Wednesday, June 30, 2010

Swiss Re on sceptic arguments re AGW

A comprehensive refutation of sceptic arguments from an unexpected source - Swiss Re, a reinsurance firm. Here is their topic list:

Thursday, June 24, 2010

Index of posts by category


An interactive topic index for all Moyhu posts.

A full chronological listing is given below. But you can click on any topic in the table to generate a list of posts, in reverse time order, relevant to that topic.

Topics

TempLS v1TempLS v2 StatisticsTime SeriesSpatial distribution
GHCNGHCN V3 GSOD BESTCRUTEM3
GCMsSST, HADSST3R, RGHCNV3Fourier Transforms Navier-Stokes,Fluid
Temp MeasTemp Indices Recent ObservationsPaleo, ProxiesWegman
PhysicsThermodynamics Greenhouse Lapse Rate AGW
Clouds OceanArctic, JAXAAntarctica Venus
GraphicsHTML 5JavascriptKML - Google EarthCondensation
BloggersHousekeepingEvents

Posts relevant to topic (newest first)

A list of posts in time order (newest first)

Interactive JS plotter data listInteractive JS plotter Ver 2
GISS Temp for March 2012 - comparisonUS temperature trends.
March 2012 temperatures up 0.1°CInteractive climate plotting news.
Autocorrelation, regression and temperaturesInteractive JS climate plotter (update)
February GISS up 0.05°C - comparisonFeb TempLS temperature down 0.05°C
Lindzen's misrepresentation at House of CommonsMiracles of 2LoT
Hansen's 1988 predictions - a JS explorer.Combined GMST trend viewer.
GISS Temp for Jan 2012 - and TempLSJanuary 2012 TempLS down 0.14°C
GHCN V3 homogeneity adjustments - revised data. A study of GHCN V3 homogeneity adjustments.
Visualizing 2011 temperature anomaliesReykjavik and GHCN adjustments.
December 2011 temp, small changes for NOAA, GISS...Lapse rates and entropy.
December TempLS surface temp - little changeCherrypicker's guide to station trends
Merry Christmas - and a new galleryNovember GISS is out - down 0.07°C from October
Significant trends in Foster/RahmstorfSignificant trend differences
Google Map problem fixedNov temps displayed with HTML 5
A Javascript Gallery Google Maps display of GHCN stations, with Javas...
Giss, TempLS and the October GMSTObserved SST and model trends
A picture of statistically significant warming.A numbers puzzle.
A JS gadget for viewing temperature trends.GMST trends - a cherrypicker's guide.
Stopped warming? Paused?GWPF is wrong, warming has not stopped.
A Javascript index for MoyhuBEST has same data as GHCN pre-1850
World coverage by decade of BEST, GHCN, GSOD and C...A combined KMZ file for BEST, GHCN, GSOD and CRUTE...
A KMZ file for the BEST stationsThe Berkeley Surface Temperature (BEST) analysis
GISS Sep 11 - down 0.13°CSeptember GMST - TempLS down from 0.444 to 0.42
FeedbackA faulty tapering
Green's Functions and Dessler's dataFeedback, frequency and Bart's comments at CA
August GISS - Very small rise - TempLS map compari...FFT, impulse response, clouds, GMST
Impulse responses and the Spencer/Dessler Cloud mo...August GMST - TempLS no change
JAXA Ice extent and JSA Javascript worldview for surface temp.
GISS spatial map, July 2011 Comparisons of TempLS with reader MP's July 2011 ...
GISS July temp up 0.09°C.Global surface temp for July - no change.
Global surface temperature coverage.Cell weighting schemes for the Earth.
First TempLS CRUTEM3 reconstructions.Missing Lat/Lons in CRUTEM3
More on CRUTEM3 stations in Google EarthCRUTEM3 stations in Google Earth
A first look at CRUTEM3 station dataUsing RghcnV3 - a very simple TempLS
TempLS V2.2 and the June 2011 global averageNOAA for June - up from .497 to .579
Global surface temperature reconstructions with th...Updating Arctic ice and global temperature data
More proxy plots with JavascriptMore proxy temperature reconstruction plots
Northern Hemisphere proxy plots - last milleniumSteven Mosher's GHCN V3 R Package
Time series plots - using animationMore visible time series plots
Cheerful colors for time seriesEffect of selection in the Wegman Report
Chladni patternsLatex now works here
The Woody Guthrie Award - Bart VerheggenQuiet time
TempLS Ver 2.1 releaseBlogger's spam filter
What's it like to run out of data?Keith Briffa and the Renaissance
Trends in Antarctica Area weighting and a 60 stations global temperatu...
Area weighted trends for AntarcticaNew Blogger - Isaac Held
Voronoi weighting of temperature stationsSpatial weighting and Voronoi tessellation.
Antarctic, RO10, Steig and TempLS.Ryan's code - testing.
Ryan's code - S09 with more PCsRyan's code
On wordsTropical Cyclone Yasi
Lisbon MeetingAway
Hadcrut is in - final temperature summary for 2010...On ;Mannian; Smoothing
GISS shows 2010 as record hot for land/ocean - NOA...RSS posts December average - 2010 lagged 1998.
On greenhouses and the Greenhouse Effect2010
Merry Christmas and happy holidays to allAcidification
New text and file repositoryWoody Guthrie Award - thanks, Science of Doom
Condensation - the Eulerian ViewCondensation and expansion
A ramble on GCMs Navier-Stokes equations.Wahl & Ammann proxy calculations
Can downwelling infrared warm the ocean?An entropy budget for the Earth
Beta version of GHCN v3 is out.GISS for August 0.53C, same as July
A very different gridding method - TempLSTempLS V2 Math basis
TempLS Version 2 ReleaseNew Blogger.
Hottest year? 2010?Warming trends in the Himalaya
Underestimate of variability in McKitrick et al Reduction of station numbers in GHCN
GE visualisation of changes to GHCN stations 1990...GHCN KML visualisation by years
If it's hot in Washington, how about Montreal?Still more KML and Google Earth
More Google Earth station dataFun with Google Earth, GSOD and KML
Spatial coverage of the GHCN and GSOD station sets...Monitoring global temperatures daily.
Global Land/Ocean - GSOD and GHCN data comparedGreenland and GSOD
Revisiting BoliviaArctic Trends using GSOD Temperature data with Tem...
Using TempLS on an alternative land temperature da...Jaxa Arctic Ice data and UAH AMSU temperatures
Swiss Re on sceptic arguments re AGWIndex of posts by category
Venus temperatures and the adiabatic pump.A success for open coding!
A temperature data collection for comparisonsFallacy in the Knappenberger et al study
What a difference four months makes!The Greenhouse Effect and the Adiabatic Lapse Rate...
Scale of spatial correlationJust 60 stations?.
Ver 2 - Regional spatial variation.Spatial Temperature distributions in TempLS v2
Plotting spatial trends in TempLSAn update on global land/sea reconstructions
The ;Bolivia effect;V1.4 with maps, conjugate gradients
The Math description of TempLSTempLS - what's been happening, and where it's goi...
TempLS Version 1.3 - SST, Land/Sea index, distanc...Big City Trends
Incorporating SST and Land/Ocean modelsThe impact of war on GHCN observations
Continents and trendsLatitudinal temperature histories and trends
Ver 1.2.1 - Various resultsVersion 1.2 of GHCN processor
Tips on File DownloadMore GHCN results.
Comparison of GHCN resultsGHCN processing algorithm
On Polynomial Cointegration and the death of AGWA blatant fiddle in the D'Aleo/Watts SPPI report.
GISS UHI adjustmentsIrregular updates to GHCN
Updating GHCN - Stations aren't ;dying;Testing the performance of GCM models
Carbon dioxide feedback.GHCN Stations warming?
GHCN Station selection.What if there were no Greenhouse Effect?
2009 Darwin and GHCN Adjustments












Wednesday, June 23, 2010

Venus temperatures and the adiabatic pump.

Science of Doom discussed recent theorizing on the causes of high temperatures on Venus, as did Chris Colose. On both blogs, Leonard Weinstein proposed a thought experiment in which a shell was placed over Venus - he believed that the high surface temperatures would remain. SoD took up this idea here, and there was further discussion. This post sets out my ideas on the problem, with some background.

It follows my earlier post on Venus and lapse rates, and a post from a while ago on the adiabatic heat pump.


Heat transfer mechanisms

This may seem elementary, but in the atmosphere, some aspects of heat transfer are different:
Conduction
Molecular conduction carries very minor flux, in accordance with Fourier's Law.
Radiation
Transmission of thermal range IR in an atmosphere like Venus is not like transmission in a vacuum. At most wavelengths the absorption length L is fairly small relative to the depth of the atmosphere. However, heat balance requires that absorption is balanced by emission. This gas-to-gas transmission is very dependent on the temperature gradient.

The Rosseland model of IR transmission works for high opacity gas. If you simplify by neglecting scattering, then
F/F0 = (16/3)*(L/*T0) ∇ T
where F0 is BB (black body) emission at ambient T0, F is flux.
This is a Fourier Law with a conductivity that can be quite high. For example, if L is 1 km, T0=400K, the flux is about 0.13 times BB flux.

You can visualise how this transmission works. Imagine yourself with IR vision in such an atmosphere with a lapse rate. The gas below is hotter than the gas above. If you hold out your hand, it's warmer below than above. There is a nett heat flux upward.

How much? Well, it's proportional to the lapse rate, which determines the temperature difference that you see. But it depends on the absorption length too. If that is higher, you can see further, and see hotter (and colder) gas, because of the linear lapse rate. This effect is proportional to L, hence the Rosseland expression.

As L increases further, things change. Looking down, the gas gets denser, so you can't see as far. The hotness increases less than linearly. Looking up, you see further. The coolness increases more than linearly. They cancel somewhat, so changes to the flux are second order in L. But eventually there is a deviation from Fourier's Law. And if you see far enough, the ground will have an effect too.

Eventually, when L is large relative to the depth of the troposphere, a substantial fraction of IR is transmitted without any absorption. This is an atmospheric window, and the relation between transmission and lapse rate diminishes.

But up to that point, Fourier's Law is a good way of thinking about IR transmission.

Convection

This also has a wrinkle. On a room scale, turbulent convection is also often thought of as obeying a Fourier Law. Gravity-forced changes of pressure are negligible. The adiabatic lapse rate is about 0.01 C/m.

However, on the atmospheric scale, compression with gravity is important in modifying the temperature. Compression warms. Fourier's Law applies not to the gradient of temperature, but of potential temperature:
θ=T(P/P0)^(-ν)
where ν=R/cp and P0 is a reference pressure.

The important thing here is that unlike with conductive and radiative transport, convective transport is zero at the dry adiabat lapse rate, rather than zero gradient, and is proportional to the difference between the actual lapse rate and the adiabat. Below the adiabat, the gas is convectively stable, and energy is used making it go up and down. Above the adiabat, it is unstable, and the temperature gradient adds energy to the motion, and accelerates the transport.


Atmospheric fluxes


Now that we've outlined the modes, and agreed to ignore conduction, we have a downflux of sunlight on Venus, after allowing for albedo, of about 160 W/m2. Not much of that actually reaches the surface - it is absorbed at various depths. But once absorbed, the heat has to get out again, and the modes available for it to reach the tropopause to be radiated away are IR transfer and convection.

Suppose the convective component were small. The lapse rate would then be determined by a Fourier Law, as given by the Rosseland model. This might be more or less than the adiabat.

If more, then the gas would be convectively unstable. Motions would be induced which would increase convective transport. Since the overall flux is determined by the sunlight, that means the proportion carried by IR would reduce, lowering the lapse rate. This argument, made by DeWitt in the first SoD thread, gives a mechanism whereby the adiabat lapse rate cannot be much exceeded.

But there is an analogous argument from below. If the IR flux caused a temperature gradient less than the adiabat, then the gas would be convectively stable, and it could remain at that. However, there are likely to be other effects inducing motion. Polar regions (and the long nights on Venus) emit more heat than they receive - sunny regions emit less. This heat has to be transported by the atmosphere, and the temperature difference drives a heat engine. The resulting circulation conveys the heat.

But the motion then affects vertical convection. I described earlier how motion pumps heat downward. This then augments the heat that must come up conveyed by IR. This in turn increases the lapse rate towards to adiabat. In doing so, it extracts KE from the air to drive the pump.

Leonard Weinstein's problem



At SoD, Leonard Weinstein proposed a thought experiment where a shell was placed around Venus at about the altitude where IR is currently emitted to space. This is high in the troposphere, where the temperature is about 230K. The shell is opaque to all radiation, but a good conductor. He contended that the temperature profile in the atmosphere would remain much the same. My initial thought was that it would become isothermal, as did others.

I now think a reasonably quantitative analysis is possible. About 160W/m2 sunlight penetrates the atmosphere, and is balanced by an IR and a convective flux. I don't know how much is radiative, but the total is known.

The shell blocks that 160W/m2. If the atmosphere remains the same, then the same IR transport upwards must continue, because it is determined by the unchanged temperature gradient. The convection would also continue, since it is determined by the gradient and the motion. But there's no longer heat supplied, so something has to change. Net upflux must go to zero.

The lapse rate must drop well below the adiabat, to ensure that convective flow is downward, by the heat pumping mechanism. If it does, there will be a balance when the downflux matches the IR upflux, giving the required zero. But how low would the lapse rate go?

Heat pump arithmetic


In this scenario, the heat pump must pump downward about 160 W/m2 than it did before the shell. It has to pump it to depths comparable to where sunlight formerly penetrated. Figures get rough here, but let's say the average depth is where the temperature has risen from 230K to 460K.

Thermodynamics tells us the energy needed to do that. It's 160*(δ T)/T, or about 80 W/m2. That comes from the KE in the gas, which must in turn have come from a heat engine somewhere.

But the equator to pole differential, which is a promising source of energy for a heat engine, is much too small. The actual flux of heat redistributed horizontally can only be a small fraction of the 160 W/m2 arriving. And the temperature differential, even with reduced circulation, is never going to match the factor of 2 difference between surface and depth ( without a shell, it's very small).

In fact, there just isn't that sort of energy available anywhere. It's half of total average incoming solar.

So what would happen?


If the convective heat pump can't replace the blocked sunlight, the lapse rate must just reduce until the IR Fourier's Law flux can be matched by the very limited energy available from a realistic heat engine. This needs a rather elaborate but doable calculation which would consider differential insolation over a sphere, and a plausible temperature differential around the shell. The resulting regional discrepancy between sunlight in and IR out would determine the horizontal fluxes, and the temperatures would determine the energy available as KE to the atmosphere. Then some unknown fraction of that could pump heat down, making possible an IR upflux and a positive lapse rate.

So a SWAG? Dunno, but surface temperatures warmer than the shell, but Earth-like rather than Venus-like.


Saturday, June 19, 2010

A success for open coding!



Steven Mosher was looking through the code for TempLS v1.4 when he saw something puzzling and asked me about it. And sure enough, it was an error. One of those things a fresh pair of eyes can notice.



It was in the section that takes the station list and assigns them to a cell number, depending on what 5x5 lat/lon block they fall into. It doesn't matter what the number is, as long as the stations in the block have the same number and others have different numbers. The stations with that number are counted, and the sum is used in the least squares weighting.

Here are the relevant lines of code:

d = floor(tv[,5:6]/5);    # station lat and long;

cellnums = unlist(d[1]*36+d[2]+1333);  # 5 deg x 5 deg cells numbered 1 to 72*36=2592;


The first line divides the lat/lon by 5 and rounds to an integer. So each station has an integer pair identifying its cell.

They range from (-18,-36) to (17,35) - there are 2592 cells.

The next line tries to number these from 1 to 2592. Numbering a rectangle array is normally done either by row or by column. The 1333 is an offset to bring them to a positive range. I'm numbering in rows, so 36 should be the number of columns. But alas, there are 36 rows and 72 columns. I made the mistake because lat/lon, on the map, is actually in (y,x) order rather than the conventional (x,y).

I'll post the revised code at Ver 1.41. Ver 2.0 should be out soon.

What is the effect?

Basically, two cells get assigned to every number. It turns out that they are opposite in longitude. This affects the weighting, which is meant to correct for station concentration. When an area is well covered by stations, the least squares process downweights each individual station, so the area does not get undue attention. But by combining with a cell on the other side of the world, this downweight could be out by a factor of two.

I should hastily say that it doesn't mean that temperatures from the other side of the world are modifying the readings. They don't - they only modify the weight given to the readings.

Fortunately, it won't matter for any regional studies. The reason is that there are then no stations included from the other side of the world. The weights are unaffected.

A digression on weighting


Another way of seeing the use of weighting by inverse density is that it does what you would do if you were trying to use the stations in a numerical integral expression. As such. it has a fault when grid cells can be empty. The natural thing to do with an empty cell is just to leave it out, as GISS does, and I suspect the other indices. So did I. But leaving it out, when compiling a global average, is effectively the same as including it with a value artificially assigned to it, which is equal to the global average. And when you look at spatial patterns of anomalies etc, it's clear this often isn't optimal. You might have regions like the Arctic where the cells that do have stations have high (or low) values, but many are missing. Implicitly assigning these to have average value biases the result down (or up).


GIStemp get criticised for extrapolating over wide regions in the Arctic. But it's the best thing to do (in a bad situation). leaving the regions out seems more conservative, but it isn't.


Anyway I've been looking at ways of using irregular triangular subdivisions instead of a regular grid. The idea is that when stations get sparse, you just make the subdivisions bigger. No part of the Earth lacks a representative cover. As I mentioned, sparseness is a problem - here the triangles get big, so the coverage degrades. But it's better than none.

Back to the error

I recalculated some of the main global plots of recent posts. Here old and new are contrasted. As you'll see, the discrepancies are noticeable but not major. Mainly the error made peaks and valleys more extreme. I don't believe the revised data would show out on Zeke's combined blogger plots. At the bottom of each plot the modified trend is shown. The changes are small.


There are sometimes some biggish changes in late 1930's. I think this reflects the fact that these years were hot in ConUS, which were then overweighted in the older version.




Global Land and Sea

Land Only

Global Sea (SST)

Rural stations

61 station subset


Sunday, June 6, 2010

A temperature data collection for comparisons


I have been collecting global surface data series for comparison with models. In the process I've made a big table, which is handy for reading into R. It contains:
1. The surface land and ocean indices - Hadcrut3, GIStemp and NOAA (current to April 2010)
2. The two satellite lower trop indices - UAH and MSU RSS (likewise)
3. A collection of model results for surface air temp (SAT). There are 24 models, using SRES A1B (scenario), and a total of 57 runs.
The table goes from 1850 to 2300, although of course most columns have blanks before and after the real data.

The model run results were collected from Geert's KNMI site using Steve McIntyre's script.

There are two auxiliary files which give data describing the columns, and a readme.txt. They are all in the modeltable.zip file in the document repository.