Tropical–extratropical teleconnections play a central role in shaping the North American winter climate, with most previous studies focusing on influences from the tropical Pacific. Recent studies suggest a wintertime Indian Ocean–North America (INA) teleconnection associated with the Indian Ocean Basin Mode (IOBM), yet its robustness across climate models remains uncertain. By employing multiple atmospheric reanalysis datasets and AMIP6 models, this study evaluates the atmospheric circulation and surface climate responses linked to the IOBM. Reanalysis datasets consistently reveal a coherent North Pacific–North American circulation pattern, accompanied by a zonally asymmetric surface temperature response. The AMIP multi-model ensemble mean reproduces the overall spatial structure and vertical coherence of this teleconnection, while exhibiting regional differences in amplitude. Using self-organizing map analysis, the AMIP models are classified into four distinct circulation response patterns, which may be associated with variations in the strength and axial position of the upper-tropospheric jet. These results support the robustness of the INA teleconnection across models and offer insight into the representation of Indian Ocean–driven climate variability in atmospheric simulations.
The Indian Ocean Dipole (IOD) is a dominant mode of interannual variability in the Indian Ocean with significant influences globally on weather, climate, and society. Despite extensive research, current numerical models and surface-based deep learning models are limited in their ability to predict IOD events beyond five to seven months. To address this gap, we introduce the novel Multi-dimensional Air–Sea Coupled Deep Learning Model (MAS-Net), which integrates three-dimensional multivariable data to deliver accurate predictions of the ocean field and the Dipole Mode Index (DMI) up to eight months in advance. Sensitivity and interpretability analyses reveal that subsurface temperature anomalies are critical in improving IOD prediction skill. By integrating subsurface dynamics into a deep learning framework, this work hence significantly advances understanding of IOD predictability and provides a robust tool for both operational forecasting and scientific exploration of the IOD. The MAS-Net framework sets a new standard for predictive modeling, offering a versatile approach that can be extended to other climate phenomena.
To improve understanding of the global carbon cycle, China is preparing to launch the next-generation carbon monitoring satellite, TanSat-2, which will collect column measurements of carbon dioxide (CO2) and methane (CH4). We investigate the potential of TanSat-2 observations for assessing anthropogenic carbon emissions and ecosystem carbon sinks. To achieve this objective, we introduce a new carbon flux estimation method that combines atmospheric measurements of CO2 and solar-induced fluorescence (SIF) to jointly constrain net primary productivity (NPP) and fossil fuel combustion (FF). We apply EOF (empirical orthogonal function) analysis to the NPP and FF inventories to identify dominant spatiotemporal patterns to reduce the size of the state vector and to help disaggregate the natural and FF sources of CO2. Using observing system simulation experiments (OSSEs)—we find that TanSat-2 CO2 and SIF observations lead to an NPP error reduction of up to 95
This study analyzes the characteristics of pre-monsoon (March–April–May) seasonal heatwaves across two periods: P1 (1976–97) and P2 (1998–2024) in the northwestern South Asia (NWSA) region. We observe a statistically significant abrupt shift (Pettitt Test, p < 0.05) in 1997 for the time series of area-averaged maximum temperature (Tmax), number of heatwave events, heatwave frequency (HWF), and cumulative heatwave intensity (HWI) over NWSA. The area-averaged Tmax, HWF, and HWI increased by 1.4°C, 5.9 days, and 9.9°C in P2 compared to P1 over NWSA. In P2, the mean spatial distributions of Tmax, HWF, and HWI increased by ∼1°C–3°C, ∼3–12 days, and ∼12°C–24°C over the NWSA region, respectively. The regime shifts in Tmax and heatwave characteristics were associated with an abrupt increase in geopotential height at 200/500 hPa, outgoing longwave radiation, and soil temperature level 1, along with an abrupt decrease in total precipitation, total cloud cover, and soil moisture over NWSA in 1997/98. An increase in atmospheric thickness, reduced subtropical westerlies, and increased subsidence over NWSA in P2 exacerbated heatwaves over NWSA. This research provides mechanistic insights into the increasing heatwave trend, which will be useful for regional heatwave prediction and for formulating adaptive strategies for future heatwave risks.
Desert ecosystems, which cover extensive global regions, play an essential yet often-overlooked role in sequestering atmospheric CO2 at substantial yet gradual rates, primarily due to insufficient monitoring, data scarcity, and limited research attention. Current assessments of terrestrial carbon sinks often exclude desert areas, creating critical “data gaps” that undermine the completeness and accuracy of global carbon budget evaluations. Focusing on the Taklimakan Desert, we developed a high-resolution CO2 flux dataset (2000–22) for shifting sand areas, featuring hourly temporal resolution and 0.25° × 0.25° spatial coverage, through an empirical estimation scheme incorporating internal carbon sequestration processes, ERA5 multi-layer soil temperature/moisture data, and extensive field-measured soil properties (soil organic carbon, pH). Our analysis reveals that spatiotemporal CO2 flux patterns are jointly controlled by shifting sand temperature, moisture, and soil characteristics, with the shifting sand area of the entire desert sequestering approximately 1.05 × 106 t CO2 annually. While two-thirds of the area functions as carbon sinks, the northwestern margins (one-third of the total area) exhibit carbon-source behavior (peaking at 64.49 g m−2 yr−1) due to higher precipitation and organic carbon content. The ongoing warm–wetting trend in northwestern China is reducing both the stability and rate of carbon sequestration in these shifting sands—processes that may accelerate through climate change feedbacks. These findings provide critical scientific evidence for assessing the role of the desert in climate mitigation and inform policy-making for carbon neutrality strategies.
The dual crises of climate change and air pollution are creating a dangerous convergence. Climate change is already fueling more frequent, intense, and longer-lasting heat waves worldwide. When these extreme heat events combine with polluted air, they produce a synergistic effect, posing health threats far greater than either would alone. India has emerged as a critical hotspot for these compound risks, with Delhi alone experiencing 388 heat–PM2.5 co-extreme [PM2.5 > 75 µg m−3 and daily maximum 2-m air temperature (Tmax) > 35°C] days during 2017–24. However, such co-extremes have received limited attention. By integrating surface and satellite observations with GEOS-Chem modeling, we find a marked revival of pre-monsoon heat–PM2.5 co-extremes over India after 2020. The regional mean reaches 17.5 days in April 2022, accounting for over 70
The Arctic climate is sensitive to aerosol abundance, making accurate historical records of atmospheric aerosols essential for understanding the recent rapid Arctic warming. We present 235-year ice-core records of sulfate, nitrate, chloride and other impurities from central Greenland, and compare them with emission inventories in the surrounding continents. The comparisons reveal a high correlation between the ice-core records and North American emissions, consistent with North America being the dominant pollution source region to Greenland. Since around 1970, sulfate, nitrate and chloride exhibit divergent patterns relative to their pre-1970 increasing trends, in parallel with anthropogenic emissions. In particular, sulfate declined in step with North American SO2 emissions but decreased more rapidly after ∼1990, probably due to combined effects from the enhanced sulfate loss in the source regions due to intensified in-cloud sulfur oxidation and the reduced transport efficiency. Nitrate tracked NOx emissions since ∼1900 until 1990, but remained high after 1990 when anthropogenic emissions in all source regions decreased. This post-1990 pattern may arise from the increasing natural NOx emissions in the Arctic, while feedbacks of atmospheric chemistry to a changing atmospheric acidity during this period may also contribute through affecting the phase partitioning and then long-range transport of nitrate. Chloride also paralleled with anthropogenic emissions since the 1960s but was further modulated by acid displacement processes, as indicated by the covariation of Cl− excess with reconstructed snow acidity. Our results demonstrate that Arctic aerosols reflect both emission controls and their modulation by atmospheric chemistry and transport.
High-resolution atmospheric thermodynamic information is essential for resolving localized weather processes in complex urbanized regions such as the Yangtze River Delta (YRD). However, existing global reanalysis products are limited by coarse spatial resolution, constraining their ability to represent fine-scale thermodynamic structures. In this study, we develop YRD1km, a 1-km hourly regional thermodynamic reanalysis dataset for the YRD, generated through dynamical downscaling of ERA5 using the WRF model with a three-level nested configuration. The framework integrates optimized physical parameterizations, a combined observation and analysis nudging strategy, and updated high-resolution land-use information. The current dataset covers the summer seasons (June–August) from 2021 to 2023, a period characterized by frequent convection and strong land–atmosphere interactions. Comprehensive validation against dense surface and radiosonde observations demonstrates that YRD1km consistently outperforms ERA5 in near-surface temperature, relative humidity, and surface pressure, with reductions of approximately 35
Sea ice is crucial for modulating Antarctic air–sea fluxes, and its thickness (SIT) is the primary factor controlling the exchange of heat, moisture, and momentum. Although CryoSat-2 is commonly used for SIT retrieval, conventional algorithms rely on empirical parameters and auxiliary data that introduce substantial uncertainties. In this study, we developed a novel SIT dataset for 2010–24, derived directly from radar parameters using the Light Gradient Boosting Machine (LightGBM) machine learning method. Intercomparisons show that the LightGBM-derived SIT shows better consistency with the ICESat-2 product than conventional algorithm results. Validation against shipborne observations indicates that LightGBM-based monthly gridded SIT achieves a mean absolute error of 0.558 m, which is lower than with conventional methods (0.823 m). Temporal comparisons reveal that the LightGBM-derived sea ice volume (SIV) exhibits a more realistic seasonal cycle, with the maximum value occurring in September, compared to the conventional method, which shows a peak in August. This new SIT dataset provides a robust basis for estimating SIV with reduced uncertainty, investigating sea ice variability mechanisms, and assessing the impact of sea ice changes.
This study analyzes 40 years (1981–2020) of ERA5 data to investigate the impact of winter tropopause folds (TFs) on precipitation in the Yangtze River valley. While previous studies have focused on local effects of TFs over the Qinghai–Tibet Plateau (QTP), our results reveal that TFs are systematically propagating weather events. Originating in the QTP, TFs propagate eastward by thousands of kilometers and significantly affect precipitation downstream. During intense TF years, the averaged total winter precipitation in the Yangtze River valley increases by 30–50 mm. TFs induce a zonal dipole pattern of precipitation, with positive anomalies to the downstream and negative anomalies to the upstream, propagating downstream with TFs. With the passage of a TF, positive precipitation anomalies of 3–5 mm d−1 are observed in the Yangtze River Basin. Further analysis indicates that TFs will trigger an intrusion of high vortex air in the upper troposphere, and will a cold vortex downstream and a warm high-pressure anomaly upstream at around 500 hPa, which will further disrupt the water vapor in the lower troposphere. In addition, the strong boundary of the TF impedes the westerly jet and alters the vertical motion of the troposphere, leading to subsidence upstream and convective rise downstream. It is proposed that the systematic eastward propagation of TFs influences local advection and convergence–divergence processes, which anchors the precipitation patterns in the Yangtze River valley. These findings emphasize the importance of TFs as a moving weather system that exerts a cross-regional influence on Yangtze River valley precipitation.
Extreme heat occurrences are primarily driven by the abnormal heating of the atmosphere and land surface. Interestingly, throughout the lifecycle of extreme heat events, cooling-induced suppression processes frequently occur, including both the pre-event interruption of heat accumulation and the termination of extreme heat. This study analyzes the characteristics and mechanisms of these two cooling types using 68 extreme heat events in the Yangtze River basin over the period spanning from 1979 to 2024. Results indicate that terminative cooling exhibits strong cooling rates, with 92
High-precision retrieval of cloud microphysical parameters is crucial for advancing cloud physics and improving weather and climate models. To address observational data scarcity, this study develops a multi-frequency residual attention network (MiRA-Net) based on forward simulations from X/Ka/W-band cloud radars. The model retrieves liquid water content (LWC), ice water content (IWC), effective radius (Re), and particle type (Ptype), and is evaluated against polynomial fitting (polyfit) and random forest (RF) methods. Results demonstrate that multi-frequency observations significantly enhance retrieval accuracy. MiRA-Net outperforms the others, achieving an RMSE of 0.178 for LWC, substantially lower than RF (0.251) and polyfit (0.256). With dual-frequency (W/Ka) linear depolarization ratio (LDR), MiRA-Net’s IWC retrieval RMSE reaches 0.038—a 63
The tropical easterly jet (TEJ) is a key upper-tropospheric circulation feature that influences regional climate and tropical cyclone (TC) activity. This study examines how the TEJ responds to distinct global warming patterns and the implications for future TC activity using high-resolution simulations from an atmospheric general circulation model. We find that La Niña-like warming strengthens the TEJ over the western Pacific but weakens it over the Indian Ocean, whereas El Niño-like warming weakens the TEJ in both regions. These contrasting responses are linked to pattern-dependent changes in the Walker circulation and associated convection heating patterns. Basin-separation experiments under El Niño-like warming further reveal that Indian Ocean warming exerts dominant control over the western Pacific TEJ (WP_TEJ), Pacific Ocean warming has the strongest influence on the Indian Ocean TEJ (IO_TEJ), while Atlantic SST anomalies provide a secondary influence on both jets through cross-basin teleconnections. A weakened Indian Ocean IO TEJ reduces easterly vertical wind shear, creating a more favorable environment for TC activity in the northern Indian Ocean during the transition seasons, and supporting more frequent intense storms. Over the western North Pacific, a weakened WP TEJ under El Niño-like warming is associated with reduced upper-level divergence, whereas La Niña-like warming strengthens this jet and is linked to a more favorable large-scale environment for TC formation. These findings emphasize that the spatial structure of sea surface temperature change, and the relative roles of individual ocean basins, must be accounted for in reliable projections of regional atmospheric circulation and future TC risk.
Ocean heat content (OHC) changes in the Tropical Indian Ocean (TIO) broadly affect the monsoon, tropical cyclogenesis, regional sea level, and marine heatwaves. While El Niño is known to induce basin-wide TIO warming, the OHC response to the Indian Ocean Dipole (IOD), the dominant climate mode in the Indian Ocean, remains unclear. Our analysis of the observational data reveals that extreme positive IOD (pIOD) events also drive a substantial increase in TIO OHC, with a pronounced warming in the western basin. During the record-breaking 2019 pIOD event, the 0–2000 m TIO OHC rose by ∼8.2 ZJ (1 ZJ = 1021 J), comparable to signatures of strong El Niño events. Ocean model experiments and heat budget analysis diagnostics indicate that ∼80
Arctic sea ice has changed significantly in recent decades, with the East Siberian–Chukchi–Beaufort Seas (ECB) region exhibiting high interannual variability that plays a crucial role in the Arctic system. This study finds that the relationship between early autumn (August–September) ECB sea ice concentration (SIC) anomalies and tropical Indian Ocean (TIO) sea surface temperature anomalies has significantly strengthened since the mid-1990s. Post-1990s, a shift in the TIO spatial pattern accompanied by a warmer background triggers a wave activity flux (WAF) extending from the TIO to the southern ECB (Bering Sea). The altered North Pacific pressure pattern facilitates the establishment of a low-pressure system over the Bering Sea, intensifying southerly winds across the warm North Pacific and consequently accelerating SIC loss. The enhanced WAF mechanisms after the mid-1990s involve tropical divergent wind anomalies changing the Rossby wave source, regulating the location and intensity of the Rossby wave trains. Furthermore, both the conversion of local kinetic energy and available potential energy contribute to maintaining the stable propagation of the wave trains. Transient eddies strengthen East Asian circulation anomalies through positive feedback mechanisms, ultimately reinforcing the WAF. In contrast, before the mid-1990s, the lack of these synergistic mechanisms resulted in a weakened WAF that inhibited low-pressure system development over the Bering Sea. Instead, a high-pressure system with northerly wind anomalies hindered poleward warm air transport. Numerical experiments employing a linear baroclinic model further confirm the influence of the TIO warming after the mid-1990s on the atmospheric circulation response, validating the proposed mechanisms.
Wildfires play a critical role in the Earth system, affecting climate, carbon cycling, air quality, and human livelihoods. Phase 6 of the Coupled Model Intercomparison Project (CMIP6) provides multi-model simulations of historical and future wildfire carbon emissions, yet substantial uncertainties remain. Here, we evaluate fire carbon emissions from 10 CMIP6 models over 2003–23 using three satellite-based datasets (GFED, GFAS, QFED) across 14 subregions. The multi-model ensemble (MME) largely reproduces the spatial patterns, with a pattern correlation coefficient of 0.64 and normalized standard deviation of 1.00, but overestimates emissions in most regions, particularly the tropics, leading to inflated global totals. Only 5 out of 14 subregions show good agreement with observations. Seasonality is reasonably captured, with most models simulating peak fire months within ±1 month. Sensitivity analysis reveals strong regional variation in fire responses to near-surface air temperature (TAS) and surface soil moisture (SM). The MME correctly identifies TAS as the dominant driver in most high and middle latitudes but misrepresents the relative influence of TAS and SM in key tropical regions. Observationally, widespread declines in tropical fire emissions contribute to an overall global decrease (−0.02 PgC yr−2), but CMIP6 models fail to capture these trends, instead simulating spurious increases that drive unrealistic upward global trends (0.03 PgC yr−2). These discrepancies are largely attributable to inadequate representation of anthropogenic fire suppression and overestimated temperature sensitivity. Our results highlight the need for improved fire-process parameterizations to enhance the reliability of future projections of fire-related carbon emissions under climate change.
The long-term trends over the past three decades of surface eddy kinetic energy (EKE) in the South China Sea (SCS) were investigated using satellite altimetry datasets. Here, the linear and nonlinear trends derived from the ordinary least-squares and multidimensional ensemble empirical mode decomposition methods, respectively, are presented and compared. The comparison indicated that EKE has undergone a nonlinear evolutionary process during 1993–2023. In the first decade, the SCS exhibited a dominant decline in EKE, while there was an accelerating upward trend in the last decade. The long-term trends were inhomogeneous in spatial structure, and the accelerated growth of EKE primarily occurred north of 10°N. Eady theory suggested that the enhanced baroclinic instability over the past three decades may have served as the primary trigger for the boosted mesoscale variability. In addition, the trends of EKE in the SCS exhibited noteworthy seasonality, showing a prominent increase in winter and a decrease in the other three seasons. Although the nonlinear trends of the annual mean EKE failed the 95
Using stratospheric nudging experiments in a global climate model, we examined the role of sudden stratospheric warming (SSW) in driving Eurasian cooling on subseasonal time scales. Nudging was applied to extratropical stratospheric conditions derived from observations, imposing the full observed stratospheric state including zonal asymmetries. Two nudging experiments were performed with different lower boundaries within the stratosphere (∼88 hPa vs ∼200 hPa). Both experiments successfully reproduced Eurasian cooling following SSW events, linked to cold air advection into Eurasia driven by changes in upper-tropospheric circulation resulting from stratosphere–troposphere coupling. The results were generally insensitive to the vertical extent of nudging, especially in cases with strong coupling. However, the nudging experiments also exhibited adverse effects. Most notably, a pronounced cold bias developed over Alaska, attributed to the model’s tendency to produce overly strong coupling between the stratosphere and upper troposphere in this region compared to observations. This bias was mitigated when nudging was extended deeper into the lower stratosphere. These findings underscore the importance of accurately representing stratosphere–troposphere coupling strength to reliably capture the surface impacts of stratospheric variability.
The Arctic stratosphere during the first half of the 2024/25 winter was characterized by an exceptionally strong polar vortex, comparable to the coldest winter of 2019/20 with record ozone depletion. In mid-February 2025, enhanced propagation of planetary wave activity over northeastern Eurasia led to a deceleration of the wind in the upper stratosphere, followed by downward wave reflection into the troposphere over Canada, northern USA, and northwestern Eurasia. This stratosphere-troposphere interaction resulted in significant surface cooling in these regions and drove the Arctic Oscillation (AO) index to a winter minimum of −5. Such strong AO anomalies, exceeding −2σ, have been observed in February over the past 25 years only in 2010, 2021, and 2025. A major sudden stratospheric warming (SSW) event in early March was preceded by a prolonged preconditioning stage of the polar vortex, characterized by intensified stratospheric vacillations between zonal winds and planetary waves. During the SSW, enhanced wave activity propagation into the stratosphere was identified over northeastern Eurasia and Europe. The amplified upward wave flux over Europe was linked to the eastward redistribution of wave activity fluxes in the upper troposphere over the North Atlantic, originating from the wave reflection region over North America. Using lidar sounding and spectral measurements of excited hydroxyl molecules OH* temperature, the stratosphere and upper mesosphere temperature variations in February–March 2025 were analyzed. Simulation with the CCM (chemistry–climate model) SOCOLv3 estimated the total chemical ozone loss in the Arctic stratosphere in winter 2024/25 as ∼50
This study examines the dynamics and systematic forecast drifts of wintertime Rossby wave breaking (RWB) events over the Northern Hemisphere in 15-day forecasts. Using ECMWF and NCEP data from the THORPEX TIGGE archive, RWBs are classified into cyclonic (CWB) and anticyclonic (AWB) types and quantified using two metrics: breaking extent (BE) and breaking amplitude (BA). Both models consistently underestimate BE and BA, with especially large biases for CWBs over the Pacific and AWBs over western North America, indicating persistent errors along major storm tracks. These drifts are largely driven by the loss of small, frequent breakings, while larger events are better preserved. To understand the dynamical origin of these biases, we define a barotropic shear index based on meridional wind shear across the jet and assess its relationship to RWB. The impact of shear depends on wave evolution: when waves remain on one side of the jet, local shear there is most influential; when waves cross the jet, weak shear on the origin side facilitates transition into a more favorable shear zone. Conditional composites show that shear regimes influence regional jet structure and associated circulation. Moreover, large-scale climate modes such as ENSO, NAO, and PNA modulate shear and thus RWB predictability. ECMWF outperforms NCEP in capturing sharp meridional wind gradients and RWB evolution, likely due to its higher resolution. These findings offer a physically grounded explanation for systematic RWB forecast errors and underscore the importance of accurately representing jet dynamics in medium-range prediction.
