Airborne Fraction of CO2 is the ratio of the amount observed in the atmosphere to the amount emitted. I have been writing (here and here) about how it seems to be extraordinarily stable. In saying this I define and plot it in a different way to the usual, in which it appears more variable, leading to speculation about trend. I'll say more about this different way below. But I think I have worked out the explanation for the stability, and it isn't obvious.
People tend to think first of Henry's Law, which suggests a fixed partition of a solute (including gas) between two phases. This is a material property, and refers to equilibrium, which does not apply to CO2 in air/sea. It applies even less to the land sink, which is quite important.
In this note, I will show that the constancy, perversely, depends on the dynamics, and is a result of the near exponential increase in CO2 emissions. This effect is mostly independent of the actual mechanism for the sinks. It is really a consequence of linearity with exponential increase.
Since this post is something of a math proof, here is a TOC:
Saturday, November 28, 2015
Wednesday, November 25, 2015
GWPF Temperature Adjustments inquiry - no news.
Two months ago, I wrote about the inquiry announced by the Global Warming Policy Foundation. You know, the one fanfared in the Telegraph. "Top Scientists Start To Examine Fiddled Global Warming Figures"
The news then was that after receiving submissions on June 30, they decided that they wouldn't write a report re the terms of reference, but maybe some papers. They had however said that they would publish the submissions. So I thought I should look in every two months or so to see what has happened.
But this time, no news. Just the report of September 29, confirming intended inaction. I'll check again next year.
The news then was that after receiving submissions on June 30, they decided that they wouldn't write a report re the terms of reference, but maybe some papers. They had however said that they would publish the submissions. So I thought I should look in every two months or so to see what has happened.
But this time, no news. Just the report of September 29, confirming intended inaction. I'll check again next year.
Monday, November 23, 2015
Using NOMADS data - movies
I've cracked the system for efficiently using NOMADS data. I'm using an R package rNOMADS. It's a system where you can quiz and selectively download a large set of gridded data. It gives me access to many new resources for high frequency data, including reanalysis.
Anyway, as a first experiment, I downloaded GFS 0.5° surface relative humidity data ("gfs_0p50","rh2m"). This data is prepared for the forecast system, and held for about 12 days. So here we have a movie of the data from 11th to 23rd November, at 6 hour intervals. This is all experimental; the movie is ogg, so should show on Firefox and Chrome, but probably not on IE or Safari. Update - I've made an alternative mp4 version, which works in Safari at least. It has the usual controls - you may need to pass the mouse pointer below the picture to make them show. Blue is high humidity, red dry. A color key will appear at some stage.
Anyway, as a first experiment, I downloaded GFS 0.5° surface relative humidity data ("gfs_0p50","rh2m"). This data is prepared for the forecast system, and held for about 12 days. So here we have a movie of the data from 11th to 23rd November, at 6 hour intervals. This is all experimental; the movie is ogg, so should show on Firefox and Chrome, but probably not on IE or Safari. Update - I've made an alternative mp4 version, which works in Safari at least. It has the usual controls - you may need to pass the mouse pointer below the picture to make them show. Blue is high humidity, red dry. A color key will appear at some stage.
Thursday, November 19, 2015
NOAA October 0.98°C!
The NOAA report is out, and shows the global anomaly rose from 0.90°C in September to 0.98°C in October. The report says that it was the hottest measured October, but in fact it was the highest anomaly of any month in the record by quite a long way, ahead of 0.9°C in just the previous month.
As expected, the rise was less than for GISS. The reason is coverage of Antarctica. Antarctica had been very cold, and switched to warm in October. GISS, which weights by total area, is very sensitive to this, and so lagged in Sept, followed by a big jump. My TempLS grid does the same. But NOAA only counts the grid cells with information, which are few in Antarctica. Consequently, it rose to a record anomaly in September, with a relatively smaller rise in October. Still, that means it upped the record by 0.08°C.
TempLS grid behaved in much the same way as NOAA, as it usually does. It rose by 0.06. The regional pattern of warmth described in the NOAA report is much as described in the TempLS report.
Update. I'll comment further on some questions in the TempLS October post. First, as Olof noted, Barzil and Greenland, not then in, had a considerable warming effect. So the rise in TempLS mesh, at 0.24°C, ended up exactly the same as GISS. And TempLS grid at 0.06 was very close to the 0.08°C rise for NOAA. This is the usual correspondence, relating to the respective methods.
There was also the question of SST. TempLS, in its attribution analysis, showed a small contribution from SST. But HADSST3 had actually declined. This made me more cautious about TempLS-based prediction. But the NOAA report showed NOAA ocean at 0.85°C, a rise of 0.04, very similar to the TempLS attribution. And that figure was also a record for any month, improving on the previous month's record.
Update. I've shown below the latest recent plot, from here. It shows the global indices set to a common anomaly period of 1981-2010. You can see how TempLS and GISS are currently moving in tandem, as are TempLS grid and NOAA.
As expected, the rise was less than for GISS. The reason is coverage of Antarctica. Antarctica had been very cold, and switched to warm in October. GISS, which weights by total area, is very sensitive to this, and so lagged in Sept, followed by a big jump. My TempLS grid does the same. But NOAA only counts the grid cells with information, which are few in Antarctica. Consequently, it rose to a record anomaly in September, with a relatively smaller rise in October. Still, that means it upped the record by 0.08°C.
TempLS grid behaved in much the same way as NOAA, as it usually does. It rose by 0.06. The regional pattern of warmth described in the NOAA report is much as described in the TempLS report.
Update. I'll comment further on some questions in the TempLS October post. First, as Olof noted, Barzil and Greenland, not then in, had a considerable warming effect. So the rise in TempLS mesh, at 0.24°C, ended up exactly the same as GISS. And TempLS grid at 0.06 was very close to the 0.08°C rise for NOAA. This is the usual correspondence, relating to the respective methods.
There was also the question of SST. TempLS, in its attribution analysis, showed a small contribution from SST. But HADSST3 had actually declined. This made me more cautious about TempLS-based prediction. But the NOAA report showed NOAA ocean at 0.85°C, a rise of 0.04, very similar to the TempLS attribution. And that figure was also a record for any month, improving on the previous month's record.
Update. I've shown below the latest recent plot, from here. It shows the global indices set to a common anomaly period of 1981-2010. You can see how TempLS and GISS are currently moving in tandem, as are TempLS grid and NOAA.
Tuesday, November 17, 2015
GISS October 1.04°C, record month anomaly.
GISS is out, a bit late. But it is at or above expectations. At 1.04°C, up by 0.24°C from September 0.8°C. That is well clear of the previous highest, 0.97°C in Jan 2007, which was itself something of an outlier.
Update. Needless to say, 2015 is pulling away in the progress to hottest year. Sou has the story here, with her updated chart. Looks like 2015 will be at least 0.1°C hotter than any previous year. It will be hard to gin up uncertainty about that.
The rise is almost the same as TempLS mesh, 0.235°C. Here is a plot of the last 20 years, monthly, with annual average overlaid:
Graphs are below the jump.
Update. Needless to say, 2015 is pulling away in the progress to hottest year. Sou has the story here, with her updated chart. Looks like 2015 will be at least 0.1°C hotter than any previous year. It will be hard to gin up uncertainty about that.
The rise is almost the same as TempLS mesh, 0.235°C. Here is a plot of the last 20 years, monthly, with annual average overlaid:
Graphs are below the jump.
Monday, November 16, 2015
Airborne fraction CO2 and the Bern model
We've been discussing IPCC projections and RCPs in relation to the airborne fraction (AF) of CO2. The AF is the fraction of CO2 emitted that remains in the air, relative to the amount emitted. In an earlier post I showed that if you plotted cumulative emissions against total CO2, it was very linear, implying a close to constant AF. You get different constants depending on whether you look at just FF emissions, or total, including land use.
At first constant AF might seem to be a consequence of Henry's Law. But that gives a fixed phase partitioning at equilibrium, which we don't have (not to mention acid/base chemistry). It might seem surprising that the time varying sink uptake could give that result.
So I tried with the dynamics of the Bern model. This is what the IPCC would probably use if they were to express an opinion on the future of AF (which they don't). The Bern model yields an impulse response function for a pulse of CO2 injected into the atmosphere. In this version, it makes that out of the sum of decaying exponentials of periods ∞, 171, 18 and2.57 years. If you filter total emissions with this function (reversed), that gives the modelled growth of the amount of CO2 in the air at any time. A caveat; fitting observed AF was no doubt one of the considerations in designing the Bern model.
So I did that. I'll show the results below. Plotting accumulated CO2 against cumulative emissions, the result is still remarkably linear. And the slope, at 0.436, is remarkably close to what I found with observed CO2 (0.439). OK the caveat applies, but the constancy is the real result.
At first constant AF might seem to be a consequence of Henry's Law. But that gives a fixed phase partitioning at equilibrium, which we don't have (not to mention acid/base chemistry). It might seem surprising that the time varying sink uptake could give that result.
So I tried with the dynamics of the Bern model. This is what the IPCC would probably use if they were to express an opinion on the future of AF (which they don't). The Bern model yields an impulse response function for a pulse of CO2 injected into the atmosphere. In this version, it makes that out of the sum of decaying exponentials of periods ∞, 171, 18 and2.57 years. If you filter total emissions with this function (reversed), that gives the modelled growth of the amount of CO2 in the air at any time. A caveat; fitting observed AF was no doubt one of the considerations in designing the Bern model.
So I did that. I'll show the results below. Plotting accumulated CO2 against cumulative emissions, the result is still remarkably linear. And the slope, at 0.436, is remarkably close to what I found with observed CO2 (0.439). OK the caveat applies, but the constancy is the real result.
Tuesday, November 10, 2015
Google Map for GHCN V4
I have a series of Google Maps tools, which you can read about on the Gallery page. The latest (till now) is here, on adjustments. In this post, I have made one in similar style for GHCN V4 Beta.
The tool shows a Google map with the usual facilities, but with markers for the stations in GHCN V4. You can click on a marker to bring up some details. The main utility is that you can choose different marker colors for different subsets. An important color is "Invis". It earlier stood for invisible, though it is now implemented by removing the marker from the map (you can get it back).
At the right you can see an orange-background table of the color options, and a cyan table of selection criteria. The selections come with a more/less sign that you can toggle, and a textbox where you can write a criterion number. Tick the left checkbox to make it live. The color table has radio buttons to make the color happen, and on the other side a count of how many of that color there are.
Nothing happens until you then click on a color radio button. When you do, the markers are sorted according to the live (checked) selection options, and those that qualify change to that color. This is "and" logic; all criteria have to be satisfied. You'll find it useful to sometimes switch to negative logic - make Invisible the options you don't want. If no checkboxes are ticked, everything will change.
I've included Lat and Lon so that you can look at a restricted area. I was thinking of performance there - the map can get sluggish if too many markers are visible. In fact, I haven't had performance problems, but that may be because my computer has upgraded.
At the bottom a selection box allows you to make color itself an option. You can subset from the classes you have discriminated.
Some examples of things you might want to do:
The pop-up tags give dates, name and the GHCN Inventory number. The first two letters of that are an abbreviation for the country (details here).
The tool shows a Google map with the usual facilities, but with markers for the stations in GHCN V4. You can click on a marker to bring up some details. The main utility is that you can choose different marker colors for different subsets. An important color is "Invis". It earlier stood for invisible, though it is now implemented by removing the marker from the map (you can get it back).
At the right you can see an orange-background table of the color options, and a cyan table of selection criteria. The selections come with a more/less sign that you can toggle, and a textbox where you can write a criterion number. Tick the left checkbox to make it live. The color table has radio buttons to make the color happen, and on the other side a count of how many of that color there are.
Nothing happens until you then click on a color radio button. When you do, the markers are sorted according to the live (checked) selection options, and those that qualify change to that color. This is "and" logic; all criteria have to be satisfied. You'll find it useful to sometimes switch to negative logic - make Invisible the options you don't want. If no checkboxes are ticked, everything will change.
I've included Lat and Lon so that you can look at a restricted area. I was thinking of performance there - the map can get sluggish if too many markers are visible. In fact, I haven't had performance problems, but that may be because my computer has upgraded.
At the bottom a selection box allows you to make color itself an option. You can subset from the classes you have discriminated.
Some examples of things you might want to do:
- Set endyr>2014 to pink the stations currently reporting. Or set endyr<2015 and then Invis to remove those that are not
- Begin by making everything Invis (just click it).
- Set duration >100 to pink (or cyan etc) long duration stations.
- Set startyr>1850 and Invis to leave only the very early stations
The pop-up tags give dates, name and the GHCN Inventory number. The first two letters of that are an abbreviation for the country (details here).
Monday, November 9, 2015
The Ice Age scare
We are often told by sceptics that "alarmist scientists" have swung from predicting an Ice age in the 1970's to later warnings of AGW. It is, of course, not true. Stoat has tracked the myth over the years - paper here.
Recently an old 1978 TV episode has been doing the rounds. But this is of course not a scientific paper, nor even a regular documentary, even if narrated by Leonard Nimoy. It's from a sensationalist "believe it or not" weekly series.
I saw an amusing recurrence a few weeks ago. Lamb 1974 was cited in support. But like many such citations, it turns out to be not a prediction of imminent ice, but rather talking about the progress of the interglacial in future millennia. And it ends up with this summary:
A sub-report headed "Atmospheric Carbon Dioxide" was written by big name scientists of the time - Roger Revelle, Wallace Broecker, Charles Keeling, Harmon Craig, and J Smagorisnky. It lists in detail the "other" consequences, as headings -
And so on since - the dominant scientific thinking has focussed on the greenhouse effect. I experienced this myself, in 1976. I had been transferred by CSIRO from Canberra to Perth. CSIRO then, although federal, tended to work closely with the states where it was located, especially in remote WA. The WA government was pondering a scientific issue.
Most of WA is dry, but the Southwest, then, got reliable winter rainfall from the "Roaring Forties" belt of westerlies, which came north at that time, then leaving for a dry summer. This is ideal for wheat farming, and the "Wheat Belt" was very important. It had substantial infrastructure (eg rail) to move the harvest.
Industry economics had changed with automation, and for those with capital, it was viable to plant crops in marginal (mostly North fringe) areas, even if every second crop failed. So there was pressure to spend money on expanded infrastructure, to make that possible. WA had heard about the coming non-Ice Age, and asked CSIRO.
I was a recently arrived junior scientist, and like most of the ags and mining folk there, knew little about such matters. But I did have some contacts at Atmospheric Physics in Victoria, so I was asked to enquire. I did.
The story, unanimously, what that global warming was on the way, and would have particular effects on WA. The Hadley cell which drove those winds would expand, pushing them further south. Dryer times. Bad idea.
That was our recommendation, and the expansion didn't happen. What did happen was three very hot, dry summers, so our recommendation was looking good. And the rainfall never really recovered.
Recently an old 1978 TV episode has been doing the rounds. But this is of course not a scientific paper, nor even a regular documentary, even if narrated by Leonard Nimoy. It's from a sensationalist "believe it or not" weekly series.
I saw an amusing recurrence a few weeks ago. Lamb 1974 was cited in support. But like many such citations, it turns out to be not a prediction of imminent ice, but rather talking about the progress of the interglacial in future millennia. And it ends up with this summary:
“The question of whether a lasting increase of glaciation and permanent shift of the climatic belts results from any given one of these episodes must depend critically on the radiation available during the recovery phase of the 200-year and other, short-term fluctuations. An influence which may be expected to tip the balance rather more towards warming – and possibly inconveniently rapid warming – in the next few centuries is the increasing output of carbon dioxide and artificially generated heat by Man (MITCHELL 1972).”That was the general scientific view. I am writing about this now because I see, via SkS, that the AAAS is noting the 50th anniversary of a rather remarkable report to the then President, Lyndon Johnson, of a panel of his Scientific Advisory Committee. You can't get a more authoritative statement of the consensus scientific view of the time than that. LBJ signed it.
A sub-report headed "Atmospheric Carbon Dioxide" was written by big name scientists of the time - Roger Revelle, Wallace Broecker, Charles Keeling, Harmon Craig, and J Smagorisnky. It lists in detail the "other" consequences, as headings -
- Melting Antarctic Ice Caps
- Rise of sea level
- Warming of Sea Water
- Increased Acidity of fresh waters
- Increase in photosynthesis
And so on since - the dominant scientific thinking has focussed on the greenhouse effect. I experienced this myself, in 1976. I had been transferred by CSIRO from Canberra to Perth. CSIRO then, although federal, tended to work closely with the states where it was located, especially in remote WA. The WA government was pondering a scientific issue.
Most of WA is dry, but the Southwest, then, got reliable winter rainfall from the "Roaring Forties" belt of westerlies, which came north at that time, then leaving for a dry summer. This is ideal for wheat farming, and the "Wheat Belt" was very important. It had substantial infrastructure (eg rail) to move the harvest.
Industry economics had changed with automation, and for those with capital, it was viable to plant crops in marginal (mostly North fringe) areas, even if every second crop failed. So there was pressure to spend money on expanded infrastructure, to make that possible. WA had heard about the coming non-Ice Age, and asked CSIRO.
I was a recently arrived junior scientist, and like most of the ags and mining folk there, knew little about such matters. But I did have some contacts at Atmospheric Physics in Victoria, so I was asked to enquire. I did.
The story, unanimously, what that global warming was on the way, and would have particular effects on WA. The Hadley cell which drove those winds would expand, pushing them further south. Dryer times. Bad idea.
That was our recommendation, and the expansion didn't happen. What did happen was three very hot, dry summers, so our recommendation was looking good. And the rainfall never really recovered.
Saturday, November 7, 2015
Temperature records broken in TempLS October
I've been following some very large spikes in the NCEP/NCAR index during October, with an eventual rise of about 0.2°C. Then followed big rises in the satellite indices, with UAH up about 0.18°C and RSS up 0.07. Here is the first surface index. TempLS mesh (report here) rose by 0.21°C, and TempLS grid by 0.03°C. There is rather a contrast here, which I'll say more about. However, TempLS grid had drifted well ahead of mesh, so this represents a degree of catch up. The result is that both are the hottest month (of all months) in their respective records, by quite a long way (plots below).
The reason for disparity seems to involve SST, and maybe sea ice. TempLS grid uses lat/lon grid cells, and has several without data. In this respect is resembles HADCRUT and NOAA, and often follows them closely. TempLS mesh has a full triangular mesh, so interpolates everywhere, more like GISS. In October, you can see a breakdown of the contributions to TempLS Mesh in the report (scroll down). A main feature is the reversal of Antarctica from cold to warm. The grid methods underweight this.
A paradox is that most indications are that SST did not rise. HADSST shows a small decrease. TempLS mesh shows an increased contribution from SST.
You can see the regional patterns in the report, and in more detail here. Antaarctica and Australia were big hotspots. Brazil is still missing. The heat in Australia is noted here.
So what does this all mean? I think there will be substantial rises in the main indices, setting more records for hottest anomaly. The rises probably won't be as high as the reanalysis, since SST will be a larger damping component. Below the fold, I'll show plots of the last 20 years of TempLS monthly, and a WebGL global plot of the differences going from September to October. Again Antarctica and Australia are the big factors.
Here is TempLS mesh
October stands out, though in TempLS there was also a big spike in 1998 - more so than in other indices. Here is TempLS grid:
Obviously the rise from September is less, but it followed a larger earlier build up. Now here is the WebGL gadget. It shows, in color shading, the changes going from September to October. Again Antarctica and Australia stand out, with seemingly no change in SST. SST changes are small and fairly even, so they tend not to show on the scale of the more volatile land. Europe cooled, central Asia warmed. As usual, the global is a trackball - right mouse button to zoom.
The reason for disparity seems to involve SST, and maybe sea ice. TempLS grid uses lat/lon grid cells, and has several without data. In this respect is resembles HADCRUT and NOAA, and often follows them closely. TempLS mesh has a full triangular mesh, so interpolates everywhere, more like GISS. In October, you can see a breakdown of the contributions to TempLS Mesh in the report (scroll down). A main feature is the reversal of Antarctica from cold to warm. The grid methods underweight this.
A paradox is that most indications are that SST did not rise. HADSST shows a small decrease. TempLS mesh shows an increased contribution from SST.
You can see the regional patterns in the report, and in more detail here. Antaarctica and Australia were big hotspots. Brazil is still missing. The heat in Australia is noted here.
So what does this all mean? I think there will be substantial rises in the main indices, setting more records for hottest anomaly. The rises probably won't be as high as the reanalysis, since SST will be a larger damping component. Below the fold, I'll show plots of the last 20 years of TempLS monthly, and a WebGL global plot of the differences going from September to October. Again Antarctica and Australia are the big factors.
Here is TempLS mesh
October stands out, though in TempLS there was also a big spike in 1998 - more so than in other indices. Here is TempLS grid:
Obviously the rise from September is less, but it followed a larger earlier build up. Now here is the WebGL gadget. It shows, in color shading, the changes going from September to October. Again Antarctica and Australia stand out, with seemingly no change in SST. SST changes are small and fairly even, so they tend not to show on the scale of the more volatile land. Europe cooled, central Asia warmed. As usual, the global is a trackball - right mouse button to zoom.
Thursday, November 5, 2015
Weekly SST indices
I've been looking at SST data that I could track on a sub-monthly timescale, in the way I use NCEP reanalysis. I initially tried the daily AVHRR data which I download and plot here. That worked, but oiSST V2 draws on a wider range of sources, and is widely used for monthly indices. NOAA produces weekly gridded data, and gives tables of weekly data for the various NINO regions.
But they don't seem to give weekly averages for the globe, nor for latitude bands, and I think that would be useful. So I have been experimenting with downloading and integrating the grids, as with NCEP, with a view to adding to the latest data page.
I tested the NINO region integrals against the NOAA figures here, and they match to the one decimal place that NOAA provides. So I think the anomaly formation, using the NOAA daily climatology here, and the spatial integration are OK.
In this post, I'll show an active plot for the weeks to date of 2015 for the globe, the main NINO regions (details below) and four latitude bands (SH 60°-tropic, SH tropics, and NH likewise). Later I hope to give longer duration plots, and WebGL global maps.
The NINO regions are (NOAA map here):
The plot is a version of the active plot in the data page. I doubt that you'll need the x-axis drag facility, with only 43 weeks, and the trend won't be meaningful. The plot starts with everything showing, but you can toggle off data that you don't want. You may need to stretch the y-axis by draging (vertically) to the left of it. The anomaly base is 1981-2010. Here is is:
But they don't seem to give weekly averages for the globe, nor for latitude bands, and I think that would be useful. So I have been experimenting with downloading and integrating the grids, as with NCEP, with a view to adding to the latest data page.
I tested the NINO region integrals against the NOAA figures here, and they match to the one decimal place that NOAA provides. So I think the anomaly formation, using the NOAA daily climatology here, and the spatial integration are OK.
In this post, I'll show an active plot for the weeks to date of 2015 for the globe, the main NINO regions (details below) and four latitude bands (SH 60°-tropic, SH tropics, and NH likewise). Later I hope to give longer duration plots, and WebGL global maps.
The NINO regions are (NOAA map here):
- NINO4 (5S-5N, 160E-150W)
- NINO3.4 (5S-5N, 170W-120W)
- NINO3 (5S-5N, 150W-90W)
- NINO1+2 (10S-0, 90W-80W)
The plot is a version of the active plot in the data page. I doubt that you'll need the x-axis drag facility, with only 43 weeks, and the trend won't be meaningful. The plot starts with everything showing, but you can toggle off data that you don't want. You may need to stretch the y-axis by draging (vertically) to the left of it. The anomaly base is 1981-2010. Here is is:
Tuesday, November 3, 2015
NCEP/NCAR index up 0.2°C in October
I posted earlier about a big spike in the Moyhu NCEP/NCAR index in early October. That index is one that I derive by integrating NCEP/NCAR reanalysis data, as explained here. The index came back from the peak, but only back to levels that would have been seen as very high in earlier months, and stayed high right to end month. So the average finished at 0.567°C, as compared to September 0.368°C. These numbers are relative to base years 1994-2013.
That makes October by far the highest monthly anomaly in the record; in fact, it beats the previous record (Jan 2007) by 0.15°C. That can be seen in the following graph of all monthly anomalies since 1994:
Relative to the 1951-80 base of GISS, October would be 1.18°C, and on the NOAA 20th Cen base, it would be 1.14°C. I wouldn't expect to see those indices rise so high, because they have been somewhat lagging the NCEP/NCAR index recently. In September, GISS was only 0.81°C. Still, there is clearly a possibility of GISS reaching 1°C, and a very strong probability of being the highest anomaly ever, in all indices.
In a related news item, Australia's October was the hottest month ever. Also very dry, where I am. We had a very unusual heat wave at the start of the month, and it continued mostly warm and sunny. It looks like a dangerous fire season coming.
That makes October by far the highest monthly anomaly in the record; in fact, it beats the previous record (Jan 2007) by 0.15°C. That can be seen in the following graph of all monthly anomalies since 1994:
Relative to the 1951-80 base of GISS, October would be 1.18°C, and on the NOAA 20th Cen base, it would be 1.14°C. I wouldn't expect to see those indices rise so high, because they have been somewhat lagging the NCEP/NCAR index recently. In September, GISS was only 0.81°C. Still, there is clearly a possibility of GISS reaching 1°C, and a very strong probability of being the highest anomaly ever, in all indices.
In a related news item, Australia's October was the hottest month ever. Also very dry, where I am. We had a very unusual heat wave at the start of the month, and it continued mostly warm and sunny. It looks like a dangerous fire season coming.
Sunday, November 1, 2015
Coverage of GHCN V4 compared
In an earlier post, I took a first look at GHCN V4 beta. Details, sources etc are there. I've been looking with a practical eye, because at some stage I will have to adapt TempLS to use it. As remarked there, by me and others, GHCN V4 has a lot of extra stations, but noy proportionately better coverage. It's merit may be with homogenisation, rather than a better global average.
In this post I'll look in more detail at that issue of coverage. In the back of my mind, I am thinking about how to use a reduced set. This is not just to save computing time; big disparities in station density actually create accuracy problems.
I will compare using the cubed sphere, that I described recently. It gives a grid of almost equal area cells. I since noticed that it has recently been adopted by GFDL, and is described here.
I'll show a WebGL plot (16x16 faces) with cells colored by the number of datapoints within, for the data of August 2015. You can switch between the data I currently use and the GHCN V4 data with full ERSST. It shows cells with zero (and sparse) data, and also cells with a great deal. I'll also show histograms for comparison. I'll then briefly discuss strategies for rationalization.
Here is the WebGL plot. As usual, the Earth is a trackball that you can rotate. The "Switch" Button switches between V3 and V4 data. Cells are colored by number of data points (dots) within. The key shows the number of data per cell. I'll discuss below the plot.
The V3 picture is influenced by my thinning of the ERSST data from 2°x2° to 4°x4°, basically to match the land density. So ocean cells have typically 1 or 2 datapoints (but not 0). I think for SST this is quite satisfactory, since spatial variability is modest. With V4 I haven't thinned, so ocean cells have a lot of data. It's better for comparison to focus on the land.
Because it is SH winter, there are large areas of sea ice. ERSST assigns these a value of -1.8°C (freezing point of sea water), but I remove them. This creates a lot of empty cells. Six months earlier, the Arctic would have appeared thus. The main land area to compare is Africa. V4 does have fewer empty cells, but still some.
Here are histogram plots of numbers in cells. The right blocks embrace a range, of which the one shown is the minimum. With V4, there is a big block of cells with about 6-9 stations. This happens because of the regular SST grid, which tends to give 9 points per cell, but with frequent variation. With V3, with thinned SST, the ocean majority are in the 1-3 range.
I think a reasonable number of data per cell to aim for is four. That gives about half the standard error of mean, and about 6000 data in total. I would probably create a reduced set to be used for all months, so to cover back to 1900, there would be more than four needed in total. But there is ample scope for being choosy in many places.
In this post I'll look in more detail at that issue of coverage. In the back of my mind, I am thinking about how to use a reduced set. This is not just to save computing time; big disparities in station density actually create accuracy problems.
I will compare using the cubed sphere, that I described recently. It gives a grid of almost equal area cells. I since noticed that it has recently been adopted by GFDL, and is described here.
I'll show a WebGL plot (16x16 faces) with cells colored by the number of datapoints within, for the data of August 2015. You can switch between the data I currently use and the GHCN V4 data with full ERSST. It shows cells with zero (and sparse) data, and also cells with a great deal. I'll also show histograms for comparison. I'll then briefly discuss strategies for rationalization.
Here is the WebGL plot. As usual, the Earth is a trackball that you can rotate. The "Switch" Button switches between V3 and V4 data. Cells are colored by number of data points (dots) within. The key shows the number of data per cell. I'll discuss below the plot.
The V3 picture is influenced by my thinning of the ERSST data from 2°x2° to 4°x4°, basically to match the land density. So ocean cells have typically 1 or 2 datapoints (but not 0). I think for SST this is quite satisfactory, since spatial variability is modest. With V4 I haven't thinned, so ocean cells have a lot of data. It's better for comparison to focus on the land.
Because it is SH winter, there are large areas of sea ice. ERSST assigns these a value of -1.8°C (freezing point of sea water), but I remove them. This creates a lot of empty cells. Six months earlier, the Arctic would have appeared thus. The main land area to compare is Africa. V4 does have fewer empty cells, but still some.
Here are histogram plots of numbers in cells. The right blocks embrace a range, of which the one shown is the minimum. With V4, there is a big block of cells with about 6-9 stations. This happens because of the regular SST grid, which tends to give 9 points per cell, but with frequent variation. With V3, with thinned SST, the ocean majority are in the 1-3 range.
| Version 4 GHCN with ERSST | V3 GHCN with reduced ERSST |
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