Articles | Volume 10, issue 4
https://doi.org/10.5194/esd-10-729-2019
© Author(s) 2019. 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-10-729-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluating climate emulation: fundamental impulse testing of simple climate models
Adria K. Schwarber
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA
Steven J. Smith
Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD 20742, USA
Joint Global Change Research Institute, 5825 University Research Ct, College Park, MD 20740, USA
Corinne A. Hartin
Joint Global Change Research Institute, 5825 University Research Ct, College Park, MD 20740, USA
Benjamin Aaron Vega-Westhoff
Joint Global Change Research Institute, 5825 University Research Ct, College Park, MD 20740, USA
Ryan Sriver
Department of Atmospheric Sciences, University of Illinois
Urbana–Champaign, Champaign, IL 61820, USA
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Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter A. Raymond, Pierre Regnier, Josep G. Canadell, Robert B. Jackson, Prabir K. Patra, Philippe Bousquet, Philippe Ciais, Edward J. Dlugokencky, Xin Lan, George H. Allen, David Bastviken, David J. Beerling, Dmitry A. Belikov, Donald R. Blake, Simona Castaldi, Monica Crippa, Bridget R. Deemer, Fraser Dennison, Giuseppe Etiope, Nicola Gedney, Lena Höglund-Isaksson, Meredith A. Holgerson, Peter O. Hopcroft, Gustaf Hugelius, Akihiko Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul B. Krummel, Ronny Lauerwald, Tingting Li, Xiangyu Liu, Kyle C. McDonald, Joe R. Melton, Jens Mühle, Jurek Müller, Fabiola Murguia-Flores, Yosuke Niwa, Sergio Noce, Shufen Pan, Robert J. Parker, Changhui Peng, Michel Ramonet, William J. Riley, Gerard Rocher-Ros, Judith A. Rosentreter, Motoki Sasakawa, Arjo Segers, Steven J. Smith, Emily H. Stanley, Joël Thanwerdas, Hanqin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido R. van der Werf, Douglas E. J. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, https://doi.org/10.5194/essd-17-1873-2025, 2025
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Methane (CH4) is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). A consortium of multi-disciplinary scientists synthesise and update the budget of the sources and sinks of CH4. This edition benefits from important progress in estimating emissions from lakes and ponds, reservoirs, and streams and rivers. For the 2010s decade, global CH4 emissions are estimated at 575 Tg CH4 yr-1, including ~65 % from anthropogenic sources.
William Lamb, Robbie Andrew, Matthew Jones, Zebedee Nicholls, Glen Peters, Chris Smith, Marielle Saunois, Giacomo Grassi, Julia Pongratz, Steven Smith, Francesco Tubiello, Monica Crippa, Matthew Gidden, Pierre Friedlingstein, Jan Minx, and Piers Forster
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-188, https://doi.org/10.5194/essd-2025-188, 2025
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This study explores why global greenhouse gas (GHG) emissions estimates vary. Key reasons include different coverage of gases and sectors, varying definitions of anthropogenic land use change emissions, and the Paris Agreement not covering all emission sources. The study highlights three main ways emissions data is reported, each with different objectives and resulting in varying global emission totals. It emphasizes the need for transparency in choosing datasets and setting assessment scopes.
Diego Guizzardi, Monica Crippa, Tim Butler, Terry Keating, Rosa Wu, Jacek W. Kamiński, Jeroen Kuenen, Junichi Kurokawa, Satoru Chatani, Tazuko Morikawa, George Pouliot, Jacinthe Racine, Michael D. Moran, Zbigniew Klimont, Patrick M. Manseau, Rabab Mashayekhi, Barron H. Henderson, Steven J. Smith, Rachel Hoesly, Marilena Muntean, Manjola Banja, Edwin Schaaf, Federico Pagani, Jung-Hun Woo, Jinseok Kim, Enrico Pisoni, Junhua Zhang, David Niemi, Mourad Sassi, Annie Duhamel, Tabish Ansari, Kristen Foley, Guannan Geng, Yifei Chen, and Qiang Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-601, https://doi.org/10.5194/essd-2024-601, 2025
Preprint under review for ESSD
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The global air pollution emission mosaic HTAP_v3.1 is the state-of-the-art database for addressing the evolution of a set of policy-relevant air pollutants over the past 2 decades. The inventory is made by the harmonization and blending of seven regional inventories, gapfilled using the most recent release of EDGAR (EDGARv8). By incorporating the best available local information, the HTAP_v3.1 mosaic inventory can be used for policy-relevant studies at both regional and global levels.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
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Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Antonin Soulie, Claire Granier, Sabine Darras, Nicolas Zilbermann, Thierno Doumbia, Marc Guevara, Jukka-Pekka Jalkanen, Sekou Keita, Cathy Liousse, Monica Crippa, Diego Guizzardi, Rachel Hoesly, and Steven J. Smith
Earth Syst. Sci. Data, 16, 2261–2279, https://doi.org/10.5194/essd-16-2261-2024, https://doi.org/10.5194/essd-16-2261-2024, 2024
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Anthropogenic emissions are the result of transportation, power generation, industrial, residential and commercial activities as well as waste treatment and agriculture practices. This work describes the new CAMS-GLOB-ANT gridded inventory of 2000–2023 anthropogenic emissions of air pollutants and greenhouse gases. The methodology to generate the emissions is explained and the datasets are analysed and compared with publicly available global and regional inventories for selected world regions.
Fei Liu, Steffen Beirle, Joanna Joiner, Sungyeon Choi, Zhining Tao, K. Emma Knowland, Steven J. Smith, Daniel Q. Tong, Siqi Ma, Zachary T. Fasnacht, and Thomas Wagner
Atmos. Chem. Phys., 24, 3717–3728, https://doi.org/10.5194/acp-24-3717-2024, https://doi.org/10.5194/acp-24-3717-2024, 2024
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Using satellite data, we developed a coupled method independent of the chemical transport model to map NOx emissions across US cities. After validating our technique with synthetic data, we charted NOx emissions from 2018–2021 in 39 cities. Our results closely matched EPA estimates but also highlighted some inconsistencies in both magnitude and spatial distribution. This research can help refine strategies for monitoring and managing air quality.
Hamza Ahsan, Hailong Wang, Jingbo Wu, Mingxuan Wu, Steven J. Smith, Susanne Bauer, Harrison Suchyta, Dirk Olivié, Gunnar Myhre, Hitoshi Matsui, Huisheng Bian, Jean-François Lamarque, Ken Carslaw, Larry Horowitz, Leighton Regayre, Mian Chin, Michael Schulz, Ragnhild Bieltvedt Skeie, Toshihiko Takemura, and Vaishali Naik
Atmos. Chem. Phys., 23, 14779–14799, https://doi.org/10.5194/acp-23-14779-2023, https://doi.org/10.5194/acp-23-14779-2023, 2023
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We examine the impact of the assumed effective height of SO2 injection, SO2 and BC emission seasonality, and the assumed fraction of SO2 emissions injected as SO4 on climate and chemistry model results. We find that the SO2 injection height has a large impact on surface SO2 concentrations and, in some models, radiative flux. These assumptions are a
hiddensource of inter-model variability and may be leading to bias in some climate model results.
Monica Crippa, Diego Guizzardi, Tim Butler, Terry Keating, Rosa Wu, Jacek Kaminski, Jeroen Kuenen, Junichi Kurokawa, Satoru Chatani, Tazuko Morikawa, George Pouliot, Jacinthe Racine, Michael D. Moran, Zbigniew Klimont, Patrick M. Manseau, Rabab Mashayekhi, Barron H. Henderson, Steven J. Smith, Harrison Suchyta, Marilena Muntean, Efisio Solazzo, Manjola Banja, Edwin Schaaf, Federico Pagani, Jung-Hun Woo, Jinseok Kim, Fabio Monforti-Ferrario, Enrico Pisoni, Junhua Zhang, David Niemi, Mourad Sassi, Tabish Ansari, and Kristen Foley
Earth Syst. Sci. Data, 15, 2667–2694, https://doi.org/10.5194/essd-15-2667-2023, https://doi.org/10.5194/essd-15-2667-2023, 2023
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This study responds to the global and regional atmospheric modelling community's need for a mosaic of air pollutant emissions with global coverage, long time series, spatially distributed data at a high time resolution, and a high sectoral resolution in order to enhance the understanding of transboundary air pollution. The mosaic approach to integrating official regional emission inventories with a global inventory based on a consistent methodology ensures policy-relevant results.
Robin N. Thor, Mariano Mertens, Sigrun Matthes, Mattia Righi, Johannes Hendricks, Sabine Brinkop, Phoebe Graf, Volker Grewe, Patrick Jöckel, and Steven Smith
Geosci. Model Dev., 16, 1459–1466, https://doi.org/10.5194/gmd-16-1459-2023, https://doi.org/10.5194/gmd-16-1459-2023, 2023
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We report on an inconsistency in the latitudinal distribution of aviation emissions between two versions of a data product which is widely used by researchers. From the available documentation, we do not expect such an inconsistency. We run a chemistry–climate model to compute the effect of the inconsistency in emissions on atmospheric chemistry and radiation and find that the radiative forcing associated with aviation ozone is 7.6 % higher when using the less recent version of the data.
Steven J. Smith, Erin E. McDuffie, and Molly Charles
Atmos. Chem. Phys., 22, 13201–13218, https://doi.org/10.5194/acp-22-13201-2022, https://doi.org/10.5194/acp-22-13201-2022, 2022
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Emissions into the atmosphere of greenhouse gases (GHGs) and air pollutants, quantified in emission inventories, impact human health, ecosystems, and the climate. We review how air pollutant and GHG inventory activities have historically been structured and their different uses and requirements. We discuss the benefits of increasing coordination between air pollutant and GHG inventory development efforts, but also caution that there are differences in appropriate methodologies and applications.
Sumanta Sarkhel, Gunter Stober, Jorge L. Chau, Steven M. Smith, Christoph Jacobi, Subarna Mondal, Martin G. Mlynczak, and James M. Russell III
Ann. Geophys., 40, 179–190, https://doi.org/10.5194/angeo-40-179-2022, https://doi.org/10.5194/angeo-40-179-2022, 2022
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A rare gravity wave event was observed on the night of 25 April 2017 over northern Germany. An all-sky airglow imager recorded an upward-propagating wave at different altitudes in mesosphere with a prominent wave front above 91 km and faintly observed below. Based on wind and satellite-borne temperature profiles close to the event location, we have found the presence of a leaky thermal duct layer in 85–91 km. The appearance of this duct layer caused the wave amplitudes to diminish below 91 km.
Fei Liu, Zhining Tao, Steffen Beirle, Joanna Joiner, Yasuko Yoshida, Steven J. Smith, K. Emma Knowland, and Thomas Wagner
Atmos. Chem. Phys., 22, 1333–1349, https://doi.org/10.5194/acp-22-1333-2022, https://doi.org/10.5194/acp-22-1333-2022, 2022
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In this work, we present a novel method to infer NOx emissions and lifetimes based on tropospheric NO2 observations together with reanalysis wind fields for cities located in polluted backgrounds. We evaluate the accuracy of the method using synthetic NO2 observations derived from a high-resolution model simulation. Our work provides an estimate for uncertainties in satellite-derived emissions inferred from chemical transport model (CTM)-independent approaches.
Jan C. Minx, William F. Lamb, Robbie M. Andrew, Josep G. Canadell, Monica Crippa, Niklas Döbbeling, Piers M. Forster, Diego Guizzardi, Jos Olivier, Glen P. Peters, Julia Pongratz, Andy Reisinger, Matthew Rigby, Marielle Saunois, Steven J. Smith, Efisio Solazzo, and Hanqin Tian
Earth Syst. Sci. Data, 13, 5213–5252, https://doi.org/10.5194/essd-13-5213-2021, https://doi.org/10.5194/essd-13-5213-2021, 2021
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We provide a synthetic dataset on anthropogenic greenhouse gas (GHG) emissions for 1970–2018 with a fast-track extension to 2019. We show that GHG emissions continued to rise across all gases and sectors. Annual average GHG emissions growth slowed, but absolute decadal increases have never been higher in human history. We identify a number of data gaps and data quality issues in global inventories and highlight their importance for monitoring progress towards international climate goals.
Kalyn Dorheim, Steven J. Smith, and Ben Bond-Lamberty
Geosci. Model Dev., 14, 365–375, https://doi.org/10.5194/gmd-14-365-2021, https://doi.org/10.5194/gmd-14-365-2021, 2021
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Simple climate models are frequently used in research and decision-making communities because of their tractability and low computational cost. Simple climate models are diverse, including highly idealized and process-based models. Here we present a hybrid approach that combines the strength of two types of simple climate models in a flexible framework. This hybrid approach has provided insights into the climate system and opens an avenue for investigating radiative forcing uncertainties.
Erin E. McDuffie, Steven J. Smith, Patrick O'Rourke, Kushal Tibrewal, Chandra Venkataraman, Eloise A. Marais, Bo Zheng, Monica Crippa, Michael Brauer, and Randall V. Martin
Earth Syst. Sci. Data, 12, 3413–3442, https://doi.org/10.5194/essd-12-3413-2020, https://doi.org/10.5194/essd-12-3413-2020, 2020
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Global emission inventories are vital to understanding the impacts of air pollution on the environment, human health, and society. We update the open-source Community Emissions Data System (CEDS) to provide global gridded emissions of seven key air pollutants from 1970–2017 for 11 source sectors and multiple fuel types, including coal, solid biofuel, and liquid oil and natural gas. This dataset includes both monthly global gridded emissions and annual national totals.
Zebedee R. J. Nicholls, Malte Meinshausen, Jared Lewis, Robert Gieseke, Dietmar Dommenget, Kalyn Dorheim, Chen-Shuo Fan, Jan S. Fuglestvedt, Thomas Gasser, Ulrich Golüke, Philip Goodwin, Corinne Hartin, Austin P. Hope, Elmar Kriegler, Nicholas J. Leach, Davide Marchegiani, Laura A. McBride, Yann Quilcaille, Joeri Rogelj, Ross J. Salawitch, Bjørn H. Samset, Marit Sandstad, Alexey N. Shiklomanov, Ragnhild B. Skeie, Christopher J. Smith, Steve Smith, Katsumasa Tanaka, Junichi Tsutsui, and Zhiang Xie
Geosci. Model Dev., 13, 5175–5190, https://doi.org/10.5194/gmd-13-5175-2020, https://doi.org/10.5194/gmd-13-5175-2020, 2020
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Computational limits mean that we cannot run our most comprehensive climate models for all applications of interest. In such cases, reduced complexity models (RCMs) are used. Here, researchers working on 15 different models present the first systematic community effort to evaluate and compare RCMs: the Reduced Complexity Model Intercomparison Project (RCMIP). Our research ensures that users of RCMs can more easily evaluate the strengths, weaknesses and limitations of their tools.
Cited articles
Aamaas, B., Peters, G. P., and Fuglestvedt, J. S.: Simple emission metrics
for climate impacts, Earth Syst. Dynam., 4, 145–170,
https://doi.org/10.5194/esd-4-145-2013, 2013.
Allen, M. R., Shine, K. P., Fuglestvedt, J. S., Millar, R., Cain, M., Frame,
D. J., and Macey, A. H.: A solution to the misrepresentations of
CO2-equivalent emissions of 2 short-lived climate pollutants under ambitious mitigation, Clim. Atmos. Sci., 1, 16, https://doi.org/10.1038/s41612-018-0026-8, 2018.
Berntsen, T. and Fuglestvedt, J.: Global temperature responses to current
emissions from the transport sectors, P. Natl. Acad. Sci. USA, 105,
19154–19159, https://doi.org/10.1073/pnas.0804844105, 2008.
Calel, R. and Stainforth, D. A.: On the physics of three integrated
assessment models, B. Am. Meteorol. Soc., 98, 1199–1216, https://doi.org/10.1175/BAMS-D-16-0034.1, 2017.
Clune, T. L. and Rood, R. B.: Software Testing and Verification in Climate Model Development, IEEE Softw., 28, 49–55, https://doi.org/10.1109/MS.2011.117, 2011.
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W., Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P., Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.: Evaluation of Climate Models, in: Climate Change 2013: The Physical Science Basis, Contribution of Working Group I to the Fifth Assess-ment 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, Cambridge, UK and New York, NY, USA, 2013.
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A.,
Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 climate projections
due to carbon cycle feedbacks, J. Climate, 27, 511–526,
https://doi.org/10.1175/JCLI-D-12-00579.1, 2014.
Fuglestvedt, J. S., Berntsen, T. K., Godal, O., Sausen, R., Shine, K. P., and
Skodvin, T.: Metrics of climate change: Assessing radiative forcing and
emission indices, Climatic Change, 58, 267–331, https://doi.org/10.1023/A:1023905326842, 2003.
Fuglestvedt, J. S., Shine, K. P., Berntsen, T., Cook, J., Lee, D. S., Stenke, A., Skeie, R. B., Velders, G. J. M. and Waitz, I. A.: Transport impacts on atmosphere and climate: Metrics, Atmos. Environ., 44, 4648–4677, https://doi.org/10.1016/j.atmosenv.2009.04.044, 2010.
Gasser, T., Peters, G. P., Fuglestvedt, J. S., Collins, W. J., Shindell, D. T., and Ciais, P.: Accounting for the climate–carbon feedback in emission metric, Earth Syst. Dynam., 8, 235–253, https://doi.org/10.5194/esd-8-235-2017, 2017.
Geoffroy, O., Saint-martin, D., Olivié, D. J. L., Voldoire, A., Bellon,
G., and Tytéca, S.: Transient climate response in a two-layer
energy-balance model. Part I: Analytical solution and parameter calibration
using CMIP5 AOGCM experiments, J. Climate, 26, 1841–1857,
https://doi.org/10.1175/JCLI-D-12-00195.1, 2013.
Good, P., Gregory, J. M., and Lowe, J. A.: A step-response simple climate
model to reconstruct and interpret AOGCM projections, Geophys. Res. Lett.,
38, L01703, https://doi.org/10.1029/2010GL045208, 2011.
Harmsen, M. J. H. M., Van Vuuren, D. P., Van Den Berg, M., Hof, A. F., Hope,
C., Krey, V., Lamarque, J.-F., Marcucci, A., Shindell, D. T., and Schaeffer,
M.: How well do integrated assessment models represent non-CO2 radiative forcing?, Climatic Change, 133, 565–582, https://doi.org/10.1007/s10584-015-1485-0, 2015.
Hartin, C. A., Patel, P., Schwarber, A., Link, R. P., and Bond-Lamberty, B. P.: A simple object-oriented and open-source model for scientific and policy
analyses of the global climate system – Hector v1.0, Geosci. Model Dev.,
8, 939–955, https://doi.org/10.5194/gmd-8-939-2015, 2015.
Hartin, C. A., Bond-Lamberty, B., Patel, P., and Mundra, A.: Ocean acidification over the next three centuries using a simple global climate carbon-cycle model: projections and sensitivities, Biogeosciences, 13, 4329–4342, https://doi.org/10.5194/bg-13-4329-2016, 2016.
Hooss, G., Voss, R., Hasselmann, K., Maier-Reimer, E., and Joos, F.: A
nonlinear impulse response model of the coupled carbon cycle-climate system (NICCS), Clim. Dynam., 18, 189–202, https://doi.org/10.1007/s003820100170, 2001.
Hope, C.: The Marginal Impact of CO2 from PAGE2002: An Integrated Assessment Model Incorporating the IPCC's Five Reasons for Concern, Integr. Assess. J., 6, 16–56, https://doi.org/10.1016/j.jns.2003.09.014, 2006.
Joos, F. and Bruno, M.: Pulse response functions are cost-efficient tools to
model the link between carbon emissions, atmospheric CO2 and global warming, Phys. Chem. Earth, 21, 471–476, https://doi.org/10.1016/S0079-1946(97)81144-5, 1996.
Joos, F., Müller-Fürstenberger, G., and Stephan, G.: Correcting the
carbon cycle representation: How important is it for the economics of climate change?, Environ. Model. Assess., 4, 133–140, https://doi.org/10.1023/A:1019004015342, 1999.
Joos, F., Roth, R., Fuglestvedt, J. S., Peters, G. P., Enting, I. G., Von Bloh, W., Brovkin, V., Burke, E. J., Eby, M., Edwards, N. R., Friedrich, T., Frölicher, T. L., Halloran, P. R., Holden, P. B., Jones, C., Kleinen,
T., Mackenzie, F. T., Matsumoto, K., Meinshausen, M., Plattner, G. K.,
Reisinger, A., Segschneider, J., Shaffer, G., Steinacher, M., Strassmann, K., Tanaka, K., Timmermann, A., and Weaver, A. J.: Carbon dioxide and climate
impulse response functions for the computation of greenhouse gas metrics: A
multi-model analysis, Atmos. Chem. Phys., 13, 2793–2825,
https://doi.org/10.5194/acp-13-2793-2013, 2013.
Khodayari, A., Wuebbles, D. J., Olsen, S. C., Fuglestvedt, J. S., Berntsen, T., Lund, M. T., Waitz, I., Wolfe, P., Forster, P. M., Meinshausen, M., Lee,
D. S., and Lim, L. L.: Intercomparison of the capabilities of simplified
climate models to project the effects of aviation CO2 on climate, Atmos. Environ., 75, 321–328, https://doi.org/10.1016/J.ATMOSENV.2013.03.055, 2013.
Knutti, R. and Sedláček, J.: Robustness and uncertainties in the new
CMIP5 climate model projections, Nat. Clim. Change, 3, 1–5, https://doi.org/10.1038/nclimate1716, 2012.
Knutti, R., Allen, M. R., Friedlingstein, P., Gregory, J. M., Hegerl, G. C.,
Meehl, G. A., Meinshausen, M., Murphy, J. M., Plattner, G. K., Raper, S. C.
B., Stocker, T. F., Stott, P. A., Teng, H., and Wigley, T. M. L.: A review of
uncertainties in global temperature projections over the twenty-first
century, J. Climate, 21, 2651–2663, https://doi.org/10.1175/2007JCLI2119.1, 2008.
Kriegler, E.: Imprecise Probability Analysis for Integrated Assessment of
Climate Change, Time, available at:
https://publishup.uni-potsdam.de/opus4-ubp/frontdoor/index/index/docId/497
(last access: 29 October 2017), 2005.
Lucarini, V.: Revising and Extending the Linear Response Theory for Statistical Mechanical Systems: Evaluating Observables as Predictors and
Predictands, J. Stat. Phys., 173, 1698, https://doi.org/10.1007/s10955-018-2151-5, 2018.
Lucarini, V. and Sarno, S.: A statistical mechanical approach for the computation of the climatic response to general forcings, Nonlin. Processes
Geophys., 18, 7–28, https://doi.org/10.5194/npg-18-7-2011, 2011.
Meinshausen, M., Meinshausen, N., Hare, W., Raper, S. C. B., Frieler, K.,
Knutti, R., Frame, D. J., and Allen, M. R.: Greenhouse-gas emission targets
for limiting global warming to 2 ∘C, Nature, 458, 1158–1162, https://doi.org/10.1038/nature08017, 2009.
Meinshausen, M., Raper, S. C. B. and Wigley, T. M. L.: Emulating coupled
atmosphere–ocean and carbon cycle models with a simpler model, MAGICC6 –
Part 1: Model description and calibration, Atmos. Chem. Phys., 11,
1417–1456, https://doi.org/10.5194/acp-11-1417-2011, 2011.
Millar, J. R., Nicholls, Z. R., Friedlingstein, P., and Allen, M. R.: A
modified impulse-response representation of the global near-surface air
temperature and atmospheric concentration response to carbon dioxide
emissions, Atmos. Chem. Phys., 17, 7213–7228,
https://doi.org/10.5194/acp-17-7213-2017, 2017.
Millar, R. J., Otto, A., Forster, P. M., Lowe, J. A., Ingram, W. J., and Allen, M. R.: Model structure in observational constraints on transient
climate response, Climatic Change, 131, 199–211, https://doi.org/10.1007/s10584-015-1384-4, 2015.
Monckton, C., Soon, W. W. H., Legates, D. R., and Briggs, W. M.: Why models
run hot: results from an irreducibly simple climate model, Sci. Bull.,
60, 122–135, https://doi.org/10.1007/s11434-014-0699-2, 2015.
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K.,
van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., Meehl,
G. A., Mitchell, J. F. B., Nakicenovic, N., Riahi, K., Smith, S. J., Stouffer, R. J., Thomson, A. M., Weyant, J. P., and Wilbanks, T. J.: The next
generation of scenarios for climate change research and assessment, Nature,
463, 747–756, https://doi.org/10.1038/nature08823, 2010.
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and
Natural Radiative Forcing, in: Clim. Chang. 2013 Phys. Sci. Basis. Contrib.
Work. Gr. I to Fifth Assess. Rep. Intergov. Panel Clim. Chang., Cambridge University Press, Cambridge, 659–740,
https://doi.org/10.1017/CBO9781107415324.018, 2013.
National Academies of Sciences, Engineering, and Medicine: Assessment of
Approaches to Updating the Social Cost of Carbon: Phase 1 Report on a Near-Term Update, The National Academies Press, Washington, D.C.,
https://doi.org/10.17226/21898, 2016.
Ortiz, R. A. and Markandya, A.: Integrated Impact Assessment Models of Climate Change with an Emphasis on Damage Functions: a Literature Review,
Basqu. Cent. Clim. Chang., October 2009, 1–35, available at:
http://ideas.repec.org/p/bcc/wpaper/2009-06.html#download (last access: 5 August 2018), 2009.
Peters, G. P., Aamaas, B., Berntsen, T., and Fuglestvedt, J. S.: The integrated global temperature change potential (iGTP) and relationships
between emission metrics, Environ. Res. Lett., 6, 044021,
https://doi.org/10.1088/1748-9326/6/4/044021, 2011.
Raper, S. C. B., Wigley, T. M. L., and Warrick, R. A.: Sea-Level Rise and Coastal Subsidence: Causes, Consequences and Strategies, edited by: Milliman, J. D. and Haq, B. U., Kluwer, Dordrecht, the Netherlands, 11–45, 1996.
Rogelj, J., Meinshausen, M., and Knutti, R.: Global warming under old and new
scenarios using IPCC climate sensitivity range estimates, Nat. Clim. Change,
2, 248–253, https://doi.org/10.1038/nclimate1385, 2012.
Rogelj, J., Schaeffer, M., Meinshausen, M., Shindell, D. T., Hare, W., Klimont, Z., Velders, G. J. M., Amann, M., and Schellnhuber, H. J.:
Disentangling the effects of CO2 and short-lived climate forcer mitigation, P. Natl. Acad. Sci. USA, 111, 16325–16330, https://doi.org/10.1073/pnas.1415631111, 2014.
Ruelle, D.: A review of linear response theory for general differentiable
dynamical systems, Nonlinearity, 22, 855–870, https://doi.org/10.1088/0951-7715/22/4/009, 2009.
Sand, M., Berntsen, T. K., Von Salzen, K., Flanner, M. G., Langner, J., and
Victor, D. G.: Response of Arctic temperature to changes in emissions of
short-lived climate forcers, Nat. Clim. Change, 6, 286–289,
https://doi.org/10.1038/nclimate2880, 2016.
Sarofim, M. C. and Giordano, M. R.: A quantitative approach to evaluating the GWP timescale through implicit discount rates, Earth Syst. Dynam., 9, 1013–1024, https://doi.org/10.5194/esd-9-1013-2018, 2018.
Sausen, R. and Schumann, U.: Estimates of the Climate Response to Aircraft
CO2 and NOx Emissions Scenarios, Climatic Change, 44, 27–58, https://doi.org/10.1023/A:1005579306109, 2000.
Schneider, S. H. and Thompson, S. L.: V. A Simple Climate Model Used in Economic Studies of Global Change, Integr. Assess., 59–80, https://doi.org/10.1.1.423.2895, 2000.
Shindell, D.: Inhomogeneous forcing and transient climate sensitivity, Nat.
Clim. Change, 4, 274–277, https://doi.org/10.1038/nclimate2136, 2014.
Smith, S. J. and Bond, T. C.: Two hundred fifty years of aerosols and climate: The end of the age of aerosols, Atmos. Chem. Phys., 14, 537–549, https://doi.org/10.5194/acp-14-537-2014, 2014.
Stjern, C. W., Samset, B. H., Myhre, G., Forster, P. M., Hodnebrog, Ø.,
Andrews, T., Boucher, O., Faluvegi, G., Iversen, T., Kasoar, M., Kharin, V.,
Kirkevåg, A., Lamarque, J. F., Olivié, D., Richardson, T., Shawki, D., Shindell, D., Smith, C. J., Takemura, T., and Voulgarakis, A.: Rapid
Adjustments Cause Weak Surface Temperature Response to Increased Black Carbon Concentrations, J. Geophys. Res.-Atmos., 122, 11462–11481,
https://doi.org/10.1002/2017JD027326, 2017.
Strassmann, K. M. and Joos, F.: The Bern Simple Climate Model (BernSCM) v1.0: an extensible and fully documented open-source re-implementation of the Bern reduced-form model for global carbon cycle–climate simulations, Geosci. Model Dev., 11, 1887–1908, https://doi.org/10.5194/gmd-11-1887-2018, 2018.
Tanaka, K., Kriegler, E., Bruckner, T., Hooss, C., Knorr, W., and Raddatz, T.: Aggregated Carbon Cycle, Atmospheric Chemistry, and Climate Model (ACC2) – description of the forward and inverse models, Max Planck Institute for Meteorology, Hamburg, Germany, 1–188, 2007.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the experiment design, B. Am. Meteorol. Soc., 93, 485–498,
https://doi.org/10.1175/BAMS-D-11-00094.1, 2012.
Thompson, M. V. and Randerson, J. T.: Impulse response functions of terrestrial carbon cycle models: Method and application, Global Change Biol.,
5, 371–394, https://doi.org/10.1046/j.1365-2486.1999.00235.x, 1999.
Thompson, T. M.: Modeling the climate and carbon systems to estimate the social cost of carbon, Wiley Interdiscip. Rev. Clim. Change, 9, e532, https://doi.org/10.1002/wcc.532, 2018.
Thomson, A. M., Calvin, K. V., Smith, S. J., Kyle, G. P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M. A., Clarke, L. E., and
Edmonds, J. A.: RCP4.5: A pathway for stabilization of radiative forcing by 2100, Climatic Change, 109, 77–94, https://doi.org/10.1007/s10584-011-0151-4, 2011.
van Vuuren, D. P., Meinshausen, M., Plattner, G.-K., Joos, F., Strassmann, K. M., Smith, S. J., Wigley, T. M. L., Raper, S. C. B., Riahi, K., de la Chesnaye, F., den Elzen, M. G. J., Fujino, J., Jiang, K., Nakicenovic, N.,
Paltsev, S., and Reilly, J. M.: Temperature increase of 21st century mitigation scenarios, P. Natl. Acad. Sci. USA, 105, 15258–15262,
https://doi.org/10.1073/pnas.0711129105, 2008.
van Vuuren, D. P., Lowe, J., Stehfest, E., Gohar, L., Hof, A. F., Hope, C.,
Warren, R., Meinshausen, M., and Plattner, G. K.: How well do integrated
assessment models simulate climate change?, Climatic Change, 104, 255–285,
https://doi.org/10.1007/s10584-009-9764-2, 2011a.
van Vuuren, D. P., Edmonds, J., Kainuma, M., Riahi, K., Thomson, A., Hibbard, K., Hurtt, G. C., Kram, T., Krey, V., Lamarque, J.-F., Masui, T., Meinshausen, M., Nakicenovic, N., Smith, S. J., and Rose, S. K.: The
representative concentration pathways: an overview, Climatic Change, 109,
5–31, https://doi.org/10.1007/s10584-011-0148-z, 2011b.
Wigley, T. M. L. and Raper, S. C. B.: Reasons for Larger Warming Projections in the IPCC Third Assessment Report sponding warming range spanning uncertainties in both, J. Climate, 15, 2945–2952, 2002.
Yang, Y., Smith, S. J., Wang, H., Mills, C. M., and Rasch, P. J.: Variability, timescales, and nonlinearity in climate responses to black carbon emissions, Atmos. Chem. Phys., 19, 2405–2420, https://doi.org/10.5194/acp-19-2405-2019, 2019.
Short summary
Simple climate models (SCMs) underlie many important scientific and decision-making endeavors. This illustrates the need for their use to be rooted in a clear understanding of their fundamental responses. In this study, we provide a comprehensive assessment of model performance by evaluating the fundamental responses of several SCMs. We find biases in some responses, which have implications for decision science. We conclude by recommending a standard set of validation tests for any SCM.
Simple climate models (SCMs) underlie many important scientific and decision-making endeavors....
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