# Presentation: 054651

##  Atmospheric Feedbacks Over the Pacific Cold-tongue: Results From Models and Observations

- De-Zheng Sun and Tao Zhang
- CIRES/Climate Diagnostics Center
- & ESRL/Physical Science Division
- _[http://www.cdc.noaa.gov/~ds](http://www.cdc.noaa.gov/~ds)_
- In collaboration with
- Curt Covey and Steve Klein
- Lawrence Livermore National Laboratory
- Bill Collins, Jim Hack, Jeff Kiehl, and Jerry Meehl
- National Center for Atmospheric Research
- Isaac Held
- Geophysical Fluid Dynamics Laboratory
- Max Suarez
- National Aronics and Space Administration

**Notes:**
I will present some results from a joint effort with major modeling centers to evaluate and improve feedbacks in climate models. The focus in this talk will be on atmospheric feedbacks over the equatorial Pacific cold-tongue

## Why feedbacks?

- Feedbacks determine the sensitivity of the climate system to an external perturbation
- Feedbacks determine the amplitude of the natural variability in the climate system
- Feedbacks determine the equilibrium state of the climate system--the time-mean climate

**Notes:**
Feedbacks are at the core of major climatic issues we are concerned with. Feedbacks are involved in determining the sensitivity of the climate system to an external perturbation. Feedbacks are involved in determining the amplitude of the natural variability.  Feedbacks also determines the time-mean climate. With wrong feedbacks, a coupled model can drift to a climate that is very different from the observed.    Feedbacks are the true building blocks of the climate system.  The importance of evaluating and understanding feedbacks in our models can not be over-emphasized. 

## Why feedbacks over the Pacific cold-tongue?

- The cold-tongue plays a key role in the heat and carbon balance of the climate system and is a major source region of climate variability
- Climate models do not simulate the cold-tongue properly: they have a tendency to develop an excessive cold-tongue.

**Notes:**
Our initial focus has been on the tropical Pacific cold-tongue. The cold-tongue plays a fundamental rule in the heat in the climate system, and is a key source region of climate variability. ENSO corresponds to interannual achanges in the intensity of the cold-tongue. If climate varibility is indeed can be viewed as a conflict between the tropical warmth and polar chill, the cold-tonge is a major front at this conflict takes place.  So the modeling community have identified simulating the climate of this region as a priority. Despite the effort, coupled state of the art of the models have difficulty in simulating even its climatological intensity and position. All coupled models, when flux adjustment is not used, tend to produce an excessive cold-tongue. The cold-tongue extends too far to the west. 

## The Problem 

- Annual Mean SST from observations and CGCMs

**Notes:**
 This figures shows the mean SST distribution from observations-top left corner, and the distribution of SST  from  nine coupled GCMs that does not use flux adjustment.  All of them have an excessive cold-tongue. The central equatorial Pacific is colder and  cold-tongue in the models extends too far to the west. Many other problems with GCM simualtions of the tropical Pacific climate, like the doulbe ITCZ sydrome are associated with this cold-tongue problem

## Ideas about the causes of the excessive cold-tongue

- Insufficient resolution of the ocean model (Stockdale et al. 1998, Kessler et al. 1998)
- Lack of phytoplankton in the ocean model (Murtugudde et al. 2002)
- A weaker regulating effect from the model atmosphere (Sun et al. 2003)

**Notes:**
Here are the major published ideas about the possible causes of the excessive cold-tongue. Early versions of the ocean model tend to have low vertical and horizonal resolution, so it was thought that the excessive cooling from the subsurface due to  insufficient veritical resolution or the reduced heating from the instability waves might be a cause. However, higher resolution models do not have significant improvement in simualting the cold-tongue. The lack of the representation of phytoplanton in the ocean models may also give a cooling bias in the ocean, but this result varies with the ocean model used. Based on an analysis of the feedbacks in an earlier version of the NCAR model, we suspect that  regulating effect from the model atmosphere over the underlying SST over the equatorial Pacific may be too weak.

## The idea of a weaker regulating effect from the model atmosphere?

- Could it be that the strength of one or more negative feedbacks in the model underestimated? Or alternatively, could it be that the strength of the one or more positive feedbacks in the model is over estimated?

**Notes:**
 There are known strong  negative  feedbacks over the central equatorial Pacific that regulate the underlying sst. For example, a cold SST bias  in the equatorial central   Pacific would result  in a reduction in convection, and  allows more solar radiation to reach the ocean’s surface. At the same time,  atmospheric circulation transports more energy into this region. These negative feedbacks will limits the growth of the SST bias and its westward expansion.  If the strength of these negative feedbacks is underestimated in the models, a initial cooling bias in the ocean due to inadeqayte reslution or a lack of represenation of biology could grow to a size that is  otherwise impposible. At the central equatorial Pacific, the cold-SST bias only needs to grow about 1 degree to complete eliminate deep convection in that region and lock the system to a permenent cold-state, caussing an excessive cold-tongue in the equatorial Pacific. 

## The effect of a less negative net atmospheric  feedback on the cold-tongue SST bias

- Atmospheric Feedbacks

- M

- SST Bias

- Net flux error at *T**0*

- Ocean feedback

**Notes:**
To illistrate the effect of a weaker regulating effect from the atmosphere on the magnitude of cold SST bias more quantatively, let us consider  a linear feedback system for the the equatorial ocean-atmosphere the potential effect of  errors in the atmospherithe cold-tongue SST bias  The bias of sst in such a model is given by this equation. T is the actual SST, and To us the observed SST.  H(To) is a flux error the model has at the observed SST. The partial deritives are various feedbacks. the system is subject to a  cooling, so it has a tendency to develop a bias when the system is set free to interact to find its equilibrium. How big the bias the system eventually has depends on the feedbacks. Clearly, the bias of the equilibrium SST depends on the initial error in the model components as well as the feedbacks.   The bottom figure shows the SST bias as a function of the net atmospheric feedback. If the model has the observed feedback, the bias is only 0.5 K, but with the feedback simulated by CAM1, the errors become1 K. The difference is even larger when a less negative feedback from the ocean transport is used in the calculation. 

## Methodology

- Use ENSO as the forcing signal and obtain the feedbacks by examining the response of various energy fluxes to SST changes associated with El Nino and La Nina

**Notes:**
Our methods to obtain the feedbacks are  very simple. We take advantage of the existence of a well observed signal in this region, the El Nino-Southern Oscillation. We will quantify the response of the various energy fluxes to the ENSO forcing. 

## Data

- Observational data: ERBE, NCEP/NCAR reanalysis, ISCCP
- Model outputs: AMIP runs of climate models

**Notes:**
The radiation data for our initial analysis  are from ERBE. The atmospheric  transport data   derived from the NCEP reanalysis. We have also made use of the ISCCP data in the analysis of the cloud field.
The model outputs are the AMIP runs of climate models. The AMIP run has the SST fixed as the observed, and thus the model has the same SST forcing as the real atmosphere is subject to in Nature. 

## The Physical Processes

**Notes:**
This cartoon summarizes the physical processes that we have quantified. S is the net solar radiation entering the coupled system at the top of the atmosphere. It consists of two parts, the clear sky solar radiation and the short-wave cloud forcing. F is the outgoing long-wave radiation. It is a function of surface emission, the greenhouse effect of water vapor and the greenhouse effect of clouds or the long-wave forcing of clouds. Nt is the n et energy flux into the coupled system. Da is the transport of total energy by the atmospheric circulations, vertically averaged. With Nt and Da, one can obtain the net surface heat flux into the ocean, considering the energy balance in the atmosphere.
Explain Cs, Cl, Ga, Nt, Da and Fs
Data source for the radiative and dynamical fluxes

## The Water Vapor Feedback

**Notes:**
I will first present results from the NCAR models and I will start with the water vapor feedback.  The upper panel shows the variations of the greenhouse effect of water vapor, averaged over the equatorial Pacific (5N-5S, 150E--100W), over the ERBE period  The solid line is for the observations and the dashed lines for for the simulations by three NCAR models. The lower panel shows the corresponding SST signal. In response to the El Nino warming of 1986-87, the greenhouse effect of water vapor increases. So over this region and on the ENSO time-scale,  water vapor feedback is positive, and this feedback is largely captured by the models.

## The Dynamical Feedback

**Notes:**
This  figure shows the variations of Da--the total energy advected into the equatorial Pacific by the atmospheric circulations. Again, the sold line is for the observations and the dashed line are for the model simulations. A negative response in Da represents a decrease in the energy converged into this region or an increase in the energy exporting out of this region. So the transport serves as a negative feedback. The models clealry have this feedback, though the strength appears to be somewhat weaker. 

## The Cloud Feedbacks

- ERBE

- CAM1

**CAM1**

**Notes:**
More significant discrepancies are found in the cloud feedbacks. The upper figures shows the variations of Cl and Cs from the observations and the bottom figure shows the corresponding quantities from NCAR CAM1. Cl is in red and Cs is in blue. In response to a warmer SST, the long-wave cloud forcing increases while the short-wave cloud forcing decreases. The two feedbacks have the opposite signs and largely cancel each other. Such an cancellation is not simulated in the model. The negative feedback from the short-wave forcing in the model is clearly too weak. 

## The Cloud Feedbacks

**Notes:**
The two subsequent  versions at the same spatial resolution  have no improvements in this regard. In fact, the sign of the cloud albedo feedbacks in these two models is now opposite of the observations. The response of long-wave forcing are also much too weaker compared to ERBE observations. CAM2 and CAM3 have a more sophisticated, and arguabley more physcially based treatment of cloud water. 

## The Cloud Feedbacks

**Notes:**
With a much higher spatial resolutions, the NCAR CAM3 simulates more successfully the two cloud feedbacks. The response of Cl and Cs to ENSO forcing  now nearly cancel as in  the observations, though they are still somewhat weaker than observations.

## Response of Cs to El Nino warming

- (W/m2/K)

**Notes:**
This slides gives a basin-wide view  of the response of Cs to El Nino warming.  Show are linear regression coefficients of Cs  at each grid point to to cold-tongue SST. From the top to bottom are respectively the results from observations, CAM1,CAM2, CAM3 and CAM3 at T85. The response of Cs near the date line is clearly improved in CAM3 at T85. The problem with  response near the east coast remains.

## Response of Precipitation to El Nino Warming

**Notes:**
 The slides shows precipiation response in CAM2 and CAM3 at the standard resolution is clearly too weak, explaining both the weak response in Cs and Cl in these two models. CAM1 also has a decent precipitation response. Its problem is with its vertical distribution of clouds.

## Response of Cloud Cover in CAM1

- CAM1

**Notes:**
This figure shows the response of upper, middle, and low cloud cover response to the SST forcing. the response in the cloud cover at mid and lower level much  weaker than the observations, but clouds of these levels contribute more feedback from the short-wave forcing. High clouds tend to cotnrubute more  to the long-wave forcing.   As in many other models, CAM1  lacks a realistic vertical distribution of clouds. How to generate enough mid level clouds against surfaces as a major issue.The low level clouds are probably too much constrained to the vertical stratification.  

## Atmospheric feedbacks in observations and the NCAR models

**Notes:**
This table summarizes more quantitatively the differences between feedbacks in the NCAR models and the feedbacks inferred from observations. These numbers are obtained through linear regression of the various fluxes to the SST variations averaged over the equatorial Pacific. The coatal area was exluded in this calcualtion. The fist three terms are Water vapor feedback, the long-wave cloud feedback, the short-wave cloud feedback. Explan the Fa.  This is strogn neagtive number in the observations, and it is not so in the models. In terms of the feedback from the surface heating, In terms of the feedback from the net surface heat flux from the ocean, the result implies a drift in SST in a coupled model will be more constrained if the model atmosphere has the observed feedbacks. The cloud feedbacks in CAM3 at T85 resolution are fairly closed to the observed. Note that as the cloud feedbacks get improved, so is the feedback from the transport. The overestimate of water vapor feedback, however,  becomes more serious. As we wll see later, it is a general problem that convection in the model tend to moist the upper troposphere more than it should do.

## How are other models doing in simulating the feedbacks?

**Notes:**
How are the models of other leading modeling groups doing in simulating the feedbacks? We have done the same analysis of four other GCMs,  the model from the Hadley center, the NASA NSIPP model, and two GFDL models. We have extended the analysis to mode models. This table summarizes the results from the models of GFDL,NASA, and the Hadley center. We choose them because these are the models who have a corresponding coupled simualtion of the tropical Pacific climate without the use of flux adjustement. The NASA and the Hadley Center model suffers from the same problems as the NCAR model at the T42 resolution, but to a less degree.  The two GFDL models now have a net feedback that is quite comparable to the observed value. The  negative feedbacks from the cloud albedo and the atmospheric transport in the two GFDL models are now even somewhat stronger than the observations, but this excess is offset by a stronger water vapor feedback. As in the NCAR series, these models all have a stronger water vapor feedback than that indicated in ERBE observations. The encouraging news is that we found that two models that have a net feedback that is comparable to the observed value. The discouraging news is that these two models also have a cold-tongue, as I show you near the beging of the talk, though the errors are alleaviated compared to some NCAR models. 

## Precipitation Response to El Nino Warming

**Notes:**
. This figure shows the response of the precipitation to the El Nino warming from observations and the 8 models we have analyzed. The two GFDL models have the strongest precipitation response over the equator. In fact, the precipitation  near the dateline is even stronger than the observations.  In connection with the earlier results we obtained from the four NCAR models, the stronger precipiation clearly plays a role in boosting the strength of the negative feedbacks. The vertical distribution of clouds in the GFDL model is not significantly better than the NCAR models. It is our impression that to get the convection in the right place and produce the right amount of rain may still be the leading order problem in cumulus paramterization. 

## Summary and Conclusion

- The net atmospheric feedback over the equatorial Pacific in the two GFDL models is comparable to the observed value.  All other models have a weaker negative net feedback from the atmosphere—a weaker regulating effect on the underlying SST than the real atmosphere.
- Most models underestimate the strength of the negative feedbacks of cloud albedo and atmospheric transport. All models  overestimate of the positive feedback from water vapor.
- These results confirm the suspicion that an underestimate of the regulating effect from the atmosphere is a prevalent problem. The results also suggest, however, that a weaker regulatory effect from the model atmosphere is unlikely solely responsible for the development of an excessive cold-tongue in all models.  The need to validate the feedback from the ocean transport is therefore highlighted.

**Notes:**
(1) I brefly remaked on the relationship between precipitation and the strenght of the negatitive feedbacks. I did not have time to discuss why models tend to overestimate the water vapor feedback. We have noted that convection in the models deliver too much moiture to the upper to. The ocean transport depends on the wind respone to SST, so the feedback from the ocean is not entirely independent of the results we see here. Such an effort also necessarily involve assessing the role of ENSO on the time-mean climate. We have found through modeling studies that ENSO inhas a warming effect on the equatorial central Pacfic, implying the cold-tongue bias could be in part due to the poor simualtion of ENSO as well. I will talk about this research in a separate seminar because of its other implication. In additon to providing a qunatitive picture of the feedbacks in the region of concern, the present study hihglihts that we still have some distance for answer some classical questions concerning the behavior of the tropical atmosphere. 

##  Atmospheric Issues

- What are the processes that control the equatorial precipitation?
- How is the vertical distribution of clouds determined?
- Why does convection in the models have a stronger moistening effect on  the upper troposphere?

**Notes:**
The present study also highlight that we are still some distance from answering some classical questions about tropical convection and its climatic impact.  Read questions. We plan to take advantage of the differences in the models and understand these issues better through a more detailed comparison among models and with observations.we would like to look a more carefully the relationship between clouds, water vapor, and precipitation. Effort of such necessarily involves a better understanding of factors controlling the vertical distribution of clouds. We are also interested in evaluating the feedback from the ocean transport.. Ultimately, we would like to have a better understanding of the role of clouds and water vapor feedbacks in the climate system.

## Oceanic Issues

-  Do models overestimate the  positive Bjerknes feedback?
- Are the off-equatorial and subtropical downward pumping of heat in the models too weak?
- Do models underestimate the negative feedback from the poleward ocean heat transport
- What’s the effect of ENSO on the time-mean upper ocean temperature?

**Notes:**
These are the oceanic issues we are working on. First we would like to know whether models overestimate the strengh of the Bjerknes feedback. This includes the response of windstress to an SST anomaly, the response of upwelling as well the depth of the thermocline to a change in the wind stres. This effort necessarily involves assessing the effect of ENSO on the mean climate or more generally its interaction with the mean. We have made some progress on this topic. Traditionally, it is thought that an exceesive cold-tongue is a cause of poor simualtion of ENSO. Our results indicate that the excessive cold-tongue is more of a consequence of a poor simualtion of ENSO, which is in turn due to a poor representation of the radiative feedbacks in the model. 

## Response of Da to El Nino warming

## Response of Fs to El Nino warming

## Feedbacks calculated from surface data

## The response in cloud cover in CAM3 T85

## The response in cloud cover in CAM2

**Notes:**
Part of the deficiency seen in the response in the radiative forcing is clearly linked to the cloud cover response. The response in the middle level and low level clouds in the model is just too weak. Right off the equator, the low cloud response has the wrong sign.

## The response of cloud cover in CAM3

## The response of Cl and Cs in CAM1

**Notes:**
Here shows the regression maps of cloud forcing, giving a basin wide view of the response of cloud forcing to El Nino warming.  Plotted are the regression coefficients of Cl and Cs with respect to the cold-tongue SST. The location in the  response of Cl and Cs to the SST forcing appears to be all right. It is just that the magnitude of the response  is wrong. 

## The response of Cl and Cs in CAM2

**Notes:**
The figures gives a more detailed look of the response of the cloud radiative forcing. Again, shown here are the regression maps of Cl and Cs to the cold-tongue SST. There is not much similarity in the response of Cs to El Nino warming in the model to that in Nature.

## The response of Cl and Cs in CAM3

## Zonal wind stress response to El Nino warming

## A further assessment of the effect of feedback errors on the cold-tongue SST

- Perturbed run with feedbacks from CAM1

- Perturbed run with observed feedbacks

- Control run

- Coupled model used: NCAR Pacific basin model coupled with an empirical atmosphere (Sun 2003)

**Notes:**
We have further assessed the impact of a less negative atmospheric feedback on the coupled simulation of the equatorial Pacific SST using a more sophisticated model. In this model, the ocean component is a GCM. Therefore the ocean feedback is explicitly calculated. Time series of cold-tongue SST from three coupled runs are shown here: a control run, a perturbed run with the observed atmospheric feedbacks, and a perturbed run with the feedbacks from CAM. Again, the run with the feedbacks from CAM settled to a colder state.