This summary is from Dr. Pielke at the University of Colorado in his words. I’ll have my own post on some detail not covered here, with links to the SI – data code, etc we are preparing in a day or two. Some may ask why I am not lead author. That was my choice, because the strength is in the statistical analysis, and I wanted it clear that the paper is about that joint work and not about any one person’s efforts. – Anthony
UPDATE: Also, two other posts, by co-author Dr. John Nielsen-Gammon that are must reads are:
The surfacestations paper – statistics primer
Something for Everyone: Fall et al. 2011
Guest post by Dr. Roger Pielke Sr.
Our paper
Fall, S., A. Watts, J. Nielsen-Gammon, E. Jones, D. Niyogi, J. Christy, and R.A. Pielke Sr., 2011: Analysis of the impacts of station exposure on the U.S. Historical Climatology Network temperatures and temperature trends. J. Geophys. Res., in press. Copyright (2011) American Geophysical Union.
has been accepted and is now in press. Below I have presented a summary of the study and its major messages from my perspective. While the other authors of our paper have read and provided input on the information given below, the views presented below are mine. I will be posting on the history of my involvement on this subject in a follow-up post in a few days.
Volunteer Study Finds Station Siting Problems Affect USA Multi-Decadal Surface Temperature Measurements
We found that the poor siting of a significant number of climate reference sites (USHCN) used by NOAA’s National Climatic Data Center (NCDC) to monitor surface air temperatures has led to inaccuracies and larger uncertainties in the analysis of multi-decadal surface temperature anomalies and trends than assumed by NCDC.
NCDC does recognize that this is an issue. In the past decade, NCDC has established a new network, the Climate Reference Network (CRN), to measure surface air temperatures within the United States going forward. According to our co-author Anthony Watts:
“The fact that NOAA itself has created a new replacement network, the Climate Reference Network, suggests that even they have realized the importance of addressing the uncertainty problem.”
The consequences of this poor siting on their analyses of multi-decadal trends and anomalies up to the present, however, has not been adequately examined by NCDC.
We are seeking to remedy this shortcoming in our study.
The placement of the USHCN sites can certainly affect the temperatures being recorded—both an area of asphalt (which is warmer than the surroundings on a sunny day or irrigated lawns (which is cooler than surrounding bare soil on a sunny day) situated near a station, for example, will influence the recorded surface air temperatures.
NOAA has adopted siting criteria for their climate reference stations: CRN 1 stations are the least likely to being influenced by nearby sources of heat or cooling, while CRN 5 stations are the most likely to be contaminated by local effects. These local effects include nearby buildings, parking lots, water treatment plants irrigated lawns, and other such local land features.
To determine how the USHCN stations satisfied the CRN siting criteria and also whether the station siting affected temperature trend characteristics, Anthony Watts of IntelliWeather set up the Surface Stations project in 2007. More than 650 volunteers nationwide visually inspected (and rated) 1007 of the 1221 USHCN stations. The volunteers wrote reports on the surroundings of each station and supplemented these reports with photographs. Further analysis by Watts and his team used satellite and aerial map measurements to confirm distances between the weather station sensors and nearby land features.
The Surface Stations project is truly an outstanding citizen scientist project under the leadership of Anthony Watts! The project did not involve federal funding. Indeed, these citizen scientists paid for the page charges for our article. This is truly an outstanding group of committed volunteers who donated their time and effort on this project!
Analyzing the collected data, as reported in our paper, we found that only 80 of the 1007 sites surveyed in the 1221 station network met the criteria of CRN 1 or CRN 2 sites – those deemed appropriate for measuring climate trends by NCDC. Of the remaining, 67 sites attained a CRN 5 rating – the worst rating. While the 30-year and 115-year trends, and all groups of stations, showed warming trends over those periods, we found that the minimum temperature trends appeared to be overestimated and the maximum warming trends underestimated at the poorer sites.
This discrepancy matters quite a bit. Wintertime minimum temperatures help determine plant hardiness, for example, and summertime minimum temperatures are very important for heat wave mortality. The use of temperature trends from poorly sited climate stations, therefore, introduces an uncertainly in our ability to quantify these key climate metrics.
While all groups of stations showed warming trends over those periods, there is evidence to suggest a higher level of uncertainty in the trends since it was found, as one example, that according to the best-sited stations, the 24 hour temperature range in the lower 48 states has no century-scale trend, while the poorly sited locations have a significantly smaller diurnal temperature range. This raises a red flag to avoid poorly sited locations since clearly station measurement siting affects the quality of the surface temperature measurements.
The inaccuracies in the maximum and minimum temperature trends do matter also in the quantification of global warming. The inaccuracies of measurements from poorly sited stations are merged with the well sited stations in order to provide area average estimates of surface temperature trends including a global average. In the United States, where this study was conducted, the biases in maximum and minimum temperature trends are fortuitously of opposite sign, but about the same magnitude, so they cancel each other and the mean trends are not much different from siting class to siting class. This finding needs to be assessed globally to see if this also true more generally.
However, even the best-sited stations may not be accurately measuring trends in temperature or, more generally, in trends in heat content of the air which includes the effect of water vapor trends (which is the more correct metric to assess surface air warming and cooling; see). Also, most of the best sited stations are at airports, which are subject to encroaching urbanization, and/or use a different set of automated equipment designed for aviation meteorology, but not climate monitoring. Additionally, the NCDC corrections for station moves or other inhomogeneities use data from poorly-sited stations for determining adjustments to better-sited stations, thus muddling the cleaner climate data. We are looking at these issues for our follow-on paper.
However, we know from our study that the use of these poorly sited locations in constructing multi-decadal surface temperature trends and anomalies has introduced an uncertainty in our quantification of the magnitude of how much warming has occurred in the United States during the 20th and early 21st century.
One critical question that needs to be answered now is; does this uncertainty extend to the worldwide surface temperature record? In our paper
Montandon, L.M., S. Fall, R.A. Pielke Sr., and D. Niyogi, 2011: Distribution of landscape types in the Global Historical Climatology Network. Earth Interactions, 15:6, doi: 10.1175/2010EI371
we found that the global average surface temperature may be higher than what has been reported by NCDC and others as a result in the bias in the landscape area where the observing sites are situated. However, we were not able to look at the local siting issue that we have been able to study for the USA in our new paper.
Appendix- Summary of Trend Analysis Results
Temperature trend estimates do indeed vary according to site classification. Assuming trends from the better-sited stations (CRN 1 and CRN 2) are most accurate:
- Minimum temperature warming trends are overestimated at poorer sites
- Maximum temperature warming trends are underestimated at poorer sites
- Mean temperature trends are similar at poorer sites due to the contrasting biases of maximum and minimum trends
- The trend of the “diurnal temperature range” (the difference between maximum and minimum temperatures) is most strongly dependent on siting quality. For 1979-2008 for example, the magnitude of the linear trend in diurnal temperature range is over twice as large for CRN 1&2 (0.13ºC/decade) as for any of the other CRN classes. For the period 1895-2009, the adjusted CRN 1&2 diurnal temperature range trend is almost exactly zero, while the adjusted CRN 5 diurnal temperature range trend is about -0.5°C/century.
- Vose and Menne[2004, their Fig. 9] found that a 25-station national network of COOP stations, even if unadjusted and unstratified by siting quality, is sufficient to estimate 30-yr temperature trends to an accuracy of +/- 0.012°C/yr compared to the full COOP network. The statistically significant trend differences found here in the central and eastern United States for CRN 5 stations compared to CRN 1&2 stations, however, are as large (-0.013°C/yr for maximum temperatures, +0.011°C/yr for minimum temperatures) or larger (-0.023°C/yr for diurnal temperature range) than the uncertainty presented by Menne at al (2010).
More detailed results are found in the paper, including analyses for different periods, comparisons of raw and adjusted trends, and comparisons with an independent temperature data set.
Questions and Answers
Q: So is the United States getting warmer?
A: Yes in terms of the surface air temperature record. We looked at 30-year and 115-year trends, and all groups of stations showed warming trends over those periods.
Q: Has the warming rate been overestimated?
A: The minimum temperature rise appears to have been overestimated, but the maximum temperature rise appears to have been underestimated.
Q: Do the differing trend errors in maximum and minimum temperature matter?
A: They matter quite a bit. Wintertime minimum temperatures help determine plant hardiness, for example, and summertime minimum temperatures are very important for heat wave mortality. Moreover, maximum temperature trends are the better indicator of temperature changes in the rest of the atmosphere, since minimum temperature trends are much more a function of height near the ground and are of less value in diagnosing heat changes higher in the atmosphere; e.g see .
Q: What about mean temperature trends?
A: In the United States the biases in maximum and minimum temperature trends are about the same size, so they cancel each other and the mean trends are not much different from siting class to siting class. This finding needs to be assessed globally to see if this also true more generally.
However, even the best-sited stations may not be accurately measuring trends in temperature or, more generally, in trends in heat content of the air which includes the effect of water vapor trends. Also, most are at airports, are subject to encroaching urbanization, and use a different set of automated equipment. The corrections for station moves or other inhomogeneities use data from poorly-sited stations for determining adjustments to better-sited stations.
Q: What’s next?
A: We also plan to look specifically at the effects of instrument changes and land use issues, among other things. The Surface Stations volunteers have provided us with a superb dataset, and we want to learn as much about station quality from it as we can.
================================================================
UPDATE: Since some people seemed unable to divine the link upstream, the pre-print version of the paper, posted on Dr. Pielke’s website is available here:
http://pielkeclimatesci.files.wordpress.com/2011/05/r-3671.pdf
– Anthony
UPDATE2: Dr. John Nielsen-Gammon, Texas State Climatologist and co-author, weighs in with his post:
Something for Everyone: Fall et al. 2011
As you may have heard, the long-awaited peer-reviewed analysis of the results of the SurfaceStations.org project has finally been released. I can’t wait to see the dueling headlines. Some will argue that the take-home message should be: Poor Station Siting Strongly Effects Temperature Trend Measurements, and will laugh at the idea that we can say with sufficient accuracy what has happened to our climate. Others will argue that the take-home message should be: Poor Station Siting Has No Effect on Temperature Trend Measurements, and will laugh at all the effort expended on a null result. Both sides will find solid evidence for their points of view in the paper. How can that be? How can one paper support opposing conclusions?
…
Here, in brief, are the answers: The poorest sites tend to be warmer. The minimum temperatures are warming faster at poorer sites than at better sites. The maximum temperatures are warming slower at poorer sites than at better sites. The adjustments reduce the differences by about half. The two effects are roughly equal and opposite so the mean temperature is rising at about the same rate across sites of different quality while the diurnal temperature range shows the biggest difference across sites.
On the one hand, this seems to be confirmation of the quality of the temperature record. All types of sites show the same mean temperature trend, so there’s no change necessary to our estimates of observed historical temperature trends in the United States.
On the other hand, there are several warning flags raised by this study. First, station siting is indeed important for the maximum and minimum temperature measurements. Second, the adjustments are only partly correcting the temperature record. Third, since the adjustments use data from all surrounding stations, there’s the danger that the mean trends are dominated by data from the poorer stations. (Less than ten percent of the USHCN stations are sited well enough to be considered appropriate for climate trend measurements.) Finally, and perhaps most important, are we really so lucky that the rest of the world would also have its poorly-sited stations have erroneous maximum and minimum temperature trends that just happen to be equal and opposite to each other?
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A heat sink has these properties ie reduces variability, make coldest- warmer and hottest- colder. Is this not the case with buildings and artificial ground surfaces. The analysis performed appears to confirm this. The question comes back to how it affects the mean. If the diurnal variation is even then it probably doesn’t.
I suspect if this “hypothesis” is true we will find similar effects if the same data analysis is performed world wide (all other things being equal).
The next issue is then if the weather or climate or whatever has more variability (for whatever reason) we could put in an argument for it is both getting hotter and colder at the same time. As we know cherry picking data to strengthen a point is easy to do.
So is the mean daily temperature averaged over time the correct number to choose. I think the majority of “persons in the street” would say yes (a concerted effort to stop reporting of simplistic cherry picking data is required).
So we then come back to the $64 question if the mean temperature is rising how much is attributable to man made CO2.
Can I just query my understanding (or not)?
My interpretation is that the best and worst sited weather stations have their own inherent biases which the research has highlighted and although from a superficial averaging they cancel each other out, this would be a misunderstanding of the data.
Rather, the fact that the best and worst each have biases means rather than canceling them out they compound the uncertainty of any trend. In effect the margins of error are now expanded and the concept of a measured or understood trend in US temps is now demonstrably more moot than previously considered?
Is that roughly right, accepting gross oversimplification?
Congratulations.
“even the best-sited stations may not be accurately measuring trends in temperature or, more generally, in trends in heat content of the air which includes the effect of water vapor trends. Also, most are at airports, are subject to encroaching urbanization,..” Most of the best-sited stations are at airports????
Twenty six years ago I first heard the term, “airport effect” as a cause of most of the measured warming.
So the study really didn’t address land use. This is a point that needs to be made.
Fred Peterson asks :
>> if the mean temperature is rising how much is attributable to man made CO2.
The further question is this : are the temperatures rising, or are the mean
temperatures rising … there is a difference … after all, you can increase the
mean temp. by extending the ‘warm’ season … this need not entail any actual
increase in temperatures
Further, if any increases in the mean are caused by rising temperatures,
are these increases in the minima, or increases in the maxima, or both.
After all, it is possible to have an increasing annual mean, coupled with
decreasing maximum temperatures.
I think that the concept of a “mean annual global temperature” is inutile.
Surely important climate changes are (1) seasonal, and (2) regional ?
( or perhaps my ‘surely’ is special pleading ! )
From a beautiful autumn day in Wellington, NZ
Thanks, Anthony and your co-authors for tackling a big data handling job and making sense of the numbers so they could be used in a published paper. Thanks also, Anthony to you and your cast of hundreds of volunteers who visited and reviewed the measurement sites. You had a big job just to document the data in a usable manner.
Please take pride in what you do for climate science. You are one of the good’uns as are your volunteers.
I have one concern about using poor data when homogenization is used to adjust temperature readings up to 1200 km distance. As I understand it, this could result in using bad readings to adjust good readings and vice versa to a lesser degree. The whole homogenization effort leaves me cold.
I have used a bad thermometer to measure process temperatures many times, and then tested the same sites with a good thermometer when I could find a good one. The level of the readings of the bad thermometer is not correct but the differential temperature is usually close enough to draw conclusions about what the process is doing. Even given this, I would prefer an accurate temperature measurement – always.
Anthony, earlier in this post someone commented on the rarity in climate “science” of people actually collecting and analyzing data so that some conclusions can be drawn about that data.
I’d like to echo that comment and join the choir of people that commend all of the volunteers and authors on this project, for gathering and interpreting this data for initial conclusions and so that it could be analyzed from a variety of perspectives.
Why aren’t others in this lucrative game doing this type of basic data based research that requires the collection and analysis of data?
I realize that this is a rhetorical question, but my hope is that legitimate inquiring minds that “know” of the scientific method (maybe they learned it in 8th grade?) will research the term, and apply it to today’s climate “science” to help slow down this conclusion dominated – model based (not data based)- prediction factory that is now being funded to prove a political/religious point.
High fives to all!!
To the other “Mike” posting on this topic…
I am a meteorologist and one of Anthony’s surfacestation volunteer surveyors. I suspected that the warming trend would be overstated as a result of poor siting of so many of the stations. That has only partially turned out to be correct (for the mins), a result I didn’t expect.
BUT THAT IS HOW GOOD SCIENCE IS DONE. One does investigations and publishes the results regardless the personal expectations or beliefs of the person doing the research. You should be congratulating Anthony and company rather than making sarcastic postings. We have identified one more piece in the climate puzzle. Hooray.
Mike Smith
Airports:
“” Most of the best-sited stations are at airports????
Twenty six years ago I first heard the term, “airport effect” as a cause of most of the measured warming.
So the study really didn’t address land use. This is a point that needs to be made.”
The case of airports versus non airports has been tested. Globally there is no difference.
We have looked at a case with only non airports. no difference.
I have seen a study done of japan which showed cooling at airports. One explanation is that airports tend to be constructed in areas with very low obstructions. airports tend to be constructed where there is a long fetch. Since UHI tends to infect Tmin rather than Tmax, one important thing to realize is that when windspeed gets above 7m/sec there is no UHI. Also, one major paper on UHI (parker) used a high percentage of airports and found no UHI signal.
So don’t expect a lot by controlling for the airport variable. This does not mean that you will find that every airport site is good. what it means is that globally the airport effect is small. IF it were large we would see clear differences between airport/non airport. we dont. If it were large we would see a big difference between all airport and UHA. we dont.
Again, do not expect all the warming we see (~1C over land) to vanish. It wont. It wont because its real. we know its real because of what UHA tells us, because SST has gone up since 1850 and because we can see other changes in proxies for temperature.
LazyTeenager: So it’s time to do an experiment instead of relying on mistaken common sense. Build two weather stations side-by-side. One perfect and the other with asphalt and run both for a year. See then if the max and min trends cancel out.
Go for it! Too lazy to actually build stations? Not to worry, somebody else already has. Take a look at COOPs #244560 and #244558 (both at the Kalispell airport). Let us know what you come up with.
Latitude.
So its the same temperature today as it was in the LIA?
or is it warmer now?
Is roy spencer lying about UHA?
http://www.woodfortrees.org/plot/uah/from:1979/to:2011/every/offset/plot/uah/from:1979/to:2011/trend/plot/hadcrut3vgl/from:1979/to:2011/trend/plot/none
What you see here is a warming planet. UHA is free of UHI. Its free of adjustments.
And look at the trend for CRU.
Question: if CRU has loads of UHI why isnt its trend different from CTU?
If CRU is adjusted to warm of cool planet why does it match?
Here another question. why does my temperature reconstruction that does not have any of CRU adjustments track CRU within .1C?
It’s warmer. The sea level rise tells us that. UHA tells us that. the glaciers tell that story. no story, no data, no theory, suggests that it cooler now than in the LIA.
Mosh
It was sometimes almost as warm at times in the LIA- depending on where you were- but generally it was cooler then than it is now, sometimes very markedly so.
Averages mask so many intersting variations when temperature records are really lots of peaks and troughs but I find it wholly unremarkable that the planet generally is warmer (thank goodness) now than it was then.
I put the low point as 1607 with a general-but not consistent-warming since then.
tonyb
Another difference between UAH and land based measurements was identified by Frank Lasner a while ago. Sadly, it hadn’t been discussed when he posted it. I’ll try to do it here:
UAH land only and UAH sea surface only trends tend to converge to the same value from time to time. Land based land only and sea surface trends don’t.
The difference between land only and sea surface measurements has therefore been increasing all the time (excluding short term variations).
This is an unexpected behaviour. Of course, the land mass reacts quicker but as the land mass is strongly coupled with the oceans, a certain temperature difference would require a convergence of the trends.
In the simplest model
m(land) = k1*m(sea) – k2*(temp(land)-temp(sea))
where m is the linear trend slope
k1 accounts for the faster respones of the land mass
k2 accounts for the coupling of land mass and oceans.
A convergence of trends m(land) = m(sea) should then happen at a temperature difference of
temp(land)-temp(sea) = (k1-1)*m(sea) / k2.
As this hasn’t happened in ground based measurements. either k2, the coupling between oceans and land is extremely small and land response times to ocean temperatures are in the 100 years plus range or so. This is against all experience.
The other explanation would be a trend inflation affecting specifically the land only trend.
It would be interesting to look at the nature of the instrument error. Temperatures recorded descending through max to min, I would say would be different from recordings made going from Tmin to Tmax. Is it assumed that the Tmax error is equal to the Tmin error ?
I am reminded of it in measuring blood pressure, if the sensor is not located properly, systolic always cuts off late, diastolic cuts in early resulting in an underestimate of the high end and an overestimate of the the lower reading. Not the same as temperature reading but an uncanny resemblance.
This comment goes to the heart of the ‘warming’ issue and should not be forgotten.
steven mosher>> UAHCRU correlates well, yes.
None of them seem to correlate all that well with GISS:
http://www.woodfortrees.org/plot/hadcrut3vgl/from:1990/plot/uah/offset:0.2/from:1990/plot/gistemp/from:1990
A (not very appropriate but anyway) extrapolation of those curves would probably yield quite different global means in 100 years.
My “analysis” here is 100% amateurish, I know, but have you made comparisons with GISS and if so, what have you concluded?
There are many errors in the data stream and all will have differing adjustments so how can we get any meaningful answer to the problem?
Far better to have sited these stations correctly in the first place.
Sorry I would like to leave a political comment.
‘Power to the people’.
There the ‘People’ that spent ‘Their’ time and money on a project like this because they care about the world they live in and not because they get a grant or have some other motive.
To every single person involved in this please take my total gratitude.
Anthony, huge congratulations to the entire team for getting this massive job done and getting it through peer review to publication, all without government or institutional support. I have only skim-read the paper, but I take it that you and your team have low confidence in the veracity of the data overall due to the very small number of surface stations that comply with best practice and have a significant recording history over time; or did I get that wrong?
Hi Anthony,
Do you think there is enough station classification data to put together a ‘best stations’ network for the US land mass?
Secondly, have you been approached by anyone wanting to do the same, from other countries? China and India would be good to study.
Robert Morris – Thank you for correctly summarizing one of the implications of our study. As you write (with one edit]
“…the fact that the best and worst each have biases means rather than canceling them out they compound the uncertainty of any trend. In effect the margins of error are now expanded and the concept of a measured or understood trend in US temps is now demonstrably more [uncertain] than previously considered.”
Congratulations Anthony! And well done to all of the authors of the paper. Excellent work.
Congrats and GJ all involved. I quess the basic outcome has been apparent from early statistical runs so kudos on pulling it through the process anyways. I can only assume a whole lot of climate science gets it’s plug pulled when the results do not obey the politics – so well done indeed.
steven mosher says:
May 11, 2011 at 10:30 pm
Latitude.
So its the same temperature today as it was in the LIA?
or is it warmer now?
Is roy spencer lying about UHA?
=====================================================
Mosh, that’s not what I was talking about at all, and you know it.
But since you brought it up….
If you start those stupid temperature graphs at 1700 (LIA) like you mentioned, you get
the exact same slope we’re getting now. 1700-1800
That is natural global warming. Anything on top of that slope “could” be man made global warming.
But since both slopes are exactly the same, show me the man made signature.
The only way you can increase that slope is by adjusting past temperatures down, faking it, lying about past temperatures.
(sorry, I do not speak perfectly well english)
I noticed some weakness in the report of statistical results in the article.
– P values are not systematically reported. p=0.04 and p=0.00000001 are both significant at 0.05 level, but do not represent the same level of confidence..
– There is multiple comparaisons, what increase the risk of type I error. Examining 20 associations will produce one result that is “significant at P = 0.05” by
chance alone. This issue is not addressed.
– There is no assesment of effect size. Effect size allows the evaluation of the strength of an association, not only its statistical significance. Very small difference can be statistically significant, but without any practical importance…
References:
Sifting the evidence—what’s wrong with significance tests?
Jonathan A C Sterne, George Davey Smith
http://www.rni.helsinki.fi/~kja/epid10/article2.pdf
Effect size, confidence interval and statistical significance: a practical guide for biologists
Shinichi Nakagawa1,* and Innes C. Cuthill
http://www.kyb.mpg.de/bethge/teaching/introstats11/nakagawa2007.pdf
Martin says:
“Congratulations.
“even the best-sited stations may not be accurately measuring trends in temperature or, more generally, in trends in heat content of the air which includes the effect of water vapor trends. Also, most are at airports, are subject to encroaching urbanization,..” Most of the best-sited stations are at airports????
Twenty six years ago I first heard the term, “airport effect” as a cause of most of the measured warming.
So the study really didn’t address land use. This is a point that needs to be made.”
I think this is very important. So how does a site that was a field 50 years ago and now is a big city could now have underestimated (lower) max temps? My car thermometer says it is hotter in town than in rural areas during the hottest time of the day. Maybe land use changes have not been addressed. Maybe the many reasons for bad siting washes out any land use changes and maybe the statistical methods used also contribute to this washing out.
I haven’t read the paper yet but a thought occurred to me that I haven’t seen mentioned.
The results may be reasonable from a purely scientific perspective. If heat is being captured during the day and released at night, then that heat is not present during the day to influence the high temperature for the day. Something to consider.
In addition, I have always assumed that weather stations for the most part are not in the heart of the cities. They exist mostly on the outskirts (airports). In my own situation I happen to live at the edge of a medium sized city. When I go into town I see typically see the temperature go up (especially in winter). However, when I go into the country I also see the temperature go down, usually much more than when I go into town.
Clearly, there is a UHI effect. However, the biggest part of the UHI effect is probably missing from our weather station data base. I realize this paper is trying to deal with microsite issues only, but growth in and around the microsite is probably just as big a factor if one is trying to understand the real warming.