The world has warmed on land by up to 1.5 C since pre-industrial times, but what does that really mean? Most people assume it means extreme temperatures are increasing. However that is not the full story. The daily change in temperature from night to day we experience on land is far higher than that from global warming. Scientists use temperature anomalies to estimate climate change based on whether any location has warmed relative to a standard 30 year normalisation period. All reference data (CRU, GHCN, BERKELEY) are based on the 12 monthly anomalies of average temperatures (Tav) for each station, where Tav = (Tmax+Tmin)/2 processed from the original station records. The only source for these original raw weather station data that I know of is GHCN Daily. This is a huge archive (33600 stations) of measurements dating back to 1750. Each station recorded the maximum temperature and minimum temperature each day of operation. These also measure the diurnal temperature range because Tmin occurs at night and Tmax occurs around midday.
Tav ( average Daily temperature) = (Tmax+Tmin)/2
Trange (Daily Diurnal Range) = Tmax – Tmin
Robert Rhode from Berkeley Earth was the first person AFAIK to calculate global the Land temperature anomaly based solely on this daily data. I then repeated the calculation using an icosahedral gridding method. First here are the Berkeley Earth Results.
Daily temperature anomalies. Traditional BEST temperatures are shown in red
The reduction in the temperature range is clearly visible in the annual data. This becomes clearer if we plot annual avergaes
Tav and Trange anomalies (Berkeley Earth) Trends
It is clear the the diurnal temperature range has decreased by about 0.6C since 1880. My analysis of GHCN Daily is described here. The next two plots shows my independently calculated result.
GHCN-Daily monthly anomalies. In red are shown the TMAX-TMIN (Diurnal) anomalies.
Temperature and Range anomalies for full time range- Annual averages.
Again we see that Trange (TMax – TMin) has reduced significantly since 1880. The early data actually seem to show an increase prior to 1850 but stations data are sparse in this early period. I also find a reduction in Trange of about 0.6C since 1900.
Further evidence that minimum temperatures are warming faster than maximum temperatures can be seen in the Australia ACORN daily station data of 112 stations. For each station I take the highest temperature recorded each year and the minimum temperature recorded each year. I then average both of these over the area of Australia based on station locations.
Average Maximum and Minimum temperatures in Australia
So in general the extreme maximum temperatures have not increased significantly in Australia whereas the coldest temperature (at night in winter) have.
All these examples essentially imply that 20th century warming has been mostly occurring at night. Nights are warming faster than days. I think this can be explained as follows.
During the day the sun heats the surface which then cools mainly by convection through the troposphere up to where it can radiate to space. At night the inverse is true. Without any solar heating and clear skies, the surface cools until convection slows or stops and surface radiative cooling becomes more important. The surface cools as the radiation balance changes. That is also how fog and ground frost occur. So increasing CO2 levels reduce radiative transfer to space more at night than it does during the day, when convection is more important. The increase in the effective emission height affects both maximum and minimum temperatures but proportionally more so at night than during the day. This is illustrated below
Figure from Richard Lindzen. Pure radiative equilibrium would be the temperature gradient without convection. The surface temperature would be >20C warmer than today ! Thermodynamics drives the lapse rate towards the moist adiabatic lapse rate
Nights shifts the temperature gradient towards Pure radiative cooling.
Great insight into the mechanics and real impact of global warming due to CO2 in the atmosphere. Thanks for sharing the article.
typo on 8th line? Tav = (Tmax – Tmin)/2 => Tav = (Tmax + Tmin)/2
as private eye would say shum mistake ed?
True indeed!
I should proof read it before publishing!
A very good article. Your graphs are labelled all of Australia… How many of the data points are in urban areas and how many are in rural ones? Urbanisation does seem to have a greater role in raising minimum temperatures rather than changing maximum ones. In fact I have seen analysis of the USA showing maximum temperatures falling steadily from their peak in the 1920s.
These are the standard ACORN station, so yes they include an urbanisation effect. Several are within major cities.
http://www.bom.gov.au/climate/data/acorn-sat/stations/#/23090
Many people discuss nights warming faster than days as part of the greenhouse effect but papers which have looked into the phenomenon have found that’s not really the case. It’s more attributed to declining surface solar radiation absorption, primarily due to water vapor absorbing it in the atmosphere, plus anthropogenic aerosols and clouds. Also often attributed to change in soil moisture.
But should also be considered that station changes and, as a consequence, adjustments typically affect Tmax/Tmin differences much more than they do Tavg. It’s a general finding of homogenisation methods that Tmax is biased high in the past, and it’s also generally found in method testing that there’s a tendency to under-adjust: The high bar for statistical significance means there are more Type II errors than Type I. Consequently it seems more likely than not that our current records are overestimating the historical decline in DTR.
Aerosols may play a role and higher humidity may inhibit radiative loss to space, but solar radiation is zero at night and at high latitudes in winter. Are you saying that homogenisation algorithms work on Tmax and Tmin independently? I thought they worked only on Tavg, although I may be wrong.
Sorry, bit of a delay. Happy New Year!
Regarding lack of solar radiation at night, the point is that the day/night difference seems to be mostly due to days warming slower rather than nights warming faster, if you get what I mean.
Yeah, all the major homogenisation algorithms work on Tmax and Tmin separately.
I agree.
days are warming slower and nights warming faster.
Happy New Year !
Hi Clive, does your analysis hold true for rural stations only? I assume that heat island effects, pollution etc over large towns and cities would significantly affect Tmin?
The UHI is definitely real but still smaller than this. I think it is an energy balance problem.
https://clivebest.com/blog/?p=3434
Whether homogenization affects Tmax and to what degree depends on the homogenization method applied. KNMI in the Netherlands did a homogenization at the daily temperatures of primary station De Bilt between 1901 and 1951. That affected Tmax the most: from the 23 original heat waves measured between 1901-1951 no less than 16 heat waves disappeared because of that homogenization!
Very interesting how you’ve correlated TMAX with DTR using land station data. I’ve also been exploring the GHCN-daily dataset, and I put it up on Climatebinge.com so that you can chart out TMAX for single weather stations by calendar day (x) and by year (y). The chart can be viewed as a heat map or as a contour chart. Other measurements such as TMIN and precipitation are coming soon. I’ve found that climate trends are highly local. For example, Caribbean stations show dramatic climate change:
https://climatebinge.com/?loadchart=true&firstyear=1959&lastyear=2018&station=GP000078897
while trends in other places are harder to see:
https://climatebinge.com/?loadchart=true&firstyear=1762&lastyear=1820&station=ITE00100554
Data is pretty sparse up until about 1950, but there are a few stations that go back to the mid 18th century, like Oxford and Milan.