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William F. Stern

Abstract

Using a 12-component spectral model derived from the barotropic vorticity equation, updating experiments were performed at intervals ranging from one to six time steps, where one time step was equivalent to about 2.4 hr. The Monte-Carlo and stochastic dynamic methods were used to determine a representative initial error growth for the model. In the first groups of experiments, it was found that when all 12 components were updated at intervals of five time steps or less, the rms vorticity errors eventually converged to zero; but when the updating interval was increased to six time steps, there was no tendency for convergence. In the second group of experiments the effect of not updating the smallest scales (sub-synoptic or mesoscale) was considered. The general result was that it was still possible to determine large-scale features rather well with fairly infrequent updating (three and four time steps) and model resolution to the intermediate (synoptic) scales, but intermediate-scale features could be recovered with good accuracy only when updating was done quite rapidly (every time step) or if smaller scale resolution was retained.

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Jeffrey L. Anderson
and
William F. Stern

Abstract

A method is presented for determining when an ensemble of model forecasts has the potential to provide some useful information. An ensemble forecast of a particular scale quantity is said to have potential predictive utility when the ensemble forecast distribution is significantly different from an appropriate climatological distribution. Here, the potential predictive utility is measured using Kuiper's statistical test for comparing two discrete distributions. More traditional measures of the potential usefulness of an ensemble forecast based on ensemble mean or variance discard possibly valuable information by making implicit assumptions about the distributions being compared.

Application of the potential predictive utility to long integrations of an atmospheric general circulation model in a boundary value problem (an ensemble of Atmospheric Model Intercomparison Project integrations) reveals a number of features about the response of a GCM to observed sea surface temperatures. In particular, the ensemble of forecasts is found to have potential predictive utility over large geographic areas for a number of atmospheric fields during strong El Niño-Southern Oscillation anomalous events. Unfortunately, there are only limited areas of potential predictive utility for near-surface fields and precipitation outside the regions of the tropical oceans. Nevertheless, the method presented here can identify all areas where the GCM ensemble may provide useful information, whereas methods that make assumptions about the distribution of the ensemble forecast variables may not be able to do so.

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Charles T. Gordon
and
William F. Stern

Abstract

A multi-level, global, spectral transform model of the atmosphere, based upon spherical harmonies, has been developed at GFDL. The basic model has nine sigma levels in the vertical and rhomboidal spectral truncation at wavenumber 30. However, finer spectral or vertical resolution versions are available as well. The model's efficient semi-implicit time differencing scheme does not appear to adversely affect medium range predictions. The model has physical processes commonly associated with grid point GCM'S. Two unique features are a linearized virtual temperature correction and an optional, spectrally-computed non-linear horizontal diffusion scheme. A parameterization of vertical mixing based upon the turbulent closure method is also optional.

The GFDL spectral model has been widely utilized at GFDL for extended range weather prediction experiments. In addition, it has been adapted and applied to climate studies, four-dimensional data assimilation experiments and even to the atmosphere of Venus. These applications are briefly reviewed.

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Xiu-Qun Yang
,
Jeffrey L. Anderson
, and
William F. Stern

Abstract

An approach to assess the potential predictability of the extratropical atmospheric seasonal variations in an ensemble of atmospheric general circulation model (AGCM) integrations has been proposed in this study by isolating reproducible forced modes and examining their contributions to the local ensemble mean. The analyses are based on the monthly mean output of an eight-member ensemble of 10-yr Atmospheric Model Intercomparison Project integrations with a T42L18 AGCM.

An EOF decomposition applied to the ensemble anomalies shows that there exist some forced modes that are less affected by the internal process and thus appear to be highly reproducible. By reconstructing the ensemble in terms of the more reproducible forced modes and by developing a quantitative measure, the potential predictability index (PPI), which combines the reproducibility with the local variance contribution, the local ensemble mean over some selective geographic areas in the extratropics was shown to result primarily from reproducible forced modes rather than internal chaotic fluctuations. Over those regions the ensemble mean is potentially predictable. Extratropical potentially predictable regions are found mainly over North America and part of the Asian monsoon regions. Interestingly, the potential predictability over some preferred areas such as Indian monsoon areas and central Africa occasionally results primarily from non-ENSO-related boundary forcing, although ENSO forcing generally dominates over most of the preferred areas.

The quantitative analysis of the extratropical potential predictability with PPI has shown that the preferred geographic areas have obvious seasonality. For the 850-hPa temperature, for example, potentially predictable regions during spring and winter are confined to Alaska, northwest Canada, and the southeast United States, the traditional PNA region, while during summer and fall they are favored over the middle part of North America. It has also been shown that the boreal summer season (June–August) possesses the largest potentially predictable area, which seems to indicate that it is a favored season for the extratropical potential predictability. On the contrary, boreal winter (December–February) appears to have a minimum area of extratropical potential predictability.

The results have been compared with the more traditional statistical tests for potential predictability and with observations from the National Centers for Environmental Prediction reanalysis, which indicates that the PPI analysis proposed here is successful in revealing extratropical potential predictability determined by the external forcing.

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Liwei Jia
,
Gabriel A. Vecchi
,
Xiaosong Yang
,
Richard G. Gudgel
,
Thomas L. Delworth
,
William F. Stern
,
Karen Paffendorf
,
Seth D. Underwood
, and
Fanrong Zeng

Abstract

This study investigates the roles of radiative forcing, sea surface temperatures (SSTs), and atmospheric and land initial conditions in the summer warming episodes of the United States. The summer warming episodes are defined as the significantly above-normal (1983–2012) June–August 2-m temperature anomalies and are referred to as heat waves in this study. Two contrasting cases, the summers of 2006 and 2012, are explored in detail to illustrate the distinct roles of SSTs, direct radiative forcing, and atmospheric and land initial conditions in driving U.S. summer heat waves. For 2012, simulations with the GFDL atmospheric general circulation model reveal that SSTs play a critical role. Further sensitivity experiments reveal the contributions of uniform global SST warming, SSTs in individual ocean basins, and direct radiative forcing to the geographic distribution and magnitudes of warm temperature anomalies. In contrast, for 2006, the atmospheric and land initial conditions are the key drivers. The atmospheric (land) initial conditions play a major (minor) role in the central and northwestern (eastern) United States. Because of changes in radiative forcing, the probability of areal-averaged summer temperature anomalies over the United States exceeding the observed 2012 anomaly increases with time over the early twenty-first century. La Niña (El Niño) events tend to increase (reduce) the occurrence rate of heat waves. The temperatures over the central United States are mostly influenced by El Niño/La Niña, with the central tropical Pacific playing a more important role than the eastern tropical Pacific. Thus, atmospheric and land initial conditions, SSTs, and radiative forcing are all important drivers of and sources of predictability for U.S. summer heat waves.

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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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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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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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