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Lakshmi Krishnamurthy, Gabriel A. Vecchi, Rym Msadek, Hiroyuki Murakami, Andrew Wittenberg, and Fanrong Zeng

Abstract

Tropical cyclone (TC) activity in the North Pacific and North Atlantic Oceans is known to be affected by the El Niño–Southern Oscillation (ENSO). This study uses the GFDL Forecast Oriented Low Ocean Resolution Model (FLOR), which has relatively high resolution in the atmosphere, as a tool to investigate the sensitivity of TC activity to the strength of ENSO events. This study shows that TCs exhibit a nonlinear response to the strength of ENSO in the tropical eastern North Pacific (ENP) but a quasi-linear response in the tropical western North Pacific (WNP) and tropical North Atlantic. Specifically, a stronger El Niño results in disproportionate inhibition of TCs in the ENP and North Atlantic, and leads to an eastward shift in the location of TCs in the southeast of the WNP. However, the character of the response of TCs in the Pacific is insensitive to the amplitude of La Niña events. The eastward shift of TCs in the southeast of the WNP in response to a strong El Niño is due to an eastward shift of the convection and of the associated environmental conditions favorable for TCs. The inhibition of TC activity in the ENP and Atlantic during El Niño is attributed to the increase in the number of days with strong vertical wind shear during stronger El Niño events. These results are further substantiated with coupled model experiments. Understanding of the impact of strong ENSO on TC activity is important for present and future climate as the frequency of occurrence of extreme ENSO events is projected to increase in the future.

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James A. Carton, Stuart A. Cunningham, Eleanor Frajka-Williams, Young-Oh Kwon, David P. Marshall, and Rym Msadek
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Xiaosong Yang, Anthony Rosati, Shaoqing Zhang, Thomas L. Delworth, Rich G. Gudgel, Rong Zhang, Gabriel Vecchi, Whit Anderson, You-Soon Chang, Timothy DelSole, Keith Dixon, Rym Msadek, William F. Stern, Andrew Wittenberg, and Fanrong Zeng

Abstract

The decadal predictability of sea surface temperature (SST) and 2-m air temperature (T2m) in the Geophysical Fluid Dynamics Laboratory (GFDL) decadal hindcasts, which are part of the Fifth Coupled Model Intercomparison Project experiments, has been investigated using an average predictability time (APT) analysis. Comparison of retrospective forecasts initialized using the GFDL Ensemble Coupled Data Assimilation system with uninitialized historical forcing simulations using the same model allows identification of the internal multidecadal pattern (IMP) for SST and T2m. The IMP of SST is characterized by an interhemisphere dipole, with warm anomalies centered in the North Atlantic subpolar gyre region and North Pacific subpolar gyre region, and cold anomalies centered in the Antarctic Circumpolar Current region. The IMP of T2m is characterized by a general bipolar seesaw, with warm anomalies centered in Greenland and cold anomalies centered in Antarctica. The retrospective prediction skill of the initialized system, verified against independent observational datasets, indicates that the IMP of SST may be predictable up to 4 (10) yr lead time at 95% (90%) significance level, and the IMP of T2m may be predictable up to 2 (10) yr at the 95% (90%) significance level. The initialization of multidecadal variations of northward oceanic heat transport in the North Atlantic significantly improves the predictive skill of the IMP. The dominant roles of oceanic internal dynamics in decadal prediction are further elucidated by fixed-forcing experiments in which radiative forcing is returned abruptly to 1961 values. These results point toward the possibility of meaningful decadal climate outlooks using dynamical coupled models if they are appropriately initialized from a sustained climate observing system.

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Gabriel A. Vecchi, Rym Msadek, Whit Anderson, You-Soon Chang, Thomas Delworth, Keith Dixon, Rich Gudgel, Anthony Rosati, Bill Stern, Gabriele Villarini, Andrew Wittenberg, Xiaosong Yang, Fanrong Zeng, Rong Zhang, and Shaoqing Zhang
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Liwei Jia, Xiaosong Yang, Gabriel A. Vecchi, Richard G. Gudgel, Thomas L. Delworth, Anthony Rosati, William F. Stern, Andrew T. Wittenberg, Lakshmi Krishnamurthy, Shaoqing Zhang, Rym Msadek, Sarah Kapnick, Seth Underwood, Fanrong Zeng, Whit G. Anderson, Venkatramani Balaji, and Keith Dixon

Abstract

This study demonstrates skillful seasonal prediction of 2-m air temperature and precipitation over land in a new high-resolution climate model developed by the Geophysical Fluid Dynamics Laboratory and explores the possible sources of the skill. The authors employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land and demonstrate the predictive skill of these components. First, the improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of the Niño-3.4 index and other aspects of interest is shown. Then, the skill of temperature and precipitation in the high-resolution model for boreal winter and summer is measured, and the sources of the skill are diagnosed. Last, predictions are reconstructed using a few of the most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2-m air temperature and precipitation over land.

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Gabriel A. Vecchi, Rym Msadek, Whit Anderson, You-Soon Chang, Thomas Delworth, Keith Dixon, Rich Gudgel, Anthony Rosati, Bill Stern, Gabriele Villarini, Andrew Wittenberg, Xiasong Yang, Fanrong Zeng, Rong Zhang, and Shaoqing Zhang

Abstract

Retrospective predictions of multiyear North Atlantic Ocean hurricane frequency are explored by applying a hybrid statistical–dynamical forecast system to initialized and noninitialized multiyear forecasts of tropical Atlantic and tropical-mean sea surface temperatures (SSTs) from two global climate model forecast systems. By accounting for impacts of initialization and radiative forcing, retrospective predictions of 5- and 9-yr mean tropical Atlantic hurricane frequency show significant correlations relative to a null hypothesis of zero correlation. The retrospective correlations are increased in a two-model average forecast and by using a lagged-ensemble approach, with the two-model ensemble decadal forecasts of hurricane frequency over 1961–2011 yielding correlation coefficients that approach 0.9. These encouraging retrospective multiyear hurricane predictions, however, should be interpreted with care: although initialized forecasts have higher nominal skill than uninitialized ones, the relatively short record and large autocorrelation of the time series limits confidence in distinguishing between the skill caused by external forcing and that added by initialization. The nominal increase in correlation in the initialized forecasts relative to the uninitialized experiments is caused by improved representation of the multiyear tropical Atlantic SST anomalies. The skill in the initialized forecasts comes in large part from the persistence of a mid-1990s shift by the initialized forecasts, rather than from predicting its evolution. Predicting shifts like that observed in 1994/95 remains a critical issue for the success of multiyear forecasts of Atlantic hurricane frequency. The retrospective forecasts highlight the possibility that changes in observing system impact forecast performance.

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Rong Zhang, Thomas L. Delworth, Rowan Sutton, Daniel L. R. Hodson, Keith W. Dixon, Isaac M. Held, Yochanan Kushnir, John Marshall, Yi Ming, Rym Msadek, Jon Robson, Anthony J. Rosati, MingFang Ting, and Gabriel A. Vecchi

Abstract

Identifying the prime drivers of the twentieth-century multidecadal variability in the Atlantic Ocean is crucial for predicting how the Atlantic will evolve in the coming decades and the resulting broad impacts on weather and precipitation patterns around the globe. Recently, Booth et al. showed that the Hadley Centre Global Environmental Model, version 2, Earth system configuration (HadGEM2-ES) closely reproduces the observed multidecadal variations of area-averaged North Atlantic sea surface temperature in the twentieth century. The multidecadal variations simulated in HadGEM2-ES are primarily driven by aerosol indirect effects that modify net surface shortwave radiation. On the basis of these results, Booth et al. concluded that aerosols are a prime driver of twentieth-century North Atlantic climate variability. However, here it is shown that there are major discrepancies between the HadGEM2-ES simulations and observations in the North Atlantic upper-ocean heat content, in the spatial pattern of multidecadal SST changes within and outside the North Atlantic, and in the subpolar North Atlantic sea surface salinity. These discrepancies may be strongly influenced by, and indeed in large part caused by, aerosol effects. It is also shown that the aerosol effects simulated in HadGEM2-ES cannot account for the observed anticorrelation between detrended multidecadal surface and subsurface temperature variations in the tropical North Atlantic. These discrepancies cast considerable doubt on the claim that aerosol forcing drives the bulk of this multidecadal variability.

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Gabriel A. Vecchi, Rym Msadek, Whit Anderson, You-Soon Chang, Thomas Delworth, Keith Dixon, Rich Gudgel, Anthony Rosati, Bill Stern, Gabriele Villarini, Andrew Wittenberg, Xiasong Yang, Fanrong Zeng, Rong Zhang, and Shaoqing Zhang
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Xiaosong Yang, Gabriel A. Vecchi, Rich G. Gudgel, Thomas L. Delworth, Shaoqing Zhang, Anthony Rosati, Liwei Jia, William F. Stern, Andrew T. Wittenberg, Sarah Kapnick, Rym Msadek, Seth D. Underwood, Fanrong Zeng, Whit Anderson, and Venkatramani Balaji

Abstract

The seasonal predictability of extratropical storm tracks in the Geophysical Fluid Dynamics Laboratory’s (GFDL)’s high-resolution climate model has been investigated using an average predictability time analysis. The leading predictable components of extratropical storm tracks are the ENSO-related spatial patterns for both boreal winter and summer, and the second predictable components are mostly due to changes in external radiative forcing and multidecadal oceanic variability. These two predictable components for both seasons show significant correlation skill for all leads from 0 to 9 months, while the skill of predicting the boreal winter storm track is consistently higher than that of the austral winter. The predictable components of extratropical storm tracks are dynamically consistent with the predictable components of the upper troposphere jet flow for both seasons. Over the region with strong storm-track signals in North America, the model is able to predict the changes in statistics of extremes connected to storm-track changes (e.g., extreme low and high sea level pressure and extreme 2-m air temperature) in response to different ENSO phases. These results point toward the possibility of providing skillful seasonal predictions of the statistics of extratropical extremes over land using high-resolution coupled models.

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Rym Msadek, T. L. Delworth, A. Rosati, W. Anderson, G. Vecchi, Y.-S. Chang, K. Dixon, R. G. Gudgel, W. Stern, A. Wittenberg, X. Yang, F. Zeng, R. Zhang, and S. Zhang

Abstract

Decadal prediction experiments were conducted as part of phase 5 of the Coupled Model Intercomparison Project (CMIP5) using the GFDL Climate Model, version 2.1 (CM2.1) forecast system. The abrupt warming of the North Atlantic Subpolar Gyre (SPG) that was observed in the mid-1990s is considered as a case study to evaluate forecast capabilities and better understand the reasons for the observed changes. Initializing the CM2.1 coupled system produces high skill in retrospectively predicting the mid-1990s shift, which is not captured by the uninitialized forecasts. All the hindcasts initialized in the early 1990s show a warming of the SPG; however, only the ensemble-mean hindcasts initialized in 1995 and 1996 are able to reproduce the observed abrupt warming and the associated decrease and contraction of the SPG. Examination of the physical mechanisms responsible for the successful retrospective predictions indicates that initializing the ocean is key to predicting the mid-1990s warming. The successful initialized forecasts show an increased Atlantic meridional overturning circulation and North Atlantic Current transport, which drive an increased advection of warm saline subtropical waters northward, leading to a westward shift of the subpolar front and, subsequently, a warming and spindown of the SPG. Significant seasonal climate impacts are predicted as the SPG warms, including a reduced sea ice concentration over the Arctic, an enhanced warming over the central United States during summer and fall, and a northward shift of the mean ITCZ. These climate anomalies are similar to those observed during a warm phase of the Atlantic multidecadal oscillation, which is encouraging for future predictions of North Atlantic climate.

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