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 30ยบ 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?

Tuesday, August 15, 2017

Increasing Clouds at Minchumina?

Some weeks ago when I was exploring the artificial warming reported by RAWS thermometers on sunny days, I noted a remarkable reduction in the RAWS warm bias at Lake Minchumina over the past several years.  The chart I showed earlier is reproduced below, with the solid lines indicating the difference in monthly means of daily high temperature (RAWS minus airport AWOS).  In 2009-2012 the RAWS high temperatures averaged about 4-5°F warmer in May through July, but in 2015 and 2016 the difference was only around 2°F.


In the previous post I suggested that the systematic trend towards a smaller warm bias could be related to increasing cloudiness; if solar radiation has been lower in recent summers, then there would be less artificial warming of the RAWS thermometer.

To explore this hypothesis, I looked at solar radiation data from the Lake Minchumina RAWS for May through August - see the chart below.  To minimize issues related to missing data, I first calculated the mean solar radiation for each hour of the day in a given year and month, and then I took the 7am-7pm mean while requiring fewer than 10% missing data points to arrive at a valid monthly number.


The results are rather striking, with a pronounced drop-off in solar radiation since 2014-2015 at the Lake Minchumina RAWS.  Broadly speaking the decrease in reported solar radiation corresponds to the diminution of the RAWS warm bias, so this seems to be physically consistent.  But is it possible that the solar radiation can have dropped off so significantly and in such a sustained manner in each month from May through August?  This would imply a really significant shift in the summer climate and I'm inclined to be skeptical: a more likely explanation, perhaps, might be that a systematic error has been developing in the solar radiation measurements.  For example, perhaps the sensor has somehow become progressively more obscured or inefficient.

We might have a lot more confidence in the Minchumina RAWS data if other nearby RAWS sites showed a similar reduction in solar radiation.  The map below shows the area we're dealing with.


Looking first at data from Fairbanks, there is little evidence of a sustained increase in summer cloudiness and in fact the overall trend in solar radiation is slightly upward since 2007.  But of course Fairbanks is a long way from Lake Minchumina.


The data from the Telida RAWS (southwest of Minchumina and at similar elevation) do suggest a downward trend in solar insolation in each of the summer months - see below.  The magnitude of the trend is less than at Minchumina, and in particular June and July of this year were comparable to earlier years, which is very different from the situation reported at Minchumina.  So the Telida data seem only mildly supportive of the Minchumina trend.


Looking farther to the southwest, the Farewell RAWS has also reported a modest decrease in summer sunshine over the past 10 years, but again the change in the past few years is nowhere near as dramatic as at Minchumina.



The McKinley River and Wein Lake RAWS also show some indications of diminished solar radiation, but the trends are not pronounced and consistent across all months as at Minchumina.  So again, this is not conclusive.




What about other independent sources of information on cloudiness and humidity?  The airport AWOS at Lake Minchumina has reported cloud coverage at sub-hourly intervals for some years, so this should be of some value - see below.  No significant trend is evident, but we must bear in mind that the AWOS ceilometer can't detect high-altitude cloudiness; and moreover the bi-annual oscillation in the cloud cover is very odd and more than a little suspicious, so I'm not sure the data are trustworthy.


Finally, a quick look at reanalysis data suggests that, contrary to expectations, relative humidity has been lower than the long-term normal over our area of interest in recent years.  Of course, the NCEP global reanalysis is incapable of reproducing the local flow patterns around the Alaska Range and so its broad analysis may not correspond to local trends, but nevertheless it does not support the idea of increased summer cloudiness.



In conclusion, the situation at Lake Minchumina unfortunately remains a puzzle.  The significant reported decrease in solar radiation is quite consistent with the dramatic reduction in the RAWS warm bias, suggesting that recent summers have been consistently and significantly more cloudy than earlier summers; but data from other sites and sources give a mixed message as to whether we should believe the Minchumina trend.  Judging from the multi-site consensus of the RAWS data, it does seem that summers have been relatively cloudy of late in the upper Kuskokwim valley, but that may be the only conclusion we can make.

Saturday, August 12, 2017

The Fairbanks Flood of 1967: The Rainfall

Hi, Rick T. here. You don't have to live in Fairbanks very long to hear about the great flood of August 1967. Even for a community long accustomed to significant flooding, this was extreme. Something like 95% of the city was flooded, causing millions of dollars in damage. Rasmussen Library at UAF has posted a number of videos from the flood on Alaska Film Archives YouTube channel. The NWS Fairbanks Forecast Office and the Alaska-Pacific River Forecaster Center have also put together a very nice online storybook with a short description of the meteorology and hydrology and lots of photos. There has also been work by the NWS, the City of Fairbanks and FNSB Borough to survey and put up high water mark signs around town, and many of these have been installed in the past couple weeks, such as this one downtown on the north bank of the Chena River.


In this post, I want to look at the rainfall that lead to this flooding from a climate perspective. The rainfall August 11-13, 1967 stands out as the highest of record for daily and multi-day totals. The only rainfall records this event does not hold are short duration records, which are all thunderstorm related. To set the stage, here is the background: the second half of July 1967 saw well normal rainfall: 3.07" fell between July 16 and 31. That's still the fifth highest "second half of July" total. However, that was followed by a dry start to August: only 0.02" of rain fell during the first week of the month. But then the skies opened. Below is a plot of the hourly and cumulative rainfall for the week of August 8-15, 1967 (data extracted from Fairbanks August 1967 Local Climatological Data). More than half an inch of rain fell on the 9th. This was followed by about 36 hours with very little rain. Starting late in the afternoon on the 11th, moderate rain fell without much of a break until the morning of the 13th, though light rain continued to dribble on into the 15th before the fire hose finally shut down.
The following totals were recorded at the Airport, all of which still stand as the highest of record:
  • 24 hour: 3.44" 11pm AKST August 11 to 11pm AKST August 12
  • 36 hour: 4.40" 4pm AKST August 11 to 4am AKST August 13 
  • 48 hour: 4.76" 3pm AKST August 11 to 3pm AKST August 13
  • Single calendar day: 3.42" August 12  
  • Two consecutive calendar days: 4.29" August 11-12
  • Three consecutive  calendar days: 4.98" August 11-13
So beyond "highest of record", what's the climatological context? Was this a one-in-a-million event, or is there some reasonable likelihood it will be broken?  To answer this I've compiled annual extremes of a few of these parameters and then fitted a generalized extreme value (GEV) distribution. If that's greek, no worries: GEV is a standard technique for analyzing extreme event frequency and generating estimates of return periods.

Maximum 24-hour precipitation (not necessarily calendar day) has been recorded in Fairbanks since the Weather Bureau office opened in the summer of 1929. For two and three day totals, I've included the cooperative data from the Ag Experiment Station starting with 1915, when daily precipitation began to be regularly recorded. In the graphic below I show an example of the annual times series, in this case the maximum 24 hour precipitation (upper left) and then the GEV analysis for the annual  maximum 24 hour precip (upper right) and annual maximum two and three day consecutive days (bottom row). The red line shows the fitted return period, while the open circles are the observations (which are plotted in rank order).  I should point out that there is no significant trend in any of the annual values.
So what are the return periods for the precipitation amounts that occurred in August 1967?

With  87 years of data:
  • 3.44" in 24 hours is expected to occur on average once in 203 years
With 102 years of data:
  • 4.29" in two consecutive days is expected to occur on average once in 269 years
  • 4.98"  in three consecutive days is expected to occur on average once in 352 years
I'm actually not a fan of return periods. It's not technically wrong, but many people would look at those numbers and say "gee, I'll never see that." In fact, the return periods are just an alternate way of expressing the probability of occurrence. So if I tell you that there is 13% chance that Fairbanks Airport will receive precipitation totaling 3.44" or more in 24 hours hours once in the next 30 years, that's equivalent to saying the return period is 203 years, but the take-home message is different. Now 13% in 30 years is not high (and that makes the dubious assumption that there is no change in extreme precipitation events in a warming world), but it is hardly unthinkably rare.

Postscript:
The GEV analysis I've presented here differs somewhat from that published in the 2012 NOAA Atlas 14 primarily in that I used a longer period of record (for extremes analysis, the longer the better), and, as near as I can tell, the maximum 24 hour precipitation (as distinct from calendar day) was not used in compiling that work.

The maximum 24 hour precipitation for August 1967 is listed as 3.42" in the August 1967 Local Climatlogical Data publication and has been carried through ever since. In fact the hourly precipitation data in the same publication shows that the correct amount is 3.44", 11pm on the 11th to 11pm on the 12th.



Thursday, August 3, 2017

Extent of Humid July

In the previous post a question arose as to whether the very high humidity observations from Fairbanks airport in July were accurate or if perhaps the sensor might be malfunctioning to some extent.  To shed a bit more light on this, I looked at other sites around the interior and also - thanks to a suggestion from Rick T. - looked at the surface dewpoint reported twice per day on the Fairbanks upper-air sounding.

The sounding data clearly support the record July humidity reported by the ASOS instrument - see the chart below.  Last year saw the most humid July on record at the Fairbanks upper-air site (adjacent to the airport), and this July was even more humid.

Looking at hourly data from Fort Wainwright and Eielson AFB, the same thing is observed; both of these sites also saw a record high monthly mean dewpoint.  This is a record for any calendar month, not just for July; and at all three sites the previous record was in July 2016.


I also pulled out the historical data for several other sites across the interior to see how far afield the record moist airmass extended.  The charts below show the July mean dewpoint for 12 sites divided broadly into eastern and western groupings, with the record high values indicated by markers.  Among the "eastern" sites, Nenana and Northway observed record humidity in July 2017, although the 1973-74 data from Delta Junction and the 1962 record from Fort Yukon look highly suspect, and if we take these out then the record also occurred in 2017 at these sites.


Farther to the west, July 1998 was the most humid July (and calendar month) on record at Galena, Indian Mountain, and Bettles, and July 2004 was the most humid month at McGrath, but a new record was set last month at Minchumina and Tanana.


In conclusion, the data suggest that record humidity occurred last month at least throughout the Tanana River valley, as new calendar month record dewpoints were observed at every site I looked at from Northway down to Tanana (assuming the 1974 Delta Junction data is wrong). 

The July 500mb and MSLP patterns (see below) do not show an amplified pattern over Alaska, but the modest upper-level ridge over northern and western Alaska was persistent and prevented cool, dry air from reaching the interior from the north.  Daily minimum temperatures were almost entirely above normal in Fairbanks as shown in the chart below.

 


It seems to have been the stagnant weather pattern, more than anything, that allowed humidity to pool over the Tanana River valley, although the widespread above-normal sea surface temperatures (and therefore enhanced evaporation) surrounding Alaska presumably played a role.  Here's a map of recent SST anomalies, as shown by Rick in his recent climate briefing.


Saturday, July 29, 2017

Summer Humidity

As a follow-up to recent posts on summer temperatures, let's take a quick look at summer humidity trends in Fairbanks.  This topic is timely, as Rick drew attention yesterday to a very remarkable statistic: the number of hours this July with a dewpoint of 55°F or greater in Fairbanks is higher than last year, despite the month not being over yet - and last year the number was apparently far higher than any other year in recent decades.  Here's a chart from Iowa State University.


When I first saw this I really thought there had to be a mistake somewhere, but this is what the hourly observations from the airport have recorded.  There does seem to be a chance that the ASOS sensor is malfunctioning to some extent, as the Eielson ASOS data (see below) show similarly high humidity in a number of earlier years; but there's little doubt that this month has been much more humid than normal.


For a longer term look at each of the summer months, the chart below shows monthly mean surface dewpoint and column precipitable water.  The precipitable water is the total moisture in the atmosphere above a given location and is expressed as the depth of liquid that would result if all the moisture were condensed, i.e. the amount of moisture that is theoretically "precipitable".



The long-term upward linear trends in July dewpoint and precipitable water are highly statistically significant, although for precipitable water there has been little increase since about 1980.  June has also seen increases, but less pronounced, and the long-term changes in August have been quite small.

As noted in the previous post, higher humidity provides an obvious (although perhaps not sufficient) explanation for higher daily minimum temperatures during summer in Fairbanks, because water vapor is a powerful "greenhouse" gas.  See this previous post for more discussion on the topic.


Tuesday, July 25, 2017

Changes in Summer Warmth Odds

Rick's post on the climatological chances of reaching various warm temperature thresholds in the remainder of summer led me to wonder how these "exceedance probabilities" have changed over time.  The chart below takes a very simple look at this by showing the probability curves for years prior to 1976 (solid lines) and years since 1976 (dashed lines).


Perhaps surprisingly, the 90°F threshold is the only one with a distinct systematic difference between the two periods.  Clearly 90+ temperatures have become more common in recent decades, but none of the other curves show pronounced or consistent differences throughout the summer.

Part of the explanation for the lack of difference is that summer-time daily high temperatures have not warmed very much over the long term in Fairbanks.  In terms of high temperatures, June through August in the latter period (1976-2016) was less than 1°F warmer than the earlier period; whereas daily low temperatures were nearly 3°F warmer.  If we created a similar chart for probabilities of warm nights rather than warm days, I'd expect to see greater differences.  I'll see if I can add that chart tomorrow.

[Update July 26] Here's the chart for daily minimum temperatures, so now instead of looking at the chance of a threshold high temperature being reached after each date, we're looking at the odds of daily minimum temperatures being above a threshold after each date.


As expected, the differences are more pronounced for warm nights than for hot days (the term "night" being used loosely for the height of summer).  The probability of any given summer having a daily low temperature of 60°F or above nearly doubled from 39% to 76% between the two periods, and this trend has continued in more recent years; the only year in the last 10 years without a 60+ minimum temperature was 2011.  It's worth noting also the stark difference for August: a 60+ minimum temperature was not observed in August prior to 1974, but it has happened 15 times since then.

The stronger warming trend in daily minimum temperatures is probably caused by a combination of urbanization influences (more heat-storing asphalt etc) and higher humidity derived from warmer ocean surface temperatures surrounding Alaska.  These effects are less significant during the day because the lower atmosphere is generally well-mixed and the afternoon surface temperature is closely tied to temperatures aloft and the amount of sunshine at this time of year.

Sunday, July 23, 2017

Warm Weather Prospects

Hi, Rick T. here with a short post on the (climatological) propects of warm weather the remainder of the summer. We're more than a month past summer solstice, and in the Interior, fireweed is blooming, blueberries are (finally) coming ripe and it's getting dimmer in the middle of the night. All signs that we're on the back side of summer. Summer is by no means over yet, but with each passing day, very warm temperatures becoming less likely, and we wonder,"is this the last time it will be this warm til next year?"

Of course, we can use history as a guide as at how likely a given temperature is to reoccur. Below is a temperature threshold exceedance plot for Fairbanks, constructed using the full Weather Bureau/NWS era daily data. That sounds complicated, but it really is pretty easy to use. Each colored line represents a threshold value. e.g. the blue line is for 80°F or higher.  The vertical axis represents the historical probably that a temperature equal to or higher than the threshold will occur later than the dates on the horiztonal axis. So for any given date, just go up the until you intersect the threshold of interest and read from the vertical axis the (historical) chances of exceeding that temperature later in the season. Harder to explain than to do.

By way of example, let's start on the left hand side of the plot. Say it's June 11th, and you want to know the chances it will reach or exceed 90°F the rest of the summer.  Just go up the June 11th vertical line (in this case, it's the first dashed grid line), and where it intercests the red line, read over to the vertical axis. This gives about a 24% chance that it would reach 90°F or higher. This is of course close to the climatological chance of a 90°F temperature for a summer.   An example from the middle of the chart: today (July 23) the temperature at the Fairbanks Airport reached the 80°F threshold value. What are the chances it will get at least that warm again before the snow flies? Well, just going up the July 23 verticial, we see that there is, climatologically, about a 70% chance of a temperature 80°F  or higher occurring before the end of summer. However, when I consult the handy-dandy NWS forecast, I see that there are no temperatures anywhere close to 80°F forecast for the next week. So if that works out, by the last days of July, the chances that a temperature 80°F or higher will occur later than that drops to ~55%.


Now, of course there are limitations to this approach, esepcially in regards to extremes. For example, since the temperature has been 80°F or higher as late September 4th, there is no reason to think that there is zero chance of a temperature ≥  80°F on September 5th. Also, this approach assumes that the threshold occurances are independent, which is not likely to be true, and this has the highest impact on the analysis of of rare events (e.g. any 90°F temperature, or any 80°F temperature after late August). Overall, this approach is best suited to getting a fix on the high-probability to low-probability transitions, which we are now approaching.

Tuesday, July 18, 2017

Wetter in July

Ever since the 1981-2010 standard climate normals became available in 2011, July has been "officially" the wettest month of the year in Fairbanks.  As noted by Rick Thoman back in 2011, this is a new phenomenon, as August was traditionally the wettest month of the year; in fact, all other 30-year normal periods back to 1931-1960 had August as wetter than July, and in the early decades it wasn't particularly close.

Here's a comparison of the monthly mean precipitation from 1931-1960 with the modern climate normal period; interestingly the July-August total mean rainfall is nearly unchanged, as August rainfall has decreased about as much as July rainfall has increased.


The change of monthly rankings is not just a feature of monthly means and therefore susceptible to one or two huge outliers; the monthly medians show a similar result (see below).  Note that the medians are more stable in winter; for example, the 1931-1960 means were thrown off by the outlandish January precipitation of 1937.


If we add the most recent 15 year period, 2002-2016, the recent change really jumps out; the month of July has been extraordinarily wet in recent years compared to earlier normals.


The chart below shows a more continuous view of the evolving differences between July and August rainfall.  In the earliest decades, August really was a lot wetter than July; from 1930-1947, August was wetter in 15 of 18 years.  Contrast this with the past 18 years: from 1999-2016, July was wetter in 14 of 18 years.  This is quite a profound change in the climate.


So how do we explain the recent change to higher precipitation in July?  In a nutshell, heavy rain events have become much more common in July, and while the heaviest rain events are still rare, they are occurring frequently enough to cause a dramatic increase in climatological July rainfall.

Here's a look at the running 15-year frequency of daily rainfall events of 1" or more in July and August.  There have only been 27 of these days in July and August since 1930, so this is close to the top of the daily rainfall distribution.  Remarkably, August has not seen a single such day since 1990, but July has produced 8 such days since 2003 (3 of them were in 2014).


Given that normal July rainfall used to be only about 2" in Fairbanks, an increase like this in the frequency of 1" days is bound to have a significant effect on the long-term averages.  We can quantify this by dividing up the total rainfall into categories of daily rainfall amount - see below for the July breakdown.  Prior to the past 15 years, July rainfall was obtained nearly equally from events of 0.1-0.25", 0.25-0.5", and 0.5-1", but rain events of 1" or more were so infrequent that they contributed rather little to the overall total.  In contrast, the past 15 years have seen nearly equal contributions from the top 3 categories, and the change is most pronounced for the heaviest rain events.  Despite the rarity of 1" rain events, even in the new regime, these events are now contributing a substantial fraction of the total July rainfall.


Here's the parallel analysis for August.  In the past 15 years, the 0.1-0.25" events have contributed less and the 0.5-1" events have contributed more, but oddly there have been no days with daily rainfall above 1" in recent years. 


In conclusion, July has become easily the wettest month in recent years in Fairbanks, and this is largely because of a dramatic increase in the frequency of heavy rain events (above 0.5" and especially above 1").  Interestingly, August has seen a paucity of 1-inch rain events in recent years, and this shift appears to date back all the way to the 1970s.

From a physical standpoint, the increase in July heavy rain events is very consistent with the increased capacity of a warmer atmosphere for holding water vapor, and the same trend has been found in most areas of the U.S. and in many parts of the globe: see the 2014 U.S. National Climate Assessment and the 2012 IPCC SREX report (Table 3-2) at the following links:


The absence of a similar trend in August is more difficult to explain and therefore in one sense it's a more interesting result; I'd like to look at whether similar changes have occurred at other sites and dig a little deeper into the physical mechanisms responsible for the observed trends.

Sunday, July 16, 2017

Raws Warm Bias Continued

A couple of weeks ago I presented a few results from my latest project - an attempt to adjust RAWS temperature data to remove the warm bias that occurs during strong sunshine.  The goal here is to make the RAWS temperature data more useful for climate monitoring; we want to know the spatial and temporal distribution of temperature variations across Alaska, but the RAWS measurements are heavily affected by this warm bias that varies depending on sunshine and - to a lesser extent - wind speed.

In the previous post I showed the results of a bias correction based on the hourly quantity of solar radiation, for 3 different RAWS sites that are located close to reliable FAA instruments (ASOS/AWOS).  Now let's look at the effect of wind speed, which is also measured by the RAWS platform.  The charts below show the residual differences between the RAWS and FAA temperatures after the solar adjustment has been applied, with hourly mean wind speed on the horizontal axis.  The red markers indicate the median difference for each wind speed value; note that wind speed is reported to the nearest whole number in mph.




At all 3 sites, increasing wind speeds cause the RAWS temperature to decrease relative to the ASOS temperature, which is what we expect; when a breeze is blowing, the thermometer is naturally aspirated and the airflow through the thermometer housing helps reduce the artificial warming from solar heating.  This means that the warm bias becomes less of a problem as the wind picks up, and therefore it also means that my solar adjustment is too great when the breeze is blowing: if I apply my solar adjustment without regard to wind speed, then my adjusted temperatures will be too low (as shown in the scatter plots).

The obvious next step is to model the wind speed effect in a similar manner to the solar effect, and I've done that using another analytical function to describe the relationship.  After optimizing the fit of the function for each site separately, the results look like this:




Happily we find that the average wind speed dependence has been largely removed, although of course this is not a perfect process; the temperatures still seem to be biased a bit high at low wind speeds at Eagle and Lake Minchumina.

So after all this we have a set of hourly adjusted temperatures for these RAWS sites, and we can now run a test to see whether the revised data show monthly or annual climate variations that are similar to those measured at the FAA sites.  Here we are interested not so much in the long-term average bias, which can always be removed by subtracting the long-term normals, but in the sign and magnitude of month-to-month and year-to-year changes.

Ideally we would find that such changes are very similar for each pair of sites; for example, when the adjusted Fairbanks RAWS data say that a month was 3°F warmer than normal, then we want to see that the ASOS data show the same anomaly.  If this is true, then the monthly temperature differences would remain constant over time - indeed the differences would be zero if the bias is fully removed - and then we could claim that the adjusted RAWS data provide a true estimate of the long-term temperature variations.

I'll start by showing results from Lake Minchumina, where the adjustment procedure seems to have paid off handsomely.  The first chart below shows the May, June, and July monthly means of daily high temperature before and after the RAWS adjustment; the FAA/AWOS temperature is also plotted in blue.  Note that these results are drawn only from the sample I used for the adjustment process - i.e. only "peak sunshine hours", so the high temperatures might be different from 24-hour values in some cases.  Clearly the adjusted RAWS numbers show very similar month-to-month and year-to-year changes to the AWOS data.  The unadjusted RAWS data also capture the major ups and downs, but notice that there's a trend in the differences: the unadjusted RAWS line is closer to the others in more recent years.


The chart below highlights the trend issue by showing the monthly mean differences of daily high temperature between the two sites, with the unadjusted differences indicated with solid lines and the adjusted differences shown with dashed lines.


The key thing to note here is that the adjusted differences don't change significantly over time - there is little trend and the monthly variance is much smaller than for the unadjusted data.  This means that, as we saw above, the adjusted RAWS temperatures essentially move in lockstep with the AWOS temperatures.  This is in contrast to the unadjusted RAWS data, which show a remarkable trend: the RAWS warm bias has diminished considerably in the past few years.  We might be tempted to speculate about instrumentation changes as a cause for this, but the fact that the bias correction eliminates the trend suggests that solar radiation has been reduced significantly in recent years.  Obviously I'll have to confirm whether that is the case; it would be an interesting result by itself.

In conclusion, the removal of the RAWS warm bias at Lake Minchumina appears to work very well as a means to improve the quality of the data for climate monitoring.  Unfortunately, the monthly mean temperature results are not as encouraging for Fairbanks and Eagle - see below.  The long-term average bias has been removed, but the monthly temperature differences are not significantly less variable than for the unadjusted RAWS data.





The disappointing results at Fairbanks could be related to the fact that the RAWS and airport ASOS sites are over 12km apart, in contrast to Lake Minchumina where the two sites are only a few hundred meters apart.  I looked into using Fairbanks' Fort Wainwright ASOS instead of the airport, but the Fort Wainwright historical data are not as complete.

In Eagle the problem could simply be that the solar warm bias is smaller, as shown in the first post, so there's less opportunity to improve the RAWS data.

Finally, as a measure of the degree of improvement, here are (1) the correlations of the monthly mean temperature anomalies before and after adjustment, and (2) standard deviation of the monthly mean temperature differences before and after adjustment.  The higher the correlation and the smaller the standard deviation, the better.

SiteCorrelation: before (after)Standard deviation (°F): before (after)
Lake Minchumina0.93 (0.99)1.27 (0.43)
Fairbanks0.91 (0.92)1.56 (1.37)
Eagle0.98 (0.98)0.79 (0.70)