Articles | Volume 14, issue 5
https://doi.org/10.5194/esd-14-989-2023
© Author(s) 2023. 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-14-989-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Changes in apparent temperature and PM2.5 around the Beijing–Tianjin megalopolis under greenhouse gas and stratospheric aerosol intervention scenarios
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
John C. Moore
CORRESPONDING AUTHOR
Arctic Center, University of Lapland, Rovaniemi, Finland
Liyun Zhao
CORRESPONDING AUTHOR
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Related authors
Jun Wang, John C. Moore, Liyun Zhao, Chao Yue, and Zhenhua Di
Earth Syst. Dynam., 13, 1625–1640, https://doi.org/10.5194/esd-13-1625-2022, https://doi.org/10.5194/esd-13-1625-2022, 2022
Short summary
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.
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.
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.
Jun Wang, John C. Moore, Liyun Zhao, Chao Yue, and Zhenhua Di
Earth Syst. Dynam., 13, 1625–1640, https://doi.org/10.5194/esd-13-1625-2022, https://doi.org/10.5194/esd-13-1625-2022, 2022
Short summary
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.
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.
Xiaoran Guo, Liyun Zhao, Rupert M. Gladstone, Sainan Sun, and John C. Moore
The Cryosphere, 13, 3139–3153, https://doi.org/10.5194/tc-13-3139-2019, https://doi.org/10.5194/tc-13-3139-2019, 2019
Rupert M. Gladstone, Yuwei Xia, and John Moore
The Cryosphere, 12, 3605–3615, https://doi.org/10.5194/tc-12-3605-2018, https://doi.org/10.5194/tc-12-3605-2018, 2018
Short summary
Short summary
Computer models for the simulation of marine ice sheets (ice sheets resting on bedrock below sea level) historically show poor numerical convergence for grounding line (the boundary between grounded and floating parts of the ice sheet) movement. We have further characterised the nature of the numerical problems leading to poor convergence and highlighted implications for the design of computer experiments that test grounding line movement.
Liren Wei, Duoying Ji, Chiyuan Miao, Helene Muri, and John C. Moore
Atmos. Chem. Phys., 18, 16033–16050, https://doi.org/10.5194/acp-18-16033-2018, https://doi.org/10.5194/acp-18-16033-2018, 2018
Short summary
Short summary
We analyzed streamflow and flood frequency under the stratospheric aerosol geoengineering scenario simulated by climate models. Stratospheric aerosol geoengineering appears to reduce flood risk in most regions, but the overall effects are largely determined by the large-scale geographic pattern. Over the Amazon, stratospheric aerosol geoengineering ameliorates the drying trend here under a future warming climate.
Michael J. Wolovick and John C. Moore
The Cryosphere, 12, 2955–2967, https://doi.org/10.5194/tc-12-2955-2018, https://doi.org/10.5194/tc-12-2955-2018, 2018
Short summary
Short summary
In this paper, we explore the possibility of using locally targeted geoengineering to slow the rate of an ice sheet collapse. We find that an intervention as big as existing large civil engineering projects could have a 30 % probability of stopping an ice sheet collapse, while larger interventions have better odds of success. With more research to improve upon the simple designs we considered, it may be possible to perfect a design that was both achievable and had good odds of success.
Ben Kravitz, Philip J. Rasch, Hailong Wang, Alan Robock, Corey Gabriel, Olivier Boucher, Jason N. S. Cole, Jim Haywood, Duoying Ji, Andy Jones, Andrew Lenton, John C. Moore, Helene Muri, Ulrike Niemeier, Steven Phipps, Hauke Schmidt, Shingo Watanabe, Shuting Yang, and Jin-Ho Yoon
Atmos. Chem. Phys., 18, 13097–13113, https://doi.org/10.5194/acp-18-13097-2018, https://doi.org/10.5194/acp-18-13097-2018, 2018
Short summary
Short summary
Marine cloud brightening has been proposed as a means of geoengineering/climate intervention, or deliberately altering the climate system to offset anthropogenic climate change. In idealized simulations that highlight contrasts between land and ocean, we find that the globe warms, including the ocean due to transport of heat from land. This study reinforces that no net energy input into the Earth system does not mean that temperature will necessarily remain unchanged.
Peter J. Irvine, David W. Keith, and John Moore
The Cryosphere, 12, 2501–2513, https://doi.org/10.5194/tc-12-2501-2018, https://doi.org/10.5194/tc-12-2501-2018, 2018
Short summary
Short summary
Stratospheric aerosol geoengineering, a form of solar geoengineering, is a proposal to add a reflective layer of aerosol to the upper atmosphere. This would reduce sea level rise by slowing the melting of ice on land and the thermal expansion of the oceans. However, there is considerable uncertainty about its potential efficacy. This article highlights key uncertainties in the sea level response to solar geoengineering and recommends approaches to address these in future work.
Duoying Ji, Songsong Fang, Charles L. Curry, Hiroki Kashimura, Shingo Watanabe, Jason N. S. Cole, Andrew Lenton, Helene Muri, Ben Kravitz, and John C. Moore
Atmos. Chem. Phys., 18, 10133–10156, https://doi.org/10.5194/acp-18-10133-2018, https://doi.org/10.5194/acp-18-10133-2018, 2018
Short summary
Short summary
We examine extreme temperature and precipitation under climate-model-simulated solar dimming and stratospheric aerosol injection geoengineering schemes. Both types of geoengineering lead to lower minimum temperatures at higher latitudes and greater cooling of minimum temperatures and maximum temperatures over land compared with oceans. Stratospheric aerosol injection is more effective in reducing tropical extreme precipitation, while solar dimming is more effective over extra-tropical regions.
Qin Wang, John C. Moore, and Duoying Ji
Atmos. Chem. Phys., 18, 9173–9188, https://doi.org/10.5194/acp-18-9173-2018, https://doi.org/10.5194/acp-18-9173-2018, 2018
Short summary
Short summary
(1) Genesis potential and ventilation indices are assessed in 6 ESMs running RCP4.5 and G4, in 6 tropical cyclone genesis basins.
(2) Genesis potential is reasonably well parameterized by simple surface temperature, but other factors are important in different basins and models such as relative humidity and wind shear.
(3) The Northern Hemisphere basins behave rather differently from the southern ones, and these dominate TC statistics. G4 leads to significantly fewer TCs globally than RCP4.5.
Anboyu Guo, John C. Moore, and Duoying Ji
Atmos. Chem. Phys., 18, 8689–8706, https://doi.org/10.5194/acp-18-8689-2018, https://doi.org/10.5194/acp-18-8689-2018, 2018
Short summary
Short summary
This is an examination of both the zonal and meridional tropical circulations under G1 geoengineering using eight ESMs. Drivers of the changes are examined, with meridional temperature gradient being the dominant factor. The Hadley circulation is changed under G1 differently for each hemisphere, but changes are small compared with abrupt4xCO2. Changes in the Walker circulation are subtle but potentially important in some regions, and ENSO impacts circulations only slightly differently under G1.
Liyun Zhao, John C. Moore, Bo Sun, Xueyuan Tang, and Xiaoran Guo
The Cryosphere, 12, 1651–1663, https://doi.org/10.5194/tc-12-1651-2018, https://doi.org/10.5194/tc-12-1651-2018, 2018
Short summary
Short summary
We investigate the age–depth profile to be expected of the ongoing deep ice coring at Kunlun station, Dome A, using the depth-varying anisotropic fabric suggested by the recent polarimetric measurements in a three-dimensional, thermo-mechanically coupled full-Stokes model. The model results suggest that the age of the deep ice at Kunlun is 649–831 ka, and there are large regions where 1-million-year-old ice may be found 200 m above the bedrock within 5–6 km of the Kunlun station.
Yongmei Gong, Thomas Zwinger, Jan Åström, Bas Altena, Thomas Schellenberger, Rupert Gladstone, and John C. Moore
The Cryosphere, 12, 1563–1577, https://doi.org/10.5194/tc-12-1563-2018, https://doi.org/10.5194/tc-12-1563-2018, 2018
Short summary
Short summary
In this study we apply a discrete element model capable of simulating ice fracturing. A microscopic-scale discrete process is applied in addition to a continuum ice dynamics model to investigate the mechanisms facilitated by basal meltwater production, surface meltwater and ice crack opening, for the surge in Basin 3, Austfonna ice cap. The discrete element model is used to locate the ice cracks that can penetrate though the full thickness of the glacier and deliver surface water to the bed.
Camilla W. Stjern, Helene Muri, Lars Ahlm, Olivier Boucher, Jason N. S. Cole, Duoying Ji, Andy Jones, Jim Haywood, Ben Kravitz, Andrew Lenton, John C. Moore, Ulrike Niemeier, Steven J. Phipps, Hauke Schmidt, Shingo Watanabe, and Jón Egill Kristjánsson
Atmos. Chem. Phys., 18, 621–634, https://doi.org/10.5194/acp-18-621-2018, https://doi.org/10.5194/acp-18-621-2018, 2018
Short summary
Short summary
Marine cloud brightening (MCB) has been proposed to help limit global warming. We present here the first multi-model assessment of idealized MCB simulations from the Geoengineering Model Intercomparison Project. While all models predict a global cooling as intended, there is considerable spread between the models both in terms of radiative forcing and the climate response, largely linked to the substantial differences in the models' representation of clouds.
Sainan Sun, Stephen L. Cornford, John C. Moore, Rupert Gladstone, and Liyun Zhao
The Cryosphere, 11, 2543–2554, https://doi.org/10.5194/tc-11-2543-2017, https://doi.org/10.5194/tc-11-2543-2017, 2017
Short summary
Short summary
The buttressing effect of the floating ice shelves is diminished by the fracture process. We developed a continuum damage mechanics model component of the ice sheet model to simulate the process. The model is tested on an ideal marine ice sheet geometry. We find that behavior of the simulated marine ice sheet is sensitive to fracture processes on the ice shelf, and the stiffness of ice around the grounding line is essential to ice sheet evolution.
Liyun Zhao, Yi Yang, Wei Cheng, Duoying Ji, and John C. Moore
Atmos. Chem. Phys., 17, 6547–6564, https://doi.org/10.5194/acp-17-6547-2017, https://doi.org/10.5194/acp-17-6547-2017, 2017
Short summary
Short summary
We find stratospheric sulfate aerosol injection geoengineering, G3, can slow shrinkage of high-mountain Asia glaciers by about 50 % by 2069 relative to losses from RCP8.5. The reduction in mean precipitation expected for solar geoengineering is less important than the temperature-driven shift from solid to liquid precipitation for forcing Himalayan glacier change. The termination of geoengineering in 2069 leads to temperature rise of 1.3 °C and corresponding increase in glacier volume loss rate.
Hiroki Kashimura, Manabu Abe, Shingo Watanabe, Takashi Sekiya, Duoying Ji, John C. Moore, Jason N. S. Cole, and Ben Kravitz
Atmos. Chem. Phys., 17, 3339–3356, https://doi.org/10.5194/acp-17-3339-2017, https://doi.org/10.5194/acp-17-3339-2017, 2017
Short summary
Short summary
This study analyses shortwave radiation (SW) in the G4 experiment of the Geoengineering Model Intercomparison Project. G4 involves stratospheric injection of 5 Tg yr−1 of SO2 against the RCP4.5 scenario. The global mean forcing of the sulphate geoengineering has an inter-model variablity of −3.6 to −1.6 W m−2, implying a high uncertainty in modelled processes of sulfate aerosols. Changes in water vapour and cloud amounts due to the SO2 injection weaken the forcing at the surface by around 50 %.
Wenli Wang, Annette Rinke, John C. Moore, Duoying Ji, Xuefeng Cui, Shushi Peng, David M. Lawrence, A. David McGuire, Eleanor J. Burke, Xiaodong Chen, Bertrand Decharme, Charles Koven, Andrew MacDougall, Kazuyuki Saito, Wenxin Zhang, Ramdane Alkama, Theodore J. Bohn, Philippe Ciais, Christine Delire, Isabelle Gouttevin, Tomohiro Hajima, Gerhard Krinner, Dennis P. Lettenmaier, Paul A. Miller, Benjamin Smith, Tetsuo Sueyoshi, and Artem B. Sherstiukov
The Cryosphere, 10, 1721–1737, https://doi.org/10.5194/tc-10-1721-2016, https://doi.org/10.5194/tc-10-1721-2016, 2016
Short summary
Short summary
The winter snow insulation is a key process for air–soil temperature coupling and is relevant for permafrost simulations. Differences in simulated air–soil temperature relationships and their modulation by climate conditions are found to be related to the snow model physics. Generally, models with better performance apply multilayer snow schemes.
W. Wang, A. Rinke, J. C. Moore, X. Cui, D. Ji, Q. Li, N. Zhang, C. Wang, S. Zhang, D. M. Lawrence, A. D. McGuire, W. Zhang, C. Delire, C. Koven, K. Saito, A. MacDougall, E. Burke, and B. Decharme
The Cryosphere, 10, 287–306, https://doi.org/10.5194/tc-10-287-2016, https://doi.org/10.5194/tc-10-287-2016, 2016
Short summary
Short summary
We use a model-ensemble approach for simulating permafrost on the Tibetan Plateau. We identify the uncertainties across models (state-of-the-art land surface models) and across methods (most commonly used methods to define permafrost).
We differentiate between uncertainties stemming from climatic driving data or from physical process parameterization, and show how these uncertainties vary seasonally and inter-annually, and how estimates are subject to the definition of permafrost used.
We differentiate between uncertainties stemming from climatic driving data or from physical process parameterization, and show how these uncertainties vary seasonally and inter-annually, and how estimates are subject to the definition of permafrost used.
S. Peng, P. Ciais, G. Krinner, T. Wang, I. Gouttevin, A. D. McGuire, D. Lawrence, E. Burke, X. Chen, B. Decharme, C. Koven, A. MacDougall, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, C. Delire, T. Hajima, D. Ji, D. P. Lettenmaier, P. A. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
The Cryosphere, 10, 179–192, https://doi.org/10.5194/tc-10-179-2016, https://doi.org/10.5194/tc-10-179-2016, 2016
Short summary
Short summary
Soil temperature change is a key indicator of the dynamics of permafrost. Using nine process-based ecosystem models with permafrost processes, a large spread of soil temperature trends across the models. Air temperature and longwave downward radiation are the main drivers of soil temperature trends. Based on an emerging observation constraint method, the total boreal near-surface permafrost area decrease comprised between 39 ± 14 × 103 and 75 ± 14 × 103 km2 yr−1 from 1960 to 2000.
B. Kravitz, A. Robock, S. Tilmes, O. Boucher, J. M. English, P. J. Irvine, A. Jones, M. G. Lawrence, M. MacCracken, H. Muri, J. C. Moore, U. Niemeier, S. J. Phipps, J. Sillmann, T. Storelvmo, H. Wang, and S. Watanabe
Geosci. Model Dev., 8, 3379–3392, https://doi.org/10.5194/gmd-8-3379-2015, https://doi.org/10.5194/gmd-8-3379-2015, 2015
T. Zwinger, T. Malm, M. Schäfer, R. Stenberg, and J. C. Moore
The Cryosphere, 9, 1415–1426, https://doi.org/10.5194/tc-9-1415-2015, https://doi.org/10.5194/tc-9-1415-2015, 2015
Short summary
Short summary
By deploying a large-scale high-resolution turbulent CFD simulation using the present-day topography of the Scharffenbergbotnen (SBB) valley, we show how the surrounding topography redirects incoming easterly katabatic storm fronts to impact the blue ice areas (BIA) inside the valley, where the snow cover frequently is removed. A further simulation of a reconstructed topography at the Late Glacial Maximum further reveals that the BIA at SBB must have formed after this period.
M. A. Rawlins, A. D. McGuire, J. S. Kimball, P. Dass, D. Lawrence, E. Burke, X. Chen, C. Delire, C. Koven, A. MacDougall, S. Peng, A. Rinke, K. Saito, W. Zhang, R. Alkama, T. J. Bohn, P. Ciais, B. Decharme, I. Gouttevin, T. Hajima, D. Ji, G. Krinner, D. P. Lettenmaier, P. Miller, J. C. Moore, B. Smith, and T. Sueyoshi
Biogeosciences, 12, 4385–4405, https://doi.org/10.5194/bg-12-4385-2015, https://doi.org/10.5194/bg-12-4385-2015, 2015
Short summary
Short summary
We used outputs from nine models to better understand land-atmosphere CO2 exchanges across Northern Eurasia over the period 1960-1990. Model estimates were assessed against independent ground and satellite measurements. We find that the models show a weakening of the CO2 sink over time; the models tend to overestimate respiration, causing an underestimate in NEP; the model range in regional NEP is twice the multimodel mean. Residence time for soil carbon decreased, amid a gain in carbon storage.
D. Ji, L. Wang, J. Feng, Q. Wu, H. Cheng, Q. Zhang, J. Yang, W. Dong, Y. Dai, D. Gong, R.-H. Zhang, X. Wang, J. Liu, J. C. Moore, D. Chen, and M. Zhou
Geosci. Model Dev., 7, 2039–2064, https://doi.org/10.5194/gmd-7-2039-2014, https://doi.org/10.5194/gmd-7-2039-2014, 2014
S. Sun, S. L. Cornford, Y. Liu, and J. C. Moore
The Cryosphere, 8, 1561–1576, https://doi.org/10.5194/tc-8-1561-2014, https://doi.org/10.5194/tc-8-1561-2014, 2014
R. Gladstone, M. Schäfer, T. Zwinger, Y. Gong, T. Strozzi, R. Mottram, F. Boberg, and J. C. Moore
The Cryosphere, 8, 1393–1405, https://doi.org/10.5194/tc-8-1393-2014, https://doi.org/10.5194/tc-8-1393-2014, 2014
B. Sun, J. C. Moore, T. Zwinger, L. Zhao, D. Steinhage, X. Tang, D. Zhang, X. Cui, and C. Martín
The Cryosphere, 8, 1121–1128, https://doi.org/10.5194/tc-8-1121-2014, https://doi.org/10.5194/tc-8-1121-2014, 2014
T. Zwinger, M. Schäfer, C. Martín, and J. C. Moore
The Cryosphere, 8, 607–621, https://doi.org/10.5194/tc-8-607-2014, https://doi.org/10.5194/tc-8-607-2014, 2014
J. A. Åström, T. I. Riikilä, T. Tallinen, T. Zwinger, D. Benn, J. C. Moore, and J. Timonen
The Cryosphere, 7, 1591–1602, https://doi.org/10.5194/tc-7-1591-2013, https://doi.org/10.5194/tc-7-1591-2013, 2013
L. Zhao, L. Tian, T. Zwinger, R. Ding, J. Zong, Q. Ye, and J. C. Moore
The Cryosphere Discuss., https://doi.org/10.5194/tcd-7-145-2013, https://doi.org/10.5194/tcd-7-145-2013, 2013
Revised manuscript not accepted
Z. Zhang and J. C. Moore
Ann. Geophys., 30, 1743–1750, https://doi.org/10.5194/angeo-30-1743-2012, https://doi.org/10.5194/angeo-30-1743-2012, 2012
Cited articles
Burnett, R., Pope III, C., Ezzati, M., Olives, C., Lim, S., Mehta, S., Shin,
H., Singh, G., Hubbell, B., Brauer, M., Anderson, A., Smith, K., Balmes, J.,
Bruce, N., Kan, H., Laden, F., Prüss-Ustün, A., Turner, M., Gapstur,
S., Diver, W., and Cohen, A.: An Integrated Risk Function for Estimating the
Global Burden of Disease Attributable to Ambient Fine Particulate Matter
Exposure, Environ., Health Perspect., 122, 397–403,
https://doi.org/10.1289/ehp.1307049, 2014.
Bala, G., Duffy, P. B., and Taylor, K. E.: Impact of geoengineering schemes on
the global hydrological cycle, P. Natl. Acad. Sci. USA, 105,
7664–7669, https://doi.org/10.1073/pnas.0711648105, 2008.
Chen, Z., Cai, J., Gao, B., Xu, B., Dai, S., He, B., and Xie, X.: Detecting the
causality influence of individual meteorological factors on local PM2.5
concentrations in the Jing-Jin-Ji region, Sci. Rep., 7, 40735,
https://doi.org/10.1038/srep40735, 2017.
Chen, Z., Xie, X., Cai, J., Chen, D., Gao, B., He, B., Cheng, N., and Xu, B.: Understanding meteorological influences on PM2.5 concentrations across China: a temporal and spatial perspective, Atmos. Chem. Phys., 18, 5343–5358, https://doi.org/10.5194/acp-18-5343-2018, 2018.
Chen, Z., Chen, D., Kwan, M.-P., Chen, B., Gao, B., Zhuang, Y., Li, R., and Xu, B.: The control of anthropogenic emissions contributed to 80 % of the decrease in PM2.5 concentrations in Beijing from 2013 to 2017, Atmos. Chem. Phys., 19, 13519–13533, https://doi.org/10.5194/acp-19-13519-2019, 2019.
Chen, Z., Chen, D., Zhao, C., Kwan, M., Cai, J., Zhuang, Y., Zhao, B., Wang,
X., Chen, B., Yang, J., Li, R., He, B., Gao, B., Wang, K., and Xu, B.:
Influence of meteorological conditions on PM2.5 concentrations across
China: A review of methodology and mechanism, Environ. Int., 139, 105558,
https://doi.org/10.1016/j.envint.2020.105558, 2020.
Cheng, L., Meng, F., Chen, L., Jiang, T., and Su, L.: Effects on the haze
pollution from autumn crop residue burning over the Jing-Jin-Ji Region,
China Environ. Sci., 37, 2801–2812, 2017.
Chi, X., Li, R., Cubasch, U., and Cao, W.: The thermal comfort and its changes
in the 31 provincial capital cities of mainland China in the past 30 years,
Theor. Appl. Climatol., 132, 599–619, 2018.
Chuang, M., Chou, C., Lin, N., Takami, A., Hsiao, T., Lin, T., Fu, J., Pani,
S., Lu, Y., and Yang, T.: A simulation study on PM2.5 sources and
meteorological characteristics at the northern tip of Taiwan in the early
stage of the Asian haze period, Aerosol Air Qual. Res., 17, 3166–3178,
https://doi.org/10.4209/aaqr.2017.05.0185, 2017.
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.
Dimri, A. P., Kumar, D., Choudhary, A., Maharana, P.: Future changes over
the Himalayas: Maximum and minimum temperature, Global Planet. Change,
162, 212–234, https://doi.org/10.1016/j.gloplacha.2018.01.015, 2018.
Dou, C., Ji, Z., Xiao, Y., Zhu, X., and Dong, W.: Projections of air
pollution in northern China in the two RCPs scenarios, Remote Sens., 13,
3064, https://doi.org/10.3390/rs13163064, 2021.
Eastham, D., Weisenstein, D., Keith, D., and Barrett, A.: Quantifying the
impact of sulfate geoengineering on mortality from air quality and UV-B
exposure, Atmos. Environ., 187, 424–434,
https://doi.org/10.1016/j.atmosenv.2018.05.047, 2018.
Fan, M., Zhang, Y., Lin, Y., Cao, F., Sun, Y., Qiu, Y., Xing, G., Dao, X.,
and Fu, P.: Specific sources of health risks induced by metallic elements in
PM2.5 during the wintertime in Beijing, China, Atmos. Environ., 246,
118112, https://doi.org/10.1016/j.atmosenv.2020.118112, 2021.
Fischer, E. and Knutti, R.: Robust projections of combined humidity and
temperature extremes, Nat. Clim. Change, 3, 126–130,
https://doi.org/10.1038/nclimate1682, 2013.
Foley, K. M., Roselle, S. J., Appel, K. W., Bhave, P. V., Pleim, J. E., Otte, T. L., Mathur, R., Sarwar, G., Young, J. O., Gilliam, R. C., Nolte, C. G., Kelly, J. T., Gilliland, A. B., and Bash, J. O.: Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7, Geosci. Model Dev., 3, 205–226, https://doi.org/10.5194/gmd-3-205-2010, 2010.
Fu, J., Jiang, D., and Huang, Y.: 1 km Grid Population Dataset of China, Digital
Journal of Global Change Data Repository,
https://doi.org/10.3974/geodb.2014.01.06.V1, 2014.
Garcia, F. C., Bestion, E., Warfield, R., and Yvon-Durocher, G.: Changes in
temperature alter the relationship between biodiversity and ecosystem
functioning, P. Natl. Acad. Sci. USA, 115, 10989–10999,
https://doi.org/10.1073/pnas.1805518115, 2018.
Grinsted, A., Moore, J., and Jevrejeva, S.: Projected Atlantic tropical
cyclone threat from rising temperatures, P. Natl. Acad. Sci. USA, 110, 5369–5373,
https://doi/10.1073/pnas.1209980110, 2013.
Grundstein, A. and Dowd, J.: Trends in extreme apparent temperatures over
the United States, 1949–2010, J. Appl, Meteorol. Clim., 50,
1650–1653, https://doi.org/10.1175/JAMC-D-11-063.1, 2011.
Guan, W., Zheng, X., Chung, K., and Zhong, N.: Impact of air pollution on
the burden of chronic respiratory diseases in China: time for urgent action,
Lancet, 388, 1939–1951, https://doi.org/10.1016/S0140-6736(16)31597-5, 2016.
Guo, L., Zhang, Y., Lin, H., Zeng, W., Liu, T., Xiao, J., Rutherford, S.,
You, J., and Ma, W.: The washout effects of rainfall on atmospheric particulate
pollution in two Chinese cities, Environ. Pollut., 215, 195–202,
https://doi.org/10.1016/j.envpol.2016.05.003, 2016.
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),
https://doi.org/10.24381/cds.bd0915c6, 2018.
Ho, H. C., Knudby, A., Xu, Y., Hodul, M., and Aminipouri, M.: A comparison of
urban heat islands mapped using skin temperature, air temperature, and
apparent temperature (Humidex), for the greater Vancouver area, Sci. Total Environ., 544, 929–938,
https://doi.org/10.1016/j.scitotenv.2015.12.021, 2016.
Hong, C., Zhang, Q., Zhang, Y., Davis, S., Tong, D., Zheng, Y., Liu, Z.,
Guan, D., He, K., and Schellnhuber, H. J.: Impacts of climate change on
future air quality and human health in China, P. Natl. Acad. Sci. USA, 116, 17193–17200,
https://doi.org/10.1073/pnas.1812881116, 2019.
Huang, J., Li, Q., and Song, Z.: Historical global land surface air apparent
temperature and its future changes based on CMIP6 projections, Sci. Total Environ., 816, 151656,
https://doi.org/10.1016/j.scitotenv.2021.151656, 2021.
IPCC: Climate change 2021: the physical science basis, in: Contribution of Working Group I to the
Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 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., Waterfifield, T.,
Yelekçi, O., Yu, R., and Zhou, B.,
Cambridge University Press, https://doi.org/10.1017/9781009157896, 2021.
Jacobs, S. J., Pezza, A. B., Barras, V., Bye, J., and Vihma, T.: An analysis of
the meteorological variables leading to apparent temperature in Australia:
present climate, trends, and global warming simulations, Global Planet. Change, 107, 145–156, 2013.
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi, B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution, Atmos. Chem. Phys., 15, 11411–11432, https://doi.org/10.5194/acp-15-11411-2015, 2015.
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.
Jin, H., Chen, X., Zhong, R., and Liu, M.: Influence and prediction of
PM2.5 through multiple environmental variables in China, Sci. Total
Environ., 849, 157910, https://doi.org/10.1016/j.scitotenv.2022.157910, 2022.
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's Future, 6, 230–251,
https://doi.org/10.1002/2017EF000720, 2018.
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.
Klimont, Z., Kupiainen, K., Heyes, C., Purohit, P., Cofala, J., Rafaj, P.,
Borken-Kleefeld, J., and Schöpp, W.: Global anthropogenic emissions of
particulate matter including black carbon, Atmos. Chem. Phys., 17,
8681–8723, https://doi.org/10.5194/acp-17-8681-2017, 2017.
Kong, Q. and Huber, M.: Explicit calculations of wet-bulb globe temperature compared with
approximations and why it matters for labor productivity, Earth's
Future, 10, e2021EF002334, https://doi.org/10.1029/2021EF002334,
2022.
Kraaijenbrink, P. D. A., Bierkens, M. F. P., Lutz, A. F., and Immerzeel, W.
W.: Impact of a global temperature rise of 1.5 degrees Celsius on Asia's
glaciers, Nature, 549, 257–260, https://doi.org/10.1038/nature23878, 2017.
Kravitz, B., MacMartin, D., and Caldeira, K.: Geoengineering: Whiter skies?,
Geophys. Res. Lett., 39, L11801,
https://doi.org/10.1029/2012GL051652, 2012.
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.
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, C. and Sheridan, S.: A new approach to modeling temperature-related
mortality: non-linear autoregressive models with exogenous input, Environ.
Res., 164, 53–64, https://doi.org/10.1016/j.envres.2018.02.020, 2018.
Li, D., Wu, Q., Feng, J., Wang, Y., Wang, L., Xu, Q., Sun, Y., Cao, K., and
Cheng, H.: The influence of anthropogenic emissions on air quality in
Beijing-Tianjin-Hebei of China around 2050 under the future climate
scenario, J. Cleaner Prod., 388, 135927,
https://doi.org/10.1016/j.jclepro.2023.135927, 2023.
Li, J., Chen, H., Li, Z., Wang, P., Cribb, M., and Fan, X.: Low-level
temperature inversions and their effect on aerosol condensation nuclei
concentrations under different large-scale synoptic circulations, Adv.
Atmos. Sci., 32, 898–908, https://doi.org/10.1007/s00376-014-4150-z, 2015.
Li, J., Chen, Y., Gan, T., and Lau, N.: Elevated increases in human-perceived
temperature under climate warming, Nat. Clim. Chang., 8, 43–47,
https://doi.org/10.1038/s41558-017-0036-2, 2018.
Li, K., Liao, H., Zhu, J., and Moch, J.: Implications of RCP emissions on
future PM2.5 air quality and direct radiative forcing over China, J.
Geophys. Res.-Atmos., 121, 12985–13008, https://doi.org/10.1002/2016JD025623, 2016.
Li, M., Klimont, Z., Zhang, Q., Martin, R. V., Zheng, B., Heyes, C., Cofala, J., Zhang, Y., and He, K.: Comparison and evaluation of anthropogenic emissions of SO2 and NOx over China, Atmos. Chem. Phys., 18, 3433–3456, https://doi.org/10.5194/acp-18-3433-2018, 2018.
Liao, T., Wang, S., Ai, J., Gui, K., Duan, B., Zhao, Q., Zhang, X., Jiang,
W., and Sun, Y.: Heavy pollution episodes, transport pathways and potential
sources of PM2.5 during the winter of 2013 in Chengdu (China), Sci.
Total Environ., 584, 1056–1065,
https://doi.org/10.1016/j.scitotenv.2017.01.160, 2017.
Lo, J. C. F., Lau, A. K. H., Fung, J. C. H., and Chen, F.: Investigation of enhanced cross-city transport and trapping of air pollutants by coastal and urban land-sea breeze circulations, J. Geophys. Res., 111, D14104, https://doi.org/10.1029/2005JD006837, 2006.
Luo, M. and Lau, N.-C.: Characteristics of summer heat stress in China
during 1979-2014: Climatology and long-term trends, Clim. Dynam., 53,
5375–5388, https://doi.org/10.1007/s00382-019-04871-5, 2019.
Luo, M. and Lau, N. C.: Increasing Human-Perceived Heat Stress Risks Exacerbated by Urbanization in China: A Comparative Study Based on Multiple Metrics, Earth's Future, 9, e2020EF001848, https://doi.org/10.1029/2020ef001848, 2021.
Lyon, B. and Barnston, A.: Diverse characteristics of US summer heat waves,
J. Climate, 30, 7827–7845, https://doi.org/10.1175/JCLI-D-17-0098.1,
2017.
Maji, K., Ye, W., Arora, M., and Nagendra, S.: PM2.5-related health and
economic loss assessment for 338 Chinese cities, Environ. Int., 121,
392–403, https://doi.org/10.1016/j.envint.2018.09.024, 2018.
Matthews, T., Wilby, R., and Murphy, C.: Communicating the deadly
consequences of global warming for human heat stress, P. Natl. Acad. Sci. USA, 114, 3861–3866,
https://doi.org/10.1073/pnas.1617526114, 2017.
Miao, L., Moore, J. C., Zeng, F., Lei, J., Ding, J., He, B., and Cui, X.:
Footprint of research in desertification management in China, Land Degrad.
Dev., 26, 450–457, https://doi.org/10.1002/ldr.2399, 2015.
Mishra, D., Goyal, P., and Upadhyay, A.: Artificial intelligence based
approach to forecast PM2.5 during haze episodes: a case study of Delhi,
India, Atmos. Environ., 102, 239–248,
https://doi.org/10.1016/j.atmosenv.2014.11.050, 2015.
Murray, F.: On the computation of saturation vapor pressure, J. Appl. Meteorol. Clim., 6, 203–204, https://doi.org/10.1175/1520-0450(1967)006<0203:OTCOSV>2.0.CO;2, 1966.
Nguyen, G., Shimadera, H., Uranishi, K., Matsuo, T., and Kondo, A.:
Numerical assessment of PM2.5 and O3 air quality in Continental
Southeast Asia: Impacts of future projected anthropogenic emission change
and its impacts in combination with potential future climate change impacts,
Atmos. Environ., 226, 117398,
https://doi.org/10.1016/j.atmosenv.2020.117398, 2020.
Perkins, S. and Alexander, L.: On the measurement of heat waves, J. Climate, 26, 4500–4517, https://doi.org/10.1175/JCLI-D-12-00383.1, 2013.
Ran, Q., Lee, S., Zheng, D., Chen, H., Yang, S., Moore, J., and Dong, W.:
Potential Health and Economic Impacts of Shifting Manufacturing from China
to Indonesia or India, Sci. Total Environ., 855, 158634,
https://doi.org/10.1016/j.scitotenv.2022.158634, 2023.
Ren, J., Liu, J., Li, F., Cao, X., Ren, S., Xu, B., and Zhu, Y.: A study of ambient fine particles at Tianjin International Airport, China, Sci. Total Environ., 556, 126–135, https://doi.org/10.1016/j.scitotenv.2016.02.186, 2016.
Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann,
G., Nakicenovic, N., and Rafaj, P.: RCP8.5 – A scenario of comparatively high
greenhouse gas emissions, Clim. Change, 109, 33,
https://doi.org/10.1007/s10584-011-0149-y, 2011.
Song, F., Zhang, G., Ramanathan, V., and Ruby Leung, L.: Trends in surface equivalent potential temperature: A more comprehensive metric for global warming and weather extremes, P. Natl. Acad. Sci. USA, 119, e2117832119, https://doi.org/10.1073/pnas.2117832119, 2022.
Steadman, R. G.: A universal scale of apparent temperature, J. Appl.
Meteorol., 23, 1674–1687,
https://doi.org/10.1175/1520-0450(1984)023<1674:AUSOAT>2.0.CO;2, 1984.
Steadman, R. G.: Norms of apparent temperature in Australia, Aust. Meteorol.
Mag., 43, 1–16, 1994.
Stohl, A., Aamaas, B., Amann, M., Baker, L. H., Bellouin, N., Berntsen, T. K., Boucher, O., Cherian, R., Collins, W., Daskalakis, N., Dusinska, M., Eckhardt, S., Fuglestvedt, J. S., Harju, M., Heyes, C., Hodnebrog, Ø., Hao, J., Im, U., Kanakidou, M., Klimont, Z., Kupiainen, K., Law, K. S., Lund, M. T., Maas, R., MacIntosh, C. R., Myhre, G., Myriokefalitakis, S., Olivié, D., Quaas, J., Quennehen, B., Raut, J.-C., Rumbold, S. T., Samset, B. H., Schulz, M., Seland, Ø., Shine, K. P., Skeie, R. B., Wang, S., Yttri, K. E., and Zhu, T.: Evaluating the climate and air quality impacts of short-lived pollutants, Atmos. Chem. Phys., 15, 10529–10566, https://doi.org/10.5194/acp-15-10529-2015, 2015.
Tong, C., Yim, S., Rothenberg, D., Wang, C., Lin, C., Chen, Y., and Lau, N.:
Projecting the impacts of atmospheric conditions under climate change on air
quality over the Pearl River Delta region, Atmos. Environ., 193, 79–87,
https://doi.org/10.1016/j.atmosenv.2018.08.053, 2018.
Upadhyay, A., Dey, S., Goyal, P., and Dash, S.: Projection of near-future
anthropogenic PM2.5 over India using statistical approach, Atmos.
Environ., 186, 178–188, https://doi.org/10.1016/j.atmosenv.2018.05.025,
2018.
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 Environ. Change, 41, 46–63, https://doi.org/10.1016/j.gloenvcha.2016.08.006, 2016.
Wang, J., Allen, D., Pickering, K., Li, Z., and He, H.: Impact of aerosol direct
effect on East Asian air quality during the EAST-AIRE campaign, J. Geophys.
Res.- Atmos., 121, 6534–6554, https://doi.org/10.1002/2016JD025108, 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.
Wang, J., Zhang, L., Niu, X., and Liu, Z.: Effects of PM2.5 on health
and economic loss: Evidence from Beijing-Tianjin-Hebei region of China, J.
Cleaner Prod., 257, 120605, https://doi.org/10.1016/j.jclepro.2020.120605,
2020.
Wang, J., Moore, J. C., Zhao, L., Yue, C., and Di, Z.: Regional dynamical and statistical downscaling temperature, humidity and wind speed for the Beijing region under stratospheric aerosol injection geoengineering, Earth Syst. Dynam., 13, 1625–1640, https://doi.org/10.5194/esd-13-1625-2022, 2022.
Wang, P., Luo, M., Liao, W., Xu, Y., Wu, S., Tong, X., Tian, H., Xu, F.,
and Han, Y.: Urbanization contribution to human perceived temperature changes in
major urban agglomerations of China, Urban Climate, 38, 100910,
https://doi.org/10.1016/j.uclim.2021.100910, 2021.
Wang, S., Ancell, B., Huang, G., and Baetz, B.: Improving robustness of
hydrologic ensemble predictions through probabilistic pre- and
post-processing in sequential data assimilation, Water Resour. Res., 54, 2129–2151, https://doi.org/10.1002/2018WR022546, 2018.
Wang, X., Huang, G., Lin, Q., Nie, X., Cheng, G., Fan, Y., Li, Z., Yao, Y.,
and Suo, M.: A stepwise cluster analysis approach for downscaled climate
projection – a Canadian case study, Environ. Model Softw., 49, 141–151,
https://doi.org/10.1016/j.envsoft.2013.08.006, 2013.
Wang, Y., Zhuang, G., Zhang, X., Huang, K., Xu, C., Tang, A., Chen, J., and
An, Z.: The ion chemistry, seasonal cycle, and sources of PM2.5 and TSP
aerosol in Shanghai, Atmos. Environ., 40, 2935–2952,
https://doi.org/10.1016/j.atmosenv.2005.12.051, 2006.
Wang, Y., Yao, L., Wang, L., Liu, Z., Ji, D., Tang, G., Zhang, J., Sun, Y.,
Hu, N., and Xin, J.: Mechanism for the formation of the January 2013 heavy
haze pollution episode over central and eastern China, Sci. China Earth
Sci., 57, 14–25, https://doi.org/10.1007/s11430-013-4773-4, 2014.
Wang, Y., Chen, L., Song, Z., Huang, Z., Ge, E., Lin, L., and Luo, M.:
Human-perceived-temperature changes over South China: long-term trends and
urbanization effects, Atmos. Res., 215, 116–127,
https://doi.org/10.1016/j.atmosres.2018.09.006, 2019.
Wang, Y., Hu, J., Zhu, J., Li, J., Qin, M., Liao, H., Chen, K., and Wang,
M.: Health Burden and economic impacts attributed to PM2.5 and O3
in China from 2010 to 2050 under different representative concentration
pathway scenarios, Resour. Conserv. Recy., 173, 105731,
https://doi.org/10.1016/j.resconrec.2021.105731, 2021.
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: ESM data, WCRP [data set], https://esgf-node.llnl.gov/projects/cmip5 (last access: 14
September 2023), 2022.
Wei, J., Li, Z., Lyapustin, A., Sun, L., Peng, Y., Xue, W., Su, T., and
Cribb, M.: Reconstructing 1-km-resolution high-quality PM2.5 data
records from 2000 to 2018 in China: spatiotemporal variations and policy
implications, Remote Sens. Environ., 252, 112136,
https://doi.org/10.1016/j.rse.2020.112136, 2021.
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.
Wu, D., Tie, X., Li, C., Ying, Z., Kai-Hon Lau, A., Huang, J., Deng, X., and
Bi, X.: An extremely low visibility event over the Guangzhou region: a case
study, Atmos. Environ., 39, 6568–6577,
https://doi.org/10.1016/j.atmosenv.2005.07.061, 2005.
Wu, J., Gao, X., Giorgi, F., and Chen, D.: Changes of effective temperature and
cold/hot days in late decades over China based on a high resolution gridded
observation dataset, Int. J. Climatol., 37, 788–800,
https://doi.org/10.1002/joc.5038, 2017.
Xu, J., Yao, M., Wu, W., Qiao, X., Zhang, H., Wang, P., Yang, X., Zhao, X.,
and Zhang, J.: Estimation of ambient PM2.5-related mortality burden in
China by 2030 under climate and population change scenarios: A modeling
study, Environ, Int., 156, 106733,
https://doi.org/10.1016/j.envint.2021.106733, 2021.
Xue, W., Zhang, J., Zhong, C., Li, X., and Wei, J.: Spatiotemporal
PM2.5 variations and its response to the industrial structure from 2000
to 2018 in the Beijing-Tianjin-Hebei region, J. Cleaner Prod., 279, 123742,
https://doi.org/10.1016/j.jclepro.2020.123742, 2021.
Yang, S., Ma, Y., Duan, F., He, K., Wang, L., Wei, Z., Zhu, L., Ma, T., Li,
H., and Ye, S.: Characteristics and formation of typical winter haze in Handan,
one of the most polluted cities in China, Sci. Total Environ., 613,
1367–1375, https://doi.org/10.1016/j.scitotenv.2017.08.033, 2018.
Yang, Y. and Tang, J.: Substantial Differences in Compound Long Duration
Dry and Hot Events Over China Between Transient and Stabilized Warmer Worlds
at 1.5 ∘C Global Warming, Earths Future, 11, e2022EF002994,
https://doi.org/10.1029/2022EF002994, 2023.
Yang, Y., Tang, J., Xiong, Z., Wang, S., and Yuan, J.: An intercomparison of
multiple statistical downscaling methods for daily precipitation and
temperature over China: future climate projections, Clim. Dynam., 52,
6749–6771, https://doi.org/10.1007/s00382-018-4543-2, 2019.
Yang, Y., Maraun, D., Ossó, A., and Tang, J.: Increased spatial extent and likelihood of compound long-duration dry and hot events in China, 1961–2014, Nat. Hazards Earth Syst. Sci., 23, 693–709, https://doi.org/10.5194/nhess-23-693-2023, 2023.
Yang, X., Zhao, C., Guo, J., and Wang, Y.: Intensification of aerosol
pollution associated with its feedback with surface solar radiation and
winds in Beijing, J. Geophys. Res.-Atmos., 121, 4093–4099,
https://doi.org/10.1002/2015JD024645, 2016.
Yang, X., Wu, Q., Zhao, R., Cheng, H., He, H., Ma, Q., Wang, L., and Luo,
H.: New method for evaluating winter air quality: PM2.5 assessment using
Community Multiscale Air Quality Modeling (CMAQ) in Xi'an, Atmos. Environ.,
211, 18–28, https://doi.org/10.1016/j.atmosenv.2019.04.019, 2019.
Yin, Z., Wang, H., and Chen, H.: Understanding severe winter haze events in the North China Plain in 2014: roles of climate anomalies, Atmos. Chem. Phys., 17, 1641–1651, https://doi.org/10.5194/acp-17-1641-2017, 2017.
You, T., Wu, R., Huang, G., and Fan, G.: Regional meteorological patterns for
heavy pollution events in Beijing, J. Meteorol. Res., 31, 597–611,
https://doi.org/10.1007/s13351-017-6143-1, 2017.
Yu, X., Moore, J. C., Cui, X., Rinke, A., Ji, D., Kravitz, B., and Yoon, J.:
Impacts, effectiveness and regional inequalities of the GeoMIP G1 to G4
solar radiation management scenarios, Global Planet. Change, 129,
10–22, https://doi.org/10.1016/j.gloplacha.2015.02.010, 2015.
Zhang, Q., Zheng, Y., Tong, D., Shao, M., Wang, S., Zhang, Y., Xu, X., Wang,
J., He, H., Liu, W., Ding, Y., Lei, Y., Li, J., Wang, Z., Zhang, X., Wang,
Y., Cheng, J., Liu, Y., Shi, Q., Yan, L., Geng, G., Hong, C., Li, M., Liu,
F., Zheng, B., Cao, J., Ding, A., Gao, J., Fu, Q., Huo, J., Liu, B., Liu,
Z., Yang, F., He, K., and Hao, J.: Drivers of improved PM2.5 air
quality in China from 2013 to 2017, P. Natl. Acad. Sci. USA, 116, 24463–24469,
https://doi.org/10.1073/pnas.1907956116, 2019.
Zheng, C., Zhao, C., Zhu, Y., Wang, Y., Shi, X., Wu, X., Chen, T., Wu, F., and Qiu, Y.: Analysis of influential factors for the relationship between PM2.5 and AOD in Beijing, Atmos. Chem. Phys., 17, 13473–13489, https://doi.org/10.5194/acp-17-13473-2017, 2017.
Zhou, B., Xu, Y., Wu, J., Dong, S., and Shi, Y.: Changes in temperature and
precipitation extreme indices over China: analysis of a high-resolution grid
dataset, Int. J. Climatol., 36,
1051–1066, https://doi.org/10.1002/joc.4400, 2016.
Zhu, J., Wang, S., and Huang, G.: Assessing Climate Change Impacts on
Human-Perceived Temperature Extremes and Underlying Uncertainties, J. Geophys. Res.-Atmos., 124, 3800–3821,
https://doi.org/10.1029/2018JD029444, 2019.
Zhu, X., Huang, G., Zhou, X., and Zheng, S.: Projection of apparent temperature
using statistical downscaling approach in the Pearl River Delta, Theor.
Appl. Climatol., 144, 1253–1266,
https://doi.org/10.1007/s00704-021-03603-2, 2021.
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.
Apparent temperatures and PM2.5 pollution depend on humidity and wind speed in addition to...
Altmetrics
Final-revised paper
Preprint