An assessment of scale-dependent variability and bias in global prediction models
The paper presents a method for the scale-dependent validation of the spatio-temporal variability in global weather or climate models and for their bias quantification in relation to dynamics. The method provides a relationship between the bias and simulated spatial and temporal variance by a model in comparison with verifying reanalysis data. For the low resolution (T30L8) subset of ERA-20C data, it was found that 80–90% (depending on season) of the global interannual variance is at planetary scales (zonal wavenumbers k = 0−3), and only about 1% of the variance is at scales with \(k>7\). The reanalysis is used to validate a T30L8 GCM in two configurations, one with the prescribed sea-surface temperature (SST) and another using a slab ocean model. Although the model with the prescribed SST represents the average properties of surface fields well, the interannual variability is underestimated at all scales. Similar to variability, model bias is strongly scale dependent. Biases found in the experiment with the prescribed SST are largely increased in the experiment using a slab ocean, especially in \(k=0\), in scales with missing variability and in seasons with poorly simulated energy distribution. The perfect model scenario (a comparison between the GCM coupled to a slab ocean vs. the same model with prescribed SSTs) shows that the representation of the ocean is not critical for synoptic to subsynoptic variability, but essential for capturing the planetary scales.
KeywordsSpatio-temporal variability Bias spectra Variability quantification Climate models Model validation
The authors are grateful to the comments of two anonymous reviewers that led to the paper improvements. Discussions with Grant Branstator, Akira Kasahara, Fred Kucharski, Franco Molteni and Joe Tribbia are gratefully acknowledged. Nedjeljka Žagar and Martin Horvat were partly supported by the Slovenian Research Agency, program P1-0188 and project J1-9431. Funded by the European Research Council, Grant Agreement no. 280153.
- Blažica V, Žagar N, Strajnar B, Cedilnik J (2013) Rotational and divergent kinetic energy in the mesoscale model ALADIN. Tellus 65A(18):918Google Scholar
- Castanheira JM (2000) Climatic variability of the atmospheric circulation at the global scale. PhD thesis, Department of Physics, University of Aveiro, PortugalGoogle Scholar
- Charlton-Perez AJ, Baldwin MP, Birner T, Black RX, Butler AH, Calvo N, Davis NA, Gerber EP, Gillett N, Hardiman S, Kim J, Krüger K, Lee Y, Manzini E, McDaniel BA, Polvani L, Reichler T, Shaw TA, Sigmond M, Son S, Toohey M, Wilcox L, Yoden S, Christiansen B, Lott F, Shindell D, Yukimoto S, Watanabe S (2013) On the lack of stratospheric dynamical variability in low-top versions of the cmip5 models. J Geophys Res Atmos 118:2494–2505CrossRefGoogle Scholar
- Cubasch U, Wuebbles D, Chen D, Facchini M, Frame D, Mahowald N, Winther JG (2013) Introduction. Chapter 1 in climate change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9781107415324.007 CrossRefGoogle Scholar
- Daley R (1991) Atmospheric data analysis. Cambridge University Press, Cambridge, p 460Google Scholar
- Flato G, Marotzke J, Abiodun B, Braconnot P, Chou S, Collins W, Cox P, Driouech F, Emori S, Eyring V, Forest C, Gleckler P, Guilyardi E, Jakob C, Kattsov V, Reason C, Rummukainen M (2013) Evaluation of climate models. Chapter 9 in climate change 2013: the physical science basis. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9781107415324.020 CrossRefGoogle Scholar
- Herceg-Bulić I, Kucharski F (2012) Delayed ENSO impact on spring precipitation over North/Atlantic European region. J Atmos Sci 51:2225–2237Google Scholar
- Lin JL, Kiladis G, Mapes BE, Weickmann KM, Sperber KR, Lin W, Wheeler MC, Schubert SD, Genio AD, Donner LJ, Emori S, Gueremy JF, Hourdin F, Rasch PJ, Roeckner E, Scinocca JF (2006) Tropical intraseasonal variability in 14 IPCC AR4 climate models. Part I: convective signals. J Clim 19:2665–2690CrossRefGoogle Scholar
- Machenhauer B (1977) On the dynamics of gravity oscillations in a shallow water model, with applications to normal mode initialization. Beitr Phys Atmos 50:253–271Google Scholar
- Phillips NA (1990) Dispersion processes in large-scale weather prediction. Sixth IMO lecture, World Meteorological Organization, No, p 700Google Scholar
- Polo I, Martin-Rey M, Rodriguez-Fonesca B, Kucharski F, Mechoso C (2014) Processes in the Pacific La Nina onset triggered by Atlantic Nino. Clim DynGoogle Scholar
- Scaife A, Kucharski F, Folland C, Kinter J, Bronnimann S, Fereday D, Fischer A, Grainger S, Jin E, Kang I, Knight J, Kusunoki S, Lau N, Nath M, Nakaegawa T, Pegion P, Schubert S, Sporyshev P, Syktus J, Yoon J, Zeng N, Zhou T (2009) The CLIVAR C20C project: selected twentieth century climate events. Clim Dyn 33:603–614CrossRefGoogle Scholar