Most of the discussion surrounding Kosaka and Xie (2013, Nature, doi:10.1038/nature12534) has focused on how much, according to their research, might natural variability have altered the recent global warming trend. But some of their results that haven’t received much attention might turn out to be the more interesting aspects of that paper.
They ran a global climate model (GFDL, from the Geophysical Fluid Dynamics Lab), but instead of letting all the variables roam free, they constrained sea surface temperature (SST) in the tropical east Pacific ocean (very much the el Niño region) to match observed historical data. By doing so, they constrained the model to mimic the observed pattern of el Niño fluctuations, in hopes (since el Niño is known to affect global temperature) of obtaining a better match to observed global temperature data. They dubbed their experiment POGA, for “Pacific Ocean Global Atmosphere.”
The attempt succeeded far better than expected — the match of model output to historical observations is uncanny. Nor can this be solely due to constraining SST in the el Niño region, since their chosen area of constraint is only 8.2% of the entire globe. Their research strongly suggests that the GFDL model correctly simulates the impact of forcing agents (both man-made like greenhouse gases, and natural like volcanic explosions and solar variations) on earth’s climate, so that when it’s given the right el Niño process — the strongest known agent of natural internal variability — it can reproduce temperature history remarkably well.
The principal result is that the reduced increase of global average surface air temperature (SAT) since about 2002 can be attributed to sea surface temperatures in the el Niño region. This has been posited often, and other evidence supports that view. But what I found most promising is that their constrained model does so well at reproducing not just global climate, but seasonal and regional patterns.
Seasonally, they find that SST in the el Niño region has a pronounced effect during boreal (northern-hemisphere) winter and a muted impact in boreal summer. They even identify a physical mechanism for this seasonal pattern:
The SAT hiatus is confined to the cold season13 (seasons refer to those for the Northern Hemisphere hereafter), with a decadal cooling trend for November to April, whereas the global temperature continues to rise during summer (Fig. 1c). POGA-H reproduces this seasonal cycle of the hiatus, albeit with a somewhat reduced amplitude. Although the La-Niña-like cooling trend in the tropical Pacific is similar in winter and in summer (Extended Data Fig. 4a), stationary/transient eddies, which are the dominant mechanism for meridional heat transport14, are stronger in winter than summer. As a result, the tropical cooling effect on the extratropics is most pronounced in winter (the seasonality of the temperature trend in the Southern Hemisphere extratropics is weak). The tropical influence on the Northern Hemisphere extratropics is weak during the summer, allowing the radiative forcing to continue the warming trend during the recent decade (Extended Data Fig. 4b).
Although I’m rather familiar with global temperature data, I hadn’t noticed this seasonal pattern in recent trends. As their model and their physical mechanism suggest, it’s most pronounced in the northern hemisphere. Looking at trends by season, smoothing northern-hemisphere data on about a 10-year time scale, we see it plainly:
The summer/winter contrast in recent northern hemisphere temperatures is also evident if we compute a linear trend using data from 1975 through 2000, extrapolate the trend line, and compare it to observations since 2000. For winter, temperatures have dropped significantly below the extended trend:
But for summer, there’s no departure from the prior trend at all:
Kosaka and Xie go so far as to suggest that due to the northern hemisphere extratropics being so much less influenced by el Niño, continued warming from steadily increasing climate forcing has permitted extreme heat waves to develop over northern hemisphere continents this century (such as were seen in Europe in 2003 and Russia in 2010).
They note the same seasonal behavior in simulations which do not constrain sea surface temperatures in the el Niño region. Their HIST experiments (using historical and projected forcing data but no el Niño SST constraints) don’t reproduce observed temperature nearly so well because they don’t get the timing of the el Niño southern oscillation right, but they do reproduce its enhanced influence during boreal winter:
This seasonal contrast is evident also in HIST. For 1970–2040, a period when the ensemble-mean global temperature shows a steady increase in HIST, the probability density function for the 11-year trend is similar in winter and in summer for tropical temperatures, with means both around 0.25°C (Extended Data Fig. 4c). The probability density function is much broader for winter than for summer forNorthern Hemisphere extratropical temperatures (Extended Data Fig. 4d). The chance of the 11-year temperature change falling below –0.3°C is 8% for winter but only 0.7% for summer in the Northern Hemisphere extratropics (around 4% in the tropics for both seasons). The 11-fold increase in the chance of an extratropical cooling in winter is partly because the tropical influence is stronger in winter than in summer.
Besides the match between observed and modeled seasonal patterns, there are also some impressive matches between regional patterns. This is true for some large ocean basins, and for the North American continent, although the model does not capture the regional pattern over Eurasia:
We examined regional climate change associated with the hiatus. Although models project a slowdown of the Walker circulation in global warming15, the Pacific Walker cell intensified during the past decade (Fig. 2c). POGA-H captures this circulation change, forced by the SST cooling across the tropical Pacific (Fig. 2d). As in interannual ENSO, the tropical Pacific cooling excites global teleconnections in December, January and February (DJF; the season is denoted by the first letters of the months). SST changes in POGA-H are in broad agreement with observations over the Indian, South Atlantic and Pacific oceans outside the restoring domain (Fig. 2a, b). The model reproduces the weakening of the Aleutian low as the response of the Pacific–North American pattern to tropical Pacific cooling11 (Fig. 2c, d). As a result, the SAT change over North America is well reproduced, including a pronounced cooling in the northwest of the continent. The model fails to simulate the SAT and sea-level presure (SLP) changes over Eurasia, suggesting that they are due to internal variability unrelated to tropical forcing (Extended Data Fig. 5a and c).
This is the first published research I’ve seen which does so well at simulating seasonal and regional patterns. This has very important implications for climate research, from both a theoretical and practical perspective, as the authors emphasize:
Although the radiation-forced response will become increasingly important, deviations from the forced response are substantial at any given time, especially on regional scales19. We need quantitative tools — like our POGA-H — to determine the causes of regional climate anomalies17. The current hiatus illustrates the global influence of tropical Pacific SST, and a dependency of climate sensitivity on the spatial pattern of tropical ocean warming, which itself is uncertain in observations20 and among models21,22. This highlights the need to develop predictive pattern dynamics constrained by observations.
It also points the way to other possibly fruitful research. For instance, the regression approach of Foster & Rahmstorf, or simple energy balance models like this, might be improved substantively simply by allowing for a seasonal pattern in the influence of el Niño.