Sau đó, một quy trình xử lý hậu kỳ cho các mô hình đã được chọn được thực hiện dựa trên sự điều chỉnh bản đồ phân vị. Việc lựa chọn một tập hợp con các mô hình khí hậu là một bước quan trọng khi tiến hành các nghiên cứu tác động của biến đổi khí hậu. Hiệu suất của phương pháp được đề xuất đã được đánh giá thông qua một nghiên cứu trường hợp về việc đánh giá sự khó chịu do nhiệt độ ở thành phố Prato, nằm ở Italy. Thông số khí hậu được áp dụng để đánh giá sự khó chịu do nhiệt độ cao là chỉ số humidex. Đối với trường hợp thử nghiệm cụ thể này, phương pháp được định nghĩa và sử dụng để lựa chọn một tập hợp con phù hợp của các mô hình khí hậu EURO-CORDEX để đánh giá sự thay đổi trong xu hướng của chỉ số humidex và sự không chắc chắn liên quan, đã chứng minh là hợp lệ.
Từ khóa
#mô hình khí hậu khu vực #biến đổi khí hậu #chỉ số humidex #xử lý hậu kỳ #EURO-CORDEXTài liệu tham khảo
Biemans H, Speelman LH, Ludwig F, Moors EJ, Wiltshire AJ, Kumar P, Gerten D, Kabat P (2013) Selecting global climate models for regional climate change studies. Proc Natl Acad Sci U S A 106(21):84418446. https://doi.org/10.1073/pnas.0900094106
Boeand J, Terray L, Habets F, Martin E (2007) Statistical and dynamical downscaling of the Seine basin climate for hydro-meteorological studies. Int J Climatol 27(12):16431655
Caldwell P, Bretherton C, Zelinka M, Klein S, Santer B, Sanderson B (2014) Statistical significance of climate sensitivity predictors obtained by data mining. Geophys Res Lett 41:18031808. https://doi.org/10.1002/2014GL059205
Cannon AJ (2014) Selecting GCM scenarios that span the range of changes in a multimodel ensemble: application to CMIP5 climate extremes indices. J Clim 28:12601267. https://doi.org/10.1175/JCLI-D-14-00636.1
Cannon AJ (2015) Selecting GCM scenarios that span the range of changes in a multimodel ensemble: application to CMIP5 climate extremes indices. J Clim 28:12601267. https://doi.org/10.1175/JCLI-D-14-00636.1
Collins M, Brierley CM, MacVean M, Booth BBB, Harris GR (2007) The sensitivity of the rate of transient climate change to ocean physics perturbations. J Clim 20:23315–2320
Coppola E, Sobolowski S, Pichelli E et al (2020) (2018) A first-of-its-kind multi-model convection permitting ensemble for investigating convective phenomena over Europe and the Mediterranean. Clim Dyn 55:334. https://doi.org/10.1007/s00382-018-4521-8
Dosio A (2018) PESETA III Task1: Climate change projections, bias-adjustment, and selection of model runs, Joint Research Centre (JRC) Technical Report, JRC113745, EUR 29444 EN, ISBN 978-92-79-97261-4, ISSN 1831-9424, https://doi.org/10.2760/44883.
Evans P, Ji F, Abramowitz G, Ekstrm M (2013) Optimally choosing small ensemble members to produce robust climate simulations. Environ Res Lett 8:044050. https://doi.org/10.1088/1748-9326/8/4/044050
Giorgi F, Mearns LO (2002) Calculation of average, uncertainty range, and reliability of regional climate changes from AOGCM simulations via the reliability ensemble aver- aging (REA) method. J Clim 15(10):11411158
Gudmundsson L, Bremnes JB, Haugen JE, Engen Skaugen T (2012) Technical note: downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods. Hydrol Earth Syst Sci 9:6185–6201
Hawkins E, Sutton R (2009) The potential to narrow uncertainty in regional climate predictions. Bull Am Meteorol Soc 90(1095):1107.
Hennemuth TI, Jacob D, Keup-Thiel E, Kotlarski S, Nikulin G, Otto et al J (2017) Guidance for EUROCORDEX climate projections data use. Version1.0 - 2017.08. Link: https ://euro-corde x.net/imperia/md/conte nt/csc/corde x/euro-corde x-guide lines -versi on1.0-2017.08.pdf
Hingray B, Sad M (2014) Partitioning internal variability and model uncertainty components in a multimember multimodel ensemble of climate projections. J Clim 27(6779):6798. https://doi.org/10.1175/jcli-d-13-00629.1
Houle D, Bouffard A, Duchesne L, Logan T, Harvey R (2012) Projections of future soil temperature and water content for three Southern Quebec forested sites. J Clim 25(21):76907701. https://doi.org/10.1175/JCLI-D-11-00440.1
Immerzeel WW, Pellicciotti F, Bierkens MFP (2013) Rising river flows throughout the twenty-first century in two Himalayan glacierized watersheds. Nat Geosci 6(8):14. https://doi.org/10.1038/ngeo1896
IPCC (2012) Summary for Policymakers. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. In: Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds) A special report of working Groups I and II of the intergovernmental panel on climate change (IPCC). Cambridge University Press, Cambridge, pp 3–21
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 TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds). Cambridge University Press, Cambridge
IPCC (2014a) Climate change 2014: mitigation of climate change. contribution of working Group III to the fifth assessment report of the intergovernmental panel on climate change. In: Edenhofer O, Pichs-Madruga R, Sokona Y, Farahani E, Kadner S, Seyboth K, Adler A, Baum I, Brunner S, Eickemeier P, Kriemann B, Savolainen J, Schlmer S, von Stechow C, Zwickel T , Minx JC (eds). Cambridge University Press, Cambridge
Jacob D, Petersen J, Eggert B et al (2014) EURO-CORDEX: new high-resolution climate change projections for European impact research. Reg Environ Chang 14:563578. https://doi.org/10.1007/s10113-013-0499-2
Knutti R (2008) Should we believe model predictions of future climate change? Philos Trans R Soc Lond A 366(4647):4664. https://doi.org/10.1098/rsta.2008.0169
Knutti R, Furrer R, Tebaldi C, Cermak J, Meehl G (2010) Challenges in combining projections from multiple climate models. J Clim 23:27392758. https://doi.org/10.1175/2009JCLI3361.1
Knutti R, Sedlek J (2013) Robustness and uncertainties in the new CMIP5 climate model projections. Nat Clim Change 3:369373. https://doi.org/10.1038/nclimate1716
Kotlarski S, Keuler K, Christensen OB et al (2014) Regional climate modeling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geosci Model Dev 7:12971333. https://doi.org/10.5194/gmd-7-1297-2014
Lutz AF, ter Maat HW, Biemans H, Shrestha AB, Westerd F, Immerzeel WW (2016) Selecting representative climate models for climate change impact studies: an advanced envelope-based selection approach. Int J Climatol 36:39884005. https://doi.org/10.1002/joc.4608
Maraun D (2013) Bias correction, quantile mapping, and downscaling: revisiting the inflation issue. J Clim 26:21372143. https://doi.org/10.1175/JCLI-D-12-00821.1
Martinez Gerardo Sanchez, Baccini Michela, De Ridder Koen, Hooyberghs Hans, Lefebvre Wouter, Kendrovski Vladimir, Scott Kristen, Spasenovska Margarita (2016) Projected heat-related mortality under climate change in the metropolitan area of Skopje. BMC Public Health 16:407. https://doi.org/10.1186/s12889-016-3077-y
Maslin M, Austin P (2012) Climate models at their limit? Nature 486(183):184. https://doi.org/10.1038/486183a
Masson D, Knutti R (2011) Climate model genealogy. Geophys Res Lett 38:L08703. https://doi.org/10.1029/2011GL046864
Masterson J, Richardson FA (1979) Humidex, a method of quantifying human discomfort due to excessive heat and humidity. Environment Canada, Downs view, Ontario
Meinshausen M et al (2011) The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim Change 109:213241. https://doi.org/10.1007/s10584-011-0156-z
Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, van Vuuren DP, Carter TR, Emori S, Kainuma M, Kram T, Meehl GA, Mitchell JFB, Nakicenovic N, Riahi K, Smith SJ, Stouffer RJ, Thomson AM, Weyant JP, Wilbanks TJ (2010) The next generation of scenarios for climate change research and assessment. Nature 463:747756. https://doi.org/10.1038/nature08823
Murphy JM, Sexton DMH, Barnett DN, Jones GS, Webb MJ, Collins M, Stainforth DA (2004) Quantification of modelling uncertainties in a large ensemble of climate change simulations. Nature 430(768):772. https://doi.org/10.1038/nature02771
Parker W (2013) Ensemble modelling, uncertainty and robust predictions. Wiley Interdiscip Rev Clim Change 4:213223. https://doi.org/10.1002/wcc.220
Piani C, Weedon GP, Best M, Gomes SM, Viterbo P, Hagemann S, Haerter JO (2010) Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models. J Hydrol 395(3–4):199–215
Pierce DW, Barnett TP, Santer BD, Gleckler PJ (2009) Selecting global climate models for regional climate change studies. Proc Natl Acad Sci USA. 106(21):84418446. https://doi.org/10.1073/pnas.0900094106
Pirtle Z, Meyer R, Hamilton A (2010) What does it mean when climate models agree? Environ Sci Policy 13:351361. https://doi.org/10.1016/j.envsci.2010.04.004
Sanderson B, Knutti R, Caldwell P (2015a) A representative democracy to reduce interdependency in a multimodel ensemble. J Clim 28:51715194. https://doi.org/10.1175/JCLI-D-14-00362.1
Sorg A, Huss M, Rohrer M, Stoffel M (2014) The days of plenty might soon be over in glacierized Central Asian catchments, Environ Res Lett 9(10), https://doi.org/10.1088/1748-9326/9/10/104018.
Tebaldi C, Arblaster J, Knutti R (2011) Mapping model agreement on future climate projections. Geophys Res Lett 38:L23701. https://doi.org/10.1029/2011GL049863
Teutschbein C, Seibert J (2012) Bias correction of regional climate model simulations for hydrological climate change impact studies: Review and evaluation of different methods. J Hydrol 456–457:12–29
Teutschbein C, Seibert J (2010) Regional climate models for hydrological impact studies at the catchment scale: a review of recent modeling strategies. Geograp Compass 4(7):834860. https://doi.org/10.1111/j.1749-8198.2010.00357.x
Villani V, Rianna G, Mercogliano P, Zollo AL (2015) Statistical approaches versus weather generator to downscale RCM outputs to slope scale for stability assessment: a comparison of performances. Elect J Geotech Eng 20(4):1495–1515
Warszawski L, Frieler K, Huber V, Piontek F, Serdeczny O, Schewe J (2014) The inter-sectoral impact model intercomparison project (ISI-MIP): project framework. Proc Natl Acad Sci USA 111(9):32283232. https://doi.org/10.1073/pnas.1312330110
Willows RI , Connell RK (2003) Climate Adaptation: Risk, Uncertainty and Decision-Making, (UK Climate Impacts Programme, Oxford) UKCIP Technical Report
Winkler J et al (2011) Climate scenario development and applications for local/regional climate change impact assessments an overview for the nonclimate scientist. Geograp Compass 5(6):275–300
WMO (2007) The Role of Climatological Normals in a Changing Climate, WCDMP-No. 61, WMO.TD No. 1377
