The best context for understanding decadal temperature changes comes from the world’s sea surface temperatures (SST), for several reasons:
- The ocean covers 71% of the globe and drives average temperatures;
- SSTs have a constant water content, (unlike air temperatures), so give a better reading of heat content variations;
- A major El Nino was the dominant climate feature in recent years.
HadSST is generally regarded as the best of the global SST data sets, and so the temperature story here comes from that source, the latest version being HadSST3. More on what distinguishes HadSST3 from other SST products at the end.
The Current Context
The chart below shows SST monthly anomalies as reported in HadSST3 starting in 2015 through January 2018.
Note that higher temps in 2015 and 2016 were first of all due to a sharp rise in Tropical SST, beginning in March 2015, peaking in January 2016, and steadily declining back below its beginning level. Secondly, the Northern Hemisphere added three bumps on the shoulders of Tropical warming, with peaks in August of each year. Also, note that the global release of heat was not dramatic, due to the Southern Hemisphere offsetting the Northern one.
A global cooling pattern has persisted, seen clearly in the Tropics since its peak in 2016, joined by NH and SH dropping since last August. An upward bump occurred in October, and now again in January 2018. As will be shown in the analysis below, 0.410C has been the average global anomaly since 1995 and last month remains lower at 0.376C. SH rose along with the Tropics, while NH held steady. Global and NH SSTs are the lowest since 3/2014, while Tropics SSTs are the lowest since 3/2012. SH is the lowest January since 2014.
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.
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.
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?
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.