Friday, September 15, 2017

+Myth: No Hot Spot Implies Less Global Warming and Support for Lukewarmerism

This post is part of a series addressing issues related to the hot spot. The other parts of this series are listed in the "Myths about the Hot Spot" section of the "Quick Scientific Debunking" page.

The outline for this post is as follows:
  1. The Myth and Its Flaw
  2. Context and Analysis
  3. Posts Providing Further Information and Analysis
  4. References

This is the "+References" version of this post, which means that this post contains my full list of references and citations. If you would like an abbreviated and easier to read version, then please go to the "main version" of this post.

References are cited as follows: "[#]", with "#" corresponding to the reference number given in the References section at the end of this post.

1.  The Myth and Its Flaw

Climate models predict that in moist tropical areas, a region of the lower atmosphere will warm more than Earth's surface. This is known as the "hot spot". The myth states that the lack of a hot spot implies that various factors will not substantially increase global warming to the levels seen in climate models. Thus the lack of a hot spot means that mainstream scientists over-estimate future global warming.

Proponents of this myth include David Evans [1, pages 12 - 18; 2 - 5; 73; 152; 174], Roy Spencer [6 - 8; 130; 572, page 35], Matt Ridley [12], William Happer [572, page 35], Craig Idso [572, page 35], Nicola Scafetta [572, page 35], Joseph D'Aleo [572, page 35], James Wallace III [572, page 35], Tim Ball [572, page 35], Don Easterbrook [572, page 35], Anthony Lupo [572, page 35], the Global Warming Policy Foundation [13], C3 Headlines [13; 14; 129], Friends of Science [15], Popular Social Science [16], and possibly Richard Lindzen [125, page 944]. Stefan Molyneux [9], GlobalWarming.Org [10], and the Heartland Institute [11] repeat this myth as well, though these proponents usually cite either Evans or Spencer as their support for the myth [9 - 11].

The myth's flaw: the hot spot represents a negative feedback that limits the rate of global warming [68 - 71; 133; 154; 520; 533, section 2.6.1 on page 112; 534; 558]. So the lack of a hot spot would imply greater warming, not less warming. Myth proponents claim otherwise because they make claims that contradict scientific evidence and/or they misrepresent how the hot spot forms. Myth defenders also distort or ignore the evidence supporting mainstream estimates of carbon-dioxide induced (CO2-induced) warming.

2. Context and Analysis

Earth's atmosphere contains multiple layers. The layer closest to the Earth's surface air is known as the troposphere. Tropospheric temperature decreases with increasing height; the rate of decrease is known as the tropospheric lapse rate

Climate models and basic physical theory predict that warming at Earth's surface will cause more water to evaporate, especially over tropical oceans. This evaporation increases the amount of water vapor in the air, since warmer air can hold more water vapor. The vapor-rich air then rises into the troposphere by convection. The water vapor subsequently condenses with increasing tropospheric height, since tropospheric temperature and pressure decreases with increasing height.

Condensation of water vapor releases some of the energy that went into evaporating the water; this is known as release of latent heat. This latent heat release causes tropospheric warming. So latent heat release causes more warming of the lower troposphere and even more warming of the upper troposphere, as water vapor condenses with increasing height. Thus latent heat release shrinks the rate at which tropospheric temperature decreases with increasing height; in this way latent heat release reduces the magnitude of the tropospheric lapse rate [17, pages 4 and 22; 18 - 21; 22, from 31:01 to 31:48; 69; 70; 133; 516, pages 7 and 8; 517, pages 101 and 102; 529; 530; 532], as depicted in figure 1:

Figure 1: A diagram of tropical tropospheric warming reducing the magnitude of the tropospheric lapse rate (adapted from Crok, Strengers, and Verheggen [23, page 3]). The horizontal dimension represents temperature, with temperature increasing as one goes to further right. The vertical dimension represents altitude in the troposphere, with altitude increasing as one goes further up from Earth's surface at the black line. The blue line represents the tropical temperature profile before warming, while the red line represents the tropical temperature profile after warming. Latent heat release causes more warming with increasing height [17, pages 4 and 22; 18 - 21; 22, from 31:01 to 31:48; 529; 530; 532], leading to the red line being steeper than the blue line. As a result, there is less of a temperature decrease with increasing height after tropical warming. Thus the lapse rate's magnitude is greater for the blue line than for the red line, indicative of a lapse rate reduction in response to tropical warming.

So the tropical troposphere should behave somewhat like a moist adiabat, in which the rate of warming increases with increasing height in response to water vapor condensing from vapor-saturated air [17, pages 4 and 22; 18 - 21; 22, from 31:01 to 31:48; 516, pages 7 and 8; 517, pages 101 and 102; 532]. 

The aforementioned tropical warming amplification is called the tropical tropospheric hot spot by myth defenders [6; 23, page 6]. As myth proponent Roy Spencer puts it:

"One of the most vivid predictions of global warming theory is a “hotspot” in the tropical upper troposphere, where increased tropical convection responding to warming sea surface temperatures (SSTs) is supposed to cause enhanced warming in the upper troposphere [6]."

So the hot spot I will discuss relates to amplification of warming as one goes from the tropical surface to higher in the tropical troposphere. This is different from the question of whether the amount or magnitude of observed tropospheric warming matches the amount of warming projected by climate models [23, pages 5 - 7; 24]; I address the "magnitude" issue in "Myth: Santer et al. Show That Climate Models are Very Flawed"

Figure 2 depicts a modeled hot spot (amplification of warming with increasing height in the tropics) in response to warming caused by increased solar activity or in response to warming caused by increased carbon dioxide (CO2):

Figure 2: ECHAM3/LSG model (European Center/Hamburg Model 3 / Large Scale Geostrophic coupled atmosphere-ocean climate model) simulation of the atmospheric response to (a) increased solar forcing (from increased solar output) and (b) increased CO2 forcing (from increased CO2 levels). Colored areas indicate significant responses, with darker blues indicating cooling and darker reds indicating warming. The horizontal axis represents latitude, with the tropics being between roughly 30°N and 30°[25, page 707]. The vertical axis represents altitude, with decreasing atmospheric pressure as altitude increases. The tropical troposphere lies below 150hPa, while the tropical stratosphere is above 70hPa [164]. Tropical tropospheric warming increases with height in both panels a and b, indicating that the hot spot forms in response to both solar-induced warming and CO2-induced warming. In contrast, strong tropical stratospheric cooling comes with CO2-induced warming, but not solar-induced warming. This figure is taken from a 2001 report of the United Nations Intergovernmental Panel on Climate Change (IPCC) [25, page 707].

Multiple lines of evidence show that the hot spot exists, though many "skeptics" of mainstream climate science claim otherwise, as I discuss in "Myth: The Tropospheric Hot Spot does not Exist". But for the sake of argument, suppose that the hot spot does not exist. What would that imply? That question is the subject of the myth: myth proponents claim that the lack of a hot spot would indicate that various factors are not increasing global warming as much as projected by climate models and mainstream climate science. Thus the hot spot shows that mainstream climate science over-estimates the amount of global warming, according to the myth advocates [1 - 16; 73; 129; 130; 152; 174].

The myth proponents are wrong. To see why, let's examine the mainstream scientific position on the hot spot and see what evidence supports this position.

If the hot spot does not exist, then there are two main explanations for the hot spot's absence:
  1. There is no surface warming, and thus no warming for the troposphere to amplify.
  2. The tropical troposphere does not behave somewhat like a moist adiabat.

Option 1 fails since there is clear evidence of multi-decadal surface warming for both land and oceans [26 - 37]. There are also other signs of warming, such as sea level rise resulting from melting ice and thermal expansion of water [38 - 41], increased hurricane intensity [42, page 3; 43 - 45; 306], and increased water vapor levels [46 - 52; 117 - 120; 122; 123], among other metrics [53; 54; 126; 483]. Furthermore, the absence of tropospheric amplification does not imply a lack of surface warming, since a number of regions (including deserts [55 - 57] and the Arctic [58; 59]) have surface warming with neither tropospheric amplification in the upper troposphere [55 - 59] nor amplification of warming with increasing elevation [60 - 62]. These regions are dissimilar to a moist adiabat, consistent with climate model results [63 - 67; 314, page 445]. The deserts in particular have intense, CO2-induced surface warming [57; 170; 171], with relatively little upper tropospheric warming [55 - 57]. So the aforementioned lines of evidence debunk option 1's claim of no surface warming.

That leaves option 2. If option 2 is right, then latent heat release did not significantly decrease the magnitude of the tropospheric lapse rate. If instead option 2 were wrong and the hot spot did exist, then the tropical troposphere behaves somewhat like a moist adiabat in which latent heat release by condensing water vapor transfers energy from the surface to the upper troposphere [68 - 71; 133; 529; 530]. The upper troposphere would then emit much of this energy away from Earth [69; 133; 529, figure 3c on page 5 and page 16; 533, section 2.6.1 on page 112; 534]. So the hot spot's lapse rate reduction serves as a negative feedback that limits global warming [68 - 71; 133; 154; 520; 533, section 2.6.1 on page 112; 534; 558], though one paper disputes this point by arguing that upper tropospheric warming can augment ocean warming [72].

But on the whole, the scientific literature shows that surface warming with no hot spot implies less negative feedback and greater warming [68 - 71; 133; 154; 520; 533, section 2.6.1 on page 112; 534; 558]. Even the noted climate contrarian John Christy admits that the lapse rate feedback is a negative feedback [523, page 516; 524]. Thus surface warming, in conjunction with option 2, implies that the tropics have greater surface warming and remain very different from a moist adiabat.

This makes sense in light of other regions that also radically differ from a moist adiabat, but have significant surface warming. For example, the Arctic and deserts have some of the greatest surface and lower tropospheric warming on the planet [55 - 59; 63 - 67; 170; 171], even though the Arctic and deserts lack a hot spot of amplified warming in the upper troposphere [55 - 59; 63 - 67] and thus lack a strong lapse rate feedback. So the "amplification" in the hot spot's "tropospheric amplification" is not indicative of positive feedback that amplifies global warming. Instead the hot spot's "tropospheric amplification" means 1, not 2:

  1. Tropospheric amplification: Upper tropospheric warming is amplified relative to surface warming [17, pages 4 and 22; 18 - 21; 22, from 31:01 to 31:48; 69; 70; 133].
  2. Positive feedback: Global warming (that is: total warming, including both surface warming and tropospheric warming) with the hot spot is amplified relative to global warming without the hot spot.

Maybe the myth persists because many myth advocates accept 2, due to their confusion of 2 with 1? If so, then these myth defenders are wrong, since the hot spot's negative lapse rate feedback [68 - 71; 133; 154; 520; 533, section 2.6.1 on page 112; 534; 558] means that 2 is false.

The aforementioned points result in a conundrum for the myth proponent Richard Lindzen. Lindzen claims that the hot spot's absence could be evidence of strong negative feedback or muting of the positive feedbacks that would otherwise amplify warming [125, page 944]. Therefore, all other things being equal, a model lacking the hot spot should have stronger negative feedback and thus less warming. Yet Lindzen admits that the magnitude of a model's hot spot does not correlate with how much CO2-induced warming that model projects [125, pages 942 and 943]; this projection is known as the model's climate sensitivity [64; 74; 75; 88 - 90]. So Lindzen's position appears contradictory.

One might defend Lindzen's position by claiming that his discussion of negative feedback [125, page 944] applies to the upper troposphere, while his claims on sensitivity apply to CO2-induced warming that occurs near Earth's surface. That defense fails, however, since Lindzen states that CO2-induced warming must warm the tropical upper troposphere [125, pages 941 and 942]. So his claims on negative feedback in the upper troposphere [125, page 944] would then also apply to sensitivity, thus implying that the hot spot's magnitude should correlate with climate sensitivity. But Lindzen disavows that conclusion, at least when it comes to climate models [125, pages 942 and 943]. Therefore the conundrum remains for Lindzen's position.

And in accord with Lindzen's example, myth advocates insinuate that the hot spot's absence would mean muted positive feedback [1 - 16; 73; 125, page 944; 129; 130; 152; 174; 572, page 35]. But as we just saw, the hot spot's absence would imply a muted lapse rate feedback, less negative feedback, and thus more global warming. Therefore the myth defenders are wrong.

So by clarifying the relationship between the hot spot and the lapse rate feedback, one debunks the myth proponents' claim that the hot spot's absence would imply that models over-estimate long-term global warming [1 - 16; 73; 129; 130; 152; 174]. Climate models project a decrease in the magnitude of the lapse rate reduction [235; 520], and thus an increase in CO2-induced global warming [520]. The lapse rate feedback has decreased [235], further supporting the model-based warming projections, though data on climate in the distant past (paleoclimate) suggests that climate models may under-estimate the lapse rate feedback for past climates [536].

So if the hot spot is not a sign of positive feedback, why is the hot spot important? Critics of mainstream climate science focused on the hot spot because they believed (or pretended to believe) that the hot spot's absence meant:
  • less global warming would occur, due to reduced positive feedback [1 - 16; 73; 125, page 944; 129; 130; 152; 174; 572, page 35]

Both of these beliefs are false, and thus neither belief adequately explains why the hot spot is important. Instead the hot spot is significant for other reasons, including:
  • the hot spot serves as negative feedback that limits global warming [68 - 71; 133; 154; 520; 533, section 2.6.1 on page 112; 534; 558]
  • the hot spot's tropospheric warming affects higher-elevation regions [21; 536; 538 as discussed in 539; 540; 541], melting land ice in these areas [536; 538 as discussed in 539; 540]

So discussing the hot spot is important, even if one is not interested in debunking contrarians' myths regarding the hot spot. However, I think debunking contrarian myths is also important, especially when politically-motivated parties, such as John Christy [18, page 383; 546, page 10; 547 - 551] and ICECAP [545], use hot spot myths to mislead Congress and the general public. I hope this clarifies why I spent so much discussing the hot spot and the myths surrounding it.

With that, I have stated what's necessary for debunking the myth. I could therefore stop my post right here, and you could stop reading this post at this point.

However, I want to address some of the specific claims myth advocates make in defense of the myth; I devote the rest of this post to doing that. So if you want the details on where the myth proponents go wrong, then feel free to continue reading the content below. The material below also explains why Matt Ridley's "lukewarmer" position is nonsense. This helps explain why many climate scientists think myth defenders under-estimate future CO2-induced warming.

Myth proponents typically defend the myth by linking the hot spot to positive feedback that amplifies global warming [1 - 16; 73; 125, page 944; 129; 130; 152]. Figure 3 depicts a number of positive feedbacks included in climate models, alongside the hot spot's lapse rate feedback:

Figure 3: (a) Average equilibrium temperature change (ECS) in response to a doubling of atmospheric CO2 levels in atmosphere-ocean general circulation models (GCMs) from CMIP3 (phase 3 of the Coupled Model Intercomparison Project), and the contribution of various feedbacks to this temperature change in the CMIP3 models. The Planck response represents temperature response to forcing from CO2, without taking other feedbacks into account [68; 533, section 2.6.1 on page 112]. (b) Average relative magnitude of each feedback in the CMIP3 models, with stronger positive feedbacks having a more positive value and stronger negative feedbacks having a more negative value. Error bars indicate +/- one standard deviation [68].

Figure 3 presents the average equilibrium climate sensitivity for an older set of climate models used by the United Nations Intergovernmental Panel on Climate Change (IPCC) [68; 298]. Equilibrium climate sensitivity (ECS) is climate sensitivity when Earth is in an equilibrium state where Earth releases as much energy as it takes in, and fast feedbacks (as opposed to slower acting feedbacks) have exerted their full effect [299; 300]. Transient climate sensitivity (TCS or TCR) is Earth's climate sensitivity over a shorter period of time, before Earth reaches equilibrium [299 - 301]. Different scientists give different definitions for climate sensitivity and ECS [74], but the aforementioned definitions should suffice for this blogpost.

The IPCC offers a central TCR estimate of ~1.8K, and a central ECS estimate of ~3K [298, page 871 and figure 10.20 on page 925], in agreement with figure 3. I will refer to these as high estimates of climate sensitivity in this blogpost.

Figure 3 also depicts positive feedback from:
  1. surface albedo
  2. water vapor
  3. clouds
The first feedback refers to surface albedo, which represents the proportion of light reflected by Earth's surface. Ice has a greater albedo than liquid water, implying that ice reflects more visible light from the Sun back into space than does liquid water. Melting ice therefore reduces Earth's albedo and increases the amount of radiation absorbed by Earth's surface [74; 75]. This increased in absorbed radiation causes more surface warming and therefore more ice melt; thus melting ice acts as a positive feedback amplifying warming [58; 76; 77; 346], as per the first feedback listed above.

The second and third feedbacks are linked together by water vapor, so I will discuss them together. Water vapor and CO2 are two greenhouse gases. CO2 absorbs energy at wavelengths missed by water vapor, which helps explain why CO2 can contribute to global warming even in the presence of water vapor [78 - 81; 565]. Furthermore, water vapor is a condensing greenhouse gas that condenses into liquid water at colder atmospheric temperatures [82 - 84]. This makes water vapor very responsive to atmospheric temperature changes, and thus very poor at driving up long-term temperature to very high levels in Earth's current climate [82; 84].

CO2, in contrast, is a non-condensing greenhouse gas that does not condense at the temperatures and pressures normally seen in the troposphere. This allows CO2 to accumulate in the presence of short-term atmospheric temperature fluctuations. CO2 therefore has a longer atmospheric residence time than does water vapor [82 - 84]. So in contrast to water vapor, CO2 can drive temperatures up in the long-term [82; 84], resulting in a long-term correlation between CO2 and temperature [74; 85 - 103; 564].

Since water vapor levels remain sensitive to air temperature, one might wonder how CO2-induced warming might affect water vapor levels. One can answer this query using the concepts of specific humidity and relative humidity. Relative humidity is the mass of the water vapor in the air relative to the maximum amount of water vapor the air can hold at that temperature. Relative humidity should stay about the same with long-term warming [19; 69; 119; 131; 132; 314]. Specific humidity is the mass of water vapor relative to the mass of water-vapor-containing air; specific humidity should increase with warming [19; 69; 119; 131; 132], since warmer air can hold more water vapor [68; 112; 113].

So as increased CO2 warms the atmosphere, atmospheric water vapor levels should increase in the warming air [68; 112; 113; 314]. And since water vapor acts a greenhouse gas that causes warming, increased water vapor will serve as a fast, positive feedback that amplifies the warming caused by CO2 [50; 68; 82; 131; 133; 314]. This water vapor feedback is not the same as the lapse rate feedback from the hot spot [68; 133; 154; 533, section 2.6.1 on page 112; 534; 558]; the former is a positive feedback resulting from accumulating water vapor absorbing radiation emitted by the Earth [50; 68; 82; 131; 133; 314; 558], while the latter is a negative feedback resulting from condensing water vapor releasing latent heat [68 - 71; 133] and the upper troposphere then emitting much of this energy away from Earth [69; 133; 529, figure 3c on page 5 and page 16; 533, section 2.6.1 on page 112; 534]. Even the climate contrarian Christy admits this [524], while also admitting that the lapse rate feedback is a negative feedback [523, page 516]:

"For models in general, water vapor feedback doubles the surface warming. The lapse rate feedback mitigates this somewhat at the surface [524]."

Though strongly positive water vapor feedback roughly correlates with strongly negative lapse rate feedback in regions of high precipitation [69], this relationship breaks down if precipitation is not taken into account [69; 70; 153]. In fact, some of the strongest warming on Earth occurs in deserts [55 - 57], where water vapor feedback amplifies surface warming, without a hot spot forming [55; 57]. So the hot spot (and thus the lapse rate feedback) should not be treated as an invariable marker of positive water vapor feedback. Yet many myth proponents (including David Evans [1 - 5; 152], Roy Spencer [6 - 8; 130], and Matt Ridley [12]) erroneously treat the hot spot's absence as a sign that positive water vapor feedback is missing [1 - 12; 15; 16; 129; 130; 152].

In addition to acting as a positive feedback on warming [50; 68; 82; 131; 133], water vapor can condense to form clouds. These clouds can then act as a positive feedback or as a negative feedback [82; 84; 112; 114; 115], depending on the nature of the clouds and how high the clouds are in the atmosphere: clouds can reflect solar radiation and thus act as a negative feedback, or clouds can reflect/absorb radiation emitted by the Earth and thus act as a positive feedback [112; 114; 115]. Lower level clouds tend to act as a negative feedback, while higher level clouds tend to act as a positive feedback [112; 114; 115; 160]. Climate models predict a net positive feedback from clouds due to increases in higher level clouds and reductions in lower level clouds [114; 116], though different models disagree on some aspects of this cloud feedback [70; 112].

So let's synthesize the above points into seven model-based predictions with respect to the feedbacks and projected warming in figure 3:
  1. There should be a hot spot (upper tropospheric warming greater than surface warming in the tropics), indicative of a lapse rate reduction that acts as a negative feedback on warming.
  2. Estimates of climate sensitivity using measurements of past CO2 levels and warming, should be, on average, around the value given in figure 3 and the corresponding high estimates of ECS and TCR.
  3. Water vapor levels increase during warming, with an increase in specific humidity.
  4. Relative humidity stays fairly constant.
  5. Higher water vapor levels act as a positive feedback on warming.
  6. Clouds act as a net positive feedback on warming.
  7. Melting ice should reduce Earth's albedo and act as a positive feedback on warming.

These predictions contrast with the proposals made by myth advocates:

  • Ridley [12], Evans [174], the Global Warming Policy Foundation [13], and C3 Headlines [13; 129] state that climate sensitivity is low. Ridley [12] also uses the (supposed) lack of a hot spot to justify a lukewarmer position; this position states that figure 3 over-estimates CO2-induced warming [246; 352; 456] (see "Christopher Monckton and Projecting Future Global Warming, Part 1" for more on lukewarmerism).
  • Evans [2, 4; 174] and Friends of Science [15] claim that specific humidity decreased in the mid- to upper troposphere; similarly, Ridley [12] and Spencer state that specific humidity is not increasing [6; 8; 130]. Spencer also claims that climate models failed to predict this lack of an increase in specific humidty, since the climate models may not accurately represent precipitation processes [6; 8; 130].
  • Friends of Science state that relative humidity decreased [15].
  • Many myth proponents, including Evans [1 - 5; 152; 174], Spencer [6 - 8; 130], and Ridley [12; 244 - 247], argue that much of the predicted water vapor feedback did not occur [1 - 12; 15; 16; 129; 130; 152; 174; 244 - 247; 352, page 2].
  • Evans mentions a proposal in which increased solar activity shielded Earth from cosmic rays, reducing cloud cover and thereby warming the Earth [1, page 18]. Evans also states the cloud responses are not well understood [2]. Both Evans [152] and Spencer link model failure with respect to clouds to model failures with respect to precipitation [6; 8], while Spencer [11] and Ridley [244 - 247; 352, pages 2 and 3] dispute the idea that the clouds amplify warming in a way that implies higher climate sensitivity [11]. Friends of Science also state that clouds act as a net negative feedback on warming [16].

So let's take stock of what the evidence shows regarding these predictions, starting with prediction #2 (I address prediction #1 in "Myth: The Tropospheric Hot Spot does not Exist").

Prediction #2: Climate sensitivity is relatively high

(I discuss climate sensitivity further in "Christopher Monckton and Projecting Future Global Warming, Part 1". In that series I also address lukewarmers such as myth proponents Matt Ridley [246; 352] and Roy Spencer [456]; these lukewarmers believe that figure 3 over-estimates the amount of CO2-induced warming.)

Myth defender Matt Ridley disputes the high equilibrium climate sensitivity of ~3K from figure 3 [12; 244 - 247]; he calls it "catastrophic" global warming [247], a straw man commonly offered in "skeptic"/contrarian circles [250, section 3.2.3 on page 41, pages 48 - 50]. Ridley provides at least eight arguments for a low equilibrium climate sensitivity of 1K to 2K [12; 244 - 247; 352, page 5]. His first argument is that estimates of climate sensitivity decreased overtime [12]. Ridley seems to get this idea from contrarian blogs and Patrick Michaels [244; 247]; Michaels typically depicts the decrease in sensitivity estimates using a particular image [192]. Figure 4 below presents a later version of Michaels' image [192] that was updated after Ridley made his comments regarding decreasing climate sensitivity estimates [12; 244; 247]:

Figure 4: Range of recent estimates of climate sensitivity, in comparison to the estimates used by the Interagency Working Group on the Social Cost of Carbon Climate (Roe and Baker, 2007) [192]. 

Figure 4 is problematic for at least three reasons. The figure is less comprehensive than a scientific review of the literature; this review presents recent evidence in support of figure 3's high climate sensitivity [498]. Figure 4 also excludes other studies that show higher climate sensitivity [74; 90; 161; 198 - 208; 213; 270; 449; 515; 525; 526; 531], and it excludes papers that show flaws in figure 4's studies [197, page 1375; 198; 199; 201 - 205; 499; 520; 525; 554]. These last two reasons are connected since correcting the flaws in figure 4's studies tends to increase the studies' climate sensitivity estimates [197 - 199; 201 - 205; 498, page 3; 499; 520; 554]. And matters become even worse for figure 4 when one includes other studies with climate sensitivity estimates greater than that of figure 3 [74; 161; 206; 207; 318; 498; 564]. If one were to follow Michaels' and Ridley's logic, then including these studies shows that figure 3 may under-estimate how much feedbacks amplify CO2-induced warming.

Since Michaels' figure 4 excludes these studies in order to support a lower climate sensitivity estimate, Michaels cherry-picks studies. And Ridley then relies on Michaels cherry-picking [244]. So Ridley's first argument cherry-picks, in order make it appear that estimates of climate sensitivity decreased overtime; this is not the first time critics have accused Ridley of cherry-picking climate sensitivity studies [297].

Strangely, Ridley's second argument may create tension with his aforementioned first argument: Ridley's first argument cites some studies that estimate climate sensitivity based, in part, on measurements of surface warming [247]. But Ridley's second argument claims that the surface warming may be over-estimated, due to data adjustments by an "extremist" NASA scientist [246; 352, page 2] or due to warming caused by urbanization [246; 247; 352, page 3] This urbanization-induced warming is also known as the urban heat island effect, or UHI for short; I discuss UHI further in section 3.5 of "Christopher Monckton and Projecting Future Global Warming, Part 1"). These factors may cause scientists to over-estimate the amount of CO2-induced warming, and thus artificially inflate climate sensitivity estimates [247].

Ridley's argument against surface-based records lacks merit for a number of reasons:

  • Diverse, non-NASA research groups used different methods to show about the same rate of global warming [26; 27; 282]. So Ridley is wrong when he says the warming results from adjustments by an "extremist" NASA scientist [246; 352, page 2].
  • Temperature proxies confirm the observed pattern of global warming [53; 54; 126; 483]. And there are also other signs of warming, such as increased hurricane intensity [42, page 3; 43 - 45], along with sea level rise resulting from melting ice and thermal expansion of water [38 - 41].
  • Scientists can correct for UHI by using processes known as homogenization. Homogenization corrects for artifacts / errors known as heterogeneities or inhomogeneities; these heterogeneities artificially skew temperature records [134] (I discuss homogenization in more detail in section 3.1 of "John Christy, Climate Models, and Long-term Tropospheric Warming", and in "Myth: The Tropospheric Hot Spot does not Exist" I discuss heterogeneities further). Homogenization techniques are validated for both surface [29; 53; 251 - 255] and tropospheric measurements [18; 241; 256; 269; 312; 313], to the point that even non-experts can check the accuracy of the surface-based homogenization [257 - 259]. When one corrects for UHI, there is still statistically significant surface warming and most of the observed surface warming remains [260 - 266].
  • The global warming trend is often not significantly higher in urban areas vs. rural areas [271; 272; 273; 274, figure 4 on page 2153]. When there is a significant difference, homogenization can correct for this urbanization-induced difference [253; 260 - 263; 265; 275; 276]. 
  • Wind mitigates the effect of UHI. So if UHI was responsible for much of the long-term warming, then there should be a large discrepancy in the temperature record for windy areas vs. non-windy areas. However, this predicted difference is not observed [261; 277; 278]. Thus UHI is not responsible for much of the long-term warming trend
  • Take one of the worst cases of UHI: China [263 - 266; 276; 279; 280]. UHI may account for up to around a third of the surface warming trend in China [264; 266; 280], though a number of scientists have shown that this number likely over-estimates UHI's contribution to China's surface warming [263; 265 276; 279]. But, for the sake of argument, suppose one accepts that UHI caused roughly a third of China's surface warming trend. And then suppose one removes this estimated UHI-induced warming from the Chinese warming trend. Then China's warming trend closely matches the average global warming trend [266]. So China, one of the worst cases of UHI, gives one little reason for thinking that UHI substantially skews the average, homogenized, global warming trend [266]. 

These points undermine Ridley's attempt to use UHI (and conspiracist insinuations about a NASA scientist) to discredit the surface temperature. Since Ridley distrusts the surface-based temperature records, he instead opts for satellite-based analyses of lower tropospheric warming [246; 247; 352, page 2]. And according to Ridley, satellite-based analyses from Remote Sensing Systems (RSS) and a team at the University of Alabama in Huntsville (UAH) show less warming than the surface-based analyses. So the satellite-based analyses appear to justify Ridley's claim that scientists over-estimate the surface-based warming trend [246; 247; 352, page 2].

To see the problem with Ridley's reasoning, let's start with one of Ridley's preferred sources: Judith Curry [12; 245; 247]. Curry lauds [267] the European Centre for Medium-Range Weather Forecasts Interim re-analysis (ERA-I), a temperature re-analysis that incorporates satellite data [46; 137]. She also discusses [267] a 2016 paper that compares ERA-I's lower tropospheric temperature analysis to that of RSS and UAH [268]. That paper showed that the ERA-I, UAH, and RSS lower tropospheric warming trends were very similar [268, figure 3], with all three trends being lower than surface warming trends from three sources [268, figure 1]:

Figure 5: Global, relative surface temperature trends from 1979 - 2014 for ERA-I (ECMWF), GISTEMP (Goddard Institute for Space Studies Surface Temperature Analysis), and HadCRUT4 (Hadley Centre, Climate Research Unit Temperature analysis) [268, figure 1].

Figure 6: Global, relative tropospheric temperature trends from 1979 - 2014 for ERA-I (ECMWF), UAH version 6.0, and RSS version 3.2. UAH, RSS, and ERA-I "TLT" trends represent the lower troposphere, while "ECMWF mid trop" represents the mid-troposphere [268, figure 3].

The paper suggests a number of possible explanations for this difference between tropospheric and surface warming, including urbanization biasing the surface temperature record [268, pages 1, 3 and 7]. That is the conclusion Ridley wants: that the surface records over-estimate warming when compared to the satellite records [246; 247; 352, page 2], and this over-estimation might cause scientists to over-estimate climate sensitivity.

But Ridley's reasoning collapses once one examines the scientific literature on ERA-I and RSS. In 2014 [46] and again in 2016 [26], the ERA-I team admitted that ERA-I under-estimates lower tropospheric warming. Other scientists acknowledged this point as well [518, section 2]. And in 2017, RSS published a study that corrected issues in RSS' homogenization. These corrections increased RSS' lower tropospheric warming trend [269].

Moreover, a number of weather balloon analyses support a greater tropospheric warming trend [269, figure 12], despite the fact that the weather balloon analyses likely contain heterogeneities that artificially reduce their warming trend [235]. So neither the RSS nor ERA-I analyses support Ridley's conclusion that satellite-based lower tropospheric warming trends remain lower than surface warming trends:

Figure 7: Near-global, lower tropospheric temperature trends from 1979 - 2016 for RSS and UAH, relative to a baseline of 1979 - 1981. Depicted trends come from version 3.3 and version 4.0 of the RSS analysis, along with version 5.6 and version 6.0 of the UAH analysis. RSS version 4.0 is an update of RSS version 3.3, while UAH version 6.0 is an update of UAH version 5.6. The lower lines (black, gray, red, and pink) indicate relative temperature. The upper lines (green and purple) display the difference between the relative temperature values from different analysis versions [269, figure 9a].


Figure 8: Comparison of relative, lower tropospheric temperature trends from 1979 - 2012 for satellite-based analyses and weather-balloon-based analyses, as presented by the RSS team. The figure covers specific regions where valid weather balloon data is available for each weather balloon analysis. The satellite-based analyses are RSS version 3.3, RSS version 4.0, UAH version 5.6, and UAH version 6.0. The weather balloon analyses are Hadley Center Radiosonde Temperature (HadAT), Radiosonde Observation Correction using Reanalysis (RAOBCORE), Radiosonde Innovation Composite Homogenization (RICH), and Iterative Universal Kriging (IUK) [269, figure 12]. RSS did not include Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC), since RATPAC lacked the homogenization needed for a valid comparison [269, page 7712].

That leaves only UAH to support Ridley's conclusion. But UAH has a long history of under-estimating tropospheric warming due to UAH's faulty homogenization [22, from 36:31 to 37:10; 281; 282; 283, pages 5 and 6; 284; 285; 322; 512], other scientists have critiqued UAH's homogenization methods [18; 23, pages 17 - 19; 124; 241; 286 - 289; 322; 512; 513], and UAH's satellite-based temperature analyses often diverge from analyses made by other research groups, in both the troposphere and other atmospheric layers [18; 23, pages 17 - 19; 124; 241; 286 - 290; 512]. Ridley's argument thus remains on shaky ground if all he has to rely on is Spencer's UAH analysis.

And even Carl Mears, a member of the RSS team, admits that satellite-based tropospheric temperature measurements remain less certain than surface temperature measurements [351; 552] (based on his published uncertainty estimates [555; 556]). The U.S. Global Change Research Program makes much the same point [533, Appendix A on page 637]. So Ridley's second argument fails since neither the satellite-based lower tropospheric temperature record nor UHI provide a strong reason for thinking that the surface temperature is flawed in a way that would cause scientists to significantly over-estimate climate sensitivity.

Despite Ridley's discussion of UHI and an "extremist" adjuster manipulating surface temperature records [246; 247; 352, page 2], Ridley still makes claims based on the observed surface temperature record [12; 244 - 247]. One of Ridley's arguments on this point baffles me: he claims that reduced (or non-existent) global warming since the late 1990s supports a low climate sensitivity estimate [12; 245 - 247]. So in this third argument, he seemingly assumes that high climate sensitivity entails that CO2 is the only factor significantly affecting shorter-term temperature.

But Ridley's assumption is mistaken: climate sensitivity reflects a temperature response to CO2 [64; 74; 75; 88 - 90], and a strong response to CO2 is compatible with a strong, short-term response to non-CO2 factors. A number of studies illustrate this point, by showing that high climate sensitivity remains compatible with the post-1998 temperature record [198; 201; 203; 204; 213]. This is because non-CO2 factors can drive temperature downwards of short time scales, even if there is long-term CO2-induced warming [47; 212; 213; 233; 234]. To say otherwise would be to conflate short-term trends with CO2's effect on long-term climate; it would be akin to saying that a multi-month warming trend from mid-winter to mid-summer is not real, because the current week was cooler than last week (I discuss this analogy further in section 3.4 of "John Christy, Climate Models, and Long-term Tropospheric Warming").

Ridley makes matters even worse by cherry-picking short-term trends that begin in 1997 [246; 352, page 4]. This cherry-picking has the effect of minimizing his warming trend, since 1997 and 1998 were especially warm due to the warm phase of an ocean cycle known as the El Niño-Southern Oscillation (ENSO) [18; 29; 196; 197; 213; 281; 291, page 194; 292 - 296]. And Ridley's short-term, post-1997 trend [246] becomes even more biased when one takes into account the 1998 transition in satellite temperature monitoring equipment [196, pages 2 and 3; 281, pages 69 and 72], along with RSS improving their satellite-based homogenization for the post-1998 [123, figure 9 on page 7711; 124, figure 8 on page 3642] Ridley abused. Yet Ridley still cherry-picks this short-term trend anyway [12; 245 - 247], as if neither faulty homogenization nor El Niño could skew a short-term temperature trend, in the presence of CO2-induced warming. So Ridley's third argument fails since he implicitly assumes that high climate sensitivity requires that CO2 is the only factor influencing shorter-term temperature changes he cherry-picks.

Ridley's fourth argument makes a similar mistake, but on a larger time-scale: he states that climate sensitivity is low, since ice core records show that temperature can decrease while CO2 stays high [12]. But in making this argument, he runs afoul of the fact that positive feedback and high climate sensitivity do not entail that CO2 is the only factor significantly affecting longer-term temperature. So, for example, climate sensitivity could be high, even if a reduction in solar output caused temporary global cooling in the presence of high CO2 levels. Ridley's fourth argument therefore hinges on a false assumption.

And his argument appears even weaker in light of the robust correlation between CO2 vs. temperature in ice cores and other data sources [74; 85 - 103; 218]. Figure 9 below illustrates this correlation for an ice core record:

Graph of temperature change and carbon dioxide change measured from the EPICA Dome C ice core in Antarctica

Figure 9: CO2 level and temperature change measured from an Antarctic ice core [91]. The data is taken from two published studies [96; 218]. "Years before present" (BP) for ice cores means "years before 1950" [217]. And the aforementioned data stops by about 38 BP [215; 216], which is equivalent to ~1912. So this figure does not include most of the warming and CO2 increase since the 20th century; CO2 levels are now above 405ppm [553]. When calculating climate sensitivity, 1°C of Antarctic warming translates to ~0.6°C of global warming [219].

In his fifth argument, Ridley states that climate models over-estimate observed temperature rises, and thus the models over-estimate both positive feedback and climate sensitivity [12]. But his argument collapses since other factors explain most of the difference between observed warming vs. model-based projections of warming. These factors include heterogeneities in the observations, random/internal variability in the climate system, and errors in the information inputted into the models [18; 29; 193; 194 - 196; 209 - 214]. The aforementioned explanations imply neither that the models are flawed nor that the models over-estimate climate sensitivity [195; 196, page 6], as I discuss in "Myth: Santer et al. Show That Climate Models are Very Flawed". So those explanations debunk Ridley's fifth argument for low climate sensitivity. 

The sixth argument from Ridley implies that the hot spot's absence shows that positive water vapor feedback is muted and thus climate sensitivity is low [12]. This sixth argument lacks merit since the hot spot exists, as I discuss in "Myth: The Tropospheric Hot Spot does not Exist". The evidence for the hot spot places Ridley in an awkward position, since Ridley claims to trust satellite analyses more than surface-based analyses [246; 247; 352, page 2], even though his "no tropical troposphere hot-spot [12]" claim conflicts with at least five different satellite analyses that show the hot spot [18; 241, table 4; 248, figures 8 and 10; 249, figures 12 and 13; 286; 287] (I consider only four of these analyses to be fairly reliable).

And as I discussed above, the hot spot implies a negative feedback from lapse rate reduction [68 - 71; 133; 154; 520; 533, section 2.6.1 on page 112; 534; 558], not positive feedback from water vapor. So the hot spot's absence would imply less negative feedback, more warming, and therefore a higher climate sensitivity. Ridley's argument is therefore wrong since it draws the opposite conclusion from the hot spot's (supposed) absence [12].

Ridley's seventh argument states that funding goes to scientists researching human-induced global warming, instead of to scientists investigating natural, non-human-caused (or non-anthropogenic) climate change. So, according to Ridley, climate scientists present figure 3's "dangerous [247]" climate sensitivity estimate because scientists need that estimate to exaggerate the risk of human-caused climate change and thus justify their research funding.

Ridley then tries to bolster his case by listing other occasions in which scientists (supposedly) over-estimated climate responses. His examples include predictions regarding an imminent ice age, increased hurricane intensity, and an acceleration in sea level rise [247]. Unfortunately, Ridley's argument runs afoul of scientific evidence, basic scientific reasoning, and facts regarding how the scientific enterprise works. Let's address these issues in turn.

Ridley's hurricane and sea level claims contradict evidence showing increased hurricane intensity [42, page 3; 43 - 45] and accelerating sea level rise coinciding with periods of global warming, including during post-1970s global warming [363 - 369; 559; 560] and during warming over the past decade or so [358 - 363; 559]. And there was no 1970s scientific consensus regarding an imminent ice age [370 - 372]. Instead, more scientists predicted imminent warming than imminent cooling, with warming predictions having a greater impact on the scientific literature [370]. Ridley obscures this accurate warming prediction by relying on 1970s media coverage of a (supposed) imminent ice age [247]. Thus Ridley fails to give climate scientists the credit they merit.

Despite their accurate predictions, climate scientists are not perfect; scientists, both at the IPCC and elsewhere, often under-estimate the effects of climate change [18, figure 3 on page 378; 22, from 24:56 to 26:18, and 31:47 to 33:33; 23, page 22; 196, page 5; 373 - 378]. In the case of scientists at the IPCC, their under-estimation of trends likely results from critics (often contrarians themselves) applying undue pressure to scientists [379]. This runs contrary to Ridley's claims that research funding caused climate scientists to exaggerate climate change dangers [247]. In order to make it look like climate scientists constantly exaggerate climate change risks, critics of mainstream climate science carefully cherry-pick particular instances in which the models used by the IPCC over-estimated a given tren, while disregarding instances in which the models under-estimate a trend [23, page 22]. Ridley resorts to such cherry-picking [247].

And even though climate scientists could justifiably use "alarmist" language to increase concern about CO2-induced warming [380], the tone of IPCC scientists tends to be more tentative and less "alarmist" [381], with the IPCC paying proper attention to how to talk about uncertainty [382; 383]. Many mainstream climate scientists also avoid defending hyperbolic notions such "[imminent] runaway global warming" [68; 83; 324 - 327; 340, page 90; 522, pages 17 and 24; 570] and "catastrophic anthropogenic global warming," [250] despite Ridley's insinuations to the contrary [12; 350]. This also conflicts with Ridley's claim that "the climate science establishment has a vested interest in alarm [247]," as does the fact that mainstream climate scientists often correct exaggerated media stories on climate change [384 - 390]. So Ridley seems to lack a basic understanding of how climate scientists operate.

Ridley's ignorance becomes even more clear when he insinuates that funding drives climate scientists' support of figure 3's high climate sensitivity and the associated estimates of CO2-induced, anthropogenic climate change [247]. High climate sensitivity estimates date back to at least 1896, with Arrhenius' ECS estimate of >4K or >5K [370, page 1328; 371, page 68; 391, page 17]. So it makes no sense to claim that scientists recently made up high sensitivity estimates in order to maintain their funding.

Moreover, climate scientists receive funding for research into non-anthropogenic climate change, despite Ridley's insinuating otherwise [247]. For example, climate science progressed from the ~1900s to ~1950s, without much focus on anthropogenic climate change [22, from 10:27 to 22:50; 371, 68 - 71], despite research on the subject dating back to the 1890s [22, from 5:52 to 10:56; 370, page 1328; 371, pages 68 - 71; 391, page 17]. Contrarians / myth proponents such as Richard Lindzen [393, "Acknowledgements" section], Roy Spencer [6; 394, "Acknowledgements" section; 501, "Acknowledgements" section], John Christy [501, "Acknowledgements" section; 503, "Acknowledgements" section], Roger Pielke Sr. [507, "Acknowledgements" section; 508, "Acknowledgements" section; 509, "Acknowledgements" section], Judith Curry [504, "Acknowledgements" section; 505; 506, "Acknowledgements" section], Willie Soon [500, "Acknowledgements" section; 510, "Acknowledgements" section], Craig Idso [510, "Acknowledgements" section], David Legates [509, "Acknowledgements" section; 510, "Acknowledgements" section], and Anthony Watts [509, "Acknowledgements" section] also benefited from government funding, even though some of these contrarians co-authored debunked claims that went against figure 3's high climate sensitivity estimate [18, page 379; 22, from 34:40 to 39:12; 197, page 1375; 392, page 4; 393; 502]. Yet Ridley uses Spencer's words to prop up the idea that funding organizations favor those who support the consensus on anthropogenic climate change [247], despite the fact that Spencer's own government funding [6; 394, "Acknowledgements" section; 501, "Acknowledgements" section] contradicts Spencer and Ridley's funding narrative.

To make matter even worse for their narrative, a 2013 review of abstracts for climate change papers found that most abstracts took no stance of anthropogenic global warming [395]. Another 2016 paper surveyed other studies on this subject, noting that scientists continued to publish research on topics such as ENSO and ice ages [557]. Consistent with this, Steveen Sherwood, a climate researcher unfairly berated by myth proponents such as David Evans [152], recently published paper on non-anthropogenic climate change [396]. So research on non-anthropogenic climate change (even contrarian research) receives funding. This makes sense once understands how actual funding practices differ from Ridley's and Spencer's paranoid narrative.

Funding organizations judge grant proposals based on factors such as preliminary data presented in the proposal, whether the proposal writers have access to the tools needed for their proposed project, etc., not based on whether the proposal supports a pre-determined conclusion. That applies to research in cancer just as much as it applies to research in climate science [397; 398, "Application Quality" section; 399; 400]; scientists receive funding to examine non-anthropogenic causes of cancer [401; 402], just like scientists receive funding to examine non-athropogenic causes of climate change. So saying that climate research funding only goes towards work on pre-determined conclusions regarding anthropogenic climate change, is as ignorant as saying cancer research funding only goes towards work on pre-determined conclusions regarding smoking and other anthropogenic causes of cancer. Yet Ridley displays this sort of ignorance in his discussion of climate science funding [247].

To his ignorance Ridley adds a baseless conspiracy theory: the climate science community (supposedly) exaggerates climate change risks for the sake of funding [247]. Denialists often employ these sorts of conspiracy theories against scientists who present evidence that debunks the denialists' position [372; 403 - 408; 495; 496, pages 203, 207, 213, 216, and 217]. Ridley's conspiracy theory suffers from a number of flaws, some of which I summarize below (I discuss these flaws in more depth in section 3.1 of "John Christy, Climate Models, and Long-term Tropospheric Warming"):

  • For Ridley's conspiracy theory to work, the conspiracy would need to involve (at least) hundreds of conspirators, given the broad scientific consensus on anthropogenic climate change [409; 410, page 49; 411, figure 2 on page 11; 412]. The conspiracy would also need to date back to at least the late 1800s, since high climate sensitivity estimates first arose then [370, page 1328; 371, page 68; 391, page 17]. And numerous researchers from different fields would need to be involved in the conspiracy, since evidence from these fields supports figure 3's high climate sensitivity [74; 90; 161; 198 - 208; 213; 270] (see figure 10 below; this figure depicts older climate sensitivity estimates using proxies to estimate CO2 levels and temperature in the distant past {paleoclimate evidence} or in the more recent past {instrumental evidence}). Such a conspiracy is unlikely to exist, given the enormous amount of proposed conspirators spread across more than a century [403]. Scientific evidence, not a conspiracy theory, better explains such a scientific consensus among a diverse group of experts [487], as with other evidence-based scientific consensuses [404; 488 - 494].

Figure 10: Estimates of (a) TCR and (b) ECS from the scientific literature. The histogram height is proportional to the relative probability that CS is at the value shown on the horizontal axis. For example, the bottom panel on (b) includes Aldrin et al. 2012, where the maximum value for the histogram is around 1.7K, indicating that 1.7K is the most likely value for ECS of all possible ECS values examine in Aldrin et al. 2012. Horizontal bars show the probability range and the circles mark  the median estimate. The dashed lines in (a) show estimates from a previous IPCC report (AR4). The boxes on the right-hand side indicate limitations and strengths of each line of evidence. A blue box implies an overall line of evidence that is well understood, has small uncertainty, or many studies and overall high confidence. Pale yellow indicates medium confidence, and dark red implies low confidence [298, figure 10.20 of page 925].

  • Ridley would likely reject a conspiracy theory about how scientists exaggerate the health risks of smoking, so that scientists could maintain their funding for smoking research. Therefore Ridley engages in special pleading when he accepts an analogous conspiracy theory about climate scientists exaggerating CO2-induced, anthropogenic global warming. This also rebuts Ridley's conspiracist logic, since his logic would support the rejection (or non-acceptance) of well-evidenced scientific facts that one should accept [404; 406; 407; 413; 414; 495, 496, pages 203, 207, 213, 216, and 217]. 
  • Ridley's reasoning undermines his position, since one could use his reasoning to claim that research funding motivates many climate scientists to generate low climate sensitivity estimates. Paranoid conspiracy theories often undermine themselves in an analogous way [415, page 17].
  • Ridley tries to immunize his position against falsification. To see why, note that Ridley could claim that any evidence against his low climate sensitivity must have (supposedly) been manufactured by scientists motivated by research funding. Those scientists would also either cover-up or ignore evidence of low climate sensitivity, which explains any absence of evidence for Ridley's position. And, of course, any low climate sensitivity estimates count as support for Ridley's position, no matter how flawed the estimate is. In this way, Ridley relies on an impossible burden of proof that no high climate sensitivity evidence could ever meet. That runs contrary to cogent scientific reasoning, yet denialists still resort to this type of flawed logic in order to evade falsification [403 - 408; 415 - 417]. Ridley is no different [247].

I find it sad that an accomplished science writer like Ridley would resort to such easily debunked, conspiracy-theory-mongering in his seventh argument. Unfortunately, his reasoning gets even worse: Ridley's eighth argument is so flawed that it convinced me that Ridley either deceives people or he is incompetent. Ridley argues that the past two centuries of global warming are not as rapid as warming during the medieval warm period (MWP) from around 500AD to 1000AD. Furthermore, past post-glacial warming periods show that natural factors can cause warming, even when CO2 levels do not significantly change. Thus, according to Ridley, non-anthropogenic factors can match CO2 in terms of warming, including contributing to recent temperature trends [352, page 4].

Ridley's reasoning here does nothing to change the fact that increase CO2 caused most of the recent global warming [106; 175 - 178; 336, 418 - 454], since humanity's impact on climate counter-acted natural variability [455, page 3]. Ridley admits this [247]. To say otherwise would be as implausible as saying that police cannot know that arsonists caused a recent forest fire, unless police can explain every forest fire that happened since 500AD [457]. Such a claim is implausible because police can find clear evidence that arsonists caused the fire, even if police lack an explanation for some fires in the distant past. Analogously, scientists can present evidence that CO2 caused most of the recent global warming, even if scientists lack an explanation for some warming in the distant past. This evidence of CO2-induced warming includes cooling of higher layers in the atmosphere [149; 418; 458 - 475] and atmospheric absorption of energy in the energy wavelengths CO2 is predicted to absorb [22, from 9:07 to 10:29; 336 - 339] (I discuss this more in "Myth: The Sun Caused Recent Global Warming and the Tropical Stratosphere Warmed").

Not only does Ridley's argument fail to rebut recent CO2-induced warming, but Ridley's argument rests on a misleading fabrication. To see why, take the following figure Ridley presents in support of his eighth argument:

Figure 11: Ridley's figure [352, page 4], likely lifted from the CO2 Science blog [356] and (supposedly) "adapted" from a scientific paper [355]. The figure purports to depict relative temperature up to 1999 for the northern hemisphere outside of the tropics, using temperature proxies (black lines). Blue shading represents the standard deviation, one representation of the margin of error for the proxy records. Temperature is relative to a baseline of 1961 - 1990 [352, page 4; 356]. Abbreviations are follows: RWP - Roman warm period, DACP - Dark Ages cold period, MWP - Medieval warm period, LIA - Little Ice Age, and CWP - Current warm period [357].

Figure 11 (supposedly) shows a MWP in northern hemisphere regions outside of the tropics [352, page 4; 356]. The MWP, however, was not as pronounced in the southern hemisphere [478; 485, figure 5.7 on page 409]. One can combine evidence from both hemispheres in order to generate a more global picture. This picture shows a hockey stick pattern, in which global average temperature is relatively constant, with some warming during the MWP. Then rapid warming occurs during the past two centuries, forming the blade of the hockey stick pattern [54; 477; 478; 479; 484; 485, figure 5.7 on page 409].

The hockey stick even appears in sources [298, figure 10.19 on page 918; 476; 478] that "skeptics"/contrarians [480; 486, from 4:54 to 13:35] distort, including in many regional temperature trends in the northern hemisphere [476 - 479; 481; 482; 514, figure 7; 519]. Many "skeptics"/contrarians avoid this hockey stick pattern by either cherry-picking temperature records from particular locations or by focusing on temperature records from just the northern hemisphere [486, from 4:54 to 13:35]. Ridley engages in this misleading cherry-picking with his use of figure 11 [352, page 4].

You may wonder why I use the terms the term "supposedly" when discussing figure 11. I use this terms because figure 11 is a fabrication. This becomes clear when one compares figure 11 to the original graph presented below:

Figure 12: Relative temperature up to 1999 for the northern hemisphere outside of the tropics, using temperature proxies (gray lines) and HadCRUT surface temperature record (dashed black line from 1850 - 1999). Gray shading represents the standard deviation, one representation of the margin of error for the proxy records. Temperature is relative to a baseline of 1961 - 1990, HadCRUT data serving as the baseline [355, figure 3 on page 345]

Figure 11 removes some of the recent warming shown at the end of figure 12 in the form of a dashed line. Removing this warming makes it easier for Ridley to claim that recent temperature and recent warming are not greater than during the MWP [352, page 4]. So Ridley's figure 11 partakes in the "skeptic"/contrarian/denialist tradition of fabricating graphs in an attempt to minimize recent warming relative to warming in the more distant past [486, from 4:54 to 9:10]. This returns us to my previous point regarding Ridley's incompetence or deception. Either:

  1. Ridley copied figure 11 from another source (ex: a denialist blog such as CO2 Science [356]) without bothering to check the original paper that contained figure 12. If so, then Ridley is incompetent; as a science writer and someone with a scientific degree, he should to read the scientific literature, not just denialist blogs. That is especially the case when he tries to cite graphs from scientific sources, in an attempt to critique experts working in mainstream climate science [247; 352].
  2. Or Ridley read the original paper containing figure 12, and thus Ridley knew that figure 11 was a misleading modification of figure 12. If so, then Ridley deceived his audience when he presented figure 11 without figure 12. This deception could be motivated by the financial benefits Ridley receives from leasing land for coal mining.

So that addresses Ridley's incompetent (or deceptive) attempts to critique figure 3's high climate sensitivity. In addition to Matt Ridley, a number of other myth defenders argue that climate sensitivity is low and thus positive feedback is muted. These proponents include the Global Warming Policy Foundation (GWPF) [13] and C3 Headlines [13; 129]. Neither GWPF nor C3 Headlines bring much new material to the discussion, beyond two claims.

First, GWPF and C3 Headlines say the hot spot does not appear in HadAT2 (Hadley Center Radiosonde Temperature) weather balloon data from 1996 - 2012 [13; 129]. This amounts to cherry-picking a time-frame, since the HadAT2 analysis shows the hot spot on some longer time-scales [235, figure 2c; 236, figure 10 and 11; 237, figure 3 and table 1]. Moreover, HadAT2 is an outlier analysis among the five available weather balloon analyses [235; 237].

The HadAT2 team also acknowledges the heterogeneities in HadAT2 analysis [235; 238]; they therefore recommend that researchers also examine other weather balloon analyses and the RSS satellite analysis [239]. These other analyses show the hot spot [18; 149, figures 1 and 2; 235, figure 2c; 237, figure 3 and table 1; 240, figure 9; 241], as I discuss in "Myth: The Tropospheric Hot Spot does not Exist". Yet GWPF and C3 Headlines do not mention these analyses and thus fail to follow the HadAT2's team sound advice. GWPF and C3 Headlines [13; 129] thus cherry-pick a particular weather balloon analysis and cherry-pick a time-frame within that analysis, in order to unjustifiably deny the hot spot's existence.

Second, GWPF and C3 Headlines claim that there is no positive feedback, since instead of temperature continuing ever upward, tropospheric temperature briefly spikes and then returns to normal during El Niño [13; 129]. This statement puzzles me for the same reason some of Ridley's arguments baffled me: GWPF and C3 Headlines run afoul of the fact that positive feedback and high climate sensitivity do not entail that CO2 is the only factor significantly affecting temperature. Non-CO2 factors can drive temperature downwards of short time scales, even if there is long-term CO2-induced warming [47; 212; 213; 233; 234]. So GWPF and C3 Headlines are misguided in their attempt to use a short-term response to debunk long-term positive feedback [13; 129].

In contrast to GWPF and C3 Headlines [13; 129], David Evans provides a slightly more interesting argument for low climate sensitivity [174]. Evans appeals to ice-core-based estimates that indicate that CO2 levels increased after warming occurred; that is: CO2 lags temperature. He uses to argue that there is little positive feedback on CO2-induced warming [174].

Scientists can explain the CO2-temperature lag as follows: another factor (such as a change in Earth's orbit and/or axial tilt relative to the Sun) causes some initial ocean warming, the CO2-saturated warming oceans release CO2 into the atmosphere, this atmospheric CO2 causes further ocean warming, and a positive feedback loop begins between CO2-induced ocean warming and warming-induced ocean release of CO2. Melting ice also contributes to the warming via surface albedo feedback [86; 104; 105; 242, page 1730; 243; 307, page 435; 347; 521]. So increased CO2 causes the subsequent warming, but not the the initial warming.

There are also past cases in which the atmospheric CO2 rise occurs before, or at the same time as, the initial temperature rise in particular regions [85; 86; 242, page 1730]. This is compatible with CO2 causing the initial warming in one region and most of the subsequent warming, as in the current period of global warming that began around the 19th century [106; 175 - 178]. And unlike during some the CO2-temperature lags in the distant past, warming oceans are not the cause of increasing CO2 over the past couple of centuries [106 - 108; 230 - 232], since the non-CO2-saturated oceans acted as net uptakers of CO2 [107 - 111; 230; 232]. None of this implies low climate sensitivity; in fact, sensitivity needs to be high enough for CO2 to cause much of the subsequent warming after a CO2-temperature lag, as shown in studies of CO2-induced global warming in the distant past (including ice-core-based studies) [74; 87 - 89; 571, section 5.5]. So this paleoclimate evidence rebuts Evans' claim of low climate sensitivity [174], even in the presence of a CO2-temperature lag.

The paleoclimate evidence also debunks another argument from Evans: Evans claims that figure 3's positive feedback is too close to runaway global warming [1, pages 17 and 18] in which Earth continually warms in an irreversible way. Ridley also tries to link predictions of runaway global warming to mainstream climate science from the 1980s [12; 350]. But the high sensitivity in figure 3 does not entail a runaway [68; 197, page 1372]. In fact, much of the paleoclimate evidence supports a higher climate sensitivity than shown in figure 3 [74; 522, pages 17 and 24], even though Earth's warming and cooling patterns differ from Venus' runaway warming [83; 323 - 326; 522, pages 17 and 24] (as acknowledged by the climate scientist James Hansen [327, from 1:55 to 3:36; 522, pages 17 and 24]). So positive feedback does not entail runaway global warming [68; 197, page 1372; 522]. There are at least three reasons for this.

First, positive feedback eventually ceases. For instance, positive feedback from melting ice will stop once all the ice melts. Second, once global warming ceases, positive feedbacks can drive long-term global cooling. Cooling can result from orbital forcing, which involves slight changes in Earth's orbit and tilt relative to the Sun [341; 345]. Orbital forcing causes slight cooling [341] or other factors reduce atmospheric CO2 levels [342 - 345; 349]. This initiates a positive feedback in which colder oceans take up more CO2, which results in more ocean cooling, which results in more ocean uptake of CO2, and so on. Furthermore, cooling results in more frozen ice, which acts as a positive feedback promoting cooling, which results in more frozen ice, and so on [341; 348; 349]. The Earth thus ends up in a glacial cycle.

The third reason is a bit more complex, but understanding it is crucial for understanding long-term climate on Earth. As Earth warms, Earth radiates more energy into space (as per the Stefan-Boltzmann law), instead of all the energy just accumulating [113; 328; 329]. Scientists can observe this increased radiation during a warm El Niño [330 - 332]; the radiation increase occurs largely because El Niño increases cloud cover and these clouds then reflect the solar radiation Earth would otherwise absorb [330; 333]. This cloud-based mechanism compensates [330; 332] for less emission of radiation by clouds during El Niño [334; 335].

In contrast to a temporary El Niño [191; 569], CO2 remains for much longer [74; 85 - 103; 218], absorbing radiation released by Earth's surface [78 - 81]. This radiation absorption slows the rate at which Earth released energy, creating an energy imbalance, where Earth radiates energy less energy than Earth takes in from space. This energy imbalance causes Earth to warm [336 - 339]. Thus CO2 can cause long-term global warming, as CO2 has done in the past [74; 82].

Eventually, however, CO2-induced warming stops, in part because increased radiation release by a warming Earth leads to an equilibrium in which Earth's release of energy into space equals the energy entering Earth from space [299; 300]. So even though positive feedback can augment CO2-induced warming at current and near-future atmospheric CO2 levels [74; 566 - 568], runaway warming does not occur due to this increased radiation release. Runaway warming on Earth will not occur for at least another billion years [68; 83; 324 - 326; 340, page 90; 522, pages 17 and 24; 570], when solar radiation increases enough to drive a massive energy imbalance on Earth. This rebuts Evans [1, pages 17 and 18] and Ridley's [12; 350] attempt to link mainstream climate science to a prediction of imminent, runaway global warming.

Prediction #3: Specific humidity increases

Scientists can measure specific humidity using re-analyses that incorporate data from weather balloons (radiosondes), satellites, and other sources. Researchers use homogenization to correct the data for heterogeneities in these data sources [50; 117; 119; 134; 179; 302; 303]. There are at least six homogenized re-analysis groupings, with each grouping having different re-analysis versions and updates [135; 136]. The homogenized groupings are as follows:

  • European Centre for Medium-Range Weather Forecasts Interim re-analysis (ERA-I) [46; 137]
  • Modern-Era Retrospective analysis for Research and Applications (MERRA) [138; 139]
  • National Centers for Environmental Prediction / Climate Forecast System Re-analysis (CFSR or NCEP/CFSR) [140; 141]
  • Japan Meteorological Agency Re-analysis (JRA) [142 - 144]
  • National Centers for Environmental Prediction / National Center for Atmospheric Research re-analysis (NCEP, NCEP-2, or NCEP/NCAR) [145 - 147]
  • 20th Century Re-analysis (20CR) [148]

(You can access re-analysis data using a free online tool [308; 309]. I do not present such an analysis here and instead rely on published, peer-reviewed analyses that show better familiarity with possible issues with the re-analyses than I would.)

20CR only incorporates surface data [135; 136, page 1423; 148, section 8], and thus may not be very useful for examining mid- to upper tropospheric trends [136, page 1423]. And I cannot find any papers examining CFSR's tropical tropospheric trends for humidity, though CFSR shows increasing specific humidity in an unpublished analysis [308]. So let's set aside CFSR and 20CR. That leaves us with four re-analysis groupings. The following figure presents specific humidity measurements from analyses in these four groupings:

Figure 13: Trends in specific humidity for the ECMWF-Interim (ERA-I), MERRA, JRA-25, ERA-40, and NCEP re-analyses.  NCEP does not provide data at 200hPa. Data covers 1989 - 2010 for ECMWF-Interim, and from 1979 - 2010 for the remaining re-analyses. 95% confidence intervals are shown only for ECMWF-Interim [118]. The vertical axis represents altitude, with decreasing atmospheric pressure as altitude increases [164]. Three geographic regions are displayed: the tropics (20°S to 20°N), northern hemisphere ("NH"; 20°N to 50°N), and southern hemisphere ("SH"; 20°S to 50°S). 
Top panel: Specific humidity change per year. The trend is represented as a percent; i.e. the specific humidity trend divided by the average specific humidity for a given pressure level over the entire time period.
Middle panel: Short-term specific humidity change per unit of warming. The data is filtered to remove variations with a time-scale longer than 10 years. The trend is represented as a percent, as in the top panel.
Bottom panel: Long-term specific humidity change per unit of warming. The data is filtered to remove variations with a time-scale shorter than 10 years. The trend is represented as a percent, as in the top panel [118].  

I will focus on long-term, tropical upper tropospheric trends, since myth proponents focus on this region when discussing the hot spot's long-term, tropical upper tropospheric warming. Thus I will focus on the left-most portions of the top panel and of the bottom panel in figure 13 (if you are curious about the northern hemisphere trends vs. the southern hemisphere trends, then keep in mind that the troposphere warms more in the northern hemisphere than in the southern hemisphere [149 - 151]; also note that some radiosonde data suggests that ERA-I and MERRA under-estimate increasing specific humidity in the lower troposphere [311]).

Based on the top panel of figure 13, ERA-I, MERRA, and JRA-25 show some increased specific humidity in the tropical upper troposphere at around the 300hPa level, which represents a level at which the hot spot should occur (see "Myth: The Tropospheric Hot Spot does not Exist" for evidence of the hot spot at 300hPa). In contrast, ERA-40 and NCEP do not show increased specific humidity at this level [118, figure 1]. When one shifts to examining specific humidity increases per unit of warming in the bottom panel of figure 13, ERA-I, MERRA, JRA-25, and ERA-40 show increased humidity at 300hPa in the tropics. NCEP is the only re-analysis to show a decrease. So ERA-I, MERRA and JRA-25 support the prediction of increased specific humidity, while NCEP, and to a lesser extent ERA-40, argue against the prediction.

Some individuals defend NCEP's specific humidity reduction [6; 8; 165; 174; 225], including myth proponents David Evans [174] and Roy Spencer [6; 8], along with Garth Paltridge [165; 174], who views climate science in political terms [561]. Spencer claims that climate models failed to predict the specific humidity reduction, because the models may not accurately represent precipitation processes [6]. Spencer's defense fails, however, since the models make fairly accurate predictions about precipitation patterns [172]. And it is ERA-I, not NCEP, that better represents precipitation patterns [187; 189; 227; 228]. So Spencer's appeal to precipitation patterns would actually imply support for the specific humidity increases from ERA-I and climate models.

And even setting aside Spencer's reply, there are quite a number of reasons to reject NCEP's (and to a lesser extent, ERA-40's) reduction in specific humidity and instead accept the specific humidity increase depicted in ERA-I, MERRA, and JRA-25. These reasons include:

  • Satellite analyses indicate that specific humidity increased up to the upper troposphere [48; 52; 69; 128; 173; 537], including the tropical upper troposphere [52; 69; 128; 173]. However, ERA-I [220] and MERRA [221] over-estimate these increases over the past decade or so.
  • ERA-I and MERRA tend to perform better than NCEP [117; 179; 182 - 189; 222; 226 - 228], and its improved successor NCEP-2 [50; 226], when it comes to representing atmospheric phenomena, with a couple of short-term exceptions [304]. One should therefore consider using another re-analysis instead of NCEP-2 [180; 181], let alone using NCEP-2's more flawed predecessor NCEP [136, page 1422; 145]. In comparison to NCEP and NCEP-2, ERA-I shows better agreement with precipitation data [187; 189; 227; 228] and water vapor measurements from satellite analyses [50], global positioning system radio occultation (GPS RO) [50], and homogenized radiosondes [50; 117]. Homogenized radiosonde analyses also show greater agreement with tropospheric warming trends from ERA-I and MERRA than with trends from NCEP [179]. This fits with NCEP's history of under-estimating upper tropospheric warming [182; 184; 190]. So overall, NCEP seems less credible than ERA-I when it comes to upper tropospheric warming and humidity.
  • ERA-I updates and improves ERA-40 [46; 117; 136, page 1421].
  • Unlike ERA-I and MERRA, NCEP fails to accurately represent the specific humidity increase during the 1998 El Niño. And in comparison to NCEP, ERA-I and MERRA are better designed for examining long-term specific humidity trends [118].

The above points, along with the lines of evidence presented below, debunk Evans' defense of NCEP's decreasing specific humidity trend [2; 174]:

  • Evans argues in favor of the radiosonde data used in the NCEP analysis [2; 174], as does Spencer [6; 8]. However, this data contained heterogeneities [50; 117; 118; 119; 224; 302; 303; 311]. Homogenized radiosonde data shows greater agreement with ERA-I than with NCEP/NCEP-2 when it comes to water vapor [50; 117] and tropospheric warming trends [179]. So Evans once again abuses radiosonde data that contain large heterogeneities, while he side-steps better-validated data sources (see "Myth: The Tropospheric Hot Spot does not Exist" for another example of Evans doing this).
  • Since Evans wants to use the NCEP trend to argue against positive water vapor feedback increasing climate sensitivity, Evans appeals to other lines of evidence for low climate sensitivity. In particular, he appeals to ice-core-based estimates that indicate that CO2 levels increase after warming occurs [174]. But Evan's reply fails, as I discussed in the context of prediction #2: evidence from the distant past (including ice-core-based evidence) rebuts Evans claim of low climate sensitivity [74; 87 - 89].
  • Evans claims that the hot spot's absence supports NCEP's declining specific humidity trend, since increased specific humidity should amplify warming in the upper troposphere [174]. But Evans' point here fails, since multiple lines of scientific evidence show that the hot spot exists, as I discuss in "Myth: The Tropospheric Hot Spot does not Exist". Evans reasoning also fails because positive water vapor feedback does not cause the hot spot. Instead, latent heat release by condensing water vapor causes the hot spot [68 - 71; 133; 529; 530].

Prediction #4: Relative humidity remains fairly constant

Unlike specific humidity [46; 48; 52; 118; 173], relative humidity decreases during warm El Niño years such as 1998 [46; 127; 191], as illustrated in figure 14 below for ERA-I:

Figure 14: ERA-I and ERA-40 tropical (20°S to 20°N) average from 1979 - 2012, relative to a baseline of 1979 - 2001. Black lines represent ERA-I trends, while gray shading indicates ERA-40 trends. Panels a, b, and c display tropical upper tropospheric data at an atmospheric pressure level of 300hPa, while panel d shows data two meters above Earth's surface. Warming in panel c is greater than warming in panel d [46, figure 23 on page 348], indicative of a hot spot.

The ERA-40 trends in figure 14 are less relevant than the ERA-I trends, since ERA-I updates and improves ERA-40 [46; 117; 136, page 1421]. It remains unclear whether ERA-I's slight relative humidity increase (panel a, figure 14) represents a real trend, as opposed to resulting from a heterogeneity [46, page 349; 127].

To put the ERA-I data into context, let's examine the tropical upper tropospheric trends from some of the other re-analyses (I again exclude CFSR and 20CR for the reasons discussed in the section on prediction #3, though an unpublished CFSR analysis [308] shows a fairly constant relative humidity in the tropical upper troposphere until ~2002, when relative humidity sharply decreases):

  • ERA-I: Fairly constant relative humidity, with a possible slight increasing [46, figures 23 on page 948 and figure 25 on page 950] or slight decreasing trend [127], depending on how the ERA-I data is processed.
  • MERRA: Fairly constant relative humidity until ~2002, when relative humidity sharply decreases [46, figure 25 on page 950; 127].
  • JRA-55: Fairly constant relative humidity until ~2002, when relative humidity sharply increases [127].
  • NCEP: Relative humidity strongly decreases (both globally and in the tropics) [225].

So the re-analyses conflict with one another, especially after 2002. Examining other sources helps reveal which analysis trends are less plausible. Let's start with the satellites as a source.

Satellite analyses show that upper tropospheric relative humidity is not constant in every geographical region [52; 154], though relative humidity in the tropics is fairly constant [52; 69; 127], slightly increases with warming from 2000 - 2010 [154], or slightly decreases since 1979 [127]. These satellite analyses show much better agreement with ERA-I than with MERRA; the satellites analyses conflict even more with the JRA-55's strongly increasing relative humidity trend. And the satellite analyses show no strong increase nor decrease in relative humidity in ~2002 [127; 154]. This suggests that the MERRA's ~2002 decrease and JRA-55's ~2002 increase are heterogeneities, possibly resulting from how these two analyses process their relative humidity data sources [127, page 2885].

The satellite analyses therefore cast doubt on large, long-term increases or decreases in relative humidity, while supporting ERA-I's fairly constant trend in relative humidity [127]. This conclusion is further supported by the fact that homogenized radiosonde analyses also lack a sharp increase or decrease in 2002 [119; 223], though the analyses show long-term relative humidity increases in some regions [119] and decreases in other regions [223]. So overall, both the JRA-55 and MERRA trends appear less plausible than that ERA-I trend.

That leaves the NCEP trend. NCEP humidity trends should not be trusted, for the reasons I went over with respect to prediction #3. To make matters even worse for NCEP defenders, NCEP-2 shows a global, long-term increase in relative humidity [229, figure 7], contradicting the long-term decrease seen in NCEP [225] (though an unpublished NCEP-2 analysis shows a decrease in tropical relative humidity [308]). This casts further doubt on the NCEP analysis, since NCEP-2 was meant as an improvement on NCEP [136, page 1422; 145].

One might appeal to radiosonde data in order to defend the NCEP analysis. However, radiosonde relative humidity data contained heterogeneities [119; 302; 303]. Homogenized relative humidity analyses fit better with ERA-I, MERRA, and JRA-55, than with NCEP and NCEP-2 [226]. For instance, a homogenized radiosonde analysis from just outside the tropics shows fairly constant relative humidity, in conjunction with tropospheric warming and increasing specific humidity [302].

So the homogenized radiosonde data [226] and the satellite data [127] support the ERA-I's relative humidity analysis over NCEP's analysis. This rebuts Friends of Science's attempt to show that relative humidity decreased, based on their cherry-picking of the NCEP re-analysis and their exclusion of every other re-analysis [15]. The satellite results also place myth proponent Roy Spencer in an interesting position, since Spencer [6; 130] plans to use the same satellite data that supports the ERA-I's fairly constant relative humidity trend [127].

Predictions #5, #6, and #7: Positive feedback from water vapor, clouds, and reduced surface albedo

Friends of Science use a NASA Water Vapor Project (NVAP) analysis to argue that water vapor decreased since the 1980s [15]. However, NVAP could not be used for this purpose, given unresolved heterogeneities in the NVAP analysis [353; 354]. More homogeneous analyses reveal that water vapor levels increased during recent periods of global warming [46 - 52; 69; 117 - 124; 128; 155; 302; 310; 535; 537], with much of the increase occurring in response to man-made global warming caused by increased CO2 [48; 121; 122; 155]. This water vapor increase acted as a positive feedback that amplified warming [48; 51; 154; 155; 173; 302; 305; 535; 537]. So myth defenders are wrong when they claim that much of the predicted water vapor feedback did not occur [1 - 12; 15; 16; 129; 130; 152; 174; 244 - 246; 352, page 2].

Clouds also acted as a positive feedback [116; 154; 156 - 160; 207; 319; 497; 518], consistent with model-based predictions [116; 156; 157]. Cloud responses may therefore augment CO2-induced global warming [161; 207; 318]. This evidence debunks Friends of Science's claim that clouds act as a net negative feedback on warming [16], Spencer's criticism of positive feedback from clouds [11], and Ridley's objections to positive cloud feedback [244 - 247; 352, pages 2 and 3]. Spencer [6; 8] and Evans [152] link (supposed) model failure with respect to clouds with model failures with respect to precipitation. Yet models made fairly accurate predictions about precipitation patterns [172]. So Spencer and Evans' point does not fit with the evidence.

On another occasion, Evans' takes a more moderate position by stating that cloud responses are not well understood [2]. Scientists may have gone a long way in addressing Evan's concern, given the aforementioned evidence on positive cloud feedback [116; 154; 156 - 160; 207; 319; 497], models accurately representing cloud-based responses [116; 156; 157; 320; 321; 497; 563], improvements in how climate models simulate clouds [162; 497], and reduced uncertainty in estimates of cloud feedback [163; 497].

Earth's exposure to cosmic rays also did not change in a way consistent with cosmic rays reducing cloud cover and thereby causing recent global warming [166 - 169; 511; 527; 528; 562]. This rebuts Evans' reference to the idea that increased solar activity shielded Earth from cosmic rays, reducing cloud cover and Earth's albedo, and thus warming the Earth [1, page 18]. In contrast, melting ice reduced Earth's surface albedo and acted as a positive feedback amplifying warming [154], as observed in regions such as the Arctic [58; 76; 77; 315 - 317].

Taking stock of the predictions

So let's return to the seven model-based predictions for figure 3. How did the predictions fare, based on the evidence? Well:

  1. Well-supported: There should be a hot spot, indicative of a lapse rate reduction that acts as a negative feedback on warming [69; 154; 518; 529, figure 3c on page 5 and page 16; 534] (for more on this, see "Myth: The Tropospheric Hot Spot does not Exist")
  2. Well-supported: Estimates of climate sensitivity using measurements of past CO2 levels and warming, should be, on average, around the value given in figure 3.
  3. Well-supported: Water vapor levels increase during warming, with an increase in specific humidity.
  4. Evidence is mixed: Relative humidity stays fairly constant.
  5. Well-supported: Higher water vapor levels act as a positive feedback on warming.
  6. Well-supported: Clouds act as a net positive feedback on warming.
  7. Well-supported: Melting ice should reduce Earth's albedo and act as a positive feedback on warming.

So most of the model-based predictions were borne out, with the possible exception of prediction #4 regarding relative humidity. Hopefully the evidence on prediction #4 will become clearer in the future. And even given the issues with prediction #4, the evidence on predictions #2, #5, #6, and #7 show that positive feedback is occurring, consistent with the model-based predictions. This is true regardless of whether or not the hot spot exists, as per prediction #1. Therefore these observed positive feedbacks provide another line of evidence against the myth proponents' claim that the hot spot's absence implies muted positive feedback and less global warming [1 - 16; 73; 125, page 944; 129; 130; 152; 174; 572, page 35].

3. Posts Providing Further Information and Analysis

4. References

  1. David Evans': "The Missing Hotspot"
  9. Stefan Molyneux's video: "Climate Change in 12 Minutes - The Skeptic's Case"
  17. "Response of the large-scale structure of the atmosphere to global warming"
  18. "Comparing tropospheric warming in climate models and satellite data"
  19. "Water vapor and the dynamics of climate changes"
  20. "The physical basis for increases in precipitation extremes in simulations of 21st-century climate change"
  21. "Elevation-dependent warming in mountain regions of the world"
  22. Ray Pierrehumbert's 2012 video: "Tyndall Lecture: GC43I. Successful Predictions - 2012 AGU Fall Meeting"
  23. "Extended Summary of the Climate Dialogue on the (missing) tropical hot spot"
  25. "Climate change 2001: The scientific basis; Chapter 12: Detection of climate change and attribution of causes"
  26. "A reassessment of temperature variations and trends from global reanalyses and monthly surface climatological datasets"
  27. "Estimating changes in global temperature since the pre-industrial period"
  28. "Global temperature evolution: recent trends and some pitfalls"
  29. "Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends"
  30. Hansen et al.: "Global temperature in 2015"
  31. "Assessing recent warming using instrumentally homogeneous sea surface temperature records"
  32. "Assessing the impact of satellite-based observations in sea surface temperature trends"
  33. "A review of global ocean temperature observations: Implications for ocean heat content estimates and climate change"
  34. "Possible artifacts of data biases in the recent global surface warming hiatus"
  35. "Land surface temperature over global deserts: Means, variability, and trends"
  36. "On the definition and identifiability of the alleged “hiatus” in global warming"
  37. "Global land-surface air temperature change based on the new CMA GLSAT dataset"
  38. "An apparent hiatus in global warming?"
  39. "Global sea level linked to global temperature"
  40. "Twentieth-century global-mean sea level rise: Is the whole greater than the sum of the parts?"
  41. "Reassessment of 20th century global mean sea level rise"
  42. Walsh et al. 2016: "Tropical cyclones and climate change"
  43. "Economic losses from US hurricanes consistent with an influence from climate change"
  44. Knutson et al. 2010: "Tropical cyclones and climate change"
  45. "Recent intense hurricane response to global climate change"
  46. "Estimating low-frequency variability and trends in atmospheric temperature using ERA-Interim"
  47. "Spectrally dependent CLARREO infrared spectrometer calibration requirement for climate change detection"
  48. "Upper-tropospheric moistening in response to anthropogenic warming"
  49. "Trends in U.S. Surface Humidity, 1930–2010"
  50. "Global water vapor variability and trend from the latest 36 year (1979 to 2014) data of ECMWF and NCEP reanalyses, radiosonde, GPS, and microwave satellite"
  51. "Global water vapor trend from 1988 to 2011 and its diurnal asymmetry based on GPS, radiosonde, and microwave satellite measurements"
  52. "The radiative signature of upper tropospheric moistening"
  53. "Independent confirmation of global land warming without the use of station temperatures"
  54. "A global multiproxy database for temperature reconstructions of the Common Era"
  55. "Observational evidence for desert amplification using multiple satellite datasets"
  56. "Detection and analysis of an amplified warming of the Sahara Desert"
  57. "Desert amplification in a warming climate"
  58. "The central role of diminishing sea ice in recent Arctic temperature amplification"
  59. "Amplified Arctic warming and mid-latitude weather: new perspectives on emerging connections"
  60. "Negative elevation-dependent warming trend in the Eastern Alps"
  61. "Evidence of high-elevation amplification versus Arctic amplification"
  62. "Regional air pollution brightening reverses the greenhouse gases induced warming-elevation relationship"
  63. "Arctic amplification dominated by temperature feedbacks in contemporary climate models"
  64. "Sensitivity of a global climate model to an increase of CO2 concentration in the atmosphere"
  65. "High-latitude climate change in a global coupled ocean-atmosphere-sea ice model with increased atmospheric CO2"
  66. "Processes and impacts of Arctic amplification: A research synthesis"
  67. "The atmospheric response to three decades of observed Arctic sea ice loss"
  68. "Positive feedback in climate: stabilization or runaway, illustrated by a simple experiment"
  69. "Physical mechanisms of tropical climate feedbacks investigated using temperature and moisture trends"
  70. "Regional variation of the tropical water vapor and lapse rate feedbacks"
  71. "An assessment of direct radiative forcing, radiative adjustments, and radiative feedbacks in coupled ocean–atmosphere models"
  72. "Upper tropospheric warming intensifies sea surface warming"
  74. "Climate sensitivity in the geologic past"
  75. "What caused Earth's temperature variations during the last 800,000 years? Data-based evidence on radiative forcing and constraints on climate sensitivity"
  76. "Observational determination of albedo decrease caused by vanishing Arctic sea ice"
  77. "Estimating the global radiative impact of the sea ice–albedo feedback in the Arctic"
  78. "Infra-red absorption by carbon dioxide, with special reference to atmospheric radiation"
  79. "Observational determination of surface radiative forcing by CO2 from 2000 to 2010"
  80. "Increases in greenhouse forcing inferred from the outgoing longwave radiation spectra of the Earth in 1970 and 1997"
  81. "Infrared absorption by carbon dioxide, water vapor, and minor atmospheric constituents"
  82. "Atmospheric CO2: Principal control knob governing Earth’s temperature"
  83. "Increased insolation threshold for runaway greenhouse processes on Earth like planets"
  85. "Synchronous change of atmospheric CO2 and Antarctic temperature during the last deglacial warming"
  86. "Global warming preceded by increasing carbon dioxide concentrations during the last deglaciation"
  87. "CO2 as a primary driver of Phanerozoic climate"
  88. "Can the Last Glacial Maximum constrain climate sensitivity?"
  89. "Climate sensitivity estimated from temperature reconstructions of the Last Glacial Maximum"
  90. "Deep time evidence for climate sensitivity increase with warming"
  91. "Temperature change and carbon dioxide change":
  92. "CO2-forced climate thresholds during the Phanerozoic"
  93. "Atmospheric carbon dioxide linked with Mesozoic and early Cenozoic climate change"
  94. "Atmospheric CO2 and climate on millennial time scales during the last glacial period"
  95. "Atmospheric CO2 over the last 1000 years: A high-resolution record from the West Antarctic Ice Sheet (WAIS) Divide ice core"
  96. "High-resolution carbon dioxide concentration record 650,000–800,000 years before present"
  97. "Mode change of millennial CO2 variability during the last glacial cycle associated with a bipolar marine carbon seesaw"
  98. "Carbon isotope constraints on the deglacial CO2 rise from ice cores"
  99. "Iron fertilization of the subantarctic ocean during the last ice age"
  100. "Expression of the bipolar see-saw in Antarctic climate records during the last deglaciation"
  101. "Palaeoclimate: Windows on the greenhouse"
  102. "EPICA Dome C record of glacial and interglacial intensities"
  103. "Abrupt change in atmospheric CO2 during the last ice age"
  104. "Causal feedbacks in climate change"
  105. "Positive feedback between global warming and atmospheric CO2 concentration inferred from past climate change"
  106. "On the causal structure between CO2 and global temperature"
  107. "Comment on “The phase relation between atmospheric carbon dioxide and global temperature” Humlum et al. [Glob. Planet. Change 100: 51–69.]: Isotopes ignored"
  108. "Comment on “The phase relation between atmospheric carbon dioxide and global temperature” by Humlum, Stordahl and Solheim"
  109. "A time-series view of changing ocean chemistry due to ocean uptake of anthropogenic CO2 and ocean acidification"
  110. "Ocean acidification: The other CO2 problem" [2009; doi: 10.1146/annurev.marine.010908.163834]
  111. "History of seawater carbonate chemistry, atmospheric CO2, and ocean acidification"
  112. "Processes responsible for cloud feedback"
  113. "An analysis of the dependence of clear-sky top-of-atmosphere outgoing longwave radiation on atmospheric temperature and water vapor"
  114. "Thermodynamic control of anvil cloud amount"
  115. "An analysis of the short-term cloud feedback using MODIS data"
  116. "Evidence for climate change in the satellite cloud record"
  117. "Evaluation of atmospheric precipitable water from reanalysis products using homogenized radiosonde observations over China"
  118. "Trends in tropospheric humidity from reanalysis systems"
  119. "Trends in tropospheric humidity from 1970 to 2008 over China from a homogenized radiosonde dataset"
  120. "The use of ground-based GPS precipitable water measurements over China to assess radiosonde and ERA-Interim moisture trends and errors from 1999-2015"
  121. "Attribution of observed surface humidity changes to human influence"
  122. "Identification of human-induced changes in atmospheric moisture content"
  123. "A satellite-derived lower tropospheric atmospheric temperature dataset using an optimized adjustment for diurnal effects"
  124. "Sensitivity of satellite-derived tropospheric temperature trends to the diurnal cycle adjustment"
  125. "Taking greenhouse warming seriously"
  126. "Global and hemispheric temperature reconstruction from glacier length fluctuations"
  127. "An assessment of the consistency between satellite measurements of upper tropospheric water vapor"
  128. "Three decades of intersatellite-calibrated High-Resolution Infrared Radiation Sounder upper tropospheric water vapor"
  131. "Recent climatology, variability, and trends in global surface humidity"
  132. "Recent changes in surface humidity: Development of the HadCRUH dataset"
  133. "Global warming due to increasing absorbed solar radiation"
  134. "Classic examples of inhomogeneities in climate datasets"
  136. "Introduction to the SPARC Reanalysis Intercomparison Project (S-RIP) and overview of the reanalysis systems"
  137. "The ERA-Interim reanalysis: configuration and performance of the data assimilation system"
  138. "MERRA: NASA's modern-era retrospective analysis for research and applications"
  139. "MERRA-2: Initial evaluation of the climate"
  140. "The NCEP climate forecast system reanalysis"
  141. "The NCEP climate forecast system version 2"
  142. "The JRA-25 reanalysis"
  143. "The JRA-55 reanalysis: Representation of atmospheric circulation and climate variability"
  144. "The JRA-55 reanalysis: General specifications and basic characteristics"
  145. "NCEP–DOE AMIP-II Reanalysis (R-2)"
  146. "The NCEP–NCAR 50-Year Reanalysis: Monthly means CD-ROM and documentation"
  147. "The NCEP/NCAR 40-Year Reanalysis project"
  148. "The Twentieth Century Reanalysis Project"
  149. "Atmospheric changes through 2012 as shown by iteratively homogenized radiosonde temperature and wind data (IUKv2)"
  150. "Westward shift of western North Pacific tropical cyclogenesis"
  151. "Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC): A new data set of large-area anomaly time series"
  153. "Geographical distribution of climate feedbacks in the NCAR CCSM3.0"
  154. "Observations of climate feedbacks over 2000–10 and comparisons to climate models"
  155. "Anthropogenic greenhouse forcing and strong water vapor feedback increase temperature in Europe"
  156. "Cloud feedback mechanisms and their representation in global climate models"
  157. "A net decrease in the Earth’s cloud, aerosol, and surface 340 nm reflectivity during the past 33 yr (1979–2011)"
  158. "New observational evidence for a positive cloud feedback that amplifies the Atlantic Multidecadal Oscillation"
  159. "Impact of dataset choice on calculations of the short-term cloud feedback"
  160. "Thermodynamic constraint on the depth of the global tropospheric circulation"
  161. "Observational constraints on mixed-phase clouds imply higher climate sensitivity"
  162. "Are climate model simulations of clouds improving? An evaluation using the ISCCP simulator"
  163. "Reducing the uncertainty in subtropical cloud feedback"
  164. "Tropical Tropopause Layer" [doi:10.1029/2008RG000267]
  166. "Cosmic rays, solar activity and the climate"
  167. "Solar activity and the mean global temperature"
  168. "Dynamical evidence for causality between galactic cosmic rays and interannual variation in global temperature"
  169. "Global atmospheric particle formation from CERN CLOUD measurements"
  170. "Mechanisms for stronger warming over drier ecoregions observed since 1979"
  171. "Stronger warming amplification over drier ecoregions observed since 1979"
  172. "Observed heavy precipitation increase confirms theory and early models"
  173. "Water-vapor climate feedback inferred from climate fluctuations, 2003–2008"
  175. "Sensitivity of the attribution of near surface temperature warming to the choice of observational dataset"
  176. "Testing the robustness of the anthropogenic climate change detection statements using different empirical models"
  177. "A probabilistic quantification of the anthropogenic component of twentieth century global warming"
  178. "How natural and anthropogenic influences alter global and regional surface temperatures: 1889 to 2006"
  179. "A comparison of atmospheric temperature over China between radiosonde observations and multiple reanalysis datasets"
  180. ("Expert guidance" section)
  181. "Overview of current atmospheric reanalyses"
  182. "Impacts of atmospheric temperature trends on tropical cyclone activity"
  183. "Influence of tropical tropopause layer cooling on Atlantic hurricane activity"
  184. "Validating atmospheric reanalysis data using tropical cyclones as thermometers"
  185. "On the factors affecting trends and variability in tropical cyclone potential intensity"
  186. "Evaluation and intercomparison of cloud fraction and radiative fluxes in recent reanalyses over the Arctic using BSRN surface observations"
  187. "Evaluation of multireanalysis products with in situ observations over the Tibetan Plateau"
  188. "TropFlux: Air-Sea Fluxes for the Global Tropical Oceans – Description and evaluation against observations"
  189. "Representation of tropical subseasonal variability of precipitation in global reanalyses"
  190. "Response to Comment on "Contributions of Anthropogenic and Natural Forcing to Recent Tropopause Height Changes""
  191. (accessed June 12, 2017)
  192. "At what cost? Examining the social cost of carbon"
  193. "Causes of differences in model and satellite tropospheric warming rates"
  194. "Uncertainties in climate trends: Lessons from upper-air temperature records"
  195. "A quantification of uncertainties in historical tropical tropospheric temperature trends from radiosondes"
  196. "A response to the “Data or Dogma?” hearing"
  197. "Misdiagnosis of Earth climate sensitivity based on energy balance model results"
  198. "Reconciled climate response estimates from climate models and the energy budget of Earth"
  199. "Implications for climate sensitivity from the response to individual forcings"
  200. "Implications of potentially lower climate sensitivity on climate projections and policy"
  201. "Disentangling greenhouse warming and aerosol cooling to reveal Earth’s climate sensitivity"
  202. "Inhomogeneous forcing and transient climate sensitivity"
  203. "On a minimal model for estimating climate sensitivity"
  204. "Corrigendum to "On a minimal model for estimating climate sensitivity" [Ecol. Model. 297 (2015), 20-25]"
  205. "Projection and prediction: Climate sensitivity on the rise"
  206. "Spread in model climate sensitivity traced to atmospheric convective mixing"
  207. "Long-term cloud change imprinted in seasonal cloud variation: More evidence of high climate sensitivity"
  208. "Nonlinear climate sensitivity and its implications for future greenhouse warming"
  209. "Robust comparison of climate models with observations using blended land air and ocean sea surface temperatures"
  210. "Reconciling controversies about the ‘global warming hiatus’"
  211. "Reconciling warming trends"
  212. "Forcing, feedback and internal variability in global temperature trends"
  213. "Natural variability, radiative forcing and climate response in the recent hiatus reconciled"
  214. "Investigating the recent apparent hiatus in surface temperature increases: 2. Comparison of model ensembles to observational estimates"
  215. (Data for "Orbital and millennial Antarctic climate variability over the past 800,000 years")
  216. (Data for "High-resolution carbon dioxide concentration record 650,000–800,000 years before present")
  217. ("800,000-year Ice-Core Records of Atmospheric Methane (CH4)")
  218. "Orbital and millennial Antarctic climate variability over the past 800,000 years"
  219. "Evolution of global temperature over the past two million years"
  220. "An assessment of upper-troposphere and lower-stratosphere water vapor in MERRA, MERRA2 and ECMWF reanalyses using Aura MLS observations"
  221. "Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology"
  222. "A comparison of tropopause heights over China between radiosonde and three reanalysis datasets for the period 1979–2012"
  223. "Trends in upper tropospheric water vapour over the Tibetan Plateau from remote sensing"
  224. "Assessing the quality of humidity measurements from global operational radiosonde sensors"
  225. "Trends in middle-and upper-level tropospheric humidity from NCEP reanalysis data"
  226. "Trends in the frequency of high relative humidity over China: 1979–2012"
  227. "Consistency of temperature and precipitation extremes across various global gridded in situ and reanalysis datasets"
  228. "Comparison of NCEP-NCAR and ERA-Interim over Australia"
  229. "Long-term change of the atmospheric energy cycles and weather disturbances"
  230. "Linking emissions of fossil fuel CO2 and other anthropogenic trace gases using atmospheric 14CO2"
  231. "Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks"
  232. "Is the airborne fraction of anthropogenic CO2 emissions increasing?"
  233. "Global temperature evolution 1979–2010"
  234. "Deducing multidecadal anthropogenic global warming trends using multiple regression analysis"
  235. "Internal variability in simulated and observed tropical tropospheric temperature trends"
  236. "Revisiting radiosonde upper-air temperatures from 1958 to 2002"
  237. "Reexamining the warming in the tropical upper troposphere: Models versus radiosonde observations"
  238. "Assessing bias and uncertainty in the HadAT-adjusted radiosonde climate record"
  240. "New estimates of tropical mean temperature trend profiles from zonal mean historical radiosonde and pilot balloon wind shear observations"
  241. "Removing diurnal cycle contamination in satellite-derived tropospheric temperatures: understanding tropical tropospheric trend discrepancies"
  242. "Timing of atmospheric CO2 and Antarctic temperature Changes across Termination III"
  243. "Tightened constraints on the time-lag between Antarctic temperature and CO2 during the last deglaciation"
  248. "Temperature trends at the surface and in the troposphere"
  249. "30-year atmospheric temperature record derived by one-dimensional variational data assimilation of MSU/AMSU-A observations"
  250. "Polluted discourse: Communication and myths in a climate of denial"
  251. "Homogenization of temperature data: An assessment"
  252. "Benchmarking the performance of pairwise homogenization of surface temperatures in the United States"
  253. "Evaluating the impact of U.S. Historical Climatology Network homogenization using the U.S. Climate Reference Network"
  254. "On the reliability of the U.S. surface temperature record"
  255. "Homogenization of temperature series via pairwise comparisons"
  256. "Critically reassessing tropospheric temperature trends from radiosondes using realistic validation experiments"
  260. "Quantifying the effect of urbanization on U.S. Historical Climatology Network temperature records"
  261. "Urban heat island effects on estimates of observed climate change"
  262. "Assessment of urban versus rural in situ surface temperatures in the contiguous United States: No difference found"
  263. "Urbanization effects in large-scale temperature records, with an emphasis on China"
  264. "Urbanization effects on observed surface air temperature trends in north China"
  265. "Correcting urban bias in large-scale temperature records in China, 1980–2009"
  266. "Contribution of urbanization to warming in China"
  268. "Assessing atmospheric temperature data sets for climate studies"
  269. "A satellite-derived lower tropospheric atmospheric temperature dataset using an optimized adjustment for diurnal effects"
  270. "A less cloudy future: the role of subtropical subsidence in climate sensitivity"
  271. "Influence of urban heating on the global temperature land average using rural sites identified from MODIS classifications"
  272. "Global rural temperature trends"
  273. "The effects of urbanization on the rise of the European temperature since 1960"
  274. "An analysis on the urban heat island effect using radiosonde profiles and Landsat imagery with ground meteorological data in south Florida"
  275. "Urban and rural temperature trends in proximity to large US cities: 1951–2000"
  276. "Urbanization-related warming in local temperature records: a review"
  277. "A demonstration that large-scale warming is not urban"
  278. "Climate: Large-scale warming is not urban"
  279. "Urban heat island effect on annual mean temperature during the last 50 years in China"
  280. "Observed surface warming induced by urbanization in east China"
  281. "Tropospheric temperature trends: history of an ongoing controversy"
  282. "The reproducibility of observational estimates of surface and atmospheric temperature change"
  283. "Review of the consensus and asymmetric quality of research on human-induced climate change"
  284. "The effect of diurnal correction on satellite-derived lower tropospheric temperature"
  285. "Correcting temperature data sets"
  286. "Contribution of stratospheric cooling to satellite-inferred tropospheric temperature trends"
  287. "Satellite-derived vertical dependence of tropical tropospheric temperature trends"
  288. "A bias in the midtropospheric channel warm target factor on the NOAA-9 Microwave Sounding Unit"
  289. "Reply to “Comments on 'A bias in the midtropospheric channel warm target factor on the NOAA-9 Microwave Sounding Unit'"
  290. "Stratospheric temperature changes during the satellite era"
  291. "Climate change 2013: The physical science basis; Chapter 2: Observations: Atmosphere and Surface"
  292. "Overcoming endpoint bias in climate change communication: The case of arctic sea ice trends"
  293. "Debunking the climate hiatus"
  294. "Sensitivity to factors underlying the hiatus"
  295. "Tropospheric warming over the past two decades"
  298. "Climate change 2013: Working Group I: The physical science basis; Chapter 10; Detection and attribution of climate change: from global to regional"
  299. "The equilibrium sensitivity of the Earth’s temperature to radiation changes"
  300. "Feedbacks, climate sensitivity and the limits of linear models"
  301. "Climate change 2007: Working Group I: The physical science basis; Section Definition of climate sensitivity"
  302. "An analysis of tropospheric humidity trends from radiosondes"
  303. "A new approach to homogenize daily radiosonde humidity data"
  304. "Evaluation of NCEP–CFSR, NCEP–NCAR, ERA-Interim, and ERA-40 Reanalysis datasets against independent sounding observations over the Tibetan Plateau"
  305. "Enhanced positive water vapor feedback associated with tropical deep convection: New evidence from Aura MLS"
  306. "Intensification of landfalling typhoons over the northwest Pacific since the late 1970s"
  307. "Climate and atmospheric history of the past 420,000 years from the Vostok ice core, Antarctica"
  308. "Web-based Reanalysis Intercomparison Tool: Monthly/seasonal time series"
  309. "Web-Based Reanalysis Intercomparison Tools (WRIT) for analysis and comparison of reanalyses and other datasets"
  310. "Radiosonde-based trends in precipitable water over the Northern Hemisphere: An update"
  311. "Tropospheric moisture in the Southwest Pacific as revealed by homogenized radiosonde data: climatology and decadal trend"
  312. "Temporal homogenization of monthly radiosonde temperature data. Part II: Trends, sensitivities, and MSU comparison"
  313. "Toward elimination of the warm bias in historic radiosonde temperature records—Some new results from a comprehensive intercomparison of upper-air data"
  314. "Water vapor feedback and global warming"
  315. "Quantifying snow albedo radiative forcing and its feedback during 2003–2016"
  316. "Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008"
  317. "Evidence for ice-ocean albedo feedback in the Arctic Ocean shifting to a seasonal ice zone"
  318. "Variability in modeled cloud feedback tied to differences in the climatological spatial pattern of clouds"
  319. "A determination of the cloud feedback from climate variations over the past decade"
  320. "On the response of MODIS cloud coverage to global mean surface air temperature: Ts-mediated cloud response by MODIS"
  321. "Observational evidence for a negative shortwave cloud feedback in middle to high latitudes"
  322. "Effects of orbital decay on satellite-derived lower-tropospheric temperature trends"
  323. "Runaway and moist greenhouse atmospheres and the evolution of Earth and Venus"
  324. "Delayed onset of runaway and moist greenhouse climates for Earth"
  325. "The Runaway Greenhouse: implications for future climate change, geoengineering and planetary atmospheres"
  326. "Low simulated radiation limit for runaway greenhouse climates"
  327. Youtube: "The Runaway Greenhouse Effect - James Hansen"
  328. "Global monthly precipitation estimates from satellite-observed outgoing longwave radiation"
  329. "An observationally based energy balance for the Earth since 1950"
  330. "ENSO-driven energy budget perturbations in observations and CMIP models"
  331. "Advances in understanding top-of-atmosphere radiation variability from satellite observations"
  332. "Observed changes in top-of-the-atmosphere radiation and upper-ocean heating consistent within uncertainty"
  333. "The ENSO effects on tropical clouds and top-of-atmosphere cloud radiative effects in CMIP5 models"
  335. "Does vertical temperature gradient of the atmosphere matter for El Niño development?"
  336. "Anthropogenic and natural warming inferred from changes in Earth’s energy balance"
  337. "Insights into Earth’s Energy imbalance from multiple sources"
  338. "Reconciling estimates of ocean heating and Earth’s radiation budget"
  339. "Observed and simulated full-depth ocean heat-content changes for 1970–2005"
  340. "Annex C: Cross cutting theme paper 4: Assessing the science to address UNFCCC Article 2; A concept paper relating to cross cutting theme number four"
  341. "A simple rule to determine which insolation cycles lead to interglacials"
  342. "The role of orbital forcing in the early middle pleistocene transition"
  343. "Southern Ocean dust–climate coupling over the past four million years"
  344. "Late Neogene history of deepwater ventilation in the Southern Ocean"
  345. "The 100,000-year ice-age cycle identified and found to lag temperature, carbon dioxide, and orbital eccentricity"
  346. "Insolation-driven 100,000-year glacial cycles and hysteresis of ice-sheet volume"
  347. "Orbitally forced ice sheet fluctuations in snowball Earth"
  348. "Revealing the climate of snowball Earth from ?17O systematics of hydrothermal rocks"
  349. "Re-Os geochronology and coupled Os-Sr isotope constraints on the Sturtian snowball Earth"
  351. Youtube: "Satellite Scientist: Surface Temp Measures are More Accurate"
  352. "A lukewarmer's ten tests: What it would take to persuade me that current climate policy makes sense"
  353. "Weather and climate analyses using improved global water vapor observations"
  354. "The GEWEX Water Vapor Assessment: Results from intercomparison, trend, and homogeneity analysis of total column water vapor"
  355. "A new reconstruction of temperature variability in the extra-tropical Northern Hemisphere during the last two millennia"
  358. "New estimate of the current rate of sea level rise from a sea level budget approach"
  359. "Reassessment of 20th century global mean sea level rise"
  360. "The increasing rate of global mean sea-level rise during 1993–2014"
  361. "Unabated global mean sea-level rise over the satellite altimeter era"
  362. "An increase in the rate of global mean sea level rise since 2010"
  363. "Probabilistic reanalysis of twentieth-century sea-level rise"
  364. "Considerations for estimating the 20th century trend in global mean sea level"
  365. "Twentieth-century global-mean sea level rise: Is the whole greater than the sum of the parts?"
  366. "Recent global sea level acceleration started over 200 years ago?"
  367. "An anomalous recent acceleration of global sea level rise"
  368. "A 20th century acceleration in global sea-level rise"
  369. "Sea-level rise from the late 19th to the early 21st century"
  370. "The myth of the 1970s global cooling scientific consensus"
  371. "The idea of anthropogenic global climate change in the 20th century"
  372. Farmer and Cook: "Rebuttals to climate myths"
  373. "Reexamining climate change debates: Scientific disagreement or scientific certainty argumentation methods (SCAMs)?"
  374. "Climate change prediction: Erring on the side of least drama?"
  375. "Comparison of dryland climate change in observations and CMIP5 simulations"
  376. "Accelerated dryland expansion under climate change"
  377. "Twentieth century temperature trends in CMIP3, CMIP5, and CESM-LE climate simulations: Spatial-temporal uncertainties, differences, and their potential sources"
  378. "Evaluating CMIP5 models using AIRS tropospheric air temperature and specific humidity climatology"
  379. "Global warming estimates, media expectations, and the asymmetry of scientific challenge"
  380. "The new climate discourse: Alarmist or alarming?"
  381. "The language of denial: Text analysis reveals differences in language use between climate change proponents and skeptics"
  382. "Comment on “Climate Science and the Uncertainty Monster” by J. A. Curry and P. J. Webster"
  383. "Guidance note for lead authors of the IPCC Fifth Assessment Report on consistent treatment of uncertainties"
  391. "On the influence of carbonic acid in the air upon the temperature of the ground" (alternative version here)
  392. "Global warming due to increasing absorbed solar radiation"
  393. "On the observational determination of climate sensitivity and its implications"
  394. "The role of ENSO in global ocean temperature changes during 1955-2011 simulated with a 1D climate model"
  395. "Quantifying the consensus on anthropogenic global warming in the scientific literature"
  396. "Natural variations of tropical width and recent trends"
  401. "Increased thyroid cancer incidence in a basaltic volcanic area is associated with non-anthropogenic pollution and biocontamination"
  402. "Sunburn and p53 in the onset of skin cancer"
  403. "Conspiracy theories in science"
  404. "HIV denial in the Internet era"
  405. "Science denialism: Evolution and climate change"
  406. "How the growth of denialism undermines public health"
  407. "Denialism: what is it and how should scientists respond?"
  408. "How bad ideas gain social traction"
  409. "Consensus on consensus: a synthesis of consensus estimates on human-caused global warming"
  410. "Models, manifestation and attribution of climate change"
  411. "The Bray and von Storch 5th International Survey of Climate Scientists 2015/2016"
  412. "Does it matter if the consensus on anthropogenic global warming is 97% or 99.99%?"
  413. "Science and the public: Debate, denial, and skepticism"
  414. "A blind expert test of contrarian claims about climate data"
  415. "What makes weird beliefs thrive? The epidemiology of pseudoscience"
  416. "Immunizing strategies and epistemic defense mechanisms"
  417. "How Convenient! The epistemic rationale of self-validating belief systems"
  418. "Identifying human influences on atmospheric temperature"
  419. "Solar trends and global warming"
  420. "Small influence of solar variability on climate over the past millennium"
  421. "Evidence of recent causal decoupling between solar radiation and global temperature"
  422. "Assessing the observed impact of anthropogenic climate change"
  423. "Detection and attribution of climate change: a regional perspective"
  424. "Combinations of natural and anthropogenic forcings in twentieth-century climate"
  425. "A multimodel update on the detection and attribution of global surface warming"
  426. "The detection and attribution of climate change using an ensemble of opportunity"
  427. "Estimation of natural and anthropogenic contributions to twentieth century temperature change"
  428. "Attribution of observed historical near-surface temperature variations to anthropogenic and natural causes using CMIP5 simulations"
  429. "Attributing observed SST trends and subcontinental land warming to anthropogenic forcing during 1979–2005"
  430. "Quantifying anthropogenic influence on recent near-surface temperature change"
  431. "Evidence for external forcing on 20th-century climate from combined ocean-atmosphere warming patterns"
  432. "Observed 21st century temperatures further constrain likely rates of future warming"
  433. "CMIP5 historical simulations (1850–2012) with GISS ModelE2"
  434. "Climate variability and change since 850 C.E.: An ensemble approach with the Community Earth System Model (CESM)"
  435. "Uncertainties in the attribution of greenhouse gas warming and implications for climate prediction"
  436. "Application of regularised optimal fingerprinting to attribution. Part II: application to global near-surface temperature"
  437. "A fractal climate response function can simulate global average temperature trends of the modern era and the past millennium"
  438. "Evaluating global climate responses to different forcings using simple indices"
  439. "Causes of twentieth-century temperature change near the Earth’s surface"
  440. "Causes of climate change over the past 1000 years"
  441. "Detecting climate signals in the surface temperature record"
  442. "Detecting the influence of fossil fuel and bio-fuel black carbon aerosols on near surface temperature changes"
  443. "Drivers of decadal hiatus periods in the 20th and 21st centuries"
  444. "Statistically derived contributions of diverse human influences to twentieth-century temperature changes"
  445. "A new statistical approach to climate change detection and attribution"
  446. "A contribution to attribution of recent global warming by out-of-sample Granger causality analysis"
  447. "Testing for linear Granger causality from natural/anthropogenic forcings to global temperature anomalies"
  448. "Anthropogenic and natural causes of climate change"
  449. "Improved constraints on 21st-century warming derived using 160 years of temperature observations"
  450. "Climate of the past millennium: combining proxy data and model simulations"
  451. "The role of Atlantic Multi-decadal Oscillation in the global mean temperature variability"
  452. "The Atlantic Multidecadal Oscillation as a dominant factor of oceanic influence on climate"
  453. "Return periods of global climate fluctuations and the pause"
  454. "Clarifying the roles of greenhouse gases and ENSO in recent global warming through their prediction performance"
  455. "The Medieval Quiet Period"
  457. "Climate change 2007: Working Group I: The physical science basis; FAQ 6.1; What caused the ice ages and other important climate changes before the industrial era?"
  458. "Isolating the roles of different forcing agents in global stratospheric temperature changes using model integrations with incrementally added single forcings"
  459. "Stratospheric ozone change and related climate impacts over 1850–2100 as modelled by the ACCMIP ensemble"
  460. "Models versus radiosondes in the free atmosphere: A new detection and attribution analysis of temperature"
  461. "Attributing the forced components of observed stratospheric temperature variability to external drivers"
  462. "Human and natural influences on the changing thermal structure of the atmosphere"
  463. "Towards a physical understanding of stratospheric cooling under global warming through a process-based decomposition method"
  464. "Use of SSU/MSU satellite observations to validate upper atmospheric temperature trends in CMIP5 simulations"
  465. "Executive Summary: Temperature trends in the lower atmosphere - Understanding and reconciling differences"
  466. "Postmillennium changes in stratospheric temperature consistently resolved by GPS radio occultation and AMSU observations"
  467. "Troposphere-stratosphere temperature trends derived from satellite data compared with ensemble simulations from WACCM"
  468. "A model estimate of cooling in the mesosphere and lower thermosphere due to the CO2 increase over the last 3–4 decades"
  469. "Evidence of CO2-induced progressive cooling of the middle atmosphere derived from radio observations"
  470. "Ozone and temperature decadal trends in the stratosphere, mesosphere and lower thermosphere, based on measurements from SABER on TIMED"
  471. "Why CO2 cools the middle atmosphere-a consolidating model perspective"
  472. "Effect of trends of middle atmosphere gases on the mesosphere and thermosphere"
  473. "How will changes in carbon dioxide and methane modify the mean structure of the mesosphere and thermosphere?"
  474. "Temperature trends in the midlatitude summer mesosphere"
  475. "Role of carbon dioxide in cooling planetary thermospheres"
  476. "Proxy-based reconstructions of hemispheric and global surface temperature variations over the past two millennia"
  477. "Continental-scale temperature variability during the last two millennia"
  478. "Pacific ocean heat content during the past 10,000 years"
  479. "Inter-hemispheric temperature variability over the past millennium"
  481. "Ensemble reconstruction constraints on the global carbon cycle sensitivity to climate"
  482. "Robustness of the Mann, Bradley, Hughes reconstruction of Northern Hemisphere surface temperatures: Examination of criticisms based on the nature and processing of proxy climate evidence"
  483. "Global warming in an independent record of the past 130 years"
  484. "A reconstruction of regional and global temperature for the past 11,300 years" [further discussion at:]
  485. "Climate change 2013: Working Group I: The physical science basis; Information from paleoclimate archives"
  486. Youtube: potholer54's "23 -- Medieval Warm Period -- fact vs. fiction"
  487. "When is consensus knowledge based? Distinguishing shared knowledge from mere agreement"
  488. "European evidence based consensus on the diagnosis and management of Crohn's disease: definitions and diagnosis"
  489. "The Durban Declaration"
  490. Link for "Consensus Study Report":
  491. "An overview of the last 10 years of genetically engineered crop safety research"
  493. "The pivotal role of perceived scientific consensus in acceptance of science"
  494. "Highlighting consensus among medical scientists increases public support for vaccines: evidence from a randomized experiment"
  495. "The importance of assessing and communicating scientific consensus"
  496. "Manufactured scientific controversy: Science, rhetoric, and public debate"
  497. "Clearing clouds of uncertainty"
  498. "Beyond equilibrium climate sensitivity"
  499. "Slow climate mode reconciles historical and model-based estimates of climate sensitivity"
  500. "Proxy climatic and environmental changes of the past 1000 years"
  501. "UAH version 6 global satellite temperature products: Methodology and results"
  502. "The implications for climate sensitivity of AR5 forcing and heat uptake estimates"
  503. "Time series construction of summer surface temperatures for Alabama, 1883–2014, and comparisons with tropospheric temperature and climate model simulations"
  504. "Role for Eurasian Arctic shelf sea ice in a secularly varying hemispheric climate signal during the 20th century"
  505. "Application of global weather and climate model output to the design and operation of wind-energy systems"
  506. "Climate science and the uncertainty monster"
  507. "Climate-relevant land use and land cover change policies"
  508. "Land cover changes and their biogeophysical effects on climate"
  509. "Impacts of land use/land cover change on climate and future research priorities"
  510. "Reconstructing climatic and environmental changes of the past 1000 years: a reappraisal"
  511. "Solar influence on global and regional climates"
  512. "Spurious trends in satellite MSU temperatures from merging different satellite records"
  513. "A comparative analysis of data derived from orbiting MSU/AMSU instruments"
  514. "800-yr-long records of annual air temperature and precipitation over southern Siberia inferred from Teletskoye Lake sediments"
  515. "Scaling fluctuation analysis and statistical hypothesis testing of anthropogenic warming"
  516. "The effects of doubling the CO2 concentration on the climate of a general circulation model"
  517. "On the distribution of climate change resulting from an increase in CO2 content of the atmosphere"
  518. "Climate variability and relationships between top-of-atmosphere radiation and temperatures on Earth"
  519. "Recent warming reverses long-term Arctic cooling"
  520. "Relationship of tropospheric stability to climate sensitivity and Earth's observed radiation budget"
  521. "Global climate evolution during the last deglaciation"
  522. "Climate sensitivity, sea level and atmospheric carbon dioxide"
  523. "Satellite bulk tropospheric temperatures as a metric for climate sensitivity"
  525. "The influence of internal variability on Earth's energy balance framework and implications for estimating climate sensitivity"
  526. "Emergent constraint on equilibrium climate sensitivity from global temperature variability"
  527. "Investigation of cosmic ray–cloud connections using MISR"
  528. "Are there persistent physical atmospheric responses to galactic cosmic rays?"
  529. "Relationships between outgoing longwave radiation and diabatic heating in reanalyses"
  530. "The diabatic heat budget of the upper troposphere and lower/mid stratosphere in ECMWF reanalyses"
  531. "Impact of the Atlantic Multidecadal Oscillation (AMO) on deriving anthropogenic warming rates from the instrumental temperature record"
  532. "The impact of multidecadal Atlantic meridional overturning circulation variations on the Southern Ocean"
  533. "Climate science special report: A sustained assessment activity of the U.S. Global Change Research Program"
  534. "The atmospheric energy constraint on global-mean precipitation change"
  535. "Comparison of global observations and trends of total precipitable water derived from microwave radiometers and COSMIC radio occultation from 2006 to 2013"
  536. "The tropical lapse rate steepened during the Last Glacial Maximum"
  537. "An assessment of tropospheric water vapor feedback using radiative kernels"
  538. "Recent changes in freezing level heights in the tropics with implications for the deglacierization of high mountain regions"
  539. "Savor the cryosphere"
  540. "Modern and glacial tropical snowlines controlled by sea surface temperature and atmospheric mixing"
  541. "Future warming rates over the Hawaiian Islands based on elevation‐dependent scaling factors"
  542. "Changes in the sea surface temperature threshold for tropical convection"
  543. "Decrease of tropical cyclone genesis frequency in the western North Pacific since 1960s"
  544. "Dominant role of Atlantic Multi-decadal Oscillation in the recent decadal changes in western North Pacific tropical cyclone activity"
  546. "U.S. House Committee on Science, Space & Technology, 29 Mar 2017, Testimony of John R. Christy"
  548. "On the Existence of a “Tropical Hot Spot" & The Validity of EPA’s CO2 Endangerment Finding"
  549. "On the Existence of a “Tropical Hot Spot” & The Validity of EPA’s CO2 Endangerment Finding, Abridged Research Report, Second Edition"
  551. "McNider and Christy: Why Kerry is flat wrong on climate change"
  552. ("Measurement Errors" section)
  554. "Internal variability and disequilibrium confound estimates of climate sensitivity from observations"
  555. "Assessing uncertainty in estimates of atmospheric temperature changes from MSU and AMSU using a Monte-Carlo estimation technique"
  556. "Assessing the value of Microwave Sounding Unit–radiosonde comparisons in ascertaining errors in climate data records of tropospheric temperatures"
  557. "Climate change research in view of bibliometrics"
  558. "Climate sensitivity of the Community Climate System Model Version 4"
  559. "A consistent sea-level reconstruction and its budget on basin and global scales over 1958–2014"
  560. "Climate-change–driven accelerated sea-level rise detected in the altimeter era"
  561. "The politicised science of greenhouse climate change"
  562. "Solar influences on climate" (DOI: 10.1029/2009RG000282)
  563. "Observational evidence against strongly stabilizing tropical cloud feedbacks"
  564. "Changing atmospheric CO2 concentration was the primary driver of early Cenozoic climate"
  566. "Plio-Pleistocene climate sensitivity evaluated using high-resolution CO2 records"
  567. "Addendum: Plio-Pleistocene climate sensitivity evaluated using high-resolution CO2 records"
  568. "Atmospheric CO2 concentrations during ancient greenhouse climates were similar to those predicted for A.D. 2100"
  569. "Ranking the strongest ENSO events while incorporating SST uncertainty"
  570. "Earth and Mars: evolution of atmospheres and surface temperatures"
  571. "The faint young Sun problem" (DOI: 10.1029/2011RG000375)
  572. USCA Case #09-1322, Document #1312291