Verification of regional model trends

Guest post by Geert Jan van Oldenborgh, Francisco Doblas-Reyes, Sybren Drijfhout and Ed Hawkins

Climate information for the future is usually presented in the form of scenarios: plausible and consistent descriptions of future climate without probability information. This suffices for many purposes, but for the near term, say up to 2050, scenarios of emissions of greenhouse gases do not diverge much and we could work towards climate forecasts: calibrated probability distributions of the climate in the future.

This would be a logical extension of the weather, seasonal and decadal forecasts in existence or being developed (Palmer, BAMS, 2008). In these fields a fundamental forecast property is reliability: when the forecast probability of rain tomorrow is 60%, it should rain on 60% of all days with such a forecast.

This is routinely checked: before a new model version is introduced a period in the past is re-forecast and it is verified that this indeed holds. In seasonal forecasting a reliable forecast is often constructed on the basis of a multi-model ensemble, as forecast systems tend to be overconfident (they underestimate the actual uncertainties).

As the climate change signal is now emerging from the noise in many regions of the world, the verification of regional past trends in climate models has become possible. The question is whether the recent CMIP5 multi-model ensemble, interpreted as a probability forecast, is reliable.

As there is only one trend estimate per grid point, necessarily the verification has to be done spatially, over all regions of the world. The CMIP3 ensemble was analysed in this way by Räisänen (2007) and Yokohata et al. (2012). In the last few months three papers have appeared that approach this question for the CMIP5 ensemble with different methodologies: Bhend and Whetton (2013), van Oldenborgh et al. (2013) and Knutson et al (J. Climate, to appear).

All these studies reach similar conclusions. For temperature: the ensemble is reliable if one considers the full signal, but this is due to the differing global mean temperature responses (Total Climate Responses, TCR).

When the global mean temperature trend is factored out, the ensemble becomes overconfident: the spatial variability is too low. For annual mean precipitation the ensemble is also found to be overconfident. Precipitation trends in 3-month seasons have so much natural variability compared to the trends that the overconfidence is no longer visible.

These conclusions match with earlier work using the Detection and Attribution framework showing that the continental-averaged temperature trends can be attributed to anthropogenic factors (eg Stott et al, 2003), but zonally-averaged precipitation trends are not reproduced correctly by climate models (Zhang et al, 2007).

The spatial patterns for annual mean temperature and precipitation are shown in figure 1 below. The trends are defined as regressions on the modelled global mean temperature, i.e., we plot B(x,y) in

(1) T(x,y,t) = B(x,y) Tglobal,mod(t) + η(x,y,t)

This definition excludes the TCR and minimises the noise η(x,y,t) better than a trend that is linear in time.

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  2. J. RÄISÄNEN, "How reliable are climate models?", Tellus A, vol. 59, pp. 2-29, 2007.
  3. T. Yokohata, J.D. Annan, M. Collins, C.S. Jackson, M. Tobis, M.J. Webb, and J.C. Hargreaves, "Reliability of multi-model and structurally different single-model ensembles", Clim Dyn, vol. 39, pp. 599-616, 2011.
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