Month: February 2018

Warming to 2100: A Lukewarmer Scenario

My previous post dealt with a 1D model of ocean temperature changes to 2,000m depth, optimized to match various observed quantities: deep-ocean heat storage, surface temperature warming, the observed lagged-variations between CERES satellite radiative flux and surface temperature, and warming/cooling associated with El Nino/La Nina.

While that model was meant to match global average (land+ocean) conditions, I more recently did one for oceans-only (60N-60S). I changed a few things, so the models are not directy comparable. For example, I used all of the RCP6.0 radiative forcings, but with the land use and snow albedo changes removes (since the model is ocean-only). For SST observations, I used the ERSSTv5 data.

The resulting equilibrium climate sensitivity (ECS) is 1.54 deg. C (coincidently the same as the previous, global model).

What I thought would be fun, though, would be to run the model out to 2100. This requires an estimate of ENSO activity (I used the MEI index). After examining the history of MEI, including it’s low-frequency variations (which are somewhat related to the Pacific Decadal Oscillation, PDO), I made the February 2017 MEI values onward equal to the Feb. 1929 values up to the present.

The resulting forecast shows global average SST almost reaching 1.5 C above pre-industrial times by the end of this century:

2-Layer ocean model sea surface temperature variations. See the figure inset for model assumptions and how it was tuned.

Because I used past MEI data for the future, the lack of significant warming until the late 2040s is due to reduced El Nino activity that was observed from about 1940 to the late 1970s. The enhanced warming after 2040 is analogous to the enhanced warming from stronger El Nino activity that existed from the late 1970s to the late 1990s.

Of course, this whole exercise assumes that, without humans, the climate system would have had no temperature trend between 1765-2100. That is basically the IPCC assumption — that the climate system is in long-term energy equilibrium, not only at the top-of atmosphere, but in terms of changes in ocean vertical circulation whcih can warm the surface and atmosphere without any TOA radiative forcing.

I don’t really believe the “climate stasis” assumption, because I believe the Medieval Warm Period and the Little Ice Age were real, and that some portion of recent warming has been natural. In that case, the model climate sensitivity would be lower, and the model warming by 2100 would be even less.

What would cause warming as we came out of the Little Ice Age? You don’t need any external forcing (e.g. the Sun) to accomplish it, although I know that’s a popular theory. My bet (but who knows?) is a change in ocean circulation, possibly accompanied by a somewhat different cloud regime. We already know that El Nino/La Nino represents a bifurcation in how the climate system wants to behave on interannual time scales. Why not multi-century time scale bifurcations in the deep ocean circulation? This possibility is simply swept under the rug by the IPCC.

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February 28, 2018 at 10:34AM

Challenging statistics of weather extremes

From KING ABDULLAH UNIVERSITY OF SCIENCE & TECHNOLOGY (KAUST)

By integrating previously distinct statistical paradigms into a single modeling scheme, Raphaël Huser from KAUST and Jennifer Wadsworth from Lancaster University in the UK have taken some of the guesswork out of modelling of weather extremes. This could greatly improve predictions of future extreme events.

Modeling the frequency and severity of possible weather extremes, such as intense rainfall, strong winds and heat waves, must account for nearby monitoring stations being spatially correlated. That is, heavy rain at one station often implies that there will be similarly heavy rain nearby.

However, as the severity of the event increases, this spatial dependence can weaken–the higher the rainfall intensity, for example, the less likely it is to occur across a wide region. Some extreme events may even be entirely localized around one station, with no correlation at all with those nearby.

Deciding whether the dependence changes with intensity, and to what extent, is a crucial step in the model selection process, but is often difficult to determine. For those involved in predicting weather disasters, a mismatch between model selection and the hidden character of the data can critically undermine the accuracy of predictions.

“It is very common with wind speeds or rainfall that spatial dependence weakens as events become more extreme, and eventually vanishes,” explains Huser. “If we restrict ourselves to ‘asymptotically’ dependent models, we might overestimate the spatial dependence strength of the largest extreme events; meanwhile, if we restrict ourselves to ‘asymptotically’ independent models, we might underestimate their dependence strength.”

Building on their recent work, Huser and Wadsworth have developed an integrated statistical approach that eliminates this guesswork by combining these disparate spatial dependence models on a smooth continuum.

“Our statistical model smoothly transitions between asymptotic dependence and independence in the interior of the parameter space,” explains Huser, “which greatly facilitates statistical inference and is more general than other models, covering a different class of statistical models with application to a broader range of scenarios.”

The researchers applied the modeling scheme to winter observations of extreme wave height in the North Sea, which was found in a previous study to have a high degree of ambiguity in its dependence class. The model proved to be very effective in dealing with the data, accounting for the case where there is strong spatial dependence but also strong evidence of asymptotic independence.

“Our new statistical model bridges these two usually distinct possibilities, and crucially, learning about the dependence type becomes part of the inference process,” says Wadsworth. “This means the model can be fitted without having to select the appropriate dependence class in advance, while being flexible and easy to use.”

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The paper: https://www.tandfonline.com/doi/full/10.1080/01621459.2017.1411813

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February 28, 2018 at 09:49AM

Thanks to readers, I’m at NASA now – waiting for press briefing

Thanks to readers, I’m at NASA now – waiting for press briefing

I’m at the NASA press Center on Merritt Island at this moment, writing live from the John Holliman Press Center.

As you can see above, the upcoming press conference will be carried live, so readers are encouraged to tune in. You can also ask questions using the #AskGOES hashtag on Twitter or leave me a question in comments.

I want to thank everyone for donating to help get me here. I’ll add updates to this post through the day.

-Anthony

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February 28, 2018 at 09:38AM

A DYI Climate Sensitivity Toolkit

Guest disalarmism by David Middleton

Do you ever watch the DYI Network?  The TV network where they have all the “Do It Yourself” home improvement shows?  I don’t watch it because I can’t do anything like that myself.  If a home improvement or repair project is much beyond duct tape and bungee cords, I’m on the phone to a professional in a heartbeat.  When I was a bachelor, the pipe under my kitchen sink was leaking.  So I wrapped in in duct tape and put a bowl under it.  Whenever it started to leak again, I wrapped it with more duct tape.  I actually left the roll of duct tape attached to the pipe, so I could easily wrap more duct tape.  When I got married and we renovated the house, the plumbers actually took pictures of my “handiwork.”   Is it duct tape or duck tape?  But I digress…

I may not be able to fix things around the house, but it occurred to me that if the climate (e.g. average surface temperature of the Earth) is sensitive to atmospheric CO2, there ought to be a simple DYI way to demonstrate it.  So, I broke out two of my favorite data sets: Moberg et al., 2005 (a non-hockey stick 2,000 year northern hemisphere climate reconstruction) and MacFarling-Meure et al., 2006 (a fairly high resolution CO2 record from the Law Dome, Antarctica ice cores).

For the sake of this exercise, I am going to assume that the “greenhouse” warming effect of CO2 is logarithmic.  While this is not necessarily a safe assumption, it’s a good bet that it is a diminishing returns function… So a logarithmic function is probably good enough for a DYI project.

The first thing I did was to crossplot the Moberg temperature anomalies against the MacFarling-Meure CO2 values…

Moberg_MacFarlingMeure_01

Figure 1. CO2 vs temperature 0-1979 AD. A really bad correlation below 285 ppm.

R² = 0.0741… not exactly a robust correlation.  Why is the correlation so bad below 285 ppm?  Well, that’s the data from the lower resolution DSS core.  What happens if we only use the data from the very high resolution DE08 core?

Moberg_MacFarlingMeure_02

Figure 2. CO2 vs temperature 1850-1979. A much better correlation with a very low climate sensitivity.

R² = 0.1994… Roughly a 20% explained variance… Not too shabby for noisy climate data.  We also get a climate sensitivity that is in line with other recent observation-derived estimates: 1.23 °C per doubling of atmospheric CO2.  Note that this puts the “we’re all going to die” 2.0 °C limit out to about 720 ppm CO2 and the “women, children and poor people will die” 1.5 °C limit out to about 560 ppm CO2.  So, it’s not worse than we thought, unless you’re an alarmist.  Then it’s probably worse than you will believe.  1.23 °C is very close to the IPCC TAR estimate of 1.2 °C sans feedback mechanisms.

If the amount of carbon dioxide were doubled instantaneously, with everything else remaining the same, the outgoing infrared radiation would be reduced by about 4 Wm-2. In other words, the radiative forcing corresponding to a doubling of the CO2 concentration would be 4 Wm-2. To counteract this imbalance, the temperature of the surface-troposphere system would have to increase by 1.2°C (with an accuracy of ±10%), in the absence of other changes. In reality, due to feedbacks, the response of the climate system is much more complex. It is believed that the overall effect of the feedbacks amplifies the temperature increase to 1.5 to 4.5°C. A significant part of this uncertainty range arises from our limited knowledge of clouds and their interactions with radiation. To appreciate the magnitude of this temperature increase, it should be compared with the global mean temperature difference of perhaps 5 or 6°C from the middle of the last Ice Age to the present interglacial.

IPCC TAR, 2001

Things aren’t looking to good for feedback amplification.

The next thing I DIY’ed was to calculate a “CO2 temperature” using this equation:

T = 1.7714ln(CO2) – 10.305

Moberg_MacFarlingMeure_03

Figure 3. Moberg temperature reconstruction, “CO2 temperature”, Moberg temperature minus CO2 effect and CO2.

The gray curve is the Moberg temperature reconstruction, the red dashed curve is Moberg at a constant 277 ppmv CO2.  Not much difference between the gray and red dashed curves.

Let’s now apply this to the HadCRUT4 northern hemisphere temperature series (via Wood for Trees).

Moberg_MacFarlingMeure_04

Figure 4. HadCRUT4 northern hemisphere (1979-2017), “CO2 temperature” and HadCRUT 4 minus “CO2 temperature.”

Northern hemisphere warming since 1979

  • Total: 0.91 °C (0.1 to 0.92)
  • CO2-driven: 0.33 °C (0.0 to 0.33)
  • Not CO2-driven: 0.58 °C (0.1 to 0.59)

This would suggest that anthropogenic CO2 emissions are only responsible for 36% of the warming since 1979.

Let’s now look at some RCP (representative concentration pathways) scenarios.

Moberg_MacFarlingMeure_05

Figure 5. “CO2 temperature calculations for RCP 4.5, 6.0 and Bad SyFy 8.5 along with an extrapolation of MLO CO2 and HadCRUT4 31-yr average.

With a 1.23 °C climate sensitivity, not even the Bad SyFy RCP8.5 exceeds the “we’re all going to die” 2.0 °C limit and RCP4.5 and 6.0 pretty well stay below the “women, children and poor people will die” 1.5 °C limit.  Note than an exponential extrapolation of MLO CO2 basically tracks RCP4.5.  Also note that HadCRUT4 clearly exhibits a ~60-yr cyclical variation and continued warming from the Little Ice Age (part of a ~1,000-yr cyclical variation).  For those math purists who object to my geological use of the word “cyclical,” pretend that I wrote “quasi-periodic fluctuation.”

The Phanerozoic Eon

This is all well and good for the Late Holocene; but what about the rest of the Phanerozoic Eon?  Thanks to Bill Illis, I have this great set of paleoclimate spreadsheets.  One of the paleo temperature data sets was the pH-corrected version of Veizer’s Phanerozoic reconstruction from Royer et al., 2004.  The Royer temperature series was smoothed (spline fit?) to a 10 million year sample interval matching Berner’s GeoCarb III,  thus facilitating crossplotting.

Moberg_MacFarlingMeure_06

Figure 6. Phanerozoic CO2 vs temperature.

Shocking!!! It yields a climate sensitivity of 1.28 °C.  Royer’s pH corrections were derived from CO; so it shouldn’t be too much of a surprise that the correlation was so good (R² = 0.6701)… But the low climate sensitivity is truly “mind blowing”… /Sarc.

References

Berner, R.A. and Z. Kothavala, 2001. GEOCARB III: A Revised Model of Atmospheric CO2 over Phanerozoic Time, American Journal of Science, v.301, pp.182-204, February 2001.

Hadley Centre.  Data from Hadley Centre.  http://www.metoffice.gov.uk/hadobs/hadcrut4/data/download.html Data processed by http://www.woodfortrees.org

Illis, B. 2009. Searching the PaleoClimate Record for Estimated Correlations: Temperature, CO2 and Sea Level. Watts Up With That?

MacFarling Meure, C., D. Etheridge, C. Trudinger, P. Steele, R. Langenfelds, T. van Ommen, A. Smith, and J. Elkins (2006), Law Dome CO2, CH4 and N2O ice core records extended to 2000 years BP, Geophys. Res. Lett., 33, L14810, doi:10.1029/2006GL026152.

Moberg, A., D.M. Sonechkin, K. Holmgren, N.M. Datsenko and W. Karlén. 2005.
Highly variable Northern Hemisphere temperatures reconstructed from low- and high-resolution proxy data. Nature, Vol. 433, No. 7026, pp. 613-617, 10 February 2005.

NOAA. Data from NOAA Earth System Research Laboratory. http://www.esrl.noaa.gov/gmd/ccgg/trends/ Data processed by http://www.woodfortrees.org

Royer, D. L., R. A. Berner, I. P. Montanez, N. J. Tabor and D. J. Beerling. CO2 as a primary driver of Phanerozoic climate.  GSA Today, Vol. 14, No. 3. (2004), pp. 4-10

Featured image from Wikipedia.

The DYI Climate Sensitivity Toolkit

Moberg_MacFarlingMeure

 

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February 28, 2018 at 09:21AM