Comments On “Accuracy Of Climate Change Predictions Using High Resolution” By Matsueda and Palmer 2011

In 1988 I wrote the book chapter

Pielke, R.A., 1988: Evaluation of climate change using numerical models. In “Monitoring Climate for the Effects of Increasing Greenhouse Gas Concentrations. Proceedings of a Workshop”. R.A. Pielke and T. Kittel, Eds., Cooperative Institute for Research in the Atmosphere (CIRA), Fort Collins, Colorado, August 1987, 161-172.

Included in the text is

“The dynamic accuracy of GCM models have not been adequately tested. Such models need to be used to predict short-term weather changes since skill at such forecasts is essential if the models are to demonstrate a numerical fidelity in simulating wave-wave interactions. If the GCMs have insufficient spatial resolution or physics to forecast weather as accurately as current operational weather numerical weather prediction models, what confidence should be placed on their skill at predicting long-term climate change?”

Now, finally in 2011, a paper examines part of this issue, although it equates  high spatial resolution and lower spatial resolution model runs at two different 25 year time slices with an actual scientific test of climate change, when observations are not, of course, available to test their results decades from now.  It is an informative study, however, on the effect of model resolution for the time period of 1979 to 2003.

The paper is (h/t Dallas Staley)

Matsueda, M., and T. N. Palmer (2011), Accuracy of climate change predictions using high resolution simulations as surrogates of truth, Geophys. Res. Lett., 38, L05803, doi:10.1029/2010GL046618.

The abstract reads [highlight added]

How accurate are predictions of climate change from a model which is biased against contemporary observations? If a model bias can be thought of as a state‐independent linear offset, then the signal of climate change derived from a biased climate model should not be affected substantially by that model’s bias. By contrast, if the processes which cause model bias are highly nonlinear, we could expect the accuracy of the climate change signal to degrade with increasing bias.Since we do not yet know the late 21st Century climate change signal, we cannot say at this stage which of these two paradigms describes best the role of model bias in studies of climate change. We therefore study this question using time‐slice projections from a global climate model run at two resolutions ‐ a resolution typical of contemporary climate models and a resolution typical of contemporary numerical weather prediction – and treat the high‐resolution model as a surrogate of truth, for both 20th and 21st Century climate. We find that magnitude of the regionally varying model bias is a partial predictor of the accuracy of the regional climate change signal for both wind and precipitation. This relationship is particularly apparent for the 850 mb wind climate change signal. Our analysis lends some support to efforts to weight multi‐model ensembles of climate change according to 20th Century bias, though note that the optimal weighting appears to be a nonlinear function of bias.”

They outline their model experiments in the text

“….we make use here of the “timeslice” technique, whereby an atmosphere‐only model is integrated over two periods of 25 years corresponding to the late 20th Century and the late 21st Century with prescribed sea surface temperatures (SSTs). We do, however, recognise that prescribed SST integrations are themselves subject to systematic biases due to one‐way coupling [Douville, 2005]. [7] Model integrations were conducted for the (“control”) period 1979–2003 using observed interannually‐varying HadISST SSTs and sea ice concentrations (SICs) [Rayner et al., 2003] as lower boundary conditions. For the period 2075–2099, the SST and SIC climate‐change signals are estimated by the CMIP3 [Meehl et al., 2007] multi‐model ensemble mean to which the detrended interannual variations in HadISST have been added [Mizuta et al., 2008]. In this way, both control and timeslice integrations are integrated with interannually varying SSTs and SICs. The IPCC SRES A1B scenario was assumed for future emissions of greenhouse gases.”

They describe the results for the time period 1979 to 2003, which is actually the only scientifically robust part of their paper, with the text

“Table 1 shows the 20th Century RMS bias in 850 hPa wind (U850) for all individual Giorgi regions for the low and high resolution models, for December to February (DJF) and June to August (JJA), against real data (Japanese reanalysis [Onogi et al., 2007]). Notice that in general (10 out of 16 entries in Table 1) the high resolution model has lower bias than the low resolution model against the real data ‐ in a further 3 cases the single‐member high‐resolution simulation has equal bias with the smoother low‐resolution ensemblemean field.”

The conclusion reads

“Using high resolution simulations as a surrogate of truth, we have shown that the regionally dependent 20th Century 850 hPa zonal wind and precipitation bias of a climate model is a predictor of the accuracy of its 21st Century climate change signal. In particular, in two regions where model bias was especially large, the low‐resolution model’s climate change signal was negatively correlated with the true climate change signal

The results give some support to efforts to weight multi‐model ensembles with bias, though our results suggest the weighting should depend nonlinearly with bias, and, for precipitation, may also depend on season. More generally the results in this paper lend support to aims to try to reduce model bias – the notion of a state independent linear bias offset is simply not tenable. A byproduct of our study was the finding that the bias of a model run at typical NWP resolution was typically smaller than that of an equivalent model run at typical climate resolution (though due to some changes of parameters, it cannot be stated unambiguously that the reduction of bias was uniquely due to resolution). Consistent with the seamless prediction methodology [Palmer and Webster, 1993; Palmer et al., 2008], we strongly recommend that a fully rigourous study of the impact of running climate models at today’s NWP resolutions be made using fully comprehensive coupled ocean‐atmosphere climate models, where high‐resolution ocean dynamics is also likely to be important [Shaffrey et al., 2009]. Given the demands of Earth‐System complexity and the need for ensemble integrations, this would require computational facilities with sustained multi‐petaflop performance, dedicated to climate prediction. Such facilities are currently unavailable to the climate modelling community [Palmer, 2005; Nature Editorial, 2008; Shukla et al., 2009]. Given the preeminence of the climate threat, and the need to reduce uncertainty in climate predictions, we believe this to be a matter of importance and urgency.”

This paper is an important addition to the understanding of spatial resolution in terms of the accuracy of the model predictions.  Such an examination can only be performed, however, when real world observational data is available (i.e. 1979 to 2003 in their paper). It is a necessary condition to have any confidence in multi-decadal global model predictions.

However, it is far from a sufficient test since, as I summarized in the post

The Failure Of Dynamic Downscaling As Adding Value to Multi-Decadal Regional Climate Prediction

the multi-decadal global model predictions must be able to skillfully predict the changes in the statistics of the climate system. In my post, I wrote

” Finally, There is sometimes an incorrect assumption that although global climate models cannot predict future climate change as an initial value problem, they can predict future climate statistics as a boundary value problem [Palmer et al., 2008]. With respect to weather patterns, for the downscaling regional (and global) models to add value over and beyond what is available from the historical, recent paleo-record, and worse case sequence of days, however, they must be able to skillfully predict the changes in the regional weather statistics.

 There is only value for predicting climate change, however, if they could skillfully predict the changes in the statistics of the weather and other aspects of the climate system. There is no evidence, however, that the model can predict changes in these climate statistics even in hindcast.”

This was not tested in the Matsueda and Palmer paper.  The paper is an informative addition to our understanding of the role of spatial resolution in a model of the atmospheric portion of the climate system. However, it is not a robust study of the effect of spatial resolution on model prediction skill of climate change decades from now.  Indeed, a more accurate title of their paper is

‘Accuracy Of  Model Simulations Using High Spatial Resolution In An Atmospheric General Circulation Global Model”

It is not a scientifically robust study of the accuracy of climate change predictions.

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