Articles | Volume 6, issue 4
https://doi.org/10.5194/wcd-6-1539-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-6-1539-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Process-based diagnostics using atmospheric budget analysis and nudging technique to identify sources of model systematic errors
Chihiro Matsukawa
CORRESPONDING AUTHOR
Japan Meteorological Agency, Tokyo, Japan
Met Office, Exeter, UK
José M. Rodríguez
Met Office, Exeter, UK
Sean F. Milton
University of Leeds, Leeds, UK
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Léa M. C. Prévost, Leighton A. Regayre, Jill S. Johnson, Doug McNeall, Sean Milton, and Kenneth S. Carslaw
EGUsphere, https://doi.org/10.5194/egusphere-2025-4795, https://doi.org/10.5194/egusphere-2025-4795, 2025
Short summary
Short summary
Climate models rely on uncertain adjustable parameters. We tested millions of combinations of these inputs to see how well the model matches real-world data. We found that no single set of inputs can match several observations at the same time, which suggests that the issue lies in the model itself. We developed a method to detect these conflicts and trace them back trace them to their source. The aim is to help modellers target improvements that reduce uncertainty in climate projections.
Gill M. Martin and José M. Rodríguez
Weather Clim. Dynam., 5, 711–731, https://doi.org/10.5194/wcd-5-711-2024, https://doi.org/10.5194/wcd-5-711-2024, 2024
Short summary
Short summary
Using sensitivity experiments, we show that model errors developing in the Maritime Continent region contribute substantially to the Asian summer monsoon (ASM) circulation and rainfall errors through their effects on the western North Pacific subtropical high-pressure region and the winds and sea surface temperatures in the equatorial Indian Ocean, exacerbated by local coupled feedback. Such information will inform future model developments aimed at improving model predictions for the ASM.
Gill M. Martin, Richard C. Levine, José M. Rodriguez, and Michael Vellinga
Geosci. Model Dev., 14, 1007–1035, https://doi.org/10.5194/gmd-14-1007-2021, https://doi.org/10.5194/gmd-14-1007-2021, 2021
Short summary
Short summary
Our study highlights a number of different techniques that can be employed to investigate the sources of model error. We demonstrate how this methodology can be used to identify the regions and model components responsible for the development of long-standing errors in the Asian summer monsoon. Once these are known, further work can be done to explore the local processes contributing to this behaviour and their sensitivity to changes in physical parameterisations and/or model resolution.
Cited articles
Andrews, D. G., Leovy, C. B., and Holton, J. R.: Middle Atmosphere Dynamics, Academic Press, ISBN 0120585766, 1987. a
Andrews, D. G. and Mcintyre, M. E.: Planetary waves in horizontal and vertical shear: The generalized Eliassen-Palm relation and the mean zonal acceleration, Journal of Atmospheric Sciences, 33, 2031–2048, 1976. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55, https://doi.org/10.1038/nature14956, 2015. a, b
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.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Bland, J., Gray, S., Methven, J., and Forbes, R.: Characterising extratropical near-tropopause analysis humidity biases and their radiative effects on temperature forecasts, Quarterly Journal of the Royal Meteorological Society, 147, 3878–3898, https://doi.org/10.1002/qj.4150, 2021. a, b
Brown, A., Milton, S., Cullen, M., Golding, B., Mitchell, J., and Shelly, A.: Unified modeling and prediction of weather and climate: A 25-year journey, Bulletin of the American Meteorological Society, 93, 1865–1877, https://doi.org/10.1175/BAMS-D-12-00018.1, 2012. a
Brown, A. R.: Resolution dependence of orographic torques, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, 130, 3029–3046, 2004. a
Chagnon, J. M., Gray, S. L., and Methven, J.: Diabatic processes modifying potential vorticity in a North Atlantic cyclone, Quarterly Journal of the Royal Meteorological Society, 139, 1270–1282, 2013. a
Clayton, A. M., Lorenc, A. C., and Barker, D. M.: Operational implementation of a hybrid ensemble/4D-Var global data assimilation system at the Met Office, Quarterly Journal of the Royal Meteorological Society, 139, 1445–1461, 2013. a
Cullen, M. J. P.: The unified forecast/climate model, Meteorological Magazine, 122, 81–94, 1993. a
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and Wimmer, W.: The operational sea surface temperature and sea ice analysis (OSTIA) system, Remote Sensing of Environment, 116, 140–158, 2012. a
Duynkerke, P. G., de Roode, S. R., van Zanten, M. C., Calvo, J., Cuxart, J., Cheinet, S., Chlond, A., Grenier, H., Jonker, P. J., Köhler, M., Lenderink, G., Lewellen, D., Lappen, C.-l., Lock, A. P., Moeng, C.-h., Müller, F., Olmeda, D., Piriou, J.-m., Sánchez, E., and Sednev, I.: Observations and numerical simulations of the diurnal cycle of the EUROCS stratocumulus case, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, 130, 3269–3296, 2004. a
Edwards, J. M. and Slingo, A.: Studies with a flexible new radiation code. I: Choosing a configuration for a large-scale model, Quarterly Journal of the Royal Meteorological Society, 122, 689–719, 1996. a
Elvidge, A. D., Sandu, I., Wedi, N., Vosper, S. B., Zadra, A., Boussetta, S., Bouyssel, F., van Niekerk, A., Tolstykh, M. A., and Ujiie, M.: Uncertainty in the representation of orography in weather and climate models and implications for parameterized drag, Journal of Advances in Modeling Earth Systems, 11, 2567–2585, 2019. a
Frassoni, A., Reynolds, C., Wedi, N., Bouallègue, Z. B., Caltabiano, A. C. V., Casati, B., Christophersen, J. A., Coelho, C. A., De Falco, C., Doyle, J. D., Fernandes, L. G., Forbes, R., Janiga, M. A., Klocke, D., Magnusson, L., McTaggart-Cowan, R., Pakdaman, M., Rushley, S. S., Verhoef, A., Yang, F., and Zängl, G.: Systematic errors in weather and climate models: Challenges and opportunities in complex coupled modeling systems, Bulletin of the American Meteorological Society, 104, E1687–E1693, 2023. a
Gregory, D. and Rowntree, P. R.: A mass flux convection scheme with representation of cloud ensemble characteristics and stability-dependent closure, Monthly Weather Review, 118, 1483–1506, 1990. a
Hamilton, K.: Diagnostic study of the momentum balance in the Northern Hemisphere winter stratosphere, Monthly weather review, 111, 1434–1441, https://doi.org/10.1175/1520-0493(1983)111<1434:DSOTMB>2.0.CO;2, 1983. a
Hardiman, S. C., Boutle, I. A., Bushell, A. C., Butchart, N., Cullen, M. J. P., Field, P. R., Furtado, K., Manners, J. C., Milton, S. F., Morcrette, C., O’Connor, F. M., Shipway, B. J., Smith, C., Walters, D. N., Willett, M. R., Williams, K. D., Wood, N., Abraham, N. L., Keeble, J., Maycock, A. C., Thuburn, J., and Woodhouse, M. T.: Processes controlling tropical tropopause temperature and stratospheric water vapor in climate models, Journal of Climate, 28, 6516–6535, https://doi.org/10.1175/JCLI-D-15-0075.1, 2015. a, b, c
Hartmann, D. L.: The dynamical climatology of the stratosphere in the Southern Hemisphere during late winter 1973, Journal of the Atmospheric Sciences, 33, 1789–1802, https://doi.org/10.1175/1520-0469(1976)033<1789:TDCOTS>2.0.CO;2, 1976. a, b
Hoskins, B., Fonseca, R., Blackburn, M., and Jung, T.: Relaxing the tropics to an 'observed' state: Analysis using a simple baroclinic model, Quarterly Journal of the Royal Meteorological Society, 138, 1618–1626, https://doi.org/10.1002/qj.1881, 2012. a
Iwasaki, T.: A diagnostic formulation for wave-mean flow interactions and Lagrangian-mean circulation with a hybrid vertical coordinate of pressure and isentropes, Journal of the Meteorological Society of Japan. Ser. II, 67, 293–312, 1989. a
Iwasaki, T.: General circulation diagnosis in the pressure-isentrope hybrid vertical coordinate, Journal of the Meteorological Society of Japan. Ser. II, 70, 673–687, 1992. a
Jeuken, A. B. M., Siegmund, P. C., Heijboer, L. C., Feichter, J., and Bengtsson, L.: On the potential of assimilating meteorological analyses in a global climate model for the purpose of model validation, Journal of Geophysical Research: Atmospheres, 101, 16939–16950, https://doi.org/10.1029/96JD01218, 1996. a, b
Karmalkar, A. V., Sexton, D. M., Murphy, J. M., Booth, B. B., Rostron, J. W., and McNeall, D. J.: Finding plausible and diverse variants of a climate model. Part II: development and validation of methodology, Climate Dynamics, 53, 847–877, 2019. a
Klinker, E.: Investigation of systematic errors by relaxation experiments, Quarterly Journal of the Royal Meteorological Society, 116, 573–594, https://doi.org/10.1002/qj.49711649304, 1990. a
Klinker, E. and Sardeshmukh, P. D.: The diagnosis of mechanical dissipation in the atmosphere from large-scale balance requirements, Journal of Atmospheric Sciences, 49, 608–627, https://doi.org/10.1175/1520-0469(1992)049<0608:TDOMDI>2.0.CO;2, 1992. a, b, c
Lenderink, G., Siebesma, A. P., Cheinet, S., Irons, S., Jones, C. G., Marquet, P., üLLER, F. M., Olmeda, D., Calvo, J., Sánchez, E., and Soares, P. M. M.: The diurnal cycle of shallow cumulus clouds over land: A single-column model intercomparison study, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, applied meteorology and physical oceanography, 130, 3339–3364, 2004. a
Lock, A. P., Brown, A. R., Bush, M. R., Martin, G. M., and Smith, R. N. B.: A new boundary layer mixing scheme. Part I: Scheme description and single-column model tests, Monthly Weather Review, 128, 3187–3199, 2000. a
Ma, H.-Y., Xie, S., Klein, S. A., Williams, K. D., Boyle, J. S., Bony, S., Douville, H., Fermepin, S., Medeiros, B., Tyteca, S., Watanabe, M., and Williamson, D.: On the correspondence between mean forecast errors and climate errors in CMIP5 models, Journal of Climate, 27, 1781–1798, https://doi.org/10.1175/JCLI-D-13-00474.1, 2014. a, b, c
Martin, G. M., Milton, S. F., Senior, C. A., Brooks, M. E., Ineson, S., Reichler, T., and Kim, J.: Analysis and reduction of systematic errors through a seamless approach to modeling weather and climate, Journal of Climate, 23, 5933–5957, https://doi.org/10.1175/2010JCLI3541.1, 2010. a, b, c
Martin, G. M., Levine, R. C., Rodriguez, J. M., and Vellinga, M.: Understanding the development of systematic errors in the Asian summer monsoon, Geosci. Model Dev., 14, 1007–1035, https://doi.org/10.5194/gmd-14-1007-2021, 2021. a, b, c
Martineau, P., Son, S.-W., and Taguchi, M.: Dynamical consistency of reanalysis datasets in the extratropical stratosphere, Journal of Climate, 29, 3057–3074, https://doi.org/10.1175/JCLI-D-15-0469.1, 2016. a, b
Milton, S. F. and Wilson, C. A.: The impact of parameterized subgrid-scale orographic forcing on systematic errors in a global NWP model, Monthly Weather Review, 124, 2023–2045, https://doi.org/10.1175/1520-0493(1996)124<2023:TIOPSS>2.0.CO;2, 1996. a, b, c
Palmer, T. N., Shutts, G. J., and Swinbank, R.: Alleviation of a systematic westerly bias in general circulation and numerical weather prediction models through an orographic gravity wave drag parametrization, Quarterly Journal of the Royal Meteorological Society, 112, 1001–1039, https://doi.org/10.1002/qj.49711247406, 1986. a
Peixoto, J. P. and Oort, A. H.: Physics of Climate, vol. 520, American Institute of Physics, ISBN 0883187116, 1992. a
Phillips, T. J., Potter, G. L., Williamson, D. L., Cederwall, R. T., Boyle, J. S., Fiorino, M., Hnilo, J. J., Olson, J. G., Xie, S., and Yio, J. J.: Evaluating parameterizations in general circulation models: Climate simulation meets weather prediction, Bulletin of the American Meteorological Society, 85, 1903–1916, https://doi.org/10.1175/BAMS-85-12-1903, 2004. a
Rao, J., Garfinkel, C. I., Chen, H., and White, I. P.: The 2019 new year stratospheric sudden warming and its real-time predictions in multiple S2S models, Journal of Geophysical Research: Atmospheres, 124, 11155–11174, https://doi.org/10.1029/2019JD030826, 2019. a
Rodríguez, J. M. and Milton, S. F.: East Asian summer atmospheric moisture transport and its response to interannual variability of the West Pacific subtropical high: An evaluation of the Met Office Unified Model, Atmosphere, 10, 457, https://doi.org/10.3390/atmos10080457, 2019. a
Rodwell, M. J. and Palmer, T. N.: Using numerical weather prediction to assess climate models, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, Applied Meteorology and Physical Oceanography, 133, 129–146, https://doi.org/10.1002/qj.23, 2007. a, b
Saffin, L., Methven, J., and Gray, S. L.: The non-conservation of potential vorticity by a dynamical core compared with the effects of parametrized physical processes, Quarterly Journal of the Royal Meteorological Society, 142, 1265–1275, 2016. a
Sánchez, C., Methven, J., Gray, S., and Cullen, M.: Linking rapid forecast error growth to diabatic processes, Quarterly Journal of the Royal Meteorological Society, 146, 3548–3569, 2020. a
Scaife, A. A., Butchart, N., Warner, C. D., and Swinbank, R.: Impact of a spectral gravity wave parameterization on the stratosphere in the Met Office Unified Model, Journal of the Atmospheric sciences, 59, 1473–1489, 2002. a
Senior, C. A., Arribas, A., Brown, A. R., Cullen, M. J. P., Johns, T. C., Martin, G. M., Milton, S. F., Webster, S., and Williams, K. D.: Synergies between numerical weather prediction and general circulation climate models, The development of atmospheric general circulation models, edited by: Donner, L., Schubert, W., and Somerville, R., Cambridge University Press, Cambridge, UK, ISBN 9781108445696, 2011. a
Sexton, D. M. H., Karmalkar, A. V., Murphy, J. M., Williams, K. D., Boutle, I. A., Morcrette, C. J., Stirling, A. J., and Vosper, S. B.: Finding plausible and diverse variants of a climate model. Part 1: Establishing the relationship between errors at weather and climate time scales, Climate Dynamics, 53, 989–1022, 2019. a
Smith, A. K. and Lyjak, L. V.: An observational estimate of gravity wave drag from the momentum balance in the middle atmosphere, Journal of Geophysical Research: Atmospheres, 90, 2233–2241, https://doi.org/10.1029/JD090iD01p02233, 1985. a
Svensson, G., Holtslag, A. A. M., Kumar, V., Mauritsen, T., Steeneveld, G. J., Angevine, W. M., Bazile, E., Beljaars, A., De Bruijn, E. I. F., Cheng, A., Conangla, L., Cuxart, J., Ek, M., Falk, M. J., Freedman, F., Kitagawa, H., Larson, V. E., Lock, A., Mailhot, J., Masson, V., Park, S., Pleim, J., Söderberg, S., Weng, W., and Zampieri, M.: Evaluation of the diurnal cycle in the atmospheric boundary layer over land as represented by a variety of single-column models: The second GABLS experiment, Boundary-Layer Meteorology, 140, 177–206, 2011. a
Tanaka, D., Iwasaki, T., Uno, S., Ujiie, M., and Miyazaki, K.: Eliassen–Palm flux diagnosis based on isentropic representation, Journal of the Atmospheric Sciences, 61, 2370–2383, 2004. a
Telford, P. J., Braesicke, P., Morgenstern, O., and Pyle, J. A.: Technical Note: Description and assessment of a nudged version of the new dynamics Unified Model, Atmos. Chem. Phys., 8, 1701–1712, https://doi.org/10.5194/acp-8-1701-2008, 2008. a, b
van Niekerk, A., Shepherd, T. G., Vosper, S. B., and Webster, S.: Sensitivity of resolved and parametrized surface drag to changes in resolution and parametrization, Quarterly Journal of the Royal Meteorological Society, 142, 2300–2313, https://doi.org/10.1002/qj.2821, 2016. a, b, c
van Niekerk, A., Sandu, I., Zadra, A., Bazile, E., Kanehama, T., Köhler, M., Koo, M.-S., Choi, H.-J., Kuroki, Y., Toy, M. D., Vosper, S. B., and Yudin, V.: COnstraining ORographic Drag Effects (COORDE): A model comparison of resolved and parametrized orographic drag, Journal of Advances in Modeling Earth Systems, 12, e2020MS002160, https://doi.org/10.1029/2020MS002160, 2020. a
Vosper, S. B.: Mountain waves and wakes generated by South Georgia: Implications for drag parametrization, Quarterly Journal of the Royal Meteorological Society, 141, 2813–2827, 2015. a
Walters, D., Boutle, I., Brooks, M., Melvin, T., Stratton, R., Vosper, S., Wells, H., Williams, K., Wood, N., Allen, T., Bushell, A., Copsey, D., Earnshaw, P., Edwards, J., Gross, M., Hardiman, S., Harris, C., Heming, J., Klingaman, N., Levine, R., Manners, J., Martin, G., Milton, S., Mittermaier, M., Morcrette, C., Riddick, T., Roberts, M., Sanchez, C., Selwood, P., Stirling, A., Smith, C., Suri, D., Tennant, W., Vidale, P. L., Wilkinson, J., Willett, M., Woolnough, S., and Xavier, P.: The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations, Geosci. Model Dev., 10, 1487–1520, https://doi.org/10.5194/gmd-10-1487-2017, 2017. a
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.: The Met Office Unified Model Global Atmosphere 7.0/7.1 and JULES Global Land 7.0 configurations, Geosci. Model Dev., 12, 1909–1963, https://doi.org/10.5194/gmd-12-1909-2019, 2019. a
Wehrli, K., Guillod, B. P., Hauser, M., Leclair, M., and Seneviratne, S. I.: Assessing the dynamic versus thermodynamic origin of climate model biases, Geophysical Research Letters, 45, 8471–8479, https://doi.org/10.1029/2018GL079220, 2018. a
Williams, K. D., van Niekerk, A., Best, M. J., Lock, A. P., Brooke, J. K., Carvalho, M. J., Derbyshire, S. H., Dunstan, T. D., Rumbold, H. S., Sandu, I., and Sexton, D. M. H.: Addressing the causes of large-scale circulation error in the Met Office Unified Model, Quarterly Journal of the Royal Meteorological Society, 146, 2597–2613, 2020. a
Wilson, D. R. and Ballard, S. P.: A microphysically based precipitation scheme for the UK Meteorological Office Unified Model, Quarterly Journal of the Royal Meteorological Society, 125, 1607–1636, 1999. a
Wilson, D. R., Bushell, A. C., Kerr-Munslow, A. M., Price, J. D., and Morcrette, C. J.: PC2: A prognostic cloud fraction and condensation scheme. I: Scheme description, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, Applied Meteorology and Physical Oceanography, 134, 2093–2107, 2008a. a
Wilson, D. R., Bushell, A. C., Kerr-Munslow, A. M., Price, J. D., Morcrette, C. J., and Bodas-Salcedo, A.: PC2: A prognostic cloud fraction and condensation scheme. II: Climate model simulations, Quarterly Journal of the Royal Meteorological Society: A journal of the atmospheric sciences, Applied Meteorology and Physical Oceanography, 134, 2109–2125, 2008b. a
Wood, N., Staniforth, A., White, A., Allen, T., Diamantakis, M., Gross, M., Melvin, T., Smith, C., Vosper, S., Zerroukat, M., and Thuburn, J.: An inherently mass-conserving semi-implicit semi-Lagrangian discretization of the deep-atmosphere global non-hydrostatic equations, Quarterly Journal of the Royal Meteorological Society, 140, 1505–1520, https://doi.org/10.1002/qj.2235, 2014. a
Yanai, M., Esbensen, S., and Chu, J.-H.: Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets, Journal of Atmospheric Sciences, 30, 611–627, https://doi.org/10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2, 1973. a
Yang, W., Seager, R., and Cane, M. A.: Zonal momentum balance in the tropical atmospheric circulation during the global monsoon mature months, Journal of the Atmospheric Sciences, 70, 583–599, https://doi.org/10.1175/JAS-D-12-0140.1, 2013. a
Short summary
To identify sources of the model systematic errors, we investigate northern hemisphere mid-latitude wind errors at short- to medium-range timescale using the atmospheric zonal-mean budgets analysis and the relaxation technique. These process-based diagnostics specify to what extent the individual components in the budgets contribute to the total tendency of the corresponding variable. This study shows disentanglements of compensating errors caused by mechanical and thermal forcings.
To identify sources of the model systematic errors, we investigate northern hemisphere...