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Peiqiang Xu, Lin Wang, Zizhen Dong, Yanjie Li, Xiaocen Shen, and Wen Chen

Abstract

Based on observation and reanalysis datasets, numerical experiments with a simple dynamical model, and climate model outputs, this study investigates the second leading waveguide teleconnection along the summer polar front jet (PFJ) over Eurasia on the interannual time scale, the British–Okhotsk Corridor (BOC) pattern. The BOC pattern explains 20.8% of the total variance over northern Eurasia, which is only slightly lower than the first leading teleconnection, the British–Baikal Corridor pattern. It consists of four zonally oriented action centers over the British Isles, western Russia, northern Siberia, and the Sea of Okhotsk. It is primarily confined to northern Eurasia and leads to wavelike temperature and precipitation anomalies along its routine. Besides, it is occasionally coupled to the dominant waveguide teleconnection along the subtropical jet (STJ), the Silk Road pattern (SRP). A bifurcated wavelike pattern appears over Eurasia when the coupling is strong, with two branches of waves over the PFJ and the STJ, respectively. The fluctuations of the BOC–SRP linkage play a profound role in shaping the dominant climate variability modes over Eurasia. Numerical experiments with a simple dynamical model suggest that the basic flow cannot directly influence the BOC–SRP linkage by affecting the propagation condition of Rossby waves. Nevertheless, the basic flow can indirectly influence the linkage by changing the exciting locations of the BOC pattern through modulating the wave–mean flow interaction at the exit of the Atlantic jet stream. The climate model INMCM4.0 can reproduce the observed BOC–SRP linkage and its time-varying characteristics, supporting the observation and the proposed mechanism.

Significance Statement

Over Eurasia, extreme summer weather events are often related to long-lasting stagnant atmospheric circulation anomalies in the upper troposphere, which can be well determined by a few recurring modes called atmospheric teleconnections. Atmospheric teleconnections over Eurasia usually propagate along the subtropical jet (STJ) and the polar front jet (PFJ). Although the teleconnections along the STJ have been well understood, the teleconnections along the PFJ are currently not fully understood. This paper investigates the British–Okhotsk Corridor (BOC) pattern, a newly defined major summer teleconnection along the PFJ. The BOC pattern can be occasionally coupled to a dominant teleconnection along the STJ. Fluctuations of this linkage play a profound role in shaping the dominant surface climate variability modes over Eurasia. Observation and reanalysis datasets, numerical experiments with a simple dynamical model, and climate model output are used to understand this linkage in the study.

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M. Z. Sheikh, K. Gustavsson, E. Lévêque, B. Mehlig, A. Pumir, and A. Naso

Abstract

Collisions, resulting in aggregation of ice crystals in clouds, is an important step in the formation of snow aggregates. Here, we study the collision process by simulating spheroid-shaped particles settling in turbulent flows and by determining the probability of collision. We focus on platelike ice crystals (oblate ellipsoids), subject to gravity, and to the Stokes force and torque generated by the surrounding fluid. We also take into account the contributions to the drag and torque due to fluid inertia, which are essential to understand the tendency of crystals to settle with their largest dimension oriented horizontally. We determine the collision rate between identical crystals, of diameter 300 μm, with aspect ratios in the range 0.005 ≤ β ≤ 0.05, and over a range of energy dissipation per unit mass, ε, 1 ≤ ε ≤ 250 cm2 s−3. For all values of β studied, the collision rate increases with the turbulence intensity. The dependence on β is more subtle. Increasing β at low turbulence intensity (ε16cm2s3) diminishes the collision rate, but increases it at higher ε ≈ 250 cm2 s−3. The observed behaviors can be understood as resulting from three main physical effects. First, the velocity gradients in a turbulent flow tend to bring particles together. In addition, differential settling plays a role at small ε when the particles are thin enough (β small), whereas the prevalence of particle inertia at higher ε leads to a strong enhancement of the collision rate.

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Xunshu Song, Youmin Tang, Xiaojing Li, and Ting Liu

Abstract

In this study, we investigate both the decadal variation of the Indian Ocean dipole (IOD) prediction skill and possible sources of this decadal variation. We use an ensemble long-term retrospective forecast experiment covering 1880–2017 that utilizes the Community Earth System Model (CESM). We find that the decadal variation of the IOD prediction skill is significant and that it varies with the lead time. We also find that the decadal variation of the IOD prediction skill for the target season of boreal autumn determines that for all initial conditions, regardless of the lead months. For short lead times, the decadal variations of the IOD strength and of the IOD precursor in the initial month of July are the major factors influencing the IOD prediction skill. This occurs because the IOD events are in the developmental phase, and the stronger IOD signal in the initial conditions leads to better predictions. For long lead times, the decadal variation of remote forcing by El Niño–Southern Oscillation (ENSO) and the ENSO precursor signal in the IOD influence the IOD prediction skill more significantly than do the strengths of the ENSO or the IOD. In addition, the analysis also indicated that the period with a low ENSO–IOD relationship has low predictability, not only because the ENSO little influence on IOD but also because the model biasedly overestimates the ENSO–IOD relationship.

Significance Statement

The Indian Ocean dipole (IOD) has strong climatic effects, both around the Indian Ocean and globally, which have strong impacts on human life and economic development. It is important to be able to predict IOD events accurately to mitigate those impacts. Here, we conducted a 138-yr prediction experiment using a state-of-the-art climate model to confirm the existence of a decadal variation in IOD predictability and to identify factors that influence the IOD prediction skill. The most important factors that influence the decadal variation of IOD prediction skill differ for 3-month and 6-month lead times, and additional studies will be necessary to clarify the specific factors responsible for these differences.

Open access
Yuanyuan Guo, Zhiping Wen, Yu Zhu, and Xiaodan Chen

Abstract

Tropical sea surface temperature (SST) and associated precipitation, acting as diabatic heat forcing, has far-reaching climatic impacts across the globe through exciting poleward-propagating Rossby waves. It is found that the leading mode of tropical Pacific forcing in austral autumn experiences a significant interdecadal shift from an eastern Pacific (EP) to a central Pacific (CP) type around the late 1990s. More specifically, the EP-type precipitation anomaly mode before 1998 drives a quadrupole-like teleconnection pathway emanating from the tropical Pacific to the Ross Sea and Amundsen–Bellingshausen Seas (ABS) region, whereas the CP-type mode after 1999 excites a Pacific–South American (PSA)-like teleconnection orienting along a great circle. Divergent flows induced by different precipitation anomaly modes primarily determine the generation of Rossby waves by means of the vortex stretching and vorticity advection processes. Furthermore, the synoptic high-frequency transient eddy activity along with its dynamic forcing effect differs greatly before and after 1998/99, contributing to different locations of the teleconnection lobes at mid- to high latitudes. In contrast, the subseasonal low-frequency transient eddy activity exerts a limited influence. Our findings also indicate that the EP-type (CP-type) tropical forcing mode could significantly modulate the zonal displacement (strength) of the Amundsen Sea low, which could lead to distinct climate responses of West Antarctica and the Antarctic Peninsula in austral autumn.

Open access
Jing Yang, Siyu Li, Tao Zhu, Xin Qi, Jiping Liu, Seong-Joong Kim, and Daoyi Gong

Abstract

Arctic sea ice intraseasonal variation (ISV) is crucial for understanding and predicting atmospheric subseasonal variations over the middle and high latitudes but unclear. Sea ice concentration (SIC) over the northern Barents Sea (NBS) features large ISV during the melting season (April–July). Based on the observed SIC, this study finds that the NBS SIC ISV in the melting season is dominated by 30–60-day periodicity. The composite analysis, using 34 significant 30–60-day sea ice melting events during 1989–2017, demonstrates that 30–60-day circumpolar clockwise-propagating atmospheric waves (CCPW) are concurrent with the NBS SIC ISV, which features zonal wavenumber 1 along 65°N and a typical quasi-barotropic structure. Further analysis finds that the 30–60-day surface air temperature (SAT) evidently leads the SIC variations by nearly 6 days over the NBS, which is primarily caused by low-level meridional thermal advection linked with the 30–60-day CCPW. The positive anomalies of the downward sensible heat and longwave radiative fluxes, caused by the increased SAT and atmospheric moisture, play the dominant roles in melting the sea ice on the 30–60-day time scale over the NBS. The increased atmospheric moisture is mainly ascribed to the increased horizontal moisture advection influence by the 30–60-day CCPW. This study strongly suggests that the atmospheric ISV is a crucial precursor for NBS sea ice intraseasonal changes in boreal summer, and more accurate subseasonal predictions of atmospheric circulation, temperature, and moisture are indispensable for improving sea ice subseasonal prediction over the Arctic region.

Significance Statement

Northern Barents Sea (NBS) sea ice intraseasonal variation (ISV) is crucial for understanding mid- to high-latitude climate variations as well as new trans-Arctic shipping predictions but lacks solid knowledge. This study found that the 30–60-day variation is the dominant ISV periodicity of NBS sea ice change during summer, which is essentially modulated by circumpolar clockwise-propagating atmospheric waves. The atmospheric wave-induced meridional thermal advection modulates the surface temperature and atmospheric moisture, causes the changes of downward sensible heat and longwave radiative fluxes, and eventually dominantly regulates the 30–60-day sea ice variations. The mechanism of sea ice ISV strongly suggests that accurately predicting the atmospheric fields is indispensable for obtaining more accurate sea ice subseasonal prediction.

Open access
Frank Kwasniok

Abstract

Linear inverse modeling or principal oscillation pattern (POP) analysis is a widely applied tool in climate science for extracting from data dominant spatial patterns together with their dynamics as approximated by a linear Markov model. The system is projected onto a principal linear subspace and the system matrix is estimated from data. The eigenmodes of the system matrix are the POPs, with the eigenvalues providing their decay time scales and oscillation frequencies. Usually, the subspace is spanned by the leading principal components (PCs) and empirical orthogonal functions (EOFs). Outside of climate science, this procedure is now more commonly referred to as dynamic mode decomposition (DMD). Here, we use optimal mode decomposition (OMD) to address the full linear inverse modeling problem of simultaneous optimization of the principal subspace and the linear operator. The method is illustrated on two pedagogical examples and then applied to a three-level quasigeostrophic atmospheric model with realistic mean state and variability. The OMD models significantly outperform the EOF/DMD models in predicting the time evolution of the large-scale flow modes. The advantage of the OMD models stems from finding more persistent modes as well as from better capturing the nonnormality of the linear operator and the associated nonmodal growth. The dynamics of the large-scale flow modes turn out to be markedly non-Markovian and the OMD modes are superior to the EOF/DMD modes also in a modeling setting with a higher-order vector autoregressive process. The OMD modes could also be used as basis functions for a nonlinear dynamical model although they are not optimized for that purpose. Potential applications of the OMD method in weather and climate science include ENSO or MJO prediction, reduced-rank data assimilation, and generation of initial perturbations for ensemble prediction.

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Yuna Lim and Seok-Woo Son

Abstract

The dynamical mechanism by which the quasi-biennial oscillation (QBO) might influence the temperature anomaly, associated with the Madden–Julian oscillation (MJO), in the equatorial upper troposphere and lower stratosphere (UTLS) is examined by conducting a series of initial-value experiments using a dry primitive equation model. The observed temperature response to the MJO convection becomes colder and more in phase with the convection during easterly QBO (EQBO) than westerly QBO (WQBO) phases. This QBO-dependent MJO temperature anomaly in the UTLS is qualitatively reproduced by model experiments in which EQBO or WQBO background state is artificially imposed above 250 hPa while leaving the troposphere unaltered. As in the observations, the localized cold anomaly in the UTLS becomes strengthened and steepened with EQBO-like background state than WQBO-like one. It turns out that the QBO zonal wind, instead of temperature, plays a major role in determining the localized UTLS temperature anomaly by modulating wave energy dispersion.

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Sungmin O, Ana Bastos, Markus Reichstein, Wantong Li, Jasper Denissen, Hanna Graefen, and Rene Orth

Abstract

Droughts cause serious environmental and societal impacts, often aggravated by simultaneously occurring heat waves. Climate and vegetation play key roles in the evolution of drought-associated temperature anomalies, but their relative importance is largely unknown. Here, we present the hottest temperature anomalies during drought in subhumid and tree-dominated regions using observation-based, global data over 2001–15. These anomalies are mainly driven by a drought-related net radiation surplus and further amplified by forests’ water-saving strategies that result in diminished evaporative cooling. By contrast, in semiarid and short-vegetation regions, drought-related temperature increases are smaller. The reduction of evaporative cooling is weak and net radiation increases only marginally due to high albedo over drought-stressed vegetation. Our findings highlight the importance of considering all interacting factors in understanding diverse mechanisms of concurrent drought–heat extremes across different climate regimes.

Significance Statement

Climate and vegetation have a strong influence in regulating temperature anomalies during drought. However, the physical mechanisms behind drought–heat events across different climate–vegetation regimes are not always accurately described in physically based models. Here we use global-scale, observation-based datasets to show the spatial variation of temperature anomalies during drought, with the largest anomalies in subhumid and tree-dominated regions. Further, we present observational evidence for the relative roles of climate and vegetation in shaping drought–heat extremes across space. Our study provides valuable inputs to better understand the drought–heat pathways and their spatial variations, which can inform drought adaptation and mitigation efforts.

Open access
Wenhao Dong and Yi Ming

Abstract

The ratio of snowfall to total precipitation (S/P ratio) is an important metric that is widely used to detect and monitor hydrologic responses to climate change over mountainous areas. Changes in the S/P ratio over time have proved to be reliable indicators of climatic warming. In this study, the seasonality and interannual variability of monthly S/P ratios over High Mountain Asia (HMA) have been examined during the period 1950–2014 based on a three-member ensemble of simulations using the latest GFDL AM4 model. The results show a significant decreasing trend in S/P ratios during the analysis period, which has mainly resulted from reductions in snowfall, with increases in total precipitation playing a secondary role. Significant regime shifts in S/P ratios are detected around the mid-1990s, with rainfall becoming the dominant form of precipitation over HMA after the changepoints. Attribution analysis demonstrates that increases in rainfall during recent decades were primarily caused by a transformation of snowfall to rainfall as temperature warmed. A logistic equation is used to explore the relationship between the S/P ratio and surface temperature, allowing calculation of a threshold temperature at which the S/P ratio equals 50% (i.e., precipitation is equally likely to take the form of rainfall or snowfall). These temperature thresholds are higher over higher elevations. This study provides an extensive evaluation of simulated S/P ratios over the HMA that helps clarify the seasonality and interannual variability of this metric over the past several decades. The results have important socioeconomic and environmental implications, particularly with respect to water management in Asia under climate change.

Open access
Antonios Mamalakis, Elizabeth A. Barnes, and Imme Ebert-Uphoff

Abstract

Convolutional neural networks (CNNs) have recently attracted great attention in geoscience due to their ability to capture non-linear system behavior and extract predictive spatiotemporal patterns. Given their black-box nature however, and the importance of prediction explainability, methods of explainable artificial intelligence (XAI) are gaining popularity as a means to explain the CNN decision-making strategy. Here, we establish an intercomparison of some of the most popular XAI methods and investigate their fidelity in explaining CNN decisions for geoscientific applications. Our goal is to raise awareness of the theoretical limitations of these methods and gain insight into the relative strengths and weaknesses to help guide best practices. The considered XAI methods are first applied to an idealized attribution benchmark, where the ground truth of explanation of the network is known a priori, to help objectively assess their performance. Secondly, we apply XAI to a climate-related prediction setting, namely to explain a CNN that is trained to predict the number of atmospheric rivers in daily snapshots of climate simulations. Our results highlight several important issues of XAI methods (e.g., gradient shattering, inability to distinguish the sign of attribution, ignorance to zero input) that have previously been overlooked in our field and, if not considered cautiously, may lead to a distorted picture of the CNN decision-making strategy. We envision that our analysis will motivate further investigation into XAI fidelity and will help towards a cautious implementation of XAI in geoscience, which can lead to further exploitation of CNNs and deep learning for prediction problems.

Free access