Climate Change as Seen by Trees and by Climate Modelers

Sue Ann Bowling


Introduction

Over the last few years several attempts have been made to model how CO2 warming will affect forest distribution. One of the questions raised about such studies, that of whether forests can migrate fast enough to keep up with modeled climate change, is probably not as serious as it seems -- in our human-dominated world, if forests are considered desirable, trees will be planted in tundra that has warmed enough for them to survive, even if the nearest seed source is several thousand kilometers away. The best species and provenance of trees is addressed in a number of the other papers in this Proceedings. The question examined in this paper is more basic: how trustworthy are the results of climate change models? In particular, can the results of the models be used as a guide for planting seedings that will mature 50 to 100 years in the future?

The path the models take from the observed and continuing increase in the CO2 content of the atmosphere to the geographical distribution of forests 100 years in the future is a tortuous one, with a number of pitfalls. Further, no single person is normally responsible for or even familiar with all of the steps. Because of this, it seems appropriate to review these steps, with particular emphasis on those known to be poor approximations of reality.

The physical basis of climate

Climate, in the broad sense and as it influences tree growth and survival, is not simply a matter of mean temperatures, precipitation, and wind, but is rather the sum total or ensemble of all of the weather experienced in a region. Statistics such as means and extremes are merely convenient partial descriptions of climate. This being the case, climate change needs to be looked at in terms of changes in the weather.

Weather, in turn, is nothing more than a by-product of the atmospheric transport of energy from regions of radiative surplus to those of radiative deficit, subject to conservation of mass, moisture, and momentum on a rotating planet. As human activities influence the distribution of the radiative surpluses and deficits in the atmosphere, they are influencing the driving mechanism of the whole weather (and ocean current) system.

Radiative surpluses and deficits are emphasized because electromagnetic radiation is the only way in which the Earth is able to gain or lose significant amounts of energy. Energy is transferred from the sun to the earth primarily as electromagnetic waves with wavelengths shorter than 4 micrometers. The Earth in turn radiates energy to space at an intensity depending on temperature (via the blackbody function) and emissivity, with most of this thermal radiation being at wavelengths longer than 4 micrometers (Brasseur and Solomon, 1984). In a steady state climate, the energy absorbed from solar radiation just balances that emitted to space from the earth as a whole. Locally, however, there are major imbalances. At night and at high latitudes in the winter, for instance, the incoming solar radiation is zero, which for balance would require temperatures of - 273 degrees C. At noon near the equator, on the other hand, radiative balance would require a temperature well above the boiling point of water.

Different wavelengths of electromagnetic radiation have different energies per photon, and react in different ways with the Earth's atmosphere and surface. The shortest wavelengths (X-rays and "hard" ultraviolet) are able to ionize atoms and molecules -- knock electrons free with a wide range of energies for the electrons. These wavelengths can be absorbed by a wide range of atmospheric gasses, and are almost completely absorbed above 100 km in the atmosphere, producing the ionosphere (defined on the basis of ionization) and thermosphere (roughly the same region, but defined on the basis of temperature).

Somewhat less energetic radiation -- still in the hard ultraviolet range -- is able to knock apart stable molecules such as O2 and N2, leading to free atoms of O and N which can then combine with the remaining molecules to produce relatively unstable products such as ozone, O3. These unstable molecules can be knocked apart by somewhat softer (longer wavelength) ultraviolet. Ozone in particular forms a shielding layer at around 20 to 50 km elevation against the more dangerous part of the ultraviolet radiation that is not absorbed by O2. The absorption of ultraviolet at these elevations produces a relatively warm layer in the middle and upper stratosphere.

Longer and less energetic solar wavelengths are in general not well absorbed by atmospheric gasses, and for the most part the longest ultraviolet, visible, and very near infrared radiation emitted by the sun reach either clouds or the surface of the Earth. Their absorption or reflection there depends critically on the nature of the surface. Dark coniferous forest, for instance, absorbs more than 90% of the incoming solar radiation reaching the surface, reflecting 10% or less back upward. Fresh snow, on the other hand, may reflect as much as 80% of the incoming solar radiation, leaving only 20% to be absorbed. Clouds also reflect a high percentage (around 60% to 70%) of the incident sunlight (List, 1951), and transmit much of the remainder. The fraction of sunlight reflected is called the albedo. Since the albedo itself depends on climate and season, it is apparent even at this stage that climate is a non-linear process with powerful feedback mechanisms.

As we continue to even longer wavelengths (greater than 4 micrometers), several things happen. In the first place, radiation emitted by objects at normal terrestrial temperatures begins to be more important than that received from the sun. Albedo becomes very low for almost all normal terrestrial surfaces (polished metal is an exception), so most surfaces are emitting near-blackbody radiation. Finally, a new type of absorption and emission by gasses becomes important -- that associated with rotational or vibrational energy levels of molecules. While this process is not important for simple diatomic molecules such as O2 or N2, it is very important for some atmospheric components present in smaller amounts, notably H2O, CO2, and CH4, in the natural atmosphere.

Each of these compounds absorbs at specific wavelengths and is capable of emitting at the same wavelengths. A photon emitted at one of these wavelengths, say one absorbable by CO2, at the surface will probably be absorbed by a CO2 molecule at some height in the atmosphere. This molecule will usually transfer its excess energy to the air by a collision before it reemits a photon at the same wavelength, but the warmth of the atmosphere will assure that a fraction of the CO2 molecules are kept in the excited state and thus the layer will also emit at the same wavelengths it absorbs. The intensity of the emission (which is both upward and downward) depends on the wavelength, the concentration of the gas, and the temperature of the atmospheric layer.

Actual escape of a photon to space from a specific layer is a probabilistic function depending on the absolute column number of molecules capable of absorbing the photon above the layer. But we can define a "level of escape" for any given wavelength where there is a specific probability -- say 50% -- that a photon emitted with an upward inclination will escape the Earth's atmosphere before it strikes a molecule capable of absorbing it. If the amount of an active gas in the atmosphere is increased, the amount above any pressure height will be increased by the same proportion, and the emission to space will occur from higher in the atmosphere. So far as the surface balance is concerned, the downward radiation reaching the surface in wavelengths from radiatively active gasses will come from lower and warmer layers of the atmosphere, and thus the total longwave radiation absorbed by the surface will increase. The overall direct result is a temperature increase near the surface of the earth.

Minor anthropogenic disturbances of both atmospheric gasses and surface albedo probably date back to the domestication of fire, and accelerated with the development of agriculture. The disturbances were, however, small compared with natural processes. During the last century three things have combined to make human influences on the global radiation balance significant: (1) The explosive growth of the human population, spreading agricultural modification of albedo into the remaining "natural" areas of the earth's surface, and incidentally releasing carbon stored in standing forest biomass to the atmosphere as CO2; (2) The increasing use of fossil fuels, returning carbon buried over geological time to the atmosphere as CO2 (recognized by Arrhenius, 1908, and further publicized by Callendar, 1938, 1949); and (3) The growth of the chemical industry, releasing compounds which do not occur in nature, such as the chlorofluorocarbons (CFC's) which are implicated in the destruction of ozone, into the atmospheric system. The three are of course interrelated; it is unlikely that the global population could be fed for long without fossil fuels and chemicals. The overall result, however, has been significant changes in the amounts and composition of minor atmospheric gasses.

The two changes most significant for high-latitude forest growth are (1) large fractional increases in several compounds which actively absorb and emit thermal infrared radiation, most notably CO2 and CH4, and (2) the possible reduction of the ozone layer, especially in spring at high latitudes, due to the presence of CFC's in conjunction with natural dynamical and cloud physical processes. Both CO2 and CH4 have increased measurably and significantly over the last half century, and carbon dioxide, which is produced by the burning of fossil fuels, seems likely to double by the middle of the twenty-first century. The source of the increase in methane is still controversial, which makes its continued increase harder to forecast. Since both compounds affect the distribution of energy sources and sinks, which drives the whole weather and climate system, we can reasonably expect that some climate changes will occur as a result. But what changes are important?

Climate as seen by a tree

A tree's climate as seen by a climatologist

Trees do not read thermometers. Neither do they measure precipitation, wind speed or snowfall. In particular, they have no knowledge of or interest in these parameters as measured at the nearest climatological station. This is not to imply either that trees are sentient or that climate is unimportant to a tree, but only to emphasize that a tree responds to the environment of its own living tissues, not to what is measured a few kilometers or even a few meters away from its branches or roots. While the internal environment is related to the external environment, the relationship is not one to one.

From the tree's point of view, then, the critical factors are those which affect the life processes of the tree. These certainly include:


A tree's climate as seen by the tree

All of these bits of its environment as seen by the tree are of course dependent on climate as we more normally think of it, but usually in complex ways. The temperature of a leaf or needle, for instance, is normally determined by:


Primary factors affecting the temperature of a leaf

The same is true of most parts of its environment as seen by the tree. Water balance is a function of precipitation, soil type (which itself is partly due to history), relative humidity, wind speed and temperature as well as the geometry and health of the tree's root system and the population of sap-sucking insects.

The availablility of nutrients and the uptake of toxic substances from the soil depends on soil history, precipitation (including its pH), and microorganisms which may be sensitive to soil temperature and moisture.

The physical state of soil almost always depends on temperature-dependent weathering processes as well as the past vegetation history.

In marginal continental environments mean annual temperature and surface vegetation may combine to produce permafrost, which in turn influences moisture transport and space for root growth as well as soil temperature. Shading of the soil by trees or tall grass may in turn influence the permafrost table.

Physiologically active radiation can be affected by aerosols and cloud cover as well as ozone.

Physical damage may be caused directly by wind or snow load, or indirectly by drought which leads to forest fire.

Damage by herbivores is often dependent on weather conditions which allow pests to survive. The possibility that generally improved weather could allow greater insect damage, for instance, must be kept in mind.

Numerous attempts have of course been made to link upper/poleward tree line to the mean temperature of the warmest month, growing degree days, or wind speeds. Several of the papers in this volume deal with these attempts. In fact, the limiting factor(s) for tree growth will vary with microsite and may or may not relate well to the values measured at the nearest climatological station. One promising approach is to relate growth to actual tissue temperatures, and tissue temperatures to a combination of radiation temperature, air temperature, and wind speed. (See, for instance, Grace and James in this volume.) The relationship between climate and tree growth, however, is probably the lesser part of the problem.

Climate from the point of view of the climate modeller


Climate as seen by a climate modeler

Some simple effects of increases in the so-called "greenhouse" gasses that are increasing due to human activities are relatively easy to calculate. It was recognized over a century ago (Fourier, 1827) that increased carbon dioxide with no other changes would lead to warmer temperatures. Arrhenius (1908) estimated at the beginning of this century that a doubling of carbon dioxide would result in warming the earth by about 4 degrees C, and recognised also that burning of fossil fuels could lead to such a result in the course of a few centuries. A somewhat more sophisticated analysis took explicit account of the fact that on a planet with as much free liquid water as ours, an increase in temperature would increase the amount of water vapor in the air. (Arrhenius recognized this fact, but it is unclear whether it was included in his calculations.) Since water is also a greenhouse gas, this would provide a positive feedback and increase the temperature still more.

Manabe and Wetherald (1967) calculated the warming expected from doubling carbon dioxide content with average incoming solar radiation, allowing vertical redistribution of energy through convection while holding the relative humidity constant, and obtained a warming of around 2 degrees C. Even very simple models allow us to say that if the temperature of the air column stayed the same, an increase in carbon dioxide would decrease the likelihood of radiation frosts. Since the latest spring frosts and the earliest ones in autumn are usually of the radiation frost type, this would lead to slight increases in the length of the growing season, regardless of how the average temperature changed. Schneider (1975) gives a summary of these early energy balance models and their evolution.

When the problem is looked at on a global scale, an increase in radiatively active gasses will undoubtedly produce changes in the location and intensity of the regions of positive and negative radiative balance in the atmosphere. Since it is the distribution of these regions that drives the entire weather/climate/ocean current system, it is almost certain that some changes in the climate will occur. But the climate system is extremely complex, and contains both negative and positive feedbacks. As an example, clouds play a major role in the radiation balance at both short and long wavelengths. A small increase in cloudiness, a change in the microphysics that would increase cloud brightness, or even a small change in the height of cloud tops could balance the increase in greenhouse gasses as far as global average temperature was concerned (e.g., Ramanathan et al., 1989). It is by no means certain, however, that changes would in fact be in the direction of increasing temperature leading to increasing global cloud albedo. Even if it occurred it would still represent a change in the distribution of the energy sources and sinks in the atmosphere. This in turn would lead to changes in the circulation of the atmosphere which would in turn affect regional climates.

Most modern climate modelling is carried out using general circulation models (GCM's). The goal of the climate modeller using such models is:

The physical laws governing the atmosphere are well known. They consist of:

In order to solve these equations, we need to know atmospheric composition, initial conditions, and boundary conditions. The atmospheric composition is important primarily for radiative transfer, and must include CO2, O3, CH4, some aerosols and exotics such as the CFC's as well as the major componants of the atmosphere. Parts of the atmospheric composition, notably water vapor, liquid water (clouds), and some aerosols (wind-lifted dust, for instance) should be and sometimes are calculated within the model. The model calculations of aerosols and clouds (which affect the calculations of water vapor, precipitation, and radiative transfer) are at present generally agreed to be very poor (e.g., Cess et al, 1989; Cess et al., 1991).

The initial state of the atmosphere is of great concern in short-term forecasting, which starts from the same equations. In practice, this is a major limitation to forecasting, as the equation set is extremely non-linear, and potentially subject to chaotic behavior -- i.e., two simulations with indetectably slight differences in initial conditions (which in the atmosphere are in any case only roughly and sparsely measured) may diverge to totally different states (Lorenz, 1964). In climate modelling, the usual approach is to start with something approximating a present climate and assume that if the equations are integrated long enough the initial conditions will be lost as a transient, at least so far as the statistics of the simulation are concerned. The sampling of model results starts after the model appears to have "settled down" and lost the influence of initial conditions. Initial surface conditions (such as the presence or absence of sea ice) may well be important, but these would be included as boundary conditions. Independence of long-range climate statistics on initial state (within reasonable limits) is, however, an unproven assumption, especially if the role of the oceans is taken into account.

Ignoring for the moment the possibility that chaos-like conditions may occur on climatic time scales, let us look at how these equations are solved in practice. It is obviously impossible to specify initial conditions and solve the equations at an infinite number of points in the atmosphere. In practice, initial conditions are specified and the equations are solved at a finite number of points in some sort of three-dimensional grid over the surface of the earth. The horizontal and vertical spacing of gridpoints and whether the equations are solved as waves with a finite number of wave numbers or directly at the gridpoints differ among the models. In every case, however, the topographic and thermal characteristics of the lower boundary must be smooth on the scale of the grid used.

Since to a first approximation the number of points varies inversely as the cube of the distance between points, and the model time step must also be reduced as the distance between points is reduced, the computer time needed to simulate a given amount of model time varies approximately as the fourth power of the spatial resolution. (This assumes that the vertical separation between layers is reduced in accord with the horizontal separation between points.) Since a climate model must be run for a long model time to obtain any kind of model statistics, climate models are run on a much coarser grid (usually on the order of 5 degrees of latitude for horizontal point spacing) than are weather forecasting models. This has two implications. The first is that the topography is smoothed to the point that the Himalayas and the American Cordillera are essentially the only mountain ranges entering the simulations, and these two major ranges are greatly simplified. This means that regional climates with strong orographic control cannot be simulated well with the global models. Nested regional models, in which the global model contributes the boundary conditions at the horizontal boundary of a limited region, are increasingly being used for climate studies, but the problem of the region possibly influencing the global model remains.

The second implication is that meteorological processes taking place at spatial scales less than several grid points must be parameterized, or approximated by more or less empirical equations linking the small-scale processes to what the model says is going on at larger scales. Each model is "tuned" by adjusting the constants in the parameterizations until the model more or less reproduces the present climate when run with present boundary conditions. These parameterized processes include all convective processes, with or without cloud formation and/or precipitation, on scales up to hurricane or typhoon size. They also include all cloud microphysics (including precipitation processes), boundary layer processes involving transfer of energy and water vapor into and out of the atmosphere at the surface (Garratt, 1990), and indirectly any radiative process involving surfaces or clouds. Thus the energy sources and sinks in the models all enter through parameterized processes.

The actual solution of the equations results in matrices of vector winds, vertical motion, temperature, and moisture content at each time step and grid point. The wind and temperature fields away from the lower boundary are probably the most accurate output of the models.

The moisture field has an additional source of uncertainty in the very crude modeling of cloud formation. Although details vary among models, the basic principle is that if the mixing ratio goes above saturation, any excess water vapor condenses and precipitates out. In fact, condensed water may reach the ground as precipitation, fall to a lower and drier elevation of the atmosphere and evaporate there (which some models allow), or remain as droplets or ice crystals too small to fall significantly which eventually evaporate as drier air mixes with the cloud.

Cloud density, optical properties (albedo) and lifespan as well as the formation of precipitation actually depend critically on nucleation processes for both water droplets and ice crystals, which current models neglect. The fractional cloud cover during convection (vertical overturning driven by heating at the ground surface), which in the real world may vary from near 0 to almost 100%, is often specified arbitrarily in the models. As a result of this poor parameterization of clouds, both precipitation and atmospheric moisture content predicted by any model are likely to be much less accurate than the fields of motion and temperature in the free air. In addition, since the condensation process is a source of heat and re-evaporation of clouds or precipitation an energy sink, poor parameterization of clouds distorts the energy sources and sinks driving the system and may thus have some effect on the motion and temperature fields.



Steps from modeled circulation to climatological outputs. Italicized outputs represent calculations which are not usually given as model outputs, but which could readily be obtained from the models if desired. Note that all climatological outpus must pass through the gray areas of the boundary layer and/or cloud physics.

Surface temperatures and winds are not given directly by the calculations, but require another step back through the parameterized boundary layer. This may be particularly critical at high latitudes in winter, as the parameterizations are at their worst when the surface radiative budget is negative and energy has to be transported downward. Under these circumstances both winds and temperatures at the surface are likely to be very poorly modeled. Precipitation may be even worse. The problems with cloudiness (which clearly affects both surface air temperature and radiative temperature) will also influence the climate as a tree perceives it.

Parameterizations generally differ among models, and a number of recent papers have compared models using different parameterization schemes and methods of calculation. There are difficulties with such comparisons. Differences in how well the model simulates present climate will carry over into differences in response to perturbations such as doubling CO2 (Mitchell et al., 1987). Attempts to evaluate differences in treatment of specific problem areas such as clouds are hindered by differences in the way clouds are coupled into other processes in different models. Mitchell et al. (1989) have presented results of a single model with different assumptions of cloud parameterizations, showing very high sensitivity of climate to how clouds are handled.

Nevertheless, several recent papers have addressed the problem. Cess et al. (1989, 1990, 1991) compare the effects of the cloud-climate feedbacks, snow-climate feedbacks and climate feedbacks in general in as many as 19 different climate models. Mearns et al. (1990) compare variability of modeled and observed climates for three versions of the NCAR Community Climate Model. Cunnington and Mitchell (1990) have discussed the effects of convective parameterizations, while Grotch and MacCracken (1991) and Gutowski et al. (1991) have looked at regional variations among models. Kellogg and Zhaio (1988) and Zhaio (1988) have examined the areas of agreement and disagreement among models as regards predictions of soil moisture.

Some of the models have been tested by using them to simulate past climates, such as ice age climates (e.g., Kutzbach, 1987; Rind, 1988). Unfortunately, no model has as yet been able to predict the onset of glaciation or deglaciation, though several can maintain ice age conditions as a steady state.

In addition to the problems with parameterization of cloud and boundary layer processes, almost all models are hampered on long time scales by the slow feedbacks between boundary conditions and climate. On land, these feedbacks include such factors as desertification or the replacement of grassland with forest (or forest with grassland) as well as the melting or advance of ice sheets. These processes produce notable changes in albedo and in the availability of water for evaporation at the land surface.

Ocean surface temperatures and the extent of sea ice represent a problem of even more concern. While some models do have "swamp" oceans which respond to local energy balances, the real ocean has both surface and deep water energy exchanges and in fact transports a significant fraction of the total energy necessary to balance the horizontal radiative energy imbalances (Vonder Haar and Oort, 1973). Because the time scale for the deep circulation is so long -- on the order of hundreds of years -- there are serious mathematical problems with coupling oceanic and atmospheric models. Bryan et al. (1975) and Bryan (1984) began attacking the problem. Recent studies by Rind and Chandler (1991) using oceanic and atmospheric models run separately suggest that feedbacks between the major deep oceanic circulation and the atmosphere are such as to enhance climate change -- i.e., the feedback loop, which is not accurately handled in any existing model, is positive. Several researchers have pointed out the potential for ocean-atmosphere linking to produce major steps in climate (Broeker, 1987; Broeker et al., 1985; Covey, 1991, Covey and Barron, 1988). Comparison among models cannot even evaluate the magnitude of errors of this type, as all of the current models have the same weakness.

Conclusions

I think it is clear from the above that predicting future forest distribution from the output of a single climate model is a rather questionable enterprise. This does not, however, mean that the models are of no use.
So far as the present models are concerned, a key factor is consensus among models. There are some results on which most or all the current models seem to be in agreement. A global temperature rise in response to increasing greenhouse gasses, for instance, is predicted by most models and also indicated by past correlations between carbon dioxide content and global temperature. There is considerably more disagreement among models about predicted changes in precipitation and regional climate change, while none of the present models can claim to predict precipitation with any accuracy in areas of strong relief. In general, current models can reasonably be used to predict qualitative forest boundary changes in regions where most of the state-of-the-art models are in agreement.

Beyond this, foresters need to push for improvement of the climate models in three ways.

First, models are needed that do a better job of parameterizing the processes that are related to both the energetics of the models and the relationship of the modeled surface climate to the modeled circulation. Probably the most important of these processes at the moment are:

The second major change needed is to include in the climate models processes currently treated as boundary processes, such as the circulation of the oceans. Although this is a formidable challenge mathematically, it may well be essential if we are going to be able to predict the results of our unthinking and to a large extent uncontrollable manipulation of the composition of our atmosphere.

Finally, foresters need to interact with modellers in determining what are the most useful model outputs for their particular applications. Models work by creating their own internal weather, and almost any aspect of that interal weather can be captured and recorded if the modeller so desires. If a forester needs degree days above 30 degrees C, frost days in the summer months, number of hours with surface winds above a given threshold, consecutive days without precipitation, or a similar output, this information is generated by general circulation models. Its accuracy is subject to the same caveats is are the standard climate statistics, but getting it out of the model is more a matter of communicating its importance to the modellers than of any real programming difficulties.

Ideally, future generations of climate models will be improvements on today's not only in the accuracy of their depiction of the complex, chaotic system we call climate, but in improvement in the availability of a broad range of kinds of climate data. It is up to the users of non-standard measures of climate, however, to work with the modellers in developing these outputs.


References

Arrhenius, Svante, 1908: The celestial bodies, in particular the Earth, as abodes of organisms. Chapter II in: Worlds in the Making, Harper Brothers, pp 39-63

Brasseur, Guy and Susan Solomon, 1984: Aeronomy of the Middle Atmosphere. D. Reidel, Dordecht, 441 pp.

Broecker, Wallace S., 1987: Unpleasant surprises in the greenhouse? Nature 328, 123-126.

Broecker, Wallace S., Dorothy M. Peteet and David Rind, 1985: Does the ocean-atmosphere system have more than one stable mode of operation? Nature 315, 21-25.

Bryan, Kirk, 1984: Accelerating the convergence to equilibrium of ocean-climate models. Journal of Physical Oceanography 14, 666-673.

Bryan, Kirk, Syukuro Manabe and Ronald C. Pacanowski, 1975: A global ocean-atmosphere climate model Part II: The oceanic circulation. Journal of Physical Oceanography 5, 30-46.

Callendar, G. S., 1938: The artificial production of carbon dioxide and its influence on temperature. Quarterly Journal of the Royal Meteorological Society 64, 223-237.

Callendar, G. S., 1949: Can carbon dioxide influence climate? Weather 4, 310-314.

Cess, R. D., G. L. Potter, J. P. Blanchet, G. J. Boer, S. J. Ghan, J. T. Kiehl, H. Le Treut, Z.-X. Li, X.-Z Liang, J. F. B. Mitchell, J.-J. Morcrette, D. A. Randall, M. R. Riches, E. Roeckner, U. Schlese, A. Slingo, K. E. Taylor, W. M. Washington, R. T. Wetherald, and I. Yagai, 1989; Interpretation of cloud-climate feedback as produced by 14 atmospheric general circulation models. Science 245, 513-516.

Cess, R. D., G. L. Potter, J. P. Blanchet, G. J. Boer, A. D. Del Genio, M. D_qu_, V. Dymnikov, V. Galin, W. L. Gates, S. J. Ghan, J. T. Kiehl, A. A. Lacis, H. Le Treut, Z.-X. Li, X.-Z. Liang, B. J. McAvaney, V. P. Meleshko, J. F. B. Mitchell, J.-J. Morcrette, D. A. Randall, L. Rikus, E. Roeckner, J. F. Royer, U. Schlese, D. A. Sheinin, A. Slingo, A. P. Sokolov, K. E. Taylor, W. M. Washington, R. T. Wetherald, I. Yagai and M.-H. Zhang, 1990: Intercomparison and interpretation of climate feedback processes in 19 atmospheric general circulation models. Journal of Geophysical Research 95, 16601-16615.

Cess, R. D., G. L. Potter, M.-H. Zhang, J. P. Blanchet, S. Chalita, R. Colman, D. A. Dazlich, A. D. Del Genio, V. Dymnikov, V. Galin, D. Jerrett, E. Keup, A. A. Lacis, H. Le Treut, X.-Z. Liang, J.-F. Mahfouf, B. J. McAvaney, V. P. Meleshko, J. F. B. Mitchell, J.-J. Morcrette, P. M. Norris, D. A. Randall, L. Rikus, E. Roeckner, J. F. Royer, U. Schlese, D. A. Sheinin, A. Slingo, A. P. Sokolov, K. E. Taylor, W. M. Washington, R. T. Wetherald, and I. Yagai, 1991: Interpretation of snow-climate feedback as produced by 17 general circulation models. Science 253, 888-892.

Covey, Curt, 1991: Credit the oceans? Nature 352, 196-197.

Covey, Curt and Eric Barron, 1988: The role of ocean heat transport in climate change. Earth-Science Reviews 24, 429-445.

Cunnington, W. M. and J. F. B. Mitchell, 1990: On the dependence of climate sensitivity on convective parametrization. Climate Dynamics 4, 85-93.

Fourier, M., 1827: Memoire sur les temperatures du globe terrestre et des espaces planetaires. Mem. de l'Academie Royale des Sciences de l'Institut de France 7, 569-604.

Garratt, J. R., 1990: The sensitivity of large-scale models to PBL and land-surface parameterizations. Planetary Boundary Layer Model Evaluation Workshop, Reading, UK, 14-15 Aug 1989, published as WCRP-42, WMO/TD No 378, pp 24-28.

Grace, John and Jennifer James, 1993: Physiology of trees at treeline. NATO Advanced Research Workshop on Forest Development in Cold Climets held June 18-23, 1991 in Laugarvain, Iceland. Published by Plenum in their ASI Series Series A: Live Sciences Vol 244, 1993, pages 105-114

Grotch, Stanley L. and Michael C. MacCracken, 1991: The use of general circulation models to predict regional climatic change. Journal of Climate 4, 286-303.

Gutowski, William J. Jr., David S. Gutzler and Wei-Chyung Wang, 1991: Surface energy balances of three general circulation models: implications for simulating regional climate change. Journal of Climate 4, 121-134.

Hansen, J., I. Fung, A. Lacis, D. Rind, S. Lebbedeff, R. Ruedy and G. Russell, 1988: Global climate changes as forecast by Goddard Institute for Space Studies three-dimensional model. Journal of Geophysical Research 93, 9341-9364.

Kellogg, William W. and Zong-ci Zhao, 1988: Sensitivity of soil moisture to doubling of carbon dioxide in climate model experiments. Part I: North America. Journal of Climate 1, 348-366.

Kutzbach, John E., 1987: Model simulations of the climatic patterns during the deglaciation of North America. The Geology of North America K-3, Ch 19, pp 425-446.

List, 1951: Smithsonian Meteorological Tables, sixth revised edition. Smithsonian Institution Press, Washington, 442-443.

Lorenz, Edward N., 1964: The problem of deducing the climate from the governing equations. Tellus 16, 1-11.

Manabe, Syukuro and Richard T. Wetherald, 1967: Thermal equilibrium of the atmosphere with a given distribution of relative humidity. Journal of the Atmospheric Sciences 24, 241-259.

Manabe, Syukuro and Richard T. Wetherald, 1986: Reduction in summer soil wetness induced by an increase in atmospheric carbon dioxide. Science 232, 626-628.

Mearns, L. O., S. H. Schneider, S. L. Thompson and L. R. McDaniel, 1990: Analysis of climate variability in general circulation models: comparison with observations and changes in variability in 2x CO2 experiments. Journal of Geophysical Research 95, 20469-20490.

Mitchell, J. F. B., C. A. Wilson and W. M. Cunnington, 1987: On CO2 climate sensitivity and model dependence of results. Quart. J. Roy. Met. Soc. 113, 293-322

Mitchell, J. F. B., C. A. Senior and W. J. Ingram, 1989: CO2 and climate: a missing feedback? Nature 341, 132-134

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.

Rind, David, 1988: Dependence of warm and cold climate depiction on climate model resolution. J. of Climate 1, 965-997.

Rind, D. and M. Chandler, 1991: Increased ocean heat transports and warmer climate. Journal of Geophysical Research 96, 7437-7461.

Schneider, Stephen H., 1975: On the carbon dioxide-climate confusion. Journal of the Atmospheric Sciences 32, 2060-2066.

Vonder Haar, T. H., and Oort, A. H., 1973, New estimates of annual poleward transport by Northern Hemisphere oceans. Journal of Phys. Oceanography 3, 169-172

Zhao, Zong-Ci, 1988: Sensitivity of soil moisture to doubling of carbon dioxide in climate experiments, part II: The Asian monsoon region. Journal of Climate 4, 367-378.



The original paper was presented at the NATO Advanced Research Workshop on Forest Development in Cold Climets held June 18-23, 1991 in Laugarvain, Iceland. It was published by Plenum in their ASI Series Series A: Live Sciences Vol 244, 1993, pages 189-202. The version given here is being updated somewhat from the original paper to reflect advances in modeling over the last several years. Come back in a few weeks and there will most likely be some new references.
Last update September 4, 1996.