Wednesday, September 20, 2017

Autumn Cooling

There's a slight chill in the air today across interior Alaska, with afternoon temperatures around Fairbanks only in the mid-40s at valley-level thanks to clouds and some light rain.  Temperatures are in the mid-upper 30s in the higher hills; and a glance at the calendar reveals the reason.  Here are some of the observations as of about 6pm (click to enlarge).



Yesterday was the first day with a high temperature below 50°F in Fairbanks, and this is about a week late compared to normal.  Fairbanks usually sees its first sub-50°F day more than a week earlier than Anchorage, which illustrates the contrast between the rapid cooling of the interior and the slower cooling that occurs closer to the waters of the North Pacific.  Anchorage has never seen a sub-50°F day in August during the modern historical record, but it's not too uncommon in Fairbanks and happened just two years ago.

Here are a couple of webcam views of Fairbanks; autumn colors look to be a little past peak.




At this time of year it's always interesting to be reminded of the rapid drop-off in solar radiation at northern latitudes during autumn.  Thanks to 15 years of high-quality data from the Fairbanks CRN site (actually 11 miles northeast of town), we know what the normal incoming solar radiation curve looks like - see below.  I've added the normal cloud cover from Fairbanks airport to give an idea of relative changes in cloudiness through the year, although cloudiness is presumably a bit greater at the CRN site.


Notice how quickly the solar radiation diminishes in mid to late September; the average amount of incoming solar energy drops by 50% in the last 3 weeks of the month.  Of course, at the beginning of September it's already down by about 50% from the peak in June.

Here's the equivalent chart for Barrow, also from 15 years of CRN data; there's little available solar energy by this date in Barrow, and the very high cloud cover in late summer and autumn only exacerbates the rate of decline.


Just for fun, here's the equivalent solar data from the most southerly CRN site in the continental U.S., at Everglades City in Florida, along with the normal cloud cover from nearby Fort Myers.  By September 20 the normal solar radiation has decreased by only about 20% from the solstice, although the annual peak in south Florida occurs in May prior to the wet season.  The slow decline of the solar input, along with the maritime environment, explains why September is basically still high summer in Florida.


But the most interesting aspect of the comparison may be that Barrow receives more solar energy on average in June than south Florida - even though Barrow is very much more cloudy!  This is the result of 24-hour daylight; the noon sun in Barrow is nothing like the noon sun in Florida, but the hours of sunshine really add up in the far north.

Monday, September 18, 2017

La Niña Emerging

While the attention of many meteorologists (this one included) has been drawn to the remarkable storminess in the tropical Atlantic Ocean lately, significant events have been unfolding at the same time in the tropical Pacific.  Here's a sequence of maps showing the departure from normal of the sea surface temperature (SST) at 4-week intervals; notice the expanding area of below-normal SSTs along the equator in the central and eastern Pacific.




In tandem with the clear trend towards more La Niña-like conditions, the leading seasonal computer models have shifted quite dramatically towards a La Niña outlook for winter, and in response the most recent IRI/CPC forecast showed a sudden change to a much higher probability of La Niña.  Compare the two forecasts below, issued only a few weeks apart.  This is as dramatic a shift as I can recall in what is usually a slowly-evolving assessment of long-range forecast possibilities.





So what does the prospect of La Niña mean for Alaska?  One could be forgiven for having a hazy recollection of the last La Niña episode - the last time a modest La Niña occurred in winter was 2011-2012, and the last strong episode occurred the year before that.

The key feature of a La Niña winter, from the Alaskan viewpoint, is that the normal trough of low pressure over the Bering Sea and Aleutians tends to be relatively weak, and episodes of high pressure in this area are more common than normal.  A ridge over the Bering Sea is a cold set-up for most of the state, so it follows that most of Alaska is usually colder than normal during La Niña, although the signal is strongest in the southern part of the state.

The propensity for high pressure to Alaska's west and southwest reduces the frequency of storm systems moving into southern Alaska, so the amount of snow and rain is typically lower than normal in the south.  However, the west and north of mainland Alaska tend to see above-normal snowfall.

Here are maps of the November-March pressure, temperature, and precipitation patterns that were associated with the 10 strongest La Niña episodes since 1950, based on the Multivariate ENSO Index of Klaus Wolter.




Another aspect of the La Niña influence is that the flow pattern and associated temperatures tend to be more variable than normal over Alaska, so while the mean is below normal, fluctuations from week to week tend to be large.  This means that very cold conditions (much below normal) are considerably more likely than normal, but the chance of occasional very warm conditions is not much reduced except in southern and southeast Alaska.  For more reading on this, search "ENSO variance" on this blog.

Finally, the charts below provide another viewpoint on Fairbanks winter temperatures as they relate to the ENSO phenomenon. For this analysis I've calculated the November-March temperature anomaly relative to two different climatology periods in an attempt to remove the 4.5°F systematic difference between the early (1950-1975) and later (1976-2016) periods.  The vertical colored lines show the ENSO tercile boundaries.


The cold signal associated with La Niña is evident in the lower left, but it's clear that marginal La Niña winters are not always colder than normal, and the overall correlation between the ENSO index and Nov-Mar temperatures is not particularly good.  This partly reflects the fact that the PDO phase is not always aligned with the ENSO phase, and of course the PDO is more closely connected to Alaska winter temperatures than ENSO - see the corresponding chart below for the PDO correlation.


If we re-do the ENSO chart after excluding winters with an out-of-phase PDO, then the correlation improves quite a bit as we would expect, and especially for El Niño winters.


So in conclusion, if La Niña continues to develop into a strong episode this winter, then the usual La Niña patterns will become quite likely in Alaska; but if we see a less robust La Niña, then the outcome is much less certain.  Another way of saying this is that the PDO is more important; but unfortunately it is much more difficult to predict the PDO phase.  The PDO has been bouncing around near neutral recently and shows no indication of becoming significantly negative - and indeed I would say a significantly negative phase is probably unlikely because of the lingering subsurface effects of the strongly positive PDO phase that we've experienced in recent years.



Friday, September 8, 2017

Cooler Arctic Summer

Arctic sea ice extent is now close to its seasonal minimum, but in the past few weeks there has been less ice retreat than in some recent years.  Consequently a new record minimum extent is not likely to occur this year, although the National Snow and Ice Data Center notes that the ice edge in the Beaufort Sea is currently farther north than at any other time in the satellite era.  Here's the latest map of ice extent (defined as areas with greater than 15% ice concentration).



The summer circulation pattern was quite unsettled over the Arctic Ocean, with persistently below-normal sea-level pressure, especially to the north of Siberia.  The Arctic Oscillation was generally positive, corresponding to lower than normal pressure in the high latitudes.  Here are monthly maps of sea-level pressure anomaly from June through August.



Based on the 19 Arctic coastal observing sites that I've used before on this blog (e.g. here), temperatures were quite close to the 1981-2010 normal this summer on average around the Arctic basin, and this is a marked contrast from the persistent and extreme warmth of 2016.  The first chart below shows this year's June-August mean temperature in the context of recent decades, and the second chart shows the rather sudden cooling (relative to normal) that has occurred since late winter.




Given that Arctic sea ice extent is still far below normal, it's very likely that unusual warmth will return around the Arctic basin this autumn as the wide expanse of open water provides a direct heating influence; this post from last year shows the extraordinary warming trend for the month of October.  It will be interesting to see how the winter turns out, but in light of recent years it would be surprising to see anything other than significantly above-normal temperatures again for the seasonal mean over the Arctic basin.

Saturday, September 2, 2017

Brooks Range Chill in Context

Following up briefly on the last post, temperature data from the Atigun Pass SNOTEL provide a bit more context for the rather chilly conditions observed at this high-elevation site in recent days (down to 17°F on Wednesday).  Historical temperature data go back to 1999, although prior to 2006 there is only one data point provided per day - the temperature at midnight.

The chart below shows the accumulation of freezing degree days through August and September for each year since 1999 (calculated with only the midnight temperature throughout, for consistency).  The black line, indicating this year's pace of freeze-up, is a little ahead of the 18-year normal, but only by a few days, and the early chill this year has been much less severe than in 2000 and 2002.


Looking just at August total FDDs, the data from the early years confirm what I suspected - that the apparently cooler August conditions of the past few years are not unusual relative to a longer history.  From this perspective, the relative absence of chill in a number of years from 2004-2012 seems more unusual.


A longer and higher-quality history of temperature data is available from Toolik Lake Research Station, just 40 miles farther north up the haul road - and while this site is not quite in the mountains, the elevation at Toolik is still a respectable 2500'.  The chart below shows August FDDs at Toolik, confirming that recent years have brought freezes closer to the long-term normal, although not as severe as in some of the earlier years.


What could explain the early arrival of autumn chill in the Brooks Range in recent years?  The 500mb height chart from Wednesday provides a clue: notice the strong, cold upper-level trough over the North Slope.  More analysis would be needed to be sure, but it's likely that recent years have seen this kind of feature relatively more often at about the same time on the calendar.



Finally, lest we be tempted to infer that Arctic-wide conditions are cooler now than in recent years, the latest Alaska-centric sea ice analysis shows open water for more than 300 miles north of Barrow.  Arctic sea ice extent won't set a new record low this year, but it is still far below the long-term normal, and estimated ice volume is tracking near record lows.  More on that another day.


Tuesday, August 29, 2017

Brooks Range Snow

Wintry conditions have arrived early this year in the higher elevations of the Brooks Range, as evidenced by webcam photos taken this afternoon at the Chandalar Shelf DOT site (elevation 3285'), Anaktuvuk Pass (2171'), and Toolik Field Station (2493').





The Atigun Pass SNOTEL site is reporting a healthy snow depth of 8", and this is certainly plausible based on mostly sub-freezing temperatures for several days and the webcam views above.

This will be the 10th winter with a snow depth sensor installed at the Atigun Pass SNOTEL instrument array, and so we can look at the recent history of daily snow depth in August and September - see the chart below.



With the exception of last year, snow has been reported on the ground in August of each year since 2012, and 2015 also saw 8" on August 30, so the current conditions are not particularly unusual.  The 9" snow depth that was reported earlier this month on August 15 seems a little suspicious, although temperatures were certainly low enough and a little snow was evident on regional webcams at that time:




A curious feature of the chart above is that August snow was not reported in 2008-2011, and if we plot the first date of 4" snow depth, a rather striking trend jumps out - see below.  Based on this very brief history, significant snow has arrived earlier in the most recent years; but if we had a longer history, I imagine it would show that 2008-2012 were unusual for the lateness of the snow.  Given that temperature (and of course precipitation) data go back to 1999, it would be an interesting exercise to estimate when snow arrived for earlier years.


Friday, August 25, 2017

Growing Degree Days

The persistent (though not extreme) warmth in Fairbanks-land since the middle of summer has lifted the seasonal total of growing degree days to near the top of the historical range.  The chart below shows the daily accumulation of GDDs this year (black line) compared to recent years; the growing season started on a cool note but has ended as one of the warmer summers on record.


We've looked at the long-term increase in GDDs on a previous occasion, but it's still striking to note that the 1930-1960 normal was approximately equivalent to the coolest conditions of the past 20 years (summer of 2000).

The chart above makes it look like GDDs are usually accumulated nearly evenly through most of June and July, but the daily normals from 1981-2016 show a peak at the beginning of July as we would expect (green line in the chart below).


The black line indicates the frequency with which the daily GDDs are non-zero, i.e. the daily mean temperature is above 50°F; this doesn't drop below 50% until early September, but the additional accumulation of GDDs is small after about August 20... usually.  The month of August actually has the highest variance of GDDs of any month, as normal temperatures are much cooler than June or July but variability is increased; in some years the growing season is all but over on August 1, but in other years August provides as much as 30% or more of the seasonal GDDs.

Tuesday, August 22, 2017

North Pacific Blog Post

I added a new post on the Alaska "Blob Tracker" blog today, with a brief discussion of a new paper published in the Journal of Climate.  Thanks to Brian Brettschneider for pointing out the paper to me.

https://alaskapacificblob.wordpress.com/2017/08/22/atmospheric-connection-to-the-blob/


Sunday, August 20, 2017

Climate Normals in Changing Environment

Hi, Rick T. here. One of the things that interests me is how people adjust to a changing climate. Anecdotally, it was vaguely humorous to me last winter to see how quickly many people have incorporated three consecutive mild winters into a perception of a "new normal". This was underlying the many comments I heard about how cold the winter of 2016-17 was in Alaska. Of course, through the multi-decade lens, it wasn't notably cold for the winter (through parts of the state were, by any measure, cold in March). So that got me to thinking: given that many climate variables in Alaska are changing, how can we provide estimates of "normal" and associated variability that takes into account the ongoing changes?

One approach I've been toying with to make these kinds of estimates is with the use of quantile regression. Quantile regression is something of cousin to the more familiar least-squares regression, but is computationally more tedious, so was not much utilized until the advent of modern computing. Nowadays, it's trivially simple to use on the kinds of climate datasets that I mostly work with, that is, point-based time series. So the first question you ask: what is a quantile? A quantile is, to quote Wikipedia, "…cutpoints dividing the range of a probability distribution into contiguous intervals…". Quantiles can have any value between zero and one. So, the 0.5 quantile divides a distribution into two equal sizes: half the values are above and half the values are below. You've heard of this: it's better known as the median. A quantile of 0.843 divides a distribution into two parts: the quantile is the value of the distribution for which 84.3% of the distribution is below and 15.7% above. Quantile regression is a method to estimate the quantile values of a dataset when one variable is (possibly) dependent on one or more other variables. The second question you ask: why would you want to use quantile regression? There are a couple of reasons. First quantile regression is not nearly as sensitive to outliers as ordinary linear regression, which in effect models the mean. Secondly, and most significantly for my purposes here, quantile regression allows us to generate estimates of not only the central values of a distribution, e.g. mean or median, but also allows for estimates of how other aspects of the distribution are (possibly) changing.

As an example of this approach, below is a plot of some climate data that you are probably familiar with: spring breakup dates of Tanana River at Nenana (for this version I've used  "fractional dates" which incorporate the time of breakup, which does not matter to this analysis). There is no statistically significant trend through into the 1960s, so I construct the quantile regression to have zero slope in this time period. The purple line is the segmented median (0.50 quantile) date of breakup, which in this case we're looking at the dependence of breakup date on the year (i.e. the trend). The green-shaded area represents the area between the 0.333 and 0.666 quantiles. So, this plot should partition the breakup dates into three (roughly) equal categories: one-third below the green shading (significantly early break-ups), one-third inside the green shading (near normal) and one-third above (significantly later than normal). From this, it's easy to see that break-up dates during the first days in May in the mid-20th century were solidly in the "significantly earlier than normal" category, but the same dates are now in the "significantly later than normal" category.
Below is another example. Here I've plotted the Alaska-wide January through March average temperature from the NCEI Climate Divisions data set. In this case there is no strong evidence for a change in the regression slope that would be better fit with a segmented analysis. In this plot, the purple line is again the regression of the median (0.50 quantile), but the shaded area in this case represents one standard deviation (if the season average temperatures are normally distributed) either side of the mean (approximated by the 0.159 and 0.841 quantiles). You'll notice that the median and +1 standard deviation estimates have increased more than 3°F since 1925. However, the -1 standard deviation estimate has not changed at all. This suggests that late winter temperatures have become more variable: "cold" late winters are about as cold as they were 90 plus years ago, but the warmest late winters are now significantly warmer than back in the Roaring Twenties. How can that be?


Well, in part it's a feature of my analysis. The estimated slope of the 0.159 quantile (the bottom of the shaded area) is about the same as the median. However, at the 90% confidence level, the 0.159 quantile estimate crosses zero (for all you P-value fans, in this case this is the same as saying there is insufficient support to reject the null hypothesis of "no trend"). The 90% confidence estimate does not cross zero for the median or the 0.841 quantile. My convention is: if there is not robust statistical support for a non-zero trend, plot it as zero. More important than any convention, is there something interesting going on physically? I would suggest that yes there is. The late winter season has seen no long term change in the larger regional scale cryosphere variables, i.e. late winter sea ice extent in the Bering Sea shows lots of inter-annual variability, but no trend; snow cover extent is near the seasonal maximum with no trend at high latitudes. This means that given the appropriate weather pattern it can still be cold. Since cyrosphere changes are evidently not at play, ocean temperatures and increasing greenhouse gas forcing are the obvious suspects that would support increased warmth but at this point still allow the cold "tail" to hang on.

The quantile regression I've presented here allows us to make reasonable estimates of the current  distribution of some climate variables in the face of change. This simple linear approach is not likely to be sufficient in the future. For instance, in looking at the Tanana at Nenana breakup dates, I suspect that we are starting to (or will be soon) butt up against astronomical constraints on how early breakup can be given expected terrestrial climate forcing in the next century; e.g. a solar noon sun angle of 30º above the horizon (Nenana on April 1) can only do so much heating. In that scenario, well need to employ non-linear techniques. But that's a topic for another day.

___________________________
Updated to respond to Richard's comments and questions of Aug 21.
Here's a plot of the quantile regresion slope at 0.05 increments and the associated confidence intervals (90% level) for the Alaska statewide late winter (JFM) temperatures (data plotted above). In this case both the tails show higher spread in the confidence intervals than most of the middle, which I would expect. One wonders though what's going on at the 0.60 and 0.65 quantiles.
Here is some data with more a problematic structure. This is over a century of first autumn freeze dates at the Experiment Farm at UAF. I've included the segmented median and the "near normal" category (0.333 to 0.666 quantiles):
Here the "problem" is the cluster of very late dates between 2001 to 2011. Below, the quantile regression slope and confidence levels seem reasonable until the very high end. Notice the spread of the 0.95 is lower than others above the 75th percentile. I don't think this is realistic, and must be due to that cluster of very late (top ten) dates.
If we push it out even further and make it even more fine grained (quantiles 0.02 to 0.98 every 0.01)  more artifacts emerge, such as the occasional spikes in the bounds, and then the impossibly small confidence interval above the 95th percentile. For me the moral of this story is that it's important to do this exploratory review first, especially if the focus is in the far extremes of the distributions, where potentially other tools are better suited.   








Thursday, August 17, 2017

Summer Wanes

Summer is waning quickly now in Alaska's interior, and some cooler temperatures are finally showing up.  There have been no freezes in the Fairbanks area yet, but the first freeze occurred this morning at Chicken (29°F).  This is the 3rd latest first freeze in the 21 years of data from Chicken; the record latest was on August 21.

Similarly, the Chalkyitsik RAWS saw its first freeze this morning (32°F); this is also the 3rd latest on record (19 years of data; the record latest is August 22).

Last year at about this time I commented on the persistent warmth at the Goldstream Valley Bottom (Ester 5NE) coop site near Fairbanks, and it's been a similar story this summer.  From July 1 through August 15, the lowest temperature was 38°F, compared to 40°F in the same period last year.  In every other summer in this site's 20-year history, the temperature dropped to at least 34°F in this period, and indeed the average date for the first freeze is August 2.  The unusual warmth has been very persistent in the last few weeks.



Looking back at summer conditions across the entire state, the highest reliable temperature measurement was 94°F at the CRN site southeast of Tok, although a more remarkable heat wave occurred just 12 days ago at Skagway, when the temperature rose to 93°F - an all-time record for the site.  These were the highest temperatures in Alaska since June 2013, when Talkeetna smashed its all-time heat record with an astonishing 96°F.

Wednesday, August 16, 2017

Minchumina Follow-Up

Yesterday I posted what I thought was a bit of a mystery regarding solar radiation and temperature data from the Lake Minchumina RAWS, but within just a few minutes reader Gary pointed to a possible solution: increasing shade from vegetation that may have grown up right next to the RAWS instruments.  Here's a 2004 photo from the Western Regional Climate Center website (click to enlarge):


As Gary noted, the photo faces approximately east, so the tree growing up on the right side appears to be roughly southwest of what look like the thermometer and pyranometer in the middle of the arm.  Obviously if this and other vegetation hasn't been controlled in the 10+ years since the photo was taken, then it may have provided increasing amounts of shade over the instruments in recent years; and this would explain the reduction in both solar radiation and warm bias.

Interestingly the hourly solar radiation data support the idea that shading has developed from objects to the south and southwest.  The chart below shows the mean hourly solar radiation (units of langleys) during May on a kind of polar plot; the distance away from the center indicates the radiation amount in each hour, and the angle from the vertical corresponds to the average position (azimuth) of the sun in that hour.  So over the course of the day the solar radiation starts small in the east, increases as the sun moves towards the south, and decreases as the sun goes west.  The blue line shows the averages for 2009-2013 and the red line is for 2015-2017.


The plot makes clear that the reduction in sunshine is fairly small in the morning until about 11am in May, but then it appears that the shading effect is pronounced by around 1-3pm, when the sun is just west of south.  This is nicely consistent with the apparent location of vegetation in the photo.

The charts below show similar results for June, July, and August.  Interestingly the month of June is the only month in which there appears to be no shading from the southeast, i.e. around 9am-noon, and this makes sense if we consider that the sun rises highest in the sky near the solstice; so whatever vegetation has grown up to the southeast, it's apparently not yet high enough to cause shading in June.




In conclusion, I think the problem is just about solved - it looks like the Minchumina radiation data have been seriously affected by shading in recent years, and this has also altered the temperature bias relative to the nearby airport thermometer.  Final confirmation will await a site visit: anyone want to take a field trip?