From NOAA/NCDC, something interesting: a new map showing when to expect hottest days of the year and where. The long thin line on the west coast is interesting because that is where the bulk of the population of each state lives and one might think that has something to do with it, but it is actually related to the marine layer.
Mercury Rising: When to Expect the “Warmest Day of the Year”
Following the first official day of summer, many areas in the United States are approaching their highest temperatures for the year. To give you a better idea of the warmest time of year for your area, NCDC has created a new “Warmest Day of the Year” map for the contiguous United States. The map is derived from the 1981–2010 U.S. Climate Normals, NCDC’s 30-year averages of climatological variables including the average high temperature for every day. From these values scientists can identify which day of the year, on average, has the highest maximum temperature, referred to here as the “warmest day.”
Although the amount of solar radiation reaching the earth peaked at the summer solstice on June 21 in the Northern Hemisphere, temperatures for most of the United States tend to keep increasing into July. The temperature increase after the solstice occurs because the rate of heat input from the sun during the day continues to be greater than the cooling at night for several weeks, until temperatures start to descend in late July and early August.
But, this isn’t the case everywhere! The “Warmest Day of the Year” map shows just how variable the climate of the United States can be. For instance, the June values in New Mexico and Arizona reflect the North American Monsoon, a period of increased rainfall affecting the Southwest United States. Because these areas tend to be cloudier and wetter from July through September, the temperature is highest on average in June. Similarly, the persistence of the marine layer along the Pacific Coast leads to cool temperatures in early summer with the warmest days on average later in the season.
Temperature Normals are important indicators that are used in forecasting and monitoring by many U.S. economic sectors. Knowing the probability of high temperatures can help energy companies to prepare for rising electricity demand and farmers to monitor heat-sensitive crops. They are also useful planning tools for the healthcare, construction, and tourism industries. You may want to check the Normals before planning your next event or vacation.
While the map shows warmest days of the year on average throughout the United States, this year’s actual conditions may vary widely based on weather and climate patterns.
Source: http://www.ncdc.noaa.gov/news/mercury-rising-when-expect-warmest-day-year
h/t to Tom Peterson
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The 30-year definition of “normal” was decided on long before there was such a thing as PDO, AMO, or el Nino, in the meteorological literature. Eons ago myself, perhaps 40 years ago, I read that the average of the preceding N years, with N ranging from 10 to 30 or so, had the best correlation with the current year or next year. So it was the best “predictor” of the current or near future temperature of, say, a month. For what it’s worth, because I can’t recall a reference for that at this time of night.
As for daily “normals”, they are computed from the 12 monthly normals (from 30 year averages), to which an interpolation function (a running 3-month cubic fit, or a harmonic fit) creates a smooth curve from which daily values can be read. I’m a co-op observer (Coal Creek Canyon, CO), and over the past 30 years January has averaged a little warmer than December or February. So my daily normals have an odd bump in mid-winter. You eastern Yankees might call that the January thaw, but back east that’s not a big enough event to keep January from being the coldest month. In Colorado I think it has something to do with the regional north-south gradients causing westerly chinook winds to peak in January, which keeps nighttime minima a bit higher than December or February.
I myself find that map fascinating and it’s fun to look at how the season lag varies so much and to speculate on why. The seasonal lag becomes a lead down in Big Bend, Texas, where the daily max peaks before the solstice. The early seasonal max across the southwest is due to the onset of the summer monsoon in July, which increases dew points and afternoon clouds and showers that cut off the rising temperatures before mid-afternoon.
Meanwhile, the southern plains have a late seasonal max, and the line between the early and late seasonal max temps looks like the dry line, known and beloved by tornado chasers.
Maybe they’ll put out a cold version in the Fall when folks are more interested. Since the link to the chart is http://www.ncdc.noaa.gov/file/us-warmest-day-year-mapjpg , I tried replacing “warm” with “cool” or “cold”, but no luck.
I too found the map most interesting. Having lived in three of the four corners of the CONUS (missed out on Washington/Oregon) and now in South Central Texas , the map certainly coincides with my experience. It was interesting to move from coastal Southern California where the hottest days were during the first two weeks of September, to New England, where by the third week in August it was already starting to feel like fall.
Note however, the map says nothing about the temperature magnitude of the hottest day. It is still hotter in Texas in June than it is in Connecticut.
‘Temperature Normals are important indicators that are used in forecasting and monitoring by many U.S. economic sectors’
A bugbear that I have with forecasters in the UK, and I expect they’ll be the same around the world, is that they make statements along the lines of ” tomorrows temps will be two degrees above what we would expect at this time of year”. They seem unaware of the principle that norms are an average, and that a temperature two degrees above the norm is to be expected at least part of the time. Like tabloid headlines, there is a desire to make any news sound out of the ordinary.
I didn’t think it was quite this variable across the US so for me this is interesting. I guess there are local factors which influence the day of peak summer and peak winter.
It does however indicate an important physics phenomenon. Joules of energy accumulate and drawdown on the Earth’s surface on a per second basis throughout the day and there is net imbalance in those numbers on a daily basis through the seasonal cycle. One can actually calculate the joules involved here compared to the amount of energy coming in from the Sun and it is exceedingly small number.
Energy comes in from the Sun, and it is almost immediately emitted back to space. Very, very small amounts can accumulate as the day warms up and peaks around 3:30 pm and then each day there is a small excess accumulating as it warms up after the peak of winter to the peak temperature of summer. But these accumulation rates are nothing compared to the Sun’s daily energy inflow.
Energy and the speed that energy flows in and out of the system hasn’t been addressed by climate science that I have seen. Yet it is actually what is going on.
“Because “normal” in the context of weather is defined as a 30 year average”
Well that explains why weather related “science” does such a bad job at prediction and why everyone in “climate science” is so comfortable with “harmonizing” data. In no other field of science would you decide to toss out the bulk of data for an arbitrary averaging period. And you know you could convert the larger data set into 30 year rolling averages.
This also helps to explain why the Ag department’s “improved” hardiness zones resulted in so many of us in the north with a yard full of dead plants this spring. The “improved” zone maps based on the prior 30 years wrongly coded plants that then couldn’t handle a normal winter.
When I was recording temps living in forested western VA (2700 ft elevation), the date of the highest temp varied wildly — but ended up “average” around — July 21st. High elevation forested areas are nicely cool in summer:
1987 — 90F, 8/22
1988 — 91F, 8/17
1989 — 83F, 7/11
1990 — 87F, 7/9
1991 — 86F, 7/22 & 7/23
1992 — 84F, 7/13
1993 — 85F, 7/7 & 7/9 & 7/10
1994 — 85F, 6/15
1995 — 86F, 8/17
1996 — 87F, 5/19
1997 — 86F, 8/17
1998 — 86F, 7/21 & 9/14
Avgs — 86F, 7/21
Interpretive aid — includes informative land/ocean lag & gain maps:
Stine, A.R.; & Huybers, P. (2012). Changes in the seasonal cycle of temperature and atmospheric circulation. Journal of Climate 25, 7362-7380.
http://www.people.fas.harvard.edu/~phuybers/Doc/seasons_JofC2012.pdf (final)
http://www.people.fas.harvard.edu/~phuybers/Doc/Seasons_and_circulation.pdf (preprint)
=
[…] gradients give rise to anomalous circulation, with this anomalous circulation then causing convergence or divergence of heat by acting across either the land-ocean temperature gradient (a mechanism which is particularly important in Northern Europe) or across the pole-to-equator temperature gradient (an important mechanism in Eastern North America and in East Asia).
[…] the shift toward earlier seasons is not predicted by any of 72 simulations of twentieth-century climate made using 24 different general circulation models […]
[…] general circulation models forced with the observed twentieth-century forcing not only fail to capture observed trends in temperature seasonality, as mentioned, but also generally fail to reproduce the observed trends in atmospheric circulation […]
Thus, the hypothesis that changes in atmospheric circulation are responsible for changes in the structure of the seasonal cycle is consistent with the failure of general circulation models to reproduce the trends in either […]
=
More maps exploring spatial structure of oceanality/continentality:
McKinnon, K.A.; Stine, A.R.; & Huybers, P. (2013). The spatial structure of the annual cycle in surface temperature: amplitude, phase, and lagrangian history. Journal of Climate 26, 7852-7862.
http://popo.sfsu.edu/~zan/Files/McKinnon_Stine_Huybers_2013.pdf (final)
http://www.people.fas.harvard.edu/~phuybers/Doc/McKinnon_JofC2013.pdf (preprint)
http://people.fas.harvard.edu/~mckinnon/Home/Publications_and_presentations_files/McKinnonEA2013_JClim_EOR.pdf (preprint)
One thing I noticed was that the date of the hottest day strongly correlates with how humid an area is.
Would it be possible to determine if the atmosphere has been getting more humid (as the models say it must) by tracking any changes in the average date of the hottest day?
A few more links of historical interest:
Mann, M.; & Park, J. (1996). Greenhouse warming and changes in the seasonal cycle of temperature: model versus observations. Geophysical Research Letters 23, 1111-1114.
http://www.meteo.psu.edu/holocene/public_html/shared/articles/MannPark1996GRL.pdf
Thomson, D.J. (1995). The seasons, global temperature, and precession. Science 268, 59-68.
http://www.inventus.org/posterous/file/2009/04/228341-2886492.pdf
With more careful attention to
(a) SAM (Southern Annular Mode),
(b) Sidorenkov’s (2009) section 8.7 (on equator-pole heat engines), &
(c) the works of Jean Dickey (of NASA JPL),
the error in the latter paper can be corrected.
The mainstream outlook hinges on sea ice.
This abstract succeeds in getting the message across succinctly:
Dwyer, J.G.; Biasutti, M.; & Sobel, A.H.; (2012). Projected changes in the seasonal cycle of surface temperature. Journal of Climate 25, 6359-6374.
http://www.ldeo.columbia.edu/~biasutti/papers/Dwyer_Journal_of_Climate_2012.pdf
Figure 2(a)&(d) (ERA-40) concisely put the US “hottest day” map into a global context.
Illustrating the global context as concisely as possible:
http://s24.postimg.org/cfbs6lxd1/Seasonal_Cycle_Map_ERA40.gif
= map animation:
surface temperature
seasonal cycle amplitude / phase (slowly alternating maps)
Credit:
Dwyer+ (2012) (linked immediately above)
Humm.
The long thin line on the west coast is interesting because that is where the bulk of the population of each state lives
Southern California – OK.
Northern California – Not.
Oregon Coast – Not.
Washington Coast – Not.
Randall_G says:
June 28, 2014 at 8:12 pm
Humm.
The long thin line on the west coast is interesting because that is where the bulk of the population of each state lives
Southern California – OK.
Northern California – Not.
==========================
Last I looked, we here in the Bay Area are in the thin line area for the most part. Only our inland suburbs are not.