Being a Climate Scientist for a day

So who is right, Phil Jones or Anthony Watts? Basically they disagree over the influence of town size on measured temperature. So being an experimentalist I wondered what I could do to check and see who was right. And, since this is something that you can do at home, I’m going to explain exactly what I did, since I only used the data from Missouri, and there is data available for all the states, so that those who want to can repeat for their state, what I did. (And for that reason I will explain it in excruciating detail).

Now, if you are going to analyze data it is a good idea to define what the questions are that you are seeking an answer to before you start. So let me state 3 initial hypotheses. The first is from Jones et al in 2007, which refers back to a paper by Karl and James in 1990, which says, in part

If the Canadian stations behave similarly to stations in the United States, the decrease of the DTR (diurnal temperature range) may be exaggerated by about 0.1 dg C due to urbanization.

This correlates with the 2007 paper which says

Urban-related warming over China is shown to be about 0.1°C/ decade over the period 1951–2004, with true climatic warming accounting for 0.81°C over this period.

So the hypothesis is that the rate of warming does not significantly change, as a function of the size of the community around the weather station.

The second comes from the way in which the Goddard Institute for Space Sciences classifies site sizes, calling communities below 10,000 rural. So the hypothesis is that there is no change in temperature with population below a community size of 10,000.

And the third hypothesis comes from reading Anthony Watts, and it seems to me that if his finding about the deteriorating condition of weather stations holds true, then the scatter of the data should get worse with time. So my hypothesis is that the standard deviation of the results should increase with time.

OK so I have my hypotheses – where do I get my data (you also need to have a spread sheet program, I am going to use Microsoft Excel running on a Mac) and a state map (or equivalent source for town populations). As I said I am going to look at the data for Missouri, but before I get the data I need a table to put it into. So I open Excel and create the table I want. To do this I first type titles starting in square B3, and going sequentially down, inserting the titles Station; GISS; USHCN Code; Latitude; Longitude; Elevation; and Population. I then move to square A11, and type in Calendar Year. So we start with a table that looks like this:


I am going to be putting data in from 1895 to the year 2008, and so I put 1895 into square A12, and then (=A12+1) into square A13. Then I highlight from A13 to A125 and select “Fill down” from the EDIT menu at the top of the Excel page. This now puts the years to 2008 into the A column. So now I need to get the data to put into the table. To get this I go to the US Historical Climatology Network and select Missouri from the scrolling list on the top left of the page.


I then clicked on the Map Sites button to get to the data that I wanted.


The map that now comes up shows the 26 sites that are listed for which there are records going back to 1895 in Missouri. These are listed to the right, and if you click on any one of them then the identification for it shows on the main map. I have done that for the first site on the list, Appleton City, so that you can see what I mean.


From the map information I can get the USHCN Code (230204), the latitude, longitude and elevation of the site. So I can enter those below that station name in my table, which now looks like this:


Now I need the historic temperature data for that site, and to get this I click on the “Get Monthly Data” phrase in the map balloon. This takes me to a new window.


We need to have the data in a form that will fit into the EXCEL spreadsheet, so click on the middle line in the second set of options, to create a download file. This drops you down to the bottom half of the page, and what we want (for today) is the Annual Average Mean Temperature, which is on the upper half of the screen, so I click on that box (a tick mark appears). Then I press on the submit button.



This brings up a response, which tells you the name of the file that will be downloaded to your computer, when you click the blue line, which I did.


This is the data that you want from the file (and why I included the site number when I made the table, so that I could check that I was getting each range copied into the right column, and that when I was finished I had data from all the sites). The file is downloaded into your downloads file on your computer, and when you open it in EXCEL, you get:


The data that you want is in the third column (C ) and you want to make sure that you have the Annual Average Temp so C2 should read as shown. Copy the numbers in the column (Select C3 to C116 and copy or command C). Then re-open the initial EXCEL page where we are storing the data (I call mine Missouri Annual Temp, so I will refer to it as the MAT page from now on). Place the cursor on box C13 and tell the computer to Paste (either from the Edit menu or by using command V). You should get the data pasted into the spreadsheet, as I have shown.


This is the information for the first site, and should fill down to C126. It is now a good idea to save the file.

Now we go back to the USHCN page, close the window that had the data file on it, and you should be looking at the map and list of sites again. Now select the second name on the list (in this case Bowling Green) and repeat the steps to input the site information to the spreadsheet, then select Monthly Data, and the Annual Average Mean Temperature, download the file and copy the information, and then paste it into the spreadsheet.

Keep doing this as you work down the list of stations and by the end you should have 26 station records (if you are doing Missouri – the numbers vary – Illinois has 36 for example). The right end of the EXCEL file now looks like this (except that I have put in some information in boxes W4 and Y4 that I will explain in a minute).

(and the files extend down to row 126).
Now I need the information on population, and for that I went to the State Map (since it has it all in one place), and by each town there is the population from the 2000 Census). So I then inserted this set of numbers as I moved along row 9. I had a problem when I got to Steffenville, since no population is shown. So I went to Google Earth and had a look at the place. It looks as though there is perhaps a dozen or so houses there, so I gave it a population of 30, and for Truman Dam, looking it up on Google, the Corps of Engineers Office is in Web City, so I used the population of that town for the station.


Now I’m not quite done getting data, since the Goddard Institute for Space Studies, GISS, actually doesn’t use any of these, but uses information from three of the largest cities in the state, Columbia, Springfield and Saint Louis. So I need to get the data that they use, but since they record the data in degrees C, and the rest are in degrees F, I don’t want to put their data right next to the information in the table, so while I create the station names, and put them in boxes AC3, AD4 and AE4 I am going to create the initial data columns further over in the table (actually in columns AN, AO and AP) so that after I insert the numbers I can convert them back to Farenheit.

So where do I get the GISS temperatures? Unfortunately I have forgotten the exact route I used, but you can start with the GISTEMP Station Selector page and if you click on the shape of Missouri on the map


You will get a list of the stations that they have in Missouri, though, as I said above, apparently they only use 3 of them - in part because some of the other series are not complete. (I am only going to show the top of the list down past Columbia. ) You click on the name of the station you want (we are going to use the three listed above, so the procedure is the same for each from this point).


Clicking on the station, at GISS, gives you a plot of the average temperature over the past , but you need to go one step further and click on the bottom phrase of those shown to get to the data page. Oh, and it is this page, with the populations that are less than 10,000 being considered as rural, that gave rise to the second hypothesis that we are testing. (Which I guess you might call the “and is James Hansen right?” corollary to the initial question I asked).


And being their usual helpful selves this is not easily downloadable into our table. So, having no other way to proceed, I hand-downloaded the info that I wanted into the table. I have only shown the top of the table, and the numbers that I want started out in the column on the far end, but, as I explain below, I changed the array (If you are doing this, the table can be stretched so that all the data for one year are on a single line, and then you need the data in the far right column). So the data that I am inserting in a column, starting in box AN13 is the metANN (mean temperature Annual) data. (Small hint, to check that I had the years and data correct I squeezed the frame down so that the row data for a year appeared in 2 rows with the metANN in the second row one column over, as shown, and I started with 1895 (12.05) to be consistent with the other tabulated values. (And I only copied the left half of the screen that contains what I need).


This took a little while, since the data had to be hand entered for all three sites, but after maybe an hour (remember I titled this a day as a climate scientist – this is why), we have the data from the three sites that GISS uses in Missouri entered into the table, in Centigrade. The latitude and longitude aren’t given quite as precisely, we don’t have heights, and the population values don’t match the census, but this is the GISS data, and we gratefully take what we are given.


Now we need to go over to the initial columns for these stations (AC to AE) and insert the same data, converted to deg F. (so box AC13 has the equation = 32+(AN13*9/5)). Then I filled down from AC13 to AC126, and then filled right AC to AE, and the conversion was done). In the new columns I also entered the 2000 census data for the three sites, rather than the GISS values.

Now I have the raw data that I need to do the analysis. As they say most research is in the preparation, the fun part comes a lot more rapidly. To get to that we need to add just a few calculation steps into the raw data table. The first one that I am going to add is to simply take three different sets of averages. The first is for each year for the USHCN data without the GISS stations, the second is each year for the GISS stations, and the third is for each station over the full period of the data set. (I am not going to show the screens for this since they are rather straightforward.)

To get the first average I go to column AG – call it Historic ave, without GISS) and in AG13 type =SUM(C13:AB13)/26 which calculates the average or mean value. Then I select from AG13 to AG126 and fill down from the EDIT menu. That gives me the average annual temp for the USHCN data. Then I add, in column AH, which I call Standard Deviation, and in box AH13 a statistical formula (that I get from the Insert menu > Function > STDEV ). This puts =STDEV() into the box, and I give it the range I want examined by either selecting all the boxes from C13 to AE13. Or typing that in, so that the box reads =STDEV(C13:AE13) and click ENTER. This gives me a measure of the scatter in the data for the year 1895. I then select the boxes AH13 to AH126 and filled down. This now has tabulated the change in the scatter of the data over the last 113 years. (And I’ll come back to this in a minute).

Now I want the average of the GISS stations, so I created this in column AJ, typing the formula =(AC13+AD13+AE13)/3 into that box. Then, as before, selecting and filling down the column. And then there is a final column – which I call Difference, which is the difference between the Historic data set and the GISS set. That is created in column AL, and is simply typed into AL13 as =AJ13 – AG13, then filled down to AL129. (It is extended to include the average values that are calculated next). And to provide the overall average for the state I combined all 29 station data into a combined average in column AF.

And the individual station averages are created by typing =sum(c13:C126)/114 into box C129, and then filling that right to column AG129. This formula can then be copied and pasted into box AJ129 to give the average value over the years for the stations that GISS relies on. And immediately it is clear, by looking at box AL129 that there is an average difference, over the years, of 1.19 deg F between the stations that GISS are using, which are in the larger cities, and those of the more rural stations in Missouri. (Which would seem to validate the criticism from E.M. Smith, but that, as they used to say in debate, “is not the question before the house.”)

Now you are going to have to take my word for this, but when I started making this data set I had no knowledge as to how it would turn out, though I had some expectations. Let us now see what happens when we plot the data. (I am going to use the charting function of EXCEL, and add trendlines to the data, with equations and the r-squared value, so that we can see what is happening, since the data is scattered about a bit on each individual plot).

So the first hypothesis we wanted to verify was - the rate of warming does not significantly change, as a function of the size of the community around the weather station. Given that the historic average is for smaller stations and the GISS average is for the larger communities, this would, initially suggest that a plot of difference against time should show no change, if this hypothesis is true.
(Plot of column AL against A)


Well this shows that the difference has been getting less, rather than increasing – which, if anything I suppose initially supports the hypothesis. But out of curiosity I wondered how much temperature change we have had, since the actual relationship hypothesized was about rates of change. So let’s plot average temperature against time.


Now if you look at that plot Missouri has had quite a wimpy warming, less than half a degree F over a hundred and fifteen years, so given that small range, detecting changes in the rates is perhaps not feasible. But let’s plot the difference as a function of temperature just to see if there is anything.


And still it goes down? Wonder if that is trying to tell us something?

Moving on to the second hypothesis, which comes from GISS, and is that temperature is insensitive to adjacent population below a community size of 10,000 folk. This is a plot of row 129 plotted against row 9. I am going to show the plot twice. The first time I am using a log scale for the horizontal axis to cover the range from a population of 30 to that of over a million.


And now I am going to change the scale so that the horizontal scale is linear, and truncate it so that it only shows the data up to a population of 50,000.


Notice how the temperature is much more sensitive to population BELOW a population of 10,000 relative to the sensitivity above that size. Thus the assumption that GISS makes in classifying every town below 10,000 as rural without any sensitivity to population is clearly not correct.

And interestingly this also possibly explains the decline in the temperature difference with time (although it would require inputting data from earlier years census to fully explore the topic). The assumption behind the first two hypotheses was that the larger towns had a greater sensitivity to urban heat, which is getting worse, but in reality, if the smaller towns were growing faster (and require less population change to have an impact on the measured temperature) then they would be gaining temperature, because of that growth, faster than the urban sites – hence the negative slope to the graph.

Which brings me to my hypothesis that the scatter in the data would get larger with time, given the deterioration and urbanization around the weather stations. By using standard deviation to illustrate scatter, the plot, if I am right should have an upward slope, over time.


Hmm! Well it looks as though I got that wrong – it was heading the way I thought until the 1940’s and then it started to bend the other way. Apparently the change from glass thermometers to the automated Maximum/Minimum Temperature System (MMTS) started about then and the changing shape of the curve is perhaps indicative of the spread of the new system.

In all these graphs it should be borne in mind that Missouri has had a relatively stable climate over the past hundred and fifteen years or so. There are also likely influences across the state due to changes in latitude and longitude. And since, with the data table assembled, generating additional plots is easy and relatively fast, we can take a look. It turns out that Longitude doesn’t have that much effect, but the temperature values are much more sensitive to Latitude than anything else that we have discussed.


And it may well be that dependence that hides some of the nuances of the other relationships.

Well there we are, a little exercise in climate science. Of the three hypotheses we looked into, it turned out that the second and third were wrong, and because of that it may be that the data on which the first was based was not focused sufficiently on the changes in the small size of some of the communities (if the sensitivity gets less above a town size of perhaps 15,000.

The procedures that I spelled out in such detail should allow anyone else to run this same series of steps to determine if what I found for Missouri holds true over other states in the Union, and if anyone wants a copy of the spreadsheet, let me know where to send it through comments. (While yes I work at a University and yes I acquire data, I have no clue how to store it on the master servers –all of ours, for lots of good reasons, are stored otherwise, and so I don’t know how to make the file available in other ways than by attaching it to an e-mail).

And so to summarize the exercise, which as I noted in the title took me about a day to do and write up – by analyzing the data from 29 weather stations in Missouri, which have a continuous record of temperature from 1895 to 2008 (and are still running I assume) we have shown that
a) It is not possible to decide if Anthony Watts or Phil Jones is correct, since there may have been an incorrect assumption made in the data collection, which (conclusion b) means that the wrong initial assumptions were made in parsing the data.
b) The assumption that the “urban heat island” effect gets greater with larger conurbations is not correct in Missouri, where the data suggests that the sensitivity is most critical as the community grows to a size of 15,000 people.
c) The hypothesis that the data scatter gets worse with time because of deterioration in station conditions does not hold in Missouri, when the assumption is predicated on increase in the standard deviation of the readout between stations in a community. However this assumption may have been valid where reliance was placed on glass thermometers, since it is possible that the change to automated instrumentation has, at least for the present, over-ridden that deterioration.

So much for my venture into climate science, at least for now. I just wanted to show that it is not that difficult to check things out for yourself, and, provided you have the time, it can yield some unexpected results. (Though I should point out that Anthony Watts and Joseph D’Aleo quoted Oke in their report on surface temperature records, (page 34)

Oke (1973) * found that the urban heat-island (in °C) increases according to the formula –

➢ Urban heat-island warming = 0.317 ln P, where P = population.

Thus a village with a population of 10 has a warm bias of 0.73°C. A village with 100 has a warm bias of 1.46°C and a town with a population of 1000 people has a warm bias of 2.2°C. A large city with a million people has a warm bias of 4.4°C.

It is interesting to note that his coefficient is 0.317 and the one I found is 0.396.

Which is the other thing that you learn when doing research, most of the time someone else has been there before you, and there is little that is new, under the sun.

* Oke, T.R. 1973. City size and the urban heat island. Atmospheric Environment 7: 769-779.

The EPA Endangerment Finding

In a long-anticipated announcement today the Environmental Protection Agency (EPA) announced

After a thorough examination of the scientific evidence and careful consideration of public comments, the U.S. Environmental Protection Agency (EPA) announced today that greenhouse gases (GHGs) threaten the public health and welfare of the American people. EPA also finds that GHG emissions from on-road vehicles contribute to that threat. 

GHGs are the primary driver of climate change, which can lead to hotter, longer heat waves that threaten the health of the sick, poor or elderly; increases in ground-level ozone pollution linked to asthma and other respiratory illnesses; as well as other threats to the health and welfare of Americans.

It should be noted that the statement includes that GHG “threatens” the public welfare, while it is “the primary driver of climate change.” The press release relates to two specific findings which were signed by the EPA Administrator today. Those findings are:

Endangerment Finding: The Administrator finds that the current and projected concentrations of the six key well-mixed greenhouse gases--carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6)--in the atmosphere threaten the public health and welfare of current and future generations.

Cause or Contribute Finding: The Administrator finds that the combined emissions of these well-mixed greenhouse gases from new motor vehicles and new motor vehicle engines contribute to the greenhouse gas pollution which threatens public health and welfare.

The basis for this decision is given in three sets of documents; the findings themselves; a technical support document (TSD); and eleven volumes of comments (the list is here). I'm going to go through the first of these, a 284 page document, and pull out paragraphs that I have found of interest. Unfortunately I did not, while doing this, insert all the page numbers, so for that I ask your indulgence. Though I will include a few.

Within the findings the EPA explains the legal framework on which it based its decision, the way it went about evaluating the evidence that it considered, and the resulting finding. In its opening statement it notes

the Administrator finds that greenhouse gases in the atmosphere may reasonably be anticipated both to endanger public health and to endanger public welfare.

And here the gases are specifically defined as “long-lived, well-mixed and directly emitted greenhouse gases.” The primary basis for the decision being based on assessments of the U.S. Global Climate Research Program (USGCRP), the Intergovernmental Panel on Climate Change (IPCC) and the National Research Council (NRC).

The Administrator reached her determination by considering both observed and projected effects of greenhouse gases in the atmosphere, their effect on climate, and the public health and welfare risks and impacts associated with such climate change. The Administrator’s assessment focused on public health and public welfare impacts within the United States. She also examined the evidence with respect to impacts in other world regions, and she concluded that these impacts strengthen the case for endangerment to public health and welfare because impacts in other world regions can in turn adversely affect the United States.

The evidence concerning adverse air quality impacts provides strong and clear support for an endangerment finding. Increases in ambient ozone are expected to occur over broad areas of the country, and they are expected to increase serious adverse health effects in large population areas that are and may continue to be in nonattainment. The evaluation of the potential risks associated with increases in ozone in attainment areas also supports such a finding.

The impact on mortality and morbidity associated with increases in average temperatures, which increase the likelihood of heat waves, also provides support for a public health endangerment finding. There are uncertainties over the net health impacts of a temperature increase due to decreases in cold-related mortality, but some recent evidence suggests that the net impact on mortality is more likely to be adverse, in a context where heat is already the leading cause of weather-related deaths in the United States.

The evidence concerning how human-induced climate change may alter extreme weather events also clearly supports a finding of endangerment, given the serious adverse impacts that can result from such events and the increase in risk, even if small, of the occurrence and intensity of events such as hurricanes and floods.

Additionally, public health is expected to be adversely affected by an increase in the severity of coastal storm events due to rising sea levels.

There is some evidence that elevated carbon dioxide concentrations and climate changes can lead to changes in aeroallergens that could increase the potential for allergenic illnesses. The evidence on pathogen borne disease vectors provides directional support for an endangerment finding. The Administrator acknowledges the many uncertainties in these areas. Although these adverse effects provide some support for an endangerment finding, the Administrator is not placing primary weight on these factors.

Finally, the Administrator places weight on the fact that certain groups, including children, the elderly, and the poor, are most vulnerable to these climate-related health effects.

The evidence concerning adverse impacts in the areas of water resources and sea level rise and coastal areas provides the clearest and strongest support for an endangerment finding, both for current and future generations. Strong support is also found in the evidence concerning infrastructure and settlements, as well ecosystems and wildlife. Across the sectors, the potential serious adverse impacts of extreme events, such as wildfires, flooding, drought, and extreme weather conditions, provide strong support for such a finding.

And from Page 14

The most serious potential adverse effects are the increased risk of storm surge and flooding in coastal areas from sea level rise and more intense storms. Observed sea level rise is already increasing the risk of storm surge and flooding in some coastal areas. The conclusion in the assessment literature that there is the potential for hurricanes to become more intense (and even some evidence that Atlantic hurricanes have already become more intense) reinforces the judgment that coastal communities are now endangered by human induced climate change, and may face substantially greater risk in the future. Even if there is a low probability of raising the destructive power of hurricanes, this threat is enough to support a finding that coastal communities are endangered by greenhouse gas air pollution. In addition, coastal areas face other adverse impacts from sea level rise such as land loss due to inundation, erosion, wetland submergence, and habitat loss.

The increased risk associated with these adverse impacts also endangers public welfare, with an increasing risk of greater adverse impacts in the future.

While the impacts on net energy demand may be viewed as generally neutral for purposes of making an endangerment determination, climate change is expected to result in an increase in electricity production, especially supply for peak demand. This may be exacerbated by the potential for adverse impacts from climate change on hydropower resources as well as the potential risk of serious adverse effects on energy infrastructure from extreme events. Changes in extreme weather events threaten energy, transportation, and water resource infrastructure.

Vulnerabilities of industry, infrastructure, and settlements to climate change are generally greater in high-risk locations, particularly coastal and riverine areas, and areas whose economies are closely linked with climate-sensitive resources. Climate change will likely interact with and possibly exacerbate ongoing environmental change and environmental pressures in settlements, particularly in Alaska where indigenous communities are facing major environmental and cultural impacts on their historic lifestyles.

The above ends on page 15.

However, the body of evidence points towards increasing risk of net adverse impacts on U.S. food production and agriculture over time, with the potential for significant disruptions and crop failure in the future.

For the near term, the Administrator finds the beneficial impact on forest growth and productivity in certain parts of the country from elevated carbon dioxide concentrations and temperature increases to date is offset by the clear risk from the observed increases in wildfires, combined with risks from the spread of destructive pests and disease.

And moving on to page 21.

The concern now, however, is that the changes taking place in our atmosphere as a result of the well-documented buildup of greenhouse gases due to human activities are changing the climate at a pace and in a way that threatens human health, society, and the natural environment.

Some hint of future regulation may be discerned as the document progresses

On September 15, 2009, EPA and the Department of Transportation’s National Highway Safety Administration (NHTSA) proposed a National Program that would dramatically reduce greenhouse gas emissions and improve fuel economy for new cars and trucks sold in the United States.

The combined EPA and NHTSA standards that make up this proposed National Program would apply to passenger cars, light-duty trucks, and medium-duty passenger vehicles, covering model years 2012 through 2016. They proposed to require these vehicles to meet an estimated combined average emissions level of 250 grams of carbon dioxide per mile, equivalent to 35.5 miles per gallon (MPG) if the automobile industry were to meet this carbon dioxide level solely through fuel economy improvements. Together, these proposed standards would cut carbon dioxide emissions by an estimated 950 million metric tons and 1.8 billion barrels of oil over the lifetime of the vehicles sold under the program (model years 2012-2016). The proposed rulemaking can be viewed at (74 FR 49454, September 28, 2009).

Much of the rest of the document is a justification of action relative to the host of comments that had been submitted. These 380,000-odd comments, on the proposed ruling, were described thusly.

A majority of the comments (approximately 370,000) were the result of mass mail campaigns, which are defined as groups of comments that are identical or very similar in form and content. Overall, about two-thirds of the mass mail comments received are supportive of the Findings and generally encouraged the Administrator both to make a positive endangerment determination and implement greenhouse gas emission regulations.

Of the mass mail campaigns in disagreement with the Proposed Findings most either oppose the proposal on economic grounds (e.g., due to concern for regulatory measures following an endangerment finding) or take issue with the proposed finding that atmospheric greenhouse gas concentrations endanger public health and welfare.

The recent publication of the e-mails and codes from the CRU is addressed on page 46.

Our response regarding the request to reopen the comment period due to concerns about alleged destruction of raw global surface data is discussed more fully in the Response to Comments document, Volume 11.

The commenter did not provide any compelling reason to conclude that the absence of these data would materially affect the trends in the temperature records or conclusions drawn about them in the assessment literature and reflected in the TSD. The Hadley Centre/Climate Research Unit (CRU) temperature record (referred to as HadCRUT) is just one of three global surface temperature records that EPA and the assessment literature refer to and cite. National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) also produce temperature records, and all three temperature records have been extensively peer reviewed. Analyses of the three global temperature records produce essentially the same long-term trends as noted in the Climate Change Science Program (CCSP) (2006) report "Temperature Trends in the Lower Atmosphere," IPCC (2007), and NOAA's study5 "State of the Climate in 2008". Furthermore, the commenter did not demonstrate that the allegedly destroyed data would materially alter the HadCRUT record or meaningfully hinder its replication.

The document further notes

First, the Administrator is required to protect public health and welfare, but she is not asked to wait until harm has occurred. EPA must be ready to take regulatory action to prevent harm before it occurs.

Section 202(a)(1) requires the Administrator to “anticipate” “danger” to public health or welfare. The Administrator is thus to consider both current and future risks. Second, the Administrator is to exercise judgment by weighing risks, assessing potential harms, and making reasonable projections of future trends and possibilities.

It follows that when exercising her judgment the Administrator balances the likelihood and severity of effects. This balance involves a sliding scale; on one end the severity of the effects may be of great concern, but the likelihood low, while on the other end the severity may be less, but the likelihood high. Under either scenario, the Administrator is permitted to find endangerment. If the harm would be catastrophic, the Administrator is permitted to find endangerment even if the likelihood is small.

The Administrator recognizes that the context for this action is unique. There is a very large and comprehensive base of scientific information that has been developed over many years through a global consensus process involving numerous scientists from many countries and representing many disciplines. She also recognizes that there are varying degrees of uncertainty across many of these scientific issues. It is in this context that she is exercising her judgment and applying the statutory framework.

The reason for the ruling is tied to the Massachusetts case

As the Supreme Court made clear in Massachusetts v. EPA, EPA’s judgment in making the endangerment and contribution findings is constrained by the statute, and EPA is to decide these issues based solely on the scientific and other evidence relevant to that decision. EPA may not "rest” on reasoning divorced from the statutory text," and instead EPA’s exercise of judgment must relate to whether an air pollutant causes or contributes to air pollution that endangers. Massachusetts v. EPA, 549 U.S. at 532.

and

The Administrator has determined that the body of scientific evidence compellingly supports her endangerment finding.

This evidence is included in a technical support document (TSD) including the assessment of the USGCRP. It imposes a standard on this information, and seeks to address some of the criticism that has arisen since the word on Climategate got out. Moving to page 86:

Fourth, these assessment reports undergo a rigorous and exacting standard of peer review by the expert community, as well as rigorous levels of U.S. government review and acceptance. Individual studies that appear in scientific journals, even if peer reviewed, do not go through as many review stages, nor are they reviewed and commented on by as many scientists. The review processes of the IPCC, USGCRP, and NRC (explained in fuller detail in the TSD and the Response to Comments document, Volume 1) provide EPA with strong assurance that this material has been well vetted by both the climate change research community and by the U.S. government. These assessments therefore essentially represent the U.S. government’s view of the state of knowledge on greenhouse gases and climate change.

But it does note

In addition to the significant reasons discussed above for relying on and placing primary weight on these assessment reports, EPA has been a very active part of the U.S. government climate change research enterprise, and has taken an active part in the review, writing, and approval of these assessments. EPA was the lead agency for three significant reports under the USGCRP, and recently completed an assessment addressing the climate change impacts on U.S. air quality—a report on which the TSD heavily relies for that particular issue. EPA was also involved in review of the IPCC Fourth Assessment Report, and in particular took part in the approval of the summary for policymakers for the Working Group II Volume, Impacts, Adaptation and Vulnerability.

Page 106

Thus, commenters misunderstand the role that international effects played in the proposal. The Administrator is not evaluating the impact of international effects on populations outside the United States; she is considering what impact these international effects could have on the U.S. population. That is fully consistent with the CAA's stated purpose of protecting the health and welfare of this nation’s population.

An additional parameter of the endangerment analysis is the timeframe. The Administrator’s view is that the timeframe over which vulnerabilities, risks, and impacts are considered should be consistent with the timeframe over which greenhouse gases, once emitted, have an effect on climate.

Thus the relevant time frame is decades to centuries for the primary greenhouse gases of concern. Therefore, in addition to reviewing recent observations, the underlying science upon which the Administrator is basing her findings generally considers the next several decades —the time period out to around 2100, and for certain impacts, the time period beyond 2100.

Together the six well-mixed greenhouse gases constitute the largest anthropogenic driver of climate change. Of the total anthropogenic heating effect caused by the accumulation of the six well-mixed greenhouse gases plus other warming agents (that do not meet all of the Administrator’s criteria that pertain to the six greenhouse gases) since pre-industrial times, the combined heating effect of the six well-mixed greenhouses is responsible for roughly 75 percent, and it is expected that this share may grow larger over time, as discussed below.

Warming of the climate system is unequivocal, as is now evident from observations of increases in global average air and ocean temperatures, widespread melting of snow and ice, and rising global average sea level. Global mean surface temperatures have risen by 0.74°C (1.3ºF) (±0.18°C) over the last 100 years. Eight of the 10 warmest years on record have occurred since 2001. Global mean surface temperature was higher during the last few decades of the 20th century than during any comparable period during the preceding four centuries.

There is this specific comment about recent discussion of the world no longer warming at an increasing rate.

Though most of the warmest years on record have occurred in the last decade in all available datasets, the rate of warming has, for a short time in the Hadley Center record, slowed. However, the NOAA and NASA trends do not show the same marked slowdown for the 1999-2008 period.

Year-to-year fluctuations in natural weather and climate patterns can produce a period that does not follow the long-term trend. Thus, each year may not necessarily be warmer than every year before it, though the long-term warming trend continues. The scientific evidence is compelling that elevated concentrations of heat-trapping greenhouse gases are the root cause of recently observed climate change. . . . . .
Climate model simulations suggest natural forcing alone (e.g., changes in solar irradiance) cannot explain the observed warming.

The first line of evidence arises from our basic physical understanding of the effects of changing concentrations of greenhouse gases, natural factors, and other human impacts on the climate system. The second line of evidence arises from indirect, historical estimates of past climate changes that suggest that the changes in global surface temperature over the last several decades are unusual. The third line of evidence arises from the use of computer-based climate models to simulate the likely patterns of response of the climate system to different forcing mechanisms (both natural and anthropogenic).

The claim that natural internal variability or known natural external forcings can explain most (more than half) of the observed global warming of the past 50 years is inconsistent with the vast majority of the scientific literature, which has been synthesized in several assessment reports.

Interestingly, for the United States

United States temperatures also warmed during the 20th and into the 21st century; temperatures are now approximately 0.7°C (1.3°F) warmer than at the start of the 20th century, with an increased rate of warming over the past 30 years. Both the IPCC and CCSP reports attributed recent North American warming to elevated greenhouse gas concentrations. The CCSP (2008g) report finds that for North America, "more than half of this warming [for the period 1951-2006] is likely the result of human-caused greenhouse gas forcing of climate change."

The finding also speaks to other evidence:

There is strong evidence that global sea level gradually rose in the 20th century and is currently rising at an increased rate. It is very likely that the response to anthropogenic forcing contributed to sea level rise during the latter half of the 20th century. It is not clear whether the increasing rate of sea level rise is a reflection of short-term variability or an increase in the longer-term trend.

Nearly all of the Atlantic Ocean shows sea level rise during the last 50 years with the rate of rise reaching a maximum (over 2 mm per year) in a band along the U.S. east coast running east-northeast.

Satellite data since 1979 show that annual average Arctic sea ice extent has shrunk by 4.1 percent per decade.

The size and speed of recent Arctic summer sea ice loss is highly anomalous relative to the previous few thousands of years.

It will be interesting to see how this prediction falls out.

All of the United States is very likely to warm during this century, and most areas of the United States are expected to warm by more than the global average. The largest warming is projected to occur in winter over northern parts of Alaska. In western, central and eastern regions of North America, the projected warming has less seasonal variation and is not as large, especially near the coast, consistent with less warming over the oceans.

the Administrator recognizes that black carbon is an important climate forcing agent and takes very seriously the emerging science on black carbon’s contribution to global climate change in general and the high rates of observed climate change in the Arctic in particular.

As noted in the Proposed Findings, EPA has various pending petitions under the CAA calling on the Agency to make an endangerment finding and regulate black carbon emissions.

And

EPA plans to further evaluate the issues of emissions of water that are implicated in the formation of contrails and also changes in water vapor due to local irrigation.

And they emphasize on Page 151

We received many comments suggesting global temperatures have stopped warming. The commenters base this conclusion on temperature trends over only the last decade. While there have not been strong trends over the last seven to ten years in global surface temperature or lower troposphere temperatures measured by satellites, this pause in warming should not be interpreted as a sign that the Earth is cooling or that the science supporting continued warming is in error. Year-to-year variability in natural weather and climate patterns make it impossible to draw any conclusions about whether the climate system is warming or cooling from such a limited analysis.

Historical data indicate short-term trends in long-term time series occasionally run counter to the overall trend. All three major global surface temperature records show a continuation of long-term warming.

They do note, however on page 153:

A number of commenters argue that the warmth of the late 20th century is not unusual relative to the past 1,000 years. They maintain temperatures were comparably warm during the Medieval Warm Period (MWP) centered around 1000 A.D. We agree there was a Medieval Warm Period in many regions but find the evidence is insufficient to assess whether it was globally coherent.

Our review of the available evidence suggests that Northern Hemisphere temperatures in the MWP were probably between 0.1 deg C and 0.2 deg C below the 1961-1990 mean and significantly below the level shown by instrumental data after 1980. However, we note significant uncertainty in the temperature record prior to 1600 A.D.

As one comes to some of the predictions that the literature has made on future climate effects the document paints a picture of a possible future. As such, in times to come it will be interesting to see how things turn out. But I will stop the abstracting here. At some future time I will go through some of the other documentation but these were the bits that most caught my eye on a first go through.

Dr Hansen was wrong - let's change the subject

I have pointed out in recent remarks, that if the debate on climate change were being conducted under normal scientific conventions, then the predictions that were made by Dr. Hansen back in 1988 would, by now, be considered to have been proved false, since the predictions that he made on global temperature rise, and the path that temperature would take, have not followed his models. His predictions were based upon:

Scenario A assumes continued exponential trace gas growth, scenario B assumes a reduced linear growth of trace gases, and scenario C assumes a rapid curtailment of trace gas emissions such that the net climate forcing ceases to increase after the year 2000.

The carbon dioxide path has continued to move along the path predicted for scenario A, yet the temperature rise through today has not only failed to accelerate with it, as Dr Hansen predicted, but instead is much closer to following along the temperature curve predicted for scenario C.

Comparison of Dr Hansen’s scenario predictions against actual temperature anomalies (deg C) (prediction here and actual temp from GISS) Baseline 1951-1980 mean.

I took the data from the plot of temperature predictions that Dr Hansen provided, since it allows me to easily read the predicted temperatures, and I then re-plotted the data with the actual temperatures from the GISS website as the black lines, since the original plot only went as far as 2005. I did note that the tabulated data were slightly different from those of the actual temp data plotted in the 2005 curve, and used the more up to date ones for the entire series.

If one looks at the above comparison that Dr Hansen which is extended from the one made in 2005 the difference between the actual temperature and his predictions clearly show that the actual (black) temperatures are falling away from all but the C scenario (blue) a predictive future which was only supposed to occur if we did ameliorate carbon dioxide emissions. There is no accelerated warming, and there was no amelioration of the rising carbon levels, and so his predictions fail.

However, rather than discuss this rather interesting fact, the global “warming” community has managed, yet again, to divert discussion by dragging debate over a different topic of their choosing, so that this primary point can be hidden from public debate. Their topic centers on whether the globe has been warming at all, an issue that depends very much on where your temperature starts. They might have been somewhat embarrassed to begin with 1998 (as some more realistic of the proponents recognize, following their earlier use of that temperature to show how fast the temperature was going up). However the commentators have chosen, very carefully, to attack the suggestion that the temperature might be cooling, rather than the more relevant issue that the global temperature is not following the models. When you can’t argue, then change the subject.

Now, for what it is worth I have made clear that I am more convinced by the hundreds of peer-reviewed publications (such as those referenced by Jean Grove in The Little Ice Age, or by Brian Fagan in his books) that document the Medieval Warming Period and its predecessors and the intervening cold periods, such as those of the Dark Ages and the Little Ice Age. Thus the fact that the present warming period has not reached the extent of the last warming period, let alone those before it, does not unduly disturb me. (That is evidenced by the ground conditions – such as permafrost - in the Arctic inter alia, and the positions of the ecotones in Europe). Yet it is sadly a denigration of true scientific debate to see how vociferously those arguing for AGW change the topic, or readjust the data whenever they seem at the stage of losing a point in the debate.

If one accepts that we are in this cyclic series then arguments about whether by some fraction of a degree or other the temperature was warmer in 1934 than 2005 in the United States become significantly less critical. The question then becomes whether this Warming Period is significantly less warm than the last, or the one before that. The Roman period was apparently warmer than the Medieval one (the evidence coming among other things from the Roman ruins appearing out of the ice in the Alps), and both were warmer than we are now. But we also need to go back to those periods and see what the conditions were that developed in different parts of the globe (such as the extended droughts in what is now California) and learn from that information, and take precautions accordingly, recognizing what we might be facing as the climate changes. For if we do not learn from history, then we are bound to repeat it - as once was said.

The question as to why these periods existed – or whether the intervening cooling periods are the anomalies – is probably a much more worthwhile study, but given the urge that folk have to change the way in which energy is produced, and the vast fortunes that hang on the changes in policy that are being debated, somehow I don’t see much attention being given to those questions soon.

Is global warming regional - as in the MWP?

I was looking over Anthony Watts’ web site, and noted a picture that he had posted that shows the large number of stations around the United States that were showing record temperatures.

Places in the United States showing record temperatures (From WUWT)

What struck me was that, just as one could argue that this showed how cold we were getting, those that argue that carbon dioxide is raising global temperatures could as easily point to the points in the Western part of the country as evidence for their argument.

The problem of trying to determine which arguments are more accurate is made more difficult where there is this isolation of data, so that those seeking information are only presented with that which supports one side – so cheers to Anthony for showing both.

But individual data points, while interesting, don’t really reflect the sort of long-term changes that are predicted (or not) by various climate models. There is a considerable question over the accuracy and methodology used to derive an average measure of the global temperature, with satellite data, which was supposed to be more accurate, having its own limitations, in measuring ocean temperatures for example.


If one looks at the average temperature for the contiguous United States, for example,

Average US temperatures since 1880 (GISS )

One can see that the average for the country has been tending downwards over the last decade, and has not differed that much from the temperatures of the 1930’s (which still has the record for the hottest year). It is hard to argue for AGW based on this graph alone.

Yet if once compares the data from the two hemispheres, it is the Northern Hemisphere that is warming much faster than the Southern.

Hemispheric temp changes (GISS)

So if the US landmass isn’t warming, and the Southern Hemisphere isn’t currently doing much either where is all the global warming coming from? A look at the current temperature anomalies shows that it is the Arctic and Europe that is warming the most:

Global temperature anomalies (GISS)

But it is interesting that the actual anomaly is only 0.54 degrees, since, as I have noted before, the predictive values that Dr Hansen gave in the paper that is the most cited as authoritative on this predicted that by now the temperature would be between 1.0 and 1.2 degrees higher than the datum, and that only if there was dramatic reduction in greenhouse gases would we get to 0.65 degrees by now.

Now that is still a it higher than the 0.42 degrees – which Anthony is reporting and which is, I presume, the daily difference, but as he notes, the carbon dioxide levels are at 388 ppm, and if the impact was a severe as has been modeled it should be much higher (though the GISS graph above does show that it is heading backup).

But the other thing to comment on is that back when those of us who bring up the Medieval Warming Period (MWP) first did so, the response of the AGW proponents was that we were discussing something that was only true of Europe and the Arctic. Now that we are seeing a pattern that shows a regionalization of the global warming pattern I wonder if that argument can’t be reversed?

It is only by openly discussing these changes and their implications that true scientific understanding can be achieved, not by hiding, or adjusting data in less than transparent ways. Unfortunately scientific debate takes time and evidence only accumulates slowly. How long, for example must the current apparent cooling persist, (or the accelerated increase in temperature not occur) before the model predictions are discredited? Yet without that debate we risk the frittering away of resources on valueless measures that will have no impact on the future.

Climate Hypotheses and the falsification thereof

There is much discussion in climate related circles about this topic being a scientifically based set of events, conclusions and predictions. In scientific circles when new ideas come along they are generally accorded the title of hypotheses and they are then subject to review, with the potential that they will ultimately be discarded because they have been shown to be wrong, or falsified as the technical term would describe it.

Essentially – to use the examples from Wikipedia, if I say that some men are immortal it is not possible to falsify the statement, since I just might not have met one of the immortals yet, but if I say all men are immortal, then production of the first dead body proves the hypothesis wrong.

The question then arises as to how much error a hypothesis is allowed, before it is considered wrong.

For instance I was looking at the Real Climate website, and noted their arguments about the data that they believe falsify Svensmark’s theories on the impact of Galactic Cosmic Rays on global climate. I find this a bit amusing really, since while they are quite happy to argue about falsification when it comes to theories that differ from theirs, they appear remarkably insensitive to seeing the shoe put on the other foot, when it comes to discussions of climate warming. (And incidentally there is some evidence that what Svensmark said is correct. (Which might be why folk, not generally considered to be idiots, are funding him).

RC's previous post, for example discusses the possibility of a pause in the steady progress of global warming that their theories have projected is still going on.

Trends in Global Temperature (from Real Climate – and previously from GISS)

Now the thing is that there is obviously some natural phenomenon going on that would (were greenhouse gases the villain they proclaim) negate the increasing effect of those gases over the past decade. And that has an impact on an earlier study.

It should be remembered that when the whole issue of global warming was first brought to large-scale public attention, one of the arguments made for the influence of greenhouse gases on the climate was that the models of climate behavior without consideration of the influence of greenhouse gases, showed relatively little change over time.

Predicted global temperatures from 13 models run by James Hansen (from Dire Predictions)

You can see that from 1965 the graph set is pretty flat, with all models showing no significant change over the period. Thus, if one accepts that there is something natural that is precluding the global temperature following the theoretical prediction, then one must go back to one of the underlying hypotheses that Dr Hansen proposed - namely that there are no natural phenomena that will likely have any significant effect on global temperatures over the period of time that we are all concerned about. Given that RC is now saying that there are natural causes that are transiently diminishing the effects of carbon dioxide on global temperatures, it would seem that they are concurrently arguing that the initial premise upon which Dr Hansen built his case has now been falsified.

Now when we look at the predictions with greenhouse gas in them then the graphs show that temperatures match those predicted when greenhouse gases are included.

Predicted global temperatures from Hansen’s models (blue), against actual temperatures (red) (from Dire Predictions).

However if one then looks at what Dr Hansen projects from 2000 to 2010 and beyond, one gets a very distinctive upturn in the predicted temperatures:

Dr Hansen’s predicted global temperatures under 3 scenarios (A business as usual; B moderate gas emissions and a volcanic event of significant magnitude; C a volcanic event and reductions in the emission of carbon dioxide by 2000). (Again from Dire Predictions).

Looking at where we are actually at relative to those three scenarios – which have temperature anomalies of A – 1.1 degrees; B – about 1 degree; and C about 0.65 degrees, one can see that the actual temperatures from the top graph are actually closest to following line C at the moment with an increase of somewhere around 0.55 degrees, which is below even the increase that that model predicts, although it does predict a lowering value, suggesting that we have already reached the target for 2012. . However this result, given that carbon dioxide levels have not fallen as predicted, suggests that the model predictions, once outside the range of conditions that prevailed at the time they were written, are not correct. In fact one could conclude that if Dr Hansen’s predictions are summarized into the hypothesis that, without the control of greenhouse gases exemplified by those measures he calls for in scenario C, that global temperatures will increase unacceptably, then this hypothesis has been falsified.

Now Real Climate argues that global temperatures should actually be considered higher, since there is insufficient data from the Arctic, which is considered to be warming much faster than the rest of the globe, and were that considered, then the models and readings would be much closer. . (Interestingly, however, when the ocean temperatures from the North Atlantic are looked at, they now appear to be declining, perhaps falsifying that argument before long).

North Atlantic Heat content (after Tisdale )

The problem with the RC argument is that is that it was the available temperature readings, relative to the model predictions that were touted as being so close in performance (graph 2) during the global warming period of the last part of the last century. Changing the data base as one moves along a line of predictions is generally frowned upon.

There is some growing body of opinion that projects that – just as with curve C above – the global temperature may stabilize around current temperatures for a total of as much as 30-years, before beginning to rise again. That is inconsistent with the predictions of the models used in the above work. Thus it seems only rational to conclude that if there is no discernable increase in temperature, on a consistent basis, in the near future, that the climate change arguments that hang on these graphs for justification must themselves be considered to be falsified.

That would be the scientific conclusion, and were this really a scientific debate then this would be a subject of discussion. Since, however, this has long passed beyond the point where it has become an article of faith with many folk, and the weakness of the foundations on which the Climate Change debate is really built is not to be considered, then one is left wondering how many years of obfustication we must tolerate before the recognition that it is worthy of considerable debate actually occurs.

Puzzling Graphical Comparisons raise questions of veracity

“Dire Predictions” by Michael Mann and Lee Kump, sets out to provide an illustrated guide to the findings of the IPCC. It is a relatively simple volume, full of the promised pictures, small graphs to illustrate points and condensed comments on the different aspects of the climate change debate. In short it is the sort of text that might be provided to a class in the United States to help them understand the prevailing arguments about climate change.

It has some nice initial illustrations of the way in which climate is generated that are easy to follow and which are therefore initially persuasive. But my eye was caught, from the beginning by the different graphs that are scattered throughout the chapters. I was a bit surprised by the first graph, since it didn’t quite look like the graph of the temperature plots given by NOAA, so after looking at the two separately:
From page 20 of the text

Global temperatures from 1880 to 2008 from NCDC

You can see quite a difference in the curves around 1940 – the actual peak back then has disappeared from Dr. Mann’s graph and if I superimpose them you can see how a fluctuating temperature record has been smoothed. (The heavy black line comes from the Mann and Kump curve)



The difference is more than subtle – the peak and stable or declining temperatures between 1940 and 1970 have been magically eliminated. But wait, those of you who have read the book respond – he puts a more detailed temperature plot on page 36.
Here it is:

Mann and Kump – Figure on page 36 – Trends in Global Average Surface Temperature.

But if you look at this figure – relative to the official plot you can see that while the official temperature is “debatably” flat in the official record, here the plot is steadily increasing from 1950.

Skipping forward through the book, let me pick out one more graph that caught my attention – the regional trends shown on Figure 71. Here is the Mann version:
Regional Continental Temperature Trends (after Mann and Kump) Blue are the temperatures taking into account natural trends only, pink includes both human and natural factors). Red is what they say happened.

But this is the official temperature record for North America from NASA.



Again I won’t bother superimposing the pictures, but you can see that the trends that are actually occurring don’t quite follow the curves in the book.
Having discovered which there really isn’t much point in continuing reading it, since it takes such liberties with easily verifiable figures, one is wondering what else has been “quietly adjusted” to make the facts more supportive of the argument.

Now it isn’t as though I completely agree with the official figures, given the corrections that have been imposed on the initial raw data, and that, as a result, trends appear that weren’t there before the “tweaking.’ But I do think that this book is taking the trend of “adjusting” the data just a bit too far in the process of making a point. There comes a point where this stops being Science and becomes Propaganda.