SST Forced Atmospheric Variability in an AGCMArun KumarQin ZhangPeitao PengBhaskar Jha

Climate Prediction CenterNCEP

OutlineMotivation

Data and Methodology

Results

Summary and Conclusions

MotivationHorel and Wallace, 1981: Planetary Scale Atmospheric Phenomenon Associated with the SO

MotivationWhat, then, are the prospects of utilizing information on equatorial SST anomalies to improve the quality of long-range forecasts for middle latitudes?

-- If the strength of correlations is limited by the high noise level inherent in seasonal averages then the prospects of [seasonal predictions] are not encouraging-- On the other hand, if these patterns constitute blurred images resulting from our inadvertent superposition of an ensemble of shaper patterns, , then there is hope that (seasonal prediction of) midlatitude climate anomalies with higher degree of detail and accuracy than is now (will be) possible.

Motivation

Motivation

Question: How much does the atmospheric response in the extratropical latitudes depend on details of the ENSO SST anomalies, or to SST anomalies in different ocean basins?

Data and MethodologyFor each DJF seasonal mean from 1980-2000, we have access to an 80-member ensemble of AGCM simulations forced with the observed SSTsEnsemble mean for each DJF provides a good estimate of atmospheric response to that years SST forcing

For this data set, we analyze how the ensemble mean 200-mb height response varies with SSTs

Data and MethodologyData is from Seasonal Forecast Model archives from 2002-200310-member ensemble from different atmospheric initial conditions each monthLagged ensemble from different ICs

Data and MethodologyDifference in 200-mb eddy height climatology from December and September ICs200-mb eddy height climatology for December ICs

Data and MethodologyDifference in 200-mb height variance from December and September ICs200-mb height variance for December ICs

Results

ResultsEOF1 53%

Results

ResultsEOF2 19%

ResultsEOF3 12%

ResultsFraction of Variance Explained by Modes 1-3

ResultsZ = a*SST + b*SST2ifSST+ Z+ & SST- Z-thena= (Z+ - Z-) / (2* SSTavg)andb= (Z+ + Z-) / (2* SSTavg)(Monahan & Dai 2004)

ResultsEnsemble mean (shaded); EOF1 (contour)Ensemble mean EOF1DJF 1998

ResultsEnsemble mean (shaded); EOF1 (contour)Ensemble mean EOF1DJF 1999

Results-Strong WarmColdEOF1- WarmStrong Cold

ResultsComposite based on 1980, 81, 82 & 86

ResultsAnomaly CorrelationEnsemble MeanEOF1EOF1 + EOF2EOF1:EOF3

ResultsAC(EOF1+EOF2) AC(EOF1)AC(EOF1:EOF2) AC(EOF1+EOF2)

ResultsAC (EOF1)AC (EOF1:EOF3)

Summary and ConclusionsA large fraction of extratropical variability is indeed related to high noise level inherent in seasonal averages and the prospects of [seasonal predictions] are limited.There are other modes of atmospheric response that are related to non-ENSO SSTs (e.g., EOF2), but this could be specific to the analysis period.This (and previous) analysis has shown higher order response to ENSO extremes, but it is hard to show any definite influence averaged over all SST years. This is either because of the rarity of such events, or because of incorrect simulation by the AGCMShould be repeated with other AGCMs

This work is done in collaboration withTalk is divided in 4 sections I will start with the motivation behind this studyAs for the motivation behind this study, let me start by showing you some results a classic paper from Horel and Wallace. The results from their study were synthesized by this schematic diagram. What this schematics shows is global influence of interannual variability associated with ENSO and associated rainfall pattern. Classic teleconnection pattern. Horel and Wallace also showed correlation between the 700-mb heights and a tropical index of SSTs. Correlations (and explained variance) is fairly small. Smallness of correlations was duely noted by the authors, and they had the insight to ask a remakable question:What correlation between 700-mb heights and the tropical SST index is trying to do is to extract an atmospheric signal that is common to interannual variations in SSTs. It could be small due to 2 reasons: (1) common signal is small, and noise unrelated to SSTs is large, or (2) signal is large but varies from one ENSO year to another and linear regression only captures a part of it.

Let me show you an exampleObserved SST and corresponding 500-mb height anomalies for some selected winters. This is taken from a paper from Kumar and Hoerling in 1997. The bottom panel shows SST anomalies during different ENSO events. There is considerable variation in pattern and amplitude of SST anomalies. Likewise, there is large variations in amplitude of observed 500-mb height anomalies. These variations could be due to atmospheric signal associated with SSTs or could be due to atmospheric noise superimposed on a fixed signal. For both these cases, a linear analysis will provide you with low correlations, however, as Horel and Wallace indicated, implications for predictability are enormous. So the question we are trying to analyze is:

From last 25 years we are trying to answer this question. Even if we have reached a conclusion, we dont seem to like the answer and we keep pushing back on for further enlightment. The basic problem is that we cannot answer this question from the analysis of observations alone, since for individual seasons we cannot separate signal fro noise. It could be done from the analysis of AGCM simulations where from ensemble of AGCM simulations we can separate SST forced signal from noise, and analyze how much the atmospheric varies from one year to another. One can also ask a more general question about influence of SST anomalies in different ocean basins.

This the key is to have atmospheric signal for different SSTs this is done base don

Analysis is primarily based on EOF of ensemble mean atmospheric signal

Where these simulations case from?One key to increasing the size of ensemble using lagged ensemble technique is to be able to pool runs from different ICs together or ascertaining that the statistical properties of ensemble does not depend on the initial conditionsDifference in climatology for different initial conditions (done for some other purpose) the lower panel shows the eddy height climatology and the top panel shows the difference in eddy height climatology from different ICs. ICs from Dec. are one-month lead JFM simulations and ICs from Sep are 5 month lead simulationsDifference in Variance for different initial conditions magnitude of climatological variance is ~50 meters .Start with showing you what is the ensemble mean variance and right away you can see similarity in this pattern with the schematics presented by Horel and Wallace. Variance in high in the subtropical latitudes. PNA pattern

Now this diagram shows the ration of ensemble mean variance and the intra-ensemble variance. Intra-ensemble variance is a measure of seasonal mean variability unrelated to SSTs. Large in tropics and gets smaller in the extratropical latitudes, and this monotonic decrease is because of increase in the inter-sample variability with increasing latitude.

One point to note is that ensemble variance includes variations in atmospheric response to SSTs to fullest extent possible.

Smallness of this ratio itself indicates that answer #1 in Horel and Wallace was the correct one.

But we still proceed on and analyze how the atmospheric signal varies from one year to another, and how it relates to SSTs and this analysis is based on EOF analysis of ensemble means.SST and Precip are Derived as regression patterns Looks similar to the familiar pattern of linear atmospheric response to SSTsNote small % explained, although could be bigger locallyLocally it could be large what is leftRelated to warming and cooling trends in global SSTsAtmospheric structure is more zonally asymmetric We will show that it is related to the non-linear component of SSTsThese three EOFs together explain about 84% percent of variance. We did not go beyond as even with this large ensemble for each SSTs, inter-ensemble variability can still alias on the ensemble mean variance.Rest of the talk we will focus more on the interpretation of mode3 Figure shows the non-linear component based on SST composites coefficient a represents the linear response and b represents the quadratic response.Just to illustrate it further for a strong warm event we show ensemble mean response in shading and the corresponding pattern related to EOF1. Eastward shift. The bottom panel shows the difference or what is not captured by the EOF1Same for a cold event this time response is shifted westIf an EOF analysis is done on thisthe mean response is captured by the EOF1 (middle panel), and departure from the mean response is the non-linear or the second order response that only manifests during strong ENSO yearsTo show that it is not an artifact of EOF analysis alone, and this pattern indeed is an atmospheric response to SSTs

Now the question is how real are these responses? If the responses are any reflection of what happens in the nature, then adding information from higher modes should increase the anomaly correlation between atmospheric response simulated by the AGCM and the observationsTop left panel shows the AC between ensemble means and the obs., and for this AGCM is the upper limit for ACs. Top right panel shows the AC if you reconstruct atmospheric heights based on first EOF. And a large fraction of AC is captured by the The other panels show ACs as you progressively increase the information content. A better wayBlue indicate correlation is better Adding mode 2 does indeed improves the ACs

Adding mode 3 is not so So spatial structure related to Mode 2 does improves predictions Mode 3.