Decomposition of the Atlantic Multidecadal Variability in a Historical Climate Simulation

The Atlantic multidecadal variability (AMV) modulates various climate features worldwide with enormous societal and economic implications, including variations in hurricane activity in the Atlantic, sea level, West African and Indian monsoon rainfall, European climate, and hemispheric-scale surface temperature. Leading hypotheses regarding the nature and origin of AMV focus primarily on its links with oceanic and coupled ocean–atmosphere internal variability, and on its response to external forcing. The role of another possible process, that of atmospheric noise forcing of the ocean, has received less attention. This is addressed here by means of historical coupled simulations and diagnostic experiments, which isolate the influences of external and atmospheric noise forcings. Our findings show that external forcing is an important driver of the simulated AMV. They also demonstrate that weather noise is key in driving the simulated internal AMV in the southern part (0°–60°N) of the AMV region, and that weather noise forcing is responsible for up to 10%–20% of the multidecadal internal SST variability in some isolated areas of the subpolar gyre region. Ocean dynamics independent from the weather noise forcing is found to be the dominant cause of multidecadal SST in the northern part of the AMV region.

Decomposition of the Atlantic Multidecadal Variability in a historical climate simulation

<div> <div class="gmail-page" title="Page 2"> <div class="gmail-layoutArea"> <div class="gmail-column"> <p>The Atlantic Multidecadal Variability (AMV) modulates various climate features worldwide with enormous societal and economic implications, including variations in hurricane activity in the Atlantic, sea-level changes, West African and Indian monsoon rainfall, European climate, and hemispheric‐ scale surface temperature. Leading hypotheses regarding the nature and origin of AMV focus primarily on its links with oceanic and coupled ocean-atmosphere internal variability, and on its response to external forcing. The role of another possible process, that of atmospheric noise forcing of the ocean, has received less attention. This is addressed here by means of historical coupled simulations and diagnostic experiments, which isolate the influences of external and atmospheric noise forcings. Our findings show that external forcing is an important driver of the simulated AMV. They also demonstrate that weather noise is key in driving the simulated internal AMV in the southern part of the (0o-60oN) AMV region, and that weather noise forcing is responsible for up to 10%-20% of the multidecadal internal SST variability in some isolated areas of the sub-polar gyre region. Ocean dynamics independent from the weather noise forcing is found to be the dominant cause of multidecadal SST in the northern part of the AMV region.</p> </div> </div> </div> </div> <p> </p>

Estimation of Weather Noise in Coupled Ocean–Atmosphere Systems Using Initialized Simulations

This study presents a new method to estimate atmospheric weather noise from coupled models, which is based on initialized simulations with a CGCM. In this method, the weather noise is estimated by removing the signal part, as determined from the coupled ensemble mean simulations. The weather noise estimated from coupled models is compared with that estimated from uncoupled AGCM simulations. The model used in this study is CFSv2. The initialized simulations start from each April during 1982–2009 paired with four members and extend for 6 months. To make a clear comparison between weather noise in coupled and uncoupled simulations, a set of uncoupled AGCM (the atmospheric component of CFSv2) simulations are conducted, which are forced by the daily mean SSTs from the above initialized CGCM simulations. The comparison indicates that, over the Asia–Pacific monsoon region where the local air–sea coupling is important, the noise variances are generally reduced as a result of air–sea coupling, as are the total and signal variances. This result stands in contrast to the results of previous studies that suggested that the noise variance for coupled and uncoupled models is the same. It is shown that the previous conclusion is simply an artifact of the assumption applied in the AGCM-based approach (i.e., the signal is the same between coupled and uncoupled simulations). In addition, the variance difference also exhibits a clear seasonality, with a larger difference over the monsoon region appearing toward boreal summer. Another set of AGCM experiments forced by the same SST suggests that the CGCM-based method generally remains valid in estimating weather noise within 2 months of its initial start.

Evaluation of Weather Noise and Its Role in Climate Model Simulations*

The relationship between coupled atmosphere–ocean general circulation model simulations and uncoupled simulations made with specified SST and sea ice is investigated using the Community Climate System Model, version 3. Experiments are carried out in a perfect model framework. Two closely related questions are investigated: 1) whether the statistics of the atmospheric weather noise in the atmospheric model are the same as in the coupled model, and 2) whether the atmospheric model reproduces the SST-forced response of the coupled model. The weather noise in both the coupled and uncoupled simulations is found by removing the forced response, as determined from the uncoupled ensemble, from the atmospheric field. The weather-noise variance is generally not distinguishable between the coupled and uncoupled simulations. However, variances of the total fields differ between the coupled and uncoupled simulations, since there is constructive or destructive interference between the SST-forced response and weather noise in the coupled model but no correlation between the SST-forced and weather-noise components in the uncoupled model simulations. Direct regression estimates of the forced response show little difference between the coupled and uncoupled simulations. Differences in local correlations are explained by weather noise because weather noise forces SST in the coupled simulation only. The results demonstrate and explain an important intrinsic difference in precipitation statistics between the coupled and uncoupled simulations. This difference could have consequences for the design of dynamical downscaling experiments and for tuning general circulation models.

Observed Decadal North Atlantic Tripole SST Variability. Part II: Diagnosis of Mechanisms

In Part I of this study, the atmospheric weather noise for 1951–2000 was inferred from an atmospheric analysis in conjunction with SST-forced AGCM simulations and used to force interactive ensemble coupled GCM simulations of the North Atlantic SST variability. Here, results from those calculations are used in conjunction with a simple stochastically forced coupled model of the decadal time scale North Atlantic tripole SST variability to examine the mechanisms associated with the tripole SST variability. The diagnosed tripole variability is found to be characterized by damped, delayed oscillator dynamics, similar to what has been found by other investigators. However, major differences here, affecting the signs of two of the crucial parameters of the simple model, are that the atmospheric heat flux feedback damps the tripole pattern and that a counterclockwise intergyre gyre-like circulation enhances the tripole pattern. Delayed oscillator dynamics are still obtained because the sign of the dynamically important parameter, proportional to the product of these two parameters, is unchanged. Another difference with regard to the dynamical processes included in the simple model is that the major contribution to the ocean’s dynamical heat flux response to the weather noise wind stress is through a delayed modulation of the mean gyres, rather than from the simultaneous intergyre gyre response. The power spectrum of a revised simple model forced by white noise has a less prominent decadal peak using the parameter values and dynamics diagnosed here than in previous investigations. Decadal time scale retrospective predictions made with this version of the simple model are no better than persistence.

Observed Decadal North Atlantic Tripole SST Variability. Part I: Weather Noise Forcing and Coupled Response

The mechanisms responsible for the decadal variability of the tripole mode of North Atlantic SST during the latter half of the twentieth century are diagnosed using a new technique. The SST and associated ocean variability are reconstructed by forcing an interactive ensemble coupled GCM by the surface fluxes resulting from weather noise. The weather noise surface fluxes are obtained from the NCEP–NCAR reanalysis by removing the simulated atmospheric feedback to the observed SST evolution. Simulations are performed to reconstruct and estimate the contributions of the local weather noise heat flux and wind stress to the observed evolution of the tripole pattern. The results indicate that the North Atlantic tripole pattern is forced primarily by the local weather noise surface heat flux. The roles of several types of ocean circulation variability, including gyres forced by the wind stress weather noise, the wind stress feedback to the SST, and the meridional overturning circulation, are also examined. Conclusions from this approach are expected to be model dependent. Further analysis, in the context of a simple model, of the mechanisms producing the tripole variability is presented in Part II.