{"id":282115,"date":"2023-10-06T11:28:30","date_gmt":"2023-10-06T09:28:30","guid":{"rendered":"https:\/\/climatescience.press\/?p=282115"},"modified":"2023-10-06T11:28:34","modified_gmt":"2023-10-06T09:28:34","slug":"an-unexplored-source-of-climate-change-land-evapotranspiration-changes-over-time","status":"publish","type":"post","link":"https:\/\/climatescience.press\/?p=282115","title":{"rendered":"An Unexplored Source of Climate Change: Land Evapotranspiration Changes Over Time."},"content":{"rendered":"\n
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From Watts Up With That?<\/a><\/p>\n\n\n\n

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

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  1.  Abstract<\/strong><\/li>\n<\/ol>\n\n\n\n

    The earth\u2019s seasonal oscillations in cloud cover are related to global changes in evapotranspiration, ET, from the earth\u2019s tilt and more land surface in the Northern Hemisphere. This paper presents a simple model that explores the climate change possibility of changing the Land ET over the years.<\/p>\n\n\n\n

    Evapotranspiration, ET, is the Earth\u2019s combination of Evaporation and plant Transpiration of water. \u00a0It has long been assumed that the Earth\u2019s ET and cloud fraction did not change much over time (years). Resent data has shown that anthropic land changes can cause regional ET reduction[15]<\/a>\u00a0and\u00a0[16]<\/a>.\u00a0 (These ET changes are from what the ET is now vs what the ET of the same land would have been if the anthropic change were not made.)\u00a0 Resent data has also observed cloud fraction reduction over the last 45 years\u00a0[5]<\/a>.\u00a0 This paper presents a mathematical model that shows the relationship between Anthropic land related ET change and Cloud Reduction (and Increase). The Model creates one anthropic Special Parcel of land that represents all anthropic land changes like Urban Heat Islands, UHI\u2019s, forest to cropland, mining, etc. that can have ET change over time.\u00a0\u00a0 The Model steps through the natural water cycle of this Special Parcel and the whole Earth: \u00a01) The Special Parcel has a decreased Evapotranspiration, ET, vs its natural state and this decrease in ET (on average) will result (using proven vapor pressure equations) in air rising from this Special Parcel to have increased temperature and lower relative humidity.\u00a0 2) This higher temperature and lower relative humidity air rises to where lower clouds (cumulus) form in a plume equal to or greater than the area of the Special Parcel.\u00a0 3) The plume from this special parcel mixes with the rest of the atmosphere at cloud elevation and results in a small increase in temperature and lower relative humidity (calculated at ground level).\u00a0 4) Higher temperature and lower relative humidity reduce the probability of cloud formation (or lower cloud density\/reflectivity).\u00a0 5) Less Clouds more sun.\u00a0 6)\u00a0 More Sun, the hotter it gets and the more evaporation (some increase in Transpiration too), global ET increases. \u00a0See Figure 1. \u00a0This is how a reduction in ET (local) will cause an increase in ET (global).\u00a0 Another view: The Special Parcel over time has lowered the water put into the atmosphere that would have made clouds, less clouds more sun.\u00a0<\/p>\n\n\n\n

    \"\"
    Figure 1.\u00a0 Picture of the Model<\/figcaption><\/figure>\n\n\n\n

    The Special Parcel variables in the Model are:  1) How much ET change occurs in the Special Parcel area.  2) How big is the Special Parcel.  3) How big is the air plume.  The Model shows that current possible range of these variables could possibly account for a significant part of the observed Global Warming, GW.<\/p>\n\n\n\n

    The Model uses <\/a>Clausius\u2013Clapeyron related equations <\/a>in Psychometric charts to calculate the atmospheric variables step by step in the Earth\u2019s water cycle (these equations were chosen for their very accurate water vapor pressure equations).  The Model mimics the observed increase in Specific Humidity, decrease in Relative Humidity, decrease in Cloud Cover, and increase in Temperature as the Special Parcel variables are changed.  The Model uses annual data and correlations.<\/p>\n\n\n\n

    The Model is reversable.  That is, if ET of the Special Parcel is increased (water added) each step will have the opposite results.  The Model predicts that (no matter how the GW occurred, CO2, clouds, or albedo) the GW can be reversed by adding water to the atmosphere, or stopped  by stopping the decrease in land based ET.<\/p>\n\n\n\n

    II.  Back ground<\/strong><\/p>\n\n\n\n

    The interest in Cloud Cover (fraction) and\/or Cloud albedo role in Climate Change has been growing as CERES satellite data has been analyzed over the past 20+ years.  Researchers [19]<\/a>, [20]<\/a>, [21]<\/a>, [23]<\/a>, (plus this author and many more) seem to agree that Cloud Fraction and Cloud albedo are changing, resulting in a global reduction in albedo.  Researchers (above) don\u2019t agree on how much cloud related albedo is part of the current observed climate changes \u2013 a range of 25% to 100% of the GW have been proposed, but not confirmed by the IPCC.<\/p>\n\n\n\n

    The unanswered question is: what is causing the cloud change?  This paper shows how a localized (land) anthropic reduction in ET can decrease Cloud Cover and increase global ET.  This seems to defy logic, a decrease causing and increase, or an increase causes a decrease?   Read the Model to see how.<\/p>\n\n\n\n

    UHI\u2019s and other land changes in surface albedo have already been evaluated for their contribution to GW and found to be, less than 15% [2]<\/a>.  The low relative humidity air rising from these areas has not been evaluated for its relationship to Cloud Cover, this Model will do that.<\/p>\n\n\n\n

    The IPCC is also working on models that address cloud reduction, CHIP6, which uses Radiative Forcing temperature change to reduce cloud fraction, [26]<\/a>.   The Model presented here is an alternative or addition to the IPCC efforts.<\/p>\n\n\n\n

    More recently two methods have been proposed to counter the current climate change: 1. Increase the reflectivity of clouds by adding calcium carbonate to the clouds (30)<\/a> to make them thicker and more reflective (Making Clouds thicker has the same total earth albedo effect as increasing cloud cover, [3]<\/a>)  and 2. Add \u201cartificial sunshade\u201d  to the upper atmosphere (29)<\/a> to reflect more sun. These two proposals are the same principle as the Model\u2019s adding water to the Special Parcels.<\/p>\n\n\n\n

    More info on cloud reduction by this author can be found at: <\/a>[1]<\/a>, <\/a>[2]<\/a>, and <\/a>[3]<\/a>.<\/p>\n\n\n\n

    III.  Model Variable<\/strong><\/a>s<\/strong><\/p>\n\n\n\n

    1.  Model Variable: ET (SH)<\/strong><\/p>\n\n\n\n

    ET is usually expressed as a rate (water mass transport per unit of time).  Since this water is in air, the concentration of water (or specific humidity, SH, (mass of water\/mass of dry air)) is part of the ET rate equation.  Thus, ET is proportional to SH:<\/p>\n\n\n\n

                                                                  ET ~ SH                                                         Eq 1<\/p>\n\n\n\n

    In the earth\u2019s water cycle the ET rate is hard to measure, but we know what the Specific Humidity, SH, is.  So that we can assume a -10% change in SH is a -10% change in ET rate on an annual global basis.  On a shorter time, other variables are also changing like wind speed or up-draft more information is needed to determine ET.   Using annual data, the other ET variables are relatively constant only SH is changing.   For this model we will use a % ET change is equal to a % SH change (not the actual value just the change).  This should be true for all conditions except the up-draft on an annual basis.  The model will assume that up-draft ET is proportional to the plume size variable: the faster the up-draft rate the larger the plume.<\/p>\n\n\n\n

    Lower ET (lower SH) air is produced in an UHI by having less water available for evaporation or transpiration (part of the normal rain water became run-off in a UHI) and a slightly lower albedo.  If water is not returned to the atmosphere clouds will reduce.  ET change is current vs original (virgin) land.<\/p>\n\n\n\n

    Figure 2 is an example of ET change in one day in Houston Tx showing a\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 -14% reduction in SH in \u00bd day.\u00a0 This graph is obtained by using the hourly Temperature and Relative Humidity and calculating the Specific Humidity from Eqs 2 and 4.1.\u00a0 This lower ET air produces a plume, if the plume is the same size as Houston, then the daily ET is -7% (the plume is there for half a day) of the -14%.\u00a0 If the plume is 4 time the area of Houston the daily ET is 14*4*1\/2 or -28%.\u00a0 The daily weather data does not tell us how big the plume is.\u00a0 Figure 1 is only one example to daily ET change.\u00a0 When it is raining or overcast the ET change is near 0% with no plume.\u00a0 If the parking lots and roads are wet when the sun comes out, the ET is positive.\u00a0 Daily plots of ET change in UHI\u2019s can go as low as -40% to -50%.\u00a0 In a U. of Colorado paper\u00a0[8]<\/a>\u00a0a lab experiment showed a 20-35% decrease in ET.\u00a0 Fu\u00a0[15]<\/a>\u00a0and Zheng\u00a0[16]<\/a>\u00a0show other examples of ET change in cities and forest to crop land in the -20% range.\u00a0 There is no consensus on what the global ET change is on the sum total of all the Earth\u2019s Special Parcels. \u00a0A range of 0 to -25% on the Special Parcel in this model is possible and will be tested.<\/p>\n\n\n\n

    \"\"
    Figure 2.\u00a0 Example of daily ET loss in a UHI.\u00a0<\/figcaption><\/figure>\n\n\n\n

    Some of the latest papers on ET modeling and calculation are by Rigden [24]<\/a> and Yan (25)<\/a><\/p>\n\n\n\n

    With out data on what total ET of all the Earth\u2019s Special Parcels is, ET (SH) reduction in the Special Parcel is a Model variable.<\/p>\n\n\n\n

    2.  Model Variable: The Plume<\/strong><\/p>\n\n\n\n

    No measurement of plume size could be found.  A model of plume size for Chicago by Cosgrove & Max Berkelhammer, (27)<\/a>, calculates a plume of 2 to 3 times the area of the city.  Plume size should be a function of time the sun is out, time of the year, relative humidity of the air and the albedo of the surface.   The bigger the density difference between the rising air and surrounding air the bigger the plume. With out data the Plume size is a variable in the Model<\/p>\n\n\n\n

    3.  Model Variable: Size of the Special Parcel<\/strong><\/p>\n\n\n\n

    The Special Parce is only land changes that cause the ET of that parcel to decrease.  Examples are: a city, forest to crop, forest to pasture, reduction of flooded land, draining of marsh land, post forest fires, or mining.  The model lumps all these types of land changes into one Special Parcel of land.  The purpose of this model is to show how the fundamental variables of the Special Parcel land can change the global temperature.  The sum total of all the Special Parcels is not an agreed to number.<\/p>\n\n\n\n

    For global cooing the Special Parcel may be different like new forest or new cooling towers, etc that put water into the air.  Adding water to the air may reduce plume size due the lower density difference.<\/p>\n\n\n\n

    IV.  Source of Physical Properties Equations<\/strong><\/p>\n\n\n\n

    Six atmospheric variables are linked by Clausius- Clapeyron related Psychometric chart equations.  With the use of these equations one can pick any 2 of the 6 variables and calculate the others.  This Model uses this flexibility to transition from one step to the next step in the Model.  The 6 atmospheric variables are: Temperature (T), Specific Humidify (SH), Relative Humidity (RH), Enthalpy (En), Dew Point (Td), Saturation Temperature, (Ts).  (Specific Humidity, SH, is also known as Absolut Humidity or Humidity Ratio or Mixing Ratio).  The preferred arrangement of these equation is shown in the Model\u2019s derivation.  The equations are Eq 2, 3, 4, 5, 6 and 9 (in Model Derivation)<\/p>\n\n\n\n

    The source of the equations is mainly from Vaisala Oyj [14]<\/a> who has the simplest versions of the common equations.  Similar equations can be found at [11]<\/a>, [12]<\/a>, and [13]<\/a><\/p>\n\n\n\n

    V.  The Model\u2019s basic data<\/strong><\/p>\n\n\n\n

    Any model needs to fit the observed data.\u00a0 Data from NOAA\u00a0[18]<\/a>\u00a0and HadISDH\u00a0[6]<\/a>\u00a0were both candidates for the model\u2019s derivation.\u00a0 The HadISDH\u00a0[6]<\/a>\u00a0data were used because some of the NOAA southern hemisphere data had a very low R^2 vs time.\u00a0 The HadISDH\u00a0[6]<\/a>\u00a0data were boiled down to 3 fundamental relationships plus the Climate Explorer CC data\u00a0[5]<\/a>, Table 1 and Figure 6.<\/p>\n\n\n\n

    Least squares fit vs year for HadISDH data                                           1975 to 2020<\/strong><\/td><\/tr>
    observation<\/strong><\/td>Eq vs year (x)<\/strong><\/td> <\/strong><\/td> <\/strong><\/td><\/tr>
    CC<\/strong><\/td>y = -0.0753x + 212.77<\/strong><\/td> <\/strong><\/td>R\u00b2 = 0.6486<\/strong><\/td><\/tr>
    Temp<\/strong><\/td>y = 0.0209x \u2013 27.297<\/strong><\/td> <\/strong><\/td>R\u00b2 = 0.8799<\/strong><\/td><\/tr>
    SH<\/strong><\/td>y = 0.0085x \u2013 9.1539<\/strong><\/td> <\/strong><\/td>R\u00b2 = 0.7568<\/strong><\/td><\/tr>
    RH<\/strong><\/td>y = -0.0148x + 105.99<\/strong><\/td> <\/strong><\/td>R\u00b2 = 0.4718<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
    \"\"
    Figure 6.\u00a0 Model\u2019s fit to observed data.<\/figcaption><\/figure>\n\n\n\n

    Table 1 basic data<\/p>\n\n\n\n

    To check the accuracy of the equations used in the Model four years were picked to compare calculation of the atmospheric variables to an on-line calculator <\/a>[4]<\/a> and this paper\u2019s model\u2019s equations.  Table 2 shows the calculated Temp, RH, and SH for the selected 4 years with the equations in Table 1.  The SH calculated vs Data error is shown to be good.  The calculated by an on-line calculator [4]<\/a> and this model are shown in Table 3.<\/p>\n\n\n\n

    Basic DATA<\/strong><\/td><\/tr>
    year<\/strong><\/td>CC*<\/strong><\/td>temp**<\/strong><\/td>RH**<\/strong><\/td>SH**<\/strong><\/td>SH Calc-Data error<\/strong><\/td><\/tr>
     <\/strong><\/td>%<\/strong><\/td>\u00b0C<\/strong><\/td>%<\/strong><\/td>g\/kg(da)<\/strong><\/td> <\/strong><\/td><\/tr>
    1975<\/strong><\/td>64.05<\/strong><\/td>13.981<\/strong><\/td>76.76<\/strong><\/td>7.6336<\/strong><\/td>0.15%<\/strong><\/td><\/tr>
    1982<\/strong><\/td>63.53<\/strong><\/td>14.127<\/strong><\/td>76.66<\/strong><\/td>7.6931<\/strong><\/td>0.23%<\/strong><\/td><\/tr>
    2011<\/strong><\/td>61.34<\/strong><\/td>14.733<\/strong><\/td>76.23<\/strong><\/td>7.9396<\/strong><\/td>0.45%<\/strong><\/td><\/tr>
    2020<\/strong><\/td>60.66<\/strong><\/td>14.921<\/strong><\/td>76.09<\/strong><\/td>8.0161<\/strong><\/td>0.54%<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

    *          Cloud Cover Data from Climate Explorer [5]<\/a><\/p>\n\n\n\n

    **       Average yearly least squares fit of HadISDH [6]<\/a> data<\/p>\n\n\n\n

    Table 2 basic data for four years and the error.<\/p>\n\n\n\n

    Comparison of on-line calculation vs this model\u2019s calculations.<\/p>\n\n\n\n

    Table 3 On-line data from [4]<\/a> vs result of this models calculated atmospheric variables using Equations 2, 3, 4, 5, and 6 (in Model Derivation).<\/p>\n\n\n\n

    Conclusions from Tables 1, 2, and 3 is Eq 2 thru 6 are ok to use in the Model.<\/p>\n\n\n\n

    VI.  Special Correlations used by the Model<\/strong><\/p>\n\n\n\n

    The following three relationships are used in the Model along with the atmospheric equations 2, 3, 4, 5, and 6 (in Model Derivation<\/a>).<\/p>\n\n\n\n

      \n
    1.  Clouds vs |T-Td|<\/strong><\/li>\n<\/ol>\n\n\n\n

      Cloud Cover has long been difficult to predict, see Walcek\u00a0[22]<\/a>.\u00a0\u00a0 The variable chosen to predict probability of Cloud Cover (fraction) is the difference between the measure temperature (T) and the calculated Dew point temperature (Td), |T \u2013 Td|. (Figure 3.1 shows how |T \u2013 Td| changes on a psychrometric chart.)<\/p>\n\n\n\n

      \"\"
      Figure 3.1\u00a0 |T-Td|\u00a0 on a Psychrometric Chart.\u00a0 The length of the line is inversely proportional to the probability of Cloud formation from\u00a0(3)<\/a>.<\/figcaption><\/figure>\n\n\n\n

      The Td value was obtained from HadISDH\u00a0[6]<\/a>\u00a0data from average T and SH reported values using Equations 2 and 5.\u00a0\u00a0\u00a0 The Figure 3 correlation is a little better than the conventional relative humidity correlation.\u00a0<\/p>\n\n\n\n

      \"\"
      Figure 3\u00a0 |T-Td| vs Cloud Cover for HadISDH data. First of special correlations used in the model.<\/figcaption><\/figure>\n\n\n\n

      Figure 3 shows the HadISDH [6]<\/a> data scatter plot with the linear least squares line (the insert plot in Figure 3).  The correlation is improved by adding two common sense points: 100% cloud cover at 0 |T-Td| and 0 cloud cover at 7 |T-Td| (50-0% relative humidity = no clouds).   The correlation then becomes a quadric. This quadric equation is used at each iteration of the model.  The inserted graph in Fique 3 shows the used correlation goes through the scatter plot points. (The real cloud cover vs |T-Td| is probably more complex than Figure 3)<\/p>\n\n\n\n