Climate Discussions

Yamal and temperature

2012-05-16T18:08:00.001-07:00

A recurring topic at ClimateAudit has been the use of tree ring data from the Yamal peninsula in Russia. Steve Mcintyre, the author of ClimateAudit, maintains that data from that area have been used selectively by researchers at the CRU to support the idea of a 'hockey stick' . At the RealClimate blog Gavin Schmidt claims that the results by the CRU were obtained after selection of samples following rigorous analysis of the data.

The Yamal peninsula is at around 71N 71 E. Below I plot data from two sites near to Yamal. One, Salehard, is 200 km south of the Peninsula and the other Ostrov Dickson is 200 km to the north east. The data were downloaded from the ClimateExplorer web site. There are few missing months in the data and I replaced them by the average of same calander month for the preceding and following year except for 2011 where I used the average of the temperature for appropriate calendar months.

As can be seen the observed data show no sign of a hockey stick. Both sites show a maximum in the 1940s, a minimum in the 1970s and and an increase since then - similar to the global temperature trend. One site, Ostrov-Dickson, does have a slight upturn this centruy but temperatures are still below those of the 1940s.

This comment was originally posted in the morning of 1 May 2012. It was modified during the course of day with the addition of the data for Ostrov Dickson.

Trends in cloudiness and temperature

2012-04-05T06:01:00.000-07:00

One of the fundamentals of the consensus approach to climate change is that increasing temperature should lead to increasing water vapour and cloudiness. One type of data measured but not readily available is ‘hours of bright sun’. Initially it was measured by the Campbell-Stokes solar recorder, developed in middle of the 19^th century. It uses a glass sphere to direct sunlight on to specially prepared card which shows a ‘burn’ mark when the sun is shining. Recently, radiation is measured by more modern methods. The hours of bright sun varies inversely with cloudiness.

Data on hours of bright sun, among other parameters, are posted on the website of Hungarian Met Office for four climate stations for the period 1910 to 2000. (http://owww.met.hu/eghajlat/eghajlati_adatsorok/). The following chart shows data for 4 sites for the whole of the period when they all have hours of sun data. There appears to be a break in the data measurement method around 1970; before that period there is a greater variability and less consistency between the stations, after that the data seem to be more consistent. (Note that we have presented the full data set even though part of it is not consistent with later data and could legitimately be excluded.) From 1970 to 2000 the data show a rising trend of 0.018 hours per year. This is equivalent to an increase of 0.54 hours over the 30 year period relative to average of 5.3 hours of sun per day.

Another similar data set is available from the Australian Bureau of Meteorology website at http://www.bom.gov.au/climate/data/index.shtml?bookmark=136. Here the data are expressed as daily solar exposure in MJ m^-2. In this case they are measuring solar radiation which also has an inverse relationship with cloud cover. We have chosen the 5 stations because they have data and are in different parts of Australia. The trend line shows a steady climb over the 21 years with data again indicating a reduction in cloudiness. (To create the trend line we have replaced the 2005 data which has missing data for all stations by the average of preceding and following year). The trend is equivalent to an increase of 0.45 MJm^-2 over the 21 years relative to average of 17.9 MJm^-2.

The final data set we consider is from the UK and was downloaded from the Met Office web site at http://www.metoffice.gov.uk/climate/uk/stationdata/index.html. From the stations on the web site we extracted monthly hours of sun data for 10 stations which were more-or-less complete from 1931 to 2011. The next chart shows the data for all 10 stations and the average. There is some sign of trend in the data with a minimum around 1970/80.

As this graph is a bit confusing we have plotted the average of the 10 stations on a separate graph and also a 20-year moving trend line. A moving trend line calculates the slope of the 20-year trend every year from the 20th year with data up to the end of the data set. This shows that in the 20 years up to 1966, the trend was -0.037 hours per year. In the 20 years preceding 2000 the trend was +0.041 hours a year. Given that hours of sun varies inversely with cloudiness, this demonstrates that not only does cloudiness decrease with increasing temperature but increases with decreasing temperature.

These charts using disparate data sets but they all show one thing; temperature and cloudiness are closely related.

30 year trend

2012-03-05T06:47:00.001-08:00

We have earlier commented on trends in temperature. (When is a trend not a trend?) Here we look quite simply at the 30-year trend line from three different temperature data sets and a 23-model ensemble hind-cast/projection. The trends were calculated using LINEST function in Excel. The year shown in the chart is the final year of each 30-year regression period. As can be seen the 30-year trend is positive for most of the last hundred or so years. It reached a peak in 2004 or 2005, depending on the data series, and has since declined slightly. Whilst the three data sets, not surprisingly, show very similar trends, the modelled trends are quite different.

Since 1883 there has been a peak or a trough in the trend line roughly every 30 years. Your guess as to what will happen in the future is as good as mine.

On 25 March 2012, the above paragraph was hanged with '30' replacing '25. The following was also added.

In the next graph we look at the 30-ear trend for 7 models. The choice of models was based on those listed on Table 6 of the IPCC “General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment”, Version 2, June 2007. Where a model has more than one published simulation its results were average before being include in the graph. The siulation results come from the climate explorer site.

Elswhere we have shown the results of the same seven models. Showing the difference as 30-year trends appears to demonstrate how widely different the simulations of the models were.

Sea Ice and Snow

2012-02-29T06:37:00.001-08:00

The Georgia Institute of Technology has recently released details of a study of the relationship between Arctic Sea Ice and Northern Hemisphere Snow Cover. The press release summarises it as “The researchers analyzed observational data collected between 1979 and 2010 and found that a decrease in autumn Arctic sea ice of 1 million square kilometers -- the size of the surface area of Egypt -- corresponded to significantly above-normal winter snow cover in large parts of the northern United States, northwestern and central Europe, and northern and central China.”

This goes some way to explaining the fact that whereas Arctic Sea Ice has been tending to decrease (at 53,000 km²/year) snow coverage has declined more slowly (at 22,000 km²/year). The importance of snow and ice is their role in the albedo feed-back mechanism. Snow reflects almost all the incoming energy, water and land (at least the northern boreal forests where most snow falls) reflect about 10%. So, other things being equal, the influence of snow and ice are equivalent. But, and it’s a big but, other things are not equal. Both sea ice and snow cover vary seasonally. The following chart shows average monthly values for the period 1978 to 2011. This shows that in winter snow covers a much larger area than sea ice.

However the albedo effect is only applicable when the sun is above the horizon. The next chart shows the areas adjusted for solar angle. In this case we have assumed a latitude of 80 °N for sea ice and 70 °N for snow and multiplied areas in the first chart by the sine of the sun angle at midday on the 15^th of each month. (Calculated using the tool at http://aa.usno.navy.mil/data/docs/AltAz.php). This presents a very different picture and suggests that the influence of snow and ice are equivalent – with snow being perhaps more predominant.

This calculation is subject to a large number of caveats. The sea ice area is based on NSIDC ‘sea ice extent’ which shows “the total area of ocean covered with at least 15 percent ice”. This is reasonable as a metric since sea ice is almost 90% below water but of course such ice is not reflecting radiation. The use of 70 °N and 80 °N respectively and mid-day sun angle are also only approximate. Ideally it would be necessary to track areas of ice and snow at different times of the year, at different latitudes and the energy reflected at different times of day.

The final chart shows the solar-angle adjusted ice and snow area. This was calculated as for the previous chart. It shows that despite the snow area being larger and reducing less than the albedo adjusted ice area has shown a steady decline.

Minor editorial changes on 2 March 2012.

Twenty-three climate model comparison

2012-02-26T04:21:00.000-08:00

The realclimate.org blog has a thread posted by Barry Bickmore related to an article which appeared in the Wall Street Journal (WSJ). The original article was written by a group of eminent scientists with little specific expertise in the science of climate change. To summarise in over-simplistic terms they said that they could accept Anthropogenic Global Warming (AGW) but not Catastrophic Anthropogenic Global Warming (CAGW). The WSJ published a response by a group of equally eminent climate scientists who supported CAGW.

The posting of Barry Bickmore looks at some of the claims in more detail than would be possible in a newspaper column. One point the first group of scientists had made was that climate models had not captured the recent stasis in temperatures. Bickmore’s response was that “individual models actually predict that the temperature will go up and down for a few years at a time, but the long-term slope (30 years or more) will be about what those straight lines say.” Below we show the annual temperatures expressed as degrees Celsius for 23 models (downloaded from the ClimateExplorer site) the maximum, minimum and average for these 23 models and the temperature from the HadCRU3 data series. As the HadCRU3 series only gives temperature anomalies we have adjusted it to give the same mean as the models for the period of overlap.

First of all it can be seen the chart supports Bickmore’s point. The average of the models follows the general trend of temperature from 1900 to the present and many of the individual models have periods with little or no increase even after the effect of CO2 kicked in from the mid-1970s onward. The current period may have some similarities to the period 1910 to 1970 when a strong Atlantic Multidecadal Oscillation led to an enhanced temperature increase for the first half of that period and to period of stasis for the second half. As the period of stasis, but with wide variations, lasted for about a quarter of a century we may have another decade or so of level temperatures with it still being possible to defend the models. In that period it is likely that models will improve and that the models themselves will be better able to represent such periods.

What is surprising is the difference between the models. The average temperature of the ‘hottest’ model is 15.4 °C and of the ‘coolest’ is 12.4 °C. This is, if my maths is correct, equivalent to a difference of 15.92 W/m², an order of magnitude larger than typical anthropogenic forcing estimates.

Although Bickmore doesn’t mention temperature we have produced a similar chart for precipitation.

This also shows a large difference between the models but, unlike temperature simulation, little evidence that the underlying trend has been captured. In this case the ‘wettest’ model has an average precipitation of 1184 mm/year and the ‘driest’ a precipitation of 918 mm/year. This is equivalent to 19.0 W/m², again large compared to anthropogenic forcing.

A simple regression model - with and without CO2 equivalent.

2012-02-24T08:36:00.003-08:00

All scientists are well aware of the mantra “Correlation does not imply causation” and people have identified examples to illustrate this. Two spurious correlations I remember were one between the number of missionaries sent to Jamaica and the number of illegitimate babies born the following year, and another between the number of nesting storks and the number of babies born in some German towns. That said, correlation can indicate possible links; cancer and smoking being a case in point. When a physical explanation for a phenomenon is known then regression can also indicate the relative importance of different factors.

Scafetta’s models discussed at scepticalscience.com are examples of regression models which assume that global temperature can be explained by astronomical and climatic cycles superimposed on an underlying but unexplained trend. With some justification they have been dubbed ‘climastrology’.

To look at this in a bit more detail I’ve been playing around with two simple regression models. The first used three parameters: sunspots (SS - as a proxy for solar radiation), optical thickness (OT - to represent aerosols from volcanoes and elsewhere) and Atlantic Multidecadal Oscillation (AMO, as a representative climatic cycle). This is similar to the model Foster and Rahmstorf used to determine the underlying temperature trend from 1979 to 2010 with AMO replacing the El Nino index. The most questionable parameter in both cases is the use of natural climatic oscillations as independent variables. The regression parameters were calculated using the HadCRU3 temperature base and the LINEST function in Excel.

The resulting equation was:

Temperature = 0.62 * AMO + 0.00098*SS + 0.092*OT – 0.44

The equation has an r²value of 0.19 and a standard error estimate of 0.24 °C. One anomaly is that the coefficient for the Optical Thickness is positive – implying that volcanoes would increase temperature! From these statistics you would not expect the agreement to be very good and it is not.

I then tried a four parameter regression, adding CO2. This gave the following equation.

Temperature = 0.49 * AMO + 0.00035 * SS -0.15 * OT + 0.0082 * CO2 - 2.98

In this case the r² value is 0.89 and the standard error of estimate is 0.091 °C. The coefficients are now in the right direction. For comparison I have also plotted the ensemble of IPCC climate models in this case the standard error of estimate is 0.14, rather worse than the regression model.

The regression model does as well as the IPCC ensemble in places where the IPCC ensemble performs well (the increase from 1970 to 2000)and the regression model does better in places where the IPCC ensemble is known to underperform ( the increase from 1910 to 1945, the slight decline from 1945 to 1970 and the levelling off from 2000 to 2011). It is clear that the IPCC models will improve dramatically when they are able to simulate climate oscillations.

Ater I had developed this model I remembered that CO2 is not the only greenhouse gas so I modified it to use CO2 equivalent (CO2Eqv). The data were taken mainly from the GISS site (http://data.giss.nasa.gov/modelforce/ghgases//GHGs.1850-2000.txt) updated to 2011. The new model gave the following regression:

Temperature = 0.55 * AMO + 0.000049 * SS -0.35 * OT + 0.0177 * CO2Eqv - 2.98

In this case the r² value is 0.90 and the standard error of estimate is 0.087 °C - a slight improvement. One intersting difference between the models is that in this model the influence of sunspots is considerably reduced and that of optical thickness (aerosols) is increased.

A final point – which may or may not be significant but I throw it in for fun. The coefficient for CO2Eqv is 0.0177. During that period CO2Eqv increased from 537.4 to 956.8 ppm and accounted for 0.74 °C of warming. The ratio of the increase of CO2Eqv was 1.78. This implies a CO2Eqv sensitivity of 0.89 °C for a CO2Eqv doubling (0.74 * log2)/log(1.78)), a figure close to most estimates. Given the warning about not reading too much into correlations with which I started this piece it should be treated with caution.

The modelling using CO2 equivalent was added after the initial post. The final figure with corrected labelling was replaced on 4 March.

David versus Goliath, or vice versa

2012-02-22T10:51:00.001-08:00

A very basic message concerning climate funding has been lost in the furore over Peter Gleick and the Heartland Institute documents. (If you’ve found this page you almost certainly know the details but, just in case, they are these. The Heartland Institute (HI) is a USA based think-tank which promotes free-market policies and, among other things, actively campaigns against investment based on the belief in anthropogenic global warming (AGW). Peter Gleick is water scientist who campaigned for AGW related investment. By impersonating an employee of the Heartland Institute, Peter Gleick obtained some confidential papers which he passed on to sympathetic journalists and bloggers.)

One of the documents released was the HI budget which showed the expenditure for 2011 ( $US 5.05 million) and the projected expenditure for 2012 ($US 6.53 million). Not all of this was spent on climate change issues. Identified specific activities account for $US 2.91 million of the 2012 budget. Of these $US 488,000 were for climate related activities. In addition they have earmarked $US 388,000 for HI staff work related to the Nongovernmental International Panel on Climate Change (NIPCC) and $US 305,000 for disbursement to professional contributors to the NIPCC. Adding the NIPCC items to the $US 2.91 million gives a total identified spend of $US 3.60 million, of which $US 1.18 (33%) million is climate change related. The balance of the 2012 budget is therefore $US 2.93 million (6.53-3.60). This covers general staff and office costs so if we apportion that in the same ratio as the identified cost this gives $US 0.97 million (33% of 2.93 million). The total projected HI spend on climate is therefore $US 2.15 million.

$US 2.15 million is not a trivial sum but how does it compare with other organisations on the other side of the debate. The Pacific Institute of which Peter Gleick is, as of the time of writing, the President has a budget of $2.29 million; very similar to the proportion of the HI budget estimated to be on climate change issues. The figures are however not directly comparable. Whilst it is likely that most of the employees of the Pacific Institute would be AGW protagonists their work covers a wide range of water related areas of which climate change is but one aspect.

At a wider level US government funding for climate change is several orders of magnitude larger. The following summarises projected spending for 2011. The figures come from a report by the AAAS, Chapter 15, attributed to Paul A.T. Higgins. (http://climatequotes.com/wp-content/uploads/2011/01/cc-funding2011.pdf)

Climate Change in the Federal R&D Budget (budget authority in millions of dollars)

US Global Change Res Prog,	2,561
Natl Oceanic & Atmos Admin
USGCRP	437
National Science Foundation
USGCRP	370
Geosciences	955
Atmospheric Sciences	281
Earth Sciences	199
Sci, Eng and Edu for Sustain	766
Natl Aero and Space Admin
Earth Science	1,802
Department of Energy
Office of Science	5,121
Bio and Environ Research	627
Energy R&D	2,430
Department of Interior
Climate Change Adaptation	171
Renewable Energy	73
Environ Prot Agency R&D	606
US Dept of Agriculture
Climate Change	159
Renewable Energy	179

The total is over $US 16 billion. Thisofs course includes a range of big budget items which would be necessary without climate change such as environmental satellites.

In the UK there is no exact equivalent to the HI or equivalent government figures. An approximate equivalent to HI is the Global Warming Policy Foundation (GWPF) which is concerned as much with financial aspects of climate change mitigation/adaptation as with climate change itself. Its budget is £GB 503,000 ($US 785,000).

A table produced as result of FOI request by the ‘notalotofpeopleknowthat’ blog shows UK climate change research funding as follows:

Recipient	£GB million	$US million
Universities	7.23	11.28
Engineering and Physical Sciences Research Council (EPSRC)	234.57	365.93
Met Office	12.00	18.72
Hadley Centre	1.75	2.73
Tyndall Centre	2.99	4.66

We have not totalled the columns as some of the money received by the Universities may have been provided by the EPSRC.

At a wider international level a report by Ernst & Young (Durban dynamics: navigating for progress on climate change) shows “Government spending on climate change in 2010” for 10 major economies.

Country	$US million
Germany	23,498
USA	15,936
Japan	14,201
France	10,343
Italy	6,734
UK	5,983
Spain	4,321
South Korea	2,207
Australia	2,050
South Africa	463

It is clear that the figures for climate change related government spending for different countries are measuring different things but they do start to make the Heartland Institute and the Global Warming Policy Foundation look like very small Davids facing up to very large Goliaths

So far we have only expanded one side of the equation, that of the protagonists of AGW so let’s now consider the antagonists. The following list is taken from a Wikipedia page listing companies with a world-wide revenue in excess of $US 40 billion, selecting only those referred to as ‘Oil and Gas’.

Company	Revenue $US billion
ExxonMobil	$370.125
Royal Dutch Shell	$368.056
BP	$297.107
Sinopec	$289.774
PetroChina	$221.955
Total S.A.	$212.815
Chevron	$204.928
ConocoPhillips	$198.655
Saudi Aramco	$182.396
Eni	$131.292
Petrobras	$120.052
Gazprom	$111.808
LUKoil	$104.956
Pemex	$103.538
Petróleos de Venezuela	$94.929
Rosneft	$91.975
Statoil	$90.733
Petronas	$76.822
Marathon Oil	$73.621

It can be seen that the revenues of these companies dwarf that of government spending on climate change. Of course we again are not comparing like with like. Most of the revenue comes from the expense of extraction, refining and distributing carbon based energy and is not directly related to climate change issues. Such companies have other ways of exerting influence on governments rather than supporting comparatively small think tanks. Indeed their very size gives them a certain guaranteed level of support – it would be a brave government to throw hundreds of thousands of energy workers out of a job. Often it is not the energy companies themselves who get their message across. Every advertisement for a 4x4 vehicle or a holiday in an exotic location implicitly includes the message - status and happiness depend on burning fossil fuels.

In the end I think the only conclusion is that both the protagonist and antagonists of AGW have adequate sources of funding; that the climate wars are being fought on a level battlefield. There is no David, only two Goliaths.

[Minor editorial corrections and the last two sentences added to the penultimate paragraph on 28 February 2012.]

Smooth operators

2012-02-21T08:40:00.000-08:00

In the IPCC Technical Assessment Report of 2007 many of the graphs used smoothed values of data series. This is perfectly valid as it facilitates seeing trends among the year-to-year fluctuations. The methods used are described in 'Appendix 3.A: Low-Pass Filters and Linear Trends'. They use for annual data as filter which has 13 weights 1/576 [1-6-19-42-71-96-106-96-71-42-19-6-1]. An example of this is in figure 3.8. It is interesting to note that have used a algorithm which allows smoothing right to the end of the data series.

We give below the three main global temperature series using the 13 point filter. At the end of the series we used only the part of the filter which applies retrospectively. This curve appears to shows that the rate of temperature increase has fallen off in recent years.

The same annex also mentions using regression to estimate trends. For simplicity we have done this using 13-year series and the LINEST algorithm in Excel.

Using the 13-year period for trends gives the clear impression that the rate of warming has slowed dramatically. Perhaps as a result of this, it is now being suggested that we need at least 30 years to detect a trend. (Though, it should be noted that I can find no reference to the need for a 30-year trend in IPCC report.) In the following graph we have plotted the 30-year trend lines and the 30-year trend from the average of a model ensemble.

Even using the 30-year trend it is clear that rate of temperature increase has fallen back. This graph also shows that the modelled 30-year trend was close to the observed one for the period 1975 to 2005 but outside that period diverged widely.

The Hockey Stick and the Climate Wars

2012-02-20T03:53:00.000-08:00

This book was better than I expected. My expectation was based not on Mann’s reputation/caricature, but on his previous book “Dire Predictions” co-authored with Lee Kump which we have reviewed previously.

On this site and in blog contributions I’ve recently been trying to promote two themes. The first is that the climate science community is weakening its case by trying to ignore inconvenient facts and data; it then gets doubly blamed for the cover-up and the inconsistency between their claims and the data. The second theme is that when presenting climate science to a largely lay audience, as Mann does here, a scientist has to be more careful than in a published paper. A paper will be thoroughly scrutinised by other scientists; lay people will not know if the wool is being pulled over their eyes.

Much of the book deals with the ‘climate wars’ aspect of title and the fact that few attacks on climate scientists fail to include Mann or his ‘Hockey Stick’. In the USA the whole issue of climate is much more divisive than in the UK. In the UK the Climate change Bill, mandating a reduction of 80% in CO2 emissions by 2050, was passed with only 5 votes against. At a recent lecture the Director of the Grantham Institute for Climate Change, Sir Brian Hoskins, was very frank about the shortcoming in climate science. None of this would be possible in the USA. And it goes a long way to explain why Mann devotes time to this topic.

One of my complaints of his previous book was that Mann blithely ignored any criticisms of his work. In this book he tackles some of them – even if not always head on. One example was the use of the word ‘censored’. When the data used for his original millennial temperature reconstruction was released it contained a folder called ‘censored’. This was regarded by anthropogenic global warming antagonists as proof of malfeasance; in reality it is a normal statistical term used to define a data sub-set excluded to test its importance to the overall conclusion. Another area he deals with is what he refers to as the ‘divergence problem’. This is the fact from about 1960 onwards most tree rings fail to respond to global warming. In the case of his own work he simply says that his data sets ended in the 1970s and 1980s and claims that the idea of adding the observed temperature for recent years to bring the data up to date, and increasing the hockey stick appearance, was suggested by a reviewer. Elsewhere he deals with a “high-elevation site in western United States”, without actually calling them ‘bristle cone pines’, and accepts that their growth rates could have been influenced by CO2 enhancement rather than temperature increases. This had been a criticism of his record. Another criticism of his work had been that one of the proxy records, sediments from a Lake in Finland, had not only been corrupted by upstream engineering works but had been used ‘upside down’. In one of comments on this Mann says “one of our methods didn’t assume orientation, while the other used an objective procedure for determining it”. This appears to be an admission that the orientation might not have been correct though elsewhere he says that this record did not change the overall conclusions.

So, if other climate scientists might have understood the oblique references in the book how might the public react to the book. Well of course few of them would have picked up the allusions and would quite possibly have been unaware of the significance of some of the statements. Another objection of proxy records is that, for statistical reasons, they underestimate the variability of the parameter they are estimating. Mann recognises this and explains this is a reason for the wide error bands. It is quite possible that increases in temperature such as those from 1910 to 1945 or 1975 to 2005 might have occurred in the past but not have registered in the proxy record. Again few members of the public reading this book would have understood the point and simply seen the ‘blade’ of hockey stick and not realised that the handle could have been as curvy as the blade. Another example of misleading the public is the graph he presents of a projection of temperature made in 1988 but he only includes data “available through 2005 in this analysis” even though later data were available at the time of writing the book and show the projection as been less accurate.

Elsewhere I have argued that there was need for a book in a popular style to combat the popular books of AGW antagonists – this is indeed such a book. What is now needed is a book which arbitrates between two sides.

Author: Michael E. Mann

Publisher: Columbia University Press, 2012

E-ISBN: 978-0-231-52638-8

Precipitation projections

2012-02-10T01:34:00.000-08:00

This is the time of year when climate data sets are updated to include annual totals for the preceding year (in this case 2011). Most sites concentrate on temperature - though sometimes include not just observed atmospheric temperature but also variables such as modelled projections and temperature in the oceans. One variable which is often forgotten is precipitation. After all, the positive feedback from water vapour assumes that it remains in the atmosphere rather than becoming precipitation.

On the chart below we use two data sets. The first is the NCDC 5° gridded precipitation anomaly at http://www1.ncdc.noaa.gov/pub/data/ghcn/v2/grid/grid_prcp_1900-current.dat.gz. To get a monthly global figure we averaged the data, cosine weighted on latitude to compensate for reducing grid sizes. The values are in millimetres. The second data set was of precipitation hind-cast/projected downloaded from the Climate Explorer web site at http://climexp.knmi.nl. The data set used was described as “all models, 20c3m/sresa1b” and included 23 models. These data were in mm/day so to convert then to equivalent units they were multiplied by the number of days in the month. They were adjusted to give values relative to the period 1980 to 2010. As trends were masked by month-to-month variations the five year centred moving averages are also plotted.

This shows, as stated in the IPCC TAR4 report, that the variance of the simulated precipitations is less than that of the observed values (TAR4 section 9.5.4.2.1). The difference, based on the 5-year moving average, is as high at 2 mm/month which is equivalent to 1.88 W/m². (1 mm evaporation over 1 m² weighs 1 kg. The latent heat of evaporation of 1 kg of water is 2.45 Mj. 1 kWh is 3.6 Mj.)

20 February 2012. Inaccurate labelling on chart corrected.

Fun with numbers

2012-02-04T03:43:00.000-08:00

One theme of this site over the last month or so has been the misleading use of numbers. This is very much related to the ongoing discussion of whether warming has stopped or not. One side, the Anti-AGW, says it has stopped and therefore global warming is not happening and never will, conveniently forgetting that a lot of it has already occurred. The other side, the Pro-AGW, says warming still happening and introduces new metrics to demonstrate this.

In an age when every ‘scandal’ unearthed by budding ‘Woodward and Bernsteins’ is given a ‘gate’ suffix it is easy to forget that President Nixon was impeached not for the original crime of breaking into offices of the Democratic Party but for the cover up. I wonder if something similar is happening with the Climate Science Community at the moment. I have, in earlier blogs, identified examples of presentations and sites trying to prove that the models’ predictions were accurate which have left off data from the recent years (though internal evidence shows that they could have included it.) Yes, it would be inconvenient to admit that the current climate stasis was not predicted but playing with numbers to try and disguise it opens the Climate Science Community to charges of intellectual dishonesty.

Below I ‘prove’ that there has been no increase in temperatures since 1998. I did this by assuming that temperatures from 1998 onwards have been flat and calculating the temperature perturbation from the flat line. I then regressed this perturbation, after adjusting for residual seasonality, against sunspots, aerosols and a multivariate ENSO index and got the following graph.

Wow – there’s no warming! Eat your heart out you pro-AGW crowd. But, how did I prove it? I cheated. Firstly the coefficients I derived suggested that higher solar irradiance is associated with cooling and higher aerosols are associated with warming; only the El Nino index behaved sensibly. Then I adjusted the format of the equation so the coefficient was shown to only two places of decimals. As the data are monthly this means I could have had warming of 0.049 degrees/month (0.6 degrees a year) and still shown ‘zero’ warming. What is more, if I’d played around a bit more with lag times, different indices, and different amounts of smoothing I could have produced an even more convincing, but even more dishonest, proof.

I’ve been completely upfront about what I have done but as long as the Climate Science Community tries to avoid the reality of climate stasis they will face a barrage of similar ‘proofs’ but with any underlying falsification being undisclosed.

When is a trend not a trend?

2012-02-02T12:16:00.000-08:00

There has recently been discussion on the blogosphere as to whether or not global temperatures are continuing to rise, have levelled off, or are falling. For example the ‘Met Office in the Media’ site had a rebuttal of an article in the (United Kingdom) Daily Mail which, inter alia, claimed there had been no warming for 15 years. ( http://metofficenews.wordpress.com/2012/01/29/met-office-in-the-media-29-january-2012 /) In their rebuttal the Met Office pointed out that most of the highest temperatures on record have occurred in the last 15 years. They also present a chart of temperature in 10 years blocks which showed that the first decade of the 21^st century was the warmest on record.

In the IPCC Technical Assessment Report of 2007 many of the temperature graphs are smoothed using 13 point binomial moving average (for example figure 3.8). Elsewhere they talk of decadal smoothing. One of the contributors to the blog in support of the Met Office’s position refers to the ‘Skeptical Science’ web site (http://www.skepticalscience.com/global-warming-stopped-in-1998-intermediate.htm). Here they show temperature using an unweighted 11-year moving average but do not include data after 2007. The data they do show has a positive trend. (This is strange as that page of the web site has been updated to include a 2011 paper by Foster and Rahmstorf, which we have discussed elsewhere (http://www.climatedata.info/Discussions/Discussions/opinions.php?id=3871334005763196947), which purports to compensate for the effect of solar irradiance, volcanoes and El Nino.)

In the graph below we show the HADCRU3V data set (normalised to 1979 to 2008 for compatibility with satellite temperature data series). We have include three smoothed series: 13 point binomial using the weights in the IPCC report, and 11 point unweighted moving average and a 15 year unweighted moving average.

As can be seen, only the 15 year moving average suggests that temperatures are continuing to rise. At the time of the IPCC report in 2007, the authors were happy with a 13 point smoothing or decadal smoothing. Now that these statistics no longer show a rising trend they are finding new ways of presenting the data to claim that the trend continues. I genuinely believe that rather than moving the goal posts the Met Office would better maintain its credibility by admitting that, at the very least, the rate of temperature rise has slowed. The basic truth of course is that extrapolating from the past climate says little about the future (year on year temperatures are correlated so the temperature in one year does have a bearing on following years).
[The following material was added on 3 February 2012.]

This post was, in part, prompted by the exchange on the Met Office site mentioned above. As part of the moderator's reply he referenced a Met Office document of 2010:
http://www.metoffice.gov.uk/media/pdf/m/6/evidence.pdf

This did accept that the rate of temperature rise in the past decade was slower than in the immediately preceding decades and postulated some tentative ideas as to why this might have happened. However it also contained some other metrics which were presented in such a way as to minimise the impact of this fact. Some of them I have commented on in the blog itself. One of them was a plot of decadal temperatures which showed that, despite the slowdown in the rate of temperature increase, the decade from 2000 to 2009 was the warmest on record. Below we give a plot of the decadal rate of change of temperature in C per year both the for observed temperatures (HadCRU3V annual average values) and the average of 7 climate models combining the 203cm and a1b scenarios to cover the period 1870 to 2010.

This shows two things: that while the rate of rise in the 1990s was the highest on record there were other decades in the past with rises of the same order of magnitude, the models (which get the overall rise fairly accurately) do not represent the rate of rise at a decadal time scale at all well. My purpose in this posting is not to 'rubbish' the models; it is to suggest the selecting data to agree with the science is the wrong approach; it should rather be to admit shortcomings in the science and work on improving it.