Oceans Make Climate

Empirical Proof Sun Driving Climate (Scafetta 2023)

Posted on by Ron Clutz

On June 14, 2023 Nicola Scafetta published at Science Direct Empirical assessment of the role of the Sun in climate change using balanced multi-proxy solar records.  Excerpts in italics with my bolds with exhibits from the study.

By design, climate models exclude solar forcing of earth’s climate,
and perform poorly without it.

Scafetta (2023) Highlights

•  The role of the Sun in climate change is hotly debated with diverse models.

•  The Earth’s climate is likely influenced by the Sun through a variety of physical mechanisms.

•  Balanced multi-proxy solar records were created and their climate effect assessed.

•  Factors other than direct TSI forcing account for around 80% of the solar influence on the climate.

•  Important solar-climate mechanisms must be investigated before developing reliable GCMs.

Abstract

The role of the Sun in climate change is hotly debated. Some studies suggest its impact is significant, while others suggest it is minimal. The Intergovernmental Panel on Climate Change (IPCC) supports the latter view and suggests that nearly 100% of the observed surface warming from 1850–1900 to 2020 is due to anthropogenic emissions.

However, the IPCC’s conclusions are based solely on computer simulations made with global climate models (GCMs) forced with a total solar irradiance (TSI) record showing a low multi-decadal and secular variability. The same models also assume that the Sun affects the climate system only through radiative forcing – such as TSI – even though the climate could also be affected by other solar processes.

In this paper I propose three “balanced” multi-proxy models of total solar activity (TSA) that consider all main solar proxies proposed in scientific literature. Their optimal signature on global and sea surface temperature records is assessed together with those produced by the anthropogenic and volcanic radiative forcing functions adopted by the CMIP6 GCMs. This is done by using a basic energy balance model calibrated with a differential multi-linear regression methodology, which allows the climate system to respond to the solar input differently than to radiative forcings alone, and to evaluate the climate’s characteristic time-response as well.

The proposed methodology reproduces the results of the CMIP6 GCMs when their original forcing functions are applied under similar physical conditions, indicating that, in such a scenario, the likely range of the equilibrium climate sensitivity (ECS) could be 1.4 °C to 2.8 °C, with a mean of 2.1 °C (using the HadCRUT5 temperature record), which is compatible with the low-ECS CMIP6 GCM group.

However, if the proposed solar records are used as TSA proxies and the climatic sensitivity to them is allowed to differ from the climatic sensitivity to radiative forcings, a much greater solar impact on climate change is found, along with a significantly reduced radiative effect. In this case, the ECS is found to be 0.9-1.8 °C, with a mean of around 1.3 °C. Lower ECS ranges (up to 20%) are found using HadSST4, HadCRUT4, and HadSST3.

The result also suggests that about 80% of the solar influence on the climate may not be induced by TSI forcing alone, but rather by other Sun-climate processes (e.g., by a solar magnetic modulation of cosmic ray and other particle fluxes, and/or others), which must be thoroughly investigated and physically understood before trustworthy GCMs can be created. This result explains why empirical studies often found that the solar contribution to climate changes throughout the Holocene has been significant, whereas GCM-based studies, which only adopt radiative forcings, suggest that the Sun plays a relatively modest role. The appendix includes the proposed TSA records.

Comparative Analysis

Figure 5. (A) Anthropogenic (blue) and volcanic (orange) effective radiative forcing functions adopted by the CMIP6 GCMs (Masson-Delmotte et al., 2021). (B) Comparison between the solar effective radiative forcing function from Table Annex III of the IPCC-AR6 (green) (after Masson-Delmotte et al., 2021) and the three TSI forcing functions deduced from the TSI records shown in Fig. 4. All records are anomalies relative to their values in 1850.

The solar forcing functions depicted in Fig. 5B differ in several respects. Just a very modest secular trend can be seen for the solar forcing function used by the CMIP6 GCMs, which has remained nearly constant for about 200 years. Although not depicted in Fig. 5B, the TSI forcing adopted by the CMIP6 GCMs is so flat that even the 1790–1830 Dalton grand solar minimum almost coincides with the 1890–1910 solar minimum and the 2019 solar cycle minimum (Supplementary Data Table S5). Furthermore, because this record is also based on SATIRE and the PMOD TSI satellite composite, its solar effective forcing function decreased progressively from 1970 to 2020. Thus, by using this TSI record, the CMIP6 GCMs could only conclude that solar forcing did not account for almost any of the warming observed after the pre-industrial period (1850–1900), and, particularly, from 1980 to 2020. (Masson-Delmotte et al., 2021).

On the contrary, the other three TSI records reveal a multidecadal oscillation as well as a clear increasing secular trend from the Dalton solar minimum to 2000. More specifically, the TSI forcing significantly increased from the Dalton minimum (1810–1815) to around 1870–1876, then declined until around 1890–1905; it increased rapidly between 1910 and the 1940s, and declined again between 1950 and 1975. The TSI forcing then increased again until 2000, when it began to gradually fall until 2022. This oscillating pattern is especially noteworthy because, as demonstrated below, it is closely correlated with the changes observed in total surface temperature records, which show a very similar increasing trend and multi-decadal modulation (cf.: Scafetta et al., 2004, Scafetta and West, 2006, Scafetta and West, 2008, Scafetta, 2009, Scafetta, 2010, Scafetta, 2012b, Scafetta, 2013a, Scafetta, 2021b).

Figure 8. (A) HadCRUT5 global surface temperature versus the CMIP6 GCM ensemble average; (A’) the two records are detrended with the function f(t) = a (x-1850)2. (B) HadCRUT5 global surface temperature versus the energy balance model (Eq. (16)) using the TSI proxy model #2; (B’) the same as above. The green curves in Fig. 8A and B are sketches that highlight the different multi-decadal modulation of the red curves in Fig. 8A (monotonically increasing, like the anthropogenic forcing function) and in Fig. 8B (oscillating, like the temperature records).

Fig. 8 compares the HadCRUT5 global surface temperature record to (A) the CMIP6 GCM ensemble mean record and (B) the energy balance model (Eq. (16)) using the proposed TSI proxy model #2, which does not use the GCMs’ low secular-variability TSI record. The GCM simulation depicted in Fig.8A monotonically warms up, although occasional volcanic eruptions momentarily cause cold spikes; the monotonic warming trend produced by the model is simulated by the green curve. On the contrary, the model provided in Fig. 8B indicates an oscillating pattern developing around a warming trend. The global surface temperature record shows a similar growing trend, regulated by an approximately 60-year oscillation: the period 1880–1910 experienced a global cooling; the period 1910–1940 was characterized by considerable warming; and the period 1940–1970 experienced another global cooling. From 1880 to 1970, the model in Fig. 8B reproduces this oscillating pattern more precisely (corr. coeff. r = 0.79) than the CMIP6 GCM ensemble average simulation depicted in Fig. 8A (corr. coeff. r = 0.74). Thus, Fig. 8 illustrates that the suggested model (red curve, Fig. 8B) exhibits a multidecadal modulation that correlates significantly better with the temperature record (blue curve) than the GCM ensemble average simulation (red curve, Fig. 8A).

The different performance of the CMIP6 GCM ensemble average simulation and of the proposed regression model in reproducing the temperature pattern from 1850 to 1950 becomes more evident if a quadratic upward trend is detrended from the records. The correlation coefficient is r = 0.45 using the GCM ensemble average simulation (Fig. 8A’), and r = 0.55 using the proposed regression model (Fig. 8B’).

This result suggests that the observed multi-decadal modulation of the
temperature records could have been mostly determined by solar forcings,
and not by chaotic internal oscillations of the climate system.

Conclusion

The IPCC (Solomon and et al., 2007; Stocker and et al., 2014; Masson-Delmotte et al., 2021) conclusion that the Sun’s role in climate change has been negligible since the pre-industrial period (1850–1900) derives from the fact that this organization only consider the solar climatic signature produced by the present-day GCMs. These models, however, are computer programs that can only employ equations describing physical mechanisms that are already well known. Anything unknown or ambiguous cannot be included in the GCM software. If the climatic influence of neglected physical processes is significant, the reductionist approach employed in the GCMs for assessing climate change attributions may be completely inappropriate for the task.

The CMIP6 GCMs appear to greatly underestimate the Sun’s role in climate change because of two major limitations:

(i) erroneous solar forcings have likely been integrated into the models; and
(ii) TSI alone appears to likely be not the most important solar forcing.

Additional solar-magnetism related forcings and associated mechanisms are not included in the GCMs because they are currently poorly understood, despite the fact that there are several empirical indications that they might sufficiently modulate the cloud cover system (by 5% or less) to explain a significant component of the observed climatic changes (Svensmark and Friis-Christensen, 1997, Shaviv, 2002, Svensmark et al., 2016, Easterbrook, 2019, Svensmark, 2022; ). In fact, Table 1 shows that the actual climate sensitivity to TSA variations, which is expressed by kS, can be 4-7 times greater than the climate sensitivity to radiative forcing alone, which was denoted by kA.

Thus, about 80% of the solar influence on the climate could be generated

by processes other than direct TSI forcing. If this result is correct,

several solar-climate mechanisms must be thoroughly investigated

and fully understood before reliable GCMs can be developed.

The Sun Rules, and Warming from CO2 is Impossible

A complement to Scafetta’s study is a Quadrant article by Mark Imisides explaining the geophysical realities ruling out global warming from CO2.  DIY ocean heating  (Hint:  It’s the water, isn’t it?) Excerpts in italics with my bolds.

Scarcely a day goes by without us being warned of coastal inundation by rising seas due to global warming.

Carbon dioxide, we are told, traps heat that has been irradiated by the oceans,
and this warms the oceans and melts the polar ice caps.

While this seems a plausible proposition at first glance, when one actually examines it closely a major flaw emerges. In a nutshell, water takes a lot of energy to heat up, and air doesn’t contain much. In fact, on a volume/volume basis, the ratio of heat capacities is about 3300 to 1.

This means that to heat 1 litre of water by 1˚C it would take 3300 litres of air
that was 2˚C hotter, or 1 litre of air that was about 3300˚C hotter!

This shouldn’t surprise anyone. If you ran a cold bath and then tried to heat it by putting a dozen heaters in the room, does anyone believe that the water would ever get hot?

The problem gets even stickier when you consider the size of the ocean.
Basically, there is too much water and not enough air.

The ocean contains a colossal 1,500,000,000,000,000,000,000 litres of water!  To heat it, even by a small amount, takes a staggering amount of energy. To heat it by a mere 1˚C, for example, an astonishing 6,000,000,000,000,000,000,000,000 joules of energy are required.

Let’s put this amount of energy in perspective. If we all turned off all our appliances and went and lived in caves, and then devoted every coal, nuclear, gas, hydro, wind and solar power plant to just heating the ocean, it would take a breathtaking 32,000 years to heat the ocean by just this 1˚C!

In short, our influence on our climate, even if we really tried, is miniscule!

So it makes sense to ask the question – if the ocean were to be heated by greenhouse warming of the atmosphere, how hot would the air have to get? If the entire ocean is heated by 1˚C, how much would the air have to be heated by to contain enough heat to do the job?

Well, unfortunately for every ton of water there is only a kilogram of air. Taking into account the relative heat capacities and absolute masses, we arrive at the astonishing figure of 4,000˚C.

That is, if we wanted to heat the entire ocean by 1˚C,
and wanted to do it by heating the air above it,
we’d have to heat the air to about 4,000˚C hotter than the water.

And another problem is that air sits on top of water – how would hot air heat deep into the ocean? Even if the surface warmed, the warm water would just sit on top of the cold water.

Thus, if the ocean were being heated by greenhouse heating of the air, we would see a system with enormous thermal lag – for the ocean to be only slightly warmer, the land would have to be substantially warmer, and the air much, much warmer (to create the temperature gradient that would facilitate the transfer of heat from the air to the water).

Therefore any measurable warmth in the ocean would be accompanied by a huge and obvious anomaly in the air temperatures, and we would not have to bother looking at ocean temperatures at all.

So if the air doesn’t contain enough energy to heat
the oceans or melt the ice caps, what does?

The earth is tilted on its axis, and this gives us our seasons. When the southern hemisphere is tilted towards the sun, we have more direct sunlight and more of it (longer days). When it is tilted away from the sun, we have less direct sunlight and less of it (shorter days).

The direct result of this is that in summer it is hot and in winter it is cold. In winter we run the heaters in our cars, and in summer the air conditioners. In winter the polar caps freeze over and in summer 60-70% of them melt (about ten million square kilometres). In summer the water is warmer and winter it is cooler (ask any surfer).

All of these changes are directly determined
by the amount of sunlight that we get.

When the clouds clear and bathe us in sunlight, we don’t take off our jumper because of greenhouse heating of the atmosphere, but because of the direct heat caused by the sunlight on our body. The sun’s influence is direct, obvious, and instantaneous.

If the enormous influence of the sun on our climate is so obvious, then, by what act of madness do we look at a variation of a fraction of a percent in any of these variables, and not look to the sun as the cause?

Why on earth (pun intended) do we attribute any heating of the oceans to carbon dioxide, when there is a far more obvious culprit, and when such a straightforward examination of the thermodynamics render it impossible.

El Nino Ocean Warming Abates May 2023

Posted on by Ron Clutz

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 chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through May 2023.  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 in 2015-2016 the Tropics and SH peaked in between two summer NH spikes.  That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes. By end of 2020, cooler SSTs in all regions took the Global anomaly well below the mean for this period.  In 2021 the summer NH summer spike was joined by warming in the Tropics but offset by a drop in SH SSTs, which raised the Global anomaly slightly over the mean.

Then in 2022, another strong NH summer spike peaked in August, but this time both the Tropic and SH were countervailing, resulting in only slight Global warming, later receding to the mean.   Oct./Nov. temps dropped  in NH and the Tropics took the Global anomaly below the average for this period. After an uptick in December, temps in January 2023 dropped everywhere, strongest in NH, with the Global anomaly further below the mean since 2015.

Now comes El Nino as shown by the upward spike in the Tropics since January, the anomaly nearly doubling from 0.45C to 0.83C.  SH stayed the same as March, but NH also increased 0.13, resulting in a Global anomaly of 0.85C.  However, in May 2023, both the Tropics and SH temps dropped down, reducing the Global anomaly to 0.82C dispite an upward bump in NH.

A longer view of SSTs

To enlarge, 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. There were strong cool periods before and after the 1998 El Nino event. Then SSTs in all regions returned to the mean in 2001-2. 

SSTS fluctuate around the mean until 2007, when another, smaller ENSO event occurs. There is cooling 2007-8,  a lower peak warming in 2009-10, following by cooling in 2011-12.  Again SSTs are average 2013-14.

Now a different pattern appears.  The Tropics cooled 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 were 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.  In 2021-22 there were again summer NH spikes, but in 2022 moderated first by cooling Tropics and SH SSTs, then in October to January 2023 by deeper cooling in NH and Tropics.  

Now in 2023 the Tropics flip from below to above average, and NH starts building up for a summer peak comparable to previous years. In May warming in the Tropics and SH abated, while NH showed a typical upward bump.

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.

Contemporary AMO Observations

Through January 2023 I depended on the Kaplan AMO Index (not smoothed, not detrended) for N. Atlantic observations. But it is no longer being updated, and NOAA says they don’t know its future.  So I find only the Hadsst AMO dataset has data through April.  It differs from Kaplan, which reported average absolute temps measured in N. Atlantic.  “Hadsst AMO  follows Trenberth and Shea (2006) proposal to use the NA region EQ-60°N, 0°-80°W and subtract the global rise of SST 60°S-60°N to obtain a measure of the internal variability, arguing that the effect of external forcing on the North Atlantic should be similar to the effect on the other oceans.”  So the values represent differences between the N. Atlantic and the Global ocean.

The chart above confirms what Kaplan also showed.  As August is the hottest month for the N. Atlantic, its varibility, high and low, drives the annual results for this basin.  Note also the peaks in 2010, lows after 2014, and a rise in 2021. An annual chart below is informative:

Note the difference between blue/green years, beige/brown, and purple/red years.  2010, 2021, 2022 all peaked strongly in August or September.  1998 and 2007 were mildly warm.  2016 and 2018 were matching or cooler than the global average.  2023 is starting out slightly warm.

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? 

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

 

 

El Nino Comes to Save Global Warming April 2023

Posted on by Ron Clutz

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 chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through April 2023.  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 in 2015-2016 the Tropics and SH peaked in between two summer NH spikes.  That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes. By end of 2020, cooler SSTs in all regions took the Global anomaly well below the mean for this period.  In 2021 the summer NH summer spike was joined by warming in the Tropics but offset by a drop in SH SSTs, which raised the Global anomaly slightly over the mean.

Then in 2022, another strong NH summer spike peaked in August, but this time both the Tropic and SH were countervailing, resulting in only slight Global warming, later receding to the mean.   Oct./Nov. temps dropped  in NH and the Tropics took the Global anomaly below the average for this period. After an uptick in December, temps in January 2023 dropped everywhere, strongest in NH, with the Global anomaly further below the mean since 2015.

Now comes El Nino as shown by the upward spike in the Tropics since January, the anomaly nearly doubling from 0.45C to 0.83C.  SH stayed the same as March, but NH also increased 0.13, resulting in a Global anomaly of 0.85C.  That’s above the average for this period by 0.17C, while Global January was slightly below 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. There were strong cool periods before and after the 1998 El Nino event. Then SSTs in all regions returned to the mean in 2001-2. 

SSTS fluctuate around the mean until 2007, when another, smaller ENSO event occurs. There is cooling 2007-8,  a lower peak warming in 2009-10, following by cooling in 2011-12.  Again SSTs are average 2013-14.

Now a different pattern appears.  The Tropics cooled 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 were 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.  In 2021-22 there were again summer NH spikes, but in 2022 moderated first by cooling Tropics and SH SSTs, then in October to January 2023 by deeper cooling in NH and Tropics.  

Now in 2023 the Tropics flip from below to above average, and NH starts building up for a summer peak comparable to previous years.

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.

Contemporary AMO Observations

Through January 2023 I depended on the Kaplan AMO Index (not smoothed, not detrended) for N. Atlantic observations. But it is no longer being updated, and NOAA says they don’t know its future.  So I find only the Hadsst AMO dataset has Feb. and March data.  It differs from Kaplan, which reported average absolute temps measured in N. Atlantic.  “Hadsst AMO  follows Trenberth and Shea (2006) proposal to use the NA region EQ-60°N, 0°-80°W and subtract the global rise of SST 60°S-60°N to obtain a measure of the internal variability, arguing that the effect of external forcing on the North Atlantic should be similar to the effect on the other oceans.”  So the values represent differences between the N. Atlantic and the Global ocean.

The chart above confirms what Kaplan also showed.  As August is the hottest month for the N. Atlantic, its varibility, high and low, drives the annual results for this basin.  Note also the peaks in 2010, lows after 2014, and a rise in 2021. An annual chart below is informative:

Note the difference between blue/green years, beige/brown, and purple/red years.  2010, 2021, 2022 all peaked strongly in August or September.  1998 and 2007 were mildly warm.  2016 and 2018 were matching or cooler than the global average.  2023 is starting out slightly warm.

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? 

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

 

 

Ocean Warming Spike March 2023

Posted on by Ron Clutz

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 chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through March 2023.  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 in 2015-2016 the Tropics and SH peaked in between two summer NH spikes.  That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes. By end of 2020, cooler SSTs in all regions took the Global anomaly well below the mean for this period.  In 2021 the summer NH summer spike was joined by warming in the Tropics but offset by a drop in SH SSTs, which raised the Global anomaly slightly over the mean.

Then in 2022, another strong NH summer spike peaked in August, but this time both the Tropic and SH were countervailing, resulting in only slight Global warming, later receding to the mean.   Oct./Nov. temps dropped  in NH and the Tropics took the Global anomaly below the average for this period. After an uptick in December, temps in January 2023 dropped everywhere, strongest in NH, with the Global anomaly further below the mean since 2015.

In February Global SSTs stayed cool, but in March temps everywhere spiked upward.  SH and the Tropics both showed anomalies 0.16C higher than February, pulling the Global anomaly up 0.12C. Both SH and NH are now close to the Global anomaly of 0.78C

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. There were strong cool periods before and after the 1998 El Nino event. Then SSTs in all regions returned to the mean in 2001-2. 

SSTS fluctuate around the mean until 2007, when another, smaller ENSO event occurs. There is cooling 2007-8,  a lower peak warming in 2009-10, following by cooling in 2011-12.  Again SSTs are average 2013-14.

Now a different pattern appears.  The Tropics cooled 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 were 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.  Now in 2021-22 there are again summer NH spikes, but in 2022 moderated first by cooling Tropics and SH SSTs, then in October to January 2023 by deeper cooling in NH and Tropics.  The graph shows the warming spikes since 2015, including one last month,  lifted the Global anomaly by about 0.2C above the mean since 1995 (an ENSO neutral year).

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.  Note the strength of that summer’s warming pulse, in September peaking to nearly 24 Celsius, a new record for this dataset. January 2023 starts similar to 2022. (Note:  No data updates for February or March so far.)

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? 

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

 

 

Oceans Stay Cool February 2023

Posted on by Ron Clutz

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 chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through February 2023.  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 in 2015-2016 the Tropics and SH peaked in between two summer NH spikes.  That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes. By end of 2020, cooler SSTs in all regions took the Global anomaly well below the mean for this period.  In 2021 the summer NH summer spike was joined by warming in the Tropics but offset by a drop in SH SSTs, which raised the Global anomaly slightly over the mean.

Then in 2022, another strong NH summer spike peaked in August, but this time both the Tropic and SH were countervailing, resulting in only slight Global warming, later receding to the mean.   Oct./Nov. temps dropped  in NH and the Tropics took the Global anomaly below the average for this period. After an uptick in December, temps in January 2023 dropped everywhere, strongest in NH, with the Global anomaly further below the mean since 2015. In February Global SSTs stayed the same, with slight warming in SH offset by further cooling in NH and Tropics

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. There were strong cool periods before and after the 1998 El Nino event. Then SSTs in all regions returned to the mean in 2001-2. 

SSTS fluctuate around the mean until 2007, when another, smaller ENSO event occurs. There is cooling 2007-8,  a lower peak warming in 2009-10, following by cooling in 2011-12.  Again SSTs are average 2013-14.

Now a different pattern appears.  The Tropics cooled 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 were 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.  Now in 2021-22 there are again summer NH spikes, but in 2022 moderated first by cooling Tropics and SH SSTs, then in October to January 2023 by deeper cooling in NH and Tropics.  The graph shows the warming spikes since 2015 lifted the Global anomaly by about 0.2C above the mean since 1995.

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.  Note the strength of that summer’s warming pulse, in September peaking to nearly 24 Celsius, a new record for this dataset. January 2023 starts similar to 2022.

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 will likely decline in coming months, along with ENSO also weakening will probably determine a cooler 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.

 

 

Ahoy! Cooler Ocean Ahead, January 2023

Posted on by Ron Clutz

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 chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through January 2023.  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 in 2015-2016 the Tropics and SH peaked in between two summer NH spikes.  That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes. By end of 2020, cooler SSTs in all regions took the Global anomaly well below the mean for this period.  In 2021 the summer NH summer spike was joined by warming in the Tropics but offset by a drop in SH SSTs, which raised the Global anomaly slightly over the mean.

Then in 2022, another strong NH summer spike peaked in August, but this time both the Tropic and SH were countervailing, resulting in only slight Global warming, later receding to the mean.   Oct./Nov. temps dropped  in NH and the Tropics took the Global anomaly below the average for this period. After an uptick in December, temps in January 2023 dropped everywhere, strongest in NH, with the Global anomaly further below the mean since 2015.

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. There were strong cool periods before and after the 1998 El Nino event. Then SSTs in all regions returned to the mean in 2001-2. 

SSTS fluctuate around the mean until 2007, when another, smaller ENSO event occurs. There is cooling 2007-8,  a lower peak warming in 2009-10, following by cooling in 2011-12.  Again SSTs are average 2013-14.

Now a different pattern appears.  The Tropics cooled 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 were 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.  Now in 2021-22 there are again summer NH spikes, but in 2022 moderated first by cooling Tropics and SH SSTs, then in October to January 2023 by deeper cooling in NH and Tropics.  The graph shows the warming spikes since 2015 lifted the Global anomaly by about 0.2C above the mean since 1995.

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.  Note the strength of that summer’s warming pulse, in September peaking to nearly 24 Celsius, a new record for this dataset. January 2023 starts similar to 2022.

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 will likely decline in coming months, along with ENSO also weakening will probably determine a cooler 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.

 

 

Curing Radiation Myopia Regarding Climate

Posted on by Ron Clutz

E.M. Smith provides an helpful critique of a recent incomplete theory of earth’s climate functioning in his Chiefio blog post So Close–Missing Convection and Homeostasis. Excerpts in italics with my bolds and added images.

It is Soooo easy to get things just a little bit off and miss reality. Especially in complex systems and even more so when folks raking in $Millions are interested in misleading for profit. Sigh.

Sabine Hosenfelder does a wonderful series of videos ‘explaining’ all sorts of interesting things in and about actual science and how the universe works. She is quite smart and generally “knows her stuff”. But… It looks like she has gotten trapped into the Radiative Model of Globull Warming.

The whole mythology of Global Warming depends on having you NOT think about anything but radiative processes and physics. To trap you into the Radiative Model. But the Earth is more complex than that. Much more complex. Then there’s the fact that you DO have some essential Radiative Physics to deal with, so the bait is there.   However…

It is absolutely essential to pay attention to convection in the lower atmosphere
and to the “feedback loops” or homeostasis in the system.

The system acts to restore its original state. There is NO “runaway greenhouse” or we would have never evolved into being since the early earth had astoundingly high levels of CO2 and we would have baked to death before getting out of our slime beds as microbes.

Figure 16. The geological history of CO2 level and temperature proxy for the past 400 million years. CO2 levels now are ~ 400ppm. Source: Davis, W. J. (2017).

OK, I’ll show you her video. It is quite good even with the “swing and a miss” at the end. She does 3 levels of The Greenhouse Gas Mythology so you can see the process evolving from grammar school to high school to college level of mythology. But then she doesn’t quite make it to Post-Doc Reality.

Where’s she wrong? (Well, not really wrong, but lacking…)

I see 2 major issues. First off, she talks about the “lower atmosphere warming”. Well, yes and no. It doesn’t “warm” in the sense of getting hotter, but it does speed up convection to move the added heat flow.

In English “heating” has 2 different meanings. Increasing temperature.
Increasing heat flow at a temperature.

We see this in “warm up the TV dinner in the microwave” meaning to heat it up from frozen to edible; and in the part where the frozen dinner is defrosting at a constant temperature as it absorbs heat but turns it into the heat of fusion of water. So you can “warm it up” by melting at a constant temperature of frozen water (but adding a LOT of thermal energy – “heat”) then later as increasing temperature once the ice is melted. It is very important to keep in mind that there are 2 kinds of “heating”. NOT just “increasing temperature”.

In the lower atmosphere, the CO2 window / Infrared Window is already firmly slammed shut. Sabine “gets that”. Yay! One BIG point for her! No amount of “greenhouse gas” is going to shut that IR window any more. As she points out, you get about 20 meters of transmission and then it is back to molecular vibrations (aka “heat”).

So what’s an atmosphere to do? It has heat to move! Well, it convects. It evaporates water.

Those 2 things dominate by orders of magnitude any sort of Radiative Model Physics. Yes, you have radiation of light bringing energy in, but then it goes into the ocean and into the dirt and the plants and even warms your skin on a sunny day. And it sits there. It does NOT re-radiate to any significant degree. Once “warmed” by absorption, heat trying to leave as IR hits a slammed shut window.

The hydrological cycle. Estimates of the observed main water reservoirs (black numbers in 10^3 km3 ) and the flow of moisture through the system (red numbers, in 10^3 km3 yr À1 ). Adjusted from Trenberth et al. [2007a] for the period 2002-2008 as in Trenberth et al. [2011].

So what does happen? Look around, what do you see? Clouds. Rain. Snow. (sleet hail fog etc. etc.)

Our planet is a Water Planet. It moves that energy (vibrations of atoms, NOT radiation) by having water evaporate into the atmosphere. (Yes, there are a few very dry deserts where you get some radiative effects and can get quite cold at night via radiation through very dry air, but our planet is 70% or so oceans, so those areas are minor side bars on the dominant processes). This water vapor makes the IR window even more closed (less distance to absorption). It isn’t CO2 that matters, it is the global water vapor.

What happens next?

Well, water holds a LOT of heat (vibration of atoms and NOT “temperature”) as the heat of vaporization. About 540 calories per gram (compared to 80 for melting “heat of fusion” and 1 for specific heat of a gram of water). Compare those numbers again. 1 for a gram of water. 80 for melting a gram of ice. 540 for evaporating a gram of water. It’s dramatically the case that evaporation of water matters a lot more than melting ice, and both of them make “warming water” look like an irrelevant thing.

Warming water is 1/80 as important as melting ice, and it is 1/540 th as important as evaporation of the surface of the water. Warming air is another order of magnitude less important to heat content.

So to have clue, one MUST look at the evaporation of water from the oceans as everything else is in the small change.

Look at any photo of the Earth from space. The Blue Marble covered in clouds. Water and clouds. The product of evaporation, convection, and condensation. Physical flows carrying all that heat (“vibration of atoms” and NOT temperature, remember). IF you add more heat energy, you can speed up the flows, but it will not cause a huge increase in temperature (and mostly none at all). It is mass flow that changes. The number of vibrating molecules at a temperature, not the temperature of each.

In the end, a lot of mass flow happens, lofting all that water vapor with all that heat of vaporization way up toward the Stratosphere. This is why we have a troposphere, a tropopause (where it runs out of steam… literally…) and a stratosphere.

What happens when it gets to the stratosphere boundary? Well, along the way that water vapor turns into water liquid very tiny drops (clouds) and eventually condenses to big drops of water (rain) and some of it even freezes (hail, snow, etc.). Now think about that for a minute. That’s 540 calories per gram of heat (molecular vibration NOT temperature, remember) being “dumped” way up high in the top of the troposphere as it condenses, and another 80 / gram if if freezes. 620 total. That’s just huge.

This is WHY we have a globe covered with rain, snow, hail, etc. etc. THAT is all that heat moving. NOT any IR Radiation from the surface. Let that sink in a minute. Fix it in your mind. WATER and ICE and Water Vapor are what moves the heat, not radiation. We ski on it, swim in it, have it water our crops and flood the land. That’s huge and it is ALL evidence of heat flows via heat of vaporization and fusion of water.

It is all those giga-tons of water cycling to snow, ice and rain, then falling back to be lofted again as evaporation in the next cycle. That’s what moves the heat to the stratosphere where CO2 then radiates it to space (after all, radiation toward the surface hits that closed IR window and stops.) At most, more CO2 can let the Stratosphere radiate (and “cool”) better. It can not make the Troposphere any less convective and non-radiative.

Then any more energy “trapped” at the surface would just run the mass transport water cycle faster. It would not increase the temperature.

More molecules would move, but at a limit on temperature. Homeostasis wins. We can see this already in the Sub-Tropics. As the seasons move to fall and winter, water flows slow dramatically. I have to water my Florida lawn and garden. As the seasons move to spring and summer, the mass flow picks up dramatically. Eventually reaching hurricane size. Dumping up to FEET of condensed water (that all started as warm water vapor evaporating from the ocean). It is presently headed for about 72 F today (and no rain). At the peak of hurricane season, we get to about 84 or 85 F ocean surface temperature as the water vapor cycle is running full blast and we get “frog strangler” levels of rain. That’s the difference. Slow water cycle or fast.

IF (and it is only an “if”, not a when) you could manage to increase the heat at the surface of the planet in, say, Alaska: At most you would get a bit more rain in summer, a bit more snow in winter, and MAYBE only a slight possible, of one or two days that are rain which could have been snow or sleet.

Then there’s the fact that natural cycles swamp all of that CO2 fantasy anyway. The Sun, as just one example, had a large change of IR / UV levels with both the Great Pacific Climate Shift (about 1975) and then back again in about 2000. Planetary tilt, wobble, eccentricity of the orbit and more put us in ice ages (as we ARE right now, but in an “interglacial” in this ice age… a nice period of warmth that WILL end) and pulls us out of them. Glacials and interglacials come and go on various cycles (100,000 years, 40,000 years, and 12,000 year interglacials – ours ending now, but slowly). The simple fact is that Nature Dominates, and we are just not relevant. To think we are is hubris of the highest order.

See Also  Bill Gray: H20 is Climate Control Knob, not CO2

Figure 9: Two contrasting views of the effects of how the continuous intensification of deep cumulus convection would act to alter radiation flux to space. The top (bottom) diagram represents a net increase (decrease) in radiation to space

Footnote

There are two main reasons why investigators are skeptical of AGW (anthropogenic global warming) alarm. This post intends to be an antidote to myopic and lop-sided understandings of our climate system.

  1. CO2 Alarm is Myopic: Claiming CO2 causes dangerous global warming is too simplistic. CO2 is but one factor among many other forces and processes interacting to make weather and climate.

Myopia is a failure of perception by focusing on one near thing to the exclusion of the other realities present, thus missing the big picture. For example: “Not seeing the forest for the trees.”  AKA “tunnel vision.”

2. CO2 Alarm is Lopsided: CO2 forcing is too small to have the overblown effect claimed for it. Other factors are orders of magnitude larger than the potential of CO2 to influence the climate system.

Lopsided

Lop-sided refers to a failure in judging values, whereby someone lacking in sense of proportion, places great weight on a factor which actually has a minor influence compared to other forces. For example: “Making a mountain out of a mole hill.”

 

 

Ocean Temps Warm Slightly December 2022

Posted on by Ron Clutz

 

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 chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through December 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 in 2015-2016 the Tropics and SH peaked in between two summer NH spikes.  That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes. By end of 2020, cooler SSTs in all regions took the Global anomaly well below the mean for this period.  In 2021 the summer NH summer spike was joined by warming in the Tropics but offset by a drop in SH SSTs, which raised the Global anomaly slightly over the mean.

Then in 2022, another strong NH summer spike peaked in August, but this time both the Tropic and SH were countervailing, resulting in only slight Global warming, later receding to the mean.   Oct./Nov. temps dropped  in NH and the Tropics took the Global anomaly below the average for this period. Now in December an uptick in SH has lifted the Global anomaly slightly above the mean.

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. There were strong cool periods before and after the 1998 El Nino event. Then SSTs in all regions returned to the mean in 2001-2. 

SSTS fluctuate around the mean until 2007, when another, smaller ENSO event occurs. There is cooling 2007-8,  a lower peak warming in 2009-10, following by cooling in 2011-12.  Again SSTs are average 2013-14.

Now a different pattern appears.  The Tropics cooled 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 were 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.  Now in 2021-22 there are again summer NH spikes, but in 2022 moderated first by cooling Tropics and SH SSTs, now in October and November by deeper cooling in NH and Tropics.

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.  Note the strength of this summer’s warming pulse, in September peaking to nearly 24 Celsius, a new record for this dataset. In November the SSTs were closer to the middle.

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 will likely decline in coming months, along with ENSO also weakening will probably determine a cooler 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.

 

 

Ocean Temps Dropping November 2022

Posted on by Ron Clutz

 

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 chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through November 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 in 2015-2016 the Tropics and SH peaked in between two summer NH spikes.  That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes. By end of 2020, cooler SSTs in all regions took the Global anomaly well below the mean for this period.  In 2021 the summer NH summer spike was joined by warming in the Tropics but offset by a drop in SH SSTs, which raised the Global anomaly slightly over the mean. Now in 2022, another strong NH summer spike has peaked in August, but this time both the Tropic and SH are countervailing, resulting in only slight Global warming, now receding to the mean.  Now dropping Oct./Nov. temps in NH and the Tropics have taken the Global anomaly below the average for this period. Note that 2022/11 Tropical SSTs are 0.8C below their peak in 2015.

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. There were strong cool periods before and after the 1998 El Nino event. Then SSTs in all regions returned to the mean in 2001-2. 

SSTS fluctuate around the mean until 2007, when another, smaller ENSO event occurs. There is cooling 2007-8,  a lower peak warming in 2009-10, following by cooling in 2011-12.  Again SSTs are average 2013-14.

Now a different pattern appears.  The Tropics cooled 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 were 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.  Now in 2021-22 there are again summer NH spikes, but in 2022 moderated first by cooling Tropics and SH SSTs, now in October and November by deeper cooling in NH and Tropics.

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.  Note the strength of this summer’s warming pulse, in September peaking to nearly 24 Celsius, a new record for this dataset. Now in November the SSTs are closer to the middle.

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 will likely decline in coming months, along with ENSO also weakening will probably determine a cooler 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.

 

 

NH Leads Ocean Cooler October 2022

Posted on by Ron Clutz

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 chart below shows SST monthly anomalies as reported in HadSST4 starting in 2015 through October 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 in 2015-2016 the Tropics and SH peaked in between two summer NH spikes.  That pattern repeated in 2019-2020 with a lesser Tropics peak and SH bump, but with higher NH spikes. By end of 2020, cooler SSTs in all regions took the Global anomaly well below the mean for this period.  In 2021 the summer NH summer spike was joined by warming in the Tropics but offset by a drop in SH SSTs, which raised the Global anomaly slightly over the mean. Now in 2022, another strong NH summer spike has peaked in August, but this time both the Tropic and SH are countervailing, resulting in only slight Global warming, now receding to the mean.  October shows a small SH rise, not enough to offset a sharp drop in NH and slight Tropics cooling.

A longer view of SSTs

Open image in new tab to enlarge.

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. There were strong cool periods before and after the 1998 El Nino event. Then SSTs in all regions returned to the mean in 2001-2. 

SSTS fluctuate around the mean until 2007, when another, smaller ENSO event occurs. There is cooling 2007-8,  a lower peak warming in 2009-10, following by cooling in 2011-12.  Again SSTs are average 2013-14.

Now a different pattern appears.  The Tropics cooled 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 were 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.  Now in 2021-22 there are again summer NH spikes, but in 2022 moderated first by cooling Tropics and SH SSTs, now in October by NH and Tropics cooling.

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.  Note the strength of this summer’s warming pulse, in September peaking to nearly 24 Celsius, a new record for this dataset. Now in October the SSTs are still high but closer to the middle.

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 will likely decline in coming months, along with ENSO also weakening will probably determine a cooler 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.