OPINION

Climate scientists baffled as to why Antarctica has not warmed in 70 years despite rising CO2 levels

Despite consistent rises in carbon dioxide output globally the Antarctic region has recorded no average rise in temperatures in the last 70 years and in 2021 saw its coldest 6-month winter since records began in the 1950s.

NicoElNino/Shutterstock

Tue Jan 31, 2023 – 10:19 am EST

Listen to this article

0:00 / 6:00

BeyondWords

(The Daily Sceptic) — Scientists are scrambling to explain why the continent of Antarctica has shown Net Zero warming for the last seven decades and almost certainly much longer. The lack of warming over a significant portion of the Earth undermines the unproven hypothesis that the carbon dioxide humans add to the atmosphere is the main determinant of global climate. Under “settled” science requirements, the significant debate over the inconvenient Antarctica data is of necessity being conducted well away from prying eyes in the mainstream media. Promoting the Net Zero political agenda, the Guardian recently topped up readers’ alarm levels with the notion that “unimaginable amounts of water will flow into oceans,” if temperatures in the region rise and ice buffers vanish.

Hard to believe?

The BBC green activist-in-chief Justin Rowlatt flew over parts of the region and witnessed “an epic vision of shattered ice.” He described Antarctica as the “frontline of climate change.” In 2021, the South Pole had its coldest six-month winter since records began in 1957, a fact largely ignored in the mainstream. One-off bad weather promoter Reuters subsequently “fact checked” commentary on the event in social media. It noted that a “six-month period is not long enough to validate a climate trend.” A recent paper from two climate scientists (Singh and Polvani) accepts that Antarctica has not warmed in the last seven decades, despite an increase in the atmospheric greenhouse gases. It is noted that the two polar regions present a “conundrum” for understanding present day climate change, as recent warming differs markedly between the Arctic and Antarctic. The graph below shows average Antarctica surface temperatures from 1984–2014, compared to a base period 1950–1980.

— Article continues below Petition —

Tell G20 to drop radical climate policies causing inflation and blackouts

10606 have signed the petition.

Let’s get to 12500!

Add your signature:

Show Petition Text

Keep me updated via email on this petition and related issues.

The scientists note that over the last seven decades, the Antarctica sea ice area has “modestly expanded” and warming has been “nearly non-existent” over much of the ice sheet. NASA estimates current Antarctica ice loss at 147 gigatons a year, but with 26,500,000 gigatons still to go, this works out at annual loss of 0.0005 percent. At current NASA ice loss melt, it will all be gone in about 200,000 years, although the Earth may well have gone through another ice age, or two, before then.

Most alarmist commentary centres around the cyclical loss of sea ice around the coast and some warming on parts of the west of the continent. But sea ice cover is running at levels seen around 50 years ago, as the graph below shows. Small rises and falls in the early 2010s have been followed by a reversion to the mean.

The warmth to the west, seen in the first graph, could have been caused by any number of natural localised events including warmer oceanic waters and the effects of under-water volcanic activity. It has, of course, attracted widespread alarmist interest – in particular, the fate of the Thwaites ice stream, also known as the “Doomsday Glacier.” However, recently a group of oceanographers discovered that Florida-sized Thwaites had retreated at twice the rate in the past, when human-caused CO₂ could not have been a factor. The retreat could have occurred centuries ago and is said to have been “exceptionally fast.”

Much of climate science today seems to suffer from confirmation bias. Few grants are available to those who don’t start with the premise that the climate is changing mostly, or entirely, due to humans burning fossil fuel. But many present, historic, and paleo climate observations fail to establish a clear connection between temperatures and CO₂ levels. In the past, the life-enhancing gas has occupied a space in the atmosphere up to 20 times higher, without evidence of huge temperature rises. Singh and Polvani’s explanation for expected warming in Antarctica is the depth of the continent’s ice. To this end, they use two climate models that purport to show that the “high ice sheet orography” robustly decreases the climate sensitivity to extra CO₂, and that “a flattened Antarctic ice sheet would experience significantly greater surface warming than the present-day Antarctica ice sheet.” This conclusion comes from computer models, but later in the paper is an admission that they fail to agree on significant matters. It is revealed that one of the models predicts less sea ice retreat in a flattened Antarctica when CO₂doubles, and the other one, more retreat. In the science blog No Tricks Zone there has been an interesting debate on the lack of Antarctica warming. It was noted that NASA also tends to support the role of higher elevation of the ice as an explanation. For the rest of the world, states NASA, “the greenhouse effect still works as expected.”

The science, as always, must be out. Attempting to connect every natural variation in weather and long-term climate to just one trace gas produced by humans leads to some unconvincing explanations, not least when climate models are involved.

Reprinted with permission from The Daily Sceptic.

Trends in Atmospheric Carbon Dioxide

NOTE: Starting Feb. 10, 2021, the CO₂ data are now on the WMO X2019 scale. Read about the CO₂ Scale for more details.
Can we see a change in the CO2 record because of COVID-19?

Global Monthly Mean CO₂

October 2022:	416.22 ppm
October 2021:	413.84 ppm

Last updated: Jan 05, 2023

Recent global monthly means

PDF Version

Global monthly means since 1980

PDF Version

The graphs show monthly mean carbon dioxide globally averaged over marine surface sites. The Global Monitoring Division of NOAA/Earth System Research Laboratory has measured carbon dioxide and other greenhouse gases for several decades at a globally distributed network of air sampling sites [Conway, 1994]. The last four complete years plus the current year are shown on the first graph. All years since 1980 are shown on the second graph. The last year of data are still preliminary, pending recalibrations of reference gases and other quality control checks. Data are reported as a dry air mole fraction defined as the number of molecules of carbon dioxide divided by the number of all molecules in air, including CO₂ itself, after water vapor has been removed. The mole fraction is expressed as parts per million (ppm). Example: 0.000400 is expressed as 400 ppm. The dashed red line with diamond symbols represents the monthly mean values, centered on the middle of each month. The black line with the square symbols represents the same, after correction for the average seasonal cycle. The black line is determined as a moving average of SEVEN adjacent seasonal cycles centered on the month to be corrected, except for the first and last THREE and one-half years of the record, where the seasonal cycle has been averaged over the first and last SEVEN years, respectively. A global average is constructed by first fitting a smoothed curve as a function of time to each site, and then the smoothed value for each site is plotted as a function of latitude for 48 equal time steps per year. A global average is calculated from the latitude plot at each time step [Masarie, 1995]. Go here for more details on how global means are calculated. Click for a comparison with recent trends in carbon dioxide at Mauna Loa, Hawaii, which has the longest continuous record of direct atmospheric CO₂ measurements.

NOAA/GML calculation of global means

The NOAA GML Carbon Cycle Group computes global mean surface values using measurements of weekly air samples from the Cooperative Global Air Sampling Network [Conway et al., 1994; Dlugokencky et al., 1994; Novelli et al., 1992; Trolier et al., 1996]. Global values can be computed for nearly all trace gas species and stable isotopes routinely measured by GML and the University of Colorado INSTAAR. Here we briefly describe our methodology for computing global mean surface values, illustrated using CO₂. The global estimate is based on measurements from a subset of network sites. Only sites where samples are predominantly of well-mixed marine boundary layer (MBL) air representative of a large volume of the atmosphere are considered. These “MBL” sites are typically at remote marine sea level locations with prevailing onshore winds. Measurements from sites at altitude (e.g., Mauna Loa) and from sites close to anthropogenic and natural sources and sinks (e.g., Park Falls, Wisconsin) are excluded from the global estimate. The use of MBL data results in a low-noise representation of the global trend and allows us to make the estimate directly from the data without the need for an atmospheric transport model.

Measurements

All measurements used to estimate surface global means are made by GML and CU/INSTAAR (for stable isotopes). Routine and ongoing comparison experiments within the Boulder labs help ensure that measurements are internally consistent with respect to calibration and methodology [WMO, 2009a]. All data used to construct the global estimates have been screened by the principal investigators. Only measurements determined to be free from sampling and analysis artifacts are considered for the calculation of the global estimate. Figure 1 shows all CO₂ measurements of samples collected at Ascension Island. The determination of the global mean surface time series is one of several products derived from the Data Extension methodology, which is described briefly below. A configuration file provides execution details for a specific Data Extension run including the list of designated MBL sites, which may differ for different trace gas species.

Smooth Curve fit to MBL data

To reduce noise in the determination of the global estimate due to synoptic-scale atmospheric variability and measurement gaps, we fit a smooth curve to the weekly measurements. To approximate the long-term trend and average seasonal cycle at a site (subscript “STA” for station), a function of the form

f_STA(t) = a_o + a₁t + a₂t² + ∑_k=1,4[b_2k-1sin(2πkt) + b_2kcos(2πkt)]

is fitted to the measurements [Thoning et al., 1989]. The above function includes 3 polynomial parameters (quadratic) and 8 harmonic parameters, sine and cosine which can be converted to amplitude and phase of each harmonic, if desired. The initial number of parameters chosen can vary depending on the trace gas, the site and the sampling frequency. To account for interannual variability in the seasonal cycle, the residuals, r_STA(t) = c_STA(t) – f_STA(t) where c_STA(t) denotes the actual observations, are digitally filtered through a low-pass filter with a full width at half maximum (FWHM) set to ~40 days. The smooth curve is then defined as

S_STA(t) = f_STA(t) + {r_STA(t)}_40day

Figure 2 shows the smooth curve, S(t), fitted to background CO₂ measurements from Ascension Island for 2000-2009. The curve fitting parameters including number of polynomial and harmonic terms and the FWHM setting are specified for each site in the Data Extension configuration file.

Extended Records

We define a synchronization period with 48 time steps per year (~ weekly) for which the global mean surface values will be determined. For CO₂, the synchronization period begins January 1, 1979; the ending date depends on the application. We then extract values from the smooth curve where measurements exist and gaps are less than 8 weeks in length. Measurement gaps exceeding 8 weeks are filled using the data-based, data extension methodology described by Masarie and Tans [1995] with important revisions documented in the GLOBALVIEW-CO₂ [2010]. The data extension procedure produces a set of extended records, which are synchronized in time with 48 “weekly” values per year and have no gaps. Figure 3 shows the Ascension Island extended record for 2000-2009.

Curve fit to weekly MBL data latitudinal distribution

For each week in the synchronization period, we construct a latitudinal distribution (CO₂ (ppm) versus sine (latitude) using smoothed and interpolated values from the extended records. The number of values available for the distribution can vary weekly due to measurement gaps in records or as MBL sites are added to the GML network or terminated. Confidence in values extracted from the smooth curve, S_STA(t), depends on the density of the data, the “scatter” in the data and the length of the measurement period. We use a relative weighting scheme, which assigns greater significance to sites with high signal-to-noise and consistent sampling. Relative weights range from 1 to 10 where the minimum weight of 1 is reserved for all interpolated values. A curve is then fitted to each weekly weighted latitudinal distribution. Details of the latitudinal fitting process are described by Tans et al. [1989]. Figure 4 shows the fit to the latitudinal distributions for 16 September 1980 and 2009.

Creating the NOAA GML surface

Values are extracted from each weekly latitudinal fit at intervals of 0.05 sine of latitude from 90°S to 90°N and joined together to create a 2-dimensional matrix (time versus latitude) of CO₂ values. We can then construct a three-dimensional representation of the global distribution of atmospheric CO₂ as shown in Figure 5.

The surface global mean time series

Using the above 2-dimensional matrix, we can construct a zonal average time series for any specified latitude band or extract a time series at a specific latitude. The global mean surface CO₂ time series (Figure 6) is created by computing, for each week, the latitude-weighted mean value for the band 90°S to 90°N.

Discussion

The World Meteorological Organization (WMO) World Data Center for Greenhouse Gases (WDCGG) also publishes global averages for CO₂ and other gases. WDCGG uses curve fitting and data extension methods very similar to those developed by NOAA [WMO, 2009b], but in addition to marine boundary layer sites, WDCGG includes many continental locations strongly influenced by local biospheric sources and sinks and also by fossil fuel emissions. WDCGG also includes sites from multiple independent laboratories which raises the issue of possible artifacts due to scale or measurements differences. The WDCGG global average has a positive mean offset of ~0.35 ppm and a larger seasonal cycle amplitude compared to NOAA results. The MBL estimate is expected to be lower than a full global surface average because areas with high fossil fuel loading due to recent emissions are not represented. On the other hand, the full troposphere (up to ~8-15 km altitude) and especially the stratosphere with lower CO₂ mole fraction are not represented in either approach. We observe that CO₂ is increasing at about the same rate everywhere it is measured. Because CO₂ is a long lived gas in the atmosphere, emissions anywhere will, in about one year, contribute to higher CO₂ everywhere. One cannot “hide” CO₂ emissions from the MBL sites for more than about a month. Thus the MBL gives probably the best low-noise representation of the ongoing global increase of CO₂. We continue to base our global average on MBL sites because it is not clear how to properly weight continental sites in a global average. Our evidence so far indicates that the NOAA MBL global average is representative, internally consistent and stable over time with respect to the addition of new MBL sites.

References

Conway, T.J., P.P. Tans, L.S. Waterman, K.W. Thoning, D.R. Kitzis, K.A. Masarie, and N. Zhang, 1994, Evidence for interannual variability of the carbon cycle from the NOAA/CMDL global air sampling network, J. Geophys. Res., 99, 22831-22855.
Dlugokencky, E.J., L.P. Steele, P.M. Lang, and K.A. Masarie, 1994, The growth rate and distribution of atmospheric methane, J. Geophys. Res., 99, 17,021-17,043.
GLOBALVIEW-CO₂: Cooperative Atmospheric Data Integration Project – Carbon Dioxide. NOAA ESRL, Boulder, Colorado, 2010. gml.noaa.gov/ccgg/globalview/co2/co2_intro.html.
Masarie, K.A. and P.P. Tans, 1995, Extension and Integration of Atmospheric Carbon Dioxide Data into a Globally Consistent Measurement Record, J. Geophys. Res., .100, 11593-11610.
Novelli, P.C., L.P. Steele, and P.P. Tans, 1992, Mixing ratios of carbon monoxide in the troposphere, J. Geophys. Res., 97, 20,731-20,750.
Tans, P. P., T. J. Conway and T. Nakazawa, 1989, Latitudinal distribution of the sources and sinks of atmospheric carbon dioxide derived from surface observations and atmospheric transport model, J. of Geophys. Res., 94., 5151-5172.
Thoning, K.W., P.P. Tans, and W.D. Komhyr, 1989, Atmospheric carbon dioxide at Mauna Loa Observatory, 2. Analysis of the NOAA/GMCC data, 1974 1985., J. Geophys. Res. ,94, 8549 8565.
Trolier, M., J.W.C. White, P.P. Tans, K.A. Masarie and P.A. Gemery, 1996, Monitoring the isotopic composition of atmospheric CO₂: measurements from the NOAA Global Air Sampling Network, J. Geophys. Res., 101, 25897-25916.
14th WMO/IAEA Meeting of Experts on Carbon Dioxide Concentration and Related Tracers Measurement Techniques (Helsinki, Finland, 10-13 September 2007) WMO TD No. 1487, April 2009a.
Tsutsumi, Y., M. Kazumasa, H. Takatoshi, I. Masaaki and T.J. Conway, Technical Report of Global Analysis Method for Major Greenhouse Gases by the World Data Center for Greenhouse Gases, WMO TD No. 1473, June 2009b.