Search Results (56)

The geographical response of western North Pacific subtropical high (SH) to environmental conditions such as the El Niño-Southern Oscillation (ENSO) and global warming has been one of the main concerns with respect to extreme events induced by tropical convections. By considering observed outgoing longwave radiation (OLR) as the strength of subtropical high, this study attempts to further understand the geographical response of SH strength to ENSO and global warming. Here, “SH strength” is defined as the inhibition of regional convections under SH environment. A meridional seesaw pattern among SH strength anomalies is found at 130°–175° E. In addition, the La Niña environment with weaker convections at lower latitudes is characterized by farther westward expansion of SH but with a weaker strength. Conversely, the El Niño environment with stronger convections at lower latitudes leads to shrunken SH but with a greater strength. The influence of the seesaw mechanism appears to be modulated by global warming. The western North Pacific subtropical high strengthens overall under warming in both the La Niña and El Niño environments. This suggests that the weakening effect by drier tropics is largely offset by anomalous highs induced by a warming atmosphere. It is most remarkable that the highest SH strengths appear in a warmer El Niño environment. The finding implies that every new El Niño environment may experience the driest atmosphere ever in the subtropics under global warming. The value of this study lies in the fact that OLR effectively illustrates how the ENSO variation and global warming bring the zonally undulating strength of boreal-summer SH. Full article

Earth’s energy imbalance (EEI) is a major indicator of climate change. Its metrics are top of the atmosphere radiation imbalance (EEI TOA) and net internal heat uptake. Both EEI and temperature are expected to respond gradually to forcing on annual timescales. This expectation was tested by analyzing regime changes in the inputs to EEI TOA along with increasing ocean heat content (OHC). Outward longwave radiation (OLR) displayed rapid shifts in three observational and two reanalysis records. The reanalysis records also contained shifts in surface fluxes and temperature. OLR, outward shortwave radiation (OSR) and TOA net radiation (Net) from the CERES Energy Balanced and Filled Ed-4.2.1 (2001–2023) record and from 27 CMIP5 historical and RCP4.5 forced simulations 1861–2100, were also analyzed. All variables from CERES contained shifts but the record was too short to confirm regime changes. Contributions of OLR and OSR to net showed high complementarity over space and time. EEI TOA was −0.47 ± 0.11 W m⁻² in 2001–2011 and −1.09 ± 0.11 W m⁻² in 2012–2023. Reduced OSR due to cloud feedback was a major contributor, coinciding with rapid increases in sea surface temperatures in 2014. Despite widely varying OLR and OSR, 26/27 climate models produced stable regimes for net radiation. EEI TOA was neutral from 1861, shifting downward in the 26 reliable records between 1963 and 1995, with 25 records showing it stabilizing by 2039. To investigate heat uptake, temperature and OHC 1955/57–2023 was analyzed for regime change in the 100 m, 700 m and 2000 m layers. The 100 m layer, about one third of total heat content, was dominated by regimes. Increases became more gradual with depth. Annual changes between the 700 m layer and 1300 m beneath were negatively correlated (−0.67), with delayed oscillations during lag years 2–9. Heat uptake at depth is dynamic. These changes reveal a complex thermodynamic response to gradual forcing. We outline a complex arrangement of naturally evolved heat engines, dominated by a dissipative heat engine nested within a radiative engine. EEI is a property of the dissipative heat engine. This far-from-equilibrium natural engine has evolved to take the path of least resistance while being constrained by its maximum power limit (~2 W m⁻²). It is open to the radiative engine, receiving solar radiation and emitting scattered shortwave and longwave radiation. Steady states maximize entropy within the dissipative engine by regulating spatial patterns in surface variables that influence outgoing OLR and OSR. Regime shifts to warmer climates balance the cost of greater irreversibility with increased energy rate density. The result is the regulation of EEI TOA through a form of thermodynamic metabolism. Full article

Projecting future changes in monsoon rainfall is crucial for effective water resource management, food security, and livestock sustainability in South Asia. This study assesses precipitation, total cloud cover (categorized by cloud top pressure), and outgoing longwave radiation (OLR) across the region using Coupled Model Intercomparison Project Phase 6 (CMIP6) data. A multi-model ensemble (MME) approach is employed to analyze future projections under the Shared Socio-Economic Pathway (SSP5-8.5) scenario, which assumes radiative forcing will reach 8.5 W/m² by 2100. The MME projects a ~1.5 mm/day increase in total rainfall during 2081–2100. Convective and stratiform precipitation are expected to expand spatially, with convective rainfall increasing from 3 mm/day in historical simulations to 3.302 mm/day in the far future. Stratiform precipitation also shows an increase from 0.822 mm/day to 0.962 mm/day over the same period. A notable decrease in OLR (~60 W/m² along the Western Ghats) and an increase in high cloud cover suggest intensified monsoon rainfall. The pattern correlation coefficient (PCC) reveals reduced OLR in future scenarios (PCC ~0.77 vs. ~0.81 historically), likely due to cloud feedback mechanisms. These results highlight enhanced monsoonal activity under warming scenarios, with implications for regional climate adaptation. Full article

China’s FengYun 3E (FY3E) meteorological satellite, launched in 2021, is equipped with advanced instruments for comprehensive Earth observations. In this study, we compared outgoing longwave radiation (OLR) measurements from the FY3E satellite (FY3E OLR) and from a series of satellites operated by the National Oceanic and Atmospheric Agency (NOAA, United States of America; hereafter NOAA OLR) and analyzed the spatiotemporal differences between the datasets. We designed a new correction model, “DeepFM”, implementing both a factorization machine algorithm and a deep artificial neural network to minimize daily mean differences between the datasets. Then, we evaluated the spatiotemporal consistency between the corrected FY3E OLR and NOAA OLR data. The DeepFM model effectively reduced daily mean differences: after correction, the daily mean absolute bias and root-mean-square error decreased from 7.4 W/m² to 4.2 W/m² and from 10.3 W/m² to 6.3 W/m², respectively, indicating a notably improved spatiotemporal consistency between the corrected FY3E OLR and NOAA OLR data. Subsequently, we merged these datasets to generate a long-term OLR dataset suitable for climate analyses. This study provides a robust technological basis and innovative methodology for the dedicated application of China meteorological satellites to climate science. Full article

Time series satellite data, coupled with available ground-based observations, enable geophysicists to survey earthquake precursors in areas of strong geotectonic activity. This paper is focused on pre-seismic atmospheric disturbances resulting from the stress accumulated during the seismogenic process related to the 6 February 2023 Kahramanmaras doublet earthquake sequence in Türkiye. We investigated the pre- and post-seismic anomalies of multiple precursors of different spatiotemporal patterns from MODIS Terra/Aqua and NOAA-AVHRR satellite data (air temperature at 2 m height—AT, air relative humidity—RH, and air pressure—AP, surface outgoing long-wave radiation—OLR, and land surface temperature—LST). Pre-seismic recorded anomalies of AT within seven months and OLR within one month before the main shocks suggested the existence of the preparatory process of the Kahramanmaras doublet earthquake. The 8-Day LST_Day and LST_night data evidenced pre-seismic and post-seismic thermal anomalies for both the Pazarcik and Elbistan earthquakes. The results of this study highlight that the spatiotemporal evolution of earthquake precursors can be important information for updating the seismic hazard in geotectonic active areas. Full article

The impact of the quasi-biennial oscillation (QBO) and Madden–Julian oscillation (MJO) on the dynamics of major sudden stratospheric warmings (SSWs) observed in the winters of 2018, 2019, and 2021 is investigated. Using data from the MERRA-2 reanalysis, we analyze the daily mean variability of critical atmospheric parameters at the 10 hPa level, including zonal mean polar cap temperature, zonal mean zonal wind, and the amplitudes of planetary waves 1 and 2. The results reveal dramatic increases in polar cap temperature and significant wind reversals during the SSW events, particularly in 2018. The analysis of planetary wave (PW) amplitudes demonstrates intensified wave activity coinciding with the onset of SSWs, underscoring the pivotal role of PWs in these stratospheric disruptions. Further examination of outgoing long-wave radiation (OLR) anomalies highlights the influence of QBO phases on tropical convection patterns. During westerly QBO (w-QBO) phases, enhanced convective activity is observed in the western Pacific, whereas the easterly QBO (e-QBO) phase shifts convection patterns to the maritime continent and central Pacific. This modulation by QBO phases influences the MJO’s role during SSWs, affecting tropical and extra-tropical weather patterns. The day-altitude variability of upward heat flux reveals distinct spatiotemporal patterns, with pronounced warming in the polar regions and mixed heat flux patterns in low latitudes. The differences observed between the SSWs of 2017–2018 and 2018–2019 are likely related to the varying QBO phases, emphasizing the complexity of heat flux dynamics during these events. The northern annular mode (NAM) index analysis shows varied responses to SSWs, with stronger negative anomalies observed during the e-QBO phase compared to the w-QBO phases. This variability highlights the significant role of the QBO in shaping the stratospheric and tropospheric responses to SSWs, impacting surface weather patterns and the persistence of stratospheric anomalies. Overall, the study demonstrates the intricate interactions between stratospheric dynamics, QBO, and MJO during major SSW events, providing insights into the broader implications of these atmospheric phenomena on global weather patterns. Full article

The Pattern Informatics Method (PI) was initially developed for medium-to-long-term earthquake prediction by analyzing changes in seismic activity. It has since been refined and extended to identify ionospheric anomalies associated with earthquakes. Notable advancements include the development of modified and improved methods, which have demonstrated their capability to detect significant short-term and ionospheric anomalies preceding earthquake events. In this study, the IPI method was applied to infrared satellite observation data for the first time, and a new algorithm for extracting short-term and imminent anomalies from infrared earthquakes was explored based on the IPI method, from which we obtained the MIPI (Modified Improved Pattern Informatics Method). Using 1° × 1° nighttime Outgoing Longwave Radiation (OLR) data from NOAA_18 satellites of the National Oceanic and Atmospheric Administration’s Climate Prediction Center (NOAA-CPC) of the United States, the evolution of OLR anomalies before the Ridgecrest Ms 6.9 earthquake in the United States on 6 July 2019 as recorded by the China Earthquake Networks Center (CENC) and the Maduo Ms 7.4 earthquake in China on 21 May 2021 as recorded by the China Earthquake Networks Center (CENC) were studied. In order to make the IPI method suitable for the calculation of OLR data, two modifications were made to the IPI algorithm: (1) the quartile method was applied for automatically determining the abnormal changes in the OLR observation data and they were used as the input data instead of ionospheric data; (2) the standard deviation of the multi-year OLR residual data of each grid was used instead of the maximum anomaly index used in the original method to re-assign and obtain the relative anomaly index, and finally the anomaly evolution time series diagram was drawn. The results show the following: (1) The MIPI method can effectively extract short-term and imminent OLR anomalies prior to earthquakes. (2) Short-term and imminent OLR anomalies appeared about two weeks before each earthquake and lasted until the earthquake occurrence, disappearing after the earthquake. During this process, the anomalies exhibited a certain evolutionary trend. (3) The short-term and imminent OLR anomalies prior to each earthquake were distributed near the epicenter or near the seismogenic fault, about 200 KM away from the epicenters. The above results are similar to the spatiotemporal evolution characteristics of seismic infrared short-term anomalies previously studied, which indicates that the MIPI method can effectively extract seismic infrared anomalies and might provide a practical method for the extraction of seismic infrared short-term and imminent anomalies. Full article

The current study looks at how the characteristics of Arabian Sea tropical cyclones (TCs) change over time. The results show that in the pre-monsoon (April–June) and the post-monsoon (October–December), the activity of TCs > 34 knots, including cyclonic storm (CS), severe cyclonic storm (SCS), very severe cyclonic storm (VSCS), extreme severe cyclonic storm (ESCS), and super cyclonic storm (Sup. CS), has significantly increased, while the tendency of TCs < 34 knots, depressions and deep depressions (Ds) over the Arabian Sea has only slightly increased. Most of the TC activity in the first two decades (1982–2001) over the Arabian Sea activated on the eastern side, while in the last two decades (2002–2021), there was an expansion toward the southwest region of the Arabian Sea, especially in the post-monsoon season. The composite analysis of environmental parameters over the Arabian Sea reveals that the negative anomalies of outgoing longwave radiation (OLR) and the positive anomalies of relative humidity at 500 hPa (RH–500 hPa) in the first decade (1982–1991) and the second decade (1992–2001) are more concentrated on the eastern side of the Arabian Sea, leading to increased activity for TCs. Decades three (2002–2011) and four (2012–2021) demonstrated a wide distribution of weak vertical wind shear (VWS) and strong convection (OLR and RH–500 hPa) over the Arabian Sea basin. This led to TCs occurring more frequently and stronger, especially in the post-monsoon season. SST over the Arabian Sea was sufficient for tropical storm activity (≥26.5 °C) for both typical seasons. Full article

Atmospheric gravity waves (GWs) can strongly modulate middle atmospheric circulation and can be a significant factor for the coupling between the lower atmosphere and the middle atmosphere. GWs are difficult to resolve in global atmospheric models due to their small scale; thus, GW observations play an important role in middle atmospheric studies. The climatology of GW variance and momentum in the low-latitude mesosphere and lower thermosphere (MLT) region are revealed using multiple meteor radars, which are located at Kunming (25.6°N, 103.8°E), Sanya (18.4°N, 109.6°E), and Fuke (19.5°N, 109.1°E). The climatology and longitudinal variations in GW momentum fluxes and variance over the low-latitude region are reported. The GWs show strong seasonal variations and can greatly control the mesospheric horizontal winds via modulation of the quasi-geostrophic balance and momentum deposition. The different GW activities between Kunming and Sanya/Fuke are possibly consistent with the unique prevailing surface winds over Kunming and the convective system over the Tibetan Plateau according to the European Centre for Medium-Range Weather Forecasts (ECMWF), Reanalysis v5 (ERA5) data, and outgoing longwave radiation (OLR) data. These findings provide insight for better understanding the coupling between the troposphere and mesosphere. Full article

A survey of African weather and climate extremes in the period 1970–2023 reveals spatial and temporal patterns of intense dry and wet spells, associated with meteorological conditions and consequences. Seasonal wind storms occur along coasts facing the Mozambique Channel, the Gulf of Guinea, the Mediterranean, and the Southern Ocean. Desiccating evaporation is found along the edge of the Sahara and Kalahari Deserts, as well as in lowland subtropical river valleys. The Palmer Drought Severity Index (PDSI) and net outgoing longwave radiation (OLR) reflect precipitation–evaporation balance and guide regional evaluation. Temporal fluctuations are dominated by inter-decadal oscillations and drying/moistening trends over Southeast/West Africa, respectively. Localized floods and droughts are frequent, but widespread impacts are rare, suggesting that the transfer of resources from surplus to deficit regions is possible. Various case studies focus on (i) tropical cyclone impacts, (ii) monsoon moisture flux, and (iii) coastal upwelling. African communities have become resilient in the face of extreme weather and have shown that adaptation is possible, but further mitigating efforts are needed so that macro-economic progress does not come with harmful secondary consequences. Full article

Studies have demonstrated the potential of satellite thermal infrared observations to detect anomalous signals preceding large earthquakes. However, the lack of well-defined precursory characteristics and inherent complexity and stochasticity of the seismicity continue to impede robust earthquake forecasts. This study investigates the potential of pre-seismic thermal anomalies, derived from five satellite-based geophysical parameters, i.e., skin temperature, air temperature, total integrated column water vapor burden, outgoing longwave radiation (OLR), and clear-sky OLR, as valuable indicators for global earthquake forecasts. We employed a spatially self-adaptive multiparametric anomaly identification scheme to refine these anomalies, and then estimated the posterior probability of an earthquake occurrence given observed anomalies within a Bayesian framework. Our findings reveal a promising link between thermal signatures and global seismicity, with elevated forecast probabilities exceeding 0.1 and significant probability gains in some strong earthquake-prone regions. A time series analysis indicates probability stabilization after approximately six years. While no single parameter consistently dominates, each contributes precursory information, suggesting a promising avenue for a multi-parametric approach. Furthermore, novel anomaly indices incorporating probabilistic information significantly reduce false alarms and improve anomaly recognition. Despite remaining challenges in developing dynamic short-term probabilities, rigorously testing detection algorithms, and improving ensemble forecast strategies, this study provides compelling evidence for the potential of thermal anomalies to play a key role in global earthquake forecasts. The ability to reliably estimate earthquake forecast probabilities, given the ever-present threat of destructive earthquakes, holds considerable societal and ecological importance for mitigating earthquake risk and improving preparedness strategies. Full article

This study examines the response of the thermal channel within the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) mechanism during the notable earthquake in Crete, Greece, on 27 September 2021. We analyze spatio-temporal profiles of Surface Latent Heat Flux (SLHF), Outgoing Longwave Radiation (OLR), and Atmospheric Chemical Potential (ACP) using reanalysis data from the National Oceanic and Atmospheric Administration (NOAA) satellite. Anomalies in these parameters are computed by removing the background profile for a non-seismic condition. Our findings reveal a substantial anomalous increase in these parameters near the earthquake’s epicenter 3 to 7 days before the main shock. The implications of these observations contribute to a deeper understanding of the LAIC mechanism’s thermal channel in seismic events. Full article

Earth observations from remotely sensed data have a substantial impact on natural hazard surveillance, specifically for earthquakes. The rapid emergence of diverse earthquake precursors has led to the exploration of different methodologies and datasets from various satellites to understand and address the complex nature of earthquake precursors. This study presents a novel technique to detect the ionospheric and atmospheric precursors using machine learning (ML). We examine the multiple precursors of different spatiotemporal nature from satellites in the ionosphere and atmosphere related to the Turkey earthquake on 6 February 2023 (Mw 7.8), in the form of total electron content (TEC), land surface temperature (LST), sea surface temperature (SST), air pressure (AP), relative humidity (RH), outgoing longwave radiation (OLR), and air temperature (AT). As a confutation analysis, we also statistically observe datasets of atmospheric parameters for the years 2021 and 2022 in the same epicentral region and time period as the 2023 Turkey earthquake. Moreover, the aim of this study is to find a synchronized and co-located window of possible earthquake anomalies by providing more evidence with standard deviation (STDEV) and nonlinear autoregressive network with exogenous inputs (NARX) models. It is noteworthy that both the statistical and ML methods demonstrate abnormal fluctuations as precursors within 6 to 7 days before the impending earthquake over the epicenter. Furthermore, the geomagnetic anomalies in the ionosphere are detected on the ninth day after the earthquake (Kp > 4; Dst < −70 nT; ap > 50 nT). This study indicates the relevance of using multiple earthquake precursors in a synchronized window from ML methods to support the lithosphere–atmosphere–ionosphere coupling (LAIC) phenomenon. Full article

The successive tidal force (TF) at the epicenter of the Jiashi M6.6 earthquake in Xinjiang, China, was calculated for the period from 13 December 2019 to 10 February 2020. With periodic changes in tide-generating forces, the variations in the electron density (Ne) data recorded by the China Seismo-Electromagnetic Satellite (CSES) and outgoing longwave radiation (OLR) data provided by NOAA on a large scale at N25°–N55°, E65°–E135° were studied. The results show that (1) in the four cycles during which the TF changes from trough to peak, the earthquake occurred during one peak time when the OLR changed around the epicenter via calm–rise processions and in other similar TF phases, and neither an increase in the OLR nor earthquake occurred. (2) With a change in the TF, the spatiotemporal evolution of the OLR from seismogenic processes to its occurrence was as follows: microenhancement–enhancement–microattenuation–enhancement–calmness; this is consistent with the evolution of outward infrared radiation when rocks break under stress loading: microrupture–rupture–locking–accelerated rupture–rupture. (3) Ne increased significantly during the seismogenic period and was basically consistent with OLR enhancement. The results indicate that as the TF increases, the Earth’s stress accumulates at a critical point, and the OLR increases and transfers upward. The theoretical hypothesis underlying the conducted study is that the accumulated electrons on the surface cause negatively charged electrons in the atmosphere to move upward, resulting in an increase in ionospheric Ne near the epicenter, which reveals the homology of seismic stress variations in the spatial coupling process. The quasi-synchronous change process of these three factors suggests that the TF changed the process of the stress accumulation–imbalance in the interior structure of this earthquake and has the effect of triggering the earthquake, and the spatiotemporal variations in the OLR and ionospheric Ne could be indirect reflections of in situ stress. Full article

Climate change is undeniably one of the most pressing and critical challenges facing humanity in the 21st century. In this context, monitoring the Earth’s Energy Imbalance (EEI) is fundamental in conjunction with greenhouse gases (GHGs) in order to comprehensively understand and address climate change. The French Uvsq-Sat NG pathfinder mission addresses this issue through the implementation of a Six-Unit CubeSat, which has dimensions of 111.3 × 36.6 × 38.8 cm in its unstowed configuration. Uvsq-Sat NG is a satellite mission spearheaded by the Laboratoire Atmosphères, Observations Spatiales (LATMOS), and supported by the International Satellite Program in Research and Education (INSPIRE). The launch of this mission is planned for 2025. One of the Uvsq-Sat NG objectives is to ensure the smooth continuity of the Earth Radiation Budget (ERB) initiated via the Uvsq-Sat and Inspire-Sat satellites. Uvsq-Sat NG seeks to achieve broadband ERB measurements using state-of-the-art yet straightforward technologies. Another goal of the Uvsq-Sat NG mission is to conduct precise and comprehensive monitoring of atmospheric gas concentrations (CO${}_2$ and CH${}_4$) on a global scale and to investigate its correlation with Earth’s Outgoing Longwave Radiation (OLR). Uvsq-Sat NG carries several payloads, including Earth Radiative Sensors (ERSs) for monitoring incoming solar radiation and outgoing terrestrial radiation. A Near-Infrared (NIR) Spectrometer is onboard to assess GHGs’ atmospheric concentrations through observations in the wavelength range of 1200 to 2000 nm. Uvsq-Sat NG also includes a high-definition camera (NanoCam) designed to capture images of the Earth in the visible range. The NanoCam will facilitate data post-processing acquired via the spectrometer by ensuring accurate geolocation of the observed scenes. It will also offer the capability of observing the Earth’s limb, thus providing the opportunity to roughly estimate the vertical temperature profile of the atmosphere. We present here the scientific objectives of the Uvsq-Sat NG mission, along with a comprehensive overview of the CubeSat platform’s concepts and payload properties as well as the mission’s current status. Furthermore, we also describe a method for the retrieval of atmospheric gas columns (CO${}_2$, CH${}_4$, O${}_2$, H${}_2$O) from the Uvsq-Sat NG NIR Spectrometer data. The retrieval is based on spectra simulated for a range of environmental conditions (surface pressure, surface reflectance, vertical temperature profile, mixing ratios of primary gases, water vapor, other trace gases, cloud and aerosol optical depth distributions) as well as spectrometer characteristics (Signal-to-Noise Ratio (SNR) and spectral resolution from 1 to 6 nm). Full article