2023 Update: Fossil Fuels ≠ Global Warming

gas in hands

Previous posts addressed the claim that fossil fuels are driving global warming. This post updates that analysis with the latest (2022) numbers from Energy Institute and compares World Fossil Fuel Consumption (WFFC) with three estimates of Global Mean Temperature (GMT). More on both these variables below. Note: Previously these same statistics were hosted by BP.

WFFC

2022 statistics are now available from Energy Institute for international consumption of Primary Energy sources. Statistical Review of World Energy. 

The reporting categories are:
Oil
Natural Gas
Coal
Nuclear
Hydro
Renewables (other than hydro)

Note:  Energy Institute began last year to use Exajoules to replace MToe (Million Tonnes of oil equivalents.) It is logical to use an energy metric which is independent of the fuel source. OTOH renewable advocates have no doubt pressured EI to stop using oil as the baseline since their dream is a world without fossil fuel energy.

From BP conversion table 1 exajoule (EJ) = 1 quintillion joules (1 x 10^18). Oil products vary from 41.6 to 49.4 tonnes per gigajoule (10^9 joules).  Comparing this annual report with previous years shows that global Primary Energy (PE) in MToe is roughly 24 times the same amount in Exajoules.  The conversion factor at the macro level varies from year to year depending on the fuel mix. The graphs below use the new metric.

This analysis combines the first three, Oil, Gas, and Coal for total fossil fuel consumption world wide (WFFC).  The chart below shows the patterns for WFFC compared to world consumption of Primary Energy from 1965 through 2022.

The graph shows that global Primary Energy (PE) consumption from all sources has grown continuously over nearly 6 decades. Since 1965  oil, gas and coal (FF, sometimes termed “Thermal”) averaged 88% of PE consumed, ranging from 93% in 1965 to 82% in 2022.  Note that in 2020, PE dropped 21 EJ (4%) below 2019 consumption, then increased 31 EJ in 2021.  WFFC for 2020 dropped 24 EJ (5%), then in 2021 gained back 26 EJ to slightly exceed 2019 WFFC consumption. For the 58 year period, the net changes were:

Oil 194%
Gas 525%
Coal 178%
WFFC 239%
PE 287%
Global Mean Temperatures

Everyone acknowledges that GMT is a fiction since temperature is an intrinsic property of objects, and varies dramatically over time and over the surface of the earth. No place on earth determines “average” temperature for the globe. Yet for the purpose of detecting change in temperature, major climate data sets estimate GMT and report anomalies from it.

UAH record consists of satellite era global temperature estimates for the lower troposphere, a layer of air from 0 to 4km above the surface. HadSST estimates sea surface temperatures from oceans covering 71% of the planet. HadCRUT combines HadSST estimates with records from land stations whose elevations range up to 6km above sea level.

Both GISS LOTI (land and ocean) and HadCRUT4 (land and ocean) use 14.0 Celsius as the climate normal, so I will add that number back into the anomalies. This is done not claiming any validity other than to achieve a reasonable measure of magnitude regarding the observed fluctuations.[Note: HadCRUT4 was discontinued after 2021 in favor of HadCRUT5.]

No doubt global sea surface temperatures are typically higher than 14C, more like 17 or 18C, and of course warmer in the tropics and colder at higher latitudes. Likewise, the lapse rate in the atmosphere means that air temperatures both from satellites and elevated land stations will range colder than 14C. Still, that climate normal is a generally accepted indicator of GMT.

Correlations of GMT and WFFC

The next graph compares WFFC to GMT estimates over the decades from 1965 to 2022 from HadCRUT4, which includes HadSST4.

Since 1965 the increase in fossil fuel consumption is dramatic and monotonic, steadily increasing by 239% from 146 to 494 exajoules.  Meanwhile the GMT record from Hadcrut shows multiple ups and downs with an accumulated rise of 0.8C over 56 years, 6% of the starting value.

The graph below compares WFFC to GMT estimates from UAH6, and HadSST4 for the satellite era from 1980 to 2022, a period of 43 years.

In the satellite era WFFC has increased at a compounded rate of nearly 2% per year, for a total increase of 92% since 1979. At the same time, SST warming amounted to 0.53C, or 3.7% of the starting value.  UAH warming was 0.52C, or 3.8% up from 1979.  The temperature compounded rate of change is 0.1% per year, an order of magnitude less than WFFC.  Even more obvious is the 1998 El Nino peak and flat GMT since.

Summary

The climate alarmist/activist claim is straight forward: Burning fossil fuels makes measured temperatures warmer. The Paris Accord further asserts that by reducing human use of fossil fuels, further warming can be prevented.  Those claims do not bear up under scrutiny.

It is enough for simple minds to see that two time series are both rising and to think that one must be causing the other. But both scientific and legal methods assert causation only when the two variables are both strongly and consistently aligned. The above shows a weak and inconsistent linkage between WFFC and GMT.

Going further back in history shows even weaker correlation between fossil fuels consumption and global temperature estimates:

wfc-vs-sat

Figure 5.1. Comparative dynamics of the World Fuel Consumption (WFC) and Global Surface Air Temperature Anomaly (ΔT), 1861-2000. The thin dashed line represents annual ΔT, the bold line—its 13-year smoothing, and the line constructed from rectangles—WFC (in millions of tons of nominal fuel) (Klyashtorin and Lyubushin, 2003). Source: Frolov et al. 2009

In legal terms, as long as there is another equally or more likely explanation for the set of facts, the claimed causation is unproven. The more likely explanation is that global temperatures vary due to oceanic and solar cycles. The proof is clearly and thoroughly set forward in the post Quantifying Natural Climate Change.

Footnote: CO2 Concentrations Compared to WFFC

Contrary to claims that rising atmospheric CO2 consists of fossil fuel emissions, consider the Mauna Loa CO2 observations in recent years.

Despite the drop in 2020 WFFC, atmospheric CO2 continued to rise steadily, demonstrating that natural sources and sinks drive the amount of CO2 in the air.

See also: Nature Erases Pulses of Human CO2 Emissions

Temps Cause CO2 Changes, Not the Reverse

Arctic Ice in Surplus June 2023

The animation shows Arctic ice extents on day 151 (end of May) through yesterday June 30, 2023  As usual, the Pacific basins Bering and Okhotsk (far left) became ice-free and are no longer included in these updates. Years vary as to which regions retain more or less ice.  For example, this year Hudson Bay (bottom right) lost half its ice by June 30, earlier than average.  That is a shallow basin and can quickly lose its ice in coming days.  Despite this early melting, the NH Ice extent remains greater than the 17 year average.

The graph below compares the June monthly ice extents 2007 to 2023 and compared to the 17 year average.

Clearly June ice appears as a plateau, and most years MASIE shows greater extents than SII, with differences of only a few 100k km2.  Previously 2019-20 were in deficit to average, but June 2022-3 have returned to surplus years.  More on MASIE dataset at the end.

The graph shows the melting pattern during June 2023 remained above average all month, and greatly exceeded 2007 and 2020, especially in the last 2 weeks.  June 30, 2023 was 322k km2 in surplus, and exceeded 2007 by 0.4 Wadhams (M km2).

The table below shows ice extents by regions comparing 2023 with 17-year average (2006 to 2022 inclusive) and 2007.

Region 2023181 Day 181 Average 2023-Ave. 2007181 2023-2007
 (0) Northern_Hemisphere 10072140 9750262 321878 9672969 399171
 (1) Beaufort_Sea 919937 927608 -7671 939209 -19272
 (2) Chukchi_Sea 804545 723247 81299 670088 134457
 (3) East_Siberian_Sea 1021758 1010088 11669 901963 119795
 (4) Laptev_Sea 738148 699906 38242 658742 79406
 (5) Kara_Sea 568642 542617 26025 657478 -88836
 (6) Barents_Sea 99262 117038 -17776 130101 -30839
 (7) Greenland_Sea 650550 499950 150600 548399 102152
 (8) Baffin_Bay_Gulf_of_St._Lawrence 703359 513540 189819 450461 252898
 (9) Canadian_Archipelago 743003 780546 -37543 773611 -30607
 (10) Hudson_Bay 577518 707353 -129835 718441 -140923
 (11) Central_Arctic 3241230 3204305 36925 3218999 22231

2023 is 322k km2 above average (3.3%). The main deficit is in Hudson Bay, more than offset by large  surpluses in Baffin Bay and Greenland Sea, along with additonal ice elsewhere.

Footnote on MASIE Data Sources:

MASIE reports are based on data primarily from NIC’s Interactive Multisensor Snow and Ice Mapping System (IMS). From the documentation, the multiple sources feeding IMS are:

Platform(s) AQUA, DMSP, DMSP 5D-3/F17, GOES-10, GOES-11, GOES-13, GOES-9, METEOSAT, MSG, MTSAT-1R, MTSAT-2, NOAA-14, NOAA-15, NOAA-16, NOAA-17, NOAA-18, NOAA-N, RADARSAT-2, SUOMI-NPP, TERRA

Sensor(s): AMSU-A, ATMS, AVHRR, GOES I-M IMAGER, MODIS, MTSAT 1R Imager, MTSAT 2 Imager, MVIRI, SAR, SEVIRI, SSM/I, SSMIS, VIIRS

Summary: IMS Daily Northern Hemisphere Snow and Ice Analysis

The National Oceanic and Atmospheric Administration / National Environmental Satellite, Data, and Information Service (NOAA/NESDIS) has an extensive history of monitoring snow and ice coverage.Accurate monitoring of global snow/ice cover is a key component in the study of climate and global change as well as daily weather forecasting.

The Polar and Geostationary Operational Environmental Satellite programs (POES/GOES) operated by NESDIS provide invaluable visible and infrared spectral data in support of these efforts. Clear-sky imagery from both the POES and the GOES sensors show snow/ice boundaries very well; however, the visible and infrared techniques may suffer from persistent cloud cover near the snowline, making observations difficult (Ramsay, 1995). The microwave products (DMSP and AMSR-E) are unobstructed by clouds and thus can be used as another observational platform in most regions. Synthetic Aperture Radar (SAR) imagery also provides all-weather, near daily capacities to discriminate sea and lake ice. With several other derived snow/ice products of varying accuracy, such as those from NCEP and the NWS NOHRSC, it is highly desirable for analysts to be able to interactively compare and contrast the products so that a more accurate composite map can be produced.

The Satellite Analysis Branch (SAB) of NESDIS first began generating Northern Hemisphere Weekly Snow and Ice Cover analysis charts derived from the visible satellite imagery in November, 1966. The spatial and temporal resolutions of the analysis (190 km and 7 days, respectively) remained unchanged for the product’s 33-year lifespan.

As a result of increasing customer needs and expectations, it was decided that an efficient, interactive workstation application should be constructed which would enable SAB to produce snow/ice analyses at a higher resolution and on a daily basis (~25 km / 1024 x 1024 grid and once per day) using a consolidated array of new as well as existing satellite and surface imagery products. The Daily Northern Hemisphere Snow and Ice Cover chart has been produced since February, 1997 by SAB meteorologists on the IMS.

Another large resolution improvement began in early 2004, when improved technology allowed the SAB to begin creation of a daily ~4 km (6144×6144) grid. At this time, both the ~4 km and ~24 km products are available from NSIDC with a slight delay. Near real-time gridded data is available in ASCII format by request.

In March 2008, the product was migrated from SAB to the National Ice Center (NIC) of NESDIS. The production system and methodology was preserved during the migration. Improved access to DMSP, SAR, and modeled data sources is expected as a short-term from the migration, with longer term plans of twice daily production, GRIB2 output format, a Southern Hemisphere analysis, and an expanded suite of integrated snow and ice variable on horizon. Source:  Interactive Multisensor Snow and Ice Mapping System (IMS)