Climate Science | Science Matters

Oceans Cooling May 2020

Posted on June 11, 2020 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, the latest version being HadSST3. More on what distinguishes HadSST3 from other SST products at the end.

The Current Context

The cool 2020 Spring is not just your local experience, it’s the result of Earth’s ocean cooling off after last summer’s warming in the Northern Hemisphere. The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through May 2020.
A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016. In 2019 all regions had been converging to reach nearly the same value in April.

Then NH rose exceptionally by almost 0.5C over the four summer months, in August exceeding previous summer peaks in NH since 2015. In the 4 succeeding months, that warm NH pulse has reversed sharply. May NH anomaly is up a little from March but matching last November. SH and Tropics SSTs bumped upward in March, but dropped sharply since. In May the Global anomaly is the same as December 2019.

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

And as before, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one. The major difference between now and 2015-2016 is the absence of Tropical warming driving the SSTs.

A longer view of SSTs

The graph below 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.

To enlarge open image in new tab.

1995 is a reasonable (ENSO neutral) starting point prior to the first El Nino. The sharp Tropical rise peaking in 1998 is dominant in the record, starting Jan. ’97 to pull up SSTs uniformly before returning to the same level Jan. ’99. For the next 2 years, the Tropics stayed down, and the world’s oceans held steady around 0.2C above 1961 to 1990 average.

Then comes a steady rise over two years to a lesser peak Jan. 2003, but again uniformly pulling all oceans up around 0.4C. Something changes at this point, with more hemispheric divergence than before. Over the 4 years until Jan 2007, the Tropics go through ups and downs, NH a series of ups and SH mostly downs. As a result the Global average fluctuates around that same 0.4C, which also turns out to be the average for the entire record since 1995.

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

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

The highest summer NH peak came in 2019, only this time the Tropics and SH are offsetting rather adding to the warming. Since 2014 SH has played a moderating role, offsetting the NH warming pulses. Now in January 2020 last summer’s unusually high NH SSTs have been erased. (Note: these are high anomalies on top of the highest absolute temps in the NH.)

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 warming began after 1992 up to 1998, with a series of matching years since. 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 black line shows that 2020 began slightly warm, then set records for 3 months before dropping below 2016 and 2017.

Summary

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

Footnote: Why Rely on HadSST3

HadSST3 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.

HadSST3 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

N. Atlantic May 2020

Posted on June 10, 2020 by Ron Clutz

RAPID Array measuring North Atlantic SSTs.

For the last few years, observers have been speculating about when the North Atlantic will start the next phase shift from warm to cold. The way 2018 went and 2019 followed suggested this may be the onset. However, 2020 started out against that trend, now backing off a bit. First some background.

. Source: Energy and Education Canada

An example is this report in May 2015 The Atlantic is entering a cool phase that will change the world’s weather by Gerald McCarthy and Evan Haigh of the RAPID Atlantic monitoring project. Excerpts in italics with my bolds.

This is known as the Atlantic Multidecadal Oscillation (AMO), and the transition between its positive and negative phases can be very rapid. For example, Atlantic temperatures declined by 0.1ºC per decade from the 1940s to the 1970s. By comparison, global surface warming is estimated at 0.5ºC per century – a rate twice as slow.

In many parts of the world, the AMO has been linked with decade-long temperature and rainfall trends. Certainly – and perhaps obviously – the mean temperature of islands downwind of the Atlantic such as Britain and Ireland show almost exactly the same temperature fluctuations as the AMO.

Atlantic oscillations are associated with the frequency of hurricanes and droughts. When the AMO is in the warm phase, there are more hurricanes in the Atlantic and droughts in the US Midwest tend to be more frequent and prolonged. In the Pacific Northwest, a positive AMO leads to more rainfall.

A negative AMO (cooler ocean) is associated with reduced rainfall in the vulnerable Sahel region of Africa. The prolonged negative AMO was associated with the infamous Ethiopian famine in the mid-1980s. In the UK it tends to mean reduced summer rainfall – the mythical “barbeque summer”.Our results show that ocean circulation responds to the first mode of Atlantic atmospheric forcing, the North Atlantic Oscillation, through circulation changes between the subtropical and subpolar gyres – the intergyre region. This a major influence on the wind patterns and the heat transferred between the atmosphere and ocean.

The observations that we do have of the Atlantic overturning circulation over the past ten years show that it is declining. As a result, we expect the AMO is moving to a negative (colder surface waters) phase. This is consistent with observations of temperature in the North Atlantic.

Cold “blobs” in North Atlantic have been reported, but they are usually winter phenomena. For example in April 2016, the sst anomalies looked like this

But by September, the picture changed to this

And we know from Kaplan AMO dataset, that 2016 summer SSTs were right up there with 1998 and 2010 as the highest recorded.

As the graph above suggests, this body of water is also important for tropical cyclones, since warmer water provides more energy. But those are annual averages, and I am interested in the summer pulses of warm water into the Arctic. As I have noted in my monthly HadSST3 reports, most summers since 2003 there have been warm pulses in the north atlantic, and 2019 was one of them.

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 the warmest month August beginning to rise after 1993 up to 1998, with a series of matching years since. December 2017 set a record at 20.6C, but note the plunge down to 20.2C for December 2018, matching 2011 as the coldest years since 2000. December 2019 shows an uptick but still lower than 2016-2017.

December 2019 confirmed the summer pulse weakening, along with 2018 well below other recent peak years since 1998. Then came a surprise in 2020. Because McCarthy refers to hints of cooling to come in the N. Atlantic, let’s take a closer look at some AMO years in the last 2 decades.

The 2020 North Atlantic Surprise

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 was at the bottom of all these tracks. 2019 began slightly cooler than January 2018, then tracked closely before rising in the summer months. Through December 2019 tracked warmer than 2018 but cooler than other recent years in the North Atlantic.

In 2020 following a warm January, N. Atlantic temps in February, March and April were the highest in the record. Now May 2020 temps are still warm but lower than May 2016 and 2017. That is a concern for the upcoming hurricane season, along with the lack of a Pacific El Nino providing wind shear against developing tropical storms.

More recently, temps in higher Atlantic latitudes (45N to 65N) have cooled, as shown in this graph and map from Tropical Tidbits (Levi Cowan)

Footnote: Levi Cowan’s Tropical Tidbits is an excellent source of information regarding tropical storm activity, even before disturbances are assigned names, as well as ones like tropical storm Christobal now raining over states in the midwest.

Moderate May for Land and Ocean Air Temps

Posted on June 5, 2020 by Ron Clutz

With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea. UAH has updated their tlt (temperatures in lower troposphere) dataset for May 2020. Previously I have done posts on their reading of ocean air temps as a prelude to updated records from HADSST3. This month also has a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years.

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. 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?

HadSST3 results were delayed with February and March updates only appearing together end of April. For comparison we can look at lower troposphere temperatures (TLT) from UAHv6 which are now posted for May. The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above.

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). In 2015 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 new and current dataset, Version 6.0.

To enlarge, open image in new tab.

The graph above shows monthly anomalies for ocean temps since January 2015. After all regions peaked with the El Nino in early 2016, the ocean air temps dropped back down with all regions showing the same low anomaly August 2018. Then a warming phase ensued which peaked in February 2020. As was the case in 2015-16, the warming was driven by the Tropics and NH, with SH lagging behind. After the up and down fluxes, oceans temps in May were similar to last June.

Land Air Temperatures Showing a 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 May 2020 is below.

Here we have fresh evidence of the greater volatility of the Land temperatures, along with extraordinary departures, first by NH land with SH often offsetting. The overall pattern is similar to the ocean air temps, but obviously driven by NH with its greater amount of land surface. The Tropics synchronized with NH for the 2016 event, but otherwise follow a contrary rhythm. SH seems to vary wildly, especially in recent months. Note the extremely high anomaly last November, cold in March 2020, and then again a spike in April. In May 2020, all land regions converged, erasing the earlier spikes in NH and SH, and showing anomalies comparable to previous Mays since 2017.

The longer term picture from UAH is a return to the mean for the period starting with 1995. 2019 average rose but currently lacks any El Nino to sustain it.

These charts demonstrate that underneath the averages, warming and cooling is diverse and constantly changing, contrary to the notion of a global climate that can be fixed at some favorable temperature.

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, more than 1C lower than the 2016 peak, prior to these last several months. TLT measures started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern. It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995. Of course, the future has not yet been written.

IPCC Wants a 10-year Lockdown

Posted on May 30, 2020 by Ron Clutz

You’ve seen the News:

While analysts agree the historic lockdowns will significantly lower emissions, some environmentalists argue the drop is nowhere near enough.–USA Today

Emissions Declines Will Set Records This Year. But It’s Not Good News. An “unprecedented” fall in fossil fuel use, driven by the Covid-19 crisis, is likely to lead to a nearly 8 percent drop, according to new research.–New York Times

The COVID-19 pandemic cut carbon emissions down to 2006 levels. Daily global CO2 emissions dropped 17 percent in April — but it’s not likely to last–The Verge

In fact, the drop is not even enough to get the world back on track to meet the target of the 2015 Paris Agreement, which aims for global temperature rise of no more than 1.5 degree above pre-industrial levels, said WHO S-G Taalas. That would require at least a 7% annual drop in emissions, he added.–Reuters

An article at Las Vegas Review-Journal draws the implications Environmentalists want 10 years of coronavirus-level emissions cuts. Excerpts in italics with my bolds.

It’s always been difficult for the layman to comprehend what it would entail to reduce carbon emissions enough to satisfy environmentalists who fret over global warming. Then came coronavirus.

The once-in-a-century pandemic has devastated the U.S. economy. The April unemployment rate is 14.7 percent and rising. Nevada has the highest unemployment rate in the country at 28.2 percent. One-third of families with children report feeling “food insecure,” according to the Institute for Policy Research, Northwestern University. Grocery prices saw their largest one-month increase since 1974.

To keep the economy afloat, the U.S. government has spent $2.4 trillion on coronavirus programs. Another expensive relief bill seems likely. Unless the end goal is massive inflation, this level of spending can’t continue.

Amazingly, the United States has it good compared with many other places in the world. David Beasley, director of the U.N. World Food Program, has estimated that the ongoing economic slowdown could push an additional 130 million “to the brink of starvation.”

That’s the bad news. The good news for global warming alarmists is that economic shutdowns reduce carbon emissions. If some restrictions remain in place worldwide through the end of the year, researchers writing in Nature estimate emissions will drop in 2020 by 7 percent.

For some perspective, the U.N.’s climate panel calls for a 7.6 percent reduction in emissions — every year for a decade. And now we get a real-world glimpse of the cost.

What happens if we return to the course we were on before this pandemic? A previous post shows that the past continuing into the future is not disasterous, and that panic is unwarranted.

I Want You Not to Panic

I’ve been looking into claims for concern over rising CO2 and temperatures, and this post provides reasons why the alarms are exaggerated. It involves looking into the data and how it is interpreted.

Source: Met Office Hadley Centre observations https://www.metoffice.gov.uk/hadobs/hadcrut4/data/current/download.html

First the longer view suggests where to focus for understanding. Consider a long term temperature record such as Hadcrut4. Taking it at face value, setting aside concerns about revisions and adjustments, we can see what has been the pattern in the last 120 years following the Little Ice Age. Often the period between 1850 and 1900 is considered pre industrial since modern energy and machinery took hold later on. The graph shows that warming was not much of a factor until temperatures rose peaking in the 1940s, then cooling off into the 1970s, before ending the century with a rise matching the rate of earlier warming. Overall, the accumulated warming was 0.8C.

Source: NASA GISS https://data.giss.nasa.gov/modelforce/ghgases/Fig1A.ext.txt

Then regard the record concerning CO2 concentrations in the atmosphere. It’s important to know that modern measurement of CO2 really began in 1959 with Mauna Loa observatory, coinciding with the mid-century cool period. The earlier values in the chart are reconstructed by NASA GISS from various sources and calibrated to reconcile with the modern record. It is also evident that the first 60 years saw minimal change in the values compared to the post 1959 rise after WWII ended and manufacturing was turned from military production to meet consumer needs. So again the mid-20th century appears as a change point.

It becomes interesting to look at the last 60 years of temperature and CO2 from 1959 to 2019, particularly with so much clamour about climate emergency and crisis. This graph puts together rising CO2 and temperatures for this period. Firstly note that the accumulated warming is about 0.8C after fluctuations. And remember that those decades witnessed great human flourishing and prosperity by any standard of life quality. The rise of CO2 was a monotonic steady rise with some acceleration into the 21st century.

Now let’s look at projections into the future, bearing in mind Mark Twain’s warning not to trust future predictions. No scientist knows all or most of the surprises that overturn continuity from today to tomorrow. Still, as weathermen well know, the best forecasts are built from present conditions and adding some changes going forward.

Here is a look to century end as a baseline for context. No one knows what cooling and warming periods lie ahead, but one scenario is that the next 80 years could see continued warming at the same rate as the last 60 years. That presumes that forces at play making the weather in the lifetime of many of us seniors will continue in the future. Of course factors beyond our ken may deviate from that baseline and humans will notice and adapt as they have always done. And in the back of our minds is the knowledge that we are 11,500 years into an interglacial period before the cold returns, being the greater threat to both humanity and the biosphere.

Those who believe CO2 causes warming advocate for reducing use of fossil fuels for fear of overheating, apparently discounting the need for energy should winters grow harsher. The graph shows one projection similar to that of temperature, showing the next 80 years accumulating at the same rate as the last 60. A second projection in green takes the somewhat higher rate of the last 10 years and projects it to century end. The latter trend would achieve a doubling of CO2.

What those two scenarios mean depends on how sensitive you think Global Mean Temperature is to changing CO2 concentrations. Climate models attempt to consider all relevant and significant factors and produce future scenarios for GMT. CMIP6 is the current group of models displaying a wide range of warming presumably from rising CO2. The one model closely replicating Hadcrut4 back to 1850 projects 1.8C higher GMT for a doubling of CO2 concentrations. If that held true going from 300 ppm to 600 ppm, the trend would resemble the red dashed line continuing the observed warming from the past 60 years: 0.8C up to now and another 1C the rest of the century. Of course there are other models programmed for warming 2 or 3 times the rate observed.

People who take to the streets with signs forecasting doom in 11 or 12 years have fallen victim to IPCC 450 and 430 scenarios. For years activists asserted that warming from pre industrial can be contained to 2C if CO2 concentrations peak at 450 ppm. Last year, the SR1.5 lowered the threshold to 430 ppm, thus the shortened timetable for the end of life as we know it.

For the sake of brevity, this post leaves aside many technical issues. Uncertainties about the temperature record, and about early CO2 levels, and the questions around Equilibrium CO2 Sensitivity (ECS) and Transient CO2 Sensitivity (TCS) are for another day. It should also be noted that GMT as an average hides huge variety of fluxes over the globe surface, and thus larger warming in some places such as Canada, and cooling in other places like Southeast US. Ross McKitrick pointed out that Canada has already gotten more than 1.5C of warming and it has been a great social, economic and environmental benefit.

So I want people not to panic about global warming/climate change. Should we do nothing? On the contrary, we must invest in robust infrastructure to ensure reliable affordable energy and to protect against destructive natural events. And advanced energy technologies must be developed for the future since today’s wind and solar farms will not suffice.

It is good that Greta’s demands were unheeded at the Davos gathering. Panic is not useful for making wise policies, and as you can see above, we have time to get it right.

Pick Your A-Team: Arrhenius or Ångström

Posted on May 25, 2020 by Ron Clutz

“Those who cannot remember the past are condemned to repeat it.”–George Santayana 1905

Interesting that Svante Arrhenius was elevated as the founder of AGW belief system. He was ignored for many decades after Knut Ångström and his assistant Herr Koch showed that reducing CO2 concentrations did not affect the amount of IR absorbed by the air. That’s almost as interesting as discovering that shutting down the global economy over fear of Covid19 has little effect on atmospheric CO2 concentrations.

As a fellow Scandinavian, Ångström agreed with Arrhenius that his projected warming would be a good thing, even in the lower estimates Svante made later on. Still, Ångström had two objections to Arrhenius’ conjecture about global warming from increasing CO2. In 1900, Herr J. Koch, laboratory assistant to Knut Ångström, did not observe any appreciable change in the absorption of infrared radiation by decreasing the concentration of CO2 up to a third of the initial amount. This result, in addition to the observation made a couple of years before that the superposition of the water vapour absorption bands, more abundant in the atmosphere, over those of CO2, convinced most geologists that calculations by Svante Arrhenius for CO2 warming were wrong.

Ångström’s 1900 paper (english translation) was About the importance of water vapor and carbon dioxide during the absorption of the Earth’s atmosphere Title is link to pdf. Conclusion:

Under no circumstances should carbon dioxide absorb more than 16 percent of terrestrial radiation, and the size of this absorption varies quantitatively very little, as long as there is not less than 20 percent of the existing value. The main alteration caused by a decrease in atmospheric carbon dioxide content, is that the absorption exerted by the carbon dioxide (about 16 percent of the radiation) is only completed by a thicker atmospheric layer, so that the heat is a little more dispersed in the atmosphere.

Many decades passed without reference to Arrhenius until the AGW movement took off in the 1980s and advocates wanted an ancient founder for their ideas. Obviously, Ångström’s position had to be destroyed by articles at RealClimate and Wikipedia (the same team after all.) So it is now declared that Arrhenius was right and Ångström wrong, based on some claims about “line-broadening” and the famous raised Effective Radiating Level (ERL). Ångström’s experimental results were not overturned but were deemed the” Saturation Fallacy”. Meanwhile,today’s climate realists acknowledge that the IR absorption by CO2 is logarithmic with diminishing returns. Attempts to find the raised ERL in modern satellite and balloon datasets have also failed, but alarmists are undaunted. (See reprinted post below)

Background from previous post Global Warming Theory and the Tests It Fails

Many people commenting both for and against reducing emissions from burning fossil fuels assume it has been proven that rising GHGs including CO2 cause higher atmospheric temperatures. That premise has been tested and found wanting, as this post will describe. First below is a summary of Global Warming Theory as presented in the scientific literature. Then follows discussion of several unsuccessful attempts to find evidence of the hypothetical effects from GHGs in the relevant datasets. Concluding is the alternative theory of climate change deriving from solar and oceanic fluctuations.

Scientific Theory of Global Warming

The theory is well described in an article by Kristian (okulaer) prefacing his analysis of “AGW warming” fingerprints in the CERES satellite data. How the CERES EBAF Ed4 data disconfirms “AGW” in 3 different ways by okulaer November 11, 2018. Excerpts below with my bolds. Kristian provides more detailed discussion at his blog (title in red is link).

Background: The AGW Hypothesis

For those of you who aren’t entirely up to date with the hypothetical idea of an “(anthropogenically) enhanced GHE” (the “AGW”) and its supposed mechanism for (CO2-driven) global warming, the general principle is fairly neatly summed up here.

I’ve modified this diagram below somewhat, so as to clarify even further the concept of “the raised ERL (Effective Radiating Level)” – referred to as Ze in the schematic – and how it is meant to ‘drive’ warming within the Earth system; to simply bring the message of this fundamental premise of “AGW” thinking more clearly across.
Then we have the “doubled CO2” (t1) scenario, where the ERL has been pushed higher up into cooler air layers closer to the tropopause:

So when the atmosphere’s IR opacity increases with the excess input of CO2, the ERL is pushed up, and, with that, the temperature at ALL ALTITUDE-SPECIFIC LEVELS of the Earth system, from the surface (Ts) up through the troposphere (Ttropo) to the tropopause, directly connected via the so-called environmental lapse rate, i.e. the negative temperature profile rising up through the tropospheric column, is forced to do the same.

The Expected GHG Fingerprints

How, then, is this mechanism supposed to manifest itself?

Well, as the ERL, basically the “effective atmospheric layer of OUTWARD (upward) radiation”, the one conceptually/mathematically responsible for the All-Sky OLR flux at the ToA, and from now on, in this post, dubbed rather the EALOR, is lifted higher, into cooler layers of air, the diametrically opposite level, the “effective atmospheric layer of INWARD (downward) radiation” (EALIR), the one conceptually and mathematically responsible for the All-Sky DWLWIR ‘flux’ (or “the atmospheric back radiation”) to the surface, is simultaneously – and for the same physical reason, only inversely so – pulled down, into warmer layers of air closer to the surface. This latter concept was explained already in 1938 by G.S. Callendar. Feldman et al., 2015, (as an example) confirm that this is still how “Mainstream Climate Science (MCS)” views this ‘phenomenon’:

The gist being that, when we make the atmosphere more opaque to IR by putting more CO2 into it, “the atmospheric back radiation” (all-sky DWLWIR at sfc) will naturally increase as a result, reducing the radiative heat loss (net LW) from the surface up. And do note, it will increase regardless of (and thus, on top of) any atmospheric rise in temperature, which would itself cause an increase. Which is to say that it will always distinctly increase also RELATIVE TO tropospheric temps (which are, by definition, altitude-specific (fixed at one particular level, like ‘the lower troposphere’ (LT))). That is, even when tropospheric temps do go up, the DWLWIR should be observed to increase systematically and significantly MORE than what we would expect from the temperature rise alone. Because the EALIR moves further down.

Conversely, at the other end, at the ToA, the EALOR moves the opposite way, up into colder layers of air, which means the all-sky OLR (the outward emission flux) should rather be observed to systematically and significantly decrease over time relative to tropospheric temps. If tropospheric temps were to go up, while the DWLWIR at the surface should be observed to go significantly more up, the OLR at the ToA should instead be observed to go significantly less up, because the warming of the troposphere would simply serve to offset the ‘cooling’ of the effective emission to space due to the rise of the EALOR into colder strata of air.

What we’re looking for, then, if indeed there is an “enhancement” of some “radiative GHE” going on in the Earth system, causing global warming, is ideally the following:

OLR stays flat, while TLT increases significantly and systematically over time;
TLT increases systematically over time, but DWLWIR increases significantly even more.
Effectively summed up in this simplified diagram.

Figure 4. Note, this schematic disregards – for the sake of simplicity – any solar warming at work.

However, we also expect to observe one more “greenhouse” signature.

If we expect the OLR at the ToA to stay relatively flat, but the DWLWIR at the sfc to increase significantly over time, even relative to tropospheric temps, then, if we were to compare the two (OLR and DWLWIR) directly, we’d, after all, naturally expect to see a fairly remarkable systematic rise in the latter over the former (refer to Fig.4 above).

Which means we now have our three ways to test the reality of an hypothesized “enhanced GHE” as a ‘driver’ (cause) of global warming.

Three Tests for GHG Warming in the Sky

The null hypothesis in this case would claim or predict that, if there is NO strengthening “greenhouse mechanism” at work in the Earth system, we would observe:

1. The general evolution (beyond short-term, non-thermal noise (like ENSO-related humidity and cloud anomalies or volcanic aerosol anomalies))* of the All-Sky OLR flux at the ToA to track that of Ttropo (e.g. TLT) over time;
2. The general evolution of the All-Sky DWLWIR at the surface to track that of Ttropo (Ts + Ttropo, really) over time;
3. The general evolution of the All-Sky OLR at the ToA and the All-Sky DWLWIR at the surface to track each other over time, barring short-term, non-thermal noise.

* (We see how the curve of the all-sky OLR flux at the ToA differs quite noticeably from the TLT and DWLWIR curves, especially during some of the larger thermal fluctuations (up or down), normally associated with particularly strong ENSO events. This is because there are factors other than pure mean tropospheric temperatures that affect Earth’s final emission flux to space, like the concentration and distribution (equator→poles, surface→tropopause/stratosphere) of clouds, water vapour and aerosols. These may (and do) all vary strongly in the short term, significantly disrupting the normal temperature↔flux (Stefan-Boltzmann) connection, but in the longer term, they display a remarkable tendency to even out, leaving the tropospheric temperature signal as the only real factor to consider when comparing the OLR with Ttropo (TLT). Or not. The “AGW” idea specifically contends, resting on the premise, that these other factors (and crucially also including CO2, of course) do NOT even out over time, but rather accrue in a positive (‘warming’) direction.)

Missing Fingerprint #1

The first point above we have already covered extensively. The combined ERBS+CERES OLR record is seen to track the general progression of the UAHv6 TLT series tightly, both in the tropics and near-globally, all the way from 1985 till today (the last ~33 years), as discussed at length both here and here.

Since, however, in this post we’re specifically considering the CERES era alone, this is how the global OLR matches against the global TLT since 2000:
Figure 5.

This is simply the monthly CERES OLR flux data properly scaled (x0.266), enabling us to compare it more directly to temperatures (W/m2→K), and superimposed on the UAH TLT data. Watch how closely the two curves track each other, beyond the obvious noise. To highlight this striking state of relative congruity, we remove the main sources of visual bias in Fig.5 above. Notice, then, how the red OLR curve, after the 4-year period of fairly large ENSO-events (La Niña-El Niño-La Niña) between 2007/2008 and 2011/2012, when the cyan TLT curve goes both much lower (during the flanking La Niñas) and much higher (during the central El Niño), quickly reestablishes itself right back on top of the TLT curve, just where it used to be prior to that intermediate stretch of strong ENSO influence. And as a result, there is NO gradual divergence whatsoever to be spotted between the mean levels of these two curves, from the beginning of 2000 to the end of 2015.

Missing Fingerprint #2

The second point above is just as relevant as the first one, if we want to confirm (or disconfirm) the reality of an “enhanced GHE” at work in the Earth system. We compare the tropospheric temperatures with the DWLWIRsfc ‘flux’, that is, the apparent atmospheric thermal emission to the surface:

Figure 9. Note how the scaling of the flux (W/m2) values is different close to the surface than at the ToA. Here at the DWLWIR level, down low, we divide by 5 (x0.2), while at the OLR level, up high, we divide by 3.76 (x0.266).

We once again observe a rather close match overall. At the very least, we can safely say that there is no evidence whatsoever of any gradual, systematic rise in DWLWIR over the TLT, going from 2000 to 2018. If we plot the difference between the two curves in Fig.9 to obtain the “DWLWIR residual”, this fact becomes all the more evident:

Figure 10.

Remember now how the idea of an “enhanced GHE” requires the DWLWIR to rise significantly more than Ttropo (TLT) over time, and that its “null hypothesis” therefore postulates that such a rise should NOT be seen. Well, do we see such a rise in the plot above? Nope. Not at all. Which fits in perfectly with the impression we got at the ToA, where the TLT-curve was supposed to rise systematically up and away from the OLR-curve over time, but didn’t – no observed evidence there either of any “enhanced GHE” at work.

Missing Fingerprint #3

Finally, the third point above is also pretty interesting. It is simply to verify whether or not the CERES EBAF Ed4 ‘radiation flux’ data products are indeed suggesting a strengthening of some radiatively defined “greenhouse mechanism”. We sort of know the answer to this already, though, from going through points 1 and 2 above. Since neither the OLR at the ToA nor the DWLWIR at the surface deviated meaningfully from the UAHv6 TLT series (the same one used to compare with both, after all), we expect rather by necessity that the two CERES ‘flux products’ also shouldn’t themselves deviate meaningfully overall from one another. And, unsurprisingly, they don’t:

Figure 14. Difference plot (“DWLWIR residual”)

Again, it is so easy here to allow oneself to be fooled by the visual impact of that late – obviously ENSO-related – peak, and, in this case, also a definite ENSO-based trough right at the start (you’ll plainly recognise it in Fig.14); another perfect example of how one’s perception and interpretation of a plot is directly affected by “the end-point bias”. Don’t be fooled:

If we expect the OLR at the ToA to stay relatively flat, but the DWLWIR at the sfc to increase significantly over time, even relative to tropospheric temps, then, if we were to compare the two (OLR and DWLWIR) directly, we’d […] naturally expect to see a fairly remarkable systematic rise in the latter over the former (refer to Fig.4 above).

Looking at Fig.14, and taking into account the various ENSO states along the way, does such a “remarkable systematic rise” in DWLWIR over OLR manifest itself during the CERES era?

I’m afraid not …

Four Lines of Evidence Against GHG Warming Hypothesis

The lack of GHG warming in the CERES data is added to three previous atmospheric heat radiation studies.

In 2004 Ferenc MIskolczi studied the radiosonde datasets and found that the optical density at the top of the troposphere does not change with increasing CO2, since reducing H2O maintains optimal radiating efficiency. His publication was suppressed by NASA, and he resigned from his job there. He has elaborated on his findings in publications as recently as 2014. See: The Curious Case of Dr. Miskolczi

2. Ronan and Michael Connolly studied radiosonde data and concluded in 2014:

“It can be seen from the infra-red cooling model of Figure 19 that the greenhouse effect theory predicts a strong influence from the greenhouse gases on the barometric temperature profile. Moreover, the modeled net effect of the greenhouse gases on infra-red cooling varies substantially over the entire atmospheric profile.

However, when we analysed the barometric temperature profiles of the radiosondes in this paper, we were unable to detect any influence from greenhouse gases. Instead, the profiles were very well described by the thermodynamic properties of the main atmospheric gases, i.e., N 2 and O 2 , in a gravitational field.”

While water vapour is a greenhouse gas, the effects of water vapour on the temperature profile did not appear to be related to its radiative properties, but rather its different molecular structure and the latent heat released/gained by water in its gas/liquid/solid phase changes.

For this reason, our results suggest that the magnitude of the greenhouse effect is very small, perhaps negligible. At any rate, its magnitude appears to be too small to be detected from the archived radiosonde data.” Pg. 18 of referenced research paper

See: The Physics Of The Earth’s Atmosphere I. Phase Change Associated With Tropopause

3. An important proof against the CO2 global warming claim was included in John Christy’s testimony 29 March 2017 at the House Committee on Science, Space and Technology. The text and diagram below are from that document which can be accessed he re.

IPCC Assessment Reports show that the IPCC climate models performed best versus observations when they did not include extra GHGs and this result can be demonstrated with a statistical model as well.

Figure 5. Simplification of IPCC AR5 shown above in Fig. 4. The colored lines represent the range of results for the models and observations. The trends here represent trends at different levels of the tropical atmosphere from the surface up to 50,000 ft. The gray lines are the bounds for the range of observations, the blue for the range of IPCC model results without extra GHGs and the red for IPCC model results with extra GHGs.The key point displayed is the lack of overlap between the GHG model results (red) and the observations (gray). The nonGHG model runs (blue) overlap the observations almost completely.

An Alternative Theory of Natural Climate Change

Dan Pangburn is a professional engineer who has synthesized the solar and oceanic factors into a mathematical model that correlates with Average Global Temperature (AGT). On his blog is posted a monograph Cause of Global Climate Change explaining clearly his thinking and the maths. I provided a post with some excerpts and graphs as a synopsis of his analysis, in hopes others will also access and appreciate his work on this issue. See Quantifying Natural Climate Change

Footnote on the status of an hypothetical effect too small to be measured: Bertrand Russell’s teapot

Open image in new tab to enlarge.

Postscript:

A further reference comes from the brief submiitted by Professors Happer, Koonin and Lindzen to the Climate Tutorial for Judge Alsup. The pertinent citation is as follows:

“On average, the absorption rate of solar radiation by the Earth’s surface and atmosphere is equal to emission rate of thermal infrared radiation to space. Much of the radiation to space does not come from the surface but from greenhouse gases and clouds in the lower atmosphere, where the temperature is usually colder than the surface temperature, as shown in the figure on the previous page. The thermal radiation originates from an “escape altitude” where there is so little absorption from the overlying atmosphere that most (say half) of the radiation can escape to space with no further absorption or scattering. Adding greenhouse gases can warm the Earth’s surface by increasing the escape altitude. To maintain the same cooling rate to space, the temperature of the entire troposphere, and the surface, would have to increase to make the effective temperature at the new escape altitude the same as at the original escape altitude. For greenhouse warming to occur, a temperature profile that cools with increasing altitude is required.

Over most of the CO2 absorption band (between about 580 cm-1 and 750 cm-1 ) the escape altitude is the nearly isothermal lower stratosphere shown in the first figure. The narrow spike of radiation at about 667 cm-1 in the center of the CO2 band escapes from an altitude of around 40 km (upper stratosphere), where it is considerably warmer than the lower stratosphere due heating by solar ultraviolet light which is absorbed by ozone, O3. Only at the edges of the CO2 band (near 580 cm-1 and 750 cm-1 ) is the escape altitude in the troposphere where it could have some effect on the surface temperature. Water vapor, H2O, has emission altitudes in the troposphere over most of its absorption bands. This is mainly because water vapor, unlike CO2, is not well mixed but mostly confined to the troposphere.”

A synopsis of that submission was posted as Cal Climate Tutorial: The Meat

SSTs Stay Cool April 2020

Posted on May 15, 2020 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.

The Current Context

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through April 2020.
A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016. In 2019 all regions had been converging to reach nearly the same value in April.

Then NH rose exceptionally by almost 0.5C over the four summer months, in August exceeding previous summer peaks in NH since 2015. In the last 4 months of 2019 that warm NH pulse reversed sharply. Now in 2020 the first 4 months show little change from last December. Recently anomalies in the NH and Tropics bumped upward in April, offset by a drop in the SH causing the global anomaly to dip as well.

A longer view of SSTs

To enlarge, open image in new tab.

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

The highest summer NH peak came in 2019, only this time the Tropics and SH are offsetting rather adding to the warming. Since 2014 SH has played a moderating role, offsetting the NH warming pulses. (Note: these are high anomalies on top of the highest absolute temps in the NH.)

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 black line shows that 2020 began near average, but is showing higher temperatures the last three months than any year in the record except for tracking 2010. It appears that the Atlantic is driving warming in NH and Tropics thes year.

Summary

Footnote: Why Rely on HadSST3

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

Global Land & Ocean Air Cooling in April

Posted on May 7, 2020 by Ron Clutz

With apologies to Paul Revere, this post is on the lookout for cooler weather with an eye on both the Land and the Sea. UAH has updated their tlt (temperatures in lower troposphere) dataset for April 2020. Previously I have done posts on their reading of ocean air temps as a prelude to updated records from HADSST3. This month also has a separate graph of land air temps because the comparisons and contrasts are interesting as we contemplate possible cooling in coming months and years.

HadSST3 results were delayed with February and March updates only appearing together end of last month. For comparison we can look at lower troposphere temperatures (TLT) from UAHv6 which are now posted for April. The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above.

The graph above shows monthly anomalies for ocean temps since January 2015. After all regions peaked with the El Nino in early 2016, the ocean air temps dropped back down with all regions showing the same low anomaly August 2018. Then a warming phase ensued which seems now to peak in February 2020. As was the case in 2015-16, the warming was driven by the Tropics and NH, with SH lagging behind.

Land Air Temperatures Showing a Seesaw Pattern

Here we have fresh evidence of the greater volatility of the Land temperatures, along with extraordinary departures, first by NH land with SH often offsetting. The overall pattern is similar to the ocean air temps, but obviously driven by NH with its greater amount of land surface. The Tropics synchronized with NH for the 2016 event, but otherwise follow a contrary rhythm. SH seems to vary wildly, especially in recent months. Note the extremely high anomaly last November, cold in March 2020, and then again a spike in April. With its smaller land mass, SH fluctuations have less impact on the Global results.

The longer term picture from UAH is a return to the mean for the period starting with 1995. 2019 average rose but currently lacks any El Nino to sustain it.

SSTs Flat Start 2020 March Update

Posted on April 30, 2020 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.

The Current Context

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through March 2020.
A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016. In 2019 all regions had been converging to reach nearly the same value in April.

Then NH rose exceptionally by almost 0.5C over the four summer months, in August exceeding previous summer peaks in NH since 2015. In the last 4 months of 2019 that warm NH pulse reversed sharply. Now in 2020 the first 3 months show little change from last December, particularly in the NH and Tropics. Meanwhile the SH bumped upward in March, causing the global anomaly to rise as well, slightly lower than the peak in August 2019.

A longer view of SSTs

To enlarge open image in new tab.

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

The highest summer NH peak came in 2019, only this time the Tropics and SH are offsetting rather adding to the warming. Since 2014 SH has played a moderating role, offsetting the NH warming pulses. (Note: these are high anomalies on top of the highest absolute temps in the NH.)

AMO decade 032020
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 black line shows that 2020 began near average, but is showing higher temperatures the last two months that any year in the record, including 1998 and 2010.

Summary

Footnote: Why Rely on HadSST3

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

Top Climate Model Gets Better

Posted on March 19, 2020 by Ron Clutz

Figure S7. Contributions of forcing and feedbacks to ECS in each model and for the multimodel means. Contributions from the tropical and extratropical portion of the feedback are shown in light and dark shading, respectively. Black dots indicate the ECS in each model, while upward and downward pointing triangles indicate contributions from non-cloud and cloud feedbacks, respectively. Numbers printed next to the multi-model mean bars indicate the cumulative sum of each plotted component. Numerical values are not printed next to residual, extratropical forcing, and tropical albedo terms for clarity. Models within each collection are ordered by ECS.

A previous post here discussed discovering that INMCM4 was the best CMIP5 model in replicating historical temperature records. Additional posts described improvements built into INMCM5, the next generation model included for CMIP6 testing. Later on is a reprint of the temperature history replication and the parameters included in the revised model. This post focuses on a recent report of additional enhancements by the modelers in order to better represent precipitation and extreme rainfall events.

The paper is Influence of various parameters of INM RAS climate model on the results of extreme precipitation simulation by M A Tarasevich and E M Volodin 2019. Excerpts in italics with my bolds.

Modern models of the Earth’s climate can reproduce not only the average climate condition, but also extreme weather and climate phenomena. Therefore, there arises the problem of comparing climate models for observable extreme weather events.

In [1, 2], various extreme weather and climatic situations are considered. According to the paper,27 extreme indices are defined, characterizing different situations with high and low temperatures, with heavy precipitation or with absence of precipitation.

The results of simulation of the extreme indices with the INMCM4 [3] climate model were compared with the results of other models which took part in the CMIP5 project (Coupled Model Intercomparison Project, Phase 5) [2]. The comparison demonstrates that this model performs well for most indices except for those related to daily minimum temperature. For those indices the model shows one of the worst results.

The parameterizations of physical processes in the next model version, INMCM5, were replaced or tuned [4, 5], so that changes in the extreme indices simulation are expected.

The simulation results were compared to the ERA-Interim [6] reanalysis data, which were considered as the observational data for this study. Indices averaged for the 1981–2010 year range were compared. Mann-Whitney test with 1% significance level was used to examine where changes are significant.

To evaluate the quality of simulation of extreme weather phenomena, the extreme indices were calculated [7] using the results of computations performed by two versions of the INM RAS climate model (INMCM4 and INMCM5) and the ERA Interim reanalysis. We took the root mean square deviation of the index value computed from the modeled and reanalysis data as the measure of simulation quality. The mean is averaged over the land.

Tables 1 and 2 present the names of extreme indices related to temperature and precipitation, their labels and measurement units, as well as the land only averaged standard deviations for these indices between the ERA-Interim reanalysis and different versions of the INM RAS climate model.

Table 1 shows that the simulation of almost all temperature indices has improved in the INMCM5 compared to INMCM4. In particular, the simulation of the following extreme indices related to the minimum daily temperature improved significantly (by 37–56%): the annual daily minimum temperature (TNn), the number of frost days (FD) and tropical nights (TR), the diurnal temperature range (DTR), and the growing season length (GSL).

[Comment: Note that values in these tables are standard deviations from observations as presented by ERA reanalysis. So for example, growing season length (GSL) varied from mean ERA values by 24 days in INMCM4, but improved to a 15 day differential in INMCM5.]

Table 2 shows that the simulation of the number of heavy (R10mm) and very heavy (R20mm) precipitation days, consecutive wet days (CWD), simple daily intensity (SDII), and total wet-day precipitation (PRCPTOT) noticeably improved in INMCM5. At the same time, the simulation of indices related to the intensity (RX5day) and the amount (R95p) of precipitation on very rainy days became worse.

Improvements Added to INMCM5

To improve the simulation of extreme precipitation by the INMCM5 model, the following physical processes were considered: evaporation of precipitation in the upper atmosphere; mixing of horizontal velocity components due to large-scale condensation and deep convection; air resistance acting on falling precipitation particles.

Both large-scale condensation and deep convection cause vertical motion, which redistributes the horizontal momentum between the nearby air layers. The implementation of mixing due to large-scale condensation was added to the model. For short we will refer to the INMCM5 version with these changes as INMCM5VM (INMCM5 Velocities Mixing).

Since precipitation particles (water droplets or ice crystals) move in the surrounding air, a drag force arises that carries the air along with the particles. This resistance force can be included in the right hand side of the momentum balance equation, which is part of the atmosphere hydrothermodynamic system of equations. Accurate accounting for the effect of this force requires numerical solving of an additional Poisson-type equation. For short, we will refer to the INMCM5 model version with the air resistance and vertical mixing of the horizontal velocity components as INMCM5AR (INMCM5 Air Resistance).

Figure 3. (a) RX5day index values averaged over 1981–2010 according to ERA-Interim data. (b-d) Deviations of the same average obtained from INMCM5, INMCM5VM, and INMCM5AR data. Statistically insignificant deviations are presented as white.

Table 2 shows that the quality of simulation of all precipitation-related extreme indices in INMCM5AR either improved by 3–21 % compared to INMCM5 or remained unchanged.

Figures 2d, 3d show the spatial distribution of the deviations for max 1 day (RX1day) and 5 day (RX5day) precipitation according to INMCM5AR compared to INMCM5. The model with air resistance acting on falling precipitation particles compared to INMCM5 significantly underestimates RX1day and RX5day in South Africa, South and East Asia, and slightly underestimates the indicated extreme indices in Tibet.

Taking into account the air resistance acting on falling precipitation particles significantly reduces the overestimation of RX1day and RX5day observed in INMCM5 in South Africa, South and East Asia, and leads to an improvement in the quality of extreme indices associated with the precipitation amount on very rainy days and their intensity simulation by 9–21 %. At the same time, a significant overestimation of the RX1day and RX5day indices in the Amazon basin and Southeast Asia, as well as their underestimation in West Africa, still remain.

Footnote:

A simple analysis shows if the climate sensitivity estimated by INMCM5 (1.8C per doubling of CO2) would be realized over the next 80 years, it would mean a continuation of the warming over the last 60 years. The accumulated rise in GMT would be 1.2C for the 21st Century, well below the IPCC 1.5C aspiration. See I Want You Not to Panic

Update February 4, 2020

A recent comparison of INMCM5 and other CMIP6 climate models is discussed in the post
Climate Models: Good, Bad and Ugly

Updated with October 25, 2018 Report

A previous analysis Temperatures According to Climate Models showed that only one of 42 CMIP5 models was close to hindcasting past temperature fluctuations. That model was INMCM4, which also projected an unalarming 1.4C warming to the end of the century, in contrast to the other models programmed for future warming five times the past.

In a recent comment thread, someone asked what has been done recently with that model, given that it appears to be “best of breed.” So I went looking and this post summarizes further work to produce a new, hopefully improved version by the modelers at the Institute of Numerical Mathematics of the Russian Academy of Sciences.

Institute of Numerical Mathematics, Russian Academy of Sciences, Moscow, Russia

A previous post a year ago went into the details of improvements made in producing the latest iteration INMCM5 for entry into the CMIP6 project. That text is reprinted below.

Now a detailed description of the model’s global temperature outputs has been published October 25, 2018 in Earth System Dynamics Simulation of observed climate changes in 1850–2014 with climate model INM-CM5 (Title is link to pdf) Excerpts below with my bolds.

Figure 1. The 5-year mean GMST (K) anomaly with respect to 1850–1899 for HadCRUTv4 (thick solid black); model mean (thick solid red). Dashed thin lines represent data from individual model runs: 1 – purple, 2 – dark blue, 3 – blue, 4 – green, 5 – yellow, 6 – orange, 7 – magenta. In this and the next figures numbers on the time axis indicate the first year of the 5-year mean.

Abstract

Climate changes observed in 1850-2014 are modeled and studied on the basis of seven historical runs with the climate model INM-CM5 under the scenario proposed for Coupled Model Intercomparison Project, Phase 6 (CMIP6). In all runs global mean surface temperature rises by 0.8 K at the end of the experiment (2014) in agreement with the observations. Periods of fast warming in 1920-1940 and 1980-2000 as well as its slowdown in 1950-1975 and 2000-2014 are correctly reproduced by the ensemble mean. The notable change here with respect to the CMIP5 results is correct reproduction of the slowdown of global warming in 2000-2014 that we attribute to more accurate description of the Solar constant in CMIP6 protocol. The model is able to reproduce correct behavior of global mean temperature in 1980-2014 despite incorrect phases of the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation indices in the majority of experiments. The Arctic sea ice loss in recent decades is reasonably close to the observations just in one model run; the model underestimates Arctic sea ice loss by the factor 2.5. Spatial pattern of model mean surface temperature trend during the last 30 years looks close the one for the ERA Interim reanalysis. Model correctly estimates the magnitude of stratospheric cooling.

Additional Commentary

Observational data of GMST for 1850-2014 used for verification of model results were produced by HadCRUT4 (Morice et al 2012). Monthly mean sea surface temperature (SST) data ERSSTv4 (Huang et al 2015) are used for comparison of the AMO and PDO indices with that of the model. Data of Arctic sea ice extent for 1979-2014 derived from satellite observations are taken from Comiso and Nishio (2008). Stratospheric temperature trend and geographical distribution of near surface air temperature trend for 1979-2014 are calculated from ERA Interim reanalysis data (Dee et al 2011).

Keeping in mind the arguments that the GMST slowdown in the beginning of 21st 6 century could be due to the internal variability of the climate system let us look at the behavior of the AMO and PDO climate indices. Here we calculated the AMO index in the usual way, as the SST anomaly in Atlantic at latitudinal band 0N-60N minus anomaly of the GMST. Model and observed 5 year mean AMO index time series are presented in Fig.3. The well known oscillation with a period of 60-70 years can be clearly seen in the observations. Among the model runs, only one (dashed purple line) shows oscillation with a period of about 70 years, but without significant maximum near year 2000. In other model runs there is no distinct oscillation with a period of 60-70 years but period of 20-40 years prevails. As a result none of seven model trajectories reproduces behavior of observed AMO index after year 1950 (including its warm phase at the turn of the 20th and 21st centuries). One can conclude that anthropogenic forcing is unable to produce any significant impact on the AMO dynamics as its index averaged over 7 realization stays around zero within one sigma interval (0.08). Consequently, the AMO dynamics is controlled by internal variability of the climate system and cannot be predicted in historic experiments. On the other hand the model can correctly predict GMST changes in 1980-2014 having wrong phase of the AMO (blue, yellow, orange lines on Fig.1 and 3).

Conclusions

Seven historical runs for 1850-2014 with the climate model INM-CM5 were analyzed. It is shown that magnitude of the GMST rise in model runs agrees with the estimate based on the observations. All model runs reproduce stabilization of GMST in 1950-1970, fast warming in 1980-2000 and a second GMST stabilization in 2000-2014 suggesting that the major factor for predicting GMST evolution is the external forcing rather than system internal variability. Numerical experiments with the previous model version (INMCM4) for CMIP5 showed unrealistic gradual warming in 1950-2014. The difference between the two model results could be explained by more accurate modeling of stratospheric volcanic and tropospheric anthropogenic aerosol radiation effect (stabilization in 1950-1970) due to the new aerosol block in INM-CM5 and more accurate prescription of Solar constant scenario (stabilization in 2000-2014) in CMIP6 protocol. Four of seven INM-CM5 model runs simulate acceleration of warming in 1920-1940 in a correct way, other three produce it earlier or later than in reality. This indicates that for the year warming of 1920-1940 the climate system natural variability plays significant role. No model trajectory reproduces correct time behavior of AMO and PDO indices. Taking into account our results on the GMST modeling one can conclude that anthropogenic forcing does not produce any significant impact on the dynamics of AMO and PDO indices, at least for the INM-CM5 model. In turns, correct prediction of the GMST changes in the 1980-2014 does not require correct phases of the AMO and PDO as all model runs have correct values of the GMST while in at least three model experiments the phases of the AMO and PDO are opposite to the observed ones in that time. The North Atlantic SST time series produced by the model correlates better with the observations in 1980-2014. Three out of seven trajectories have strongly positive North Atlantic SST anomaly as the observations (in the other four cases we see near-to-zero changes for this quantity). The INMCM5 has the same skill for prediction of the Arctic sea ice extent in 2000-2014 as CMIP5 models including INMCM4. It underestimates the rate of sea ice loss by a factor between the two and three. In one extreme case the magnitude of this decrease is as large as in the observations while in the other the sea ice extent does not change compared to the preindustrial ages. In part this could be explained by the strong internal variability of the Arctic sea ice but obviously the new version of INMCM model and new CMIP6 forcing protocol does not improve prediction of the Arctic sea ice extent response to anthropogenic forcing.

Previous Post: Climate Model Upgraded: INMCM5 Under the Hood

Earlier in 2017 came this publication Simulation of the present-day climate with the climate model INMCM5 by E.M. Volodin et al. Excerpts below with my bolds.

In this paper we present the ﬁfth generation of the INMCM climate model that is being developed at the Institute of Numerical Mathematics of the Russian Academy of Sciences (INMCM5). The most important changes with respect to the previous version (INMCM4) were made in the atmospheric component of the model. Its vertical resolution was increased to resolve the upper stratosphere and the lower mesosphere. A more sophisticated parameterization of condensation and cloudiness formation was introduced as well. An aerosol module was incorporated into the model. The upgraded oceanic component has a modiﬁed dynamical core optimized for better implementation on parallel computers and has two times higher resolution in both horizontal directions.

Analysis of the present-day climatology of the INMCM5 (based on the data of historical run for 1979–2005) shows moderate improvements in reproduction of basic circulation characteristics with respect to the previous version. Biases in the near-surface temperature and precipitation are slightly reduced compared with INMCM4 as well as biases in oceanic temperature, salinity and sea surface height. The most notable improvement over INMCM4 is the capability of the new model to reproduce the equatorial stratospheric quasi-biannual oscillation and statistics of sudden stratospheric warmings.

The family of INMCM climate models, as most climate system models, consists of two main blocks: the atmosphere general circulation model, and the ocean general circulation model. The atmospheric part is based on the standard set of hydrothermodynamic equations with hydrostatic approximation written in advective form. The model prognostic variables are wind horizontal components, temperature, speciﬁc humidity and surface pressure.

Atmosphere Module

The INMCM5 borrows most of the atmospheric parameterizations from its previous version. One of the few notable changes is the new parameterization of clouds and large-scale condensation. In the INMCM5 cloud area and cloud water are computed prognostically according to Tiedtke (1993). That includes the formation of large-scale cloudiness as well as the formation of clouds in the atmospheric boundary layer and clouds of deep convection. Decrease of cloudiness due to mixing with unsaturated environment and precipitation formation are also taken into account. Evaporation of precipitation is implemented according to Kessler (1969).

In the INMCM5 the atmospheric model is complemented by the interactive aerosol block, which is absent in the INMCM4. Concentrations of coarse and ﬁne sea salt, coarse and ﬁne mineral dust, SO2, sulfate aerosol, hydrophilic and hydrophobic black and organic carbon are all calculated prognostically.

Ocean Module

The oceanic module of the INMCM5 uses generalized spherical coordinates. The model “South Pole” coincides with the geographical one, while the model “North Pole” is located in Siberia beyond the ocean area to avoid numerical problems near the pole. Vertical sigma-coordinate is used. The ﬁnite-diﬀerence equations are written using the Arakawa C-grid. The diﬀerential and ﬁnite-diﬀerence equations, as well as methods of solving them can be found in Zalesny etal. (2010).

The INMCM5 uses explicit schemes for advection, while the INMCM4 used schemes based on splitting upon coordinates. Also, the iterative method for solving linear shallow water equation systems is used in the INMCM5 rather than direct method used in the INMCM4. The two previous changes were made to improve model parallel scalability. The horizontal resolution of the ocean part of the INMCM5 is 0.5 × 0.25° in longitude and latitude (compared to the INMCM4’s 1 × 0.5°).

Both the INMCM4 and the INMCM5 have 40 levels in vertical. The parallel implementation of the ocean model can be found in (Terekhov etal. 2011). The oceanic block includes vertical mixing and isopycnal diﬀusion parameterizations (Zalesny et al. 2010). Sea ice dynamics and thermodynamics are parameterized according to Iakovlev (2009). Assumptions of elastic-viscous-plastic rheology and single ice thickness gradation are used. The time step in the oceanic block of the INMCM5 is 15 min.

Note the size of the human emissions next to the red arrow.

Carbon Cycle Module

The climate model INMCM5 has а carbon cycle module (Volodin 2007), where atmospheric CO2 concentration, carbon in vegetation, soil and ocean are calculated. In soil, а single carbon pool is considered. In the ocean, the only prognostic variable in the carbon cycle is total inorganic carbon. Biological pump is prescribed. The model calculates methane emission from wetlands and has a simpliﬁed methane cycle (Volodin 2008). Parameterizations of some electrical phenomena, including calculation of ionospheric potential and ﬂash intensity (Mareev and Volodin 2014), are also included in the model.

Surface Temperatures

When compared to the INMCM4 surface temperature climatology, the INMCM5 shows several improvements. Negative bias over continents is reduced mainly because of the increase in daily minimum temperature over land, which is achieved by tuning the surface ﬂux parameterization. In addition, positive bias over southern Europe and eastern USA in summer typical for many climate models (Mueller and Seneviratne 2014) is almost absent in the INMCM5. A possible reason for this bias in many models is the shortage of soil water and suppressed evaporation leading to overestimation of the surface temperature. In the INMCM5 this problem was addressed by the increase of the minimum leaf resistance for some vegetation types.

Nevertheless, some problems migrate from one model version to the other: negative bias over most of the subtropical and tropical oceans, and positive bias over the Atlantic to the east of the USA and Canada. Root mean square (RMS) error of annual mean near surface temperature was reduced from 2.48 K in the INMCM4 to 1.85 K in the INMCM5.

Precipitation

In mid-latitudes, the positive precipitation bias over the ocean prevails in winter while negative bias occurs in summer. Compared to the INMCM4, the biases over the western Indian Ocean, Indonesia, the eastern tropical Paciﬁc and the tropical Atlantic are reduced. A possible reason for this is the better reproduction of the tropical sea surface temperature (SST) in the INMCM5 due to the increase of the spatial resolution in the oceanic block, as well as the new condensation scheme. RMS annual mean model bias for precipitation is 1.35mm day−1 for the INMCM5 compared to 1.60mm day−1 for the INMCM4.

Cloud Radiation Forcing

Cloud radiation forcing (CRF) at the top of the atmosphere is one of the most important climate model characteristics, as errors in CRF frequently lead to an incorrect surface temperature.

In the high latitudes model errors in shortwave CRF are small. The model underestimates longwave CRF in the subtropics but overestimates it in the high latitudes. Errors in longwave CRF in the tropics tend to partially compensate errors in shortwave CRF. Both errors have positive sign near 60S leading to warm bias in the surface temperature here. As a result, we have some underestimation of the net CRF absolute value at almost all latitudes except the tropics. Additional experiments with tuned conversion of cloud water (ice) to precipitation (for upper cloudiness) showed that model bias in the net CRF could be reduced, but that the RMS bias for the surface temperature will increase in this case.

A table from another paper provides the climate parameters described by INMCM5.

Climate Parameters	Observations	INMCM3	INMCM4	INMCM5
Incoming solar radiation at TOA	341.3 [26]	341.7	341.8	341.4
Outgoing solar radiation at TOA	96–100 [26]	97.5 ± 0.1	96.2 ± 0.1	98.5 ± 0.2
Outgoing longwave radiation at TOA	236–242 [26]	240.8 ± 0.1	244.6 ± 0.1	241.6 ± 0.2
Solar radiation absorbed by surface	154–166 [26]	166.7 ± 0.2	166.7 ± 0.2	169.0 ± 0.3
Solar radiation reflected by surface	22–26 [26]	29.4 ± 0.1	30.6 ± 0.1	30.8 ± 0.1
Longwave radiation balance at surface	–54 to 58 [26]	–52.1 ± 0.1	–49.5 ± 0.1	–63.0 ± 0.2
Solar radiation reflected by atmosphere	74–78 [26]	68.1 ± 0.1	66.7 ± 0.1	67.8 ± 0.1
Solar radiation absorbed by atmosphere	74–91 [26]	77.4 ± 0.1	78.9 ± 0.1	81.9 ± 0.1
Direct hear flux from surface	15–25 [26]	27.6 ± 0.2	28.2 ± 0.2	18.8 ± 0.1
Latent heat flux from surface	70–85 [26]	86.3 ± 0.3	90.5 ± 0.3	86.1 ± 0.3
Cloud amount, %	64–75 [27]	64.2 ± 0.1	63.3 ± 0.1	69 ± 0.2
Solar radiation-cloud forcing at TOA	–47 [26]	–42.3 ± 0.1	–40.3 ± 0.1	–40.4 ± 0.1
Longwave radiation-cloud forcing at TOA	26 [26]	22.3 ± 0.1	21.2 ± 0.1	24.6 ± 0.1
Near-surface air temperature, °С	14.0 ± 0.2 [26]	13.0 ± 0.1	13.7 ± 0.1	13.8 ± 0.1
Precipitation, mm/day	2.5–2.8 [23]	2.97 ± 0.01	3.13 ± 0.01	2.97 ± 0.01
River water inflow to the World Ocean,10^3 km^3/year	29–40 [28]	21.6 ± 0.1	31.8 ± 0.1	40.0 ± 0.3
Snow coverage in Feb., mil. Km^2	46 ± 2 [29]	37.6 ± 1.8	39.9 ± 1.5	39.4 ± 1.5
Permafrost area, mil. Km^2	10.7–22.8 [30]	8.2 ± 0.6	16.1 ± 0.4	5.0 ± 0.5
Land area prone to seasonal freezing in NH, mil. Km^2	54.4 ± 0.7 [31]	46.1 ± 1.1	48.3 ± 1.1	51.6 ± 1.0
Sea ice area in NH in March, mil. Km^2	13.9 ± 0.4 [32]	12.9 ± 0.3	14.4 ± 0.3	14.5 ± 0.3
Sea ice area in NH in Sept., mil. Km^2	5.3 ± 0.6 [32]	4.5 ± 0.5	4.5 ± 0.5	6.1 ± 0.5

Heat flux units are given in W/m^2; the other units are given with the title of corresponding parameter. Where possible, ± shows standard deviation for annual mean value. Source: Simulation of Modern Climate with the New Version Of the INM RAS Climate Model (Bracketed numbers refer to sources for observations)

Ocean Temperature and Salinity

The model biases in potential temperature and salinity averaged over longitude with respect to WOA09 (Antonov et al. 2010) are shown in Fig.12. Positive bias in the Southern Ocean penetrates from the surface downward for up to 300 m, while negative bias in the tropics can be seen even in the 100–1000 m layer.

Nevertheless, zonal mean temperature error at any level from the surface to the bottom is small. This was not the case for the INMCM4, where one could see negative temperature bias up to 2–3 K from 1.5 km to the bottom nearly al all latitudes, and 2–3 K positive bias at levels of 700–1000 m. The reason for this improvement is the introduction of a higher background coeﬃcient for vertical diﬀusion at high depth (3000 m and higher) than at intermediate depth (300–500m). Positive temperature bias at 45–65 N at all depths could probably be explained by shortcomings in the representation of deep convection [similar errors can be seen for most of the CMIP5 models (Flato etal. 2013, their Fig.9.13)].

Another feature common for many present day climate models (and for the INMCM5 as well) is negative bias in southern tropical ocean salinity from the surface to 500 m. It can be explained by overestimation of precipitation at the southern branch of the Inter Tropical Convergence zone. Meridional heat ﬂux in the ocean (Fig.13) is not far from available estimates (Trenberth and Caron 2001). It looks similar to the one for the INMCM4, but maximum of northward transport in the Atlantic in the INMCM5 is about 0.1–0.2 × 1015 W higher than the one in the INMCM4, probably, because of the increased horizontal resolution in the oceanic block.

Sea Ice

In the Arctic, the model sea ice area is just slightly overestimated. Overestimation of the Arctic sea ice area is connected with negative bias in the surface temperature. In the same time, connection of the sea ice area error with the positive salinity bias is not evident because ice formation is almost compensated by ice melting, and the total salinity source for these pair of processes is not large. The amplitude and phase of the sea ice annual cycle are reproduced correctly by the model. In the Antarctic, sea ice area is underestimated by a factor of 1.5 in all seasons, apparently due to the positive temperature bias. Note that the correct simulation of sea ice area dynamics in both hemispheres simultaneously is a diﬃcult task for climate modeling.

The analysis of the model time series of the SST anomalies shows that the El Niño event frequency is approximately the same in the model and data, but the model El Niños happen too regularly. Atmospheric response to the El Niño vents is also underestimated in the model by a factor of 1.5 with respect to the reanalysis data.

Conclusion

Based on the CMIP5 model INMCM4 the next version of the Institute of Numerical Mathematics RAS climate model was developed (INMCM5). The most important changes include new parameterizations of large scale condensation (cloud fraction and cloud water are now the prognostic variables), and increased vertical resolution in the atmosphere (73 vertical levels instead of 21, top model level raised from 30 to 60 km). In the oceanic block, horizontal resolution was increased by a factor of 2 in both directions.

The climate model was supplemented by the aerosol block. The model got a new parallel code with improved computational eﬃciency and scalability. With the new version of climate model we performed a test model run (80 years) to simulate the present-day Earth climate. The model mean state was compared with the available datasets. The structures of the surface temperature and precipitation biases in the INMCM5 are typical for the present climate models. Nevertheless, the RMS error in surface temperature, precipitation as well as zonal mean temperature and zonal wind are reduced in the INMCM5 with respect to its previous version, the INMCM4.

The model is capable of reproducing equatorial stratospheric QBO and SSWs.The model biases for the sea surface height and surface salinity are reduced in the new version as well, probably due to increasing spatial resolution in the oceanic block. Bias in ocean potential temperature at depths below 700 m in the INMCM5 is also reduced with respect to the one in the INMCM4. This is likely because of the tuning background vertical diﬀusion coeﬃcient.

Model sea ice area is reproduced well enough in the Arctic, but is underestimated in the Antarctic (as a result of the overestimated surface temperature). RMS error in the surface salinity is reduced almost everywhere compared to the previous model except the Arctic (where the positive bias becomes larger). As a ﬁnal remark one can conclude that the INMCM5 is substantially better in almost all aspects than its previous version and we plan to use this model as a core component for the coming CMIP6 experiment.

Summary

One the one hand, this model example shows that the intent is simple: To represent dynamically the energy balance of our planetary climate system. On the other hand, the model description shows how many parameters are involved, and the complexity of processes interacting. The attempt to simulate operations of the climate system is a monumental task with many outstanding challenges, and this latest version is another step in an iterative development.

Note: Regarding the influence of rising CO2 on the energy balance. Global warming advocates estimate a CO2 perturbation of 4 W/m^2. In the climate parameters table above, observations of the radiation fluxes have a 2 W/m^2 error range at best, and in several cases are observed in ranges of 10 to 15 W/m^2.

We do not yet have access to the time series temperature outputs from INMCM5 to compare with observations or with other CMIP6 models. Presumably that will happen in the future.

Early Schematic: Flows and Feedbacks for Climate Models

N. Atlantic 2020 Surprise

Posted on March 11, 2020 by Ron Clutz

RAPID Array measuring North Atlantic SSTs.

For the last few years, observers have been speculating about when the North Atlantic will start the next phase shift from warm to cold.The way 2018 went and 2019 followed,suggested this may be the onset. However, 2020 is starting out against that trend. First some background.

. Source: Energy and Education Canada

December 2019 confirms the summer pulse weakening, along with 2018 well below other recent peak years since 1998. Because McCarthy refers to hints of cooling to come in the N. Atlantic, let’s take a closer look at some AMO years in the last 2 decades.

The 2020 North Atlantic Surprise
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 was at the bottom of all these tracks. The black line shows that 2019 began slightly cooler than January 2018, then tracked closely before rising in the summer months, though still lower than the peak years. Through December 2019 is again tracking warmer than 2018 but cooler than other recent years in the North Atlantic.

Now in 2020 following a warm January, N. Atlantic temps in February are the highest in the record. This is consistent with reports of unusually warm February weather in the Norhern Hemisphere.

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