Articles | Volume 20, issue 2
https://doi.org/10.5194/bg-20-451-2023
© 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/bg-20-451-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
31 Jan 2023
Research article |
| 31 Jan 2023
| 31 Jan 2023
Bioclimatic change as a function of global warming from CMIP6 climate projections
Morgan Sparey
CORRESPONDING AUTHOR
Natural Sciences, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QF, UK
Peter Cox
Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QF, UK
Mark S. Williamson
Mathematics and Statistics, Faculty of Environment, Science and Economy, University of Exeter, Exeter, EX4 4QF, UK
Global Systems Institute, University of Exeter, Exeter, EX4 4QE, UK
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Isobel M. Parry, Paul D. L. Ritchie, Olivier Boucher, Peter M. Cox, James M. Haywood, Ulrike Niemeier, Roland Séférian, Simone Tilmes, and Daniele Visioni
EGUsphere, https://doi.org/10.5194/egusphere-2025-4889, https://doi.org/10.5194/egusphere-2025-4889, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
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Stratospheric aerosol injection (SAI) aims to counteract global warming by injecting aerosols into the stratosphere, thereby increasing the reflection of incoming sunlight. Despite concerns that SAI could reduce vegetation productivity by reducing the amount of sunlight at the Earth's surface and shifting rainfall patterns, SAI simulations project an increase in land carbon storage globally and in the Amazon compared to a moderate warming scenario, primarily due to increased CO2 fertilisation.
Patric J. L. Boardman, Joseph Clarke, Peter M. Cox, Chris Huntingford, Christopher D. Jones, and Mark S. Williamson
EGUsphere, https://doi.org/10.5194/egusphere-2025-4899, https://doi.org/10.5194/egusphere-2025-4899, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
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Climate sensitivity quantifies how much Earth warms for a given radiative forcing, and can be characterised by TCR. Although the IPCC estimates the most likely TCR to be 1.8 K, some models predict values >2.4 K. Record warmth in 2023–2024 raises questions as to whether the TCR may indeed be larger than previously suggested. Using up-to-date data, we estimate the TCR to be 1.81 K. We also show that future warming falls within the low–mid model range, making the 2 °C Paris target still feasible.
Hsi-Kai Chou, Anna B. Harper, Arthur P. K. Argles, Maria Carolina Duran Rojas, Emma W. Littleton, Richard A. Betts, and Peter M. Cox
EGUsphere, https://doi.org/10.5194/egusphere-2025-4536, https://doi.org/10.5194/egusphere-2025-4536, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
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Global warming and climate change caused by greenhouse gas emissions will have multiple impacts on forest ecosystems. A core part of the strategy to mitigating these impacts is to use afforestation and forestry management to implement large-scale Greenhouse Gas Removal. Here we use the JULES-RED model to evaluate the afforestation under a changing environmental condition. We project that Sitka Forest afforestation could meet the target of GGR once the plantation locations been selected properly.
Paul D. L. Ritchie, Chris Huntingford, and Peter M. Cox
Earth Syst. Dynam., 16, 1523–1526, https://doi.org/10.5194/esd-16-1523-2025, https://doi.org/10.5194/esd-16-1523-2025, 2025
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Climate tipping points are not committed upon crossing critical thresholds in global warming, as is often assumed. Instead, it is possible to temporarily overshoot a threshold without causing tipping, provided the duration of the overshoot is short. In this Idea, we demonstrate that restricting the time over 1.5 °C would considerably reduce tipping point risks.
Joseph Clarke, Chris Huntingford, Paul David Longden Ritchie, Rebecca Varney, Mark Williamson, and Peter Cox
EGUsphere, https://doi.org/10.5194/egusphere-2025-3703, https://doi.org/10.5194/egusphere-2025-3703, 2025
Short summary
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An increase in CO2 in the atmosphere warms the climate through the greenhouse effect, but also leads to uptake of CO2 by the land and ocean. However, the warming is also expected to suppress carbon uptake. If this suppression were strong enough, it could overwhelm the uptake of carbon, leading to a runaway feedback loop causing severe global warming. We find it is possible that this runaway could be relevant in complex climate models and even at the end of the last ice age.
Mark S. Williamson and Timothy M. Lenton
Earth Syst. Dynam., 15, 1483–1508, https://doi.org/10.5194/esd-15-1483-2024, https://doi.org/10.5194/esd-15-1483-2024, 2024
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Climate models have transitioned to a superrotating atmospheric state under a broad range of warm climates. Such a transition would change global weather patterns should it occur. Here we simulate this transition using an idealized climate model and look for any early warnings of the superrotating state before it happens. We find several early warning indicators that we attribute to an oscillating pattern in the windfield fluctuations.
Colin G. Jones, Fanny Adloff, Ben B. B. Booth, Peter M. Cox, Veronika Eyring, Pierre Friedlingstein, Katja Frieler, Helene T. Hewitt, Hazel A. Jeffery, Sylvie Joussaume, Torben Koenigk, Bryan N. Lawrence, Eleanor O'Rourke, Malcolm J. Roberts, Benjamin M. Sanderson, Roland Séférian, Samuel Somot, Pier Luigi Vidale, Detlef van Vuuren, Mario Acosta, Mats Bentsen, Raffaele Bernardello, Richard Betts, Ed Blockley, Julien Boé, Tom Bracegirdle, Pascale Braconnot, Victor Brovkin, Carlo Buontempo, Francisco Doblas-Reyes, Markus Donat, Italo Epicoco, Pete Falloon, Sandro Fiore, Thomas Frölicher, Neven S. Fučkar, Matthew J. Gidden, Helge F. Goessling, Rune Grand Graversen, Silvio Gualdi, José M. Gutiérrez, Tatiana Ilyina, Daniela Jacob, Chris D. Jones, Martin Juckes, Elizabeth Kendon, Erik Kjellström, Reto Knutti, Jason Lowe, Matthew Mizielinski, Paola Nassisi, Michael Obersteiner, Pierre Regnier, Romain Roehrig, David Salas y Mélia, Carl-Friedrich Schleussner, Michael Schulz, Enrico Scoccimarro, Laurent Terray, Hannes Thiemann, Richard A. Wood, Shuting Yang, and Sönke Zaehle
Earth Syst. Dynam., 15, 1319–1351, https://doi.org/10.5194/esd-15-1319-2024, https://doi.org/10.5194/esd-15-1319-2024, 2024
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We propose a number of priority areas for the international climate research community to address over the coming decade. Advances in these areas will both increase our understanding of past and future Earth system change, including the societal and environmental impacts of this change, and deliver significantly improved scientific support to international climate policy, such as future IPCC assessments and the UNFCCC Global Stocktake.
Mark S. Williamson, Peter M. Cox, Chris Huntingford, and Femke J. M. M. Nijsse
Earth Syst. Dynam., 15, 829–852, https://doi.org/10.5194/esd-15-829-2024, https://doi.org/10.5194/esd-15-829-2024, 2024
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Emergent constraints on equilibrium climate sensitivity (ECS) have generally got statistically weaker in the latest set of state-of-the-art climate models (CMIP6) compared to past sets (CMIP5). We look at why this weakening happened for one particular study (Cox et al, 2018) and attribute it to an assumption made in the theory that when corrected for restores there is a stronger relationship between predictor and ECS.
Rebecca M. Varney, Pierre Friedlingstein, Sarah E. Chadburn, Eleanor J. Burke, and Peter M. Cox
Biogeosciences, 21, 2759–2776, https://doi.org/10.5194/bg-21-2759-2024, https://doi.org/10.5194/bg-21-2759-2024, 2024
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Soil carbon is the largest store of carbon on the land surface of Earth and is known to be particularly sensitive to climate change. Understanding this future response is vital to successfully meeting Paris Agreement targets, which rely heavily on carbon uptake by the land surface. In this study, the individual responses of soil carbon are quantified and compared amongst CMIP6 Earth system models used within the most recent IPCC report, and the role of soils in the land response is highlighted.
Rebecca M. Varney, Sarah E. Chadburn, Eleanor J. Burke, Simon Jones, Andy J. Wiltshire, and Peter M. Cox
Biogeosciences, 20, 3767–3790, https://doi.org/10.5194/bg-20-3767-2023, https://doi.org/10.5194/bg-20-3767-2023, 2023
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This study evaluates soil carbon projections during the 21st century in CMIP6 Earth system models. In general, we find a reduced spread of changes in global soil carbon in CMIP6 compared to the previous CMIP5 generation. The reduced CMIP6 spread arises from an emergent relationship between soil carbon changes due to change in plant productivity and soil carbon changes due to changes in turnover time. We show that this relationship is consistent with false priming under transient climate change.
Nina Raoult, Tim Jupp, Ben Booth, and Peter Cox
Earth Syst. Dynam., 14, 723–731, https://doi.org/10.5194/esd-14-723-2023, https://doi.org/10.5194/esd-14-723-2023, 2023
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Climate models are used to predict the impact of climate change. However, poorly constrained parameters used in the physics of the models mean that we simulate a large spread of possible future outcomes. We can use real-world observations to reduce the uncertainty of parameter values, but we do not have observations to reduce the spread of possible future outcomes directly. We present a method for translating the reduction in parameter uncertainty into a reduction in possible model projections.
Paul D. L. Ritchie, Hassan Alkhayuon, Peter M. Cox, and Sebastian Wieczorek
Earth Syst. Dynam., 14, 669–683, https://doi.org/10.5194/esd-14-669-2023, https://doi.org/10.5194/esd-14-669-2023, 2023
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Complex systems can undergo abrupt changes or tipping points when external forcing crosses a critical level and are of increasing concern because of their severe impacts. However, tipping points can also occur when the external forcing changes too quickly without crossing any critical levels, which is very relevant for Earth’s systems and contemporary climate. We give an intuitive explanation of such rate-induced tipping and provide illustrative examples from natural and human systems.
Chris Huntingford, Peter M. Cox, Mark S. Williamson, Joseph J. Clarke, and Paul D. L. Ritchie
Earth Syst. Dynam., 14, 433–442, https://doi.org/10.5194/esd-14-433-2023, https://doi.org/10.5194/esd-14-433-2023, 2023
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Emergent constraints (ECs) reduce the spread of projections between climate models. ECs estimate changes to climate features impacting adaptation policy, and with this high profile, the method is under scrutiny. Asking
What is an EC?, we suggest they are often the discovery of parameters that characterise hidden large-scale equations that climate models solve implicitly. We present this conceptually via two examples. Our analysis implies possible new paths to link ECs and physical processes.
Isobel M. Parry, Paul D. L. Ritchie, and Peter M. Cox
Earth Syst. Dynam., 13, 1667–1675, https://doi.org/10.5194/esd-13-1667-2022, https://doi.org/10.5194/esd-13-1667-2022, 2022
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Despite little evidence of regional Amazon rainforest dieback, many localised abrupt dieback events are observed in the latest state-of-the-art global climate models under anthropogenic climate change. The detected dieback events would still cause severe consequences for local communities and ecosystems. This study suggests that 7 ± 5 % of the northern South America region would experience abrupt downward shifts in vegetation carbon for every degree of global warming past 1.5 °C.
Rebecca M. Varney, Sarah E. Chadburn, Eleanor J. Burke, and Peter M. Cox
Biogeosciences, 19, 4671–4704, https://doi.org/10.5194/bg-19-4671-2022, https://doi.org/10.5194/bg-19-4671-2022, 2022
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Soil carbon is the Earth’s largest terrestrial carbon store, and the response to climate change represents one of the key uncertainties in obtaining accurate global carbon budgets required to successfully militate against climate change. The ability of climate models to simulate present-day soil carbon is therefore vital. This study assesses soil carbon simulation in the latest ensemble of models which allows key areas for future model development to be identified.
Garry D. Hayman, Edward Comyn-Platt, Chris Huntingford, Anna B. Harper, Tom Powell, Peter M. Cox, William Collins, Christopher Webber, Jason Lowe, Stephen Sitch, Joanna I. House, Jonathan C. Doelman, Detlef P. van Vuuren, Sarah E. Chadburn, Eleanor Burke, and Nicola Gedney
Earth Syst. Dynam., 12, 513–544, https://doi.org/10.5194/esd-12-513-2021, https://doi.org/10.5194/esd-12-513-2021, 2021
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We model greenhouse gas emission scenarios consistent with limiting global warming to either 1.5 or 2 °C above pre-industrial levels. We quantify the effectiveness of methane emission control and land-based mitigation options regionally. Our results highlight the importance of reducing methane emissions for realistic emission pathways that meet the global warming targets. For land-based mitigation, growing bioenergy crops on existing agricultural land is preferable to replacing forests.
Andrew J. Wiltshire, Eleanor J. Burke, Sarah E. Chadburn, Chris D. Jones, Peter M. Cox, Taraka Davies-Barnard, Pierre Friedlingstein, Anna B. Harper, Spencer Liddicoat, Stephen Sitch, and Sönke Zaehle
Geosci. Model Dev., 14, 2161–2186, https://doi.org/10.5194/gmd-14-2161-2021, https://doi.org/10.5194/gmd-14-2161-2021, 2021
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Limited nitrogen availbility can restrict the growth of plants and their ability to assimilate carbon. It is important to include the impact of this process on the global land carbon cycle. This paper presents a model of the coupled land carbon and nitrogen cycle, which is included within the UK Earth System model to improve projections of climate change and impacts on ecosystems.
Bettina K. Gier, Michael Buchwitz, Maximilian Reuter, Peter M. Cox, Pierre Friedlingstein, and Veronika Eyring
Biogeosciences, 17, 6115–6144, https://doi.org/10.5194/bg-17-6115-2020, https://doi.org/10.5194/bg-17-6115-2020, 2020
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Models from Coupled Model Intercomparison Project (CMIP) phases 5 and 6 are compared to a satellite data product of column-averaged CO2 mole fractions (XCO2). The previously believed discrepancy of the negative trend in seasonal cycle amplitude in the satellite product, which is not seen in in situ data nor in the models, is attributed to a sampling characteristic. Furthermore, CMIP6 models are shown to have made progress in reproducing the observed XCO2 time series compared to CMIP5.
Cited articles
Argles, A. P. K., Moore, J. R., and Cox, P. M.: Dynamic Global Vegetation
Models: Searching for the balance between demographic process representation
and computational tractability, PLOS Climate, 1, 1–18,
https://doi.org/10.1371/journal.pclm.0000068, 2022. a
Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., and
Wood, E. F.: Present and future Köppen-Geiger climate classification maps at
1-km resolution, Sci. Data, 5, 180214, https://doi.org/10.1038/sdata.2018.214,
2018. a, b
Boisvert-Marsh, L., Périé, C., and Blois, S. D.: Shifting with climate?
Evidence for recent changes in tree species distribution at high latitudes,
Ecosphere, 5, 23, https://doi.org/10.1890/ES14-00111.1, 2014. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols,
M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Ghattas, J., Lebas, N.,
Lurton, T., Mellul, L., Musat, I., Mignot, J., and Cheruy, F.: IPSL
IPSL-CM6A-LR model output prepared for CMIP6 CMIP historical,
https://doi.org/10.22033/ESGF/CMIP6.5195, 2018. a
Boucher, O., Denvil, S., Levavasseur, G., Cozic, A., Caubel, A., Foujols,
M.-A., Meurdesoif, Y., Cadule, P., Devilliers, M., Dupont, E., and Lurton,
T.: IPSL IPSL-CM6A-LR model output prepared for CMIP6 ScenarioMIP ssp585,
https://doi.org/10.22033/ESGF/CMIP6.5271, 2019. a
CAS CAS-ESM1.0 model output prepared for CMIP6 ScenarioMIP ssp585,
http://cera-www.dkrz.de/WDCC/meta/CMIP6/CMIP6.ScenarioMIP.CAS.CAS-ESM2-0.ssp585 (last access: 23 January 2023),
2018. a
Chai, Z.: CAS CAS-ESM1.0 model output prepared for CMIP6 CMIP historical,
https://doi.org/10.22033/ESGF/CMIP6.3353, 2020. a
Cox, P., Huntingford, C., Nuttall, P., and Sparey, M.: Climate, Ticks and
Disease, CABI, https://doi.org/10.1079/9781789249637.0003, 2021. a
Danabasoglu, G.: NCAR CESM2 model output prepared for CMIP6 ScenarioMIP ssp585,
https://doi.org/10.22033/ESGF/CMIP6.7768, 2019a. a
Danabasoglu, G.: NCAR CESM2 model output prepared for CMIP6 CMIP historical,
https://doi.org/10.22033/ESGF/CMIP6.7627, 2019b. a
Danabasoglu, G.: NCAR CESM2-WACCM model output prepared for CMIP6 CMIP
historical, https://doi.org/10.22033/ESGF/CMIP6.10071, 2019c. a
Danabasoglu, G.: NCAR CESM2-WACCM model output prepared for CMIP6 ScenarioMIP
ssp585, https://doi.org/10.22033/ESGF/CMIP6.10115, 2019d. a
Danek, C., Shi, X., Stepanek, C., Yang, H., Barbi, D., Hegewald, J., and
Lohmann, G.: AWI AWI-ESM1.1LR model output prepared for CMIP6 CMIP
historical, https://doi.org/10.22033/ESGF/CMIP6.9328, 2020. a
Dix, M., Bi, D., Dobrohotoff, P., Fiedler, R., Harman, I., Law, R., Mackallah,
C., Marsland, S., O'Farrell, S., Rashid, H., Srbinovsky, J., Sullivan, A.,
Trenham, C., Vohralik, P., Watterson, I., Williams, G., Woodhouse, M.,
Bodman, R., Dias, F. B., Domingues, C. M., Hannah, N., Heerdegen, A., Savita,
A., Wales, S., Allen, C., Druken, K., Evans, B., Richards, C., Ridzwan,
S. M., Roberts, D., Smillie, J., Snow, K., Ward, M., and Yang, R.:
CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 CMIP historical,
https://doi.org/10.22033/ESGF/CMIP6.4271, 2019a. a
Dix, M., Bi, D., Dobrohotoff, P., Fiedler, R., Harman, I., Law, R., Mackallah,
C., Marsland, S., O'Farrell, S., Rashid, H., Srbinovsky, J., Sullivan, A.,
Trenham, C., Vohralik, P., Watterson, I., Williams, G., Woodhouse, M.,
Bodman, R., Dias, F. B., Domingues, C. M., Hannah, N., Heerdegen, A., Savita,
A., Wales, S., Allen, C., Druken, K., Evans, B., Richards, C., Ridzwan,
S. M., Roberts, D., Smillie, J., Snow, K., Ward, M., and Yang, R.:
CSIRO-ARCCSS ACCESS-CM2 model output prepared for CMIP6 ScenarioMIP ssp585,
https://doi.org/10.22033/ESGF/CMIP6.4332, 2019b. a
EC-Earth-Consortium: EC-Earth-Consortium EC-Earth3 model output prepared for
CMIP6 ScenarioMIP ssp585, https://doi.org/10.22033/ESGF/CMIP6.4912, 2019a. a
EC-Earth-Consortium: EC-Earth-Consortium EC-Earth3 model output prepared for
CMIP6 CMIP historical, https://doi.org/10.22033/ESGF/CMIP6.4700, 2019b. a
EC-Earth-Consortium: EC-Earth-Consortium EC-Earth3-Veg model output prepared
for CMIP6 ScenarioMIP ssp585, https://doi.org/10.22033/ESGF/CMIP6.4914,
2019c. a
EC-Earth-Consortium: EC-Earth-Consortium EC-Earth3-Veg model output prepared
for CMIP6 CMIP historical, https://doi.org/10.22033/ESGF/CMIP6.4706,
2019d. a
Every, J. P., Li, L., and Dorrell, D. G.: Köppen-Geiger climate classification
adjustment of the BRL diffuse irradiation model for Australian locations,
Renew. Energ., 147, 2453–2469, https://doi.org/10.1016/j.renene.2019.09.114, 2020. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, [data set], Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a, b, c
Feng, S., Hu, Q., Huang, W., Ho, C.-H., Li, R., and Tang, Z.: Projected climate
regime shift under future global warming from multi-model, multi-scenario
CMIP5 simulations, Global Planet. Change, 112, 41–52,
https://doi.org/10.1016/j.gloplacha.2013.11.002, 2014. a, b
Flores, B. M. and Holmgren, M.: White-Sand Savannas Expand at the Core of the Amazon After Forest Wildfires, Ecosystems, 24, 1624–1637, https://doi.org/10.1007/s10021-021-0060, 2021. a
Good, P., Sellar, A., Tang, Y., Rumbold, S., Ellis, R., Kelley, D., and
Kuhlbrodt, T.: MOHC UKESM1.0-LL model output prepared for CMIP6 ScenarioMIP
ssp585, https://doi.org/10.22033/ESGF/CMIP6.6405, 2019. a
Harris, I., Osborn, T., Jones, P., and Lister, D.: Version 4 of the CRU TS
monthly high-resolution gridded multivariate climate dataset, Sci.
Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020. a
Kayes, I. and Mallik, A.: Boreal Forests: Distributions, Biodiversity, and
Management, Part of the Encyclopedia of the UN Sustainable Development Goals book series (ENUNSDG), https://doi.org/10.1007/978-3-319-71065-5_17-1, 2020. a
Kim, J.-B. and Bae, D.-H.: The Impacts of Global Warming on Climate Zone
Changes Over Asia Based on CMIP6 Projections, Earth Space Sci., 8,
e2021EA001701, https://doi.org/10.1029/2021EA001701, 2021. a, b
Kim, Y. H., Min, S. K., Zhang, X., Sillmann, J., and Sandstad, M.: Evaluation
of the CMIP6 multi-model ensemble for climate extreme indices, Weather
Clim. Ext., 29, 100269, https://doi.org/10.1016/j.wace.2020.100269, 2020. a
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: World Map of the
Köppen-Geiger Climate Classification Updated, Meteorol. Z.,
15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006. a
Köppen, W.: Die Wärmezonen der Erde, nach der Dauer der heissen, gemässigten und kalten Zeit und nach der Wirkung der Wärme auf die
organische Welt betrachtet, Meteorol. Z., 1, 215–226,
https://doi.org/10.1007/s11367-013-0693-y, 1884. a
Köppen, W.: Das geographische System der Klimate,, Gebrüder Borntraeger,
1–44, 1936. a
Lee, W.-L. and Liang, H.-C.: AS-RCEC TaiESM1.0 model output prepared for CMIP6
ScenarioMIP ssp585, https://doi.org/10.22033/ESGF/CMIP6.9823, 2020a. a
Lee, W.-L. and Liang, H.-C.: AS-RCEC TaiESM1.0 model output prepared for CMIP6
CMIP historical, https://doi.org/10.22033/ESGF/CMIP6.9755, 2020b. a
Li, J., Miao, C., Wei, W., Zhang, G., Hua, L., Chen, Y., and Wang, X.:
Evaluation of CMIP6 Global Climate Models for Simulating Land Surface Energy
and Water Fluxes During 1979–2014, J. Adv. Model. Earth Sy., 13, e2021MS002515, https://doi.org/10.1029/2021MS002515, 2021. a
Lugo, A. E., Brown, S. L., Dodson, R., Smith, T. S., and Shugart, H. H.: The
Holdridge life zones of the conterminous United States in relation to
ecosystem mapping, J. Biogeogr., 26, 1025–1038,
https://doi.org/10.1046/j.1365-2699.1999.00329.x, 1999. a
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S., Péan, C., Berger, S.,
Caud, N., Chen, Y., Goldfarb, L., Gomis, M., Huang, M., Leitzell, K., Lonnoy,
E., Matthews, J., Maycock, T., Waterfield, T., Yelekçi, O., Yu, R., and
Zhou, B. E.: Summary for Policymakers, in: Climate Change 2021: The Physical
Science Basis, Contribution of Working Group I to the Sixth Assessment Report
of the Intergovernmental Panel on Climate Change, IPCC, Cambridge University
Press., https://doi.org/10.1017/9781009157896, 2021. a
McKenney, D. W., Pedlar, J. H., Lawrence, K., Campbell, K., and Hutchinson,
M. F.: Potential Impacts of Climate Change on the Distribution of North
American Trees, Bioscience, 57, 939–948, https://doi.org/10.1641/B571106, 2007. a
Nijsse, F. J. M. M., Cox, P. M., and Williamson, M. S.: Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models, Earth Syst. Dynam., 11, 737–750, https://doi.org/10.5194/esd-11-737-2020, 2020. a
Peel, M. C., McMahon, T. A., Finlayson, B. L., and Watson, F. G.:
Identification and explanation of continental differences in the variability
of annual runoff, J. Hydrol., 250, 224–240,
https://doi.org/10.1016/S0022-1694(01)00438-3, 2001. a
Phillips, T. J. and Bonfils, C. J. W.: Köppen bioclimatic evaluation of CMIP
historical climate simulations, Environ. Res. Lett., 10, 064005,
https://doi.org/10.1088/1748-9326/10/6/064005, 2015. a
Pörtner, H.-O., Roberts, D., Poloczanska, E., Mintenbeck, K., Tignor, M.,
Alegría, A., Craig, M., Langsdorf, S., S., L., Möller, V., and Okem, A. E.:
Summary for Policymakers, in: Climate Change 2022: Impacts, Adaptation, and
Vulnerability. Contribution of Working Group II to the Sixth Assessment
Report of the Intergovernmental Panel on Climate Change, IPCC, Cambridge
University Press., https://doi.org/10.1017/9781009325844, 2022. a
Rahimi, J., Laux, P., and Khalili, A.: Assessment of climate change over Iran:
CMIP5 results and their presentation in terms of Köppen–Geiger climate
zones, Theore. Appl. Climatol., 141, 183–199,
https://doi.org/10.1007/s00704-020-03190-8, 2020. a
Russell, R. J.: Dry climates of the United States, University of California
publications in geography, University of California Press, 1931. a
Semmler, T., Danilov, S., Rackow, T., Sidorenko, D., Barbi, D., Hegewald, J.,
Pradhan, H. K., Sein, D., Wang, Q., and Jung, T.: AWI AWI-CM1.1MR model
output prepared for CMIP6 ScenarioMIP ssp585, https://doi.org/10.22033/ESGF/CMIP6.2817,
2019. a
Sherwood, S. C., Webb, M. J., Annan, J. D., Armour, K. C., Forster, P. M.,
Hargreaves, J. C., Hegerl, G., Klein, S. A., Marvel, K. D., Rohling, E. J.,
Watanabe, M., Andrews, T., Braconnot, P., Bretherton, C. S., Foster, G. L.,
Hausfather, Z., von der Heydt, A. S., Knutti, R., Mauritsen, T., Norris,
J. R., Proistosescu, C., Rugenstein, M., Schmidt, G. A., Tokarska, K. B., and
Zelinka, M. D.: An Assessment of Earth's Climate Sensitivity Using Multiple
Lines of Evidence, Rev. Geophys., 58, e2019RG000678,
https://doi.org/10.1029/2019RG000678, 2020. a
Sillmann, J., Kharin, V. V., Zhang, X., Zwiers, F. W., and Bronaugh, D.:
Climate extremes indices in the CMIP5 multimodel ensemble: Part 1, Model
evaluation in the present climate, J. Geophys. Res.-Atmos., 118, 1716–1733, https://doi.org/10.1002/jgrd.50203, 2013. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett,
N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G.,
Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L.,
von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model
output prepared for CMIP6 CMIP historical, https://doi.org/10.22033/ESGF/CMIP6.3610,
2019a.
a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett,
N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G.,
Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L.,
von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5 model
output prepared for CMIP6 ScenarioMIP ssp585, https://doi.org/10.22033/ESGF/CMIP6.3696,
2019b. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett,
N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G.,
Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L.,
von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5-CanOE
model output prepared for CMIP6 CMIP historical,
https://doi.org/10.22033/ESGF/CMIP6.10260, 2019c. a
Swart, N. C., Cole, J. N., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett,
N. P., Anstey, J., Arora, V., Christian, J. R., Jiao, Y., Lee, W. G.,
Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Solheim, L.,
von Salzen, K., Yang, D., Winter, B., and Sigmond, M.: CCCma CanESM5-CanOE
model output prepared for CMIP6 ScenarioMIP ssp585,
https://doi.org/10.22033/ESGF/CMIP6.10276, 2019d. a
Tang, Y., Rumbold, S., Ellis, R., Kelley, D., Mulcahy, J., Sellar, A., Walton,
J., and Jones, C.: MOHC UKESM1.0-LL model output prepared for CMIP6 CMIP
historical, https://doi.org/10.22033/ESGF/CMIP6.6113, 2019. a
Viacheslav, K., Kenneth, R., and Maria, D.: Evidence of Evergreen Conifer
Invasion into Larch Dominated Forests During Recent Decades in Central
Siberia, 2007. a
Short summary
Accurate climate models are vital for mitigating climate change; however, projections often disagree. Using Köppen–Geiger bioclimate classifications we show that CMIP6 climate models agree well on the fraction of global land surface that will change classification per degree of global warming. We find that 13 % of land will change climate per degree of warming from 1 to 3 K; thus, stabilising warming at 1.5 rather than 2 K would save over 7.5 million square kilometres from bioclimatic change.
Accurate climate models are vital for mitigating climate change; however, projections often...
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