Convective Adjustment and RDC
First uploaded on 2024/09/12
Last updated on 2025/01/13
Copyright(C)2024-2025 jos <jos@kaleidoscheme.com> All rights reserved.
Convective Adjustment (CA)
The first cumulus parameterization in climate prediction was described
using "convective adjustment (CA)".
Following the keen physical insight of Nobel laureate Dr. Syukuro Manabe,
it calculates other physical quantities of the atmosphere
based on the assumption that the relative humidity in the troposphere is kept constant.
Furthermore, it assumes that the constant value does not change during warming.
Although the CA had a very simple structure,
it was able to predict warming associated
with the increasing mixing ratio of carbon dioxide in the atmosphere,
including the current accelerated warming, with great accuracy.
The reason for this is precisely
in the first hypothesis of constant relative humidity.
The saturation mixing ratio of water vapor in the atmosphere
increases rapidly with increasing temperature.
The actual mixing ratio of water vapor in the atmosphere
is this saturated water vapor multiplied by the relative humidity.
Therefore, if the relative humidity is fixed,
water vapor mixing ratio in the atmosphere
will increase rapidly with increasing temperature.
Since water vapor is a greenhouse gas like carbon dioxide,
increasing the amount of water vapor accelerates global warming.
In other words, water vapor has a POSITIVE feedback effect on global warming.
Dynamical Detrainment (DD)
With the development of computers,
models of atmospheric phenomena have evolved into more detailed ones.
Today's accurate weather forecasts are the product of this evolution.
And, CA seemed obsolete at that time.
They thought they could calculate the amount of water vapor in their models
without having to provide a fixed relative humidity.
They applied weather forecasting technique to their climate prediction models
as a matter of course:
parameterization based on the internal dynamics of cumulus clouds.
That is, the air parcel rises within the cumulus cloud due to buoyancy.
As the air parcel rises to higher altitudes,
its temperature decreases
and its saturated water vapor mixing ratio decreases,
so that excess water vapor is removed from the air parcel as precipitation
and the air parcel becomes drier and drier.
When the air parcel reaches near the top of the cumulus cloud,
it loses its buoyancy and is unable to continue its ascent
and horizontally moves out of the cumulus cloud.
The water vapor distribution in the atmosphere is determined
by the subsidence of the air parcels outside the cumulus clouds
over the vast areas of clear sky.
The water vapor mixing ratio in an air parcel does not change in the subsidence,
since the temperature increases in the downward motion.
In summary, the water vapor amount of the atmosphere in clear regions
is entirely determined by the altitude at which the air parcels leave the cumulus clouds.
In this view of "Dynamical Detrainment (DD)",
as warming increases,
the air parcel tends to be transported to higher altitudes within the cumulus
due to increased height of the cumulus.
At higher altitudes,
the mixing ratio of water vapor in the out-going air parcel becomes lower,
which means the amount of water vapor in the total atmosphere is also lower.
No matter how you tinker with it,
the amount of water vapor in the atmosphere would decrease with warming in the model.
In other words, water vapor could have a NEGATIVE feedback on global warming.
This cannot explain the accelerated warming we see today.
Not only does it not explain the "fixed relative humidity" hypothesis
used in the CA,
it gives the opposite qualitative result.
Radiatively Driven Circulation (RDC)
The RDC scheme, as well as DD,
can predict the distribution of water vapor in time and space.
However, RDC scheme is the exact opposite of DD method,
both in its basic concepts and in its results.
It calculates the mean flow components
based on the relationship between the physical quantities
required for the mean-field of the atmosphere outside the cumulus
to maintain equilibrium.
(Disturbances deviated from the mean field are treated separately
by the model's equations of motion.)
The resulting water vapor field varies in space and time,
but the averaged relative humidity becomes constant.
And the constant value does not change during warming.
This means that the RDC scheme can explain the first hypothesis
of the CA method,
"fixed relative humidity", which the DD method cannot.
And of course, the RDC scheme, as well as CA,
shows a POSITIVE water vapor feedback on global warming.
Summary
The failure of the DD method lies in the fact that
the parameterization methods used for weather prediction have been directly applied
to climate prediction.
It is usually safe to assume that all flows in meteorology are determined by dynamics,
since the precipitation and violent wind speeds that people are interested in
are determined by dynamics.
However, it is detrainment flow out of atmospheric disturbances such as cumulus clouds,
that determines the amount of water vapor in the atmosphere.
This is determined not by the dynamics within the disturbance,
but by the vast atmospheric field outside the disturbance.
Many of the people currently involved in climate modeling
came from meteorological modeling.
They have had so much success in weather forecasting
that it would be difficult for them to see that they are going in the wrong direction.
However, according to their method,
cumulus clouds can provide a negative water vapor feedback on global warming
and give false optimistic predictions for the problem.
We hope they will realize the validity of the RDC scheme as soon as possible.
Contact Us
Exhibited on 2024/09/12
Last updated on 2025/01/13
Copyright(C)2024-2025 jos <jos@kaleidoscheme.com> All rights reserved.