Plateau in Ocean Air Temps

Years ago, Dr. Roger Pielke Sr. explained why sea surface temperatures (SST) were the best indicator of heat content gained or lost from earth’s climate system.  Enthalpy is the thermodynamic term for total heat content in a system, and humidity differences in air parcels affect enthalpy.  Measuring water temperature directly avoids distorted impressions from air measurements.  In addition, ocean covers 71% of the planet surface and thus dominates surface temperature estimates.

More recently, Dr. Ole Humlum reported from his research that air temperatures lag 2-3 months behind changes in SST.  He also observed that changes in CO2 atmospheric concentrations lag behind SST by 11-12 months.  This latter point is addressed in a previous post Who to Blame for Rising CO2?

The April update to HadSST3 will appear later this month, but in the meantime we can look at lower troposphere temperatures (TLT) from UAHv.6 which are already posted for April. The temperature record is derived from microwave sounding units (MSU) on board satellites like the one pictured above.

The UAH dataset includes temperature results for air above the oceans, and thus should be most comparable to the SSTs. The graph below shows monthly anomalies for ocean temps since January 2015.
The anomalies have reached the same levels as 2015.  Taking a longer view, we can look at the record since 1995, that year being an ENSO neutral year and thus a reasonable starting point for considering the past two decades.  On that basis we can see the plateau in ocean temps is persisting. Since last October all oceans have cooled, with upward bumps in Feb. 2018, now erased.

UAHv.6 TLT 
Monthly Ocean Anomalies
Ave. Since 1995 Ocean 4/2018
Global 0.13 0.11
NH 0.16 0.27
SH 0.11 -0.01
Tropics 0.12 -0.1

As of April 2018, global ocean temps are slightly below the average since 1995.  NH remains higher, but not enough to offset much lower temps in SH and Tropics (between 20N and 20S latitudes).

The details of UAH ocean temps are provided below.  The monthly data make for a noisy picture, but seasonal fluxes between January and July are important.

Click on image to enlarge.

The greater volatility of the Tropics is evident, leading the oceans through three major El Nino events during this period.  Note also the flat period between 7/1999 and 7/2009.  The 2010 El Nino was erased by La Nina in 2011 and 2012.  Then the record shows a fairly steady rise peaking in 2016, with strong support from warmer NH anomalies, before returning to the 22-year average.

Summary

TLTs include mixing above the oceans and probably some influence from nearby more volatile land temps.  They started the recent cooling later than SSTs from HadSST3, but are now showing the same pattern.  It seems obvious that despite the three El Ninos, their warming has not persisted, and without them it would probably have cooled since 1995.  Of course, the future has not yet been written.

 

Pushing for Climate Diversity

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.

Fossil Fuels ≠ Global Warming Updated

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?

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

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

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

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?

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.

 

Man Made Warming from Adjusting Data

trends and strings

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. Excerpts of text and some images are below.  The whole essay is informative and supports his conclusion:

In previous posts and comments I had said that adjustments had added only about 0.2°C of spurious warming to the global SAT record over the last 100 years or so – not enough to make much difference. But after further review it now appears that they may have added as much as 0.4°C.

For example, these graphs show warming of the GISS dataset:

Figure 2: Comparison of “Old” and “Current” GISS meteorological station surface air temperature series, annual anomalies relative to 1950-1990 means

The current GISS series shows about 0.3°C more global warming than the old version, with about 0.2°C more warming in the Northern Hemisphere and about 0.5°C more in the Southern. The added warming trends are almost exactly linear except for the downturns after 2000, which I suspect (although can’t confirm) are a result of attempts to track the global warming “pause”. How did GISS generate all this extra straight-line warming? It did it by replacing the old unadjusted records with “homogeneity-adjusted” versions.

The homogenization operators used by others have had similar impacts, with Berkeley Earth Surface Temperature (BEST) being a case in point. Figure 3, which compares warming gradients measured at 86 South American stations before and after BEST’s homogeneity adjustments (from Reference 1) visually illustrates what a warming-biased operator does at larger scales. Before homogenization 58 of the 86 stations showed overall warming, 28 showed overall cooling and the average warming trend for all stations was 0.54°C/century. After homogenization all 86 stations show warming and the average warming trend increases to 1.09°C/century:

Figure 3: Warming vs. cooling at 86 South American stations before and after BEST homogeneity adjustments

The adjusted “current” GISS series match the global and Northern Hemisphere model trend line gradients almost exactly but overstate warming relative to the models in the Southern (although this has only a minor impact on the global mean because the Southern Hemisphere has a lot less land and therefore contributes less to the global mean than does the Northern). But the unadjusted “old” GISS series, which I independently verified with my own from-scratch reconstructions, consistently show much less warming than the models, confirming that the generally good model/observation match is entirely a result of the homogeneity adjustments applied to the raw SAT records.

 

Summary

In this post I have chosen to combine a large number of individual examples of “data being adjusted to match it to the theory” into one single example that blankets all of the surface air temperature records. The results indicate that warming-biased homogeneity adjustments have resulted in current published series overestimating the amount by which surface air temperatures over land have warmed since 1900 by about 0.4°C (Table 1), and that global surface air temperatures have increased by only about 0.7°C over this period, not by the ~1.1°C shown by the published SAT series.

Land, however, makes up only about 30% of the Earth’s surface. The subject of the next post will be sea surface temperatures in the oceans, which cover the remaining 70%. In it I will document more examples of measurement manipulation malfeasance, but with a twist. Stay tuned.

Footnote:

I have also looked into this issue by analyzing a set of US stations considered to have the highest CRN rating.  The impact of adjustments was similarly evident and in the direction of warming the trends.  See Temperature Data Review Project: My Submission

 

Ocean Cools and Air Temps Follow

June Sea Surface Temperatures (SSTs) are now available, and we can see ocean temps dropping further after a short pause and resuming 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 June 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 June a large spike upward in NH was overcome by an even larger drop in SH, now three months into a cooling phase. The Tropics also cooled off so the Global anomaly continued to decline.  Presently NH and SH are both changing strongly but in opposite directions.

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 June 2017 matches closely to June 2015, with almost the same anomalies for NH, SH and Global.  The Tropics are lower now and trending down compared to an upward trend in 2015.

June satellite measures of air over the land and oceans also shows a sharp drop.  The graph below provides UAH vs.6 TLT (lower troposphere temps) confirming the general impression from SSTs.

In contrast with SST measurements, air temps in the TLT upticked in May with all areas participating in the rise of almost 0.2C.  Then in June SH dropped 0.4C, NH down 0.2C while the Tropics declined slightly. The end result has all areas back to March values except for the Tropics.  June 2017 compares closely with July 2015 but with no signs of an impending El Nino.

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.