Guest essay by Dr. Tim Ball
Lack of temperature data is a problem, but measures of water and precipitation are much worse. Temperature changes, especially cooling, are important to a degree over the long term. Precipitation changes are much more important for short, medium and long periods. Droughts are much more devastating to flora, fauna and the human condition. Irrigation was one of the earliest technologies developed 9000 years ago to offset droughts in the Fertile Crescent, triggered by onset of Holocene warming.
McKitrick et al. and others identified the problems of determining global temperature. Instruments changed over time, but continuous records are limited to the accuracy of early measures ±0.5°C. There are the problems of the recording sites as Anthony Watt’s identified. Only 7.8 percent of the US record is accurate to less than 1°C. What does that say about the rest of the world?
Measuring precipitation accurately is the most challenging of all weather elements. It’s easy if precipitation falls straight down and is only liquid, but it doesn’t and isn’t. Measuring variable snowfall and water content is even more difficult. All precipitation amounts are much more variable than temperature. Some are not considered precipitation. Condensation is overlooked and unmeasured, yet very important.
One year in the late 1980s in Western Canada crop experts predicted a below average harvest because of low precipitation. Actual yield was average or higher in most regions. A combination of high daytime temperatures, close to 30°C, and low nighttime temperatures, close to 0°C, which was well below the Dew Point temperature, produced considerable condensation. Over a couple of weeks this provided sufficient moisture to “fill out” the crop. The moisture was more widespread and evenly spread than rainfall. Deposited at the surface at night meant reduced evaporative loss. More was available to replenish soil moisture in the root zone. All this occurs below the Stevenson screen where conditions are markedly different than at the surface. Read Geiger’s brilliant 1965 Climate Near the Ground to learn the difference.
For most areas the number of precipitation measuring stations is a fraction of the WMO recommended density. For example, two computer model predictions of monsoon rains for Africa showed completely opposite results. In Waiting for the Monsoon, (4 August 2006 VOL 313 Science) Columbia University climate scientist Alessandra Giannini says “The issue of where Sahel climate is going is contentious,” “Some models predict a wetter future; others, a drier one. “They cannot all be right.” They concluded,
“One obvious problem is a lack of data. Africa’s network of 1152 weather watch stations, which provide real-time data and supply international climate archives, is just one-eighth the minimum density recommended by the World Meteorological Organization (WMO). Furthermore, the stations that do exist often fail to report.”
It’s not surprising because the IPCC note in Chapter 8 of the 2007 Report,
In short, most AOGCMs do not simulate the spatial or intra-seasonal variation of monsoon precipitation accurately.
Precipitation events are extremely variable spatially. Most rainfall comes as showers of varying intensity so amount differ within short distances. The models gloss over limited temperature data with parameterization, but they can’t do that with precipitation. The grid is too coarse for even the massive systems of thunderstorms and midsize cyclones.
The Water Cycle is a more critical mechanism than the Carbon Cycle and knowledge of its mechanisms challenged meteorology and climatology even before the IPCC bias. For example, there are four different measures of water content of air.
Absolute Humidity: Ratio of mass or weight of water vapor per unit volume of air – grams per cubic meter.
Specific Humidity: Ratio of the mass or weight of water vapor in the air to a unit of air including the water vapor – grams of water vapor per kilogram of wet air.
Mixing Ratio: Ratio of the mass of water vapor to the mass of dry air -grams per gram or grams per kilogram.
Relative Humidity: Ratio of amount of water vapor in the air as a percentage of what it could hold.
The last is best known, most used, but most useless. It’s a function of temperature, so, for example, the same 70 percent relative humidity results from different amounts of water in the air.
Water movement is one part of the Cycle, but transport of latent heat energy is another major function. A very large part of the evaporation, transport and release of water and energy from the surplus region to the deficit region (Figure) is through the Hadley Cell and tropical cyclones.
The IPCC say,
The spatial resolution of the coupled ocean-atmosphere models used in the IPCC assessment is generally not high enough to resolve tropical cyclones, and especially to simulate their intensity.
The IPCC underplay the role of CO2 in plant growth, but there’s less focus on water because it requires discussion of natural cycles and patterns. In addition, their definition limits them to human causes of climate change. As a result there is limited funding or support for such research. Fortunately, there is a commercial and humane demand. Irrigation is the single largest use of fresh water by humans, especially in the developing world: India has more land under irrigation than any other country.
Vladimir Koppen did early work on water balance. His “B” climate group identified arid climates and recognized the “effectiveness” of precipitation on plant types and growth. More recently Charles Thornthwaite who also produced a classification system pioneered water balance. W.C. Palmer produced a drought severity index in 1965, but it only relates to meteorological droughts. The fact there are three types, meteorological, hydrological and agricultural, underscores the importance of water balance on climate and life.
All this addresses water and limitations on data and understanding of mechanism at the surface. It is even worse in the atmosphere. We know from the IPCC inability to deal with clouds of the challenge. Water exists as a gas, liquid and solid at the same temperature and can exist in a single cloud at different levels. Here are comments about measuring just water vapor.
It is very hard to quantify water vapor in the atmosphere. Its concentration changes continually with time, location and altitude. To measure it at the same location every day, you would need a hygrometer, which in earlier days made use of the moisture-sensitivity of a hair, and by now of for instance condensators. A vertical profile is obtained with a weather balloon. To get a global overview, only satellite measurements are suitable. From a satellite, the absorption of the reflecting sunlight due to water vapor molecules is measured. The results are pictures of global water vapor distributions and their changes. The measurement error, however, is still about 30 to 40%.
This was in 1996, but it was no better in 2002 as NASA noted,
Finally, water vapor plays a key role in the Earth’s hydrologic cycle. Therefore, a better understanding of its role will require long-term observations of both small and large scale water vapor features, a major goal of the National Aeronautics and Space and Administration’s (NASA’s) Mission to Planet Earth (MTPE) program.
The bottom line is we don’t know how much water vapour is in the troposphere and stratosphere. It is ignored in most assessments of atmospheric gases; they record only dry air at sea level. Why? It is the only gas with a wide variability from almost zero to 4 percent. It is by far the most important greenhouse gas, but that is something else the IPCC doesn’t want the public to know.
Here is what the IPCC say about water related issues in the computer models in Chapter 8 of the 2007 Physical Science Basis Report.
Unfortunately, the total surface heat and water fluxes are not well observed.
The evaluation of the hydrological component of climate models has mainly been conducted uncoupled from AOGCMs (Bowling et al., 2003; Nijssen et al., 2003; Boone et al., 2004). This is due in part to the difficulties of evaluating runoff simulations across a range of climate models due to variations in rainfall, snowmelt and net radiation.
For models to simulate accurately the seasonally varying pattern of precipitation, they must correctly simulate a number of processes (e.g., evapotranspiration, condensation, transport) that are difficult to evaluate at a global scale.
Since the TAR, there have been few assessments of the capacity of climate models to simulate observed soil moisture. Despite the tremendous effort to collect and homogenize soil moisture measurements at global scales (Robock et al., 2000), discrepancies between large-scale estimates of observed soil moisture remain.
Glaciers and ice caps, due to their relatively small scales and low likelihood of significant climate feedback at large scales, are not currently included interactively in any AOGCMs.
The MOC (meridional overturning circulation) is an important component of present-day climate and many models indicate that it will change in the future (Chapter 10). Unfortunately, many aspects of this circulation are not well observed.
Sun et al. (2006) investigated the intensity of daily precipitation simulated by 18 AOGCMs, including several used in this report. They found that most of the models produce light precipitation (<10 mm day–1) more often than observed, too few heavy precipitation events and too little precipitation in heavy events (>10 mm day–1). The errors tend to cancel, so that the seasonal mean precipitation is fairly realistic.
The last comment is remarkable and laughable if it was not so pathetic. They are saying the extremes are wrong, but because the average of them is close to the average it makes it correct. Beyond illogical, it assumes the average is correct, which is not possible because of totally inadequate data.
There is no justification for the IPCC claim of 95-percept certainty that human CO2 is the cause of warming and latterly climate change.
Try checking the height of the big rivers? Eg Nile, Rhine, Mississipi etc
In almost complete absence of data, IPeCaC simply assumed 1) more water vapor from more CO2 & 2) that the presumed water vapor was a powerful positive feedback. Neither assumption is in evidence & all the actual observations which are available argue against the positive feedback effect.
For most of Earth’s surface, the heating effect of water vapor totally swamps out that from CO2, above very low concentrations of carbon dioxide.
For models to simulate accurately the seasonally varying pattern of precipitation, they must correctly simulate a number of processes (e.g., evapotranspiration, condensation, transport) that are difficult to evaluate at a global scale.
So, models don’t simulate the real world, because, well, it’s too darn hard.
But, they are great and we should rely on them anyway.
🙂
They are saying the extremes are wrong, but because the average of them is close to the average it makes it correct. Beyond illogical, it assumes the average is correct, which is not possible because of totally inadequate data.
That sums up everything about the IPCC though, doesn’t it? Taking a bunch of provably incorrect assumptions and claiming the average of all these has some validity. Sigh.
This year, with average rainfall, we had a better growing season. Why? Less evaporation. While that is not rainfall (or moisture falling), it still affected the over all health of the plants.
Too many questions, too few answers.
Thanks to Dr. Tim Ball for a very informative analysis.
Lots of good points but the “climate scientists” are not listening.
I have come to the conclusion that they can safely be ignored at this point and the variability of the REAL DATA shoved into their face like a fresh dog turd will ultimately force them to eventually say uncle.
And that’s all I have to say about that !!
When Dr. Ball was invited to speak in our little town, he was the first one to open my eyes to the now glaring inconsistencies in the AGW argument. He was a climate scientist long before it became the flavour of the month and he still has a lot more to offer than many of the nouveau experts. That was a nice discussion on the complexity of water, much more thought provoking than the usual hydrological cycle talk that the hydrologists give.
If you sleep with your feet in a freezer, and your head in an oven, and think that is comfortable, on average, I guess you work for our planetary overlords in IPeCaC ( love the acronym), and are, therefore either stupid (P<0.001), or determined to keep at least your front trotters in the trough.
Look, we all 'know' the science is settled.
The good Doctor Ball has merely highlighted minor areas (water molecules are pretty small, right? Three atoms) where the certainty might be increased over, umm, 'Dunno'.
No, that doesn't quite convince me, even, that we know all there is to possibly know about the atmosphere – never mind interaction with the oceans, climate, and other possible factors.
Auto, in an airport.
PS planetary overlordism; I believe is is not hereditary, but can find no sense it is democratic, either. Is it sort of assumed, or put on, like a watermelon costume?
It is – not ‘is is’! The review function is greatly missed! Auto ( in error)
David from the UK, you are so right on. From a pure logic point of view if the models are based on reality and one of them has the physics of the world closer it should produce more accurate results most of the time. It should be possible to say that one model is working better. However, as has been shown in papers it has been shown that given one model that works best for a specific time period, that same model will not work for any other time period. Each model by itself apparently simply does a better job at some situations or part of the curve than others. Yet they have no criteria for determining which model does that for what part of the curve therefore they have no basis to say any of the models represent any physics whatsoever of the whole system. This makes the models “fits to the data”, i.e. each model is simply one complex expensive way to fit a curve to a mathematical computation. If you average all such approximations it is no wonder you get something that overall fits the assumed data for the mathematical fits that they all are trying to fit. Therefore the average of the models produces something that looks to fit the given dataset better than any one model. When confronted with this the modelers will insist they are NOT fits to the data. Obviously spending billions of dollars to do a fit would be a colossal waste of money.
Unfortunately, because these are provably no better than fits and don’t actually represent the physics of the system it means that the models cannot predict anything outside of the data they are given. Like any model it would have to be shown to match the next data point to be taken seriously. Any such model should be taken with vast skepticism because there is no reason to believe that a purely mathematical exercise would result in a provable result until it was validated with REAL NEW data that had not been fed into the fitting machine beforehand. Unless one shows that the models produce results that match future data there is no certainty that the models are anything that a lucky fit to some data. In 1998 after most of the models had been constructed they all uniformily showed a continuously rising temperature eventually hitting close to 3C higher in 2100. Unfortunately in 1999 the real data took a completely other direction and went flat. Nothing more could prove the validity of my analysis above. The instant “real” “new” “reliable” data became available that could be used to test the validity of any of the fits the data went the other way. This is virtually certain proof that the models DON”T represent the physics of the system. The fact that the average of the models and not any one model in particular fits the data virtually proves that the models are nothing more than fits to the data. The fact that they are fits means the fact that they fit the data they were given is a tautology and means nothing. The fact that when you average them they produce a better fit to the data is actually damning becuase it virtually proves that all we are dealing with here are fits to the assumed data set fed into the models to build them.
The expose about how the models don’t accurately model or consider a major major factor that is 10-100 times more important than co2 is simply icing on the cake that has been obvious for more than a decade.
The IPCC is a logical fallacy. It’s so obvious to anyone who studies this to any depth. If the models were to work it would be sheer luck. Obviously the fact the divergence started virtually the day they had the models in place and that the real world data did not conform at all to any of the models after the data that fed the models clearly shows that the almost certain case is that these models are just fits to a data series they were handed prior to 2000. A very expensive “fit” as it has taken billions and billions of dollars to accomplish a “poor, very poor” fit to data that almost any graduate student in numerous mathematical sciences could have done fat better with in a week of work.
It’s been shown that if you want to fit the data using 2 sin functions and a couple constants that you insert plus a few measured data you can find a fit to the data that is %1 accurate over the entire time period of thousands of years. Of course this all depends on what data you assume, for instance if there is a MWP or …
Lastly it is clear that the only data that is important is the data post 2000 because this is the data that hadn’t been inserted into the models already. It is as if somebody came up with an equation to predict the movement of a ball but having no knowledge of the things the ball might encounter next your model is only as good as the data you feed it. The equation you construct as the model would work up until the data you fed into the model but since you don’t know in advance what the ball might run into next, what surface it might be on or what other factors might affect the ball the very next data point may be wrong. Well, that’s the bad luck the IPCC had. They built models and the very next data point the data decided to go flat and their models showed a continuously accelerating curve upward. It’s almost funny how stupid it is. It’s like they have no idea what affects the climate and when it fails to work they keep saying just wait, the ball will return to course we are sure of it with 95% certainty because it followed the path we input into our models before with 95% accuracy therefore this deviation is just an anomaly. No, you don’t have any idea what is happening with the ball because you don’t have a clue about the environment the ball is in.
So what fraction of global surface temperature measurements are (potentially) affected by the local presence of snow and ice (as well as liquid water)? I presume the temperature record must show some kind of signature where the daily minimum or maximum is frequently close to zero Celsius.
Thanks, Dr. Ball. Very interesting article.
“The Water Cycle is a more critical mechanism than the Carbon Cycle.”
Yes, exactly!
I was surprised to read that satellite pictures of global water vapor distributions “still have measurement errors of about 30 to 40%”.
One thing to add is that the dry pan evaporation rate is tracked as well.
As for the climate models, those that rely on finite element analysis, they are probably mathematically flawed. This kind of analysis will only produce good results if the elements selected are continuous internally and processes are continuous across them. Real weather is not continuous.
Re condensation during a dry spell:
“…Deposited at the surface at night meant reduced evaporative loss. More was available to replenish soil moisture in the root zone. All this occurs below the Stevenson screen…”
Tim, I had a mixed farm in the 1970s-80s and I discovered something very interesting in just the type of situation you describe (hot dry days, cool dewy nights). Not only do you get this valuable watering of the crops, but the crops themselves are unexpectedly proactive in such extremes. One pre-dawn morning, up starting the chores, I noted that a row of cabbages’ outer leaves had exfoliated and were hanging down from their stems like hands, each with about a quarter of cup of water in them. When the sun had fully risen, the hands raised up, poured the water down the “U”-shaped channel of the stems and neatly watered the cabbages directly at the roots. There was a neat annular ring of moist soil around each plants root.
In the cornfield, the leaves were straightened out like swords sticking up at the sky at 30 to 45 degrees to the corn stalk and concavely curled across the top surfaces to form a channel. The dew similarly was efficiently channeled toward the stalk, where it ran directly down to the roots, once again leaving a dark moist ring of moisture around the base of the plant. There can be no question that these plants (and probably all plants) must have evolved through unfavorable extremes of weather to have such magnificent tools their workshops. These observations then led to explanation of the overlooked mechanism of the corn when there was copious rain. The leaves then curved downwards to funnel the water away from the roots. The plants are telling us that all these “unprecidented” conditions are a load of blarney.
Probably there is something in the agronomic literature on this stuff and like a lot of specific knowledge it has been observed millions of times, to be sure, by those observant folks going about their daily labors. Tell me it isn’t actually my discovery
Maybe this is nothing but over on another thread I commented the following.
The paper covers climate variability in the Western USA over 3,000 years and emphases the role of precipitation. I’m guessing that the area covered Michael Mann’s location but the authors might have overlooked him in error. Or maybe I missed his last name via using edit find. I had earlier mentioned that maybe his Bristlecone pines measured rain instead of temperature since the hot year of 1934 shows a down spike with Mann.
They don’t want to measure water vapor because they know that water vapor is a negative feedback. For them it is far far better to just make the assertion that water vapor is a positive feedback and then ignore the subject.
So… informally, it use to be: If CO2 increases, the Air will be wetter and cause the Earth to burst into flames.” And now it is: If CO2 increases, then we have no clue if the Air will be wetter, but humans will cause the Earth to burst into flames anyways.
I can’t seem to find in AR5 where the IPCC says words to the effect that “Water vapour is the most important greenhouse gas, and carbon dioxide (CO2) is the second-most important one”. Maybe it’s there, but I can’t find it yet. I agree that the IPCC really does not want your average Joe to know that water vapour is the most important greenhouse gas. However, don’t give ammunition to our foes. If I have misread your post then please accept my apologies.
Dear Dr Ball,
thank you for your reference to Geigers ” Climate Near the Ground”. It should be mandatory reading for anyone with an interest or expressing an view on climate. Not only does it refer to careful experimentation ( in orchards, meadows, forests inter alia) but shows the complexity of climate at a single site neverminding of attempting to model a global cliamte.
When I was station in Hawaii back in the 60’s I was told that some of the Hawaiian islands have no streams/rivers, yet they raise cattle there. The livestock get their water from the dew on the grass in the morning. And, as in this article, the grass is “watered” by that “dew” when there is no rain fall. If true, there are probably many other mid ocean islands that are “green” because of this action.
Here is a small selection of precipitation and other projections / predictions / story lines / fairy tales / what if’s / scenarios / and so on…………… from the Calamastrology model runs. You will see the following results from global warming: Amazon to be greener and browner, East Africa to get more and less rain, Indian monsoons to be wetter and drier, Sahel to get more and less rain, soils to be more and less moist and so on…………
What a great career, apply for a CAGW bit of research, get your money, press a button, publish and say we need more
moneyresearch. Ho, ho, ho.Dr. Tim Ball,
You were one of the first to expose the truth on the now well known fraudulent climate science and positions of the IPCC and other alarmists.
What others who have not heard you speak may not know is that you are an outstanding communicator……………If only we could have a series of debates between you and Al Gore(or Michael Mann).
On your article. I made a comment earlier regarding evapotranspiration that is more appropriate here. As an operational meteorologist for 32 years, trading in the agricultural and energy sectors of the commodity industry, I have noted an increase in the amount of low level moisture/water vapor as measured by dew points in the US Cornbelt during the growing seasons vs 30 years ago.
This appears to be mainly the result from the increase in evapotranspiration as technology and improved hybrids/genetics, along with other factors now allows us to have plant populations for crops like corn(very dependent on water) that are double what they were 30 years ago.
During the growing season, this has created a sort of micro climate at times that can span an area the size of several states and feature dew points up to 5 degrees higher than what was occurring 30 years ago, all other variables held constant.
The increased water vapor results in lower lifting condensation levels and clouds lower in height (cumulus) that form earlier in the day from diurnal heating. This not only blocks short wave radiation from heating the lower levels but clouds at the lower levels radiate LWR more effectively than those at higher levels because of the higher temperature of the low level clouds.
This evapotranspiration increase as a result of agriculture is occurring to some degree in many locations as other countries have also increased plant populations.
In addition, the explosive growth in the natural biosphere and vegetative health/greening of our planet from CO2 fertilization is no doubt contributing to additional evapotranspiration.
The amount is impossible to measure or even estimate with any degree of confidence.
This additional water vapor from evapotranspiration not only effects clouds and cloud height but becomes part of a change in the complicated water cycle, with contributions to precipitation, soil moisture, dew/frost and so on that are impossible to model accurately.
Many of these are likely negative feedbacks for temperature but positive feedbacks regarding moisture(increased water vapor from evapotranspiration might mean heavier morning dews and plants that evapotranspirate at a greater rate that day)
Even greater, would be the atmosphere’s use of the increased water vapor from evapotranpiration in rain events that as you indicated, are rarely uniform or captured with a high degree of accuracy when the disparities are great/erratic over short distances.
An increase in evapotranspiration means rain events capable of even greater disparities as the higher end is potentially increased, while the potential lower end is always zero.
Water…my favorite compound.
Air Psychrometrics has a lot to do with water;
http://www.engineeringtoolbox.com/air-psychrometrics-properties-t_8.html
And my favorite Phase Diagram:
http://en.wikipedia.org/wiki/File:Phase_diagram_of_water.svg
Thanks for the interesting articles and comments.
Measuring the total heat/energy within the ocean-atmosphere system, over time, is not something that human beings can, as yet, really even begin to tackle. The fact that imperfectly measured temps have been allowed to stand in for the total energy of the system is an absurdity, one I wrote a letter to the black hole I mean L.A. Times Letters section recently. Couldn’t help myself.
” … to the accuracy of early measures ±0.5°C.” This is inaccurate. The recording accuracy until recently was ±0.5°C. The overall accuracy was much worse.
@ logiclogiclogic says:
October 22, 2013 at 2:34 pm
Jeez, dude. I figger there are 2, possibly 3, statements in your 6 paragraphs which are distinct. The rest is repetitive efforts to find a clear wording for your 2 or 3 points. Mind-numbing. Prolix. Wordy.
An exercise: strike out any sentence, at random. See if the same point is made elsewhere. Continue until you always get the answer “No”. I predict less than 10% of the word count will remain.
Dr. Ball;
Your observations re dew point are very interesting, and suggest to me that a very interesting analysis might be possible using it as a “dependent variable”. The lever that plants would use to control it would be presumably latent heat of condensation/evaporation.
A huge factor is the massive(trillions of tons) amounts of aquifer and deep underground reservoir water that has been pumped to the surface and used to irrigate crops and supply the main water supply for a significant portion of the worlds population.
Enough water comes out and makes its way to the oceans so that some estimate its contribution to sea level increase as being greater than glacier melt.
It’s almost impossible to measure accurately the amount that comes out and very impossible to be able to dial in an amount that represents what the irrigated farm vs the unirrigated farm contributes to immediate evaporation and evaporatranspiration or the amount left behind in the soils, some that may stay awhile and or seep deeper into ground water table. Some of that water will become runoff and increase stream flows.
Much of it is clearly making its way into the oceans according to the most recent estimates of sea level rise from this source. An amount, probably small but impossible to know will also be coming from increased precipitation over the oceans from previously evaporated aquifer water.
1. A year or so ago there was discussion in RealCimate about the fact that many (but not all) climate models do not maintain a true water balance; of the water that evaporates from the oceans some dissappears into a numerical black hole. As condensation/evaporation of water is also a major energy exchange this has implication for all the model results.
2. “Furthermore, the stations that do exist often fail to report.”
This is very true. From my own experience of working with met data from more than 40 tropical countries there are three main problems:
– many stations that used to operate have closed down,
– of those stations which do operate, data is of doubtful accuracy or irregularly (some times just a few days a month) reported,
– some countries have periods when internal strife or civil wars led to extended periods when no data were recorded.
For those interested in rainfall measurement in the southwest:
http://rainlog.org/usprn/html/main/maps.jsp
Dr Ball, thanks! Hydrological considerations are the 800 pound gorilla in the (climate) room, so to speak.
Dr. Tim, thanks as always for an interesting post. You are quite correct about how little is actually known about all the tricks that water plays.
I was glad to see you plug “The Climate Near The Ground”. I’ve referred to it as “my bible” for a while, and it is indeed the definitive text on the subject.
Keep up the good work,
w.
I took an agricultural college class in Irrigation, learning how precipitation is measured, and different systems for irrigating crops including ditches, sprinklers and the Israeli-invented Drip Irrigation, which puts the water into perforated hoses that leak water directly to the plants. Drip irrigation is the most efficient–but you don’t see it.
Sprinklers are the most wasteful method of irrigation, and that is what you see the most of. I think agricultural sprinklers should be outlawed. They cause the USA to violate water treaties with Mexico, and they harm our aquifers, which eventually will mean famine. It’s not all that hard to fix these violations. The technology is there.
One of the fun things about rainfall is that it is not normally distributed. A log-normal distribution is quite a reasonable model, if you want something without too many parameters to work with.
Immediately Stats 101 tells you that the arithmetic average is a poor measure, because the mean is way above the mode. Yet the weather people use the arithmetic average – so you will always tend to get less rain than the reported average! Then along comes a so-called “extreme event” – it isn’t extreme, it just comes from the upper end of a very skew distribution.
I have been reviewing some attempts to determine trends in rainfall, and every one of them has made the same mistake. If you go to the raw data, and work from the actual distribution, then the apparent trends disappear.
Another thought spurred by Tim’s post arose from “The IPCC say,
The spatial resolution of the coupled ocean-atmosphere models used in the IPCC assessment is generally not high enough to resolve tropical cyclones, and especially to simulate their intensity.”
It is not difficult to work out the energy involved in a tropical cyclone. A mass of air is accelerated from a certain distance out towards the eye of the storm, then rises vertically. The dimensions can readily be approximated. In a small cyclone, if you get up to horizontal wind speeds of only 100km/h in the eye, the energy dissipated approaches PJ levels – instantaneously, as much energy as mankind generates. A decent-sized hurricane, like Sandy before approaching land, is Really Big – yet the IPCC models miss it. So by the simplest of measures we know that the models are wrong. Do we really have to argue about how wrong?
Lady Life Grows says:
October 22, 2013 at 9:58 pm
After you outlaw sprinkling, please come & show us in the 19 American states which rely on sprinkling irrigation how the drip irrigation which you learned from a class in college & watching Israelis grow boutique crops can help us better to feed the world on hundreds of millions of acres of wheat, corn, potatoes, beans & what have you. We’ve only been doing this for 150 years & could benefit from your superior experience & intelligence. Thanks.
Volcanoes excluded, there is no place on earth that is too hot for humans so long as there is fresh water. In contrast, no matter how cold Al Gore and the IPCC try and make the world, without water humans cannot survive.
The Iceman Cometh says:
October 22, 2013 at 10:47 pm
Immediately Stats 101 tells you that the arithmetic average is a poor measure, because the mean is way above the mode. Yet the weather people use the arithmetic average
============
In climatology the correct statistics are those that give the answer you want. Cherry picking the methodology is much harder to spot than cherry picking the data. It should come as no surprise that the majority of scientific papers that pass peer review are later found to be unrepeatable.
But, they keep saying, the models are based on physics! yeah, except for the parts that aren’t. You can’t model what you don’t understand. The idea that radiative transfer is all we need to know to model the earth’s climate (and impacts) is so stupid it boggles the mind.
LadyLifeGrows:
Sprinkler irrigation has two important benefits which drip irrigation lacks. First, the falling force of the water droplets allows them to penetrate more deeply into the soil, driving dissolved saline deeper than drip irrigation water, and reducing salt buildup. Second, the evaporation from sprinklers cools the plants and air, allowing farmers to plant winter crops earlier in desert climates.
Great essay by Dr. Tim Ball.
I’ve been waiting a long time for someone with “acceptable” credentials to write commentary such as that was.
Here following is commentary I authored several years ago and “posted” on discussion Forums only to be told by the silly proponents of CAGW that I didn’t know what I was talking about because I was not a Degreed Climate Scientist currently working in that field and that I had never ever published any peer-reviewed papers on the subject of Climate Change. To wit:
————
Clouds, fog and mists are all forms of water vapor which have collected into larger “droplets” of water and are visible to the naked eye, …. and are the same as humidity which can not be seen with the naked eye. And that is because of the density of the larger “droplets” of water and the fact that any source of light that strikes them will be absorbed more readily and/or reflected away from them more easily.
But now the effects of clouds, fogs and mists relative to incoming solar energy and re-emitted IR energy from the earth’s surface ….. are quite different (extremely more pronounced) than the effects of humidity. Again, this is because of their density (mass).
Clouds, fogs and mists act as a unidirectional buffer to both the incoming solar energy and the re-radiated energy from the earth’s surface. And the best way to explain this is by examples.
Night time cloud cover or fog will prevent near surface air temperatures from cooling off as fast because they per say buffer the re-radiated energy.
Day time cloud cover or morning fog will prevent near surface air temperatures from warming up as fast because they per say buffer the incoming solar energy.
And this conundrum is what confuses the ell out of climate scientists who are trying to calculate “average surface air temperatures” ….. and which wrecks havoc with their Climate Modeling Programs ….. because it is such an important but indeterminate variable. ……. And thus, because they can not accurately calculate their affect, …… they completely ignore and omit said from any of their calculations …… and attempt to CTA by blaming everything on atmospheric CO2.
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In closing, ….. I do not believe it is possible for anyone to measure the warming effect of the lesser quantity of gas (CO2 @ +-390 ppm) in a mixture of two different gases when the quantity of the greater volume of gas (H2O vapor) is constantly changing (5,000 to 40,000 ppm) from hour-to-hour and day-to-day. Especially when said greater volume of gas (H2O vapor) @ 4% has a potentially 200+ greater “warming” potential for said mixture than does the lesser volume of said gas (CO2) in said mixture.
Thermal energy in the atmosphere propogates via by both radiation and conduction.
@ferd berple excludes volcanoes and i think they might be included.Heat from any source
can stimulate cloud(water) dynamics.
http://en.wikipedia.org/wiki/Pyrocumulonimbus
Thanks again for the interesting articles and comments.
PS-and i know we can’t live in a volcano , but, IMO,they contribute to the overall physics of our planet.
Yes, the closest they seem to get to these issues is cloud albedo. Vastly more is involved! Can you imagine a cartoon diagram encompassing sea fogs?
BTW: per se
wreaked havoc
Brian H says:
October 23, 2013 at 10:59 pm
BTW: per se
wreaked havoc
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HA, I’ve been writing “per say” for so long ……
If I used “per se” I would have to re-write the sentence because my use of “per say” is in reference to the verb “buffer”, …… as in “buffering action”, …. not in reference to the clouds or fog.
Brian, my being an old computer designing dinosaur I don’t have a problem with “coining” new words with a specific definition …. or …. “coining” a new definition for an already existing word.
Iffen you all “nit picked” all the computer terminology now in use like you do my use of “per say”, …… lord a mercy, ….. the personal computer industry would never have gotten off the ground.
And ps, you are right about that cartoon. Sea fogs, lake fogs, river fogs, valley fogs, etc., are probably far more abundant in many locales than cloud cover, especially during the fall and winter months.
I live bout 2 miles down stream from a large flood-control dam and some mornings I don’t see the Sunshine until way past 9 AM. And that lake fog prevents it from “frosting” here.
And Brian, it is the fog that gave the …. Great Smoky Mountains …. their name.
http://en.wikipedia.org/wiki/Great_Smoky_Mountains
“Per say” is polyglot nonsense, borne of a kind of mal-othographic malapropism. Truly ugly, IMO.