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Variability of the Atlantic Meridional Overturning Circulation (MOC) has drawn extensive attention due to its impact on the global redistribution of heat and freshwater. Here we present the latest time series (2014–2022) of the Overturning in the Subpolar North Atlantic Program and characterize MOC interannual variability. We find that any single boundary current captures ∼30% of subpolar MOC interannual variability. However, to fully resolve MOC variability, a wide swath across the eastern subpolar basin is needed; in the Labrador Sea both boundaries are needed. Through a volume budget analysis for the subpolar basins' lower limbs, we estimate the magnitude of unresolved processes (e.g., diapycnal mixing) required to close the mean budget (∼2 Sv). We find that in the eastern subpolar basin surface‐forced transformation variability is linked to lower limb volume variability, which translates to MOC changes within the same year. In contrast, this linkage is weak in the Labrador Sea.

We evaluate the ability of the latest generation atmospheric general circulation model from State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences (namely, FAMIL2) in simulating some key characteristics (genesis location, track, number, and intensity) of tropical cyclones (TCs) in terms of their climatology and seasonal to interannual variability. A standard 1° × 1° atmospheric model intercomparison project experiment is carried out for the period 1979–2002, and the last 20 years of outputs are used for analysis. The same period from International Best Track Archive for Climate Stewardship (IBTrACS) is used as the observation for comparison purposes. The evaluations focus on TC activity at the global scale as well as in the three key regions of the northern Indian Ocean (NIO), western Pacific (WP) and northern Atlantic (NA). With respect to the simulated TC climatology, FAMIL2 shows notable ability in correctly reproducing the main characteristics of the genesis locations, tracks, and numbers of TC, particularly over the key regions of TC activity in the Northern Hemisphere; whereas, it underestimates the intensities of TC, as is the case with many state‐of‐the‐art climate models operating at a medium resolution. On seasonal‐to‐interannual time scales, meanwhile, FAMIL2 successfully reproduces the seasonal cycles of TC numbers over the NIO and WP regions, the former being characterized by double TC peaks (in May and October) and the latter by a maximum peak season in August. However, the model only captures these features approximately. For the simulated interannual variability of TC activity, the correlation coefficients of 20‐year TC numbers between FAMIL2 and IBTrACS are 0.22, 0.51 (95% confidence interval), and 0.49 (95% confidence interval) for the NIO, WP, and NA, respectively. We also examine the possible reasons behind the performance of FAMIL2 by investigating its subseasonal signs related to the Madden‐Julian Oscillation (MJO) and convectively coupled equatorial waves. The TC genesis potential index is employed to investigate the possible impacts of the large‐scale dynamic fields on the simulation of TC activity. Finally, the biases of simulated TC activity, as well as possible solutions for these biases, are discussed with respect to the horizontal resolution of the model. A TC forecasting case study is introduced as a first step in applying FAMIL2 to a TC forecasting system. Key Points Characteristics of global and regional tropical cyclone are simulated in FAMIL2 We identify potential reasons to tropical cyclone activity in FAMIL2 We discuss the impacts of spatial resolution and introduce the application development of tropical cyclone prediction

The sea ice growth (SIG) in the Kara Sea plays a crucial role in the Arctic climate system. Many studies have examined its long‐term trend, but whether its variability has changed is less clear. Using observations and reanalysis data, we observe an intensified interannual variability of the Kara Sea SIG during boreal late winter (December–March) since 2004/2005. This arises from the retreat of active ice production zones in response to the strengthened modulation of the westward‐shifted North Atlantic Oscillation (NAO). Using a sea ice concentration budget, we quantitatively partition the NAO's contribution to total SIG variability post‐2004 (∼63.3%), revealing comparably dominant roles of thermodynamic and dynamic processes. During this period, the negative NAO‐associated surface winds concurrently cool sea surface and export sea ice from the Kara Sea, thereby injecting freshwater into the lower latitudes. Our study advances the understanding of the regional air‐ice‐ocean climate feedbacks in recent Arctic.

The spatial pattern of the first mode of interannual variability associated with the East Asian summer monsoon (EASM), obtained from a multivariate Empirical Orthogonal Functions (MV-EOF) analysis, corresponds to the Pacific–Japan (PJ) pattern and is referred to as the PJ-mode. The present study investigates the interannual variation of the PJ-mode from the perspective of the intraseasonal timescale. In particular, the impact of the Madden–Julian oscillation (MJO) on the interannual variation of the PJ-mode is investigated. The results show that the MJO has a significant influence on the interannual variation of the PJ-mode mainly in the lower troposphere (850 hPa) and that the former accounts for approximately 11% of the amplitude of the latter. The major part of the contribution comes from a change in frequency of the different phases of the MJO, especially that of MJO phase 6. This suggests that intraseasonal variation of the convection anomalies over the tropical eastern Indian and western Pacific Oceans plays an important role in the interannual variation of the PJ-mode. In addition, MJO phase 7 also contributes to the interannual variability of the PJ-mode, in this case induced by both the change in frequency and the change in circulation anomalies associated with MJO phase 7.

This study aims to examine the intraseasonal variation of PM 2.5 concentration in North China and explore the potential influence of the Eurasian mid-high-latitude intraseasonal oscillation (ISO) on this variation. A statistically significant period of 10–30 days is observed for PM 2.5 concentration anomalies in North China. Further study suggests that circulation patterns and associated meteorological factors play a crucial role in pollution events in North China. These factors influence local accumulation of pollutants and hygroscopic growth conditions. Based on the ISO phases, it is found that, at the earliest, the circulation in phase 2 provides favorable conditions for pollution. The circulation involved phase 2 leads to a strong increase in surface air temperature, a decrease in sea level pressure, and an increase in humidity in the low layers. These conditions provide favorable meteorological conditions for the accumulation of PM 2.5 in North China. The interannual variability of ISO intensity and its impact on PM 2.5 concentration in North China has also been analyzed. The results show that the circulation is stronger and propagates more southeastward during strong ISO years. Under the influence of the anticyclonic circulation in Mongolia during strong ISO years, favorable moisture conditions and atmospheric stability contribute to an increase in pollution.

Global Climate Models (GCMs) are very essential and crucial for projecting future climate scenarios under different greenhouse gas emissions, incorporating uncertainties in the global warming projections. The present study evaluates the seasonal performance of 32 Coupled Model Intercomparison Project Phase 5 (CMIP5) models obtained from the Coordinated Ocean Wave Climate Project phase 2 (COWCLIP 2.0) in simulating the global and regional wave power (WP) from 1979 to 2004 using historical data, and comparing them against the ERA5 reanalysis. Three skill metrics, such as Root Mean Square Error (RMSE), Interannual Variability Skill (IVS), and M-Score were used to assess the model performance across three clusters (CSIRO, JRC, and IHC). In addition, Intra-seasonal and probability distribution is also employed to determine the cluster’s performance, including individual models. The IHC cluster, employing statistical techniques, exhibited the lowest RMSE and highest M-Score values with the least variation among models over the global as well as regional ocean basins such as the North Atlantic (NA), North Pacific (NP), Indian Ocean (IO), and Pacific Ocean (PO. Results from intra-seasonal variability and probability distribution indicate that the IHC cluster demonstrates the most stable performance in simulating intra-seasonal variability of WP as compared to other clusters.

The impact of the Madden-Julian Oscillation (MJO) on India’s intraseasonal and interannual variability of rainfall has been investigated in this study. Anomalous MJO amplitude points from 1979 to 2021 are classified into post-, pre- and normal onset years of the Indian Summer Monsoon (ISM). High MJO amplitudes, particularly those exceeding three standard deviations from the mean, correlate with significant rainfall anomalies, affecting both the timing and intensity of the monsoon. Specific MJO phases contribute differently to rainfall patterns, with phases 3, 4, and 5 generally suppressing rainfall, while phases 1 and 8 enhance it. Pre-onset years show significant rainfall anomalies driven by strong MJO activity, particularly during phases 3, 4, and 5. Normal onset years exhibit notable suppression and enhancement patterns, with significant fluctuations observed in 1996 and 2020. Post-onset years have the highest frequency of anomalies, with phases 3 and 4 prominently influencing rainfall variability. Overall, this research underscores the critical role of MJO dynamics in modulating rainfall variability in India. The findings suggest that understanding MJO phases can improve rainfall predictability and monsoon onset forecasting, aiding in better climate prediction and water resource management. This study contributes to the broader climatological discourse by elucidating the interplay between MJO activity and regional precipitation patterns.

The impact of interannual variations in sea ice area in the Okhotsk Sea was investigated through a composite analysis of years with extensive and limited sea ice areas (referred to as heavy and light ice years, respectively), using atmospheric and oceanic reanalysis data. The comparison of heavy and light ice-year composites in February revealed a substantial decrease in upward surface turbulent heat flux in the Okhotsk Sea (∼−250 W m −2 ) and a notable increase in a surprisingly extensive region in the western North Pacific (30–120 W m −2 ), spanning 2300 km from the ice edge. These differences were consistent with the decrease in surface air temperature and specific humidity, suggesting that during heavy ice years, cold and dry air blowing from Siberia to the North Pacific via the Okhotsk Sea undergoes less modification over larger sea ice areas, remaining colder and drier in the North Pacific and thereby enhancing the heat flux. Such advection can be associated with the Asian winter monsoon and migratory cyclones. Cloud cover and surface radiation flux altered consistently with these differences, although longwave and shortwave radiation largely counterbalanced each other. Additionally, the Pacific storm track exhibited variation. In accordance with the heat flux difference, sea surface temperature decreased, and the ocean mixed layer deepened around the subarctic during heavy ice years. These findings suggest that sea ice area in the Okhotsk Sea influences the lower atmosphere and surface ocean in the North Pacific. Such impacts could further affect ocean nutrient circulation, ecosystems, and atmospheric teleconnections.

Previous studies have found that under global warming, El Niño/Southern Oscillation (ENSO)-related rainfall variability will become enhanced over the tropical central-eastern Pacific and weakened over the western Pacific. The climatological sea surface temperature (SST) warming pattern exhibits a warming center in the equatorial eastern Pacific in projections. How this pattern contributes to projected changes in ENSO-driven rainfall variability has not been fully addressed. Here, we use “time-slice” experiments to investigate the response of interannual variability in tropical Pacific rainfall in boreal winter to a warming background SST and associated physical mechanisms. A high-resolution Atmosphere General Circulation Model is driven by the detrended observational SST (1979–2003) plus a warming pattern from the coupled model under the A1B emission scenario (2075–2099). The results show that precipitation interannual variability over the tropical central-eastern Pacific will be enhanced more than the surrounding regions under warming, which is mostly contributed by a faster increase in rainfall amount during the El Niño year relative to non-El Niño years. Based on a moisture budget analysis, both the dynamic and thermodynamic components in the vertical advection of climatological specific humidity contribute to the enhancement of El Niño-induced precipitation anomalies in the tropical central-eastern Pacific where the dynamic effect is dominant. Moist static energy budget analysis further illustrates that vertical velocity is enhanced due to the increased transport of moist static energy from the lower troposphere into the middle-upper troposphere and the intensified warming effect of cloud longwave radiation caused by the increase of high cloud amount and altitude.

The dynamical prediction of the Asian-Australian monsoon (AAM) has been an important and long-standing issue in climate science. In this study, the predictability of the first two leading modes of the AAM is studied using retrospective prediction datasets from the seasonal forecasting models in four operational centers worldwide. Results show that the model predictability of the leading AAM modes is sensitive to how they are defined in different seasonal sequences, especially for the second mode. The first AAM mode, from various seasonal sequences, coincides with the El Niño phase transition in the eastern-central Pacific. The second mode, initialized from boreal summer and autumn, leads El Niño by about one year but can exist during the decay phase of El Niño when initialized from boreal winter and spring. Our findings hint that ENSO, as an early signal, is conducive to better performance of model predictions in capturing the spatiotemporal variations of the leading AAM modes. Still, the persistence barrier of ENSO in spring leads to poor forecasting skills of spatial features. The multimodel ensemble (MME) mean shows some advantage in capturing the spatiotemporal variations of the AAM modes but does not provide a significant improvement in predicting its temporal features compared to the best individual models in predicting its temporal features. The BCC_CSM1.1M shows promising skill in predicting the two AAM indices associated with two leading AAM modes. The predictability demonstrated in this study is potentially useful for AAM prediction in operational and climate services.