By Andy May

Some have speculated that the distribution of relative humidity would remain roughly constant as climate changes (Allen and Ingram 2002). Specific humidity can be thought of as “absolute” humidity or the total amount of water vapor in the atmosphere. We will call this amount “TPW” or total precipitable water with units of kg/m². As temperatures rise, the Clausius-Clapeyron relationship states that the equilibrium vapor pressure above the oceans should increase and thus, if relative humidity stays the same, the total water vapor or specific humidity will increase. The precise relationship between specific humidity and temperature in the real world is unknown but is estimated to be between 0.6 to 18% (10-90%ile range) per degree Celsius from global climate model results (Allen and Ingram 2002).

Carl Mears and colleagues (Mears, et al. 2018) have recently published a satellite microwave brightness record of TPW from 1988 to 2017 showing TPW, over the world’s ice-free oceans, increasing in lockstep with global mean temperature. This surprised me since Benestad (Benestad 2016), (Partridge, Arking and Pook 2009), (Miskolczi 2014) and (Miskolczi 2010) have previously reported that TPW, as computed from weather balloon data, has gone down recently, although their time periods were earlier and longer than the record shown in Mears, et al.

CO₂ does not have a large direct effect on temperature, Ramanathan and Coakley estimated that the direct effect of doubling CO₂, with no feedbacks, would cause temperatures to rise 1.2°C, which is no big deal (Ramanathan and Coakley 1978). Water vapor is a much more powerful greenhouse gas, it has twice the radiative effect (or “greenhouse” effect) of CO₂ according to Pierrehumbert (Pierrehumbert 2011) and transports thermal energy around the Earth in ocean currents and as latent heat in water vapor via atmospheric convection. If adding man-made CO₂ to the atmosphere somehow, directly or indirectly, causes the amount of atmospheric water vapor to increase, then this “feedback” could cause temperatures to rise more than we would see from adding CO₂ alone. Water vapor is the dominant greenhouse gas, according to (Soden, et al. 2005). Likewise, if adding CO₂ somehow caused water vapor to decrease or some reflective clouds to increase, the resulting negative feedback could cause temperatures to go down or stay the same. No one really knows how much water vapor feedback, or even if it is positive or negative, is occurring. For this reason, there is considerable interest in determining the current atmospheric water vapor trend.

Figure 1 shows the NCEP weather reanalysis version 1 (Kalnay, et al. 1996) total specific humidity converted to kg/m² up to about 8 km (300 mb) as an orange line. This value is based mostly upon weather balloon, surface data and after-the-fact analysis of weather using a global weather (not climate) forecasting model. The yellow line is from the NCEP reanalysis 2 global weather model (Kanamitsu, et al. 2002), it provides a total atmosphere TPW estimate, but only goes back to 1979. The gray line is the HADCRUT version 4 global surface temperature anomaly and the blue line is the RSS ice-free ocean TPW estimate from satellite microwave measurements. The RSS estimate is much higher presumably because it only uses samples over oceans that have no sea ice. The RSS data is only available from 1988 to present. Besides the problems with sea-ice, the RSS data has missing data due to rain events and the measurements used can be affected by clouds (Vonder Harr, Bytheway and Forsythe 2012).

The two NCEP analyses are global estimates from models that are calibrated using actual measurements, thus they are “reanalyses.” Their advantage over the RSS estimate is they are truly global and have values for every map grid. The reanalysis grid for NCEP reanalysis 1 for 2017 is shown in Figure 2. The NCEP reanalysis 1 model had several problems as described in (Kanamitsu, et al. 2002), but most have been fixed as discussed in the reanalysis 1 web site. The data for all of the TPW estimates displayed here was downloaded in May or June of 2018.

The specific humidity reanalysis results are not based solely on weather balloon radiosonde data, but the NCEP reanalysis 1 is more reliant on them than the reanalysis 2 project. Both projects also use land-based weather station data, ship data, aircraft and satellite data. Some have concluded that the radiosonde humidity data prior to 1973 and north of 50°N and south of 50°S is unreliable. Paltridge, et al. excluded this data and confirmed the negative overall trends in TPW, at least in the upper troposphere.

The RSS grids are much sparser as can be seen in Figure 3. The white areas (land- and ice-covered areas) of Figure 3 have no values which, in part, explains why the average RSS TPW values are so much larger than the NCEP values. The color scales used in all the maps are the same. Besides excluding areas containing sea-ice, areas with “moderate and high rain rates” are excluded from the RSS dataset, this introduces a systemic “non-rainy” bias to the dataset (Mears, et al. 2018). However, Mear’s and colleague’s dataset is probably a fairly accurate representation of TPW over the areas sampled. The problem with it is that the land areas and most of the polar regions are excluded and it only goes back to 1988. This is very unfortunate since the AMO began to turn significantly positive in 1988, which makes the RSS comparison to global temperature look “cherry-picked.”

The NCEP internet retrieval program would not allow me to download the reanalysis 2 TPW data for 2017 for some reason, but I did get the 2017 “canned” dataset from their website, it is shown in Figure 4. The data retrieval was done from here.

Compare Figure 4 to Figure 2, they are similar, except around the Pakistan/Tibet/China border. This shows as a cool, dry area in reanalysis 2 and as a wet anomaly in reanalysis 1. The NCEP reanalysis 2 shows more water vapor in the tropics than the reanalysis 1, this makes the reanalysis 2 averages higher. Further the reanalysis 2 TPW is for the whole atmosphere, whereas the reanalysis 1 TPW is only to 300 mbar (~8 km).

All three estimates shown in Figure 1 show an increase in TPW from around 1990 to the present, but the RSS increase is more dramatic. The increase in global average temperature begins in 1976, 14-16 years earlier. In Figure 2, we can see that the NASA CO₂ record shows a rapid increase in trend beginning even earlier in the 1950s.

Because of the large differences in the various estimates of TPW, the relationship with global temperature is difficult to see. Figure 6 is a close up of the RSS TPW and HADCRUT4.

In Figure 6, we see a close correlation between global temperatures and the RSS ocean TPW measurements from satellite microwave data. Even the details match well. In Figure 7 we see the longer NCEP reanalysis 2 record compared to HADCRUT4. Again, there is a close match in detail, but the trends from 1979 to 1992 are opposite.

Finally, in Figure 8, we see the NCEP reanalysis 1 record, which goes back to 1948, compared to HADCRUT4. The records match well from the present to the early 1980s and then begin to diverge, the divergence becomes extreme in the 1950s. Roy Spencer has blamed this on the poor-quality hygrometers used in weather balloons in the early days. Perhaps, but weather balloon data is not the only data used in these reanalyses. The NCEP reanalysis 2 results are almost certainly better than the reanalysis 1 results, but they are tantalizing short, beginning in 1979. We need 20-30 years more data to see if the influence of global mean temperature can be swamped by the influence of the AMO and other ocean cycles as suggested by the reanalysis 1 results.

While surface temperature is clearly a large factor influencing TPW over the short term, there may be other factors influencing it. Figure 9 compares the smoothed AMO index of Atlantic Ocean temperatures to NCEP R1.

So, if the TPW estimates in the 1950s are accurate enough, perhaps they reveal a strong influence of the AMO cycle on TPW? It is hard to tell since many have questioned the quality of the early hygrometer data.

Over the short term, the correlation between TPW over the oceans and temperature is good, see Figure 10A. This however, is certainly not surprising. Over the longer term, using the NCEP R1 data, it is poor. As seen in Figure 10B, the correlation deteriorates. The time period and the data selected matters.

The correlations between RSS TPW and NCEP R1 versus HADCRUT4 have similar slopes, which is surprising. Both show an increase of about 2.5 kg/m² (9%-13%) per degree of global temperature increase, but the NCEP reanalysis 1 plot suggests that there are actually two slopes, thus two trends and factors other than average surface temperature influencing TPW. Compare this estimate to the earlier cited specific humidity range of 0.6% to 18% per degree Celsius (Allen and Ingram 2002). The uncertainty in the amount of increase in TPW, due to global temperature changes is large.

TPW in the Upper Troposphere

As Partridge, et al. (Partridge, Arking and Pook 2009) have noted climate models predict that specific humidity will increase in the upper troposphere as global warming continues. Yet, this is not what is seen in the NCEP reanalysis 1 data, see Figure 11. Partridge, et al. have investigated more measurement levels and report that all levels above 850 hPa (~1.4 km) have a negative trend through 2007 in the tropics and southern midlatitudes. They also found that every level above 600 hPa (~4 km) in the northern midlatitudes has a negative trend.

In many ways this negative trend is counterintuitive since the world is warming and more evaporation is expected. A warming atmosphere should cause more evaporation and a higher TPW. From Paltridge, et al.:

“Negative trends in q [TPW] as found in the NCEP data would imply that long-term water vapor feedback is negative—that it would reduce rather than amplify the response of the climate system to external forcing such as that from increasing atmospheric CO₂.”

This was also the conclusion reached by Ferenc Miskolczi (Miskolczi 2014). Others, such as Roy Spencer and Richard Lindzen, have suggested that warmer temperature will cause more clouds, which will increase the albedo of the Earth and lower temperatures or reduce the rate of warming (provide negative feedback) as a result.

Conclusions and Discussion

The various estimates of total atmosphere TPW available do not agree with one another very well. Even the two NCEP estimates, both global, vary by over 18% and these estimates are 33% lower than the RSS ocean-only estimate. However, since about 1990 all the total atmosphere estimates trend upwards. Prior to 1990, the story is more complex. The longer NCEP reanalysis 1 estimate trends down from 1948 to 1975 in sync with the AMO, but different from the HADCRUT4 trend. All datasets agree that short term changes (<30 years) in surface global temperature have a positive (if small) influence on total atmosphere TPW, but it is not clear that long-term changes (>30 years) in TPW are related solely to global surface temperatures, they might be impacted more by ocean surface temperature cycles, such as the AMO.

The global climate models predict that global warming will increase upper troposphere specific humidity, but the weather balloon data shows a decline in specific humidity and in TPW in the upper troposphere. The humidity data declines in quality with altitude and lower temperatures, but even in the tropics where water vapor concentration is high at high altitudes, this trend persists. This also contradicts satellite data, but the ability of satellites to separate the signal of the upper troposphere water vapor from the lower is unclear. The accuracy of the specific humidity calculations in the upper troposphere is also unclear. However, both the NCEP reanalysis and the European reanalysis show a decline (Benestad 2016) and (Partridge, Arking and Pook 2009).

While there is great uncertainty in the amount of TPW in the whole atmosphere and in the upper troposphere, the importance of TPW and its trend is undeniable. In the tropics, at the lower levels of the atmosphere, the large amount of water vapor already traps nearly all the IR (infra-red radiation), so adding CO₂ to this atmosphere has little effect (Pierrehumbert 2011). But, in the upper troposphere, where IR is emitted to space and additional CO₂ or water vapor may make a difference, water vapor may be decreasing, at least according to NCEP reanalysis 1. Uncertainty abounds in this critical area of research and most important, what data we have is over too short a time period. Consider this quote from Pierrehumbert (Pierrehumbert 2011):

“For present Earth conditions, CO₂ accounts for about a third of the clear-sky greenhouse effect in the tropics and for a somewhat greater portion in the drier, colder extratropics; the remainder is mostly due to water vapor. The contribution of CO₂ to the greenhouse effect, considerable though it is, understates the central role of the gas as a controller of climate. The atmosphere, if CO₂ were removed from it, would cool enough that much of the water vapor would rain out. That precipitation, in turn, would cause further cooling and ultimately spiral Earth into a globally glaciated state. It is only the presence of CO₂ that keeps Earth’s atmosphere warm enough to contain much water vapor. Conversely, increasing CO₂ would warm the atmosphere and ultimately result in greater water-vapor content – a now well understood situation known as water vapor feedback.”

So, we see the crucial role assumed for water vapor in the entire man-made climate change hypothesis. CO₂ has only a minor role to play in warming the Earth by itself. It is only the assumed, but unmeasured, feedback from water vapor that allows a large impact on our climate to be predicted. Yet, as shown above, this assumed feedback cannot be measured with any accuracy with the data we have available. In fact, over climate time scales (>30 years) we cannot even be sure the feedback is positive. There is a strong correlation between temperature and total atmospheric water vapor concentration over short time periods, especially over the oceans from 1988 to 2017, when the AMO index was rising. But, it falls apart over longer periods of time and it is negative in the crucial upper troposphere. I can offer no solutions or great insights here, only questions and problems.

Andy May is a writer and author of “Climate Catastrophe! Science or Science-Fiction?” He retired in 2016 after 42 years in the oil and gas industry as a petrophysicist.

The R code and other information, including links to the original data, used to make the figures in the post can be downloaded here.

Works Cited

Allen, Myles, and William Ingram. 2002. “Constraints on future changes in climate and the hydrologic cycle.” Nature 419. https://www.climateprediction.net/wp-content/publications/nature_insight_120902.pdf.

Benestad, Rasmus. 2016. “A Mental Picture of the Greenhouse Effect.” Theoretical and Applied Climatology 128 (3-4): 679-688. https://link.springer.com/article/10.1007/s00704-016-1732-y.

Kalnay, E., M. Kanamitsu, R. Kistler, W. Collins, D. Deaven, L. Gandin, M. Iredell, et al. 1996. “The NCEP/NCAR 40-year reanalysis project.” Bulletin of the American Meteorological Society. https://journals.ametsoc.org/doi/abs/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2.

Kanamitsu, Masao, Wesley Ebisuzaki, Jack Woollen, Shi-Keng Yang, J. Hnilo, M. Fiorino, and G. Potter. 2002. “NCEP-DOE AMIP-II Reanalysis (R-2).” BAMS. https://journals.ametsoc.org/doi/abs/10.1175/BAMS-83-11-1631.

Mears, Carl, Deborah Smith, Lucrezia Ricciardulli, Junhong Wang, Hannah Huelsing, and Frank Wentz. 2018. “Construction and Uncertainty Estimation of a Satellite-Derived Total Precipitable Water Data Record Over the World’s Oceans.” Earth and Space Science. https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2018EA000363.

Miskolczi, Ferenc. 2014. “The Greenhouse Effect and the Infrared Radiative Structure of the Earth’s Atmosphere.” Development in Earth Science. http://www.seipub.org/des/paperInfo.aspx?ID=21810.

Miskolczi, Ferenc. 2010. “The Stable Stationary Value of the Earth’s Global Average Atmospheric Planck-Weighted Greenhouse-Gas Optical Thickness.” Energy and Environment. http://journals.sagepub.com/doi/abs/10.1260/0958-305X.21.4.243.

Partridge, G., A. Arking, and M. Pook. 2009. “Trends in middle- and upper-level tropospheric humidity from NCEP reanalysis data.” Theory of Applied Climatology. https://link.springer.com/article/10.1007/s00704-009-0117-x.

Pierrehumbert, Raymond. 2011. “Infrared radiation and planetary temperature.” Physics Today, January: 33-38. http://faculty.washington.edu/dcatling/555_PlanetaryAtmos/Pierrehumbert2011_RadiationPhysToday.pdf.

Ramanathan, V., and J. Coakley. 1978. “Climate Modeling Through Radiative-Convective Models.” Reviews of Geophysics and Space Physics 16 (4). https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/RG016i004p00465.

Soden, Brian, Darren Jackson, V. Ramaswamy, M. Schwarzkopf, and Xianglei Huang. 2005. “The Radiative Signature of Upper Tropospheric Moistening.” Science. https://www.researchgate.net/profile/Xianglei_Huang2/publication/7554296_The_Radiative_Signature_of_Upper_Tropospheric_Moistening/links/00b4953c458b4cc3c7000000.pdf.

Vonder Harr, Thomas, Janice Bytheway, and John Forsythe. 2012. “Weather and climate analyses using improved global water.” Geophysical Research Letters 39. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2012GL052094.