Warming from CO2 Unlikely

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. 

A recent post at Friends of Science alerted me to an important proof against the CO2 global warming claim. It was included in John Christy’s testimony 29 Mar 2017 at the House Committee on Science, Space and Technology. The text below is from that document which can be accessed here. (My bolds)

Main Point: 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.

(5)  A simple statistical model that passed the same “scientific-method” test

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. I was coauthor of a report which produced such an analysis (Wallace, J., J. Christy, and J. D’Aleo, “On the existence of a ‘Tropical Hot Spot’ & the validity of the EPA’s CO2 Endangerment Finding – Abridged Research Report”, August 2016 (Available here ).

In this report we examine annual estimates from many sources of global and tropical deep-layer temperatures since 1959 and since 1979 utilizing explanatory variables that did not include rising CO2 concentrations. We applied the model to estimates of global and tropical temperature from the satellite and balloon sources, individually, shown in Fig. 2 above. The explanatory variables are those that have been known for decades such as indices of El Nino-Southern Oscillation (ENSO), volcanic activity, and a solar activity (e.g. see Christy and McNider, 1994, “Satellite greenhouse signal”, Nature, 367, 27Jan). [One of the ENSO explanatory variables was the accumulated MEI (Multivariate ENSO Index, see https://www.esrl.noaa.gov/psd/enso/mei/) in which the index was summed through time to provide an indication of its accumulated impact. This “accumulated-MEI” was shown to be a potential factor in global temperatures by Spencer and Braswell, 2014 (“The role of ENSO in global ocean temperature changes during 1955-2011 simulated with a 1D climate model”, APJ.Atmos.Sci. 50(2), 229-237, DOI:10.1007/s13143-014- 001-z.) Interestingly, later work has shown that this “accumulated-MEI” has virtually the same impact as the accumulated solar index, both of which generally paralleled the rise in temperatures through the 1980s and 1990s and the slowdown in the 21st century. Thus our report would have the same conclusion with or without the “accumulated-MEI.”]

The basic result of this report is that the temperature trend of several datasets since 1979 can be explained by variations in the components that naturally affect the climate, just as the IPCC inadvertently indicated in Fig. 5 above. The advantage of the simple statistical treatment is that the complicated processes such as clouds, ocean-atmosphere interaction, aerosols, etc., are implicitly incorporated by the statistical relationships discovered from the actual data. Climate models attempt to calculate these highly non-linear processes from imperfect parameterizations (estimates) whereas the statistical model directly accounts for them since the bulk atmospheric temperature is the response-variable these processes impact. It is true that the statistical model does not know what each sub-process is or how each might interact with other processes. But it also must be made clear: it is an understatement to say that no IPCC climate model accurately incorporates all of the non-linear processes that affect the system. I simply point out that because the model is constrained by the ultimate response variable (bulk temperature), these highly complex processes are included.

The fact that this statistical model explains 75-90 percent of the real annual temperature variability, depending on dataset, using these influences (ENSO, volcanoes, solar) is an indication the statistical model is useful. In addition, the trends produced from this statistical model are not statistically different from the actual data (i.e. passing the “scientific-method” trend test which assumes the natural factors are not influenced by increasing GHGs). This result promotes the conclusion that this approach achieves greater scientific (and policy) utility than results from elaborate climate models which on average fail to reproduce the real world’s global average bulk temperature trend since 1979.

The over-warming of the atmosphere by the IPCC models relates to a problem the IPCC AR5 encountered elsewhere. In trying to determine the climate sensitivity, which is how sensitive the global temperature is relative to increases in GHGs, the IPCC authors chose not to give a best estimate. [A high climate sensitivity is a foundational component of the last Administration’s Social Cost of Carbon.] The reason? … climate models were showing about twice the sensitivity to GHGs than calculations based on real, empirical data. I would encourage this committee, and our government in general, to consider empirical data, not climate model output, when dealing with environmental regulations.


Planning requires assumptions because no one has knowledge of the future, only informed opinions.  Christy makes the case that our assumptions should be based on empirical data rather than models that are driven by theoretical assumptions.  When the CO2 sensitivity assumption is removed from climate models they come much closer to observed temperature measurements.  Statistical analysis shows that at least 75% of observed warming comes from factors other than CO2.  That analysis also correlates with the accumulated effects of oceanic circulations, principally the ENSO index.

Canada  Ends Probe Against Climate Change Doubters

Billboard in Calgary Alberta gives rise to complaint to Canada’s Competition Bureau.

The vigilantes report on their setback in this National Observer article Feds halt probe of climate doubters.  Some excerpts give the flavor of their perspective.

After more than a year of investigating, the Competition Bureau abruptly dropped its inquiry earlier this summer into three groups that had displayed information in public raising doubts about the international scientific consensus on climate change.

The groups put up websites and billboards that promoted statements like, “the sun is the main driver of climate change,” and “carbon dioxide is not a pollutant,” according to an application filed with the bureau, a Canadian independent law enforcement agency.

Those views are contrary to mainstream climate science and the government of Canada itself, which states that “changes in solar irradiance have contributed to climate trends over the past century but since the industrial revolution, the effect of additions of greenhouse gases to the atmosphere has been over 50 times that of changes in the sun’s output.”

From the Bureau letter to the Complainant:

The inquiry concerned allegations that Friends of Science Society, International Climate
Science Coalition and Heartland Institute made misleading representations regarding climate change on their respective websites and, in the case of Friends of Science Society, on billboards.

At this time, considering the available evidence, the assessment of the facts in this case, and
to ensure the effective allocation of limited resources, the Commissioner has discontinued the

A statement was provided to National Observer by Tom Harris, executive director of the International Climate Science Coalition, but was incompletely quoted in the article. The full statement posted at International Science Coalition is below:

“The complainants apparently think that they know the truth about the causes and consequences of climate change. But this makes no sense. This is not just because the science is enormously complicated with scientists holding at times radically different points of view. It is also because scientific hypotheses, and even scientific theories, are not truth; they can be, and often are, wrong. Truth applies to things like mathematics or chess in which we write the rules. But truth never applies to our findings about nature, which are merely educated opinions based on scientists’ interpretations of observations.”

“When authorities promote truth about science, progress stops. I am glad the Competition Bureau did not fall into this trap. Its time to open up, not shut down, the debate about climate change, one of the most complex and costly issues of our age.”

More complete account of this encounter from Friends of Science:  Responding to the National Observer Article on the Competition Bureau/Ecojustice Call for Inquiry

Key Points:

  • First, Ecojustice asked for an honest debate. We provided one.[1] They never responded.
  • Secondly, presently there is no such thing as a ‘low-carbon economy’ because everything in the modern world is made from or powered by fossil fuels – even hydro dams, nuclear facilities and geothermal require fossil fuels to create the power plant.
  • Thirdly, climate change is mostly driven by natural factors and might only be marginally mitigated by the Paris Agreement.
  • Fourthly, despite claiming humans caused most of the recent warming, the IPCC, perhaps inadvertently, revealed in its 2013 AR5 report that climate change was mostly due to natural factors. (This point is more fully explained in post Warming from CO2 Unlikely
  • Finally, the Paris Agreement targets are unattainable and if attempted, would lead to the complete destruction of the Canadian economy. This is contrary to the UN Charter’s explicit principles. 

These are our opinions, protected by the Charter of Rights.

For examples of proven false advertising by climate activists, see Truth in Climate Advertising


The complaint was filed by an “ecojustice” lawyer. Free speech about a matter of opinion is attacked as false advertising. Sad. We are still waiting for the evidence supporting global warming claims. Keep on firing away, Tom Harris and Friends of Science.



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.



Pleasure craft spotted in a marina near Miami.

Sept. Weather Forecast Arctic & NH

aer Atmospheric and Environmental Research

September 5, 2017 Dr. Judah Cohen of AER posted his monthly forecast for the Arctic and NH based on the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO).  Excerpts below.

The AO is currently slightly negative (Figure 1), reflective of mostly positive geopotential height anomalies across the Arctic and mixed geopotential height anomalies across the mid-latitudes of the NH (Figure 2). Geopotential height anomalies are mostly negative across Greenland and Iceland (Figure 2), and therefore the NAO is slightly positive.
Figure 1. (a) The predicted daily-mean near-surface AO from the 00Z 5 September 2017 GFS ensemble. Gray lines indicate the AO index from each individual ensemble member, with the ensemble-mean AO index given by the red line with squares.

The AO is predicted to straddle neutral next week as geopotential height anomalies remain mixed across the Arctic. Similarly, with mixed geopotential height anomalies stretching across Greenland and Iceland, the NAO will likely be near neutral as well.  

(Note: AO and NAO are signed differently than one might expect; the reference point is outside the Arctic itself.  Thus negative phases of these indices mean higher pressures in the Arctic and lower outside, while positive phases indicate lower pressures in the Arctic.  Now that the Arctic sun is setting, the main issue for ice extent is storminess which requires low Arctic pressures.)


It is the first week of fall, a season of transition from summer to winter. One important sign IMO of this seasonal transition is the return of the polar vortex in the stratosphere. The models predict the possible formation of the polar vortex sometime next week. Starting in October, I will be watching variability in the polar vortex for signs of pattern changes in the weather across the NH.

Another sign of the seasonal transition is the minimum in Arctic sea ice extent, which will be achieved in the coming days and/or weeks. The trajectory of sea ice melt has slowed since early August. In my last blog I suggested the possibility that the sea ice minimum could be similar to the years 2008 and 2010 and that is looking more likely but is difficult to predict. Over the coming months, I will be following Arctic sea ice variability for signs of the severity of the upcoming winter. Our understanding for how anomalies in sea ice extent influence the weather in the mid-latitudes is still immature IMO but I do think that important progress has been made recently.

Another sign of the transition from summer to winter is the return of snowfall to the NH. Snowfall over the sea ice in August probably helped retard the melt of sea ice and snowfall is now even occurring over Siberia and Alaska but is still very regionalized. Again I will be monitoring the advance of snow cover extent across the continents for signs of the strength of the polar vortex and the possible resultant weather.

Finally I find it interesting that while the atmospheric circulation has transitioned from the dominant summer pattern across Eurasia it has not across North America. The dominant summer pattern across Eurasia was ridging across Europe (with the exception of Northern Europe) and East Asia but with troughing in Western Asia. The forecast for the coming weeks is the opposite with troughs across Europe and East Asia but ridging in Western Asia. This is an overall cooler pattern than the dominant summer pattern. However across North America there are no similar signs of transition. The dominant summer pattern was strong ridging across western North America and troughing in eastern North America and at least for now that pattern looks to continue for much of the month of September. I don’t know the reason behind the persistent western ridge/eastern trough pattern across North America but how long this pattern can persist will obviously have important implications for the weather across North America in the coming months.


Bottom line, looks like September weather will be ordinary in the Arctic with seasonal cooling in the NH.  Dr. Cohen also thinks the annual ice extent minimum will be near average for the decade.



Arctic Ice Uncertainties

Northern Hemisphere Spatial Coverage

As noted in the September Outlook Arctic Ice, NOAA’s Sea Ice Index (SII) typically shows less ice than MASIE from National Ice Center (NIC). SII is a satellite product processed from passive microwave sensors. MASIE (Multisensor Analyzed Sea Ice Extent) adds other sources such as satellite imagery and field observations to produce high resolution ice charts for navigational purposes.

A post in 2016 NOAA Is Losing Arctic Ice showed how discrepancies between the two datasets vary considerably throughout the year, usually lower in SII except for October. Walt Meier directs the SII production and published a study in October 2015 comparing SII and MASIE, also discussed in that post.

In 2016 NOAA upgraded from SII version 1 to version 2, and later to version 2.1. The latest documentation says few datapoints were changed in v2.1, and that anomalies were unchanged. My cursory look seemed to confirm that. However, on closer inspection, there are significant differences between v1 and v2 (which carry over to v2.1). This post describes those differences.

I prepared two spreadsheet arrays for SIIv1 and SIIv2.1 and then a third array to calculate the differences. The graph below shows the results for 2006 to 2015 inclusive, being the years for which datapoints can be compared with MASIE.

It is clear that V2.1 is systematically lower than V1, on average -200k km2. The differences are less than 100k km2 the first four months, then increase May, June, July, before shrinking again in August and September. The big changes come in the last months, especially October. The October correction is not surprising. The comparison by Meier and in my post discussed large SII surpluses over MASIE in October that did not appear credible.

The graph is limited to one decade since that is the period to be compared with MASIE. The spreadsheet shows that the differences are typical of the whole dataset going back to 1979, albeit with considerable variety through the years. The graph below shows the month by month differences for all years through 2015.

As stated before, the average all years difference in green is comparable to the last decade. Differences were calculated by subtracting v1 from v2, since v2 is mostly lower. However, as the Min Diff line shows, v2 was higher for some datapoints, notably in July. The Max Diff shows that some Octobers were changed by as much as 1M km2. The dotted lines show the standard deviation for the average differences, which averaged  +/- 90k km2.


It is challenging to estimate Arctic ice extents. NOAA is to be commended for recognizing the erroneous October values, and correcting them. Clearly some of that overall diminishing of extents by 200k km2 derives from removing the bogus surpluses.

Those claiming that SII is for certain and MASIE is dubious need to reconsider. MASIE has its own challenges but is reasonably consistent in recent years. Meanwhile SII had to improve its product, resulting in changes to past values in the dataset. While error ranges are not available for these statistics, the standard deviation gives some indication of the variability in the estimates.

Fortunately, it appears that the critical months of March and September have not changed much in the new SII version.  However, it is not encouraging to see SII averages for the last two months -500k km2 below MASIE.  See September Outlook Arctic Ice

It is a good thing that several agencies and methods are involved in the effort to measure and understand Arctic ice dynamics. It is not good to claim certainty for a single record or to ignore the errors that are found along the way. It is wise to remember that measuring anything in the Arctic is difficult.

September Outlook Arctic Ice

2017: August Report from Sea Ice Prediction Network

For the August Report there were 37 contributions with the median Outlook value for September 2017 Arctic sea ice extent of 4.5 million square kilometers with quartiles of 4.2 and 4.8 million square kilometers (See Figure 1 in the Overview section, below). These values are unchanged from the July Report, which is consistent with the moderating impact of summer 2017 Arctic weather. The range is 3.1 to 5.5 million square kilometers in August, unchanged from the July Outlook. To place this Outlook in context, recently observed values were 4.3 million square kilometers in 2007, 3.6 million square kilometers in 2012, and 4.7 million square kilometers in 2016. 

These are predictions for the September 2017 monthly average ice extent as reported by NOAA Sea Ice Index (SII). This post provides a look at the 2017 Year To Date (YTD) based on monthly averages comparing MASIE and SII datasets. (10 year average is 2007 to 2016 inclusive)

The graph puts 2017 into recent historical perspective. Note how 2017 was below the 10-year average for the first 4 months, then recovered to match average in May and has maintained average through August. The outlier 2012 provided the highest March maximum as well as the lowest September minimum, coinciding with the Great Arctic Cyclone that year.  2007 began the decade with the lowest minimum except for 2012.  SII 2017 is running below MASIE and is currently just below MASIE 2007 and 2012.

The table below provides the numbers for comparisons.

Monthly 2017 2017 2017 2017-10yr Ave 2017-10yr Ave 2017-
Jan 13.503 13.174 -0.329 -0.418 -0.512 -0.259
Feb 14.478 14.112 -0.366 -0.363 -0.440 -0.173
Mar 14.509 14.273 -0.236 -0.544 -0.542 -0.114
Apr 13.941 13.760 -0.180 -0.412 -0.446 0.246
May 12.838 12.618 -0.220 0.075 -0.138 0.412
June 10.975 10.720 -0.255 0.069 -0.218 0.148
July 8.383 7.901 -0.482 0.024 -0.206 0.367
Aug 6.006 5.472 -0.533 0.051 -0.185 0.421

The first two columns are the 2017 YTD shown by MASIE and SII, with the SII deficits in column three.  The difference has doubled the last two months and averaged -325k km2 for the YTD. Column four shows MASIE 2017 compared to MASIE 10 year average, while column five shows SII 2017 compared to SII 10 year average.  YTD MASIE is -190k km2 to average and SII is -336k km2 to average.  The last column shows MASIE 2017 holding an August surplus of 421k km2 over 2007.  For the YTD 2017 is 131k km2 higher than 2007, overcoming this year’s deficits in the early months.

For more on SII versions 1 and 2 differences see Arctic Ice Uncertainties


The experts involved in SIPN are expecting SII 2017 September to be higher than 2007 and slightly lower than 2016.  The way MASIE is going, this September looks to go higher than 2016 unless some bad weather intervenes.


Some people unhappy with the higher amounts of ice extent shown by MASIE continue to claim that Sea Ice Index is the only dataset that can be used. This is false in fact and in logic. Why should anyone accept that the highest quality picture of ice day to day has no shelf life, that one year’s charts can not be compared with another year? Researchers do this, including Walt Meier in charge of Sea Ice Index. That said, I understand his interest in directing people to use his product rather than one he does not control. As I have said before:

MASIE is rigorous, reliable, serves as calibration for satellite products, and continues the long and honorable tradition of naval ice charting using modern technologies. More on this at my post Support MASIE Arctic Ice Dataset

MASIE: “high-resolution, accurate charts of ice conditions”
Walt Meier, NSIDC, October 2015 article in Annals of Glaciology.

Arctic Ice September Strong

Click on image to enlarge.

The image above shows ice extents for yesterday, day 244, from 2007 to 2017.  Particularly interesting is the variation in the CAA (Canadian Arctic  Archipelago), crucial for the Northwest Passage.  (The region is located north of the word “Extent” in gold.)  While 2016 was a fine year for cruising with the passage completely open at day 244 that was not the case in 2014, and this year also has places frozen solid. By September 1, ice is still clogging some channels.

The graph of August NH ice extents shows 2017 has remained above the decadal average in recent days. (Ten-year average is for 2007 to 2016 inclusive).

This year is now 600k km2 greater than 2016 and exceeds the 10 year average by 50k km2.  SII (Sea Ice Index) 2017 is closer now, only 200k km2 lower.  2007 is running 400k km2 lower.  A previous post Beware the Arctic Storms of August discussed how late summer storms have dramatic impacts, and the graph shows both 2012 and 2016 plummeting in late August.  Note that just 2 weeks ago 2012 was tied with 2017, and then lost 1.6M km2.  2016 lost 1.3M km2 in the same period.

The table below compares 2017 with 2007 and the 10-year averages for Arctic regions.

Region 2017244 Day 244
2017-Ave. 2007244 2017-2007
 (0) Northern_Hemisphere 4934548 4884191 50357 4525136 409412
 (1) Beaufort_Sea 424479 542957 -118477 629454 -204974
 (2) Chukchi_Sea 204972 225008 -20036 96232 108740
 (3) East_Siberian_Sea 314746 348577 -33831 196 314550
 (4) Laptev_Sea 216679 182883 33796 245578 -28899
 (5) Kara_Sea 34099 45628 -11528 74307 -40208
 (6) Barents_Sea 16638 23603 -6965 11061 5577
 (7) Greenland_Sea 142702 183941 -41239 288223 -145521
 (8) Baffin_Bay_Gulf_of_St._Lawrence 55689 24864 30825 32804 22885
 (9) Canadian_Archipelago 384879 294120 90759 234389 150490
 (10) Hudson_Bay 3848 23575 -19727 28401 -24553
 (11) Central_Arctic 3135439 2988097 147342 2883201 252238

2017 has deficits mainly in BCE, especially Beaufort Sea, but those are more than offset by surpluses in Central Arctic and CAA (Canadian Arctic Archipelago).  As shown in the post Arctic Heart Beat Central Arctic and CAA are the two regions providing most of the ice extent at annual minimum.


Some people unhappy with the higher amounts of ice extent shown by MASIE continue to claim that Sea Ice Index is the only dataset that can be used.  This is false in fact and in logic.  Why should anyone accept that the highest quality picture of ice day to day has no shelf life, that one year’s charts can not be compared with another year?  Researchers do this, including Walt Meier in charge of Sea Ice Index.  That said, I understand his interest in directing people to use his product rather than one he does not control.  As I have said before:

MASIE is rigorous, reliable, serves as calibration for satellite products, and continues the long and honorable tradition of naval ice charting using modern technologies.  More on this at my post Support MASIE Arctic Ice Dataset

Bret Stephens on Harvey: Wealth and Resilience

Bret Stephens again writes insightfully regarding society and climate matters, this time in his recent article Hurricanes, climate and the capitalist offset  published in NYT reprinted in Tampa Bay Times.  Entire text below.

Texans will find few consolations in the wake of a hurricane as terrifying as Harvey. But here, at least, is one: A biblical storm has hit them, and the death toll — 38 as of this writing — is mercifully low, given its intensity.

This is not how it plays out in much of the world. In 1998, Hurricane Mitch ripped through Central America and killed anywhere between 11,000 and 19,000 people, mostly in Honduras and Nicaragua. Nearly a decade later Cyclone Nargis slammed into Myanmar, and a staggering 138,000 people perished.

Nature’s furies — hurricanes, earthquakes, landslides, droughts, infectious diseases, you name it — may strike unpredictably. But their effects are not distributed at random.

Rich countries tend to experience, and measure, the costs of such disasters primarily in terms of money. Poor countries experience them primarily in terms of lives. Between 1940 and 2016, a total of 3,348 people died in the United States on account of hurricanes, according to government data, for an average of 43 victims a year. That’s a tragedy, but compare it to the nearly 140,000 lives lost when a cyclone hit Bangladesh in 1991.

Why do richer countries fare so much better than poorer ones when it comes to natural disasters? It isn’t just better regulation. I grew up in Mexico City, which adopted stringent building codes following a devastating earthquake in 1957. That didn’t save the city in the 1985 earthquake, when we learned that those codes had been flouted for years by lax or corrupt building inspectors, and thousands of people were buried under the rubble of shoddy construction. Regulation is only as good, or bad, as its enforcement.

A better answer lies in the combination of government responsiveness and civic spiritedness so splendidly on display this week in Texas. And then there’s the matter of wealth.

Every child knows that houses of brick are safer than houses of wood or straw — and therefore cost more to build. Harvey will damage or ruin thousands of homes. But it won’t sweep away entire neighborhoods, as Typhoon Haiyan did in the Philippine city of Tacloban in 2013.

Harvey will also inflict billions in economic damage, most crushingly on uninsured homeowners. The numbers are likely to be staggering in absolute terms, but what’s more remarkable is how easily the U.S. economy can absorb the blow. The storm will be a “speed bump” to Houston’s $503 billion economy, according to Moody’s Analytics’ Adam Kamins, who told the Wall Street Journal that he expects the storm to derail growth for about two months.

On a global level, the University of Colorado’s Roger Pielke Jr. notes that disaster losses as a percentage of the world’s GDP, at just 0.3 percent, have remained constant since 1990. That’s despite the dollar cost of disasters having nearly doubled over the same time — at just about the same rate as the growth in the global economy. (Pielke is yet another victim of the climate lobby’s hyperactive smear machine, but that doesn’t make his data any less valid.)

Climate activists often claim that unchecked economic growth and the things that go with it are principal causes of environmental destruction. In reality, growth is the great offset. It’s a big part of the reason why, despite our warming planet, mortality rates from storms have declined from 0.11 per 100,000 in the 1900s to 0.04 per 100,000 in the 2010s, according to data compiled by Hannah Ritchie and Max Roser. Death rates from other natural disasters such as floods and droughts have fallen by even more staggering percentages over the last century.

That’s because economic growth isn’t just a matter of parking lots paving over paradise. It also underwrites safety standards, funds scientific research, builds spillways and wastewater plants, creates “green jobs,” subsidizes Elon Musk, sets aside prime real estate for conservation, and so on. Poverty, not wealth, is the enemy of the environment. Only the rich have the luxury of developing an ethical stance toward their trash.

The paradox of our time is that the part of the world that has never been safer from the vagaries of nature seems never to have been more terrified of them. Harvey truly is an astonishing storm, the likes of which few people can easily remember.

Then again, as meteorologist Philip Klotzbach points out, it’s also only one of four Category 4 or 5 hurricanes to make landfall in the United States since 1970. By contrast, more than twice as many such storms made landfall between 1922 and 1969. Make of that what you will, but remember that fear is often a function of unfamiliarity.

Houston will ultimately recover from Harvey’s devastation because its people are creative and courageous. They will rebuild and, when the next storm comes, as it inevitably will, be better prepared for it. The best lesson the world can take from Texas is to follow the path of its extraordinary economic growth on the way to environmental resilience.

Stephens is one of a few journalists who writes lucidly about weather and climate.  Let’s see how the progressive NYT readers enjoy his clarity.

Update: What Stephens writes is largely confirmed by a news report today from the Mayor of Houston. Houston Mayor Sylvester Turner said Thursday that the city is now “mostly dry,” after having taken an aerial tour of the city.  He also said: “One year from now when people visit Houston, they will not see signs of Harvey.”

See also Climate Adaptive Cities: The Smart Way Forward