## Fluid Systems Mechanically Coupled Through a Wall

**Method of Exact Solutions Verification Problems for Transient Compressible Flows: Two Fluid Systems Mechanically Coupled Through a Wall**

The analysis given in these previous notes:

Implicit Function Theory Introduction

Initial Method of Exact Solutions Calculations

Single Isolated Node Calculations

are expanded to include the case of coupled fluid systems. Numerical solution results are given for an illustrative application.

The mathematical model, an exact continuous analogue of the discrete approximations used in many numerical solution methods, provides analytical and numerical-benchmark problems for verification by the Method of Exact Solutions ( MES ).

I have uploaded a file.

## Implicit Function Theory Applications; Part 1: Method of Exact Solutions

In a previous post I gave some background info about implicit function theory and how it might be useful. In these notes I have used results from applications to the equation of state to develop a few exact solutions for extremely simple transient, compressible flows that include fluid-structure interaction. These notes address the case of mechanical coupling of the fluid to a deformable / flexible wall. I have also included an introduction to the case of coupling of fluid systems through a common deformable / flexible wall. Additional notes will address the case of thermal interactions for both a single fluid system and coupled systems.

I kind of ran out of steam when I got to coupled-systems part of the present notes. There’s a lot of ground to cover for this case and I’m thinking a separate report might be the way to go. With coupled systems you get more that just twice as many things to look at compared to the single-system case.

I think these solutions might be candidates for analytical, and numerical-benchmark-grade, Method of Exact Solutions ( MES ) for verification of limited aspects of coding of transient compressible fluid flow model equation systems and solution methods.

I have uploaded a file.

Consider these notes as a rough draft of a report and let me know what you think about all aspects.

## More on ODEs, MMS and Ill-Posed IVPs

First for the nomenclature: ODEs means Ordinary Differential Equations, MMS means the Method of Manufactured Solutions, and IVPs means Initial Value Problems.

This previous post provided some information on these subjects. So far as I know, that post presented the first results for application of MMS to ill-posed IVPs. That post suggested that for the numerical solution methods used therein, the original Lorenz system of 1963 has yet to be correctly solved.

I have some additional results, a summary of which is:

I think the Lorenz system has not yet been accurately integrated by any numerical solution methods. Higher-order methods plus, at the same time, higher precision representation of numbers will give results that might appear to be solutions. But, calculations for sufficiently long time spans will show that errors always increase.

I’ve uploaded a file here.

## Analytical Sensitivity Analysis

I have started working on a toy model and plan to include analytical sensitivity analysis as part of the methods. These notes, and an associated extended discussion that I have up-loaded, serve as a short introduction to the subject. The file is here.

**Update October 11, 2011.** These reports by J. R. Bates explore sensitivity and feedback of several aspects of simple climate models.

J. R. Bates, Some considerations of the concept of climate feedback, Quarterly Journal of the Royal Meteorological Society, 133: 545–560 (2007)

Published online in Wiley InterScience (www.interscience.wiley.com) DOI: 10.1002/qj.62

ABSTRACT: A conceptual study of climate feedbacks is carried out using two simple linear two-zone models and the commonly-used zero-dimensional model to which they reduce under simplifying assumptions. The term ‘feedback’ is used in many different senses in the climate literature. Two prototype usages, stability-altering feedback (defined in terms of a system’s asymptotic response to an impulsive forcing, negative when stability-enhancing) and sensitivity-altering feedback (defined in terms of a system’s steady-state response to a step-function forcing, negative when sensitivity-diminishing) have been isolated for study. These two climate feedback concepts are viewed against the background of control theory, which provides a generalized feedback perspective embracing all forms of forcing and which is often seen as providing the paradigm for the concept of feedback as used in climate studies.

The relationship between the prototype climate feedbacks is simple in the context of the zero-dimensional model. Here, the stability-altering and sensitivity-altering feedbacks provided by a given interaction are of the same sign, and the sign of the stability-altering feedback as measured by initial tendencies always coincides with its sign as measured by the defining asymptotic tendencies. Even in this simple model, however, the sign of the prototype climate feedbacks can be opposite to the sign of the system’s feedback as defined in control theory.

In the two-zone models, the relationship between the prototype climate feedbacks is not so simple. It is shown that, contrary to the common assumption, these feedbacks can be of opposite signs. Moreover, the sign of the stability-altering feedback as measured by initial tendencies can be opposite to its sign as measured by asymptotic tendencies. It is further shown that there is no simple relationship between the sign of either of the prototype climate feedbacks in the two-zone models and the sign of these models’ feedback as defined in control theory.

These results point to the need for greater precision and explicitness in the definition and use of the term ‘climate feedback’, both to facilitate interdisciplinary dialogue in relation to feedback and to guard against erroneous inferences within the climate field. Explicit definitions of the two prototype categories of climate feedback studied here are proposed.

Copyright 2007 Royal Meteorological Society

J. Ray Bates, Climate stability and sensitivity in some simple conceptual models, Climate Dynamics, 2010. Published Online December 2010. DOI 10.1007/s00382-010-0966-0

Abstract

A theoretical investigation of climate stability and sensitivity is carried out using three simple linearized models based on the top-of-the-atmosphere energy budget. The simplest is the zero-dimensional model (ZDM) commonly used as a conceptual basis for climate sensitivity and feedback studies. The others are two-zone models with tropics and extratropics of equal area; in the first of these (Model A), the dynamical heat transport (DHT) between the zones is implicit, in the second (Model B) it is explicitly parameterized. It is found that the stability and sensitivity properties of the ZDM and Model A are very similar, both depending only on the global-mean radiative response coefficient and the global-mean forcing. The corresponding properties of Model B are more complex, depending asymmetrically on the separate tropical and extratropical values of these quantities, as well as on the DHT coefficient. Adopting Model B as a benchmark, conditions are found under which the validity of the ZDM and Model A as climate sensitivity models holds. It is shown that parameter ranges of physical interest exist for which such validity may not hold. The 2 × CO2 sensitivities of the simple models are studied and compared. Possible implications of the results for sensitivities derived from GCMs and palaeoclimate data are suggested. Sensitivities for more general scenarios that include negative forcing in the tropics (due to aerosols, inadvertent or geoengineered) are also studied. Some unexpected outcomes are found in this case. These include the possibility of a negative global-mean temperature response to a positive global-mean forcing, and vice versa. [pay-walled]

J. R. Bates, On climate stability, climate sensitivity and the dynamics of the enhanced greenhouse effect, Danish Center for Earth System Science, DCESS REPORT Number 3, 2003.

Abstract

The dynamics of the enhanced greenhouse effect resulting from a CO2 increase are studied using a simple two-zone hemispheric atmosphere-ocean model on an aquaplanet that is simple enough to allow analytical solution. The model’s sensitivity to forcing is viewed against the background of its stability to free perturbations. Free perturbations in SST, regarded as representative of temperature perturbations in the mixed layers beneath, are subject to a destabilizing influence from the effects of the water vapor infrared radiative (WVIR) feedback and are stabilized by evaporation, which results in moist convection and precipitation that deposit the latent heat removed from the surface above the level of the main water vapor absorbers, whence it is radiated to space. The rate of evaporation depends on the surface wind strength and the air-sea humidity deficit. In the model, the former is parameterized in terms of the atmospheric angular momentum (AM) transport, which depends on the SSTs in both zones, and the latter in terms of the local SST through the Clausius-Clapeyron relationship. Using estimates of the parameters derived from observation and detailed radiative model calculations, the model gives an equilibrium temperature increase for a CO2 doubling that lies within the range of that given by GCMs. As in the GCMs, it is found that the warming is greatest in the extratropics. Unlike the case of the GCMs, the mechanism of the warming in the simple model can be fully understood. The model’s equilibrium sensitivity is found to be inversely proportional to the value of the stability determinant (which measures the product of the decay rates of the fast and slow normal modes) and to be strongly influenced by the strength of a ventilation feedback. Both of these factors are sensitively dependent on the strength of the extratropical WVIR feedback, and the ventilation feedback in addition depends critically on the latitudinal distribution of the surface forcing

J. R. BATES, A dynamical stabilizer in the climate system: a mechanism suggested by a simple model, Tellus (1999), 51A, 349–372.

Abstract

A simple zonally averaged hemispheric model of the climate system is constructed, based on energy equations for two ocean basins separated at 30° latitude with the surface fluxes calculated explicitly. A combination of empirical input and theoretical calculation is used to determine an annual mean equilibrium climate for the model and to study its stability with respect to small perturbations. The insolation, the mean albedos and the equilibrium temperatures for the two model zones are prescribed from observation. The principal agent of interaction between the zones is the vertically integrated poleward transport of atmospheric angular momentum across their common boundary. This is parameterized using an empirical formula derived from a multiyear atmospheric data set. The surface winds are derived from the angular momentum transport assuming the atmosphere to be in a state of dynamic balance on the climatic timescales of interest. A further assumption that the air–sea temperature difference and low level relative humidity remain fixed at their mean observed values then allows the surface fluxes of latent and sensible heat to be calculated. Results from a radiative model, which show a positive lower tropospheric water vapour/infrared radiative feedback on SST perturbations in both zones, are used to calculate the net upward infrared radiative fluxes at the surface. In the model’s equilibrium climate, the principal processes balancing the solar radiation absorbed at the surface are evaporation in the tropical zone and net infrared radiation in the extratropical zone. The stability of small perturbations about the equilibrium is studied using a linearized form of the ocean energy equations. Ice-albedo and cloud feedbacks are omitted and attention is focussed on the competing effects of the water vapour/infrared radiative feedback and the turbulent surface flux and oceanic heat transport feedbacks associated with the angular momentum cycle. The perturbation equations involve inter-zone coupling and have coefficients dependent on the values of the equilibrium fluxes and the sensitivity of the angular momentum transport. Analytical solutions for the perturbations are obtained. These provide criteria for the stability of the equilibrium climate. If the evaporative feedback on SST perturbations is omitted, the equilibrium climate is unstable due to the influence of the water vapour/infrared radiative feedback, which dominates over the effects of the sensible heat and ocean heat transport feedbacks. The inclusion of evaporation gives a negative feedback which is of sufficient strength to stabilize the system. The stabilizing mechanism involves wind and humidity factors in the evaporative fluxes that are of comparable magnitude. Both factors involve the angular momentum transport. In including angular momentum and calculating the surface fluxes explicitly, the model presented here differs from the many simple climate models based on the Budyko–Sellers formulation. In that formulation, an atmospheric energy balance equation is used to eliminate surface fluxes in favour of top-of-the-atmosphere radiative fluxes and meridional atmospheric energy transports. In the resulting models, infrared radiation appears as a stabilizing influence on SST perturbations and the dynamical stabilizing mechanism found here cannot be identified. [pay-walled]

**Update November 9, 2010.** here is additional information about applications of ASA to climate sciences.

Dan G. Cacuci, Mihaela Ionescu-Bujor, and Michael Navon, “Sensitivity and Uncertainty Analysis, Volume II: Applications to Large-Scale Systems”, CRC Press, 2005.

Chapter V. Using the ASAP to Gain New Insights into Paradigm Atmospheric Sciences Problems

Chapter VI. Adjoint Sensitivity Analysis Procedure for Operational Meteorological Applications

**Update February 8, 2010.** I have found that there are papers on this subject directly related to models of the Earth’s climate systems. I’m not surprised and especially that Dan Cacuci was on the case back in the early 1980s.

Here are a few good references:

Matthew C. G. Hall, Dan G. Cacuci and M. E. Schlesinger, “Sensitivity Analysis of a Radiative-Convective Model by the Adjoint Method”, Journal of the Atmospheric Sciences, Vol. 39, pp. 2038-2050, 1982.

Matthew C. G. Hall and Dan G. Cacuci, “Physical interpretation of the Adjoint Functions for Sensitivity Analysis of Atmospheric Models”, Journal of the Atmospheric Sciences, Vol. 40, pp. 2537-2546, 1983.

Dan G. Cacuci and Matthew C. G. Hall, “Efficient Estimation of Feedback Effects with Application to Climate Models”, Journal of the Atmospheric Sciences, Vol. 41, pp. 2063-2068, 1984.

I. M. Held and M. J. Suarez, “A Two-Level Primitive Equation Atmospheric Model Designed for Climatic Sensitivity Experiments”, Journal of the Atmospheric Sciences, Vol. 39, pp. 206-229, 1978.

S. Wanabe and R. T. Wetherald, “Thermal Equilibrium of the Atmosphere with a given Distribution of Relative Humidity”, Journal of the Atmospheric Sciences, Vol. 24, pp. 241-259, 1967.

S. Wanabe and R. T. Wetherald, “On the Distribution of Climate Change Resulting from an Increase in CO2 Content of the Atmosphere”, Journal of the Atmospheric Sciences, Vol. 37, pp. 99-118, 1980.

T. L. Bell, “Climate Sensitivity from Fluctuation Dissipation: Some Simple Model Tests”, Journal of the Atmospheric Sciences, Vol. 37, pp. 1700-1707, 1980.

Isaac M. Held and Brian J. Soden, “Water Vapor Feedback and Global Warming”, Annual Review of Energy and Environment, Vol. 25, pp. 441-475, 2000.

Brian J. Soden and Isaac M. Held, “An Assessment of Climate Feedbacks in Coupled Ocean–Atmosphere Models”, Journal of Climate, Vol. 19, pp. 3354-3360, 2006.

Brian J. Soden, Anthony J. Broccoli, and Richard S. Hemler, On the Use of Cloud Forcing to Estimate Cloud Feedback”, Journal of Climate, Vol. 17, No. 19, pp. 3661-3665, 2004.

REFERENCES

Cess, R. D., and G. L. Potter, 1988: A methodology for understanding

and intercomparing atmospheric climate feedback processes in

general circulation models. J. Geophys. Res., 93, 8305–8314.

Cess, R. D., and Coauthors, 1990: Intercomparison and interpretation of

climate feedback processes in 19 atmospheric GCMs. J. Geophys. Res., 95, 16 601–16 615.

Cess, R. D., and Coauthors, 1996: Cloud feedback in atmospheric general

circulation models: An update. J. Geophys. Res., 101, 12,791–794.

Colman, R., 2003: A comparison of climate feedbacks in GCMs.

Climate Dyn., 20, 865–873.

Colman, R., and B. J., McAvaney, 1997: A study of general circulation

model climate feedbacks determined from perturbed SST experiments.

J. Geophys. Res., 102, 19,383–19,402.

Colman, R., S. B. Power, and B. J. McAvaney, 1997: Non-linear climate

feedbacks from perturbed SST experiments. Climate Dynamics 13, 10, 717–731.

Cubasch, U., and R. D. Cess, 1990: Processes and modeling. Climate

Change: The IPCC Scientific Assessment, J. T. Houghton, G. J.

Jenkins, and J. J. Ephraums, Eds., Cambridge University Press,

365 pp.

Gates, W. L., J. F. B. Mitchell, G. J. Boer, U. Cubasch, and V. P.

Meleshko, 1992: Climate modeling climate prediction, and model

validation. Climate Change 1992: The Supplementary Report

to the IPCC Scientific Assessment, J. T. Houghton, B. A. Callander,

and S. K. Varney, Eds., Cambridge University Press, 200 pp.

GFDL Global Atmospheric Model Development Team, 2004: The

new GFDL global atmospheric and land model (AM2–LM2):

Evaluation with prescribed SST simulations. J. Climate, in press.

Held, I. M., and B. J. Soden, 2000: Water vapor feedback and global warming. Annu. Rev. Energy Environ., 25, 441–475.

Le Treut, H., Z. X. Li, and M. Forichon, 1994: Sensitivity of the

LMD general circulation model to greenhouse gas forcing associated with two different cloud water parameterizations. J. Climate, 7, 1827–1841.

Mitchell, J. F. B., and W. J. Ingram, 1992: Carbon dioxide and climate:

Mechanisms of changes in cloud. J. Climate, 5, 5–21.

Ramanathan, V., R. D. Cess, E. F. Harrison, P. Minnis, B. R. Barkstrom,

E. Ahmad, and D. Hartmann, 1989: Cloud radiative-forcing

and climate: Results from the Earth Radiation Budget Experiment.

Science, 243, 57–63.

Tsushima, Y., and S. Manabe, 2001: Influence of cloud feedback on annual variation of global-mean surface temperature. J. Geophys. Res., 106, 22 635–22 646.

Wetherald, R. T., and S. Manabe, 1988: Cloud feedback processes in a general circulation model. J. Atmos. Sci., 45, 1397–1415.

Zhang, M. H., R. D. Cess, J. J. Hack, and J. T. Kiehl, 1994: Diagnosticstudy of climate feedback processes in atmospheric GCMs. J. Geophys. Res., 99, 5525–5537.

**Summary**

These notes introduce a few of the ideas and concepts associated with sensitivity analysis for algebraic and ordinary differential equations. By sensitivity I mean what are the effects of changes in the numerical values of the parameters in a system of equations relative to a response function of interest. The response function can take any mathematical form, but I will focus on the values of the dependent variables of the equation system.

## GCMs are Consistent With Chaotic Response …

of equation systems that do not possess chaotic response.

**Executive Summary**

The original PDEs that describe the Rayleigh-Benard convection problem do not posses chaotic behavior. The chaotic response observed with Lorenz-like low-order models (LOM) obtained via mode expansions disappears whenever sufficient resolution is used in the numerical solution methods applied to the original PDEs.

The low order model of the Lorenz equations omits the terms that are responsible for interaction between smaller scales and the large scales. The very interactions that form the basis for invoking the turbulence analogy.

GCMs are consistent with the chaotic response obtained from incorrect low-order models (LOM) expansions of PDEs.

GCMs are consistent with the chaotic response obtained from incorrect solutions to ODEs and PDEs.

GCMs are consistent with the chaotic response observed whenever insufficient resolution is used with numerical solution methods.

## Chaos and ODEs Part 1d: Calculations and Results

**Introduction**

The calculations preformed with the equation systems are summarized in the following discussions. The focus had been on testing for convergence of the numerical solution methods to solutions of the continuous equations. By convergence I mean that as the size of the discrete increment for the independent variable is reduced the calculated values for all dependent variables approach limiting constant values for all values of the independent variable.

None of the systems that are said to exhibit chaotic response have shown convergence. One of those, the Terman system, exhibits periodic response, not chaotic response. The Saltzman system was never intended to be an example for chaotic response.

## Chaos and ODEs Part 1c: The Numerical Methods

The numerical solution methods that will be used to check convergence are given in a file that I uploaded.

Let me know if you see any typos or if you want to see some results for a specific equation system.

I’m thinking that Part 1d will be some numerical results.

## Chaos and ODEs Part 1b: The Equation Systems

The equation systems that will be used to check convergence are given in a file that I uploaded. I had tons o’ links and cross references and other good stuff but nothing worked out. Maybe later.

Let me know if you see any typos or if you want to see some results for a specific equation system.

I’m thinking that Part 1c will be the numerical methods.

UPDATE Nov 19, 2007: I have replaced the original uploaded file with a version that has some identification for me in it.

## Chaos and ODEs Part 1a: The Literature Sources

I have way too much material for a single post. I have spent days trying to force a good fit for all the material into a single document. I have put that aside for a while. So these discussions will be broken into several parts. At some future time I might try to tie all the pieces together by use of HTML/PDF.

## Chaos and Butterflies yet again

The NWP and GCM communities cannot think that a Butterfly will have any influence whatsoever on any physical phenomena or processes of interest. Instead the *phenomenology* of The Butterfly Effect as exhibited by the numerical calculations of some systems of ordinary differential equations is *invoked by hypothesis* into NWP and GCM models/methods/codes. I think we need to limit discussions to the Lorenz-like systems of ODEs, as these seem to be the basis for invoking the phenomenology into the NWP and GCM communities. Otherwise we will get side-tracked into discussions of the “chaotic response of complex dynamical systems” in general.