July 2021 Oceans Warm Slightly


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 year end report below showed 2020 rapidly cooling in all regions.  The anomalies then continued to drop sharply well below the mean since 1995.  This Global Cooling was also evident in the UAH Land and Ocean air temperatures ( See Adios, Global Warming)

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through July 2021. After three straight Spring 2020 months of cooling led by the tropics and SH, NH spiked in the summer, along with smaller bumps elsewhere.  Then temps everywhere dropped the last six months, hitting bottom in February 2021.  All regions were well below the Global Mean since 2015, matching the cold of 2018, and lower than January 2015. Then the spring brought more temperate waters and a return to the mean anomaly since 2015.  June Global SST anomaly cooled off back to April due to dropping temps in SH and the Tropics. Now in July warming in all regions reversed the June cooling and brought the Global temp anomaly slightly above the mean since 2015.

Hadsst072021A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016.  

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.  A fourth NH bump was lower and peaked in September 2018.  As noted above, a fifth peak in August 2019 and a sixth August 2020 exceeded the four previous upward bumps in NH.

In 2019 all regions had been converging to reach nearly the same value in April.  Then  NH rose exceptionally by almost 0.5C over the four summer months, in August 2019 exceeding previous summer peaks in NH since 2015.  In the 4 succeeding months, that warm NH pulse reversed sharply. Then again NH temps warmed to a 2020 summer peak, matching 2019.  This has now been reversed with all regions pulling the Global anomaly downward sharply, tempered by warming this year in March to May.  June dropped below the global mean anomaly since 2015, and July has reversed that.

Note that in previous years the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one. However, in 2021 the warming pattern appears in all regions, resulting in a return from cooling to the mean.  The typical NH summer pulse at this point resembles 2017 rather than the much warmer 2019 and 2020.

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.

Hadsst1995 to 0720211995 is a reasonable (ENSO neutral) 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.  NH July 2017 was only slightly lower, and a fifth NH peak still lower in Sept. 2018.

The highest summer NH peaks came in 2019 and 2020, only this time the Tropics and SH are offsetting rather adding to the warming. (Note: these are high anomalies on top of the highest absolute temps in the NH.)  Since 2014 SH has played a moderating role, offsetting the NH warming pulses. After September 2020 temps dropped off down until February 2021, then all regions rose to bring the global anomaly above the mean since 1995  June 2021 backed down before warming again in July 2021.  The present level compares with 2017.

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.

But the peaks coming nearly every summer in HadSST require a different picture.  Let’s look at August, the hottest month in the North Atlantic from the Kaplan dataset.
AMO Aug and Dec 2021The AMO Index is from from Kaplan SST v2, the unaltered and not detrended dataset. By definition, the data are monthly average SSTs interpolated to a 5×5 grid over the North Atlantic basically 0 to 70N. The graph shows August warming began after 1992 up to 1998, with a series of matching years since, including 2020.  Because the N. Atlantic has partnered with the Pacific ENSO recently, let’s take a closer look at some AMO years in the last 2 decades.
AMO decade 072021This graph shows monthly AMO temps for some important years. The Peak years were 1998, 2010 and 2016, with the latter emphasized as the most recent. The other years show lesser warming, with 2007 emphasized as the coolest in the last 20 years. Note the red 2018 line is at the bottom of all these tracks. The black line shows that 2020 began slightly warm, then set records for 3 months. then dropped below 2016 and 2017, peaked in August ending below 2016. Now in 2021, AMO is tracking the coldest years, warming slightly in June and July.

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? If the pattern of recent years continues, NH SST anomalies may rise slightly in coming months, but once again, ENSO which has weakened will probably determine the outcome.

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

 

 

June 2021 Ocean Temps Stay Cool


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 year end report below showed 2020 rapidly cooling in all regions.  The anomalies have continued to drop sharply well below the mean since 1995.  This Global Cooling was also evident in the UAH Land and Ocean air temperatures ( See March 2021 Ocean Chill Deepens) 

The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through June 2021. After three straight Spring 2020 months of cooling led by the tropics and SH, NH spiked in the summer, along with smaller bumps elsewhere.  Then temps everywhere dropped the last six months, hitting bottom in February 2021.  All regions were well below the Global Mean since 2015, matching the cold of 2018, and lower than January 2015. Now the spring is bringing more temperate waters and a return to the mean anomaly since 2015.  June Global SST anomaly cooled off back to April due to dropping temps in SH and the Tropics.  NH continued its summer rise, but only slightly and well below the last two Junes.

Hadsst062021
A global cooling pattern is seen clearly in the Tropics since its peak in 2016, joined by NH and SH cycling downward since 2016.  

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.  A fourth NH bump was lower and peaked in September 2018.  As noted above, a fifth peak in August 2019 and a sixth August 2020 exceeded the four previous upward bumps in NH.

In 2019 all regions had been converging to reach nearly the same value in April.  Then  NH rose exceptionally by almost 0.5C over the four summer months, in August 2019 exceeding previous summer peaks in NH since 2015.  In the 4 succeeding months, that warm NH pulse reversed sharply. Then again NH temps warmed to a 2020 summer peak, matching 2019.  This has now been reversed with all regions pulling the Global anomaly downward sharply, tempered by warming in March to May, and now dropping below the global mean anomaly since 2015.

And as before, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one.  Note the May warming was strongest in the Tropics, though the anomaly is quite cool compared to 2016.

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.

 

Hadsst1995to 062021

1995 is a reasonable (ENSO neutral) 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.  NH July 2017 was only slightly lower, and a fifth NH peak still lower in Sept. 2018.

The highest summer NH peaks came in 2019 and 2020, only this time the Tropics and SH are offsetting rather adding to the warming. (Note: these are high anomalies on top of the highest absolute temps in the NH.)  Since 2014 SH has played a moderating role, offsetting the NH warming pulses. After September 2020 temps dropped off down until February 2021, then all regions rose to bring the global anomaly above the mean since 1995, before backing down in June 2021.

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.

But the peaks coming nearly every summer in HadSST require a different picture.  Let’s look at August, the hottest month in the North Atlantic from the Kaplan dataset.
AMO Aug and Dec 2021The AMO Index is from from Kaplan SST v2, the unaltered and not detrended dataset. By definition, the data are monthly average SSTs interpolated to a 5×5 grid over the North Atlantic basically 0 to 70N. The graph shows August warming began after 1992 up to 1998, with a series of matching years since, including 2020.  Because the N. Atlantic has partnered with the Pacific ENSO recently, let’s take a closer look at some AMO years in the last 2 decades.

AMO decade 062021
This graph shows monthly AMO temps for some important years. The Peak years were 1998, 2010 and 2016, with the latter emphasized as the most recent. The other years show lesser warming, with 2007 emphasized as the coolest in the last 20 years. Note the red 2018 line is at the bottom of all these tracks. The black line shows that 2020 began slightly warm, then set records for 3 months. then dropped below 2016 and 2017, peaked in August ending below 2016. Now in 2021, AMO is tracking the coldest years, warming slightly in May and June.

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? If the pattern of recent years continues, NH SST anomalies may rise slightly in coming months, but once again, ENSO which has weakened will probably determine the outcome.

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

 

 

June 2021 N. Atlantic Finally Cooling?

RAPID Array measuring North Atlantic SSTs.

For the last few years, observers have been speculating about when the North Atlantic will start the next phase shift from warm to cold.

Source: Energy and Education Canada

An example is this report in May 2015 The Atlantic is entering a cool phase that will change the world’s weather by Gerald McCarthy and Evan Haigh of the RAPID Atlantic monitoring project. Excerpts in italics with my bolds.

This is known as the Atlantic Multidecadal Oscillation (AMO), and the transition between its positive and negative phases can be very rapid. For example, Atlantic temperatures declined by 0.1ºC per decade from the 1940s to the 1970s. By comparison, global surface warming is estimated at 0.5ºC per century – a rate twice as slow.

In many parts of the world, the AMO has been linked with decade-long temperature and rainfall trends. Certainly – and perhaps obviously – the mean temperature of islands downwind of the Atlantic such as Britain and Ireland show almost exactly the same temperature fluctuations as the AMO.

Atlantic oscillations are associated with the frequency of hurricanes and droughts. When the AMO is in the warm phase, there are more hurricanes in the Atlantic and droughts in the US Midwest tend to be more frequent and prolonged. In the Pacific Northwest, a positive AMO leads to more rainfall.

A negative AMO (cooler ocean) is associated with reduced rainfall in the vulnerable Sahel region of Africa. The prolonged negative AMO was associated with the infamous Ethiopian famine in the mid-1980s. In the UK it tends to mean reduced summer rainfall – the mythical “barbeque summer”.Our results show that ocean circulation responds to the first mode of Atlantic atmospheric forcing, the North Atlantic Oscillation, through circulation changes between the subtropical and subpolar gyres – the intergyre region. This a major influence on the wind patterns and the heat transferred between the atmosphere and ocean.

The observations that we do have of the Atlantic overturning circulation over the past ten years show that it is declining. As a result, we expect the AMO is moving to a negative (colder surface waters) phase. This is consistent with observations of temperature in the North Atlantic.

Cold “blobs” in North Atlantic have been reported, but they are usually a winter phenomena. For example in April 2016, the sst anomalies looked like this

But by September, the picture changed to this

And we know from Kaplan AMO dataset, that 2016 summer SSTs were right up there with 1998 and 2010 as the highest recorded.

As the graph above suggests, this body of water is also important for tropical cyclones, since warmer water provides more energy.  But those are annual averages, and I am interested in the summer pulses of warm water into the Arctic. As I have noted in my monthly HadSST3 reports, most summers since 2003 there have been warm pulses in the north atlantic.

AMO June 2021
The AMO Index is from from Kaplan SST v2, the unaltered and not detrended dataset. By definition, the data are monthly average SSTs interpolated to a 5×5 grid over the North Atlantic basically 0 to 70N.  The graph shows warming began after 1970s up to 1998, with a series of matching years since.  Since 2016, June SSTs have backed down despite an upward bump in 2020. Because McCarthy refers to hints of cooling to come in the N. Atlantic, let’s take a closer look at some AMO years in the last 2 decades.

AMO decade 062021

This graph shows monthly AMO temps for some important years. The Peak years were 1998, 2010 and 2016, with the latter emphasized as the most recent. The other years show lesser warming, with 2007 emphasized as the coolest in the last 20 years. Note the red 2018 line is at the bottom of all these tracks.  Note that 2020 tracked the 2016 highs, even exceeding those temps the first 4 months.  Now 2021 is starting tracking the much cooler 2018.

With all the talk of AMOC slowing down and a phase shift in the North Atlantic, we await SST measurements for July, August and September to confirm if cooling is starting to set in.

Why Climate Models Fail to Replicate the North Atlantic

screenshot-2021-06-07-at-12.17.38-1024x539-1

A recent paper employed expert statistical analysis to prove that currently climate models fail to reproduce fluctuations of sea surface temperatures in the North Atlantic, a key region affecting global weather and climate.  H/T to David Whitehouse at GWPF for posting a revew of the paper.  I agree with him that the analysis looks solid and the findings robust.  However, as I will show below, neither Whitehouse nor the paper explicitly drew the most important implication.

At GWPF, Whitehouse writes Climate models fail in key test region (in italics with my bolds):

A new paper by Timothy DelSole of George Mason University and Michael Tippett of Columbia University looks into this by attempting to quantify the consistency between climate models and observations using a novel statistical approach. It involves using a multivariate statistical framework whose usefulness has been demonstrated in other fields such as economics and statistics. Technically, they are asking if two time series such as observations and climate model output come from the same statistical source.

To do this they looked at the surface temperature of the North Atlantic which is variable over decadal timescales. The reason for this variability is disputed, it could be related to human-induced climate change or natural variability. If it is internal variability but falsely accredited to human influences then it could lead over estimates of climate sensitivity. There is also the view that the variability is due to anthropogenic aerosols with internal variability playing a weak role but it has been found that models that use external forcing produce inconsistencies in such things as the pattern of temperature and ocean salinity. These things considered it’s important to investigate if climate models are doing well in accounting for variability in the region as the North Atlantic is often used as a test of a climate model’s capability.

The researchers found that when compared to observations, almost every CMIP5 model fails, no matter whether the multidecadal variability is assumed to be forced or internal. They also found institutional bias in that output from the same model, or from models from the same institution, tended to be clustered together, and in many cases differ significantly from other clusters produced by other institutions. Overall only a few climate models out of three dozen considered were found to be consistent with the observations.

The paper is Comparing Climate Time Series. Part II: A Multivariate Test by DelSole and Tippett.  Excerpts in italics with my bolds.

We now apply our test to compare North Atlantic sea surface temperature (NASST) variability between models and observations. In particular, we focus on comparing multi-year internal variability. The question arises as to how to extract internal variability from observations. There is considerable debate about the magnitude of forced variability in this region, particularly the contribution due to anthropogenic aerosols (Booth et al., 2012; Zhang et al., 2013). Accordingly, we consider two possibilities: that the forced response is well represented by (1) a second-order polynomial or (2) a ninth-order polynomial over 1854-2018. These two assumptions will be justified shortly.

If NASST were represented on a typical 1◦ × 1◦ grid, then the number of grid cells would far exceed the available sample size. Accordingly, some form of dimension reduction is necessary. Given our focus on multi-year predictability, we consider only large-scale patterns. Accordingly, we project annual-mean NASST onto the leading eigenvectors of the Laplacian over the Atlantic between 0 0 60◦N. These eigenvectors form an orthogonal set of patterns that can be ordered by a measure of length  scale from largest to smallest.

DelSole Tippett fig1

Figure 1. Laplacian eigenvectors 1,2,3,4,5,6 over the North Atlantic between the equator and 60◦N,  where dark red and dark blue indicate extreme positive and negative values, respectively

The first six Laplacian eigenvectors are shown in fig. 1 (these were computed by the method of DelSole and Tippett, 2015). The first eigenvector is spatially uniform. Projecting data onto the first Laplacian eigenvector is equivalent to taking the area-weighted average in the basin. In the case of SST, the time series for the first Laplacian eigenvector is merely an AMV index (AMV stands for “Atlantic Multidecadal Variability”). The second and third eigenvectors are dipoles that measure the large-scale gradient across the basin. Subsequent eigenvectors capture smaller scale patterns.  For model data, we use pre-industrial control simulations of SST from phase 5 of the Coupled Model Intercomparison Project (CMIP5 Taylor et al., 2012). Control simulations use forcings that repeat year after year. As a result, interannual variability in control simulations come from internal dynamical mechanisms, not from external forcing.

DelSole Tippett fig2Figure 2. AMV index from ERSSTv5 (thin grey), and polynomial fits to a second-order (thick black) and ninth-order (red) polynomial.

For observational data, we use version 5 of the Extended Reconstructed SST dataset (ERSSTv5 Huang et al., 2017). We consider only the 165-year period 1854-2018. We first focus on time series for the first Laplacian eigenvector, which we call the AMV index. The corresponding least squares fit to second- and ninth-order polynomials in time are shown in fig. 2. The second-order polynomial captures the secular trend toward warmer temperatures but otherwise has weak multidecadal variability. In contrast, the ninth-order polynomial captures both the secular trend and multidecadal variability. There is no consensus as to whether this multidecadal variability is internal or forced. 

DelSole Tippett fig4

Figure 4. Deviance between ERSSTv5 1854-1935 and 82-year segments from 36 CMIP5 pre-industrial control simulations. Also shown is the deviance between ERSSTv5 1854-1935 and ERSSTv5 1937-2018 (first item on x-axis). The black and red curves show, respectively, results after removing a second- and ninth-order polynomial in time over 1854-2018 before evaluating the deviance. The models have been ordered on the x-axis from smallest to largest deviance after removing a second-order polynomial in time.

Conclusion:

The test was illustrated by using it to compare annual mean North Atlantic SST variability in models and observations. When compared to observations, almost every CMIP5 model differs significantly from ERSST. This conclusion holds regardless of whether a second- or ninth-order polynomial in time is regressed out. Thus, our conclusion does not depend on whether multidecadal NASST variability is assumed to be forced or internal. By applying a hierarchical clustering technique, we showed that time series from the same model, or from models from the same institution, tend to be clustered together, and in many cases differ significantly from other clusters. Our results are consistent with previous claims (Pennell and Reichler, 2011; Knutti et al., 2013) that the effective number of independent models is smaller than the actual number of models in a multi-model ensemble.

The Elephant in the Room

Now let’s consider the interpretation reached by model builders after failing to match observations of Atlantic Multidecadal Variability.  As an example consider INMCM4, whose results deviated greatly from the ERSST5 dataset.  In 2018, Evgeny Volodin and Andrey Gritsun published Simulation of observed climate changes in 1850–2014 with climate model INM-CM5.   Included in those simulations is a report of their attempts to replicate North Atlantic SSTs.  Excerpts in italics with my bolds.

esd-9-1235-2018-f04

Figure 4 The 5-year mean AMO index (K) for ERSSTv4 data (thick solid black); model mean (thick solid red). Dashed thin lines represent data from individual model runs. Colors correspond to individual runs as in Fig. 1.

Keeping in mind the argument that the GMST slowdown in the beginning of the 21st century could be due to the internal variability of the climate system, let us look at the behavior of the AMO and PDO climate indices. Here we calculated the AMO index in the usual way, as the SST anomaly in the Atlantic at latitudinal band 0–60∘ N minus the anomaly of the GMST. The model and observed 5-year mean AMO index time series are presented in Fig. 4. The well-known oscillation with a period of 60–70 years can be clearly seen in the observations. Among the model runs, only one (dashed purple line) shows oscillation with a period of about 70 years, but without significant maximum near year 2000. In other model runs there is no distinct oscillation with a period of 60–70 years but a period of 20–40 years prevails. As a result none of the seven model trajectories reproduces the behavior of the observed AMO index after year 1950 (including its warm phase at the turn of the 20th and 21st centuries).

One can conclude that anthropogenic forcing is unable to produce any significant impact on the AMO dynamics as its index averaged over seven realization stays around zero within one sigma interval (0.08). Consequently, the AMO dynamics are controlled by the internal variability of the climate system and cannot be predicted in historic experiments. On the other hand, the model can correctly predict GMST changes in 1980–2014 having the wrong phase of the AMO (blue, yellow, orange lines in Figs. 1 and 4).

esd-9-1235-2018-f01

Figure 1 The 5-year mean GMST (K) anomaly with respect to 1850–1899 for HadCRUTv4 (thick solid black); model mean (thick solid red). Dashed thin lines represent data from individual model runs: 1 – purple, 2 – dark blue, 3 – blue, 4 – green, 5 – yellow, 6 – orange, 7 – magenta. In this and the next figures numbers on the time axis indicate the first year of the 5-year mean.

The Bottom Line

Since the models incorporate AGW in the form of CO2 sensitivity, they are unable to replicate Atlantic Multidecadal Variability.  Thus, the logical conclusion is that variability of North Atlantic SSTs is an internal, natural climate factor.

The-Elephant-in-the-RoomOMC

Arctic Sea Ice Linked to Little Ice Age

The Dutch artist Hendrick Avercamp painted winter activity on the ice during the first half of the 17th century, when it was quite cold in Central and Northern Europe. (Image: Henrik Avercamp / Wikimedia Commons)

Elise Kjørstad writes at Science Norway What actually started the Little Ice Age? Excerpts in italics with my bolds.

It all may have started with sea ice, and the changes may have happened all by themselves without the influence of volcanoes or the Sun, researchers behind a new study say.

The ninth century seems to have experienced a warmer climate, which has been called the Medieval Warm Period.

But from the 14th century things were different. It rained “without stopping” in 1315, and grain didn’t ripen. The situation was much the same the following year. Later in the 14th century there were several episodes of wild weather and cold periods.

The Little Ice Age can be divided into two phases, according to an article in The New Yorker. It began with a cooling period in 1300 – 1400. The coldest period was from the end of the 1500s to 1850.

This cooling caused glaciers to expand in Scandinavia, the Alps, in Iceland, Alaska, China, in the southern Andes and in New Zealand.

Generally speaking, the Little Ice Age is said to have begun because of an increase in volcanism and reduced activity of the Sun.

“The timing agrees quite well with the great eruptions from the 13th century. So there is good empirical evidence that this could be true,” said Martin Miles, a researcher at NORCE Norwegian Research Centre, and the Bjerknes Centre for Climate Research in Bergen, and at the University of Colorado at Boulder in the USA.

But in a new study, Miles and his colleagues have looked at another possibility.

The strait between Greenland and Svalbard is the only deep connection between the Arctic Ocean and the world’s oceans. (Image: Bdushaw / CC BY-SA 3.0 / Wikimedia Commons)

Lots of ice on the go

In their new study, Miles and his colleagues looked at the transport of sea ice from the Arctic over a 1400 year period.

They compiled data from seabed samples from areas outside Greenland, the eastern part of the Fram Strait, the Greenland Sea and off Iceland. The samples contained small fossils that give researchers information about sea temperatures and loose material that sea ice had carried with it.

In several of these areas, ice will only be found if there is an especially large amount flowing out of the Arctic Ocean. This is particularly true during cold periods and when there is also a lot of sea ice formation.

“We discovered that an unusually large amount of sea ice flowed out of the Arctic Ocean from the beginning of the 14th century. It is very interesting, and the biggest event we found during the last 1400 years,” says Miles.

Fig. 2 Arctic sea ice and polar waters from Fram Strait to the Greenland Sea. Sea-ice and ocean reconstructions from marine sediment cores. (A) Eastern Fram Strait, based on IP25 (21). (B) Eastern Fram Strait, based on IRD (22). (C) Northeast Greenland shelf, based on IP25 (21). (D) Northeast Greenland shelf, based on benthic foraminifera (23). (E) Central Greenland Sea, based on IRD (24). Blue shading represents the period of increased sea ice spanning the 1300s CE.

Can’t explain everything

Miles says sea ice may have affected the climate in Europe in the 14th century in this way.

The ice that melts and turns into fresh water can affect ocean currents, which in turn affect the atmosphere and climate, he says.

“Ocean currents are very important for transporting heat to Europe. If the currents weaken a little, it will be much colder than usual,” he said.

Sea ice is not only a reaction to climate change, but can also trigger climate change, Miles says.

The paper is Evidence for extreme export of Arctic sea ice leading the abrupt onset of the Little Ice Age  Martin W. Miles et. al. (2020).

Abstract

Arctic sea ice affects climate on seasonal to decadal time scales, and models suggest that sea ice is essential for longer anomalies such as the Little Ice Age. However, empirical evidence is fragmentary. Here, we reconstruct sea ice exported from the Arctic Ocean over the past 1400 years, using a spatial network of proxy records. We find robust evidence for extreme export of sea ice commencing abruptly around 1300 CE and terminating in the late 1300s. The exceptional magnitude and duration of this “Great Sea-Ice Anomaly” was previously unknown. The pulse of ice along East Greenland resulted in downstream increases in polar waters and ocean stratification, culminating ~1400 CE and sustained during subsequent centuries. While consistent with external forcing theories, the onset and development are notably similar to modeled spontaneous abrupt cooling enhanced by sea-ice feedbacks. These results provide evidence that marked climate changes may not require an external trigger.

Fig. 3 Arctic sea ice and polar waters downstream in the subarctic North Atlantic. Sea-ice and ocean reconstructions from marine sediment cores: (A) Nansen Fjord, East Greenland, based on foraminifera (25), inverted scale. (B) North Iceland shelf, sea ice based on IP25 (13). (C) North Iceland shelf sea surface temperatures (SSTs) based on alkenones (29), inverted scale. (D) South Greenland Fjord, based on diatoms (31). (E) West Greenland shelf, based on diatoms, five-point running average (33). Blue shading represents the period of increased polar waters and sea ice spanning the 1300s CE.

Background Post with Supporting Information

The Climate System is Self-Oscillating: Sea Ice Proves It.

Scientists have studied the Arctic for a long time at the prestigious AARI: Arctic and Antarctic Research Institute St. Petersburg, Russia. V. F. Zakharov has published a complete description supported by research findings under this title: Sea Ice In the Climate System A Russian View (here)

Below I provide excerpts from this extensive analysis to form a synopsis of their view: Component parts of the climate system interact so that Arctic Sea Ice varies within a range constrained by those internal forces.

Self-Oscillating Sea Ice System

Self-Oscillating Sea Ice System

The most probable regulator of the physical geographical process can be found from analysis of the relationships between the components of the climate system. It is not necessary to investigate the cause-effect relationships between all these components in succession. It is sufficient to choose one of them, let us say sea ice, and consider its direct interaction with the atmosphere and the ocean – in the climate system and the significance of internal mechanisms in the natural process. Pg 1

The idea that the ice area growth at present can be achieved by changes in only the haline structure of the upper ocean layer, as a result of surface Arctic water overflowing onto warmer but more saline water, is supported both by calculations and empirical data. Pg. 46

First of all, it should be noted that the signs of temperature and salinity anomalies coincide in most cases: a decreased salinity corresponds to enhanced temperature and vice versa. Such similarity in the change of these parameters is impossible to explain from the point of view of the governing role of thermal conditions in the atmosphere with regard to the ocean, as the air temperature increase and decrease can result only in the change of the thermal state of sea surface layer not its salinity. Pgs. 48-49

Thus, the presented facts suggest that the most significant cause of changes in the ice cover extent are the changes in the vertical water structure in the upper ocean layer, rather than the changes of thermal conditions in the atmosphere. These changes are induced by fluctuations in the horizontal dimensions of the halocline, which are governed in turn by the expansion or reduction of the surface Arctic water mass. Pg. 49

It follows from the above that, under present day conditions, the changes in the area of the Arctic sea ice during the colder period of the year can be induced only by the change in the haline structure of the upper ocean layer. Indirectly, this change will also affect the thermal state of the atmosphere. Pg. 56

It is important to note that the ice effect on the atmosphere is not limited to the thermal effect. That it can produce a significant effect on atmospheric circulation is already evident from the fact that the Arctic anticyclone, considered by Viese [13] as a regulator of atmospheric processes in the Northern polar region, could form as a pressure formation only in the conditions of the ice regime in the Arctic. Pg. 56

 

Zacharov fig.24

Zakharov fig.24

An analysis of cause-effect relationships does not leave any doubt in what direction and in what order the climate signal propagates in the atmosphere-ocean-polar ice system. This is not the direction and order usually assumed to cause present climate change. When it has become clear that the changes in the ocean, caused by disturbances of its freshwater balance, precede changes in the extent of sea ice, and the latter the changes in the atmosphere, then there was nothing left but for us to acknowledge self oscillation to be the most probable explanation for the development of the natural process. Pg. 58

Maybe the most convincing evidence of the Arctic sea ice stability is its preservation during the last 700,000 years despite vast glacial- interglacial fluctuations. The surface air temperature in the Arctic during the interglacial periods was higher by several degrees than present day temperatures. Pg. 44

Conclusion:

The remarkable stability of our planetary climate system derives from feedbacks between internal parts of the system, providing the oscillations we observe as natural variability. Arctic Sea Ice is a prime example. Bottom line:  A bit less ice in the Arctic indicates that we are not yet slipping into an ice age, little or otherwise. 

See also The Great Arctic Ice Exchange

Figure 4.12. Mean resulting ice-drift pattern for summer (a) and winter (b) during the warm epoch and the difference between ice-drift vectors during the warm and cold epochs for summer (c) and winter (d).

How Water Warms Our Planet

The hydrological cycle. Estimates of the observed main water reservoirs (black numbers in 10^3 km3 ) and the flow of moisture through the system (red numbers, in 10^3 km3 yr À1 ). Adjusted from Trenberth et al. [2007a] for the period 2002-2008 as in Trenberth et al. [2011].

This site has long asserted that “Oceans Make Climate”. Now a recent study reveals the dynamics by which water influences temperatures over land as well. The paper is Testing the hypothesis that variations in atmospheric water vapour are the main cause of fluctuations in global temperature by Ivan R. Kennedy and Migdat Hodzic, published in Periodicals of Engineering and Natural Sciences, August 2019.  Excerpts in italics with my bolds. H/T Notrickszone.

Introduction

Global warming issues have caused intensive research work in related areas, from land use, to urban environment to data science use in order to understand its effects better [25], [26], [27]. In this paper we focus on water related effects on global warming. Although water is recognised as the main cause of the greenhouse effect warming the Earth 33 oC above its black body temperature, water vapour is usually given a secondary role in global models, as a positive feedback from warming by all other causes. Despite its dominant effect in generating the weather, changes related to water are not seen as having a primary role in climate change, the focus being primarily on CO2. With positive feedback from primary warming, the effect of increasing CO2 is trebled [15] by water vapour increase. This conclusion is based on the perception that there are no significant trends in the hydrological cycle that could cause climate forcing. But this overlooks the effect of more than 3500 km3 of extra surface and ground water used annually in irrigation [17] to grow food for the human population. This quantity of extra water increases steadily year by year, well correlated with increasing atmospheric CO2, growing about 60% of world food requirements. Even so, the amount used in irrigation probably only adds about 3% to the annual hydrological cycle [9] of 113,000 km3. Is this sufficient to exert a significant extra greenhouse effect? Here we advance the hypothesis that it does and should be included in climate models.

A critical assumption of the IPCC consensus of global warming is that an increasing concentration of CO2 causes more retention of radiant heat near the top of the atmosphere, largely as a result of reduced emission of its spectral wavelengths centred on 15 microns. The radiative-convective model assumes that the lowered emissions at reduced pressure, number density and higher, colder altitudes from this GHG now provides an independent and sustained forcing exceeding 1-2 W per m2. It is assumed that once this reduction in OLR in the air column from increasing CO2 has occurred it must be compensated by increased OLR at different wavelengths elsewhere, maintaining balance with incoming radiation.

This critical assumption still lacks empirical confirmation.

Water Drives Atmospheric Warming

The importance of water in helping to keep the Earth’s atmosphere warm in the short term is beyond dispute. Table 1 summarises previously estimated rates for thermal energy flows into and out of the atmosphere [23]. As shown in the table, more than 80% of the power by which the temperature of air is maintained above the Earth’s black body temperature of -18 C is facilitated by water. Most significant of these air warming inputs from water is the greenhouse effect by which water vapour absorbs longwave radiation emitted from the surface, retaining more energy in air. However, warming from absorption of specific quanta by water vapour of incoming short wave solar radiation (ISR) and the latent heat of condensation of water vapour, exceeding the cooling effect of vertical convection, also contribute to warming of air.

Thus, the greenhouse gas (GHG) content of the atmosphere effectively provides resistance to heat flow to space increasing the transient storage of solar energy, with a warming effect analogous to resistances in an electrical circuit. By comparison to water, other polyatomic greenhouse gases like CO2 play a minor role in this process, totalling less than 20% of warming. Furthermore, the fact that the minor GHGs are relatively well-mixed by the turbulence in the troposphere, unlike water, means that we cannot expect to observe spatial variations in their effects. Furthermore, the heat capacity of non-greenhouse gases provides some 99% of the thermal inertia of the troposphere, although only greenhouse gases capable of longwave radiation by vibrational and rotational quanta can contribute to cooling by radiation through the top of the atmosphere as OLR. Figure 1 contrasts schematically the typical variation of outgoing longwave radiation (OLR) over marine and terrestrial environments.

On well-watered land such as southern China much less direct emission of OLR to space occurs, in contrast to Quetta, Pakistan, on the same latitude with similar incoming shortwave radiation (ISR). In contrast to humid atmospheres on land and tropical seas, relatively arid regions such as the Sahara, the Middle East and Australia provide heat vents effectively cooling the Earth, solely as a result of the radiant emissions from GHGs as OLR. The varying global emissions of OLR estimated for typical marine and terrestrial regions shown in Figure 2 mirror this scheme.

Clearly, water vapour is the most critical factor in the mechanism by which the air column of the lower troposphere is charged with heat energy. It is of interest from this figure and in Table 1 that the exact sum of the effects of all greenhouse gases in directly warming air, including conduction from the surface, charges the lower atmosphere with sufficient heat to generate the downwelling radiation from greenhouse gases directed towards the surface [12]. Water is the main source of this back radiation [18], well understood to be responsible for keeping the surface air warmer in humid atmospheres, thus raising the minimum temperature.

None of the variation in OLR in Figure 1 can be attributed to the well-mixed GHGs such as CO2.

Furthermore, unlike the greenhouse effect of CO2, which is regarded as increasing only in in a logarithmic manner as its concentration rises, the greenhouse effect of water on retaining heat in the atmosphere should vary more linearly, even in the case of absorption of surface radiation, as its vapour spreads into dryer atmospheres; this potential is illustrated in Fig.1 in the descending zones of Hadley cells at sub-tropical latitudes.

Fig. 1 Global values of mean OLR from 2003-2011 (downloaded August 2, 2017, AIRS OLR 2003-2011 average htpp://mirador.gsfc.nasa.gov/ estimated by Giorgio, G.P., June 24, 2014). The russet areas show regions of greater OLR, with outgoing radiation above the average of ca. 240 W per m2, thus tending to cool the Earth. Note how the upper troposphere above arid continental regions provides a vent for the greatest rate of cooling.

Thermal Effects from Water are Direct and Linear

An approximately linear response in increasing air temperature to changes in atmospheric water content is reasonable. Unlike the well-mixed CO2, there are marked spatial and temporal variations in atmospheric water content, with much of the Earth’s surface in significant deficit, particularly in the sub-tropical zone subject to Hadley cell recycling, emphasised over semi-arid land. To the extent that additional water vapour spills over into these dryer regions on land the greater the area of the Earth that is subject to the greenhouse effect. This response can be contrasted to the effect of increasing CO2, which has a logarithmic relationship between climate forcing and concentration in the atmosphere [14], [15], each doubling causing a similar increase in temperature. Because there is no obvious regional effect of CO2 on the weather or regional climate, the effect of any increases in its concentration can only be theoretically inferred. If additional heat is retained in the atmosphere by increasing greenhouse effects from CO2 or water, the air temperature near the surface is expected to increase to keep global values of ISR and OLR in balance. A critical assumption of the IPCC consensus for climate change is that increasing CO2 causes more retention of heat in air near the top of the troposphere, largely as reduced emission from the edges of its spectral peak centred on 15 microns. This edge effect is predicted to be visible from space as a cooling of its spectrum, providing a negative forcing of 1-2 W per m2. It is assumed that this forcing must be compensated by increased OLR at different wavelengths as a result of the increased temperature.

Fig. 3 Satellite measurements of global-zonal OLR (http://www.cpc.ncep.noaa.gov/data/indices/olr NOAA website, downloaded August 20, 2017). The 1998-2000 El Nino peaked at about 1.03 C above the minimum temperature in the preceding La Nina, with zonal OLR varying approximately 4 W/m2; see also (8)

This is regarded as a result of convective elevation of the maritime atmosphere, reducing the outgoing longwave radiation (OLR) about 100 W/m2 locally and 4 W/m2 globally from an increase in global water vapour of about 4%. This suggests a linear response from greenhouse warming to increased water vapour content of the atmosphere. Note that the extra heat in the atmosphere during an El Nino is controlled by all these sources of warming, as shown in Figure 2. Whatever the source of extra heat in the ocean, by moving extra water into the atmosphere as vapour it warms the atmosphere by the resultant greenhouse effect, reducing OLR, as well as direct warming by sunlight in the air column. In Table 4, another estimate of the possible effect of irrigation on global warming by comparison with the El Nino-La Nina cycle [22] is made. Consistent with the irrigation water hypothesis the El Nino has been long known to significantly reduce the OLR over the Pacific Ocean up to 25% [3], recognised as a result of elevation of emission of the OLR from water being elevated and therefore a colder altitude. Assuming 60% of irrigation water becomes vapour in the troposphere and a longer rain-out time of 15 days in dry regions compared to less than a week over the oceans with a global average of 8.5 days [19], a steady state of about 100 km3 of extra water vapour results from irrigation.

This estimate also suggests an increase in temperature near 0.2C from 0.84 W/m2 of forcing based on the data given in Figure 3. This is consistent with the total effect of water vapour on global warming exceeding 25 C.

It should be noted that this dynamic effect of water on warming air includes heat pumping by evapotranspiration as well as significant warming by direct absorption of short wave solar radiation (see Fig. 2), also contributing to a more linear effect by water on warming. Since this increase estimates a primary forcing effect of new water, a positive feedback is also anticipated from increased evaporation of the ocean, suggesting that the total increase from irrigation could be of the order of 0.5 oC in the 20th century.

These global results may have more accuracy than the results obtained from the numerous grid points in global circulation models, given the additivity of errors.

Empirical Proof Comparing Dry and Irrigated Land

In Figure 4, using the same modelling as in Figure 2, the predicted steady state greenhouse effect of adding irrigation water in a comparison between dryland and irrigated land. In fact the effect of water on heat transfer to the atmospheric column is not only a result of the greenhouse effect given in the equation in the figure but also from direct absorption by water of short wave ISR and evapotranspiration, similar in total magnitude. These latter effects will be a linear function of the water vapour involved. The evaporative effect cools the surface but must transfer a similar amount of heat to the atmosphere as infrared radiation (ca. 6 microns) associated with condensation of water vapour into droplets under convective cooling as in [21]. Paradoxically, the modelling paper in [6] failed to account for any of these effects, specifically dismissing significant transfer of water vapour into the atmosphere from growth of irrigated crop growth as noted above. This provides a clue to the possible flaw in their models. Except for environments already very humid where evapotranspiration is limited, this cannot be true.

Fig. 4 Comparison of dryland and irrigated land for effect of water on heat retention in the atmosphere as an enhanced greenhouse effect. The El Nino condition of enhanced evaporation from the ocean known to strongly reduce OLR In [3] is shown as an analogue.

NCEP/ NCAR Reanalyses Coincident with the Periodic Flooding of Lake Eyre

Fig. 5 Variation in OLR from flooding of lake Eyre using NCEP-NCAR reanalysis datasets. a.Difference in OLR values between 1978 and 1974, dry and wet years. b. Difference in OLR values between 1978 and 1973, two dry years.

Rarely, during the La Nina phase of the climate cycle, the dry interior of northern Australia overlying the Great Artesian Basin may flood. Lacking riverine exits to the ocean, the massive runoff caused flows southwards, mainly accumulating in the depression below sea level in central South Australia known as Lake Eyre. In late January and February in the early months of 1974 Lake Eyre filled to a depth of six metres, its surface only returning to its hot, dry state three years later in 1977-78. This was the greatest flood ever recorded. The hypothesis in [4] suggests that this flooding should also lead to persistent elevated water vapour content of the atmosphere, predominantly downwind from the Lake Eyre basin. Using the NCEP-NCAR reanalysis datasets, which are informed by Nimbus and other satellite observations since 1970, the OLR emissions to space and the variation in humidity from this region comparing 12 months of 1974 with the same period in 1978 by subtraction of one year from the other. A significant elevation of OLR when the lake was dry by more than 10 W/m2 was observed for the 12-month period (Figure 5). This result is accompanied by increases in specific humidity consistent with an elevated greenhouse effect such as would be experienced in semi-arid areas when irrigated. The area affected downwind also showing elevated humidity is estimated as 35 times the flooded area, showing that the magnitude of this regional greenhouse effect was indeed significant.

Conclusion:  Thankfully, A Wet World is a Warm World

The neglect of the possible effect of irrigation as a significant source of anthropogenic climate change may have been a result of reluctance to consider the relatively small amount of irrigation in the hydrological cycle. Because water has been considered as providing positive feedback to warming primarily from CO2 its possible forcing effect has been overlooked. But as shown here by several different means, the more potent effect of applying water previously in the ocean or deep in the ground to dry surfaces with air in strong water deficit can be sufficient to affect global temperature. Clearly, the water vapour content of the troposphere is the major cause of the natural greenhouse effect, contributing up to two-thirds of the 33 oC warming.

Spatial and temporal variations in soil moisture and relative humidity of the atmosphere are the main factors controlling the regional outgoing longwave radiation (OLR), in contrast to the more even effects from well-mixed greenhouse gases such as CO2.

This is well illustrated in the 4-6 year El Nino cycles, resulting in a global mean temperature variation approaching 1 oC compared with La Nina years. Longer term, the proposed Milankovitch glaciations of paleoclimates result in declines of atmospheric temperature around 10 oC, consistent with the major reduction in tropospheric water vapour approaching 50%. Weather conditions and climate as illustrated in the greenhouse effect are clearly demonstrated in the distribution of water, particularly on land. The apparently linear relationship between the water content of the atmosphere is direct verification of the greenhouse warming effect of this greenhouse gas. By contrast, other than by correlation, there is no such direct verification possible for the greenhouse effect of CO2. We rely on the forcing equation of 5.3ln[(CO2)t /(CO2)o] to estimate the climate sensitivity with respect to varying concentration (ppmv) of this greenhouse gas. Early hopes that a clear spectral signal was available showing significantly reduced OLR from increasing CO2, proving the hypothesis of climate forcing by permanent GHGs, have not been realised [5]. A focus using new satellites on the longer wavelength OLR associated with rotations of water might help resolve this question. Up till now, OLR is estimated for this region based on shorter wavelengths. The natural experiment provided by the flooding of Lake Eyre of the greenhouse effect by significantly reducing the OLR provides confirmation that irrigation water typically applied to dry land will have a measurable greenhouse effect.

One year time lapse of precipitable water (amount of water in the atmosphere) from Jan 1, 2016 to Dec 31, 2016, as modeled by the GFS. The Pacific ocean rotates into view just as the tropical cyclone season picks up steam.

Ocean Oxygen Misdirection

Warmists consistently do recycling, especially alarming stories coming back for encore media appearances.  This week it’s the suffocating ocean meme, which taps into our caring about the seas, but conflates impacts from human maritime activities with subtle temperature changes, i.e climate change (AKA emergency, chaos, crisis etc.).  Of course COP 25 is the trigger for this.  I won’t list the alarming headlines since they are little different from last time, covered in a previous post reprinted below.  Below are two typical recent quotes showing how an actual ocean concern is exploited for fossil fuel activism.

“A healthy ocean with abundant wildlife is capable of slowing the rate of climate breakdown substantially,” said Dr Monica Verbeek, the executive director of the group Seas at Risk. “To date, the most profound impact on the marine environment has come from fishing. Ending overfishing is a quick, deliverable action which will restore fish populations, create more resilient ocean ecosystems, decrease CO2 pollution and increase carbon capture, and deliver more profitable fisheries and thriving coastal communities.”

“Ending overfishing would strengthen the ocean, making it more capable of withstanding climate change and restoring marine ecosystems – and it can be done now,” explained Rashid Sumaila, professor and director of the fisheries economics research unit at the University of British Columbia. “The crisis in our fisheries and in our oceans and climate are not mutually exclusive problems to be addressed separately – it is imperative that we move forward with comprehensive solutions to address them.”

Previous post from last year

The climate scare machine is promoting again the fear of suffocating oceans. For example, an article this week by Chris Mooney in Washington Post, It’s Official, the Oceans are Losing Oxygen.

A large research synthesis, published in one of the world’s most influential scientific journals, has detected a decline in the amount of dissolved oxygen in oceans around the world — a long-predicted result of climate change that could have severe consequences for marine organisms if it continues.

The paper, published Wednesday in the journal Nature by oceanographer Sunke Schmidtko and two colleagues from the GEOMAR Helmholtz Centre for Ocean Research in Kiel, Germany, found a decline of more than 2 percent in ocean oxygen content worldwide between 1960 and 2010.

Climate change models predict the oceans will lose oxygen because of several factors. Most obvious is simply that warmer water holds less dissolved gases, including oxygen. “It’s the same reason we keep our sparkling drinks pretty cold,” Schmidtko said.

But another factor is the growing stratification of ocean waters. Oxygen enters the ocean at its surface, from the atmosphere and from the photosynthetic activity of marine microorganisms. But as that upper layer warms up, the oxygen-rich waters are less likely to mix down into cooler layers of the ocean because the warm waters are less dense and do not sink as readily.

And of course, other journalists pile on with ever more catchy headlines.

The World’s Oceans Are Losing Oxygen Due to Climate Change

How Climate Change Is Suffocating The Oceans

Overview of Oceanic Oxygen

Once again climate alarmists/activists have seized upon an actual environmental issue, but misdirect the public toward their CO2 obsession, and away from practical efforts to address a real concern. Some excerpts from scientific studies serve to put things in perspective.

k2_g_sauerstoffmischung_meer_2_e_en

2.14 > Oxygen from the atmosphere enters the near-surface waters of the ocean. This upper layer is well mixed, and is thus in chemical equilibrium with the atmosphere and rich in O2. It ends abruptly at the pyncnocline, which acts like a barrier. The oxygenrich water in the surface zone does not mix readily with deeper water layers. Oxygen essentially only enters the deeper ocean by the motion of water currents, especially with the formation of deep and intermediate waters in the polarregions. In the inner ocean, marine organisms consume oxygen. This creates a very sensitive equilibrium.

How the Ocean Breathes

Variability in oxygen and nutrients in South Pacific Antarctic Intermediate Water by J. L. Russell and A. G. Dickson

The Southern Ocean acts as the lungs of the ocean; drawing in oxygen and exchanging carbon dioxide. A quantitative understanding of the processes regulating the ventilation of the Southern Ocean today is vital to assessments of the geochemical significance of potential circulation reorganizations in the Southern Hemisphere, both during glacial-interglacial transitions and into the future.

Traditionally, the change in the concentration of oxygen along an isopycnal due to remineralization of organic material, known as the apparent oxygen utilization (AOU), has been used by physical oceanographers as a proxy for the time elapsed since the water mass was last exposed to the atmosphere. The concept of AOU requires that newly subducted water be saturated with respect to oxygen and is calculated from the difference between the measured oxygen concentration and the saturated concentration at the sample temperature.

ocean oxygen

This study has shown that the ratio of oxygen to nutrients can vary with time. Since Antarctic Intermediate Water provides a necessary component to the Pacific equatorial biological regime, this relatively high-nutrient, high-oxygen input to the Equatorial Undercurrent in the Western Pacific plays an important role in driving high rates of primary productivity on the equator, while limiting the extent of denitrifying bacteria in the eastern portion of the basin. 

Uncertain Measures of O2 Variability and Linkage to Climate Change

A conceptual model for the temporal spectrum of oceanic oxygen variability by Taka Ito and Curtis Deutsch

Changes in dissolved O2 observed across the world oceans in recent decades have been interpreted as a response of marine biogeochemistry to climate change. Little is known however about the spectrum of oceanic O2 variability. Using an idealized model, we illustrate how fluctuations in ocean circulation and biological respiration lead to low-frequency variability of thermocline oxygen.

Because the ventilation of the thermocline naturally integrates the effects of anomalous respiration and advection over decadal timescales, shortlived O2 perturbations are strongly damped, producing a red spectrum, even in a randomly varying oceanic environment. This background red spectrum of O2 suggests a new interpretation of the ubiquitous strength of decadal oxygen variability and provides a null hypothesis for the detection of climate change influence on oceanic oxygen. We find a statistically significant spectral peak at a 15–20 year timescale in the subpolar North Pacific, but the mechanisms connecting to climate variability remain uncertain.

The spectral power of oxygen variability increases from inter-annual to decadal frequencies, which can be explained using a simple conceptual model of an ocean thermocline exposed to random climate fluctuations. The theory predicts that the bias toward low-frequency variability is expected to level off as the forcing timescales become comparable to that of ocean ventilation. On time scales exceeding that of thermocline renewal, O2 variance may actually decrease due to the coupling between physical O2 supply and biological respiration [Deutsch et al., 2006], since the latter is typically limited by the physical nutrient supply.

k2_wk_sauerstoffmangel_e_en

2.15 > Marine regions with oxygen deficiencies are completely natural. These zones are mainly located in the mid-latitudes on the west sides of the continents. There is very little mixing here of the warm surface waters with the cold deep waters, so not much oxygen penetrates to greater depths. In addition, high bioproductivity and the resulting large amounts of sinking biomass here lead to strong oxygen consumption at depth, ­especially between 100 and 1000 metres.

Climate Model Projections are Confounded by Natural Variability

Natural variability and anthropogenic trends in oceanic oxygen in a coupled carbon cycle–climate model ensemble by T. L. Frolicher et al.

Internal and externally forced variability in oceanic oxygen (O2) are investigated on different spatiotemporal scales using a six-member ensemble from the National Center for Atmospheric Research CSM1.4-carbon coupled climate model. The oceanic O2 inventory is projected to decrease significantly in global warming simulations of the 20th and 21st centuries.

The anthropogenically forced O2 decrease is partly compensated by volcanic eruptions, which cause considerable interannual to decadal variability. Volcanic perturbations in oceanic oxygen concentrations gradually penetrate the ocean’s top 500 m and persist for several years. While well identified on global scales, the detection and attribution of local O2 changes to volcanic forcing is difficult because of unforced variability.

Internal climate modes can substantially contribute to surface and subsurface O2 variability. Variability in the North Atlantic and North Pacific are associated with changes in the North Atlantic Oscillation and Pacific Decadal Oscillation indexes. Simulated decadal variability compares well with observed O2 changes in the North Atlantic, suggesting that the model captures key mechanisms of late 20th century O2 variability, but the model appears to underestimate variability in the North Pacific.

Our results suggest that large interannual to decadal variations and limited data availability make the detection of human-induced O2 changes currently challenging.

The concentration of dissolved oxygen in the thermocline and the deep ocean is a particularly sensitive indicator of change in ocean transport and biology [Joos et al., 2003]. Less than a percent of the combined atmosphere and ocean O2 inventory is found in the ocean. The O2 concentration in the ocean interior reflects the balance between O2 supply from the surface through physical transport and O2 consumption by respiration of organic material.

Our modeling study suggests that over recent decades internal natural variability tends to mask simulated century-scale trends in dissolved oxygen from anthropogenic forcing in the North Atlantic and Pacific. Observed changes in oxygen are similar or even smaller in magnitude than the spread of the ensemble simulation. The observed decreasing trend in dissolved oxygen in the Indian Ocean thermocline and the boundary region between the subtropical and subpolar gyres in the North Pacific has reversed in recent years [McDonagh et al., 2005; Mecking et al., 2008], implicitly supporting this conclusion.

The presence of large-scale propagating O2 anomalies, linked with major climate modes, complicates the detection of long-term trends in oceanic O2 associated with anthropogenic climate change. In particular, we find a statistically significant link between O2 and the dominant climate modes (NAO and PDO) in the North Atlantic and North Pacific surface and subsurface waters, which are causing more than 50% of the total internal variability of O2 in these regions.

To date, the ability to detect and interpret observed changes is still limited by lack of data. Additional biogeo-chemical data from time series and profiling floats, such as the Argo array (http://www.argo.ucsd.edu) are needed to improve the detection of ocean oxygen and carbon system changes and our understanding of climate change.

The Real Issue is Ocean Dead Zones, Both Natural and Man-made

Since 1994, he and the World Resources Institute (report here) in Washington,D.C., have identified and mapped 479 dead zones around the world. That’s more than nine times as many as scientists knew about 50 years ago.

What triggers the loss of oxygen in ocean water is the explosive growth of sea life fueled by the release of too many nutrients. As they grow, these crowds can simply use up too much of the available oxygen.

Many nutrients entering the water — such as nitrogen and phosphorus — come from meeting the daily needs of some seven billion people around the world, Diaz says. Crop fertilizers, manure, sewage and exhaust spewed by cars and power plants all end up in waterways that flow into the ocean. Each can contribute to the creation of dead zones.

Ordinarily, when bacteria steal oxygen from one patch of water, more will arrive as waves and ocean currents bring new water in. Waves also can grab oxygen from the atmosphere.

Dead zones develop when this ocean mixing stops.

Rivers running into the sea dump freshwater into the salty ocean. The sun heats up the freshwater on the sea surface. This water is lighter than cold saltier water, so it floats atop it. When there are not enough storms (including hurricanes) and strong ocean currents to churn the water, the cold water can get trapped below the fresh water for long periods.

Dead zones are seasonal events. They typically last for weeks or months. Then they’ll disappear as the weather changes and ocean mixing resumes.

Solutions are Available and do not Involve CO2 Emissions

Helping dead zones recover

The Black Sea is bordered by Europe and Asia. Dead zones used to develop here that covered an area as large as Switzerland. Fertilizers running off of vast agricultural fields and animal feedlots in the former Soviet Union were a primary cause. Then, in 1989, parts of the Soviet Union began revolting. Two years later, this massive nation broke apart into 15 separate countries.

The political instability hurt farm activity. In short order, use of nitrogen and phosphorus fertilizers by area farmers declined. Almost at once, the size of the Black Sea’s dead zone shrunk dramatically. Now if a dead zone forms there it’s small, Rabalais says. Some years there is none.

Chesapeake Bay, the United State’s largest estuary, has its own dead zone. And the area affected has expanded over the past 50 years due to pollution. But since the 1980s, farmers, landowners and government agencies have worked to reduce the nutrients flowing into the bay.

Farmers now plant cover crops, such as oats or barley, that use up fertilizer that once washed away into rivers. Growers have also established land buffers to absorb nutrient runoff and to keep animal waste out of streams. People have even started to use laundry detergents made without phosphorus.

In 2011, scientists reported that these efforts had achieved some success in shrinking the size of the bay’s late-summer dead zones.

The World Resources Institute lists 55 dead zones as improving. “The bottom line is if we take a look at what is causing a dead zone and fix it, then the dead zone goes away,” says Diaz. “It’s not something that has to be permanent.”

Summary

Alarmists/activists are again confusing the public with their simplistic solution for a complex situation. And actual remedies are available, just not the agenda preferred by climatists.


Waste Management Saves the Ocean

 

H20 the Gorilla Climate Molecule

In climate discussions, someone is bound to say: Climate is a lot more than temperatures. And of course, they are right. So let’s consider the other major determinant of climate, precipitation.

The chart above is actually a screen capture of real-time measurements of precipitable water in the atmosphere.  The 24-hour animation can be accessed at MIMIC-TPW ver.2 .  H/T Ireneusz Palmowski, who commented:  “I do not understand why scientists deal with anthropogenic CO2, although the entire convection in the troposphere is driven by water vapor (and ozone in high latitudes).”

These images show that H2O is driving the heat engine through its phase changes (liquid to vapor to liquid (and sometimes ice crystals as well).  And as far as radiative heat transfer is concerned, 95% of it is done by water molecules.  Below is an essay going into the dynamics of precipitation, its variability over the earth’s surface, and its role in determining regional, and even microclimates.  The post was originally titled “Here Comes the Rain Again, inspired by the Eurythmics classic song

The global story on rain is straightforward:

“Precipitation is a major component of the water cycle, and is responsible for depositing the fresh water on the planet. Approximately 505,000 cubic kilometres (121,000 cu mi) of water falls as precipitation each year; 398,000 cubic kilometres (95,000 cu mi) of it over the oceans. Given the Earth’s surface area, that means the globally averaged annual precipitation is 990 millimetres (39 in). Climate classification systems such as the Köppen climate classification system use average annual rainfall to help differentiate between differing climate regimes.

http://en.wikipedia.org/wiki/Precipitation_(meteorology)

Globally, average precipitation can vary from +/-5% yearly, but there is no particular trend in the history of observations. But rain is one of those things where averages don’t tell you much. For starters, look at where it’s coming down:

So about 1 meter a year is the nominal average of all rain over all surfaces. Some places get up to 10 meters of rain (about 400 inches ) and others get near none. 47% of the earth is considered dryland, defined as anyplace where the rate of evaporation/transpiration exceeds the rate of precipitation. A desert is defined as a dryland with less than 25 cm of precipitation. In the image above, polar deserts are remarkably defined. It just does not have much hope of precipitation as there is little heat to move the water. More heat in, more water movement. Less heat in, less water movement.

Then there’s the seasonal patterns. The band of maximum rains moves with the sun: More north in June, more south in December. More sun, more heating, more rain. Movement in sync with the sun, little time delay. Equatorial max solar heat has max rains. Polar zones minimal heating, minimal precipitation. It’s a very tightly coupled system with low time lags.

The other obvious thing is how central land areas get dry desert conditions if they are not in the equatorial band nor near a warm water current. Brazil, in particular, benefits from warm coastal waters and near equatorial rains. The Gulf Stream rescues Europe from a much drier climate, but I fear the Gulf Stream shifting of zones also puts parts of Saharan Africa out of the equatorial wet. (In some times during history it DOES get a load of water, though…)
From E.M. Smith
https://chiefio.wordpress.com/2011/11/01/what-does-precipitation-say-about-heat-flow/

How do Oceans Make Rain

Here I am taking direction from A. M Makarieva and her colleagues. She explains:

“Water vapor originating by evaporation sustained by solar radiation represents a source of ordered potential energy that is available for generation of atmospheric circulation, including the biotic pump. We will further consider details of this process.

As we can see, early in its life the cloud expands in all directions, meanwhile the air continues to converge towards the (growing) condensation area. This process is at the core of condensation-induced dynamics: as condensation occurs and local pressure drops, this initiates convergence and ascent. They, in their turn, feedback positively on condensation intensity, such that the air pressure lowers further, convergence becomes more extensive and so on — as long as there is enough water vapor around to feed the process.

And where does the water vapor come from? Ocean evaporation, 87%, Plant transpiration 10% , Other evaporation, lakes, rivers, etc. 3%.

Air circulation without condensation (A) and with condensation (B). Gray squares are the air volumes, which in case (B) contain water vapor shown by small blue squares inside gray ones. White squares indicate those air volumes that have lost their water vapor owing to condensation. Blue arrows at the Earth’s surface represent evaporation that replenishes the store of water vapor in the circulating air.

On Fig. B we can see a circulation accompanied by water vapor condensation (water vapor is shown by blue squares). At a certain height water vapor condenses leaving the gaseous phase, while the remaining air continues to circulate deprived of water vapor (this depletion is shown by empty white squares): it first rises and then descends. As one can see, in such a circulation total mass of the rising air would be larger than total mass of the descending air (cf. an escalator transporting people up). The motor driving such a circulation would not only have to compensate the friction losses, but also have to work against gravity that is acting on the ascending air.

One can see from Fig. B that the difference between the cumulative masses of the ascending and descending air parcels grows with increasing height where condensation occurs. This difference also grows with increasing amount of water vapor in the air (i.e. with increasing size of the blue squares). The dynamic power of condensation, on the other hand, is also proportional to the amount of water vapor, but it is practically independent of condensation height.

Condensation height (a proxy for precipitation pathlength) grows with increasing temperature of the Earth’s surface. It is shown in the paper that power losses associated with precipitation of condensate particles become equal to the total dynamic power of condensation at surface temperatures around 50 degrees Celsius. Since the observed power of condensation-driven winds is equal to the total dynamic power of condensation (the “motor”) minus the power spent on compensating precipitation, at such temperatures the observed circulation power becomes zero and the circulation must stop. For commonly observed values of surface temperature these losses do no exceed 40% of condensation power and cannot arrest the condensation-induced circulation. Over 60% of condensation power is spent on friction at the Earth’s surface.

Why Some Places Get More Rain Than Others

This figure shows the “tug-of-war” between the forest and the ocean for the right to become a predominant condensation zone. In Fig. a: on average the Amazon and Congo forests win this war: annual precipitation over forests is two to three times larger than the precipitation over the Atlantic Ocean at the same latitude. Note the logarithmic scale on the vertical axis: “1” means that the land/ocean precipitation ratio is equal to e = 2.718, “2” means it is equal to e2 ≈ 7.4; “0” means that this ratio is unity (equal precipitation on land and the ocean); “-1” means this ratio is 1/e ≈ 0.4; and so on.

In Fig. b: the Eurasian biotic pump. In winter the forest sleeps, so the ocean wins, and all moisture remains over the ocean and precipitates there. In summer, when trees are active, moisture is taken from the ocean and distributed regularly over seven thousand kilometers. The forest wins! (compare the red and black lines) As a result, precipitation over the ocean in summer is lower than it is in winter, despite the temperature in summer is higher.

Finally, in panel (c): an unforested Australia. One can often hear that Australia is so dry because it is situated in the descending branch of the Hadley cell. But this figure shows that such an interpretation does not hold. Both in wet and dry seasons precipitation over Australia is four to six times lower than over the ocean. There is no biotic pump there. Being unforested, oceanic moisture cannot penetrate to the Australian continent irrespective of how much moisture there is over the ocean; during the wet season it precipitates in the coastal zones causing floods. Gradually restoring natural forests in Australia from coast to interior will recover the hydrological cycle on the continent.

http://www.bioticregulation.ru/pump/pump9.php

biotic pump

The Biotic Pump A. M Makarieva et al

Water cycle on land owes itself to the atmospheric moisture transport from the ocean. Properties of the aerial rivers that ensure the “run-in” of water vapor inland to compensate for the gravitational “run-off” of liquid water from land to the ocean are of direct relevance for the regional water availability. The biotic pump concept clarifies why the moist aerial rivers flow readily from ocean to land when the latter gives home to a large forest — and why they are reluctant to do so when the forest is absent.

While it is increasingly common to blame global change for any regional water cycle disruption, the biotic pump evidence suggests that the burden of responsibility rather rests with the regional land use practices. On large areas on both sides of the Atlantic Ocean, temperate and boreal forests are intensely harvested for timber and biofuel. These forests are artificially maintained in the early successional stages and are never allowed to recover to the natural climax state. The water regulation potential of such forests is low, while their susceptibility to fires and pests is high.

https://2s3c.wordpress.com/2012/04/22/taac/

Conclusion

So the oceans make rain, and together with the forests the land receives its necessary fresh water. There is a threat from human activity, but it has nothing to do with CO2. Land use practices leading to deforestation have the potential to disrupt this process. Without trees attracting the moist air from the ocean there is desert.

Ironically, modern societies burn fossil fuels instead of burning the forests as in the past.

For more on climate science related to H2O, see Bill Gray: H20 is Climate Control Knob, not CO2

May 2018 Ocean Cooling On Hold

globpop_countriesThe 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 May 2018.

Hadsst052018

Open image in new tab to enlarge.

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. Upward bumps occurred last October, in January and again in March and April 2018.  Five months of 2018 now show slight warming since the low point of December 2017, led by steadily rising NH. May 2018  temps in all regions are slightly lower than 5/2015, except for the Tropics being much lower. Since 4/2018 SH and Tropics cooled slightly while NH pulled the Global anomaly upwards.

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.

With ocean temps positioned the same as three years ago, we can only wait and see whether the previous cycle will repeat or something different appears.  As the analysis belows shows, the North Atlantic has been the wild card bringing warming this decade, and cooling will depend upon a phase shift in that region.

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.

Hadsst1995to2018

Open image in new tab to enlarge.

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.”  As this month shows, nature moves in cycles, not straight lines, and human forecasts and projections are tenuous at best.

einsteinalbert-integratesempirically800px

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

 

Mar. 2018 Ocean Cooling? Wait and See

 

globpop_countriesThe 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 March 2018.
HadSST032018

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. Upward bumps occurred last October, in January and again in March 2018.  Three months of 2018 now show slight warming since the low point of December 2017.  Only the Tropics are showing temps the lowest in this time frame.  Globally, and in both hemispheres anomalies closely match March 2015.

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.

With ocean temps positioned the same as three years ago, we can only wait and see whether the previous cycle will repeat or something different appears.  As the analysis belows shows, the North Atlantic has been the wild card bringing warming this decade, and cooling will depend upon a phase shift in that region.

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.

HadSST1995to032018

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.”  As this month shows, nature moves in cycles, not straight lines, and human forecasts and projections are tenuous at best.

einsteinalbert-integratesempirically800px

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