Climate Models, Climate Change and Feedback Processes
First let’s take a quick look at reliability of the models used to
make projections of future climate change:
Global mean near-surface temperatures over the 20th century from observations (black) and as obtained from 58
simulations produced by 14 different climate models driven by both natural and human-caused factors that
influence climate (yellow). The mean of all these runs is also shown (thick red line). Temperature anomalies are
shown relative to the 1901 to 1950 mean. Vertical grey lines indicate the timing of major volcanic eruptions.
Next let’s take a quick look at the projections of future climate
change that these models make (IPCC, 2007):
The figure below show the multi-model averages and assessed ranges for surface warming and
the table summarizes the projected global average surface warming and sea level rise at the end
of the 21st century. In the figure, solid lines are multi-model global averages of surface warming
(relative to 1980–1999) for the scenarios A2, A1B and B1, shown as continuations of the 20th
century simulations. Shading denotes the ±1 standard deviation range of individual model annual
averages. The orange line is for the experiment where concentrations were held constant at year
2000 values. The grey bars at right indicate the best estimate (solid line within each bar) and the
likely range assessed for the six SRES marker scenarios. The assessment of the best estimate and
likely ranges in the grey bars includes the AOGCMs in the left part of the figure, as well as
results from a hierarchy of independent models and observational constraints.
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The text box below lists the scenarios that are in current use.
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THE EMISSION SCENARIOS OF THE IPCC SPECIAL REPORT ON EMISSION
SCENARIOS (SRES)
A1. The A1 storyline and scenario family describes a future world of very rapid economic growth,
global population that peaks in mid-century and declines thereafter, and the rapid introduction of new
and more efficient technologies. Major underlying themes are convergence among regions, capacity
building and increased cultural and social interactions, with a substantial reduction in regional
differences in per capita income. The A1 scenario family develops into three groups that describe
alternative directions of technological change in the energy system. The three A1 groups are
distinguished by their technological emphasis: fossil-intensive (A1FI), non-fossil energy sources
(A1T) or a balance across all sources (A1B) (where balanced is defined as not relying too heavily on
one particular energy source, on the assumption that similar improvement rates apply to all energy
supply and end use technologies).
A2. The A2 storyline and scenario family describes a very heterogeneous world. The underlying
theme is self reliance and preservation of local identities. Fertility patterns across regions converge
very slowly, which results in continuously increasing population. Economic development is primarily
regionally oriented and per capita economic growth and technological change more fragmented and
slower than other storylines.
B1. The B1 storyline and scenario family describes a convergent world with the same global
population, that peaks in mid-century and declines thereafter, as in the A1 storyline, but with rapid
change in economic structures toward a service and information economy, with reductions in material
intensity and the introduction of clean and resource-efficient technologies. The emphasis is on global
solutions to economic, social and environmental sustainability, including improved equity, but without
additional climate initiatives.
B2. The B2 storyline and scenario family describes a world in which the emphasis is on local solutions
to economic, social and environmental sustainability. It is a world with continuously increasing global
population, at a rate lower than A2, intermediate levels of economic development, and less rapid and
more diverse technological change than in the B1 and A1 storylines. While the scenario is also
oriented towards environmental protection and social equity, it focuses on local and regional levels.
An illustrative scenario was chosen for each of the six scenario groups A1B, A1FI, A1T, A2, B1 and
B2. All should be considered equally sound. The SRES scenarios do not include additional climate
initiatives, which means that no scenarios are included that explicitly assume implementation of the
United Nations Framework Convention on Climate Change or the emissions targets of the Kyoto
Protocol.
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How well do we Understand Climate Feedback Processes?
A Review Article by: Sandrine Bony, Robert Colman, Vladimir M. Kattsov, Richard P. Allan,
Christopher S. Bretherton, Jean-Louis Dufresne, Alex Hall, Stephane Hallegatte, Marika M.
Holland, William Ingram, David A. Randall, Brian J. Soden, George Tselioudis, and Mark J.
Webb. Journal of Climate, Vol. 19, 3445-3482, August 2006.
1. Abstract/Introduction
This review addresses the physical mechanisms involved in climate feedbacks. Climate
feedbacks are processes (internal to the climate system) that either amplify or damp the climate
response to an external perturbation. The focus of the review is on the feedbacks associated with
climate variables (i) that directly affect the top-of-the-atmosphere (TOA) radiation budget, and
(ii) that respond to surface temperature mostly through physical (rather than chemical or
biochemical – this excludes feedbacks that might be associated with the carbon cycle or with
aerosols, for example, and with soil moisture and ocean processes) processes.
Review of Climate Feedbacks
Water Vapor
Feedback (+)
Snow/ice – Albedo
Feedback (+)
IR flux – Temp.
Feedback (–)
Clouds Feedbacks
????????
Climate sensitivity estimates depend strongly on radiative feedbacks associated with the
interaction of the earth’s radiation budget with water vapor, clouds, temperature lapse rate, and
surface albedo in snow and sea ice regions – all feedbacks have a well-established role in
General Circulation Models (GCMs) and their estimates of climate sensitivity.


Water vapor and temperature – positive feedback
Snow and sea ice and temperature – positive feedback
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
lapse-rate and temperature – negative (positive) radiative feedback if a warming is larger
(smaller) in the upper troposphere than at low levels, compared to a uniform temperature
change
 the stronger the decrease of temperature with height, the larger the greenhouse effect –
discuss
 cloud feedbacks – can be positive or negative, uncertain for the most part
Compare quantitative estimates of climate feedbacks – Figure 1:





water vapor is the strongest feedback (magnitude)
cloud feedbacks next to WV in strength, followed by surface albedo and lapse rate
inter-model mean spread is substantial in all cases
range in strength of response is large for WV and cloud feedbacks
spread of combined WV and lapse rate feedbacks is ~ ½ of individual feedbacks (anticorrelated)
Summary: cloud feedbacks are very important and are responsible for a large part of the
uncertainties associated in climate sensitivity estimates.
It is important to look at the physical mechanisms behind the global estimates of climate
feedbacks because that would help us (i) to understand the reasons why climate feedbacks differ
or not among models, (ii) to assess the reliability of the feedbacks produced by the different
models, and (iii) to guide the development of strategies of model–data comparison relevant for
observationally constraining some components of the global feedbacks.
2. Clouds feedbacks
So, where are the clouds?
Fig. 2 illustrates that the atmospheric dynamics (this is: the large-scale organization of the
atmosphere) is a strong function of latitude. In the Tropics, large-scale overturning circulations
prevail. These are associated with narrow cloudy convective regions and widespread regions of
sinking motion in the midtroposphere (generally associated with a free troposphere void of
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clouds and a cloud-free or cloudy planetary boundary layer). In the extratropics (midlatitudes
and such), the atmosphere is organized in large-scale baroclinic disturbances.
FIG. 2. Composite of instantaneous infrared imagery from geostationary satellites (at 1200 UTC 29 Mar 2004)
showing the contrast between the large-scale organization of the atmosphere and of the cloudiness in the Tropics and
in the extratropics. [From SATMOS (©METEO-FRANCE and Japan Meteorological Agency).
The Tropics:
FIG. 3. Two conceptual representations of the relationship between cloudiness and large-scale atmospheric
circulation in the Tropics: (a) structure of the tropical atmosphere, showing the various regimes, approximately as a
function of SST (decreasing from left to right) or mean large-scale vertical velocity in the midtroposphere (from
mean ascending motions on the left to large-scale sinking motions on the right). [From Emanuel (1994).] (b) Twobox model of the Tropics used by Larson et al. (1999). The warm pool has high convective clouds and the cold pool
has boundary layer clouds. Air is rising in the warm pool and sinking across the inversion in the cold pool.
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 compare above with mantle convection – here intense and narrow regions of “upwelling”
(convective, tall, towering clouds) and broad regions of “downwellings”. Both systems
heated from below, mantle has internal heating source also.
A continuous look at the same conceptual model is summarized in the figure below. To study
vertical motion in the atmosphere one can use the vertical velocity w (defined as dz / dt in a
Cartesian coordinate system, where z is the vertical coordinate) or omega ≡ ω, which is the
pressure change (here one uses pressure surfaces instead of height surfaces) following the motion
of a parcel, defined as   dp / dt . It can be shown (see the classical textbook by Holton: “An
Introduction to Dynamic Meteorology”) that to a very good approximation (namely, for most
practical purposes) there is a simple and direct equivalency between w and  :    gw , where
ρ is the density and g is gravity. Pressure can be measured in pounds/in2 = psi, Pascals, Pa
(Newton/m2) or bars (and millibars = mbar). Common in meteorology is the hPa (hecto Pascal)
which is equivalent to mbar. Basically, the 500 mbar is the 500 hPa surface.
Here the large-scale vertical velocity ω is used as a proxy for the large scale vertical motions in
the atmosphere and to look at the Hadley-Walker circulation in the tropics, assigning statistical
weight to the different circulation regimes (ascending versus descending motions and their
associated atmospheric vertical structure).
sinking – more areas
of descending motion
at 500 mb in the
Tropics
ascending motion
large amounts of
precipitation associated
with ascending motion
over warm pool
low precipitation
regime
lower clouds – eastern
parts of ocean basins
clouds over warm pool –
high cloud tops/tall clouds
(convective), large anvils
FIG. 4. (a) PDF Pω of the 500-hPa monthly mean large-scale vertical velocity ω500 in the Tropics (30°S–30°N)
derived from ERA-40 meteorological reanalyses, and composite of the monthly mean (b) GPCP precipitation and
(c) ERBE-derived longwave and shortwave (multiplied by -1) cloud radiative forcing in different circulation
regimes defined from ERA-40 ω500 over 1985–89. Vertical bars show the seasonal standard deviation within each
regime. [After Bony et al. (2004).]
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Considering dynamical regimes defined from ω allows us to classify the tropical regions
according to their convective activity, and to segregate in particular regimes of deep convection
from regimes of shallow convection. ‘Convection’ is used to describe thermally driven turbulent
mixing in the atmosphere. ‘Deep convection’ refers to convection where vertical motion takes
parcels from the lower atmosphere to above 500 hPa and it requires low level convergence/upper
level divergence, upper level relative humidity in excess of 70%, unstable layer and a triggering
mechanism. ‘Shallow convection’ refers to convection where vertical lifting is capped at about
500 hPa, it requires basically the same ingredients as deep convection except that all have to take
place in the lower atmosphere and it is then not necessary to have upper level divergence but a
mid level cap (a local inversion, for example).
In general terms we might consider the panels as corresponding to the western and eastern parts
of the ocean basin, to the left of 0 and to the right of 0, respectively. Panels (b) and (c) are the
precipitation and clouds fields (from observations) composited with the regimes determined from
the ω fields. The blue arrows in the figure indicate the extreme regimes (tails of the PDF): small
weight for areas of strong ascending and areas of the strongest descending motions. This
basically says that although there is intense large-scale ascending and sinking motions in the
atmosphere, the areas over which that happens are small and so their importance to the general
circulation (hence to climate) may not be that important. It is the impact of the largest areas, over
which motions may not be so intense, that matters. Panels (b) and (c) must be looked at in
conjunction with (a). Panel (c) is the CRF (Cloud Radiative Forcing) in the short wave and long
wave parts of the spectrum. The CRF is a very useful quantity (and hence the importance of
these composited diagrams) because it integrates all possible effects of clouds: low, high, thick,
thin, etc., and hence it tells us about the impact of clouds on climate (not just a sense of amount
of clouds, for example). For example, looking at panel (c) we can see that there are areas with a
very high value of CRF (both in the SW and LW, the left side of the panel) but when we look at
the corresponding values of Pω we see that the values of CRF happen over small areas of the
tropics. The same can be said for the other tail-end of the CRF composite with Pω. What the Pω
curve is also showing is a maximum occurring over areas of slow descending motion, generally
over the eastern part of the ocean basin, (recall the Walker circulation pattern over the Pacific
basin). There we also see that the values of CRF are not so big but these areas are very
important climatologically. In sum: these studies have been able to show that the most vigorous,
convectively speaking, regions may not be the most important climatologically in terms of
climate change.
The Extra-tropics:
At midlatitudes, the atmosphere is mostly organized in synoptic weather systems. An idealized
baroclinic disturbance is represented in Fig. 5a, (included below) showing the warm and cold
fronts outward from the low-level pressure center of the disturbance, together with the
occurrence of sinking motion behind the cold front and rising motion ahead of the warm front.
The different parts of the system are associated with specific cloud types, ranging from thin lowlevel cumulus clouds behind the cold front, thin upper-level clouds ahead of the warm front, and
thick precipitating clouds over the fronts (Fig. 5b). For good discussion of extratropical weather
systems (and much more!), see the book by Wallace and Hobbs (1977).
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

Barotropic Atmosphere – one in which the density depends only on the pressure, so that
isobaric surfaces are also surfaces of constant density.
Baroclinic Atmosphere – an atmosphere in which the density depends on both the
temperature and the pressure. In a baroclinic atmosphere the geostrophic wind generally
has vertical shear and this shear is related to horizontal temperature gradients.
FIG. 5. (top) Schematic of a mature extratropical cyclone represented in the horizontal plane. Shaded areas are
regions of precipitation. [From Cotton (1990).] (bottom) Schematic vertical cross section through an extratropical
cyclone along the dashed line reported in the top showing typical cloud types and precipitation. [From Cotton
(1990), after Houze and Hobbs (1982).]
Notice the transition in the cloud types as you pass through the warm front, cirrus and
cirrostratus (green house effect dominates) to alto-stratus and nimbostratus (sheet like, thick
cloud base) overall producing a warming effect. On the backside of the low pressure system you
have a cold front where the cloud types generally consist of cumulous and cumulonimbus. Once
the high pressure settles in cumulus clouds dominate bright high albedo, overall cooling effect.
Given the strong connection between the large-scale atmospheric circulation and the distribution
of water vapor and clouds, understanding cloud (and water vapor) feedbacks under climate
change requires the examination of at least two main issues: 1) how might the large-scale
circulation change under global warming and how might that affect the global mean radiation
budget (even without any specific change in the atmospheric properties under given dynamic
conditions), and 2) how might the global climate warming affect the water vapor and cloud
distributions under specified dynamic conditions.
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Understanding of cloud feedback processes:
The Tropics and the extratropics are associated with a large spectrum of cloud types, ranging
from low-level boundary layer clouds to deep convective clouds and anvils. Because of their
different top altitudes and optical properties, the different cloud types affect the earth’s radiation
budget in various ways. Therefore, understanding cloud radiative feedbacks requires an
understanding of how a change in climate may affect the distribution of the different cloud types
and their radiative properties, and an estimate of the impact of such changes on the earth’s
radiation budget.
The main point here is that because the occurrence of the cloud types is controlled by (1) the
large-scale atmospheric circulation and by (2) other factors such as surface boundary conditions,
boundary layer stratification and wind shear, making the background relationship between cloud
properties and large-scale circulation more explicit, it becomes easier to isolate other influences
such as the impact of a change in surface temperature or in the thermodynamic structure of the
troposphere. The analysis of cloud response (namely, cloud changes) to a climate change can be
then explained by the response due to (1) changes in the large-scale flow, and (2) and on the by
other factors, such as an intrinsic dependence of cloud properties on temperature. In the Tropics,
the dynamics are known to control to a large extent changes in cloudiness and cloud radiative
forcing at the regional scale. At the Tropics-wide scale, a change in circulation may change the
tropically averaged cloud radiative forcing and radiation budget (even in the absence of any
change in cloud properties, namely even in the absence of changes in the thermodynamics) if
circulation changes are associated with a global strengthening or a weakening of the Hadley–
Walker circulation.
The baroclinicity of the atmosphere dominates the dynamics of midlatitudes. Changes in the
baroclinic state can be induced by changing meridional temperature gradients and land–sea
temperature contrasts and of increasing water vapor in the atmosphere, all of which are projected
to happen with climate change. The focus of studying cloud feedbacks under climate change in
the extratropics is (1) on using observations to investigate how a change in the dynamics of
midlatitudes could affect the cloud radiative forcing and (2) on investigating how a change in
temperature under given dynamical conditions could affect cloud properties and/or the cloud
radiative forcing.
Deep convective clouds:
Several climate feedback mechanisms involving convective clouds have been examined with
observations and climate models of various complexity, but it seems that there are no definitive
results and hence “more research is necessary” might summarize the state of studying how deep
convective clouds might enter/alter climate feedbacks in a warming climate.
From observations it is found that the LW and SW CRF of clouds in tropical oceanic deep
convective regimes nearly cancel each other out, suggesting that may be their impact may not be
so important overall. This does not appear to be happening globally and there is a lot of
discussion of what might be the mechanisms that is responsible for the cancellation. (simple
coincidence?, dynamical feedbacks in the ocean–atmosphere system so as to keep the radiation
budget of convective regions close to that of adjacent nonconvective regions?). Then there is the
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rather polemical Lindzen iris’s hypothesis: according to Lindzen and co-workers data support
this hypothesis, according to every body else (or so it seems!) data do not support this
hypothesis. The iris hypothesis is a theory proposed by Prof. Richard Lindzen in 2001 that
suggested increased sea surface temperature in the tropics would result in reduced cirrus clouds
(high, thin, large extent) and thus more infrared radiation leakage from Earth's atmosphere. This
suggested infrared radiation leakage was hypothesized to be a negative feedback which would
have an overall cooling effect. The consensus view is that increased sea surface temperature
would result in increased cirrus clouds which would have the effect of warming the sea surface
further and thus there would be positive feedback. And finally another hypothesis is mentioned,
one that proposes that the emission temperature of tropical anvil clouds is essentially
independent of the surface temperature, and that it will thus remain unchanged during climate
change.
Low-latitude boundary-layer clouds:
Boundary layer clouds have a strongly negative CRF and cover a very large fraction of the area
of the Tropics, therefore understanding how they may change in a perturbed climate is vital to an
understanding of the cloud feedback problem. As with deep convective clouds, it seems that here
also there are more questions than answers.
Recall: more low clouds, high albedo, more SW reflected, low level = higher T so higher LW
outgoing radiation, both then lead to cooling the climate – basically the albedo effect dominates.
It is argued that in a warmer climate, water clouds of a given thickness would hold more water
and have a higher albedo. But observations show evidence of decreasing cloud optical depth and
liquid water path with temperature in low latitude boundary layer clouds, which indicates a
decrease in the albedo (hence more SW in and a general tendency toward warming). Yet some
studies (models) find results that indicate a substantial increase in low cloud cover in a warmer
climate across the Tropics, which produces a strong negative feedback.
These factors that affect amount and types of boundary layer clouds are of synoptic (regional)
and large-scale nature. Namely, these factors depend on local physical processes but also on
remote influences, such as the effect of changing deep convective activity (changes in deep
clouds discussed above) on the free tropospheric humidity of subsidence regions
Extra-tropical cloud systems:
Extra-tropical cloud systems are controlled by the large scale atmospheric dynamics in both
hemispheres. Extra-tropical cyclones generate thick high top frontal clouds in regions of synoptic
ascent (warm fronts and warm air mass) and low level clouds under regions of synoptic descent
(just behind the cold front). Remember from our previous discussion low level clouds, in this
case brighter, higher albedo are also radiating upward (to space) at a higher T.
In general think about the scales of change (mainly cloud radiating force) that are being
described in this paper. Variations in feedback processes ranging from -1.5 to just over 2
W/m2/K. Small changes in radiating forces have important consequences for climate. Now
consider how different cloud types associated with synoptic cyclones influence the radiation
budget and the orders of magnitude associated with the low and high pressure regions.
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

Differences in short wave fluxes between low and high pressure regimes ranged between
-5 and -50 W/m2.
Differences in long wave fluxes between low and high pressure regimes ranged between
5 and 35 W/m2.
Net flux differences between regimes resulted in wintertime warming of low pressure
regimes (5-15 W/m2) and cooling all other seasons (10-40 W/m2).
General Circulation Models predict a decrease in storm frequency and an increase in storm
intensity under the current regime of climate change. Fewer organized systems would result in
less cloud cover, more shortwave warming and more longwave cooling. Stronger storms, result
in more vigorous convection, thicker clouds, larger region?, longer duration?, shortwave cooling
and longwave warming. It is expected that changes in mid-latitude clouds under climate change
may generate a positive feedback and is likely to be associated with a latitudinal shift of the
storm tracks.
Polar clouds:
Polar regions are extremely sensitive to climate change, with clouds exerting a large influence on
the surface radiation budget. Remember, polar regions have high surface albedo (ice sheets,
glaciers, sea ice) which will be reduced by cloud cover. An increase in polar cloud cover is
expected with warming temperatures. This will have a positive feedback as longwave radiation
the surface increases.
3. Water vapor-lapse rate feedbacks
This section is about two feedbacks that sometimes work as one, sometimes as two: the water
vapor (WV) feedback (always positive) and the lapse rate (LR) feedback (always negative)
Physical Processes:
Water vapor absorption is strong across much of the longwave spectrum, generally with a
logarithmic dependence on concentration. Additionally, the water vapor–holding capacity of the
atmosphere increases quasi-exponentially as temperature rises. These facts combined predict a
strongly positive water vapor feedback providing that the relative humidity remains
unchanged, this is: provided the water vapor concentration remains at roughly the same fraction
of the saturation specific humidity.
more WV  more LW absorbed (and hence a rise in the local T)
Also: ** with RH approximately constant **
higher T  more WV in air  more LW absorbed (and emitted!)  leading to
increasing T further  strong positive feedback
note that changes in humidity does not necessarily means changes in RH
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Global warming associated with a carbon dioxide doubling is amplified by nearly a factor of 2 by
the water vapor feedback considered in isolation from other feedbacks and possibly by as much
as a factor of 3 or more when interactions with other feedbacks are considered (see Fig. 1).
The water vapor feedback plays an important role in determining the magnitude of natural
variability in coupled models (atmosphere-ocean), therefore understanding the physics of that
feedback and assessing its simulation in climate models is crucial for climate predictions.
This section is a lot about humidity in the atmosphere, its measures and its variability. In
particular about the issue of changes in relative humidity (does it change?, where in the
atmosphere?, by how much?, in response to what perturbation?) as much of what is understood
and projected relies on the crucial assumption that it is invariant under climate change.
Specific humidity (g of WV/kg of air) is measured as the weight of water vapor in the air per
unit weight of air, which includes the weight of water vapor. This does not depend on changes in
volume as absolute humidity (g of WV/m3 of air) does. Relative humidity is the ratio of the
amount of water vapor in the air to its saturation point; or the ration of the actual amount of
water vapor in the air to “how much it can hold” at a given temperature.
The tropospheric temperature lapse rate is controlled by radiative, convective, and dynamical
processes. In response to global warming, at low latitudes (tropical atmosphere) GCMs predict a
larger tropospheric warming at altitudes than near the surface, and thus a negative lapse rate
feedback. At mid- and high latitudes, on the other hand, they predict a larger warming near the
surface than at altitude (i.e., a positive lapse rate feedback). On average over the globe, the
tropical lapse rate response dominates over the extratropical response, and the climate change
lapse rate feedback is negative in most or all the GCMs (Fig. 1). Model differences in global
lapse rate feedback estimates are primarily attributable to differing meridional patterns of surface
warming: the larger the ratio of tropical over global warming, the larger the negative lapse
rate feedback.
The free troposphere is particularly critical for the water vapor feedback, because humidity
changes higher up have more radiative effect (see Fig. 12). In the Tropics, the upper troposphere
is also where the temperature change associated with a given surface warming is the largest,
owing to the dependence of moist adiabats on temperature. If relative humidity changes little, a
warming of the tropical troposphere is thus associated with a negative lapse rate feedback and a
positive upper-tropospheric water vapor feedback.
The diagram below is helpful to think through these concepts.
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σT4: atmosphere at lower T, hence less OLR from here
σT4: atmosphere at higher T, hence more OLR from here
The greenhouse dominates higher up in the atmosphere. Changes in humidity here will have a
higher radiative effect. Also changes in Ts show up at high levels in the troposphere more than
at other latitudes, hence we have an increase in T here which affects the OLR by increasing it
(more LW out) therefore reducing the greenhouse effect (lessening T) of the warming that we
would have related to a higher T. This is then a lapse rate – radiative feedback that is negative.
But more WV with higher T leads to more absorbed LW hence to less OLR! And this leads to
warming (greenhouse effect) and it is a positive feedback. And here we can understand why the
issue of whether RH stays the same or not is so important for the magnitude of the combined
water vapor–lapse rate feedbacks: a change in relative humidity alters the radiative compensation
between the water vapor and lapse rate variations, so that:
increase RH enhances WV feedback
decrease RH lessen WV feedback
relative to lapse rate feedback
Of course changes of RH get us back into having to look at how that affects the cloud feedbacks!
The distribution of humidity within the troposphere is controlled by surface fluxes (latent and
sensible), cloud microphysics (conversion of cloud water to precipitation, reevaporation of
precipitation and entrainment due to turbulent mixing between cloud and environment), the
detrainment of moisture from convective systems (which depends on the penetration height of
convective cells and cloud microphysical processes) and the large-scale atmospheric circulation.
Figure 13 illustrates all of these.
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FIG. 13. Illustration of Lagrangian trajectories through the atmosphere, showing the importance of microphysical
processes in determining the water content of air. Diagram extends from (left) equator to (right) high latitudes and
extends from surface to lower stratosphere. White clouds represent cumuli while the dark cloud represents sloping
ascent in baroclinic systems. The total water content of air flowing out of clouds is set by the fraction of condensed
water converted to precipitation, and subsequent moistening in the general subsiding branch is governed by
detrainment from shallower clouds and by evaporation of precipitation. [From Emanuel and Zivkovic-Rothman
(1999).]
The two main points of the rest of this section are: (1) confidence in GCMs’ water vapor
feedbacks depends crucially on how critical the (parameterized) details of cloud and convective
microphysics are for simulating the relative humidity distribution and its change under global
warming; and (2) two main factors may potentially affect tropospheric RH distribution under
climate change: (a) changes in large-scale atmospheric circulation and (b) changes in the
temperature at which convective detrainment occurs, i. e., the minimum temperature of saturated
parcels.
The final question in this section relates to the amount and variability of WV in the lower
stratosphere under climate change. One might expect that this will affect the upper troposphere
directly, particularly because it is believed that it is the temperature of the tropical tropopause
that controls the WV in the lower stratosphere. Two questions are posed: “How may the lowerstratospheric water vapor be affected by a global climate change?” and “To what extent do lower
stratospheric water vapor changes contribute to climate feedbacks?” These are discussed in some
detail in subsection 3f and the main point of the discussion is that the contribution of lowerstratospheric water vapor changes to climate sensitivity is highly dependent on the nature of the
climate perturbation. From this discussion the conclusion is: water vapor changes in the lower
stratosphere may not greatly affect the magnitude of the global water vapor feedback if we are
considering forcing to be a CO2 doubling but that when it comes to prediction and evolution of
the chemical composition (which might then affect WV in the troposphere) it is important to
include the mechanisms that control the change in stratospheric water vapor.
Please note here (3f) the distinction made between what constitutes radiative forcing and climate
response and feedbacks (see Appendix for definitions of forcing, response and feedbacks).
Informally: is the stratosphere and what happens there part of a forcing to what goes on in the
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troposphere that might be considered climate response and feedbacks or we include the lower
stratosphere in the ‘upper troposphere’ region?
So, does RH change or not in the real atmosphere?
Observation at large-scale of interannual variations of the column-integrated water vapor with
sea surface temperature over the last decade suggest a thermodynamic coupling consistent with
close to invariant relative humidity; this behavior is also found in current climate models. Not so
clear at a regional scale.
Could models have an in-built artifact that leads to an ‘invariant RH’ in all cases?
The question comes because models containing a wide range of parameterizations predict a small
variation in the global-mean relative humidity under climate change, inducing a strongly positive
water vapor feedback.
And the answer is: no, they seem to be doing fine, although models do not explicitly fix relative
humidity at a constant value, their prediction of a nearly unchanged mean relative humidity
under climate warming is a robust feature of climate change predictions.
So, are we done with the issue of changes in RH?
The answer is ‘no’, because since the water vapor feedback represents the strongest positive
feedback of the climate system, uncertainties about how small relative humidity changes should
be, or how accurate the magnitude of the correlation between humidity and temperature should
be, can matter for the spread in water vapor–lapse rate feedback and for the magnitude of climate
sensitivity (Remember Fig. 1).
4. Cryosphere feedbacks
The general response of GCM to increases in atmospheric concentrations of greenhouse gasses is
the poleward retreat of snow and ice, and the polar amplification of increases in lowertropospheric temperature. Both southern and northern latitudes exhibit this behavior however,
increases in lower-tropospheric temperatures in the southern hemisphere are moderated by high
ocean heat uptake. (Figure 17) This amplification is generally attributed to feedbacks within the
climate system, and cryosphere processes (in particular snow and sea ice feedbacks) which are
strongly coupled to both ocean and atmosphere, are of particular importance. The sensitive polar
regions exhibit the largest intermodel scatter in quantification of the greenhouse gas-induced
climate change.
a. Snow Feedbacks: The typical simulated feedback associated with snow is an increase in
absorbed solar radiation resulting from a retreat of highly reflective snow in a warmer climate
(snow albedo feedback). Snow albedo feedback is stronger in the Northern hemisphere
accounting for approximately half the simulated annual-mean increase in solar radiation. The
southern hemisphere is more susceptible to sea-ice retreat as much of the snow remains frozen
year round. The snow albedo feedback in the Northern Hemisphere is positive in the real world,
this in part was proven when trying to simulate realistic ENSO climate impacts which required
positive feedback mechanisms in order to reproduce the correct magnitude of surface air
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anomalies over North America. Recent attempts to quantify snow albedo feedback in GCMs for
comparison with observations involved breaking it down into its two constituent components, 1)
relating surface albedo anomalies in snow regions to planetary albedo anomalies, and 2) relating
the magnitude of snow albedo anomalies to the surface air temperature anomalies associated
with them.
Surface albedo anomalies are attenuated at the top of the atmosphere due to other solar
interactions, resulting in the planetary albedo anomaly being about half as large as that of the
surface. Because the clear sky atmosphere is responsible for more than half the attenuation
effect, the magnitude of the attenuation effect changed little between GCMs simulations of future
climate. This implies the relationship between snow albedo and planetary albedo is a reasonably
well-know quantity, is unlikely to change much in the future, and is therefore not a significant
source of error in simulations of snow albedo feedback.
The relationship between surface albedo and temperature in snow regions, arising from the way
GCMs parameterize snow albedo (mainly as a monotonic function of snow age and depth), is the
main source of divergence and error in models. Many other processes/properties are still not
considered in GCMs including: snow-vegetation canopy interactions, the effect of subgrid-scale
snow –free surfaces, snow crystals restructuring/recoloring, and the effect of impurities such as
soot. In addition, unlike some of the other feedback mechanisms discussed, the snow albedo
feedback simulated in the context of the present-day seasonal cycle is an excellent predictor of
snow albedo feedback in the transient clime change context. This suggests that GCMs should be
constrained to reproduce the observed behavior of snow albedo feedback which should lead to
better intermodel agreement.
b. sea ice feedbacks: Changes in sea ice contribute to a projected amplification of climate
warming in the Arctic region (Positive feedback) as well as to the global mean warming. In
addition the possibility of threshold behavior contributes to the uncertainty of the system in part
resulting in the largest spread in model projections of Arctic surface air temperature change.
The most important sea ice feedback is the influence of the ice area and surface state on the
surface albedo. As sea ice melts under a climate warming scenario, the highly reflective surface
is lost and the ocean will absorb more solar radiation (Positive feedback). This sea ice albedo
feedback may be responsible for about half the high-latitude response to a doubling of CO2. In
addition, sea ice insulates the overlying atmosphere from the relatively warm ocean. The
insolating process is a function of both the geographic extent and thickness of the sea ice. Less
and/or thinner sea ice will result in more heat being transferred from the ocean to the atmosphere
(Positive feedback), as well as more solar energy going into heating of the ocean as less energy is
consumed in changing the state of water from solid to liquid (latent heat).
A number of more recent GCMs have improved sea-ice albedo parameterizations to represent
(sub-grid) features such as regions of snow-covered ice, surface meltwater ponds, drainage
channels, and pressure ridges. However, this has not significantly improved the overall climate
simulations because sea ice model physics are secondary to feedbacks and biases involving the
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atmosphere and ocean. The true benefit of these improved parameterizations will not be fully
appreciated until we have better atmosphere-ocean models.
5. Summary and conclusions
Climate sensitivity estimates critically depend on the magnitude of climate feedbacks, and global
feedback estimates still differ among GCMs despite steady progress in climate modeling. This
constitutes a major source of uncertainty for climate change projections. This paper shows that
the numerous observational, numerical, and theoretical studies carried out over the last few years
have led to some progress in our understanding of the physical mechanisms behind the global
estimates of climate feedbacks, that offer promising avenues for the evaluation of the realism of
the climate change feedbacks produced by GCMs. The paper discusses the important
contributions of the studies that have led to major improvements in the identification of the
reasons for differing global climate feedbacks among GCMs, and therefore that guide the work
on how to evaluate the GCMs’ feedbacks using observations. This is due to both, major model
improvements and improvements in the methodology of model simulations, and to
improvements in the observations and the methodology to analyze data. Finally, this paper also
identifies many issues requiring further investigation.
Homework:
List and describe briefly the major points in the Summary and Conclusions section.
Appendix – Definition of feedbacks
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