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Topics - ChrisReynolds

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This follows on from a throw-away justification of a statement in my most recent blog post....

How do we define seasonally ice free? The ultimate definition is no ice at all, but that is too long a prospect for me. So I turn to virtually sea ice free...

The common definition seems to be total NH sea ice area extent of less than 1M km^2. But how might we apply this to the regional seas of the Arctic?

Average September extent for the 1980s in Wipneus's data set (near as damn it NSIDC Extent), was 7.219M km^2. 15% seems to be a good demarcation for working out extent, so let's try applying that to overall Arctic extent. 7.219M X 0.15 = 1.083M km^2. That's only a bit above the 1M km^2 level below which the Arctic Ocean can be considered virtually sea ice free.

So I propose that September extent of below 15% of 1980s September extent means that sea/region is virtually sea ice free.

Looking at the regions available:

Okhostk - ice free in summer most of the record - discounted.
Bering - ice free in summer most of the record - discounted.

Beaufort 1980s average is 0.34M km^2 - OK
Chukchi 1980s average is 0.25M km^2 - OK
ESS 1980s average is 0.67M km^2 - OK
Laptev 1980s average is 0.34M km^2 - OK
Kara 1980s average is 0.30M km^2 - OK

Barents - 1980s average only 0.07M km^2 - discounted.

Greenland Sea 1980s average is 0.30M km^2 - OK
Central Arctic 1980s average is 4.40M km^2 - OK
CAA 1980s average is 0.44M km^2 - OK

Baffin - ice free in summer most of the record - discounted.
Hudson - ice free in summer most of the record - discounted.
St Lawrence - ice free in summer most of the record - discounted.

Of the regions accepted as having a reasonable amount of ice in the 1980s during September, Central, Greenland and the CAA have no years with less than 15% of the September average. That leaves us with Beaufort round to Laptev, the peripheral seas of the Arctic Basin, and the Kara Sea.

The list below shows the regions concerned that do show a (virtually) ice free state in September.
1 means ice all year (>15% of 1980s September extent). 0 means ice free (<15% of 1980s September extent)

   Beaufort   Chukchi   ESS   Laptev   Kara
1979   1   1   1   1   1
1980   1   1   1   1   1
1981   1   1   1   1   1
1982   1   1   1   1   1
1983   1   1   1   1   1
1984   1   1   1   1   1
1985   1   1   1   1   1
1986   1   1   1   1   1
1987   1   1   1   1   1
1988   1   1   1   1   1
1989   1   1   1   1   1
1990   1   1   1   1   1
1991   1   1   1   1   1
1992   1   1   1   1   1
1993   1   0   1   1   1
1994   1   1   1   1   1
1995   1   1   1   0   0
1996   1   1   1   1   1
1997   1   1   1   1   1
1998   0   1   1   1   1
1999   1   0   1   1   1
2000   1   1   1   1   1
2001   1   1   1   1   1
2002   1   0   1   1   1
2003   1   0   1   1   1
2004   1   0   1   1   1
2005   1   0   1   1   1
2006   1   1   1   1   1
2007   1   0   0   1   1
2008   0   0   0   1   1
2009   1   0   1   1   1
2010   1   0   1   1   1
2011   1   0   1   0   1
2012   0   0   0   0   1
2013   1   0   1   0   1
2014   1   0   1   0   1

Arctic sea ice / The June Area Anomaly Cliff and Melt Ponding.
« on: August 25, 2014, 09:18:44 PM »
New rather long blog post posted.

A quick and necesarily incomplete precis...

June losses are related to temperature.

Though not overwhelmingly so.

Regions outside of the Arctic Ocean play a role in recent large June losses.

However June losses have mainly increased due to June 1st ice concentrations associated with the ice edge, not the pack away from the ice edge where melt ponds should make their presence felt.

In total 70% of the increase in loss between the two averages is associated with ice near the ice edge, not within the centre of the pack.

Looking at the June anomaly crashes for the years in which they have been strongest.

A sharp drop is seen in June, i.e. well above average losses of area.

Take Arctic Ocean area alone and they are nowhere near as strong a feature.

So a role for melt ponding 'fooling' satellite sensors into seeing a drop in concentration is not dismissed, I was too strong in an earlier comment on the forum. But I do not think that the evidence adds up to the recent June cliffs being from the spread of melt ponds at in early June. It seems that some 70% of the increased loss of area in the recent Junes with 'cliffs' is actually due to ice associated with the ice edge, and other regions play a non-negligible role.

Arctic sea ice / Is the Arctic being geoengineered (in secret)?
« on: August 13, 2014, 07:48:22 PM »
This refers to geoengineering to stop the effects of AGW, not AGW itself.

So, is the Arctic being geoengineered in secret?

No. It is not.  8)

Arctic sea ice / The Slow Transition
« on: July 24, 2014, 06:37:47 PM »
I'm starting this thread to take the activity out of the 2014 Melting Season thread.

I'm going to see how much bother it is to copy comments over here. If you reply before I post the 'Comments completed' message you'll probably not be noticed, so hold off for now.

Arctic sea ice / What is the cause of the PIOMAS volume loss?
« on: January 13, 2014, 08:46:10 PM »
Zhang and Rothrock have done a paper in 2005 covering the period 1948 to 1999, pdf here. In a nutshell; during that early period they find that the volume loss is due to loss of thinner mechanically undeformed ice, in other words - thinning of younger ice.

Such a thinning has continued into the PIOMAS record for this century, but most of the volume loss this century has come from the central Arctic and loss of thick, mechanically deformed multi year ice. I can provide graphics if needed, but for now I want to crack on.

So what's been happening to cause volume loss in PIOMAS last century is different from after 1995, when the large rapid drop in volume happened, in short Zhang and Rothrock 2005 is of no use because the mechanism has changed. It's possible that some of the other gridded data from PIOMAS might be able to help, but I'm trying to see if there's a quick and easy way to the answer before I a) ask Dr Zhang, b) try a more advanced approach using the other gridded PIOMAS data.

I can break the year into two discrete periods with their own processes going on, again I can go into why this is valid, but want to crack on with this train of thought. So I have the melt season and the freeze season. Being able to tie the loss to either would be advantageous as I could bring other research to bear in the search for the cause of the volume loss. (I have been playing around with this since before Christmas!)

Using PIOMAS monthly averages calculated from their main season I use April to September as the melt season, September to April as the freeze season, with both seasons stated for the year in which April falls. This gives me a series of numbers for volume gain and volume loss.

Year   Freeze   Melt
1980   15.334   15.925
1981   14.431   17.937
1982   16.165   15.468
1983   16.887   15.195
1984   15.137   15.705
1985   16.244   16.294
1986   16.359   14.863
1987   15.721   16.440
1988   15.842   16.214
1989   15.128   15.347
1990   15.139   16.090
1991   16.930   17.153
1992   16.056   14.565
1993   15.348   17.985
1994   17.292   15.877
1995   14.578   17.208
1996   16.220   13.495
1997   15.416   16.146
1998   16.188   17.795
1999   16.834   17.414
2000   16.117   16.074
2001   16.554   15.367
2002   15.159   16.587
2003   16.401   16.963
2004   15.473   15.716
2005   16.012   16.772
2006   15.830   16.002
2007   14.656   17.236
2008   18.468   17.750
2009   17.717   17.986
2010   16.267   18.658
2011   17.188   17.564
2012   17.520   18.358
2013   18.309   16.637

Taking these as zig zagging through the years the first month used is September 1979, the last September 2013, over that period there's been a volume loss of 11.866k km^3. If I sum the above columns I get: 548.920 and 560.786, subtract those numbers and the result is -11.866, no surprise there, the total loss is as a result of an imbalance between volume gains over autumn/winter and losses over the spring/summer.

The problem is I don't know whether freeze season volume gains are less than they 'should' be, melt season losses greater than they 'should' be, or a combination of those two factors.

I make up two synthetic series of volume loss, one using a melt season that is losing 0.5k km^3 per year more than the freeze season gains, the other with a freeze season that produces 0.5k km^3 less ice than is lost in the melt season. This illustrates the two exclusive possibilities, loss of volume due to freeze season processes, and loss of volume due to melt season processes.

Say I fix the nominal freeze and melt season to be 15k km^3, so without an offset the peak volume stays at the initial value, which I could set to 30k km^3. When I apply the 0.5k km^3 offset to either the melt or freeze season I get the same result, the melt season losses are larger than the freeze season gains, either because I've set the melt season to be 0.5k km^3 larger, or the freeze season to be 0.5k km^3 smaller. The point is that the observation that total melt season losses exceed total freeze season gains, by the amount of volume lost, does not tell us whether the losses have been from the melt season, freeze season or indeed both.

Anyone got any ideas as to how I might seperate out the relative roles of melt and freeze seasons? Or indeed is it likely to be impossible?

In the Zhang Rothrock paper I linked to above they say that losses may be from either melt or freeze season processes, suggesting they've not been able to determine which - or perhaps just didn't have the time to do the extra digging into the far more detailed data they had...

Developers Corner / PIOMAS Region Mask
« on: November 24, 2013, 10:05:13 PM »
I've worked out a region mask for the PIOMAS gridded data grid boxes. The data is in a flat file format, and is a sequence of bytes in the same order as the sequence of single precision numbers used in PIOMAS thickness files.

The region mask itself is available here:

I've plotted it, some of the rough edges (spikes) show the limitation of my plotting technique, i.e. the dots plotted for each grid point are larger than the grid boxes in the central region.

The regions are numbered as follows:

0 Non Regional Ocean
1 Sea of Okhotsk and Japan
2 Bering Sea
3 Beaufort Sea, 
4 Chukchi Sea
5 East Siberian Sea
6 Laptev Sea 
7 Kara Sea
8 Barents Sea
9 Greenland Sea
10 Central Arctic
11 Canadian Arctic Archipelago
12 Baffin Bay/Newfoundland Sea
13 Hudson Bay
14 Gulf Of St Lawrence
15 Land

All regions derived from the region graphic on the Crysophere Today main page (which is an NSIDC format 25km grid), land derived from the PIOMAS landmask.

Science / Francis/Vavrus paper flawed.
« on: October 01, 2013, 08:59:00 PM »
I've just come across research that shows that the Francis/Vavrus paper on larger meanders and slowing of the Jetstream is in doubt.

Barnes 2013, Revisiting the evidence linking Arctic Amplification to extreme weather in midlatitudes.
Previous studies have suggested that Arctic Amplification has caused planetary-scale waves to elongate meridionally and slow-down, resulting in more frequent blocking patterns and extreme weather. Here, trends in the meridional extent of atmospheric waves over North America and the North Atlantic are investigated in three reanalyses, and it is demonstrated that previously reported positive trends are an artifact of the methodology. No significant decrease in planetary-scale wave phase speeds are found except in OND, but this trend is sensitive to the analysis parameters. Moreover, the frequency of blocking occurrence exhibits no significant increase in any season in any of the three reanalyses, further supporting the lack of trends in wave speed and meridional extent. This work highlights that observed trends in midlatitude weather patterns are complex and likely not simply understood in terms of Arctic Amplification alone.

To quote from my recent blog post:
The Francis/Vavrus paper linking Arctic Amplification to increased amplitude of waves in the Jetstream and 'stuck' weather patterns is now looking like it is wrong. In the Barnes paper it is noted that "metrics that focus on a narrow range of isopleths to track the ridges and troughs of a passing wave will incorrectly interpret a shift of the geopotential height field as a change in wave extent. When this shift is accounted for, no significant trend is found."

 In other words, with the warming of the northern hemisphere the atmosphere has been expanding, shifting geopotential heights upwards (geopotential height being the height at which a certain pressure level is found). And causing a northward shift of geopotential height. Barnes attacks the problem in a very intelligent way, she defines two indices, SeaMaxMin is the seasonal excursion of peaks and troughs in the 500mb GPH field, where GPH is 5700m up in the atmosphere, DayMaxMin is the same index but on a daily basis. In line with Francis and Vavrus SeaMaxMin has an upward trend, but crucially DayMaxMin doesn't. This is fatal problem for the Francis/Vavrus paper, because if their hypothesis is correct both indices should show an upwards trend.

EDIT - I've changed the first line to say the paper is flawed.
Discussion at my blog with Kevin O'Neill has tempered my conclusion, the blog post and comments are here:

Arctic sea ice / Comparison of PIOMAS and Quikscat MYI
« on: August 25, 2013, 08:34:10 AM »
In my most recent blog post I set out to compare Quikscat multi-year ice and PIOMAS ice thicker than 2m, both in January, the post digressed so I am presenting the comparison here.

PIOMAS doesn't explicitly provide ice age, but using ice thickness as a proxy for ice age it is possible to compare modelled and observed MYI distribution.

Ice thickens from open water or thin ice at the end of the summer towards 2m thick by April, MYI by January can already be in excess of 2m thick. I use this assumption and compare the white region of Quikscat to the blue region (>2m thick) in PIOMAS. Quikscat was the satellite 'radar' system that preceded ASCAT. In it young ice (<2 years approx) shows up as grey, old ice (>3 years approx) shows up as white.

The coloured images that follow are PIOMAS, black and white are Quikscat. And  we're comparing the blue region in PIOMAS and the white sea ice region in Quikscat.











This series of images shows what a good job PIOMAS is doing in terms of the spatial positioning of the thicker older ice, indeed I am still staggered at how good PIOMAS is. The images also show the decline of older thicker multi-year ice in the first part of this century, and this is shown not just by a model, but by satellite data from a system originally designed to monitor winds.

There's also a similar comparison using ASCAT here.

Arctic sea ice / The Cause of the Muted Melt of 2013?
« on: August 08, 2013, 10:27:15 PM »
UPDATE I've placed a question mark in the title of this thread, my reasons are explained here. Basically I am in the process of questioning whether ice dynamics or the Arctic Dipole are the major driver to the post 2007 increase in annual range.

The reason for the muted melt this year lies in the atmosphere. I should clarify muted, there are various indices and measures that clearly place 2013 in the set of post 2010 years, after 2010's volume loss. However the melt this year is not what it could have been, 2013 seems unlikely to challenge 2011 or 2007 and there is no realistic prospect of it challenging 2012.

Here is why.

First, here's the average sea level pressure for June to August (JJA) for the pre 2007 period.

Now the June to August average pressure for the period 2007 to 2012, it will become apparent why I've left out 2013 (aside from us not yet having August data).

These two patterns can be used as reference patterns using the numeric data behind them. Correlation can be carried out between these two reference patterns and each year's JJA average pressure maps. The result is two timeseries of correlations. A correlation ranges between -1 and 1, figures near zero show little agreement between the two patterns being correlated, figures near -1 show the pattern is the reverse of the reference pattern, figures near +1 show the pattern is a good match for the reference.

Here are the correlations for the last year, 2013 using the June/July average, not June to August.

2007 to 2012 show strong correlations, 2010 being reduced due to absence of the summer pattern in July (which is reflected in monthly anomaly changes for CT Area). These strong correlations and anti-correlations mark the post 2007 period out as unusual. However in 2013 June and July show negative correlations, the SLP pattern was reversed from the preceding six years.

The message is clear, take away the summer pattern that has been characteristic of the post 2007 summers, except 2013, and you don't get a massive melt, you get a muted melt. This is because the Dipole Anomaly it causes across the Arctic is absent.

Here is the atmospheric cross section and meridional (North/South) winds for June/July 2007 to 2012.

Note the red at 180deg (Chukchi) indicating strong northward inflow. Note the blue/purple outflow southwards between 120E and 30W.

Now 2013 June and July.

Note the near absence of inflow and outflow in the regions indicated above.

What 2013 is showing is that ice dynamics are not enough, the atmosphere has been playing a strong role in the enhanced melt seasons after 2007.

Arctic sea ice / The Summer Acceleration
« on: May 23, 2013, 09:06:16 PM »
I've just blogged about the summer acceleration of sea ice area.

This summer acceleration is that summer decline in area has been faster than winter decline in area in recent years.

Basically I argue that before this decline started both summer and winter were declining at similar rates. So why is summer declining faster than winter?

This is seen by comparing the decline in ice volume of PIOMAS grid boxes over 2m thick with the CT Area daily minimum, both curves track each other closely.

It is the decline in volume that is driving the decline in area. This has significance because April average thickness is now below 2m.

And it is around 2m that the percentage of melt to open water as a function of April thickness begins to increase rapidly.

We will see increasing volatility in summer melts in the years to come. And unless autumn growth of ice tempers the loss of April thickness, we will see this volatility manifest itself as a series of crashes.

Developers Corner / Excel - Spreadsheeting sea ice basics.
« on: March 24, 2013, 10:36:45 AM »
Excel - Handling Sea Ice Data.

The source Excel workbook for this is available on Google Docs here. It's best not to view it from Google Docs, select File>Download and save or open direct.

There are 6 sheets in the spreadsheet.
  • RawData - the point of entry for new data.
  • Area - Cryosphere Today area tabulated data.
  • Volume - PIOMAS daily volume tabulated data.
  • CalcThick - Calculated thickness tabulated data.
  • ThickSeries - Calculated thickness time series.
  • Monthly - Monthly average values of various indices of sea ice.

In this first post I'll explain the nuts and bolts of how this spreadsheet is constructed. In this series my posts will be headed with a bold title so that they stand out from any discussion.

The first place to start is on the sheet RawData, click on the bottom tab and it will open. Note that between lines 10 and 11 there is a break, scrolling below that line moves the data up and down but keeps the headers of the columns visible, they don't disappear off the top of the screen. This is done using Freeze Panes (View > FreezePanes).

You'll see that there are five columns:
  • Area.
  • DataTable.
  • Volume.
  • Extent.

The URLs from which to obtain the source data are given above each column, apart from DataTable, which will be explained. I could use data links to update these source data columns (Data > Get External Data), however I've found them to be awkward to keep working through regular updates, so my update method is to do it manually. I'll be keeping that Spreadsheet up to date when PIOMAS data comes out each month. The way to update is described in a following comment, for now I want to keep on track with how all this works.

The key to the RawData sheet, and to subsequent sheet interactions with RawData is the DataTable table (columns G to L).

The table itself is in blue and the key column is the DateNum column. This obtains the date number index from the Area columns (Columns B to E), specifically from the Date column (column B) which is from the original Cryosphere Today area data index as downloaded from the stated webpage. The Year (G) is worked out as =INT(Bxx), this cuts off the CT date index to leave only the integer part. The day number (day of the year) is a bit more complex, that uses:


ROUND(X,Y) rounds the number X, to the number of digits Y, for example ROUND(12.347386,2) produces 12.35 as a result. Bxx-INT(Bxx) simply removes the whole number part of a number leaving the part to the right of the decimal point, for example 12.347386 becomes .347386. This is then multiplied by 365 because it's a fraction of one year, and one is added because the first day of the year is '.000'.

The above formulae makes two columns, one of year and one of date. These are combined in column DateNum (I) to make a date index, which is essential to the operation of the whole spreadsheet. The combination is simply done by =Gxx&"\"&Hxx, which concatenates to make the string seen in column I, e.g. 1979/4.

Area is simply copied in cell by cell, to do this in a new table (Insert > Table) one need only put the formula into the first table entry, when the table is resized by clicking on a lower corner and dragging down, the formula copies down that column.

In Extent and Volume (K & L) we see the first use of the key formula behind the whole sheet; the VLOOKUP function. I'll ignore Extent as that's not maintained, and once you understand how volume works you'll be able to start maintaining Extent should you wish to. In a simlar way to the DataTable table, volume is built into a series in a volume table, which is seen in columns U and V. The values in this table are then referenced in column L, the volume column of our main data table.

VLOOKUP works as follows:

VLOOKUP(Reference, Source Data, Column Number, Match Type)

  • Reference - the item VLOOKUP will search for in the source data.
  • Source Data - the range of the spreadsheet that VLOOKUP will search in, actually it looks down the first column of that range.
  • Column Number - the column number in the Source Data range that VLOOKUP returns a result from.
  • Match - the type of match, we want to use an exact match so we'll use FALSE for this.

Because we'll be adding new data to the sheet the best was to declare our source data is with a named table. This is why the DataTable table has been built up. VLOOKUP will search down the DateNum column (I) until it finds a match for the Reference it's looking for, then it will use Column Number to tell it how many columns from the first column of the Source Data range it should go (to the right) to find the value to display.

So in L11 we have:


This means - look for the value in I11 in the table process volume, and when you find a match go to the same row as the match, but get the value from column 2. Because FALSE is selected get an exact match, or return an error. In this way the DateNum index of the Volume table (U & V) is searched for the dates listed in the DateNum column of DataTable and the relevant values of volume are copied into the DataTable. Once again; as you resize the table downwards the VLOOKUP formula copies down, so every date gets the correct data.

The result is a table of dates, and for each date are the matched Area (Extent) and Volume, although I'm not using Extent further.

But a list of numbers is not really of use, what we need to do next is get these values into a useable form. I'll go over that in my next comment.

Arctic sea ice / ASCAT, PIOMAS and the DAM
« on: February 22, 2013, 10:32:09 PM »
I won't make a habit of starting threads by linking to my blog. But I thought people might find this blog post interesting:

I balk at the idea of posting all those graphics on a post here.

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