{"id":252816,"date":"2023-04-14T13:16:46","date_gmt":"2023-04-14T11:16:46","guid":{"rendered":"https:\/\/climatescience.press\/?p=252816"},"modified":"2023-04-14T13:16:50","modified_gmt":"2023-04-14T11:16:50","slug":"more-on-cloud-reduction-co2-is-innocent-but-clouds-are-guilty","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=252816","title":{"rendered":"More on Cloud Reduction.\u00a0 CO2 is innocent but Clouds are guilty."},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

<\/p>\n\n\n\n

From Watts Up With That?<\/a><\/p>\n\n\n\n

By Charles Blaisdell PhD ChE<\/strong><\/p>\n\n\n\n

Abstract<\/strong><\/p>\n\n\n\n

This is a continuation of previous papers (1)<\/a> and (2)<\/a> on Cloud Reduction.  Further analysis of cloud data has revealed four new observations:<\/p>\n\n\n\n

    \n
  1.  Mount Pinatubo ash in the atmosphere and Amazonia deforestation may be seen in the cloud data.<\/li>\n\n\n\n
  2. A correlation of measured \u201cTemperature \u2013 Dew point Temperature\u201d, T-Td, to Cloud Cover was found.<\/li>\n\n\n\n
  3. The Temperature \u2013 Dew point Temperature variable suggests Cloud Reduction has been going on before 1975.<\/li>\n\n\n\n
  4. A simple model shows that Clouds either by reduced Cloud Fraction, decreased Cloud Albedo (lower reflectivity), or both can account for most of the observed Radiation and the associated Global Warming, GW.<\/li>\n<\/ol>\n\n\n\n

    CO2 is innocent but Clouds are guilty.<\/p>\n\n\n\n

    Introduction<\/strong><\/p>\n\n\n\n

    Climate change leaves a multi variable data finger print in the Atmosphere that is useful in drawing conclusions and testing theories.  The first of these finger prints is shown in Figure 1 where Cloud Cover, Temperature, Specific Humidity, and Relative Humidity (ground and 850mb) are shown on the same time scale.  None of Figure 1 graphs is a flat line any theory on GW should account for all these observations.  Figure 1 is NOAA data from \u201cNOAA Physical Science Laboratory\u201d, (3)<\/a> average Northern and Southern Hemisphere.  In Figure 1 note that relative humidity at 1000mb is much less sensitive than the relative humidity at 850mb(where cumulus cloud are).  Cloud Data is from Climate Explorer, <\/a>(11)<\/a><\/p>\n\n\n\n

     Another data finger-print data set is shown in Figure 2 from \u201cMet Office Climate Dashboard\u201d (\u201cHadISDH\u201d data), (4)<\/a>  (station and buoy data).  Note that the Met Data has a much better relative humidity correlation.  The relative humidity is significant variable in the Dew Point temperature calculation, Figure 2 (e).<\/p>\n\n\n\n

    Methods and data Calculations<\/strong><\/p>\n\n\n\n

    The raw data from Figure 1 and 2 graph sets was tabulated in excel.  The actual (vs published temperature anomalies) temperature needed for the Dew Point temperature was obtained by adding 13.7 to the temperature anomaly data.  The Dew Point was obtained from \u201cOnline Psychrometric Chart\u201d, (5)<\/a>  The HadISDH data for Relative Humidity (1000mb) was used for the T-Td calculation.<\/p>\n\n\n\n

    Pinatubo and Amazonia<\/strong><\/p>\n\n\n\n

                The cloud data from Climate Explorer, (11)<\/a> can be displayed by Northern and Southern Hemisphere, see Figure 3.  Mount Pinatubo erupted in 1991, Pinatubo\u2019s ash remained circulating in the atmosphere for 3 years, (6)<\/a> In Figure 3 the Pinatubo ash perturbation is seen in the Northern Hemisphere but not in the Southern Hemisphere graphs, as expected (Mount Pinatubo is in the Northern Hemisphere).  The Figure 1 finger print graphs all show some sign of a perturbation in that time period.  The ash could have made cloud formation a higher probability.  The Ash could also have been viewed by the satellite as clouds. (Or both.)  Since the Figure 2 graphs all show some expected response to more clouds it suggests the ash helped make more clouds. (Expected response to less clouds are: increased temperature, increased specific humidity, decreased relative humidity, and increased T-Td.   All reversed for cloud increase)<\/p>\n\n\n\n

    Logging in the Amazon rain forest has been going on since before 1970.  From 1977 to about 1998 most of the logging was a sustainable selective type of logging that left some forest canopy and allowed the forest to regrow and did not upset the natural water cycle.  Started in about 1998-1999 to 2004 a clear-cutting practice was started, (12)<\/a>. Clear-cutting destroyed any remaining forest canopy and drastically altered the water cycle.  Crop and pastureland replaced the rain forest after 2004.  Current farm and pasture land made from that deforestation is about 70,000km^2  (the size of West Virginia) but only 0.05% of the earths land mass.  This clear-cutting may have been captured in the Southern Hemisphere graph in Figure 4.  Does a plume of warm low humidity air rise out of this area and have a greater effect in the upper atmosphere (where the clouds are) than the area it came from?   We can see plumes from forest fires and cooling towers rising to cloud level then making a downwind plume much bigger than the area they came from.  We cannot see warm lower humidity air plumes. All the other graphs in Figure 2 move in the expected direction (less clouds) with the 1999 to 2004 Amazon event.  Figure 2 (d) the T-Td graph seems to capture the Amazon event.<\/p>\n\n\n\n

    Models of Urban Heat Islands, HI\u2019s, warm dry air plumes suggest plumes 2-4 times larger than the area they came from (7)<\/a><\/p>\n\n\n\n

    D\u00fcbal (8)<\/a> suggest the 1998-2004 inflection in the Cloud Fraction data is related to changes in the \u201cAtlantic Multi-decadal Oscillation\u201d, AMO.  Inspection of the AMO cycles in the 1998-2004 period reveals little activity; but the \u201cPacific Decadal Oscillation\u201d, PDO does have activity in that time frame.<\/p>\n\n\n\n

    Loeb (9)<\/a> shows a red spot (his Figure 3(e)) in humidity change pictures of South America where the Amazon clear-cuts would be, indicating a big humidity change occurred at that location.<\/p>\n\n\n\n

    T-Td an indicator of cloud cover<\/strong><\/p>\n\n\n\n

    The variable (Temp -Temp(dew)) is a common-sense variable representing how close the temperature is to the saturation point; which, should be related to the cloud point. (T-Td is nothing new, a T-Td correlation is used by pilots to get an idea of probable cloud ceiling, (10)<\/a> )  The cloud point is not an exact variable, aerosols, particulates, and cosmic rays can cause the cloud point to occur sooner while in a lack of these items the atmosphere can become supersaturated and delay cloud formation.  The variable Temp-Temp(dew) should be thought of as a probability function.  As the T-Td decrease the probability of cloud formation increases.<\/p>\n\n\n\n

    Figure 4 is graphical representation of the sensitivity of T-Td to atmosphere variables.  Note that the probability of cloud formation decreases with temperature and increasing relative humidity.  Also note the same probability of cloud formation can occur at equal T-Td\u2019s combination of lower temperature \u2013 low relative humidity and high temperature \u2013 high relative humidity.<\/p>\n\n\n\n

    The Cloud data in Climate Explorer, (11)<\/a>, has an oscillation with the seasons.  The oscillations are opposite each other in the two hemispheres; therefore, looking at each hemisphere separately reveals a good correlation of T-Td to cloud Cover over short time periods, see Figure 5.   Figure 5 groups the monthly data in years 1983-6 and years 2016-8 to show the T-Td vs Cloud Fraction line in the Northern Hemisphere is shifting with time.  Figure 6 uses the average yearly data to show the T-Td correlation for the 36 years of cloud data.  This correlation can be used in Climate Change models to test theories on how local changes in water balances can change global water balance.<\/p>\n\n\n\n

    The T-Td variable is used in Figure 7 to show Cloud Cover has been changing before 1975.<\/p>\n\n\n\n

    In the use of the T-Td variable it is noted that T-Td is confounded with Temperature and Relative Humidity and only slightly improves Cloud Cover correlations.  The higher sensitivity to relative humidity at 850mb (where the changing clouds are) suggest a T-Td at 850mb may be a better indicator of Cloud Cover.<\/p>\n\n\n\n

    Relating Change in Cloud Cover to the Earth\u2019s Short Wave Radiation.<\/strong><\/p>\n\n\n\n

                The following relies on the data and correlations published by Hans-Rolf D\u00fcbal and Fritz Vahrenholt <\/a>(8)<\/a>.   Norman G. Loeb,Gregory C. Johnson,Tyler J. Thorsen,John M. Lyman,Fred G. Rose,Seiji Kato (9)<\/a> data could just as easily been used.   Table 1 shows the data used for the following calculations.  In the Hans-Rolf D\u00fcbal and Fritz Vahrenholt (8)<\/a> paper the Cloudy Areas Radiation (CAR) is a calculated number from Total Radiation to the Earth (TR), Clear Sky Radiation (CSR), and the Cloud Fraction(Cover) (CC) :<\/p>\n\n\n\n

     (Eq 1)<\/strong> CAR = ( TR \u2013 (1-CC) * CSR ) \/ CC <\/p>\n\n\n\n

    The cloud fraction(cover) used by D\u00fcbal and the cloud fraction from Climate Explorer, (11)<\/a>, is show in Table 1.   The D\u00fcbal cloud fraction change is -0.1%\/decade for Years 2001 to 2020 and the Climate Explorer cloud fraction change is -0.75%\/decade for 1982 to 2018 both are statistically correct for their data source.   Figure 1(a) has a very low slope from 2001 to 2020.  Both will be analyzed.<\/p>\n\n\n\n

                This analysis will only deal with the Short Wave Radiation, SWR, in and out.  Table 2 shows the total earth albedo calculation of the CERES data and extrapolation to 1975.  The observed temperature anomalies from NASA data remain proportional to the albedo change at 0.27\u2019C\/Wm^2, in Table 1.  (The 0.27\u2019C\/Wm^2 assumption is used assuming LW out and the calculated EEI are proportional to the SW in. This assumption is only valid if CO2 is not affecting Long Wave out radiation (CO2 is innocent).  Figure 8 shows they are not exactly proportional but are (with in statistical error) statistically proportional.<\/p>\n\n\n\n

    In calculating the Cloud Fraction (Cover) contribution to albedo change the nature of the collected data gives three possible sources of radiation change:<\/p>\n\n\n\n

      \n
    1.  Clear Sky albedo change, Acs  (could be related to land albedo changes)<\/li>\n\n\n\n
    2. Cloudy Areas albedo change, Aca (could be cloud reflectivity, or cloud thinness, or cloud temperature as D\u00fcbal points out, or all)<\/li>\n\n\n\n
    3. Cloud Fraction (Cover) change, CC (clear sky vs cloudy areas)<\/li>\n<\/ol>\n\n\n\n

      The Earth\u2019s Short Wave Radiation, SWR, balance can be calculated two way:<\/p>\n\n\n\n

      (Eq 2) <\/strong>SWR(earth) = SWR(sun) * (1- Ae)                 <\/p>\n\n\n\n

      ( Eq 3)<\/strong> SWR(earth) = SWR(sun) *((1-Aca) * CC + (1-1Acs) * (1- CC))<\/p>\n\n\n\n

      Where:<\/p>\n\n\n\n

      SWR(sun) = Short Wave Radiation flux from the Sun from D\u00fcbal data.<\/p>\n\n\n\n

      Ae = total Albedo of the earth from D\u00fcbal data<\/p>\n\n\n\n

      Aca = Albedo of cloudy areas calculated from D\u00fcbal  or Climate Explorer data<\/p>\n\n\n\n

      Acs = Albedo of clear sky area from D\u00fcbal data<\/p>\n\n\n\n

      CC = Cloud fraction (cover) from D\u00fcbal or Climate Explorer data<\/p>\n\n\n\n

      Table 3 Model calculates the Radiation totals from Climate Explorer data with a 0.75%\/decade change in Cloud Cover.  Clear Sky albedo and Cloudy area albedo are calculated from D\u00fcbal data (note Cloudy area albedo mathematically becomes constant because Cloud Cover change is high enough to account for all the radiation change).  Table 4 Model calculates radiation from a much lower Cloud cover change (D\u00fcbal\u2019s) which mathematically causes the Cloudy area albedo to change more.  In either table the total SW radiation to the earth is about the same.  The pie charts below the Tables shows the big shift in where the radiation change is coming from; Change in Cloud Fraction or Change in Cloud albedo or both.  In either case clouds have caused a change in the earth\u2019s albedo that is not considered by the IPCC.<\/p>\n\n\n\n

                  The LW radiation from cloudy areas can also be calculated from Equation 3 and has the same change in slope as seen with the SW radiation.  In fact, some LW radiations in cloudy areas reverse slope when using Climate Explorer vs Dubal cloud cover data, not shown.<\/p>\n\n\n\n

      Extrapolation of D\u00fcbal data shows the uncertainty in the CERES data.<\/strong><\/p>\n\n\n\n

                  Figure 8 show the D\u00fcbal data (least squares fit) extrapolated to 1975.  Note the Short Wave to the Earth\u2019s surface line crosses the LW radiation out line.  This should not be the case.  The LW radiation out should always be less than the SW radiation to the earth.  As D\u00fcbal noted this could be the result of subtracting large members.  But, Figures like 8 add to the uncertainty of using the data.<\/p>\n\n\n\n

      Conclusions.<\/strong><\/p>\n\n\n\n

      The variable T-Td is a useful tool in predicting cloud fraction.  (not perfect but useful)<\/p>\n\n\n\n

      The Climate Explorer, (11)<\/a> data in Figure 3 may have captured the Mount Pinatubo ash in the atmosphere and the clear-cut deforestation in the Amazon.  If the clear-cut observation is true (related to the 1998-2004 reduction in cloud cover), it supports the theory proposed in (2)<\/a> that: if a localized change in the evapotranspiration, ET is big enough (including it\u2019s plume) the low relative humidity and warmer temperatures from that location (and ones like it) could mix in the atmosphere where clouds form and cause Cloud Reduction Global Warming, CRGW.<\/p>\n\n\n\n

      Simple models using cloud reduction and calculated cloud albedo data showed that either or both could be related to the Earth\u2019s albedo change observed by CERES data.   If low relative humidity \u2013 warm air plums from areas like the Amazon clear-cuts raise to cloud altitude in the atmosphere it could cause either cloud reduction or thinner less reflective clouds (lower albedo) or both at different conditions.  If this is true, cloud reduction and lower cloud albedo should be thought of as one variable.<\/p>\n\n\n\n

      Statistical uncertainty in the CERES and Cloud data seem to retard acceptance of alternative GW theories.<\/p>\n\n\n\n

      The IPCC should try this type of model in one of their Global Circulation Models, GCM\u2019s.<\/p>\n\n\n\n

      CO2 is innocent but Clouds are guilty.<\/p>\n\n\n\n

      \n\n\n\n

      Tables<\/strong><\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Table 1.\u00a0 Basic Data used for this paper.\u00a0 All cloudy area data is calculated from Clear Sky, All Sky, and Cloud Cover (Fraction) data.<\/em><\/p>\n\n\n\n

      <\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Table 2.\u00a0 Using equation 2 to calculate the SW radiation to the earth with D\u00fcbal total earth albedo.<\/em><\/p>\n\n\n\n

      <\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Table 3.\u00a0 Calculating the SW in distribution of radiation using Climate Explorer data. Of -.75%\/decade.\u00a0 Note this amount of Cloud Change is enough to make the Cloud albedo constant.<\/em><\/p>\n\n\n\n

      <\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Table 4.\u00a0 SW in radiation distribution using Dubal cloud fraction.\u00a0 Note this -1%\/decade cloud fraction change causes the cloud albedo to change over time.<\/em><\/p>\n\n\n\n

      <\/p>\n\n\n\n

      Pie chart for Table 3<\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      \u00a0\u00a0 Pie chart for Table 4<\/p>\n\n\n\n

      <\/p>\n\n\n\n

      Figures<\/strong><\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Figure 1.\u00a0 \u00a0Atmospheric Finger Print of Cloud data from Climate Explorer and atmospheric data from NOAA.\u00a0 Yellow area is years Mount Pinatubo ash was in the atmosphere and green area is years of clear-cut logging in Amazonia.<\/em><\/p>\n\n\n\n

      <\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Figure 2.\u00a0 Atmospheric Finger Print of Cloud data from Climate Explorer and atmospheric data from HadISDH.\u00a0 Yellow area is years Mount Pinatubo ash was in the atmosphere and green area is years of clear-cut logging in Amazonia.<\/em><\/p>\n\n\n\n

      <\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Figure 3.\u00a0 Cloud Fraction (Cover) for Northern and Southern Hemispheres from Climate Explorer.\u00a0 Note a perturbation in the Northern Hemisphere that may be related to the Mount Pinatubo ash circulating in the atmosphere but not in the Southern Hemisphere, as expected.\u00a0\u00a0 A strong perturbation in the Southern Hemisphere may be related to a switch to clear-cut logging in the Amazon rain forest and is not so strong in the Northern Hemisphere, as expected.<\/em><\/p>\n\n\n\n

      <\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Figure 4.\u00a0 Temperature vs Specific Humidity with constant Relative Humidity line (a psychrometric chart) showing the Temperature \u2013 Temperature(dew), T-Td.<\/em><\/p>\n\n\n\n

      <\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Figure 5.\u00a0 T-Td vs Cloud Fraction for the beginning and the end of the Cloud data. The normal seasonal variation of Cloud Fraction is shifting with time.<\/em><\/p>\n\n\n\n

      <\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Figure 6.\u00a0 T-Td vs Cloud Fraction.\u00a0 Useful correlation in Models (wish it was better R^2)<\/em><\/p>\n\n\n\n

      <\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Figure 7.\u00a0 T-Td vs time from HadISDH data.\u00a0 Cloud change has been going before the 1970\u2019s<\/em><\/p>\n\n\n\n

      <\/p>\n\n\n\n

      \"\"<\/figure>\n\n\n\n

      Figure 8.\u00a0 D\u00fcbal CERES data extrapolated to 1975.\u00a0 The three least squares fit of the data do not come together exactly in 1975 but they are statistically close.\u00a0 The LW radiation should always be less than the SW radiation.<\/em><\/p>\n\n\n\n

      <\/p>\n\n\n\n

      Bibliography<\/strong><\/p>\n\n\n\n

        \n
      1. Where have all the Clouds gone and why care? \u2013 Watts Up With That?<\/a>  <\/li>\n\n\n\n
      2. CO2 is Innocent but Clouds are Guilty.  New Science has Created a \u201cBlack Swan Event\u201d** \u2013 Watts Up With That?<\/a><\/li>\n\n\n\n
      3. Monthly Mean Timeseries: NOAA Physical Sciences Laboratory<\/a><\/li>\n\n\n\n
      4. Humidity | Climate Dashboard (metoffice.cloud)<\/a><\/li>\n\n\n\n
      5. Free Online Interactive Psychrometric Chart (flycarpet.net)<\/a><\/li>\n\n\n\n
      6. Mount Pinatubo: Eruption and Climate Change \u2013 Philippines Tour Guide (phtourguide.com)<\/a><\/a><\/a><\/a><\/li>\n\n\n\n
      7. Downwind footprint of an urban heat island on air and lake temperatures | npj Climate and Atmospheric Science (nature.com)<\/a><\/li>\n\n\n\n
      8. <\/a>Hans-Rolf D\u00fcbal and Fritz Vahrenholt<\/a>  web link:  Atmosphere | Free Full-Text | Radiative Energy Flux Variation from 2001\u20132020 | HTML (mdpi.com)<\/a><\/li>\n\n\n\n
      9. Norman G<\/a>. Loeb,Gregory <\/a>C. Johnson,Tyler J. Thorsen,John M. Lyman,Fred G. Rose,Seiji Kato  web link  Satellite and Ocean Data Reveal Marked Increase in Earth\u2019s Heating Rate \u2013 Loeb \u2013 2021 \u2013 Geophysical Research Letters \u2013 Wiley Online Library<\/a><\/li>\n\n\n\n
      10. \u201cRelative Humidity and Dew Point as a Function of Altitude \u2014 A Way to Estimate Cloud Ceilings\u201d  by David Burch Navigation Blog  web link: <\/a>David Burch Navigation Blog: Relative Humidity and Dew Point as a Function of Altitude \u2014 A Way to Estimate Cloud Ceilings<\/a><\/li>\n\n\n\n
      11. Climate Explorer: Select a monthly field (knmi.nl)<\/a>  go to \u201cCloud Cover\u201d  click \u201cEUMETSAT CM-SAF 0.25\u00b0 cloud fraction\u201d  click \u201cselect field\u201d at top of page on next page enter latitude (-90 to 90) and longitude (-180 to 180) for whole earth.<\/li>\n\n\n\n
      12. Selective logging leads to clear-cutting in Amazon (scidev.net)<\/a><\/li>\n<\/ol>\n\n\n\n

        <\/p>\n","protected":false},"excerpt":{"rendered":"

        CO2 is innocent but Clouds are guilty.<\/p>\n","protected":false},"author":121246920,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_crdt_document":"","advanced_seo_description":"","jetpack_seo_html_title":"","jetpack_seo_noindex":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[1],"tags":[691818431,691818432,691818076],"class_list":["post-252816","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-cloud-reduction","tag-clouds","tag-co2","has-post-thumbnail","fallback-thumbnail"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_likes_enabled":true,"jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paxLW1-13LG","jetpack-related-posts":[{"id":338762,"url":"https:\/\/climatescience.press\/?p=338762","url_meta":{"origin":252816,"position":0},"title":"VPD, Vapor Pressure Deficit a Correlation to Global Cloud Fraction?","author":"uwe.roland.gross","date":"08\/08\/2024","format":false,"excerpt":"The term \u201cVapor Pressure Deficit\u201d, VPD is not a new term it has been used in agricultural management for many years with correlations to plant growth and CO2 absorption. VPD is the difference between the atmospheric saturated water partial pressure, Psw, and the actual water vapor pressure, Pw. It is\u2026","rel":"","context":"In \"CHIMP6\"","block_context":{"text":"CHIMP6","link":"https:\/\/climatescience.press\/?tag=chimp6"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/08\/0-clouds-over-the-ocean.jpeg?fit=1200%2C750&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/08\/0-clouds-over-the-ocean.jpeg?fit=1200%2C750&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/08\/0-clouds-over-the-ocean.jpeg?fit=1200%2C750&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/08\/0-clouds-over-the-ocean.jpeg?fit=1200%2C750&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/08\/0-clouds-over-the-ocean.jpeg?fit=1200%2C750&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":362499,"url":"https:\/\/climatescience.press\/?p=362499","url_meta":{"origin":252816,"position":1},"title":"Cloud Reduction Global Warming, CRGW 101.\u00a0 A Competitive Theory to CO2 Related Global Warming","author":"uwe.roland.gross","date":"17\/01\/2025","format":false,"excerpt":"The Cloud Reduction Global Warming, CRGW, theory is a cascading natural process that only since about 1970 has become significant in Climate Change. The basic elements of CRGW theory have been around forever, it is the size (% of the earth affected) that has increased to the point that this\u2026","rel":"","context":"In \"carbon dioxide (CO\u2082)\"","block_context":{"text":"carbon dioxide (CO\u2082)","link":"https:\/\/climatescience.press\/?tag=carbon-dioxide-co%e2%82%82"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/01\/0clouds-vienna-scaled-1.jpg?fit=1200%2C832&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/01\/0clouds-vienna-scaled-1.jpg?fit=1200%2C832&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/01\/0clouds-vienna-scaled-1.jpg?fit=1200%2C832&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/01\/0clouds-vienna-scaled-1.jpg?fit=1200%2C832&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/01\/0clouds-vienna-scaled-1.jpg?fit=1200%2C832&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":374008,"url":"https:\/\/climatescience.press\/?p=374008","url_meta":{"origin":252816,"position":2},"title":"More Evidence on Vapor Pressure Deficit, Cloud Reduction, and Climate Change","author":"uwe.roland.gross","date":"07\/04\/2025","format":false,"excerpt":"In addition to WUWT, more and more web sites are mentioning cloud reduction as a source of climate change but offer no source of the cloud reduction.\u00a0 WUWT was the first to published this author\u2019s theory: Cloud Reduction Global Warming, CRGW,\u00a0(1).\u00a0 A critical part of CRGW theory is the relationship\u2026","rel":"","context":"In \"carbon dioxide (CO\u2082)\"","block_context":{"text":"carbon dioxide (CO\u2082)","link":"https:\/\/climatescience.press\/?tag=carbon-dioxide-co%e2%82%82"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/04\/0-clouds-33.jpeg?fit=1200%2C675&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/04\/0-clouds-33.jpeg?fit=1200%2C675&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/04\/0-clouds-33.jpeg?fit=1200%2C675&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/04\/0-clouds-33.jpeg?fit=1200%2C675&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/04\/0-clouds-33.jpeg?fit=1200%2C675&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":230680,"url":"https:\/\/climatescience.press\/?p=230680","url_meta":{"origin":252816,"position":3},"title":"CO2 is Innocent but Clouds are Guilty.\u00a0 New Science has Created a \u201cBlack Swan Event\u201d **","author":"uwe.roland.gross","date":"23\/11\/2022","format":false,"excerpt":"By Charles Blaisdell PhD ChE\u00a0\u00a0 **From web sources: \u201c\u2026\u00a0in 1697 the Dutch explorer Willem de Vlamingh discovered black swans in Australia, upending the belief\u201d (that all swans were white) \u201cand transforming how we understand the natural world.\u00a0 \u2026the phrase \u201cblack swan event\u201d came to refer to an event that suddenly\u2026","rel":"","context":"Similar post","block_context":{"text":"Similar post","link":""},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/11\/image-817.png?fit=1200%2C675&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/11\/image-817.png?fit=1200%2C675&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/11\/image-817.png?fit=1200%2C675&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/11\/image-817.png?fit=1200%2C675&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2022\/11\/image-817.png?fit=1200%2C675&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":366010,"url":"https:\/\/climatescience.press\/?p=366010","url_meta":{"origin":252816,"position":4},"title":"Underestimating Clouds: A Climate Mistake We Cannot Afford","author":"uwe.roland.gross","date":"13\/02\/2025","format":false,"excerpt":"Carbon dioxide (CO\u2082) has been predominantly portrayed as the chief culprit driving global warming. For decades, this misconception has guided international policies, prompted ambitious targets for reducing CO2\u00a0emissions and driven a shift from reliable and affordable energy resources like coal, oil, and natural gas toward problematic wind and solar sources.","rel":"","context":"In \"carbon dioxide (CO\u2082)\"","block_context":{"text":"carbon dioxide (CO\u2082)","link":"https:\/\/climatescience.press\/?tag=carbon-dioxide-co%e2%82%82"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/02\/0104901.jpg?fit=1200%2C750&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/02\/0104901.jpg?fit=1200%2C750&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/02\/0104901.jpg?fit=1200%2C750&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/02\/0104901.jpg?fit=1200%2C750&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2025\/02\/0104901.jpg?fit=1200%2C750&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":342979,"url":"https:\/\/climatescience.press\/?p=342979","url_meta":{"origin":252816,"position":5},"title":"Studies That \u2018Confirm\u2019 Humans Cause Climate Rely On Imaginary-World Conditions In Their Calculations","author":"uwe.roland.gross","date":"14\/09\/2024","format":false,"excerpt":"To claim that anthropogenic CO2 emissions drive global warming, radiative forcing modeling studies must assume 1) clouds do not ever change, 2) cloud albedo is constant, and\/or 3) clouds do not exist. None of these are real-world conditions.","rel":"","context":"In \"anthropogenic CO2\"","block_context":{"text":"anthropogenic CO2","link":"https:\/\/climatescience.press\/?tag=anthropogenic-co2"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/09\/0clouds-hd.jpg?fit=1200%2C675&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/09\/0clouds-hd.jpg?fit=1200%2C675&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/09\/0clouds-hd.jpg?fit=1200%2C675&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/09\/0clouds-hd.jpg?fit=1200%2C675&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/climatescience.press\/wp-content\/uploads\/2024\/09\/0clouds-hd.jpg?fit=1200%2C675&ssl=1&resize=1050%2C600 3x"},"classes":[]}],"_links":{"self":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/252816","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/users\/121246920"}],"replies":[{"embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=252816"}],"version-history":[{"count":13,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/252816\/revisions"}],"predecessor-version":[{"id":252843,"href":"https:\/\/climatescience.press\/index.php?rest_route=\/wp\/v2\/posts\/252816\/revisions\/252843"}],"wp:attachment":[{"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=252816"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=252816"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/climatescience.press\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=252816"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}