CPP has Three Fatal Flaws

 

Captains of industry contending with a sea of Obama era regulations.

Thanks to Rich Lowry’s article at National Review and some other sources, we can see clearly the three fatal flaws bringing down the Clean Power Plan in its entirety. Lowry wrote The Great Regulatory Rollback. Excerpts below with my bolds and images.

1. No federal law governs CO2 emissions.

Lowry: The Clean Power Plan, which sought to reduce U.S. carbon emissions by 32 percent below 2005 levels by 2030, was government by the administrative state on a scale that has never been attempted before. The EPA took a dubious reading of a portion of the Clean Air Act (Section 111, which arguably prevented the EPA from taking this action rather than empowered it to do so) and used it to mandate that the states adopt far-reaching plans to reduce carbon emissions, under threat of the loss of federal highway funds.

In an August ruling of the DC Court of Appeals, the justices put it in writing:

However, EPA’s authority to regulate ozone-depleting substances under Section 612 and other statutes does not give EPA authority to order the replacement of substances that are not ozone depleting but that contribute to climate change. Congress has not yet enacted general climate change legislation. Although we understand and respect EPA’s overarching effort to fill that legislative void and regulate HFCs, EPA may act only as authorized by Congress. Here, EPA has tried to jam a square peg (regulating non-ozone depleting substances that may contribute to climate change) into a round hole (the existing statutory landscape).

The Supreme Court cases that have dealt with EPA’s efforts to address climate change have taught us two lessons that are worth repeating here. See, e.g., Utility Air Regulatory Group v. EPA, 134 S. Ct. 2427 (2014). First, EPA’s well intentioned policy objectives with respect to climate change do not on their own authorize the agency to regulate. The agency must have statutory authority for the regulations it wants to issue. Second, Congress’s failure to enact general climate change legislation does not authorize EPA to act. Under the Constitution, congressional inaction does not license an agency to take matters into its own hands, even to solve a pressing policy issue such as climate change.
From the Court Document On Petitions for Review of Final Action by the United States Environmental Protection Agency. Additional discussion at DC Appeals Court Denies EPA Climate Rules

2. EPA regulates sites, not the Energy Sector.

Lowry: The presumption of the plan was jaw-dropping. The EPA usually targets pollutants; carbon dioxide isn’t one (although the Supreme Court erroneously said that it meets the definition in the case of Massachusetts v. EPA). The EPA has always regulated specific power plants; in this scheme, it went “outside the fence” to mandate broader actions by the states, e.g., the adoption of quotas for renewable energy. The EPA once considered its mandate to be protecting clear air and water for Americans; with the Clean Power Plan, it sought to adjust the global thermostat for the good of all of humanity.

From the EPA document Repeal of Carbon Pollution Emission Guidelines

That the CPP depends on the employment of measures that cannot be applied at and to an individual source is evident from its treatment of coal-fired power plants. The rule established performance standards for coal-fired plants assuming a uniform emissions rate well below that which could be met by existing units through any retrofit technology of reasonable cost available at the time. This means that, in order to comply, many owners or operators of existing coal-fired units were expected to shift generation from such units to gas-fired units or to renewable generation. Similarly, the rule contemplated that gas-fired units would shift generation to renewable generation. The rule therefore is formulated in reliance on and anticipation of actions taken across the electric grid, rather than actions taken at and applied to individual units. Pp 8-9

The EPA is proposing to repeal the CPP in its entirety. The EPA proposes to take this action because it proposes to determine that the rule exceeds its authority under the statute, that those portions of the rule which arguably do not exceed its authority are not severable and separately implementable, and that it is not appropriate for a rule that exceeds statutory authority—especially a rule of this magnitude and with this level of impact on areas of traditional state regulatory authority—to remain in existence pending a potential, successive rulemaking process.Pg 12

After reconsidering the statutory text, context, and legislative history, and in consideration of the EPA’s historical practice under CAA section 111 as reflected in its other existing CAA section 111 regulations, the Agency proposes to return to a reading of CAA section 111(a)(1) (and its constituent term, “best system of emission reduction”) as being limited to emission-reduction measures that can be applied to or at an individual stationary source. That is, such measures must be based on a physical or operational change to a building, structure, facility, or installation at that source, rather than measures that the source’s owner or operator can implement on behalf of the source at another location. The EPA believes that this is the best construction of CAA section 111(a)(1), as explained in detail below, for several reasons.pg 14

Therefore, the EPA proposes that the BSER be limited to measures that physically or operationally can be applied to or at the source itself to reduce its emissions. Generation shifting—which accounts for a significant percentage of the emissions reductions projected in the CPP and without which individual sources could not meet the CPP’s requirements—fails to comply with this limitation. Accordingly, the EPA proposes to repeal the CPP.pg25-26

In addition, while the EPA is authorized to regulate emissions from sources in the power sector and to consider the impact of its standards on the generation mix in setting standards to avoid negative energy impacts, regulation of the nation’s generation mix itself is not within the Agency’s authority. Regulation of the energy sector qua energy sector is generally undertaken by the Federal Energy Regulatory Commission (FERC) and States, depending on which markets are being regulated. Pg.27

3. CPP costs are huge, while benefits are marginal.

Lowry: The last gets to the absurdity of the Clean Power Plan on its own terms — it did virtually nothing to affect global warming. As Benjamin Zycher of the American Enterprise Institute points out, the Obama administration’s Climate Action Plan (which includes the Clean Power Plan) would reduce the global temperature by 15 one-thousandths of a degree by 2100. The point wasn’t to fight climate change per se, but to signal our climate virtue in the hopes of catalyzing action by other nations and, not incidentally, hobble the U.S. coal industry in favor of more politically palatable sources of energy, namely wind and solar.

An irony emerges on this third point. In order to propose a regulatory change, the EPA must present calculations pertaining to the “Social Cost of Carbon (SCC)”, now renamed “Social Cost of CO2 (SC-CO2)”. In the document released by EPA, this Regulatory Impact Analysis (RIA), begins on page 30 with several tables.

Methodology Considerations:

In addition to presenting results from the 2015 CPP RIA, this RIA uses two additional quantitative approaches to analyze the effects of the CPP in order to present information on the potential effects of the proposed repeal of the CPP. The first approach involves a modest reworking of the 2015 CPP RIA to increase transparency and illuminate the uncertainties associated with assessing benefits and costs of the CPP, as reflected in the 2015 analysis, as well as analyzing the potential effects of the CPP repeal. More specifically, this analysis increases transparency of the 2015 CPP analysis by presenting the energy efficiency cost savings as a benefit rather than a cost reduction and provides a bridge to future analyses that the agency is committed to performing. The current analysis also provides alternative approaches for examining the foregone benefits, including more clearly distinguishing the direct benefits from the co-benefits and exploring alternative ways to illustrate the impacts on the total net benefits of the uncertainty in health co-benefits at various PM2.5 cutpoints. This approach shifts the focus to the domestic (rather than global) social cost of carbon, and employs both 3 percent and 7 percent discount rates. Finally, we consider that how changing market conditions and technologies may have affected future actions that may have been undertaken by states to comply with the CPP and how these changes may affect the potential benefits and costs of the CPP repeal. Pg. 30

As the RIA analyzes costs and benefits applying a variety of different methods and discount rates, there is a relatively large number of results. We present the full suite of avoided compliance cost, forgone benefit, and net benefit results discussed in the RIA in Tables 1 through 3. Pg 33

Therefore, in Tables 4 and 5 we offer another perspective on the costs and benefits of this rule by presenting a comparison of the forgone benefits from the targeted pollutant – CO2 – (the costs of this proposed rule) with the avoided compliance cost (the benefits of this proposed rule). Excluded from this comparison are the forgone benefits from the SO2 and NOX emission reductions that were also projected to accompany the CO2 reductions. However, had those SO2 and NOX reductions been achieved through other means, then they would have been represented in the baseline for this proposed repeal (as well as for the 2015 Final CPP), which would have affected the estimated costs and benefits of controlling CO2 emissions alone. Pg.37

Table 5 Gives the Bottom Line (in billions of US$)

Year Discount 
Rate
Compliance Costs 
Avoided
Forgone Domestic 
Climate Benefits
2020 3% ($0.30) $0.10
7% ($0.30) $0.00
2025 3% $14.50 $1.30
7% $14.50 $0.20
2030 3% $14.40 $2.50
7% $14.40 $0.40

 

Summary

There will be lots of pushback on these numbers since they show billions of compliance cost against miniscule benefits.

Lowry: If Congress had authorized the EPA to remake the nation’s energy economy, we would presumably be aware of it and recall an impassioned congressional debate over this radical and costly change. In fact, the opposite is true. Congress has declined to enact laws limiting carbon emissions, including when Democrats held both houses of Congress under President Obama. If the future of the planet is at stake and it requires a generational effort to save it, surely it is not too much to ask that a statute or two be enacted by Congress explicitly committing the country to the task. Yes, this requires winning elections and gaining democratic assent, but such are the challenges of living in a republic and a nation of laws.

 

For background on SCC, now termed SC-CO2:

Social Cost of Carbon: Origins and Prospects

Six Reasons to Rescind Social Cost of Carbon

SBC: Social Benefits of Carbon

 

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Hurricane Science, not Fiction

We continue to see activist journalism claiming recent hurricanes prove global warming and the need for efforts like the Paris accord. People writing these articles seem oblivious to the meteorological science pertaining to tropical storms. The intentional deception is discussed more fully in the post Media Duping Scandal.

Joseph D’Aleo of WeatherBELL comes to the rescue with a primer for the public to gain literacy on this topic.

What Made This Hurricane Season So Active in the Atlantic? summarizes for all of us what is common sense weather knowledge, with graphs and images to enhance understanding of this science. Excerpts below with my bolds.

What a hurricane season! It started very early with Arlene in April but the real action held off until the last week of August, when Hurricane Harvey flooded Texas and Louisiana. Harvey was the first hurricane to make landfall in Texas since Ike in 2008 and the first Category 4 hurricane in Texas since Carla in 1961.

(D’Aleo summarizes the sequence of Irma, Jose, Maria and Nate, then digs into the issues.)

Before the landfall of two major storms on the U.S. we had gone just short of 12 years without a major hurricane landfall, the longest such lull since the 1860s.

The quiet period came after three big years. Isabel made landfall on the Mid Atlantic in 2003. Charley, Frances, Ivan and Jeanne in 2004 and Dennis, Katrina, Rita and Wilma in 2005 all made landfall on the mainland. Emily in 2005 was another major hurricane but turned west into Mexico. 2005 holds the record for five Category 4 or greater and four Category 5 impact storms. Some speculated this was the new norm for the Atlantic before nature gave us that 12-year break.

So what causes long quiet spells and then big years like 2004 and 2005 and now 2017?

(D’Aleo then describes the historical context regarding these storms.)

Okay, major hurricanes have occurred even during cold periods, but is there a trend in the modern record?

The Accumulated Cyclone Energy index measures seasonal tropical activity.

The Accumulated Cyclone Energy index takes into account the number, duration and strength of all tropical storms in a season. The ACE index is a wind energy index, defined as the sum of the squares of the maximum sustained surface wind speed (knots) measured every six hours for all named storms while they are at least tropical storm strength.

The ACE index for the Atlantic shows a cyclical behavior with no long-term trend but with spikes in 1893, 1926, 1933 and 1950 then again in 1995, 2004 and 2005. 2017 ranks 8th now with still weeks to go this season.

So what causes long breaks and then big years like 2004 and 2005 and now 2017?

OCEAN TEMPERATURE AND PRESSURE PATTERNS

The North Atlantic, like the Pacific, undergoes multi-decadal changes in ocean temperature and pressure patterns. It has long been known that when the Atlantic is in what is called its warm mode, there are more storms. Since 1995, when the current warm Atlantic mode began, we have average 14.6 named storms per year, more than five greater than the long-term 1851-2017 average.

An important factor that affects whether hurricanes affect the United States is El Niño and La Niña. When El Niños develop, more storms develop in the eastern and central Pacific, threatening Mexico, Hawaii and sometimes in weakened forms Arizona and California.

These storms enhance high-level winds that cross into the Atlantic. These winds produce shear that disrupts developing storms, causing them to weaken or dissipate and/or turn harmlessly north into the North Atlantic. Storms can still develop near the coast where the water is warm like in the Gulf and near the Gulf Stream off the southeast coast.

When La Ninas develop there are usually fewer storms in the eastern Pacific and less shear to disrupt the Atlantic storms.

In warm Atlantic years, that means trouble as the storms can track the entire basin with more time to turn into major hurricanes. Even the East Coast is more vulnerable to a landfalling hurricane. We had eight high-impact East Coast hurricanes from 1938 to 1960 and nine from 1988 to 2012.

The last important La Niña stretch was in 2010/11 to 2011/12. We avoided a major hurricane hit, though major hurricanes at sea made final landfall in the NYC metro — Irene (as a tropical storm) in 2011 and Sandy in 2012 (as a post-tropical cyclone). They caused massive flooding (from rains with Irene in upstate NY and Vermont and from a storm surge with Sandy in New York City and New Jersey).

We are still in the latest Atlantic warm period. This year, a spring attempt at an El Niño failed and La Niña-like conditions developed. Had El Niño succeeded we may have had Harvey, which developed near the Texas coast, and Nate, which came out of the bath water in the western Caribbean, but maybe Irma and Maria would have been weakened or deflected. But with La Niña conditions developing, no shear and warm Atlantic water we saw a return to big storms just as we saw in 2004 and 2005.

Summary

So when we get a year like 2017 or back-to-back bad years like 2004 and 2005, we have to accept that is how the weather works. Permadroughts ended with record wet years for Texas and California this decade. The record nearly 12-year major hurricane “drought” ended with 2017.

Joe D’Aleo is currently a senior co-chief meteorologist with WeatherBELL Analytics. Joe is a CCM, fellow of the AMS and former chair of the AMS Committee on Weather Analysis and Forecasting. He was a college professor of meteorology/climatology, the co-founder and first director of meteorology at The Weather Channel and chief meteorologist with three companies. He is the executive director of Icecap.us since 2007.

Climate Science: Put Up or Shut Up

That’s the theme of an article by Rowan Dean in The Courier-Mail, Australia:  Time for climate scientists to produce evidence that carbon dioxide emissions affect climate  Full text below with my bolds and images.

IT’S time for so-called climate scientists to either cough up one single, solitary shred of genuine scientific evidence that proves that the climate is being changed by mankind’s carbon dioxide emissions, or ‘fess up and admit that the whole thing is a gigantic hoax.

That’s the bottom line.

Asked at the beginning of this year for one of those “predictions for 2017”, I claimed that this would be the year the Australian public wakes up and realises they are being hoodwinked by the whole climate change/renewables scam.

I told Paul Murray’s lively late night TV show on Sky News that 2017 would be the year the climate con comes to an end. So how is my prediction going?

Well, so far this year two extraordinary books have come out, and one insightful film, that support my argument that the public is indeed waking up to the tricks of the climate change/renewables fraud.

Climate Change: The Facts 2017, a series of essays published by the Institute of Public Affairs, not only debunks the entire scare campaign about the Great Barrier Reef, but in a piece of superb investigative work Dr Jennifer Marohasy exposes the Bureau of Meteorology’s embarrassing manipulation of temperature data.

The book has sold out three print runs and gained serious attention overseas. Then came the surprise hit film Climate Hustle by sceptic Marc Morano, which was, ironically, more popular than the scaremongering Al Gore film it challenged.

And this week a new book is coming out by Australia’s Ian Plimer, one of our greatest geologists.

Called Climate Change Delusion and the Great Electricity Rip-off it’s a must-read for anyone who still believes they’re saving the planet by paying through the nose for electricity.  Because you’re not. The planet is doing just fine with or without your financial impoverishment, and whatever changes may or may not be occurring to our planet’s climate, it almost certainly has nothing to do with your gas bill.

As Plimer points out, Australia is blessed with an abundance of the cheapest and cleanest energy on the planet, yet we are paying the highest electricity prices on earth.

Put simply, that doesn’t add up. And when something smells fishy, it’s because it is.

Australian taxpayers are being ripped off by deluded luvvies (Turnbull is one of the worst) pandering to the voracious leeches of the renewables industry and their greedy investors gorging on a bloated smorgasbord of your cash which they siphon up via subsidies, targets and bills.

Yet, as Plimer points out, it’s all in vain. With rigorous scientific and geological data, Plimer provides evidence that the climate “experts” fail to provide. He shows that Earth has frequently warmed up, cooled down, and warmed up again, but this process has never had anything to do with CO2.

Indeed, the geological evidence is that Earth’s coldest periods often had far higher atmospheric CO2 levels than we do now. What’s more, the mild warming we may currently be experiencing (we are, geologically speaking, still in an Ice Age and moving slowly out of it) has always been associated in human history with increased health, wealth, fertility and prosperity.

Mankind’s most successful times have been in periods such as the Roman era or medieval warming when the Earth was warmer than it is now.

Indeed, we are currently seeing flora around the globe getting greener and more fertile as CO2 levels increase.

Meanwhile, desperately trying to reinvigorate the whole tiresome climate change alarmist nonsense, this year we got Al Gore’s latest horror flick-cum-ad for his own renewables investments An Inconvenient Sequel (what an unoriginal title).

Showing suitably terrifying footage of storms, floods and hurricanes, the film was a box-office flop that received lacklustre reviews at best. Oh, and the other day an ANU “climate scientist” made the hysterical (and unprovable) claim that Sydney and Melbourne “could” roast in 50 degree summers by the end of the century.

Global Mean Temperature from land and ocean expressed in absolute degrees F.

That’s it. And still no proof that man-made carbon dioxide emissions are warming the planet. Still no proof that a warmer planet can be avoided, or would actually be a bad thing. Still no proof that removing civilisation’s reliance on coal is even remotely feasible. Still no proof that even if we did do all the things climate fanatics want us to do and destroy our economies and lifestyles, it would make the slightest difference to global temperatures. And still no proof that we even need to.

The biggest con of all is that Australian voters are denied any political leadership courageous enough to call out this scaremongering for what it is, cancel all our subsidies, targets and the Paris Agreement, which only enrich renewables carpetbaggers, and return us to a land blessed with cheap, abundant energy.

 

Climate Model Upgraded: INMCM5 Under the Hood

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

Earlier this year 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

Degrees of Climate Truth

Previous posts have dealt with science as a mode of inquiry, and described the process of theory and observation by which scientific knowledge is obtained. This post presents work by Andy May to classify the degrees of scientific certainty or truth, and apply these to climate claims.

The essay Facts and Theories comes from his blog Andy May Petrophysicist.
Excerpts below with my bolds.

Categories of Scientific Knowledge

Newton provided us with his descriptive “Law of Gravitation.” Newton’s law tells us what gravity does and it is very useful, but it tells us nothing about how it works. For that we need Einstein’s theory of relativity. Theories and laws are not necessarily related in science. A law simply describes what happens without describing why. A scientific theory attempts to explain why a relationship holds true.

In the scientific community, for both a law and a theory, a single conflicting experiment or observation invalidates them. Einstein once said:

“No amount of experimentation can ever prove me right; a single experiment can prove me wrong.”

So, let’s examine our topics in that light. Newton’s descriptive law of gravity, based on mass and distance, are there any exceptions? Not to my knowledge, except possibly on galactic sized scales, black holes and probably on very, very small sub-atomic scales. In everyday life, Newton’s law works fine. How about Einstein’s theory of gravity (Relativity), any exceptions? None that I know of at any scale.

How about evolution? Species evolve, we can see that in the geological record. We can also watch it happen in some quickly reproducing species. Thus we could describe evolution as a fact. It happens, but we cannot describe how without more work. Early theories of the evolutionary process include Darwin’s theory of natural selection and Lamarck’s theory of heritable species adaptation due to external stresses. Due to epigenetic research we now know that Darwin and Lamarck were both right and that evolution involves both processes. For a summary of recent research into the epigenetic component of evolution see this Oxford Journal article. Thus well-established facts and scientific laws rarely change but theories do evolve. I might add that while facts and laws don’t often change, they are easily dismissed when contradictory data are gathered. The modern theory of evolution is a good example of where competing theories can merge into one.

Most scientific theories begin as hypotheses. A hypothesis is best described as an idea of what might be causing a specific event to occur. A proper scientific hypothesis, like a theory, must be falsifiable. That is, we must be able to design an experiment or foresee an observation that will make the hypothesis false. “Climate change” is not falsifiable, it is not a scientific hypothesis or a theory. “Man-made climate change” is a scientific hypothesis since it is falsifiable. Hypotheses and theories are evolving things, new facts and observations cause them to change. In this way we build the body of science. Science is mostly skepticism. We look for what does not fit, we poke at established facts and laws, at theories and hypotheses. We try and find flaws, we check the numbers. Worse, science done properly means we spend more time proving ourselves wrong than we do proving we are right. Life is tough sometimes.

So how does this fit with the great climate change debate. I’ve made a table of phrases and identified each common phrase as a fact, theory, law, hypothesis, or simply an idea. These are my classifications and certainly open for debate.

In Table 1 we can see that the comparison of man-made climate change and the possibility of a man-made climate catastrophe are not really comparable to the theories of gravity and evolution. Man-made climate change is more than an idea, it is based on some observations and reasonable models of the process have been developed and can be tested. But, none of the models have successfully predicted any climatic events. Thus, they are still a work-in-progress and not admissible as evidence supporting a scientific theory.

The idea of man-made climate change causing a catastrophe at the scale of Islamic terrorism is pure speculation. The models used to compute man’s influence don’t match any observations, this is easily seen in Figure 1 which is Dr. John Christy’s graph of the computer model’s predictions versus satellite and weather balloon observations. I should mention that satellite and weather balloon measurements are independent of one another and they are independent of the various surface temperature datasets, like HADCRUT and GHCN-M. All of the curves on the plot have been smoothed with five year averages.

The purple line going through the observations is the Russian model “INM-CM4.” It is the only model that comes close to reality. INM-CM4, over longer periods, does very well at hindcasting observed temperatures. This model uses a CO2 forcing response that is 37% lower than the other models, a much higher deep ocean heat capacity (climate system inertia) and it exactly matches lower tropospheric water content and is biased low above that. The other models are biased high. The model predicts future temperature increases at a rate of about 1K/century, not at all alarming and much lower than the predictions of the other models. (See Temperatures According to Climate Models)

One can consider each model to be a digital experiment. It is clear that the range of values from these digital experiments exceeds the predicted average temperature increase. This does not give us much confidence in the accuracy of the models. Yet, the IPCC uses the difference between the mean model temperature anomalies and observed surface temperatures since 1950 to compute man’s influence on climate.   (See Climate Models Explained)

In particular Soon, Connolly and Connolly (SCC15) believe that the IPCC chose an inappropriate model of the variation in the sun’s output (TSI or total solar irradiance). There are many models of solar variation in the peer reviewed literature and it is a topic of vigorous debate. Eight recent models are presented in Figure 8 of SCC15 (see Figure 3). Only low solar variability models (those on the right of Figure 3) are used by the IPCC to compute man’s influence on climate although just as much evidence exists for the higher variability models on the left. The scales used in the graphs are all the same, but the top and bottom values vary. At minimum, the IPCC should have run two cases, one for high variability and one for low. SCC15 clearly shows that the model used makes a big difference.

Any computer Earth model must establish a track record before it is used in calculations. The Earth is simply too complex and natural climate cycles are poorly understood. If natural cycles cannot be predicted they cannot be subtracted from observations to give us man’s influence on climate. The debate is not whether man influences climate, the debate is over how much man contributes and whether or not the additional warming dangerous. This observer, familiar with the science, would say the jury is still out. Certainly, the case for an impending catastrophe has not been made as this requires two speculative jumps. First, we need to assume that man is the dominant driver of climate, second we need to assume this will lead to a catastrophe. One can predict a possible catastrophe if the most extreme climate models are correct, but the record shows they are not. Only INM-CM4 matches observations reasonably well and INM-CM4 does not predict anything remotely close to a catastrophe.

In the study of the process of evolution the problem is the same. Some believe that the dominant process is natural selection and epigenetic change is minor. Some believe the opposite. Everyone believes that both play a role. As in climate science, figuring out which process is dominant is tough.

Recent climate history (the “pause” in warming) suggests that we have plenty of time to get our arms around this problem before doing anything drastic like destroying the fossil fuel industry and sending billions of people into poverty due to a lack of affordable energy.

Summary:  Scientific vs. Social Proof

In the IPCC reports certainty is presented in terms of social proofs. For example, an assertion is rated as Very High Confidence, or 95% Certain, meaning almost all consulted experts held that opinion. A claim rated as Moderate Confidence is in fact 50% Certain, meaning it is regarded as equally unlikely. Statements regarded as Low Confidence are thought to be so improbable that one wonders why they are presented. In any case, these are not scientific assessments, but rather opinion polling of people thought to be knowledgeable.

In contrast, Andy May demonstrates how scientific proof is obtained: A law or theory stands as along as no exceptions have been found. Or the law or theory is modified to stipulate more clearly under what conditions it operates. His classification of climate facts, theories and ideas sits well with me and helps to clarify what is presently known and unknown in this field.

As shown above, a theory or hypothesis falls if exceptions are observed.  Are there exceptions to the hypothesis of man-made global warming?  Yes, indeed there are many.  One of the most important disproofs (it only takes one) is actually provided by the climate models.

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.

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.

Full explanation at Warming from CO2 Unlikely

For more on measurements and science see Data, Facts and Information

 

Sorry Climate Science

h/t Scottish Skeptic for referring to this article by James Delingpole in the Sun UK

How scientists got their global warming sums wrong — and created a £1TRILLION-a-year green industry that bullied experts who dared to question the figures

The summary puts the point clearly.  The scientists who produce those doomsday reports for the Intergovernmental Panel on Climate Change finally come clean. The planet has stubbornly refused to heat up to predicted levels.  Read the whole thing.  Favorite excerpts below with my bolds.

I’VE just discovered the hardest word in science.

Not pneumonoultramicroscopicsilicovolcanoconiosis (inflammation of the lungs caused by inhalation of silica dust). Nor palmitoyloleoylphosphatidylethanolamine (a lipid bilayer found in nerve tissue).

No, the actual hardest word — which scientists use so rarely it might as well not exist — is “Sorry”.

Which is a shame because right now the scientists owe us an apology so enormous that I doubt even a bunch of two dozen roses every day for the rest of our lives is quite enough to make amends for the damage they’ve done.

Thanks to their bad advice on climate change our gas and electricity bills have rocketed.

So too have our taxes, our car bills and the cost of flying abroad, our kids have been brainwashed into becoming tofu-munching eco-zealots, our old folk have frozen to death in fuel poverty, our countryside has been blighted with ranks of space-age solar panels and bat-chomping, bird-slicing eco-crucifixes, our rubbish collection service hijacked by hectoring bullies, our cities poisoned with diesel fumes . . .

And all because a tiny bunch of ­scientists got their sums wrong and scared the world silly with a story about catastrophic man-made global warming.

This scare story, we now know, was at best an exaggeration, at worst a ­disgraceful fabrication. But while a handful of reviled and derided sceptics have been saying this for years, it’s only this week that those scientists have fessed up to their mistake.

One scientist has described the ­implications of the new Nature Geoscience report as “breathtaking”. He’s right. What it effectively does is scotch probably the most damaging ­scientific myth of our age — the notion that man-made carbon dioxide (CO2) is causing the planet to warm at such dangerous and ­unprecedented speeds that only massive government intervention can save us.

For a quarter of a century now — it all really got going in 1992 when 172 nations signed up to the Rio Earth Summit — our politicians have believed in and acted on this discredited theory.

Doomsday was predicted, but midnight passed without disaster.

In the name of saving the planet, war was declared on carbon dioxide, the benign trace gas which we exhale and which is so good for plant growth it has caused the planet to “green” by an extraordinary 14 per cent in the last 30 years.

This war on CO2 has resulted in a massive global decarbonisation industry worth around $1.5trillion (£1.11trillion) a year. Though it has made a handful of green crony capitalists very rich, it has made most of us much poorer, by forcing us to use expensive “renewables” instead of cheap, abundant fossil fuels.

So if the science behind all this ­nonsense was so dodgy, why did no one complain all these years?

Well, a few of us did. Some — such as Johnny Ball and David Bellamy — were brave TV celebrities, some — Graham Stringer, Peter Lilley, Owen Paterson, Nigel (now Lord) Lawson — were ­outspoken MPs, some were bona fide scientists. But whenever we spoke out, the response was the same — we were bullied, vilified, derided and dismissed as scientifically illiterate loons by a powerful climate alarmist establishment which brooked no dissent.

Unfortunately this alarmist establishment has many powerful media allies. The BBC has a huge roster of eco-activist reporters and science “experts” who believe in man-made global warming, and almost never gives sceptics air time.

It comes as little consolation to those of us who’ve been right all along to say: “I told you so.”

In the name of promoting the global warming myth, free speech has been curtailed, honest science corrupted and vast economic and social damage done. That ­apology is long overdue.

Footnote:

For a short course in how climate science was exploited, Richard Lindzen provides details and names in this post Climate Science Was Broken

Again Falsely Linking Smoking and Climate Science

Sarah Myhre is at it again, claiming climate science links storms to CO2 as certainly as smoking causes cancer.  Fossil fuel activists are obsessed with the smoking analogy, not least because oil companies have even deeper pockets than tobacco companies.

The analogy actually works against her on both sides.  Storms and CO2 are not correlated in the statistics, and she exaggerates the extent to which smoking results in cancer.  A previous post explains.

Original Post:  Climate Risky Business

A new theme emerging out of the IPCC Fifth Report was the emphasis on selling the risk of man-made climate change. The idea is that scientists should not advocate policy, but do have a responsibility to convince the public of the risks resulting from burning fossil fuels.

An article illustrates how this approach shapes recent public communications in support of actions on global warming/climate change.  Treading the Fine Line Between Climate Talk and Alarmism (Op-Ed)  By Sarah E. Myhre, Ph.D. | June 23, 2017.  Excerpts:

What is our role in public leadership as scientists? I would suggest a few action items: Work to reduce risk and cost for the public; steward the public’s interest in evidence; and be steady and committed to the scientific process of dissent, revision and discovery. This means communicating risk when necessary. We would never fault an oncologist for informing patients about the cancer risks that come with smoking. Why would we expect Earth scientists to be any different, when we’re just as certain?

As a public scholar with expertise in paleoclimate science, I communicate alarming, difficult information about the consequences to Earth and ocean systems that have come with past events of abrupt climate warming. As the saying goes, the past is the key to the future. 

We are living through a crisis of trust between the American public and climate scientists, and we must extend ourselves, as scientists and public servants, to rebuild transparency and trust with the public. I will start: I want the global community to mitigate the extreme risk of the warmest future climate scenarios. And, I want my kid to eat salmon and ski with his grandkids in the future. I am invested in that cooler, safer, more sustainable future — for your kids and for mine. Just don’t call me an alarmist.

This provides a teachable moment concerning the rhetorical maneuver to present climate as a risky business. The technique typically starts with a particular instance of actual risk and then makes a gross generalization so that the risk is exaggerated beyond reason.  From the article above:

Climate scientists are just as certain as oncologists are.

Herein lies the moral of this tale. The particular risk is the convincing epidemiological evidence linking lung cancer to smokers. The leap was claiming second-hand smoke puts non-smokers at risk of cancer. The statistical case was never conclusive, but the public was scared into enacting all kinds of smoke-free spaces.

Very few passive smoking/lung cancer studies are published these days compared to the glut of the 1980s and 1990s, but the handful that have appeared in recent years continue to support the null hypothesis. For all the campaigners’ talk of “overwhelming evidence”, the link between secondhand smoke and lung cancer has always been very shaky. It tends to be the smaller, case-control studies which find the associations while the larger, cohort studies do not (and, as the JNCI report notes, case-control studies “can suffer from recall bias: People who develop a disease that might be related to passive smoking are more likely to recall being exposed to passive smoking.”)

Gerard Silvestri, MD, of the Medical University of South Carolina, a member of NCI’s PDQ Screening and Prevention Editorial Board said (here):

“We’ve gotten smoking out of bars and restaurants on the basis of the fact that you and I and other nonsmokers don’t want to die,” said Silvestri. “The reality is, we probably won’t.”

To be clear, I don’t want smokers fouling my space in restaurants, and the policies are beneficial to me esthetically. But there was never any certainty about my risk of cancer, just the spoiling of clean air around me.  What was a matter of opinion and personal preference was settled politically by asserting scientific certainty of my health risk.

To draw the point finely, secondhand smoke shows how science is used by one group (anti-smoking activists) against another group (smokers) by mobilizing support for regulations on the basis of a generalized risk, raising concerns among the silent majority who otherwise were not particularly interested in the issue.

Climate as a Risky Business

Environmentalists have often employed risk exaggeration, beginning with Rachel Carson’s Silent Spring full of innuendo about DDT without any actual epidemiological proof. Currently Junk Science provides a list of EPA exaggerations about environmental pollution, for example The scientific fraud that claims air pollution is killing people

In the climate field, any flat Polynesian island is of course at risk of flooding, and thus by extension they produce images of Manhattan under water. Global risk is trumpeted, ignoring all the local particularities of land subsidence, tidal gauge records, terrain drainage features, infrastructure, precipitation patterns, etc.

Any storm, drought, flood, or unusual weather likewise presents a particular risk in the locale where it occurs. The gross exaggeration is to claim that we are increasing the risk of all these events, and by stopping burning fossil fuels we can prevent them from happening.

Sarah Myhre’s research focuses on ocean dead zones (oxygen-depleted waters), which is a real and long-studied risk. Then comes her leap into the fearful future:

The surface and deep ocean will continue to absorb heat and CO2 from the atmosphere. The heating of the ocean will increase the stratification of water (i.e. ocean mixing will be reduced, as will the strength of thermohaline circulation). Ocean heating will also drive the thermal expansion of the interior of the ocean – this is one of the primary contributors to sea level rise.

The absorption of CO2 from the atmosphere will drive changes in the chemistry of surface and deep waters – there are significant biological consequences to acidifying the global surface ocean. Basically, we are looking at the fundamental reorganization of biological communities and ecological provinces in the ocean. These physical drivers (warming, stratification, acidification) all area associated with significant biological consequences.

This is a continuation of a scare called Climate Change Is Suffocating The Oceans.  Once again climate alarmists/activists have seized upon an actual environmental issue, but misdirect the public toward their CO2 obsession, and away from practical efforts to address a real concern. Some excerpts from scientific studies serve to put things in perspective.  See Ocean Oxygen Misdirection

As a paleoclimate expert the author knows the climate and sea levels have changed many times in the past, and often shifted quickly in geological terms.

And yet the evidence shows clearly that CO2 follows as an effect of changing temperatures, not the cause.

Summary

Warmists are of the opinion that because of burning fossil fuels, our modern climate no longer compares to paleoclimates, a claim in fact that humans are overriding natural forces. But the message from the ice cores is clear: Through the ages, CO2 responds to temperatures and not the other way around.

The other message is also clear: Climates change between warm and cool, and warm has always been good for humans and the biosphere. We should concern ourselves with Adaptation, preparing for the cold times with robust infrastructure and reliable, affordable energy.

See also CO2 and Climate Change for the Ages

See also Claim: Fossil Fuels Cause Global Warming
Updated 2017  Fossil Fuels ≠ Global Warming

Footnote:

Actuaries are accountants specialized in risk statistics like morbidity and mortality, usually working in the insurance industry.

Question:  What is the difference between an Actuary and an Auditor?
Answer:  The Auditor is the one with a sense of humor.
(Old joke from days working at KPMG)

See also:  Cavemen Climate Comics

Autumnal Climate Change 2017

 

geese-in-v-formation

Seeing a lot more of this lately, along with hearing the geese  honking. And in the next month or so, we expect that trees around here will lose their leaves. It definitely is climate change of the seasonal variety.

Interestingly, the science on this is settled: It is all due to reduction of solar energy because of the shorter length of days (LOD). The trees drop their leaves and go dormant because of less sunlight, not because of lower temperatures. The latter is an effect, not the cause.

Of course, the farther north you go, the more remarkable the seasonal climate change. St. Petersburg, Russia has their balmy “White Nights” in June when twilight is as dark as it gets, followed by the cold, dark winter and a chance to see the Northern Lights.

And as we have been monitoring, the Arctic ice has been melting from sunlight in recent months, but will now begin to build again in the darkness to its maximum in March.

We can also expect in January and February for another migration of millions of Canadians (nicknamed “snowbirds”) to fly south in search of a summer-like climate to renew their memories and hopes. As was said to me by one man in Saskatchewan (part of the Canadian wheat breadbasket region): “Around here we have Triple-A farmers: April to August, and then Arizona.” Here’s what he was talking about: Quartzsite Arizona annually hosts 1.5M visitors, mostly between November and March.

Of course, this is just North America. Similar migrations occur in Europe, and in the Southern Hemisphere, the climates are changing in the opposite direction, Springtime currently. Since it is so obviously the sun causing this seasonal change, the question arises: Does the sunlight vary on longer than annual timescales?

The Solar-Climate Debate

And therein lies a great, enduring controversy between those (like the IPCC) who dismiss the sun as a driver of multi-Decadal climate change, and those who see a connection between solar cycles and Earth’s climate history. One side can be accused of ignoring the sun because of a prior commitment to CO2 as the climate “control knob”.

The other side is repeatedly denounced as “cyclomaniacs” in search of curve-fitting patterns to prove one or another thesis. It is also argued that a claim of 60-year cycles can not be validated with only 150 years or so of reliable data. That point has weight, but it is usually made by those on the CO2 bandwagon despite temperature and CO2 trends correlating for only 2 decades during the last century.

One scientist in this field is Nicola Scafetta, who presents the basic concept this way:

“The theory is very simple in words. The solar system is characterized by a set of specific gravitational oscillations due to the fact that the planets are moving around the sun. Everything in the solar system tends to synchronize to these frequencies beginning with the sun itself. The oscillating sun then causes equivalent cycles in the climate system. Also the moon acts on the climate system with its own harmonics. In conclusion we have a climate system that is mostly made of a set of complex cycles that mirror astronomical cycles. Consequently it is possible to use these harmonics to both approximately hindcast and forecast the harmonic component of the climate, at least on a global scale. This theory is supported by strong empirical evidences using the available solar and climatic data.”

He goes on to say:

“The global surface temperature record appears to be made of natural specific oscillations with a likely solar/astronomical origin plus a noncyclical anthropogenic contribution during the last decades. Indeed, because the boundary condition of the climate system is regulated also by astronomical harmonic forcings, the astronomical frequencies need to be part of the climate signal in the same way the tidal oscillations are regulated by soli-lunar harmonics.”

He has concluded that “at least 60% of the warming of the Earth observed since 1970 appears to be induced by natural cycles which are present in the solar system.” For the near future he predicts a stabilization of global temperature and cooling until 2030-2040.

 

For more see Scafetta vs. IPCC: Dueling Climate Theories

A Deeper, but Accessible Presentation of Solar-Climate Theory

I have found this presentation by Ian Wilson to be persuasive while honestly considering all of the complexities involved.

The author raises the question: What if there is a third factor that not only drives the variations in solar activity that we see on the Sun but also drives the changes that we see in climate here on the Earth?

The linked article is quite readable by a general audience, and comes to a similar conclusion as Scafetta above: There is a connection, but it is not simple cause and effect. And yes, length of day (LOD) is a factor beyond the annual cycle.

http://www.lavoisier.com.au/articles/greenhouse-science/solar-cycles/IanwilsonForum2008.pdf

It is fair to say that we are still at the theorizing stage of understanding a solar connection to earth’s climate. And at this stage, investigators look for correlations in the data and propose theories (explanations) for what mechanisms are at work. Interestingly, despite the lack of interest from the IPCC, solar and climate variability is a very active research field these days.

A summary of recent studies is provided at NoTricksZone: Since 2014, 400 Scientific Papers Affirm A Strong Sun-Climate Link

Ian Wilson has much more to say at his blog: http://astroclimateconnection.blogspot.com.au/

Once again, it appears that the world is more complicated than a simple cause and effect model suggests.

Fluctuations in observed global temperatures can be explained by a combination of oceanic and solar cycles.  See engineering analysis from first principles Quantifying Natural Climate Change.

For everything there is a season, a time for every purpose under heaven.

What has been will be again, what has been done will be done again;
there is nothing new under the sun.
(Ecclesiastes 3:1 and 1:9)

Original post in 2015 included this commentary with Dr. Arnd Bernaerts

ArndB comments:

Fine writing, Ron, well done!
No doubt the sun is the by far the most important factor for not living on a globe with temperatures down to minus 200°C. That makes me hesitating to comment on „solar and climate variability” or “the sun drives climate” (currently at NTZ – link above), but today merely requesting humbly that the claimed correlation should be based at least on some evidence showing that the sun has ever caused a significant climatic shift during the last one million years, which was not only a bit air temperature variability due to solar cycles that necessarily occur in correlation with the intake and release of solar-radiation by the oceans and seas.

Interestingly the UK MetOffice just released a report (Sept.2015, pages 21) titled:
“Big Changes Underway in the Climate System?” by attributing the most possible and likely changes to the current status of El Niño, PDO, and AMO, and – of course – carbon dioxide -, and a bit speculation on less sun-energy (see following excerpt at link)
http://www.metoffice.gov.uk/media/pdf/8/c/Changes_In_The_Climate_System.pdf

From p. 13: “It is well established that trace gases such as carbon dioxide warm our planet through the “greenhouse effect”. These gases are relatively transparent to incoming sunlight, but trap some of the longer-wavelength radiation emitted by the Earth. However, other factors, both natural and man-made, can also change global temperatures. For example, a cooling could be caused by a downturn of the amount of energy received from the sun, or an increase in the sunlight reflected back to space by aerosol particles in the atmosphere. Aerosols increase temporarily after volcanic eruptions, but are also generated by pollution such as sulphur dioxide from factories.
These “external” factors are imposed on the climate system and may also affect the ENSO, PDO and AMO variations……

My Reply:

Thanks Arnd for engaging in this topic.

My view is that the ocean makes the climate by means of its huge storage of solar energy, and the fluctuations, oscillations in the processes of distributing that energy globally and to the poles. In addition, the ocean is the most affected by any variation in the incoming solar energy, both by the sun outputting more or less, and also by clouds and aerosols blocking incoming radiation more or less (albedo or brightness variability).  See Nature’s Sunscreen

The oscillations you mention, including the present El Nino (and Blob) phenomenon, show natural oceanic variability over years and decades. Other ocean cycles occur over multi-decadal and centennial scales, and are still being analyzed.

At the other end of the scale, I am persuaded that the earth switches between the “hot house” and the “ice house” mainly due to orbital cycles, which are an astronomical phenomenon. These are strong enough to overwhelm the moderating effect of the ocean thermal flywheel.

The debate centers on the extent to which solar activity has contributed to climate change over the last 3000 years of our current interglacial period, including current solar cycles.

 

CO2 Also Explains Fair Weather?

Typical weather on Miami Beach

Ross McKitrick raises some interesting questions in his Washington Examiner article Despite Hurricanes Harvey and Irma, science has no idea if climate change is causing more (or fewer) powerful hurricanes  h/t GWPF

Why is global warming/climate change invoked only to explain bad weather (storms)? What about crediting CO2 for storms that didn’t happen? And how is a storm that could not be predicted proof of something after the fact? Do storms in 2017 fulfill predictions made every year since Katrina in 2005?  Excerpts below (my bolds)

After Hurricane Harvey hit Texas, it didn’t take long for climate alarmists to claim they knew all along it would happen. Politico’s Eric Holthaus declared “We knew this would happen, decades ago.” Naomi Klein stated “these events have long been predicted by climate scientists.” Joe Romm at ThinkProgress wrote, “the fact is that Harvey is exactly the kind of off-the-charts hurricane we can expect to see more often because of climate change.”

According to these and other authors, rising greenhouse gas levels are at least partly to blame for the occurrence and severity of Harvey, and probably for Hurricane Irma as well. But after-the-fact guesswork is not science. If any would-be expert really knew long ago that Harvey was on its way, let him or her prove it by predicting what next year’s hurricane season will bring.

Don’t hold your breath: Even the best meteorologists in the world weren’t able to predict the development and track of Hurricane Harvey until a few days before it hit.

This is why the idea of climate science being “settled” is so ludicrous, at least as regards the connection between global warming and tropical cyclones. A settled theory makes specific predictions that can, in principle, be tested against observed data. A theory that only yields vague, untestable predictions is, at best, a work in progress.

The climate alarmists offer a vague prediction: Hurricanes may or may not happen in any particular year, but when they do, they will be more intense than they would have been if GHG levels were lower. This is a convenient prediction to make because we can never test it. It requires observing the behaviour of imaginary storms in an unobservable world. Good luck collecting the data.

Climate scientists instead use computer models to simulate the alternative world. But the models project hundreds of possible worlds, and predict every conceivable outcome, so whatever happens it is consistent with at least one model run. After Hurricane Katrina hit New Orleans in 2005, some climate modelers predicted such storms would be more frequent in a warmer world, while others predicted the opposite, and still others said there was no connection between warming and hurricanes.

What ensued was an historically unprecedented 12-year absence of major (category 3 or higher) hurricanes making landfall in the United States, until Harvey, which ties for 14th-most intense hurricane since 1851. The events after 2005 were “consistent with” some projections, but any other events would have been as well.

The long absence of landfalling hurricanes also points to another problem when opinion writers connect GHGs to extreme weather. Science needs to be concerned not only with conspicuous things that happened, but with things that conspicuously didn’t happen. Like the famous dog in the Sherlock Holmes story, the bark that doesn’t happen can be the most important of all.

It is natural to consider a hurricane a disruptive event that demands an explanation. It is much more difficult to imagine nice weather as a disruption to bad weather that somehow never happened.

Suppose a hurricane would have hit Florida in August 2009, but GHG emissions prevented it and the weather was mild instead. The “event,” pleasant weather, came and went unnoticed and nobody felt the need to explain why it happened. It is a mistake to think that only bad events call for an explanation, and only to raise the warming conjecture when bad weather happens. If we are going to tie weather events to GHGs, we have to be consistent about it. We should not assume that any time we have pleasant weather, we were going to have it anyway, but a storm is unusual and proves GHG’s control the climate.

I am grateful to the scientists who work at understanding hurricane and typhoon events, and whose ability to forecast them days in advance has saved countless lives. But when opinion writers tacitly assume all good weather is natural and GHGs only cause bad weather, or claim to be able to predict future storms, but only after they have already occurred, I reserve the right to call their science unsettled.

Ross McKitrick is a professor of economics at the University of Guelph and an adjunct scholar of the Cato Institute.

Another claim people are making: Several major storms in a row for sure proves global warming/climate change.  Well, no.  Not according to Gerry Bell, the lead seasonal hurricane forecaster with the Climate Prediction Center, a part of the National Oceanic and Atmospheric Administration.  He explains in a NYT article First Harvey, Then Irma and Jose. Why? It’s the Season. h/t GWPF Excerpts below.

Hurricane experts say that the formation of several storms in rapid succession is not uncommon, especially in August, September and October, the most active months of the six-month hurricane season.

“This is the peak,” said Gerry Bell, the lead seasonal hurricane forecaster with the Climate Prediction Center, a part of the National Oceanic and Atmospheric Administration. “This is when 95 percent of hurricanes and major hurricanes form.”

Dr. Bell and his team at NOAA had forecast that this season would be a busy one, and that is how it is playing out, he said.

“With above normal seasons, you have even more activity mainly in August through October,” he said. “We’re seeing the activity we predicted.”

Dr. Bell said that in the late summer and early fall, conditions in the tropical Atlantic off Africa become just right for cyclonic storms to form. Among those conditions, he said, are warming waters, which fuel the growth of storms, and a relative lack of abrupt wind shifts, called wind shear, that tend to disrupt storm formation.

“There’s a whole combination of conditions that come together,” he said.

Storms that form in the Gulf of Mexico, as Katia did this week, are also not uncommon, Dr. Bell said.

Dr. Bell said his group does not consider climate change in developing its forecasts.

Instead, he said, they consider longer-term cycles of hurricane activity based on a naturally occurring climate pattern called the Atlantic multidecadal oscillation, which affects ocean surface temperatures over 25 to 40 years.

Footnote

boat-climate-change

Pleasure craft spotted in a marina near Miami.

X-Weather is Back! Harvey edition

With Hurricane Harvey making landfall in Texas as a Cat 4, the storm drought is over and claims of linkage to climate change can be expected.  So far (mercifully) articles in Time and Washington Post have been more circumspect than in the past.  Has it become more respectable to look at the proof supporting wild claims?  This post provides background on X-Weathermen working hard to claim extreme weather as proof of climate change.

In the past the media has been awash with claims of “human footprints” in extreme weather events, with headlines like these:

“Global warming is making hot days hotter, rainfall and flooding heavier, hurricanes stronger and droughts more severe.”

“Global climate change is making weather worse over time”

“Climate change link to extreme weather easier to gauge”– U.S. Report

“Heat Waves, Droughts and Heavy Rain Have Clear Links to Climate Change, Says National Academies”

That last one refers to a paper released by the National Academy of Sciences Press: Attribution of Extreme Weather Events in the Context of Climate Change (2016)

And as usual, the headline claims are unsupported by the actual text. From the NAS report (here): (my bolds)

Attribution studies of individual events should not be used to draw general conclusions about the impact of climate change on extreme events as a whole. Events that have been selected for attribution studies to date are not a representative sample (e.g., events affecting areas with high population and extensive infrastructure will attract the greatest demand for information from stakeholders) P 107

Systematic criteria for selecting events to be analyzed would minimize selection bias and permit systematic evaluation of event attribution performance, which is important for enhancing confidence in attribution results. Studies of a representative sample of extreme events would allow stakeholders to use such studies as a tool for understanding how individual events fit into the broader picture of climate change. P 110

Correctly done, attribution of extreme weather events can provide an additional line of evidence that demonstrates the changing climate, and its impacts and consequences. An accurate scientific understanding of extreme weather event attribution can be an additional piece of evidence needed to inform decisions on climate change related actions. P. 112

The Indicative Without the Imperative

extreme-weather-events

The antidote to such feverish reporting is provided by Mike Hulme in a publication: Attributing Weather Extremes to ‘Climate Change’: a Review (here).

He has an insider’s perspective on this issue, and is certainly among the committed on global warming (color him concerned). Yet here he writes objectively to inform us on X-weather, without advocacy: real science journalism and a public service, really. Excerpts below with my bolds.

Overview

In this third and final review I survey the nascent science of extreme weather event attribution. The article proceeds by examining the field in four stages: motivations for extreme weather attribution, methods of attribution, some example case studies and the politics of weather event Attribution.

The X-Weather Issue

As many climate scientists can attest, following the latest meteorological extreme one of the most frequent questions asked by media journalists and other interested parties is: ‘Was this weather event caused by climate change?’

In recent decades the meaning of climate change in popular western discourse has changed from being a descriptive index of a change in climate (as in ‘evidence that a climatic change has occurred’) to becoming an independent causative agent (as in ‘climate change caused this event to happen’). Rather than being a descriptive outcome of a chain of causal events affecting how weather is generated, climate change has been granted power to change worlds: political and social worlds as much as physical and ecological ones.

To be more precise then, what people mean when they ask the ‘extreme weather blame’ question is: ‘Was this particular weather event caused by greenhouse gases emitted from human activities and/or by other human perturbations to the environment?’ In other words, can this meteorological event be attributed to human agency as opposed to some other form of agency?

The Motivations

Hulme shows what drives scientists to pursue the “extreme weather blame” question, noting four motivational factors.

Why have climate scientists over the last ten years embarked upon research to provide an answer beyond the stock phrase ‘no individual weather event can directly be attributed to greenhouse gas emissions’?  There seem to be four possible motives.

1.Curiosity
The first is because the question piques the scientific mind; it acts as a spur to develop new rational understanding of physical processes and new analytic methods for studying them.

2.Adaptation
A second argument, put forward by some, is that it is important to know whether or not specific instances of extreme weather are human-caused in order to improve the justification, planning and execution of climate adaptation.

3.Liability
A third argument for pursuing an answer to the ‘extreme weather blame’ question is inspired by the possibility of pursuing legal liability for damages caused. . . If specific loss and damage from extreme weather can be attributed to greenhouse gas emissions – even if expressed in terms of increased risk rather than deterministically – then lawyers might get interested.

The liability motivation for research into weather event attribution also bisects the new international political agenda of ‘loss and damage’ which has emerged in the last two years. . . The basic idea is to give recognition that loss and damage caused by climate change is legitimate ground for less developed countries to gain access to new international climate adaptation funds.

4. Persuasion
A final reason for scientists to be investing in this area of climate science – a reason stated explicitly less often than the ones above and yet one which underlies much of the public interest in the ‘extreme weather blame’ question – is frustration with and argument about the invisibility of climate change. . . If this is believed to be true – that only scientists can make climate change visible and real –then there is extra onus on scientists to answer the ‘extreme weather blame’ question as part of an effort to convince citizens of the reality of human-caused climate change.

Attribution Methods

Attributing extreme weather events to human influences requires different approaches, of which four broad categories can be identified.

1. Physical Reasoning
The first and most general approach to attributing extreme weather phenomena to rising greenhouse gas concentrations is to use simple physical reasoning.

General physical reasoning can only lead to broad qualitative statements such as ‘this extreme weather is consistent with’ what is known about the human-enhanced greenhouse effect. Such statements offer neither deterministic nor stochastic answers and clearly underdetermine the ‘weather blame question.’ It has given rise to a number of analogies to try to communicate the non-deterministic nature of extreme event attribution. The three most widely used ones concern a loaded die (the chance of rolling a ‘6’ has increased, but no single ‘6’ can be attributed to the biased die), the baseball player on steroids (the number of home runs hit increases, but no single home run can be attributed to the steroids) and the speeding car-driver (the chance of an accident increases in dangerous conditions, but no specific accident can be attributed to the fast-driving).

2. Classical Statistical Analysis
A second approach is to use classical statistical analysis of meteorological time series data to determine whether a particular weather (or climatic) extreme falls outside the range of what a ‘normal’ unperturbed climate might have delivered.

All such extreme event analyses of meteorological time series are at best able to detect outliers, but can never be decisive about possible cause(s). A different time series approach therefore combines observational data with model simulations and seeks to determine whether trends in extreme weather predicted by climate models have been observed in meteorological statistics (e.g. Zwiers et al., 2011, for temperature extremes and Min et al., 2011, for precipitation extremes). This approach is able to attribute statistically a trend in extreme weather to human influence, but not a specific weather event. Again, the ‘weather blame question’ remains underdetermined.

slide20

3. Fractional Attributable Risk (FAR)
Taking inspiration from the field of epidemiology, this method seeks to establish the Fractional Attributable Risk (FAR) of an extreme weather (or short-term climate) event. It asks the counterfactual question, ‘How might the risk of a weather event be different in the presence of a specific causal agent in the climate system?’

The single observational record available to us, and which is analysed in the statistical methods described above, is inadequate for this task. The solution is to use multiple model simulations of the climate system, first of all without the forcing agent(s) accused of ‘causing’ the weather event and then again with that external forcing introduced into the model.

The credibility of this method of weather attribution can be no greater than the overall credibility of the climate model(s) used – and may be less, depending on the ability of the model in question to simulate accurately the precise weather event under consideration at a given scale (e.g. a heatwave in continental Europe, a rain event in northern Thailand) (see Christidis et al., 2013a).

4. Eco-systems Philosophy
A fourth, more philosophical, approach to weather event attribution should also be mentioned. This is the argument that since human influences on the climate system as a whole are now clearly established – through changing atmospheric composition, altered land surface characteristics, and so on – there can no longer be such a thing as a purely natural weather event. All weather — whether it be a raging tempest or a still summer afternoon — is now attributable to human influence, at least to some extent. Weather is the local and momentary expression of a complex system whose functioning as a system is now different to what it would otherwise have been had humans not been active.

Results from Weather Attribution Studies

Hulme provides a table of numerous such studies using various methods, along with his view of the findings.

It is likely that attribution of temperature-related extremes using FAR methods will always be more attainable than for other meteorological extremes such as rainfall and wind, which climate models generally find harder to simulate faithfully at the spatial scales involved. As discussed below, this limitation on which weather events and in which regions attribution studies can be conducted will place important constraints on any operational extreme weather attribution system.

Political Dimensions of Weather Attribution

Hulme concludes by discussing the political hunger for scientific proof in support of policy actions.

But Hulme et al. (2011) show why such ambitious claims are unlikely to be realised. Investment in climate adaptation, they claim, is most needed “… where vulnerability to meteorological hazard is high, not where meteorological hazards are most attributable to human influence” (p.765). Extreme weather attribution says nothing about how damages are attributable to meteorological hazard as opposed to exposure to risk; it says nothing about the complex political, social and economic structures which mediate physical hazards.

And separating weather into two categories — ‘human-caused’ weather and ‘tough-luck’ weather – raises practical and ethical concerns about any subsequent investment allocation guidelines which excluded the victims of ‘tough-luck weather’ from benefiting from adaptation funds.

Contrary to the claims of some weather attribution scientists, the loss and damage agenda of the UNFCCC, as it is currently emerging, makes no distinction between ‘human-caused’ and ‘tough-luck’ weather. “Loss and damage impacts fall along a continuum, ranging from ‘events’ associated with variability around current climatic norms (e.g., weather-related natural hazards) to [slow-onset] ‘processes’ associated with future anticipated changes in climatic norms” (Warner et al., 2012:21). Although definitions and protocols have not yet been formally ratified, it seems unlikely that there will be a role for the sort of forensic science being offered by extreme weather attribution science.

Conclusion

Thank you Mike Hulme for a sane, balanced and expert analysis. It strikes me as being another element in a “Quiet Storm of Lucidity”.

Is that light the end of the tunnel or an oncoming train?