Why are U.S. Taxpayers Supporting Numerous Duplications of Effort in the US Global Change Research Program (USGCRP)?

There are numerous duplications of effort in the US Global Change Research Program (USGCRP): from multiple U.S.-funded climate-modeling groups, to multiple producers of the same climate-related data. Why are taxpayers supporting all of those duplications of effort?


According to the 2014 Climate Change Expenditures Report from The White House, costs to U.S. taxpayers of the US Global Change Research Program (USGCRP) run about $2.5 billion annually.

CLIMATE MODELS – Duplication of Effort Example 1

Climate models are the basis for long-term prognostications of the impacts of human-induced global warming and climate change. Unknown to most climate laypersons, climate models are not simulating Earth’s climate as it existed in the past, as it exists now, or as it might exist in the future.  This reality came to light back in 2007 with the blog post Predictions of climate written for Nature.com by Dr. Kevin Trenberth of the National Center for Atmospheric Research (NCAR). Now consider that Dr. Trenberth is not a skeptic of human-induced global warming. He was a lead author of the IPCC’s 3rd and 4th Assessment Reports.  In that Nature.com article, Dr. Trenberth wrote (my boldface and underline):

None of the models used by IPCC are initialized to the observed state and none of the climate states in the models correspond even remotely to the current observed climate. In particular, the state of the oceans, sea ice, and soil moisture has no relationship to the observed state at any recent time in any of the IPCC models. There is neither an El Niño sequence nor any Pacific Decadal Oscillation that replicates the recent past; yet these are critical modes of variability that affect Pacific rim countries and beyond. The Atlantic Multidecadal Oscillation, that may depend on the thermohaline circulation and thus ocean currents in the Atlantic, is not set up to match today’s state, but it is a critical component of the Atlantic hurricanes and it undoubtedly affects forecasts for the next decade from Brazil to Europe. Moreover, the starting climate state in several of the models may depart significantly from the real climate owing to model errors. I postulate that regional climate change is impossible to deal with properly unless the models are initialized.

The outputs of the recent generation of models are stored in an archive called the Coupled Model Intercomparison Phase 5 (CMIP5).  The CMIP5-archived model outputs were used by the Intergovernmental Panel on Climate Change (IPCC) for their 5th Assessment Report (AR5) in 2013.  Referring to the CMIP5 webpage here, of the 25 modeling centers/institutions around the world, 5 are located in the United States.  They include:

  • Center for Ocean-Land-Atmosphere Studies at George Mason University and NOAA’s National Centers for Environmental Prediction (1 model)
  • NASA Goddard Institute for Space Studies (4 models)
  • NASA Global Modeling and Assimilation Office (1 model)
  • NOAA Geophysical Fluid Dynamics Laboratory (6 models)
  • National Science Foundation, Department of Energy, National Center for Atmospheric Research (5 models)

Note:  There is another U.S. modeling center listed on that CMIP5 webpage: National Center for Atmospheric Research (1 model). It is my understanding that the CCSM4 model from NCAR is no longer being updated.  NCAR is now relying on a model they share with the NSF and DOE.   [End note.]

U.S. taxpayers are paying for 17 climate models from 5 climate-modeling groups—and the resulting model-based studies—even though those climate models are simulating fantasy climates, not climate as it exists on this planet.   Keep in mind there are more than a dozen other modeling groups around the globe and many models share computer code, which means they are not independent of one another.  Why do U.S. taxpayers pay for 17 models of fantasy climate?


For this discussion, we’ll define climate model-based studies as scientific research of Earth’s climate and the long-term impacts of manmade greenhouse gases on climate, on flora and fauna and on human health and wellbeing that are based solely on the outputs of climate models.

Those studies can rely on the outputs of selected (cherry-picked) models that meet the researchers’ needs. Others studies can rely on the outputs of the full ensemble of models stored in a CMIP archive.  For yet even other studies, the models are specially programmed for a specific variable, output and result.

We discussed above that climate models are not simulating climate as it exists on Earth. Yet each year U.S. taxpayers pay for hundreds of research studies of fictional model-based climate.

Climate model-based studies are conjectural at best, misleading at other times. Why do we keep pumping U.S. taxpayer dollars into science fiction?


Both NASA’s Goddard Institute of Space Studies (GISS) and NOAA’s National Centers for Environmental Information (NCEI) manufacture global surface temperature anomaly products. (GISS data here and NCEI data here.) Both use the much-adjusted data from NOAA/NCEI: GHCN.v3 near-land surface temperature data and ERSST.v4 sea surface temperature data, the latter of which is NOAA’s “pause-buster” data (see the posts here and here for discussions of the curiosities in the NOAA’s ERSST.v4 sea surface temperature dataset). GISS also includes the Antarctic surface temperature data from The Scientific Committee on Antarctic Research (SCAR) to better capture the surface temperatures of that ice-covered continent, which represents less than 3% of the surface area of the globe.

Figure 1 compares the annual global land+ocean surface temperature anomalies for the full terms of data from both suppliers, along with their difference and their linear trends. You’ll note that I’ve used the full terms of the data (1880 to 2015) as the reference period for anomalies.  That was done so that the base years didn’t skew the curve of the difference.

Figure 1

Figure 1

Based on their linear trends, their global warming rates are basically the same at approximately +0.07 deg C/decade. The minor differences in annual wiggles result from:

  • the additional Antarctic data used by GISS,
  • the additional adjustments made by GISS to the NOAA land surface temperature data,
  • the different methods used to infill portions of the continental land masses without temperature data and
  • how the suppliers handle the surface temperatures above sea ice in the polar oceans.

Even so, there are only minor differences in the annual wiggles. The correlation between the two datasets is almost perfect at 0.996.

The UK Met Office (webpage here), the Japan Meteorological Agency (webpage here), the privately funded Berkeley Earth (webpage here), and a privately funded, modified version of the UKMO HADCRUT4 data from the University of York (webpage here) also produce global surface temperature products so there are numerous other sources of similar information for research and verification purposes.

Why do U.S. taxpayers pay for two global surface temperature products that furnish the same information and are based on the same source data from NOAA?

NOTE:  The U.S. also produces two satellite-derived atmospheric temperature datasets, one from Remote Sensing Systems and the other from the University of Alabama at Huntsville.   No other countries produce similar datasets so there are no additional sources of the same information.  Therefore, they are both required for research and verification purposes.

CLIMATE DATA – Satellite- and Rain Gauge-Based Global Precipitation – Duplication of Effort Example 3

NASA and NOAA produce three global precipitation datasets based on rain gauges and satellite observations. Those datasets are available in easy-to-use form at the KNMI Climate Explorer.  They include:

  • CAMS-OPI – NOAA’s Climate Anomaly Monitoring System (“CAMS”) and OLR Precipitation Index(“OPI”) – Starts in 1983 at KNMI Climate Explorer,
  • CMAP – NOAA’s CPC Merged Analysis of Precipitation, and
  • GPCP v2.2 – NASA’s Global Precipitation Climatology Project Version 2.2 (also supported by other groups around the globe) – Ends February 2015 at KNMI Climate Explorer.

Figure 2 compares the annual global precipitation values from those three datasets for the period of 1983 to 2014. The data are presented in absolute form, not anomalies.

Figure 2

Figure 2

Unlike global surface temperature datasets, there is little agreement among the global precipitation datasets.  They all show declining global precipitation over the satellite era, but the rates at which they decline are noticeably different.   Annual variations show few agreements. That is, the three datasets correlate very poorly.  Keep that in mind the next time you hear of a consensus on climate change.

Do U.S. taxpayers need to support 3 global precipitation datasets, especially when they show the climate science community has a very limited understanding of how precipitation varies around the globe?

OTHER CLIMATE DATA – Duplication of Effort Example 4

There are numerous other duplications of effort.  Sea ice for example. Sea ice data are produced in the U.S. by:

The sea ice products can vary between research centers: some produce sea ice extent data, others produce sea ice area data, while others produce sea ice volume data. But there are numerous duplications of effort.  Do taxpayers really need to pay for 5 research centers, all studying polar sea ice?


Pick a climate-related variable.  There are numerous datasets of that metric.  And when data from direct measurements do not exist, there are multiple modeling groups attempting to simulate it with special models called reanalyses.

Are they all necessary?


For further insights into how poorly climate models simulate Earth’s climate, see the series of posts titled Climate Models are NOT Simulating Earth’s Climate.  There are numerous other discussions about climate model flaws in my free ebook On Global Warming and the Illusion of Control – Part 1 (700+page, 25MB .pdf).

About Bob Tisdale

Research interest: the long-term aftereffects of El Niño and La Nina events on global sea surface temperature and ocean heat content. Author of the ebook Who Turned on the Heat? and regular contributor at WattsUpWithThat.
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