Evaluation of CMIP6 AMIP climate simulations with the ACCESS-AM2 model
R. W. Bodman A B C E , D. J. Karoly B C , M. R. Dix C , I. N. Harman D , J. Srbinovsky C , P. B. Dobrohotoff C and C. Mackallah CA School of Earth Sciences, The University of Melbourne, Parkville, Melbourne, Vic. 3010, Australia.
B NESP Earth Systems and Climate Change Hub, CSIRO, Aspendale, Australia.
C Climate Science Centre, Oceans and Atmosphere, CSIRO, Aspendale, Australia.
D Climate Science Centre, Oceans and Atmosphere, CSIRO, Canberra, Australia.
E Corresponding author. Email: rwbodman@unimelb.edu.au
Journal of Southern Hemisphere Earth Systems Science 70(1) 166-179 https://doi.org/10.1071/ES19033
Submitted: 13 December 2019 Accepted: 18 March 2020 Published: 4 September 2020
Journal Compilation © BoM 2020 Open Access CC BY-NC-ND
Abstract
The most recent version of the ACCESS-AM2 atmosphere-only climate model is introduced with results from the CMIP6 Atmospheric Model Intercomparison Project (AMIP) experiments configured with two land-surface models: CABLE and JULES. AMIP simulations are required as part of the CMIP6 core experiments. They are forced by prescribed time-varying observed sea surface temperature and sea-ice variations as well as variations in natural and anthropogenic external forcings. We evaluate the performance of the two configurations using three historical realisations for each. Model biases are estimated both globally and for the Australian region. The model shows close agreement with observed interannual variations of global-mean temperature across the latitude range 65°N–65°S. This is also true for the land-only temperature for 65°N–65°S, and a more stringent test of the model is driven by specified observed sea surface temperatures. Patterns of mean precipitation are simulated reasonably well, although there are biases in the amount and distribution of precipitation, typical of longstanding problems in representing this aspect of the climate. Selected features of the atmospheric circulation are discussed, including air temperatures and wind speeds. For the Australian region, in addition to examining the climatological patterns of temperature and precipitation, important drivers of climate variability are reviewed: El Niño-Southern Oscillation, the Indian Ocean Dipole and the Southern Annular Mode. In general, the correlation patterns for precipitation simulated by ACCESS-AM2 are somewhat weaker than in observations, although the ensemble means show better agreement than individual ensemble members. Overall, the two different land-surface schemes perform similarly. ACCESS-AM2 has reduced root mean square errors for both temperature and precipitation of around 15–20% at the global scale compared to the older CMIP5 versions of the model: ACCESS 1.0 and ACCESS 1.3.
Keywords: ACCESS-AM2 atmosphere-only climate model, Australia, climate model evaluation, CMIP6 AMIP simulations, El Niño, global scale.
References
Adler, R. F., Gu, G., Sapiano, M., Wang, J.-J., and Huffman, G. J. (2017). Global Precipitation: Means, Variations and Trends During the Satellite Era (1979–2014). Surv. Geophys. 38, 679–699.| Global Precipitation: Means, Variations and Trends During the Satellite Era (1979–2014).Crossref | GoogleScholarGoogle Scholar |
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J. (2011). The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes. Geosci. Model Dev. 4, 677–699.
| The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes.Crossref | GoogleScholarGoogle Scholar |
Bi, D., Dix, M., Marsland, S., O’Farrell, S., Sullivan, A., Bodman, R., Law, R., Harman, I., Srbinovsky, J., Rashid, H., Dobrohotoff, P., Mackallah, C., Woodhouse, M., and Fiedler, R. (2020). Configuration and spinup of ACCESS-CM2, the new generation Australian Community Climate and Earth System Simulator Coupled Model. J. South. Hemisph. Earth Sys. Sci. , .
| Configuration and spinup of ACCESS-CM2, the new generation Australian Community Climate and Earth System Simulator Coupled Model.Crossref | GoogleScholarGoogle Scholar |
Cai, W., Rensch, P. V., Cowan, T., and Hendon, H. H. (2011). Teleconnection Pathways of ENSO and the IOD and the Mechanisms for Impacts on Australian Rainfall. J. Clim. 24, 3910–3923.
| Teleconnection Pathways of ENSO and the IOD and the Mechanisms for Impacts on Australian Rainfall.Crossref | GoogleScholarGoogle Scholar |
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J., Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding, R. J., Huntingford, C., and Cox, P. M. (2011). The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722.
| The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics.Crossref | GoogleScholarGoogle Scholar |
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., Martin, G., O’Connor, F., Rae, J., Senior, C., Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S. (2011). Development and evaluation of an Earth-System model – HadGEM2. Geosci. Model Dev. 4, 1051–1075.
| Development and evaluation of an Earth-System model – HadGEM2.Crossref | GoogleScholarGoogle Scholar |
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J. J., Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N., and Vitart, F. (2011). The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quart. J. Roy. Meteorol. Soc. 137, 553–597.
| The ERA-Interim reanalysis: configuration and performance of the data assimilation system.Crossref | GoogleScholarGoogle Scholar |
Durack, P., Taylor, K. E. and others. (2019). CMIP6 Forcing Datasets Summary. (v6.2.37). Available online: http://goo.gl/r8up31 [Accessed 25 November 2019].
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958.
| Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization.Crossref | GoogleScholarGoogle Scholar |
Fogt, R. L., Perlwitz, J., Monaghan, A. J., Bromwich, D. H., Jones, J. M., and Marshall, G. J. (2009). Historical SAM Variability. Part II: Twentieth-Century Variability and Trends from Reconstructions, Observations, and the IPCC AR4 Models. J. Climate 22, 5346–5365.
| Historical SAM Variability. Part II: Twentieth-Century Variability and Trends from Reconstructions, Observations, and the IPCC AR4 Models.Crossref | GoogleScholarGoogle Scholar |
Gong, D., and Wang, S. (1999). Definition of Antarctic Oscillation index. Geophys. Res. Lett. 26, 459–462.
| Definition of Antarctic Oscillation index.Crossref | GoogleScholarGoogle Scholar |
Harman, I. N., Bodman, R. W., Dix, M. and Srbinovsky, J. (2019). CABLE within ACCESS-CM2. CSIRO client report.
Hendon, H. H., Thompson, D. W. J., and Wheeler, M. C. (2007). Australian Rainfall and Surface Temperature Variations Associated with the Southern Hemisphere Annular Mode. J. Climate 20, 2452–2467.
| Australian Rainfall and Surface Temperature Variations Associated with the Southern Hemisphere Annular Mode.Crossref | GoogleScholarGoogle Scholar |
Jin, L., Zhang, H., Moise, A., Martin, G., Milton, S., and Rodriguez, J. (2019). Australia-Asian monsoon in two versions of the UK Met Office Unified Model and their impacts on tropical–extratropical teleconnections. Clim. Dyn. 53, 4717–4741.
| Australia-Asian monsoon in two versions of the UK Met Office Unified Model and their impacts on tropical–extratropical teleconnections.Crossref | GoogleScholarGoogle Scholar |
Jones, D. A., Wang, W., and Fawcett, R. (2009). High-quality spatial climate data-sets for Australia. Aust. Meteorol. Oceanogr. J. 58, 233–248.
| High-quality spatial climate data-sets for Australia.Crossref | GoogleScholarGoogle Scholar |
Karoly, D. J. (1990). The role of transient eddies in low-frequency zonal variations of the Southern Hemisphere circulation. Tellus A 42, 41–50.
| The role of transient eddies in low-frequency zonal variations of the Southern Hemisphere circulation.Crossref | GoogleScholarGoogle Scholar |
Kowalczyk, E. A., Stevens, L., Law, R. M., Dix, M., Wang, Y. P., Harman, I. N., Haynes, K., Srbinovsky, J., Pak, B., and Ziehn, T. (2013). The land surface model component of ACCESS: description and impact on the simulated climatology. Aust. Meteorol. Oceanogr. J. 63, 65–82.
| The land surface model component of ACCESS: description and impact on the simulated climatology.Crossref | GoogleScholarGoogle Scholar |
Lorenz, R., Pitman, A. J., Donat, M. G., Hirsch, A. L., Kala, J., Kowalczyk, E. A., Law, R. M., and Srbinovsky, J. (2014). Representation of climate extreme indices in the ACCESS1.3b coupled atmosphere–land surface model. Geosci. Model Dev. 7, 545–567.
| Representation of climate extreme indices in the ACCESS1.3b coupled atmosphere–land surface model.Crossref | GoogleScholarGoogle Scholar |
Mann, G. W., Carslaw, K. S., Ridley, D. A., Spracklen, D. V., Pringle, K. J., Merikanto, J., Korhonen, H., Schwarz, J. P., Lee, L. A., Manktelow, P. T., Woodhouse, M. T., Schmidt, A., Breider, T. J., Emmerson, K. M., Reddington, C. L., Chipperfield, M. P., and Pickering, S. J. (2012). Intercomparison of modal and sectional aerosol microphysics representations within the same 3-D global chemical transport model. Atmos. Chem. Phys. 12, 4449–4476.
| Intercomparison of modal and sectional aerosol microphysics representations within the same 3-D global chemical transport model.Crossref | GoogleScholarGoogle Scholar |
Mann, G. W., Carslaw, K. S., Spracklen, D. V., Ridley, D. A., Manktelow, P. T., Chipperfield, M. P., Pickering, S. J., and Johnson, C. E. (2010). Description and evaluation of GLOMAP-mode: a modal global aerosol microphysics model for the UKCA composition-climate model. Geosci. Model Dev. 3, 519–551.
| Description and evaluation of GLOMAP-mode: a modal global aerosol microphysics model for the UKCA composition-climate model.Crossref | GoogleScholarGoogle Scholar |
Marshall, G. (2003). Trends in the Southern Annular Mode from Observations and Reanalyses. J. Climate 16, 4134–4143.
| Trends in the Southern Annular Mode from Observations and Reanalyses.Crossref | GoogleScholarGoogle Scholar |
Morice, C. P., Kennedy, J. J., Rayner, N. A., and Jones, P. D. (2012). Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set. J. Geophys. Res. Atmos. 117, D08101.
| Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set.Crossref | GoogleScholarGoogle Scholar |
Osborn, T. J., and Jones, P. D. (2014). The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth. Earth Syst. Sci. Data 6, 61–68.
| The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth.Crossref | GoogleScholarGoogle Scholar |
Rashid, H., Hirst, A., and Dix, M. (2013). Atmospheric circulation features in the ACCESS model simulations for CMIP5: historical simulation and future projections. Aust. Meteorol. Oceanogr. J. 63, 145–160.
| Atmospheric circulation features in the ACCESS model simulations for CMIP5: historical simulation and future projections.Crossref | GoogleScholarGoogle Scholar |
Risbey, J. S., Pook, M. J., McIntosh, P. C., Wheeler, M. C., and Hendon, H. H. (2009). On the Remote Drivers of Rainfall Variability in Australia. Mon. Wea. Rev. 137, 3233–3253.
| On the Remote Drivers of Rainfall Variability in Australia.Crossref | GoogleScholarGoogle Scholar |
Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T. (2017). MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6. Geosci. Model Dev. 10, 433–452.
| MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6.Crossref | GoogleScholarGoogle Scholar |
Walters, D., Baran, A. J., Boutle, I., Brooks, M., Earnshaw, P., Edwards, J., Furtado, K., Hill, P., Lock, A., Manners, J., Morcrette, C., Mulcahy, J., Sanchez, C., Smith, C., Stratton, R., Tennant, W., Tomassini, L., Van Weverberg, K., Vosper, S., Willett, M., Browse, J., Bushell, A., Carslaw, K., Dalvi, M., Essery, R., Gedney, N., Hardiman, S., Johnson, B., Johnson, C., Jones, A., Jones, C., Mann, G., Milton, S., Rumbold, H., Sellar, A., Ujiie, M., Whitall, M., Williams, K., and Zerroukat, M. (2019). The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations. Geosci. Model Dev. 12, 1909–1963.
| The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations.Crossref | GoogleScholarGoogle Scholar |
Williams, K. D., Copsey, D., Blockley, E. W., Bodas-Salcedo, A., Calvert, D., Comer, R., Davis, P., Graham, T., Hewitt, H. T., Hill, R., Hyder, P., Ineson, S., Johns, T. C., Keen, A. B., Lee, R. W., Megann, A., Milton, S. F., Rae, J. G. L., Roberts, M. J., Scaife, A. A., Schiemann, R., Storkey, D., Thorpe, L., Watterson, I. G., Walters, D. N., West, A., Wood, R. A., Woollings, T., and Xavier, P. K. (2018). The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations. J. Adv. Model. Earth Syst. 10, 357–380.
| The Met Office Global Coupled Model 3.0 and 3.1 (GC3.0 and GC3.1) Configurations.Crossref | GoogleScholarGoogle Scholar |
Xie, P., and Arkin, P. A. (1997). Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs. Bull. Amer. Meteorol. Soc. 78, 2539–2558.
| Global Precipitation: A 17-Year Monthly Analysis Based on Gauge Observations, Satellite Estimates, and Numerical Model Outputs.Crossref | GoogleScholarGoogle Scholar |