The decline in the sea ice extent in May and June of 2010 appeared to be extremely fast. According to NSIDC,
Arctic sea ice extent averaged 13.10 million square kilometers (5.06 million square miles) for the month of May, 500,000 square kilometers (193,000 square miles) below the 1979 to 2000 average. The rate of ice extent decline for the month was -68,000 kilometers (-26,000 square miles) per day, almost 50% more than the average rate of -46,000 kilometers (18,000 square miles) per day. This rate of loss is the highest for the month of May during the satellite record.
However, later on the same page, they also state under Conditions in Context:
As we noted in our May post, several regions of the Arctic experienced a late-season spurt in ice growth. As a result, ice extent reached its seasonal maximum much later than average, and in turn the melt season began almost a month later than average. As ice began to decline in April, the rate was close to the average for that time of year.
In sharp contrast, ice extent declined rapidly during the month of May. Much of the ice loss occurred in the Bering Sea and the Sea of Okhotsk, indicating that the ice in these areas was thin and susceptible to melt. Many polynyas, areas of open water in the ice pack, opened up in the regions north of Alaska, in the Canadian Arctic Islands, and in the Kara and Barents and Laptev seas.
This latter observation that the seasonal maximum was reached later in the season and the melt season started later is important. Regardless of specific annual weather conditions, May and June are melt season months in the Arctic. Furthermore, if there is more ice available, then it stands to reason that more melting will take place. What might a better way to look at the data than simply plotting the total extent?
From the JAXA site:
Why not graph the rate of change, as well? In particular, because a wider extent will naturally imply a higher areal melt under the same melting conditions, it makes sense to look at the daily percentage change.
To do this, I downloaded the JAXA daily ice data into R (from 2002 to the present). For convenience purposes, December 31 was deleted from both 2004 and 2008 to reduce the number of days to 365. The percentage change was calculated for each day for which the corresponding data was available. No infilling was done for missing data. The data was plotted:
(Click graph for larger version)
Here, all of the years prior to 2010 are plotted in gray and the current year in red. The plot gives graphic insight into the patterns of thawing and freezing: the thaw season goes from roughly mid-March to mid-September. The very high variability in October is likely due to a reasonably similar annual speed of recovery which is expressed as a percentage of quite varied minima starting points in September.
How does 2010 compare in May and June? For May, it is somewhat toward the lower part the combined record, but I would not classify it as extreme in any way. June was definitely below the other recent years during three periods of several days each. What will July and August look like? I guess we will have to wait and see…
The R script follows:
#get latest JAXA extent data
iceurl = url("http://www.ijis.iarc.uaf.edu/seaice/extent/plot.csv")
latest = read.csv(iceurl,header=F,na.strings="-9999")
colnames(latest) = c("month","day","year","ext")
#remove Dec 31, 2004 and 2008 (extra leap year day) for convenience
#fill in with missing values for early part of 2002 (for convenience)
arc.ext = latest$ext
which((latest$month==12)&(latest$day==31)) # 214 579 945 1310 1675 2040 2406 2771 3136
arc.ext = arc.ext[-c(945,2406)]
arc.ext = c(rep(NA,365-214),arc.ext)
#length(arc.ext)/365 # 9
#calculate changes as % of current value
#form matrix with 9 columns (one for each year)
pct.change = matrix(100*c(diff(arc.ext),NA)/arc.ext,ncol=9)
#years 2002 to 2009 as gray background
#year 2010 in red
#add month boundaries
modays = c(31,28,31,30,31,30,31,31,30,31,30,31)
matplot(pct.change[,1:9],type="l",main ="Arctic ice Extent Change Relative to Area",xlab="Day",
ylab="Daily % Change", col=c(rep("grey",8),"red"),lty=1)
text(x =14+c(0,cumsum(modays)[-12]),y =c(rep(3,9),rep(-1,3)), labels=month.abb,col="blue")