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Mitchell Bushuk, Michael Winton, F. Alexander Haumann, Thomas Delworth, Feiyu Lu, Yongfei Zhang, Liwei Jia, Liping Zhang, William Cooke, Matthew Harrison, Bill Hurlin, Nathaniel C. Johnson, Sarah B. Kapnick, Colleen McHugh, Hiroyuki Murakami, Anthony Rosati, Kai-Chih Tseng, Andrew T. Wittenberg, Xiaosong Yang, and Fanrong Zeng

Abstract

Compared to the Arctic, seasonal predictions of Antarctic sea ice have received relatively little attention. In this work, we utilize three coupled dynamical prediction systems developed at the Geophysical Fluid Dynamics Laboratory to assess the seasonal prediction skill and predictability of Antarctic sea ice. These systems, based on the FLOR, SPEAR_LO, and SPEAR_MED dynamical models, differ in their coupled model components, initialization techniques, atmospheric resolution, and model biases. Using suites of retrospective initialized seasonal predictions spanning 1992–2018, we investigate the role of these factors in determining Antarctic sea ice prediction skill and examine the mechanisms of regional sea ice predictability. We find that each system is capable of skillfully predicting regional Antarctic sea ice extent (SIE) with skill that exceeds a persistence forecast. Winter SIE is skillfully predicted 11 months in advance in the Weddell, Amundsen/Bellingshausen, Indian, and west Pacific sectors, whereas winter skill is notably lower in the Ross sector. Zonally advected upper-ocean heat content anomalies are found to provide the crucial source of prediction skill for the winter sea ice edge position. The recently developed SPEAR systems are more skillful than FLOR for summer sea ice predictions, owing to improvements in sea ice concentration and sea ice thickness initialization. Summer Weddell SIE is skillfully predicted up to 9 months in advance in SPEAR_MED, due to the persistence and drift of initialized sea ice thickness anomalies from the previous winter. Overall, these results suggest a promising potential for providing operational Antarctic sea ice predictions on seasonal time scales.

Open access
Mingna Wu, Tianjun Zhou, and Xiaolong Chen

Abstract

The western North Pacific anomalous anticyclone (WNPAC) is a key bridge that links El Niño and East Asian climate variability. Future projections of ENSO-related WNPAC changes under global warming are highly uncertain across climate models. Based on a 40-member ensemble from the Community Earth System Model Large Ensemble (CESM-LE) project, we investigate the effects of internal variability on the El Niño–related WNPAC projection. Here, we first develop a decomposition method to separate the contributions of El Niño amplitude change and nonamplitude change from the leading uncertainty in the El Niño–related WNPAC projection. Based on the decomposition, approximately 23% of the uncertainty in the El Niño–related WNPAC projection is attributed to the El Niño amplitude change, while the remaining 77% is from the nonamplitude change, which is mainly related to the change in the El Niño decaying pace. A larger (smaller) El Niño amplitude can enhance (weaken) the WNPAC through a stronger (weaker) tropical Indian Ocean capacitor effect. For nonamplitude change, a faster (slower) El Niño decaying pace can also enhance (weaken) the WNPAC through descending Rossby waves in response to cold sea surface temperature anomalies over the tropical central-eastern Pacific. The decomposition method can be generalized to investigate the sources of uncertainty related to El Niño properties in climate projections and to help improve the understanding of changes in the interannual variability of East Asian–western Pacific climate under global warming.

Open access
Jong-Hoon Jeong, Jiwen Fan, and Cameron R. Homeyer

Abstract

Following on our study of hail for the southern Great Plains (SGP), we investigated the spatial and temporal hail trends and variabilities for the northern Great Plains (NGP) and the contributing factors for summers (June–August) focusing on the period of 2004–16 using two independent hail datasets. Analysis for an extended period (1994–2016) with the hail reports was also conducted to more reliably investigate the contributing factors. Both severe hail (diameter between 1 and 2 inches) and significant severe hail (SSH; diameter > 2 inches) were examined and similar results were obtained. The occurrence of hail over the NGP demonstrated a large interannual variability, with a positive slope overall. Spatially, the increase is mainly located in the western part of Nebraska, South Dakota, and North Dakota. We find the three major dynamical factors that most likely contribute to the hail interannual variability in the NGP are El Niño–Southern Oscillation (ENSO), the North Atlantic subtropical high (NASH), and the low-level jet (LLJ). With a thermodynamical variable integrated water vapor transport that is strongly controlled by LLJ, the four factors can explain 78% of the interannual variability in the number of SSH reports. Hail occurrences in the La Niña years are higher than the El Niño years since the jet stream is stronger and NASH extends farther into the southeastern United States, thereby strengthening the LLJ and in turn water vapor transport. Interestingly, the important factors impacting hail interannual variability over the NGP are quite different from those for the SGP, except for ENSO.

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Matthew Watts, Wieslaw Maslowski, Younjoo J. Lee, Jaclyn Clement Kinney, and Robert Osinski

Abstract

The Arctic sea ice response to a warming climate is assessed in a subset of models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6), using several metrics in comparison with satellite observations and results from the Pan-Arctic Ice Ocean Modeling and Assimilation System and the Regional Arctic System Model. Our study examines the historical representation of sea ice extent, volume, and thickness using spatial analysis metrics, such as the integrated ice edge error, Brier score, and spatial probability score. We find that the CMIP6 multimodel mean captures the mean annual cycle and 1979–2014 sea ice trends remarkably well. However, individual models experience a wide range of uncertainty in the spatial distribution of sea ice when compared against satellite measurements and reanalysis data. Our metrics expose common and individual regional model biases, which sea ice temporal analyses alone do not capture. We identify large ice edge and ice thickness errors in Arctic subregions, implying possible model specific limitations in or lack of representation of some key physical processes. We postulate that many of them could be related to the oceanic forcing, especially in the marginal and shelf seas, where seasonal sea ice changes are not adequately simulated. We therefore conclude that an individual model’s ability to represent the observed/reanalysis spatial distribution still remains a challenge. We propose the spatial analysis metrics as useful tools to diagnose model limitations, narrow down possible processes affecting them, and guide future model improvements critical to the representation and projections of Arctic climate change.

Open access
Jing Sun, Mojib Latif, and Wonsun Park

Abstract

There is a controversy about the nature of multidecadal climate variability in the North Atlantic (NA) region, concerning the roles of ocean circulation and atmosphere–ocean coupling. Here we describe NA multidecadal variability from a version of the Kiel Climate Model, in which both subpolar gyre (SPG)–Atlantic meridional overturning circulation (AMOC) coupling and atmosphere–ocean coupling are essential. The oceanic barotropic and meridional overturning streamfunctions and the sea level pressure are jointly analyzed to derive the leading mode of Atlantic sector variability. This mode accounting for 23.7% of the total combined variance is oscillatory with an irregular periodicity of 25–50 years and an e-folding time of about a decade. SPG and AMOC mutually influence each other and together provide the delayed negative feedback necessary for maintaining the oscillation. An anomalously strong SPG, for example, drives higher surface salinity and density in the NA’s sinking region. In response, oceanic deep convection and AMOC intensify, which, with a time delay of about a decade, reduces SPG strength by enhancing upper-ocean heat content. The weaker gyre leads to lower surface salinity and density in the sinking region, which reduces deep convection and eventually AMOC strength. There is a positive ocean–atmosphere feedback between the sea surface temperature and low-level atmospheric circulation over the southern Greenland area, with related wind stress changes reinforcing SPG changes, thereby maintaining the (damped) multidecadal oscillation against dissipation. Stochastic surface heat flux forcing associated with the North Atlantic Oscillation drives the eigenmode.

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Yonghui Lei, Jiancheng Shi, Chuan Xiong, and Dabin Ji

Abstract

In this study, the net water flux (precipitation minus evaporation) over the Tibetan Plateau (TP) and its 12 drainage basins is estimated using ERA5. The terrestrial branch of the water cycle is investigated using the total water storage anomalies (TWSAs) derived from GRACE (Gravity Recovery and Climate Experiment) data and daily streamflow records collected in Zhimenda and Tangnaihai (two hydrological stations located in the upper Yangtze River Basin and upper Yellow River Basin). This work provides a preliminary assessment of discrepancies between model-derived and space-based observations in the atmospheric–terrestrial water cycle over the TP and its drainage basins. The results show that the net water fluxes occurring over the TP and the scale of its drainage basins are closely tied to local dynamics and physical processes and to large-scale circulation and atmospheric water vapor. ERA5 maintains the atmospheric water balance over the TP. ERA5-derived net water flux anomalies constitute a major component of the water cycle and correspond to GRACE-derived TWSAs. The water budget–based approach with the ERA5 and ITSG-Grace2018 datasets constrains the atmospheric–terrestrial water cycle over the TP and its drainage basins. Both the ERA5- and GRACE-derived estimates contain consistent long- and short-term variations over the TP. Discrepancies are evident at the drainage basin, while the ratio of signal to noise in both the ERA5 and GRACE datasets might cause discrepancies between estimates over relatively small or arid basins. Nevertheless, the observed good correspondence between ERA5- and GRACE-derived atmospheric–terrestrial water cycles over the TP highlights the potential value of the rational application of water resource information.

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Zeyu Cui, Guang J. Zhang, Yong Wang, and Shaocheng Xie

Abstract

The wrong diurnal cycle of precipitation is a common weakness of current global climate models (GCMs). To improve the simulation of the diurnal cycle of precipitation and understand what physical processes control it, we test a convective trigger function described in Xie et al. with additional optimizations in the NCAR Community Atmosphere Model version 5 (CAM5). The revised trigger function consists of three modifications: 1) replacing the convective available potential energy (CAPE) trigger with a dynamic CAPE (dCAPE) trigger, 2) allowing convection to originate above the top of planetary boundary layer [i.e., the unrestricted air parcel launch level (ULL)], and 3) optimizing the entrainment rate and threshold value of the dynamic CAPE generation rate for convection onset based on observations. Results from 1° resolution simulations show that the revised trigger can alleviate the long-standing GCM problem of too early maximum precipitation during the day and missing the nocturnal precipitation peak that is observed in many regions, including the U.S. southern Great Plains (SGP). The revised trigger also improves the simulation of the propagation of precipitation systems downstream of the Rockies and the Amazon region. A further composite analysis over the SGP unravels the mechanisms through which the revised trigger affects convection. Additional sensitivity tests show that both the peak time and the amplitude of the diurnal cycle of precipitation are sensitive to the entrainment rate and dCAPE threshold values.

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Benjamin D. Santer, Stephen Po-Chedley, Carl Mears, John C. Fyfe, Nathan Gillett, Qiang Fu, Jeffrey F. Painter, Susan Solomon, Andrea K. Steiner, Frank J. Wentz, Mark D. Zelinka, and Cheng-Zhi Zou

Abstract

We compare atmospheric temperature changes in satellite data and in model ensembles performed under phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). In the lower stratosphere, multidecadal stratospheric cooling during the period of strong ozone depletion is smaller in newer CMIP6 simulations than in CMIP5 or satellite data. In the troposphere, however, despite forcing and climate sensitivity differences between the two CMIP ensembles, their ensemble-average global warming over 1979–2019 is very similar. We also examine four properties of tropical behavior governed by basic physical processes. The first three are ratios between trends in water vapor (WV) and trends in sea surface temperature (SST), lower-tropospheric temperature (TLT), and mid- to upper-tropospheric temperature (TMT). The fourth property is the ratio between TMT and SST trends. All four ratios are tightly constrained in CMIP simulations but diverge markedly in observations. Model trend ratios between WV and temperature are closest to observed ratios when the latter are calculated with datasets exhibiting larger tropical warming of the ocean surface and troposphere. For the TMT/SST ratio, model–data consistency depends on the combination of observations used to estimate TMT and SST trends. If model expectations of these four covariance relationships are realistic, our findings reflect either a systematic low bias in satellite tropospheric temperature trends or an overestimate of the observed atmospheric moistening signal. It is currently difficult to determine which interpretation is more credible. Nevertheless, our analysis reveals anomalous covariance behavior in several observational datasets and illustrates the diagnostic power of simultaneously considering multiple complementary variables.

Open access
Claudia Gessner, Erich M. Fischer, Urs Beyerle, and Reto Knutti

Abstract

Heat waves such as the one in Europe 2003 have severe consequences for the economy, society, and ecosystems. It is unclear whether temperatures could have exceeded these anomalies even without further climate change. Developing storylines and quantifying the highest possible temperature levels is challenging given the lack of a long homogeneous time series and methodological framework to assess them. Here, we address this challenge by analyzing summer temperatures in a nearly 5000-yr preindustrial climate model simulation, performed with the Community Earth System Model CESM1. To assess how anomalous temperatures could get, we compare storylines generated by three different methods: 1) a return-level estimate, deduced from a generalized extreme value distribution; 2) a regression model, based on dynamic and thermodynamic heat wave drivers; and 3) a novel ensemble boosting method, generating large samples of reinitialized extreme heat waves in the long climate simulation. All methods provide consistent temperature estimates, suggesting that historical exceptional heat waves such as those in Chicago in 1995, Europe in 2003, and Russia in 2010 could have been substantially exceeded even in the absence of further global warming. These estimated unseen heat waves are caused by the same drivers as moderate observed events, but with more anomalous patterns. Moreover, altered contributions of circulation and soil moisture to temperature anomalies include amplified feedbacks in the surface energy budget. The methodological framework of combining different storyline approaches of heat waves with magnitudes beyond the observational record may ultimately contribute to adaptation and to the stress testing of ecosystems or socioeconomic systems to increase resilience to extreme climate stressors.

Open access
Yangchen Lai, Jianfeng Li, Xihui Gu, Cancan Liu, and Yongqin David Chen

Abstract

During simultaneous or successive occurrences of precipitation and storm surges, the interplay of the two types of extremes can exacerbate the impact to a greater extent than either of them in isolation. The compound flood hazards from precipitation and storm surges vary across regions of the world because of the various weather conditions. By analyzing in-situ observations of precipitation and storm surges across the globe, we found that the return periods of compound floods with marginal values exceeding the 98.5th percentile (i.e., equivalent to a joint return period of 12 years if the marginal variables are independent) are < 2 years in most areas, while those in northern Europe are > 8 years due to weaker dependence. Our quantitative assessment shows that cyclones (i.e., tropical cyclones (TCs) and extratropical cyclones (ETCs)) are the major triggers of compound floods. More than 80% of compound floods in East Asia and > 50% of those in the Gulf of Mexico and northern Australia are associated with TCs, while in northern Europe and the higher latitude coast of North America, ETCs contribute to the majority of compound floods (i.e., 80%). Weather patterns characterized by deep low pressure, cyclonic wind, and abundant precipitable water content are conducive to the occurrence of compound floods. Extreme precipitation and extreme storm surges over Europe tend to occur in different months, which explains the relatively lower probability of compound floods in Europe. The comprehensive hazard assessment of global compound floods in this study serves as an important reference for flood risk management in coastal regions across the globe.

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