Thursday, December 18, 2014

Winter Analogs

Regular reader Eric suggested that it would be useful to look at some analogs to see if we can find any indication on how long the unusual warmth may or may not persist this winter in Fairbanks.  Under certain circumstances, such as when major climate forcing mechanisms can be identified, analog forecasts can be at least as useful as very-long-range computer model forecasts.

The first analog I looked at was based on past years in which the PDO was significantly positive (at least +0.5 PDO index value) in both November and December.  I also required the November 1 - December 15 mean temperature in Fairbanks to be at least 3 °F above the preceding 30-year normal.  The chart below shows the daily temperature anomalies in thin gray lines and the median of the 10 years in black.  There is a wide range of temperature outcomes for January, but the median is generally on the warm side as we would expect.  I wouldn't regard any of the January peaks and troughs as being statistically significant, but the peak in early February is larger and it certainly looks like warmth is favored in the first third of February.

If we consider the fact that El NiƱo is under way at present, and combine that with the positive PDO in December, we find a slightly more robust indication of warmth through most of December and the first part of January, and again in early February.  It is interesting to note that large cold anomalies are notably infrequent among the analog years for most of December and February, but cold shows up for some of the years in January.

Another aspect of the current climate phase space is that the Quasi-Biennial Oscillation (QBO) is strongly negative.  Combining this criterion with the positive PDO in December produces a forecast that is generally similar, although the warm signal in January looks slightly more significant (see below).  The median temperature is considerably above normal for much of January, but cold extremes also show up in some of the years.

The similarity of the PDO-QBO forecast to the others suggests that perhaps the QBO has only a marginal influence.  I tested this by also finding analogs with positive PDO and negative QBO, i.e. the opposite QBO phase.  Under these conditions the warm signal seems notably less pronounced for January (see below), so perhaps we shouldn't discard the QBO as a useful factor in the forecast.

Finally, there has been much discussion lately in forecasting circles about the very large anomaly in Siberian snow cover this autumn.  In the past decade or so, it has been discovered that snow cover over Eurasia in October is a skillful predictor of the subsequent winter's Arctic Oscillation (see e.g. here), and October 2014 produced a near-record snow cover extent in Eurasia.  The implication is that the AO phase is very likely to be negative this winter (especially after January 1st), which would imply higher than normal pressure over the Arctic and lower than normal pressure in parts of the mid-latitudes, including the North Pacific; this in turn would suggest warm conditions in southern Alaska.  I looked at 7 analog years and found only small signals for Fairbanks (see below), although warmth in early February again appears more likely than cold.

Putting it all together, the grand ensemble of all the analog years produces the result shown below.  Warm conditions are the most likely outcome for most of January and the first half of February, and the probability of warmth appears to be highest for early February.  The range of uncertainty is large for January, so it shouldn't be a surprise if unusual cold shows up for at least a time.

In terms of monthly mean temperatures, the overall ensemble of 23 distinct analog years produces above-normal temperatures 65% of the time in January, 74% of the time in February, and 57% of the time in March.

Monday, December 15, 2014

Seasonality of PDO Influence

Mean daily temperatures in Fairbanks have been almost constantly above normal since the beginning of November, and the PDO phase has become strongly positive (see charts below).  Given the recent PDO behavior, the persistence of unusual warmth is no surprise, as we discussed in a post on November 27.

I thought it would be interesting to look in a bit more detail at the seasonal variations in the PDO influence on Fairbanks temperatures - for example, does the PDO influence peak in December, or is it equally large at other times of the year?

To address this question, I first looked at monthly data and examined the outcome in months for which the PDO index was in the top or bottom quartile of the 1930-2013 historical distribution.  This gives a sample size of 21 on both ends of the PDO distribution.  The first chart below shows the percentage of months in which the Fairbanks temperature anomaly had the same sign as the PDO index, and the second chart shows the median monthly temperature anomalies as well as the 25th and 75th percentiles of the temperature anomalies.  Note that I used the "normal" for the entire history (1930-2013) and did not adjust for long-term trends.

Looking first at the positive PDO, the first chart shows that December is indeed the month in which the positive PDO phase most reliably causes above-normal temperatures in Fairbanks (18 of 21 years), but the second chart indicates that the median anomaly is not as high in December as in November or January through March.  Also, the second chart reveals that there are large and interesting differences in the width of the conditional temperature distribution during the positive PDO phase.  For example, in November the positive PDO temperature distribution is quite narrow, with over 80% of years having a mean anomaly above +3 °F, but in January the temperature distribution is wide during positive PDO years.  We infer that the positive PDO is a much more useful predictor for Fairbanks temperature in November than in January; but the PDO signal becomes quite robust again in February.

The temperature behavior during the negative PDO phase is largely a mirror image of the positive PDO behavior, except during June-August when the negative PDO has almost no influence on Fairbanks temperature.  November is clearly the month in which the negative PDO most reliably causes below-normal temperatures in Fairbanks.  The negative PDO signal is much weaker in January, then picks up again in February, although with a large amount of temperature variance.  Overall, winter temperatures are more variable when the PDO is negative, leading to the conclusion that a negative PDO phase is somewhat less useful as a seasonal forecast predictor than a positive PDO phase.

Another way of looking at the data is to examine the distribution of daily temperature anomalies during positive and negative PDO phases (see charts below).  Here I used the prior 30-year climatology of daily mean temperature to obtain the daily anomalies, and I ran the calculation over sliding 21-day windows throughout the year, with the PDO index interpolated from the monthly values to the center of each date window.  The purpose of doing this is to see if there are any notable sub-monthly features of the climate during positive or negative PDO phases.

The charts above show quite a number of interesting features, but I'll only mention a few.  First, the response of daily Fairbanks temperature to the positive PDO phase seems to be relatively stable in winter (especially early winter) and does not show either a November peak or a January dip in PDO influence, as indicated by the monthly charts.  It's not necessarily surprising that daily probabilities behave very differently from monthly probabilities, but I'll have to think some more about possible explanations for the difference.  Interestingly the annual peak in probability for positive temperature anomalies with the positive PDO phase falls at the end of April, and the annual minimum is less than a month later; however, the median temperature is always above normal.

The daily chart for the negative PDO clearly shows the peak in negative PDO influence in November, but then there is a fascinating spike up in December, and the median temperature anomaly is above zero on about December 20.  It would be interesting to look at the historical years in more detail to see how this plays out; I suspect it is a reliable feature of the subseasonal evolution in the wake of a cold PDO-induced trough over Alaska in November.

It's also interesting to note that daily temperatures are more likely to be above normal than below normal in most of June, August, and September during the negative PDO phase.

Friday, December 12, 2014

January 1937 snow depth

My co-author and climate detective extraordinaire, Brian, sent me a couple of scanned images yesterday from the Fairbanks News-Miner of January 21, 1937.  This was the day after 0.99 inches of rain had fallen on top of 26 inches of new snow; over 50 inches of snow had fallen in the previous 14 days.  Not surprisingly, the newspaper article describes snow loads as reaching critical levels for buildings in the city.  Fortunately, there were only light snowfalls for the rest of the winter.

The chart below shows the daily temperatures, accumulated snowfall amounts, and snow depth for the winter of 1936-37; the snow depth reached the all-time record of 57" on February 11.   January was by far the wettest winter month on record, also the snowiest, and was the second warmest January (1930-present), with the temperature rising above freezing on 9 days.  (While remarkable, this is nowhere near as warm as January 1981, with 15 days above freezing.)

Thursday, December 11, 2014

Intra-Annual Climate Variability

If you don't like the weather, just wait 15 minutes – unknown

What parts of the U.S. have the most temperature and precipitation variability? This question is actually not so difficult to answer. The National Climate Data Center (NCDC) publishes temperature and precipitation values for nearly 10,000 stations across the country. Figure 1 shows the location of those station.

Figure 1. Location of 9,708 stations that NCDC publishes daily normal temperatures and/or daily normal precipitation. 5,869 stations have temperature data and 8,533 stations have precipitation data.

Temperature variability

There are a myriad of methods for computing the variability of temperatures at a location. For temperatures, the NCDC does all of the heavy lifting for us. One of the temperature variables that they compute in addition to the daily normal temperature is a daily normal standard deviation. For the statistically uninitiated, standard deviation is a measure of dispersion from the mean (average). In theory, 68% of daily temperatures at a station will be within 1 standard deviation from the daily mean. For example, is a station's normal temperature on October 1st is 60°F and the daily standard deviation is 8°F on that date, we expect that the temperature on October 1st will fall between 52°F and 68°F on 68% of years. We also expect temperatures to fall within 2 standard deviations approximately 95% of the time. Therefore, mapping the average daily standard deviation (of all 365 days) for all stations is a direct measure of temperature variability. Figure 2 shows the average published NCDC standard deviation for 5,869 stations and Figure 3 shows only stations in Alaska.

Figure 2. Average daily standard deviation for 5,869 stations based on NCDC published values.

Figure 3. Average daily standard deviation forAlaska stations based on NCDC published values.

The largest values of temperature variability are in interior Alaska north of the Alaska Range. Umiat, Alaska, wind the variability contest. On average, Umiat has a daily temperature standard deviation of 12.2°F. In the Contiguous U.S., the largest values are in Montana and North Dakota. Powers Lake , North Dakota has that largest value in the Contiguous U.S. (10.8°F). Stations with the largest values are subject to the widest variations of temperature whereas stations with the lowest values have very constant temperatures. The 44 lowest variability stations are all in Hawaii. The Ohe'O 256 station in Hawaii has a standard deviation of 1.5°F. In the Contiguous U.S., the lowest values are right around San Franciso, California. Several station there have standard deviations under 3.5°F. In Alaska, Shemya has the lowest average annual standard deviation of 2.7°F.

Precipitation variability

Unlike temperature variability, precipitation variability is much more difficult to measure. Since precipitation does not fall on every day, the distribution of precipitation events has a skewed distribution. If a station averages 30" of precipitation a year that falls on 100 days a year, that works out to a daily average of  0.08" per day. What that also means is that on 265 days, when no precipitation fell, they are below normal in terms of precipitation. Probable 30 or 40 of the other days had under 0.08" so those days were below normal too. This is why the NCDC does not compute a precipitation daily standard deviation.

A much better method is to look at monthly precipitation values and see how much they change over the course of the year. In many cases there are substantial difference between wet and dry months. Some stations in California and Alaska receive 60% of their annual precipitation in a three-month window. On the flip-side, many stations in the Northeast and mid-Atlantic have precipitation evenly distributed across all months.

Figure 4 shows the month-to-month variability in precipitation values across the year for the entire U.S. and Figure 5 shows Alaska only.. To make this map we calculated the difference between the NCDC normal precipitation for each month and compared it to the value that would occur if each month received 1/12th of the annual precipitation. For example, image two stations that each average 24" of precipitation per year. One station averages 2" for each of the 12 months. The other station receives 80% of their annual precipitation between May and August. In this hypothetical, the first station has very low monthly precipitation variability while the second station has very large precipitation variability. This type of assessment is called a goodness-of-fit test. In this case we used the chi square goodness-of-fit-test. The values produced by this test are unitless and are evaluated against a table of significance values. To avoid confusion, the values are left off the map and substituted with "high" to "low" labels.

Figure 4. Intra-annual precipitation variability based on monthly totals of 8,533 stations. Stations with consistent precipitation values throughout the year are shown in green and stations with large month-to-month variation (e.g., distinct wet and dry seasons) are shown in red.

Figure 5. Intra-annual precipitation variability based on monthly totals of Alaska stations. Stations with consistent precipitation values throughout the year are shown in green and stations with large month-to-month variation (e.g., distinct wet and dry seasons) are shown in red.

As you can see, some areas have low month-to-month variability and others have quite a bit. The precipitation variability winners are mostly in California. Of the 188 stations with the most variability, 185 are in California. This is due to the strong seasonal concentration of precipitation during just a few winter months. The highest variability a non-California station is Kuparuk. The second highest non-California station is Northway. On the flip side, the stations with the least precipitation variability are in eastern New England and the North Carolina Piedmont. In Alaska Kodiak and Kitoi Bay have the lowest monthly precipitation variability.

I had assumed that all cold regions would have low winter precipitation values due to the moisture capacity of the air being greatly reduced. However, that is only the case in the Northern Great Plains and Alaska – not in New England. The other quite surprising finding is the low month-to-month variability in the Great Basin. Perhaps this is an artifact of multiple synoptic-scale parameters in other regions that all converge in this region.

Overall variability

So which regions have the overall highest variability? To combine the maps, we need to make a few assumptions and do a few calculations. First we need to arbitrarily declare that 50% of a station's variability is based on the precipitation variability and 50% is based on the temperature variability. On the calculation side of the equation, we have a problem combining datasets with different units – especially since the precipitation variability calculation is unitless! Therefore, we scaled all temperature variability values (standard deviations) to a maximum score of 50 and scaled all temperature variability values to a maximum score of 50. We then added the two together and rescaled the results on a scale from that maxes out at 100 (8 to 100). Figure 6 shows the final variability score for the entire U.S. and Figure 7 shows the score for Alaska only.

Figure 6. Precipitation-climatology combined variability score. Values are scaled up to 100.

Figure 7. Precipitation-climatology combined variability score for Alaska only. Values are scaled up to 100.

At the large scale, much of California, most of Alaska, and a large part of the northern Great Plains have high values of variability. The very highest values are in northern Alaska. The lowest values of climate variability are found across all of Hawaii, the western Aleutian Islands of Alaska, and the northern coast of the Gulf of Mexico.

The station with the highest annual climate variability is the U.S. is Kuparuk – their value is 100. They have the 34th largest temperature variability (of 5,869 stations) and the 57th largest precipitation variability (of 8,533 station). Their precipitation variability is the largest of any non-California station. In the Contiguous U.S., Sandberg, California, has the highest climate variability. They are located at 4,000' in the high desert east of Los Angeles. Sandberg has a pronounced winter precipitation concentration and due to their elevation, they have a surprisingly large annual temperature variation.

The station with the lowest value is Ohe'O 256 on the island of Maui. Their combined value was 8.3. All of Hawaii has uniform temperatures and this portion of Hawaii has very consistent precipitation. Outside of Hawaii and the Aleutian Islands in Alaska, the station with the lowest value is Dauphin Island, Alabama. Their combined value is 26.5.

Greatest and Least Variability by State

Earlier we noted which stations had the greatest and lowest values. However, if we limit the analysis to cities with at least 25,000 people, it becomes a little easier for people to relate to. Table 1 below shows the station with the largest variability score (max =100) for each state. Santa Clarita, California, has the largest value of any city in the nation. At the bottom of the list is Hilo, Hawaii. Their variability score is less than half that of the second lowest statewide value.

Table 1. Largest variability score for each state when looking at cities with at least 25,000 people.

Table 2. Smallest variability score for each state when looking at cities with at least 25,000 people.

Greatest and Least in Alaska

That analysis was limited to cities with over 30,000 people. However, Alaska has only three such cities. Therefore, a proper analysis of Alaska needs to drop the threshold substantially. In this case, we decided on a value of 100. Table 3 shows the list of the 25 cities with the larges values (left side) and the 25 cities with the smallest values (right side).

Table 3. Largest (left) and smallest (right) variability scores for Alaska cities with at least 100 people. These are "cities" as defined by the U.S. Census Bureau. We make no distinction between a city, Census defined place (CDC), a village, or a Native village.

The maps in Figures 6 and 7 clearly show maximum variability along the North Slope and the eastern interior. Why is there so much variability in these locations? The answer is twofold. First, interior areas have an extreme continental climate. This results in very large temperature differences between summer and winter. Also, winter temperatures can vary by 100°F or more from one year to the next on the same calendar date. This is reflected in the very large temperature standard deviation values (see Figures 2 and 3). On the precipitation side of the equation, the extreme cold of winter dramatically reduces precipitation in areas with monthly temperatures below 0°F. Most of these areas receive 60% to 80% of their total annual precipitation in 3 to 4 months. The extreme concentration of precipitation in a small number of months gives those places large precipitation variability scores.


Some stations live up to the "just wait 15 minutes" saying and others don't. The variability in the northern Great Plains is not surprising but the variability in much of Alaska and especially California was somewhat unexpected. At the other end of the scale, the low measures of variability in the Great Basin was entirely unexpected. This region has nearly even precipitation throughout the entire year. The very low precipitation variability overwhelmed the modest temperature variability. Was there anything here that surprised you?

Tuesday, December 9, 2014

Climate of the 1930s

In last Wednesday's post I made a remark about the 1930's being an interesting time for climate in Alaska, so of course this idea deserves a post of its own (and probably many more).  I'll focus here on the winter climate and perhaps look at other seasons at another time; I'll also deal mostly with Fairbanks.

Readers of this blog may have noticed, as I have, that the 1930's often show up in comparisons between current and historical weather events in Fairbanks, because many records set in the 1930's are still standing.  For example, December 1934 saw the greatest winter chinook in Fairbanks history, with high temperatures in the 50s for five consecutive days resulting in a complete snow melt-out and the only brown Christmas in the city's history.  The winters of 1935-1936 and 1936-1937 saw significant rain on five separate occasions, including 0.99" of rain in January 1937 (contributing to the extraordinary monthly precipitation total cited in the previous post).  In 1938, extraordinary warmth in late October (60 °F on the 21st) resulted in the latest arrival of a 1" snow depth on record.

Of course, there was plenty of very cold weather too in the 1930's.  If we look at the daily record low temperatures for Fairbanks (1930-present), half of the daily records for cold in December-February were set or tied in the 1930's (45 out of 91 days).  However, this is partly an artifact of the much smaller urban warming effect in the early years.

A quick re-examination of the mean temperature and total precipitation from each Fairbanks winter since 1930-1931 (see below) shows that the 1930's were not particularly warm or cold overall, and except for the record wet winter of 1936-1937, the decade was not particularly wet or dry.  Therefore the mean winter climate doesn't stand out as being especially different from the long-term mean in Fairbanks.  However, with many extreme weather events having occurred in that decade, we might hypothesize that the climate was more volatile on sub-seasonal timescales.

To explore the idea that short-term weather extremes may have been prevalent in the 1930's, I calculated the frequency with which weekly average conditions fell within the top or bottom 1% for sliding 31-day windows throughout the year.  For example, for the date window centered on January 16, I pulled out all the weeks with central dates in the range January 1-31 and then sorted the ~2600 weekly average temperature and weekly total precipitation values.  The top and bottom 1% of temperature and the top 1% of precipitation were flagged as the "extremes" for the January 16 window.  After doing this for every day of the year, the total number of extreme values were added up for each winter (November through March); the results are shown below.

The colored columns show the November-March counts of extremes for temperature in the top 1% (red), temperature in the bottom 1% (blue), and precipitation in the top 1% (green), and the lines show the corresponding 10-year trailing mean for each category.

Several features are notable: first, the dramatic increase in warm extremes and decrease in cold extremes in the late 1970's, related to the PDO phase change.  Warm extremes have continued to occur in the most recent 20 years, but cold extremes have become rare.  In terms of wet extremes, the winters of 1962-1971 stand out, but the 1930's also saw a lot of very wet weeks.  It's interesting to note that warm, cold, and wet extremes were all quite frequent in the 1930's, which supports the hypothesis of highly volatile weather in that decade.

The chart below shows the sums of the counts for all three categories and confirms that the five winters of 1931-32 and 1933-34 through 1936-37 brought a sustained high frequency of extreme weather.  The decade of winters ending in 1971 was also quite extreme, but in that case the decadal mean is skewed by the outlier winter of 1970-1971.  Another notable aspect of the chart is the relatively subdued (non-extreme) winter climate of the past two decades in Fairbanks.

I also looked at monthly extremes of temperature and precipitation to see whether the same patterns are evident for the calendar months of winter.  The columns in the chart below show the number of times each winter that one of the calendar months fell into the top 5 or bottom 5 for temperature, or the top 5 for precipitation (1930-present).  The red line shows the 10-year trailing mean.  As in the weekly chart, we see a peak in about 1971 for the preceding decade - the 1960's was an extreme decade for both weekly and monthly averages - but the 1930's do not show up as particularly extreme on a calendar month basis.

In conclusion, initial analysis suggests that Fairbanks winters in the 1930's were characterized by relatively frequent weather extremes on a weekly timescale, though not on a monthly timescale.  What might explain increased volatility of daily and weekly weather?  My only hypothesis at this point - and it is really only speculation - is that this could be related to some unusual behavior of ENSO during the 1930's.  I've long been interested in the fact that ENSO volatility was particularly low during the 1930's, as seen in the chart below.  Between March 1931 and May 1938 there was not a single month in which the ENSO index exceeded a magnitude of 1.0, and the 10-year trailing standard deviation dropped to 0.48 in April 1938 (less than half the 1981-2010 standard deviation of 1.04).  My qualitative hypothesis is that the lack of tropical forcing - perhaps counterintuitively - allowed for unusually large fluctations in the jet stream position over Alaska during the 1930's, thereby causing extreme weather in Fairbanks.  In other words, with no major ENSO forcing to lock the winter jet stream into preferred orientations, it oscillated from one extreme to another with unusual frequency and amplitude.  I hope to investigate this idea soon using the 20th century reanalysis data.

Another feature of the global climate in the 1930's that may or may not have influenced Alaska climate is that the North Atlantic sea surface temperatures were relatively warm compared to other oceans, i.e. the Atlantic Multidecadal Oscillation was strongly positive (see e.g. here).  The relationship between the AMO and Alaska climate is another topic worthy of investigation.

For the sake of curiosity, I also computed the frequency of weekly and monthly extremes for Nome and Barrow, see below.  Nome saw many wet extremes in the winters of the 1930's and 1940's, but those decades were less notable for unusual warmth and cold.  Barrow saw a remarkable concentration of winter extremes from about 1950 to 1975 (mainly cold and wet), and there has been an uptick in wet extremes in the past 4 winters in Barrow.  Remarkably, Barrow has not seen a winter week in the bottom 1% for temperature since 1994-95.

Sunday, December 7, 2014

-40° Reached

The first measured -40° temperature of the season (as far as I'm aware) was observed in Alaska this morning, as the thermometer at Toolik Field Station registered -42 °F at 2am AKST.  A couple of sites came close to this benchmark of winter cold on November 28 (-36 °F at Arctic Village, -35 °F at Chicken).  Here's an infrared image from the Suomi-NPP satellite at 0516 AKST this morning, with the location of Toolik Lake indicated; it looks like many locations north of the Brooks Range were at least as cold.

It's late in the year to be observing the first -40° temperature, but there have been 7 years since 1950 in which it occurred even later; the 1950-2013 record latest was in 2002, when -40° was not reached until December 30.

The first -40° temperature of the season is most often observed at places such as Allakaket, Bettles, Umiat, Chandalar Lake, and Northway.  Historical data from Toolik Field Station began in 1988, and that year was the only other year in which Toolik was the first place to reach -40°.

Here's the Toolik webcam image looking south an hour before solar noon today.

Wednesday, December 3, 2014

Temperature-Precipitation Correlation

As snow fell across Fairbanks-land yesterday, I found myself wondering about the joint distribution of precipitation and temperature during the winter months.  We've seen before that temperature and precipitation are slightly inversely correlated in Fairbanks in early winter, i.e. unusually warm conditions are more associated with dry than wet weather.  This is not true for most of Alaska, but the prevalence of chinook winds in the eastern interior creates anomalous behavior in the climate statistics.

To delve into this a little more deeply, I calculated the rank correlation coefficient for monthly mean temperature and monthly total precipitation for 1930-2013 in Fairbanks, and for 1996-2013 at Keystone Ridge.  For the Fairbanks calculation I removed the long-term linear trend in temperature (by month) to help obtain a truer comparison across the decades.

The chart above shows that the monthly correlations are quite weak for Fairbanks in the winter months.  Keystone Ridge shows somewhat more of an inverse correlation in November and December, and both stations show more substantial negative correlations in summer and autumn.

A slightly different picture is obtained when the correlations are derived from weekly data.  The chart below shows the rank correlation for weekly-average temperature and weekly-total precipitation for each day of the year, based on a +/- 15 day window centered on each date.  No adjustment for long-term trends is included here.  Based on the weekly data, there is a weak positive correlation between temperature and precipitation at Fairbanks airport from November through February, but the correlation remains negative at Keystone Ridge through virtually the whole year.

The weekly and monthly correlations show opposite signs for Fairbanks in December, which is interesting; this suggests that warm weeks tend to be relatively snowy, but warm Decembers are more often drier than average.  However, the correlations are weak, and it is a bit dangerous to draw conclusions from summary statistics like this.  The charts below show the joint distribution for each month from November through March, and it's very evident that the climate actually produces a peak in precipitation amounts for near-normal temperatures, with drier conditions during both warm and cold extremes of temperature.  This is not difficult to explain: very warm conditions arise from southerly chinook flow, which is dry, and very cold conditions arise from northerly flow, which is also dry.  Therefore it's overly simplistic to think in terms of a monotonic relationship between winter temperature and precipitation in Fairbanks.

By the way, the extreme outlier for precipitation in January is not an error.  This month (January 1937) was more than twice as wet as any other winter month, and was the second wettest calendar month on record in Fairbanks (1930-present).  An extraordinary 66" of snow was received.  Only three times has Fairbanks received an inch or more of precipitation in a single calendar day from November through May - all in January 1937.  The 1930s sure was an interesting time for climate in Alaska (and elsewhere).