The about face (really mid-course correction) of US policy toward the out-of-control Climate Science Complex is front-page news. Such could not be more timely (although decades late). The 25,000 registration for the American Geophysical Society meeting in Washington, DC several months ago indicated a self-perpetuating government industry at work.
But what about the plea that we don’t know what is going on with global climate and need for resources? Such was the plea of Gavin Schmidt and Zeke Hausfather in a New York Times editorial several months ago, “We Study Climate Change. We Can’t Explain What We’re Seeing” (Nov. 13, 2024). Their intention was to alarm by insinuating: we don’t know what is happening, and it must be ‘climate change’ run amuck. Global weirding. Name your term.
But another view is that yes, climate science is unsettled. Its practitioners do not know what they don’t know. No basis for climate policy except for the Deep Ecologists who dread all things anthropogenic.
“Yet the unusual jump in global temperatures starting in mid-2023 appears to be higher than our models predicted (even as they generally remain within the expected range,” the article begins.
While there have been many partial hypotheses — new low-sulfur fuel standards for marine shipping, a volcanic eruption in 2022, lower Chinese aerosol emissions and El Niño perhaps behaving differently than in the recent past — we remain far from a consensus explanation even more than a year after we first noticed the anomalies. And that makes us uneasy.
Some humility perhaps? Nope, just a call for more money.
Why is it taking so long for climate scientists to grapple with these questions? It turns out that we do not have systems in place to explore the significance of shorter-term phenomena in the climate in anything approaching real time. But we need them badly. It’s now time for government science agencies to provide more timely updates in response to the rapid changes in the climate.
“climate science research is more used to working on approximately seven-year cycles to produce reports that summarize the evolving science about the long-term changes in climate. The data that went into the latest round of climate model simulations are based on observations that only run through 2014, and so they don’t reflect recent changes such as newer pollution controls, volcanic eruptions or even the effects of Covid. Similarly, the forecasts are stuck with scenarios that were common in the early 2000s. Business (and everything else) has changed sharply since then.”
As a result of all of this, a gap has opened up between what the general public and policymakers want and what is available.
To fix this, we need to create a better way for climate models to reflect new observations. That means more comprehensive and faster data gathering from satellites, in situ measurements and economic statistics, converted by analysts for the climate and weather models. This needs to be matched by a commitment by the roughly 30 labs around the world that maintain the models of the earth’s climate system to update their simulations each year to reflect the latest data.
Some of the information that goes into climate models currently takes years to produce. For instance, while data on greenhouse gas levels and energy from the sun are available within weeks of observations’ being taken, emissions of industrial and agricultural air pollutants need to be estimated from economic data, and this can take years to collect and process.
Scientists should be able to provide “good enough” estimates of these inputs faster using reasonable assumptions. Just as economic analysts frequently update statistics after an initial announcement, such as a quarterly jobs report, scientists could provide data for industrial emissions of pollutants, the activity of the sun, the impacts of volcanoes and greenhouse gas levels on two or more tracks — an initial estimate using as much data as is available quickly, and a fully revised estimate later once more data is in.
We think that a goal of analyzing data in under six months is achievable if the data-gathering and climate-modeling labs prioritize it. This entails a small shift by tU.S. agencies, such as the National Oceanic and Atmospheric Administration and the Department of Energy, and international agencies such as Copernicus, the European climate service provider, toward sustained funding instead of one-off research grants.
Other groups, such as weather forecasters, would also be able to take advantage of this new data stream. When they’re doing seasonal or longer-term forecasts, they are also not working from the most up-to-date information and would be able to use this to improve the forecasts.
“The public would benefit from more definitive knowledge on what is going on, too. Water-resource managers and urban planners could be more confident that they are using the most current scenarios and projections, helping them avoid underestimating or over-preparing for future change. If climate projections were better calibrated to recent changes, we could narrow the likely range of future impacts.
Some of the unease that people feel about climate change comes from a sense that things are out of our control — that the climate is changing faster than we can adapt. However, many of the most dire risks lie not with the most likely outcomes but the worst-case possibilities, for example, the collapse of the West Antarctic ice sheet, or the drying up of the Amazon and other potential tipping points. But there is a lot we don’t know about if or when those tipping points will come to pass.
The good news is that climate science could easily become more agile in understanding the rapid changes we are seeing in the real world, incorporating them into our projections of the future and, hopefully, reducing that uncertainty.
Ed. note: With the US-led demotion (if not demise) of Net Zero and ‘energy transformation’, prior attempts to come to grips with climate futility and energy reality are worth revisiting. This article, “Three Decades of Climate Mitigation: Why Haven’t We Bent the Global Emissions Curve?” is an example of too many academics (17 coauthors) and too much obtuse research (think budget cuts).
Despite three decades of political efforts and a wealth of research on the causes and catastrophic impacts of climate change, global carbon dioxide emissions have continued to rise and are 60% higher today than they were in 1990. Exploring this rise through nine thematic lenses—covering issues of climate governance, the fossil fuel industry, geopolitics, economics, mitigation modeling, energy systems, inequity, lifestyles, and social imaginaries—draws out multifaceted reasons for our collective failure to bend the global emissions curve.
However, a common thread that emerges across the reviewed literature is the central role of power, manifest in many forms, from a dogmatic political-economic hegemony and influential vested interests to narrow techno-economic mindsets and ideologies of control. Synthesizing the various impediments to mitigation reveals how delivering on the commitments enshrined in the Paris Agreement now requires an urgent and unprecedented transformation away from today’s carbon- and energy-intensive development paradigm.
Despite three decades of political efforts and scientifically informed warnings of the likely catastrophic effects of climate change, CO2emissions have continued to rise globally and are 60% higher today than they were in 1990.
Since the first IPCC report was published in 1990, more anthropogenic fossil CO2 has been released into the atmosphere than previously throughout all of human history.
The failure of leadership, particularly from within high-emitting countries, sectors, corporations, and individuals, has locked in intra- and intergenerational suffering and long-term existential threats to livelihoods and ecosystems.
Entrenched geopolitical, industrial, and military power and associated mindsets are fundamental barriers to effective mitigation.
Orthodox schools of thought and research traditions (including highly constrained forms of modeling), particularly in the fields of economics, energy, and climate mitigation, need to be challenged and replaced with, or complemented by, more heterodox approaches.
Three decades of choosing to fail on mitigation have shifted the climate challenge from a technocratic adjustment to business as usual to requiring a rapid, system-level change within both industrialized and industrializing societies.
Transformations toward more sustainable and just futures require a radical reconfiguration of long-run sociocultural and political-economic norms and institutions currently reproducing the very problems driving climate change.
Attention to equity, high-carbon lifestyles, and conditions for enabling new social imaginaries has the potential to disrupt dominant, high-carbon development pathways.
How could geopolitical competition over energy resources and ideologies of control that frame dominant responses to climate change be challenged and overcome?
How have mainstream economics and neoliberal responses to climate change (e.g., carbon markets and a broader financialization of the environment) become so pervasive, and what opportunities are there for alternative or complementary approaches?
How can research approaches currently dominating advice and underpinning climate mitigation policy (such as integrated assessment modeling) be complemented with a more varied array of approaches and perspectives?
How could approaches that rapidly reduce energy-related emissions be realized (e.g., actively displacing and disassembling fossil fuel–based energy systems, and energy demand management practices)?
How can the large asymmetry in responsibility for emissions within, as well as between, nations be addressed in climate policy and governance?
How can fossil fuel–based, high-carbon lifestyles, practices, and visions of incremental mitigation be rapidly replaced by sustainable alternatives and profound system change, informed by a timely response to the Paris temperature and equity commitments?
How can knowledge systems and institutions currently reproducing the very problems driving climate change be transformed?
How can existing and new social movements mobilize popular power and social imaginaries in a way that effectively challenges the status quo and helps drive structural change at the scale and pace required?
This article has demonstrated that, while the reasons for 30 years of failure to bend the global emissions curve are multifaceted, a common and strong thread is woven through them all. In various guises and to differing degrees, the centralization of power and the privileges that accompany it have coalesced around a particular worldview. Through recent decades, the central tenets of this worldview have evolved into a wider global Zeitgeist whereby development and progress are reduced to economic growth and defined by increasingly narrow financial metrics and indices.
Coincident with this financial reductionism and economic characterization of nations and societies has been a growing recognition that the “system” externalities are set to undermine the very tenets of the system. Thus far, however, the power and inertia of the existing system have been sufficient to give the impression of ongoing control. The challenges are “recognized” and “internalized,” and through promised technical futures that are carefully costed in elaborate models, the existing power structures remain unchallenged.
Part I of this series focussed on the sources of data substantiating an energy imbalance, the magnitude of said energy imbalance, and the likely uncertainty of this magnitude. Measurements suggest this magnitude is most likely around 0.76W/m2 but the uncertainty is optimistically stated as being as small as 0.1. Even the authors of scientific reports admit their uncertainty does not include all factors, especially instrumentation and processing biases. Most (89%) of this energy imbalance ends up warming the oceans.
Part II examines climate feedback, its potential magnitude, and likely uncertainty. Part III, when I get around to it, will focus on whether or not the Earth possesses a regulator; that is, whether there is some physical process that will limit response to such an imbalance continuing on into the future. In particular, Part III will explore the curious status of Le Chatelier’s principle, which people often invoke lately to suggest there must be such a regulating mechanism.
The Standard View
It’s best to start with the standard argument for why increasing CO2 will raise surface temperature. Then critique this argument. Figure 1 is from a review article by Held and Soden (hereafter H&S), from 2000[1] but which was largely repeated in 2006.[2] I can do no better to explain than to just paraphrase H&S themselves. Their review article, I think, explains the standard view as clearly, and simply as anything I have read.
To maintain an energy balance, the Earth must radiate back to space the 240 W/m2 portion of absorbed solar radiation it receives. To balance this a black body radiator would have to have a temperature of 255 K (240 = σ Te4) which we will call Te the emission temperature. This temperature occurs at a height above the surface which we call Ze. As pictured in Figure 1, one might think of the average infrared photon escaping to space from near this level.
As H&S say “It is an oversimplification to assume that temperature gradients within the troposphere do not change as the climate warms, but this simple assumption has proven to be a very useful point of reference…”
With a fixed Te and fixed gradient (Lapse rate Γ) surface temperature then becomes; Ts = Te + ΓZe. In this simple model only changes in Ze matter. Now the argument takes the following path.
An increased concentration of CO2 in the atmosphere makes the atmosphere more opaque to outgoing infrared radiation from the surface. Thus, to have a CO2-doubled atmosphere equally transparent above Ze to enable escape of the average photon, Ze must reside higher in the atmosphere. A doubling of CO2 makes the more opaque atmosphere equally transparent above at Ze+150m. However, the invariant gradient of 6.5K/km means the temperature at Ze+150m is lower by about 1K, and according to the Stefan-Boltzmann law this amounts to a reduction in outgoing radiation by about 4W/m2(236.3 = σ 2544). There is an energy imbalance that warms the entire atmosphere and surface.
Water Vapor Concentration
The effects do not stop at this point. The entire atmosphere is now 1K warmer, and at the Earth’s surface this higher temperature, according to the Clausius-Clapeyron relationship, will lead to an increase in water vapor pressure at saturation of about 7%. This, in turn, makes the atmosphere more opaque still, and raises Ze again. The process repeats, but converges to a new equilibrium at surface temperature enhancement of 1/(1-βH2O), where βH2O=0.4 is the feedback factor for water vapor. As H&S say in their 2006 paper, “ a number of important aspects of the hydrological response to warming are a direct consequence of the increase in lower-tropospheric water vapor.”
My Critique
Recognizing that H&S admitted this model is an oversimplification, let’s nonetheless critique its main elements in order of their appearance.
There is no emission surface at 255K
First, people appear to literally believe in an emission surface in the middle troposphere with a temperature of 255K that radiates as a black body. In other words, they view the problem as akin to a typical boundary value problem with the surface acting as one boundary and some imagined layer above acting as the other. While the surface does behave as a near black body (emissivity=0.97), the clear atmosphere is nowhere so emissive that a thin layer will act as a black body. As H&S, themselves, say in a different publication[3], “…Owing to its much
larger emissivity, the surface contribution is an order of magnitude larger than that from any individual 100-mb atmospheric layer.” What happens instead is that the compensating outgoing LWIR escapes over a broad vertical region of the atmosphere that begins right at the surface for some wavelengths.[4] The upper boundary of this problem is complex.
Instead of a single degree of freedom, Ze, establishing surface temperature, there are many different configurations that will do the task. The emission surface has a complicated, and ever changing, configuration. While the idea of an increase in height of the emission surface is one possible response, an atmosphere dehumidfied from above, which is what precipitation accomplishes, could place the average emission surface lower into the atmosphere without changing the surface temperature much if at all.[5]
Radiative-convective equilibrium doctrine
Second, the unvarying 6.5K/km gradient value of radiative-convective equilibrium is not helpful. Anyone who has examined temperature profiles knows that they are hugely more complex than just a constant gradient. Figure 2 shows a number of model atmospheric temperature profiles drawn from MODTRAN. Note that the only constant 6.5 lapse rate in the set is the U.S. Standard 1976 Atmosphere – a made-up profile of atmospheric non-structure designed by committee.[6] The other examples actually have some structure to them which tell us something about the dynamics of heat transfer in various locales.
Figure 2.
Evaporation
Water vapor assumes primacy in this simplified model, especially in the tropics. Invoking the Clausius-Clapeyron (hereafter CC) relationship means that each 1K rise in surface temperature adds 7% more water vapor into the lower troposphere – it’s a geometrical increase of the most powerful greenhouse gas.
To promote CC scaling, H&S rest their analysis on an atmospheric dynamics relationship for the evaporation process.[7] I have argued in a few instances here and elsewhere that this CC scaling is wrong by reason that the distribution of water vapor throughout the atmosphere is non-equilibrium and dependent on transport processes. It is energy constrained; whereas atmospheric dynamics models of evaporation simply assume the energy constraint vanishes.[8]
Engineering hydrology concerns itself with evaporation from surface storage.[9] Of the expressions for evaporation which they have developed from this focus, some are atmospheric dynamics based; others are energy balance based; others are a combination of the two.[10] Atmospheric dynamics based expressions work well enough, but must have a scale-size issue because they don’t consider energy balance. Without energy balance the process is unphysical.
As Landsay, et al, say in regard to energy used for evaporation.
“In Deep lakes with capacity for considerable heat storage, sudden changes in wind and humidity have longer lasting effects; heat into or from storage assists in balancing energy demands. Thus by using stored excess energy excessive evaporation during a dry, windy week can reduce evaporation which would otherwise occur in subsequent weeks.”[10]
Energy balance provides a constraint. I think atmospheric dynamics is a weak argument. Evaporation (depth of open water evaporated per unit time) based on an energy balance would look something like this:
E=(Qn+Qv-Q0)/(ρHv(1+R) ); where,
R is Bowen’s ratio, Qn=net all wave radiation, Q0 is energy going into storage, Qv=energy advected, and Hv is latent heat of vaporization.
Ignoring observations in favor of theory
Regarding the feedback enhancement from water vapor, H&S, say this:
“…There is no simple physical argument of which we are aware from which one could have concluded beforehand that βH2O was less than unity. The value of βH2O does, in fact, increase as the climate warms if the relative humidity is fixed. On this basis, one might expect runaway conditions to develop eventually if the climate warms sufficiently.”
One might respond this way. There is no simple physical argument except that the precursor to water vapor, liquid water, has covered a majority of the surface of Earth for 4 billion years, under widely varying conditions, including enhanced CO2, and we have not observed anything remotely like a runaway greenhouse effect. In fact, we more commonly observed excursions into exceptional cold.
Climate modellers seem more impressed with agreement among their models than they seem to be with observations. I am not a climate modeller, but I am not impressed with proof of correctness through consistency among models. I have some experience modeling heat transport. I have translated complex codes for all sorts of purposes from one programming language to another, and debugged the results. It was common enough for me to find the same mistakes in different platforms to suggest common ancestry of codes; sometimes agreement is just lack of independence.
Closing the water vapor controversy
Fourth, as H&S say, closing the water-vapor controversy requires comparison with data.
“Given the acceleration of the trends predicted by many models, we believe that an additional 10 years may be adequate, and 20 years will very likely be sufficient, for the combined satellite and radiosonde network to convincingly confirm or refute the predictions of increasing vapor in the free troposphere and its effects on global warming.”
How well do we know the underlying physics?
Bob Irvine wrote about feedback two years ago. He showed data similar to, but independent of that in Figure 3. Figure 3 shows Era5 reanalysis from the tropics plotting 2m temperature and dew point data against one another. There is a rise in dew point temperatures, specific humidity or mixing ratio, as one prefers, all show a modest rise in absolute humidity of about 3% over the past two decades. The observed rise does not support CC scaling and certainly not a constant relative humidity.
Figure 3.
Irvine’s essay provided a Table comparing AR4 to AR6 feedback values. Of particular interest are the large changes in the combined feedback values for water vapor+lapse rate. In AR4 (2007) this value is 0.96 ± 0.08 W/Km2. In AR6 (2019) it is stated as 1.30 (1.15 to 1.47). Perhaps noting that water vapor has not kept pace with CC scaling in the two decades from 2000 to 2019 caused the revision. Of greater interest is the stated uncertainty.
Consider AR4 uncertainty of 0.08 as pertaining to a coverage factor of 1.0, and the interval for AR6 as the 90% confidence interval. This places central values four standard deviations apart, meaning that each estimate is highly unlikely in view of the other. In addition, a graphic in reference [3] shows the water vapor and lapse rate feedback values separately. As was often noted in Part I with regard to energy imbalance, the uncertainty of combined quantities becomes smaller than uncertainty of its components. How does this happen? Possibly model biases in estimates of water vapor feedback are anticorrelated with biases in lapse rate feedback.
Cloud Feedback
Everyone recognizes that clouds are a weakness of global climate models. Everyone may not recognize the tremendous variability of clouds day-to-day. Figure 4 below shows what total downward welling solar radiation looks like on two closely spaced days along the Colorado front range. The raw data is by UTC day, so these plots are patched together. Yet what they show is patently clear. The partially cloudy day has enhanced downward radiation when cumulus or cumulonimbus north and northwest of the observatory redirect scattered light toward the observatory, but more often reduced radiation to winter-time subarctic conditions when they shade the observatory. The change in daily received solar (downwelling total solar) is from 34,000kJ/m2 on the clear day, to 23,000kJ/m2 on the other. That’s huge by anyone’s estimation. Even the surface albedo (ratio of blue curve to red) changes from 18.8% to 19.3% simply because of the redirection of sunlight.
Figure 4.
Figure 5 is from data taken just north of Laramie, Wyoming, at an elevation of 2200m on a clear summer day. There is no SURFRAD site here, but I own numerous radiometers and was testing/calibrating one. By pure serendipity, I caught the sheerest of clouds – Subvisual cirrus so insubstantial that I could not see them by eye. However, the radiometer detected them, and occasionally when the cirrus formed a wisp that could be discerned by eye which passed in front of the Sun, I could correlate it with the radiometer.
Lynch [12] suggests these clouds have an optical depth near τ=0.03, which would translate into a power density variation in the neighborhood of 1000e-0.03=970 or decline of 30 W/m2. Just about what Figure 5 shows. Thus, even in this instance of the least substantial clouds one could imagine, the effect is ten times as large as that of a degree K change in surface temperature.
Clouds present a large climate forcing.
Figure 5
Nevertheless, this climate forcing is not what “feedback” means in the context of climate science. Feedback is the effect a warmer surface has on the radiative difference between clear sky and total sky. It is the change in cloud forcing (clear sky less total sky), and complicating the matter is that clear sky is a calculation from theory again. AR4 lists the feedback effect of clouds as 0.69 ± 0.38 W/K-m2; AR6 lists 0.42 (-0.1 to 0.94).
Exploring Feedback
Models result generally in positive feedback. I have no basis for arguing with that. What I disbelieve is that a relatively tiny differential quantity, calculated as the difference between two other large variable quantities which are, themselves, differences of large variable quantities, aren’t swamped by uncertainty. This is especially so given the lack of resolution in climate models, plus the parameterizations of things like clouds, convection and precipitation that aren’t calculated directly from physics.
I am not an opponent of climate modeling, but I do wish the results of modeling could be grounded by comparison with observations. I fully recognize that observations can be so encumbered with problems of calibration and data reduction schemes that what results has large uncertainty also. Nonetheless, I want to see a comparison now and then. So, what do we do about feedback?
Let’s consider feedback calculation schemes. They’re done with models. The challenge with doing the same things with observations is having long enough runs of days to approximate climate. Can any schemes be mimicked with observations? Soden et al, outline schemes based on modeling.[3] How would they translate to observations?
Scheme 1: Think of the net energy imbalance at the top of the atmosphere (TOA) as a function of just a few items. Figuratively call this R( w, T, a, c); where w stands for water vapor, T for surface temperature, a for surface albedo, and c for clouds. Run multiple models changing only one item, c for example, at a time and compare to the unperturbed state. This is very difficult to do with observations because it is difficult to search for and find extended runs of days that are identical in all respects except for one item.
Scheme 2: Separate feedback into two factors. The first, the “radiative kernel,” depends only on the radiative algorithm and the other is simply the change in the climatology of the feedback of interest of two comparator states. The product of the two is the feedback. This method is not pertinent to observation. Yet, radiative kernels are interesting for a different reason in Part III.
Scheme 3: Perturb the climate model with a step change in sea surface temperature. Then infer the climate sensitivity from resulting computed changes in radiative fluxes.
The scheme most amenable to observations is scheme 3. The sea surface perturbations available naturally are ENSO, PDO, AMO, and so forth. In addition, we might think about organizing the effort like the factorial experiments we do in engineering; we purposely change multiple variables in each successive run because that is what the weather will do. Build a table of contrasts with four factors (w,T,a,c) and note how each changes in each successive change of PDO or ENSO. Eventually we will fill in the entire table of contrasts and have a rough idea not only of factors of feedback but also interactions among them.[13]
Conclusion
Just as in Part I, in Part II I find numbers applied to fundamental concepts that are small but with uncertainty estimates I cannot reconcile nor find entirely credible. The entire topic of climate change appears to be like this; guided by numbers and measurements that need to be certain within 0.1% but are often 10 or 100 times worse. Is the cloud feedback positive? I don’t know. There is a lot of weather to sort through to find out. However, if it is positive that is not necessarily a bad thing as explained in Part III.
References and Notes
1-Held, I. M., and B. J. Soden, 2000: Water vapor feedback and global warming. Annu. Rev. Energy Environ., 25, 441–475.
2-Isaac Held, Brian J. Soden, 2006, Robust Responses of the Hydrological Cycle to Global Warming, J. Climate, V. 19, p.5686
3-Brian J. Soden, et al, 2008, Quantifying Climate Feedbacks Using Radiative Kernels, J. Climate, V. 21, p3504
4-Even in the moist tropical atmosphere, clear sky is over 80% transmissive to many segments of the IR spectrum as wide as 2 inverse centimeters in wavenumber.
5- This is an element of Lindzen’s iris hypothesis.
6- This mean profile may be a case of an average that is never actually observed – like the 3.5 average of dice rolls. Of further interest, the band 8,9, and 10 water vapor satellite images are reduced using the U.S. standard atmosphere. Perhaps use of this model in this context could be explored in some future post.
7-An atmospheric dynamics model from H&S: “ … evaporation E from the ocean can be
modeled as proportional to the difference between the saturation vapor pressure at
the surface temperature T* and the vapor pressure in the atmosphere at some small
convenient reference height…”
8-An analogous situation occurs in electronic feedback circuits. The power supply is almost never shown explicitly in such circuits under the assumption that the power supply is capable of providing whatever the feedback circuitry demands. Clipping occurs when output reaches close to the power supply rails. This doesn’t occur in models even though it happens physically.
9-I worked as a USGS hydrologist for three years in the 1970s. Employment is a capable teacher.
10-Ray K. Lindsay, Jr., et al, 1975, Hydrology for Engineers, 2nd ed, Mcgraw-Hill
12- I had a bit of fun with generative AI. I asked if ENSO could be used as an analog of climate change. It said “no”. I then asked if El Niño could be. AI said “yes”.
13-At a conference in 2002 I suggested this as a method of making history itself, and historical sciences, look more like experimental science. No one, to my knowledge, has taken up the idea.
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A recent study published in Geophysical Research Letters, has turned up a surprising result: global ocean evaporation, a cornerstone of the hydrological cycle, has been declining since the late 2000s despite steadily warming sea surfaces. This runs counter to the widely held view that a warmer climate should boost evaporation rates. For those tracking discrepancies in climate science, this is worth a closer look.
The researchers analyzed satellite data spanning 1988 to 2017, drawing from four independent products: J-OFURO3, SeaFlux, HOAPS, and IFREMER. Their findings show that global ocean evaporation—which supplies about 85% of the atmosphere’s water vapor—rose sharply over the first two decades of the period, peaking around 2008. Then, the trend flipped. From 2008 to 2017, the global average dipped slightly, with two-thirds of the ocean experiencing reduced evaporation. This was validated, where possible, against buoy observations from the Global Tropical Moored Buoy Array, though coverage is sparse beyond the tropics.
Abstract
Ocean evaporation (Eo) is the major source of atmospheric water vapor and precipitation. While it is widely recognized that Eo may increase in a warming climate, recent studies have reported a diminished increase in the global water vapor since ∼2000s, raising doubts about recent changes in Eo. Using satellite observations, here we show that while global Eo strongly increased from 1988 to 2017, the upward trend reversed in the late 2000s. Since then, two-thirds of the ocean have experienced weakened evaporation, leading to a slight decreasing trend in global-averaged Eo during 2008–2017. This suggests that even with saturated surface, a warmer climate does not always result in increased evaporation. The reversal in Eo trend is primarily attributed to wind stilling, which is likely tied to the Northern Oscillation Index shifting from positive to negative phases. These findings offer crucial insights into diverse responses of global hydrological cycle to climate change.
What’s driving this? The study points to “wind stilling”—a measurable drop in near-surface wind speeds, particularly pronounced in the Southern Hemisphere. They link this to the Northern Oscillation Index (NOI), a measure of pressure differences between the North Pacific High and near Darwin, Australia, shifting from positive to negative phases after 2008. This phase change aligns with weakened trade winds and a broader slowdown in atmospheric circulation, outweighing the evaporation boost expected from rising sea surface temperatures (SSTs).
This challenges a core assumption in climate science: that evaporation scales predictably with temperature via the Clausius-Clapeyron relationship, where warmer air holds more vapor and thus pulls more water from the surface. The study’s data shows SSTs climbing as expected, increasing the air-sea humidity deficit—a key driver of evaporation. Yet wind speed’s decline overpowered that effect, cutting evaporation anyway. The authors quantify this using a multiple regression model, finding wind speed accounted for 62% of the post-2008 drop, compared to 38% from humidity changes.
The ramifications are significant. First, a weaker evaporation signal could reduce the moisture feeding precipitation, especially for land regions relying on ocean-derived vapor. The study notes spatial variation—declines were strongest in the southern tropical Pacific, Indian Ocean, and extratropical Atlantic—suggesting uneven impacts on rainfall patterns. Second, it could alter ocean salinity trends. Evaporation typically concentrates salt at the surface, but a slowdown might ease that process, particularly in subtropical zones where it dominates over precipitation. This could subtly influence density-driven ocean currents, though the study doesn’t claim dramatic shifts like an Atlantic Meridional Overturning Circulation disruption.
For climate modeling, this is a wrinkle. Most global circulation models (GCMs) project an intensified hydrological cycle under warming—more evaporation, more rain, more extremes. But if wind speed can override temperature effects, those projections might overshoot reality. The authors frame this as a possible natural cycle tied to decadal oscillations like the NOI or Pacific Decadal Oscillation, not a permanent climate change signature. Still, they leave the door open: if human-driven warming (e.g., polar amplification) is quietly slowing winds, this could signal a longer-term shift. With data ending in 2017, we’re stuck waiting for updates to settle that question.
Uncertainties linger. The satellite datasets aren’t flawless—HOAPS, for instance, shows a steeper drop, possibly due to quirks in its microwave sensor platforms (SSMI/SSMIS), while the others temper that signal by blending multiple sources. Buoy validation helps, but it’s spotty outside tropical zones, leaving high-latitude trends less certain. The study also sidesteps intra-annual shifts and secondary drivers like radiation or upwelling, focusing narrowly on wind and humidity. Future work, they suggest, could refine this with better measurements—think next-gen satellites or ocean drones.
It’s another case where observations don’t match the script. The “warming equals wetter” narrative takes a hit when a natural factor like wind can call the shots. It’s not a knockout blow to climate orthodoxy—evaporation rose for decades before 2008, tracking warming—but it exposes gaps in the models and the risk of over-relying on simplified physics. Is this a strong indicator that atmospheric dynamics are more in charge than CO2-driven forecasts admit? The study doesn’t preach; it just lays out the numbers. That’s plenty of fuel for discussion.
