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Fuqing Zhang and Dandan Tao

Abstract

Through cloud-resolving simulations, this study examines the effect of vertical wind shear and system-scale flow asymmetry on the predictability of tropical cyclone (TC) intensity during different stages of the TC life cycle. A series of ensemble experiments is performed with varying magnitudes of vertical wind shear, each initialized with an idealized weak TC-like vortex, with small-scale, small-amplitude random perturbations added to the initial conditions. It is found that the environmental shear can significantly affect the intrinsic predictability of tropical cyclones, especially during the formation and rapid intensification stage. The larger the vertical wind shear, the larger the uncertainty in the intensity forecast, primarily owing to the difference in the timing of rapid intensification.

In the presence of environmental shear, initial random noise may result in changes in the timing of rapid intensification by as much as 1–2 days through the randomness (and chaotic nature) of moist convection. Upscale error growth from differences in moist convection first alters the tilt amplitude and angle of the incipient tropical storms, which leads to significant differences in the timing of precession and vortex alignment. During the precession process, both the vertical tilt of the storm and the effective (local) vertical wind shear are considerably decreased after the tilt angle reaches 90° to the left of the environmental shear. The tropical cyclone intensifies immediately after the tilt and the effective local shear reach their minima. In some instances, small-scale, small-amplitude random noise may also limit the intensity predictability through altering the timing and strength of the eyewall replacement cycle.

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Fulu Tao and Zhao Zhang

Abstract

The impact of climate change on rice productivity in China remains highly uncertain because of uncertainties from climate change scenarios, parameterizations of biophysical processes, and extreme temperature stress in crop models. Here, the Model to Capture the Crop–Weather Relationship over a Large Area (MCWLA)-Rice crop model was developed by parameterizing the process-based general crop model MCWLA for rice crop. Bayesian probability inversion and a Markov chain Monte Carlo technique were then applied to MCWLA-Rice to analyze uncertainties in parameter estimations and to optimize parameters. Ensemble hindcasts showed that MCWLA-Rice could capture the interannual variability of the detrended historical yield series fairly well, especially over a large area. A superensemble-based probabilistic projection system (SuperEPPS) coupled to MCWLA-Rice was developed and applied to project the probabilistic changes of rice productivity and water use in eastern China under scenarios of future climate change. Results showed that across most cells in the study region, relative to 1961–90 levels, the rice yield would change on average by 7.5%–17.5% (from −10.4% to 3.0%), 0.0%–25.0% (from −26.7% to 2.1%), and from −10.0% to 25.0% (from −39.2% to −6.4%) during the 2020s, 2050s, and 2080s, respectively, in response to climate change, with (without) consideration of CO2 fertilization effects. The rice photosynthesis rate, biomass, and yield would increase as a result of increases in mean temperature, solar radiation, and CO2 concentration, although the rice development rate could accelerate particularly after the heading stage. Meanwhile, the risk of high-temperature stress on rice productivity would also increase notably with climate change. The effects of extreme temperature stress on rice productivity were explicitly parameterized and addressed in the study.

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Fulu Tao and Zhao Zhang

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Rising atmospheric CO2 concentration CO2 and climate change are expected to have a major effect on terrestrial ecosystem hydrological cycles and plant water stress in the coming decades. The present study investigates the potential responses of terrestrial ecosystem hydrological cycles and plant water stress across China to elevated CO2 and climate change in the twentieth and twenty-first centuries using the calibrated and validated Lund–Potsdam–Jena dynamic global vegetation model (LPJ-DGVM) and eight climate change scenarios. The spatiotemporal change patterns of estimated evapotranspiration (ET), soil moisture, runoff, and plant water stress due to climate change and elevated CO2 are plotted singly and in combination. Positive future trends in ET, soil moisture, and runoff—although differing greatly among regions—are projected. Resultant plant water stress over China’s terrestrial ecosystem generally could be eased substantially through the twenty-first century under the climate scenarios driven by emission scenarios that consider economic concerns. By contrast, under the climate scenarios driven by emission scenarios that consider environmental concerns, plant water stress could be eased until 2060, then begin to fluctuate until 2100. The net impact of physiological and structural vegetation responses to elevated CO2 could result in an increasing trend in runoff in southern and northeastern China, and a decreasing trend in runoff in northern and northwestern China in the twentieth century. It is projected to reduce ET by 1.5 × 109 to 6.5 × 109 m3 yr−1 on average, and increase runoff by 1.0 × 109 to 5.4 × 109 m3 yr−1 during 2001–2100 across China’s terrestrial ecosystems, although the spatial change pattern could be quite diverse. These findings, in partial contradiction to previous results, present an improved understanding of transient responses of China’s terrestrial ecosystem hydrological cycles and plant water stress to climate change and elevated CO2 in the twentieth and twenty-first centuries.

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Dandan Tao and Fuqing Zhang

Abstract

This study explores the spatial and temporal changes in tropical cyclone (TC) thermodynamic and dynamic structures before, near, and during rapid intensification (RI) under different vertical wind shear conditions through four sets of convection-permitting ensemble simulations. A composite analysis of TC structural evolution is performed by matching the RI onset time of each member. Without background flow, the axisymmetric TC undergoes a gradual strengthening of the inner-core vorticity and warm core throughout the simulation. In the presence of moderate environmental shear (5–6 m s−1), both the location and magnitude of the asymmetries in boundary layer radial flow, relative humidity, and vertical motion evolve with the tilt vector throughout the simulation. A budget analysis indicates that tilting is crucial to maintaining the midlevel vortex while stretching and vertical advection are responsible for the upper-level vorticity generation before RI when strong asymmetries arise. Two warm anomalies are observed before the RI onset when the vortex column is tilted. When approaching the RI onset, these two warm anomalies gradually merge into one. Overall, the most symmetric vortex structure is found near the RI onset. Moderately sheared TCs experience an adjustment period from a highly asymmetric structure with updrafts concentrated at the down-tilt side before RI to a more axisymmetric structure during RI as the eyewall updrafts develop. This adjustment period near the RI onset, however, is found to be the least active period for deep convection. TC development under a smaller environmental shear (2.5 m s−1) condition displays an intermediate evolution between ensemble experiments with no background flow and with moderate shear (5–6 m s−1).

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Tao Zhang and De-Zheng Sun

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The El Niño–La Niña asymmetry is evaluated in 14 coupled models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). The results show that an underestimate of ENSO asymmetry, a common problem noted in CMIP3 models, remains a common problem in CMIP5 coupled models. The weaker ENSO asymmetry in the models primarily results from a weaker SST warm anomaly over the eastern Pacific and a westward shift of the center of the anomaly. In contrast, SST anomalies for the La Niña phase are close to observations.

Corresponding Atmospheric Model Intercomparison Project (AMIP) runs are analyzed to understand the causes of the underestimate of ENSO asymmetry in coupled models. The analysis reveals that during the warm phase, precipitation anomalies are weaker over the eastern Pacific, and westerly wind anomalies are confined more to the west in most models. The time-mean zonal winds are stronger over the equatorial central and eastern Pacific for most models. Wind-forced ocean GCM experiments suggest that the stronger time-mean zonal winds and weaker asymmetry in the interannual anomalies of the zonal winds in AMIP models can both be a contributing factor to a weaker ENSO asymmetry in the corresponding coupled models, but the former appears to be a more fundamental factor, possibly through its impact on the mean state. The study suggests that the underestimate of ENSO asymmetry in the CMIP5 coupled models is at least in part of atmospheric origin.

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De-Zheng Sun, Yongqiang Yu, and Tao Zhang

Abstract

By comparing the response of clouds and water vapor to ENSO forcing in nature with that in Atmospheric Model Intercomparison Project (AMIP) simulations by some leading climate models, an earlier evaluation of tropical cloud and water vapor feedbacks has revealed the following two common biases in the models: 1) an underestimate of the strength of the negative cloud albedo feedback and 2) an overestimate of the positive feedback from the greenhouse effect of water vapor. Extending the same analysis to the fully coupled simulations of these models as well as other Intergovernmental Panel on Climate Change (IPCC) coupled models, it is found that these two biases persist. Relative to the earlier estimates from AMIP simulations, the overestimate of the positive feedback from water vapor is alleviated somewhat for most of the coupled simulations. Improvements in the simulation of the cloud albedo feedback are only found in the models whose AMIP runs suggest either a positive or nearly positive cloud albedo feedback. The strength of the negative cloud albedo feedback in all other models is found to be substantially weaker than that estimated from the corresponding AMIP simulations. Consequently, although additional models are found to have a cloud albedo feedback in their AMIP simulations that is as strong as in the observations, all coupled simulations analyzed in this study have a weaker negative feedback from the cloud albedo and therefore a weaker negative feedback from the net surface heating than that indicated in observations. The weakening in the cloud albedo feedback is apparently linked to a reduced response of deep convection over the equatorial Pacific, which is in turn linked to the excessive cold tongue in the mean climate of these models. The results highlight that the feedbacks of water vapor and clouds—the cloud albedo feedback in particular—may depend on the mean intensity of the hydrological cycle. Whether the intermodel variations in the feedback from cloud albedo (water vapor) in the ENSO variability are correlated with the intermodel variations of the feedback from cloud albedo (water vapor) in global warming has also been examined. While a weak positive correlation between the intermodel variations in the feedback of water vapor during ENSO and the intermodel variations in the water vapor feedback during global warming was found, there is no significant correlation found between the intermodel variations in the cloud albedo feedback during ENSO and the intermodel variations in the cloud albedo feedback during global warming. The results suggest that the two common biases revealed in the simulated ENSO variability may not necessarily be carried over to the simulated global warming. These biases, however, highlight the continuing difficulty that models have in simulating accurately the feedbacks of water vapor and clouds on a time scale of the observations available.

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Tao Zhang, Xiaolu Shao, and Shuanglin Li

Abstract

An evaluation of El Niño–La Niña asymmetry is conducted in the two recent NCAR coupled models (CCSM4 and CESM1) sharing the same ocean component. Results show that two coupled models generally underestimate observed ENSO asymmetry, mainly owing to an overestimate of the cold SST anomaly during the La Niña phase. The weaker ENSO asymmetry corresponds to a cold bias in mean SST climatology that is more severe in CESM1 than in CCSM4, despite a better performance in simulating ENSO asymmetry in the former. Corresponding AMIP (CAM4 and CAM5) runs are examined to probe the origin of the weaker ENSO asymmetry in coupled models. The analysis reveals a stronger time mean zonal wind in AMIP models, favoring a cold bias in mean SST. The bias of the stronger mean wind, associated with changes in mean precipitation, is more significant in CAM5 than in CAM4. The simulated skewness of the interannual variability of zonal winds is weaker than observations but somewhat improved in CAM5 compared to CAM4, primarily resulting from a more westward shift of easterly wind anomalies tied to the displacement of precipitation anomalies during the cold phase. Wind-forced ocean GCM experiments confirm that the bias in AMIP model winds can weaken ENSO asymmetry, with the contribution from the wind interannual variability being larger than from the mean winds. This demonstrates that the bias in ENSO asymmetry in coupled models can be traced back to the bias in the stand-alone atmosphere models to a large extent. The results pinpoint a pathway to reduce the bias in ENSO asymmetry in coupled models.

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Jie Cao, Wei-kang Zhang, and Yun Tao

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This paper describes the study of the relationship between the thermal configuration of the Bay of Bengal (BOB)–Tibetan Plateau (TP) region and the precipitation anomaly in Yunnan, a province in China, in May using ERA-Interim data and precipitation data for May from 125 meteorological stations across Yunnan for 1979–2014. Results from the analysis indicate that the interannual variability of May precipitation in Yunnan is significantly modulated by the BOB–TP thermal configuration. Model runs with a linear baroclinic model suggest physical consistency. The thermal conditions over the BOB mainly impact the May precipitation anomaly in Yunnan via changes in water vapor transport from the eastern BOB northeastward to southwestern Yunnan. The second factor influencing precipitation anomalies relates to the characteristics and variability of cold air transport from the TP to northeastern Yunnan. When the BOB (the TP) is occupied by positive (negative) diabatic heating, a thermal gradient with a warmer (colder) center over the BOB and a colder (warmer) center over the TP is established, and more-than-normal (less than normal) precipitation in Yunnan will occur in May. This relationship can persist from April to the following May to some extent; therefore, the BOB–TP thermal configuration in April could be used to forecast May precipitation in Yunnan.

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Qigang Wu, Jing Zhang, Xiangdong Zhang, and Wei Tao

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The Beaufort Sea high (BSH) plays an important role in forcing Arctic sea ice and the Beaufort Gyre. This study examines the variability and long-term trends of atmospheric circulation over the Chukchi and Beaufort Seas using the ECMWF Interim Re-Analysis (ERA-Interim) for the period 1979–2012. Because of the mobility of the BSH through the year, EOF analysis is applied to the sea level pressure (SLP) field in order to investigate the principal patterns of BSH variability. In each season, the three leading EOF modes explain nearly 90% of the total variance and reflect a strengthened or weakened BSH centered over the western Arctic Ocean (EOF1), a north–south dipole-like SLP anomaly (EOF2), and a west–east dipole-like SLP anomaly (EOF3), respectively. These three EOF modes offer distinct influences on local climate in each season and have different connections with the large-scale climate variability modes in winter. In particular, the second principal component (PC2) associated with EOF2 in the autumn exhibits a tendency toward high-index polarity significant at the 5% level, and is related to strongly reduced sea ice extent.

Further, the authors have detected significant anticyclonic trends among surface wind fields associated with a strengthened BSH during summer and autumn, but significant cyclonic trends associated with a weakened BSH during early midwinter, consistent with significant trends in SLP gradients between western Arctic Ocean and the adjoining landmass. Comparison with forced trends of surface winds from various simulations from the IPCC Fifth Assessement Report (AR5) indicates that summertime changes in atmospheric circulation cannot be explained by natural external forcing or lower boundary forcings and may instead be attributable to external anthropogenic forcing.

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Hongbo Zhang, Fan Zhang, Tao Che, Wei Yan, and Ming Ye

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Though the use of reanalysis datasets to analyze snow changes is increasingly popular, the snow depth variability in China simulated by multiple reanalysis datasets has not been well evaluated. Also, the extent of regional snow depth variability and its driving mechanisms are still unknown. In this study, monthly snow depth observations from 325 stations during the period of 1981–2018 were taken to evaluate the ability of five reanalysis datasets (JRA-55, MERRA-2, GLDAS2, ERA5, and ERA5L) to simulate the spatial and temporal variability of snow depth in China. The evaluation results indicate that MERRA-2 has the lowest root-mean-square deviation of snow depth and a high spatial correlation coefficient with observations. This may be partly related to the high accuracy of precipitation and temperature in MERRA-2. Also, the 31 combinations of the five reanalysis datasets do not yield better accuracy in snow depth than MERRA-2 alone. This is because the other four datasets have larger uncertainty. Based on MERRA-2, four hot-spot regions with significant snow depth changes from 1981 to 2018 were identified, including the central Xinjiang (XJ-C), the southern part of the northeastern plain and mountain (NPM-S), and the southwestern (TP-SW) and southeastern (TP-SE) portions of the Tibetan Plateau. Snow depth changes mostly occurred in spring in TP-SW and winter in XJ-C, NPM-S, and TP-SE. The snow depth increase in XJ-C, NPM-S, and TP-SW is mainly caused by increased seasonal precipitation, while the snow depth decrease in TP-SE is attributed to the combined effects of decreased precipitation and warming temperature in winter.

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