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Jia-Bei Fang and Xiu-Qun Yang

Abstract

Following Goodman and Marshall (hereinafter GM), an improved intermediate midlatitude coupled ocean–atmosphere model linearized around a basic state is developed. The model assumes a two-layer quasigeostrophic atmosphere overlying a quasigeostrophic upper ocean that consists of a constant-depth mixed layer, a thin entrainment layer, and a thermocline layer. The SST evolution is determined within the mixed layer by advection, entrainment, and air–sea flux. The atmospheric heating is specified at midlevel, which is parameterized in terms of both the SST and atmospheric temperature anomalies. With this coupled model, the dynamical features of unstable ocean–atmosphere interactions in the midlatitudes are investigated. The coupled model exhibits two types of coupled modes: the coupled oceanic Rossby wave mode and the SST-only mode. The SST-only mode decays over the entire range of wavelengths, whereas the coupled oceanic Rossby wave mode destabilizes over a certain range of wavelengths (∼10 500 km) when the atmospheric response to the heating is equivalent barotropic. The relative roles of different physical processes in destabilizing the coupled oceanic Rossby wave are examined. The main processes emphasized are the influence of entrainment and advection for determining SST evolution, and the atmospheric heating profile. Although either entrainment or advection can lead to unstable growth of the coupled oceanic Rossby wave with similar wavelength dependence for each case, the advection process is found to play the more important role, which differs from GM’s results in which the entrainment process is dominant. The structure of the unstable coupled mode is sensitive to the atmospheric heating profile. The inclusion of surface heating largely reduces the growth rate and stabilizes the coupled oceanic Rossby wave. In comparison with observations, it is demonstrated that the structure of the growing coupled mode derived from the authors’ model is closer to reality than that from the previous model.

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Yong-Fei Zhang, Mitchell Bushuk, Michael Winton, Bill Hurlin, Xiaosong Yang, Tom Delworth, and Liwei Jia

Abstract

The current GFDL seasonal prediction system achieved retrospective sea ice extent (SIE) skill without direct sea ice data assimilation. Here we develop sea ice data assimilation, shown to be a key source of skill for seasonal sea ice predictions, in GFDL’s next-generation prediction system, the Seamless System for Prediction and Earth System Research (SPEAR). Satellite sea ice concentration (SIC) observations are assimilated into the GFDL Sea Ice Simulator version 2 (SIS2) using the ensemble adjustment Kalman filter (EAKF). Sea ice physics is perturbed to form an ensemble of ice–ocean members with atmospheric forcing from the JRA-55 reanalysis. Assimilation is performed every 5 days from 1982 to 2017 and the evaluation is conducted at pan-Arctic and regional scales over the same period. To mitigate an assimilation overshoot problem and improve the analysis, sea surface temperatures (SSTs) are restored to the daily Optimum Interpolation Sea Surface Temperature version 2 (OISSTv2). The combination of SIC assimilation and SST restoring reduces analysis errors to the observational error level (~10%) from up to 3 times larger than this (~30%) in the free-running model. Sensitivity experiments show that the choice of assimilation localization half-width (190 km) is near optimal and that SIC analysis errors can be further reduced slightly either by reducing the observational error or by increasing the assimilation frequency from every 5 days to daily. A lagged-correlation analysis suggests substantial prediction skill improvements from SIC initialization at lead times of less than 2 months.

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Shi-Xin Wang, Hong-Chao Zuo, Fen Sun, Li-Yang Wu, Yixing Yin, and Jing-Jia Luo

Abstract

Dynamics of the East Asian spring rainband are investigated with a reanalysis dataset and station observations. Here, it is revealed that the rainband is anchored by external forcings. The midtropospheric jet core stays quasi-stationary around Japan. It has two branches in its entry region, which originate from the south and north flanks of the Tibetan Plateau and then run northeastward and southeastward, respectively. The southern branch advects warm air from the Tibetan–Hengduan Plateau northeastward, forming a rainband over southern China through causing adiabatic ascent motion and triggering diabatic feedback. The rainband is much stronger in spring than in autumn due to the stronger diabatic heating over the Tibetan–Hengduan Plateau, a more southward-displaced midtropospheric jet, and the resulting stronger warm advection over southern China. The northern jet branch forms a zonally elongated cold advection belt, which reaches a maximum around northern China, and then weakens and extends eastward to east of Japan. The westerly jet also steers strong disturbance activities roughly collocated with the cold advection belt via baroclinic instability. The high disturbance activities belt causes large cumulative warm advection (CWA) through drastically increasing extremely warm advection days on its eastern and south flanks, where weak cold advection prevails. CWA is more essential for monthly/seasonally rainfall than conventionally used time-average temperature advection because it is shown that strengthened warm advection can increase rainfall through positive diabatic feedback, while cold advection cannot cause negative rainfall. Thus, the rainband is collocated with the large CWA belt instead of the warm advection south of it. This rainband is jointed to the rainband over southern China, forming the long southwest–northeast-oriented East Asian spring rainband. Increasing moisture slightly displaces the rainband southeastward.

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Gan Zhang, Hiroyuki Murakami, Xiaosong Yang, Kirsten L. Findell, Andrew T. Wittenberg, and Liwei Jia

Abstract

Climate models often show errors in simulating and predicting tropical cyclone (TC) activity, but the sources of these errors are not well understood. This study proposes an evaluation framework and analyzes three sets of experiments conducted using a seasonal prediction model developed at the Geophysical Fluid Dynamics Laboratory (GFDL). These experiments apply the nudging technique to the model integration and/or initialization to estimate possible improvements from nearly perfect model conditions. The results suggest that reducing sea surface temperature (SST) errors remains important for better predicting TC activity at long forecast leads—even in a flux-adjusted model with reduced climatological biases. Other error sources also contribute to biases in simulated TC activity, with notable manifestations on regional scales. A novel finding is that the coupling and initialization of the land and atmosphere components can affect seasonal TC prediction skill. Simulated year-to-year variations in June land conditions over North America show a significant lead correlation with the North Atlantic large-scale environment and TC activity. Improved land–atmosphere initialization appears to improve the Atlantic TC predictions initialized in some summer months. For short-lead predictions initialized in June, the potential skill improvements attributable to land–atmosphere initialization might be comparable to those achievable with perfect SST predictions. Overall, this study delineates the SST and non-oceanic error sources in predicting TC activity and highlights avenues for improving predictions. The nudging-based evaluation framework can be applied to other models and help improve predictions of other weather extremes.

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Liping Zhang, Thomas L. Delworth, Xiaosong Yang, Richard G. Gudgel, Liwei Jia, Gabriel A. Vecchi, and Fanrong Zeng

Abstract

This study explores the potential predictability of the Southern Ocean (SO) climate on decadal time scales as represented in the GFDL CM2.1 model using prognostic methods. Perfect model predictability experiments are conducted starting from 10 different initial states, showing potentially predictable variations of Antarctic bottom water (AABW) formation rates on time scales as long as 20 years. The associated Weddell Sea (WS) subsurface temperatures and Antarctic sea ice have potential predictability comparable to that of the AABW cell. The predictability of sea surface temperature (SST) variations over the WS and the SO is somewhat smaller, with predictable scales out to a decade. This reduced predictability is likely associated with stronger damping from air–sea interaction. As a complement to this perfect predictability study, the authors also make hindcasts of SO decadal variability using the GFDL CM2.1 decadal prediction system. Significant predictive skill for SO SST on multiyear time scales is found in the hindcast system. The success of the hindcasts, especially in reproducing observed surface cooling trends, is largely due to initializing the state of the AABW cell. A weak state of the AABW cell leads to cooler surface conditions and more extensive sea ice. Although there are considerable uncertainties regarding the observational data used to initialize the hindcasts, the consistency between the perfect model experiments and the decadal hindcasts at least gives some indication as to where and to what extent skillful decadal SO forecasts might be possible.

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Lakshmi Krishnamurthy, Gabriel A. Vecchi, Xiaosong Yang, Karin van der Wiel, V. Balaji, Sarah B. Kapnick, Liwei Jia, Fanrong Zeng, Karen Paffendorf, and Seth Underwood

Abstract

Unprecedented high-intensity flooding induced by extreme precipitation was reported over Chennai in India during November–December of 2015, which led to extensive damage to human life and property. It is of utmost importance to determine the odds of occurrence of such extreme floods in the future, and the related climate phenomena, for planning and mitigation purposes. Here, a suite of simulations from GFDL high-resolution coupled climate models are used to investigate the odds of occurrence of extreme floods induced by extreme precipitation over Chennai and the role of radiative forcing and/or large-scale SST forcing in enhancing the probability of such events in the future. The climate of twentieth-century experiments with large ensembles suggest that the radiative forcing may not enhance the probability of extreme floods over Chennai. Doubling of CO2 experiments also fails to show evidence for an increase of such events in a global warming scenario. Further, this study explores the role of SST forcing from the Indian and Pacific Oceans on the odds of occurrence of Chennai-like floods. Neither El Niño nor La Niña enhances the probability of extreme floods over Chennai. However, a warm Bay of Bengal tends to increase the odds of occurrence of extreme Chennai-like floods. In order to trigger a Chennai like-flood, a conducive weather event, such as a tropical depression over the Bay of Bengal with strong transport of moisture from a moist atmosphere over the warm Bay, is necessary for the intense precipitation.

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Hiroyuki Murakami, Gabriel A. Vecchi, Thomas L. Delworth, Andrew T. Wittenberg, Seth Underwood, Richard Gudgel, Xiaosong Yang, Liwei Jia, Fanrong Zeng, Karen Paffendorf, and Wei Zhang

Abstract

The 2015 hurricane season in the eastern and central Pacific Ocean (EPO and CPO), particularly around Hawaii, was extremely active, including a record number of tropical cyclones (TCs) and the first instance of three simultaneous category-4 hurricanes in the EPO and CPO. A strong El Niño developed during the 2015 boreal summer season and was attributed by some to be the cause of the extreme number of TCs. However, according to a suite of targeted high-resolution model experiments, the extreme 2015 EPO and CPO hurricane season was not primarily induced by the 2015 El Niño tropical Pacific warming, but by warming in the subtropical Pacific Ocean. This warming is not typical of El Niño, but rather of the Pacific meridional mode (PMM) superimposed on long-term anthropogenic warming. Although the likelihood of such an extreme year depends on the phase of natural variability, the coupled GCM projects an increase in the frequency of such extremely active TC years over the next few decades for EPO, CPO, and Hawaii as a result of enhanced subtropical Pacific warming from anthropogenic greenhouse gas forcing.

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Liwei Jia, Xiaosong Yang, Gabriel Vecchi, Richard Gudgel, Thomas Delworth, Stephan Fueglistaler, Pu Lin, Adam A. Scaife, Seth Underwood, and Shian-Jiann Lin

Abstract

This study explores the role of the stratosphere as a source of seasonal predictability of surface climate over Northern Hemisphere extratropics both in the observations and climate model predictions. A suite of numerical experiments, including climate simulations and retrospective forecasts, are set up to isolate the role of the stratosphere in seasonal predictive skill of extratropical near-surface land temperature. It is shown that most of the lead-0-month spring predictive skill of land temperature over extratropics, particularly over northern Eurasia, stems from stratospheric initialization. It is further revealed that this predictive skill of extratropical land temperature arises from skillful prediction of the Arctic Oscillation (AO). The dynamical connection between the stratosphere and troposphere is also demonstrated by the significant correlation between the stratospheric polar vortex and sea level pressure anomalies, as well as the migration of the stratospheric zonal wind anomalies to the lower troposphere.

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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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