There is a new paper on the level of skill of multi-decadal climate models to accurately simulate the climate system [h/t to Dan Hughes]. The paper is
Sen Gupta et al, 2012: Climate Drift in the CMIP3 Models. Journal of Climate;doi: http://dx.doi.org/10.1175/JCLI-D-11-00312.1
The abstract reads [abstract added]
“Even in the absence of external forcing, climate models often exhibit long-term trends that cannot be attributed to natural variability. This so called ‘climate drift’ arises for various reasons including: perturbations to the climate system on coupling component models together and deficiencies in model physics and numerics. When examining trends in historical or future climate simulations, it is important to know the error introduced by drift so that action can be taken where necessary. This study assesses the importance of drift for a number of climate properties at global and local scales. To illustrate this we have focused on simulated trends over the second half of the 20th century. While drift in globally-averaged surface properties is generally considerably smaller than observed and simulated 20th century trends, it can still introduce non-trivial errors in some models. Furthermore, errors become increasingly important at smaller scales. The direction of drift is not systematic across different models or variables; as such drift is considerably reduced in the multi-model mean. Despite drift being primarily associated with ocean adjustment, it is also apparent in atmospheric variables. For example, most models have local drift magnitudes that are typically between 15 and 35% of the 20th century simulation trend magnitudes for 1950-2000. Below depths of 1000 to 2000m, drift dominates over any forced trend in most regions. As such steric sea-level is strongly affected and for some models and regions the sea-level trend direction is reversed. Thus depending on the application, drift may be negligible or may make up an important part of the simulated trend.”
My Comment: This is a useful paper but they are committing an error by assuming that there are no systematic biases in the real world observed data. When they write that the “drift in globally-averaged surface properties is generally considerably smaller than observed…..20th century trends“, they are making an assumption when they write “generally considered”. They are referring to Tom Karl, Tom Peterson and associates at NCDC, Jim Hansen at GISS, and Phil Jones who make this “consideration”. As we have discussed in several of our papers; e.g. see
Pielke Sr., R.A., C. Davey, D. Niyogi, S. Fall, J. Steinweg-Woods, K. Hubbard, X. Lin, M. Cai, Y.-K. Lim, H. Li, J. Nielsen-Gammon, K. Gallo, R. Hale, R. Mahmood, S. Foster, R.T. McNider, and P. Blanken, 2007: Unresolved issues with the assessment of multi-decadal global land surface temperature trends. J. Geophys. Res., 112, D24S08, doi:10.1029/2006JD008229
their remain uncertainties with respect to how accurately are the global averaged surface trends.
Extracts from the conclusion of the Sen Gupta et al 2012 paper reads
“Below ~1 to 2km, the drift generally dominates over any forced trend. Any study examining subsurface processes or depth-integrated properties like steric sea-level rise, must pay careful attention to how drift is treated. The drift in sea-level can be large enough to reverse the sign of the forced change both regionally and in some models for the global average. Conclusions drawn from such studies may be sensitive to the method by which the drift is corrected for.
My Comment: With respect to the statement that “[b]elow ~1 to 2km, the drift generally dominates over any forced trend”, this is where some are now claiming the “missing heat” is going (e.g. see). Certainly, based on the Sen Gupta et al paper, the models are not providing much guidance on this issue.
The extract from the conclusion continues with
As surface drift is spatially heterogeneous, the regional importance of drift for individual models can be much larger than the global figures suggest.
My Comment: The statement that “the regional importance of drift for individual models can be much larger than the global figures suggest” further supports the lack of any skill in the multi-decadal regional climate predictions as we discuss in our article
Pielke Sr., R.A., and R.L. Wilby, 2012: Regional climate downscaling – what’s the point? Eos Forum, 93, No. 5, 52-53, doi:10.1029/2012EO050008.
The extract from the conclusion continues
Studies examining ocean fields from coupled climate models routinely take climate drift into account through some form of correction. As far as we are aware, this is not generally the case for the analysis of atmospheric fields. However, surface ocean drift will necessarily propagate to the atmosphere via air-sea coupling. This is evident in the strong correlations that exist between globally averaged SST drift magnitude and the drift magnitude of SAT and precipitation across the models. As such, drift in atmospheric properties e.g. SAT and precipitation, can make up a significant proportion of 20C3M trends. As an example, for precipitation (Fig. 3d) in 13 out of 21 models the error incurred by ignoring drift at a given location typically exceeds 20% for 1950-2000. Consideration of regional drift is particularly important as there is an increasing effort to use regional and local-scale information from individual climate models to inform regional impact studies.
As evidenced by the fact that drift can diminish with time, climate drift is sensitive to the mean state of a model. The mean state can change because of external forcing or because of natural variability. This implies that drift in the pre-industrial control will not be a perfect proxy for the drift within a transient simulation. While we offer no solution to this problem, it is important to recognise that this introduces some degree of uncertainty into any drift corrected forced trend estimate.
Long spin-up simulations can greatly reduce the rate of climate drift. However, this comes at a cost. A long integration necessarily means that the climate state has more time to diverge from the initial observed’ state. This has implications for the evaluation of climate models i.e. assessing their realism in simulating the observed system. It is often assumed that a ‘good’ model is simply one that can adequately reproduce a realistic mean state. Such an assessment is often used to select models or even weight models to provide a best estimate of future projections (see Knutti 2010 for a review). However, historical simulations and subsequent projections are branched from spin-up integrations of very different lengths. As such, a physically realistic model that has been integrated for a long period of time (to reduce any drift) may exhibit a poorer mean state than a less physically realistic model that has had only a short spin-up time and so retains a strong memory of the observational data used in the model initialisation. Knowledge of the rate of a model’s drift during the spin-up phase may in itself be a useful indicator of model realism, as a realistic model would have a final state that is close to the observationally derived initial state (assuming the initialised observed fields are dynamically consistent). The relative importance of a stable climate versus a realistic mean state must be carefully considered.
In the absence of a clear direction forward to alleviate climate drift in the near term, it seems important to keep open the question of flux-adjustment within climate models that suffer from considerable drift. Flux-adjustments are non-physical and therefore inherently undesirable. They may also fundamentally alter the evolution of a transient climate response (Neelin and Dijkstra 1995; Tziperman 2000). Nevertheless, flux-adjustment can alleviate climate drift, at least in surface temperature, which is also non physical and inherently undesirable.”
My Comment: The Sen Gupta et al 2012 paper is quite an indictment on the quality of the global climate models when used to assess the role of human climate forcings in altering the climate system. They are having problems in even accurately simulating the current climate system. Skillful predictions of changes in the climate system due to human activity cannot be achieved until this first hurdle is solved. Skillfully predicting changes in the regional climate is an even more daunting challenge. As I have written before (e.g. see), the use of the multi-decadal climate model predictions from regional and local impact studies is a waste of money and this study further supports that view.