Half of 21st Century Warming Due to El Nino

Reblogged from Dr.RoySpencer.com  [HiFast bold]

May 13th, 2019 by Roy W. Spencer, Ph. D.

A major uncertainty in figuring out how much of recent warming has been human-caused is knowing how much nature has caused. The IPCC is quite sure that nature is responsible for less than half of the warming since the mid-1900s, but politicians, activists, and various green energy pundits go even further, behaving as if warming is 100% human-caused.

The fact is we really don’t understand the causes of natural climate change on the time scale of an individual lifetime, although theories abound. For example, there is plenty of evidence that the Little Ice Age was real, and so some of the warming over the last 150 years (especially prior to 1940) was natural — but how much?

The answer makes as huge difference to energy policy. If global warming is only 50% as large as is predicted by the IPCC (which would make it only 20% of the problem portrayed by the media and politicians), then the immense cost of renewable energy can be avoided until we have new cost-competitive energy technologies.

The recently published paper Recent Global Warming as Confirmed by AIRS used 15 years of infrared satellite data to obtain a rather strong global surface warming trend of +0.24 C/decade. Objections have been made to that study by me (e.g. here) and others, not the least of which is the fact that the 2003-2017 period addressed had a record warm El Nino near the end (2015-16), which means the computed warming trend over that period is not entirely human-caused warming.

If we look at the warming over the 19-year period 2000-2018, we see the record El Nino event during 2015-16 (all monthly anomalies are relative to the 2001-2017 average seasonal cycle):

21st-century-warming-2000-2018-550x733
Fig. 1. 21st Century global-average temperature trends (top) averaged across all CMIP5 climate models (gray), HadCRUT4 observations (green), and UAH tropospheric temperature (purple). The Multivariate ENSO Index (MEI, bottom) shows the upward trend in El Nino activity over the same period, which causes a natural enhancement of the observed warming trend.

We also see that the average of all of the CMIP5 models’ surface temperature trend projections (in which natural variability in the many models is averaged out) has a warmer trend than the observations, despite the trend-enhancing effect of the 2015-16 El Nino event.

So, how much of an influence did that warm event have on the computed trends? The simplest way to address that is to use only the data before that event. To be somewhat objective about it, we can take the period over which there is no trend in El Nino (and La Nina) activity, which happens to be 2000 through June, 2015 (15.5 years):

21st-century-warming-2000-2015.5-550x733
Fig. 2. As in Fig. 1, but for the 15.5 year period 2000 to June 2015, which is the period over which there was no trend in El Nino and La Nina activity.

Note that the observed trend in HadCRUT4 surface temperatures is nearly cut in half compared to the CMIP5 model average warming over the same period, and the UAH tropospheric temperature trend is almost zero.

One might wonder why the UAH LT trend is so low for this period, even though in Fig. 1 it is not that far below the surface temperature observations (+0.12 C/decade versus +0.16 C/decade for the full period through 2018). So, I examined the RSS version of LT for 2000 through June 2015, which had a +0.10 C/decade trend. For a more apples-to-apples comparison, the CMIP5 surface-to-500 hPa layer average temperature averaged across all models is +0.20 C/decade, so even RSS LT (which usually has a warmer trend than UAH LT) has only one-half the warming trend as the average CMIP5 model during this period.

So, once again, we see that the observed rate of warming — when we ignore the natural fluctuations in the climate system (which, along with severe weather events dominate “climate change” news) — is only about one-half of that projected by climate models at this point in the 21st Century. This fraction is consistent with the global energy budget study of Lewis & Curry (2018) which analyzed 100 years of global temperatures and ocean heat content changes, and also found that the climate system is only about 1/2 as sensitive to increasing CO2 as climate models assume.

It will be interesting to see if the new climate model assessment (CMIP6) produces warming more in line with the observations. From what I have heard so far, this appears unlikely. If history is any guide, this means the observations will continue to need adjustments to fit the models, rather than the other way around.

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Curious Correlations

Reblogged from Watts Up With That:

Guest Post by Willis Eschenbach

I got to thinking about the relationship between the Equatorial Pacific, where we find the El Nino/La Nina phenomenon, and the rest of the world. I’ve seen various claims about what happens to the temperature in various places at various lag-times after the Nino/Nina changes. So I decided to take a look.

To do that, I’ve gotten the temperature of the NINO34 region of the Equatorial Pacific. The NINO34 region stretches from 90°W, near South America, out to 170° West in the mid-Pacific, and from 5° North to 5° South of the Equator. I’ve calculated how well correlated that temperature is with the temperatures in the whole world, at various time lags.

To start with, here’s the correlation of what the temperature of the NINO34 region is doing with what the rest of the world is doing, with no time lag. Figure 1 shows which areas of the planet move in step with or in opposition to the NINO34 region with no lag.

Figure 1. Correlation of the temperature of the NINO34 region (90°-170°W, 5°N/S) with gridcell temperatures of the rest of the globe. Correlation values greater than 0.6 are all shown in red.

Now, perfect correlation is where two variables move in total lockstep. It has a value of 1.0. And if there is perfect anti-correlation, meaning whenever one variable moves up the other moves down, that has a value of minus 1.0.

There are a couple of interesting points about that first look, showing correlations with no lag. The Indian Ocean moves very strongly in harmony with the NINO34 region (red). Hmmm. However, the Atlantic doesn’t do that. Again hmmm. Also, on average northern hemisphere land is positively correlated with the NINO34 region (orange), and southern hemisphere land is the opposite, negatively correlated (blue).

Next, with a one-month lag to give the Nino/Nina effects time to start spreading around the planet, we see the following:

Figure 2. As in Figure 1, but with a one month lag between the NINO34 temperature and the rest of the world. In other words, we’re comparing each month’s temperature with the previous month’s NINO34 temperature.

Here, after a month, the North Pacific and the North Atlantic both start to feel the effects. Their correlation switches from negative (blues and greens) to positive (red-orange). Next, here’s the situation after a two-month lag.

Figure 3. As in previous figures, but with a two month lag.

I found this result most surprising. Two months after a Nino/Nina change, the entire Northern Hemisphere strongly tends to move in the same direction as the NINO34 region moved two months earlier … and at the same time, the entire Southern Hemisphere moves in opposition to what the NINO34 region did two months earlier.

Hmmm …

And here’s the three-month lag:

Figure 4. As in previous figures, but with a three month lag.

An interesting feature of the above figure is that the good correlation of the north-eastern Pacific Ocean off the west coast of North America does not extend over the continent itself.

Finally, after four months, the hemispherical pattern begins to fall apart.

Figure 5. As in previous figures, but with a four & five month lag.

Even at five months, curious patterns remain. In the northern hemisphere, the land is all negatively correlated with NINO34, and the ocean is positively correlated. But in the southern hemisphere, the land is all positively correlated and the ocean negative.

Note that this hemispheric land-ocean difference with a five-month lag is the exact opposite of the land-ocean difference with no lag shown in Figure 1.

Now … what do I make of all this?

The first thing that it brings up for me is the astounding complexity of the climate system. I mean, who would have guessed that the two hemispheres would have totally opposite strong responses to the Nino/Nina phenomenon? And who would have predicted that the land and the ocean would react in opposite directions to the Nino/Nina changes right up to the very coastlines?

Second, it would seem to offer some ability to improve long-range forecasting for certain specific areas. Positive correlation with Hawaii, North Australia, Southern Africa, and Brazil is good up to four-five months out.

Finally, it strikes me that I can run this in reverse. By that, I mean I can find all areas of the planet that are able to predict the future temperature at some pre-selected location. Like, say, what areas of the globe correlate well with whatever the UK will be doing two months from now?

Hmmm indeed …

Warmest regards to all, the mysteries of this wondrous world are endless.

w.

Emperor Penguins “Wiped Out”

NOT A LOT OF PEOPLE KNOW THAT

By Paul Homewood

image

Thousands of emperor penguin chicks drowned when the sea-ice on which they were being raised was destroyed in severe weather.

The catastrophe occurred in 2016 in Antarctica’s Weddell Sea.

Scientists say the colony at the edge of the Brunt Ice Shelf has collapsed with adult birds showing no sign of trying to re-establish the population.

And it would probably be pointless for them to try as a giant iceberg is about to disrupt the site.

The dramatic loss of the young emperor birds is reported by a team from the British Antarctic Survey (BAS).

Drs Peter Fretwell and Phil Trathan noticed the disappearance of the so-called Halley Bay colony in satellite pictures.

It is possible even from 800km up to spot the animals’ excrement, or guano, on the white ice and then to estimate the likely size of any gathering.

But the Brunt population, which had sustained…

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A Simple Model of the Atmospheric CO2 Budget

Reblogged from Dr. Roy Spencer:

April 11th, 2019 by Roy W. Spencer, Ph. D.

SUMMARY: A simple model of the CO2 concentration of the atmosphere is presented which fairly accurately reproduces the Mauna Loa observations 1959 through 2018. The model assumes the surface removes CO2 at a rate proportional to the excess of atmospheric CO2 above some equilibrium value. It is forced with estimates of yearly CO2 emissions since 1750, as well as El Nino and La Nina effects. The residual effects of major volcanic eruptions (not included in the model) are clearly seen. Two interesting finding are that (1) the natural equilibrium level of CO2 in the atmosphere inplied by the model is about 295 ppm, rather than 265 or 270 ppm as is often assumed, and (2) if CO2 emissions were stabilized and kept constant at 2018 levels, the atmospheric CO2 concentration would eventually stabilize at close to 500 ppm, even with continued emissions.

A recent e-mail discussion regarding sources of CO2 other than anthropogenic led me to revisit a simple model to explain the history of CO2 observations at Mauna Loa since 1959. My intent here isn’t to try to prove there is some natural source of CO2 causing the recent rise, as I think it is mostly anthropogenic. Instead, I’m trying to see how well a simple model can explain the rise in CO2, and what useful insight can be deduced from such a model.

The model uses the Boden et al. (2017) estimates of yearly anthropogenic CO2 production rates since 1750, updated through 2018. The model assumes that the rate at which CO2 is removed from the atmosphere is proportional to the atmospheric excess above some natural “equilibrium level” of CO2 concentration. A spreadsheet with the model is here.

Here’s the assumed yearly CO2 inputs into the model:

1
Fig. 1. Assumed yearly anthropogenic CO2 input into the model atmosphere.

I also added in the effects of El Nino and La Nina, which I calculate cause a 0.47 ppm yearly change in CO2 per unit Multivariate ENSO Index (MEI) value (May to April average). This helps to capture some of the wiggles in the Mauna Loa CO2 observations.

The resulting fit to the Mauna Loa data required an assumed “natural equilibrium” CO2 concentration of 295 ppm, which is higher than the usually assumed 265 or 270 ppm pre-industrial value:

2Fig. 2. Simple model of atmospheric CO2 concentration using Boden et al. (2017) estimates of yearly anthropogenic emissions, an El Nino/La Nina natural source/sink, after fitting of three model free parameters.

Click on the above plot and notice just how well even the little El Nino- and La Nina-induced changes are captured. I’ll address the role of volcanoes later.

The next figure shows the full model period since 1750, extended out to the year 2200:

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Fig. 3. As in Fig. 2, but for the full model period, 1750-2200.

Interestingly, note that despite continued CO2 emissions, the atmospheric concentration stabilizes just short of 500 ppm. This is the direct result of the fact that the Mauna Loa observations support the assumption that the rate at which CO2 is removed from the atmosphere is directly proportional to the amount of “excess” CO2 in the atmosphere above a “natural equilibrium” level. As the CO2 content increases, the rate or removal increases until it matches the rate of anthropogenic input.

We can also examine the removal rate of CO2 as a fraction of the anthropogenic source. We have long known that only about half of what is emitted “shows up” in the atmosphere (which isn’t what’s really going on), and decades ago the IPCC assumed that the biosphere and ocean couldn’t keep removing excess CO2 at such a high rate. But, in fact, the fractional rate of removal has actually been increasing, not decreasing.And the simple model captures this:

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Fig. 4. Rate of removal of atmospheric CO2 as a fraction of the anthropogenic source, in the model and observations.

The up-and-down variations in Fig. 4 are due to El Nino and La Nina events (and volcanoes, discussed next).

Finally, a plot of the difference between the model and Mauna Loa observations reveals the effects of volcanoes. After a major eruption, the amount of CO2 in the atmosphere is depressed, either because of a decrease in natural surface emissions or an increase in surface uptake of atmospheric CO2:

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Fig. 5. Simple model of yearly CO2 concentrations minus Mauna Loa observations (ppm), revealing the effects of volcanoes which are not included in the model.

What is amazing to me is that a model with such simple but physically reasonable assumptions can so accurately reproduce the Mauna Loa record of CO2 concentrations. I’ll admit I am no expert in the global carbon cycle, but the Mauna Loa data seem to support the assumption that for global, yearly averages, the surface removes a net amount of CO2 from the atmosphere that is directly proportional to how high the CO2 concentration goes above 295 ppm. The biological and physical oceanographic reasons for this might be complex, but the net result seems to follow a simple relationship.

2019 ENSO forecast

Climate Etc.

by Judith Curry and Jim Johnstone

CFAN’s 2019 ENSO forecast is for a transition away from El Niño conditions as the summer progresses. The forecast for Sept-Oct-Nov 2019 calls for 60% probability of ENSO neutral conditions, with 40% probability of weak El Niño conditions. – Forecast issued 3/25/19

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Hurricanes & climate change: 21st century projections

Climate Etc.

by Judith Curry

Final installment in my series on hurricanes and climate change.

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Hurricanes & climate change: recent U.S. landfalling hurricanes

BLUF:  6.6   Conclusions

Convincing detection and attribution of individual extreme weather events such as hurricanes requires:

  • a very long time series of high-quality observations of the extreme event
  • an understanding of the variability of extreme weather events associated with multi-decadal ocean oscillations, which requires at least a century of observations
  • climate models that accurately simulate both natural internal variability on timescales of years to centuries and the extreme weather events

Of the four hurricanes considered here, only the rainfall in Hurricane Harvey passes the detection test, given that it is an event unprecedented in the historical record for a continental U.S. landfalling hurricane. Arguments attributing the high levels of rainfall to near record ocean heat content in the western Gulf of Mexico are physically plausible. The extent to which the high value of ocean heat content in the western Gulf of Mexico can be attributed to manmade global warming is debated. Owing to the large interannual and decadal variability in the Gulf of Mexico (e.g. ENSO), it is not clear that a dominant contribution from manmade warming can be identified against the background internal climate variability (Chapter 4).

Climate Etc.

by Judith Curry

An assessment of whether any of the impacts of recent  U.S. landfalling hurricanes were exacerbated by global warming.

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