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Isaac Davis
and
Brian Medeiros

Abstract

The Community Earth System Model, version 2 (CESM2) has a very high climate sensitivity driven by strong positive cloud feedbacks. To evaluate the simulated clouds in the present climate and characterize their response with climate warming, a clustering approach is applied to three independent satellite cloud products and a set of coupled climate simulations. Using k-means clustering with a Wasserstein distance cost function, a set of typical cloud configurations is derived for the satellite cloud products. Using satellite simulator output, the model clouds are classified into the observed cloud regimes in both current and future climates. The model qualitatively reproduces the observed cloud configurations in the historical simulation using the same time period as the satellite observations, but it struggles to capture the observed heterogeneity of clouds which leads to an overestimation of the frequency of a few preferred cloud regimes. This problem is especially apparent for boundary layer clouds. Those low-level cloud regimes also account for much of the climate response in the late 21st Century in four Shared Socioeconomic Pathway simulations. The model reduces the frequency of occurrence of these low-cloud regimes, especially in tropical regions under large-scale subsidence, in favor of regimes that have weaker cloud radiative effects.

Restricted access
Olivia Gozdz
,
Martha W. Buckley
, and
Timothy DelSole

Abstract

The impact of interactive ocean dynamics on internal variations of Atlantic sea surface temperature (SST) is investigated by comparing preindustrial control simulations of a fully coupled atmosphere-ocean-ice model to the same atmosphere-ice model with the ocean replaced by a motionless slab layer (henceforth slab ocean model). Differences in SST variability between the two models are diagnosed by an optimization technique that finds components whose variance differs as much as possible. This technique reveals that Atlantic SST variability differs significantly between the two models. The two components with the most extreme enhancement of SST variance in the slab ocean model resemble the tripole SST pattern associated with the North Atlantic Oscillation (NAO) and the Atlantic Multidecadal Variability (AMV) pattern. This result supports previous claims that ocean dynamics are not necessary for the AMV, although ocean dynamics lead to slight increases in the memory of both the AMV and the NAO tripole. The component with the most extreme enhancement of SST variance in the fully coupled model resembles the Atlantic Niño pattern, confirming the ability of our technique to isolate physical modes known to require ocean dynamics. The second component with more variance in the fully coupled model is a mode of subpolar SST variability. Both reemergence of SST anomalies and changes in ocean heat transport lead to increased SST variance and memory in the subpolar Atlantic. Despite large differences in the mean and variability of SST, atmospheric variability is quite similar between the two models, confirming that most atmospheric variability is generated by internal atmospheric dynamics.

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Sandrine Trotechaud
,
Bruno Tremblay
,
James Williams
,
Joy Romanski
,
Anastasia Romanou
,
Mitchell Bushuk
,
William Merryfield
, and
Rym Msadek

Abstract

Observations show predictive skill of the minimum sea ice extent (Min SIE) from late winter anomalous off-shore ice drift along the Eurasian coastline – leading to local ice thickness anomalies at the onset of the melt season – a signal then amplified by the ice-albedo feedback. We assess whether the observed seasonal predictability of September sea ice extent (Sept SIE) from Fram Strait Ice Area Export (FSIAE, a proxy for Eurasian coastal divergence) is present in Global Climate Model (GCM) large ensembles, namely the CESM2-LE, GISS-E2.1-G, FLOR-LE, CNRM-CM6-1, and CanESM5. All models show distinct periods where winter FSIAE anomalies are negatively correlated with the May sea ice thickness (May SIT) anomalies along the Eurasian coastline, and the following Sept Arctic SIE, as in observations. Counter-intuitively, several models show occasional periods where winter FSIAE anomalies are positively correlated with the following Sept SIE anomalies when the mean ice thickness is large or late in the simulation when the sea ice is thin, and/or when internal variability increases. More importantly, periods with weak correlation between FSIAE and the Sept SIE dominate, suggesting that summer melt processes dominate over late winter preconditioning and May SIT anomalies. In general, we find that the coupling between the FSIAE and ice thickness anomalies along the Eurasian coastline is an ubiquitous feature of GCMs and that the relationship with the following Sept SIE is dependent on the mean Arctic sea ice thickness.

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Xiuzhen Li
,
Donghai Wang
, and
Wen Zhou

Abstract

South China encountered an exceptionally heavy pre-summer rainy season in 2022 with the regional precipitation ranking first in the past 44 years. This study aims to analyze the multiple-time-scale variations of precipitation in this pre-summer rainy season to shed light on the complex dynamics influencing pre-summer precipitation over South China. The findings reveal that the variation of precipitation was dominated by the 10–20-day oscillation during April–May, while interannual variation and trend during May–June. The 10–20-day oscillation of precipitation in pre-summer rainy season in South China demonstrates a strong association with cold-air activity, which can be traced back to the propagation of disturbances along a teleconnection, which represents the dominant mode of intraseasonal atmospheric circulation over Eurasia in high latitudes during April–May. This teleconnection plays a crucial role in facilitating cold-air invasion and triggering precipitation over East China and South China. The interannual component of abnormal precipitation is strong during May–June of 2022. It is primarily attributed to the abnormal highs in the lower troposphere over the subtropical western North Pacific and Japan. These abnormal highs are likely stimulated by the combined influences of Eurasian teleconnection propagation and cooling sea surface temperature anomalies (SSTAs) over the tropical central and eastern Pacific in the third year of a consecutive La Niña event. However, the universality of the impact of Eurasian teleconnection propagation on the abnormal high over Japan on interannual scale necessitates further investigation. Furthermore, there is a significant upward trend in pre-summer rainfall over South China, accounting for 38% of the total anomaly observed in 2022.

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Víctor C. Mayta
and
Ángel F. Adames Corraliza

Abstract

Observations of column water vapor in the tropics show significant variations in space and time, indicating that it is strongly influenced by the passage of weather systems. It is hypothesized that many of the influencing systems are moisture modes, systems whose thermodynamics are governed by moisture. On the basis of four objective criteria, results suggest that all oceanic convectively coupled tropical depression (TD)-like waves and equatorial Rossby waves are moisture modes. These modes occur where the horizontal column moisture gradient is steep and not where the column water vapor content is high. Despite geographical basic-state differences, the moisture modes are driven by the same mechanisms across all basins. The moist static energy (MSE) anomalies propagate westward by horizontal moisture advection by the trade winds. Their growth is determined by the advection of background moisture by the anomalous meridional winds and anomalous radiative heating. Horizontal maps of column moisture and 850-hPa streamfunction show that convection is partially collocated with the low-level circulation in nearly all the waves. Both this structure and the process of growth indicate that the moisture modes grow from moisture–vortex instability. Last, space–time spectral analysis reveals that column moisture and low-level meridional winds are coherent and exhibit a phasing that is consistent with a poleward latent energy transport. Collectively, these results indicate that moisture modes are ubiquitous across the tropics. That they occur in regions of steep horizontal moisture gradients and grow from moisture–vortex instability suggests that these gradients are inherently unstable and are subject to continuous stirring.

Significance Statement

Over the tropics, column water vapor has been found to be highly correlated with precipitation, especially in slowly evolving systems. These observations and theory support the hypothesis that moisture modes exist, a type of precipitating weather system that does not exist in dry theory. In this study, we found that all oceanic tropical depression (TD)-like waves and equatorial Rossby waves are moisture modes. These systems exist in regions where moisture varies greatly in space, and they grow by transporting air from the humid areas of the tropics toward their low pressure center. These results indicate that the climatological-mean distribution of moisture in the tropics is unstable and is subject to stirring by moisture modes.

Open access
Feihong Zhou
,
Daniel Fiifi Tawia Hagan
,
Guojie Wang
,
X. San Liang
,
Shijie Li
,
Yuhao Shao
,
Emmanuel Yeboah
, and
Xikun Wei

Abstract

The land surface and atmosphere interaction forms an integral part of the climate system. However, this intricate relationship involves many complicated interactions and feedback effects between multiple variables. As a result, relying solely on traditional linear regression analysis and correlation analysis to distinguish between multivariate complex “driver–response” relations can be challenging, since they do not have the needed asymmetry to establish causality. The Liang–Kleeman (LK) information flow theory provides a strict nonparametric causality measurement for identifying the causality between any given time series, and its recent extension from bivariate to multivariate form provides a powerful tool for causal inference in complex multivariate systems. However, the multivariate LK information flow also assumes stationarity in time and requires a sufficiently long time series to ensure statistical sufficiency. To remedy this challenge, we rely on the square-root Kalman filter to estimate the time-varying form of the multivariate LK information flow causality. The results from theoretical and real-world applications show that the new algorithm provides a valuable tool for characterizing time-varying causal relationships in land–atmosphere interactions, even when the time series are short and highly correlated.

Significance Statement

Causality in land–atmosphere interactions is generally characterized by seasonal and intraseasonal changes that are usually not captured with commonly used approaches, because most approaches assume the time series are stationary. In this study, we extend the recently proposed multivariate Liang–Kleeman information flow causality (MtvLK) to handle nonstationary systems such as those in land–atmosphere interactions. By considering nonstationarity, we aim to unravel time-varying causal structures that are usually masked out in commonly used methods. Validating the MtvLK with synthetic models showed that the MtvLK is able to obtain the expected causal structures. Furthermore, real-world applications reveal novel findings of the time-varying causal structures between soil moisture, vapor pressure deficit, and the gross primary product.

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Clara Deser
,
Adam S. Phillips
,
Michael. A. Alexander
,
Dillon J. Amaya
,
Antonietta Capotondi
,
Michael G. Jacox
, and
James D. Scott

Abstract

The future evolution of sea surface temperature (SST) extremes is of great concern, not only for the health of marine ecosystems and sustainability of commercial fisheries, but also for precipitation extremes fueled by moisture evaporated from the ocean. This study examines the projected influence of anthropogenic climate change on the intensity and duration of monthly SST extremes, hereafter termed marine heat waves (MHWs) and marine cold waves (MCWs), based on initial-condition large ensembles with seven Earth system models. The large number of simulations (30–100) with each model allows for robust quantification of future changes in both the mean state and variability in each model. In general, models indicate that future changes in variability will cause MHW and MCW events to intensify in the northern extratropics and weaken in the tropics and Southern Ocean, and to shorten in duration in many areas. These changes are generally symmetric between MHWs and MCWs, except for the longitude of duration change in the tropical Pacific and sign of duration change in the Arctic. Projected changes in ENSO account for a large fraction of the variability-induced changes in MHW and MCW characteristics in each model and are responsible for much of the intermodel spread as a result of differences in future ENSO behavior. The variability-related changes in MHW and MCW characteristics noted above are superimposed upon large mean-state changes. Indeed, their contribution to the total change in SST during MHW and MCW events is generally <10% except in polar regions where they contribute upward of 50%.

Open access
Qianru Wang
and
Shuhua Zhang

Abstract

Solar radiation balances significantly affect Earth’s surface energy balance and climate change. Studying top-of-the-atmosphere (TOA) albedo changes is of great significance for understanding Earth’s energy budget and atmospheric circulation. The Loess Plateau (LP), located in the middle reaches of the Yellow River in China, is one of the most severely eroded areas in the world. In this paper, long-term remote sensing data were used to analyze the changes in the TOA albedo in the LP from 1982 to 2016. The results showed that the TOA albedo, its atmospheric contribution (AC), and surface contribution (SC) exhibited decreasing trends: −0.0012, −0.0010, and −0.0003 a−1. The spatial pattern of the TOA albedo was similar to AC, which indicates that AC dominates the change in the TOA albedo. We detected driving factors for AC and SC and found that the cloud fraction (CF) was the main driving factor of the AC, whereas the soil moisture (SM) dominated the SC. The driving factors of two typical regions with a significantly decreasing trend in the TOA albedo were also detected. The Mu Us Desert, where vegetation improved significantly, showed a decreasing trend in the TOA albedo, and we found that NDVI was the main driving factor for the change in the SC of the TOA albedo. However, the Eastern Qilian Mountains, where snow cover decreased in recent years, also showed a significant decreasing trend in the TOA albedo; the SC here was mainly driven by the changes in snow cover days (SCD). These results indicate that changes in the surface environment alter the radiation balance.

Significance Statement

The Loess Plateau in China is one of the most severe cases of soil erosion in the world, and ecological restoration projects have been carried out to recover the fragile ecological environment. Our study was designed to explore changes in the top-of-the-atmosphere (TOA) albedo of the Loess Plateau between 1982 and 2016 using a long time series of multisource satellite products, and driving factors in the atmosphere and at the surface were analyzed. We concluded that the TOA albedo of the Loess Plateau decreased over 35 years, and its atmospheric contribution dominated the change in the TOA albedo. However, the significant ecological improvement in the Loess Plateau, especially in the central vegetation recovery region, such as the Mu Us Desert, was also strongly related to the regional changes in the surface contribution of the TOA albedo. The climate changes had a considerable impact on the eastern branch of the Qilian Mountains in the Qinghai region, where the decline in snow cover days affected the local Alpine meadow ecosystems; therefore, snow cover days also played a decisive role in the local variation of the surface contribution of the TOA albedo.

Restricted access
Weizhen Chen
,
Chang-Hoi Ho
,
Song Yang
,
Zeming Wu
, and
Hongjing Chen

Abstract

The Madden–Julian oscillation (MJO) and the quasi-biweekly oscillation (QBWO) are prominent components of the intraseasonal oscillations over the tropical Indo-Pacific Ocean. This study examines the tropical cyclone (TC) genesis over the Bay of Bengal (BOB) and the South China Sea (SCS) on an intraseasonal scale in May–June during 1979–2021. Results show that the convection associated with the two types of intraseasonal oscillations simultaneously modulates TC genesis in both ocean basins. As the MJO/QBWO convection propagated, TCs form alternately over the two basins, with a significant increase (decrease) in TC genesis frequency in the convective (nonconvective) MJO/QBWO phase. Based on the anomalous genesis potential index associated with the MJO/QBWO, an assessment of the influence of various factors on TC genesis is further assessed. Middle-level relative humidity and lower-level relative vorticity play key roles in the MJO/QBWO modulation on TC genesis. The MJO primarily enhances large-scale cross-equatorial moisture transport, resulting in significant moisture convergence, while the QBWO generally strengthens the monsoon trough and induces the retreat of the western North Pacific subtropical high, increasing the regional lower-level relative vorticity. The potential intensity and vertical wind shear make small or negative contributions. This study provides insights into the neighboring basin TC relationship at intraseasonal scales, which has a potential to improve the short-term prediction of regional TC activity.

Significance Statement

The Madden–Julian oscillation (MJO) and the quasi-biweekly oscillation (QBWO) are two types of intraseasonal tropical atmospheric oscillations. The development of tropical cyclones (TCs) is often accompanied by intraseasonal convection. This study highlights the distinct roles of MJO and QBWO in TC genesis over the South Asian marginal seas (e.g., Bay of Bengal and South China Sea). The QBWO can co-regulate TC genesis along with the background of the MJO, where the large-scale MJO mainly provides moisture, while the small-scale QBWO mainly contributes to vorticity. These findings provide useful information for subseasonal TCs forecasting. There are many developing countries along the South Asian marginal seacoast; therefore, further research on regional TC climate would help effectively reduce casualties and property damage.

Open access
Elena Saggioro
,
Theodore G. Shepherd
, and
Jeff Knight

Abstract

Skilful prediction of the Southern Hemisphere (SH) eddy-driven jet is crucial for representation of mid-to-high latitude SH climate variability. In the austral spring-to-summer months, the jet and the stratospheric polar vortex variabilities are strongly coupled. Since the vortex is more predictable and influenced by long-lead drivers one month or more ahead, the stratosphere is considered a promising pathway for improving forecasts in the region on sub-seasonal to seasonal (S2S) timescales. However, a quantification of this predictability has been lacking, as most modelling studies address only one of the several interacting drivers at a time, while statistical analyses quantify association but not skill. This methodological gap is addressed through a knowledge-driven probabilistic causal network approach, quantified with seasonal ensemble hindcast data. The approach enables to quantify the jet’s long-range predictability arising from known late-winter drivers, namely El Niño Southern Oscillation, Indian Ocean Dipole, upward wave activity flux and polar night jet oscillation, mediated by the vortex variability in spring. Network-based predictions confirm the vortex as determinant for skilful jet predictions, both for the jet’s poleward shift in late spring and its equatorward shift in early summer. ENSO, IOD, late-winter wave activity flux and polar night jet oscillation only provide moderate prediction skill to the vortex. This points to early spring sub-monthly variability as important for determining the vortex state leading up to its breakdown, creating a predictability bottle-neck for the jet. The method developed here offers a new avenue to quantify the predictability provided by multiple, interacting drivers on S2S timescales.

Open access