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Providing Insight
Into Climate Change
Climate Change Science Essay page 6

Water Vapour Feedback

Relative humidity is the fraction of water vapour in a small parcel of air relative to the total amount of water vapour the air could contain at the given temperature and pressure. All the General Circulation Models, also known as Global Climate Models (GCM), just set various evaporation and precipitation parameters to achieve approximately the result:   Relative humidity = constant.

Box 8.1 of 4AR Chapter 8 page 632 states:

The radiative effect of absorption by water vapour is roughly proportional to the logarithm of its concentration, so it is the fractional change in water vapour concentration, not the absolute change, that governs its strength as a feedback mechanism. Calculations with GCMs suggest that water vapour remains at an approximately constant fraction of its saturated value (close to unchanged relative humidity (RH)) under global-scale warming (see Section Under such a response, for uniform warming, the largest fractional change in water vapour, and thus the largest contribution to the feedback, occurs in the upper troposphere.

The assumption of constant relative humidity is not correct. Here is a graph of global average annual relative humidity at various elevations in the atmosphere expressed in millibars (mb) from 300 mb to 700 mb for the period 1970 to 2019. [Standard atmospheric pressure = 1013 mb. 1 mb = 1 hectopascal (hPa)] The data is from the NOAA Earth System Research Laboratory here.

Global relative humidity


This graph shows that the relative humidity has been dropping, especially at higher altitudes allowing more heat to escape to space. The curve labelled 300 mb is at about 9 km altitude, which is in the middle of the predicted (but missing) tropical troposphere hot-spot. This is the critical elevation as this is where radiation can start to escape without being recaptured. The average relative humidity at this altitude has declined by 11% (or 5.4 percentiles) from 1970 to 2019!

This is no logical reason to expect relative humidity to remain constant with increasing CO2 above the cloud layer. Relative humidity in a cloud is exactly 100% because the water droplets that make up the cloud are in equilibrium with the air. Likewise, relative humidity immediately above the oceans is 100%. The relative humidity in air parcels moving up over mountains will increase up to 100% causing rainfall. This saturation limit controls the average humidity in the atmosphere up to the top of the cloud layer. But the relative humidity at 400 mbars averages only 36% globally, or 30% in the tropics, and rarely gets anywhere near the saturation limit except in high thunderstorm clouds. The saturation limit therefore plays little role in determining the water vapour content of the upper atmosphere.

Doubling the amount of CO2 would increase temperatures by only about 1 degree Celsius if nothing else changed according to the IPCC. But the amount of water vapour will change in response to a CO2 induced temperature increase. Warmer air can hold more water vapour, so if relative humidity remains constant, the amount of water vapour increases with increasing temperatures. More water vapour, being a powerful greenhouse gas, would cause a further temperature increase, which is called a positive feedback. Most of the IPCCs projected warming is due to this water vapour feedback.

But the above graph shows falling relative humidity where the IPCC says changing water vapour content is most important. If relative humidity declines with increasing CO2 concentrations, the amount of water vapour in the upper troposphere may not increase, but might decline instead, resulting in a negative water vapour feedback.

Here is a graph of specific humidity, or the actual water vapour content, in grams of water vapour per kilogram of air, at the 400 mb level (about 8 km altitude).

specific humidity 400 mb


This shows that the actual water vapour content in the upper troposphere has declined to 2012 then increase through 2020. The trend indicates a decline of 7% (best fit line) from 1960 to 2020 at the 400 mb pressure level. The climate models predict that humidity will increase in the upper troposphere, but the data shows a large decrease to 2012; just where water vapour changes have the greatest effect on global temperatures.

The NASA water vapour project (NVAP) uses multiple satellite sensors to create a standard climate dataset to measure long-term variability of global water vapour. The Heritage NVAP merges data from several satellites and radiosonde water vapour products for the years 1988 to 2001. The graph below left was presents at the GEWEX/ESA Due GlobVapour workshop March 8, 2011 here. Water vapour content of an atmospheric layer is represented by the height in millimeters (mm) that would result from precipitating all the water vapour in a vertical column to liquid water.

zonal water vapor 500 to 300 hPa


The graph shows a significant decline in global water vapour in the atmosphere layer from 500 to 300 hPa, about 6 to 9 km altitude.

Animation water vapor


The animation above shows the amount of water vapor over the earth in the 500 to 300 mbar pressure layer. The Heritage NVAP global water vapour data (1988 to 2001) by layer is available from a NASA website here.  

The global annual average precipitable water vapour by atmospheric layer and by hemisphere is shown in the follow graph. 

precipitable water vapor by layer


The graph is presented on a logarithmic scale so the vertical change of the curves approximately represents the forcing effect of the change. The water content of the L1 layer, surface to 700 mb, is about 20 times greater than in the L3 layer, 500 to 300 mb, whereas the forcing effect of a change in the L3 is approximately 14.5 times the same change in the L1. From 1990 to 2001, the water vapour changed by: L3   -0.55 mm, L2   -0.57 mm, L1  +1.73 mm.  The decrease in L3 is equivalent to an 8 mm reduction in L1. The water vapor decline in the L2 and L3 layers overwhelms the forcing effect of the water vapor increase in the L1 layer, so the water vapor feedback is negative. The upper atmosphere (L2 and L3) water vapor content of the southern hemisphere is less than, and has declined more than the water vapor content of the northern hemisphere.

PWV by layer 1991

The above graph shows the precipitable water vapour by layer versus latitude by one degree bands.The highest water vapour content at each atmospheric layer occurs near the equator.

Dr. Ferenc Miskolczi performed computations using the HARTCODE line-by-line radiative code to determine the sensitivity of OLR to a 0.3 mm change in precipitable water vapor in each of 5 layers of the NVAP-M project.

Sensitivity of water vapor change on OLR


The results show that a water vapor change in the 500-300 mb layer has 29 times the effect on OLR than the same change in the 1013-850 mb near-surface layer. A water vapor change in the 300-200 mb layer has 81 times the effect on OLR than the same change in the 1013-850 mb near-surface layer.

The table below shows the precipitable water vapor for the three layers of the Heritage NVAP and the CO2 content for the years 1990 and 2001, and the change.


L1 near-surface

L2 middle

L3 upper






































Calculations show that the cooling effect of the water vapor changes on OLR is 16 times the warming effect of CO2 during this 11-year period. The cooling effect of the two upper layers is 5.8 times the warming effect of the lowest layer.

These results highlight the fact that changes in the total water vapor column, from surface to the top of the atmosphere, is of little relevance to climate change because the sensitivity of OLR to water vapor changes in the upper atmosphere overwhelms changes in the lower atmosphere. See here.

The NVAP-M project extends the analysis to 2009 and reprocesses the Heritage NVAP data.

The global total precipitable water vapour column from here is given below. Climate models assume that water vapor increases with increasing CO2 concentrations, but the NVAP-M data, using the best available satellite data, shows no increase in the total water vapor column.

global total precipitable water vapour


The most obvious way to determine the water vapour feedback due to CO2 changes, ie. the effect that CO2 changes have on upper atmosphere water vapour, is to plot the annual water vapour specific humidity versus CO2 concentrations. Annual data is used to eliminate the seasonal signal. The climate models show that the maximum predicted water vapour feedback is at about the 400 mbar pressure level, which is in the predicted but missing tropical troposphere hot spot as shown in the Heating of the Troposphere section.

It has been suggested that the early NOAA Earth System Research Laboratory data is unreliable due to poor coverage and calibration issues. Water vapour in air immediately above the ocean is in equilibrium with the water, so the air is near 100% relative humidity, regardless of the temperature. Water vapour over land is expected to vary proportionally with water vapour over the oceans, resulting in a near constant global average relative humidity near the surface with global warming. Data before 1960 is considered less reliable because the surface relative humidity is too high and would result in a declining relative humidity trend. The graph below shows the relative humidity near the surface at 1000 mbar pressure from the NOAA database from 1960 to 2020. The best fit trend line shows no trend confirming that the NOAA water vapour data from 1960 has no drying bias near the surface. Therefore, we use only the data from 1960 in the analysis.

RH near the surface


The graph below shows the annual specific humidity at the 400 mbar pressure level by three latitude bands.  Note that in the tropics there is a significant drying trend to 2013. There is very little trend in either the northern or southern mid-latitude regions.

SH at 400 mb by latitude range


The graph below shows the global average annual specific humidity at the 400 mbar pressure level versus CO2 concentration from 1960 to 2014.

specific humidity 400 mb vs CO2 global


The blue line shows that as CO2 increases, water vapour decreases, which is opposite to climate model predictions. The brown line shows what the specific humidity would have been at the actual measured temperature assuming the relative humidity was held constant at the 1960 value.

The graph below shows the annual specific humidity in the tropics from 30 degrees North to 30 degrees South latitude at the 400 mbar pressure level versus CO2 concentration from 1960 to 2014. This is in the middle of the predicted but missing tropical hot spot.

specific humidity 400 mb vs CO2 tropics


Note the greater discrepancy between the actual data and the constant relative humidity assumption in the tropics versus the discrepancy for the global average. In the topics, the specific humidity best fit line has declined by 0.12 g/kg, or 14%, from 1960 to 2014, while the global average specific humidity best fit line has declined by 0.05 g/kg. There is a remarkably high correlation in the tropics between specific humidity and CO2 concentration with a R-squared (R2) factor of 0.73. (The coefficient of determination R2 is a measure of how well the data fits the linear regression straight line.)  The brown line shows what the specific humidity would have been assuming a constant relative humidity. The actual climate model projections would show a much greater increase in specific humidity than indicated by the brown line because the climate models, in addition to the incorrect constant relative humidity assumption, also project the temperature increase in the upper atmosphere to be four times greater than the actual temperature trend determined by radiosonde and satellite measurements.

To compare this correlation to the climate model assumptions, the following graph shows the annual specific humidity in the tropics from 30 degrees North to 30 degrees South latitude at the 400 mbar pressure level versus temperature from 1960 to 2013. The climate models assume that water vapour changes only in response to a temperature change. If this were true, this graph should show a very strong correlation of increasing humidity with temperature. The graph is a phase space plot of the data points connected in time sequence. Over short time periods, an increase in temperature causes an increase in specific humidity. The annual data shows linear striations increasing from bottom left to top right, confirming that higher temperatures relate to higher specific humidity over short time intervals. But the overall trend is down, proving that specific humidity in the upper atmosphere declines with increasing temperatures over longer time scales.

specific humidity 400 mb vs temperature tropics

The graph not only shows a very poor correlation of specific humidity to temperature at the 400 mbar pressure level, but the trend is negative rather than strongly positive as assumed in the climate models. Increasing CO2 would initially cause a slight warming before considering a water vapour or cloud response. In climate models this warming causes an increase in upper atmosphere water vapour because the models assume that water vapour can only change in response to a temperature change. But the data shows that water vapour declines with increasing CO2 at a R2 correlation of 0.73 and shows that water vapour declines with temperature at a R2 correlation of only 0.027. Obviously specific humidity is not only responding to temperature changes. In the long-term, factors other than temperature determine upper atmosphere humidity. Temperature has little effect on the long-term upper atmosphere specific humidity contrary to climate model assumptions. CO2 emissions are causing a decline in upper atmosphere water vapour thereby allowing heat to escape to space. We believe that the long-term specific humidity in the upper atmosphere is determined by the maximum entropy principle, not temperature. The atmosphere is able to maximize the loss of heat to space subject to the constraint of the saturation limit in the lower atmosphere by decreasing the water vapour content in the upper atmosphere in response to increasing CO2 concentrations.

The NOAA humidity data is here in Excel format.

The graph below compares the IPCC AR5 hindcast/forecast mult-model mean to the NOAA total precipitable water vapour column anomally. It also shows that water vapour changes lag behind ENSO by about 3 months. The graph is from a blog comment by Bill Illis here.

ENSO lagged 3 month vs water vapor


The AGW theory is essentially the idea that an increase in CO2 will cause water vapour to increase causing an enhanced greenhouse effect. The graph shows that the models roughly agrees with observation to 1984, then the models significantly over estimates the total water vapour content of the atmosphere. The modellers apparently make no attempt to match observations after 1984.

Greenhouse gases absorb long-wave radiation, making the atmosphere opaque at those wave lengths.  Dr. Ferenc M. Miskolczi has developed a program called High-resolution Atmospheric Radiative Transfer Code (HARTCODE) that uses thousands of measured absorption lines and is capable of doing accurate radiative flux calculations. The calculations are independent of any greenhouse theory and contain no assumptions on how the greenhouse effect works, other than the fact that greenhouse gases absorb and emit radiation.

Water vapour is the most important greenhouse gas. HARTCODE simulations show that a 10% increase in CO2 concentration has the same effect as a uniform 1.80% change in water vapour on the out-going longwave radiation (OLR). A uniform 1% change in water vapor has 5.4 times the effect that a 1% change in CO2 has on OLR. A doubling of CO2 can be offset by a 12.3% reduction in H2O. This is shown in the following graph.

water vapor and CO2 on OLR


The radiation balance is determined at the top of the troposphere. The HARTCODE was used to determine the effect of changes of water vapour at the upper atmosphere versus near the surface. The graph below shows that changing the water vapour content in an atmospheric layer from the 300 mb to the 400 mb level has 30 times the effect on out-going long-wave radiation (OLR) as the same small change near the surface. So water vapour changes in the upper atmosphere are more important than changes in the lower atmosphere.

Effect of H2O by altitude on OLR


Optical depth is a measure of how opaque the atmosphere is to long-wave radiation, and so is a measure of the strength of the greenhouse effect. Miskolczi used HARTCODE to compute the optical depth from 1948 to 2008 using the measured CO2 content at Mauna Loa, Hawaii and the global average water vapour content from the NOAA Earth System Research Laboratory. The optical depths are calculated for each greenhouse gas and summed line-by-line across the electromagnetic spectrum. The resulting optical depth curve is a measure of the total greenhouse gases by effect over the last 61 years. The result is given below.

Optical depth change


The blue line of the graph shows the optical depth of the atmosphere with changing CO2 and water vapour content. The green line is the linear trend of this data which indicates an insignificant trend. The pink line is the effect of increasing CO2 with water vapour held constant. It shows a small upward trend. The difference of these trends is the water vapour feedback. Recall that the IPCC assumes that water vapour provides a large positive feedback, which implies that the green line would be increasing much steeper than the pink line. The HARTCODE results shows the opposite. It shows a large negative feedback, where the changing water vapour offsets most of the warming effect of CO2. 

The results show that the total effective amount of greenhouse gasses in the atmosphere has not significantly increased over the last 60 years.

The IPCC claims that the warming over the last half century was due to an increase in the quantity of greenhouse gases in the atmophere. But the HARTCODE result shows that CO2 replaces water vapour as a greenhouse gas, so it can't be responsible for global warming.

Here is the GCM error of specific humidity as reported by the IPCC's 4AR, Chapter 8-Suppl page 54:

specific humidity model error

This chart shows the multi-model mean fractional error, expressed as a percent (i.e., simulated minus observed, divided by observed and multiplied by 100). The observational estimate is from the 40-year European Reanalysis (ERA40, Uppala et al., 2005) based on observations over the period 1980-1999. The model results are from the same period of the CMIP3 20th Century simulations.

Note that the chart shows that the model's errors in specific humidity at the altitude where the largest contribution of the feedback is predicted to occur is between 20% to 40% too high!  If the specific humidity were corrected in the models at this critical altitude, the positive feedback would change to a strong negative feedback.

The strength of the greenhouse effect is undetermined in the current theory utilized by climate models. Parameters are just set to match the current temperatures. A new greenhouse effect theoryby Ferenc Miskolczi shows that the current greenhouse effect equations are incomplete because they do not include all the necessary energy constraints. When these constraints are included in a new theory, the strength of the GHE is determined analytically. The new theory presented in Miskolczi's paper shows that the atmosphere maintains a saturated greenhouse effect, controlled by water vapor content. There is a near infinite supply of greenhouse gases available to the atmosphere in the form of water vapor from the ocean to provide the greenhouse effect, but the atmosphere takes up only a portion of the water vapour it could hold due to energy balance constraints. Adding CO2 to the atmosphere just replaces an equivalent amount of water vapour to maintain an almost constant greenhouse effect and has negligible effect on global temperatures. See here for more information.


Cloud Feedback


Climate models are limited by our understanding of cloud formation. While scientists have a basic understanding of cloud formation, the details controlling how bright they are, how dense and how large they become is poorly understood. We lack the detailed understanding of clouds required to make accurate climate models. Clouds have a major role in climate by reflecting sunlight back into space, trapping heat, and producing precipitation.

As the Earth warms, there is more evaporation from the oceans, therefore more water vapour in the atmosphere available for cloud formation. But low clouds reflect sunlight back into space resulting in a strong cooling effect, negating most of the initial temperature increase.

Researchers at the University of Alabama in Huntsville (UAH) reported in August 2007 that individual tropical warming cycles that served as proxies for global warming saw a decrease in the coverage of heat-trapping [high altitude] cirrus clouds, says Dr. Roy Spencer, a principal research scientist in UAHuntsville's Earth System Science Center.

"All leading climate models forecast that as the atmosphere warms there should be an increase in high altitude cirrus clouds, which would amplify any warming caused by manmade greenhouse gases," he said. "That amplification is a positive feedback. What we found in month-to-month fluctuations of the tropical climate system was a strongly negative feedback. As the tropical atmosphere warms, cirrus clouds decrease. That allows more infrared heat to escape from the atmosphere to outer space."

"While low clouds have a predominantly cooling effect due to their shading of sunlight, most cirrus clouds have a net warming effect on the Earth," Spencer said. With high altitude ice clouds their infrared heat trapping exceeds their solar shading effect.  If computer models incorporated this enhanced cooling effect due to such a reduction of high clouds, "it would reduce estimates of future warming by over 75 percent," Spencer said.

 A review of Dr. Spencer's paper is in ScienceDaily here and the paper is available here.

The modelers only do crude analysis of feedback from satellite data. They observe that low clouds tend to decrease with warming and assumed that the warming caused the low clouds to decrease.  But cloud changes also cause temperatures to change.  When a cloud moves to block the Sun, temperatures fall.  The amount of clouds can change in response to a general ocean circulation change. So cloud changes are sometimes a cause of temperature change, and sometimes an effect of temperature change.  The false assumption that all cloud changes are the effect of temperature changes led modelers to vastly over estimate the feedback from clouds.

Dr. Roy Spencer has developed a method to separate cause and effect of cloud variability.  His technique is to plot quarterly average temperature and net flux readings from satellite data on a graph. These averages are plotted every day allowing the time evolution to be visualized.  He found that the plots have two types of patterns a set of linear striations with a common slope, and superimposed slower random spiral patterns.

To understand these patterns, Spencer has developed a simple computer model where he can specify the amount of feedback, and can input radiative forcing that might be caused by random cloud changes. The model shows that the slope of the linear striations corresponds to the feedback in the climate system.  These striations are due to changes in evaporation and precipitation which causes temperature changes. The temperature changes cause cloud changes, which is the cloud feedback signal we are looking for. The spiral patterns are caused by radiative forcing that might be due to changing the low cloud cover which varies the solar radiation warming the surface.

Spencer has analyzed the temperature-radiative patterns of the NASA Terra satellite.  The Terra data starts in March 2000, and its temperature-radiative plot is shown below.

NASA Terra satellite

The plot shows two types of patterns; linear striations and random spiral patterns. The usual interpretation of this data by climate modelers would be to use the best fit line which shows a slope of 0.7 W/m2/C, which is a very high positive feedback.  The actual feedback should be determined by the slope of the linear striations, which is 8 W/m2/C, which is a very high negative feedback.  A value of 3.3 W/m2/C corresponds to no feedback.  (No feedback means if the temperature of the atmosphere were uniformly increased by 1 C and nothing else changed, the top of the atmosphere would radiate 3.3 W/m2 more radiation to space.)  The feedback is observed to occur on shorter time scales in response to evaporation and precipitation events, which are superimposed upon a more slowly varying background of radiative imbalance due to natural fluctuation in cloud cover changing the rate of solar heating Earths surface.

The satellite data shows that over short time scales, clouds provide strong negative feedbacks. Spencer also analyzed the radiative flux and temperature variations from climate models used by the IPCC to determine if the short term negative feedback found in the satellite data is also applicable to long term feedback.  He found that the short term linear striations and the spiral patterns show up all 18 climate models that he analyzed. Spencer says the slopes of the linear striations do indeed correspond to the long term feedbacks diagnosed from these models response to anthropogenic greenhouse gas forcing.  This strongly suggests that the short term negative feedback shown in satellite data also applies to long term global climate change.

The feedback estimate for a hypothetical doubling of carbon dioxide, using the Terra satellite data gives a climate sensitivity of 0.46 C.

Changes in cloud cover cause changes in the amount of sunlight reaching the surface. The graph below shows the measurements of downward shortwave solar radiation at Potsdam, Germany during the period 1937 to 2010. The changes in solar radiation that reaches the surface mimics the changes in surface temperatures. Dr. Spencer suggests, "natural changes in cloud cover have caused the temperature changes, and cloud feedbacks are in reality negative rather than positive." See here.

Germany SW surface radiation

See here for a more detailed discussion of cloud feedbacks.



Aerosols are a suspension of fine particles in the atmosphere and include smoke, oceanic haze, smog, etc. The most significant aerosols from human sources that affect climate are sulphate and black carbon aerosols. Sulphate aerosols are primarily from the burning of fossil fuels and generally cause a cooling effect by reflecting solar radiation. Black carbon aerosols are from burning of biomass, and generally have a warming effect as it absorbs solar radiation.

Three recent papers discussed below show that change in aerosols account for a much larger portion of recent climate change than assumed in climate computer models, implying that the effect of CO2 is much less than what the climate models show. The sun is likely the main cause of the global warming of the 20th century with aerosol changes providing a significant contribution. When one combines the effects of aerosols with the Sun, ocean cycles and the urban heat island effects, there is no climate change left for CO2 to explain.

A paper published in Journal of Geophysical Research in June, 2009 shows that changes in the amount of aerosols in the atmosphere over the 20th century has had a much larger impact on global temperatures than they are given credit for in the climate computer models. Martin Wild of the Institute for Atmospheric and Climate Science, Zurich, Switzerland, shows that the increase of sulphate aerosols from fossil fuels caused a global solar dimming effect from the 1950s to the 1980s and contributed to global cooling. Air pollution control measures have reduced sulphate aerosols from the 1980s to the 2000s, resulting is solar brightening which significantly contributed to global warming. Air pollution controls allowed more solar radiation to warm the surface. However, on a global basis the effect of aerosols has been stable since 2000 and there has been no global warming this century. Wild says satellite data and Earthshine observations both show a stable planetary albedo after 2000. See here.

A paper published in the journal Science in July, 2009 reports that a careful study of satellite data show the assumed cooling effect of aerosols in the atmosphere to be significantly less than previously estimated. Gunnar Myhre of the Centre for International Climate and Environmental Research, Oslo, Norway, states that previous values for aerosol cooling are too high by as much as 40 percent, implying the IPCC's model sensitivity for CO2 are too high. The main anthropogenic aerosols that cause cooling are sulphate, nitrate, and organic carbon, whereas black carbon absorbs solar radiation. Myhre argues that since preindustrial times, black carbon soot particle concentrations have increased much more than other aerosols. See here.

NASA research published in Nature Geoscience in April, 2009 suggests that much of the atmospheric warming observed in the Arctic since 1976 may be due to changes aerosol particles. Scientists led by Drew Shindell of NASA found that the mid and high latitudes are especially responsive to changes in the level of aerosols. The research suggests aerosols likely account for 45 percent or more of the warming that has occurred in the Arctic during the last thirty years to 2005. (Arctic temperatures have been falling since 2005.) Since decreasing amounts of sulphates and increasing amounts of black carbon in the Arctic both encourage warming, temperature increases can be especially rapid. In the Antarctic, in contrast, the impact of sulphates and black carbon is minimized because of the continents isolation from major population centres. Antarctica temperatures have not increased over the last 30 years. See here.

A study published in March 2007 uses the longest uninterrupted satellite record of aerosols in the lower atmosphere, a unique set of global estimates funded by NASA. Satellite measurements show large, short-lived spikes in global aerosols caused by major volcanic eruptions in 1982 and 1991, but a gradual decline since about 1990. By 2005, global aerosols had dropped as much as 20 percent from the relatively stable level between 1986 and 1991.



Sun-blocking aerosols around the world steadily declined (red line) since the 1991 eruption of Mount Pinatubo, according to satellite estimates.

Credit: Michael Mishchenko, NASA. See here.

Since 2005 China has had a major effort to install state-of-the-art desulphurisation in its coal-fired plants installing more such units than the rest of the world combined. At the end of 2008, 66% of the Chinas coal-fired power plant capacity is equiped with flue gas desulphurisation. Today 75% of all desulphurisation systems are being installed in China. See here. The reduction of aerosols, especially over China, allows more sunlight through the atmosphere to warm the Earth's surface, contributing to global warming.

China's SO2 emissions have declined 14.3% from 2006 to 2011 according to the 2010 and 2011 reports on the state of the environment in China. See 2010 here and 2011 here.

China aerosols

Many studies have shown that aerosols associated with biological activity provide a negative feedback to climate change. An initial warming stimulates production of marine phytoplankton. These micro-organisms emit greater volumes of dimethyl sulphide, or DMS. The DMS is oxidized in the atmosphere creating acidic aerosols that function as cloud condensation nuclei. Tiny water droplets form around these aerosols leading to the creation of more and brighter clouds that reflect more incoming solar radiation back to space, thereby providing a cooling effect.

Land plants emit greater amounts of carbonyl sulfide gas in response to CO2 fertilization and temperature rise, which is transformed into sulfate aerosol particles, which have a cooling effect. See here for more information.

Cern's CLOUD chamberThe effect of aerosols on clouds is one of the largest sources of uncertainty in climate science. Recent experiments using the large CLOUD (Cosmics Leaving Outdoor Droplets) chamber at the CERN, the European Organization for Nuclear Research, show that organic vapours emitted by trees produce abundant aerosol particle in the atmosphere and these particle can rapidly grow to sizes big enough to seed cloud droplets. Climate modelers assumed that sulfuric acid is the key player in cloud formation so that the pre-industrial sky were less cloudy than now due to a lack of sulfur emissions from the use of fossil fuel. The new results show that modelers can’t offset as much CO2 forcing with aerosol cooling. The studies suggest that future temperature increases from greenhouse gas emissions will be much less than currently projected by climate models. The CLOUD experiments also finds that ions from galactic cosmic rays strongly enhance the production rate of the biogenic particles. The cosmic rays are modulated by changing solar activity, so they affect cloudiness and global temperatures. See the Sciencemag article here.


Climate Sensitivity

Climate sensitivity refers to the equilibrium change in global mean surface temperature following a doubling of the atmospheric CO2 concentration. Since pre-industrial times, atmospheric CO2 has increased from 280 ppmv to 400 ppmv. There are many estimates of climate sensitivity. When the Earth warms, it emits more infrared radiation to outer space. This natural cooling effect amounts to an average of 3.3 Watts per square meter for every 1 °C (W/m2/°C) that the Earth warms. This is often expressed in the reciprocal form as a gray body Earth sensitivity of 0.30 °C/(W/m2) as given here. According to the IPCC, a doubling of CO2 concentration would cause a radiation flux forcing of 3.71 W/m2, assuming no feedbacks. Therefore, a doubling of CO2 would cause 3.71 W/m2 / 3.3 W/m2/°C = 1.1 Celsius global surface temperature increase, assuming no feedbacks. This sensitivity assumes that the amount of water vapour, cloud cover, vegetation and ice cover does not change.

There is a wide range of estimates of the climate sensitivity with feedbacks. The IPCC assumes that clouds and water vapour cause a positive feedback, while other scientists say that clouds and water vapour cause a strong negative feedback.

The table below shows estimates of climate sensitivity from various sources. The climate sensitivity is shown as temperature change in degrees Celsius per doubling of CO2 concentration (C/CO2X2), and as temperature change per radiation flux (°C/W/m2). The last column shows the final estimated global surface temperature change from pre-industrial time to 2015 due to the human caused increase in atmospheric CO2 of 120 ppmv.

Climate Sensitivity Estimates


Climate Sensitivity

Climate Sensitivity

Temperature Change 280 - 400 ppm

  (°C/CO2X2) (°C/W/m2) (°C)











































The Bates 2016 estimate used a two zone energy balance model, where the radiative responses in the tropics (30 N to 30 S) and extratropics are estimated separately, and the dynamic heat transport from the tropics to the extratropics are explicitly estimated that depends linearly on the difference between the tropical and extratropical temperature perturbations.

The Lewis-Curry 2018 estimate used a one zone energy balance model using the climate forcings from the IPCC AR5 report and temperature histories from 1869 to 2016. This estimate assumes all of the warming was caused by anthopogenic forcing and does not account for the natural warming since the Little Ice Age or the urban heat island effect contamination of the land surface temperature record.

The Gregory estimate builds on the Lewis-Curry estimate by including the effects of natural climate change and the urban heat island effect included in the historical  temperature record.

The Spencer estimates are based on satellite temperature change observations. The Lindzen estimate is based on short term changes in outgoing longwave radiation as measured by satellites and sea surface temperature changes. The Schwartz and Chylek estimates both assume that the Sun has no effect on the temperature increase, and attributes the 20th century temperature change to CO2, modified by aerosols. This assumption greatly over-estimates the climate sensitivity due to CO2. The estimates also rely on the surface temperature record, which is contaminated by the urban heat island effect. 

The IPCC determined climate sensitivity by two methods:

  • comparing short term temperature variation with the radiation emission from the top of the atmosphere from satellite data, and
  • by interpreting indirect clues from the geological record

Climate sensitivity estimates used by the IPCC assumed that observed temperature variability caused the observed cloud variability. But causation also flows in the opposite direction with cloud variability causing temperature variability. A temperature change caused by cloud variability would be incorrectly interpreted as a positive feedback. This error causes the estimates to have a built-in bias toward high climate sensitivity. We know that the Sun can cause a change in lower cloud cover which cause a temperature change. The IPCC does not consider possible climate change from the Sun as its mandate is to investigate man-made climate change. The analysis of indirect clues from the geological record is very uncertain. The IPCC 4AR gives a range of climate sensitivity of 2 to 4.5 °C/W/m2, with a best estimate of 3 °C/W/m2. The IPCC 5AR gives a range of climate sensitivity of 1.5 to 4.5 °C/W/m2, with no best estimate due to a lack of consensus.

The following chart from a presentation by Dr. Richard Lindzen shows prediction results from a number of climate models and satellite data. The horizontal axis shows the change in sea surface temperatures per year as measured over various time intervals. The vertical axis is the change in outgoing longwave radiation at the top of the atmosphere as predicted by several climate models.

A positive correlation (slope from bottom left to top right) indicates that there is a negative feedback loop in SST change such that the hotter the sea gets the more heat is radiated away to space, which reduces the temperature rise. A negative correlation (slope from top left to bottom right) indicates that there is a positive feedback loop in that the atmosphere inhibits heat loss to space, which increases the temperature further.

Lindzen models vs ERBE observations


The first correlation labeled ERBE is the actual data as measured by the Earth Radiation Budget Experiment (ERBE) satellite. The slope of the line indicates a strong negative feedback which offsets the initial temperature rise. The eleven other correlations are from climate models. They all show negative correlations corresponding to positive feedbacks, which amplifies the initial temperature rise. All the models have the feedback in the wrong direction, confirming that the models are fundamentally wrong.

In the following graph, each climate model's predicted climate sensitivity is plotted versus the slope of the correlations shown above, which correspond to the amount of the temperature feedback.  The curved black line shows the relation between the feedback and the climate sensitivity to doubling the amount of carbon dioxide in the atmosphere. The large errors in the feedback factors cause a large range of predicted equilibrium climate sensitivities. The model results show the climate sensitivity could range from 1.3 °C to over 5 °C considering the range of feedback factors. But the ERBE satellite data tells a completely different story. It shows a climate sensitivity of 0.4 to 0.5 °Celsius. This small temperature change would not cause any problem and it there is no reason to be concerned about our CO2 emissions.  See here or here for further information.

Lindzen Climate Sensitivity

Ocean Oscillations

The oceans contain about 1000 times as much heat energy as does the atmosphere, so changes in ocean circulation patterns can have a large impact on global atmospheric temperatures. The oceans currents move enormous quantities of heat from the tropics to the exo-tropics, where heat can more easily radiate to space. There are several ocean oscillations identified that are important to climate. Over short time scales, the, El Nino Southern Oscillation (ENSO) dominates climate variations. The Pacific Decadal Oscillation (PDO) and the Atlantic Multi-decadal Oscillation (AMO) varies on roughly 60 year cycles. There may also be longer time scale cycles.

The suite of climate models used by the IPCC to predict future climates have been adjusted to generally match the increase in global average temperatures from 1975 to 2000, assuming that almost all of the temperature change was due to human caused greenhouse gas emissions. However, much of that temperature increase was due to the warming phase of ocean oscillations, rather than greenhouse gasses. 

Here is a brief description of only the three most important ocean oscillations:

El Niño - Southern Oscillation

The El Niño - Southern Oscillation (ENSO) is an ocean - atmosphere oscillation in the tropical Pacific ocean characterized by variation in the ocean surface temperatures in the eastern tropical Pacific ocean and air pressure variations in the western tropical Pacific ocean. The warm phase is El Niño (from Spanish meaning "the boy" or "Christ child") and the cool phase is La Niña (from Spanish meaning "the girl"). During the warm El Niño, there is high air pressure in the western Pacific and high sea surface temperatures in the eastern Pacific. It typically last for 6 to 18 months. During the cool La Niña, there is low air pressure in the western Pacific and low sea surface temperatures in the eastern Pacific. The Southern Oscillation refers to changes in sea level air pressure patterns in the Southern Pacific Ocean between Tahiti and Darwin, Australia. Sea surface temperatures are monitors in four regions show:

El Nino zones

During normal conditions, easterly trade wind blowing to the west caused warm water to pile up in the western Pacific such that the sea level is 0.5 m higher at Indonesia than in Peru. The winds push surface water to the west, then it descends and return at depth to the east. Cool deep water upwells near South America. The diagram below show the normal conditions.

ENSO neutral

During the El Niño phase, the trade winds weakens, which reduces the transport of water to the west and reduces the upwelling of cold deep water in the east Pacific. This makes the eastern Pacific sea surface temperature warmer than normal. During the La Niña phase, the trade winds are stronger than normal, cause more upwelling of cold water in the eastern Pacific.

The multivariate ENSO Index (MEI) is based on the six main observed variables over the tropical Pacific. These six variables are: sea-level pressure, zonal and meridional components of the surface wind, sea surface temperature, surface air temperature, and total cloudiness fraction of the sky. The graph below shows the MEI since 1979.

Multivariate ENSO index

See NOAA Jetstream here, ESRL here.

The Pacific Decadal Oscillation

The Pacific Decadal Oscillation (PDO) is a long-lived El Niño-like pattern of Pacific climate variability. The PDO index is defined as the leading principal component of North Pacific monthly sea surface temperature variability (poleward of 20N) after the global average sea surface temperature has been removed. It is not a measure of sea surface temperature, but rather its pattern. However, during its warm phase, the Pacific sea surface is warm along the west coast of North America, and it is there cool during the cold phase. The pattern of sea surface temperature (colours) and surface winds (arrows) are shown;

            Warm Phase                                                    Cool Phase

PDO Warm and Cool Phase

The PDO index is shown below.

PDO Index 1900 - 2013

The PDO changes every 30 years or so, making it important for climate change. A change in PDO induced ocean circulation and weather patterns can change global cloudiness, which can have a major effect on global warming because clouds reflect sunlight.

Dr. Roy Spencer writes, "a change in cloudiness associated with the PDO might explain most of the climate change we’ve seen in the last 100 years or more. For instance, after the “Great Climate Shift of 1977″ when the PDO went from its negative to positive phase, the Arctic region began to warm."

See JISAO here, NOAA here, Spencer here, Appinsys here.

Atlantic Multi-Decadal Oscillation

The Atlantic Multi-Decadal Oscillation (AMO) is a fluctuation in de-trended sea surface temperatures in the North Atlantic Ocean. AMO index is the detrended Atlantic sea surface temperature anomalies from the equator to 70 N. It is usually presented as annual or 10-year moving averages and has a cycle length of about 65 years.

Central and Eastern North America temperatures and droughts are correlated to the AMO. The two most severe droughts in recent history, during the dust bowl of the 1930s and the 1950s, occurred during the warm phase of the AMO. The Pacific Northwest tends to be wetter during the AMO warm phase.

AMO detrended annual

See ESRL here, create plots here.

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