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Bin Wang and Qin Zhang

Abstract

The anomalous Philippine Sea anticyclone (PSAC) conveys impacts of El Niño to east Asian climate during the mature and decay of an El Niño (from the winter to ensuing summer). It is shown that the anomalous PSAC forms in fall about one season prior to the peak El Niño; its strength increases with the El Niño intensity and its sign reverses during a La Niña. The PSAC formation concurs with abnormal deepening of the east Asian trough and with increasing number of northward recurvature of tropical storms in the western Pacific. The PSAC establishment is abrupt, coupling with a swing from a wet to dry phase of an intraseasonal oscillation (ISO) and often concurrent with early retreat of the east Asian summer monsoon. The ISO becomes inactive after PSAC establishment.

The development of the PSAC is attributed to combined effects of the remote El Niño forcing, tropical–extratropical interaction, and monsoon–ocean interaction. The developing El Niño induces off-equatorial ascending Rossby wave responses and land surface cooling in northeast Asia; both deepen the east Asian trough in fall and induces vigorous tropical–extratropical exchange of air mass and heat, which enhances the cold air outbreak and initiation of the PSAC. Through exciting descending Rossby waves, the El Niño–induced Indonesian subsidence generates low-level anticyclonic vorticity over south Asia, which is advected by mean monsoon westerly, instigating the anomalous PSAC. The ISO interacting with the underlying ocean plays a critical role in the abrupt establishment of PSAC. The wind–evaporation/entrainment feedback tends to amplify (suppress) ISO before (after) winter northeasterly monsoon commences, suggesting the roles of atmosphereocean interaction and the seasonal march of background winds in changing the Philippine Sea ISO intensity and maintaining PSAC.

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Ming Zhang, Yonggang Liu, Jian Zhang, and Qin Wen

Abstract

North Africa was green during the mid-Holocene [about 6000 years ago (6 ka)] and emitted much less dust to the atmosphere than in the present day. Here we use a fully coupled atmosphere–ocean general circulation model, CESM1.2.2, to test the impact of dust reduction and greening of the Sahara on the Atlantic meridional overturning circulation (AMOC) during this period. Results show that dust removal leads to a decrease of AMOC by 6.2% while greening of the Sahara with 100% shrub (100% grass) cover causes an enhancement of the AMOC by 6.1% (4.8%). The AMOC is increased by 5.3% (2.3%) when both the dust reduction and green Sahara with 100% shrub (100% grass) are considered. The AMOC changes are primarily due to the precipitation change over the west subtropical North Atlantic, from where the salinity anomaly is advected to the deep-water formation region. Global-mean surface temperature increases by 0.09° and 0.40°C (0.25°C) when global dust is removed and when North Africa and the Arabian region are covered by shrub (grass), respectively, showing a dominating effect of vegetation over dust. The comparison between modeled and reconstructed sea surface temperature is improved when the effect of vegetation is considered. The results may have implications for climate impact of future wetting over North Africa, either through global warming or through building of solar farms and wind farms.

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Arun Kumar, Qin Zhang, Peitao Peng, and Bhaskar Jha

Abstract

From ensembles of 80 AGCM simulations for every December–January–February (DJF) seasonal mean in the 1980–2000 period, interannual variability in atmospheric response to interannual variations in observed sea surface temperature (SST) is analyzed. A unique facet of this study is the use of large ensemble size that allows identification of the atmospheric response to SSTs for each DJF in the analysis period. The motivation of this study was to explore what atmospheric response patterns beyond the canonical response to El Niño–Southern Oscillation (ENSO) SST anomalies exist, and to which SST forcing such patterns may be related. A practical motivation for this study was to seek sources of atmospheric predictability that may lead to improvements in seasonal predictability efforts.

This analysis was based on the EOF technique applied to the ensemble mean 200-mb height response. The dominant mode of the atmospheric response was indeed the canonical atmospheric response to ENSO; however, this mode only explained 53% of interannual variability of the ensemble means (often referred to as the external variability). The second mode, explaining 19% of external variability, was related to a general increase (decrease) in the 200-mb heights related to a Tropicwide warming (cooling) in SSTs. The third dominant mode, explaining 12% of external variability, was similar to the mode identified as the “nonlinear” response to ENSO in earlier studies.

The realism of different atmospheric response patterns was also assessed from a comparison of anomaly correlations computed between different renditions of AGCM-simulated atmospheric responses and the observed 200-mb height anomalies. For example, the anomaly correlation between the atmospheric response reconstructed from the first mode alone and the observations was compared with the anomaly correlation when the atmospheric response was reconstructed including modes 2 and 3. If the higher-order atmospheric response patterns obtained from the AGCM simulations had observational counterparts, their inclusion in the reconstructed atmospheric response should lead to higher anomaly correlations. Indeed, at some geographical regions, an increase in anomaly correlation with the inclusion of higher modes was found, and it is concluded that the higher-order atmospheric response patterns found in this study may be realistic and may represent additional sources of atmospheric seasonal predictability.

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Emily Becker, Huug van den Dool, and Qin Zhang

Abstract

Forecast skill and potential predictability of 2-m temperature, precipitation rate, and sea surface temperature are assessed using 29 yr of hindcast data from models included in phase 1 of the North American Multimodel Ensemble (NMME) project. Forecast skill is examined using the anomaly correlation (AC); skill of the bias-corrected ensemble means (EMs) of the individual models and of the NMME 7-model EM are verified against the observed value. Forecast skill is also assessed using the root-mean-square error. The models’ representation of the size of forecast anomalies is also studied. Predictability was considered from two angles: homogeneous, where one model is verified against a single member from its own ensemble, and heterogeneous, where a model’s EM is compared to a single member from another model. This study provides insight both into the physical predictability of the three fields and into the NMME and its contributing models.

Most of the models in the NMME have fairly realistic spread, as represented by the interannual variability. The NMME 7-model forecast skill, verified against observations, is equal to or higher than the individual models’ forecast ACs. Two-meter temperature (T2m) skill matches the highest single-model skill, while precipitation rate and sea surface temperature NMME EM skill is higher than for any single model. Homogeneous predictability is higher than reported skill in all fields, suggesting there may be room for some improvement in model prediction, although there are many regional and seasonal variations. The estimate of potential predictability is not overly sensitive to the choice of model. In general, models with higher homogeneous predictability show higher forecast skill.

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Arun Kumar, Bhaskar Jha, Qin Zhang, and Lahouari Bounoua

Abstract

Predictability limits for seasonal atmospheric climate variability depend on the fraction of variability that is due to factors external to the atmosphere (e.g., boundary conditions) and the fraction that is internal. From the analysis of observed data alone, however, separation of the total seasonal atmospheric variance into its external and internal components remains a difficult and controversial issue. In this paper a simple procedure for estimating atmospheric internal variability is outlined. This procedure is based on the expected value of the mean square error between the observed and the general circulation model simulated (or predicted) seasonal mean anomaly. The end result is a spatial map for the estimate of the observed seasonal atmospheric internal (or unpredictable) variability. As improved general circulation models become available, mean square error estimated from the new generation of general circulation models can be easily included in the procedure proposed herein, bringing the estimate for the internal variability closer to its true estimate.

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Qin Zhang, Arun Kumar, Yan Xue, Wanqiu Wang, and Fei-Fei Jin

Abstract

Simulations from the National Centers for Environmental Prediction (NCEP) coupled model are analyzed to document and understand the behavior of the evolution of the El Niño–Southern Oscillation (ENSO) cycle. The analysis is of importance for two reasons: 1) the coupled model used in this study is also used operationally to provide model-based forecast guidance on a seasonal time scale, and therefore, an understanding of the ENSO mechanism in this particular coupled system could also lead to an understanding of possible biases in SST predictions; and 2) multiple theories for ENSO evolution have been proposed, and coupled model simulations are a useful test bed for understanding the relative importance of different ENSO mechanisms.

The analyses of coupled model simulations show that during the ENSO evolution the net surface heat flux acts as a damping mechanism for the mixed-layer temperature anomalies, and positive contribution from the advection terms to the ENSO evolution is dominated by the linear advective processes. The subsurface temperature–SST feedback, referred to as thermocline feedback in some theoretical literature, is found to be the primary positive feedback, whereas the advective feedback by anomalous zonal currents and the thermocline feedback are the primary sources responsible for the ENSO phase transition in the model simulation. The basic mechanisms for the model-simulated ENSO cycle are thus, to a large extent, consistent with those highlighted in the recharge oscillator. The atmospheric anticyclone (cyclone) over the western equatorial northern Pacific accompanied by a warm (cold) phase of the ENSO, as well as the oceanic Rossby waves outside of 15°S–15°N and the equatorial higher-order baroclinic modes, all appear to play minor roles in the model ENSO cycles.

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Song Yang, Yundi Jiang, Dawei Zheng, R. Wayne Higgins, Qin Zhang, Vernon E. Kousky, and Min Wen

Abstract

Variations of U.S. regional precipitation in both observations and free-run experiments with the NCEP Climate Forecast System (CFS) are investigated. The seasonality of precipitation over the continental United States and the time–frequency characteristics of precipitation over the Southwest (SW) are the focus. The differences in precipitation variation among different model resolutions are also analyzed.

The spatial distribution of U.S. precipitation is characterized by high values over the East and the West Coasts, especially over the Gulf Coast and southeast states, and low values elsewhere except over the SW in summer. A large annual cycle of precipitation occurs over the SW, northern plains, and the West Coast. Overall, the CFS captures the above features reasonably well, except for the SW. However, it overestimates the precipitation over the western United States, except the SW in summer, and underestimates the precipitation over the central South, except in springtime. It also overestimates (underestimates) the precipitation seasonality over the intermountain area and Gulf Coast states (SW, West Coast, and northern Midwest). The model using T126 resolution captures the observed features more realistically than at the lower T62 resolution over a large part of the United States.

The variability of observed SW precipitation is characterized by a large annual cycle, followed by a semiannual cycle, and the oscillating signals on annual, semiannual, and interannual time scales account for 41% of the total precipitation variability. However, the CFS, at both T62 and T126 resolution, fails in capturing the above feature. The variability of SW precipitation in the CFS is much less periodic. The annual oscillation of model precipitation is much weaker than that observed and it is even much weaker than the simulated semiannual oscillation. The weakly simulated annual cycle is attributed by the unrealistic precipitation simulations of all seasons, especially spring and summer. On the annual time scale, the CFS fails in simulating the relationship between the SW precipitation and the basinwide sea surface temperature (SST) and the overlying atmospheric circulation. On the semiannual time scale, the model exaggerates the response of the regional precipitation to the variations of SST and atmospheric circulation over the tropics and western Atlantic, including the Gulf of Mexico. This study also demonstrates a challenge for the next-generation CFS, at T126 resolution, to predict the variability of North American monsoon climate.

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Panfeng Zhang, Guoyu Ren, Yan Xu, Xiaolan L. Wang, Yun Qin, Xiubao Sun, and Yuyu Ren

Abstract

This paper presents an analysis of changes in global land extreme temperature indices (1951–2015) based on the new global land surface daily air temperature dataset recently developed by the China Meteorological Administration (CMA). The linear trends of the gridpoint time series and global land mean time series were calculated by using a Mann–Kendall method that accounts for the lag-1 autocorrelation in the time series of annual extreme temperature indices. The results, which are generally consistent with previous studies, showed that the global land average annual and seasonal mean extreme temperature indices series all experienced significant long-term changes associated with warming, with cold threshold indices (frost days, icing days, cold nights, and cold days) decreasing, warm threshold indices (summer days, tropical nights, and warm days) increasing, and all absolute indices (TXx, TXn, TNx, and TNn) also increasing, over the last 65 years. The extreme temperature indices series based on daily minimum temperatures generally had a stronger and more significant trend than those based on daily maximum temperatures. The strongest warming occurred after the mid-1970s, and a few extreme temperature indices showed no significant trend over the period from 1951 to the mid-1970s. Most parts of the global land experienced significant warming trends over the period 1951–2015 as a whole, and the largest trends appeared in mid- to high latitudes of the Eurasian continent.

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Li-Chuan Chen, Huug van den Dool, Emily Becker, and Qin Zhang

Abstract

In this study, precipitation and temperature forecasts during El Niño–Southern Oscillation (ENSO) events are examined in six models in the North American Multimodel Ensemble (NMME), including the CFSv2, CanCM3, CanCM4, the Forecast-Oriented Low Ocean Resolution (FLOR) version of GFDL CM2.5, GEOS-5, and CCSM4 models, by comparing the model-based ENSO composites to the observed. The composite analysis is conducted using the 1982–2010 hindcasts for each of the six models with selected ENSO episodes based on the seasonal oceanic Niño index just prior to the date the forecasts were initiated. Two types of composites are constructed over the North American continent: one based on mean precipitation and temperature anomalies and the other based on their probability of occurrence in a tercile-based system. The composites apply to monthly mean conditions in November, December, January, February, and March as well as to the 5-month aggregates representing the winter conditions. For anomaly composites, the anomaly correlation coefficient and root-mean-square error against the observed composites are used for the evaluation. For probability composites, a new probability anomaly correlation measure and a root-mean probability score are developed for the assessment. All NMME models predict ENSO precipitation patterns well during wintertime; however, some models have large discrepancies between the model temperature composites and the observed. The fidelity is greater for the multimodel ensemble as well as for the 5-month aggregates. February tends to have higher scores than other winter months. For anomaly composites, most models perform slightly better in predicting El Niño patterns than La Niña patterns. For probability composites, all models have superior performance in predicting ENSO precipitation patterns than temperature patterns.

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Arun Kumar, Qin Zhang, J-K. E. Schemm, Michelle L’Heureux, and K-H. Seo

Abstract

For the uncoupled atmospheric general circulation model (AGCM) simulations, the quantification of errors due to the lack of coupled ocean–atmospheric evolution on the characteristics of the atmospheric interannual variability is important for various reasons including the following: 1) AGCM simulations forced with specified SSTs continue to be used for understanding atmospheric interannual variability and 2) there is a vast knowledge base quantifying the global atmospheric influence of tropical SSTs that traditionally has relied on the analysis of AGCM-alone simulations. To put such results and analysis in a proper context, it is essential to document errors that may result from the lack of a coupled ocean–atmosphere evolution in the AGCM-alone integrations.

Analysis is based on comparison of tier-two (or uncoupled) and coupled hindcasts for the 1982–2005 period, and interannual variability for the December–February (DJF) seasonal mean is analyzed. Results indicate that for the seasonal mean variability, and for the DJF seasonal mean, atmospheric interannual variability between coupled and uncoupled simulations is similar. This conclusion is drawn from the analysis of interannual variability of rainfall and 200-mb heights and includes analysis of SST-forced interannual variability, analysis of El Niño and La Niña composites, and a comparison of hindcast skill between tier-two and coupled hindcasts.

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