Evaluation of climate variability and change in ACCESS historical simulations for CMIP6
Harun A. Rashid
A * , Arnold Sullivan A , Martin Dix A , Daohua Bi A , Chloe Mackallah A , Tilo Ziehn A , Peter Dobrohotoff A , Siobhan O’Farrell A , Ian N. Harman B , Roger Bodman A and Simon Marsland A
+ Author Affiliations
- Author Affiliations
A CSIRO Oceans and Atmosphere, 107–121 Station Street, Aspendale, Vic. 3195, Australia.
B CSIRO Oceans and Atmosphere, Canberra, Australia.
* Correspondence to: harun.rashid@csiro.au
Journal of Southern Hemisphere Earth Systems Science 72(2) 73-92 https://doi.org/10.1071/ES21028
Submitted: 17 November 2021 Accepted: 31 March 2022 Published: 14 July 2022
© 2022 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of BoM. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)
Abstract
We analyse and document the historical simulations performed by two versions of the Australian Community Climate and Earth System Simulator (ACCESS-CM2 and ACCESS-ESM1.5) for the Coupled Model Intercomparison Project Phase 6 (CMIP6). Three ensemble members from each model are used to compare the simulated seasonal-mean climate, climate variability and climate change with observations over the historical period. Where appropriate, we also compare the ACCESS model results with the results from 36 other CMIP6 models. We find that the simulations of the winter and summer mean climates (over the global domain) by the two ACCESS models are similar to or better than most of the other CMIP6 models for surface temperature, precipitation and surface specific humidity. For sea-level pressure, both ACCESS models perform worse than most other models. The spatial structures of the prominent climate variability modes (ENSO, IOD, IPO and AMO) also compare favourably with the corresponding observed structures. However, the results for the simulation of the models’ temporal variability are mixed. In particular, whereas ACCESS-ESM1.5 simulates ENSO events with ~3-year periods (that are closer to the observed periods of 3–7 years), the ACCESS-CM2 simulates ENSO events having quasi-biennial periods. However, ACCESS-CM2 has a much smaller bias (−0.1 W m−2) in present-day top-of-the-atmosphere energy balance than ACCESS-ESM1.5 (−0.6 W m−2). The ACCESS models simulate the anthropogenic climate change signal in historical global-mean surface temperature reasonably well, although the simulated signal variances are ~10% weaker than the observed signal variance (a common bias in most CMIP6 models). Both models also well simulate the major features of observed surface temperature changes, as isolated using a multiple regression model. Despite some identified biases, the two ACCESS models provide high-quality climate simulations that may be used in further analyses of climate variability and change.
Keywords: ACCESS-CM2, ACCESS-ESM1.5, aerosols, climate change, climate variability modes, CMIP6, coupled climate model, earth system model, evaluation, greenhouse gases, historical simulation.
References
Adler RF, Gu G, Sapiano M, Wang JJ, Huffman GJ (2017) Global precipitation: means, variations and trends during the satellite era (1979–2014). Surveys in Geophysics 38, 679–699.| Global precipitation: means, variations and trends during the satellite era (1979–2014).Crossref | GoogleScholarGoogle Scholar |
Adler RF, Sapiano MRP, Huffman GJ, Wang J-J, Gu G, Bolvin D, et al. (2018) The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere 9, 138
| The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation.Crossref | GoogleScholarGoogle Scholar | 30013797PubMed |
Allan R, Ansell T (2006) A new globally complete monthly historical gridded mean sea level pressure dataset (HadSLP2): 1850–2004. Journal of Climate 19, 5816–5842.
| A new globally complete monthly historical gridded mean sea level pressure dataset (HadSLP2): 1850–2004.Crossref | GoogleScholarGoogle Scholar |
Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419, 228–232.
| Constraints on future changes in climate and the hydrologic cycle.Crossref | GoogleScholarGoogle Scholar | 12226678PubMed |
Behrangi A, Song Y (2020) A new estimate for oceanic precipitation amount and distribution using complementary precipitation observations from space and comparison with GPCP. Environmental Research Letters 15, 124042
| A new estimate for oceanic precipitation amount and distribution using complementary precipitation observations from space and comparison with GPCP.Crossref | GoogleScholarGoogle Scholar |
Bi D, Dix M, Marsland SJ, O’Farrell S, Rashid HA, Uotila P, et al. (2013) The ACCESS coupled model: description, control climate and evaluation. Australian Meteorological and Oceanographic Journal 63, 41–64.
| The ACCESS coupled model: description, control climate and evaluation.Crossref | GoogleScholarGoogle Scholar |
Bi D, Dix M, Marsland S, O’Farrell S, Sullivan A, Bodman R, et al. (2020) Configuration and spin-up of ACCESS-CM2, the new generation Australian Community Climate and Earth System Simulator Coupled Model. Journal of Southern Hemisphere Earth Systems Science 70, 225–251.
| Configuration and spin-up of ACCESS-CM2, the new generation Australian Community Climate and Earth System Simulator Coupled Model.Crossref | GoogleScholarGoogle Scholar |
Bi D, Wang G, Cai W (2022) Improved simulation of ENSO variability through feedback from the equatorial Atlantic in a Pacemaker Experiment. Geophysical Research Letters 49, e2021GL096887
| Improved simulation of ENSO variability through feedback from the equatorial Atlantic in a Pacemaker Experiment.Crossref | GoogleScholarGoogle Scholar |
Bodman RW, Karoly DJ, Dix MR, Harman IN, Srbinovsky J, Dobrohotoff PB, Mackallah C (2020) Evaluation of CMIP6 AMIP climate simulations with the ACCESS-AM2 model. Journal of Southern Hemisphere Earth Systems Science 70, 166–179.
| Evaluation of CMIP6 AMIP climate simulations with the ACCESS-AM2 model.Crossref | GoogleScholarGoogle Scholar |
Booth BBB, Dunstone NJ, Halloran PR, Andrews T, Bellouin N (2012) Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability. Nature 484, 228–232.
| Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability.Crossref | GoogleScholarGoogle Scholar |
Cai W, van Rensch P, Cowan T, Hendon HH (2011) Teleconnection pathways of ENSO and the IOD and the mechanisms for impacts on Australian rainfall. Journal of Climate 24, 3910–3923.
| Teleconnection pathways of ENSO and the IOD and the mechanisms for impacts on Australian rainfall.Crossref | GoogleScholarGoogle Scholar |
Cowtan K, Way RG (2014) Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Quarterly Journal of the Royal Meteorological Society 140, 1935–1944.
| Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends.Crossref | GoogleScholarGoogle Scholar |
Eyring V, Bony S, Meehl GA, Senior CA, Stevens B, Stouffer RJ, Taylor KE (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development 9, 1937–1958.
| Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization.Crossref | GoogleScholarGoogle Scholar |
Frankcombe LM, England MH, Mann ME, Steinman BA (2015) Separating internal variability from the externally forced climate response. Journal of Climate 28, 8184–8202.
| Separating internal variability from the externally forced climate response.Crossref | GoogleScholarGoogle Scholar |
Gillett NP, Shiogama H, Funke B, Hegerl G, Knutti R, Matthes K, et al. (2016) The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6. Geoscientific Model Development 9, 3685–3697.
| The Detection and Attribution Model Intercomparison Project (DAMIP v1.0) contribution to CMIP6.Crossref | GoogleScholarGoogle Scholar |
Goyal R, Jucker M, Sen Gupta A, Hendon HH, England MH (2021) Zonal wave 3 pattern in the Southern Hemisphere generated by tropical convection. Nature Geoscience 14, 732–738.
| Zonal wave 3 pattern in the Southern Hemisphere generated by tropical convection.Crossref | GoogleScholarGoogle Scholar |
Hawcroft M, Haywood JM, Collins M, Jones A, Jones AC, Stephens G (2017) Southern Ocean albedo, inter-hemispheric energy transports and the double ITCZ: global impacts of biases in a coupled model. Climate Dynamics 48, 2279–2295.
| Southern Ocean albedo, inter-hemispheric energy transports and the double ITCZ: global impacts of biases in a coupled model.Crossref | GoogleScholarGoogle Scholar |
Hawkins E, Sutton R (2016) Connecting climate model projections of global temperature change with the real world. Bulletin of the American Meteorological Society 97, 963–980.
| Connecting climate model projections of global temperature change with the real world.Crossref | GoogleScholarGoogle Scholar |
Hegerl GC, Brönnimann S, Cowan T, Friedman AR, Hawkins E, Iles C, et al. (2019) Causes of climate change over the historical record. Environmental Research Letters 14, 123006
| Causes of climate change over the historical record.Crossref | GoogleScholarGoogle Scholar |
Hendon HH (2003) Indonesian rainfall variability: impacts of ENSO and local air-sea interaction. Journal of Climate 16, 1775–1790.
| Indonesian rainfall variability: impacts of ENSO and local air-sea interaction.Crossref | GoogleScholarGoogle Scholar |
Henley BJ, Gergis J, Karoly DJ, Power S, Kennedy J, Folland CK (2015) A Tripole Index for the Interdecadal Pacific Oscillation. Climate Dynamics 45, 3077–3090.
| A Tripole Index for the Interdecadal Pacific Oscillation.Crossref | GoogleScholarGoogle Scholar |
Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J, et al. (2020) The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999–2049.
| The ERA5 global reanalysis.Crossref | GoogleScholarGoogle Scholar |
Hewitt HT, Copsey D, Culverwell ID, Harris CM, Hill RSR, Keen AB, et al. (2011) Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system. Geoscientific Model Development 4, 223–253.
| Design and implementation of the infrastructure of HadGEM3: the next-generation Met Office climate modelling system.Crossref | GoogleScholarGoogle Scholar |
Hwang Y-T, Frierson DMW (2013) Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean. Proceedings of the National Academy of Sciences of the United States of America 110, 4935–40.
| Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean.Crossref | GoogleScholarGoogle Scholar | 23493552PubMed |
Joshi MM, Lambert FH, Webb MJ (2013) An explanation for the difference between twentieth and twenty-first century land–sea warming ratio in climate models. Climate Dynamics 41, 1853–1869.
| An explanation for the difference between twentieth and twenty-first century land–sea warming ratio in climate models.Crossref | GoogleScholarGoogle Scholar |
Kosaka Y, Xie SP (2016) The tropical Pacific as a key pacemaker of the variable rates of global warming. Nature Geoscience 9, 669–673.
| The tropical Pacific as a key pacemaker of the variable rates of global warming.Crossref | GoogleScholarGoogle Scholar |
Loeb NG, Doelling DR, Wang H, Su W, Nguyen C, Corbett JG, et al. (2018) Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product. Journal of Climate 31, 895–918.
| Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) top-of-atmosphere (TOA) edition-4.0 data product.Crossref | GoogleScholarGoogle Scholar |
Mackallah C, Chamberlain MA, Law R, Dix M, Ziehn T, Bi D, Bodman R, Brown J, Dobrohotoff P, Druken K, Evans B, Harman IN, Hayashida H, Holmes R, Kiss A, Lenton A, Liu Y, Marsland S, Meissner K, Menviel L, O Farrell S, Rashid HA, Ridzwan S, Savita A, Srbinovsky J, Sullivan A, Trenham C, Vohralik P, Wang Y, Williams G, Woodhouse M, Yeung N (2022) ACCESS datasets for CMIP6: methodology and idealised experiments. Journal of Southern Hemisphere Earth Systems Science. [Published online XX July 2022].
| Crossref |
McIntosh PC, Hendon HH (2018) Understanding Rossby wave trains forced by the Indian Ocean Dipole. Climate Dynamics 50, 2783–2798.
| Understanding Rossby wave trains forced by the Indian Ocean Dipole.Crossref | GoogleScholarGoogle Scholar |
McKenna S, Santoso A, Gupta A, Sen , Taschetto AS, Cai W (2020) Indian Ocean Dipole in CMIP5 and CMIP6: characteristics, biases, and links to ENSO. Scientific Reports 10, 11500
| Indian Ocean Dipole in CMIP5 and CMIP6: characteristics, biases, and links to ENSO.Crossref | GoogleScholarGoogle Scholar | 32661240PubMed |
Meehl GA, Goddard L, Murphy J, Stouffer RJ, Boer G, Danabasoglu G, et al. (2009) Decadal prediction. Bulletin of the American Meteorological Society 90, 1467–1485.
| Decadal prediction.Crossref | GoogleScholarGoogle Scholar |
Mo KC, Higgins RW (1998) The Pacific–South American modes and tropical convection during the Southern Hemisphere winter. Monthly Weather Review 126, 1581–1596.
| The Pacific–South American modes and tropical convection during the Southern Hemisphere winter.Crossref | GoogleScholarGoogle Scholar |
Morice CP, Kennedy JJ, Rayner NA, Jones PD (2012) Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 data set. Journal of Geophysical Research – D. Atmospheres 117, D08101
| Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: the HadCRUT4 data set.Crossref | GoogleScholarGoogle Scholar |
Notz D (2020) Arctic Sea Ice in CMIP6. Geophysical Research Letters 47, e2019GL086749
| Arctic Sea Ice in CMIP6.Crossref | GoogleScholarGoogle Scholar |
Pendergrass AG, Hartmann DL (2014) The atmospheric energy constraint on global-mean precipitation change. Journal of Climate 27, 757–768.
| The atmospheric energy constraint on global-mean precipitation change.Crossref | GoogleScholarGoogle Scholar |
Pezza AB, Rashid HA, Simmonds I (2012) Climate links and recent extremes in antarctic sea ice, high-latitude cyclones, Southern Annular Mode and ENSO. Climate Dynamics 38, 57–73.
| Climate links and recent extremes in antarctic sea ice, high-latitude cyclones, Southern Annular Mode and ENSO.Crossref | GoogleScholarGoogle Scholar |
Planton YY, Guilyardi E, Wittenberg AT, Lee J, Gleckler PJ, Bayr T, McGregor S, McPhaden MJ, Power S, Roehrig R, Vialard J, Voldoire A (2021) Evaluating climate models with the CLIVAR 2020 ENSO metrics package. Bulletin of the American Meteorological Society 102, E193–E217.
| Evaluating climate models with the CLIVAR 2020 ENSO metrics package.Crossref | GoogleScholarGoogle Scholar |
Power S, Casey T, Folland C, Colman A, Mehta V (1999) Inter-decadal modulation of the impact of ENSO on Australia. Climate Dynamics 15, 319–324.
| Inter-decadal modulation of the impact of ENSO on Australia.Crossref | GoogleScholarGoogle Scholar |
Purich A, Cai W, England MH, Cowan T (2016) Evidence for link between modelled trends in Antarctic sea ice and underestimated westerly wind changes. Nature Communications 7, 10409
| Evidence for link between modelled trends in Antarctic sea ice and underestimated westerly wind changes.Crossref | GoogleScholarGoogle Scholar | 26842498PubMed |
Rashid HA (2020) Factors affecting ENSO predictability in a linear empirical model of tropical air-sea interactions. Scientific Reports 10, 3931
| Factors affecting ENSO predictability in a linear empirical model of tropical air-sea interactions.Crossref | GoogleScholarGoogle Scholar | 32127554PubMed |
Rashid HA (2021) Diverse responses of global‐mean surface temperature to external forcings and internal climate variability in observations and CMIP6 models. Geophysical Research Letters 48, e2021GL093194
| Diverse responses of global‐mean surface temperature to external forcings and internal climate variability in observations and CMIP6 models.Crossref | GoogleScholarGoogle Scholar |
Rayner NA, Parker DE, Horton EB, Folland CK, Alexander LV, Rowell DP, et al. (2003) Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. Journal of Geophysical Research – D. Atmospheres 108, 4407
| Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century.Crossref | GoogleScholarGoogle Scholar |
Roach LA, Dörr J, Holmes CR, Massonnet F, Blockley EW, Notz D, et al. (2020) Antarctic sea ice area in CMIP6. Geophysical Research Letters 47, e2019GL086729
| Antarctic sea ice area in CMIP6.Crossref | GoogleScholarGoogle Scholar |
Santoso A, Hendon H, Watkins A, Power S, Dommenget D, England MH, et al. (2019) Dynamics and predictability of El Niño–Southern Oscillation: an Australian perspective on progress and challenges. Bulletin of the American Meteorological Society 100, 403–420.
| Dynamics and predictability of El Niño–Southern Oscillation: an Australian perspective on progress and challenges.Crossref | GoogleScholarGoogle Scholar |
Schneider T, Bischoff T, Haug GH (2014) Migrations and dynamics of the intertropical convergence zone. Nature 513, 45–53.
| Migrations and dynamics of the intertropical convergence zone.Crossref | GoogleScholarGoogle Scholar | 25186899PubMed |
Spreen G, Kaleschke L, Heygster G (2008) Sea ice remote sensing using AMSR-E 89-GHz channels. Journal of Geophysical Research – C. Oceans 113, C02S03
| Sea ice remote sensing using AMSR-E 89-GHz channels.Crossref | GoogleScholarGoogle Scholar |
Sun S, Eisenman I (2021) Observed Antarctic sea ice expansion reproduced in a climate model after correcting biases in sea ice drift velocity. Nature Communications 12, 1060
| Observed Antarctic sea ice expansion reproduced in a climate model after correcting biases in sea ice drift velocity.Crossref | GoogleScholarGoogle Scholar | 33594079PubMed |
Swart NC, Cole JNS, Kharin VV, Lazare M, Scinocca JF, Gillett NP, et al. (2019) The Canadian Earth System Model version 5 (CanESM5.0.3). Geoscientific Model. Development 12, 4823–4873.
| The Canadian Earth System Model version 5 (CanESM5.0.3). Geoscientific Model.Crossref | GoogleScholarGoogle Scholar |
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research – D. Atmospheres 106, 7183–7192.
| Summarizing multiple aspects of model performance in a single diagram.Crossref | GoogleScholarGoogle Scholar |
Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society 93, 485–498.
| An overview of CMIP5 and the experiment design.Crossref | GoogleScholarGoogle Scholar |
Thompson DWJ, Kennedy JJ, Wallace JM, Jones PD (2008) A large discontinuity in the mid-twentieth century in observed global-mean surface temperature. Nature 453, 646–649.
| A large discontinuity in the mid-twentieth century in observed global-mean surface temperature.Crossref | GoogleScholarGoogle Scholar |
Tian B, Dong X (2020) The Double-ITCZ Bias in CMIP3, CMIP5, and CMIP6 models based on annual mean precipitation. Geophysical Research Letters 47, e2020GL087232
| The Double-ITCZ Bias in CMIP3, CMIP5, and CMIP6 models based on annual mean precipitation.Crossref | GoogleScholarGoogle Scholar |
Trenberth KE, Fasullo JT (2009) Global warming due to increasing absorbed solar radiation. Geophysical Research Letters 36, L07706
| Global warming due to increasing absorbed solar radiation.Crossref | GoogleScholarGoogle Scholar |
Turner J, Hosking JS, Bracegirdle TJ, Marshall GJ, Phillips T (2015) Recent changes in Antarctic sea ice. Philosophical Transactions of the Royal Society – A. Mathematical, Physical and Engineering Sciences 373, 20140163
| Recent changes in Antarctic sea ice.Crossref | GoogleScholarGoogle Scholar |
van Loon H, Jenne RL (1972) The zonal harmonic standing waves in the southern hemisphere. Journal of Geophysical Research 77, 992–1003.
| The zonal harmonic standing waves in the southern hemisphere.Crossref | GoogleScholarGoogle Scholar |
Vinnikov KY, Robock A, Stouffer RJ, Walsh JE, Parkinson CL, Cavalieri DJ, et al. (1999) Global warming and Northern Hemisphere sea ice extent. Science 286, 1934–1937.
| Global warming and Northern Hemisphere sea ice extent.Crossref | GoogleScholarGoogle Scholar | 10583952PubMed |
Voldoire A, Saint-Martin D, Sénési S, Decharme B, Alias A, Chevallier M, et al. (2019) Evaluation of CMIP6 DECK experiments with CNRM-CM6-1. Journal of Advances in Modeling Earth Systems 11, 2177–2213.
| Evaluation of CMIP6 DECK experiments with CNRM-CM6-1.Crossref | GoogleScholarGoogle Scholar |
Walters D, Baran AJ, Boutle I, Brooks M, Earnshaw P, Edwards J, et al. (2019) The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations. Geoscientific Model Development 12, 1909–1963.
| The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations.Crossref | GoogleScholarGoogle Scholar |
Wilcox LJ, Highwood EJ, Dunstone NJ (2013) The influence of anthropogenic aerosol on multi-decadal variations of historical global climate. Environmental Research Letters 8, 024033
| The influence of anthropogenic aerosol on multi-decadal variations of historical global climate.Crossref | GoogleScholarGoogle Scholar |
Yamazaki K, Sexton DMH, Rostron JW, McSweeney CF, Murphy JM, Harris GR (2021) A perturbed parameter ensemble of HadGEM3-GC3.05 coupled model projections: part 2: global performance and future changes. Climate Dynamics 56, 3437–3471.
| A perturbed parameter ensemble of HadGEM3-GC3.05 coupled model projections: part 2: global performance and future changes.Crossref | GoogleScholarGoogle Scholar |
Ziehn T, Chamberlain MA, Law RM, Lenton A, Bodman RW, Dix M, et al. (2020) The Australian Earth System Model: ACCESS-ESM1.5. Journal of Southern Hemisphere Earth Systems Science 70, 193–214.
| The Australian Earth System Model: ACCESS-ESM1.5.Crossref | GoogleScholarGoogle Scholar |
