Ocean Acidification: Natural Cycles and Ubiquitous Uncertainties

In 2002, Scripps’ esteemed oceanographer Walter Munk argued for the establishment of an Ocean Observation System reporting,  “much of the twentieth century could be called a “century of undersampling” in which “physical charts of temperature, salinity, nutrients, and currents were so unrealistic that they could not possibly have been of any use to the biologists. Similarly, scientists could find experimental support for their favorite theory no matter what the theory they claimed.” Due to that undersampling MIT’s oceanographer Carl Wunsch (2006) likewise noted, “Among the more troublesome distortions now widely accepted, one must include the notion that the ocean circulation is a simple “conveyor belt” and that the Gulf Stream is in danger of ‘turning off’.”

Another such favorite theory, mistakenly offered as a fact, speculates we are now witnessing increasing anthropogenic ocean acidification, despite never determining if current pH trends lie within the bounds of natural variability. Claims of acidification are based on an “accepted scientific paradigm” that “anthropogenic CO₂ is entering the ocean as a passive thermodynamic response to rising atmospheric CO₂.” Granted when all else is equal, higher atmospheric CO₂ concentrations result in more CO₂ entering the oceans and declining pH. But the ever-changing conditions of surface waters exert far more powerful effects. Whether we examine seasonal, multi-decadal, millennial or glacial/interglacial time frames, ocean surfaces are rarely in equilibrium with atmospheric CO₂. Relative to atmospheric CO₂, seasonal surface water can range up to 60% oversaturated due to rising acidic deep water. Due to the biological pump, CO₂ concentrations can be drawn down, leaving surface waters as much as 60% under‑saturated (Takahashi 2002). Thus we cannot simply attribute trends in surface water pH to equilibration with atmospheric CO₂. We must first fully account for natural ocean cycles that raise acidic waters from deeper layers and the biological responses that pump CO₂ back to ocean depths.

[note: in this essay I use “acidic” in a relative sense. For example, although the pH of ocean water is 7.8 at 250 meters depth and is technically alkaline, those waters are “more acidic” relative to the surface pH of 8.1.]

To appreciate the importance of pH altering dynamics, consider the fact that pure water has a neutral pH of 7.0. Rainfall quickly equilibrates with atmospheric CO₂, and pH falls to ~5.5. Dark‑water rivers such as the Rio Negro drop to pH 5.1. In contrast, due to a combination of biological activities and geochemical buffering, the average pH of ocean surfaces (and some rivers) rises to ~8.1. In other words, after equilibration with atmospheric CO₂, powerful factors combine to remove 99.8% of all acidifying hydrogen ions from rainwater. The balance between upwelled acidic waters versus carbon sequestration and export by the “biological pump” is the key factor maintaining high pH in oceanic surface waters, and the communities of plankton that operate that pump undergo changes on seasonal, multidecadal and millennial time scales; changes we are just beginning to understand.

In Bates 2014, A Time-Series View of Changing Surface Ocean Chemistry Due to Ocean Uptake of Anthropogenic CO₂ and Ocean Acidification, they simplistically argued declining ocean pH is “consistent with rising atmospheric CO₂”. But a closer examination of each site used in their synthesis suggests their anthropogenic attribution is likely misplaced.  For example, at the Hawaiian oceanic station known as HOT, based on 10 samplings a year since 1988, researchers reported a declining pH trend. But that trend was not consistent with invasions from atmospheric CO₂. An earlier paper (Dore 2009) had observed, “Air-sea CO₂ fluxes, while variable, did not appear to exert an influence on surface pH variability. For example, low fluxes of CO₂ into the sea from 1998–2002 corresponded with low pH and relatively high fluxes during 2003–2005 were coincident with high pH; the opposite pattern would be expected if variability in the atmospheric CO₂ invasion was the primary driver of anomalous DIC accumulation.” (DIC is the abbreviation for Dissolved Inorganic Carbon referring to the combined components derived from aqueous CO₂, including bicarbonate and carbonate ions.)

Those higher fluxes of CO₂ into the surface likely stimulated a more efficient biological pump resulting in higher pH. That rise in pH is consistent with experimental evidence demonstrating CO₂ is often a limiting nutrient (Riebesell 2007), and adding CO₂ stimulates photosynthesis. That most photosynthesizing plankton have CO₂ concentrating mechanisms suggests CO₂ is often in chronic short supply.

The greatest concentrations of CO₂ upwell from depth to invade surface waters. As seen below in the illustration by Byrne 2010 from the northern Pacific, the ocean’s pH (thus the store of DIC) rapidly drops from 8.1 at the surface to 7.3 at 1000 meters depth. Dynamics such as upwelling bring deeper waters to the surface reducing pH, while dynamics such as the biological pump shunt carbon back to deeper depths and raise surface pH.  At the risk of oversimplifying a myriad of complex dynamics, oceans basically undergo a 4-phase cycle that determines the average annual surface pH. Any adjustments to this cycle will alter trends in pH over decadal to millennial time periods.

 

Vertical profile ocean pH

Phase 1: Varied rates of upwelling and winter mixing raises acidic water to the sunlit surface  

and lowers pH.

Phase 2: Specific plankton communities, largely diatoms respond quickly to the arrival of

nutrients in the surface waters, and rapidly sequester and export carbon back to depth. Phase-2 productivity also generates dissolved and suspended organic carbon that is transported laterally to other regions. When community photosynthesis absorbs CO₂ faster than respiration releases it or upwelling injects it, surface pH rises.

Phase 3: As available nutrients are depleted, diatom populations dwindle and other plankton

communities dominate such as coccolithophores and photosynthesizing bacteria. Instead of rapidly exporting carbon, this plankton community is better at retaining and utilizing nutrients. The utilization of suspended and dissolved organic carbon and increased grazing by populations of zooplankton increase respiration rates relative to new photosynthesis, so pH declines.

Phase 4: A “regional equilibrium” is established as accumulated organic carbon from previous

phases is depleted and new, but lower, levels of productivity are balanced by community respiration. That balance raises pH. This equilibrium is fleeting and lasts until a new burst of nutrients reaches sunlit waters. The supply of nutrients rising to the surface cycles seasonally as well as over decades, millennia and glacial/interglacial intervals, so that short interval trends are embedded in much longer trends. This is one reason why computed pH trends by Bates 2014 statistically explained only a minor portion of pH variability even after removing seasonal trends.

 

Diatoms 

First consider that oceans store 50 times more CO₂ than the atmosphere. A small change in the rate by which deep acidic water reaches the surface is the major determinant of surface pH trends. Nutrients, acidity, and density increase with depth, but not all depths contain a balanced supply of nutrients critical for photosynthesis. To bring denser water to the surface requires a significant input of energy that is primarily provided by the winds or tides (Wunsch 2004). Stronger winds generate more upwelling and winter mixing.  Thus cycles of oceanic and atmospheric circulation that strength and weaken winds, raise varied combinations and concentrations of nutrients to the surface, which accordingly affects the biological pump and pH.

For example in temperate oceans, winter cooling of surface waters allows winds and storms to mix surface waters with CO₂ rich waters from as deep as 500 meters. This lowers surface pH, so that relatively insignificant inputs from atmospheric CO₂ are undetectable. (Takahashi 2002, 1993). Several researchers have observed significant correlations between winter mixing and the North Atlantic Oscillation (Ullman 2009, Steinberg 2012). A positive NAO is associated with stronger westerly winds and also correlates with a stronger subpolar gyre. Counter-clockwise gyres in the northern hemisphere increase regional upwelling when they strengthen. So changes in NAO-driven upwelling cause multi-decadal oscillations in the plankton communities and pH.

In the Pacific, El Nino years strengthen the Aleutian Low and the Pacific subpolar gyre, similarly increasing regional upwelling. In contrast during La Nina years, gyre upwelling decreases but trade winds speed up and intensify coastal and equatorial upwelling. The frequency of El Niño’s vs La Niña’s varies over 40 to 60 year cycles of the Pacific Decadal Oscillation. Although periods of increased upwelling decreases pH, due to undersampling it is not clear how this extrapolates across the whole Pacific Basin during the 20^th century.

Upwelling also varies on millennial scales. During the Roman Warm Period, Medieval Warm Period and the Current Warm Period, La Nina-like conditions with stronger trade winds dominated (Salvatteci 2014) causing above average upwelling and higher productivity. During cooler periods like the Dark Ages and Little Ice Age, the Pacific was dominated by El Nino-like conditions with less upwelling and lower productivity. Claims that oceans have acidified since the Little Ice Age due to anthropogenic CO₂ (Caldeira 2003) may be true, but the uncertainties are huge. It is just as likely increased upwelling caused more acidic modern oceans, or it is equally possible that modern oceans are less acidic if increased upwelling stimulated a biological pump that sequestered and exported enough carbon to offset acidic upwelling.

Global ocean acidification is determined by averaging sink regions with out‑gassing source regions. Opposing regional trends add significant uncertainty when determining global calculations. As illustrated by the yellows and reds in the Martinez-Boti (2015) illustration below, there are vast regions where so much DIC is upwelled, on average the ocean is exhaling CO₂. Regions that are net sources of out-gassing CO₂ experience lower pH solely due to upwelling of ancient waters, and the pH is lower than predicted from simple equilibration with the atmosphere.

Oceanic regions of outgassing CO2 sources and CO2 sinks

Paradoxically, oceans also experience acidification if weakening winds reduce upwelling. For example due to changing locations and strength of the InterTropical Convergence Zone (ITCZ), trade winds over northern Venezuela’s Cariaco Basin undergo decadal and centennial shifts in strength. When the ITCZ moved south during the Little Ice Age, upwelling and productivity in the Cariaco Basin declined. At the end of the LIA, the ITCZ began moving northward and upwelling and productivity increased (Gutierrez 2009). Recently the ITCZ moved further northward due to more La Niña’s and the negative Pacific Decadal Oscillation, and regional winds declined. Consequently researchers reported anomalously shallow seasonal upwelling that brought more DIC to the surface but fewer critical nutrients that reside at lower depths. This resulted in decreased productivity and a decrease in diatom populations. Less productivity and less carbon export did not offset upwelled DIC, so the regional pH declined (Astor 2013). Despite Astor serving as a co-author, Bates 2014 oddly failed to mention this pH altering dynamic, choosing to attribute Cariaco’s declining pH trend to rising anthropogenic CO₂.

In contrast to the Cariaco Basin, a negative Pacific Decadal Oscillation increases upwelling along the Americas west coast, stimulating the highly productive/high carbon-export community of phase-2. Upwelled DIC is quickly sequestered and exported by large single-celled diatoms. With their relatively heavy siliceous shells, dead diatoms rapidly sink carrying carbon to the sea floor. Larger zooplankton graze on diatoms and their large fecal pellets and carcasses also carry carbon rapidly to depth.  Diatom blooms along California and Oregon spark increased krill and anchovy populations, which attract feeding humpback whales from Costa Rica and seabirds like the Sooty Shearwater from New Zealand, confounding any attempts accurately measure the carbon budget.

As illustrated in the Evans et al graph below, coastal upwelling can over‑saturate the surface waters to 1000 matm, 2.5 times above atmospheric pCO₂ (represented by dashed horizontal line). Within weeks the biological response sequesters and exports that carbon so that concentrations of surface water CO₂ fall as low as 200 matm; a concentration that would still be under-saturated relative to the Little Ice Age’s atmosphere. Relative to these rapid seasonal changes in pH, fears that marine organisms cannot adapt quickly enough to the relatively slower changes wrought by anthropogenic CO₂ seem overblown.

Upwelling and the Biological pump along the Oregon Coast

Still such fears filter researchers’ interpretations. Along the west coast of North America, planktonic sea snails called pteropods, begin life feeding on algal blooms ignited by seasonal coastal upwelling. As illustrated in scanning electron micrograph “a”, shown below from (Bednarsek 2014), pteropod shells are heavily dissolved during the first few weeks of life due to acidic upwelled water. Picture  “b” shows a larger more mature shell with the outer part of the shell experiencing no dissolution. As the snails matured, either upwelled acidic waters subsided or the snail was transported seaward to less acidic waters by the same currents that promoted upwelling. The result is pteropod shell dissolution is a very localized, short duration phenomenon.

Nonetheless in a study sponsored by NOAA’s Ocean Acidification Program Bednarsek 2014 argued those examples of shell dissolution were caused by anthropogenic carbon writing, “We estimate that the incidence of severe pteropod shell dissolution owing to anthropogenic OA has doubled in near shore habitats since pre-industrial conditions across this region and is on track to triple by 2050.” But such “conclusions” are unsupported speculation at best. The study failed to determine if upwelled waters were any more acidic now than during any other seasonal or La Nina upwelling event. Most studies suggest upwelling declined during the Little Ice Age, and the resumption of stronger upwelling is the result of a natural cycle. But Bednarsek (2014) simply used a formula equilibrating past and present atmospheric CO₂ to compute surface water pH. But such methodology is meaningless. No net CO₂ diffusion from the atmosphere to surface waters occurs when upwelling has oversaturated surface pCO₂, and as shown in the Evans et al graph, due to the biological pump surface waters remained undersaturated relative to both current and LIA atmospheric CO₂. Shame on those NOAA scientists for such biased interpretations.

Dissolution of pteropod shells from Bednarsek 2014

On all time frames, when upwelling subsides and nutrients and carbon become scarce, diatom populations dwindle and oceans transition to Phase 3. Coccolithophore and bacterial communities that were relatively minor constituents, begin to dominate. Smaller bacteria remain suspended in the surface layers and export much less carbon. Grazing on increasingly abundant bacteria and accumulated organic carbon, promotes greater zooplankton populations.  As a result, community respiration rates increase, and higher CO₂ concentrations lower surface pH.

Coccolithophores

Coccolithophores are large single-celled alga encased by several ornate calcium-carbonate “coccoliths”, so that sinking dead individuals do export carbon relatively quickly. However the construction of coccoliths metabolically increases surface pCO₂, lowers pH and counteracts the “biological pump”. When calcium combines with carbonate ions to form coccoliths, the reaction releases acidifying CO₂. Likewise the growth of pteropods’ calcium carbonate shells also increases CO₂. It seems paradoxical that one of the greatest fears of ocean acidification is the dissolution of carbonate shells, yet the very process of creating those shells increases acidification and lowers surface alkalinity.

Several researchers suggest coccolith formation evolved to provide much needed CO₂ for photosynthesis in under-saturated waters. Experimental evidence reveals higher concentrations of CO₂ results in lower rates of coccolith formation but proponents of worrisome acidification argue this is evidence of acidification’s deleterious effects. However the same response would be expected if the rate of coccolith formation responds to the available supply of CO₂ required for photosynthesis. Furthermore if they are so vulnerable to acidification, how did coccolithophores evolve and survive over 200 million years ago, when atmospheric CO₂ was at least 2 to 3 times higher than today?

Without copious supplies of nutrients from upwelling, productivity in subtropical gyres is much lower and diatoms constitute a minor component of that plankton community. But they still undergo cyclic changes. In the Atlantic, Steinberg (2012) describes a 113% decrease in diatoms between 1990 and 2007 in contrast to stable coccolithophore populations and a rapidly increasing community of photosynthesizing bacteria. In turn rapidly increasing communities of small zooplankton graze on the bacteria resulting in increased community respiration rates. Three sites from Bates 2014 are located in subtropical gyres: HOT near Hawaii, BATS near the Bermuda and ESTOC near the Canary Islands. And all three are exhibiting these classic phase-3 patterns with increasing respiration rates (Lomas 2010, Gonzalez-Davila 2003, Peligri 2005, Steinberg 2012), which accounts for declining pH trends. As shown by Steinberg 2012, those trends are significantly correlated with multi-decadal climate indices – the North Atlantic Oscillation plus three different Pacific Ocean climate indices”.

Global pH decreased when oceans transitioned from the Last Glacial Maximum (LGM) to the current interglacial Teleconnections between the Atlantic and Pacific have been confirmed  as warm periods in the Greenland ice core correlate with periods of extended periods of upwelling along the California coast (Ortiz 2005). Recent research also links simultaneous multi‑millennial cycles of upwelling and higher productivity in the sub‑Antarctic Atlantic and equatorial Pacific. Most research suggests that at the end of the LGM, Antarctic began to warm followed by a rise in atmospheric CO₂. Although the precise mechanism of CO₂ out‑gassing during the deglacial period has been under debate, there is a growing consensus that circulation changes caused aged waters rich in nutrients to upwell in subpolar Antarctic waters. Via oceanic tunneling, those deep Antarctic waters also upwelled in the equatorial eastern Pacific. Using foraminifera proxy data, the graphic below from Martinez-Boti (2015) shows periodic upwelling of subpolar Antarctic waters (on the left in blue) caused regional pH to decline from the LGM maximum of 8.4 to about 8.1 at the beginning of the Holocene. Due to the biological pump and/or reduced upwelling during the early and mid Holocene, pH rises and bounces between 8.25 and 8.15.

Based on CO₂ concentrations determined from Antarctic ice cores, Martinez‑Boti also constructed a green “Equilibrium pH” trend indicating the surface pH if it had simply equilibrated with atmospheric CO₂.  For most of the past 20,000 years, surface waters were not in atmospheric equilibrium and more acidic, so those regional oceans were typically a source of out‑gassing CO₂. The graphs on the right (in red) show the same pattern for the equatorial eastern Pacific but with data that extends further into the LGM.

Ocane pH variability over pst 20,000 years from Martinez‑Boti 2015

Calvo 2011 examined ocean sediments to determine the strength of upwelling versus the biological pump plus the relationship between diatoms and coccolithophores over the past 40,000 years. Their research found lower productivity during the LGM and lower diatom abundance relative to coccolithopheres. As upwelling increased around 20,000 years ago so did ocean productivity and the proportion of diatoms. They concluded upwelling enhanced the biological pump but it was “not sufficient to counteract the return to the atmosphere of large amounts of CO₂ delivered by the oceans through an enhanced ventilation of deep water.”

Ratio of Diatoms to Coccolithophores from Calvo 2011

Finally examining sediments in the eastern equatorial Pacific, Carbacos 2014 found “a clear prevalence of dominant La Niña-like conditions during the early Holocene, with an intense upwelling and high primary productivity.” High levels of productivity persisted through the Holocene Optimum until productivity dramatically declined around 5,500 years ago. Since that time Carbacos 2014 reports, “An alternation between El Niño-like and La Niña-like dominant conditions occurred during the late Holocene, characterized by a clear trend toward prevailing El Niño-like conditions, with a low primary productivity.” During the past 5,000 years, that lower productivity coincided with increased dominance of coccolithophores and declining proportions of diatoms. That suggests the oceans have been in a phase-3 multi-millennial decline in pH superimposed on multidecadal cycles driven by the Pacific Decadal and Atlantic Multidecadal Oscillations.

It is also worth noting, as seen in the graph below, throughout the Holocene changes in atmospheric CO₂ did not correlate with temperature. However atmospheric CO₂ did track changing plankton communities. During the early and late Holocene, atmospheric CO₂ concentrations were relatively low and stable during periods of high productivity with higher ratios of diatoms. When ocean productivity crashed overall around 5,000 years ago, the proportion of CO₂ producing coccolithophores increased and atmospheric CO₂ likewise increased by about 20 matm. A similar annual increase in CO₂ has been observed in modern oceans and similarly attributed to increased proportions of coccolithophores (Bates 1996). 

So where are the oceans headed? If history repeats itself, declining solar insolation will result in less upwelling, lower productivity, a reduced biological pump and higher pH. Or perhaps higher levels of atmospheric CO₂ will increase productivity as observed in several experiments, or perhaps rising CO₂ will cause a deleterious decline in pH?  The ubiquitous uncertainties from the current undersampling of oceans allows anyone to “find experimental support for their favorite theory no matter what the theory they claimed.” But I can say for sure, I would not trust any predictions that failed to account for changes in upwelling and the various responses of the biological pump.

Contrasting Holocene Temperatures and Atmospheric CO2

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?