Temperature Trend Analysis

Pushing for Climate Diversity

Posted on by Ron Clutz

Amidst all the concerns for social diversity, let’s raise a cry for scientific diversity. No, I am not referring to the gender or racial identities of people doing science, but rather acknowledging the diversity of climates and their divergent patterns over time. The actual climate realities affecting people’s lives are hidden within global averages and abstractions. A previous post Concurrent Warming and Cooling presented research findings that on long time scales maritime climates can shift toward inland patterns including both colder winters and warmer summers.

It occurred to me that Frank Lansner had done studies on weather stations showing differences depending on exposure to ocean breezes or not. That led me to his recent publication Temperature trends with reduced impact of ocean air temperature Lansner and Pederson March 21, 2018. Excerpts in italics with my bolds.

Abstract

Temperature data 1900–2010 from meteorological stations across the world have been analyzed and it has been found that all land areas generally have two different valid temperature trends. Coastal stations and hill stations facing ocean winds are normally more warm-trended than the valley stations that are sheltered from dominant oceans winds.

Thus, we found that in any area with variation in the topography, we can divide the stations into the more warm trended ocean air-affected stations, and the more cold-trended ocean air-sheltered stations. We find that the distinction between ocean air-affected and ocean air-sheltered stations can be used to identify the influence of the oceans on land surface. We can then use this knowledge as a tool to better study climate variability on the land surface without the moderating effects of the ocean.

We find a lack of warming in the ocean air sheltered temperature data – with less impact of ocean temperature trends – after 1950. The lack of warming in the ocean air sheltered temperature trends after 1950 should be considered when evaluating the climatic effects of changes in the Earth’s atmospheric trace amounts of greenhouse gasses as well as variations in solar conditions.

As a contrast to the OAS stations, we compare with what we designate as ocean air affected (OAA) stations, which are more exposed to the influence of the ocean, see Figure 1. The optimal OAA locations are defined as positions with potential first contact with ocean air. In general, stations where the location offers no shelter in the directions of predominant winds are best categorized as OAA stations.

Conversely, the optimal OAS area is a lower point surrounded by mountains in all directions. In this case, the existence of predominant wind directions is not needed. Only in locations with a predominant wind direction, the leeward side of the mountains can also form an OAS region.

Figure 2. The optimal OAA and OAS locations with respect to dominating wind direction.

A total of 10 areas were chosen for this work to present the temperature trends of OAS areas (typically valley areas) and OAA areas from Scandinavia, Central Siberia, Central Balkan, Midwest USA, Central China, Pakistan/North India, the Sahel Area, Southern Africa, Central South America, and Southeast Australia. In this work, we have only considered an area as “OAS” or “OAA” if it comprises at least eight independent temperature sets. In the following, temperature data 1900–2010 from individual areas are discussed.

As an example, we show in Figure 3 the results for the Scandinavian area where we have used a total of 49 OAS stations and 18 OAA stations. The large number of stations available is due to the use of meteorological yearbooks as supplement to data sources such as ECA&D climate data and Nordklim database.

Figure 3. OAS and OAA temperature stations, Scandinavia.

The upper set of curves is from the OAS areas: Here the blue lines show one-year mean temperature averages for each temperature station, the red lines show the average of all stations of the area, and the thick black line is a five-year running mean of the station average. The reference period is 1951–1980. The middle set of curves is from the OAA areas. Here the orange lines show one-year mean temperature averages for each temperature station, the red lines show average of the stations of the area, and the thick black line is a five-year running mean of the station average. The reference period is 1951–1980.

On the lower set of curves labeled “OAS vs. OAA areas,” a comparison of the two data sets of stations is shown. The blue lines are the one-year average of OAS stations of the area and the red lines are the one-year average of OAA stations of the area. The reference period is 1995–2010. We note that these Scandinavian OAS stations are not well shielded from easterly winds.

Although easterly winds are not frequent (see Figure 2), the OAS area used cannot be characterized as an optimal OAS area. Despite this, we find a difference between the OAS and OAA area temperature data. While the general five-year running mean temperature curves (left panel in Figure 3) show resemblance in warming/cooling cycles, the OAA stations show less variation than the OAS stations.

We also find the absolute temperature anomalies for the Scandinavian OAS areas deviate from the OAA area with the OAS stations showing less warming than the OAA stations during the 20th century. For the years 1920–1950, we thus find temperatures in the OAS area to be up to 1 K warmer than temperature in the OAA area. In recent years, there is a closer agreement between OAS and OAA trends and even though the Scandinavian OAS data generally are warmer than OAA data for 1920–1950, we also note that in some very cold years, OAS temperatures are slightly colder than the OAA temperatures.

The paper presents all ten regions analyzed, but I will include here the USA example to see how it compares with other depictions of US regions. For example, see the map at the top shows the dramatic difference between temperature records in Eastern versus Western US stations. Here is the assessment from Lansner and Pederson. Note the topographical realities.

For the USA (Figure 6), we defined the OAS area as consisting of eight boxes, each of size 5° X 5°. The boxes were defined as 40–45N X 100–95 W, 40–45N  X 95–90W, 35– 40N X 100–95W, 35–40N X 95–90 W, 35–40N X 90–85W, 35–30N X 100– 95W, 35–30N X 95–90W, and 35–30N X 90–85W. A total of 236 temperature stations were used from this area. Full 5 X 5 grids were not found to be suited as OAA areas, but 27 stations indicated on the map were used for the OAA data set. All data were taken from GHCN v2 raw data. The OAS area in the US Midwest is well protected against westerly oceanic (Pacific) winds due to the Rocky Mountains. The US Midwest is also to some degree sheltered against easterly winds due to the Appalachian mountain range. Again the temperature trends from the OAS area as defined above show the 1920–1955 period in most years to be around 1 K warmer than temperature trends from the OAA areas.

Summation

Figure 13. OAS and OAA temperature averages, Northern Hemisphere.

In Figure 13 we have combined average temperature trends for all seven NH OAS areas (blue curves) and OAA areas (brown curves) were areas are divided into low (0–45N) and high (45–90N) latitudes (dark colors are used for low and light colors for high latitudes). Both for the OAS areas and the OAA areas we see that the seven NH areas have similar development of temperature trends for 1900–2010. The larger variation in data from high latitudes (45–90N) is likely to reflect the Arctic amplification of temperature variations. OAS temperature stations further away from the Arctic (0–45N) seem to show less temperature increase during 1980–2010 than the OAS areas most affected by the Arctic (45– 90N). The NH OAS data all reveal a period of heating of the Earth surface 1920–1950 that the OAA data do not reflect well.

Figure 19. OAS and OAA temperatures, all regions.

Conclusion

Bromley et al. raise shifts in seasonality as a factor in climate change. Now Lansner and Pederson show differences in temperature trends due to ocean exposure, and also greater fluctuations with higher latitudes. Note that the cooling in the USA is replicated in the pattern shown worldwide in OAS regions. The key factor is the hotter temperatures prior to 1950s appearing in OAS records but not in OAA records.

Despite all the clamor about global warming (or recently global cooling since the hiatus), it all depends on where you are.  Recognizing the diversity of local and regional climates is the sort of climate justice I can support.

Footnote:

I do not subscribe to Arctic “Amplification” to explain latitudinal differences.  Since earth’s climate system is always working to transport energy from the equator to poles, any additional heat shows up in higher latitudes by meridional transport.  Previous posts have noted how anomalies give a distorted picture since temperatures are more volatile at higher (colder) NH latitudes.

See: Temperature Misunderstandings

Clive Best provides this animation of recent monthly temperature anomalies which demonstrates how most variability in anomalies occur over northern continents.

Concurrent Warming and Cooling

Posted on by Ron Clutz

Rannoch Moor and Glencoe Landscape. Scotland Images by Nigel For is a photograph I by Nigel Forster which was uploaded on May 30th, 2019.

This post highlights recent interesting findings regarding past climate change in NH, Scotland in particular. The purpose of the research was to better understand how glaciers could be retreating during the Younger Dryas Stadia (YDS), one of the coldest periods in our Holocene epoch.

The lead researcher is Gordon Bromley, and the field work was done on site of the last ice fields on the highlands of Scotland. 14C dating was used to estimate time of glacial events such as vegetation colonizing these places. Bromley explains in an article Shells found in Scotland rewrite our understanding of climate change at siliconrepublic. Excerpts in italics with my bolds.

By analysing ancient shells found in Scotland, the team’s data challenges the idea that the period was an abrupt return to an ice age climate in the North Atlantic, by showing that the last glaciers there were actually decaying rapidly during that period.

The shells were found in glacial deposits, and one in particular was dated as being the first organic matter to colonise the newly ice-free landscape, helping to provide a minimum age for the glacial advance. While all of these shell species are still in existence in the North Atlantic, many are extinct in Scotland, where ocean temperatures are too warm.

This means that although winters in Britain and Ireland were extremely cold, summers were a lot warmer than previously thought, more in line with the seasonal climates of central Europe.

“There’s a lot of geologic evidence of these former glaciers, including deposits of rubble bulldozed up by the ice, but their age has not been well established,” said Dr Gordon Bromley, lead author of the study, from NUI Galway’s School of Geography and Archaeology.

“It has largely been assumed that these glaciers existed during the cold Younger Dryas period, since other climate records give the impression that it was a cold time.”

He continued: “This finding is controversial and, if we are correct, it helps rewrite our understanding of how abrupt climate change impacts our maritime region, both in the past and potentially into the future.”

The recent report is Interstadial Rise and Younger Dryas Demise of Scotland’s Last Ice Fields
G. Bromley A. Putnam H. Borns Jr T. Lowell T. Sandford D. Barrell  First published: 26 April 2018.(my bolds)

Abstract

Establishing the atmospheric expression of abrupt climate change during the last glacial termination is key to understanding driving mechanisms. In this paper, we present a new 14C chronology of glacier behavior during late‐glacial time from the Scottish Highlands, located close to the overturning region of the North Atlantic Ocean. Our results indicate that the last pulse of glaciation culminated between ~12.8 and ~12.6 ka, during the earliest part of the Younger Dryas stadial and as much as a millennium earlier than several recent estimates. Comparison of our results with existing minimum‐limiting 14C data also suggests that the subsequent deglaciation of Scotland was rapid and occurred during full stadial conditions in the North Atlantic. We attribute this pattern of ice recession to enhanced summertime melting, despite severely cool winters, and propose that relatively warm summers are a fundamental characteristic of North Atlantic stadials.

Plain Language Summary

Geologic data reveal that Earth is capable of abrupt, high‐magnitude changes in both temperature and precipitation that can occur well within a human lifespan. Exactly what causes these potentially catastrophic climate‐change events, however, and their likelihood in the near future, remains frustratingly unclear due to uncertainty about how they are manifested on land and in the oceans. Our study sheds new light on the terrestrial impact of so‐called “stadial” events in the North Atlantic region, a key area in abrupt climate change. We reconstructed the behavior of Scotland’s last glaciers, which served as natural thermometers, to explore past changes in summertime temperature. Stadials have long been associated with extreme cooling of the North Atlantic and adjacent Europe and the most recent, the Younger Dryas stadial, is commonly invoked as an example of what might happen due to anthropogenic global warming. In contrast, our new glacial chronology suggests that the Younger Dryas was instead characterized by glacier retreat, which is indicative of climate warming. This finding is important because, rather than being defined by severe year‐round cooling, it indicates that abrupt climate change is instead characterized by extreme seasonality in the North Atlantic region, with cold winters yet anomalously warm summers.

The complete report is behind a paywall, but a 2014 paper by Bromley discusses the evidence and analysis in reaching these conclusions. Younger Dryas deglaciation of Scotland driven by warming summers  Excerpts with my bolds.

Significance: As a principal component of global heat transport, the North Atlantic Ocean also is susceptible to rapid disruptions of meridional overturning circulation and thus widely invoked as a cause of abrupt climate variability in the Northern Hemisphere. We assess the impact of one such North Atlantic cold event—the Younger Dryas Stadial—on an adjacent ice mass and show that, rather than instigating a return to glacial conditions, this abrupt climate event was characterized by deglaciation. We suggest this pattern indicates summertime warming during the Younger Dryas, potentially as a function of enhanced seasonality in the North Atlantic.

Surface temperatures range from -30C to +30C

Fig. 1. Surface temperature and heat transport in the North Atlantic Ocean.  The relatively mild European climate is sustained by warm sea-surface temperatures and prevailing southwesterly airflow in the North Atlantic Ocean (NAO), with this ameliorating effect being strongest in maritime regions such as Scotland. Mean annual temperature (1979 to present) at 2 m above surface (image obtained using University of Maine Climate Reanalyzer, http://www.cci-reanalyzer.org). Locations of Rannoch Moor and the GISP2 ice core are indicated.

Thus the Scottish glacial record is ideal for reconstructing late glacial variability in North Atlantic temperature (Fig. 1). The last glacier resurgence in Scotland—the “Loch Lomond Advance” (LLA)—culminated in a ∼9,500-km2 ice cap centered over Rannoch Moor (Fig. 2A) and surrounded by smaller ice fields and cirque glaciers.

Fig. 2. Extent of the LLA ice cap in Scotland and glacial geomorphology of western Rannoch Moor. (A) Maximum extent of the ∼9,500 km2 LLA ice cap and larger satellite ice masses, indicating the central location of Rannoch Moor. Nunataks are not shown. (B) Glacial-geomorphic map of western Rannoch Moor. Distinct moraine ridges mark the northward active retreat of the glacier margin (indicated by arrow) across this sector of the moor, whereas chaotic moraines near Lochan Meall a’ Phuill (LMP) mark final stagnation of ice. Core sites are shown, including those (K1–K3) of previous investigations (14, 15).

When did the LLA itself occur? We consider two possible resolutions to the paradox of deglaciation during the YDS. First, declining precipitation over Scotland due to gradually increasing North Atlantic sea-ice extent has been invoked to explain the reported shrinkage of glaciers in the latter half of the YDS (18). However, this course of events conflicts with recent data depicting rapid, widespread imposition of winter sea-ice cover at the onset of the YDS (9), rather than progressive expansion throughout the stadial.

Loch Lomond

Furthermore, considering the gradual active retreat of LLA glaciers indicated by the geomorphic record, our chronology suggests that deglaciation began considerably earlier than the mid-YDS, when precipitation reportedly began to decline (18). Finally, our cores contain lacustrine sediments deposited throughout the latter part of the YDS, indicating that the water table was not substantially different from that of today. Indeed, some reconstructions suggest enhanced YDS precipitation in Scotland (24, 25), which is inconsistent with the explanation that precipitation starvation drove deglaciation (26).

We prefer an alternative scenario in which glacier recession was driven by summertime warming and snowline rise. We suggest that amplified seasonality, driven by greatly expanded winter sea ice, resulted in a relatively continental YDS climate for western Europe, both in winter and in summer. Although sea-ice formation prevented ocean–atmosphere heat transfer during the winter months (10), summertime melting of sea ice would have imposed an extensive freshwater cap on the ocean surface (27), resulting in a buoyancy-stratified North Atlantic. In the absence of deep vertical mixing, summertime heating would be concentrated at the ocean surface, thereby increasing both North Atlantic summer sea-surface temperatures (SSTs) and downwind air temperatures. Such a scenario is analogous to modern conditions in the Sea of Okhotsk (28) and the North Pacific Ocean (29), where buoyancy stratification maintains considerable seasonal contrasts in SSTs. Indeed, Haug et al. (30) reported higher summer SSTs in the North Pacific following the onset of stratification than previously under destratified conditions, despite the growing presence of northern ice sheets and an overall reduction in annual SST. A similar pattern is evident in a new SST record from the northeastern North Atlantic, which shows higher summer temperatures during stadial periods (e.g., Heinrich stadials 1 and 2) than during interstadials on account of amplified seasonality (30).

Our interpretation of the Rannoch Moor data, involving the summer (winter) heating (cooling) effects of a shallow North Atlantic mixed layer, reconciles full stadial conditions in the North Atlantic with YDS deglaciation in Scotland. This scenario might also account for the absence of YDS-age moraines at several higher-latitude locations (12, 36–38) and for evidence of mild summer temperatures in southern Greenland (11). Crucially, our chronology challenges the traditional view of renewed glaciation in the Northern Hemisphere during the YDS, particularly in the circum-North Atlantic, and highlights our as yet incomplete understanding of abrupt climate change.

Summary

Several things are illuminated by this study. For one thing, glaciers grow or recede because of multiple factors, not just air temperature. The study noted that glaciers require precipitation (snow) in order to grow, but also melt under warmer conditions. For background on the complexities of glacier dynamics see Glaciermania

Also, paleoclimatology relies on temperature proxies who respond to changes over multicentennial scales at best. C14 brings higher resolution to the table.

Finally, it is interesting to consider climate changing with respect to seasonality.  Bromley et al. observe that during Younger Dryas, Scotland shifted from a moderate maritime climate to one with more seasonal extremes like that of inland continental regions. In that light, what should we expect from cooler SSTs in the North Atlantic?

Note also that our modern warming period has been marked by the opposite pattern. Many NH temperature records show slight summer cooling along with somewhat stronger warming in winter, the net being the modest (fearful?) warming in estimates of global annual temperatures.

It seems that climate shifts are still events we see through a glass darkly.

 

Media Raises False Alarms of Ocean Cooling

Posted on by Ron Clutz

The RAPID moorings being deployed. Credit: National Oceanography Centre.

The usual suspects, such as BBC, the Guardian, New York Times, Washington Post etc., are reporting that the Atlantic gulf stream is slowing down due to climate change, threatening an ice age.  That’s right, warmists are now claiming fossil fuels do cooling when they are not warming.  As usual the headlines are not supported by the details.

The AMOC is back in the news following a recent Ocean Sciences meeting.  This update adds to the theme Oceans Make Climate. Background links are at the end, including one where chief alarmist M. Mann claims fossil fuel use will stop the ocean conveyor belt and bring a new ice age.  Actual scientists are working away methodically on this part of the climate system, and are more level-headed.  H/T GWPF for noticing the recent article in Science Ocean array alters view of Atlantic ‘conveyor belt’  By Katherine Kornei Feb. 17, 2018 . Excerpts with my bolds.

The powerful currents in the Atlantic, formally known as the Atlantic meridional overturning circulation (AMOC), are a major engine in Earth’s climate. The AMOC’s shallower limbs—which include the Gulf Stream—transport warm water from the tropics northward, warming Western Europe. In the north, the waters cool and sink, forming deeper limbs that transport the cold water back south—and sequester anthropogenic carbon in the process. This overturning is why the AMOC is sometimes called the Atlantic conveyor belt.

Fig. 1. Schematic of the major warm (red to yellow) and cold (blue to purple) water pathways in the NASPG (North Atlantic subpolar gyre ) credit: H. Furey, Woods Hole Oceanographic Institution): Denmark Strait (DS), Faroe Bank Channel (FBC), East and West Greenland Currents (EGC and WGC, respectively), NAC, DSO, and ISO.

In February at the American Geophysical Union’s (AGU’s) Ocean Sciences meeting, scientists presented the first data from an array of instruments moored in the subpolar North Atlantic. The observations reveal unexpected eddies and strong variability in the AMOC currents. They also show that the currents east of Greenland contribute the most to the total AMOC flow. Climate models, on the other hand, have emphasized the currents west of Greenland in the Labrador Sea. “We’re showing the shortcomings of climate models,” says Susan Lozier, a physical oceanographer at Duke University in Durham, North Carolina, who leads the $35-million, seven-nation project known as the Overturning in the Subpolar North Atlantic Program (OSNAP).

Fig. 2. Schematic of the OSNAP array. The vertical black lines denote the OSNAP moorings with the red dots denoting instrumentation at depth. The thin gray lines indicate the glider survey. The red arrows show pathways for the warm and salty waters of subtropical origin; the light blue arrows show the pathways for the fresh and cold surface waters of polar origin; and the dark blue arrows show the pathways at depth for waters that originate in the high-latitude North Atlantic and Arctic.

The research and analysis is presented by Dr. Lozier et al. in this publication Overturning in the Subpolar North Atlantic Program: A New International Ocean Observing System Images above and text excerpted below with my bolds.

For decades oceanographers have assumed the AMOC to be highly susceptible to changes in the production of deep waters at high latitudes in the North Atlantic. A new ocean observing system is now in place that will test that assumption. Early results from the OSNAP observational program reveal the complexity of the velocity field across the section and the dramatic increase in convective activity during the 2014/15 winter. Early results from the gliders that survey the eastern portion of the OSNAP line have illustrated the importance of these measurements for estimating meridional heat fluxes and for studying the evolution of Subpolar Mode Waters. Finally, numerical modeling data have been used to demonstrate the efficacy of a proxy AMOC measure based on a broader set of observational data, and an adjoint modeling approach has shown that measurements in the OSNAP region will aid our mechanistic understanding of the low-frequency variability of the AMOC in the subtropical North Atlantic.

Fig. 7. (a) Winter [Dec–Mar (DJFM)] mean NAO index. Time series of temperature from the (b) K1 and (c) K9 moorings.

Finally, we note that while a primary motivation for studying AMOC variability comes from its potential impact on the climate system, as mentioned above, additional motivation for the measure of the heat, mass, and freshwater fluxes in the subpolar North Atlantic arises from their potential impact on marine biogeochemistry and the cryosphere. Thus, we hope that this observing system can serve the interests of the broader climate community.

Fig. 10. Linear sensitivity of the AMOC at (d),(e) 25°N and (b),(c) 50°N in Jan to surface heat flux anomalies per unit area. Positive sensitivity indicates that ocean cooling leads to an increased AMOC—e.g., in the upper panels, a unit increase in heat flux out of the ocean at a given location will change the AMOC at (d) 25°N or (e) 50°N 3 yr later by the amount shown in the color bar. The contour intervals are logarithmic. (a) The time series show linear sensitivity of the AMOC at 25°N (blue) and 50°N (green) to heat fluxes integrated over the subpolar gyre (black box with surface area of ∼6.7 × 10 m2) as a function of forcing lead time. The reader is referred to Pillar et al. (2016) for model details and to Heimbach et al. (2011) and Pillar et al. (2016) for a full description of the methodology and discussion relating to the dynamical interpretation of the sensitivity distributions.

In summary, while modeling studies have suggested a linkage between deep-water mass formation and AMOC variability, observations to date have been spatially or temporally compromised and therefore insufficient either to support or to rule out this connection.

Current observational efforts to assess AMOC variability in the North Atlantic.

The U.K.–U.S. Rapid Climate Change–Meridional Overturning Circulation and Heatflux Array (RAPID–MOCHA) program at 26°N successfully measures the AMOC in the subtropical North Atlantic via a transbasin observing system (Cunningham et al. 2007; Kanzow et al. 2007; McCarthy et al. 2015). While this array has fundamentally altered the community’s view of the AMOC, modeling studies over the past few years have suggested that AMOC fluctuations on interannual time scales are coherent only over limited meridional distances. In particular, a break point in coherence may occur at the subpolar–subtropical gyre boundary in the North Atlantic (Bingham et al. 2007; Baehr et al. 2009). Furthermore, a recent modeling study has suggested that the low-frequency variability of the RAPID–MOCHA appears to be an integrated response to buoyancy forcing over the subpolar gyre (Pillar et al. 2016). Thus, a measure of the overturning in the subpolar basin contemporaneous with a measure of the buoyancy forcing in that basin likely offers the best possibility of understanding the mechanisms that underpin AMOC variability. Finally, though it might be expected that the plethora of measurements from the North Atlantic would be sufficient to constrain a measure of the AMOC within the context of an ocean general circulation model, recent studies (Cunningham and Marsh 2010; Karspeck et al. 2015) reveal that there is currently no consensus on the strength or variability of the AMOC in assimilation/reanalysis products.

Atlantic Meridional Overturning Circulation (AMOC). Red colours indicate warm, shallow currents and blue colours indicate cold, deep return flows. Modified from Church, 2007, A change in circulation? Science, 317(5840), 908–909. doi:10.1126/science.1147796

In addition we have a recent report from the United Kingdom Marine Climate Change Impacts Partnership (MCCIP) lead author G.D. McCarthy Atlantic Meridional Overturning Circulation (AMOC) 2017.

Figure 1: Ten-day (colours) and three month (black) low-pass filtered timeseries of Florida Straits transport (blue), Ekman transport (green), upper mid-ocean transport (magenta), and overturning transport (red) for the period 2nd April 2004 to end- February 2017. Florida Straits transport is based on electromagnetic cable measurements; Ekman transport is based on ERA winds. The upper mid-ocean transport, based on the RAPID mooring data, is the vertical integral of the transport per unit depth down to the deepest northward velocity (~1100 m) on each day. Overturning transport is then the sum of the Florida Straits, Ekman, and upper mid-ocean transports and represents the maximum northward transport of upper-layer waters on each day. Positive transports correspond to northward flow.

The RAPID/MOCHA/WBTS array (hereinafter referred to as the RAPID array) has revolutionized basin scale oceanography by supplying continuous estimates of the meridional overturning transport (McCarthy et al., 2015), and the associated basin-wide transports of heat (Johns et al., 2011) and freshwater (McDonagh et al., 2015) at 10-day temporal resolution. These estimates have been used in a wide variety of studies characterizing temporal variability of the North Atlantic Ocean, for instance establishing a decline in the AMOC between 2004 and 2013.

Summary from RAPID data analysis

MCCIP reported in 2006 that:

  • a 30% decline in the AMOC has been observed since the early 1990s based on a limited number of observations. There is a lack of certainty and consensus concerning the trend;
  • most climate models anticipate some reduction in strength of the AMOC over the 21st century due to increased freshwater influence in high latitudes. The IPCC project a slowdown in the overturning circulation rather than a dramatic collapse.And in 2017 that:
  • a substantial increase in the observations available to estimate the strength of the AMOC indicate, with greater certainty, a decline since the mid 2000s;
  • the AMOC is still expected to decline throughout the 21st century in response to a changing climate. If and when a collapse in the AMOC is possible is still open to debate, but it is not thought likely to happen this century.

And also that:

  • a high level of variability in the AMOC strength has been observed, and short term fluctuations have had unexpected impacts, including severe winters and abrupt sea-level rise;
  • recent changes in the AMOC may be driving the cooling of Atlantic ocean surface waters which could lead to drier summers in the UK.

Conclusions

  • The AMOC is key to maintaining the mild climate of the UK and Europe.
  • The AMOC is predicted to decline in the 21st century in response to a changing climate.
  • Past abrupt changes in the AMOC have had dramatic climate consequences.
  • There is growing evidence that the AMOC has been declining for at least a decade, pushing the Atlantic Multidecadal Variability into a cool phase.
  • Short term fluctuations in the AMOC have proved to have unexpected impacts, including being linked
    with severe winters and abrupt sea-level rise.

Background:

Oceans Make Climate: SST, SSS and Precipitation Linked

Climate Pacemaker: The AMOC

Evidence is Mounting: Oceans Make Climate

Mann-made Global Cooling

 

 

Ocean Temps Falling Feb. 2018

Posted on by Ron Clutz

globpop_countries

The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • A major El Nino was the dominant climate feature in recent years.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source, the latest version being HadSST3.  More on what distinguishes HadSST3 from other SST products at the end.

The Current Context

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through February 2018.
Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back below its beginning level. Secondly, the Northern Hemisphere added three bumps on the shoulders of Tropical warming, with peaks in August of each year. Also, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one.

A global cooling pattern has persisted, seen clearly in the Tropics since its peak in 2016, joined by NH and SH dropping since last August. An upward bump occurred last October, and again in January 2018.  Now the cooling has resumed in February with only the NH showing a slight increase.  As will be shown in the analysis below, 0.4C has been the average global anomaly since 1995 and this month remains lower at 0.349C.  SH erased the January bump while the tropics reached a new low of 0.155 for this period.

Global and NH SSTs are the lowest since 3/2014, while SH and Tropics SSTs are the lowest since 3/2012.

A longer view of SSTs

The graph below  is noisy, but the density is needed to see the seasonal patterns in the oceanic fluctuations.  Previous posts focused on the rise and fall of the last El Nino starting in 2015.  This post adds a longer view, encompassing the significant 1998 El Nino and since.  The color schemes are retained for Global, Tropics, NH and SH anomalies.  Despite the longer time frame, I have kept the monthly data (rather than yearly averages) because of interesting shifts between January and July.

Open image in new tab for sharper detail.

1995 is a reasonable starting point prior to the first El Nino.  The sharp Tropical rise peaking in 1998 is dominant in the record, starting Jan. ’97 to pull up SSTs uniformly before returning to the same level Jan. ’99.  For the next 2 years, the Tropics stayed down, and the world’s oceans held steady around 0.2C above 1961 to 1990 average.

Then comes a steady rise over two years to a lesser peak Jan. 2003, but again uniformly pulling all oceans up around 0.4C.  Something changes at this point, with more hemispheric divergence than before. Over the 4 years until Jan 2007, the Tropics go through ups and downs, NH a series of ups and SH mostly downs.  As a result the Global average fluctuates around that same 0.4C, which also turns out to be the average for the entire record since 1995.

2007 stands out with a sharp drop in temperatures so that Jan.08 matches the low in Jan. ’99, but starting from a lower high. The oceans all decline as well, until temps build peaking in 2010.

Now again a different pattern appears.  The Tropics cool sharply to Jan 11, then rise steadily for 4 years to Jan 15, at which point the most recent major El Nino takes off.  But this time in contrast to ’97-’99, the Northern Hemisphere produces peaks every summer pulling up the Global average.  In fact, these NH peaks appear every July starting in 2003, growing stronger to produce 3 massive highs in 2014, 15 and 16, with July 2017 only slightly lower.  Note also that starting in 2014 SH plays a moderating role, offsetting the NH warming pulses. (Note: these are high anomalies on top of the highest absolute temps in the NH.)

What to make of all this? The patterns suggest that in addition to El Ninos in the Pacific driving the Tropic SSTs, something else is going on in the NH.  The obvious culprit is the North Atlantic, since I have seen this sort of pulsing before.  After reading some papers by David Dilley, I confirmed his observation of Atlantic pulses into the Arctic every 8 to 10 years as shown by this graph:

The data is annual averages of absolute SSTs measured in the North Atlantic.  The significance of the pulses for weather forecasting is discussed in AMO: Atlantic Climate Pulse

But the peaks coming nearly every July in HadSST require a different picture.  Let’s look at August, the hottest month in the North Atlantic from the Kaplan dataset.Now the regime shift appears clearly. Starting with 2003, seven times the August average has exceeded 23.6C, a level that prior to ’98 registered only once before, in 1937.  And other recent years were all greater than 23.4C.

Summary

The oceans are driving the warming this century.  SSTs took a step up with the 1998 El Nino and have stayed there with help from the North Atlantic, and more recently the Pacific northern “Blob.”  The ocean surfaces are releasing a lot of energy, warming the air, but eventually will have a cooling effect.  The decline after 1937 was rapid by comparison, so one wonders: How long can the oceans keep this up?

To paraphrase the wheel of fortune carnival barker:  “Down and down she goes, where she stops nobody knows.”

Postscript:

In the most recent GWPF 2017 State of the Climate report, Dr. Humlum made this observation:

“It is instructive to consider the variation of the annual change rate of atmospheric CO2 together with the annual change rates for the global air temperature and global sea surface temperature (Figure 16). All three change rates clearly vary in concert, but with sea surface temperature rates leading the global temperature rates by a few months and atmospheric CO2 rates lagging 11–12 months behind the sea surface temperature rates.”

Footnote: Why Rely on HadSST3

HadSST3 is distinguished from other SST products because HadCRU (Hadley Climatic Research Unit) does not engage in SST interpolation, i.e. infilling estimated anomalies into grid cells lacking sufficient sampling in a given month. From reading the documentation and from queries to Met Office, this is their procedure.

HadSST3 imports data from gridcells containing ocean, excluding land cells. From past records, they have calculated daily and monthly average readings for each grid cell for the period 1961 to 1990. Those temperatures form the baseline from which anomalies are calculated.

In a given month, each gridcell with sufficient sampling is averaged for the month and then the baseline value for that cell and that month is subtracted, resulting in the monthly anomaly for that cell. All cells with monthly anomalies are averaged to produce global, hemispheric and tropical anomalies for the month, based on the cells in those locations. For example, Tropics averages include ocean grid cells lying between latitudes 20N and 20S.

Gridcells lacking sufficient sampling that month are left out of the averaging, and the uncertainty from such missing data is estimated. IMO that is more reasonable than inventing data to infill. And it seems that the Global Drifter Array displayed in the top image is providing more uniform coverage of the oceans than in the past.

uss-pearl-harbor-deploys-global-drifter-buoys-in-pacific-ocean

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

 

Fossil Fuels ≠ Global Warming Updated

Posted on by Ron Clutz

Note: This Analysis was updated with 2019 statistics in the post 2020 Update: Fossil Fuels ≠ Global Warming

Previous posts addressed the claim that fossil fuels are driving global warming. This post updates that analysis with the latest (2016) numbers from BP Statistics and compares World Fossil Fuel Consumption (WFFC) with three estimates of Global Mean Temperature (GMT). More on both these variables below.

WFFC

2016 statistics are now available from BP for international consumption of Primary Energy sources. Statistical Review of World Energy.  2017 numbers should be available this summer.

The reporting categories are:
Oil
Natural Gas
Coal
Nuclear
Hydro
Renewables (other than hydro)

This analysis combines the first three, Oil, Gas, and Coal for total fossil fuel consumption world wide. The chart below shows the patterns for WFFC compared to world consumption of Primary Energy from 1965 through 2016.

WFFC 2016 BP

The graph shows that Primary Energy consumption has grown continuously for 5 decades. Over that period oil, gas and coal (sometimes termed “Thermal”) averaged 90% of PE consumed, ranging from 94% in 1965 to 86% in 2016.  MToe is millions of tons of oil equivalents.

Global Mean Temperatures

Everyone acknowledges that GMT is a fiction since temperature is an intrinsic property of objects, and varies dramatically over time and over the surface of the earth. No place on earth determines “average” temperature for the globe. Yet for the purpose of detecting change in temperature, major climate data sets estimate GMT and report anomalies from it.

UAH record consists of satellite era global temperature estimates for the lower troposphere, a layer of air from 0 to 4km above the surface. HadSST estimates sea surface temperatures from oceans covering 71% of the planet. HADCRUT combines HadSST estimates with records from land stations whose elevations range up to 6km above sea level.

Both GISS LOTI (land and ocean) and HADCRUT4 (land and ocean) use 14.0 Celsius as the climate normal, so I will add that number back into the anomalies. This is done not claiming any validity other than to achieve a reasonable measure of magnitude regarding the observed fluctuations.

No doubt global sea surface temperatures are typically higher than 14C, more like 17 or 18C, and of course warmer in the tropics and colder at higher latitudes. Likewise, the lapse rate in the atmosphere means that air temperatures both from satellites and elevated land stations will range colder than 14C. Still, that climate normal is a generally accepted indicator of GMT.

Correlations of GMT and WFFC

The next graph compares WFFC to GMT estimates over the five decades from 1965 to 2016 from HADCRUT4, which includes HadSST3.

WFFC HadGMT 2016

Over the last five decades the increase in fossil fuel consumption is dramatic and monotonic, steadily increasing by 223% from 3.5B to 11.4 B oil equivalent tons.  Meanwhile the GMT record from Hadcrut shows multiple ups and downs with an accumulated rise of 0.9C over 51 years, 7% of the starting value.

The second graph compares to GMT estimates from UAH6, and HadSST3 for the satellite era from 1979 to 2016, a period of 37 years.

WFFC HadSST UAH 2016

In the satellite era WFFC has increased at a compounded rate of nearly 2% per year, for a total increase of 84% since 1979. At the same time, SST warming amounted to 0.55C, or 3.9% of the starting value.  UAH warming was 0.72, or 5.5% up from 1979.  The temperature compounded rate of change is 0.1% per year, an order of magnitude less.  Even more obvious is the 1998 El Nino peak and flat GMT since.

Summary

The climate alarmist/activist claim is straight forward: Burning fossil fuels makes measured temperatures warmer. The Paris Accord further asserts that by reducing human use of fossil fuels, further warming can be prevented.  Those claims do not bear up under scrutiny.

It is enough for simple minds to see that two time series are both rising and to think that one must be causing the other. But both scientific and legal methods assert causation only when the two variables are both strongly and consistently aligned. The above shows a weak and inconsistent linkage between WFFC and GMT.

Going further back in history shows even weaker correlation between fossil fuels consumption and global temperature estimates:

wfc-vs-sat

Figure 5.1. Comparative dynamics of the World Fuel Consumption (WFC) and Global Surface Air Temperature Anomaly (ΔT), 1861-2000. The thin dashed line represents annual ΔT, the bold line—its 13-year smoothing, and the line constructed from rectangles—WFC (in millions of tons of nominal fuel) (Klyashtorin and Lyubushin, 2003). Source: Frolov et al. 2009

In legal terms, as long as there is another equally or more likely explanation for the set of facts, the claimed causation is unproven. The more likely explanation is that global temperatures vary due to oceanic and solar cycles. The proof is clearly and thoroughly set forward in the post Quantifying Natural Climate Change.

Background context for today’s post is at Claim: Fossil Fuels Cause Global Warming.

What is Global Temperature? Is it warming or cooling?

Posted on by Ron Clutz

H/T graeme for asking a good question.

This blog features a monthly update on ocean SST averages from HadSST3 (latest is Oceans Cool Off Previous 3 Years). Graeme added this comment:
I came across this today. Can you comment as your studies seem to show the reverse! Regards, Graeme Weber
https://www.carbonbrief.org/category/science/temperature/global-temperature

While thinking about a concise, yet complete response, I put together this post. This is how I see it, to the best of my knowledge.

The question could be paraphrased in these words: Why are there differences between various graphs that report changes in global temperatures?

The short answer is: The differences arise both from what is measured and how the measurements are processed.

For example, consider HadSST3 as one example and GISTEMP as another. All climate temperature products divide the earth surface into grid cells for analysis. This is necessary because a global average can be biased by some regions being much more heavily sampled, eg. North America or North Atlantic. HadSST takes in measurements only from cells containing ocean, while GISTEMP uses data files from NOAA GHCN v3 (meteorological stations), ERSST v5 (ocean areas), and SCAR (Antarctic stations).

Beyond this, HadSST3 is properly termed a temperature data product, while GISTEMP is a temperature reconstruction product. The distinction goes to how the product team deals with missing data. HadSST3 calculates averages each month from grid cells with sufficient samples of observations, and excludes cells with inadequate samples for the month.

GISTEMP estimates temperature values for cells lacking data by referring to cells that are observed sufficiently. The estimates are a best guess as to what temperatures would have been recorded had there been fully functional sensors operating. This process is called interpolation, resulting in a product combining observations with estimates, ie an admixture of data and guesses.

I rely on HadSST3 because I know their results are based upon observational data. I am doubtful of GISTEMP results because many studies, including some of my own, show that interpolation produces strange and unconvincing results which come to light when you look at changes in the local records themselves.

One disturbing thing is that GISTEMP keeps on changing the past, and always in the direction of adding warming.  What you see today differs from yesterday, and tomorrow who knows?

Roger Andrews does a thorough job analyzing the effects of adjustments upon Surface Air Temperature (SAT) datasets. His article at Energy Matters is Adjusting Measurements to Match the Models – Part 1: Surface Air Temperatures.

Another thing is that temperature patterns are altered so that places that show cooling trends on their own are converted to warming after processing.

Figure 3: Warming vs. cooling at 86 South American stations before and after BEST homogeneity adjustments  This shows results from BEST, another reconstruction product demonstrating how an entire continent is presented differently by means of processing.

Then there is the problem that more and more places are showing estimates rather than observations. Years ago, Dr. McKitrick noticed that the decreasing number of stations reporting coincided with the rising GMT reports last century.   Below is his graph showing the correlation between Global Mean Temperature (Average T) and the number of stations included in the global database. Source: Ross McKitrick, U of Guelph

Ave. T vs. No. Stations

Currently it is clear that a great many places are estimated, and it is even the case that active station records are ignored in favor of estimates.

For these reasons I am skeptical of these land+ocean temperature reconstructions. HadSST3 deals with the ocean in a reasonable way, without inventing data.

When it comes to land surface stations, it is much more reasonable to compute the change derivative for each station (i.e. slope) and average the slopes as an indication of regional, national or global temperature change. This form of Temperature Trend Analysis deals with missing data in the most direct way: by putting unobserved months at a specific station on the trendline of the months that are observed at that station–no infilling, no homogenization.

Several of my studies using this approach are on this blog under the category Temperature Trend Analysis. A guideline to these resources is at Climate Compilation Part I Temperatures

The method of analysis is demonstrated by a post as Temperature Data Review Project-My Submission.which also confirms the problems noted above.

A peer-reviewed example of this way of analyzing climate temperature change is the paper Arctic temperature trends from the early nineteenth century to the present W. A. van Wijngaarden, Theoretical & Applied Climatology (2015) here

Is the globe warming or cooling?

Despite the difficulties depicting temperature changes noted above, we do observe periods of warming and cooling at different times and places.  Interpreting those fluctuations is a matter of context.  For example, consider GISTEMP estimated global warming in the context of the American experience of temperature change during a typical year.

 

Global Ocean Cooling in September

Posted on by Ron Clutz

September Sea Surface Temperatures (SSTs) are now available, and we see downward spikes in ocean temps everywhere, led by sharp decreases in the Tropics and SH, reversing the bump upward last month. The Tropical cooling in particular factors into forecasters favoring an unusually late La Nina appearance in coming months.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source, the latest version being HadSST3.

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through September 2017.

The August bump upward was overcome with the Global average matching the lowest level in the chart at February 2015.  September NH temps almost erased a three-month climb; even so 9/2017 is well below the previous two years.  Meanwhile SH and the Tropics are setting new lows for this period.  With current reports from the El Nino 3.4 grid sector, it seems likely October will go even lower, with downward moves across all oceans.

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back to its beginning level. Secondly, the Northern Hemisphere added two bumps on the shoulders of Tropical warming, with peaks in August of each year. Also, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one.

Note:  Last month someone asked about HadSST calculations, especially as the Global appeared to be a simple average of NH and SH, which would be misleading.  My queries to Met Office received these clarifying responses:

My colleague in the Climate Monitoring and Research team has advised the following:

For HadSST3, we take an area-weighted average of all the grid boxes with data in to calculate the global average. We don’t calculate the two hemispheric series and then average them. In the case of SST, this wouldn’t work because the southern hemisphere ocean area is larger than the northern hemisphere.

The uncertainty that arises from incomplete sampling is estimated and incorporated into the global average SST files. Coverage varies throughout the record with the northern hemisphere being generally better observed, but at other times, coverage is concentrated other places, dictated by where shipping happened to be at those times. Since the mid 2000s drifting buoys have provided a more uniform sampling of the world’s oceans. When we compare to other data sets, we typically compare where both data sets have data which minimizes the coverage problems.

Kind regards,  Misha,  Weather Desk Climate Advisor

Summary

We have seen lots of claims about the temperature records for 2016 and 2015 proving dangerous man made warming.  At least one senator stated that in a confirmation hearing.  Yet HadSST3 data for the last two years show how obvious is the ocean’s governing of global average temperatures.

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

The best context for understanding these two years comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • A major El Nino was the dominant climate feature these years.

Solar energy accumulates massively in the ocean and is variably released during circulation events.

 

Tropics Lead Ocean Warming in August

Posted on by Ron Clutz

August Sea Surface Temperatures (SSTs) are now available, and we see an upward spike in ocean temps everywhere, led by sharp increases in the Tropics and SH, reversing for now the downward trajectory from the previous 12 months.  It seems likely the Tropical warming in particular factored into the active hurricane season peaking this month and next.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source, the latest version being HadSST3.

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through August 2017.

In May despite a slight rise in the Tropics, declines in both hemispheres and globally caused SST cooling to resume after an upward bump in April.  Then in July a large drop showed in both in the Tropics and in SH, declining over 4 months.  The sharp upturn in August in the Tropics is the unusual feature this month, along with SH rising, resulting in a global average matching the previous two Augusts. Meanwhile the NH is peaking in August as in the past two years, but somewhat lower.  Despite the August warming, ENSO has gone below neutral toward La Nina, and no one expects a rise like 2015 in the coming months.

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back to its beginning level. Secondly, the Northern Hemisphere added two bumps on the shoulders of Tropical warming, with peaks in August of each year. Also, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one.

Note:  Last month someone asked about HadSST calculations, especially as the Global appeared to be a simple average of NH and SH, which would be misleading.  My query to Met Office received this clarifying response:

My colleague in the Climate Monitoring and Research team has advised the following:

For HadSST3, we take an area-weighted average of all the grid boxes with data in to calculate the global average. We don’t calculate the two hemispheric series and then average them. In the case of SST, this wouldn’t work because the southern hemisphere ocean area is larger than the northern hemisphere.

Kind regards,  Misha,  Weather Desk Climate Advisor

Summary

We have seen lots of claims about the temperature records for 2016 and 2015 proving dangerous man made warming.  At least one senator stated that in a confirmation hearing.  Yet HadSST3 data for the last two years show how obvious is the ocean’s governing of global average temperatures.

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

The best context for understanding these two years comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • A major El Nino was the dominant climate feature these years.

Solar energy accumulates massively in the ocean and is variably released during circulation events.

 

Tropics Lead Ocean Cooling

Posted on by Ron Clutz

July Sea Surface Temperatures (SSTs) are now available, and we can see further ocean cooling led by plummeting temps in the  Tropics and SH, continuing the downward trajectory from the previous 12 months.

HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source, the latest version being HadSST3.

The chart below shows the last two years of SST monthly anomalies as reported in HadSST3 including July 2017.

In May despite a slight rise in the Tropics, declines in both hemispheres and globally caused SST cooling to resume after an upward bump in April.  Now in July a large drop is showing both in the Tropics and in SH, declining the last 4 months.  Meanwhile the NH is peaking in July as usual, but well down from the previous July.  The net of all this is a slightly lower Global anomaly but with likely additional future cooling led by the Tropics and also SH hitting new lows for this period.

Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back to its beginning level. Secondly, the Northern Hemisphere added two bumps on the shoulders of Tropical warming, with peaks in August of each year. Also, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one. Note that Global anomaly for July 2017 matches closely to April 2015.  However,  SH and the Tropics are lower now and trending down compared to an upward trend in 2015.

We have seen lots of claims about the temperature records for 2016 and 2015 proving dangerous man made warming.  At least one senator stated that in a confirmation hearing.  Yet HadSST3 data for the last two years show how obvious is the ocean’s governing of global average temperatures.

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

The best context for understanding these two years comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • A major El Nino was the dominant climate feature these years.

Solar energy accumulates massively in the ocean and is variably released during circulation events.

 

How Trustworthy are SSTs?

Posted on by Ron Clutz

Roger Andrews as promised has published his analysis of SST (Sea Surface Temperatures) datasets, based on some years of research. The essay is Making the Measurements Match the Models – Part 2: Sea Surface Temperatures and well worth a look.

Some years ago while reading to get up to speed on climate science, I was struck by a Roger Pielke Sr. comment. He said that surface temperatures are serving as a proxy for changes in heat content of the earth climate system, which is the real concern.  And air temperatures are contaminated by fluctuations in water content, such that a degree difference in the humid tropics involves much more additional heat than does the same change in extremely dry polar air.

For those who want to see the math, here it is from the Engineering Toolbox.

The enthalpy of humid air at 25C with specific moisture content x = 0.0203 kg/kg (saturation), can be calculated as 76.9 (kJ/kg). . .The same calculation for moist air at 20C gives a heat capacity of 58.2, so the 5C increase requires 18.7 kj/kg for moist air vs. 5.0 kj/kg for dry air, or a ratio of 1:3.7. Similar ratios apply at all air temperatures above 0C. Subzero air, like that in the Arctic most of the year, shows little difference in heat content between dry or saturated, since cold air doesn’t hold much water vapor. See Arctic Amplication?

One implication is that polar air temperatures lacking moisture are 2-3 times more volatile, leading to the “Arctic Amplication” effect. Even so, a thorough look into weather station records around the Arctic circle undermines fears on that account. See Arctic Warming Unalarming.

The larger point made by Pielke Sr. was that a much better proxy for global warming or cooling is provided by SSTs. Measuring temperature changes in the water itself is a much better idea, giving a more exact indication of changes in heat content. There is also the point that SSTs cover 71% of the planet surface.

Andrews knows well the difficulties in assembling SST datasets, including the bucket era and the engine intake era. He addresses directly the problematic WWII measurements, suggesting they can simply be excluded as bad data without affecting the pattern. He also compares the International Comprehensive Ocean-Atmosphere Data Set (ICOADS) raw global SST series used to generate the global HadSST3 series, which is the most widely cited of the currently-published SST series.

There he finds that prior to 1940, there was systematic warming adjustments making HadSST temps higher than ICOADS. He attributes this to the long-standing belief that Night Marine Air Temperatures (NMATs) should synchronize with SSTs. That assumes that air moisture over the water should be fairly consistent from one location to another, and that marine air would be in thermal equilibrium with the water.

But apparently no studies have proven that assumption. I know of one empirical study of the ocean-air interface which shows considerable fluctuation in both the heat exchange and evaporation rates. See Empirical Evidence: Oceans Make Climate

The graph displays measures of heat flux in the sub-tropics during a 21-day period in November. Shortwave solar energy shown above in green labeled radiative is stored in the upper 200 meters of the ocean. The upper panel shows the rise in SST (Sea Surface Temperature) due to net incoming energy. The yellow shows latent heat cooling the ocean, (lowering SST) and transferring heat upward, driving convection. From An Investigation of Turbulent Heat Exchange in the Subtropics James B. Edson

Thanks to Roger’s work on this, we can conclude that SSTs prior to 1950 have issues, but can be encouraged that HadSST3 since then is reasonably consistent with the raw data. And in the future the ARGO record will become long enough for us to follow the trends.

USS Pearl Harbor deploys Global Drifter Buoys in Pacific Ocean

Summary

The best context for understanding global temperature effects in recent years comes from the world’s sea surface temperatures (SST), for several reasons:

  • The ocean covers 71% of the globe and drives average temperatures;
  • SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
  • Major El Ninos have been the dominant climate features these decades.

Solar energy accumulates massively in the ocean and is variably released during circulation events.