About Climate Confusion and Clarity

Christelle Lagace-Babim, left, and Elise Lagace walk along Rue Jacques-Cartier Friday, after checking out their home in Gatineau, Que., as significant rainfall continues to cause flooding. (Justin Tang/Canadian Press)

A lot of verbage about global warming/climate change is worse than useless because the parties are using terms whose meaning is vague or equivocal, and thus no meaningful interaction occurs. Alarmists/activists claim climate change is real, man-made, and dangerous (Obama tweet). Skeptics/doubters respond that climate is always changing, has been both warmer and cooler in the past, long before humans did anything.

In addition, climate confusion causes statements like this one recently in the CBC: Gatineau flooding ‘tip of the iceberg,’ climate scientist warns

Swollen rivers and streams have threatened hundreds of homes in the Outaouais thanks to recent heavy rainfall — three times the normal amount since April 1.

University of Ottawa climate scientist Paul Beckwith says that’s due to a changing climate, and says we’re seeing its effects “on a day-to-day basis” in weather patterns.

Beckwith points to an increase in extreme weather events across North America as proof. “We’ve changed the chemistry of the atmosphere and the oceans with our greenhouse gases, so we’re seeing the consequences of this now,” he added. “It’s only the tip of the iceberg, so to speak.”

Such reports mislead people to think of the climate acting like some kind of agent causing the weather to change in ways unfavorable to us. That confuses the relation between climate and weather, as we shall see below.

What is “Weather”?

Fortunately in science things get defined not theoretically but by observations and measurements. In science, weather is defined as the behavior we measure on a daily basis. In fact today’s automated weather equipment monitors the weather constantly. Let us consider an operational definition of weather to be the variables for which data is reported into global databases.

Each National Weather Service has its own additional particulars they track, but the common global definition of weather can be seen in the defined elements from the ECA&D weather data dictionary (European Climate Assessment & Dataset)

Weather Measurement Elements

What is weather: Eight variables are measured globally–Sunshine, Sea Level Pressure, Humidity, Cloud cover, Wind, Precipitation, Snow Depth, Temperature. With multiple measures of some variables, weather datasets consist of 13 common elements.

Sunshine (SS) in units of 0.1 hour. Total daily SS plus measures of hours for intervals during the day.

Sea Level Pressure (PP) in units of 0.1hPa Daily average PP plus measures for specific times and parts of the day.

Humidity (HU) in units of 1% of relative humidity. Daily average HU plus measures for specific times and parts of the day.

Cloud Cover (CC) in oktas (0 being clear sky, 8 being completely overcast). Daily average CC plus measures for specific times and parts of the day.

Wind Direction (DD) in degrees azimuth for the wind source (that is, a southerly wind comes from 180 degrees.) Daily average DD plus measures for different times of day, and the direction of maximum gust.

Wind Speed (FG) in units of 0.1 m/s. Daily average FG plus measures for speeds at different times and parts of the day.

Wind Gust (FX) in units of 0.1m/s.  Daily average FX (24 hourly gusts) plus measures for maximums of different durations. (2 to 15 minutes).

Precipitation Amount (RR) in units of 0.1 mm. Daily total RR plus measures of amounts for intervals during the day.

Maximum Hourly Precipitation (MXR) in units of 0.1 mm. MXR for the day plus measures of amounts for intervals during the day.

Snow Depth (SD) in units of 1 cm. Mean daily SD plus measures of depths for intervals during the day.

Mean Temperature (TG) in units of 0.1C. Daily TG plus measures of various ways of calculating TG.

Minimum Temperature (TN) in units of 0.1C. Daily TN plus measures for different times and parts of the day.

Maximum Temperature (TX) in units of 0.1C. Daily TX plus measures for different times and parts of the day.

What is “Climate”?

Change in Frequency of Frost Days in Europe in the Period 1976-2006

To sort out the confusion between “weather” and “climate”, we can also look at how climate is measured and thereby defined. From the same ECA&D source is a climate indices database which is termed Indices of Extremes.

There is one datafile for each index. Each datafile gives information for all available stations in the ECA&D database. The indices are aggregated over the year, the winter-half (ONDJFM), the summer-half (AMJJAS), winter (DJF), spring (MAM), summer (JJA), autumn (SON) and each of the calendar months.

There are 74 indices grouped into twelve categories corresponding with different aspects of climate change. Some categories come directly from weather elements, while others are derivations.

The 74 indices are statistics built upon weather data, adding patterns of interest to humans. For example, temperature is greatly emphasized by adding various concerns with heat and cold on top of temperature records. Also, a compound category focuses on temperature and precipitation combinations and their favorability to humans.

What is Climate: Categories and Indices

Note that climate is operationally defined as statistical patterns of weather data. Some indices are simply averages of daily weather over long term periods. By convention, a 30-year average is used to define a climate baseline for a location.

Other climate indices are based on value judgments according to human interests. For example, heat and cold include many examples like growing days, good tourism days, heating degree days. In fact, a feature of climate is the imposition of human expectations upon nature, other examples being the sunshine indices Mostly Sunny and Mostly Cloudy days.

Andrew John Herbertson, a British geographer and Professor at Oxford, wrote in a textbook from 1901:

By climate we mean the average weather as ascertained by many years’ observations. Climate also takes into account the extreme weather experienced during that period. Climate is what on an average we may expect, weather is what we actually get.

Mark Twain, who is often credited with that last sentence, actually said:

Climate lasts all the time and weather only a few days.

The point is, weather consists of events occurring in real time, while climate is a statistical artifact. Weather is like a baseball player swinging in the batter’s box, climate is his batting average, RBIs, bases on balls, etc.

What is “Climate Change”?

The usefulness of climate indices is suggested by the last category called compound, where temperature and precipitation patterns are combined. In fact those two factors are sufficient to define distinctive local climate zones..

Based on empirical observations, Köppen (1900) established a climate classification system which uses monthly temperature and precipitation to define boundaries of different climate types around the world. Since its inception, this system has been further developed (e.g. Köppen and Geiger, 1930; Stern et al., 2000) and widely used by geographers and climatologists around the world.

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Köppen climate zones as they appear in the 21st Century.

As an example, consider how the island of Hawaii looks with its climate zones indicated:

Note: This image comes from an interactive tool and uses a different color scheme than the global map above.  The table below shows the thresholds by which zones are defined.

Zones Zones Description Thresholds
A Tropical climates Tmin ≥ +18 °C
Af Tropical rain forest Pmin ≥ 60 mm
Am Tropical monsoon Pann ≥ 25(100 – Pmin) mm
As Tropical savannah with dry summer Pmin < 60 mm in summer
Aw Tropical savannah with dry winter Pmin < 60 mm in winter
B Dry climates Pann < 10 Pth
BW Desert (arid) Pann ≤ 5 Pth
BS Steppe (semi-arid) Pann > 5 Pth
C Mild temperate -3 °C < Tmin < +18 °C
Cs Mild temperate with dry summer Psmin < Pwmin, Pwmax > 3 Psmin, Psmin < 40 mm
Cw Mild temperate with dry winter Psmax > 10 Pwmin, Pwmin < Psmin
Cf Mild temperate, fully humid Not Cs or Cw
D Snow Tmin ≤ -3 °C
Ds Snow with dry summer Psmin < Pwmin, Pwmax > 3 Psmin, Psmin < 40 mm
Dw Snow with dry winter Psmax > 10 Pwmin, Pwmin < Psmin
Df Snow, fully humid Not Ds or Dw
E Polar Tmax < +10 °C
ET Tundra Tmax ≥ 0 °C
EF Frost Tmax < 0 °C

Köppen and Climate Change

The focus is on differentiating vegetation regimes, which result primarily from variations in temperature and precipitation over the seasons of the year. Now we have an interesting study that considers shifts in Köppen climate zones over time in order to identify changes in climate as practical and local/regional realities.  The paper is: Using the Köppen classification to quantify climate variation and change: An example for 1901–2010 By Deliang Chen and Hans Weiteng Chen Department of Earth Sciences, University of Gothenburg, Sweden

Hans Chen has built an excellent interactive website (here): The purpose of this website is to share information about the Köppen climate classification, and provide data and high-resolution figures from the paper Chen and Chen, 2013:  For more details on Chen and Chen see the post: Data vs. Models 4: Climates Changing

Summary:  Climate Change Defined

Chen and Chen provide a data-based definition of “climate change”. Climate zones are defined by past temperature and precipitation ranges observed by humans. The weather datasets and climate indices inform us whether or not the patterns in a place are moving outside the norm for that location. Climate change appears as a shift in zonal boundaries so that one place starts to resemble a neighboring zone with a different classification.  The table above shows the defined zones and thresholds.

The Chen and Chen analysis shows that almost half of climates around the world will get a year of weather outside of their normal ranges. Getting a decade of abnormal weather is much rarer. True climate change would be a shift enduring over a 30 year period which has been observed in less than 10% of all climate zones.

Summary: The Myth of “Global” Climate Change

Climate is a term to describe a local or regional pattern of weather. There is a widely accepted system of classifying climates, based largely on distinctive seasonal variations in temperature and precipitation. Depending on how precisely you apply the criteria, there can be from 6 to 13 distinct zones just in South Africa, or 8 to 11 zones only in Hawaii.

Each climate over time experiences shifts toward warming or cooling, and wetter or drier periods. One example: Fully a third of US stations showed cooling since 1950 while the others warmed. It is nonsense to average all of that and call it “Global Warming” because the net is slightly positive. Only in the fevered imaginations of CO2 activists do all of these diverse places move together in a single march toward global warming.

For more on measurements and science see Data, Facts and Information

Footnote:

weather10seylanbax_2079151i

This post was focused on the distinction between weather and climate, so extreme weather events were not discussed, since by definition such events are weather. Still the quote at the beginning shows that activists are working hard to attribute attention-grabbing events as proof of global warming/climate change.

Mike Hulme wrote a series of articles describing the unsuccessful effort to link extreme weather to climate change and said this:
In recent decades the meaning of climate change in popular western discourse has changed from being a descriptive index of a change in climate (as in ‘evidence that a climatic change has occurred’) to becoming an independent causative agent (as in ‘climate change caused this event to happen’). Rather than being a descriptive outcome of a chain of causal events affecting how weather is generated, climate change has been granted power to change worlds: political and social worlds as much as physical and ecological ones.

More at X-Weathermen are Back 

SH and Tropics Keep Mild Ocean Temps August 2022


The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • A major El Nino was the dominant climate feature in recent years.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source. Previously I used HadSST3 for these reports, but Hadley Centre has made HadSST4 the priority, and v.3 will no longer be updated.  HadSST4 is the same as v.3, except that the older data from ship water intake was re-estimated to be generally lower temperatures than shown in v.3.  The effect is that v.4 has lower average anomalies for the baseline period 1961-1990, thereby showing higher current anomalies than v.3. This analysis concerns more recent time periods and depends on very similar differentials as those from v.3 despite higher absolute anomaly values in v.4.  More on what distinguishes HadSST3 and 4 from other SST products at the end. The user guide for HadSST4 is here.

The Current Context

The 2021 year end report included below showed rapid cooling in all regions.  The anomalies then continued in 2022 to remain near the mean since 2015.  This Global Cooling was also evident in the UAH Land and Ocean air temperature (Cooler Air over Land and Ocean August 2022 )

The chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through July 2022.  A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016. 

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back below its beginning level. Secondly, the Northern Hemisphere added three bumps on the shoulders of Tropical warming, with peaks in August of each year.  A fourth NH bump was lower and peaked in September 2018.  As noted above, a fifth peak in August 2019 and a sixth August 2020 exceeded the four previous upward bumps in NH. A smaller NH rise in 2021 peaked in September of that year.

 

Note that in 2015-2016 the Tropics peaked with an upward SH bump along with two summer NH spikes.  That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes.  Now in 2021-2022  the last two summer NH summer spikes are not joined by warming in the Tropics or in SH, which in August resulted in a Global anomaly close to the mean for this period.

A longer view of SSTs

To enlarge image open in new tab.

 

The graph above is noisy, but the density is needed to see the seasonal patterns in the oceanic fluctuations.  Previous posts focused on the rise and fall of the last El Nino starting in 2015.  This post adds a longer view, encompassing the significant 1998 El Nino and since.  The color schemes are retained for Global, Tropics, NH and SH anomalies.  Despite the longer time frame, I have kept the monthly data (rather than yearly averages) because of interesting shifts between January and July.1995 is a reasonable (ENSO neutral) starting point prior to the first El Nino.  The sharp Tropical rise peaking in 1998 is dominant in the record, starting Jan. ’97 to pull up SSTs uniformly before returning to the same level Jan. ’99.  For the next 2 years, the Tropics stayed down, and the world’s oceans held steady around 0.5C above 1961 to 1990 average.

Then comes a steady rise over two years to a lesser peak Jan. 2003, but again uniformly pulling all oceans up around 0.5C.  Something changes at this point, with more hemispheric divergence than before. Over the 4 years until Jan 2007, the Tropics go through ups and downs, NH a series of ups and SH mostly downs.  As a result the Global average fluctuates around that same 0.5C, which also turns out to be the average for the entire record since 1995.

2007 stands out with a sharp drop in temperatures so that Jan.08 matches the low in Jan. ’99, but starting from a lower high. The oceans all decline as well, until temps build peaking in 2010.

Now again a different pattern appears.  The Tropics cool sharply to Jan 11, then rise steadily for 4 years to Jan 15, at which point the most recent major El Nino takes off.  But this time in contrast to ’97-’99, the Northern Hemisphere produces peaks every summer pulling up the Global average.  In fact, these NH peaks appear every July starting in 2003, growing stronger to produce 3 massive highs in 2014, 15 and 16.  NH July 2017 was only slightly lower, and a fifth NH peak still lower in Sept. 2018.

The highest summer NH peaks came in 2019 and 2020, only this time the Tropics and SH are offsetting rather adding to the warming. (Note: these are high anomalies on top of the highest absolute temps in the NH.)  Since 2014 SH has played a moderating role, offsetting the NH warming pulses. After September 2020 temps dropped off down until February 2021, then all regions rose to bring the global anomaly above the mean since 1995  June 2021 backed down before warming again slightly in July and August 2021, then cooling slightly in September.  The present 2022 level compares with 2014 and also 2018.

What to make of all this? The patterns suggest that in addition to El Ninos in the Pacific driving the Tropic SSTs, something else is going on in the NH.  The obvious culprit is the North Atlantic, since I have seen this sort of pulsing before.  After reading some papers by David Dilley, I confirmed his observation of Atlantic pulses into the Arctic every 8 to 10 years.

But the peaks coming nearly every summer in HadSST require a different picture.  Let’s look at August, the hottest month in the North Atlantic from the Kaplan dataset.

The AMO Index is from from Kaplan SST v2, the unaltered and not detrended dataset. By definition, the data are monthly average SSTs interpolated to a 5×5 grid over the North Atlantic basically 0 to 70N. The graph shows August warming began after 1992 up to 1998, with a series of matching years since, including 2020, dropping down in 2021.  Because the N. Atlantic has partnered with the Pacific ENSO recently, let’s take a closer look at some AMO years in the last 2 decades.

 

This graph shows monthly AMO temps for some important years. The Peak years were 1998, 2010 and 2016, with the latter emphasized as the most recent. The other years show lesser warming, with 2007 emphasized as the coolest in the last 20 years. Note the red 2018 line is at the bottom of all these tracks. The heavy blue line shows that 2022 started warm, dropped to the bottom and stayed near the lower tracks, before reaching one of the highest peaks in August.

Summary

The oceans are driving the warming this century.  SSTs took a step up with the 1998 El Nino and have stayed there with help from the North Atlantic, and more recently the Pacific northern “Blob.”  The ocean surfaces are releasing a lot of energy, warming the air, but eventually will have a cooling effect.  The decline after 1937 was rapid by comparison, so one wonders: How long can the oceans keep this up? If the pattern of recent years continues, NH SST anomalies may rise slightly in coming months, but once again, ENSO which has weakened will probably determine the outcome.

Footnote: Why Rely on HadSST4

HadSST is distinguished from other SST products because HadCRU (Hadley Climatic Research Unit) does not engage in SST interpolation, i.e. infilling estimated anomalies into grid cells lacking sufficient sampling in a given month. From reading the documentation and from queries to Met Office, this is their procedure.

HadSST4 imports data from gridcells containing ocean, excluding land cells. From past records, they have calculated daily and monthly average readings for each grid cell for the period 1961 to 1990. Those temperatures form the baseline from which anomalies are calculated.

In a given month, each gridcell with sufficient sampling is averaged for the month and then the baseline value for that cell and that month is subtracted, resulting in the monthly anomaly for that cell. All cells with monthly anomalies are averaged to produce global, hemispheric and tropical anomalies for the month, based on the cells in those locations. For example, Tropics averages include ocean grid cells lying between latitudes 20N and 20S.

Gridcells lacking sufficient sampling that month are left out of the averaging, and the uncertainty from such missing data is estimated. IMO that is more reasonable than inventing data to infill. And it seems that the Global Drifter Array displayed in the top image is providing more uniform coverage of the oceans than in the past.

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

Footnote Rare Triple Dip La Nina Likely This Winter

Here’s Where a Rare “Triple Dip La Niña” Might Drop the Most Snow This Winter Ski Mag

The unusual weather phenomenon might result in the snowiest season in years for some parts of the country.

The long-range winter forecast could be good news for skiers living in the certain parts of the U.S. and Canada. The National Oceanic and Atmospheric Administration (NOAA) estimates that the chance of a La Niña occurring this fall and early winter is 86 percent, and the main beneficiary is expected to be mountains in the Northwest and Northern Rockies.

If NOAA’s predictions pan out, this will be the third La Niña in a row—a rare phenomenon called a “Triple Dip La Niña.” Between now and 1950, only two Triple Dips have occurred.

Smith also notes that winters on the East Coast are similarly tricky to predict during La Niña years. “In the West, you’re simply looking for above-average precipitation, which typically translates to above-average snowfall, but in the East, you have temperature to worry about as well … that adds another complication.” In other words, increased precip could lead to more rain if the temperatures aren’t cooperative.

The presence of a La Niña doesn’t always translate to higher snowfall in the North, either, as evidenced by last ski season, which saw few powder days.

However, in consecutive La Niña triplets, one winter usually involves above-average snowfall. While this historical pattern isn’t tied to any documented meteorological function, it could mean that the odds of a snowy 2022’-’23 season are higher, given the previous two La Niñas didn’t deliver the goods.

 

 

Cooler Air over Land and Ocean August 2022

The post below updates the UAH record of air temperatures over land and ocean.  But as an overview consider how recent rapid cooling  completely overcame the warming from the last 3 El Ninos (1998, 2010 and 2016).  The UAH record shows that the effects of the last one were gone as of April 2021, again in November 2021, and in February and June 2022  (UAH baseline is now 1991-2020).

For reference I added an overlay of CO2 annual concentrations as measured at Mauna Loa.  While temperatures fluctuated up and down ending flat, CO2 went up steadily by ~55 ppm, a 15% increase.

Furthermore, going back to previous warmings prior to the satellite record shows that the entire rise of 0.8C since 1947 is due to oceanic, not human activity.

gmt-warming-events

The animation is an update of a previous analysis from Dr. Murry Salby.  These graphs use Hadcrut4 and include the 2016 El Nino warming event.  The exhibit shows since 1947 GMT warmed by 0.8 C, from 13.9 to 14.7, as estimated by Hadcrut4.  This resulted from three natural warming events involving ocean cycles. The most recent rise 2013-16 lifted temperatures by 0.2C.  Previously the 1997-98 El Nino produced a plateau increase of 0.4C.  Before that, a rise from 1977-81 added 0.2C to start the warming since 1947.

Importantly, the theory of human-caused global warming asserts that increasing CO2 in the atmosphere changes the baseline and causes systemic warming in our climate.  On the contrary, all of the warming since 1947 was episodic, coming from three brief events associated with oceanic cycles. 

Update August 3, 2021

Chris Schoeneveld has produced a similar graph to the animation above, with a temperature series combining HadCRUT4 and UAH6. H/T WUWT

image-8

 

mc_wh_gas_web20210423124932

See Also Worst Threat: Greenhouse Gas or Quiet Sun?

August Update Cooler Air over Land and Sea 

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With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea.  While you will hear a lot about 2020-21 temperatures matching 2016 as the highest ever, that spin ignores how fast the cooling set in.  The UAH data analyzed below shows that warming from the last El Nino was fully dissipated with chilly temperatures in all regions. May NH land and SH ocean showed temps matching March, reversing an upward blip in April, and then June was virtually the mean since 1995.

UAH has updated their tlt (temperatures in lower troposphere) dataset for August 2022.  Previously I have done posts on their reading of ocean air temps as a prelude to updated records from HadSST3 (which is now discontinued). So I have separately posted on SSTs using HadSST4 SH and Tropics Lead Ocean Cooling July 2022.   This month also has a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years. Sometimes air temps over land diverge from ocean air changes.  However, July showed air temps over all ocean regions warmed sharply, lifting up Global ocean temps. Now in August air over both land and ocean cooled off again.

Note:  UAH has shifted their baseline from 1981-2010 to 1991-2020 beginning with January 2021.  In the charts below, the trends and fluctuations remain the same but the anomaly values change with the baseline reference shift.

Presently sea surface temperatures (SST) are the best available indicator of heat content gained or lost from earth’s climate system.  Enthalpy is the thermodynamic term for total heat content in a system, and humidity differences in air parcels affect enthalpy.  Measuring water temperature directly avoids distorted impressions from air measurements.  In addition, ocean covers 71% of the planet surface and thus dominates surface temperature estimates.  Eventually we will likely have reliable means of recording water temperatures at depth.

Recently, Dr. Ole Humlum reported from his research that air temperatures lag 2-3 months behind changes in SST.  Thus the cooling oceans now portend cooling land air temperatures to follow.  He also observed that changes in CO2 atmospheric concentrations lag behind SST by 11-12 months.  This latter point is addressed in a previous post Who to Blame for Rising CO2?

After a change in priorities, updates are now exclusive to HadSST4.  For comparison we can also look at lower troposphere temperatures (TLT) from UAHv6 which are now posted for August.  The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above. Recently there was a change in UAH processing of satellite drift corrections, including dropping one platform which can no longer be corrected. The graphs below are taken from the revised and current dataset.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. There is the additional feature that ocean air temps avoid Urban Heat Islands (UHI).  The graph below shows monthly anomalies for ocean air temps since January 2015.

 

Note 2020 was warmed mainly by a spike in February in all regions, and secondarily by an October spike in NH alone. In 2021, SH and the Tropics both pulled the Global anomaly down to a new low in April. Then SH and Tropics upward spikes, along with NH warming brought Global temps to a peak in October.  That warmth was gone as November 2021 ocean temps plummeted everywhere. After an upward bump 01/2022 temps reversed and plunged downward in June.  After an upward spike in July, ocean air everywhere cooled in August.

Land Air Temperatures Tracking Downward in Seesaw Pattern

We sometimes overlook that in climate temperature records, while the oceans are measured directly with SSTs, land temps are measured only indirectly.  The land temperature records at surface stations sample air temps at 2 meters above ground.  UAH gives tlt anomalies for air over land separately from ocean air temps.  The graph updated for July is below.

 

Here we have fresh evidence of the greater volatility of the Land temperatures, along with extraordinary departures by SH land.  Land temps are dominated by NH with a 2021 spike in January,  then dropping before rising in the summer to peak in October 2021. As with the ocean air temps, all that was erased in November with a sharp cooling everywhere. Land temps dropped sharply for four months, even more than did the Oceans. March and April saw some warming, reversed In May when all land regions cooled pulling down the global anomaly. Then in June Tropics land dropped sharply while SH land rose, NH cooled slightly leaving the Global land anomaly little changed. In July, Tropics and SH land rose sharply, NH slightly, pulling up the Global land anomaly. In August that was reversed downward.

The Bigger Picture UAH Global Since 1980

 

The chart shows monthly Global anomalies starting 01/1980 to present.  The average monthly anomaly is -0.06, for this period of more than four decades.  The graph shows the 1998 El Nino after which the mean resumed, and again after the smaller 2010 event. The 2016 El Nino matched 1998 peak and in addition NH after effects lasted longer, followed by the NH warming 2019-20.   A small upward bump in 2021 has been reversed with temps having returned close to the mean as of 2/2022.  March and April brought warmer Global temps, reversed in May and the June anomaly was almost zero. The upward spike in July was almost 0.3C, now lower in August.

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  Clearly NH and Global land temps have been dropping in a seesaw pattern, nearly 1C lower than the 2016 peak.  Since the ocean has 1000 times the heat capacity as the atmosphere, that cooling is a significant driving force.  TLT measures started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern.  It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

SH and Tropics Lead Ocean Cooling July 2022


The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • A major El Nino was the dominant climate feature in recent years.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source. Previously I used HadSST3 for these reports, but Hadley Centre has made HadSST4 the priority, and v.3 will no longer be updated.  HadSST4 is the same as v.3, except that the older data from ship water intake was re-estimated to be generally lower temperatures than shown in v.3.  The effect is that v.4 has lower average anomalies for the baseline period 1961-1990, thereby showing higher current anomalies than v.3. This analysis concerns more recent time periods and depends on very similar differentials as those from v.3 despite higher absolute anomaly values in v.4.  More on what distinguishes HadSST3 and 4 from other SST products at the end. The user guide for HadSST4 is here.

The Current Context

The 2021 year end report included below showed rapid cooling in all regions.  The anomalies then continued in 2022 to remain near the mean since 2015.  This Global Cooling was also evident in the UAH Land and Ocean air temperature ( Tropics Lead Remarkable Cooling June 2022 )

The chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through July 2022.  A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016. 

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back below its beginning level. Secondly, the Northern Hemisphere added three bumps on the shoulders of Tropical warming, with peaks in August of each year.  A fourth NH bump was lower and peaked in September 2018.  As noted above, a fifth peak in August 2019 and a sixth August 2020 exceeded the four previous upward bumps in NH. A smaller NH rise in 2021 peaked in September of that year.

After an upward bump in August, the 2021 yearend Global temp anomaly dropped below the mean, driven by sharp declines in the Tropics and NH. 2022 started with all regions remaining cool and the Global anomaly lower than the mean for this period. Despite an upward bump in NH May to July, other regions remained cool leaving the Global anomaly little changed. This year the summer NH upward bump is not joined by warming in the Tropics or in SH, which in July resulted in a cooler Global anomaly offsetting NH warming.

A longer view of SSTs

The graph above is noisy, but the density is needed to see the seasonal patterns in the oceanic fluctuations.  Previous posts focused on the rise and fall of the last El Nino starting in 2015.  This post adds a longer view, encompassing the significant 1998 El Nino and since.  The color schemes are retained for Global, Tropics, NH and SH anomalies.  Despite the longer time frame, I have kept the monthly data (rather than yearly averages) because of interesting shifts between January and July.1995 is a reasonable (ENSO neutral) starting point prior to the first El Nino.  The sharp Tropical rise peaking in 1998 is dominant in the record, starting Jan. ’97 to pull up SSTs uniformly before returning to the same level Jan. ’99.  For the next 2 years, the Tropics stayed down, and the world’s oceans held steady around 0.5C above 1961 to 1990 average.

Then comes a steady rise over two years to a lesser peak Jan. 2003, but again uniformly pulling all oceans up around 0.5C.  Something changes at this point, with more hemispheric divergence than before. Over the 4 years until Jan 2007, the Tropics go through ups and downs, NH a series of ups and SH mostly downs.  As a result the Global average fluctuates around that same 0.5C, which also turns out to be the average for the entire record since 1995.

2007 stands out with a sharp drop in temperatures so that Jan.08 matches the low in Jan. ’99, but starting from a lower high. The oceans all decline as well, until temps build peaking in 2010.

Now again a different pattern appears.  The Tropics cool sharply to Jan 11, then rise steadily for 4 years to Jan 15, at which point the most recent major El Nino takes off.  But this time in contrast to ’97-’99, the Northern Hemisphere produces peaks every summer pulling up the Global average.  In fact, these NH peaks appear every July starting in 2003, growing stronger to produce 3 massive highs in 2014, 15 and 16.  NH July 2017 was only slightly lower, and a fifth NH peak still lower in Sept. 2018.

The highest summer NH peaks came in 2019 and 2020, only this time the Tropics and SH are offsetting rather adding to the warming. (Note: these are high anomalies on top of the highest absolute temps in the NH.)  Since 2014 SH has played a moderating role, offsetting the NH warming pulses. After September 2020 temps dropped off down until February 2021, then all regions rose to bring the global anomaly above the mean since 1995  June 2021 backed down before warming again slightly in July and August 2021, then cooling slightly in September.  The present 2022 level compares with 2014 and also 2018.

What to make of all this? The patterns suggest that in addition to El Ninos in the Pacific driving the Tropic SSTs, something else is going on in the NH.  The obvious culprit is the North Atlantic, since I have seen this sort of pulsing before.  After reading some papers by David Dilley, I confirmed his observation of Atlantic pulses into the Arctic every 8 to 10 years.

But the peaks coming nearly every summer in HadSST require a different picture.  Let’s look at August, the hottest month in the North Atlantic from the Kaplan dataset.

The AMO Index is from from Kaplan SST v2, the unaltered and not detrended dataset. By definition, the data are monthly average SSTs interpolated to a 5×5 grid over the North Atlantic basically 0 to 70N. The graph shows August warming began after 1992 up to 1998, with a series of matching years since, including 2020, dropping down in 2021.  Because the N. Atlantic has partnered with the Pacific ENSO recently, let’s take a closer look at some AMO years in the last 2 decades.

This graph shows monthly AMO temps for some important years. The Peak years were 1998, 2010 and 2016, with the latter emphasized as the most recent. The other years show lesser warming, with 2007 emphasized as the coolest in the last 20 years. Note the red 2018 line is at the bottom of all these tracks. The heavy blue line shows that 2022 started warm, dropped to the bottom and now is near the lower tracks pictured.

Summary

The oceans are driving the warming this century.  SSTs took a step up with the 1998 El Nino and have stayed there with help from the North Atlantic, and more recently the Pacific northern “Blob.”  The ocean surfaces are releasing a lot of energy, warming the air, but eventually will have a cooling effect.  The decline after 1937 was rapid by comparison, so one wonders: How long can the oceans keep this up? If the pattern of recent years continues, NH SST anomalies may rise slightly in coming months, but once again, ENSO which has weakened will probably determine the outcome.

Footnote: Why Rely on HadSST4

HadSST is distinguished from other SST products because HadCRU (Hadley Climatic Research Unit) does not engage in SST interpolation, i.e. infilling estimated anomalies into grid cells lacking sufficient sampling in a given month. From reading the documentation and from queries to Met Office, this is their procedure.

HadSST4 imports data from gridcells containing ocean, excluding land cells. From past records, they have calculated daily and monthly average readings for each grid cell for the period 1961 to 1990. Those temperatures form the baseline from which anomalies are calculated.

In a given month, each gridcell with sufficient sampling is averaged for the month and then the baseline value for that cell and that month is subtracted, resulting in the monthly anomaly for that cell. All cells with monthly anomalies are averaged to produce global, hemispheric and tropical anomalies for the month, based on the cells in those locations. For example, Tropics averages include ocean grid cells lying between latitudes 20N and 20S.

Gridcells lacking sufficient sampling that month are left out of the averaging, and the uncertainty from such missing data is estimated. IMO that is more reasonable than inventing data to infill. And it seems that the Global Drifter Array displayed in the top image is providing more uniform coverage of the oceans than in the past.

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

Footnote Rare Triple Dip La Nina Likely This Winter

Here’s Where a Rare “Triple Dip La Niña” Might Drop the Most Snow This Winter Ski Mag

The unusual weather phenomenon might result in the snowiest season in years for some parts of the country.

The long-range winter forecast could be good news for skiers living in the certain parts of the U.S. and Canada. The National Oceanic and Atmospheric Administration (NOAA) estimates that the chance of a La Niña occurring this fall and early winter is 86 percent, and the main beneficiary is expected to be mountains in the Northwest and Northern Rockies.

If NOAA’s predictions pan out, this will be the third La Niña in a row—a rare phenomenon called a “Triple Dip La Niña.” Between now and 1950, only two Triple Dips have occurred.

Smith also notes that winters on the East Coast are similarly tricky to predict during La Niña years. “In the West, you’re simply looking for above-average precipitation, which typically translates to above-average snowfall, but in the East, you have temperature to worry about as well … that adds another complication.” In other words, increased precip could lead to more rain if the temperatures aren’t cooperative.

The presence of a La Niña doesn’t always translate to higher snowfall in the North, either, as evidenced by last ski season, which saw few powder days.

However, in consecutive La Niña triplets, one winter usually involves above-average snowfall. While this historical pattern isn’t tied to any documented meteorological function, it could mean that the odds of a snowy 2022’-’23 season are higher, given the previous two La Niñas didn’t deliver the goods.

 

 

July 2022 UAH Rebound from June Cooling

The post below updates the UAH record of air temperatures over land and ocean.  But as an overview consider how recent rapid cooling  completely overcame the warming from the last 3 El Ninos (1998, 2010 and 2016).  The UAH record shows that the effects of the last one were gone as of April 2021, again in November 2021, and in February and June 2022  (UAH baseline is now 1991-2020).

For reference I added an overlay of CO2 annual concentrations as measured at Mauna Loa.  While temperatures fluctuated up and down ending flat, CO2 went up steadily by ~55 ppm, a 15% increase.

Furthermore, going back to previous warmings prior to the satellite record shows that the entire rise of 0.8C since 1947 is due to oceanic, not human activity.

gmt-warming-events

The animation is an update of a previous analysis from Dr. Murry Salby.  These graphs use Hadcrut4 and include the 2016 El Nino warming event.  The exhibit shows since 1947 GMT warmed by 0.8 C, from 13.9 to 14.7, as estimated by Hadcrut4.  This resulted from three natural warming events involving ocean cycles. The most recent rise 2013-16 lifted temperatures by 0.2C.  Previously the 1997-98 El Nino produced a plateau increase of 0.4C.  Before that, a rise from 1977-81 added 0.2C to start the warming since 1947.

Importantly, the theory of human-caused global warming asserts that increasing CO2 in the atmosphere changes the baseline and causes systemic warming in our climate.  On the contrary, all of the warming since 1947 was episodic, coming from three brief events associated with oceanic cycles. 

Update August 3, 2021

Chris Schoeneveld has produced a similar graph to the animation above, with a temperature series combining HadCRUT4 and UAH6. H/T WUWT

image-8

 

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See Also Worst Threat: Greenhouse Gas or Quiet Sun?

July Update Land and Sea Temps Rebound from June Cooling

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With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea.  While you will hear a lot about 2020-21 temperatures matching 2016 as the highest ever, that spin ignores how fast the cooling set in.  The UAH data analyzed below shows that warming from the last El Nino was fully dissipated with chilly temperatures in all regions. May NH land and SH ocean showed temps matching March, reversing an upward blip in April, and then June was virtually the mean since 1995.

UAH has updated their tlt (temperatures in lower troposphere) dataset for July 2022.  Previously I have done posts on their reading of ocean air temps as a prelude to updated records from HadSST3 (which is now discontinued). So I have separately posted on SSTs using HadSST4 Ocean SSTs Stay Mild June 2022  This month also has a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years. Sometimes air temps over land diverge from ocean air changes.  However, last month showed air temps over all ocean regions warmed sharply, lifting up Global ocean temps. Land temps also rose, especially in SH, resulting in a higher Global land anomaly. 

Note:  UAH has shifted their baseline from 1981-2010 to 1991-2020 beginning with January 2021.  In the charts below, the trends and fluctuations remain the same but the anomaly values change with the baseline reference shift.

Presently sea surface temperatures (SST) are the best available indicator of heat content gained or lost from earth’s climate system.  Enthalpy is the thermodynamic term for total heat content in a system, and humidity differences in air parcels affect enthalpy.  Measuring water temperature directly avoids distorted impressions from air measurements.  In addition, ocean covers 71% of the planet surface and thus dominates surface temperature estimates.  Eventually we will likely have reliable means of recording water temperatures at depth.

Recently, Dr. Ole Humlum reported from his research that air temperatures lag 2-3 months behind changes in SST.  Thus the cooling oceans now portend cooling land air temperatures to follow.  He also observed that changes in CO2 atmospheric concentrations lag behind SST by 11-12 months.  This latter point is addressed in a previous post Who to Blame for Rising CO2?

After a change in priorities, updates are now exclusive to HadSST4.  For comparison we can also look at lower troposphere temperatures (TLT) from UAHv6 which are now posted for July.  The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above. Recently there was a change in UAH processing of satellite drift corrections, including dropping one platform which can no longer be corrected. The graphs below are taken from the revised and current dataset.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. There is the additional feature that ocean air temps avoid Urban Heat Islands (UHI).  The graph below shows monthly anomalies for ocean temps since January 2015.

Note 2020 was warmed mainly by a spike in February in all regions, and secondarily by an October spike in NH alone. In 2021, SH and the Tropics both pulled the Global anomaly down to a new low in April. Then SH and Tropics upward spikes, along with NH warming brought Global temps to a peak in October.  That warmth was gone as November 2021 ocean temps plummeted everywhere. After an upward bump 01/2022 temps reversed and plunged downward in June.  Now July shows an upward spike everywhere, with NH, SH and Global anomalies all up to 0.3C, and the tropics up from -0.4C to +0.1C.

Land Air Temperatures Tracking Downward in Seesaw Pattern

We sometimes overlook that in climate temperature records, while the oceans are measured directly with SSTs, land temps are measured only indirectly.  The land temperature records at surface stations sample air temps at 2 meters above ground.  UAH gives tlt anomalies for air over land separately from ocean air temps.  The graph updated for July is below.

Here we have fresh evidence of the greater volatility of the Land temperatures, along with extraordinary departures by SH land.  Land temps are dominated by NH with a 2021 spike in January,  then dropping before rising in the summer to peak in October 2021. As with the ocean air temps, all that was erased in November with a sharp cooling everywhere. Land temps dropped sharply for four months, even more than did the Oceans. March and April saw some warming, reversed In May when all land regions cooled pulling down the global anomaly. Then in June Tropics land dropped sharply while SH land rose, NH cooled slightly leaving the Global land anomaly little changed.  Now in July, Tropics and SH land rose sharply, NH slightly, pulling up the Global land anomaly. Still summer 2022 is peaking lower than the previous two.

The Bigger Picture UAH Global Since 1980

The chart shows monthly Global anomalies starting 01/1980 to present.  The average monthly anomaly is -0.06, for this period of more than four decades.  The graph shows the 1998 El Nino after which the mean resumed, and again after the smaller 2010 event. The 2016 El Nino matched 1998 peak and in addition NH after effects lasted longer, followed by the NH warming 2019-20.   A small upward bump in 2021 has been reversed with temps having returned close to the mean as of 2/2022.  March and April brought warmer Global temps, reversed in May and the June anomaly was almost zero. The upward spike is July is almost 0.3C.

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  Clearly NH and Global land temps have been dropping in a seesaw pattern, nearly 1C lower than the 2016 peak.  Since the ocean has 1000 times the heat capacity as the atmosphere, that cooling is a significant driving force.  TLT measures started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern.  It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

Ocean SSTs Stay Mild June 2022


The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • A major El Nino was the dominant climate feature in recent years.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source. Previously I used HadSST3 for these reports, but Hadley Centre has made HadSST4 the priority, and v.3 will no longer be updated.  HadSST4 is the same as v.3, except that the older data from ship water intake was re-estimated to be generally lower temperatures than shown in v.3.  The effect is that v.4 has lower average anomalies for the baseline period 1961-1990, thereby showing higher current anomalies than v.3. This analysis concerns more recent time periods and depends on very similar differentials as those from v.3 despite higher absolute anomaly values in v.4.  More on what distinguishes HadSST3 and 4 from other SST products at the end. The user guide for HadSST4 is here.

The Current Context

The 2021 year end report included below showed rapid cooling in all regions.  The anomalies then continued in 2022 to remain near the mean since 2015.  This Global Cooling was also evident in the UAH Land and Ocean air temperature ( Tropics Lead Remarkable Cooling June 2022 )

The chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through June 2022.  A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016. 

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back below its beginning level. Secondly, the Northern Hemisphere added three bumps on the shoulders of Tropical warming, with peaks in August of each year.  A fourth NH bump was lower and peaked in September 2018.  As noted above, a fifth peak in August 2019 and a sixth August 2020 exceeded the four previous upward bumps in NH. A smaller NH rise in 2021 peaked in September of that year.

After an upward bump in August, the 2021 yearend Global temp anomaly dropped below the mean, driven by sharp declines in the Tropics and NH. Now in 2022 all regions remain cool and the Global anomaly remains lower than the mean for this period. Despite an upward bump May and June in NH, other regions remained cool leaving the Global anomaly little changed. This year the summer NH upward bump is not joined by warming in the Tropics.

A longer view of SSTs

To enlarge, double click or open image in new tab.

The graph above is noisy, but the density is needed to see the seasonal patterns in the oceanic fluctuations.  Previous posts focused on the rise and fall of the last El Nino starting in 2015.  This post adds a longer view, encompassing the significant 1998 El Nino and since.  The color schemes are retained for Global, Tropics, NH and SH anomalies.  Despite the longer time frame, I have kept the monthly data (rather than yearly averages) because of interesting shifts between January and July.1995 is a reasonable (ENSO neutral) starting point prior to the first El Nino.  The sharp Tropical rise peaking in 1998 is dominant in the record, starting Jan. ’97 to pull up SSTs uniformly before returning to the same level Jan. ’99.  For the next 2 years, the Tropics stayed down, and the world’s oceans held steady around 0.5C above 1961 to 1990 average.

Then comes a steady rise over two years to a lesser peak Jan. 2003, but again uniformly pulling all oceans up around 0.5C.  Something changes at this point, with more hemispheric divergence than before. Over the 4 years until Jan 2007, the Tropics go through ups and downs, NH a series of ups and SH mostly downs.  As a result the Global average fluctuates around that same 0.5C, which also turns out to be the average for the entire record since 1995.

2007 stands out with a sharp drop in temperatures so that Jan.08 matches the low in Jan. ’99, but starting from a lower high. The oceans all decline as well, until temps build peaking in 2010.

Now again a different pattern appears.  The Tropics cool sharply to Jan 11, then rise steadily for 4 years to Jan 15, at which point the most recent major El Nino takes off.  But this time in contrast to ’97-’99, the Northern Hemisphere produces peaks every summer pulling up the Global average.  In fact, these NH peaks appear every July starting in 2003, growing stronger to produce 3 massive highs in 2014, 15 and 16.  NH July 2017 was only slightly lower, and a fifth NH peak still lower in Sept. 2018.

The highest summer NH peaks came in 2019 and 2020, only this time the Tropics and SH are offsetting rather adding to the warming. (Note: these are high anomalies on top of the highest absolute temps in the NH.)  Since 2014 SH has played a moderating role, offsetting the NH warming pulses. After September 2020 temps dropped off down until February 2021, then all regions rose to bring the global anomaly above the mean since 1995  June 2021 backed down before warming again slightly in July and August 2021, then cooling slightly in September.  The present 2022 level compares with 2014 and also 2018.

What to make of all this? The patterns suggest that in addition to El Ninos in the Pacific driving the Tropic SSTs, something else is going on in the NH.  The obvious culprit is the North Atlantic, since I have seen this sort of pulsing before.  After reading some papers by David Dilley, I confirmed his observation of Atlantic pulses into the Arctic every 8 to 10 years.

But the peaks coming nearly every summer in HadSST require a different picture.  Let’s look at August, the hottest month in the North Atlantic from the Kaplan dataset.

The AMO Index is from from Kaplan SST v2, the unaltered and not detrended dataset. By definition, the data are monthly average SSTs interpolated to a 5×5 grid over the North Atlantic basically 0 to 70N. The graph shows August warming began after 1992 up to 1998, with a series of matching years since, including 2020, dropping down in 2021.  Because the N. Atlantic has partnered with the Pacific ENSO recently, let’s take a closer look at some AMO years in the last 2 decades.

This graph shows monthly AMO temps for some important years. The Peak years were 1998, 2010 and 2016, with the latter emphasized as the most recent. The other years show lesser warming, with 2007 emphasized as the coolest in the last 20 years. Note the red 2018 line is at the bottom of all these tracks. The heavy blue line shows that 2022 started warm, dropped to the bottom and now is in the middle of all the tracks pictured.

Summary

The oceans are driving the warming this century.  SSTs took a step up with the 1998 El Nino and have stayed there with help from the North Atlantic, and more recently the Pacific northern “Blob.”  The ocean surfaces are releasing a lot of energy, warming the air, but eventually will have a cooling effect.  The decline after 1937 was rapid by comparison, so one wonders: How long can the oceans keep this up? If the pattern of recent years continues, NH SST anomalies may rise slightly in coming months, but once again, ENSO which has weakened will probably determine the outcome.

Footnote: Why Rely on HadSST4

HadSST is distinguished from other SST products because HadCRU (Hadley Climatic Research Unit) does not engage in SST interpolation, i.e. infilling estimated anomalies into grid cells lacking sufficient sampling in a given month. From reading the documentation and from queries to Met Office, this is their procedure.

HadSST4 imports data from gridcells containing ocean, excluding land cells. From past records, they have calculated daily and monthly average readings for each grid cell for the period 1961 to 1990. Those temperatures form the baseline from which anomalies are calculated.

In a given month, each gridcell with sufficient sampling is averaged for the month and then the baseline value for that cell and that month is subtracted, resulting in the monthly anomaly for that cell. All cells with monthly anomalies are averaged to produce global, hemispheric and tropical anomalies for the month, based on the cells in those locations. For example, Tropics averages include ocean grid cells lying between latitudes 20N and 20S.

Gridcells lacking sufficient sampling that month are left out of the averaging, and the uncertainty from such missing data is estimated. IMO that is more reasonable than inventing data to infill. And it seems that the Global Drifter Array displayed in the top image is providing more uniform coverage of the oceans than in the past.

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

 

 

By the Numbers: CO2 Mostly Natural

This post compiles several independent proofs which refute those reasserting the “consensus” view attributing all additional atmospheric CO2 to humans burning fossil fuels.

The IPCC doctrine which has long been promoted goes as follows. We have a number over here for monthly fossil fuel CO2 emissions, and a number over there for monthly atmospheric CO2. We don’t have good numbers for the rest of it-oceans, soils, biosphere–though rough estimates are orders of magnitude higher, dwarfing human CO2. So we ignore nature and assume it is always a sink, explaining the difference between the two numbers we do have. Easy peasy, science settled.

The non-IPCC paradigm is that atmospheric CO2 levels are a function of two very different fluxes. FF CO2 changes rapidly and increases steadily, while Natural CO2 changes slowly over time, and fluctuates up and down from temperature changes. The implications are that human CO2 is a simple addition, while natural CO2 comes from the integral of previous fluctuations.

1.  History of Atmospheric CO2 Mostly Natural

This proof is based on the 2021 paper World Atmospheric CO2, Its 14C Specific Activity, Non-fossil Component, Anthropogenic Fossil Component, and Emissions (1750–2018) by Kenneth Skrable, George Chabot, and Clayton French at University of Massachusetts Lowell.

The analysis employs ratios of carbon isotopes to calculate the relative proportions of atmospheric CO2 from natural sources and from fossil fuel emissions. 

The specific activity of 14C in the atmosphere gets reduced by a dilution effect when fossil CO2, which is devoid of 14C, enters the atmosphere. We have used the results of this effect to quantify the two components: the anthropogenic fossil component and the non-fossil component.  All results covering the period from 1750 through 2018 are listed in a table and plotted in figures.

These results negate claims that the increase in total atmospheric CO2 concentration C(t) since 1800 has been dominated by the increase of the anthropogenic fossil component. We determined that in 2018, atmospheric anthropogenic fossil COrepresented 23% of the total emissions since 1750 with the remaining 77% in the exchange reservoirs. Our results show that the percentage of the total CO2 due to the use of fossil fuels from 1750 to 2018 increased from 0% in 1750 to 12% in 2018, much too low to be the cause of global warming.

The graph above is produced from Skrable et al. dataset Table 2. World atmospheric CO2, its C‐14 specific activity, anthropogenic‐fossil component, non fossil component, and emissions (1750 ‐ 2018).  The purple line shows reported annual concentrations of atmospheric CO2 from Energy Information Administration (EIA)  The starting value in 1750 is 276 ppm and the final value in this study is 406 ppm in 2018, a gain of 130 ppm.

The red line is based on EIA estimates of human fossil fuel CO2 emissions starting from zero in 1750 and the sum slowly accumulating over the first 200 years.  The estimate of annual CO2 emitted from FF increases from 0.75 ppm in 1950 up to 4.69 ppm in 2018. The sum of all these annual emissions rises from 29.3 ppm in 1950 (from the previous 200 years) up to 204.9 ppm (from 268 years).  These are estimates of historical FF CO2 emitted into the atmosphere, not the amount of FF CO2 found in the air.

Atmospheric CO2 is constantly in two-way fluxes between multiple natural sinks/sources, principally the ocean, soil and biosphere. The annual dilution of carbon 14 proportion is used to calculate the fractions of atmospheric FF CO2 and Natural CO2 remaining in a given year. The blue line shows the FF CO2 fraction rising from 4.03 ppm in 1950 to 46.84 ppm in 2018.  The cyan line shows Natural CO2 fraction rising from 307.51 in 1950 to 358.56 in 2018.

The details of these calculations from observations are presented in the two links above, and the logic of the analysis is summarized in my previous post On CO2 Sources and Isotopes.  The table below illustrates the factors applied in the analysis.

C(t) is total atm CO2, S(t) is Seuss 14C effect, CF(t) is FF atm CO2, CNF(t) is atm non-FF CO2, DE(t) is FF CO2 emissions

Summary

Despite an estimated 205 ppm of FF CO2 emitted since 1750, only 46.84 ppm (23%) of FF CO2 remains, while the other 77% is distributed into natural sinks/sources. As of 2018 atmospheric CO2 was 405, of which 12% (47 ppm) originated from FF.   And the other 88% (358 ppm) came from natural sources: 276 prior to 1750, and 82 ppm since.  Natural CO2 sources/sinks continue to drive rising atmospheric CO2, presently at a rate of 2 to 1 over FF CO2.

2.  Analysis of CO2 Flows Confirms Natural Dominance

Independent research by Dr. Ed Berry focused on studying flows and level of CO2 sources and sinks.  The above summary chart from his published work presents a very similar result.

The graph above summarizes Dr. Berry’s findings. The lines represent CO2 added into the atmosphere since the 1750 level of 280 ppm. Based on IPCC data regarding CO2 natural sources and sinks, the black dots show the CO2 data. The small blue dots show the sum of all human CO2 emissions since they became measurable, irrespective of transfers of that CO2 from the atmosphere to land or to ocean.

Notice the CO2 data is greater than the sum of all human CO2 until 1960. That means nature caused the CO2 level to increase prior to 1960, with no reason to stop adding CO2 since. In fact, the analysis shows that in the year 2020, the human contribution to atmospheric CO2 level is 33 ppm, which means that from a 2020 total of 413 ppm, 280 is pre-industrial and 100 is added from land and ocean during the industrial era.

My synopsis of his work is IPCC Data: Rising CO2 is 75% Natural

A new carbon cycle model shows human emissions cause 25% and nature 75% of the CO2 increase is the title (and link) for Dr. Edwin Berry’s paper accepted in the journal Atmosphere August 12, 2021.

3. Nature Erases Pulses of Human CO2 Emissions  

Those committed to blaming humans for rising atmospheric CO2 sometimes admit that emitted CO2 (from any source) only stays in the air about 5 years (20% removed each year)  being absorbed into natural sinks.  But they then save their belief by theorizing that human emissions are “pulses” of additional CO2 which persist even when particular molecules are removed, resulting in higher CO2 concentrations.  The analogy would be a traffic jam on the freeway which persists long after the blockage is removed.

A recent study by Bud Bromley puts the fork in this theory.  His paper is A conservative calculation of specific impulse for CO2.  The title links to his text which goes through the math in detail.  Excerpts are in italics here with my bolds.

In the 2 years following the June 15, 1991 eruption of the Pinatubo volcano, the natural environment removed more CO2 than the entire increase in CO2 concentration due to all sources, human and natural, during the entire measured daily record of the Global Monitoring Laboratory of NOAA/Scripps Oceanographic Institute (MLO) May 17, 1974 to June 15, 1991. Then, in the 2 years after that, that CO2 was replaced plus an additional increment of CO2.

The data and graphs produced by MLO also show a reduction in slope of total CO2 concentration following the June 1991 eruption of Pinatubo, and also show the more rapid recovery of total CO2 concentration that began about 2 years after the 1991 eruption. This graph is the annual rate of change (i.e., velocity or slope) of total atmosphere CO2 concentration. This graph is not human CO2.

More recently is his study Scaling the size of the CO2 error in Friedlingstein et al.  Excerpts in italics with my bolds.

Since net human emissions would be a cumulative net of two fluxes, if there were a method to measure it, and since net global average CO2 concentration (i.e., NOAA Mauna Loa) is the net of two fluxes, then we should compare these data as integral areas. That is still an apples and oranges comparison because we only have the estimate of human emissions, not net human emissions. But at least the comparison would be in the right order of magnitude.

That comparison would look something like the above graphic. We would be comparing the entire area of the orange quadrangle to the entire blue area, understanding that the tiny blue area shown is much larger than actually is because the amount shown is human emissions only, not net human emissions. Human CO2 absorptions have not been subtracted. Nevertheless, it should be obvious that (1) B is not causing A, and (2) the orange area is enormously larger than the blue area.

Human emissions cannot be driving the growth rate (slope) observed in net global average CO2 concentration.

4.  Setting realistic proportions for the carbon cycle.

Hermann Harde applies a comparable perspective to consider the carbon cycle dynamics. His paper is Scrutinizing the carbon cycle and CO2 residence time in the atmosphere. Excerpts with my bolds.

Different to the IPCC we start with a rate equation for the emission and absorption processes, where the uptake is not assumed to be saturated but scales proportional with the actual CO2 concentration in the atmosphere (see also Essenhigh, 2009; Salby, 2016). This is justified by the observation of an exponential decay of 14C. A fractional saturation, as assumed by the IPCC, can directly be expressed by a larger residence time of CO2 in the atmosphere and makes a distinction between a turnover time and adjustment time needless.

Based on this approach and as solution of the rate equation we derive a concentration at steady state, which is only determined by the product of the total emission rate and the residence time. Under present conditions the natural emissions contribute 373 ppm and anthropogenic emissions 17 ppm to the total concentration of 390 ppm (2012). For the average residence time we only find 4 years.

The stronger increase of the concentration over the Industrial Era up to present times can be explained by introducing a temperature dependent natural emission rate as well as a temperature affected residence time. With this approach not only the exponential increase with the onset of the Industrial Era but also the concentrations at glacial and cooler interglacial times can well be reproduced in full agreement with all observations.

So, different to the IPCC’s interpretation the steep increase of the concentration since 1850 finds its natural explanation in the self accelerating processes on the one hand by stronger degassing of the oceans as well as a faster plant growth and decomposition, on the other hand by an increasing residence time at reduced solubility of CO2 in oceans. Together this results in a dominating temperature controlled natural gain, which contributes about 85% to the 110 ppm CO2 increase over the Industrial Era, whereas the actual anthropogenic emissions of 4.3% only donate 15%. These results indicate that almost all of the observed change of CO2 during the Industrial Era followed, not from anthropogenic emission, but from changes of natural emission. The results are consistent with the observed lag of CO2 changes behind temperature changes (Humlum et al., 2013; Salby, 2013), a signature of cause and effect. Our analysis of the carbon cycle, which exclusively uses data for the CO2 concentrations and fluxes as published in AR5, shows that also a completely different interpretation of these data is possible, this in complete conformity with all observations and natural causalities.

5.  More CO2 Is Not a Problem But a Blessing

William Happer provides a framework for thinking about climate, based on his expertise regarding atmospheric radiation (the “greenhouse” mechanism).  But he uses plain language accessible to all.  The Independent Institute published the transcript for those like myself who prefer reading for full comprehension.  Source: How to Think about Climate Change  

His presentation boils down to two main points:  More CO2 will result in very little additional global warming. But it will increase productivity of the biosphere.  My synopsis is: Climate Change and CO2 Not a Problem  Brief excerpts in italics with my bolds.

This is an important slide. There is a lot of history here and so there are two historical pictures. The top picture is Max Planck, the great German physicist who discovered quantum mechanics. Amazingly, quantum mechanics got its start from greenhouse gas-physics and thermal radiation, just what we are talking about today. Most climate fanatics do not understand the basic physics. But Planck understood it very well and he was the first to show why the spectrum of radiation from warm bodies has the shape shown on this picture, to the left of Planck. Below is a smooth blue curve. The horizontal scale, left to right is the “spatial frequency” (wave peaks per cm) of thermal radiation. The vertical scale is the thermal power that is going out to space. If there were no greenhouse gases, the radiation going to space would be the area under the blue Planck curve. This would be the thermal radiation that balances the heating of Earth by sunlight.

In fact, you never observe the Planck curve if you look down from a satellite. We have lots of satellite measurements now. What you see is something that looks a lot like the black curve, with lots of jags and wiggles in it. That curve was first calculated by Karl Schwarzschild, who first figured out how the real Earth, including the greenhouse gases in its atmosphere, radiates to space. That is described by the jagged black line. The important point here is the red line. This is what Earth would radiate to space if you were to double the CO2 concentration from today’s value. Right in the middle of these curves, you can see a gap in spectrum. The gap is caused by CO2 absorbing radiation that would otherwise cool the Earth. If you double the amount of CO2, you don’t double the size of that gap. You just go from the black curve to the red curve, and you can barely see the difference. The gap hardly changes.

The message I want you to understand, which practically no one really understands, is that doubling CO2 makes almost no difference.

The alleged harm from CO2 is from warming, and the warming observed is much, much less than predictions. In fact, warming as small as we are observing is almost certainly beneficial. It gives slightly longer growing seasons. You can ripen crops a little bit further north than you could before. So, there is completely good news in terms of the temperature directly. But there is even better news. By standards of geological history, plants have been living in a CO2 famine during our current geological period.

So, the takeaway message is that policies that slow CO2 emissions are based on flawed computer models which exaggerate warming by factors of two or three, probably more. That is message number one. So, why do we give up our freedoms, why do we give up our automobiles, why do we give up a beefsteak because of this model that does not work?

Takeaway message number two is that if you really look into it, more CO2 actually benefits the world. So, why are we demonizing this beneficial molecule that is making plants grow better, that is giving us slightly less harsh winters, a slightly longer growing season? Why is that a pollutant? It is not a pollutant at all, and we should have the courage to do nothing about CO2 emissions. Nothing needs to be done.

Footnote:  The Core of the CO2 Issue Update July 15

An adversarial comment below goes to the heart of the issue:

“The increase of the CO2 level since 1850   are more than accounted for by manmade emissions.
Nature remains a net CO2 sink, not a net emitter.”

The data show otherwise.  Warming temperatures favor natural sources/sinks emitting more CO2 into the atmosphere, while previously captured CO2 shifts over time into long term storage as bicarbonates.  In fact, rising temperatures are predictive of rising CO2, as shown mathematically.

Temps Cause CO2 Changes, Not the Reverse. June 2022 Update

It is the ongoing natural contribution to atmospheric CO2 that is being denied.

 

 

Tropics Lead Remarkable Cooling June 2022

The post below updates the UAH record of air temperatures over land and ocean.  But as an overview consider how recent rapid cooling  completely overcame the warming from the last 3 El Ninos (1998, 2010 and 2016).  The UAH record shows that the effects of the last one were gone as of April 2021, again in November 2021, February 2022 and now in June (UAH baseline is now 1991-2020).

For reference I added an overlay of CO2 annual concentrations as measured at Mauna Loa.  While temperatures fluctuated up and down ending flat, CO2 went up steadily by ~55 ppm, a 15% increase.

Furthermore, going back to previous warmings prior to the satellite record shows that the entire rise of 0.8C since 1947 is due to oceanic, not human activity.

gmt-warming-events

The animation is an update of a previous analysis from Dr. Murry Salby.  These graphs use Hadcrut4 and include the 2016 El Nino warming event.  The exhibit shows since 1947 GMT warmed by 0.8 C, from 13.9 to 14.7, as estimated by Hadcrut4.  This resulted from three natural warming events involving ocean cycles. The most recent rise 2013-16 lifted temperatures by 0.2C.  Previously the 1997-98 El Nino produced a plateau increase of 0.4C.  Before that, a rise from 1977-81 added 0.2C to start the warming since 1947.

Importantly, the theory of human-caused global warming asserts that increasing CO2 in the atmosphere changes the baseline and causes systemic warming in our climate.  On the contrary, all of the warming since 1947 was episodic, coming from three brief events associated with oceanic cycles. 

Update August 3, 2021

Chris Schoeneveld has produced a similar graph to the animation above, with a temperature series combining HadCRUT4 and UAH6. H/T WUWT

image-8

 

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See Also Worst Threat: Greenhouse Gas or Quiet Sun?

June Update Tropics Lead Dramatic Ocean Cooling

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With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea.  While you will hear a lot about 2020-21 temperatures matching 2016 as the highest ever, that spin ignores how fast the cooling set in.  The UAH data analyzed below shows that warming from the last El Nino was fully dissipated with chilly temperatures in all regions. May NH land and SH ocean showed temps matching March, reversing an upward blip in April, and now June is virtually the mean since 1995.

UAH has updated their tlt (temperatures in lower troposphere) dataset for June 2022.  Previously I have done posts on their reading of ocean air temps as a prelude to updated records from HadSST3 (which is now discontinued). So I have separately posted on SSTs using HadSST4 Ocean SSTs Keep Cool May 2022.  This month also has a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years. Sometimes air temps over land diverge from ocean air changes.  However, last month showed air temps over Tropical ocean cooled sharply, along with strong cooling over NH and SH, taking Global ocean temps down.  Tropical land also dropped, and NH less so, while SH land rose leaving Global land average little changed

Note:  UAH has shifted their baseline from 1981-2010 to 1991-2020 beginning with January 2021.  In the charts below, the trends and fluctuations remain the same but the anomaly values change with the baseline reference shift.

Presently sea surface temperatures (SST) are the best available indicator of heat content gained or lost from earth’s climate system.  Enthalpy is the thermodynamic term for total heat content in a system, and humidity differences in air parcels affect enthalpy.  Measuring water temperature directly avoids distorted impressions from air measurements.  In addition, ocean covers 71% of the planet surface and thus dominates surface temperature estimates.  Eventually we will likely have reliable means of recording water temperatures at depth.

Recently, Dr. Ole Humlum reported from his research that air temperatures lag 2-3 months behind changes in SST.  Thus the cooling oceans now portend cooling land air temperatures to follow.  He also observed that changes in CO2 atmospheric concentrations lag behind SST by 11-12 months.  This latter point is addressed in a previous post Who to Blame for Rising CO2?

After a change in priorities, updates are now exclusive to HadSST4.  For comparison we can also look at lower troposphere temperatures (TLT) from UAHv6 which are now posted for June.  The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above. Recently there was a change in UAH processing of satellite drift corrections, including dropping one platform which can no longer be corrected. The graphs below are taken from the revised and current dataset.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. There is the additional feature that ocean air temps avoid Urban Heat Islands (UHI).  The graph below shows monthly anomalies for ocean temps since January 2015.

Note 2020 was warmed mainly by a spike in February in all regions, and secondarily by an October spike in NH alone. In 2021, SH and the Tropics both pulled the Global anomaly down to a new low in April. Then SH and Tropics upward spikes, along with NH warming brought Global temps to a peak in October.  That warmth was gone as November 2021 ocean temps plummeted everywhere. After an upward bump 01/2022 temps have reversed and plunged downward in June.  Tropics ocean anomaly cooled 0.4C the lowest in this period.

Land Air Temperatures Tracking Downward in Seesaw Pattern

We sometimes overlook that in climate temperature records, while the oceans are measured directly with SSTs, land temps are measured only indirectly.  The land temperature records at surface stations sample air temps at 2 meters above ground.  UAH gives tlt anomalies for air over land separately from ocean air temps.  The graph updated for June is below.

Here we have fresh evidence of the greater volatility of the Land temperatures, along with extraordinary departures by SH land.  Land temps are dominated by NH with a 2021 spike in January,  then dropping before rising in the summer to peak in October 2021. As with the ocean air temps, all that was erased in November with a sharp cooling everywhere. Land temps dropped sharply for four months, even more than did the Oceans. March and April saw some warming, reversed In May when all land regions cooled pulling down the global anomaly. Now in June Tropics land dropped sharply while SH land rose, NH cooled slightly leaving the Global land anomaly little changed

The Bigger Picture UAH Global Since 1980

The chart shows monthly Global anomalies starting 01/1980 to present.  The average monthly anomaly is -0.06, for this period of more than four decades.  The graph shows the 1998 El Nino after which the mean resumed, and again after the smaller 2010 event. The 2016 El Nino matched 1998 peak and in addition NH after effects lasted longer, followed by the NH warming 2019-20.   A small upward bump in 2021 has been reversed with temps having returned close to the mean as of 2/2022.  March and April brought warmer Global temps, reversed in May and now the June anomaly is almost zero.

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  Clearly NH and Global land temps have been dropping in a seesaw pattern, nearly 1C lower than the 2016 peak.  Since the ocean has 1000 times the heat capacity as the atmosphere, that cooling is a significant driving force.  TLT measures started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern.  It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

Ocean SSTs Keep Cool May 2022


The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • A major El Nino was the dominant climate feature in recent years.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source. Previously I used HadSST3 for these reports, but Hadley Centre has made HadSST4 the priority, and v.3 will no longer be updated.  HadSST4 is the same as v.3, except that the older data from ship water intake was re-estimated to be generally lower temperatures than shown in v.3.  The effect is that v.4 has lower average anomalies for the baseline period 1961-1990, thereby showing higher current anomalies than v.3. This analysis concerns more recent time periods and depends on very similar differentials as those from v.3 despite higher absolute anomaly values in v.4.  More on what distinguishes HadSST3 and 4 from other SST products at the end. The user guide for HadSST4 is here.

The Current Context

The 2021 year end report below showed rapid cooling in all regions.  The anomalies then continued in 2022 to remain well below the mean since 2015.  This Global Cooling was also evident in the UAH Land and Ocean air temperatures (Still No Global Warming, Milder March Land and Sea).

The chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through May 2022.  A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016. 

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back below its beginning level. Secondly, the Northern Hemisphere added three bumps on the shoulders of Tropical warming, with peaks in August of each year.  A fourth NH bump was lower and peaked in September 2018.  As noted above, a fifth peak in August 2019 and a sixth August 2020 exceeded the four previous upward bumps in NH. A smaller NH rise in 2021 peaked in September of that year.

After three straight Spring 2020 months of cooling led by the tropics and SH, NH spiked in the summer, along with smaller bumps elsewhere.  Then temps everywhere dropped for six months, hitting bottom in February 2021.  All regions were well below the Global Mean since 2015, matching the cold of 2018, and lower than January 2015. Then the spring and summer brought more temperate waters and a July return to the mean anomaly since 2015.  After an upward bump in August, the 2021 yearend Global temp anomaly dropped below the mean, driven by sharp declines in the Tropics and NH. Now in 2022 all regions remain cool and the Global anomaly remains lower than the mean for this period. Despite an upward bump in NH, other regions cooled leaving the Global anomaly little changed.

A longer view of SSTs

 

Open in new tab to enlarge image.

The graph above is noisy, but the density is needed to see the seasonal patterns in the oceanic fluctuations.  Previous posts focused on the rise and fall of the last El Nino starting in 2015.  This post adds a longer view, encompassing the significant 1998 El Nino and since.  The color schemes are retained for Global, Tropics, NH and SH anomalies.  Despite the longer time frame, I have kept the monthly data (rather than yearly averages) because of interesting shifts between January and July.1995 is a reasonable (ENSO neutral) starting point prior to the first El Nino.  The sharp Tropical rise peaking in 1998 is dominant in the record, starting Jan. ’97 to pull up SSTs uniformly before returning to the same level Jan. ’99.  For the next 2 years, the Tropics stayed down, and the world’s oceans held steady around 0.5C above 1961 to 1990 average.

Then comes a steady rise over two years to a lesser peak Jan. 2003, but again uniformly pulling all oceans up around 0.5C.  Something changes at this point, with more hemispheric divergence than before. Over the 4 years until Jan 2007, the Tropics go through ups and downs, NH a series of ups and SH mostly downs.  As a result the Global average fluctuates around that same 0.5C, which also turns out to be the average for the entire record since 1995.

2007 stands out with a sharp drop in temperatures so that Jan.08 matches the low in Jan. ’99, but starting from a lower high. The oceans all decline as well, until temps build peaking in 2010.

Now again a different pattern appears.  The Tropics cool sharply to Jan 11, then rise steadily for 4 years to Jan 15, at which point the most recent major El Nino takes off.  But this time in contrast to ’97-’99, the Northern Hemisphere produces peaks every summer pulling up the Global average.  In fact, these NH peaks appear every July starting in 2003, growing stronger to produce 3 massive highs in 2014, 15 and 16.  NH July 2017 was only slightly lower, and a fifth NH peak still lower in Sept. 2018.

The highest summer NH peaks came in 2019 and 2020, only this time the Tropics and SH are offsetting rather adding to the warming. (Note: these are high anomalies on top of the highest absolute temps in the NH.)  Since 2014 SH has played a moderating role, offsetting the NH warming pulses. After September 2020 temps dropped off down until February 2021, then all regions rose to bring the global anomaly above the mean since 1995  June 2021 backed down before warming again slightly in July and August 2021, then cooling slightly in September.  The present 2022 level compares with 2014.

What to make of all this? The patterns suggest that in addition to El Ninos in the Pacific driving the Tropic SSTs, something else is going on in the NH.  The obvious culprit is the North Atlantic, since I have seen this sort of pulsing before.  After reading some papers by David Dilley, I confirmed his observation of Atlantic pulses into the Arctic every 8 to 10 years.

But the peaks coming nearly every summer in HadSST require a different picture.  Let’s look at August, the hottest month in the North Atlantic from the Kaplan dataset.

The AMO Index is from from Kaplan SST v2, the unaltered and not detrended dataset. By definition, the data are monthly average SSTs interpolated to a 5×5 grid over the North Atlantic basically 0 to 70N. The graph shows August warming began after 1992 up to 1998, with a series of matching years since, including 2020, dropping down in 2021.  Because the N. Atlantic has partnered with the Pacific ENSO recently, let’s take a closer look at some AMO years in the last 2 decades.

 

This graph shows monthly AMO temps for some important years. The Peak years were 1998, 2010 and 2016, with the latter emphasized as the most recent. The other years show lesser warming, with 2007 emphasized as the coolest in the last 20 years. Note the red 2018 line is at the bottom of all these tracks. The heavy blue line shows that 2022 started warm, dropped to the bottom and now is in the middle of all the tracks pictured.

Summary

The oceans are driving the warming this century.  SSTs took a step up with the 1998 El Nino and have stayed there with help from the North Atlantic, and more recently the Pacific northern “Blob.”  The ocean surfaces are releasing a lot of energy, warming the air, but eventually will have a cooling effect.  The decline after 1937 was rapid by comparison, so one wonders: How long can the oceans keep this up? If the pattern of recent years continues, NH SST anomalies may rise slightly in coming months, but once again, ENSO which has weakened will probably determine the outcome.

Footnote: Why Rely on HadSST4

HadSST is distinguished from other SST products because HadCRU (Hadley Climatic Research Unit) does not engage in SST interpolation, i.e. infilling estimated anomalies into grid cells lacking sufficient sampling in a given month. From reading the documentation and from queries to Met Office, this is their procedure.

HadSST4 imports data from gridcells containing ocean, excluding land cells. From past records, they have calculated daily and monthly average readings for each grid cell for the period 1961 to 1990. Those temperatures form the baseline from which anomalies are calculated.

In a given month, each gridcell with sufficient sampling is averaged for the month and then the baseline value for that cell and that month is subtracted, resulting in the monthly anomaly for that cell. All cells with monthly anomalies are averaged to produce global, hemispheric and tropical anomalies for the month, based on the cells in those locations. For example, Tropics averages include ocean grid cells lying between latitudes 20N and 20S.

Gridcells lacking sufficient sampling that month are left out of the averaging, and the uncertainty from such missing data is estimated. IMO that is more reasonable than inventing data to infill. And it seems that the Global Drifter Array displayed in the top image is providing more uniform coverage of the oceans than in the past.

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

 

 

How to FLICC Off Climate Alarms

John Ridgway has provided an excellent framework for skeptics to examine and respond to claims from believers in global warming/climate change.  His essay at Climate Scepticism is Deconstructing Scepticism: The True FLICC.  Excerpts in italics with my bolds and added comments.

Overview

I have modified slightly the FLICC components to serve as a list of actions making up a skeptical approach to an alarmist claim.  IOW this is a checklist for applying critical intelligence to alarmist discourse in the public arena. The Summary can be stated thusly:

♦  Follow the Data
Find and follow the data and facts to where they lead

♦  Look for full risk profile
Look for a complete assessment of risks and costs from proposed policies

♦  Interrogate causal claims
Inquire into claimed cause-effect relationships

♦  Compile contrary explanations
Construct an organized view of contradictory evidence to the theory

♦  Confront cultural bias
Challenge attempts to promote consensus story with flimsy coincidence

A Case In Point

John Ridgway illustrates how this method works in a comment:

No sooner have I’ve pressed the publish button, and the BBC comes out with the perfect example of what I have been writing about:  Climate change: Rising sea levels threaten 200,000 England properties

It tells of a group of experts theorizing that 200,000 coastal properties are soon to be lost due to climate change. Indeed, it “is already happening” as far as Happisburg on the Norfolk coast is concerned. Coastal erosion is indeed a problem there.

But did the experts take into account that the data shows no acceleration of erosion over the last 2000 years? No.

Have they acknowledge the fact that erosion on the East coast is a legacy of glaciation? No.

[For the US example of this claim, see my post Sea Level Scare Machine]

The FLICC Framework

Below is Ridgway’s text regarding this thought process, followed by a synopsis of his discussion of the five elements. Text is in italics with my bolds.

As part of the anthropogenic climate change debate, and when discussing the proposed plans for transition to Net Zero, efforts have been made to analyse the thinking that underpins the typical sceptic’s position. These analyses have universally presupposed that such scepticism stubbornly persists in the face of overwhelming evidence, as reflected in the widespread use of the term ‘denier’. Consequently, they are based upon taxonomies of flawed reasoning and methods of deception and misinformation.1 

However, by taking such a prejudicial approach, the analyses have invariably failed to acknowledge the ideological, philosophical and psychological bases for sceptical thinking. The following taxonomy redresses that failing and, as a result, offers a more pertinent analysis that avoids the worst excesses of opinionated philippic. The taxonomy identifies a basic set of ideologies and attitudes that feature prominently in the typical climate change sceptic’s case. For my taxonomy I have chosen the acronym FLICC:2

  • Follow data but distrust judgement and speculation

     i.e. value empirical evidence over theory and conjecture.

  • Look for the full risk profile

      i.e. when considering the management of risks and uncertainties, demand that those associated        with mitigating and preventative measures are also taken into account.

  • Interrogate causal arguments

      i.e. demand that both necessity and sufficiency form the basis of a causal analysis.

  • Contrariness

      i.e. distrust consensus as an indicator of epistemological value.

  • Cultural awareness

       i.e. never underestimate the extent to which a society can fabricate a truth for its own purposes.

All of the above have a long and legitimate history outside the field of climate science. The suggestion that they are not being applied in good faith by climate change sceptics falls beyond the remit of taxonomical analysis and strays into the territory of propaganda and ad hominem.

The five ideologies and attitudes of climate change scepticism introduced above are now discussed in greater detail.

Following the data

Above all else, the sceptical approach is characterized by a reluctance to draw conclusions from a given body of evidence. When it comes to evidence supporting the idea of a ‘climate crisis’, such reluctance is judged by many to be pathological and indicative of motivated reasoning. Cognitive scientists use the term ‘conservative belief revision’ to refer to an undue reluctance to update beliefs in accordance with a new body of evidence. More precisely, when the individual retains the view that events have a random pattern, thereby downplaying the possibility of a causative factor, the term used is ‘slothful induction’. Either way, the presupposition is that the individual is committing a logical fallacy resulting from cognitive bias.

However, far from being a pathology of thinking, such reluctance has its legitimate foundations in Pyrrhonian philosophy and, when properly understood, it can be seen as an important thinking strategy.3 Conservative belief revision and slothful induction can indeed lead to false conclusions but, more importantly, the error most commonly encountered when making decisions under uncertainty (and the one with the greatest potential for damage) is to downplay unknown and possibly random factors and instead construct a narrative that overstates and prejudges causation. This tendency is central to the human condition and it lies at the heart of our failure to foresee the unexpected – this is the truly important cognitive bias that the sceptic seeks to avoid.

The empirical sceptic is cognisant of evidence and allows the formulation of theories but treats them with considerable caution due to the many ways in which such theories often entail unwarranted presupposition.

The drivers behind this problem are the propensity of the human mind to seek patterns, to construct narratives that hide complexities, to over-emphasise the causative role played by human agents and to under-emphasise the role played by external and possibly random factors. Ultimately, it is a problem regarding the comprehension of uncertainty — we comprehend in a manner that has served us well in evolutionary terms but has left us vulnerable to unprecedented, high consequence events.

It is often said that a true sceptic is one who is prepared to accept the prevailing theory once the evidence is ‘overwhelming’. The climate change sceptic’s reluctance to do so is taken as an indication that he or she is not a true sceptic. However, we see here that true scepticism lies in the willingness to challenge the idea that the evidence is overwhelming – it only seems overwhelming to those who fail to recognise the ‘theorizing disease’ and lack the resolve to resist it. Secondly, there cannot be a climate change sceptic alive who is not painfully aware of the humiliation handed out to those who resist the theorizing.

In practice, the theorizing and the narratives that trouble the empirical sceptic take many forms. It can be seen in:

♦  over-dependence upon mathematical models for which the tuning owes more to art than science.

♦  readiness to treat the output of such models as data resulting from experiment, rather than the hypotheses they are.

♦  lack of regard for ontological uncertainty (i.e. the unknown unknowns which, due to their very nature, the models do not address).

♦  emergence of story-telling as a primary weapon in the armoury of extreme weather event attribution.

♦  willingness to commit trillions of pounds to courses of action that are predicated upon Representative Concentration Pathways and economic models that are the ‘theorizing disease’ writ large.

♦  contributions of the myriad of activists who seek to portray the issues in a narrative form laden with social justice and other ethical considerations.

♦  imaginative but simplistic portrayals of climate change sceptics and their motives; portrayals that are drawing categorical conclusions that cannot possibly be justified given the ‘evidence’ offered. And;

♦  any narrative that turns out to be unfounded when one follows the data.

Climate change may have its basis in science and data, but this basis has long since been overtaken by a plethora of theorizing and causal narrative that sometimes appears to have taken on a life of its own. Is this what settled science is supposed to look like?

Looking for the full risk profile

Almost as fundamental as the sceptic’s resistance to theorizing and narrative is his or her appreciation that the management of anthropogenic warming (particularly the transition to Net Zero) is an undertaking beset with risk and uncertainty. This concern reflects a fundamental principle of risk management: proposed actions to tackle a risk are often in themselves problematic and so a full risk analysis is not complete until it can be confirmed that the net risk will decrease following the actions proposed.7

Firstly, the narrative of existential risk is rejected on the grounds of empirical scepticism (the evidence for an existential threat is not overwhelming, it is underwhelming).

Secondly, even if the narrative is accepted, it has not been reliably demonstrated that the proposal for Net Zero transition is free from existential or extreme risks.

Indeed, given the dominant role played by the ‘theorizing disease’ and how it lies behind our inability to envisage the unprecedented high consequence event, there is every reason to believe that the proposals for Net Zero transition should be equally subject to the precautionary principle. The fact that they are not is indicative of a double standard being applied. The argument seems to run as follows: There is no uncertainty regarding the physical risk posed by climate change, but if there were it would only add to the imperative for action. There is also no uncertainty regarding the transition risk, but if there were it could be ignored because one can only apply the precautionary principle once!

This is precisely the sort of inconsistency one encounters when uncertainties are rationalised away in order to support the favoured narrative.

The upshot of this double standard is that the activists appear to be proceeding with two very different risk management frameworks depending upon whether physical or transition risk is being considered. As a result, risks associated with renewable energy security, the environmental damage associated with proposals to reduce carbon emissions and the potentially catastrophic effects of the inevitable global economic shock are all played down or explained away.

Looking for the full risk profile is a basic of risk management practice. The fact that it is seen as a ploy used only by those wishing to oppose the management of anthropogenic climate change is both odd and worrying. It is indeed important to the sceptic, but it should be important to everyone.

Interrogating causal arguments

For many years we have been told that anthropogenic climate change will make bad things happen. These dire predictions were supposed to galvanize the world into action but that didn’t happen, no doubt partly due to the extent to which such predictions repeatedly failed to come true (as, for example, with the predictions of the disappearance of Arctic sea ice).  .  .This is one good reason for the empirical sceptic to distrust the narrative,8 but an even better one lies in the very concept of causation.

A major purpose of narrative is to reduce complexity so that the ‘truth’ can shine through. This is particularly the case with causal narratives. We all want executive summaries and sound bites such as ‘Y happened because of X’. But very few of us are interested in examining exactly what we mean by such statements – very few except, of course, for the empirical sceptics. In a messy world in which many factors may be at play, the more pertinent questions are:

♦  To what extent was X necessary for Y to happen?
♦  To what extent was X sufficient for Y to happen?

The vast majority of the extreme weather event attribution narrative is focused upon the first question and very little attention is paid to the second; at least not in the many press bulletins issued. Basically, we are told that the event was virtually impossible without climate change, but very little is said regarding whether climate change on its own was enough.

This problem of oversimplification is even more worrying once one starts to examine consequential damages whilst failing to take into account man-made failings such as those that exacerbate the impacts of floods and forest fires.9   The oversimplification of causal narrative is not restricted to weather-related events, of course. Climate change, we are told, is wreaking havoc with the flora and fauna and many species are dying out as a result. However, when such claims are examined more closely,10 it is invariably the case that climate change has been lumped in with a number of other factors that are destroying habitat.

When climate change sceptics point this out they are, of course, accused of cherry-picking. The truth, however, is that their insistence that the extended causal narrative of necessity and sufficiency should be respected is nothing more than the consequence of following the data and looking for the full risk profile.

Contrariness

The climate change debate is all about making decisions under uncertainty, so it is little surprise that gaining consensus is seen as centrally important. Uncertainty is reduced when the evidence is overwhelming and it is tempting to believe that the high level of consensus amongst climate scientists surely points towards there being overwhelming evidence. If one accepts this logic then the sceptic’s refusal to accept the consensus is just another manifestation of his or her denial.

Except, of course, an empirical sceptic would not accept this logic. Consensus does not result from a simple examination of concordant evidence, it is instead the fruit of the tendentious theorizing and simplifying narrative that the empirical sceptic intuitively distrusts. As explained above, there are a number of drivers that cause such theories and narratives to entail unwarranted presupposition, and it is naïve to believe that scientists are immune to such drivers.

However, the fact remains that consensus on beliefs is neither a sufficient nor a necessary condition for presuming that these beliefs constitute shared knowledge. It is only when a consensus on beliefs is uncoerced, uniquely heterogeneous and large, that a shared knowledge provides the best explanation of a given consensus.11 The notion that a scientific consensus can be trusted because scientists are permanently seeking to challenge accepted views is simplistic at best.

It is actually far from obvious that in climate science the conditions have been met for consensus to be a reliable indicator of shared knowledge.

Contrariness simply comes with the territory of being an empirical sceptic. The evidence of consensus is there to be seen, but the amount of theorizing and narrative required for its genesis, together with the social dimension to consensus generation, are enough for the empirical sceptic to treat the whole matter of consensus with a great deal of caution.

Cultural awareness

There has been a great deal said already regarding the culture wars surrounding issues such as the threat posed by anthropogenic climate change. Most of the concerns are directed at the sceptic, who for reasons never properly explained is deemed to be the instigator of the conflict. However, it is the sceptic who chooses to point out that the value-laden arguments offered by climate activists are best understood as part of a wider cultural movement in which rationality is subordinate to in-group versus outgroup dynamics.

Psychological, ethical and spiritual needs lie at the heart of the development of culture and so the adoption of the climate change phenomenon in service of these needs has to be seen as essentially a cultural power play. The dangers of uncritically accepting the fruits of theorizing and narrative are only the beginning of the empirical sceptic’s concerns. Beyond that is the concern that the direction the debate is taking is not even a matter of empiricism – data analysis has little to offer when so much depends upon whether the phenomenon is subsequently to be described as warming or heating. It is for this reason that much of the sceptic’s attention is directed towards the manner in which the science features in our culture rather than the science itself. Such are our psychological, ethical and spiritual needs, that we must not underestimate the extent to which ostensibly scientific output can be moulded in their service.

Conclusions

Taxonomies of thinking should not be treated too seriously. Whilst I hope that I have offered here a welcome antidote to the diatribe that often masquerades as a scholarly appraisal of climate change scepticism, it remains the case that the form that scepticism takes will be unique to the individual. I could not hope to cover all aspects of climate change scepticism in the limited space available to me, but it remains my belief that there are unifying principles that can be identified.

Central to these is the concept of the empirical sceptic and the need to understand that there are sound reasons to treat theorizing and simplifying narratives with extreme caution. The empirical sceptic resists the temptation to theorize, preferring instead to keep an open mind on the interpretation of the evidence. This is far from being self-serving denialism; it is instead a self-denying servitude to the data.

That said, I cannot believe that there would be any activist who, upon reading this account, would see a reason to modify their opinions regarding the bad faith and irrationality that lies behind scepticism. This, unfortunately, is only to be expected given that such opinions are themselves the result of theorizing and simplifying narrative.

Footnote:

While the above focuses on climate alarmism, there are many other social and political initiatives that are theory-driven, suffering from inadequate attention to analysis by empirical sceptics.  One has only to note corporate and governmental programs based on Critical Race or Gender theories.  In addition, COVID policies in advanced nations ignored the required full risk profiling, as well as overturning decades of epidemiological knowledge in favor of models and experimental gene therapies proposed by Big Pharma.