Month: January 2025

Ocean Leads Cooling UAH December 2024

Posted on by Ron Clutz

The post below updates the UAH record of air temperatures over land and ocean. Each month and year exposes again the growing disconnect between the real world and the Zero Carbon zealots.  It is as though the anti-hydrocarbon band wagon hopes to drown out the data contradicting their justification for the Great Energy Transition.  Yes, there was warming from an El Nino buildup coincidental with North Atlantic warming, but no basis to blame it on CO2.  

As an overview consider how recent rapid cooling  completely overcame the warming from the last 3 El Ninos (1998, 2010 and 2016).  The UAH record shows that the effects of the last one were gone as of April 2021, again in November 2021, and in February and June 2022  At year end 2022 and continuing into 2023 global temp anomaly matched or went lower than average since 1995, an ENSO neutral year. (UAH baseline is now 1991-2020). Now we have had an usual El Nino warming spike of uncertain cause, unrelated to steadily rising CO2 and now dropping steadily.

For reference I added an overlay of CO2 annual concentrations as measured at Mauna Loa.  While temperatures fluctuated up and down ending flat, CO2 went up steadily by ~60 ppm, a 15% increase.

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

gmt-warming-events

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

Importantly, the theory of human-caused global warming asserts that increasing CO2 in the atmosphere changes the baseline and causes systemic warming in our climate.  On the contrary, all of the warming since 1947 was episodic, coming from three brief events associated with oceanic cycles. And now in 2024 we have seen an amazing episode with a temperature spike driven by ocean air warming in all regions, along with rising NH land temperatures, now dropping below its peak.

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

image-8

 

mc_wh_gas_web20210423124932

See Also Worst Threat: Greenhouse Gas or Quiet Sun?

December 2024 Ocean Leads Global Cooling banner-blog

With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea.  While you heard a lot about 2020-21 temperatures matching 2016 as the highest ever, that spin ignores how fast the cooling set in.  The UAH data analyzed below shows that warming from the last El Nino had fully dissipated with chilly temperatures in all regions. After a warming blip in 2022, land and ocean temps dropped again with 2023 starting below the mean since 1995.  Spring and Summer 2023 saw a series of warmings, continuing into October, followed by cooling in November and December.

UAH has updated their TLT (temperatures in lower troposphere) dataset for December 2024. Due to one satellite drifting more than can be corrected, the dataset has been recalibrated and retitled as version 6.1 Graphs here contain this updated 6.1 data.  Posts on their reading of ocean air temps this month are ahead of the update from HadSST4.  I posted recently on SSTs Ocean Remains Cooler November 2024. These posts have a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years.

Sometimes air temps over land diverge from ocean air changes. In July 2024 all oceans were unchanged except for Tropical warming, while all land regions rose slightly. In August we saw a warming leap in SH land, slight Land cooling elsewhere, a dip in Tropical Ocean temp and slightly elsewhere.  September showed a dramatic drop in SH land, overcome by a greater NH land increase. In October, ocean and land temps in both NH and Tropics dropped, pulling the global anomaly down. Now in November and December there was cooling everywhere, except only SH and Tropics land temps.

Note:  UAH has shifted their baseline from 1981-2010 to 1991-2020 beginning with January 2021.   v6.1 data was recalibrated also starting with 2021. In the charts below, the trends and fluctuations remain the same but the anomaly values changed with the baseline reference shift.

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

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

After a change in priorities, updates are now exclusive to HadSST4.  For comparison we can also look at lower troposphere temperatures (TLT) from UAHv6.1 which are now posted for December.  The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above. Recently there was a change in UAH processing of satellite drift corrections, including dropping one platform which can no longer be corrected. The graphs below are taken from the revised and current dataset.

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

In 2021-22, SH and NH showed spikes up and down while the Tropics cooled dramatically, with some ups and downs, but hitting a new low in January 2023. At that point all regions were more or less in negative territory. 

After sharp cooling everywhere in January 2023, there was a remarkable spiking of Tropical ocean temps from -0.5C up to + 1.2C in January 2024.  The rise was matched by other regions in 2024, such that the Global anomaly peaked at 0.95C in May, Since then the Tropics and the Global anomaly have cooled down to 0.5C, as well as SH dropping down to 0.4C in December.

Land Air Temperatures Tracking in Seesaw Pattern

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

Here we have fresh evidence of the greater volatility of the Land temperatures, along with extraordinary departures by SH land.  The seesaw pattern in Land temps is similar to ocean temps 2021-22, except that SH is the outlier, hitting bottom in January 2023. Then exceptionally SH goes from -0.6C up to 1.4C in September 2023 and 1.8C in  August 2024, with a large drop in between.  In November, SH and the Tropics pulled the Global Land anomaly further down despite a bump in NH land temps. December showed an upward rebound in SH and Tropics land temps, offset by a NH drop, leaving the Global land anomaly little changed.

The Bigger Picture UAH Global Since 1980

The chart shows monthly Global Land and Ocean anomalies starting 01/1980 to present.  The average monthly anomaly is -0.03, for this period of more than four decades.  The graph shows the 1998 El Nino after which the mean resumed, and again after the smaller 2010 event. The 2016 El Nino matched 1998 peak and in addition NH after effects lasted longer, followed by the NH warming 2019-20.   An upward bump in 2021 was reversed with temps having returned close to the mean as of 2/2022.  March and April brought warmer Global temps, later reversed

With the sharp drops in Nov., Dec. and January 2023 temps, there was no increase over 1980. Then in 2023 the buildup to the October/November peak exceeded the sharp April peak of the El Nino 1998 event. It also surpassed the February peak in 2016. In 2024 March and April took the Global anomaly to a new peak of 0.94C.  The cool down started with May dropping to 0.9C, and in June a further decline to 0.8C.  October went down to 0.7C,  November and December dropped to 0.6C. 

The graph reminds of another chart showing the abrupt ejection of humid air from Hunga Tonga eruption.

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  Clearly NH and Global land temps have been dropping in a seesaw pattern, nearly 1C lower than the 2016 peak.  Since the ocean has 1000 times the heat capacity as the atmosphere, that cooling is a significant driving force.  TLT measures started the recent cooling later than SSTs from HadSST4, but are now showing the same pattern. Despite the three El Ninos, their warming had not persisted prior to 2023, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

Lacking Data, Climate Models Rely on Guesses

Posted on by Ron Clutz

A recent question was posed on  Quora: Say there are merely 15 variables involved in predicting global climate change. Assume climatologists have mastered each variable to a near perfect accuracy of 95%. How accurate would a climate model built on this simplified system be?  Keith Minor has a PhD in organic chemistry, PhD in Geology, and PhD in Geology & Paleontology from The University of Texas at Austin.  He responded with the text posted below in italics with my bolds and added images.

I like the answers to this question, and Matthew stole my thunder on the climate models not being statistical models. If we take the question and it’s assumptions at face value, one unsolvable overriding problem, and a limit to developing an accurate climate model that is rarely ever addressed, is the sampling issue. Knowing 15 parameters to 99+% accuracy won’t solve this problem.

The modeling of the atmosphere is a boundary condition problem. No, I’m not talking about frontal boundaries. Thermodynamic systems are boundary condition problems, meaning that the evolution of a thermodynamic system is dependent not only on the conditions at t > 0 (is the system under adiabatic conditions, isothermal conditions, do these conditions change during the process, etc.?), but also on the initial conditions at t = 0 (sec, whatever). Knowing almost nothing about what even a fraction of a fraction of the molecules in the atmosphere are doing at t = 0 or at t > 0 is a huge problem to accurately predicting what the atmosphere will do in the near or far future. [See footnote at end on this issue.]

Edward Lorenz attempted to model the thermodynamic behavior of the atmosphere by using models that took into account twelve variables (instead of fifteen as posed by the questioner), and found (not surprisingly) that there was a large variability in the models. Seemingly inconsequential perturbations would lead to drastically different results, which diverged (euphemism for “got even worse”) the longer out in time the models were run (they still do). This presumably is the origin of Lorenz’s phrase “the butterfly effect”. He probably meant it to be taken more as an instructive hypothetical rather than a literal effect, as it is too often taken today. He was merely illustrating the sensitivity of the system to the values of the parameters, and not equating it to the probability of outcomes, chaos theory, etc., which is how the term has come to be known. This divergence over time is bad for climate models, which try to predict the climate decades from now. Just look at the divergence of hurricane “spaghetti” models, which operate on a multiple-week scale.

The sources of variability include:

♦  the inability of the models to handle water (the most important greenhouse gas in the atmosphere, not CO2) and processes related to it;
♦  e.g., models still can’t handle the formation and non-formation of clouds;
♦  the non-linearity of thermodynamic properties of matter (which seem to be an afterthought, especially in popular discussions regarding the roles that CO2 plays in the atmosphere and biosphere), and
♦  the always-present sampling problem.

While in theory it is possible to know what a statistically significant number of the air and water molecules are doing at any point in time (that would be a lot of atoms and molecules!), a statistically significant sample of air molecules is certainly not being sampled by releasing balloons twice a day from 90 some odd weather stations in the US and territories, plus the data from commercial aircraft, plus all of the weather data from around the World. Doubling this number wouldn’t help, i.e wouldn’t make any difference. Though there are some blind spots, such as northeast Texas that might benefit from having a radar in the area. So you have to weigh the cost of sampling more of the atmosphere versus the 0% increase in forecasting accuracy (within experimental error) that you would get by doing so.

I’ll go out on a limb and say that the NWS (National Weather Service) is actually doing pretty good job in their 5-day forecasts with the current data and technologies that they have (e.g., S-band radar), and the local meteorologists use their years of experience and judgment to refine the forecasts to their viewing areas. The old joke is that a meteorologist’s job is the one job where you can be wrong more than half the time and still keep your job, but everyone knows that they go to work most, if not all, days with one hand tied behind their back, and sometimes two! The forecasts are not that far off on average, and so meteorologists get my unconditional respect.

In spite of these daunting challenges, there are certainly a number of areas in weather forecasting that can be improved by increased sampling, especially on a local scale. For example, for severe weather outbreaks, the CASA project is being implemented using multiple, shorter range radars that can get multiple scan directions on nearby severe-warned cells simultaneously. This resolves the problem caused by the curvature of the Earth as well as other problems associated with detecting storm-scale features tens or hundreds of miles away from the radar. So high winds, hail, and tornadoes are weather events where increasing the local sampling density/rate might help improve both the models and forecasts.

Prof. Wurman at OU has been doing this for decades with his pioneering work with mobile radar (the so-called “DOW’s”). Let’s not leave out the other researchers who have also been doing this for decades. The strategy of collecting data on a storm from multiple directions at short distances, coupled with supercomputer capabilities, has been paying off for a number of years. As a recent example, Prof. Orf at UW Madison, with his simulation of the May 24th, 2011 El Reno, OK tornado (you’ve probably seen it on the Internet), has shed light on some of the “black box” aspects to how tornadoes form. [Video below is Leigh Orf 1.5 min segment for 2018 Blue Waters Symposium plenary session. This segment summarizes, in 90 seconds, some of the team’s accomplishments on the Blue Waters supercomputer over the past five years.]

Prof. Orf’s simulation is just that, and the resolution is around ~10 m (~33 feet), but it illustrates how increased targeted sampling can be effective in at least understanding the complex, thermodynamic processes occurring within a storm. Colleagues have argued that the small improvements in warning times in the last couple of decades are really due more to the heavy spotter presence these days rather than case studies of severe storms. That may be true. However, in test cases of the CASA system, it picked out the subtle boundaries along which the storms fired that did go unnoticed with the current network of radars. So I’m optimistic about increased targeted sampling for use in an early warning system.

These two examples bring up a related problem-too much data! As commented on by a local meteorologist at a TESSA meeting, one of the issues with CASA that will have to be resolved is how to handle/process the tremendous amounts of data that will be generated during a severe weather outbreak. This is different from a research project where you can take your data back to the “lab”. In a real-time system, such as CASA, you need to have the ability to process the volumes of data rapidly so a meteorologist can quickly make a decision and get that life-saving info to the public. This data volume issue may be less of a problem for those using the data to develop climate models.

So back to the Quora question, with regard to a cost-effective (cost-effect is the operational term) climate model or models (say an ensemble model) that would “verify” say 50 years from now, the sampling issue is ever present, and likely cost-prohibitive at the level needed to make the sampling statistically significant. And will the climatologist be around in 50 years to be “hoisted with their own petard” when the climate model is proven to be wrong? The absence of accountability is the other problem with these long-range models into which many put so much faith.

But don’t stop using or trying to develop better climate models. Just be aware of what variables they include, how well they handle the parameters, and what their limitations are. How accurate would a climate model built on this simplified system [edit: of 15 well-defined variables (to 95% confidence level)] be? Not very!

My Comment

As Dr. Minor explains, powerful modern computers can process detailed observation data to simulate and forecast storm activity.  There are more such tools for preparing and adapting to extreme weather events which are normal in our climate system and beyond our control.  He also explains why long-range global climate models presently have major limitations for use by policymakers.

Footnote Regarding Initial Conditions Problem

What About the Double Pendulum?

Trajectories of a double pendulum

A comment by tom0mason at alerted me to the science demonstrated by the double compound pendulum, that is, a second pendulum attached to the ball of the first one. It consists entirely of two simple objects functioning as pendulums, only now each is influenced by the behavior of the other.

Lo and behold, you observe that a double pendulum in motion produces chaotic behavior. In a remarkable achievement, complex equations have been developed that can and do predict the positions of the two balls over time, so in fact the movements are not truly chaotic, but with considerable effort can be determined. The equations and descriptions are at Wikipedia Double Pendulum

Long exposure of double pendulum exhibiting chaotic motion (tracked with an LED)

But here is the kicker, as described in tomomason’s comment:

If you arrive to observe the double pendulum at an arbitrary time after the motion has started from an unknown condition (unknown height, initial force, etc) you will be very taxed mathematically to predict where in space the pendulum will move to next, on a second to second basis. Indeed it would take considerable time and many iterative calculations (preferably on a super-computer) to be able to perform this feat. And all this on a very basic system of known elementary mechanics.

Our Chaotic Climate System

 

 

Ocean Remains Cooler November 2024

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;
  • Major El Ninos have been 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 2024.  A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016, followed by rising temperatures in 2023 and 2024.

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.  A small warming was driven by NH summer peaks in 2021-22, but offset by cooling in SH and the tropics, By January 2023 the global anomaly was again below the mean.

Now in 2023-24 came an event resembling 2015-16 with a Tropical spike and two NH spikes alongside, all higher than 2015-16. There was also a coinciding rise in SH, and the Global anomaly was pulled up to 1.1°C last year, ~0.3° higher than the 2015 peak.  Then NH started down autumn 2023, followed by Tropics and SH descending 2024 to the present. After 10 months of cooling in SH and the Tropics, the Global anomaly was back down, led by NH cooling the last 3 months from its peak in August.

Comment:

The climatists have seized on this unusual warming as proof their Zero Carbon agenda is needed, without addressing how impossible it would be for CO2 warming the air to raise ocean temperatures.  It is the ocean that warms the air, not the other way around.  Recently Steven Koonin had this to say about the phonomenon confirmed in the graph above:

El Nino is a phenomenon in the climate system that happens once every four or five years.  Heat builds up in the equatorial Pacific to the west of Indonesia and so on.  Then when enough of it builds up it surges across the Pacific and changes the currents and the winds.  As it surges toward South America it was discovered and named in the 19th century  It iswell understood at this point that the phenomenon has nothing to do with CO2.

Now people talk about changes in that phenomena as a result of CO2 but it’s there in the climate system already and when it happens it influences weather all over the world.   We feel it when it gets rainier in Southern California for example.  So for the last 3 years we have been in the opposite of an El Nino, a La Nina, part of the reason people think the West Coast has been in drought.

It has now shifted in the last months to an El Nino condition that warms the globe and is thought to contribute to this Spike we have seen. But there are other contributions as well.  One of the most surprising ones is that back in January of 2022 an enormous underwater volcano went off in Tonga and it put up a lot of water vapor into the upper atmosphere. It increased the upper atmosphere of water vapor by about 10 percent, and that’s a warming effect, and it may be that is contributing to why the spike is so high.

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.  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.  

Then in 2023 the Tropics flipped from below to well above average, while NH produced a summer peak extending into September higher than any previous year.  Despite El Nino driving the Tropics January 2024 anomaly higher than 1998 and 2016 peaks, following months cooled in all regions, and the Tropics continued cooling in April, May and June along with SH dropping.  After July and August NH warming again pulled the global anomaly higher, September and October resumed cooling in all regions.

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 that ERSSTv5 AMO dataset has current data.  It differs from Kaplan, which reported average absolute temps measured in N. Atlantic.  “ERSST5 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 sst anomaly 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 variability, high and low, drives the annual results for this basin.  Note also the peaks in 2010, lows after 2014, and a rise in 2021. Then in 2023 the peak was holding at 1.4C before declining.  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 started out slightly warm, then rose steadily to an  extraordinary peak in July.  August to October were only slightly lower, but by December cooled by ~0.4C.

Then in 2024 the AMO anomaly started higher than any previous year, then leveled off for two months declining slightly into April.  Remarkably, May showed an upward leap putting this on a higher track than 2023, and rising slightly higher in June.  In July, August and September 2024 the anomaly declined, and despite a small rise in October, is now lower than the peak reached in 2023.

The pattern suggests the ocean may be demonstrating a stairstep pattern like that we have also seen in HadCRUT4. 

The purple line is the average anomaly 1980-1996 inclusive, value 0.18.  The orange line the average 1980-202404, value 0.39, also for the period 1997-2012. The red line is 2013-202409, value 0.69. As noted above, these rising stages are driven by the combined warming in the Tropics and NH, including both Pacific and Atlantic basins.

See Also:

2024 El Nino Collapsing

Curiosity:  Solar Coincidence?

The news about our current solar cycle 25 is that the solar activity is hitting peak numbers now and higher  than expected 1-2 years in the future.  As livescience put it:  Solar maximum could hit us harder and sooner than we thought. How dangerous will the sun’s chaotic peak be?  Some charts from spaceweatherlive look familar to these sea surface temperature charts.

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? And is the sun adding forcing to this process?

Space weather impacts the ionosphere in this animation. Credits: NASA/GSFC/CIL/Krystofer Kim

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