GISS-solar

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Can IPCC Coupled Atmospheric-Ocean General
Circulation Models Simulate the 11-Year Solar-Cycle
Response?
(Intended for Climate Dynamics (?) Ed Schneider Editor)
1. Introduction
Is it true that the coupled atmosphere-ocean general circulation models (AOGCMs)
assessed in the latest Fourth Assessment Report (AR4) of the Intergovernmental Panel on
Climate Change (IPCC) cannot simulate the tropospheric response to the 11-year
variation in solar forcing? If so, then one can possibly conclude that the required
mechanism for producing a detectable response similar to the observed one is perhaps
more exotic than those included in these models, such as the effect of cosmic ray’s
variation on cloud condensation nuclei, or that the tropospheric response is caused by a
top-down influence of an intricate interaction between the UV production of ozone and
its transport, not adequately resolved by the current generation of GCMs with low
resolution in the stratosphere. Gray et al. [2005] provided a long table listing many of
these mechanisms. Also, if true, it leads to the conclusion that our current understanding
of Earth’s response to solar forcing---not only to the cyclic but also to the secular forcing-- is “low”. This in fact was one of the major conclusions of IPCC AR4 (Solomon et al.
[2007] ).
It turns out, as we will show, that the “failure” of the current class of AOGCMs in
producing the solar-cycle response comparable to the observed is caused by some less
exotic reasons. First of all, it is not so much that the IPCC models cannot simulate the
solar-cycle response as that it has not been unambiguously demonstrated that they did
using only archived data.
Table 1 lists the 22 IPCC AR4 AOGCMs whose model output is archived in (CMIP3
reference…..). Eleven of them incorporated the 11-year solar-cycle variation in their
solar forcing, and the other 11 did not (clarify two of these). Our Figure 1 (Perhaps we
should refer to our AGU poster, with Albert and Duane and Run-Lie as coauthors, to give
them credit) shows that the models with solar-cycle forcing (the left panels), by and large,
produce a spectral peak in their global surface temperature above 95% confidence level
near the 11-year period (or as close as the model spectral resolution allows; we used the
output from 1959-2000 in the 120-year simulation (1880-2000), to compare with the
observation). The other 11 models, those without solar-cycle forcing, do not have such a
decadal peak. Furthermore, the amplitude of this spectral peak in many of the models
with solar forcing is comparable to the observed peak shown in Figure 2, constructed
using NCEP data, from 1959-2004 (?). This figure furthermore shows (in magenta line)
that this peak coincides with the peak in the total solar irradiance (TSI).
The problem is, most of the 11 models that included solar-cycle forcing also included
volcano aerosol forcing, while most of the 11 that did not have solar-cycle forcing also
did not have volcano aerosol forcing (see Table 1). Figure 2 also shows that the volcano
aerosol index (Sato et al. [1993] ) has a spectral peak close to the 11-year solar cycle
(will add aerosol line in a different color; this was done previously by Fai; I need to look
it up). That is, the evidence for the existence of the solar-cycle response in these 11
models is probably contaminated by the volcano aerosol forcing that was also
incorporated; the latter happened to have a significant decadal component during the
period examined.
Table 1. The AOGCMs assessed by AR4 (reference?)
Figure 1: Model spectra for the
22 AOGCMs. For convenience the
10-year period is indicated in a dashed
vertical line.
Figure 2. Observed spectrum using NCEP surface temperature (1959-2004?), annually
and globally averaged. The magenta line is the spectrum of the aerosol index for the
same period (?)
In order to unambiguously diagnose the solar-cycle response, we take one of the IPCC
models above, the GISS-EH AOGCM, which has been released to the public, suppress
the volcanoes and in fact keep all aerosols and greenhouse gases constant as in their
preindustrial condition, impose an 11-year periodic sinusoidal variation in the solar
radiation, and run the model for a long time until stable statistics is established.
3. Model setup
We conduct an atmosphere-ocean coupled climate simulation using the released version
of GISS-HYCOM (GISS-EH) atmosphere-ocean coupled climate model (Sun and Bleck
Clim. Dyn. 2006). The configuration used here has a horizontal resolution of 4 by 5
degrees (latitude by longitude) with 20 vertical layers (extending to 0.1 mbar plus 3
additional radiation layers) in the atmosphere (GISS-E) and 2 by 2 cos (latitude) with 16
vertical layers in the ocean (HYCOM). To isolate the response due to solar forcing,
anthropogenic (greenhouse gases, aerosols, and human-made chemicals) and volcanic
emissions are not considered; the amount of greenhouse gases and atmospheric aerosol
loading is kept at pre-industrial level. A pure sinusoidal solar forcing is imposed at a
period of eleven years with peak-to-peak amplitude of 0.1% for total insolation and 2%
for solar flux at wavelengths less than 295 nm. Ozone concentration varies accordingly
with the flux of dissociative UV photons (Shindell et al. GRL 2006); no anthropogenic
source is included. (need more detail description of the ozone treatment).The model used
here is same as that used in the AR4 study, with one adjustment to the eddy vertical
mixing coefficient in the model. Specifically, the value of is set to 0.05 cm2/s, which is
half of that used in the AR4 study. The lower ocean mixing runs produce a more realistic
low-frequency internal variability in the SST, such as ENSO and longer period
oscillations than the standard model. In the standard model a typical ENSO-like response
during the first and second years of the solar peak discussed previously by Meehl and
Arblaster [2009 ]; Meehl et al. [2009 ]; Van Loon and Meehl [2008 ]; Van Loon et al.
[2007] , is much muted, and generally the SST response more sluggish. A more detailed
comparison of the two models is given in a separate paper (Liang et al, 2010).
About 3000 years of simulations were performed. The need for such a long run in the
coupled mode was not previously appreciated. The models are initiated with preindustrial
conditions (at Year1850) the same way as in IPCC AR4, but, instead of stopping at year
2000 as for the AR4 report, the runs are continued for 2000 more years. We note in
Figure 3 the model climate drift during the first few hundred years of the run. This
feature was mentioned previously (Sun and Bleck Clim. Dyn. 2006). In the standard
model run (with AR4 parameters), there is a secular cooling trend for the first 200
hundred years, despite the fact that all the forcings are either constant or periodic. During
this period, the global mean surface temperature cools by 0.8 C, coincidentally about the
same magnitude as the observed warming since 1880 (IPCC AR4). The model then takes
800 years to recover, and reaches approximate “equilibrium” at around Year 2900-3000.
The FFT spectrum shows a low-frequency variability of around 500 year period (above
99% confidence level). By design there is a very clear and sharp 11-year oscillation in
global mean surface temperature response to the 11-year solar-cycle forcing. There is
also power at the 2-6 year period, corresponding to ENSO oscillations.
The reduced ocean mixing run was started off the standard run at Year 2900, when the
standard run is near equilibration. It is then run for another 2000 years. The initial 0.8 C
of cooling is probably caused by the adjustment to the reduction of the ocean mixing
parameter.
This adjustment took about 100 years.
From then on there is a slow
downward drift of temperature of about 0.2 C over 2000 years, which is small enough for
us to consider that the model has equilibrated. The FFT spectrum shows that the power
at the sub-decadal frequency consists of a wider bandwidth ENSO type signal. In the
centennial periods, there are statistically significant peaks at 150, 200 and 400 years. The
solar cycle response at 11 years is also very clear and sharp. The general impression is
that the model with reduced ocean mixing has more interannual variability, and that the
low-frequency variability is also richer in its spectrum of periods. These features are
closer to the observed than those in the standard run (Heavens et al, 2009)
Figure 3: Time series of global mean surface temperature and FFT spectra for model
runs with standard- ---in(a) and (b) ---- and reduced---- (c) and (d)----oceanic eddy
mixing coefficients.
4. Model result.
Figure 4 shows the spatial pattern and time series of the surface temperature response to
the solar-cycle radiative forcing. The time series of the response has an 11-year period,
although it is not a pure sinusoid. The correlation coefficient of the retrieved time series
and the solar radiation is highly statistically significant, with  = 0.83, establishing the
causal relationship between the solar response and solar forcing, a conclusion that is
always difficult to obtain using observational data only. When the spatial pattern of the
response is normalized so that its global mean is one (not shown), the time series in the
lower panel then measures the global mean amplitude of the response. The  response is
the amplitude of the global surface temperature response regressed against the variation
in solar constant as defined in Camp and Tung [2007] , and is found for the current model
to be 0.11C per W m-2. This is close to the observationally derived value of 0.12 C per
W m-2 in three datasets (Tung et al JGR 2008). (The 4th dataset studied by Tung et al is
NCEP Reanalysis, and that yielded a larger amplitude of 0.17 C per W m-2.)
The spatial pattern associated with the derived time series is shown in the upper panel of
Figures 4. The observed spatial pattern from Tung et al. [2008] is shown in Figure 5.
Figures 4 and 5 show a striking resemblance, suggesting that this model is able to
reproduce both the magnitude and the following spatial characteristics of the observed
response, previously mentioned in Camp and Tung [2007] , including: (1) warming in the
continents more than over the oceans, (2) polar amplification of warming: the Arctic and
the Anarctic warm more than the tropics by a factor of 2-3. (3) Large warming over the
eastern US and Canada, and the Pacific Northwest Passage. (4) There is a prominent El
Nino warm tongue in the eastern equatorial Pacific.
This last feature is controversial: it is opposite to that found previously (Meehl et al
2009). Figure 6 shows it in more detail for the current model: The winter (DJF) average
of the solar peak years (minus the DJF mean of all years in the same record) shows an El
Nino like pattern of warm tongue in the equatorial Pacific, changing to a La Nina like
cold tongue one year later. We attribute the difference from the Meehl et al model result
to the fact that their model runs were for short durations of less than one hundred years,
not long enough for their model ocean to equilibrate with the atmosphere module. The
statistical behaviour of the transient runs is unstable during the first two hundred years of
our model run. During that time behaviour similar to their results can be found, only to
change later when the model is run for another hundred years. (need to check this).
Observationally, Zhou and Tung (2009) found a warm tongue, similar to our model, but
cautioned that even 150 years of instrumental SST data is not long enough to statistically
rule out one sign or the other since it is occurring in a region of large ENSO variability.
This is no longer a concern with our long model data. Zhou and Tung (2009) also reexamined the previous observational results of van Loon et al 2007, van Loon and Meehl
2008, and showed that theirs were not statistically significant in the equatorial Pacific.
Figure 4. The spatial pattern of the surface temperature response to the solar forcing and
its time behavior, as determined by LDA (I suggest that we redo this figure using CMD,
because it is easier for readers to understand.)
Figure 5. Same as Figure 4 except for the observed data. (I will redo the figures in
CMD.)
Figure 6: SST response to solar forcing, calculated for the December-January-February
mean of the solar peak years minus the climatology mean for the same record (top panel)
and one year after the solar peak years (bottom panel).
Figure 7 shows the amplitude of the response, as measured by , as a function of lag. It
shows that the largest response occurs for lags of less than 1 year (practically zero lag),
indicating a small ocean inertia and shallow ocean involvement.
5. Mechanisms
5.1 Global mechanisms, radiative-dynamical considerations:
From solar min to solar max, the solar constant varies by 0.1%, which is 1.3 Wm-2 in the
present model setup. When divided by 4 to take into constant the different geometry
between a circular disk---on which the solar constant is measured----and the spherical
earth, and multiplied by (1-, where ~0.3 is the earth’s mean Albedo, we arrive at 0.23
Wm-2 as the top of the atmosphere radiative forcing from the solar cycle phenomenon. In
this model, there is a percentage-wise larger variation (2%) of the radiation below 295nm
in the course of 11 years. Most of the shorter wavelength radiation is absorbed in the
stratosphere, with a concomitant production of ozone, when then warms the stratosphere
slightly. When the stratosphere has adjusted, there is some cancellation of the ozone
absorption of the UV radiation and its long wave downward radiation produced by the
resulting ozone warming of the stratosphere.
The net radiative forcing (RF, as defined in IPCC AR4) at the top of the troposphere as
calculated by this model in shown in Figure 8 (Need this figure from Jimmy Lin: lat-long
plot of radiative flux at the tropopause or an approximate pressure level). The global
mean of the RF is 0.2 Wm-2 (?). In the absence of ocean heat uptake and climate
feedback processes, a globally averaged of surface warming of 0.1 C will result
(RF/B~0.2 Wm-2/1.9 Wm-2/C~0.1 C). In their presence, our model produced a global
mean surface warming of  times 1.3 Wm-2~0.11x 1.3 C~0.14 C. So there is a net 40%
gain over a radiative equilibrium response.
The GISS model has an Equilibrium Climate Sensitivity (ECS) of 2.7 C, implying a
climate gain factor of g=2.7/1.2~2.2 (IPCC AR4). The RF from solar-cycle forcing
would have, at equilibrium, given rise to a warming of 0.22 C. However, since the
phenomenon of solar cycle is periodic, the transient response is affected by the ocean
heat uptake, the latter takes 0.08 C of the potential warming.
The more relevant transient behavior of the model is measured by the so-called Transient
Climate Response (TCR), which is defined as the transient model response to 1% per
year increase in CO2 concentration in the atmosphere at the time of doubling. For the
standard GISS-EH model, TCR=1.6 C (IPCC AR4), implying a transient climate gain
factor of g=1.6/1.2~1.33, much less than the equilibrium gain factor of 2.2 for the same
model. The ocean heat uptake accounts for the reduction of net climate gain. With
reduced ocean eddy mixing, our current model has a slightly higher TCR of 1.8 C (?.
Need to redo this calculation using 1% per year compound increase in CO2). This
implies a transient climate gain factor of g=1.8/1.2~1.5. This 50% gain in the transient
climate response over radiative equilibrium is close to the 40% gain diagnosed for the
solar-cycle phenomenon, with the slight difference accountable by the secular vs periodic
nature of the two forcings.
The model spatial pattern for the solar-cycle response look remarkably similar to that
produced for the greenhouse gas warming phenomenon by the same GISS model (show
figure if available from some reference). Both show larger warming over the Arctic and
Antarctic than over the tropics. The mechanisms responsible for the polar amplification
of warming with regards to the greenhouse warming problem have been discussed in
detail by Cai [2005 ]; Cai [2006 ]; Cai and Lu [2007] . This mechanism works essentially
the same way for the solar-cycle warming: Due to the low static stability of the tropical
troposphere, radiative heating of the surface leads mostly to more evaporation and
vertical convection with not much realized surface warming. The latent heat is deposited
by vertical convection at around 200 hPa, where it is released through condensation. The
warming aloft creates an enhanced (negative) poleward temperature gradient, giving rise
to an enhanced poleward transport of heat, thus warming the polar regions at that level.
Downward long-wave radiation then warms the surface in the polar regions, which have
high static stability that enables the trapping of heat near the surface. Ice-snow albedo
feedback enhances further the polar warming. The one difference between the
greenhouse-gas and solar radiative heating lies in the fact that the latter is more
concentrated over the tropics, which should lead to a more vigorous poleward transport of
heat, still resulting in polar amplification of warming.
5.2 Regional Responses
Equatorial Pacific Ocean
In the northern subtropics, the east coast of the Pacific is cold while the west coast of the
Pacific is warm. This is consistent with the observation analysis of SST by Zhou and
Tung (2009), although the latter shows a stronger warming in the northwestern Pacific. In
the equatorial Pacific, there is a prominent El Nino type of warming in the eastern
Pacific. This equatorial warming appears to be explainable by the fact that the eastern
equatorial Pacific is climatologically cloud free because of the subsidence of the Walker
circulation over the shallower ocean thermocline. More solar radiation reaches the
surface in the eastern equatorial Pacific than over the western equatorial Pacific, where it
is very cloudy. Because of the cold equatorial Pacific SST, maintained by the ocean
upwelling of cold deep water, evaporation anomaly is not as strong as in the west Pacific
because of the nonlinear Clausius-Clapeyron relationship. With reduced evaporative
feedback, solar radiative heating leads to regional SST warming in the eastern equatorial
Pacific, unlike that mentioned previously for the tropical oceans in general. In the
western equatorial Pacific, not much SST heating is realized because of the same
mechanism, now applied to a much warmer climatological SST.
Figure 8 shows the model cloud distribution. There is a positive correlation between
cloud cover and realized warming, supporting the interpretation above.
The first part of the mechanism proposed by Meehl et al. [2009] works essentially the
same way, but somehow leads to a cold anomaly during the solar peak years. They
proposed, like us here, that the solar heating penetrates more to the surface in the eastern
Pacific because it is relatively cloud free. However, instead of heating giving rise to SST
warming in the eastern Pacific, they claimed that it leads to evaporation that is
transported to the western Pacific by the prevailing easterlies of the Walker circulation,
where the latent heating is deposited and the SST warmed in the west. Their mechanism
produces a La Nina like pattern of cold equatorial Pacific in the east and warm in the
west, while we find a more El Nino like pattern. The difference in the two model results
may lie in how vigorous the model Walker circulation is in transporting surface heating
westward.
Indian Ocean
The Indian Ocean is a small, shallow (?) ocean, and so the annual mean response to
basin-wide radiative heating should be a basin-wide warming. And this is indeed found
in the present model., consistent with the observational result on SST of Zhou and Tung
(2009).
Atlantic Ocean.
References
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amplification. Part II: meridional and vertical asymmetries of the global warming. Clim.
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