The number of species at time step t is given in equation E.2:

where and are “expert guesses”, is a scaling parameter and is the temperature change with regard to the previous year.

A brief examination of the formula lets one guess, that the exponential decay given by the parameter will dominate the evolution of species numbers, but let’s have a look. I have assumed a simplistic temperature model: temperature change after 2000 follows an arcus tangens function with an initial slope of 2°C/century and levels off at various . Here’s the result.

As expected, the exponential decay dominates the behaviour of all curves, has rather little influence. By the year 3000 the model rates the number of species at 4% to 5% of todays level. I expect this result to be robust with regard to changes in the temperature model.

• 2010: Application GB1014917.7

  Date Lodged: 08.09.2010
  Title: THERAPEUTIC TREATMENTS
  Applicant(s): Monckton, Christopher
  ***** END *****

• 2010: Application GB1014918.5
  

  Date Lodged: 08.09.2010
  Title: THERAPEUTIC TREATMENTS
  Applicant(s): Monckton, Christopher
  ***** END *****

• 2009: Application GB0915801.5
  

  Date Lodged: 09.09.2009
  Title: THERAPUTIC TREATMENT
  Applicant(s): Monckton, Christopher
  Application terminated on 3rd September 2010
  ***** END *****

• 2009: Application GB0915802.3
  

  Date Lodged: 09.09.2009
  Title: THERAPEUTIC TREATMENTS
  Applicant(s): Monckton, Christopher
  Application terminated on 3rd September 2010
  ***** END *****

• 2008: Application GB0816300.8
  

  Date Lodged: 05.09.2008
  Title: THERAPEUTIC TREATMENTS
  Applicant(s): Monckton, Christopher
  Application terminated on 2nd September 2009
  ***** END *****

• 2008: Application GB0816301.6
  

  Date Lodged: 05.09.2008
  Title: THERAPEUTIC TREATMENTS
  Applicant(s): Monckton, Christopher
  Application terminated on 2nd September 2009
  ***** END *****

Date Lodged: 05.09.2008
Title: THERAPEUTIC TREATMENTS
Applicant(s): Monckton, Christopher
Application terminated on 2nd September 2009
***** END *****

Application GB0816301.6

Date Lodged: 05.09.2008
Title: THERAPEUTIC TREATMENTS
Applicant(s): Monckton, Christopher
Application terminated on 2nd September 2009
***** END *****

Application GB0915801.5

Date Lodged: 09.09.2009
Title: THERAPUTIC TREATMENT
Applicant(s): Monckton, Christopher
Application terminated on 3rd September 2010
***** END *****

Application GB0915802.3

Date Lodged: 09.09.2009
Title: THERAPEUTIC TREATMENTS
Applicant(s): Monckton, Christopher
Application terminated on 3rd September 2010
***** END *****

Here they are:

In both graphics the slope of the “IPCC scenario” as depicted by Monckton is way higher than observed at present. However, if one simply overlays the NOAA data on the IPCC plot using identical scaling one ends up with the following:

The NOAA data corresponds very closely to the IPCC A2 scenario.

IPCC scenario atmospheric CO₂ concentrations can be found here:

http://www.ipcc-data.org/ancilliary/tar-isam.txt

An overlay on Lord Monckton’s plot shows, that he is not depicting scenario A2

In January 2009 the SPPI started publishing its “SPPI Monthly CO2 Report” (since March/April 2010 they seem to be published as double issues). The editor of the reports is Christopher Monckton. In these reports information can be gleaned about the “SPPI global temperature index”, that seems to be the basis for Monckton’s own charts, where he compares “IPCC projections” to “global temperature”. It turns out, that this “temperature index” is simply the mean of a varying pool of temperature anomalies (both terrestrial and satellite, namely HadCRUt3, NCDC, RSS, and UAH) and zeroed to the minimum value over the time period of the plot it is used in. Its pool has varied considerably since January 2009 and its offset depends on the time period of the graph it is used in.

The varying pool of anomalies

All reports include a graph about the 29-year global warming trend. The caption of this graph spells out, which anomaly datasets were used in the “SPPI global temperature index”. Here is a compilation of evolution of the data source attribution of the graph over time.

January through June 2009:

“Data source: SPPI index, compiled from HadCRUt3, NCDC, RSS, and UAH.”

July through October 2009 (NCDC is dropped from the index, no explanation given):

“Data source: SPPI index, compiled from HadCRUt3, RSS, and UAH.”

November 2009 through March 2010:

“Data source: SPPI index, compiled from RSS, and UAH. SPPI no longer uses any terrestrial-temperature datasets, because they have become near-universally discredited as unreliable.”

April and June 2010:

“Data source: SPPI index, compiled from RSS Inc. UAH has not reported data for two months and has been excluded from this graph, but will be relied upon again when data become available. SPPI no longer uses any terrestrial-temperature datasets, because they have become discredited as unreliable.”

How the index is calculated

The first mention of how the index is calculated can be found in a figure caption in the February 2009 issue:

“Using the SPPI global temperature index, which is the mean of two terrestrial and two satellite global mean temperature anomaly datasets,”

Starting in the June 2009 issue the calculation of the index is explained in detail. In the following compilation the changes to previous versions are highlighted.

Our global-temperature graphs show changes in real-world temperature at or near the Earth’s surface. Each temperature graph represents the mean of two surface and two satellite datasets: the monthly surface temperature anomalies from the Hadley Center in the UK and the National Climatic Data Center in the US, and the lower-troposphere anomalies from the satellites of Remote Sensing Systems, Inc., and of the University of Alabama at Huntsville.

On each graph, the anomalies are zeroed to the least element in the dataset. For clarity, the IPCC’s range of predictions is zeroed to the start-point of the least-squares linear-regression trend on the real-world data. Since late 2001, global temperature has been falling fast.

From the July 2009 to October 2009 issue:

Our global-temperature graphs show changes in real-world temperature at or near the Earth’s surface. Each temperature graph represents the mean of one surface and two satellite datasets: the monthly surface temperature anomalies from the Hadley Center in the UK and the National Climatic Data Center in the US, and the lower-troposphere anomalies from the satellites of Remote Sensing Systems, Inc., and of the University of Alabama at Huntsville. We do not use the NCDC/GISS datasets.

On each graph, the anomalies are zeroed to the least element in the dataset. For clarity, the IPCC’s range of predictions is zeroed to the start-point of the least-squares linear-regression trend on the realworld data. Since late 2001, global temperature has been falling fast.

From November 2009 to April 2010 issue:

Our global-temperature graphs show changes in real-world temperature at or near the Earth’s surface. Each temperature graph represents the mean of one surface and two satellite datasets: the monthly surface temperature anomalies from the Hadley Center in the UK, and the lower-troposphere anomalies from the satellites of Remote Sensing Systems, Inc., and of the University of Alabama at Huntsville. We do not use the Hadley/CRU or NCDC/GISS datasets: the Climate-gate scandal has shown these to be mere science fiction.

On each graph, the anomalies are zeroed to the least element in the dataset. For clarity, the IPCC’s range of predictions is zeroed to the start-point of the least-squares linear-regression trend on the real-world data. Since late 2001, global temperature has been falling fast.

June 2010:

Our global-temperature graphs show changes in real-world temperature at or near the Earth’s surface. Each temperature graph represents the mean of two satellite datasets: the monthly lower-troposphere anomalies from the satellites of Remote Sensing Systems, Inc., and of the University of Alabama at Huntsville. We do not use the Hadley/CRU or NCDC/GISS datasets: the Climate-gate scandal has shown these to be unreliable.

On each graph, the anomalies are zeroed to the least element in the dataset. For clarity, the IPCC’s range of predictions is zeroed to the start-point of the least-squares linear-regression trend on the real-world data. Since late 2001, global temperature has been falling fast.

Note that the figure caption as of April 2010 claims that UAH was temporarily dropped from the index, so the index should simply be a shifted version of RSS.

• Falsification of the Atmospheric CO₂ Greenhouse Effects within the Frame of Physics.
  http://dx.doi.org/10.1142/S021797920904984X
  post-print version on arXiv
  http://arxiv.org/abs/0707.1161
• Comment on the above by Halpern et.al.
  http://dx.doi.org/10.1142/S021797921005555X
  Free copy hosted by William Connoley
  http://scienceblogs.com/stoat/upload/2010/05/halpern_etal_2010.pdf
• Reply by G&T to Halpern et.al.
  http://dx.doi.org/10.1142/S0217979210055573

One of the authors of the comment has opened a forum thread for the discussion.
http://climatephysicsforums.com/topic/3292392/1
On page two parts of the G&T reply are addressed.

Download VirtualDub and unzip it to where you lateron want to use this program.

Download and install Avisynth. It is a frame server for VirtualDub and is working by passing an avs-script to VirtualDub. Documentation can be found in the project’s wiki, you can skip that for now.

Download the images you want to process and organize them in subfolders.

Copy and paste the following script into a text-editor and save the file in the base folder of your image data using an extension ‘.avs’

## read in a series of images. the substring "%03d" is place holder for a zero-padded three-digit number. it will run from 111 to 114. the frames will be shown at 5 frames per second
v1=ImageReader(file = "vid1\Arctic_r05c03.2010%03d.terra.4km.jpg",start = 70, end = 177, fps = 5)
## you could have a second set of images you want to show alongside the first clip
# v2=ImageReader(file = "vid2\img%d.jpg",start = 0, end = 21, fps = 5)
## Have a single image span a series of frames, e.g. as backdrop for subtitles.
# v3=ImageSource(file = "footer.jpg", start = 1, end = 21, fps = 5)

## Stack the named clips. The need to be of the same width. StackHorizontal stacks in horizontal direction, here clips need to have the same height. A stacked clip is again just one clip, so you can use a series of stack commands to assemble a table of clips.
# StackVertical(v1,v2,v3) ## This command would stack the three videos above and return it as the current clip

## set v1 to be the current clip
v1

## subtitles need to be done one at a frame, see documentation for the "align" paramenter. Best done in a spreadsheet and copied here.
subtitle("day 111", align=2, first_frame=0, halo_color=$00FF00FF, text_color=$0000FF00, size=36, last_frame=0)
subtitle("day 112", align=2, first_frame=1, halo_color=$00FF00FF, text_color=$0000FF00, size=36, last_frame=1)
subtitle("day 113", align=2, first_frame=2, halo_color=$00FF00FF, text_color=$0000FF00, size=36, last_frame=2)
subtitle("day 114", align=2, first_frame=3, halo_color=$00FF00FF, text_color=$0000FF00, size=36, last_frame=3)

Adapt the filename-mask and parameters in the second line (starting “v1=ImageReader”) to match your images. Adapt the subtitles.

Start VirtualDub and open that avs-script. Select ‘Audio/No Audio’, select ‘Video/Compression…’ and select a codec like “Microsoft Video 1″ or XVid (the latter requires an extra download and install). Select ‘File/Save as AVI…’ and enjoy the result.

Now for more detailed explanations. The syntax for the commands used in the script can be found at the wiki: ImageReader/ImageSource (IR for multiple, IS for single images) and Subtitle. Each command creates a clip, i.e. a sequence of frames. It can either be assigned to an identifier (e.g. “v1″) or simply put out as the current clip. The script needs to end with a clip, so the last line needs to be a command or a clip identifier. “#” comments out lines. Commands act either on the clip named as the first parameter or the current video. As long as parameters are left in the correct order, you can drop the descriptors, like so:

v1=ImageReader("vid1\Arctic_r05c03.2010%3d.terra_4km.jpg", 111, 114, 5)
# v2=ImageReader("vid2\img%d.jpg",start = 0, 3, 5)
# v3=ImageSource("footer.jpg", start = 0, 3, 5)