Articles | Volume 14, issue 3
https://doi.org/10.5194/esd-14-697-2023
© 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-14-697-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
30 Jun 2023
Research article |
| 30 Jun 2023
| 30 Jun 2023
Modelling the effect of aerosol and greenhouse gas forcing on the South Asian and East Asian monsoons with an intermediate-complexity climate model
Lucy G. Recchia
CORRESPONDING AUTHOR
Department of Mathematics and Statistics, University of Reading, Reading, Berkshire, RG6 6AH, UK
Centre for the Mathematics of Planet Earth, University of Reading, Reading, Berkshire, RG6 6AH, UK
Valerio Lucarini
Department of Mathematics and Statistics, University of Reading, Reading, Berkshire, RG6 6AH, UK
Centre for the Mathematics of Planet Earth, University of Reading, Reading, Berkshire, RG6 6AH, UK
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Valerio Lembo, Federico Fabiano, Vera Melinda Galfi, Rune Grand Graversen, Valerio Lucarini, and Gabriele Messori
Weather Clim. Dynam., 3, 1037–1062, https://doi.org/10.5194/wcd-3-1037-2022, https://doi.org/10.5194/wcd-3-1037-2022, 2022
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Eddies in mid-latitudes characterize the exchange of heat between the tropics and the poles. This exchange is largely uneven, with a few extreme events bearing most of the heat transported across latitudes in a season. It is thus important to understand what the dynamical mechanisms are behind these events. Here, we identify recurrent weather regime patterns associated with extreme transports, and we identify scales of mid-latitudinal eddies that are mostly responsible for the transport.
Valerio Lucarini, Larissa Serdukova, and Georgios Margazoglou
Nonlin. Processes Geophys., 29, 183–205, https://doi.org/10.5194/npg-29-183-2022, https://doi.org/10.5194/npg-29-183-2022, 2022
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In most of the investigations on metastable systems, the stochastic forcing is modulated by Gaussian noise. Lévy noise laws, which describe jump processes, have recently received a lot of attention, but much less is known. We study stochastic versions of the Ghil–Sellers energy balance model, and we highlight the fundamental difference between how transitions are performed between the competing warm and snowball states, depending on whether Gaussian or Lévy noise acts as forcing.
Yumeng Chen, Alberto Carrassi, and Valerio Lucarini
Nonlin. Processes Geophys., 28, 633–649, https://doi.org/10.5194/npg-28-633-2021, https://doi.org/10.5194/npg-28-633-2021, 2021
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Chaotic dynamical systems are sensitive to the initial conditions, which are crucial for climate forecast. These properties are often used to inform the design of data assimilation (DA), a method used to estimate the exact initial conditions. However, obtaining the instability properties is burdensome for complex problems, both numerically and analytically. Here, we suggest a different viewpoint. We show that the skill of DA can be used to infer the instability properties of a dynamical system.
Veronika Eyring, Lisa Bock, Axel Lauer, Mattia Righi, Manuel Schlund, Bouwe Andela, Enrico Arnone, Omar Bellprat, Björn Brötz, Louis-Philippe Caron, Nuno Carvalhais, Irene Cionni, Nicola Cortesi, Bas Crezee, Edouard L. Davin, Paolo Davini, Kevin Debeire, Lee de Mora, Clara Deser, David Docquier, Paul Earnshaw, Carsten Ehbrecht, Bettina K. Gier, Nube Gonzalez-Reviriego, Paul Goodman, Stefan Hagemann, Steven Hardiman, Birgit Hassler, Alasdair Hunter, Christopher Kadow, Stephan Kindermann, Sujan Koirala, Nikolay Koldunov, Quentin Lejeune, Valerio Lembo, Tomas Lovato, Valerio Lucarini, François Massonnet, Benjamin Müller, Amarjiit Pandde, Núria Pérez-Zanón, Adam Phillips, Valeriu Predoi, Joellen Russell, Alistair Sellar, Federico Serva, Tobias Stacke, Ranjini Swaminathan, Verónica Torralba, Javier Vegas-Regidor, Jost von Hardenberg, Katja Weigel, and Klaus Zimmermann
Geosci. Model Dev., 13, 3383–3438, https://doi.org/10.5194/gmd-13-3383-2020, https://doi.org/10.5194/gmd-13-3383-2020, 2020
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The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool designed to improve comprehensive and routine evaluation of earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP). It has undergone rapid development since the first release in 2016 and is now a well-tested tool that provides end-to-end provenance tracking to ensure reproducibility.
Valerio Lembo, Frank Lunkeit, and Valerio Lucarini
Geosci. Model Dev., 12, 3805–3834, https://doi.org/10.5194/gmd-12-3805-2019, https://doi.org/10.5194/gmd-12-3805-2019, 2019
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The Thermodynamic Diagnostic Tool (TheDiaTo v1.0) is a collection of diagnostics for the study of the thermodynamics of the climate system in climate models. This is fundamental in order to understand where the imbalances affecting climate projections come from and also to allow for easy comparison of different scenarios and atmospheric regimes. The tool is currently being developed for the assessment of models that are part of the next phase of the Coupled Model Intercomparison Project (CMIP).
Mallory Carlu, Francesco Ginelli, Valerio Lucarini, and Antonio Politi
Nonlin. Processes Geophys., 26, 73–89, https://doi.org/10.5194/npg-26-73-2019, https://doi.org/10.5194/npg-26-73-2019, 2019
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We explore the nature of instabilities in a well-known meteorological toy model, the Lorenz 96, to unravel key mechanisms of interaction between scales of different resolutions and time scales. To do so, we use a mathematical machinery known as Lyapunov analysis, allowing us to capture the degrees of chaoticity associated with fundamental directions of instability. We find a non-trivial group of such directions projecting significantly on slow variables, associated with long term dynamics.
Gabriele Vissio and Valerio Lucarini
Nonlin. Processes Geophys., 25, 413–427, https://doi.org/10.5194/npg-25-413-2018, https://doi.org/10.5194/npg-25-413-2018, 2018
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Constructing good parametrizations is key when studying multi-scale systems. We consider a low-order model and derive a parametrization via a recently developed statistical mechanical approach. We show how the method allows for seamlessly treating the case when the unresolved dynamics is both faster and slower than the resolved one. We test the skill of the parametrization by using the formalism of the Wasserstein distance, which allows for measuring how different two probability measures are.
Lesley De Cruz, Sebastian Schubert, Jonathan Demaeyer, Valerio Lucarini, and Stéphane Vannitsem
Nonlin. Processes Geophys., 25, 387–412, https://doi.org/10.5194/npg-25-387-2018, https://doi.org/10.5194/npg-25-387-2018, 2018
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The predictability of weather models is limited largely by the initial state error growth or decay rates. We have computed these rates for PUMA, a global model for the atmosphere, and MAOOAM, a more simplified, coupled model which includes the ocean. MAOOAM has processes at distinct timescales, whereas PUMA surprisingly does not. We propose a new programme to compute the natural directions along the flow that correspond to the growth or decay rates, to learn which components play a role.
Tamás Bódai, Valerio Lucarini, and Frank Lunkeit
Earth Syst. Dynam. Discuss., https://doi.org/10.5194/esd-2018-30, https://doi.org/10.5194/esd-2018-30, 2018
Revised manuscript not accepted
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We establish a framework to conduct a scenario analysis of the best possible outcomes under geoengineering. The scenarios may consist of scenarios of greenhouse gas emission the choice of the quantity that we want to keep under control. The motivation is the desire of an efficient way of assessing the side-effects of geoengineering, concerning the unwanted and uncontrolled changes. Countering CO2 emission by modulating insolation, we find considerable changes in local temperatures or rainfall.
S. Hasson, V. Lucarini, M. R. Khan, M. Petitta, T. Bolch, and G. Gioli
Hydrol. Earth Syst. Sci., 18, 4077–4100, https://doi.org/10.5194/hess-18-4077-2014, https://doi.org/10.5194/hess-18-4077-2014, 2014
S. Hasson, V. Lucarini, S. Pascale, and J. Böhner
Earth Syst. Dynam., 5, 67–87, https://doi.org/10.5194/esd-5-67-2014, https://doi.org/10.5194/esd-5-67-2014, 2014
R. Deidda, M. Marrocu, G. Caroletti, G. Pusceddu, A. Langousis, V. Lucarini, M. Puliga, and A. Speranza
Hydrol. Earth Syst. Sci., 17, 5041–5059, https://doi.org/10.5194/hess-17-5041-2013, https://doi.org/10.5194/hess-17-5041-2013, 2013
S. Hasson, V. Lucarini, and S. Pascale
Earth Syst. Dynam., 4, 199–217, https://doi.org/10.5194/esd-4-199-2013, https://doi.org/10.5194/esd-4-199-2013, 2013
Related subject area
Topics: Climate change | Interactions: Other interactions | Methods: Earth system and climate modeling
Understanding pattern scaling errors across a range of emissions pathways
Role of mean and variability change in changes in European annual and seasonal extreme precipitation events
Christopher D. Wells, Lawrence S. Jackson, Amanda C. Maycock, and Piers M. Forster
Earth Syst. Dynam., 14, 817–834, https://doi.org/10.5194/esd-14-817-2023, https://doi.org/10.5194/esd-14-817-2023, 2023
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There are many possibilities for future emissions, with different impacts in different places. Complex models can study these impacts but take a long time to run, even on powerful computers. Simple methods can be used to reduce this time by estimating the complex model output, but these are not perfect. This study looks at the accuracy of one of these techniques, showing that there are limitations to its use, especially for low-emission future scenarios.
Raul R. Wood
Earth Syst. Dynam., 14, 797–816, https://doi.org/10.5194/esd-14-797-2023, https://doi.org/10.5194/esd-14-797-2023, 2023
Short summary
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The change in extreme-event occurrence is influenced by both a shift in the mean and a change in variability. How large the individual contributions are remains largely unknown. Large-ensemble climate simulations and probability risk ratio are used to partition the change in extreme precipitation events into contributions from a change in the mean and variability. The results reveal that the change in variability can be equally as important as or even more important than the mean change.
Cited articles
Almazroui, M., Saeed, S., Saeed, F., Islam, M. N., and Ismail, M.: Projections
of precipitation and temperature over the South Asian countries in CMIP6,
Earth Systems and Environment, 4, 297–320, https://doi.org/10.1007/s41748-020-00157-7,
2020. a, b
Annamalai, H., Taguchi, B., McCreary, J. P., Nagura, M., and Miyama, T.:
Systematic Errors in South Asian Monsoon Simulation: Importance of
Equatorial Indian Ocean Processes, J. Climate, 30, 8159–8178,
https://doi.org/10.1175/JCLI-D-16-0573.1, 2017. a
Ayantika, D. C., Krishnan, R., Singh, M., Swapna, P., Sandeep, N., Prajeesh,
A. G., and Vellore, R.: Understanding the combined effects of global warming
and anthropogenic aerosol forcing on the South Asian monsoon,
Clim. Dynam., 56, 1643–1662, https://doi.org/10.1007/s00382-020-05551-5, 2021. a, b, c
Boers, N., Goswami, B., Rheinwalt, A., Bookhagen, B., Hoskins, B., and Kurths,
J.: Complex networks reveal global pattern of extreme-rainfall
teleconnections, Nature, 566, 373–377, https://doi.org/10.1038/s41586-018-0872-x,
2019. a, b
Bollasina, M. A., Ming, Y., and Ramaswamy, V.: Anthropogenic aerosols and the
weakening of the South Asian summer monsoon, Science, 334, 502–505,
https://doi.org/10.1126/science.1204994, 2011. a, b
Bollasina, M. A., Ming, Y., Ramaswamy, V., Schwarzkopf, M. D., and Naik, V.:
Contribution of local and remote anthropogenic aerosols to the twentieth
century weakening of the South Asian Monsoon,
Geophys. Res. Lett.,
41, 680–687, https://doi.org/10.1002/2013GL058183, 2014. a
Boos, W. R. and Storelvmo, T.: Near-linear response of mean monsoon strength to
a broad range of radiative forcings,
P. Natl. Acad. Sci. USA, 113, 1510–1515, https://doi.org/10.1073/pnas.1517143113, 2016. a, b
Boschi, R. and Lucarini, V.: Water Pathways for the Hindu-Kush-Himalaya and an
Analysis of Three Flood Events, Atmosphere, 10, 489,
https://doi.org/10.3390/atmos10090489, 2019. a, b
Boschi, R., Lucarini, V., and Pascale, S.: Bistability of the climate around
the habitable zone: A thermodynamic investigation, Icarus, 226, 1724–1742,
https://doi.org/10.1016/j.icarus.2013.03.017, 2013. a
Chakraborty, A., Satheesh, S. K., Nanjundiah, R. S., and Srinivasan, J.: Impact of absorbing aerosols on the simulation of climate over the Indian region in an atmospheric general circulation model, Ann. Geophys., 22, 1421–1434, https://doi.org/10.5194/angeo-22-1421-2004, 2004. a, b
Chakraborty, A., Nanjundiah, R. S., and Srinivasan, J.: Local and remote
impacts of direct aerosol forcing on Asian monsoon,
Int. J. Climatol., 34, 2108–2121, https://doi.org/10.1002/joc.3826, 2014. a, b, c
Chen, Z., Zhou, T., Zhang, L., Chen, X., Zhang, W., and Jiang, J.: Global Land
Monsoon Precipitation Changes in CMIP6 Projections,
Geophys. Res. Lett., 47, e2019GL086902, https://doi.org/10.1029/2019GL086902, 2020. a
Cherchi, A., Alessandri, A., Masina, S., and Navarra, A.: Effects of increased
CO2 levels on monsoons, Clim. Dynam., 37, 83–101,
https://doi.org/10.1007/s00382-010-0801-7, 2011. a, b
Chou, C., Ryu, D., Lo, M.-H., Wey, H.-W., and Malano, H. M.:
Irrigation-Induced Land–Atmosphere Feedbacks and Their Impacts on Indian
Summer Monsoon, J. Climate, 31, 8785–8801,
https://doi.org/10.1175/JCLI-D-17-0762.1, 2018. a
Choudhury, B. A., Rajesh, P. V., Zahan, Y., and Goswami, B. N.: Evolution of
the Indian summer monsoon rainfall simulations from CMIP3 to CMIP6 models,
Clim. Dynam., 58, 2637–2662, https://doi.org/10.1007/s00382-021-06023-0, 2022. a, b
Ding, Q. and Wang, B.: Circumglobal Teleconnection in the Northern Hemisphere
Summer, J. Climate, 18, 3483–3505, https://doi.org/10.1175/JCLI3473.1, 2005. a, b
Dong, B., Wilcox, L. J., Highwood, E. J., and Sutton, R. T.: Impacts of recent
decadal changes in Asian aerosols on the East Asian summer monsoon: roles of
aerosol-radiation and aerosol-cloud interactions, Clim. Dynam., 53,
3235–3256, https://doi.org/10.1007/s00382-019-04698-0, 2019. a, b
Douville, H., Royer, J. F., Polcher, J., Cox, P., Gedney, N., D. B., S., and
P. J., V.: Impact of CO2 Doubling on the Asian Summer
Monsoon: Robust Versus Model-Dependent Responses,
J. Meteorol. Soc. Jpn Ser. II, 78, 421–439,
https://doi.org/10.2151/jmsj1965.78.4_421, 2000. a, b
Duque-Villegas, M., Salazar, J. F., and Rendón, A. M.: Tipping the ENSO into a permanent El Niño can trigger state transitions in global terrestrial ecosystems, Earth Syst. Dynam., 10, 631–650, https://doi.org/10.5194/esd-10-631-2019, 2019. a
Enomoto, T., Hoskins, B. J., and Matsuda, Y.: The formation mechanism of the
Bonin high in August,
Q. J. Roy. Meteor. Soc.,
129, 157–178, https://doi.org/10.1256/qj.01.211, 2003. a, b
Fadnavis, S., Sabin, T. P., Rap, A., Müller, R., Kubin, A., and Heinold,
B.: The impact of COVID-19 lockdown measures on the Indian summer monsoon,
Environ. Res. Lett., 16, 074054, https://doi.org/10.1088/1748-9326/ac109c,
2021. a, b
Fraedrich, K., Jansen, H., Kirk, E., Luksch, U., and Lunkeit, F.: The Planet
Simulator: Towards a user friendly model, Meteorologische Z., 14,
299–304, https://doi.org/10.1127/0941-2948/2005/0043, 2005. a, b
Francis, P. A. and Gadgil, S.: Intense rainfall events over the west coast of
India, Meteorol. Atmos. Phys., 94, 27–42,
https://doi.org/10.1007/s00703-005-0167-2, 2006. a
Francis, P. A. and Gadgil, S.: Towards understanding the unusual Indian
monsoon in 2009, J. Earth Syst. Sci., 119, 397–415,
https://doi.org/10.1007/s12040-010-0033-6, 2010. a
Gadgil, S. and Gadgil, S.: The Indian Monsoon, GDP and Agriculture,
Econ. Polit. Weekly, 41, 4887–4895, 2006. a
Gadgil, S., Srinivasan, J., Nanjundiah, R. S., Krishna Kumar, K., Munot, A. A.,
and Rupa Kumar, K.: On forecasting the Indian summer monsoon: the
intriguing season of 2002, Current Science, 83, 394–403, 2002. a
Galfi, V. M. and Lucarini, V.: Fingerprinting Heatwaves and Cold Spells and
Assessing Their Response to Climate Change Using Large Deviation Theory,
Phys. Rev. Lett., 127, 058701, https://doi.org/10.1103/PhysRevLett.127.058701,
2021. a
Ganguly, D., Rasch, P. J., Wang, H., and Yoon, J.-h.: Fast and slow responses
of the South Asian monsoon system to anthropogenic aerosols, Geophys. Res. Lett., 39, L18804, https://doi.org/10.1029/2012GL053043, 2012. a
Garreaud, R. D., Molina, A., and Farias, M.: Andean uplift, ocean cooling and
Atacama hyperaridity: A climate modeling perspective,
Earth Planet. Sci. Lett., 292, 39–50, https://doi.org/10.1016/j.epsl.2010.01.017, 2010. a
Ghil, M. and Lucarini, V.: The physics of climate variability and climate
change, Rev. Mod. Phys., 92, 035002,
https://doi.org/10.1103/RevModPhys.92.035002, 2020. a
Gillett, N. P., Wehner, M. F., Tett, S. F. B., and Weaver, A. J.: Testing the
linearity of the response to combined greenhouse gas and sulfate aerosol
forcing, Geophys. Res. Lett., 31, L14201, https://doi.org/10.1029/2004GL020111, 2004. a
Gómez-Leal, I., Kaltenegger, L., Lucarini, V., and Lunkeit, F.: Climate
Sensitivity to Carbon Dioxide and the Moist Greenhouse Threshold of
Earth-like Planets under an Increasing Solar Forcing,
The Astrophysical Journal, 869, 129, https://doi.org/10.3847/1538-4357/aaea5f, 2018. a
Guo, L., Turner, A. G., and Highwood, E. J.: Local and remote impacts of
aerosol species on Indian summer monsoon rainfall in a GCM, J. Climate, 29, 6937–6955, https://doi.org/10.1175/JCLI-D-15-0728.1, 2016. a, b, c
Gusain, A., Ghosh, S., and Karmakar, S.: Added value of CMIP6 over CMIP5
models in simulating Indian summer monsoon rainfall, Atmos. Res.,
232, 104680, https://doi.org/10.1016/j.atmosres.2019.104680, 2020. a, b, c
Hazra, A., Taraphdar, S., Halder, M., Pokhrel, S., Chaudhari, H. S., Salunke,
K., Mukhopadhyay, P., and Rao, S. A.: Indian summer monsoon drought 2009:
role of aerosol and cloud microphysics, Atmos. Sci. Lett., 14,
181–186, https://doi.org/10.1002/asl2.437, 2013. a
He, C., Zhou, W., Li, T., Zhou, T., and Wang, Y.: East Asian summer monsoon
enhanced by COVID-19, Clim. Dynam., 59, 2965–2978, https://doi.org/10.1007/s00382-022-06247-8,
2022. a
Held, I. M. and Soden, B. J.: Robust Responses of the Hydrological Cycle to
Global Warming, J. Climate, 19, 5686–5699,
https://doi.org/10.1175/JCLI3990.1, 2006. a, b, c
Holden, P. B., Edwards, N. R., Garthwaite, P. H., Fraedrich, K., Lunkeit, F., Kirk, E., Labriet, M., Kanudia, A., and Babonneau, F.: PLASIM-ENTSem v1.0: a spatio-temporal emulator of future climate change for impacts assessment, Geosci. Model Dev., 7, 433–451, https://doi.org/10.5194/gmd-7-433-2014, 2014. a
Holden, P. B., Edwards, N. R., Fraedrich, K., Kirk, E., Lunkeit, F., and Zhu, X.: PLASIM–GENIE v1.0: a new intermediate complexity AOGCM, Geosci. Model Dev., 9, 3347–3361, https://doi.org/10.5194/gmd-9-3347-2016, 2016. a, b
Intergovermental Panel on Climate Change (IPCC): IPCC Climate
Change 2013: The Physical Science Basis. Contribution of Working Group I to
the Fifth Assessment Report of the Intergovernmental Panel on Climate
Change, edited by: Stocker, T.
F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A.,
Xia, Y., Bex, V., and Midgley, P. M., Cambridge University Press, https://www.ipcc.ch/report/ar5/wg1/ (last access: 4 Januar 2021), 2013. a
Intergovermental Panel on Climate Change (IPCC): IPCC, 2021:
Climate Change 2021: The Physical Science Basis. 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., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press, https://www.ipcc.ch/report/ar6/wg1/ (last access: 6 January 2022), 2021. a, b, c, d, e, f
Jiang, D., Hu, D., Tian, Z., and Lang, X.: Differences between CMIP6 and CMIP5
Models in Simulating Climate over China and the East Asian Monsoon, Adv. Atmos. Sci., 37, 1102–1118, https://doi.org/10.1007/s00376-020-2034-y,
2020. a, b
Katzenberger, A., Schewe, J., Pongratz, J., and Levermann, A.: Robust increase of Indian monsoon rainfall and its variability under future warming in CMIP6 models, Earth Syst. Dynam., 12, 367–386, https://doi.org/10.5194/esd-12-367-2021, 2021. a
Khadka, D., Babel, M. S., Abatan, A. A., and Collins, M.: An evaluation of
CMIP5 and CMIP6 climate models in simulating summer rainfall in the Southeast
Asian monsoon domain, Int. J. Climatol., 42, 1181–1202,
https://doi.org/10.1002/joc.7296, 2022. a, b, c
Kitoh, A.: The Asian Monsoon and its Future Change in Climate Models: A
Review, J. Meteorol. Soc. Jpn. Ser. II, 95, 7–33
https://doi.org/10.2151/jmsj.2017-002, 2017. a
Konda, G. and Vissa, N. K.: Evaluation of CMIP6 models for simulations of
surplus/deficit summer monsoon conditions over India, Clim. Dynam.,
60, 1023–1042, https://doi.org/10.1007/s00382-022-06367-1, 2023. a, b
Kovilakam, M. and Mahajan, S.: Confronting the “Indian summer monsoon
response to black carbon aerosol” with the uncertainty in its radiative
forcing and beyond, J. Geophys. Res.-Atmos., 121,
7833–7852, https://doi.org/10.1002/2016JD024866, 2016. a, b
Kripalani, R., Ha, K.-J., Ho, C.-H., Oh, J.-H., Preethi, B., Mujumdar, M., and
Prabhu, A.: Erratic Asian summer monsoon 2020: COVID-19 lockdown initiatives
possible cause for these episodes?, Clim. Dynam., 59, 1339–1352,
https://doi.org/10.1007/s00382-021-06042-x, 2022. a, b
Krishna Kumar, K., Rupa Kumar, K., Ashrit, R. G., Deshpande, N. R., and Hansen,
J. W.: Climate impacts on Indian agriculture, Int. J. Climatol., 24, 1375–1393, https://doi.org/10.1002/joc.1081, 2004. a
Krishnamurti, T. N., Thomas, A., Simon, A., and Kumar, V.: Desert air
incursions, an overlooked aspect, for the dry spells of the Indian summer
monsoon, J. Atmos. Sci., 67, 3423–3441,
https://doi.org/10.1175/2010JAS3440.1, 2010. a
Krishnan, R., Sanjay, J., Gnanaseelan, C., Mujumdar, M., Kulkarni, A., and
Chakraborty, S.: Assessment of Climate Change over the Indian Region: A
Report of the Ministry of Earth Sciences (MoES), Government of India,
Springer Nature, https://doi.org/10.1007/978-981-15-4327-2, 2020. a, b
Kumar, S. and Devara, P. C. S.: A long-term study of aerosol modulation of
atmospheric and surface solar heating over Pune, India, Tellus B, 64, 18420,
https://doi.org/10.3402/tellusb.v64i0.18420, 2012. a
Lau, N.-C. and Nath, M. J.: A model study of the air–sea interaction
associated with the climatological aspects and interannual variability of the
south Asian summer monsoon development, J. Climate, 25, 839–857,
https://doi.org/10.1175/JCLI-D-11-00035.1, 2012. a
Lau, W. K. M. and Kim, K.-M.: Observational relationships between aerosol and
Asian monsoon rainfall, and circulation, Geophys. Res. Lett., 33, L21810,
https://doi.org/10.1029/2006GL027546, 2006. a
Lau, W. K. M. and Kim, K.-M.: Fingerprinting the impacts of aerosols on
long-term trends of the Indian summer monsoon regional rainfall,
Geophys. Res. Lett., 37, L16705, https://doi.org/10.1029/2010GL043255, 2010. a
Lau, W. K.-M. and Kim, K.-M.: Competing influences of greenhouse warming and
aerosols on Asian summer monsoon circulation and rainfall, Asia-Pacific
J. Atmos. Sci., 53, 181–194,
https://doi.org/10.1007/s13143-017-0033-4, 2017. a
Lee, S.-S., Chu, J.-E., Timmermann, A., Chung, E.-S., and Lee, J.-Y.: East
Asian climate response to COVID-19 lockdown measures in China, Sci.
Rep., 11, 1–9, https://doi.org/10.1038/s41598-021-96007-1, 2021. a
Lenton, T. M., Held, H., Kriegler, E., Hall, J. W., Lucht, W., Rahmstorf, S.,
and Schellnhuber, H. J.: Tipping elements in the Earth's climate system,
P. Natl. Acad. Sci. USA, 105, 1786–1793,
https://doi.org/10.1073/pnas.0705414105, 2008. a
Levermann, A., Schewe, J., Petoukhov, V., and Held, H.: Basic mechanism for
abrupt monsoon transitions, P. Natl. Acad. Sci. USA,
106, 20572–20577, https://doi.org/10.1073/pnas.0901414106, 2009. a, b
Li, X., Ting, M., Li, C., and Henderson, N.: Mechanisms of Asian Summer
Monsoon Changes in Response to Anthropogenic Forcing in CMIP5 Models,
J. Climate, 28, 4107–4125, https://doi.org/10.1175/JCLI-D-14-00559.1, 2015. a, b
Li, Z., Lau, W. K.-M., Ramanathan, V., Wu, G., Ding, Y., Manoj, M. G., Liu, J.,
Qian, Y., Li, J., Zhou, T., Fan, J., Rosenfeld, D., Ming, Y., Wang, Y.,
Huang, J., Wang, B., Xu, X., Lee, S.-S., Cribb, M., Zhang, F., Yang, X.,
Zhao, C., Takemura, T., Wang, K., Xia, X., Yin, Y., Zhang, H., Guo, J., Zhai,
P. M., Sugimoto, N., Babu, S. S., and Brasseur, G. P.: Aerosol and monsoon
climate interactions over Asia, Rev. Geophys., 54, 866–929,
https://doi.org/10.1002/2015RG000500, 2016. a
Liu, Y., Sun, J., and Yang, B.: The effects of black carbon and sulphate
aerosols in China regions on East Asia monsoons, Tellus B, 61, 642–656, https://doi.org/10.1111/j.1600-0889.2009.00427.x,
2009. a
Lu, Q., Liu, C., Zhao, D., Zeng, C., Li, J., Lu, C., Wang, J., and Zhu, B.:
Atmospheric heating rate due to black carbon aerosols: Uncertainties and
impact factors, Atmos. Res., 240, 104891,
https://doi.org/10.1016/j.atmosres.2020.104891, 2020. a
Lucarini, V., Fraedrich, K., and Lunkeit, F.: Thermodynamics of climate change: generalized sensitivities, Atmos. Chem. Phys., 10, 9729–9737, https://doi.org/10.5194/acp-10-9729-2010, 2010. a
Lucarini, V., Pascale, S., Boschi, R., Kirk, E., and Iro, N.: Habitability and
Multistability in Earth-like Planets, Astron. Nachr., 334,
576–588, https://doi.org/10.1002/asna.201311903, 2013. a
Lucarini, V., Ragone, F., and Lunkeit, F.: Predicting Climate Change Using
Response Theory: Global Averages and Spatial Patterns,
J. Stat. Phys., 166, 1036–1064, https://doi.org/10.1007/s10955-016-1506-z, 2017. a, b
Lunkeit, F., Borth, H., Bottinger, M., Fraedrich, K., Jansen, H., Kirk, E.,
Kleidon, A., Luksch, U., Paiewonsky, P., Schubert, S., Sielmann, F., and Wan,
H.: Planet Simulator Reference Manual, Version 16, Meteorological Institute
of the University of Hamburg, Hamburg, Germany,
https://www.mi.uni-hamburg.de/en/arbeitsgruppen/theoretische-meteorologie/modelle/plasim.html (last access: 5 April 2022),
2011. a, b
Margazoglou, G., Grafke, T., Laio, A., and Lucarini, V.: Dynamical landscape
and multistability of a climate model, P. R. Soc. A,
477, 20210019, https://doi.org/10.1098/rspa.2021.0019, 2021. a
McKenna, S., Santoso, A., Gupta, A. S., Taschetto, A. S., and Cai, W.: Indian
Ocean Dipole in CMIP5 and CMIP6: characteristics, biases, and links to ENSO,
Sci. Rep., 10, 1–13, https://doi.org/10.1038/s41598-020-68268-9, 2020. a
Meehl, G. A., Arblaster, J. M., and Collins, W. D.: Effects of black carbon
aerosols on the Indian monsoon, J. Climate, 21, 2869–2882,
https://doi.org/10.1175/2007JCLI1777.1, 2008. a
Menon, A., Levermann, A., Schewe, J., Lehmann, J., and Frieler, K.: Consistent increase in Indian monsoon rainfall and its variability across CMIP-5 models, Earth Syst. Dynam., 4, 287–300, https://doi.org/10.5194/esd-4-287-2013, 2013. a
Mishra, A. K.: Observing a severe flooding over southern part of India in
monsoon season of 2019, J. Earth Syst. Sci., 130, 1–8,
https://doi.org/10.1007/s12040-020-01509-7, 2021. a
Monerie, P.-A., Wilcox, L. J., and Turner, A. G.: Effects of Anthropogenic
Aerosol and Greenhouse Gas Emissions on Northern Hemisphere Monsoon
Precipitation: Mechanisms and Uncertainty, J. Climate, 35,
2305–2326, https://doi.org/10.1175/JCLI-D-21-0412.1, 2022. a, b, c
Moon, S. and Ha, K.-J.: Future changes in monsoon duration and precipitation
using CMIP6, npj Climate and Atmospheric Science, 3, 1–7,
https://doi.org/10.1038/s41612-020-00151-w, 2020. a
Ogata, T., Ueda, H., Inoue, T., Hayasaki, M., Yoshida, A., Watanabe, S., Kira,
M., Ooshiro, M., and Kumai, A.: Projected Future Changes in the Asian
Monsoon: A Comparison of CMIP3 and CMIP5 Model Results, J.
Meteorol. Soc. Jpn Ser. II, 92, 207–225,
https://doi.org/10.2151/jmsj.2014-302, 2014. a, b
Platov, G., Krupchatnikov, V., Martynova, Y., Borovko, I., and Golubeva, E.: A
new earth’s climate system model of intermediate complexity,
PlaSim-ICMMG-1.0: description and performance, in: IOP Conference Series:
Earth and Environmental Science, vol. 96, p. 012005, IOP Publishing,
https://doi.org/10.1088/1755-1315/96/1/012005, 2017. a, b
Polson, D., Bollasina, M., Hegerl, G. C., and Wilcox, L. J.: Decreased monsoon
precipitation in the Northern Hemisphere due to anthropogenic aerosols,
Geophys. Res. Lett., 41, 6023–6029, https://doi.org/10.1002/2014GL060811,
2014. a
Ragone, F., Lucarini, V., and Lunkeit, F.: A new framework for climate
sensitivity and prediction: a modelling perspective, Clim. Dynam., 46,
1459–1471, https://doi.org/10.1007/s00382-015-2657-3, 2016. a, b
Ramarao, M. V. S., Ayantika, D. C., Krishnan, R., Sanjay, J., Sabin, T. P.,
Mujumdar, M., and Singh, K. K.: Signatures of aerosol-induced decline in
evapotranspiration over the Indo-Gangetic Plain during the recent decades,
MAUSAM, 74, 297–310, https://doi.org/10.54302/mausam.v74i2.6031, 2023. a, b, c
Recchia, L. G. and Lucarini, V.: Supplementary material for Recchia & Lucarini 2023, Figshare [data set], https://doi.org/10.6084/m9.figshare.23554896.v1, 2023. a
Recchia, L. G., Griffiths, S. D., and Parker, D. J.: Controls on propagation of
the Indian monsoon onset in an idealised model, Q. J. Roy. Meteor. Soc., 147, 4010–4031, https://doi.org/10.1002/qj.4165, 2021. a
Samset, B. H., Sand, M., Smith, C. J., Bauer, S. E., Forster, P. M.,
Fuglestvedt, J. S., Osprey, S., and Schleussner, C.-F.: Climate Impacts From
a Removal of Anthropogenic Aerosol Emissions, Geophys. Res. Lett.,
45, 1020–1029, https://doi.org/10.1002/2017GL076079, 2018. a, b, c
Sanap, S. D. and Pandithurai, G.: The effect of absorbing aerosols on Indian
monsoon circulation and rainfall: A review, Atmos. Res., 164,
318–327, https://doi.org/10.1016/j.atmosres.2015.06.002, 2015. a
Sarangi, C., Kanawade, V. P., Tripathi, S. N., Thomas, A., and Ganguly, D.:
Aerosol-induced intensification of cooling effect of clouds during Indian
summer monsoon, Nat. Commun., 9, 1–9,
https://doi.org/10.1038/s41467-018-06015-5, 2018. a
Schewe, J., Levermann, A., and Cheng, H.: A critical humidity threshold for monsoon transitions, Clim. Past, 8, 535–544, https://doi.org/10.5194/cp-8-535-2012, 2012. a, b
Sherman, P., Gao, M., Song, S., Archibald, A. T., Abraham, N. L., Lamarque, J.-F., Shindell, D., Faluvegi, G., and McElroy, M. B.: Sensitivity of modeled Indian monsoon to Chinese and Indian aerosol emissions, Atmos. Chem. Phys., 21, 3593–3605, https://doi.org/10.5194/acp-21-3593-2021, 2021. a, b, c
Shifter, B., von Hardenberg, J., Borth, H., and Lunkeit, F.: jhardenberg/PLASIM: Plasim-LSG TNG (1.0), Zenodo [code], https://doi.org/10.5281/zenodo.4041462, 2020. a, b
Shindell, D. T., Voulgarakis, A., Faluvegi, G., and Milly, G.: Precipitation response to regional radiative forcing, Atmos. Chem. Phys., 12, 6969–6982, https://doi.org/10.5194/acp-12-6969-2012, 2012. a
Siler, N. and Roe, G.: How will orographic precipitation respond to surface
warming? An idealized thermodynamic perspective, Geophys. Res. Lett., 41, 2606–2613, https://doi.org/10.1002/2013GL059095, 2014. a
Song, F., Zhou, T., and Qian, Y.: Responses of East Asian summer monsoon to
natural and anthropogenic forcings in the 17 latest CMIP5 models,
Geophys. Res. Lett., 41, 596–603, https://doi.org/10.1002/2013GL058705, 2014. a, b, c
Sperber, K. R., Annamalai, H., Kang, I.-S., Kitoh, A., Moise, A., Turner, A.,
Wang, B., and Zhou, T.: The Asian summer monsoon: an intercomparison of
CMIP5 vs. CMIP3 simulations of the late 20th century, Clim. Dynam.,
41, 2711–2744, https://doi.org/10.1007/s00382-012-1607-6, 2013. a
Swaminathan, R., Parker, R. J., Jones, C. G., Allan, R. P., Quaife, T., Kelley,
D. I., De Mora, L., and Walton, J.: The Physical Climate at Global Warming
Thresholds as Seen in the U.K. Earth System Model, J. Climate, 35,
29–48, https://doi.org/10.1175/JCLI-D-21-0234.1, 2022. a, b, c
Swapna, P., Krishnan, R., Sandeep, N., Prajeesh, A. G., Ayantika, D. C.,
Manmeet, S., and Vellore, R.: Long-Term Climate Simulations Using the IITM
Earth System Model (IITM-ESMv2) With Focus on the South Asian Monsoon,
J. Adv. Model. Earth Sy., 10, 1127–1149,
https://doi.org/10.1029/2017MS001262, 2018. a
Thomson, J. R., Holden, P. B., Anand, P., Edwards, N. R., Porchier, C. A., and
Harris, N. B. W.: Tectonic and climatic drivers of Asian monsoon evolution,
Nat. Commun., 12, 1–10, https://doi.org/10.1038/s41467-021-24244-z, 2021. a
Tran, G. T., Oliver, K. I. C., Sóbester, A., Toal, D. J. J., Holden, P. B.,
Marsh, R., Challenor, P., and Edwards, N. R.: Building a traceable climate
model hierarchy with multi-level emulators, Advances in Statistical
Climatology, Meteorology and Oceanography, 2, 17–37,
https://doi.org/10.5194/ascmo-2-17-2016, 2016. a
Turner, A. G. and Annamalai, H.: Climate change and the South Asian summer
monsoon, Nat. Clim. Change, 2, 587–595, https://doi.org/10.1038/nclimate1495,
2012. a
Ueda, H., Iwai, A., Kuwako, K., and Hori, M. E.: Impact of anthropogenic
forcing on the Asian summer monsoon as simulated by eight GCMs,
Geophys. Res. Lett., 33, L06703, https://doi.org/10.1029/2005GL025336, 2006. a, b
Ul Hasson, S., Pascale, S., Lucarini, V., and Böhner, J.: Seasonal cycle
of precipitation over major river basins in South and Southeast Asia: A
review of the CMIP5 climate models data for present climate and future
climate projections, Atmos. Res., 180, 42–63,
https://doi.org/10.1016/j.atmosres.2016.05.008, 2016. a
Undorf, S., Polson, D., Bollasina, M. A., Ming, Y., Schurer, A., and Hegerl,
G. C.: Detectable Impact of Local and Remote Anthropogenic Aerosols on the
20th Century Changes of West African and South Asian Monsoon Precipitation,
J. Geophys. Res.-Atmos., 123, 4871–4889,
https://doi.org/10.1029/2017JD027711, 2018. a, b
Vaishya, A., Babu, S. N. S., Jayachandran, V., Gogoi, M. M., Lakshmi, N. B., Moorthy, K. K., and Satheesh, S. K.: Large contrast in the vertical distribution of aerosol optical properties and radiative effects across the Indo-Gangetic Plain during the SWAAMI–RAWEX campaign, Atmos. Chem. Phys., 18, 17669–17685, https://doi.org/10.5194/acp-18-17669-2018, 2018. a
Varghese, S. J., Surendran, S., Rajendran, K., and Kitoh, A.: Future
projections of Indian Summer Monsoon under multiple RCPs using a high
resolution global climate model multiforcing ensemble simulations, Clim.
Dynam., 54, 1315–1328, https://doi.org/10.1007/s00382-019-05059-7, 2020. a, b
Wang, B., Jin, C., and Liu, J.: Understanding Future Change of Global Monsoons
Projected by CMIP6 Models, J. Climate, 33, 6471–6489,
https://doi.org/10.1175/JCLI-D-19-0993.1, 2020. a
Wang, B., Biasutti, M., Byrne, M. P., Castro, C., Chang, C.-P., Cook, K., Fu,
R., Grimm, A. M., Ha, K.-J., Hendon, H., Kitoh, A., Krishnan, R., Lee, J.-Y.,
Li, J., Liu, J., Moise, A., Pascale, S., Roxy, M. K., Seth, A., Sui, C.-H.,
Turner, A., Yang, S., Yun, K.-S., Zhang, L., and Zhou, T.: Monsoons Climate
Change Assessment, B. Am. Meteorol. Soc., 102,
E1–E19, https://doi.org/10.1175/BAMS-D-19-0335.1, 2021. a, b, c
Wang, C., Zhang, L., Lee, S.-K., Wu, L., and Mechoso, C. R.: A global
perspective on CMIP5 climate model biases, Nat. Clim. Change, 4,
201–205, https://doi.org/10.1038/nclimate2118, 2014. a
Wang, L. and Gu, W.: The Eastern China flood of June 2015 and its causes,
Sci. Bull., 61, 178–184, https://doi.org/10.1007/s11434-015-0967-9, 2016. a
Wang, Z., Zhang, H., and Zhang, X.: Projected response of East Asian summer
monsoon system to future reductions in emissions of anthropogenic aerosols
and their precursors, Clim. Dynam., 47, 1455–1468,
https://doi.org/10.1007/s00382-015-2912-7, 2016. a, b, c
Wester, P., Mishra, A., Mukherji, A., and Shrestha, A. B., eds.: The Hindu
Kush Himalaya assessment: Mountains, Climate Change, Sustainability and
People, Springer Nature, https://doi.org/10.1007/978-3-319-92288-1, 2019. a
Wilcox, L. J., Liu, Z., Samset, B. H., Hawkins, E., Lund, M. T., Nordling, K., Undorf, S., Bollasina, M., Ekman, A. M. L., Krishnan, S., Merikanto, J., and Turner, A. G.: Accelerated increases in global and Asian summer monsoon precipitation from future aerosol reductions, Atmos. Chem. Phys., 20, 11955–11977, https://doi.org/10.5194/acp-20-11955-2020, 2020. a, b
Xin, X., Wu, T., Zhang, J., Yao, J., and Fang, Y.: Comparison of CMIP6 and
CMIP5 simulations of precipitation in China and the East Asian summer
monsoon, Int. J. Climatol., 40, 6423–6440,
https://doi.org/10.1002/joc.6590, 2020. a, b, c, d
Zhang, L. and Zhou, T.: Drought over East Asia: A Review, J. Climate,
28, 3375–3399, https://doi.org/10.1175/JCLI-D-14-00259.1, 2015. a
Zhang, X., Obringer, R., Wei, C., Chen, N., and Niyogi, D.: Droughts in India
from 1981 to 2013 and Implications to Wheat Production, Sci. Rep.,
7, 1–12, https://doi.org/10.1038/srep44552, 2017. a
Zhou, T., Zhang, W., Zhang, L., Zhang, X., Qian, Y., Peng, D., Ma, S., and
Dong, B.: The dynamic and thermodynamic processes dominating the reduction of
global land monsoon precipitation driven by anthropogenic aerosols emission,
Science China Earth Sciences, 63, 919–933, https://doi.org/10.1007/s11430-019-9613-9,
2020. a
Zickfeld, K., Knopf, B., Petoukhov, V., and Schellnhuber, H. J.: Is the
Indian summer monsoon stable against global change?, Geophys. Res. Lett., 32, L15707, https://doi.org/10.1029/2005GL022771, 2005. a, b, c
Short summary
Simulations are performed with an intermediate-complexity climate model, PLASIM, to assess the future response of monsoons to changing concentrations of aerosols and greenhouse gases. The aerosol loading is applied to India, Southeast Asia, and eastern China, both concurrently and independently, to assess linearity. The primary effect of increased aerosol loading is a decrease in summer precipitation in the vicinity of the applied forcing, although the regional response varies significantly.
Simulations are performed with an intermediate-complexity climate model, PLASIM, to assess the...
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