By coincidence, after I posted
the seminar scheduled in Boulder, Colorado titled
On the reliability of climate models: How well do they describe observed trends?
by Geert Jan van Oldenborgh was anounced. Geert works at KNMI (Royal Netherlands Meteorological Institute) in De Bilt, Netherlands and his seminar is on Tuesday, January 31, 10:00 am in room 1D403 of the David Skaggs Research Center.
The abstract reads
Climate models are widely used to construct local projections of future climate changes. For these to be used as “forecasts” the ensemble of climate models has to be reliable in the sense that the projected probability of outcomes should correspond with the realised probability. In weather and seasonal forecasts this is verified over a set of past forecasts. Since the local climate change signal is now emerging from the weather noise in many regions of the world, the reliability of climate model ensembles can be estimated by comparing the observed and modelled trends in temperature and precipitation over the past 50 to 100 years. The spatial dimension is used to gather the necessary statistics.
My Comment: Implicit in this statement is that there is a background climate signal from which a local effect is expected to emerge. In reality, climate is very nonlinear, and as illustrated later in the abstract, the demonstration of model predictive (explanatory) skill is not clearly shown. Indeed, in his paper
Oldenborgh, G.J. van, F.J. Doblas-Reyes, B. Wouters and W. Hazeleger, 2012: Skill in the trend and internal variability in a multi-model decadal prediction ensemble. accepted, Clim. Dyn.
they write
The modelled trends agree well with observations in the global mean, but the agreement is not so good at the local scale
and
The skill assessment does not take into account the considerable biases and drift of the models.
The abstract continues
Although global and continental trends are represented well, it is shown that in many regions of the world the observed local trends are not within the ensemble of modelled trends. These areas are larger than would be expected on the basis of chance fluctuations and are therefore a consequence of either misrepresentation of the trends or underestimation of low-frequency variability in climate models. Downscaling with regional climate models does not change this conclusion beyond the addition of orographic details.
My Comment: His report that “Downscaling with regional climate models does not change this conclusion beyond the addition of orographic details” provides further support to our conclusion in the paper
Pielke Sr., R.A., R. Wilby, D. Niyogi, F. Hossain, K. Dairuku, J. Adegoke, G. Kallos, T. Seastedt, and K. Suding, 2012: Dealing with complexity and extreme events using a bottom-up, resource-based vulnerability perspective. AGU Monograph on Complexity and Extreme Events in Geosciences, in press
that regional downscaling does not add any value beyond what can be achieved by just interpolating the global model results to a finer resolution grid resolution of such surface features as terrain.
The abstract continues
For European temperature and precipitation trend we have investigated the causes of the discrepancies. In winter, both temperature and precipitation have increased much faster than modelled due to an increase in westerly circulation associated with a significant increase in air pressure over the Mediterranean. In spring and summer the faster rise of temperature is over the land areas of southern Europe. In the Netherlands it is associated with a large increase in global radiation. The concomitant rise in East Atlantic SST causes an increase in coastal precipitation that is absent in the climate models. This is partially explainable by a wrong ocean current system in the North Atlantic Ocean, which is a well-known deficiency of coarse resolution ocean models. Finally, the decrease of mist and fog caused by decreased air pollution is not represented in climate models. None of these factors is associated with known modes of low-frequency variability, leading to the conclusion that the biases are more likely in the trend than in the variability.
My Comment: His paragraph further confirms the importance of variations in regional atmospheric and ocean circulations even with respect to long term means. As we concluded in our paper
Pielke Sr., R., K. Beven, G. Brasseur, J. Calvert, M. Chahine, R. Dickerson, D. Entekhabi, E. Foufoula-Georgiou, H. Gupta, V. Gupta, W. Krajewski, E. Philip Krider, W. K.M. Lau, J. McDonnell, W. Rossow, J. Schaake, J. Smith, S. Sorooshian, and E. Wood, 2009: Climate change: The need to consider human forcings besides greenhouse gases. Eos, Vol. 90, No. 45, 10 November 2009, 413. Copyright (2009) American Geophysical Union
where we wrote
Unfortunately, the 2007 Intergovernmental Panel on Climate Change (IPCC) assessment did not sufficiently acknowledge the importance of these other human climate forcings in altering regional and global climate and their effects on predictability at the regional scale. It also placed too much emphasis on average global forcing from a limited set of human climate forcings.
The abstract concludes with
Time permitting, extreme hourly precipitation trends are discussed. Plotting these as a function of dew point temperature gives a common scaling behavior, between De Bilt and Hong Kong, two stations with long hourly time series. In the Netherlands this allows for an attribution of the increase of hourly extremes to local temperature rise. In Hong Kong this attribution cannot be made and other factors, such as possibly urbanisation, must be responsible for the observed increase.
My Comment: This statement illustrates why attribution studies must move beyond CO2 and a few other greenhouse gases in order to explain long term climate trends. Landscape change is certainly one of the major, under-examined attributions as we discuss in our paper
Pielke Sr., R.A., A. Pitman, D. Niyogi, R. Mahmood, C. McAlpine, F. Hossain, K. Goldewijk, U. Nair, R. Betts, S. Fall, M. Reichstein, P. Kabat, and N. de Noblet-Ducoudré, 2011: Land use/land cover changes and climate: Modeling analysis and observational evidence. WIREs Clim Change 2011, 2:828–850. doi: 10.1002/wcc.144.
Climate Science: Roger Pielke Sr. 
