Lots of hubbub around the blogosphere recently about precipitation in the UK and U.S. The UK had lots of rain in 2012 and the U.S. had too little. (Hey, you guys! Give us our rain back, will ya?) So I decided to take a look at how well the CMIP5 climate models, which were prepared for the IPCC’s upcoming 5th Assessment Report (AR5), simulated precipitation during the satellite era. Considering how poorly the models performed on global and regional bases (See Post Here), one would probably guess that the models performed poorly with country-size areas. That guess would be correct.
Figure 1 presents the UK model-data comparison and Figure 2 is for the U.S. Because the model-mean is an average of the dozens of models found in the archive, we would not expect the year-to-year variations in the models to match the observational data. We should, however, expect the modeled linear trends to at least be the same sign as the trends of the data. For both the UK and U.S., precipitation has decreased over the past 34 years, but that’s not the case for the models.
The observations-based dataset is NOAA’s CAMS-OPI. It is a merged dataset based on ground-based rain gauge observations from the Climate Anomaly Monitoring System (CAMS) and based on satellite rainfall estimates from the Outgoing Longwave Radiation (OLR) Precipitation Index (OPI). It has global (pole to pole) coverage, with a resolution of 2.5 X 2.5 deg longitude and latitude. The dataset runs from January 1979 to present. We’ve compared those observations to the multi-model ensemble mean of the precipitation simulations from climate models stored in the CMIP5 archive. That’s the archive used by the IPCC for its upcoming 5th Assessment Report. I’ve used the RCP6.0 scenario outputs, which are very similar to the SRES A1B scenario that was well-cited in past IPCC reports. The CAMS-OPI precipitation data and the outputs of the climate model simulations of precipitation are available through the KNMI Climate Explorer.
STANDARD NOTE ABOUT THE USE OF THE MODEL MEAN
We’ve published numerous posts that include model-data comparisons. If history repeats itself, proponents of manmade global warming will complain in comments that I’ve only presented the model mean in the above graphs and not the full ensemble. In an effort to suppress their need to complain once again, I’ve borrowed parts of the discussion from the post Blog Memo to John Hockenberry Regarding PBS Report “Climate of Doubt”.
The model mean provides the best representation of the manmade greenhouse gas-driven scenario—not the individual model runs, which contain noise created by the models. For this, I’ll provide two references:
The first is a comment made by Gavin Schmidt (climatologist and climate modeler at the NASA Goddard Institute for Space Studies—GISS). He is one of the contributors to the website RealClimate. The following quotes are from the thread of the RealClimate post Decadal predictions. At comment 49, dated 30 Sep 2009 at 6:18 AM, a blogger posed this question:
If a single simulation is not a good predictor of reality how can the average of many simulations, each of which is a poor predictor of reality, be a better predictor, or indeed claim to have any residual of reality?
Gavin Schmidt replied:
Any single realisation can be thought of as being made up of two components – a forced signal and a random realisation of the internal variability (‘noise’). By definition the random component will uncorrelated across different realisations and when you average together many examples you get the forced component (i.e. the ensemble mean).
To paraphrase Gavin Schmidt, we’re not interested in the random component (noise) inherent in the individual simulations; we’re interested in the forced component, which represents the modeler’s best guess of the effects of manmade greenhouse gases on the variable being simulated.
The second reference is a similar statement by the National Center for Atmospheric Research (NCAR). I’ve quoted the following in numerous blog posts and in my recently published ebook. Sometime over the past few months, NCAR elected to remove that educational webpage from its website. Luckily the Wayback Machine has a copy. NCAR wrote on that FAQ webpage that had been part of an introductory discussion about climate models (my boldface):
Averaging over a multi-member ensemble of model climate runs gives a measure of the average model response to the forcings imposed on the model. Unless you are interested in a particular ensemble member where the initial conditions make a difference in your work, averaging of several ensemble members will give you best representation of a scenario.
In summary, we are not interested in the models’ internally created noise, and we are not interested in the results of individual responses of ensemble members to initial conditions. So, in the graphs, we exclude the visual noise of the individual ensemble members and present only the model mean, because the model mean is the best representation of how the models are programmed and tuned to respond to manmade greenhouse gases.
There may be a couple of El Niño-Southern Oscillation (ENSO)-related precipitation variations in the two graphs. The large dip in the U.S. rainfall in the late 1990s and early 2000s may be associated with the 1998-2001 La Niña. Likewise, the early 1980s peak looks as though it could be associated with the 1982/83 El Niño. But there are few other agreements.
There is, however, a very strong relationship between strong El Niño events and the rise in global sea surface temperatures during the satellite era. That is, satellite-era sea surface temperature records indicate El Niño and La Niña events are responsible for the warming of global sea surface temperature anomalies over the past 30 years, not manmade greenhouse gases. I’ve searched sea surface temperature records for more than 4 years, and I can find no evidence of an anthropogenic greenhouse gas signal. That is, the warming of the global oceans has been caused by Mother Nature, not anthropogenic greenhouse gases. Mother Nature’s handiwork is also obvious in the warming of Ocean Heat Content data.
I’ve recently published my e-book (pdf) about the phenomena called El Niño and La Niña. It’s titled Who Turned on the Heat? with the subtitle The Unsuspected Global Warming Culprit, El Niño Southern Oscillation. It is intended for persons (with or without technical backgrounds) interested in learning about El Niño and La Niña events and in understanding the natural causes of the warming of our global oceans for the past 30 years. Because land surface air temperatures simply exaggerate the natural warming of the global oceans over annual and multidecadal time periods, the vast majority of the warming taking place on land is natural as well. The book is the product of years of research of the satellite-era sea surface temperature data that’s available to the public via the internet. It presents how the data accounts for its warming—and there are no indications the warming was caused by manmade greenhouse gases. None at all.
Who Turned on the Heat?was introduced in the blog post Everything You Ever Wanted to Know about El Niño and La Niña… …Well Just about Everything. The Free Preview includes the Table of Contents; the Introduction; the beginning of Section 1, with the cartoon-like illustrations; the discussion About the Cover; and the Closing. The book was updated recently to correct a few typos.
Please buy a copy. Credit/Debit Card through PayPal. You do NOT need to open a PayPal account. Simply scroll down to the “Don’t Have a PayPal Account” purchase option. It’s only US$8.00.
U.S. precipitation was also presented in the Video: Drought, Hurricanes and Heat Waves – 2012 in Perspective.
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