Climate: about which temperature are we talking about?

by S. Furfari and H. Masson, July 26, 2019 in ScienceClimateEnergie

Is it the increase of temperature during the period 1980-2000 that has triggered the strong interest for the climate change issue? But actually, about which temperatures are we talking, and how reliable are the corresponding data?

1/ Measurement errors

Temperatures have been recorded with thermometers for a maximum of about 250 years, and by electronic sensors or satellites, since a few decades. For older data, one relies on “proxies” (tree rings, stomata, or other geological evidence requiring time and amplitude calibration, historical chronicles, almanacs, etc.). Each method has some experimental error, 0.1°C for a thermometer, much more for proxies. Switching from one method to another (for example from thermometer to electronic sensor or from electronic sensor to satellite data) requires some calibration and adjustment of the data, not always perfectly documented in the records. Also, as shown further in this paper, the length of the measurement window is of paramount importance for drawing conclusions on a possible trend observed in climate data. Some compromise is required between the accuracy of the data and their representativity.

2/ Time averaging errors

If one considers only “reliable” measurements made using thermometers, one needs to define daily, weekly, monthly, annually averaged temperatures. But before using electronic sensors, allowing quite continuous recording of the data, these measurements were made punctually, by hand, a few times a day. The daily averaging algorithm used changes from country to country and over time, in a way not perfectly documented in the data; which induces some errors (Limburg, 2014) . Also, the temperature follows seasonal cycles, linked to the solar activity and the local exposition to it (angle of incidence of the solar radiations) which means that when averaging monthly data, one compares temperatures (from the beginning and the end of the month) corresponding to different points on the seasonal cycle. Finally, as any experimental gardener knows, the cycles of the Moon have also some detectable effect on the temperature (a 14 days cycle is apparent in local temperature data, corresponding to the harmonic 2 of the Moon month, Frank, 2010); there are circa 13 moon cycle of 28 days in one solar year of 365 days, but the solar year is divided in 12 months, which induces some biases and fake trends (Masson, 2018).

3/ Spatial averaging

Figs. 12, 13 and 14 : Linear regression line over a single period of a sinusoid.




  1. IPCC projections result from mathematical models which need to be calibrated by making use of data from the past. The accuracy of the calibration data is of paramount importance, as the climate system is highly non-linear, and this is also the case for the (Navier-Stokes) equations and (Runge-Kutta integration) algorithms used in the IPCC computer models. Consequently, the system and also the way IPCC represent it, are highly sensitive to tiny changes in the value of parameters or initial conditions (the calibration data in the present case), that must be known with high accuracy. This is not the case, putting serious doubt on whatever conclusion that could be drawn from model projections.

  2. Most of the mainstream climate related data used by IPCC are indeed generated from meteo data collected at land meteo stations. This has two consequences:(i) The spatial coverage of the data is highly questionable, as the temperature over the oceans, representing 70% of the Earth surface, is mostly neglected or “guestimated” by interpolation;(ii) The number and location of theses land meteo stations has considerably changed over time, inducing biases and fake trends.

  3. The key indicator used by IPCC is the global temperature anomaly, obtained by spatially averaging, as well as possible, local anomalies. Local anomalies are the comparison of present local temperature to the averaged local temperature calculated over a previous fixed reference period of 30 years, changing each 30 years (1930-1960, 1960-1990, etc.). The concept of local anomaly is highly questionable, due to the presence of poly-cyclic components in the temperature data, inducing considerable biases and false trends when the “measurement window” is shorter than at least 6 times the longest period detectable in the data; which is unfortunately the case with temperature data

  4. Linear trend lines applied to (poly-)cyclic data of period similar to the length of the time window considered, open the door to any kind of fake conclusions, if not manipulations aimed to push one political agenda or another.

  5. Consequently, it is highly recommended to abandon the concept of global temperature anomaly and to focus on unbiased local meteo data to detect an eventual change in the local climate, which is a physically meaningful concept, and which is after all what is really of importance for local people, agriculture, industry, services, business, health and welfare in general.

Urban Heat Island (or UHI)

by Roger A. Pielke Sr,  July 25, 2019

The urban heat island effect is a well-documented example of inadvertent modification of climate by human activities in the form of increased temperatures of urban areas compared to a city’s rural surroundings. It is a fine example of how changing the energy balance of a region can affect the regional climate.

On average, the city is warmer than the countryside because of differences between the energy gains and losses of each region. There are a number of factors that contribute to the relative warmth of cities, such as heat from industrial activity, the thermal properties of buildings, and the evaporation of water. For example, the heat produced by heating and cooling city buildings and running planes, trains, buses, and automobiles contributes to the warmer city temperatures. Heat generated by these objects eventually makes its way into the atmosphere, adding as much as one third of the heat received from solar energy. The architecture of cities intensifies UHI effect. The canyon shape of the tall buildings and the narrow space between them magnifies the longwave energy gains. During the day, solar energy is trapped by multiple reflections off the many closely spaced, tall buildings, reducing heat losses by longwave radiation (See schematic below). Pollution in the city’s air also modifies the absorption of longwave and shortwave radiation of the atmosphere.


by Cap Allon, July 23, 2019 in Electroverse

With the few days of ‘eastern heat’ taking all the headlines, the anomalous and long-lasting cold infecting vast swathes of North America is again being swept under the sustainably-sourced non-synthetic-petroleum-derived-fiber carpet. Record cold hit Montana over the weekend with several cities and towns setting new all-time record lows, but I’ll bet my diesel-guzzling L200 you never heard about it.

I’ve listed a few of the new record lows below:

  • Utica set a new record low of -1.1C (30F) on Saturday July 20.
  • Kalispell’s 2.2C (36F) busted the previous record of 3.3C (38F) set in 1996 (solar minimum of cycle 22).


You can view it from this link :

CO2 Is So Powerful It Can Cause Global Warming To Pause For Decades

by Joanna Nova, July 24, 2019 in ClimateChangeDispatch

It’s all so obvious. If researchers start with models that don’t work, they can find anything they look for — even abject nonsense which is the complete opposite of what the models predicted.

Holy Simulation! Let’s take this reasoning and run with it  — in the unlikely event, we actually get relentless rising temperatures, that will imply that the climate sensitivity of CO2 is lower. Can’t see that press release coming…

Nature has sunk so low these days it’s competing with The Onion.

The big problem bugging believers was that global warming paused, which no model predicted, and which remains unexplained still, despite moving goalposts, searching in data that doesn’t exist, and using error bars 17 times larger than the signal.

The immutable problem is that energy shalt not be created nor destroyed, so The Pause still matters even years after it stopped pausing.

The empty space still shows the models don’t understand the climate — CO2 was supposed to be heating the world, all day, every day.

Quadrillions of Joules have to go somewhere, they can’t just vanish, but models don’t know where they went. If we can’t explain the pause, we can’t explain the cause, and the models can’t predict anything.

In studies like these, the broken model is not a bug, it’s a mandatory requirement — if these models actually worked, it wouldn’t be as easy to produce any and every conclusion that an unskeptical scientist could hope to “be surprised” by.

The true value of this study, if any, is in 100 years time when some psychology Ph.D. student will be able to complete an extra paragraph on the 6th-dimensional flexibility of human rationalization and confirmation bias.

Busted climate models can literally prove anything. The more busted they are, the better.