Updated: Global Warming Ends 2021

The animation is an update of a previous analysis from Dr. Murry Salby.  These graphs use Hadcrut4 and include the 2016 El Nino warming event.  The exhibit shows since 1947 GMT warmed by 0.8 C, from 13.9 to 14.7, as estimated by Hadcrut4.  This resulted from three natural warming events involving ocean cycles. The most recent rise 2013-16 lifted temperatures by 0.2C.  Previously the 1997-98 El Nino produced a plateau increase of 0.4C.  Before that, a rise from 1977-81 added 0.2C to start the warming since 1947.

Importantly, the theory of human-caused global warming asserts that increasing CO2 in the atmosphere changes the baseline and causes systemic warming in our climate.  On the contrary, all of the warming since 1947 was episodic, coming from three brief events associated with oceanic cycles. Moreover, the UAH record shows that the effects of the last one are now gone as of January 2021.

The 2016 El Nino persisted longer than 1998, and was followed by warming after effects in NH.  The monthly anomaly as 2021 begins is nearly the 0.18C average since 1995, an ENSO neutral year prior to the second warming event discussed above. With a quiet sun and cooling oceans, the prospect is for cooler times ahead.

Postscript:  Article by Dr. Arnd Bernaerts regarding ENSO and Climate Models

At Oceans Govern Climate Arnd writes Instead of El Niño, La Niña 2020/21 came. 

He summarizes in this way (in italics with my bolds):

Although ENSO is a long-known climate phenomenon, climatologists still follow the view of the meteorologists 100 years ago, according to which the atmosphere is at the center of all-weather events. They are generously willing to acknowledge that the oceans play an important role, but not that ocean temperatures and their contribution to atmospheric humidity are the most crucial factors. This can be seen in the example of ENSO. Although small in oceanic proportions, the weather above can have long distance effects. Once it happen, e.g. due to a lack of trade winds, the triggering cause remains the changes in equatorial water temperatures.

The attempt to use computer models and weather observation data, by atmosphere-ocean coupling, ENSO forecasts failed with the 2020/2021 forecast and will not achieve what would be necessary in the future either.

What is needed is twofold: (a) much more ocean data , and (b) acknowledging the supremacy of the oceans in climatic change matters. 

No ocean area is as intensive observed as the Equatorial Eastern Pacific (EEP), well over 40 years. Since recently the Tropical Pacific Observing System, TPOS 2020, sustained sampling network is the “backbone” of the system, (Details: WMO). Whether this system can even provide nearly enough oceanic data to make predictions about what is going on under the sea surface cannot be judged here, but it is unlikely and for a long time.

So the other problem remains, the climatologists’ narrow view on the atmosphere. The authors of the El Nino forecast for 2020/21 failed because they lacked the insight that without comprehensive marine data, their model calculations are at best speculations. At least this conclusion should be drawn from their dramatic false prognosis.

In conclusion climatology should realize, that any ocean space, whether in size of a few hundred square miles or as covered by ENSO, plays an important role in climate matters, and that the latter should be regarded as a gift, to understand the mechanism quicker, on who is driving the climate.

 

2020 Update: US Coasts Not Flooding as Predicted

 

Previous Post Updated with 2020 Statistics

In 2018 climatists applied their considerable PR skills and budgets swamping the media with warnings targeting major coastal cities, designed to strike terror in anyone holding real estate in those places. Example headlines included:

Sea level rise could put thousands of homes in this SC county at risk, study says The State, South Carolina

Taxpayers in the Hamptons among the most exposed to rising seas Crain’s New York Business

Adapting to Climate Change Will Take More Than Just Seawalls and Levees Scientific American

The Biggest Threat Facing the City of Miami Smithsonian Magazine

What Does Maryland’s Gubernatorial Race Mean For Flood Management? The Real News Network

Study: Thousands of Palm Beach County homes impacted by sea-level rise WPTV, Florida

Sinking Land and Climate Change Are Worsening Tidal Floods on the Texas Coast Texas Observer

Sea Level Rise Will Threaten Thousands of California Homes Scientific American

300,000 coastal homes in US, worth $120 billion, at risk of chronic floods from rising seas USA Today

That last gets the thrust of the UCS study Underwater: Rising Seas, Chronic Floods, and the Implications for US Coastal Real Estate (2018)

Sea levels are rising. Tides are inching higher. High-tide floods are becoming more frequent and reaching farther inland. And hundreds of US coastal communities will soon face chronic, disruptive flooding that directly affects people’s homes, lives, and properties.

Yet property values in most coastal real estate markets do not currently reflect this risk. And most homeowners, communities, and investors are not aware of the financial losses they may soon face.

This analysis looks at what’s at risk for US coastal real estate from sea level rise—and the challenges and choices we face now and in the decades to come.

The report and supporting documents gave detailed dire warnings state by state, and even down to counties and townships. As example of the damage projections is this table estimating 2030 impacts:

State  Homes at Risk  Value at Risk Property Tax at Risk  Population in 
at-risk homes 
AL  3,542 $1,230,676,217 $5,918,124  4,367
CA  13,554 $10,312,366,952 $128,270,417  33,430
CT  2,540 $1,921,428,017 $29,273,072  5,690
DC  – $0 $0  –
DE  2,539 $127,620,700 $2,180,222  3,328
FL  20,999 $7,861,230,791 $101,267,251  32,341
GA  4,028 $1,379,638,946 $13,736,791  7,563
LA  26,336 $2,528,283,022 $20,251,201  63,773
MA  3,303 $2,018,914,670 $17,887,931  6,500
MD  8,381 $1,965,882,200 $16,808,488  13,808
ME  788 $330,580,830 $3,933,806  1,047
MS  918 $100,859,844 $1,392,059  1,932
NC  6,376 $1,449,186,258 $9,531,481  10,234
NH  1,034 $376,087,216 $5,129,494  1,659
NJ  26,651 $10,440,814,375 $162,755,196  35,773
NY  6,175 $3,646,706,494 $74,353,809  16,881
OR  677 $110,461,140 $990,850  1,277
PA  138 $18,199,572 $204,111  310
RI  419 $299,462,350 $3,842,996  793
SC  5,779 $2,882,357,415 $22,921,550  8,715
TX  5,505 $1,172,865,533 $19,453,940  9,802
VA  3,849 $838,437,710 $8,296,637  6,086
WA  3,691 $1,392,047,121 $13,440,420  7,320

The methodology, of course is climate models all the way down. They explain:

Three sea level rise scenarios, developed by the National Oceanic and Atmospheric Administration (NOAA) and localized for this analysis, are included:

  • A high scenario that assumes a continued rise in global carbon emissions and an increasing loss of land ice; global average sea level is projected to rise about 2 feet by 2045 and about 6.5 feet by 2100.
  • An intermediate scenario that assumes global carbon emissions rise through the middle of the century then begin to decline, and ice sheets melt at rates in line with historical observations; global average sea level is projected to rise about 1 foot by 2035 and about 4 feet by 2100.
  • A low scenario that assumes nations successfully limit global warming to less than 2 degrees Celsius (the goal set by the Paris Climate Agreement) and ice loss is limited; global average sea level is projected to rise about 1.6 feet by 2100.

Oh, and they did not forget the disclaimer:

Disclaimer
This research is intended to help individuals and communities appreciate when sea level rise may place existing coastal properties (aggregated by community) at risk of tidal flooding. It captures the current value and tax base contribution of those properties (also aggregated by community) and is not intended to project changes in those values, nor in the value of any specific property.

The projections herein are made to the best of our scientific knowledge and comport with our scientific and peer review standards. They are limited by a range of factors, including but not limited to the quality of property-level data, the resolution of coastal elevation models, the potential installment of defensive measures not captured by those models, and uncertainty around the future pace of sea level rise. More information on caveats and limitations can be found at http://www.ucsusa.org/underwater.

Neither the authors nor the Union of Concerned Scientists are responsible or liable for financial or reputational implications or damages to homeowners, insurers, investors, mortgage holders, municipalities, or other any entities. The content of this analysis should not be relied on to make business, real estate or other real world decisions without independent consultation with professional experts with relevant experience. The views expressed by individuals in the quoted text of this report do not represent an endorsement of the analysis or its results.

The need for a disclaimer becomes evident when looking into the details. The NOAA reference is GLOBAL AND REGIONAL SEA LEVEL RISE SCENARIOS FOR THE UNITED STATES NOAA Technical Report NOS CO-OPS 083

Since the text emphasizes four examples of their scenarios, let’s consider them here. First there is San Francisco, a city that sued oil companies over sea level rise. From tidesandcurrents comes this tidal gauge record
It’s a solid, long-term record providing more than a century of measurements from 1900 through 2020.  The graph below compares the present observed trend with climate models projections out to 2100.

Since the record is set at zero in 2000, the difference in 21st century expectation is stark. Instead of  the existing trend out to around 20 cm, models project 2.5 meters rise by 2100.

New York City is represented by the Battery tidal gauge:


Again, a respectable record with a good 20th century coverage.  And the models say:


The red line projects 2500 mm rise vs. 287 mm, almost a factor of 10 more.  The divergence is evident even in the first 20 years.

Florida comes in for a lot of attention, especially the keys, so here is Key West:


A similar pattern to NYC Battery gauge, and here is the projection:


The pattern is established: Instead of a rise of about 25 cm, the models project 250 cm.

Finally, probably the worst case, and already well-known to all is Galveston, Texas:


The water has been rising there for a long time, so maybe the models got this one close.

The gap is less than the others since the rising trend is much higher, but the projection is still nearly four times the past.  Galveston is at risk, all right, but we didn’t need this analysis to tell us that.

A previous post Unbelievable Climate Models goes into why they are running so hot and so extreme, and why they can not be trusted.

Footnote Regarding Alarms in Other Places

Recently there was a flap over future sea levels at Rhode Island, so I took a look at Newport RI, the best tidal gauge record there.  Same Story: Observed sea levels already well below projections that are 10 times the tidal gauge trend.

Another city focused upon urban flooding is Philadelphia.  As with other coastal settlements, claims of sea level rise from global warming are unfounded.

Philadelphia is a great example where a real concern will not be addressed by reducing CO2 emissions.  See Urban Flooding: The Philadelphia Story

Arctic Building Ice Inventory Mid January

At this point in the Arctic refreezing phase, LIFO inventory accounting comes into play.  Last-In, First-out is one accepted way to price the value of a company’s inventory.  For Arctic ice, it means that basins that are last to freeze over in winter are the first to melt out in the summer.  For example, in Mid January 2021, total NH ice extent is 91% of last March maximum, so most basins have long been covered with ice.  The last 9% will be added in four places (present % of max is noted):

Bering Sea        62%
Okhotsk Sea     70%
Barents Sea      58%
Baffin Bay         66%

In the Pacific animation above, Bering on the right adds ice extent from 261k km2 to 513k km2 since Jan. 1, while Okhotsk goes from 500k km2 to 800k km2.  Together they will likely add ~650k km2 more by March maximum.  

On the Atlantic side, Barents Sea added only ~100k km2 so far in January.  More interesting on the right side is the Baltic Sea quadrupled from 9K km2 to 42k km2.  While the Baltic extent is not large by comparison, it is already 38% greater than last March maximum, so that is surprising.

Normally, ice in the Yellow Sea is insignificant, but this year is different.  Perhaps you saw reports like this one from gcaptain Sea Ice Slows Ships In North China Ports  Excerpts in italics with my bolds.

By Muyu Xu and Chen Aizhu (Reuters) – Chinese ports and marine safety authorities are on high alert as an expansion of sea ice makes it tougher for ships to berth and discharge at key energy product import terminals along the coast of northern Bohai Bay.

A cold wave sweeping the northern hemisphere has plunged temperatures across China to their lowest in decades, boosting demand for power and fuel to historic highs in the world’s largest energy consumer.

Bohai Bay appears in the upper right corner, with Beijing nearby. Yellow Sea extent doubled in January up to 28,000 km2, which is twice the maximum last March.

Background on Okhotsk Sea

NASA describes Okhotsk as a Sea and Ice Factory. Excerpts in italics with my bolds.

The Sea of Okhotsk is what oceanographers call a marginal sea: a region of a larger ocean basin that is partly enclosed by islands and peninsulas hugging a continental coast. With the Kamchatka Peninsula, the Kuril Islands, and Sakhalin Island partly sheltering the sea from the Pacific Ocean, and with prevailing, frigid northwesterly winds blowing out from Siberia, the sea is a winter ice factory and a year-round cloud factory.

The region is the lowest latitude (45 degrees at the southern end) where sea ice regularly forms. Ice cover varies from 50 to 90 percent each winter depending on the weather. Ice often persists for nearly six months, typically from October to March. Aside from the cold winds from the Russian interior, the prodigious flow of fresh water from the Amur River freshens the sea, making the surface less saline and more likely to freeze than other seas and bays.


Map of the Sea of Okhotsk with bottom topography. The 200- and 3000-m isobars are indicated by thin and thick solid lines, respectively. A box denotes the enlarged portion in Figure 5. White shading indicates sea-ice area (ice concentration ⩾30%) in February averaged for 2003–11; blue shading indicates open ocean area. Ice concentration from AMSR-E is used. Color shadings indicate cumulative ice production in coastal polynyas during winter (December–March) averaged from the 2002/03 to 2009/10 seasons (modified from Nihashi and others, 2012, 2017). The amount is indicated by the bar scale. Source: Cambridge Core

Bering Sea Ice is Highly Variable

The animation above shows Bering Sea ice extents at April 2 from 2007 to 2020.  The large fluctuation is evident, much ice in 2012 -13 and almost none in 2018, other years in between.  Given the alarmist bias, it’s no surprise which two years are picked for comparison:

Source: Seattle Times ‘We’ve fallen off a cliff’: Scientists have never seen so little ice in the Bering Sea in spring.

Taking a boat trip from Hokkaido Island to see Okhotsk drift ice is a big tourist attraction, as seen in the short video below.  Al Gore had them worried back then, but not now.

Drift ice in Okhotsk Sea at sunrise.

Global Warming Ends

The animation is an update of a previous analysis from Dr. Murry Salby.  These graphs use Hadcrut4 and include the 2016 El Nino warming event.  The exhibit shows since 1947 GMT warmed by 0.8 C, from 13.9 to 14.7, as estimated by Hadcrut4.  This resulted from three natural warming events involving ocean cycles. The most recent rise 2013-16 lifted temperatures by 0.2C.  Previously the 1994-98 El Nino produced a plateau increase of 0.4C.  Before that, a rise from 1977-81 added 0.2C to start the warming since 1947.

Importantly, the theory of human-caused global warming asserts that increasing CO2 in the atmosphere changes the baseline and causes systemic warming in our climate.  On the contrary, all of the warming since 1947 was episodic, coming from three brief events associated with oceanic cycles. Moreover, the UAH record shows that the effects of the last one are now gone.

The 2016 El Nino persisted longer than 1998, and was followed by warming after effects in NH.  The monthly anomaly at 2020 year end is nearly the 0.18C average since 1995, an ENSO neutral year prior to the second warming event discussed above. With a quiet sun and cooling oceans, the prospect is for cooler times ahead.

Arctic Ice Year-End 2020

At  the bottom is a discussion of statistics on year-end Arctic Sea Ice extents.  The values are averages of the last five days of each year.  End of December is a neutral point in the melting-freezing cycle, midway between September minimum and March maximum extents.

Background from Previous Post Updated to Year-End

Some years ago reading a thread on global warming at WUWT, I was struck by one person’s comment: “I’m an actuary with limited knowledge of climate metrics, but it seems to me if you want to understand temperature changes, you should analyze the changes, not the temperatures.” That rang bells for me, and I applied that insight in a series of Temperature Trend Analysis studies of surface station temperature records. Those posts are available under this heading. Climate Compilation Part I Temperatures

This post seeks to understand Arctic Sea Ice fluctuations using a similar approach: Focusing on the rates of extent changes rather than the usual study of the ice extents themselves. Fortunately, Sea Ice Index (SII) from NOAA provides a suitable dataset for this project. As many know, SII relies on satellite passive microwave sensors to produce charts of Arctic Ice extents going back to 1979.  The current Version 3 has become more closely aligned with MASIE, the modern form of Naval ice charting in support of Arctic navigation. The SII User Guide is here.

There are statistical analyses available, and the one of interest (table below) is called Sea Ice Index Rates of Change (here). As indicated by the title, this spreadsheet consists not of monthly extents, but changes of extents from the previous month. Specifically, a monthly value is calculated by subtracting the average of the last five days of the previous month from this month’s average of final five days. So the value presents the amount of ice gained or lost during the present month.

These monthly rates of change have been compiled into a baseline for the period 1980 to 2010, which shows the fluctuations of Arctic ice extents over the course of a calendar year. Below is a graph of those averages of monthly changes during the baseline period. Those familiar with Arctic Ice studies will not be surprised at the sine wave form. December end is a relatively neutral point in the cycle, midway between the September Minimum and March Maximum.

The graph makes evident the six spring/summer months of melting and the six autumn/winter months of freezing.  Note that June-August produce the bulk of losses, while October-December show the bulk of gains. Also the peak and valley months of March and September show very little change in extent from beginning to end.

The table of monthly data reveals the variability of ice extents over the last 4 decades.

The values in January show changes from the end of the previous December, and by summing twelve consecutive months we can calculate an annual rate of change for the years 1979 to 2019.

As many know, there has been a decline of Arctic ice extent over these 40 years, averaging 40k km2 per year. But year over year, the changes shift constantly between gains and losses.

Moreover, it seems random as to which months are determinative for a given year. For example, much ado has been printed about October 2020 being slower than expected to refreeze and add ice extents. As it happens in this dataset, October has the highest rate of adding ice. The table below shows the variety of monthly rates in the record as anomalies from the 1980-2010 baseline. In this exhibit a red cell is a negative anomaly (less than baseline for that month) and blue is positive (higher than baseline).

Note that the  +/ –  rate anomalies are distributed all across the grid, sequences of different months in different years, with gains and losses offsetting one another.  Yes, October 2020 recorded a lower than average gain, but higher than 2016. The loss in July 2020 was the largest of the year, during the hot Siberian summer.  Note November 2020 ice gain anomaly exceeded the October deficit anomaly by more than twice as much.  December added more surplus so that the anomaly for the year was nothing. The bottom line presents the average anomalies for each month over the period 1979-2020.  Note the rates of gains and losses mostly offset, and the average of all months in the bottom right cell is virtually zero.

A final observation: The graph below shows the Yearend Arctic Ice Extents for the last 30 years.

Note: SII daily extents file does not provide complete values prior to 1988.

Year-end Arctic ice extents (last 5 days of December) show three distinct regimes: 1989-1998, 1998-2010, 2010-2019. The average year-end extent 1989-2010 is 13.4M km2. In the last decade, 2009 was 13.0M km2, and ten years later, 2019 was 12.8M km2. So for all the the fluctuations, the net loss was 200k km2, or 1.5%. Talk of an Arctic ice death spiral is fanciful.

These data show a noisy, highly variable natural phenomenon. Clearly, unpredictable factors are in play, principally water structure and circulation, atmospheric circulation regimes, and also incursions and storms. And in the longer view, today’s extents are not unusual.

 

 

Illustration by Eleanor Lutz shows Earth’s seasonal climate changes. If played in full screen, the four corners present views from top, bottom and sides. It is a visual representation of scientific datasets measuring Arctic ice extents.

 

Arctic Freezing Fast Mid-Dec. 2020

 

As noted in a previous post, alarms were raised over slower than average Arctic refreezing in October.  Those fears were laid to rest firstly when ice extents roared back in November, and now with the Arctic freezing fast in December. The image above shows the ice gains over the last two weeks, from Dec. 5 to 17, 2020.  In November, 3.5 Wadhams of sea ice were added during the month.  (The metric 1 Wadham = 1 M km2 comes from the professor’s predictions of an ice-free Arctic, meaning less than 1 M km2 extent). So far in December a further 1.9 Wadhams have been added with another two weeks to go in 2020.

Some years ago reading a thread on global warming at WUWT, I was struck by one person’s comment: “I’m an actuary with limited knowledge of climate metrics, but it seems to me if you want to understand temperature changes, you should analyze the changes, not the temperatures.” That rang bells for me, and I applied that insight in a series of Temperature Trend Analysis studies of surface station temperature records. Those posts are available under this heading. Climate Compilation Part I Temperatures

This post seeks to understand Arctic Sea Ice fluctuations using a similar approach: Focusing on the rates of extent changes rather than the usual study of the ice extents themselves. Fortunately, Sea Ice Index (SII) from NOAA provides a suitable dataset for this project. As many know, SII relies on satellite passive microwave sensors to produce charts of Arctic Ice extents going back to 1979.  The current Version 3 has become more closely aligned with MASIE, the modern form of Naval ice charting in support of Arctic navigation. The SII User Guide is here.

There are statistical analyses available, and the one of interest (table below) is called Sea Ice Index Rates of Change (here). As indicated by the title, this spreadsheet consists not of monthly extents, but changes of extents from the previous month. Specifically, a monthly value is calculated by subtracting the average of the last five days of the previous month from this month’s average of final five days. So the value presents the amount of ice gained or lost during the present month.

These monthly rates of change have been compiled into a baseline for the period 1980 to 2010, which shows the fluctuations of Arctic ice extents over the course of a calendar year. Below is a graph of those averages of monthly changes during the baseline period. Those familiar with Arctic Ice studies will not be surprised at the sine wave form. December end is a relatively neutral point in the cycle, midway between the September Minimum and March Maximum.

The graph makes evident the six spring/summer months of melting and the six autumn/winter months of freezing.  Note that June-August produce the bulk of losses, while October-December show the bulk of gains. Also the peak and valley months of March and September show very little change in extent from beginning to end.

The table of monthly data reveals the variability of ice extents over the last 4 decades.

The values in January show changes from the end of the previous December, and by summing twelve consecutive months we can calculate an annual rate of change for the years 1979 to 2019.

As many know, there has been a decline of Arctic ice extent over these 40 years, averaging 40k km2 per year. But year over year, the changes shift constantly between gains and losses.

Moreover, it seems random as to which months are determinative for a given year. For example, much ado has been printed about October 2020 being slower than expected to refreeze and add ice extents. As it happens in this dataset, October has the highest rate of adding ice. The table below shows the variety of monthly rates in the record as anomalies from the 1980-2010 baseline. In this exhibit a red cell is a negative anomaly (less than baseline for that month) and blue is positive (higher than baseline).

Note that the  +/ –  rate anomalies are distributed all across the grid, sequences of different months in different years, with gains and losses offsetting one another.  Yes, October 2020 recorded a lower than average gain, but higher than 2016. The loss in July 2020 was the largest of the year, during the hot Siberian summer.  Note November 2020 ice gain anomaly exceeded the October deficit anomaly by more than twice as much.  The bottom line presents the average anomalies for each month over the period 1979-2020.  Note the rates of gains and losses mostly offset, and the average of all months in the bottom right cell is virtually zero.

Combining the months of October and November shows 2020 828k km2 more ice than baseline for the two months and matching 2019 ice recovery.

The average December adds 2M km2 of sea ice according to SII dataset, and in the first 17 days of December 2020 ice increased by 1.9M km2, with 2 weeks of futher freezing to come.

A final observation: The graph below shows the Yearend Arctic Ice Extents for the last 30 years.

Note: SII daily extents file does not provide complete values prior to 1988.

Year-end Arctic ice extents (last 5 days of December) show three distinct regimes: 1989-1998, 1998-2010, 2010-2019. The average year-end extent 1989-2010 is 13.4M km2. In the last decade, 2009 was 13.0M km2, and ten years later, 2019 was 12.8M km2. So for all the the fluctuations, the net loss was 200k km2, or 1.5%. Talk of an Arctic ice death spiral is fanciful.

These data show a noisy, highly variable natural phenomenon. Clearly, unpredictable factors are in play, principally water structure and circulation, atmospheric circulation regimes, and also incursions and storms. And in the longer view, today’s extents are not unusual.

 

 

Illustration by Eleanor Lutz shows Earth’s seasonal climate changes. If played in full screen, the four corners present views from top, bottom and sides. It is a visual representation of scientific datasets measuring Arctic ice extents.

Wacky “War on Nature”

The usual suspects reported U.N. Secretary General Antonio Guterres announcing that humans are at war upon nature.  For example, NY Daily News (in italics with my bolds):

The United Nations is calling on people worldwide to stop “waging war on nature” as the planet achieves disturbing milestones in the battle against climate change.

In a speech at Columbia University, UN Secretary-General Antonio Guterres said, “The state of the planet is broken. … This is suicidal,” The Associated Press reported Wednesday.

Guterres pointed to “apocalyptic fires and floods, cyclones and hurricanes” that have only become more frequent in recent years, and in particular, during 2020, one of the three hottest years on record.

“Human activities are at the root of our descent towards chaos,” he explained, noting this also means humans are the ones who “can solve it.”

John Osbourne explains at Real Markets how crazy is this latest meme in his article The Utterly Nonsensical View That Humanity Is Waging War on Nature. Excerpts in italics with my bolds.

The narrative that humanity is waging a ‘war on nature’ is nonsense.

At Columbia University on December 2, 2020, U.N. Secretary General Antonio Guterres claimed that humanity’s “war” on the environment was coming to a head. Guterres said, “We are facing a devastating pandemic, new heights of global heating, new lows of ecological degradation and new setbacks in our work towards global goals for more equitable, inclusive and sustainable development… To put it simply, the state of the planet is broken.”

Is humanity facing the crisis that Guterres claims? Is the planet ‘broken’? Most importantly, is there any scientific basis for these claims of doom and gloom?

To answer these questions: No. Global living standards continue to rise, despite alarmists constant failed predictions of a dreary future. Greater prosperity has allowed developed countries to devote time and money to remediating existing damage and improving the environment; developing countries have no such luxury. Contrary to alarmists’ claims, climate and temperature changes are extensively documented and perfectly natural. Guterres’ belief that man is at war with nature is unsubstantiated.

Facts, rather than beliefs, should be the foundation of public policy.

In his speech, Guterres highlights nearly every woe in the world. He implies that any problem in the natural world, be it fires, flooding, cyclones, hurricanes, pollution, disease, changes in sea ice and ocean temperatures, can be blamed on climate change. And in Guterres’ view, humanity is the sole driver of climate change. This couldn’t be further from the truth.

Nor is Guterres the sole proponent of such ideas. Media outlets such as CNN, the Huffington Post, and NPR repeat these tropes while rarely citing facts for their claims of impending calamity.

An examination of the science and history of natural disasters will show that deaths from natural disasters are at one of the lowest rates in history. While natural disasters are more expensive than they once were, the reasons are mundane and expected.

Climate has changed throughout history, well before humanity had any significant impact. Even with CO2’s warming effect, UN IPCC and U.S. Government data show no increase in rates most natural disasters from the period of natural warming (1900 to 1950) to the period the IPCC claims is one of largely human-caused warming (1950 to 2018).

This calls into question not just claims of current CO2-driven “climate crisis” but projections of future damage by those who promote the ‘war on nature’ narrative.

Guterres continues, “Air and water pollution are killing 9 million people annually.” Not only is his number wrong, but his reasoning is flawed. In developed countries (which have access to cheap reliable energy), air and water are cleaner than ever because of the economic growth driven by fossil fuels. Furthermore, his solutions condemn the populations of less developed countries to continue to suffer public health problems arising from lack of affordable energy. Nearly half of the deaths cited in the WHO’s number are caused by cooking indoors with dirty fuels. In the CO2 Coalition’s latest white paper, New-Tech American Coal Fired Electricity for Africa: Clean Air, Indoors and Out we offer a solution to the polluted indoor air: cheap, reliable energy using local resources. President Trump could reverse by executive order the Obama-era ban on U.S. exports of clean-coal technology to coal-rich Africa, saving thousands of lives.

As to ocean health, Guterres says, “The carbon dioxide they absorb is acidifying the seas…” Again, he misses the mark. The CO2 Coalition has analyzed decades of research and found that CO2, which is plankton food that enriches sea life, does not cause “ocean acidification” and that the term itself is misleading.

Guterres concludes by advocating humanity “flick the green switch” and transform the world’s economy by using ‘renewable energy’ to drive sustainability. While this sounds wonderful, the reality is that so-called ‘renewables’ are anything but renewable. Flipping the green switch requires us to depend on energy that is unreliable, expensive, and requires the use of dangerous pollutants. His proposed solution would be harmful to both health and global prosperity.

One thing is certain: the General Secretary is wrong on the science and wrong on the economics. His ‘war on nature’ narrative is bunk.

 

.

 

 

Fear Not Rising Temperatures or Ocean Levels


Dominick T. Armentano writes at the Independent Institute Are Temperatures and Ocean Levels Rising Dangerously? Not Really. Excerpts in italics with my bolds.  H/T John Ray

There are two widely held climate-change beliefs that are simply not accurate. The first is that there has been a statistically significant warming trend in the U.S. over the last 20 years. The second is that average ocean levels are rising alarmingly due to man-made global warming. Neither of these perspectives is true; yet both remain important, nonetheless, since both are loaded with very expensive public policy implications.

To refute the first view, we turn to data generated by the National Oceanic and Atmospheric Administration (NOAA) for the relevant years under discussion. The table below reports the average mean temperature in the continental U.S. for the years 1998 through 2019*:

1998 54.6 degrees
1999 54.5 degrees
2000 54.0 degrees
2001 54.3 degrees
2002 53.9 degrees
2003 53.7 degrees
2004 53.5 degrees
2005 54 degrees
2006 54.9 degrees
2007 54.2 degrees
2008 53.0 degrees
2009 53.1 degrees
2010 53.8 degrees
2011 53.8 degrees
2012 55.3 degrees
2013 52.4 degrees
2014 52.6 degrees
2015 54.4 degrees
2016 54.9 degrees
2017 54.6 degrees
2018 53.5 degrees
2019 52.7 degrees

*National Climate Report – Annual 2019

It is apparent from the data that there has been no consistent warming trend in the U.S. over the last 2 decades; average mean temperatures (daytime and nighttime) have been slightly higher in some years and slightly lower in other years. On balance–and contrary to mountains of uninformed social and political commentary—annual temperatures on average in the U.S. were no higher in 2019 than they were in 1998.

The second widely accepted climate view—based on wild speculations from some op/ed writers and partisan politicians–is that average sea levels are increasing dangerously and rationalize an immediate governmental response. But as we shall demonstrate below, this perspective is simply not accurate.

There is a wide scientific consensus (based on satellite laser altimeter readings since 1993) that the rate of increase in overall sea levels has been approximately .12 inches per year.

To put that increase in perspective, the average sea level nine years from now (in 2029) is likely to be approximately one inch higher than it is now (2020). One inch is roughly the distance from the tip of your finger to the first knuckle. Even by the turn of the next century (in 2100), average ocean levels (at that rate of increase) should be only a foot or so higher than they are at present.

NYC past & projected 2020

None of this sounds particularly alarming for the general society and little of it can justify any draconian regulations or costly infrastructure investments. The exception might be for very low- lying ocean communities or for properties (nuclear power plants) that, if flooded, would present a wide-ranging risk to the general population. But even here there is no reason for immediate panic. Since ocean levels are rising in small, discrete marginal increments, private and public decision makers would have reasonable amounts of time to prepare, adjust and invest (in flood abatement measures, etc.) if required.

But are sea levels actually rising at all? Empirical evidence of any substantial increases taken from land-based measurements has been ambiguous. This suggests to some scientists that laser and tidal-based measurements of ocean levels over time have not been particularly accurate.

For example, Professor Niles-Axel Morner (Stockholm University) is infamous in climate circles for arguing–based on his actual study of sea levels in the Fiji Islands–that “there are no traces of any present rise in sea levels; on the contrary, full stability.” And while Morner’s views are controversial, he has at least supplied peer reviewed empirical evidence to substantiate his nihilist position on the sea-level increase hypothesis.

The world has many important societal problems and only a limited amount of resources to address them. What we don’t need are overly dramatic climate-change claims that are unsubstantiated and arrive attached to expensive public policies that, if enacted, would fundamentally alter the foundations of the U.S. economic system.

DOMINICK T. ARMENTANO is a Research Fellow at the Independent Institute and professor emeritus in economics at the University of Hartford (CT).

Update Dec.11: USCRN Comparable Temperature Results

In response to Graeme Weber’s Question, this information is presented:

Anthony Watts:

NOAA’s U.S. Climate Reference Network (USCRN) has the best quality climate data on the planet, yet it never gets mentioned in the NOAA/NASA press releases. Commissioned in 2005, it has the most accurate, unbiased, and un-adjusted data of any climate dataset.

The USCRN has no biases, and no need for adjustments, and in my opinion represents a ground truth for climate change.

In this graph of the contiguous United States updated for 2019 comes out about 0.75°F cooler than the start of the dataset in 2005.

See Also Fear Not For Fiji

Setting the Global Temperature Record Straight

Arctic Ice Fears Erased in November

As noted in a previous post, alarms were raised over slower than average Arctic refreezing in October.  Those fears are now laid to rest by ice extents roaring back in November.  The image above shows the ice gains completed from October 31 to November 30, 2020. In fact 3.5 Wadhams of sea ice were added during the month.  (The metric 1 Wadham = 1 M km2 comes from the professor’s predictions of an ice-free Arctic, meaning less than 1 M km2 extent)

Some years ago reading a thread on global warming at WUWT, I was struck by one person’s comment: “I’m an actuary with limited knowledge of climate metrics, but it seems to me if you want to understand temperature changes, you should analyze the changes, not the temperatures.” That rang bells for me, and I applied that insight in a series of Temperature Trend Analysis studies of surface station temperature records. Those posts are available under this heading. Climate Compilation Part I Temperatures

This post seeks to understand Arctic Sea Ice fluctuations using a similar approach: Focusing on the rates of extent changes rather than the usual study of the ice extents themselves. Fortunately, Sea Ice Index (SII) from NOAA provides a suitable dataset for this project. As many know, SII relies on satellite passive microwave sensors to produce charts of Arctic Ice extents going back to 1979.  The current Version 3 has become more closely aligned with MASIE, the modern form of Naval ice charting in support of Arctic navigation. The SII User Guide is here.

There are statistical analyses available, and the one of interest (table below) is called Sea Ice Index Rates of Change (here). As indicated by the title, this spreadsheet consists not of monthly extents, but changes of extents from the previous month. Specifically, a monthly value is calculated by subtracting the average of the last five days of the previous month from this month’s average of final five days. So the value presents the amount of ice gained or lost during the present month.

These monthly rates of change have been compiled into a baseline for the period 1980 to 2010, which shows the fluctuations of Arctic ice extents over the course of a calendar year. Below is a graph of those averages of monthly changes during the baseline period. Those familiar with Arctic Ice studies will not be surprised at the sine wave form. December end is a relatively neutral point in the cycle, midway between the September Minimum and March Maximum.

The graph makes evident the six spring/summer months of melting and the six autumn/winter months of freezing.  Note that June-August produce the bulk of losses, while October-December show the bulk of gains. Also the peak and valley months of March and September show very little change in extent from beginning to end.

The table of monthly data reveals the variability of ice extents over the last 4 decades.

The values in January show changes from the end of the previous December, and by summing twelve consecutive months we can calculate an annual rate of change for the years 1979 to 2019.

As many know, there has been a decline of Arctic ice extent over these 40 years, averaging 40k km2 per year. But year over year, the changes shift constantly between gains and losses.

Moreover, it seems random as to which months are determinative for a given year. For example, much ado has been printed about October 2020 being slower than expected to refreeze and add ice extents. As it happens in this dataset, October has the highest rate of adding ice. The table below shows the variety of monthly rates in the record as anomalies from the 1980-2010 baseline. In this exhibit a red cell is a negative anomaly (less than baseline for that month) and blue is positive (higher than baseline).

Note that the  +/ –  rate anomalies are distributed all across the grid, sequences of different months in different years, with gains and losses offsetting one another.  Yes, October 2020 recorded a lower than average gain, but higher than 2016. The loss in July 2020 was the largest of the year, during the hot Siberian summer.  Note November 2020 ice gain anomaly exceeded the October deficit anomaly by more than twice as much.  The bottom line presents the average anomalies for each month over the period 1979-2020.  Note the rates of gains and losses mostly offset, and the average of all months in the bottom right cell is virtually zero.

Combining the months of October and November shows 2020 828k km2 more ice than baseline for the two months and matching 2019 ice recovery.

A final observation: The graph below shows the Yearend Arctic Ice Extents for the last 30 years.

Note: SII daily extents file does not provide complete values prior to 1988.

Year-end Arctic ice extents (last 5 days of December) show three distinct regimes: 1989-1998, 1998-2010, 2010-2019. The average year-end extent 1989-2010 is 13.4M km2. In the last decade, 2009 was 13.0M km2, and ten years later, 2019 was 12.8M km2. So for all the the fluctuations, the net loss was 200k km2, or 1.5%. Talk of an Arctic ice death spiral is fanciful.

These data show a noisy, highly variable natural phenomenon. Clearly, unpredictable factors are in play, principally water structure and circulation, atmospheric circulation regimes, and also incursions and storms. And in the longer view, today’s extents are not unusual.

 

 

Illustration by Eleanor Lutz shows Earth’s seasonal climate changes. If played in full screen, the four corners present views from top, bottom and sides. It is a visual representation of scientific datasets measuring Arctic ice extents.

Oh No! Greenland Melts in Virtual Reality “Experiments”

Phys.org sounds the alarm:  Greenland ice sheet faces irreversible melting.  But as is usual with announcements from that source, discretion and critical intelligence are advised.  The back story is a study that takes outputs from climate models projecting incredible warming and feeds them into ice models that assume melting from higher temperatures. Warning:  The paper has 93 references to “experiments” and all of them are virtual reality manipulations in the computers they programmed. Excerpts in italics with my bolds.

Professor Jonathan Gregory, Climate Scientist from the National Centre for Atmospheric Science and University of Reading, said: “Our experiments underline the importance of mitigating global temperature rise. To avoid partially irreversible loss of the ice sheet, climate change must be reversed—not just stabilized—before we reach the critical point where the ice sheet has declined too far.”

The paper is Large and irreversible future decline of the Greenland ice sheet.  And obviously, it is models all the way down.  Excerpts in italics with my bolds.

We run a set of 47 FiG experiments to study the SMB change (ΔSMB), rate of mass loss and eventual steady state of the Greenland ice sheet using the three different choices of FAMOUS–ice snow-albedo parameters, with 20-year climatological monthly mean sea surface BCs taken from the four selected CMIP5 AOGCMs for five climate scenarios (Table 2). These five are the late 20th century (1980–1999, called “historical”), the end of the 21st century under three representative concentration pathway (RCP) scenarios (as in the AR5; van Vuuren et al., 2011) and quadrupled pre-industrial CO2 (abrupt4xCO2, warmer than any RCP). The experiments have steady-state climates. This is unrealistic, but it simplifies the comparison and is reasonable since no-one can tell how climate will change over millennia into the future. Our simulations should be regarded only as indicative rather than as projections. Each experiment begins from the FiG spun-up state for MIROC5 historical climate with the appropriate albedo parameter. Although in most cases there is a substantial instantaneous change in BCs when the experiment begins, the land and atmosphere require only a couple of years to adjust.

Under constant climates that are warmer than the late 20th century, the ice sheet loses mass, its surface elevation decreases, and its surface climate becomes warmer. This gives a positive feedback on mass loss, but it is outweighed by the negative feedbacks due to declining ablation area and increasing cloudiness over the interior as the ice sheet contracts. In the ice sheet area integral, snowfall decreases less than ablation because the precipitation on the margins is enhanced by the topographic gradient and moves inland as the ice sheet retreats. Consequently, after many millennia under a constant warm climate, the ice sheet reaches a reduced steady state. Final GMSLR is less than 1.5 m in most late 21st-century RCP2.6 climates and more than 4 m in all late 21st-century RCP8.5 climates. For warming exceeding 3 K, the ice sheet would be mostly lost, and its contribution to GMSLR would exceed 5 m.

The reliability of our conclusions depends on the realism of our model. There are systematic uncertainties arising from assumptions made in its formulation. The atmosphere GCM has low resolution and comparatively simple parametrization schemes. The ice sheet model does not simulate rapid ice sheet dynamics; this certainly means that it underestimates the rate of ice sheet mass loss in coming decades, but we do not know what effect this has on the eventual steady states, which are our focus. The SMB scheme uses a uniform air temperature lapse rate and omits the phase change in precipitation in the downscaling from GCM to ice sheet model. The snow albedo is a particularly important uncertainty; with our highest choice of albedo, removal of the ice sheet is reversible.

What about observations instead of imaginary projections?  Previous post: Greenland Ice Varies, Don’t Panic

The scare du jour is about Greenland Ice Sheet (GIS) and how it will melt out and flood us all.  It’s declared that GIS has passed its tipping point, and we are doomed.  Typical is the Phys.org hysteria: Sea level rise quickens as Greenland ice sheet sheds record amount:  “Greenland’s massive ice sheet saw a record net loss of 532 billion tonnes last year, raising red flags about accelerating sea level rise, according to new findings.”

Panic is warranted only if you treat this as proof of an alarmist narrative and ignore the facts and context in which natural variation occurs. For starters, consider the last four years of GIS fluctuations reported by DMI and summarized in the eight graphs above.  Note the noisy blue lines showing how the surface mass balance (SMB) changes its daily weight by 8 or 10 gigatonnes (Gt) around the baseline mean from 1981 to 2010.  Note also the summer decrease between May and August each year before recovering to match or exceed the mean.

The other four graphs show the accumulation of SMB for each of the last four years including 2020.  Tipping Point?  Note that in both 2017 and 2018, SMB ended about 500 Gt higher than the year began, and way higher than 2012, which added nothing.  Then came 2019 dropping below the mean, but still above 2012.  Lastly, this year is matching the 30-year average.  Note also that the charts do not integrate from previous years; i.e. each year starts at zero and shows the accumulation only for that year.  Thus the gains from 2017 and 2018 do not result in 2019 starting the year up 1000 Gt, but from zero.

The Truth about Sliding Greenland Ice

Researchers know that the small flows of water from surface melting are not the main way GIS loses ice in the summer.  Neil Humphrey explains in this article from last year Nate Maier and Neil Humphrey Lead Team Discovering Ice is Sliding Toward Edges Off Greenland Ice Sheet  Excerpts in italics with my bolds.

While they may appear solid, all ice sheets—which are essentially giant glaciers—experience movement: ice flows downslope either through the process of deformation or sliding. The latest results suggest that the movement of the ice on the GIS is dominated by sliding, not deformation. This process is moving ice to the marginal zones of the sheet, where melting occurs, at a much faster rate.

“The study was motivated by a major unknown in how the ice of Greenland moves from the cold interior, to the melting regions on the margins,” Neil Humphrey, a professor of geology from the University of Wyoming and author of the study, told Newsweek. “The ice is known to move both by sliding over the bedrock under the ice, and by oozing (deforming) like slowly flowing honey or molasses. What was unknown was the ratio between these two modes of motion—sliding or deforming.

“This lack of understanding makes predicting the future difficult, since we know how to calculate the flowing, but do not know much about sliding,” he said. “Although melt can occur anywhere in Greenland, the only place that significant melt can occur is in the low altitude margins. The center (high altitude) of the ice is too cold for the melt to contribute significant water to the oceans; that only occurs at the margins. Therefore ice has to get from where it snows in the interior to the margins.

“The implications for having high sliding along the margin of the ice sheet means that thinning or thickening along the margins due to changes in ice speed can occur much more rapidly than previously thought,” Maier said. “This is really important; as when the ice sheet thins or thickens it will either increase the rate of melting or alternatively become more resilient in a changing climate.

“There has been some debate as to whether ice flow along the edges of Greenland should be considered mostly deformation or mostly sliding,” Maier says. “This has to do with uncertainty of trying to calculate deformation motion using surface measurements alone. Our direct measurements of sliding- dominated motion, along with sliding measurements made by other research teams in Greenland, make a pretty compelling argument that no matter where you go along the edges of Greenland, you are likely to have a lot of sliding.”

The sliding ice does two things, Humphrey says. First, it allows the ice to slide into the ocean and make icebergs, which then float away. Two, the ice slides into lower, warmer climate, where it can melt faster.

While it may sound dire, Humphrey notes the entire Greenland Ice Sheet is 5,000 to 10,000 feet thick.

In a really big melt year, the ice sheet might melt a few feet. It means Greenland is going to be there another 10,000 years,” Humphrey says. “So, it’s not the catastrophe the media is overhyping.”

Humphrey has been working in Greenland for the past 30 years and says the Greenland Ice Sheet has only melted 10 feet during that time span.

Summary

The Greenland ice sheet is more than 1.2 miles thick in most regions. If all of its ice was to melt, global sea levels could be expected to rise by about 25 feet. However, this would take more than 10,000 years at the current rates of melting.

Background from Previous Post: Greenland Glaciers: History vs. Hysteria

The modern pattern of environmental scares started with Rachel Carson’s Silent Spring claiming chemicals are killing birds, only today it is windmills doing the carnage. That was followed by ever expanding doomsday scenarios, from DDT, to SST, to CFC, and now the most glorious of them all, CO2. In all cases the menace was placed in remote areas difficult for objective observers to verify or contradict. From the wilderness bird sanctuaries, the scares are now hiding in the stratosphere and more recently in the Arctic and Antarctic polar deserts. See Progressively Scaring the World (Lewin book synopsis)

The advantage of course is that no one can challenge the claims with facts on the ground, or on the ice. Correction: Scratch “no one”, because the climate faithful are the exception. Highly motivated to go to the ends of the earth, they will look through their alarmist glasses and bring back the news that we are indeed doomed for using fossil fuels.

A recent example is a team of researchers from Dubai (the hot and sandy petro kingdom) going to Greenland to report on the melting of Helheim glacier there.  The article is NYUAD team finds reasons behind Greenland’s glacier melt.  Excerpts in italics with my bolds.

First the study and findings:

For the first time, warm waters that originate in the tropics have been found at uniform depth, displacing the cold polar water at the Helheim calving front, causing an unusually high melt rate. Typically, ocean waters near the terminus of an outlet glacier like Helheim are at the freezing point and cause little melting.

NYUAD researchers, led by Professor of Mathematics at NYU’s Courant Institute of Mathematical Sciences and Principal Investigator for NYU Abu Dhabi’s Centre for Sea Level Change David Holland, on August 5, deployed a helicopter-borne ocean temperature probe into a pond-like opening, created by warm ocean waters, in the usually thick and frozen melange in front of the glacier terminus.

Normally, warm, salty waters from the tropics travel north with the Gulf Stream, where at Greenland they meet with cold, fresh water coming from the polar region. Because the tropical waters are so salty, they normally sink beneath the polar waters. But Holland and his team discovered that the temperature of the ocean water at the base of the glacier was a uniform 4 degrees Centigrade from top to bottom at depth to 800 metres. The finding was also recently confirmed by Nasa’s OMG (Oceans Melting Greenland) project.

“This is unsustainable from the point of view of glacier mass balance as the warm waters are melting the glacier much faster than they can be replenished,” said Holland.

Surface melt drains through the ice sheet and flows under the glacier and into the ocean. Such fresh waters input at the calving front at depth have enormous buoyancy and want to reach the surface of the ocean at the calving front. In doing so, they draw the deep warm tropical water up to the surface, as well.

All around Greenland, at depth, warm tropical waters can be found at many locations. Their presence over time changes depending on the behaviour of the Gulf Stream. Over the last two decades, the warm tropical waters at depth have been found in abundance. Greenland outlet glaciers like Helheim have been melting rapidly and retreating since the arrival of these warm waters.

Then the Hysteria and Pledge of Alligiance to Global Warming

“We are surprised to learn that increased surface glacier melt due to warming atmosphere can trigger increased ocean melting of the glacier,” added Holland. “Essentially, the warming air and warming ocean water are delivering a troubling ‘one-two punch’ that is rapidly accelerating glacier melt.”

My comment: Hold on.They studied effects from warmer ocean water gaining access underneath that glacier. Oceans have roughly 1000 times the heat capacity of the atmosphere, so the idea that the air is warming the water is far-fetched. And remember also that long wave radiation of the sort that CO2 can emit can not penetrate beyond the first millimeter or so of the water surface. So how did warmer ocean water get attributed to rising CO2? Don’t ask, don’t tell.  And the idea that air is melting Arctic glaciers is also unfounded.

Consider the basics of air parcels in the Arctic.

The central region of the Arctic is very dry. Why? Firstly because the water is frozen and releases very little water vapour into the atmosphere. And secondly because (according to the laws of physics) cold air can retain very little moisture.

Greenland has the only veritable polar ice cap in the Arctic, meaning that the climate is even harsher (10°C colder) than at the North Pole, except along the coast and in the southern part of the landmass where the Atlantic has a warming effect. The marked stability of Greenland’s climate is due to a layer of very cold air just above ground level, air that is always heavier than the upper layers of the troposphere. The result of this is a strong, gravity-driven air flow down the slopes (i.e. catabatic winds), generating gusts that can reach 200 kph at ground level.

Arctic air temperatures

Some history and scientific facts are needed to put these claims in context. Let’s start with what is known about Helheim Glacier.

Holocene history of the Helheim Glacier, southeast Greenland

Helheim Glacier ranks among the fastest flowing and most ice discharging outlets of the Greenland Ice Sheet (GrIS). After undergoing rapid speed-up in the early 2000s, understanding its long-term mass balance and dynamic has become increasingly important. Here, we present the first record of direct Holocene ice-marginal changes of the Helheim Glacier following the initial deglaciation. By analysing cores from lakes adjacent to the present ice margin, we pinpoint periods of advance and retreat. We target threshold lakes, which receive glacial meltwater only when the margin is at an advanced position, similar to the present. We show that, during the period from 10.5 to 9.6 cal ka BP, the extent of Helheim Glacier was similar to that of todays, after which it remained retracted for most of the Holocene until a re-advance caused it to reach its present extent at c. 0.3 cal ka BP, during the Little Ice Age (LIA). Thus, Helheim Glacier’s present extent is the largest since the last deglaciation, and its Holocene history shows that it is capable of recovering after several millennia of warming and retreat. Furthermore, the absence of advances beyond the present-day position during for example the 9.3 and 8.2 ka cold events as well as the early-Neoglacial suggest a substantial retreat during most of the Holocene.

Quaternary Science Reviews, Holocene history of the Helheim Glacier, southeast Greenland
A.A.Bjørk et. Al. 1 August 2018

The topography of Greenland shows why its ice cap has persisted for millenia despite its southerly location.  It is a bowl surrounded by ridges except for a few outlets, Helheim being a major one.

And then, what do we know about the recent history of glacier changes. Two Decades of Changes in Helheim Glacier

Helheim Glacier is the fastest flowing glacier along the eastern edge of Greenland Ice Sheet and one of the island’s largest ocean-terminating rivers of ice. Named after the Vikings’ world of the dead, Helheim has kept scientists on their toes for the past two decades. Between 2000 and 2005, Helheim quickly increased the rate at which it dumped ice to the sea, while also rapidly retreating inland- a behavior also seen in other glaciers around Greenland. Since then, the ice loss has slowed down and the glacier’s front has partially recovered, readvancing by about 2 miles of the more than 4 miles it had initially ­retreated.

NASA has compiled a time series of airborne observations of Helheim’s changes into a new visualization that illustrates the complexity of studying Earth’s changing ice sheets. NASA uses satellites and airborne sensors to track variations in polar ice year after year to figure out what’s driving these changes and what impact they will have in the future on global concerns like sea level rise.

Since 1997, NASA has collected data over Helheim Glacier almost every year during annual airborne surveys of the Greenland Ice Sheet using an airborne laser altimeter called the Airborne Topographic Mapper (ATM). Since 2009 these surveys have continued as part of Operation IceBridge, NASA’s ongoing airborne survey of polar ice and its longest-running airborne mission. ATM measures the elevation of the glacier along a swath as the plane files along the middle of the glacier. By comparing the changes in the height of the glacier surface from year to year, scientists estimate how much ice the glacier has lost.

The animation begins by showing the NASA P-3 plane collecting elevation data in 1998. The laser instrument maps the glacier’s surface in a circular scanning pattern, firing laser shots that reflect off the ice and are recorded by the laser’s detectors aboard the airplane. The instrument measures the time it takes for the laser pulses to travel down to the ice and back to the aircraft, enabling scientists to measure the height of the ice surface. In the animation, the laser data is combined with three-dimensional images created from IceBridge’s high-resolution camera system. The animation then switches to data collected in 2013, showing how the surface elevation and position of the calving front (the edge of the glacier, from where it sheds ice) have changed over those 15 years.

Helheim’s calving front retreated about 2.5 miles between 1998 and 2013. It also thinned by around 330 feet during that period, one of the fastest thinning rates in Greenland.

“The calving front of the glacier most likely was perched on a ledge in the bedrock in 1998 and then something altered its equilibrium,” said Joe MacGregor, IceBridge deputy project scientist. “One of the most likely culprits is a change in ocean circulation or temperature, such that slightly warmer water entered into the fjord, melted a bit more ice and disturbed the glacier’s delicate balance of forces.”

Update September 1, 2020 Greenland Ice Math

Prompted by comments from Gordon Walleville, let’s look at Greenland ice gains and losses in context.  The ongoing SMB (surface mass balance) estimates ice sheet mass net from melting and sublimation losses and precipitation gains.  Dynamic ice loss is a separate calculation of calving chunks of ice off the edges of the sheet, as discussed in the post above.  The two factors are combined in a paper Forty-six years of Greenland Ice Sheet mass balance from 1972 to 2018 by Mouginot et al. (2019) Excerpt in italics. (“D” refers to dynamic ice loss.)

Greenland’s SMB averaged 422 ± 10 Gt/y in 1961–1989 (SI Appendix, Fig. S1H). It decreased from 506 ± 18 Gt/y in the 1970s to 410 ± 17 Gt/y in the 1980s and 1990s, 251 ± 20 Gt/y in 2010–2018, and a minimum at 145 ± 55 Gt/y in 2012. In 2018, SMB was above equilibrium at 449 ± 55 Gt, but the ice sheet still lost 105 ± 55 Gt, because D is well above equilibrium and 15 Gt higher than in 2017. In 1972–2000, D averaged 456 ± 1 Gt/y, near balance, to peak at 555 ± 12 Gt/y in 2018. In total, the mass loss increased to 286 ± 20 Gt/y in 2010–2018 due to an 18 ± 1% increase in D and a 48 ± 9% decrease in SMB. The ice sheet gained 47 ± 21 Gt/y in 1972–1980, and lost 50 ± 17 Gt/y in the 1980s, 41 ± 17 Gt/y in the 1990s, 187 ± 17 Gt/y in the 2000s, and 286 ± 20 Gt/y in 2010–2018 (Fig. 2). Since 1972, the ice sheet lost 4,976 ± 400 Gt, or 13.7 ± 1.1 mm SLR.

Doing the numbers: Greenland area 2.1 10^6 km2 80% ice cover, 1500 m thick in average- That is 2.5 Million Gton. Simplified to 1 km3 = 1 Gton

The estimated loss since 1972 is 5000 Gt (rounded off), which is 110 Gt a year.  The more recent estimates are higher, in the 200 Gt range.

200 Gton is 0.008 % of the Greenland ice sheet mass.

Annual snowfall: From the Lost Squadron, we know at that particular spot, the ice increase since 1942 – 1990 was 1.5 m/year ( Planes were found 75 m below surface)
Assume that yearly precipitation is 100 mm / year over the entire surface.
That is 168000 Gton. Yes, Greenland is Big!
Inflow = 168,000Gton. Outflow is 168,200 Gton.

So if that 200 Gton rate continued, (assuming as models do, despite air photos showing fluctuations), that ice loss would result in a 1% loss of Greenland ice in 800 years. (H/t Bengt Abelsson)

Comment:

Once again, history is a better guide than hysteria.  Over time glaciers advance and retreat, and incursions of warm water are a key factor.  Greenland ice cap and glaciers are part of the Arctic self-oscillating climate system operating on a quasi-60 year cycle.