by Dr. Roy Spencer, June 7, 2019 in WUWT
Abstract: A simple time-dependent model of Earth surface temperatures over the 24 hr day/night cycle at different latitudes is presented. The model reaches energy equilibrium after 1.5 months no matter what temperature it is initialized at. It is shown that even with 1,370 W/m2 of solar flux (reduced by an assumed albedo of 0.3), temperatures at all latitudes remain very cold, even in the afternoon and in the deep tropics. Variation of the model input parameters over reasonable ranges do not change this fact. This demonstrates the importance of the atmospheric “greenhouse” effect, which increases surface temperatures well above what can be achieved with only solar heating and surface infrared loss to outer space.
by Anthony Watts, May 9, 2019 in ClimateChangeDispatch
That’s an indication of the personal bias of co-author Schmidt, who in the past has repeatedly maligned the UAH dataset and its authors because their findings didn’t agree with his own GISTEMP dataset.
In fact, Schmidt’s bias was so strong that when invited to appear on national television to discuss warming trends, in a fit of spite, he refused to appear at the same time as the co-author of the UAH dataset, Dr. Roy Spencer.
A breakdown of several climate datasets, appearing below in degrees centigrade per decade, indicates there are significant discrepancies in estimated climate trends:
- AIRS: +0.24 (from the 2019 Susskind et al. study)
- GISTEMP: +0.22
- ECMWF: +0.20
- RSS LT: +0.20
- Cowtan & Way: +0.19
- UAH LT: +0.18
- HadCRUT4: +0.17
Which climate dataset is the right one? Interestingly, the HadCRUT4 dataset, which is managed by a team in the United Kingdom, uses most of the same data GISTEMP uses from the National Oceanic and Atmospheric Administration’s Global Historical Climate Network.
by D. Rosenfeld et al., February 8, 2019 in Science
Reflections on cloud effects
How much impact does the abundance of cloud condensation nuclei (CCN) aerosols above the oceans have on global temperatures? Rosenfeld et al.analyzed how CCN affect the properties of marine stratocumulus clouds, which reflect much of the solar radiation received by Earth back to space (see the Perspective by Sato and Suzuki). The CCN abundance explained most of the variability in the radiative cooling. Thus, the magnitude of radiative forcing provided by these clouds is much more sensitive to the presence of CCN than current models indicate, which suggests the existence of other compensating warming effects.
by David Whitehouse, February 7, 2019 in GWPF
Average global temperature has been falling for the last 3 years, despite rising atmospheric CO2 levels.
2018 was the fourth warmest year of the instrumental period (started 1850) having a temperature anomaly of 0.91 +/- 0.1 °C – cooler than 2017 and closer to the fifth warmest year than the third. But of course there are those that don’t like to say the global surface temperature has declined.
by Marc Morano ,February 6, 2019 in ClimatDepot
Another year, another claim of “hottest” or “warmest years.” So-called “Hottest year” claims are purely political statements designed to persuade the public that the government needs to take action on man-made climate change. Once again, the media and others are hyping temperature changes year-to-year so small as to be within the margin of error.
Such temperature claims are based on year-to-year temperature data that differs by only a few hundredths of a degree to up to a few tenths of a degree—differences that were within the margin of error in the surface data.
Here are the AP’s and NASA’s claims out today: (A full debunking of these “hottest year”claims follows below.)
by Ph.D. Roy Spencer, January 2, 2019 in GlobalWarming
2018 was 6th Warmest Year Globally of Last 40 Years
The Version 6.0 global average lower tropospheric temperature (LT) anomaly for December, 2018 was +0.25 deg. C, down a little from +0.28 deg. C in November.
by P. Homewood, December 20, 2018 via GWPF
The significance of this new GWPF report by Prof Ray Bates of the Meteorology and Climate Centre at University College Dublin cannot really be overstated:
GWPF Briefing 36
This is the press release:
London, 20 December: One of Europe’s most eminent climate scientists has documented the main scientific reasons why the recent UN climate summit failed to welcome the IPCC’s report on global warming of 1.5°C.
In a paper published today by the Global Warming Policy Foundation Professor Ray Bates of University College Dublin explains the main reasons for the significant controversy about the latest IPCC report within the international community.
The IPCC’s Special Report on a Global Warming of 1.5°C (SR1.5) was released by the Intergovernmental Panel on Climate Change (IPCC) in advance of the recent COP24 meeting in Katowice, Poland, but was not adopted by the meeting due to objections by a number of governments.
by Bob Tisdale, December 13, 2018 in WUWT
This post comes a couple of weeks after the post EXAMPLES OF HOW AND WHY THE USE OF A “CLIMATE MODEL MEAN” AND THE USE OF ANOMALIES CAN BE MISLEADING(The WattsUpWithThat cross post is here.)
I was preparing a post using Berkeley Earth Near-Surface Land Air Temperature data that included the highest-annual TMAX temperatures (not anomalies) for China…you know, the country with the highest population here on our wonder-filled planet Earth. The graph was for the period of 1900 to 2012 (FYI, 2012 is the last full year of the local TMAX and TMIN data from Berkeley Earth). Berkeley Earth’s China data can be found here, with the China TMAX data here. For a more-detailed explanation, referring to Figure 1, I was extracting the highest peak values for every year of the TMAX Data for China, but I hadn’t yet plotted the graph in Figure 1, so I had no idea what I was about to see.
Figure 1 The results are presented in Figure 2, and they were a little surprising, to say the least.
by Bob Tisdale, December 8, 2018 in WUWT
In this post, we’re going to present monthly TMIN and TMAX Near-Land Surface Air Temperature data for the Northern and Southern Hemispheres (not in anomaly form) in an effort to add a little perspective to global warming. And at the end of this post, I’m asking for your assistance in preparing a post especially for you, the visitors to this wonderful blog WattsUpWithThat.
INTRODUCTION FOR THE “GLOBAL WARMING IN PERSPECTIVE” SERIES
A small group of international unelected bureaucrats who serve the United Nations now wants to limit the rise of global land+ocean surface temperatures to no more 1.5 deg C from pre-industrial times…even though we’ve already seen about 1.0 deg C of global warming since then. So we’re going to put that 1.0 deg C change in global surface temperatures in perspective by examining the ranges of surface temperatures “we’ve been used to” on our lovely shared home Earth.
The source of the quote in the title of this post is Gavin Schmidt, who is the Director of the NASA GISS (Goddard Institute of Space Studies). It is from a 2014 post at the blog RealClimate, and, specifically, that quote comes from the post Absolute temperatures and relative anomalies (Archived here.). The topic of discussion for that post at RealClimate was the wide span of absolute global mean temperatures [GMT, in the following quote] found in climate models. Gavin wrote (my boldface):
by Bob Tisdale, December 3, 2018 in WUWT
Most of us are familiar with the World Meteorological Organization (WMO)-recommended 30-year period for “normals”, which are also used as base years against which anomalies are calculated. Most, but not all, climate-related data are referenced to 30-year periods. Presently the “climatological standard normals” period is 1981-2010. These “climatological standard normals” are updated every ten years after we pass another year ending in a zero. That is, the next period for “climatological standard normals” will be 1991-2020, so the shift to new “climatological standard normals” will take place in a few years.
But were you aware that the WMO also has another recommended 30-year period for “normals”, against which anomalies are calculated? It’s used for the “reference standard normals” or “reference normals”. The WMO-recommended period for “reference normals” is 1961-1990. And as many of you know, of the primary suppliers of global mean surface temperature data, the base years of 1961-1990 are only used by the UKMO.
by M.D., 3 décembre 2018 in MythesManciesMathématiques
Que savait-on en décembre 2015, que sait-on en décembre 2018 ?
Températures globales depuis 1979 selon trois sources (1979 est l’année origine des relevés par satellites).
by Mark Fife, November 30, 2018 in WUWT
We have looked at quality, long term records from three different regions. Two of these are on opposite sides of the North Atlantic, one is in the South Pacific. The two regions bordered by the North Atlantic are similar, but not identical. The record from Australia is only similar in that temperature has varied over time and has warmed in the recent past.
In all three regions there is no evidence of any strong correlation to CO2. There is ample evidence to support a conjecture of little to no influence.
There is ample evidence, widely shown in other studies, of localized influence due to development and population growth. The CET record has a correlation of temperature to CO2 of 0.54, which is the highest correlation of any individual record in this study. This area is also the most highly developed. While this does not constitute proof, it does tend to support the supposition the weak CO2 signal is enhanced by a coincidence between rising CO2 and rising development and population.
The efficacy of combining US records with those records from Greenland, Iceland, and the UK may be subject to opinion. However, there is little doubt combining records from Australia would create an extremely misleading record. Like averaging a sine curve and a cosine curve.
It appears the GISS data set does a poor job of estimating the history of temperature in all three regions. It shows a near perfect correlation to CO2 levels which is simply not reflected in any of the individual or regional records. There are probably numerous reasons for this. I would conjecture the reasons would include the influence of short-term temperature record bias, development and population growth bias, and data estimation bias. However, a major source of error could be attributed to the simple mistake of averaging regions where the records simply are too dissimilar for an average to yield useful information.
by Nick Stokes, November 14, 2018 in WUWT
There is much criticism here of the estimates of global surface temperature anomaly provided by the majors – GISS, NOAA and HADCRUT. I try to answer these specifically, but also point out that the source data is readily available, and it is not too difficult to do your own calculation. I point out that I do this monthly, and have done for about eight years. My latest, for October, is here (it got warmer).
Last time CharlesTM was kind enough to suggest that I submit a post, I described how Australian data made its way, visible at all stages, from the 30-minute readings (reported with about 5 min delay) to the collection point as a CLIMAT form, from where it goes unchanged into GHCN unadjusted (qcu). You can see the world’s CLIMAT forms here; countries vary as to how they report the intermediate steps, but almost all the data comes from AWS, and is reported at the time soon after recording. So GHCN unadjusted, which is one of the data sources I use, can be verified. The other, ERSST v5, is not so easy, but there is a lot of its provenance available.
My calculation is based on GHCN unadjusted. That isn’t because I think the adjustments are unjustified, but rather because I find adjustment makes little difference, and I think it is useful to show that.
I’ll describe the methods and results, but firstly I should address that much-argued question of why use anomalies.