Antarctic sea ice plays a critical role in modulating global climate variability and in supporting polar ecosystems. However, sea ice seasonal forecasts continue to show limited skill in capturing interannual variability across key regions and seasons. Here, we evaluate seasonal predictions of Antarctic sea ice extent (SIE) from six Copernicus Climate Change Service (C3S) forecast systems, focusing on the Weddell Sea, where springtime skill deteriorates rapidly with lead time. We identify two systematic sources of model error. First, systems show excessive persistence of winter sea ice anomalies during spring compared to observations, suggesting an overdependence on ocean conditions. Second, they fail to adequately represent large-scale atmospheric circulation anomalies associated with El Niño–Southern Oscillation (ENSO) and Southern Annular Mode (SAM) interactions, underestimating Amundsen Sea Low pressure anomalies and related wind patterns. These circulation-related biases appear to originate from misrepresented atmospheric responses to tropical Pacific sea surface temperatures. Our results prompt us to revisit sea-ice–ocean couplings and better capture tropical–Antarctic teleconnections in dynamical models to improve Antarctic sea ice prediction.
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% over Siberia and the Amazon, and to error reductions of about 80% for FF emissions over Siberia, North Asia, the United States, and South Africa, if measurement bias could be eliminated. However, our OSSEs show that the presence of even a small XCO2 bias can cause serious distortion or misallocation of CO2 sources and sinks. Therefore, systematic observation bias needs to be properly addressed in retrieval and data application. We also explore the importance of adopting a large across-track swath in delivering robust CO2 flux estimates and develop an error matrix tool that could be used in future mission assessment. (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (TanSat-2). (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (CO2) (sic)(sic)(sic) (CH4) (sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic) TanSat-2 (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) CO2 (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (SIF) (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (NPP) (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (FF) (sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic)(sic) NPP (sic) FF (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (EOF) (sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (OSSEs), (sic)(sic)(sic)(sic)(sic) TanSat-2 CO2 (sic) SIF (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), TanSat-2 (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) NPP (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) 95%, (sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) CO2 (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) 80%. (sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) (XCO2) (sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic) CO2(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic). (sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic) CO2 (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic), (sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).
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 & micro;g m(-3) and daily maximum 2-m air temperature (T-max) > 35 degrees 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% of recent PM2.5 extremes. Satellite observations reveal substantial increases in major PM2.5 precursors during this time, and GEOS-Chem sensitivity experiments show that changing meteorological conditions act as primary amplifiers. The meteorological shift features a strengthened anticyclonic circulation over northern India, inducing extreme heat and southeasterly anomalies that facilitate greater pollutant transport. As a result, secondary organic aerosol formation increases markedly and organic carbon yields rise by similar to 2.3 times, thus dominating these co-extremes. These insights provide an important early warning about how circulation shifts could drive future increases in co-extremes in India.
Extreme rainfall (ER) produced by convective storms moving from mountains to plain areas in North China has long been a great challenge in short-term forecasting, since the production of rainfall is closely related to the fine structure of convection. This type of ER event struck northern Beijing on 26 July 2025, causing widespread community concern. Based on multi-source observations and convection-permitting simulation, this study attempts to reveal the thermodynamic and microphysical structures in the meso-β-scale convection system (MCS-k) that produced the ER. Observational facts show that the weakening of cold outflow together with the southeast wind in the warm sector hindered the movement of the MCS-k after travelling downhill, and the more abundant water vapor and stronger convective instability in plain areas led to its back-building development. The content of liquid water significantly increased, and more large raindrops were produced by coalescence, indicating more intense warm-rain processes. Simulation experiments show that direct contribution of the warm-rain processes dominated the production of ER, since they provided 77.8
Wildfire danger in Southern California is strongly influenced by episodic offshore wind events and extreme atmospheric dryness, conditions that are not fully captured by the standard Canadian Fire Weather Index (CFWI), which was developed for boreal forest environments using noon-based meteorological inputs and has been widely applied by various climatic and ecological regimes. This study presents a regionally calibrated adaptation of the CFWI to improve representation of wind-driven fire danger in San Diego County’s chaparral-dominated landscapes. Using high-resolution observation-based meteorological data, the CFWI was recalibrated by replacing noon observations with daily maximum temperature and wind speed, and daily minimum relative humidity, applying a region-specific day-length factor, and introducing conditional modifiers based on wind direction and humidity thresholds. Evaluation over the 2000–09 period, including extreme fire years such as 2003 and 2007, shows that the recalibrated CFWI substantially improves the ability to distinguish large wildfire days, particularly during peak Santa Ana wind seasons. A conditional Initial Spread Index adjustment based on easterly (negative zonal) winds and low relative humidity improves model performance, particularly during autumn and winter. Results also highlight strong seasonal and spatial variability, supporting a seasonally adaptive calibration strategy. This work demonstrates that physically motivated regional recalibration improves the use of the CFWI to wind-dominated fire regimes, providing a more reliable tool for operational fire danger assessment and wildfire preparedness in locations prone to extreme wind-driven wildfires.
