Climate Horror Stories That Wont Die: The Case of the Pika (Stewart, 2015)

pika

Because most people can’t fathom how 0.8 degrees of warming over a century can be lethal compared to far greater changes on a daily and seasonal basis, advocates of CO2 warming have littered the media and scientific literature with apocryphal stories statistically linking cherry-picked data with that small temperature rise and suggest wide spread future extinctions (i.e. Polar Bears,  Walrus, Emperor Penguins, Edith Checkerspot, Moose, Golden Toad ). Pikas are another species that have been repeatedly targeted as an icon of impending climate doom. Pikas, or boulder bunnies, inhabit talus slopes (boulder fields) throughout western North America’s mountainous regions. Some suggest warming has been driving pikas up the mountain slopes, and they will soon be driven over the edge into the extinction abyss.

The doomsday stories of the pikas’ “impending extinction” began with a few contentious papers by Dr. Erik Beever. He re-surveyed a small subset of Nevada’s pika populations and reported 28% (7 of 25)  of pika territories, which had been occupied at the beginning of the 20^th century, were now vacant. He suggested those 7 populations had gone extinct possibly due to climate change. That claim was then trumpeted by groups like the National Wildlife Federation with articles like “No Room at the Top.”

As they had done for polar bears and penguins, the Center of Biological Diversity argued climate change was threatening species with extinction and sued for pikas to be listed as federally endangered once, and as California endangered twice. The CBD alarmingly exaggerated Beever’s small survey to falsely report, “We've already lost almost half of the pikas that once inhabited the Great Basin.”  But to the credit of official wildlife experts, they rejected those lawsuits due to insufficient evidence. Dismayed that bad science had been rejected, the CBD called Obama a denier and Joe Romm bemoaned, “So long pika, we hardly knew ya.”

Now once again, dubious science is pushing pikas as another canary in the climate coal mine. Although the evidence has not supported the pika’s demise, Stewart (2015) constructed a model that would and published their projections in Revisiting The Past To Foretell The Future: Summer Temperature And Habitat Area Predict Pika Extirpations In California. These researchers predict “that by 2070 pikas will be extirpated from “39% to 88%” of California’s historical sites.  And once again the media is hyping that pikas are being pushed up the mountains to their doom.

In contrast to the hype, Dr. Andrew Smith, the International Union for the Conservation of Nature pika expert, has testified that pikas are thriving in California and should not be listed.  Although an avid defender of the Endangered Species Act, he argued that incorrectly listing the pika as endangered (see his letter here) would only subject the ESA to greater criticism and denigrate conservation science.

Due to possible climate change concerns, the US Forest Service was obligated to extensively survey pika habitat throughout the national forests of the Sierra Nevada and the Great Basin. Supporting Dr. Smith’s views, in 2010 they too reported thriving pikas. Overall, only 6% of observed pika territories were vacant. Due to the lack of connectivity with other pika territories, when a pika dies the smaller more isolated territories suffer longer periods of vacancy. Accordingly, the USFS reported that vacancy rates increased as surveys moved from the Sierra Nevada with its large interconnected talus slopes to more isolated habitat in the Great Basin. In the Sierra Nevada the vacancy rate was just 2%, in the southwestern Great Basin vacancies increased to 17%, and vacancies were highest, 50%, in more isolated habitat of the central Great Basin ranges. The larger percentage of unoccupied sites east of the Sierra Nevada crest was typically due to the greater difficulty of finding and re‑colonizing relatively small and isolated habitat.

USFS surveys provided more damning evidence that would lead to rejecting the CBD’s lawsuits. The benchmark for wildlife abundance and distribution in California had been Joseph Grinnell’s surveys from the early 1900s. Contrary to global warming theory, the USFS survey found many new active pika colonies several hundred meters lower than Grinnell had documented. In total, 19% of the currently known populations are at lower elevations than ever documented by any study during the cooler 1900s. Further north in the Columbia River Gorge, another independent researcher also found pikas at much lower elevations, surviving at temperatures much higher than the models had predicted.

Beever’s 2011 paper tried to counter those findings by arguing there was a nearly “five-fold increase in the rate of local extinctions and an 11-fold increase in the rate of upslope range retraction during the last ten years.” But Beever had badly manipulated his data. Surveying his 25 sites, he too had found 10 examples where pikas now inhabited lower elevations than previously documented. But he decided not to use those observations in his calculations. He, the editors and peer-reviewers unapologetically published his biased calculations to create his “11-fold increase in the rate of upslope range retraction”. Beever defended this statistical blasphemy by arguing pikas had likely always lived at those lower elevations, but had escaped detection by earlier observers (the equivalent of climate science infilling). Perhaps. It was possible. But by eliminating all new observations of pikas at warmer, lower elevations, he guaranteed their statistical upslope retreat.

Here’s an example of his calculations:   At Cougar Peak, a 1925 record documented the lowest elevation that pikas had inhabited was 2416 meters. Beever’s more recent surveys detected pikas living even lower on Cougar Peak at 2073 meters in the late 1990s, and at 2222 metes in  follow‑up surveys in 2003. Despite the fact that recent observations were all lower than 1925 by about 200 meters, Beever ignored the historical record. He simply subtracted the 1990s elevation from the 2003 elevation, to report climate had pushed pikas 149 meters higher. Furthermore, the Cougar Peak site was one of the sites Beever had initially reported as extinct.  Follow-up surveys found a robust population.

Vacant pika territories are natural and to be expected. Pika are very territorial and each year they drive their young away. Because pika live no longer than 7 years, (averaging 3 to 4 years in the wild), there is constant turnover at each site. A site remains vacant until a young pika, driven from another territory, randomly scampers into that vacancy and claims ownership. Without knowing how often a talus pile alternates between occupied and vacant, simply reporting observations of a vacant site tells us nothing about 1) why it is vacant, 2) when it was vacated, and 3) if it will soon be recolonized. Unfortunately vacancies have been misleadingly called extinctions. To illustrate, in the most recent paper by Stewart, his team initially found 15 vacancies, but a re‑survey the following year, found that 5 of those sites were now re‑colonized, a 33% reduction in “extinct locations” in just one year.

Re‑colonization has similarly undermined other classic doomsday stories. Parmesan’s iconic 1996 paper reported global warming had increased extinctions for the Edith’s Checkerspot butterfly, but most of those extinct colonies in the Sierra Nevada have now been re‑colonized. Unfortunately the re‑colonization information was never published. (read here and here).

The IUCN’s Dr. Andrew Smith is the only researcher with results from long term pika monitoring that actually provides insight into the natural frequency of “extinction” and re‑colonization.

Pika on Bodie ore piles

In California’s abandoned desert mining town of Bodie, pika have colonized discarded ore piles. Dr. Smith tracked the vacancy rates of 76 ore piles from 1972 to 2009. As expected, during those 37 years Smith observed 107 local extinctions, balanced by 106 re-colonizations. Like pika habitat elsewhere in the Great Basin, on average 30% of the ore piles were unoccupied at any given time, but that vacancy rate was highly variable. Some years the vacancy rate was as high as 52%, and other years as low as 11% (see chart below). In his first survey in 1972, Smith found that 82.3% of the ore piles were occupied by pika. In 2009, pika again occupied 82.8% of their possible sites. Coincidentally Stewart (2015) found 85% (57/67) of his re-surveyed sites are now occupied. Without accounting for such a wide range of variability, the percentage of vacant territories tells us precious little about any climate effects. But in contrast to Smith’s analysis, Stewart presented vacant territories as evidence of global warming caused “extinctions”.

Pika Colonizaton and Extinctions at Bodie

Although Smith’s research establishes a natural frequency of vacancy rates, it still doesn’t tell us why a site became vacant. In Beever’s 2003 paper, the seven “extinct” sites he attributed to climate change had other more plausible explanations. One site had half of the talus removed for road maintenance, another site had become a dump site, and a third site had scattered shotgun shells throughout the talus.

Like rabbits, and a truly endangered species of pika in China, pikas have been hunted and poisoned because they compete with livestock for vegetation. All of Beever’s extinct  sites were heavily grazed. Furthermore pikas do not hibernate. They create hay piles to sustain them through the winter. Any significant loss of vegetation will likely cause pikas to abandon their talus. Although studies have reported significant effects from grazing competition, Stewart (2015) did not include grazing as a variable in his climate change model.

Stewart (2015) created a model that only included 1) area of talus and 2) summer mean temperature as the determinants of local pika extinctions. Assuming that model represents reality, they then argued that according to projected warming from CO2 driven models, pika will become increasingly “extirpated from 39% to 88% of these historical sites”.

But talus area is the more critical variable, and the average summer temperature is highly questionable. Larger talus areas sustain more pika territories, and provide protection for dispersing young looking for vacancies. With more adjacent territories, there are more young pika who can immediately occupy any abandoned territory. In contrast the smallest talus areas, often sustaining just a single territory, are islands that lack connectivity to other territories. Vacant territories must wait to be randomly colonized by dispersing young from some distance talus. As the distance between isolated territories increases it is less likely that randomly dispersing young will re‑colonize a vacated territory. But the degree of connectivity was also never considered in Stewart’s model. As seen in his diagram below (I added the red lines for reference), the vacancies can be readily explained just by the talus area and random dispersal.

Stewart 2015 pika model

If the size of the talus area had been modeled as the only predictor of pika vacancies, any large talus area, (areas above the upper red line), would correctly predict full occupancy, accounting for 31% of the sites (20 of 67), regardless of temperature. Small talus area (areas below the lower red line) would correctly account for 70% of the vacancies (7 of 10 vacancies) regardless of temperature. In talus of intermediate areas, only 7% of the sites were vacant (3 of 39) which is close to the overall 6% finding of the USFS surveys. That 7% vacancy rate is easily accounted for by random extinction/colonization events, and the percentage is far better than vacancy rates Dr. Smith reported for Bodie’s ore piles. 

The higher temperatures reported at the 3 vacancies with intermediate talus areas may have been the result of a more barren dry landscape typical of the eastside of the Sierra Nevada. If so, lack of food, not higher temperatures may be the critical factor. Stewart never asks if the vacancies are due to higher temperatures, less reliable vegetation, or distance from other territories. Stewart’s model statistically linked higher temperatures to pika vacancies, but that link depends on what sites he includes or omits in his database.

Beever’s data had similarly suggested higher temperatures were killing pika, but his analysis excluded data from nearby populations thriving at warmer and lower elevations just 93 miles away from 71% of Beever’s extinct sites. At Lava Beds pika were flourishing at an average elevation 900 feet lower than the average elevation of three nearby extinct sites. Temperatures at Lava Beds also averaged an additional 3.6°F higher, and precipitation was 24% less. But Beever analyzed those sites separately. Likewise Stewart was clearly more interested in a connection to global warming. In his introduction he speculated, “climate change forces range contractions, species may effectively be ‘pushed off’ the tops of mountains by warming climate.” He also referenced Parmesan’s bad climate science connection for support. To create a link to global warming, Stewart needed to use average summer temperature as the other model variable.

During high temperatures, heat-sensitive pika will seek refuge beneath the cooler talus. However Stewart argues such behavior reduces critical foraging time and thus possibly reduces winter survival. Perhaps. During extreme warm days, pika are known to become crepuscular, restricting their foraging to the twilight hours. However if that is the key mechanism, then using the average temperature is simply wrong. The average temperature is amplified by minimum temperatures of the early morning when overheating is not a problem. If Stewart was sincerely concerned about induced heat stress, then the correct metric would be the afternoon maximum temperatures. But maximums were not even considered in Stewart’s choice of models.

Not considering maximum temperatures would seem shamefully negligent, but Stewart was aware that other studies had already determined no correlation with maximum temperatures. Stewart referenced Beever (2010) who wrote, ““Although pikas have been shown to perish quickly when experimentally subjected to high temperatures, our metric of acute heat stress was the poorest predictor of pika extirpations.” Because maximum temperatures had revealed no acute heat stress, Beever adopted the term “chronic heat stress” which was just a more alarming way to say the average temperature.  But even using average temperature,  Beever still concluded, “Climate change metrics were by far the poorest predictor of pika extirpation.” Stewart’s own data supported the conclusion that climate metrics provided poor explanatory power. 

Stewart also cherry-picked a start date to argue, “documented 1°C increases in California-wide summer temperature over the past century, strongly suggest that pikas have experienced climate-mediated range contraction in California over the past century.” However if one examines the data Stewart links to for northeastern California, where most of their “extinctions” were observed, recent summer maximum temperatures have not exceeded the 1920s and 30s. If pika extinctions were truly “climate-mediated”, then the high temperatures of the 20s and 30s should have been the main driver. Furthermore during that 20s and 30s, pika experienced the most rapid temperature increases of about 2°C (4°F) in just 3 decades.

Northeast California Maximum Temperatures

Stewart made one more feeble attempt to justify using average summer temperatures. He reported that a 2005 paper by Grayson revealed pika have been forced to move ever upwards as climate warmed throughout the Holocene. (See graph below) But Stewart seems unaware that he damaged is own argument. Several studies, using proxies and models, have shown the Great Basin was warmer during the Middle Holocene by 1 to 2.5°C.  Using Stewart’s logic, as global warming approaches temperatures seen in the mid Holocene, pika should descend to lower elevations.

Although summer temperature data has very little predictive power regards pika biology, it was Stewart’s only link to CO2 climate models. Using that dubious link to summer temperatures, he projects impending climate doom and widespread pika extinctions. But if Stewart was truly concerned about preserving pikas, instead of preserving CO2 theory, then all the data suggests small talus areas that are subjected to grazing are the relevant concern. To protect the pikas’ forage, simply fencing off livestock from the edge of those small talus slopes would be a simple affordable solution. Stewart’s own data also suggests, along with the USFS surveys,  that wherever there is large talus area, there has been nothing to suggest imminent extinctions. So why does the pikas’ climate change extinction story persist?

Grayson's depiction of elevations of pika habitat in the Holocene

Are we Victims of Virtual Vacuousness?

To be good stewards of the environment we need climate data we can trust. Because organisms always respond to local temperatures, ecologists never use homogenized data because the process obliterates natural variability as I had written about before. Now it is coming to light that much of the "warmest year ever" is the product of a fabricated virtual reality some are calling The Great Temperature Manipulation Scandal because homogenization obliterated warm peaks in the 30s and 40s. I would not have given such blog headlines any more attention than a supermarket tabloid, except for the fact my own research observed the exact same thing. It is so outrageous I would urge you all to demand a congressional investigation.

When I began researching the causes of a bird life collapse at one of my  meadows in the Sierra Nevada, I had to examine possible connections between biological competition, parasitism from cowbirds, affects of grazing, landscape changes, the altered hydrology, and climate change. I dismissed climate change because in the Sierra Nevada the maximum temperatures were greatest in the 30s and 40s and that observation held true from Tahoe City to Yosemite to Death Valley. Apparently the Sierra Nevada were not very sensitive to rising CO2. Below is the graph downloaded from the US Historical Climate Network for Yosemite.

USHCN Maximum Temperatures at Yosemite National Park

Later, Peter Meisler, a rabid believer in CO2 caused global warming, who runs a small blog dedicated to smearing all skeptics, began stalking and attacking me in his blog.  When he saw my version of Yosemite's temperature trend in my IEEE presentation, his cognitive dissonance was so great he launched a smear campaign suggesting "my" Yosemite trends were a fraud. But that would only be true if the US Historical Climate Network is also fraudulent. Unfortunately based on recent revelations, the USHCN is coming under increasing suspicion due to The Great Temperature Manipulation Scandal! Meisler proved to be just another whacko internet sniper, as Yosemite's trend is nearly identical to my presentation. But I soon realized that graphs of temperature trends I had created for my book in 2011 were being continuously  adjusted making them warmer and warmer over a period of just 5 years even though the data had been quality controlled over a decade ago.

For example, I had downloaded Death Valley's maximum temperature data (graph on right, maximum temperature is solid line)in 2010 and published the graph in my book. The new USHCN trend for Death Valley downloaded January 2015 (on left) manipulated  a cooling trend into a warming trend.

When I was investigating IPCC's Camille Parmesan's bogus claims that global warming had killed the Eidth's Checkerspot butterfly, I had examined all the USHCN stations in southern California and downloaded USHCN data in 2010. So I was curious if other stations had also been manipulated warmer. Cuyamaca is a high quality weather station that had never moved or change instrumentation, so it should serve as a standard. But for no reason its trends was also manipulated, and manipulated repeatedly from its raw data.

Here's how the Cuyamaca was adjusted since that time and I had to wonder how much of the "warmest year on record" was the result of such data tampering. It didn't take long to find others were observing the same manipulations.

Steve Goddard has posted to his website Real Science the following observations.

For Reykjavik Iceland

Paul Homewood on his blog A Lot of People Don't Know That posted similar manipulations from Paraguay. 

And the same in Russia

And in Australia

Another independent researcher, Bill Illis, posted this Iceland graph (below) to my blog post at WUWT writing "The top right panel is the quality controlled temperatures from Iceland Met which they insist requires no further adjustment for any station moves, TOBs or polar bears or anything. The second right panel is the temperatures the NCDC reports to the public and the bottom right panel is the adjustment they apply to each year. I mean “reports to the public” is a government agency supplying temperature data to the whole world. 75% of the stations from the NCDC have this same pattern.

Bill suggests that about 75% of the global data has been manipulated in a very similar way, so I suspect we will soon see a growing wealth of evidence that adjusted data we are fed in the headlines are not to be trusted. 

To be good stewards of the environment we need data we can trust. Write your congressman and ask for an investigations!

My interview with Heartland's Dr. H. Sterling Burnett

 Link to  Janurary 27, 2015 Podcast

 interview with Jim Steele: An Environmentalist's Journey to Climate Skepticism

What Does NOAA’s Warmest Year Tell Us About Climate Sensitivity to CO2?

A friend of mine who works for the EPA emailed me a link to NASA’s Earth Observatory page pitching 2014 as the warmest year on record, and asked if  “I dismiss their findings.” The following is an edited version of my reply suggesting the Global Average Chimera tells us precious little about the climate’s sensitivity to CO2, and the uncertainty is far greater than the error bars illustrated in Anthony Watts post 2014: The Most Dishonest Year on Record.

I simply asked my friend to consider all the factors involved in Gavin Schmidt’s making of the global average temperature trend, and ask you all to do the same. Then decide for yourselves the scientific value of the graph and if there was any political motivation.

1. Consider the greatest warmth anomalies are over the Arctic Ocean because more heat is ventilating through thinner ice. Before the Arctic Oscillation removed thick insulating sea ice, air temperatures were declining. Read Kahl, J., et al., (1993) Absence of evidence for greenhouse warming over the Arctic Ocean in the past 40 years. Nature 361, 335 – 337.

 

NOAA's 2014 Temperature Anomalies

  

After subfreezing winds removed thick ice, then air temperatures rose. Read Rigor, I.G., J.M. Wallace, and R.L. Colony (2002), Response of Sea Ice to the Arctic Oscillation, J. Climate, v. 15, no. 18, pp. 2648 – 2668. They concluded, “it can be inferred that at least part of the warming that has been observed is due to the heat released during the increased production of new ice, and the increased flux of heat to the atmosphere through the larger area of thin ice.”

CO2 advocates suggest CO2 leads to “Arctic amplification” arguing dark open oceans absorb more heat. But the latest estimates show the upper 700 meter of the Arctic Ocean are cooling (see illustration below), which again supports the notion ventilating heat raised air temperatures. Read Wunsch, C., and P. Heimbach, (2014) Bidecadal Thermal Changes in the Abyssal Ocean, J. Phys. Oceanogr., http://dx.doi.org/10.1175/JPO-D-13-096.1.

So how much of the global warming trend is due to heat ventilating from a cooling Arctic ocean???

Change in top 700 meters of Ocean Heat Content between 1993 and 2011

2. Consider that NOAA’s graph is based on homogenized data. Researchers analyzing homogenization methods reported “results cast some doubts in the use of homogenization procedures and tend to indicate that the global temperature increase during the last century is between 0.4°C and 0.7°C, where these two values are the estimates derived from raw and adjusted data, respectively.”

Read Steirou, E., and Koutsoyiannis, D. (2012) Investigationof methods for hydroclimatic data homogenization. Geophysical Research Abstracts, vol. 14, EGU2012-956-1.

So how much of the recent warming trend is due to the virtual reality of homogenized data???

3. Consider the results from Menne. M., (2009) The U.S. HistoricalClimatology Network Monthly Temperature Data, version 2. The Bulletin for the American Meteorological Society, in which they argued their temperature adjustments provided a better understanding of the underlying climate trend. Notice the “adjusted” anomalies in their graph below removes/minimizes observed cooling trends. More importantly ask why does Menne (2009) report a cooling trend for the eastern USA from 1895to 2007, but NASA shows a graph (below Menne’s) with a slight warming trend for all of the USA from 1950-2014?  Does that discrepancy indicate more homogenization, or that they cherry-picked a cooler period to start their warming trend? 

How homogenization warmed the USA

NOAA's 1950-2014 global warming trend 

4. Consider that most of the warming in North America as illustrated by Menne 2009 (above) happened in the montane regions of the American west. Now consider the paper Oyler (2015) Artificial amplification of warming trends across the mountains of thewestern United States, in which they conclude, “Here we critically evaluate this network’s temperature observations and show that extreme warming observed at higher elevations is the result of systematic artifacts and not climatic conditions. With artifacts removed, the network’s 1991–2012 minimum temperature trend decreases from +1.16°C/decade to +0.106°C/decade.

So how much of the recent warming trend is due to these systematic  artifacts???

5. Consider that NOAA’s graph is based on adjusted data and the fact that NOAA now homogenizes temperature data every month and climate trends change from month to month, and year to year. As an example, below is a graph I created from the US Historical Climate Network Cuyamaca weather station in southern California; a station that never altered its location or instrumentation. In 2011 the raw data temperature trend does not differ much from the homogenized trends (Maximum Adj.)

US Historical Network raw and homogenized maximum temperatures at Cuyamaca

Just 2 years later, the 2011 homogenized century warming trend (in black ) increased by more than 2°F the 2015 trend (in red.) I have archived several other similar examples of this USHCN datamanipulation. Then ask your self which is more real? The more cyclical changes observed in non-homogenized data or the rising trend created by homogenized virtual reality?

Cuyamaca's rapidly warming trend created by homogenization

6. Consider that climate change along western North America has been completely explained by the Pacific Decadal Oscillation and the associated cycles of ventilation and absorption of heat. Read: Johnstone and Mantua (2014) Atmospheric controls on northeast Pacific temperature variability and change, 1900–2012.  Such research suggests non-homogenized data may better represent climate reality.

Knowing that the upper 10 feet of the oceans contain more heat than the entire atmosphere ask yourself if decadal warming trends are simply artifacts of the redistribution of heat.

7. Consider that increasingly temperature data is now collected at airports. A 2010 paper by Imhoff, “Remote sensing of the urban heat island effectacross biomes in the continental USA”, published in Remote Sensing of Environment 114 (2010) 504–513 concluded that “We find that ecological context significantly influences the amplitude of summer daytime urban–rural temperature differences, and the largest (8 °C average) is observed for cities built in biomes dominated by temperate broadleaf and mixed forest. For all cities combined, Impervious Surface Area is the primary driver for increase in temperature explaining 70% of the total variance. On a yearly average, urban areas are substantially warmer than the non-urban fringe by 2.9 °C, except for urban areas in biomes with arid and semiarid climates.”

So how much of this recent warming trend can be attributed to increases in Impervious Surface Area in and around weather stations in rural, suburban and urban settings?

8. Consider that direct satellite observations show lost vegetation has a warming effect, and transitions from forest to shrub land, or grassland to urban area raise skin surface temperatures by 10 to 30°F.  Satellite data reveals the canopies of the world’s forests averaged about 86°F, and in the shade beneath the canopy, temperatures are much lower. Grassland temperatures are much higher, ranging from 95 to 122°F, while the average temperatures of barren ground and deserts can reach 140°F. Read Mildrexler, D., et al. (2011) A global comparison betweenstation air temperatures and MODIS land surface temperatures reveals thecooling role of forests. J. Geophys. Res., 116, G03025, doi:10.1029/2010JG001486.

Ask yourself, “how much of the warming trend is due to population effects that remove vegetation??” How much is due to citizens of poorer nations removing trees and shrubs for fuel for cooking and heating or slash and burn agriculture?

9. Consider that neither of the satellite data sets suggest 2014 was the warmest ever recorded.

Global temperature trend from satellite data 

10. Consider that none of the tree ring data shows a warming that exceeds that 1940s as exemplified by Scandinavian tree ring data (from Esper, J. et al. (2012) Variability and extremes of northern Scandinavian summer temperatures over the past two millennia. Global and Planetary Change 88–89 (2012) 1–9.)

Tree ring and Scandinavian instrumental data show show warmest decade in the 1930s

Consider international tree ring experts have concluded, “No current tree ring based reconstruction of extratropical Northern Hemisphere temperatures that extends into the 1990s captures the full range of late 20th century warming observed in the instrumental record.”  Read Wilson R., et al., (2007) Matter of divergence: tracking recent warming at hemispheric scalesusing tree-ring data. Journal of Geophysical Research–A, 112, D17103, doi: 10.1029/2006JD008318.

In summary, after acknowledging the other factors contributing to local temperature change, and after recognizing that data homogenization has lowered the peak warming of the 30s through the 50s in many original data sets by as much as 2 to 3°F, (a peak warming also observed in many proxy data sets less tainted by urbanization effects), ask yourself, does NOAA’s graph and record 2014 temperatures really tell us anything about climate sensitivity or heat accumulation from rising CO2? Or does it tell us more about climate politics and data manipulation?

NOAA's Global Temperature Trend: What does it tell us about the causes?

On Migrating Moose and Migrating Temperature Trends

 

Bull Moose

The biggest threat to the integrity of environmental science is bad science, exaggeration and fear mongering. The recent hype about declining moose populations is just one more example of global warming advocates hijacking and denigrating ecological science. All organisms act locally, yet global warming advocates quickly characterize any local wildlife declines as the dastardly work of global warming.

In northeastern Minnesota, moose populations reached an historic high abundance of 8,840 in 2006, and then rapidly declined to 4,230 in 2012. Most recently in a 2013-2014 survey, estimates dropped to 2,760 moose. Cause for alarm? Perhaps. But moose are a species known to naturally exhibit booms and busts when their habitat can no longer sustain a rapidly growing population. Instead of deeper discussions on the ecological complexities, but reminiscent of the fearful headlines that “childrenwill no longer know what snow is”, the NationalWildlife Federation (NWF) bellowed “People never forget seeing their first moose. But due in part to the effects of climate change, it could well be their last. Moose are being hurt by overheating, disease and tick infestation – all tied to warming temperatures.”  And to magically save the moose, the NWF encourages you to sign their petition to the EPA to curb CO2 emissions, and for just $20 to $50 you can adopt a moose from the NWF. Presumably the $50 moose is in its prime and carries fewer ticks.

The Audubon Society similarly published MysteriousMoose Die-Offs Could be Linked to Global Warming and climate scientists like MichaelMann, who has hitched his scientific status to “dire predictions”, wrongly connect declining moose populations to rising CO2. There are so many reasons to be revolted by their fear mongering and its denigration of ecological science, it’s hard to know where to start. For instance, the greatest spike in moose mortality happens in March at the end of severe winters. Milder winters can be beneficial. While alarmists blame moose deaths on “global warming”, the rapid decline in northeastern Minnesota has happened in a region experiencing bouts of record breaking low temperatures. Nearby International Falls, MN broke its record January low of -37°F set in 2010, by dropping to -41°F in 2014, which followed December’s record setting 8 days with a temperature of less than -30°F. Averaging local temperatures is likely as useless as referring to the global temperature.

Furthermore moose die‑offs are not global. In adjacent habitat of southernOntario, moose are stable or increasing. Estimates of moose populations have traditionally been based on hunters’ harvest and in Scandinavia, the annual harvest was less than 10,000 in the early 1900s. After a century of global warming the moose population reached an all time high with annual harvests increasing20­‑fold to 200,000. Similarly in the 1900s, moose from British Columbia expanded into Alaska and multiplied as the climate warmed. In New England, moose were once more abundant than deer when the Pilgrims arrived. But due to deforestation for farmland and overhunting, moose have been absent from Massachusetts and Vermont for 200 years. In 1901 less than 20 moose were believed to inhabit New Hampshire. But in contrast to fearful globalwarming  theory and species range, since 1980 moose have migrated south from New Hampshire into Massachusetts and Connecticut, despite temperatures that average 4 to 6° F warmer.

Scandinavian biologists suspect the moose population may begin to decline, but their reasons illustrate the complex ecology. Increasing moose densities strain food supplies resulting in lower body mass, lower reproductive success, and lower resiliency. Moose thrive on vegetation common in regenerating forests that have been cleared by insect outbreaks, fires or logging. Scandinavian logging increased during the 20^th century but has now peaked and will decline, and so moose will lose habitat as closed forest canopies reclaim the landscape . Except in eastern Finland, depredation by wolves has been minimal, but wolf populations are now rebounding.

The beststudied moose population exists just east of Minnesota’s northeast border on Isle Royale in Lake Superior and illustrates the boom and bust nature of moose populations. As moose populations globally expanded in the 1900s, they soon colonized Isle Royale around 1912 and rapidly grew to over 3000 by early 1930s. Rapid population growth diminished food supplies and a starvation crash happened in1934. Extensive forest fires in 1936 increased their preferred vegetation and feasting on young vegetation in a regenerating forest, the population rebounded until it peaked , followed by increasing winter starvation. To add another factor governing moose population, in the 1940s wolves colonized Isle Royale.

Wolves attacking a moose

Virtually every college ecology text discusses the predator-prey interactions illustrated by the wolves and moose of Isle Royale. As observed elsewherein the Great Lakes region, moose populations remained low until they began increasing in the 1950s. As seen in the diagram below, moose populations rose but also ebbed and flowed inversely with wolf populations. In contrast to suggestions that global warming is killing moose, during the rapid warming from the 80s to 90s, Isle Royale moose doubled their population, approaching a peak not observed since the 1930s, then suddenly crashing to just 500 in 1997. Moose have slowly rebounded since 2007 and are now at levels 50% higher than the 1950s.

Relationship between moose and wolf populations at Isle Royale

In response to the dramatic decline of moose in northeastern Minnesota, over 100 moose were equipped with radio-collars that could alert biologists to the moose’s impending death, allowing biologiststo account for the deaths of 35 calves and 19 adults.

- 16 calves (46%) were killed by wolves

- 13 calves (37%) calves died due to mother abandonment. Eleven were caused when the mothers abandoned the calve during the act of attaching the collars,  2 were abandoned later.

- 4 calves (11%)  were eaten by bears

- One calf drowned and 1 calf died of unknown causes.

- Of the 19 adults, 10 (53%) were killed directly or indirectly by wolves.

Oddly given those results, biologist received a new $750,000 grant to study the effects of “global warming” on declining moose. I suspect it is politically more convenient to blame declining moose on global warming rather than to blame natural boom and busts, rebounding wolf populations, or researcher induced casualties.

Similar fear mongering blamed global warming for recent declines in New Hampshire’s moose. On a PBSNewshour, the interviewer interspersed interviews with researchers and Eric Orff of the National Wildlife Federation who insinuated that it’s all about climate change. Like the debunked claims of Parmesanthat global warming is killing animals in the south, Orff highlighted dwindling moose populations on the southern end of their range, concluding, “we need to put this earth on a diet of carbs, carbon, and bring back winter.” But New Hampshire’s average temperature has little meaning. Moose can respond to temperature changes by moving to different microclimates. Between a gravel road, open shrub lands, ponds, and closed canopies of deep evergreen forest, temperatures will vary by 20° to 40°F. A mosaic of habitats is more critical than a 1° degree change in average temperature.

In addition, Orff failed to mention that moose have been migrating from New Hampshire southward and thriving where climates averaged 4°F to 6°F warmer and winters are much milder. Orff also failed to inform the public about normal population boom and busts. New Hampshire’s moose population stagnated at fewer than 15 individuals since the mid 1800s and did not begin to rebound until the 1970s. As the climate warmed numbers exploded, by 1988 growing to 1600, and then 7500 by the late 1990s. That increase resulted in more moose‑car collisions and a public clamor for increased moose hunts.  Perhaps because the public would be less likely to “adopt a moose” that needed to be hunted, Orff failed to   mention that according to Fishand Game about half of New Hampshire’s recent population drop from 7500 to 4000 moose was due to a public safety management decision to increase hunting.

New Hampshire’s remaining decline has been blamed on moose ticks, which some suggest have increased due to milder winters. Perhaps. But moose also survive better during milder winters. On average moose are covered with 30,000 ticks and each tick can lay a thousand eggs. When moose populations explode so do the ticks. Unprecedented tick abundance coincides with unprecedented moose populations. Besides biologists have observed such parasite‑driven booms and busts for over a century.

Growing up in Massachusetts, moose were unheard of so far south. We travelled north to Baxter State Park in Maine to canoe the streams with hopes of seeing moose. Moose are indeed sensitive to warmer temperatures, so why would moose migrate southward to a warmer region that was also experiencing rapid “global warming”. Homogenized data suggested a rapid warming trend but as an ecologist, I knew homogenized temperatures are worthless for wildlife studies because the process eliminates natural temperature variations and alters the actual mean temperatures. However I also understood that trends determined from raw data can suffer from changes in instrumentation and/or changes in location.

I first looked at minimum temperatures for the only 2 USHCN weather stations in western Massachusetts where moose populations had been thriving since the 1980s. Both stations exhibited peak warming around the 1950s in the raw data, but after homogenization, that peak was lowered. For Amherst the peak dropped by 4°F. Onto the graphs downloaded on January 10 from the USHCN, I superimposed changes in instrumentation (designated by the vertical red lines) and changes in location (designated by the blue arrows). But those changes did not logically or intuitively explain the newly fabricated warming trend or the cooling of the 1950s peak. (raw data on left, homogenized on right)

Raw (left) and Homogenized (right) Minimum Temperatures for Great Barrington and Amherst MA

So I looked for a USHCN station with no such changes. Only one Massachusetts station, West Medway (below), had not moved and did not change thermometers. I assumed it would serve as the best standard with which to constrain any trend adjustments at other stations.  Yet West Medway was also homogenized (below on right) creating the same virtual warming trend. More importantly, West Medway’s raw minimum temperature trend had the same basic curve as the 2 western stations. 

The homogenization process for both NOAA and BEST creates a “regional expectation” based on similarities among neighboring stations, which in turn guides their temperature adjustments. But if USHCN stations are deemed to be of the highest quality with the fewest gaps and relocations, what data (likely much less reliable) was being used to re-create West Medway’s warming trend. If West Medway’s raw data shared similar trends with nearby stations, wouldn’t Medway’s trend be a reliable “regional expectation”? More troubling, the homogenization process undeservedly altered observed temperature peaks. Like Amherst, homogenization lowered West Medway’s 1950’s peak by 3 to 4°F, a lowering that was also applied to many other stations such as the Reading station (raw data left, homogenized right).

 

Raw and Homogenized Minimum Temperatures at West Medway, MA

Raw and Homogenized Minimum Temperatures at Reading MA

So I was curious how the raw data from West Medway’s nearby USHCN stations compared and affected the regional expectation. The Blue Hill Observatory (below left) sits just 28 miles east of West Medway and is a historical landmark that has not moved. Its trend agrees with West Medway, peaking in around 1950 and then cooling until 1980. However after 1980, due to changes in instrumentation, it is not clear how much of the exaggerated rising trend is due to climatic factors (natural or CO2) or the result of a warming bias caused by new instruments.

Taunton (below right) is located 29 miles southeast of West Medway.  It too exhibits a peak around 1950 and a cooling trend to 1980. However once again the cause of the subsequent warming trend is obscured by the change in the measuring system. However there was one nearby station, Walpole that maintained the same equipment.

Raw Minimum Temperatures at Blue Hill Observatory and Taunton MA

Walpole (below) is situated just 12 miles east of West Medway and just west of the Blue Hill observatory. But Walpole exhibited a warming trend more similar to Massachusetts’ homogenized trend. Of which of those stations should anchor a “regional expectation”? Walpole’s raw data had an odd curve not shared by most of the other stations. Although all stations experienced a warming spike during the 1972-74 El Nino/La Nina event, that peak was typically a degree lower than the 1950s. However Walpole reported an unusually higher 70s peak suggesting that after 1950 the weather station had moved to a warmer microclimate. But NOAA’s metadata did not specifically mention any relocation. Thinking I had missed that information, I rechecked. Although a relocation was not specifically mentioned, the GPS coordinates revealed a significant move in 1973. Yet comparing the raw data (below left) to the homogenized data (below right red), Since 1915 Walpoles raw data remained un-homogenized. Did the warming bias from the 1980s instrumental changes, create a confirmation bias for Walpole?

Raw Minimum Temperatures(left) and Quality Controlled vs Homogenized TMIN at Walpole

What I assume is a most reasonable method to quality control for a location change,  I compared the differences between West Medway (the only unaltered site) and Walpole’s minimum temperatures before and after Walpole’s location change. Between 1905-1973 Walpole averaged 0.3596 +/- 1.07°F warmer than Plymouth. Walpole could vary between 2° cooler one year to 4.6° warmer another. This great variability is natural and expected. Depending on how far east winter storm tracks travel up the east coast, the battle line between cold arctic air masses to the west and warm Atlantic air to the east causes significant temperature changes. Depending on the depth and extent of the cold air mass, the overriding warm Atlantic air can cause different parts of the state to simultaneously experience rain, freezing ice, sleet and snow.

After the station moved, between 1974-2004 Walpole temperatures averaged 2.89 +/- 1.29F warmer than Medway, but with similar year-to-year variability ranging between 1.5 cooler one year to 4.5 warmer another. It is impossible to adjust for such local variations. But to extract a climate trend, it is reasonable to subtract the difference in mean temperatures before and after the relocation. So I subtracted 2.53 F (2.89-0.35) from all Wapole temperatures after 1973 to create my “quality controlled” trend (blue) and plotted that against the USHCN homogenized trend (red in graph above right). Unsurprisingly Walpole’s “quality controlled” data and West Medway’s raw data exhibit very similar trends with peaks and valleys coinciding with the Atlantic Multidecadal Oscillation (AMO).

So I more carefully checked the data from Plymouth about 52 miles to the southeast. Unfortunately data from the Plymouth weather station does not extend back to the landing of the Pilgrims, which marked the beginning of the end for moose in Massachusetts. But after adjusting for Plymouth’s 2 obvious location changes, in 1966 and 1990 (blue arrows), Plymouth’s “quality controlled” data revealed a trend very similar to West Medway and a “regional expectation” related to the AMO. Most interesting, once Plymouth’s location change was accounted for there was no instrumental warm bias. As discussed by Davey and Pielke, a warming bias if often associated with MMTS temperature instruments, because new instruments and a new location happened simultaneously. Insignificant location changes could cause a warming bias when weather stations were moved closer to a building and subjected to a warmer micro-climate.

Raw Minimum Temperatures(left) and Quality Controlled vs Homogenized TMIN at Plymouth MA

It is highly likely that due to its effect on storm tracks and competing air masses, the AMO can explain most of the east coast’s temperature trends in a manner similar to how the Pacific Decadal Oscillation controls the USA’s west coast trends as published by Johnstone 2014.  Unfortunately this relationship has been obscured by a highly questionable homogenization process.

To be clear, I am not suggesting a conspiracy of data manipulation by climate scientists. I am arguing that the homogenization process is ill-conceived and erroneously applied. Many local dynamics are overlooked  by a one-size fits all digital make over. Monthly homogenization can amplify those mistakes and has changed trends from one year to the next (as discussed for Death Valley). Homogenization has failed to adjust for documented location changes, yet created adjustments to untainted data where none were needed. Before we conclude that global warming is killing moose and creating unusually warmer winters, we need examine more closely local dynamics and their relationship to landscape changes and natural ocean cycles much more closely. Understanding local micro-climates are more important that a nebulous global climate. While it may be wise to think globally all organisms react locally, as do all weather stations.

NOAA's Atlantic Multidecadal Oscillation Index