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Cloud
parameterization: is an important subject because some clouds are too
small to be resolved by climate and weather forecast models, even with the
most powerful of presentday computers. Hence models need to account
for the smallscale cloud variability that they cannot represent explicitly.
CAMCLUBB
papers In the following 7 papers,
the implementation of CLUBB in CAM is tested using a variety of
configurations and observational datasets. Bogenschutz, P.A., A. Gettelman, H. Morrison, Vincent E. Larson, C.
Craig, and D. P. Schanen (2013). ``Highorder turbulence closure and its
impact on climate simulations in the Community Atmosphere Model."
J Climate. 26, 96559676. In this
paper, CLUBB is implemented in CAM and tested in global simulations. CLUBB is used in these simulations to
parameterize all shallow (stratocumulus and cumulus) clouds. Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Schanen, D. P., Meyer, N., et al. (2012). “Unified parameterization of the planetary
boundary layer and shallow convection with a higherorder turbulence closure
in the Community Atmosphere Model: singlecolumn experiments.” Geoscientific
Model Development, 5, 14071423. This paper
simulates a variety of boundarylayer cloud types using the singlecolumn
version of CAMCLUBB. The solutions
are fairly robust to changes in time step and vertical grid spacing. Wang,
M., V. E. Larson, S. Ghan, M. Ovchinnikov, D. P. Schanen,
H. Xiao, X. Liu, P. Rasch, and Z. Guo (2014). “A Multiscale Modelling Framework model
(Superparameterized CAM5) with a higherorder turbulence closure: model
description and low cloud simulations” Submitted to Journal of Advances in Modeling Earth Systems. Here CLUBB
is implemented in a cloudresolving model with 4km horizontal grid spacing,
which in turn is implemented in CAM5.
The model behavior is similar to CAMCLUBB at 100km horizontal grid
spacing. This indicates that CLUBB
behaves similarly over a range of horizontal grid spacings. Kubar, T., Stephens, G. L., Lebsock, M., Larson, V. E., and Bogenschutz, P. A., (2014). Regional Assessments of
Low Clouds Against LargeScale Stability in CAM5 and CAMCLUBB Using MODIS
and ECMWFInterim Reanalysis Data, Accepted to J. Climate. Here,
CAMCLUBB’s depiction of low clouds is evaluated using satellite data. CAMCLUBB simulates an improved transition
between marine stratocumulus and shallow cumulus clouds. Guo, Z., Wang, M., Qian, Y., Larson, V.
E., Ghan, S., Ovchinnikov, M., et al. (2014). A sensitivity analysis of cloud
properties to CLUBB parameters in the single‐column Community Atmosphere Model (SCAM5). Journal
of Advances in Modeling Earth Systems, 6, 829858. Guo, Z., Wang, M., Qian, Y., Larson, V.
E., Ghan, S., Ovchinnikov, M., et al. (2014). Parametric Behaviors
of CLUBB in Simulations of Low Clouds in the Community Atmosphere Model
(CAM). Submitted to Journal of Advances
in Modeling Earth Systems. In these two
papers, the sensitivity of CAMCLUBB to changes in parameter values is tested
using singlecolumn and global simulations.
These papers provide valuable guidance not only on the practical issue
of tuning CAMCLUBB, but also on the issue of understanding how changes in
the strength of various smallscale processes affects the emergent cloud
behavior. Implementation
of CLUBB in cloudresolving and regional models: The fact that CLUBB works
in host models with a wide range of grid spacings
(4 to 100 km) suggests that CLUBB is relatively insensitive to horizontal
grid spacing. 2012:
``PDF Parameterization of boundary layer
clouds in models with horizontal grid spacings from
2 to 16 km." V. E. Larson, D. P. Schanen,
M. Wang, M. Ovchinnikov, and Ghan, S.
Mon. Wea.
Rev., 140, 285306. This paper
implements CLUBB in a convectionpermitting model, SAM. The use of CLUBB in SAM is tested for
various boundarylayer cloud cases. We
introduce a simple method for damping CLUBB's effects at high resolution,
thereby reducing undesirable sensitivities to horizontal grid spacing. We find that the use of CLUBB can improve
the simulations for grid spacings of 4 km or
greater. 2013:
“SILHS: A Monte Carlo interface between
clouds and microphysics."
V. E. Larson, C. Harlass, and J. Höft.
Preprints, Fourteenth Annual WRF Users’ Workshop, Boulder, CO, Natl. Cent. for Atmos. Res. 2012:
“Implementation and early tests of a PDF
parameterization in WRF."
V. E. Larson, C. Harlass, and J. Höft.
Preprints, Thirteenth Annual WRF Users’ Workshop, Boulder, CO, Natl. Cent. for Atmos. Res. These
conference papers show simulations of a marine stratocumulus case using CLUBB
implemented in a weatherforecast model, WRF, at moderate resolution. Coupling
CLUBB to microphysical variability: Larson,
V. E., B. J. Nielsen, J. Fan, and M. Ovchinnikov (2011).
``Parameterizing correlations between
hydrometeor species in mixedphase Arctic clouds."
J. Geophys.
Res., 116, D00T02, doi:10.1029/2010JD015570. In order to
drive microphysics using subgrid variability, we
need to know the correlations between hydrometeor species. For instance, the correlation between cloud
water and rain water influences the rate of accretion of cloud droplets by
rain drops. If cloud and rain are
correlated, then cloud and rain coexist, and accretion occurs rapidly. This paper proposes a method to diagnose
correlations based on information that is typically available in cloud
models. Larson,
V. E., and B. M. Griffin (2013). ``Analytic upscaling of a local
microphysics scheme. Part I: Derivation." Quart. J. Roy. Meteor. Soc., 139, 4657. Griffin,
B. M., and V. E. Larson (2013). ``Analytic upscaling of a local
microphysics scheme. Part II: Simulations." Quart. J. Roy. Meteor. Soc., 139, 5869. One reason
to predict the subgrid PDF is to drive
microphysical parameterizations more accurately. For instance, once we know the subgrid PDF, then we know what percentage of a grid box
is precipitating strongly, and so forth.
In these papers, we integrate a microphysics scheme analytically over
CLUBB's PDF. We are able to do this
exactly for the drizzle parameterization of Khairoutdinov
and Kogan, which is relatively simple in
formulation. We find that, for a
marine stratocumulus case, accounting for subgrid
variability leads to significantly more simulated drizzle at the ocean
surface. Larson,
V. E., J.C. Golaz, H. Jiang, and W. R. Cotton (2005). ``Supplying local microphysics parameterizations
with information about subgrid variability: Latin
hypercube sampling." J.
Atmos. Sci., 62, 40104026. (See also slides 3660 of the following presentation.) Larson,
V. E. , and D. P. Schanen
(2013). “The Subgrid
Importance Latin Hypercube Sampler (SILHS): a multivariate subcolumn generator." Geosci. Model Dev., 6, 1813–1829. The most accurate
way to drive microphysics using a PDF is to integrate the relevant
microphysical formulas analytically over the PDF. However, this may be intractable for some
microphysics schemes or may require rewriting the microphysics code. To avoid this, one may draw sample points
from the PDF and input them into the microphysics code one at a time. This allows the use of existing
microphysics codes, but it also introduces statistical noise due to imperfect
sampling. To reduce the noise, sample
points may be spread out in a quasirandom fashion using "Latin
hypercube sampling," and the sample points may be clustered in important
regions, such as cloud. Chowdhary, K., Salloum,
M., Debusschere, B., and Larson, V. E. (2014).
Quadrature Methods for the Calculation of Subgrid
Microphysics Moments. Submitted to Mon. Wea. Rev. Analytic
integration over microphysics is restricted in applicability, and Monte Carlo
sampling introduces sampling noise.
Here, the integration is performed using a third alternative: deterministic
quadrature. This method is more
general than analytic integration and more accurate than Monte Carlo
integration. Storer, R. L., B. M. Griffin, J. Höft, J. K. Weber, E. Raut, V.
E. Larson, M. Wang, and P. J. Rasch (2014). “Parameterizing deep convection using the
assumed probability density function method." Geosci. Model Dev. Discuss., 7, 3803–3849. In this
paper, variability in ice is included in CLUBB’s subgrid
PDF, and a fully unified cloud parameterization is created. CLUBB’s single equation set is used to do
singlecolumn simulations of stratocumulus, shallow cumulus, and deep cumulus
layers. Participation
by CLUBB in singlecolumn model intercomparisons: In singlecolumn intercomparisons, CLUBB has been tested in a wide variety
of cloud regimes. 2011:
“Evaluation of the diurnal cycle in the
atmospheric boundary layer over land as represented by a variety of single column
models — the second GABLS experiment." G. Svensson, A.A.M. Holtslag, V. Kumar, T. Mauritsen,
G. J. Steeneveld, W. M. Angevine, E. Bazile, A. Beljaars, E.I.F. de Bruijn, A.
Cheng, L. Conangla, J. Cuxart,
M. Ek, M. J. Falk, F. Freedman, H. Kitagawa, V. E.
Larson, A. Lock, J. Mailhot, V. Masson, S. Park, J.
Pleim, S. Soderberg, M. Zampieri, and W. Weng, Bound. Layer Met., 140, 177–206. 2014:
“The third GABLS intercomparison
case for evaluation studies of boundarylayer models: Part B: results and process
understanding." F. C. Bosveld et al. (including V. E. Larson), Bound. Layer
Met., 152, 157–187. These two intercomparisons demonstrate that CLUBB can simulate
stable boundary layers, including those that form at night after the
occurrence of daytime boundarylayer turbulence. 2009:
``Intercomparison of model simulations of mixedphase
clouds observed during the ARM MixedPhase Arctic Cloud Experiment. Part I: Single layer cloud."
S. A. Klein and Coauthors (including V. E. Larson). Quart.
J. Royal Met. Soc., 135, 9791002.
2009:
``Intercomparison of model simulations of mixedphase
clouds observed during the ARM MixedPhase Arctic Cloud Experiment. Part II: Multilayer cloud."
H. Morrison and Coauthors (including V. E. Larson). Quart.
J. Royal Met. Soc., 135, 10031019.
Clouds in
the Arctic are often mixedphase: that is, they often contain both liquid and
ice. Longlived mixedphase clouds are
difficult to simulate because ice naturally tends to grow at the expense of
liquid. Models may overdeplete
liquid unless the ice particles are limited in number and sediment out of
cloud base rapidly enough. Our cloud
parameterization, CLUBB, was used to simulate mixedphase clouds during the
MPACE experiment. CLUBB was able to
maintain liquid water in these clouds, as was observed. 2013:
``A singlecolumn model ensemble approach
applied to the TWPICE experiment."
L. A. Davies and Coauthors (including V. E. Larson). J. Geophys. Res., 118, 65446563. This paper
compares several internationally recognized parameterizations of deep
convection. The simulated observations
were obtained during the Tropical Warm Pool International Cloud Experiment
(TWPICE) near Darwin, Australia.
CLUBB simulated this deep convective case using the same configuration
that is used to simulate boundarylayer clouds. CLUBB's results for TWPICE are competitive
with those of the other participating parameterizations. The results suggest that CLUBB contains
enough physics to serve as a unified parameterization of both shallow and
deep clouds. 2013:
“CGILS: Results from the first phase of an
international project to understand the physical mechanisms of low cloud
feedbacks in single column models."
M. Zhang et al. (including V. E. Larson), J.
Adv. Model. Earth Syst., 5, 826–842. This intercomparison demonstrates that CLUBB can simulate
marine shallow clouds that are driven to equilibrium in monthlong
simulations. 2007:
``A singlecolumn model intercomparison
of a heavily drizzling stratocumulustopped boundary layer."
M. C. Wyant and CoAuthors. J. Geophys. Res., 112, D24204, doi:10.1029/2007JD008536. This paper
compared the output from numerous singlecolumn model
that were set up identically to simulate a cloud layer observed during the
DYCOMSII field experiment. Part of
the challenge was simulating drizzle.
In order to couple drizzle to the cloud fields, instead of drawing
sample points from the PDF using the Latin hypercube method discussed above,
we analytically integrated over the PDF. Formulation
of CLUBB: The following papers
discuss the formulation of the core of CLUBB.
2002:
``A PDFBased Model for Boundary Layer
Clouds. Part I: Method and Model
Description."
J.C. Golaz, V. E. Larson, W. R. Cotton. J.
Atmos. Sci., 59, 35403551. 2002:
``A PDFBased Model for Boundary Layer
Clouds. Part II: Model Results."
J.C. Golaz, V. E. Larson, W. R. Cotton. J.
Atmos. Sci., 59, 35523571. (See also slides 1335 of
the following presentation,
and this short
conference paper.) Traditionally, cloud parameterization has been
viewed as a multiplicity of tasks.
Such tasks include the prediction of heat flux, moisture flux, cloud
fraction, and liquid water. In
contrast, the papers above adopt the alternative viewpoint that the goal of
parameterization consists largely of a single task: the prediction of the
joint PDF of vertical velocity, heat, and moisture. Once the PDF is given, the fluxes, cloud
fraction, and liquid water can be diagnosed.
The above papers present a parameterization that can
model both stratocumulus and cumulus clouds without casespecific
adjustments. This avoids the
difficulty of having to construct a ``trigger function" that determines
which cloud type should be modeled under which meteorological conditions. 2002:
``SmallScale and Mesoscale Variability in
Cloudy Boundary Layers: Joint Probability Density Functions."
V. E. Larson, J.C. Golaz, W. R. Cotton. J.
Atmos. Sci., 59, 35193539. (See also the following short
conference paper.) Whereas the
prior paper discusses onedimensional PDFs of cloud water and humidity, this
paper discusses joint PDFs that include the vertical velocity. Joint PDFs allow us to diagnose the
buoyancy flux, which is the means by which convection generates
turbulence. Joint PDFs also allow us
to diagnose fluxes of heat and moisture.
Therefore, joint PDFs can serve as the foundation of cloud and
turbulence parameterizations in numerical models, as proposed and explored in
the two following papers. 2005:
``Using Probability Density Functions to
Derive Consistent Closure Relationships among HigherOrder Moments."
V. E. Larson and J.C. Golaz.
Mon. Wea.
Rev., 133, 10231042. (See also slides 2627 of the following presentation.) The
aforementioned papers show that if we choose an accurate PDF family, then we
can solve for many of the unknowns in our onedimensional cloud
parameterization. For some of these
unknown terms, the present paper lists simple, analytic approximations. All approximated formulas are based on the
same PDF and hence are consistent with each other. A PDF may be
constructed from a set of means, variances, and other moments of velocity,
moisture, and temperature. It is
possible that a particular set of moments does not correspond to any real PDF
in the family. We call such a set of
moments ``specifically unrealizable."
For instance, a set that includes asymmetric moments is specifically
unrealizable with respect a PDF family of symmetric, bellshaped curves. This is because the bell shape family is
too restrictive to include asymmetric moments. We show that a broad class of moments is
specifically realizable with respect to our PDF family. That is, our PDF family is not restrictive. 2007:
``Elucidating model inadequacies in a cloud
parameterization by use of an ensemblebased calibration framework."
J.C. Golaz, V. E. Larson, J. A. Hansen, D. P. Schanen, and B. M. Griffin. Mon. Wea. Rev., 135, 40774096. (See also the
following oral
presentation or slides,
and this conference
paper.) It is often
easy to see when an atmospheric model disagrees with data. It is usually much
harder to locate the ultimate sources of model error. It is
particularly difficult to diagnose errors in a model's structure, that is,
errors in the functional form of the model equations. One technique that may
help is parameter estimation, that is, the optimization of model parameter
values. Typically, parameter estimation is used solely to improve the fit
between a model and observational data. In the process, however, parameter
estimation may cover up structural model errors. In a quite
opposite application, parameter estimation may be used to uncover the ways in
which a model is wrong. The basic idea is to separately optimize model
parameters to two different data sets, and then identify parameter values
that differ between the two optimizations. When no single value of a
particular parameter fits both datasets, then there must exist
a related structural error. Carbon cycle: is important for climate
studies because not all the carbon dioxide that is emitted by humans remains
in the atmosphere. Rather, some CO_{2} is taken up by
vegetation or dissolved in the oceans.
2008: ``An idealized model of the onedimensional carbon dioxide rectifier effect." V. E. Larson and H. Volkmer. Tellus B, 60B, 525536. (See also this shorter conference paper.)
The net flux of carbon dioxide (CO_{2})
from the land surface into the atmospheric boundary layer has a diurnal
cycle. Drawdown of CO_{2} occurs during daytime photosynthesis,
and return of CO_{2} to the atmosphere occurs during night.
Even when the net diurnalaverage surface flux vanishes, the
diurnalaverage profile of atmospheric CO_{2} mixing ratio is
usually not vertically uniform. This is because of the diurnal rectifier
effect, by which atmospheric vertical transport and the surface flux conspire
to produce a surplus of CO_{2} near the ground and a deficit aloft. This paper constructs an
idealized, 1D, eddydiffusivity model of the rectifier effect and provides
an analytic series solution. When nondimensionalized,
the intensity of the rectifier effect is related solely to a single
‘rectifier parameter’. Alto clouds: could be called the ``forgotten clouds" of meteorology because they are less studied than other cloud types. But we think they are well worth remembering! 2002: ``Observed
microphysical structure of midlevel, mixedphase clouds." R. P. Fleishauer, V. E. Larson, and T. H. Vonder
Haar. J. Atmos. Sci.,
59, 17791804. (See also this related presentation.) Altostratocumulus (ASc)
clouds are not merely very high stratocumulus clouds. ASc are distinctive because they are often mixedphase
and also because they are often decoupled from surface fluxes of heat,
moisture, and momentum. This paper presents some observations from the
CLEX5 field experiment. In most cases we examined, there were weak
temperature inversions and wind shears at cloud top. This contrasts
with many observations of lowlevel stratocumulus clouds. We conjecture
that the differences are related to the fact that ASc
clouds are usually not sustained by surface moisture fluxes, and they are
usually not frictionally coupled to the ground by turbulent updrafts and
downdrafts. CLEX5 frequently encountered alto
clouds containing both liquid and ice. In the thin, singlelayer clouds
that we observed, we found that near cloud top, where the cloud is coldest,
liquid predominates over ice. Near cloud bottom, where the cloud is
warmest, ice predominates. Prior authors have found the same vertical
structure. Presumably it is due to gravitational settling of the ice
crystals. 2001: ``The death of an
altocumulus cloud." V. E. Larson, R. P. Fleishauer,
J. A. Kankiewicz, D. L. Reinke,
and T. H. Vonder Haar. Geophys. Res. Lett., 28, 26092612. This is a case study of an altostratocumulus cloud that ``died," or dissipated,
as an aircraft observed it. There are four mechanisms that can cause an
ASc to die: solar heating, incorporation into the
cloud of dry air from outside, heating induced by largescale subsidence of
air, and precipitation. In this particular case, subsidence seemed to
be the major culprit. Solar radiative heating was weak because the
cloud formed over Montana in November. 2006: ``What determines
altocumulus dissipation time?" V. E.
Larson, A. J. Smith, M. J. Falk, K. E. Kotenberg, and J.C. Golaz.
J. Geophys. Res., 111, D19207, doi:10.1029/2005JD007002. (See also the following
two animations, courtesy of David Schanen. The
first shows dissipation of liquid water, with
redder colors representing higher amounts of liquid; notice the strong
turbulence. The second movie shows the evolution
of cloud top and cloud base surfaces; notice that although the cloud base
rises, the cloud remains overcast (100% cloud cover) until near the end of
the simulation.) This paper further investigates
the causes of altostratocumulus death using
numerical simulations. A particular subject of study is feedbacks or
interactions between the 4 aforementioned processes: solar heating,
incorporation into the cloud of dry air from outside, heating induced by
largescale subsidence of air, and precipitation of ice. To quantify
these, we construct a "budget term feedback matrix." It shows
that precipitation of ice is a negative feedback on the other
processes. For instance, if solar heating dissipates the cloud,
precipitation of ice dissipates the cloud less than it would have otherwise,
thereby diminishing the effectiveness of solar heating on cloud dissipation
rate. 2009: ``Processes
that generate and deplete liquid water and snow in midlevel, mixedphase
clouds" A. J. Smith,
V. E. Larson, J. Niu, J. A. Kankiewicz,
L. D. Carey. J. Geophys. Res., 114, D12203,
doi:10.1029/2008JD013131. This paper extends the study of
Larson et al. (2006) by adding simulations of two new observed mixedphase altostratocumulus cases and by constructing budgets of
snow. As before, the new clouds, in both observations and simulations,
consist of a mixedphase layer with a quasiadiabatic profile of liquid, and
a virga layer below that consists of snow. The
snow budgets show that snow grows by deposition in and below the liquid
(mixedphase) layer, and sublimates in the remainder of the virga region below. The deposition and sublimation are
balanced primarily by sedimentation, which transports the snow from the
growth region to the sublimation region below. 2009: ``An analytic scaling law for glaciation rate in mixedphase layer clouds." V. E. Larson and A. J. Smith. J. Atmos. Sci., 66, 26202639. In various
practical problems, such as assessing the threat of aircraft icing or
calculating radiative transfer, it is important to know whether or not
mixedphase clouds contain significant liquid water content. Some mixedphase
clouds remain predominantly liquid for an extended time, and others glaciate,
or become converted to ice, quickly. The
glaciation rate of mixedphase layer clouds is thought to depend on various
factors. This paper attempts to quantify some of these factors by deriving
scaling laws (i.e.~power
laws) for the amount of snow at cloud base. The scaling laws are derived from
the governing equation for snow concentration. The scaling
laws agree adequately with highresolution simulations over one order of
magnitude for snow flux and over two orders of magnitude for snow mixing
ratio. They indicate, for instance, that cloud base snow amount increases
faster than linearly with increasing cloud thickness and supersaturation
with respect to ice. By varying
the exponents and prefactors of the scaling laws, one may explore the
sensitivity of glaciation rate to ice particle shape. The relationship is
complex, but for our cloud cases, dendrites tend to glaciate cloud more
rapidly than plates. 2007: ``What causes partial
cloudiness to form in multilayered midlevel clouds? A simulated case
study." M. J. Falk
and V. E. Larson. J. Geophys. Res., 112,
D12206, doi:10.1029/2006JD007666. (See
also the following short conference
paper.) At first glance, one might expect
that a lower cloud layer would be little affected by a separated upper cloud
layer that does not deposit snow or other quantities into it. However,
we find that the cloud fraction of such a lower layer can increase from 15%
to 100% if the upper layer is removed. The reason is that the removal
of the upper layer allows cloudtop radiative cooling in the lower layer,
thereby stabilizing it. 2007: ``An analytic
longwave radiation formula for liquid layer clouds." V. E. Larson,
K. E. Kotenberg, and N. B. Wood. M. Wea. Rev., 135, 689699. (See also the
following short conference
paper.) This paper discusses an idealized
longwave radiative transfer parameterization that is used in two papers
above, Falk and Larson (2007) and Larson et al. (2006). This radiation
parameterization is easy to implement in a numerical model, rendering it
especially useful for numerical model intercomparisons.
Dry atmospheres in
radiativeconvective equilibrium: The goal of the two papers below is to move theory
one step away from RayleighBenard convection,
which has proved so fruitful for understanding of buoyant fluids, and one
step closer to atmospheric convection. The problem considered here adds
infrared radiation to the classical problem of fluid flow between two plates,
the lower being heated and the upper being cooled. When radiation is
added, the stability properties do not change qualitatively as long as one
substitutes a radiative Rayleigh number for the classical Rayleigh
number. However, when fluid motion occurs, the turbulent heat flux does
change because the heat flux is strongly constrained by radiation. (The following article has been made available by the permission of Dynamics of Atmospheres and Oceans. Single copies of the following article can be downloaded and printed for the reader's personal research and study.) 2001: ``The effects of thermal radiation on dry
convective instability.'' V.
E. Larson. Dynamics of Atmospheres and Oceans, 34, 4571. 2000: “Stability properties of and scaling laws
for a dry radiativeconvective atmosphere.” V. E. Larson. Q. J. R. Meteorol.
Soc., 126, 145171. 1319: “The relationship between the transilient matrix and the Green’s function for the
advectiondiffusion equation.” V.
E. Larson. J. Atmos. Sci., 56, 24472453. (See also slide 7 of the following presentation.) The point of this paper is that if a singlecolumn model contains information only about horizontal averages, it discards crucial information about horizontal structure. For instance, a singlecolumn model may predict the average concentration of a pollutant at some altitude perfectly. But a model that only predicts averages doesn't know whether the pollutant resides in an updraft or downdraft. Hence the model doesn't know whether to transport the pollutant up or down at the next time step. Therefore, the transport prediction degrades rapidly. This problem was termed ``convective structure memory" by Roland Stull. It can be quantified using Green's function theory. 