Articles | Volume 13, issue 4
https://doi.org/10.5194/esd-13-1625-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-13-1625-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Nov 2022
Research article |
| 18 Nov 2022
| 18 Nov 2022
Regional dynamical and statistical downscaling temperature, humidity and wind speed for the Beijing region under stratospheric aerosol injection geoengineering
College of Global Change and Earth Systems Science, Beijing Normal
University, 100875 Beijing, China
John C. Moore
CORRESPONDING AUTHOR
College of Global Change and Earth Systems Science, Beijing Normal
University, 100875 Beijing, China
Arctic Centre, University of Lapland, Rovaniemi, Finland
Liyun Zhao
CORRESPONDING AUTHOR
College of Global Change and Earth Systems Science, Beijing Normal
University, 100875 Beijing, China
College of Global Change and Earth Systems Science, Beijing Normal
University, 100875 Beijing, China
Zhenhua Di
State Key Laboratory of Earth Surface Processes and Resource Ecology,
Faculty of Geographical Science, Beijing Normal University, 100875 Beijing,
China
Related authors
Jun Wang, John C. Moore, and Liyun Zhao
Earth Syst. Dynam., 14, 989–1013, https://doi.org/10.5194/esd-14-989-2023, https://doi.org/10.5194/esd-14-989-2023, 2023
Short summary
Short summary
Apparent temperatures and PM2.5 pollution depend on humidity and wind speed in addition to surface temperature and impact human health and comfort. Apparent temperatures will reach dangerous levels more commonly in the future because of water vapor pressure rises and lower expected wind speeds, but these will also drive changes in PM2.5. Solar geoengineering can significantly reduce the frequency of extreme events relative to modest and especially
business-as-usualgreenhouse scenarios.
Junshun Wang, Liyun Zhao, Michael Wolovick, and John C. Moore
EGUsphere, https://doi.org/10.5194/egusphere-2025-3296, https://doi.org/10.5194/egusphere-2025-3296, 2025
This preprint is open for discussion and under review for The Cryosphere (TC).
Short summary
Short summary
Ice sheet models adjust basal sliding with assumed ice temperatures so that surface speeds match observations, leading to inconsistencies between basal thermal state and sliding fields. We propose a method to quantify these inconsistencies without requiring any subglacial measurements. This method is applied to ice sheet model of Totten Glacier using eight geothermal heat flux (GHF) datasets, yielding rankings of GHF that align with those based on radar data.
Yiliang Ma, Liyun Zhao, Rupert Gladstone, Thomas Zwinger, Michael Wolovick, and John C. Moore
EGUsphere, https://doi.org/10.5194/egusphere-2024-1102, https://doi.org/10.5194/egusphere-2024-1102, 2024
Short summary
Short summary
Totten Glacier in Antarctica holds a sea level potential of 3.85 m. Basal sliding and sub-shelf melt rate have important impact on ice sheet dynamics. We simulate the evolution of Totten Glacier using an ice flow model with different basal sliding parameterizations as well as sub-shelf melt rates to quantify their effect on the projections. We found the modelled glacier retreat and mass loss is sensitive to the choice of basal sliding parameterizations and maximal sub-shelf melt rate.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
Short summary
Short summary
This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Abolfazl Rezaei, Khalil Karami, Simone Tilmes, and John C. Moore
Earth Syst. Dynam., 15, 91–108, https://doi.org/10.5194/esd-15-91-2024, https://doi.org/10.5194/esd-15-91-2024, 2024
Short summary
Short summary
Water storage (WS) plays a profound role in the lives of people in the Middle East and North Africa as well as Mediterranean climate "hot spots". WS change by greenhouse gas (GHG) warming is simulated with and without stratospheric aerosol intervention (SAI). WS significantly increases in the Arabian Peninsula and decreases around the Mediterranean under GHG. While SAI partially ameliorates GHG impacts, projected WS increases in dry regions and decreases in wet areas relative to present climate.
Yan Huang, Liyun Zhao, Michael Wolovick, Yiliang Ma, and John C. Moore
The Cryosphere, 18, 103–119, https://doi.org/10.5194/tc-18-103-2024, https://doi.org/10.5194/tc-18-103-2024, 2024
Short summary
Short summary
Geothermal heat flux (GHF) is an important factor affecting the basal thermal environment of an ice sheet and crucial for its dynamics. But it is poorly defined for the Antarctic ice sheet. We simulate the basal temperature and basal melting rate with eight different GHF datasets. We use specularity content as a two-sided constraint to discriminate between local wet or dry basal conditions. Two medium-magnitude GHF distribution maps rank well, showing that most of the inland bed area is frozen.
Chencheng Shen, John C. Moore, Heri Kuswanto, and Liyun Zhao
Earth Syst. Dynam., 14, 1317–1332, https://doi.org/10.5194/esd-14-1317-2023, https://doi.org/10.5194/esd-14-1317-2023, 2023
Short summary
Short summary
The Indonesia Throughflow is an important pathway connecting the Pacific and Indian oceans and is part of a wind-driven circulation that is expected to reduce under greenhouse gas forcing. Solar dimming and sulfate aerosol injection geoengineering may reverse this effect. But stratospheric sulfate aerosols affect winds more than simply ``shading the sun''; they cause a reduction in water transport similar to that we simulate for a scenario with unabated greenhouse gas emissions.
Jun Wang, John C. Moore, and Liyun Zhao
Earth Syst. Dynam., 14, 989–1013, https://doi.org/10.5194/esd-14-989-2023, https://doi.org/10.5194/esd-14-989-2023, 2023
Short summary
Short summary
Apparent temperatures and PM2.5 pollution depend on humidity and wind speed in addition to surface temperature and impact human health and comfort. Apparent temperatures will reach dangerous levels more commonly in the future because of water vapor pressure rises and lower expected wind speeds, but these will also drive changes in PM2.5. Solar geoengineering can significantly reduce the frequency of extreme events relative to modest and especially
business-as-usualgreenhouse scenarios.
Abolfazl Rezaei, Khalil Karami, Simone Tilmes, and John C. Moore
Atmos. Chem. Phys., 23, 5835–5850, https://doi.org/10.5194/acp-23-5835-2023, https://doi.org/10.5194/acp-23-5835-2023, 2023
Short summary
Short summary
Teleconnection patterns are important characteristics of the climate system; well-known examples include the El Niño and La Niña events driven from the tropical Pacific. We examined how spatiotemporal patterns that arise in the Pacific and Atlantic oceans behave under stratospheric aerosol geoengineering and greenhouse gas (GHG)-induced warming. In general, geoengineering reverses trends; however, the changes in decadal oscillation for the AMO, NAO, and PDO imposed by GHG are not suppressed.
Daniele Visioni, Ben Kravitz, Alan Robock, Simone Tilmes, Jim Haywood, Olivier Boucher, Mark Lawrence, Peter Irvine, Ulrike Niemeier, Lili Xia, Gabriel Chiodo, Chris Lennard, Shingo Watanabe, John C. Moore, and Helene Muri
Atmos. Chem. Phys., 23, 5149–5176, https://doi.org/10.5194/acp-23-5149-2023, https://doi.org/10.5194/acp-23-5149-2023, 2023
Short summary
Short summary
Geoengineering indicates methods aiming to reduce the temperature of the planet by means of reflecting back a part of the incoming radiation before it reaches the surface or allowing more of the planetary radiation to escape into space. It aims to produce modelling experiments that are easy to reproduce and compare with different climate models, in order to understand the potential impacts of these techniques. Here we assess its past successes and failures and talk about its future.
Yangxin Chen, Duoying Ji, Qian Zhang, John C. Moore, Olivier Boucher, Andy Jones, Thibaut Lurton, Michael J. Mills, Ulrike Niemeier, Roland Séférian, and Simone Tilmes
Earth Syst. Dynam., 14, 55–79, https://doi.org/10.5194/esd-14-55-2023, https://doi.org/10.5194/esd-14-55-2023, 2023
Short summary
Short summary
Solar geoengineering has been proposed as a way of counteracting the warming effects of increasing greenhouse gases by reflecting solar radiation. This work shows that solar geoengineering can slow down the northern-high-latitude permafrost degradation but cannot preserve the permafrost ecosystem as that under a climate of the same warming level without solar geoengineering.
Aobo Liu, John C. Moore, and Yating Chen
Earth Syst. Dynam., 14, 39–53, https://doi.org/10.5194/esd-14-39-2023, https://doi.org/10.5194/esd-14-39-2023, 2023
Short summary
Short summary
Permafrost thaws and releases carbon (C) as the Arctic warms. Most earth system models (ESMs) have poor estimates of C stored now, so their future C losses are much lower than using the permafrost C model with climate inputs from six ESMs. Bias-corrected soil temperatures and plant productivity plus geoengineering lowering global temperatures from a no-mitigation baseline scenario to a moderate emissions level keep C in the soil worth about USD 0–70 (mean 20) trillion in climate damages by 2100.
Haoran Kang, Liyun Zhao, Michael Wolovick, and John C. Moore
The Cryosphere, 16, 3619–3633, https://doi.org/10.5194/tc-16-3619-2022, https://doi.org/10.5194/tc-16-3619-2022, 2022
Short summary
Short summary
Basal thermal conditions are important to ice dynamics and sensitive to geothermal heat flux (GHF). We estimate basal thermal conditions of the Lambert–Amery Glacier system with six GHF maps. Recent GHFs inverted from aerial geomagnetic observations produce a larger warm-based area and match the observed subglacial lakes better than the other GHFs. The modelled basal melt rate is 10 to hundreds of millimetres per year in fast-flowing glaciers feeding the Amery Ice Shelf and smaller inland.
Mengdie Xie, John C. Moore, Liyun Zhao, Michael Wolovick, and Helene Muri
Atmos. Chem. Phys., 22, 4581–4597, https://doi.org/10.5194/acp-22-4581-2022, https://doi.org/10.5194/acp-22-4581-2022, 2022
Short summary
Short summary
We use data from six Earth system models to estimate Atlantic meridional overturning circulation (AMOC) changes and its drivers under four different solar geoengineering methods. Solar dimming seems relatively more effective than marine cloud brightening or stratospheric aerosol injection at reversing greenhouse-gas-driven declines in AMOC. Geoengineering-induced AMOC amelioration is due to better maintenance of air–sea temperature differences and reduced loss of Arctic summer sea ice.
Yijing Lin, Yan Liu, Zhitong Yu, Xiao Cheng, Qiang Shen, and Liyun Zhao
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-325, https://doi.org/10.5194/tc-2021-325, 2021
Preprint withdrawn
Short summary
Short summary
We introduce an uncertainty analysis framework for comprehensively and systematically quantifying the uncertainties of the Antarctic mass balance using the Input and Output Method. It is difficult to use the previous strategies employed in various methods and the available data to achieve the goal of estimation accuracy. The dominant cause of the future uncertainty is the ice thickness data gap. The interannual variability of ice discharge caused by velocity and thickness is also nonnegligible.
Chao Yue, Louise Steffensen Schmidt, Liyun Zhao, Michael Wolovick, and John C. Moore
The Cryosphere Discuss., https://doi.org/10.5194/tc-2021-318, https://doi.org/10.5194/tc-2021-318, 2021
Revised manuscript not accepted
Short summary
Short summary
We use the ice sheet model PISM to estimate Vatnajökull mass balance under solar geoengineering. We find that Stratospheric aerosol injection at the rate of 5 Tg yr−1 reduces ice cap mass loss by 4 percentage points relative to the RCP4.5 scenario. Dynamic mass loss is a significant component of mass balance, but insensitive to climate forcing.
Rupert Gladstone, Benjamin Galton-Fenzi, David Gwyther, Qin Zhou, Tore Hattermann, Chen Zhao, Lenneke Jong, Yuwei Xia, Xiaoran Guo, Konstantinos Petrakopoulos, Thomas Zwinger, Daniel Shapero, and John Moore
Geosci. Model Dev., 14, 889–905, https://doi.org/10.5194/gmd-14-889-2021, https://doi.org/10.5194/gmd-14-889-2021, 2021
Short summary
Short summary
Retreat of the Antarctic ice sheet, and hence its contribution to sea level rise, is highly sensitive to melting of its floating ice shelves. This melt is caused by warm ocean currents coming into contact with the ice. Computer models used for future ice sheet projections are not able to realistically evolve these melt rates. We describe a new coupling framework to enable ice sheet and ocean computer models to interact, allowing projection of the evolution of melt and its impact on sea level.
Cited articles
Aswathy, V. N., Boucher, O., Quaas, M., Niemeier, U., Muri, H., Mülmenstädt, J., and Quaas, J.: Climate extremes in multi-model simulations of stratospheric aerosol and marine cloud brightening climate engineering, Atmos. Chem. Phys., 15, 9593–9610, https://doi.org/10.5194/acp-15-9593-2015, 2015.
Bao, J., Feng, J., and Wang, Y.: Dynamical downscaling simulation and future
projection of precipitation over China, J. Geophys. Res.-Atmos., 120,
8227–8243, https://doi.org/10.1002/2015JD023275, 2015.
Brewer, M. C. and Mass, C. F.: Projected Changes in Heat Extremes and
Associated Synoptic- and Mesoscale Conditions over the Northwest United
States, J. Climate, 29, 6383–6400,
https://doi.org/10.1175/JCLI-D-15-0641.1, 2016.
Chen, B., Stein, A. F., Castell, N., de la Rosa, J. D., de la Campa, A. M.,
S., Gonzalez-Castanedo, Y., and Draxler, R. R.: Modeling and surface
observations of arsenic dispersion from a large Cu-smelter in southwestern
Europe, Atmos. Environ., 49, 114–122,
https://doi.org/10.1016/j.atmosenv.2011.12.014, 2012.
Chen, F. and Dudhia, J.: Coupling an Advanced Land Surface–Hydrology Model
with the Penn State–NCAR MM5 Modeling System, Part I: Model Implementation
and Sensitivity, Mon. Weather Rev., 129, 569–585,
https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Chen, S., Cao, R., Xie, Y., Zhang, Y., Tan, W., Chen, H., Guo, P., and Zhao, P.: Study of the seasonal variation in Aeolus wind product performance over China using ERA5 and radiosonde data, Atmos. Chem. Phys., 21, 11489–11504, https://doi.org/10.5194/acp-21-11489-2021, 2021.
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C., Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S.: Development and evaluation of an Earth-System model – HadGEM2, Geosci. Model Dev., 4, 1051–1075, https://doi.org/10.5194/gmd-4-1051-2011, 2011.
Curry, C. L., Sillmann, J., Bronaugh, D., Alterskjaer, K., Cole, J. N. S.,
Ji, D., Kravitz, B., Kristjánsson, J. E., Moore, J. C., Muri, H.,
Niemeier, U., Robock, A., Tilmes, S., and Yang, S.: A multi-model
examination of climate extremes in an idealized geoengineering experiment,
J. Geophys. Res.-Atmos., 119, 3900–3923,
https://doi.org/10.1002/2013JD020648, 2014.
Dou, J., Bornstein, R., Miao, S., and Zhang, Y.: Observation and Simulation
of a Bifurcating Thunderstorm over Beijing, J. Appl. Meteorol. Climatol.,
59, 2129–2148, https://doi.org/10.1175/JAMC-D-20-0056.1, 2020.
Eyring, V., Gillett, N. P., Achuta Rao, K. M., Barimalala, R., Barimalala
Parrillo, M., Bellouin, N., Cassou, C., Durack, P. J., Kosaka, Y., McGregor,
S., Min, S., Morgenstern, O., Ying, S. Human Influence on the Climate
System. In Climate Change 2021: The Physical Science Basis, Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental
Panel on Climate Change, 2nd ed., edited by: Masson-Delmotte, V., Zhai, P., Pirani, A.,
Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L.,
Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R.,
Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R. and Zhou, B.,
Cambridge University Press, UK, In Press, 2021.
Gong, Y., Yang, S., Yin, J., Wang, S., Pan, X., Li, D., and Yi, X.: Validation of the Reproducibility of Warm-Season Northeast China
Cold Vortices for ERA5 and MERRA-2 Reanalysis, J. Appl. Meteorol. Clim., 61, 1349–1366, https://doi.org/10.1175/JAMC-D-22-0052.1, 2022.
Hempel, S., Frieler, K., Warszawski, L., Schewe, J., and Piontek, F.: A trend-preserving bias correction – the ISI-MIP approach, Earth Syst. Dynam., 4, 219–236, https://doi.org/10.5194/esd-4-219-2013, 2013.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A.,
Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I.,
Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J-N.: ERA5 hourly
data on pressure levels from 1979 to present, Copernicus Climate Change
Service (C3S) Climate Data Store (CDS), cds [data set],
https://doi.org/10.24381/cds.bd0915c6, 2018.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hong, S. Y. and Lim, J. O. J.: The WRF single-moment 6-class microphysics
scheme (WSM6), J. Korean Meteor. Soc., 42, 129–151, 2006.
Huo, L., Wang, J., Jin, D., Luo, J., Shen, H., Zhang, X., Min, J.,
and Xiao, Y.: Increased summer electric power demand in Beijing driven by preceding spring tropical North Atlantic warming, Atmos. Ocean. Sci. Lett., 15, 100146, https://doi.org/10.1016/j.aosl.2021.100146, 2022.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S.
A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases:
Calculations with the AER radiative transfer models, J. Geophys. Res.-Atmos., 113, D13, https://doi.org/10.1029/2008JD009944, 2008.
Ji, D., Wang, L., Feng, J., Wu, Q., Cheng, H., Zhang, Q., Yang, J., Dong, W., Dai, Y., Gong, D., Zhang, R.-H., Wang, X., Liu, J., Moore, J. C., Chen, D., and Zhou, M.: Description and basic evaluation of Beijing Normal University Earth System Model (BNU-ESM) version 1, Geosci. Model Dev., 7, 2039–2064, https://doi.org/10.5194/gmd-7-2039-2014, 2014.
Ji, D., Fang, S., Curry, C. L., Kashimura, H., Watanabe, S., Cole, J. N. S., Lenton, A., Muri, H., Kravitz, B., and Moore, J. C.: Extreme temperature and precipitation response to solar dimming and stratospheric aerosol geoengineering, Atmos. Chem. Phys., 18, 10133–10156, https://doi.org/10.5194/acp-18-10133-2018, 2018.
Jiang, Y., Xu, X., Liu, H., Dong, X., Wang, W., and Jia, G.: The
underestimated magnitude and decline trend in near-surface wind over China,
Atmos. Sci. Lett., 18, 475–483, 2017.
Jimenez, P. and Dudhia, J.: Improving the representation of resolved and
unresolved topographic effects on surface wind in the WRF model, J. Appl.
Meteorol. Climatol., 51, 300–316, 2012.
Jones, A. C., Hawcroft, M. K., Haywood, J. M., Jones, A., Guo, X., and
Moore, J. C.: Regional climate impacts of stabilizing global warming at 1.5 K using solar geoengineering, Earth. Fut., 6, 230–251,
https://doi.org/10.1002/2017EF000720, 2018.
Kain, J. S.: The Kain-Fritsch convective parameterization: an update, J.
Appl. Meteorol., 43, 170–181, https://doi.org/10.1175/1520-0450(2004)043<0170:TKCPAU>2.0.CO;2, 2004.
Kawase, H., Yoshikane, T., Hara, M., Kimura, F., Yasunari, T., Ailikun, B.,
Ueda, H., and Inoue, T.: Intermodel variability of future changes in the
Baiu rainband estimated by the pseudo global warming downscaling method, J.
Geophys. Res.-Atmos., 114, D24, https://doi.org/10.1029/2009JD011803, 2009.
Kim, D. H., Shin, H. J., and Chung, I. U.: Geoengineering: Impact of marine
cloud brightening control on the extreme temperature change over East
Asia, Atmosphere, 11, 1345, https://doi.org/10.3390/atmos11121345, 2020.
Kong, X., Wang, A., Bi, X., and Wang, D.: Assessment of temperature extremes
in China using RegCM4 and WRF, Advan. Atmos. Sci., 36, 363–377,
https://doi.org/10.1007/s00376-018-8144-0, 2019.
Kravitz, B., Robock, A., Boucher, O., Schmidt, H., Taylor, K. E.,
Stenchikov, G., and Schulz, M.: The geoengineering model intercomparison
project (GeoMIP), Atmos. Sci. Lett., 12,
162–167, https://doi.org/10.1002/asl.316, 2011.
Kravitz, B., Robock, A., Tilmes, S., Boucher, O., English, J. M., Irvine, P. J., Jones, A., Lawrence, M. G., MacCracken, M., Muri, H., Moore, J. C., Niemeier, U., Phipps, S. J., Sillmann, J., Storelvmo, T., Wang, H., and Watanabe, S.: The Geoengineering Model Intercomparison Project Phase 6 (GeoMIP6): simulation design and preliminary results, Geosci. Model Dev., 8, 3379–3392, https://doi.org/10.5194/gmd-8-3379-2015, 2015.
Kuswanto, H., Kravitz, B., Miftahurrohmah, B., Fauzi, F., Sopahaluwaken, A.,
and Moore, J. C.: Impact of solar geoengineering on temperatures over the
Indonesian Maritime Continent, Int. J. Climatol., 42, 2795–2814,
https://doi.org/10.1002/joc.7391, 2021.
Lee, S., Lung, S., Chiu, P., Wang, W., Tsai, I., and Lin, T.: Northern
Hemisphere Urban Heat Stress and Associated Labor Hour Hazard from ERA5
Reanalysis, Int. J. Environ. Res. Public Health, 19, 8163,
https://doi.org/10.3390/ijerph19138163, 2022.
Lee, S.-H., Kim, S.-W., Angevine, W. M., Bianco, L., McKeen, S. A., Senff, C. J., Trainer, M., Tucker, S. C., and Zamora, R. J.: Evaluation of urban surface parameterizations in the WRF model using measurements during the Texas Air Quality Study 2006 field campaign, Atmos. Chem. Phys., 11, 2127–2143, https://doi.org/10.5194/acp-11-2127-2011, 2011.
Lenton, T. M. and Vaughan, N. E.: The radiative forcing potential of different climate geoengineering options, Atmos. Chem. Phys., 9, 5539–5561, https://doi.org/10.5194/acp-9-5539-2009, 2009.
Li, X. and Babovic, V.: Multi-site multivariate downscaling of global
climate model outputs: an integrated framework combining quantile mapping,
stochastic weather generator and Empirical Copula approaches, Clim.
Dyn., 52, 5775–5799, https://doi.org/10.1007/s00382-018-4480-0, 2019.
Liu, Y., Xu, Y., Zhang, F., and Shu, W.: A preliminary study on the
influence of Beijing urban spatial morphology on near-surface wind
speed, Urban Clim., 34, 100703, https://doi.org/10.1016/j.uclim.2020.100703,
2020.
MacMartin, D. G. and Kravitz, B.: Dynamic climate emulators for solar geoengineering, Atmos. Chem. Phys., 16, 15789–15799, https://doi.org/10.5194/acp-16-15789-2016, 2016.
McSweeney, C. F. and Jones, R. G.: How representative is the spread of
climate projections from the 5 CMIP5 GCMs used in ISI-MIP?, Clim.
Services, 1, 24–29, https://doi.org/10.1016/j.cliser.2016.02.001, 2016.
Meng, X. G., Guo, J. J., and Han, Y. Q.: Preliminarily assessment of ERA5
reanalysis data, J. Mar. Meteor., 38, 91–99,
https://doi.org/10.19513/j.cnki.issn2096-3599.2018.01.011, 2018, in
Chinese.
Michalakes, J., Chen, S., Dudhia, J., Hart, L., Klemp, J., Middlecoff, J.,
and Skamarock, W.: Development of a next-generation regional weather
research and forecast model, Developments in Teracomputing, edited by: Zwieflhafer, W. and Kreitz, N., World Scientific, 269–296, https://doi.org/10.1142/9789812799685_0024, 2001.
Moore, J. C., Yue, C., Zhao, L., Guo, X., Watanabe, S., and Ji, D.:
Greenland ice sheet response to stratospheric aerosol injection
geoengineering, Earth's Future, 7, 1451–1463,
https://doi.org/10.1029/2019EF001393, 2019.
Ngan, F., Kim, H., Lee, P., Al-Wali, K., and Dornblaser, B.: A study of
nocturnal surface wind speed overprediction by the WRF-ARW model in
southeastern Texas, J. Appl. Meteor., 52, 2638–2653,
https://doi.org/10.1175/JAMC-D-13-060.1, 2013.
Niemeier, U. and Timmreck, C.: What is the limit of climate engineering by stratospheric injection of SO2?, Atmos. Chem. Phys., 15, 9129–9141, https://doi.org/10.5194/acp-15-9129-2015, 2015.
Noh, Y., Cheon, W. G., Hong, S. Y., and Raasch, S.: Improvement of the
K-profile model for the planetary boundary layer based on large eddy
simulation data, Bound.-Lay. Meteorology, 107, 401–427,
https://doi.org/10.1023/A:1022146015946, 2003.
Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer,
W., Christ, R., Church, J. A., Clarke, L., Dahe, Q., Dasgupta,
P., Dubash, N. K., Edenhofer, O., Elgizouli, I., Field, C. B., Forster,
P., Friedlingstein, P., Fuglestvedt, J., Gomez-Echeverri,
L., Hallegatte, S., Hegerl, G., Howden, M., Jiang, K., Jimenez
Cisneroz, B., Kattsov, V., Lee, H., Mach, K. J., Marotzke,
J., Mastrandrea, M. D., Meyer, L., Minx, J., Mulugetta, Y., O'Brien,
K., Oppenheimer, M., Pereira, J. J., Pichs-Madruga, R., Plattner, G.
K., Pörtner, H. O., Power, S. B., Preston, B., Ravindranath, N.
H., Reisinger, A., Riahi, K., Rusticucci, M., Scholes, R., Seyboth,
K., Sokona, Y., Stavins, R., Stocker, T. F., Tschakert, P., van Vuuren,
D., and van Ypserle, J. P.: 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, edited by: Pachauri, R. and Meyer, L., Geneva, Switzerland, IPCC, p. 151, ISBN 978-92-9169-143-2,
2014.
Paulson, C. A.: The mathematical representation of wind speed and
temperature profiles in the unstable atmospheric surface layer, J. Appl.
Meteorol., 9, 857–861,
https://doi.org/10.1175/1520-0450(1970)009<0857:TMROWS>2.0.CO;2, 1970.
Pielke Sr, R. A.: Climate vulnerability: understanding and addressing
threats to essential resources, Elsevier, 2013.
Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann,
G., Nakicenovic, N., and Rafaj, P.: RCP 8.5 – A scenario of comparatively
high greenhouse gas emissions, Clim. Chang., 109, 33–57,
https://doi.org/10.1007/s10584-011-0149-y, 2011.
Robock, A., Marquardt, A., Kravitz, B., and Stenchikov, G.: Benefits, risks,
and costs of stratospheric geoengineering, Geophys. Res. Lett., 36, L19703,
https://doi.org/10.1029/2009GL039209, 2009.
Salvi, K., Kannan, S., and Ghosh, S.: Statistical downscaling and
bias-correction for projections of Indian rainfall and temperature in climate change studies, In 4th International Conference on Environmental and Computer Science, 19, 16–18 pp., IACSIT Press, Singapore, 2011.
Schmidt, H., Alterskjær, K., Bou Karam, D., Boucher, O., Jones, A., Kristjánsson, J. E., Niemeier, U., Schulz, M., Aaheim, A., Benduhn, F., Lawrence, M., and Timmreck, C.: Solar irradiance reduction to counteract radiative forcing from a quadrupling of CO2: climate responses simulated by four earth system models, Earth Syst. Dynam., 3, 63–78, https://doi.org/10.5194/esd-3-63-2012, 2012.
Schoof, J. T., Pryor, S. C., and Ford, T. W.: Projected changes in united
states regional extreme heat days derived from bivariate quantile mapping of
cmip5 simulations, J. Geophys. Res.-Atmos., 124, 5214–5232,
https://doi.org/10.1029/2018JD029599, 2019.
Shepherd, J.: Geoengineering the climate: Science, governance, and
uncertainty, Royal Society Policy document 10/09, 82 pp., 2009.
Taylor K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192, 2001.
Thomson, A. M., Calvin, K. V., Smith, S. J., Kyle, G. P., Volke, A., Patel,
P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M. A., Clarke, L. E., and
Edmonds, J. A.: RCP4.5: a pathway for stabilization of radiative forcing by
2100, Climatic Change, 109, 77, https://doi.org/10.1007/s10584-011-0151-4,
2011.
Tilmes, S., Fasullo, J., Lamarque, J. F., Marsh, D. R., Mills, M.,
Alterskjaer, K., Muri, H., Kristjánsson, J. E., Boucher, O., Schulz, M.,
Cole, J. N. S., Curry, C. L., Jones, A., Haywood, J., Irvine, P. J., Ji, D.,
Moore, J. C., Karam, D. B., Kravitz, B., Rasch, P. J., Singh, B., Yoon, J. H.,
Niemeier, U., Schmidt, H., Robock, A., Yang, S. and Watanabe, S.: The
hydrological impact of geoengineering in the Geoengineering Model
Intercomparison Project (GeoMIP), J. Geophys. Res.-Atmos., 118,
11036–11058, https://doi.org/10.1002/jgrd.50868, 2013.
Vandyck, T., Keramidas, K., Saveyn, B., Kitous, A., and Vrontisi, Z.: A
global stocktake of the Paris pledges: implications for energy systems and
economy, Global Environmental Change, 41, 46–63,
https://doi.org/10.1016/j.gloenvcha.2016.08.006, 2016.
Wang, J., Feng, J., Yan, Z., Hu, Y., and Jia, G.: Nested high-resolution
modeling of the impact of urbanization on regional climate in three vast
urban agglomerations in China, J. Geophys. Res.-Atmos., 117, D21103,
https://doi.org/10.1029/2012JD018226, 2012.
Watanabe, S., Hajima, T., Sudo, K., Nagashima, T., Takemura, T., Okajima, H., Nozawa, T., Kawase, H., Abe, M., Yokohata, T., Ise, T., Sato, H., Kato, E., Takata, K., Emori, S., and Kawamiya, M.: MIROC-ESM 2010: model description and basic results of CMIP5-20c3m experiments, Geosci. Model Dev., 4, 845–872, https://doi.org/10.5194/gmd-4-845-2011, 2011.
WCRP: Coupled Model Intercomparison Project (Phase 6), CMIP6, LLNL [data set], https://esgf-node.llnl.gov/projects/cmip6, last access: 14 July 2021.
Wilby, R. L. and Dawson, C. W.: Using SDSM version 3.1 – A decision support
tool for the assessment of regional climate change impacts, User manual, 8,
2004.
Wilcke, R. A. I., Mendlik, T., and Gobiet, A.: Multi-variable error
correction of regional climate models, Clim. Chang., 120, 871–887,
https://doi.org/10.1007/s10584-013-0845-x, 2013.
Xu, Z. and Yang, Z. L.: An improved dynamical downscaling method with GCM
bias corrections and its validation with 30 years of climate simulations, J.
Clim., 25, 6271–6286, https://doi.org/10.1175/JCLI-D-12-00005.1, 2012.
Yang, P., Ren, G., and Hou, W.: Temporal–spatial patterns of relative
humidity and the urban dryness island effect in Beijing City, J. Appl.
Meteorol., 56, 2221–2237, https://doi.org/10.1175/JAMC-D-16-0338.1, 2017.
Yu, J., Zhou, T., Jiang, Z., and Zou, L.: Evaluation of Near-surface Wind
Speed Changes during 1979 to 2011 over China Based on Five Reanalysis
Datasets, Atmosphere, 10, 804, https://doi.org/10.3390/atmos10120804, 2019.
Zha, J., Wu, J., Zhao, D., and Fan, W.: Future projections of the
near-surface wind speed over eastern China based on CMIP5 datasets, Clim.
Dynam., 54, 2361–2385, 2020.
Zhang, G., Azorin-Molina, C., Wang, X., Chen, D., McVicar, T., Guijarro, J.,
Chappell, A., Deng, K., Minola, L., Kong, F., Wang, S., and Shi, P.: Rapid
urbanization induced daily maximum wind speed decline in metropolitan areas:
A case study in the Yangtze River Delta (China), Urban Climate, 43, 101147,
https://doi.org/10.1016/j.uclim.2022.101147, 2022.
Zhang, J., Zhao, T., Li, Z., Li, C., Li, Z., Ying, K., Shi, C., Jiang, L.,
and Zhang, W.: Evaluation of Surface Relative Humidity in China from the
CRA-40 and Current Reanalyses, Adv. Atmos. Sci., 38, 1958–1976, 2021.
Zhao, T., Bennett, J. C., Wang, Q. J., Schepen, A., Wood, A. W., Robertson,
D. E., and Ramos, M. H.: How suitable is quantile mapping for postprocessing
GCM precipitation forecasts?, J. Clim., 30, 3185–3196,
https://doi.org/10.1175/JCLI-D-16-0652.1, 2017.
Short summary
We examine how geoengineering using aerosols in the atmosphere might impact urban climate in the greater Beijing region containing over 50 million people. Climate models have too coarse resolutions to resolve regional variations well, so we compare two workarounds for this – an expensive physical model and a cheaper statistical method. The statistical method generally gives a reasonable representation of climate and has limited resolution and a different seasonality from the physical model.
We examine how geoengineering using aerosols in the atmosphere might impact urban climate in the...
Altmetrics
Final-revised paper
Preprint