Evolution and customisation of the RegCM model for urban climate studies: Addressing multifaceted challenges and advancing climate science

Naushin Yasmin , Safi Ullah , Sami G. Al-Ghamdi

Geography and Sustainability ›› 2024, Vol. 5 ›› Issue (4) : 607 -624.

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Geography and Sustainability ›› 2024, Vol. 5 ›› Issue (4) :607 -624. DOI: 10.1016/j.geosus.2024.08.005
Research Article
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Evolution and customisation of the RegCM model for urban climate studies: Addressing multifaceted challenges and advancing climate science

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Abstract

The Regional Climate Model (RegCM) proves valuable for climate analysis and has been applied to a wide range of climate change aspects and other environmental issues at a regional scale. The model also demonstrated success in diverse areas of urban research, including urban heat island studies, extreme climate events analysis, assessing urban resilience, and evaluating urbanization impacts on climate and air quality. Recently, more studies have been conducted in utilizing RegCM to address climate change in cities, due to its enhanced ability over the years to capture meteorological phenomena at city scales. However, there are many challenges associated with its implementation in meso-scale research, which are attributed to various shortcomings and thus create room for further improvement in the model. This paper presents a comprehensive overview of the evolution of the RegCM over the years and its customisation across various parameters, demonstrating its versatility in urban climate studies and underscoring the model’s pivotal role in addressing multifaceted challenges in urban environments. By addressing these aspects, the paper offers valuable insights and recommendations for researchers seeking to enhance the accuracy and efficacy of urban climate simulations using the RegCM system, thereby contributing to the advancement of urban climate science and sustainability.

Keywords

RegCM / Urban climate / Air quality / Urban heat island effect / Urban design / Climate extremes

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Naushin Yasmin, Safi Ullah, Sami G. Al-Ghamdi. Evolution and customisation of the RegCM model for urban climate studies: Addressing multifaceted challenges and advancing climate science. Geography and Sustainability, 2024, 5(4): 607-624 DOI:10.1016/j.geosus.2024.08.005

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CRediT authorship contribution statement

Naushin Yasmin: Writing – review & editing, Writing – original draft, Visualization, Formal analysis, Data curation, Conceptualization. Safi Ullah: Writing – review & editing, Visualization, Data curation, Conceptualization. Sami G. Al-Ghamdi: Writing – review & editing, Supervision, Resources, Project administration, Funding acquisition, Conceptualization.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We thank the anonymous reviewers for their helpful comments.

References

[1]

Abiodun, B. J., Ojumu, A. M., Jenner, S, Ojumu, T. V., 2014. The transport of atmospheric NOx and HNO3 over Cape Town. Atmos. Chem. Phys., 14, pp. 559-575. doi: doi: 10.5194/acp-14-559-2014.

[2]

Abulibdeh, A., 2021. Analysis of urban heat island characteristics and mitigation strategies for eight arid and semi-arid gulf region cities. Environ. Earth Sci., 80, p. 259. doi: 10.1007/s12665-021-09540-7.

[3]

Abuwaer, N, Ullah, S, Al-Ghamdi, S. G., 2024. Building climate resilience through urban planning: strategies, challenges, and opportunities. S. Al-Ghamdi (Ed.), Sustainable Cities in a Changing Climate: Enhancing Urban Resilience, John Wiley & Sons Ltd, pp. 185-206. doi: 10.1002/9781394201532.ch12.

[4]

Ahmad, B, Ali, S, Khan, T. M., ul Hasson, S, Bukhari, A., 2021. Simulating the urban heat island augmented with a heat wave episode using ICTP RegCM4.7 in a mega-urban structure of Karachi, Pakistan. J. Soft Comput. Civil Eng., 5, pp. 49-61. doi: 10.22115/scce.2021.237606.1243.

[5]

Ajay, P, Pathak, B, Bhuyan, P. K., Solmon, F, Giorgi, F., 2021. Sectoral emissions contributions to anthropogenic aerosol scenarios over the Indian subcontinent and effects of mitigation on air quality, climate, and health. Clim. Res., 85, pp. 21-33. doi: 10.3354/cr01671.

[6]

Ali, H, Mishra, V, Pai, D. S., 2014. Observed and projected urban extreme rainfall events in India. J. Geophys. Res. Atmos., 119, pp. 12621-12641. doi: 10.1002/2014jd022264.

[7]

Altinsoy, H, Ozturk, T, Turkes, M, Kurnaz, M. L., 2012. Springer, Berlin, Heidelberg, pp. 365-370. doi: 10.1007/978-3-642-29172-2_51.

[8]

Anthes, R. A., 1977. A cumulus parameterization scheme utilizing a one-dimensional cloud model. Mon. Weather Rev., 105, pp. 270-286. doi: 10.1175/1520-0493(1977)105<0270:ACPSUA>2.0.CO;2.

[9]

Baklanov, A, Lawrence, M, Pandis, S, Mahura, A, Finardi, S, Moussiopoulos, N, Beekmann, M, Laj, P, Gomes, L, Jaffrezo, J. L., Borbon, A, Coll, I, Gros, V, Sciare, J, Kukkonen, J, Galmarini, S, Giorgi, F, Grimmond, S, Esau, I, Stohl, A, Denby, B, Wagner, T, Butler, T, Baltensperger, U, Builtjes, P, Collins, B, Schluenzen, H, Kulmala, M, Zilitinkevich, S, Sokhi, R, Friedrich, R, Theloke, J, Kummer, U, Jalkinen, L, Halenka, T, Wiedensholer, A, Pyle, J, Rossow, W. B., 2010. MEGAPOLI: concept of multi-scale modelling of megacity impact on air quality and climate. Adv. Sci. Res., 4, pp. 115-120. doi: 10.5194/asr-4-115-2010.

[10]

Bal, P. K., Kar, S. C., 2020. Characteristics of the Indian summer monsoon rainfall simulated by the regional climate model (RegCM4). Pure Appl. Geophys., 177, pp. 6007-6028. doi: 10.1007/s00024-020-02597-8.

[11]

Bhaskaran, B, Jones, R. G., Murphy, J. M., Noguer, M., 1996. Simulations of the Indian summer monsoon using a nested regional climate model: domain size experiments. Clim. Dyn., 12, pp. 573-587. doi: 10.1007/bf00216267.

[12]

Bracht, M. K., Olinger, M. S., Krelling, A. F., Gonçalves, A. R., Melo, A. P., Lamberts, R., 2024. Multiple regional climate model projections to assess building thermal performance in Brazil: understanding the uncertainty. J. Build. Eng., 88, Article 109248. doi: 10.1016/j.jobe.2024.109248.

[13]

Brewster, M, Li, X., 2021. Analysis of radiation-induced cooling and growth of mist and cloud droplets. Int. J. Heat Mass Transf., 166, Article 120674. doi: 10.1016/j.ijheatmasstransfer.2020.120674.

[14]

Briegleb, B. P., 1992. Delta-eddington approximation for solar radiation in the NCAR community climate model. J. Geophys. Res., 97, pp. 7603-7612. doi: 10.1029/92jd00291.

[15]

Castelli, S. T., Bisignano, A, Donateo, A, Landi, T. C., Martano, P, Malguzzi, P., 2019. Evaluation of the turbulence parametrization in the MOLOCH meteorological model. Q. J. R. Meteorol. Soc., 146, pp. 124-140. doi: 10.1002/qj.3661.

[16]

Chapman, S, Salazar, A, Thatcher, M, McAlpine, C. A., 2017. The impact of urbanization and climate change on urban temperatures: a systematic review. Landsc. Ecol., 32, pp. 1921-1935. doi: 10.1007/s10980-017-0561-4.

[17]

Chen, F., 2014. Urban morphology and citizens’ life. A.C. Michalos (Ed.), Encyclopedia of Quality of Life and Well-Being Research, Springer, Dordrecht, pp. 6850-6855. doi: 10.1007/978-94-007-0753-5_4080.

[18]

Chen, F, Kusaka, H, Bornstein, R. D., Ching, J, Grimmond, S, Grossman-Clarke, S, Loridan, T, Manning, K. W., Martilli, A, Miao, S, Sailor, D. J., Salamanca, F, Taha, H, Tewari, M, Wang, X, Wyszogrodzki, A. A., Zhang, C., 2011. The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems. Int. J. Climatol., 31, pp. 273-288. doi: 10.1002/joc.2158.

[19]

Chen, X, Zhang, H, Chen, W, Huang, G., 2021. Urbanization and climate change impacts on future flood risk in the pearl river delta under shared socioeconomic pathways. Sci. Total Environ., 762, Article 143144. doi: 10.1016/j.scitotenv.2020.143144.

[20]

Collins, W. D., Rasch, P. J., Boville, B. A., Hack, J. J., McCaa, J. R., Williamson, D. L., Briegleb, B. P., Bitz, C. M., Lin, S. J., Zhang, M., 2006. The formulation and atmospheric simulation of the community atmosphere model version 3 (CAM3). J. Clim., 19, pp. 2144-2161. doi: 10.1175/jcli3760.1.

[21]

Das, S, Mohanty, U. C., Sharma, O. P., 1988. Study of Kuo-type cumulus parameterizations during different epochs of the Asian summer monsoon. Mon. Weather Rev., 116, pp. 715-729. doi: 10.1175/1520-0493(1988)116<0715:SOKTCP>2.0.CO;2.

[22]

Dash, S. K., Saraswat, V, Panda, S. K., Pattnayak, K. C., 2022. Temperature extremes and their future projections in selected Indian cities along with their meteorological subdivisions and temperature homogeneous zones. Urban Clim., 41, Article 101057. doi: 10.1016/j.uclim.2021.101057.

[23]

Dickinson, R. E., Errico, R. M., Giorgi, F, Bates, G. T., 1989. A regional climate model for the western United States. Clim. Change, 15, pp. 383-422. doi: 10.1007/bf00240465.

[24]

Doan, Q, Kusaka, H, Nguyen, T. M., 2019. Roles of past, present, and future land use and anthropogenic heat release changes on urban heat island effects in Hanoi, Vietnam: numerical experiments with a regional climate model. Sustain. Cities Soc., 47, Article 101479. doi: 10.1016/j.scs.2019.101479.

[25]

Doury, A, Somot, S, Gadat, S, Ribes, A, Corre, L., 2022. Regional climate model emulator based on deep learning: concept and first evaluation of a novel hybrid downscaling approach. Clim. Dyn., 60, pp. 1751-1779. doi: 10.1007/s00382-022-06343-9.

[26]

Driouech, F, ElRhaz, K, Moufouma-Okia, W, Arjdal, K, Balhane, S., 2020. Assessing future changes of climate extreme events in the CORDEX-MENA region using regional climate model ALADIN-Climate. Earth Syst. Environ., 4, pp. 477-492. doi: 10.1007/s41748-020-00169-3.

[27]

Eguiluz-Gracia, I, Mathioudakis, A. G., Bartel, S, Fuertes, E, Comberiati, P, Cai, Y. S., Tomazic, P. V., Diamant, Z, Vestbo, J, Galan, C, Hoffmann, B., 2020. The need for clean air: the way air pollution and climate change affect allergic rhinitis and asthma. Allergy, 75, pp. 2170-2184. doi: 10.1111/all.14177.

[28]

Elguindi, N, Rauscher, S. A., Giorgi, F., 2012. Historical and future changes in maximum and minimum temperature records over Europe. Clim. Change, 117, pp. 415-431. doi: 10.1007/s10584-012-0528-z.

[29]

Emanuel, K. A., 1991. A scheme for representing cumulus convection in large-scale models. J. Atmos. Sci., 48, pp. 2313-2329. doi: 10.1175/1520-0469(1991)048<2313:ASFRCC>2.0.CO;2.

[30]

Gao, X, Giorgi, F., 2017. Use of the RegCM system over East Asia: review and perspectives. Engineering, 3, pp. 766-772. doi: 10.1016/j.eng.2017.05.019.

[31]

Garuma, G. F., 2018. Review of urban surface parameterizations for numerical climate models. Urban Clim., 24, pp. 830-851. doi: 10.1016/j.uclim.2017.10.006.

[32]

Ghosh, S, Dey, S, Das, S, Riemer, N, Giuliani, G, Ganguly, D, Venkataraman, C, Giorgi, F, Tripathi, S. N., Ramachandran, S, Rajesh, T. A., Gadhavi, H, Srivastava, A., 2023. Towards an improved representation of carbonaceous aerosols over the Indian monsoon region in a regional climate model: RegCM. Geosci. Model Dev., 16, pp. 1-15. doi: 10.5194/gmd-16-1-2023.

[33]

Ghosh, S, Riemer, N, Giuliani, G, Giorgi, F, Ganguly, D, Dey, S., 2021. Sensitivity of Carbonaceous aerosol properties to the implementation of a dynamic aging parameterization in the regional climate model RegCM. J. Geophys. Res. Atmos., 126 . doi: 10.1029/2020jd033613.

[34]

Giorgi, F., 2019. Thirty years of regional climate modeling: where are we and where are we going next?. J. Geophys. Res. Atmos., 124, pp. 5696-5723. doi: 10.1029/2018jd030094.

[35]

Giorgi, F, Coppola, E, Giuliani, G, Ciarlo, J. M., Pichelli, E, Nogherotto, R, Raffaele, F, Malguzzi, P, Davolio, S, Stocchi, P, Drofa, O., 2023. The fifth generation regional climate modeling system, RegCM5: description and illustrative examples at parameterized convection and convection-permitting resolutions. J. Geophys. Res. Atmos., 128 . doi: 10.1029/2022jd038199.

[36]

Giorgi, F, Coppola, E, Solmon, F, Mariotti, L, Sylla, M. B., Bi, X, Elguindi, N, Diro, G. T., Nair, V, Giuliani, G, Turuncoglu, U. U., Cozzini, S, Güttler, I, OBrien, T. A., Tawfik, A. B., Shalaby, A, Zakey, A. S., Steiner, A. L., Stordal, F, Sloan, L. C., Brankovic, C., 2012. RegCM4: model description and preliminary tests over multiple CORDEX domains. Clim. Res., 52, pp. 7-29. doi: 10.3354/cr01018.

[37]

Goswami, J, Choudhury, A., 2014. A comparative study of high resolution weather model WRF & ReGCM weather model. Int. J. Eng. Res. Gen. Sci., 2, 366-374.

[38]

Grell, G. A., 1993. Prognostic evaluation of assumptions used by cumulus parameterizations. Mon. Weather Rev., 121, pp. 764-787. doi: 10.1175/1520-0493(1993)121<0764:PEOAUB>2.0.CO;2.

[39]

Grimmond, C. S. B., Blackett, M, Best, M. J., Baik, J. J., Belcher, S. E., Beringer, J, Bohnenstengel, S. I., Calmet, I, Chen, F, Coutts, A, Dandou, A, Fortuniak, K, Gouvea, M. L., Hamdi, R, Hendry, M, Kanda, M, Kawai, T, Kawamoto, Y, Kondo, H, Krayenhoff, E. S., Lee, S. H., Loridan, T, Martilli, A, Masson, V, Miao, S, Oleson, K, Ooka, R, Pigeon, G, Porson, A, Ryu, Y. H., Salamanca, F, Steeneveld, G. J., Tombrou, M, Voogt, J. A., Young, D. T., Zhang, N., 2010. Initial results from Phase 2 of the international urban energy balance model comparison. Int. J. Climatol., 31, pp. 244-272. doi: 10.1002/joc.2227.

[40]

Halenka, T, Belda, M, Huszár, P, Karlický, J, Nováková, T., 2019. Žák. On the comparison of urban canopy effects parameterisation. Int. J. Environ. Pollut., 65, p. 177. doi: 10.1504/ijep.2019.101840.

[41]

Hoffmann, B., 2018. Air pollution in cities: urban and transport planning determinants and health in cities. In: Nieuwenhuijsen, M., Khreis, H. (Eds.), Integrating Human Health Into Urban and Transport Planning. Springer, Cham, pp. 425–441. doi: 10.1007/978-3-319-74983-9_21.

[42]

Hung, W. T., Alessandrini, S, Kumar, R, Lin, C. A., 2021. The impacts of transported wildfire smoke aerosols on surface air quality in New York State: a multi-year study using machine learning. Atmos. Environ., 259, Article 118513. doi: 10.1016/j.atmosenv.2021.118513.

[43]

Huovila, A, Siikavirta, H, Rozado, C. A., Rökman, J, Tuominen, P, Paiho, S, Hedman, Å, Ylén, P., 2022. Carbon-neutral cities: critical review of theory and practice. J. Clean. Prod., 341, Article 130912. doi: 10.1016/j.jclepro.2022.130912.

[44]

Huszár, P., Belda, M., Halenka, T., 2016. On the long-term impact of emissions from central European cities on regional air quality. Atmos. Chem. Phys. 16, 1331–1352. doi: 10.5194/acp-16-1331-2016.

[45]

Huszár, P, Belda, M, Karlický, J, Bardachova, T, Halenka, T, Pisoft, P., 2018. Impact of urban canopy meteorological forcing on aerosol concentrations. Atmos. Chem. Phys., 18, pp. 14059-14078. doi: 10.5194/acp-18-14059-2018.

[46]

Huszár, P, Halenka, T, Belda, M, Sindelarova, K, Mikšovský, J., 2014. Regional climate model assessment of the urban land-surface forcing over central Europe. Atmos. Chem. Phys., 14, pp. 12393-12413. doi: 10.5194/acp-14-12393-2014.

[47]

Huszár, P, Karlický, J, Nováková, T, Belda, M, Halenka, T, Pišoft, P., 2020. Urban canopy meteorological forcing and its impact on ozone and PM2.5: role of vertical turbulent transport. Atmos. Chem. Phys., 20, pp. 1977-2016. doi: 10.5194/acp-20-1977-2020.

[48]

Huszár, P, Karlický, J, Marková, J, Nováková, T, Liaskoni, M, Bartík, L., 2021. The regional impact of urban emissions on air quality in Europe: the role of the urban canopy effects. Atmos. Chem. Phys., 21, pp. 14309-14332. doi: 10.5194/acp-21-14309-2021.

[49]

Huszár, P, Patricio, A, Bartík, L, Karlický, J, Villalba-Pradas, A., 2024. Impact of urbanization on fine particulate matter concentrations over central Europe. Atmos. Chem. Phys., 24, pp. 397-425. doi: 10.5194/acp-24-397-2024.

[50]

Karlický, J, Huszár, P, Halenka, T, Belda, M, Pišoft, P, Mikšovský, J., 2018. Multi-model comparison of urban heat island modelling approaches. Atmos. Chem. Phys., 18, pp. 10655-10674. doi: 10.5194/acp-18-10655-2018.

[51]

Karlický, J, Huszár, P, Nováková, T, Belda, M, Halenka, T., 2020. The “urban meteorology island”: a multi-model ensemble analysis. Atmos. Chem. Phys., 20, pp. 15061-15077. doi: 10.5194/acp-20-15061-2020.

[52]

KhayatianYazdi, F, Kamali, G, Mirrokni, S. M., Memarian, M. H., 2021. Mirrokni, M.H. Memarian. Sensitivity evaluation of the different physical parameterizations schemes in regional climate model RegCM4.5 for simulation of air temperature and precipitation over North and West of Iran. Dyn. Atmos. Oceans, 93, Article 101199. doi: 10.1016/j.dynatmoce.2020.101199.

[53]

Kiehl, J. T., Hack, J. J., Bonan, G. B., Boville, B. A., Williamson, D. L., Rasch, P. J., 1998. The national center for atmospheric research community climate model: CCM3. J. Clim., 11, pp. 1131-1149. doi: 10.1175/1520-0442(1998)011<1131:TNCFAR>2.0.CO;2.

[54]

Kumar, D, Dimri, A. P., 2019. Sensitivity of convective and land surface parameterization in the simulation of contrasting monsoons over CORDEX-South Asia domain using RegCM-4.4.5.5. Theor. Appl. Climatol., 139, pp. 297-322. doi: 10.1007/s00704-019-02976-9.

[55]

Kuzu, S. L., Yavuz, E., 2021. Comparison of RegCM dust schemes by monitoring an aeolian dust transport episode. Air Qual. Atmos. Health, 14, pp. 2047-2057. doi: 10.1007/s11869-021-01073-z.

[56]

Langendijk, G. S., Rechid, D, Jacob, D., 2019. Urban areas and urban–rural contrasts under climate change: what does the EURO-CORDEX ensemble tell us?—Investigating near surface humidity in Berlin and its surroundings. Atmosphere, 10, p. 730. doi: 10.3390/atmos10120730.

[57]

Lin, B. B., Ossola, A, Alberti, M, Andersson, E, Bai, X, Dobbs, C, Elmqvist, T, Evans, K. L., Frantzeskaki, N, Fuller, R. A., Gaston, K. J., Haase, D, Jim, C. Y., Konijnendijk, C, Nagendra, H, Niemelä, J, McPhearson, T, Moomaw, W. R., Parnell, S, Pataki, D, Ripple, W. J., Tan, P. Y., 2021. Integrating solutions to adapt cities for climate change. Lancet Planet. Health, 5, pp. e479-e486. doi: 10.1016/s2542-5196(21)00135-2.

[58]

Luiza, A, Tavares, M, Scavarda, A. J., 2018. Sustainable urban infrastructure: a review. Resour. Conserv. Recycl., 128, pp. 360-372. doi: 10.1016/j.resconrec.2016.07.017.

[59]

Mishra, V, Ganguly, A. R., Nijssen, B, Lettenmaier, D. P., 2015. Changes in observed climate extremes in global urban areas. Environ. Res. Lett., 10, p. 24005. doi: 10.1088/1748-9326/10/2/024005.

[60]

Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., Clough, S. A., 1997. Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102, pp. 16663-16682. doi: 10.1029/97jd00237.

[61]

Mostafa, A. N., Zakey, A. S., Alfaro, S. C., Wheida, A, Monem, S. A., Mubarak, M., 2019. Validation of RegCM-CHEM4 model by comparison with surface measurements in the Greater Cairo (Egypt) megacity. Environ. Sci. Pollut. Res., 26, pp. 23524-23541. doi: 10.1007/s11356-019-05370-0.

[62]

Nair, M, Bherwani, H, Kumar, S, Gulia, S, Goyal, S, Kumar, R., 2020. Assessment of contribution of agricultural residue burning on air quality of Delhi using remote sensing and modelling tools. Atmos. Environ., 230, Article 117504. doi: 10.1016/j.atmosenv.2020.117504.

[63]

Neupane, S, Shrestha, S, Ghimire, U, Mohanasundaram, S, Ninsawat, S., 2021. Evaluation of the CORDEX regional climate models (RCMs) for simulating climate extremes in the Asian cities. Sci. Total Environ., 797, Article 149137. doi: 10.1016/j.scitotenv.2021.149137.

[64]

Nguyen-Xuan, T, Im, E. S., 2023. Assessing the performance of the non-hydrostatic RegCM4 with the improved urban parameterization over Southeastern China. Urban Clim., 49, Article 101527. doi: 10.1016/j.uclim.2023.101527.

[65]

Nguyen-Xuan, T, Lam, S. L., Giorgi, F, Coppola, E, Giuliani, G, Gao, X, Im, E. S., 2021. Evaluation of the performance of the non-hydrostatic RegCM4 (RegCM4-NH) over Southeastern China. Clim. Dyn., 58, pp. 1419-1437. doi: 10.1007/s00382-021-05969-5.

[66]

Oke, T. R., Cleugh, H. A., 1987. Urban heat storage derived as energy balance residuals. Bound. Layer Meteor., 39, pp. 233-245. doi: 10.1007/bf00116120.

[67]

Oleson, K, Lawrence, M, Bonan, B, Drewniak, B, Huang, M, Koven, D, Levis, S, Li, F, Riley, J, Subin, M, Swenson, S, Thornton, E, Bozbiyik, A, Fisher, R, Heald, L, Kluzek, E, Lamarque, J. F., Lawrence, J, Leung, R, Lipscomb, W, Muszala, P, Ricciuto, M, Sacks, J, Sun, Y, Tang, J, Yang, Z. L., 2013. Technical description of version 4.5 of the Community Land Model (CLM). NCAR/TN-503+STR. National Center for Atmospheric Research, Boulder. . doi: 10.5065/d6rr1w7m.

[68]

Oleson, K. W., Feddema, J., 2020. Parameterization and surface data improvements and new capabilities for the community land model urban (CLMU). J. Adv. Model Earth Syst., 12, Article e2018ms001586. doi: 10.1029/2018ms001586.

[69]

Oleson, K. W., Niu, G. Y., Yang, Z. L., Lawrence, D. M., Thornton, P. E., Lawrence, P. J., Stockli, R, Dickin-son, R. E., Bonan, G. B., Levis, S, Dai, A, Qian, T., 2008. Improvements to the community land model and their impact on the hydrological cycle. J. Geophys. Res., 113, 1021-1026.

[70]

Pernigotto, G, Prada, A, Gasparella, A., 2019. 13, pp. 152-166. doi: 10.1080/19401493.2019.1585477.

[71]

Pernigotto, G., Prada, A., Gasparella, A., 2019. Extreme reference years for building energy performance simulation. J. Build. Perform. Simul. 13, 152–166. doi: 10.1080/19401493.2019.1585477.

[72]

Pu, X, Wang, T. J., Huang, X, Melas, D, Zanis, P, Papanastasiou, D, Poupkou, A., 2017. Enhanced surface ozone during the heat wave of 2013 in Yangtze River Delta region, China. Sci. Total Environ., 603–604, pp. 807-816. doi: 10.1016/j.scitotenv.2017.03.056.

[73]

Qian, Y, Giorgi, F, Huang, Y, Chameides, W. L., Luo, C., 2001. Regional simulation of anthropogenic sulfur over East Asia and its sensitivity to model parameters. Tellus B, 53, p. 171. doi: 10.3402/tellusb.v53i2.16573.

[74]

Qin, P, Xie, Z, Jia, B, Han, R, Liu, B., 2023. Predicting changes in population exposure to precipitation extremes over Beijing–Tianjin–Hebei urban agglomeration with regional climate model RegCM4 on a convection-permitting scale. Sustainability, 15, p. 11923. doi: 10.3390/su151511923.

[75]

Reuter, L, Graf, A, Goergen, K, Döscher, N, Leuchner, M., 2023. Modelling climate analogue regions for a central European city. Clim. Change, 176 . doi: 10.1007/s10584-023-03531-2.

[76]

Rizwan, A. M., Leung, D. Y. C., Liu, C., 2008. A review on the generation, determination and mitigation of urban heat Island. J. Environ. Sci., 20, pp. 120-128. doi: 10.1016/s1001-0742(08)60019-4.

[77]

Roshan, G., 2020. Comparison of impact of climate change on building energy-saving design for two different climates; Metropolitans of Moscow and Tehran. J. Earth Space Phys., 45, pp. 189-202. doi: 10.22059/jesphys.2019.266270.1007040.

[78]

Ryu, Y, Baik, J, Kwak, K. H., Kim, S, Moon, N., 2013. Impacts of urban land-surface forcing on ozone air quality in the Seoul metropolitan area. Atmos. Chem. Phys., 13, pp. 2177-2194. doi: 10.5194/acp-13-2177-2013.

[79]

Sachindra, D. A., Ullah, S, Zaborski, P, Nowosad, M, Dobek, M., 2022. Temperature and urban heat island effect in Lublin city in Poland under changing climate. Theor. Appl. Climatol., 151, pp. 667-690. doi: 10.1007/s00704-022-04285-0.

[80]

Salimi, M, Al-Ghamdi, S. G., 2020. Climate change impacts on critical urban infrastructure and urban resiliency strategies for the Middle East. Sustain. Cities Soc., 54, Article 101948. doi: 10.1016/j.scs.2019.101948.

[81]

Shahid, S, Wang, X. J., Harun, S. B., Shamsudin, S. B., Ismail, T, Minhans, A., 2015. Climate variability and changes in the major cities of Bangladesh: observations, possible impacts and adaptation. Reg. Environ. Change, 16, pp. 459-471. doi: 10.1007/s10113-015-0757-6.

[82]

Shalaby, A, Rappenglueck, B, Eltahir, E. A. B., 2015. The climatology of dust aerosol over the arabian peninsula. Atmos. Chem. Phys. Discuss., 15, pp. 1523-1571. doi: 10.5194/acpd-15-1523-2015.

[83]

Slingo, A., 1989. A GCM parameterization for the shortwave radiative properties of water clouds. J. Atmos. Sci., 46, pp. 1419-1427. doi: 10.1175/1520-0469(1989)046<1419:AGPFTS>2.0.CO;2.

[84]

Solmon, F., Giorgi, F., Liousse, C., 2006. Aerosol modelling for regional climate studies: application to anthropogenic particles and evaluation over a European/African domain. Tellus B 58, 51. doi: 10.1111/j.1600-0889.2005.00155.x.

[85]

Sperotto, A, Torresan, S, Gallina, V, Coppola, E, Critto, A, Marcomini, A., 2016. A multi-disciplinary approach to evaluate pluvial floods risk under changing climate: the case study of the municipality of Venice (Italy). Sci. Total Environ., 562, pp. 1031-1043. doi: 10.1016/j.scitotenv.2016.03.150.

[86]

Steiner, A. L., Tawfik, A. B., Shalaby, A, Zakey, A. S., Abdel-Wahab, M. M., Salah, Z, Solmon, F, Sillman, S, Zaveri, R. A., 2014. Climatological simulations of ozone and atmospheric aerosols in the Greater Cairo region. Clim. Res., 59, pp. 207-228. doi: 10.3354/cr01211.

[87]

Tahir, F, Al-Ghamdi, S. G., 2023. Climatic change impacts on the energy requirements for the built environment sector. Energy Rep., 9, pp. 670-676. doi: 10.1016/j.egyr.2022.11.033.

[88]

Tahir, F, Dieng, F. N., Al-Ghamdi, S. G., 2023. Building resilient health policies: incorporating climate change impacts for sustainable adaptation. S.G. Al-Ghamdi (Ed.), Sustainable Cities in a Changing Climate: Enhancing Urban Resilience, John Wiley & Sons Ltd, pp. 251-262. doi: 10.1002/9781394201532.ch15.

[89]

Tapiador, F. J., Navarro, A, Moreno, R, Sánchez, J. L., García-Ortega, E., 2020. Regional climate models: 30 years of dynamical downscaling. Atmos. Res., 235, Article 104785. doi: 10.1016/j.atmosres.2019.104785.

[90]

Tesfaye, M., Tsidu, G.M., Botai, J., Sivakumar, V., de W. Rautenbach, C.J., 2015. Mineral dust aerosol distributions, its direct and semi-direct effects over South Africa based on regional climate model simulation. J. Arid Environ. 114, 22–40. doi: 10.1016/j.jaridenv.2014.11.002 .

[91]

Tiedtke, M., 1989. A comprehensive mass flux Scheme for cumulus parameterization in large-scale models. Mon. Weather Rev., 117, pp. 1779-1800. doi: 10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2.

[92]

Trinh, T, Do, N, Nguyen, V. T., Carr, K., 2021. Modeling high-resolution precipitation by coupling a regional climate model with a machine learning model: an application to Sai Gon–Dong Nai Rivers Basin in Vietnam. Clim. Dyn., 57, pp. 2713-2735. doi: 10.1007/s00382-021-05833-6.

[93]

Ullah, S, Aldossary, A, Ullah, W, Al-Ghamdi, S. G., 2024. Al-Ghamdi. Augmented human thermal discomfort in urban centers of the arabian peninsula. Sci. Rep., 14, p. 3974. doi: 10.1038/s41598-024-54766-7.

[94]

Ullah, S, You, Q, Chen, D, Sachindra, D. A., AghaKouchak, A, Kang, S, Li, M, Zhai, P, Ullah, W., 2022. Future population exposure to daytime and nighttime heat waves in South Asia. Earths Future, 10, Article e2021EF002511. doi: 10.1029/2021ef002511.

[95]

Ullah, S, You, Q, Ullah, W, Fiifi, D, Ali, A, Ali, G, Zhang, Y, Jan, M. A., Bhatti, A. S., Xie, W., 2019. Daytime and nighttime heat wave characteristics based on multiple indices over the China–Pakistan economic corridor. Clim. Dyn., 53, pp. 6329-6349. doi: 10.1007/s00382-019-04934-7.

[96]

Ullah, S, You, Q, Ullah, W, Sachindra, D. A., Ali, A, Bhatti, A. S., Ali, G., 2023. Climate change will exacerbate population exposure to future heat waves in the China-Pakistan economic corridor. Weather Clim. Extremes, 40, Article 100570. doi: 10.1016/j.wace.2023.100570.

[97]

Usha, K. H., Nair, V. S., Babu, S. S., 2020. Modeling of aerosol induced snow albedo feedbacks over the Himalayas and its implications on regional climate. Clim. Dyn., 54, pp. 4191-4210. doi: 10.1007/s00382-020-05222-5.

[98]

Vinodhkumar, B, Ullah, S, Kumar, L, Al-Ghamdi, S. G., 2024. Amplification of temperature extremes in Arabian Peninsula under warmer worlds. Sci. Rep., 14, p. 16604. doi: 10.1038/s41598-024-67514-8.

[99]

Wang, M, Liu, M, Zhang, D, Qi, J, Fu, W, Zhang, Y, Rao, Q, Bakhshipour, A. E., Tan, S. K., 2023. Bakhshipour, S.K. Tan. Assessing and optimizing the hydrological performance of grey-green infrastructure systems in response to climate change and non-stationary time series. Water Res., 232, Article 119720. doi: 10.1016/j.watres.2023.119720.

[100]

Zeleke, T. T., Giorgi, F, Diro, G. T., Zaitchik, B. F., Giuliani, G, Ayal, D. Y., Kassahun, T, Sintayehu, W. D., Demissie, T., 2023. Effect of urbanization on East African climate as simulated by coupled urban-climate model. Clim. Serv., 31, Article 100398. doi: 10.1016/j.cliser.2023.100398.

[101]

Zender, C. S., 2003. Mineral dust entrainment and deposition (DEAD) model: description and 1990s dust climatology. J. Geophys. Res., 108, p. 4416. doi: 10.1029/2002jd002775.

[102]

Zhang, G, Azorín-Molina, C, Wang, X, Chen, D, McVicar, T. R., Guijarro, J. A., Chappell, A, Deng, K, Minola, L, Kong, F, Wang, S, Shi, P., 2022. Rapid urbanization induced daily maximum wind speed decline in metropolitan areas: a case study in the Yangtze River Delta (China). Urban Clim., 43, Article 101147. doi: 10.1016/j.uclim.2022.101147.

[103]

Zhang, H, Yang, Z, Cai, Y, Qiu, J, Huang, B., 2021. Impacts of climate change on urban drainage sstems by future short-duration design rainstorms. Water, 13, p. 2718. doi: 10.3390/w13192718.

[104]

Zhang, R, Fujimori, S., 2020. The role of transport electrification in global climate change mitigation scenarios. Environ. Res. Lett., 15, p. 34019. doi: 10.1088/1748-9326/ab6658.

[105]

Zhao, L, Lee, X, Smith, R. B., Oleson, K., 2014. Strong contributions of local background climate to urban heat islands. Nature, 511, pp. 216-219. doi: 10.1038/nature13462.

[106]

Zhuang, B, Chen, H. M., Li, S, Wang, T, Liu, J, Zhang, L. J., Liu, H. N., Xie, M, Chen, P. L., Li, M. M., Zhao, M., 2019. The direct effects of black carbon aerosols from different source sectors in East Asia in summer. Clim. Dyn., 53, pp. 5293-5310. doi: 10.1007/s00382-019-04863-5.

[107]

Velikou, K, Tsikaloudaki, K., 2021. Evaluating the combined effect of climate change and urban microclimate on buildings’ heating and cooling energy demand in a mediterranean city. Energies, 14, p. 5799. doi: 10.3390/en14185799.

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