Abstract
The globally averaged diurnal temperature range (DTR) has shrunk since the mid-20th century, and climate models project further shrinking. Observations indicate a slowdown or reversal of this trend in recent decades. Here, we show that DTR has a minimum for average temperatures close to 0°C. Observed DTR shrinks strongly at colder temperature, where warming shifts the average temperature toward the DTR minimum, and expands at warmer temperature, where warming shifts the average temperature away from the DTR minimum. Most, but not all climate models reproduce the minimum DTR close to average temperatures of 0°C and a stronger DTR shrinking at colder temperature. In models that reproduce the DTR minimum, DTR shrinking slows down significantly in recent decades. Models project that the global-mean DTR will shrink over the 21st century, and models with a DTR minimum close to 0°C project slower shrinking than other models.
Abstract
Wet and cold biases on the Tibetan Plateau (TP) commonly exist in global and regional climate simulations. Previous studies have explored the possible causes of wet and cold biases and contributed to reducing these biases. However, the connection between wet and cold biases remains insufficiently addressed. Our research indicates that the TP wet bias converts into positive snow amount bias not continually but efficiently and concentratedly, under the control of snow phenology in different regions. Furthermore, the complex relationship between snow amount, snow coverage and surface albedo restricts the transformation of snow amount to surface albedo bias, and thus to cold bias. Our research highlights the spatio-temporally divergent transformation of wet to cold bias on the TP during snow season, providing a novel perspective to understand the intrinsic connection between wet and cold biases and improve climate simulations on the TP.
Abstract
Microbial acetate metabolism is an important part of marine carbon cycling. We present a comprehensive study to constrain microbial acetate metabolism and its regulation in surface seawater of the northwest Pacific Ocean. We found that acetate oxidation (rate constant k: 0.016–0.506 day−1) accounted for 77.6%–99.4% of the total microbial acetate uptake, suggesting that acetate was predominantly used as a microbial energy source. Acetate also served as a significant biomass carbon source, as reflected by the elevated contribution of acetate assimilation to bacterial carbon production. Acetate turnover was largely influenced by water mass mixing and nutrient conditions. Atmospheric deposition was a source of acetate in surface water and this process can also impact the microbial acetate uptake. Microbial utilization of acetate could account for up to 25.9% of the bacterial carbon demand, suggesting the significant role of acetate metabolism in microbial carbon cycling in the open ocean.
No abstract is available for this article.
Abstract
In this paper, we demonstrate the applicability of the data-driven solar energetic particle (SEP) model, SOlar-wind with FIeld-lines and Energetic-particles (SOFIE), to simulate the acceleration and transport processes of SEPs and make forecast of the energetic proton flux at energies ≥10 MeV that will be observed near 1 AU. The SOFIE model is built upon the Space Weather Modeling Framework developed at the University of Michigan. In SOFIE, the background solar wind plasma in the solar corona and interplanetary space is calculated by the Stream-Aligned Aflvén Wave Solar-atmosphere Model(-Realtime) driven by the near-real-time hourly updated Global Oscillation Network Group solar magnetograms. In the background solar wind, coronal mass ejections (CMEs) are launched by placing an force-imbalanced magnetic flux rope on top of the parent active region, using the Eruptive Event Generator using Gibson-Low model. The acceleration and transport processes are modeled by the Multiple-Field-Line Advection Model for Particle Acceleration. In this work, nine SEP events (Solar Heliospheric and INterplanetary Environment challenge/campaign events) are modeled. The three modules in SOFIE are validated and evaluated by comparing with observations, including the steady-state background solar wind properties, the white-light image of the CMEs, and the flux of solar energetic protons, at energies of ≥10 MeV.
Abstract
In this study, the thermospheric mass density (TMD) features observed by the CHAllenging Minisatellite Payload between 2002 and 2010 were extracted using deep learning (DL) technology; the TMD features were then mapped and modeled with the Interplanetary environment information (IEI), solar radiation, and geomagnetic indices. The DL model was used to simulate the TMD features during Day of Year (DOY) 222–241 in 2014, a period that experienced complex solar-terrestrial environmental variations. We explore the TMD features under different solar-terrestrial environmental conditions and discuss the effects of various inputs by comparing the DL simulation results with satellite observations from Gravity Recovery and Climate Experiment-A and Swarm-A, as well as the simulation results from Jacchia-Bowman 2008, Naval Research Laboratory Mass Spectrometer Incoherent Scatter radar model 2.1, and Drag Temperature Model 2013. These results show that the DL model can better capture the TMD features after adding IEI. Part of these TMD features, including the high-latitude TMD enhancement during the space hurricane event (DOY 232, 2014) and global TMD variations under complex solar-terrestrial environmental disturbances (DOY 222–225, 2014), cannot be well described by the geomagnetic indices. The DL model indicates that the east-west component of the interplanetary magnetic field (IMF By) has a great impact on TMD variations, and its modulation is different from the typical energy injection process during storms. Our results emphasize the crucial influence of IEI on TMD under both geomagnetic disturbances and quiet conditions.
Abstract
The Earth's ionosphere undergoes regular intra-annual variations (IAVs) characterized by two peaks and troughs around the equinoxes and solstices. This phenomenon is crucial for analyzing the ionospheric response to geomagnetic storms. This study presents a comprehensive analysis of the IAVs contributed by diurnal and semidiurnal migrating tides (DW1 and SW2) using Global Ionospheric Maps (GIMs) data from 2017 to 2021. Through data stacking techniques, the seasonal variability and splitting phenomenon of DW1 and SW2 across different latitudes are examined. The findings indicate that the splitting of these tides can be attributed to their quasi-periodic variations, predominantly composed of annual oscillation (AO) and semiannual oscillation (SAO). The combination of DW1, SW2, and their side-band harmonics results in beats with annual and semiannual periodicities, enabling the restoration of the seasonal variations in DW1 and SW2. The ionospheric day-to-day variations were reconstructed by superimposing DW1 and SW2, and their IAVs were evaluated using the envelope method. Comparison with IAVs driven by Earth's orbital geometry reveals that tide-driven IAVs are more significant, and both exhibit solar activity dependence. The results advance the understanding of ionospheric variability, emphasizing the critical role of tidal contributions.
Abstract
This paper investigates time-lag effects of ionospheric response to two severe geomagnetic storms (Kp = 8) on the degradation of kinematic precise point positioning (PPP) solutions, utilizing over 5500 Global Navigation Satellite Systems (GNSS) stations distributed worldwide. Focusing on these two severe geomagnetic storms that occurred during solar cycle 24, the study employs an open-source positioning software package, namely RTKLIB, to derive the PPP solutions. The findings reveal significant variations in time lags across different magnetic latitudes. These variations are driven by ionospheric responses to a southward interplanetary magnetic field and subsequent decreases in the SMY-H index during the 2015 St. Patrick's Day Storm and the 2017 September 7–8 Storm. Specifically, at high latitudes, PPP degradation primarily manifests during the main phase of the storm, resulting in delays spanning from several minutes to 1–2 hr after the sudden onset of the storm. In contrast, mid- and low latitudes exhibit a wider range of delays extending up to tens of hours. Notably, rapid positioning degradation is observed predominantly at the magnetic local time noon and midnight sectors. The study discusses these time lag effects concerning the intensity of various ionospheric disturbances triggered by the interactions among the solar wind, magnetosphere, and ionosphere during geomagnetic storms. The insights obtained from this research have the potential to be integrated into physics-based and machine-learning models to enhance forecasting capabilities of space weather impacts.
No abstract is available for this article.
Abstract
In this study, we investigate the impacts of the 17 March 2013 strong geomagnetic storm on the wholly coupled Solar Wind-Magnetosphere-Ionosphere-Thermosphere system-of-systems. Obtained from multipoint observations, our new results show (1) the solar-wind Alfven waves propagating antisunward in the sheath region and (2) oscillating solar wind interplanetary magnetic field (IMF) and electric (E) field (IEF EY) that powered (3) rigorous dayside and nightside flux transfer events (FTEs) when (4) the nightside-reconnection-related short circuiting led to fast-time Subauroral Ion Drifts (SAID) and Subauroral Polarization Streams (SAPS) E field development across the inner-magnetosphere plasmapause where the solar-wind Alfven waves (4) transitioned into kinetic Alfven waves (5) fueling the hot zone. Also, the antisunward solar-wind Alfven waves (6) drove enhanced large-scale region-1 field-aligned currents creating (7) undershielding conditions (8) allowing the dawn-to-dusk convection E field's earthward penetration, and (9) generated increased solar-wind kinetic energy, which became deposited (10) to the ionosphere increasing the ionospheric electron temperature (by the downward flowing suprathermal electron fluxes) and (11) to the thermosphere oscillating the neutral winds and increasing the neutral temperature, and finally leading to (12) the development of bright stable auroral red (SAR) arcs in (13) the enhanced SAID/SAPS flow channels (FCs) developed during FTEs, (14) demonstrated with FC-2 and FC-3 events, in the enhanced polar convection that (15) the Rice Convection Model could reproduce. Finally, we conclude the antisunward-propagating large-amplitude solar-wind Alfven waves' ultimate significant role in creating the favorable conditions for the various phenomena documented with the new observational results (1–14).
No abstract is available for this article.
Abstract
The low frictional strength of smectite minerals, such as montmorillonite, is thought to play a critical role in controlling the rheology and the stability of clay-rich faults. In this study, we perform molecular dynamics simulations on a model clay system. Clay platelets are simplified as oblate ellipsoids interacting via the Gay-Berne potential. We study the rheology and structural development during shear in this model system, which is sheared at constant strain rates for 10 strains after compression and equilibrium. We find that the system exhibits velocity-strengthening behavior over a range of normal stresses from 1.68 to 56.18 MPa and a range of strain rates from 6.93 × 105 to 6.93 × 108/s. The relationship between shear stress and strain rate follows the Herschel-Bulkley model. Shear localization is observed at lower strain rates despite the velocity-strengthening friction, while homogeneous shear is realized at higher strain rates. The structure change due to shear is analyzed from various aspects: the porosity, particle orientation, velocity profile, and the parallel radial distribution function. We find that particle rearrangement and compaction dominate at the early stage of shear when the shear stress increases. The shear band starts to form in the later stage as the shear stress decreases and relaxes to a steady-state value. The structural development at low strain rates is similar to previous experimental observations. The stacking structure is reduced during shear and restores logarithmically with time in the rest period.
Abstract
The doldrums are regions of low wind speeds and variable wind directions in the deep tropics that have been known for centuries. Although the doldrums are often associated with the Intertropical Convergence Zone (ITCZ), the exact relationship remains unclear. This study re-examines the relationship between low-level convergence and the Atlantic doldrums. By analyzing the frequency distribution of low wind speed events in reanalysis and buoy data, we show that the doldrums are largely confined between the edges of the ITCZ marked by enhanced surface convergence. While the region between the edges is a region of high time-mean precipitation, low wind speed events occur in the absence of precipitation. Based on these results, we hypothesize that low wind speed events occur in regions of low level divergence rather than convergence.
Abstract
This study examines the differences related to microphysical properties of ice in thunderstorms over the Amazon and Congo Basin using the Precipitation Feature (PF) data sets derived from passive microwave and radar observations from the Tropical Rainfall Measuring Mission and Global Precipitation Mission Core Satellites. Analysis reveals that Amazon thunderstorms are likely composed of ice crystals smaller but more numerous than those in the Congo Basin, resulting in half as many flashes per PF on average in the Amazon, for similar Ice Water Content (IWC) or Area of 30 dBZ at −10°C (Acharge). The increase of the flash count following an increase of the IWC (Acharge) is only 72% (61%) as effective in the Amazon as it would be in the Congo Basin area. PFs with similar 30 dBZ radar echo top heights exhibit lower Brightness Temperatures (TBs) in the 85/89, 165, and 183 GHz frequencies over the Amazon, indicating more numerous smaller ice particles compared to those over the Congo Basin, which tend to show colder TBs at 37 GHz, possibly due to more numerous large graupel or hail particles. Comparisons of TBs in PFs with similar 30 dBZ echo top temperature between the Amazon and 3 × 3º global grids show that the median TB in Amazon is higher than that in most oceanic areas but is comparable to areas having high oceanic lightning activity (e.g., South Pacific Convergence Zone). It suggests that systems in the Amazon have similarities with maritime precipitation systems, yet with distinct characteristics indicative of land systems.
A comprehensive evaluation of enhanced temperature influence on gas and aerosol chemistry in the lamp-enclosed oxidation flow reactor (OFR) system
Tianle Pan, Andrew T. Lambe, Weiwei Hu, Yicong He, Minghao Hu, Huaishan Zhou, Xinming Wang, Qingqing Hu, Hui Chen, Yue Zhao, Yuanlong Huang, Doug R. Worsnop, Zhe Peng, Melissa A. Morris, Douglas A. Day, Pedro Campuzano-Jost, Jose-Luis Jimenez, and Shantanu H. Jathar
Atmos. Meas. Tech., 17, 4915–4939, https://doi.org/10.5194/amt-17-4915-2024, 2024
This study systematically characterizes the temperature enhancement in the lamp-enclosed oxidation flow reactor (OFR). The enhancement varied multiple dimensional factors, emphasizing the complexity of temperature inside of OFR. The effects of temperature on the flow field and gas- or particle-phase reaction inside OFR were also evaluated with experiments and model simulations. Finally, multiple mitigation strategies were demonstrated to minimize this temperature increase.
ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data
Frédéric P. A. Vogt, Loris Foresti, Daniel Regenass, Sophie Réthoré, Néstor Tarin Burriel, Mervyn Bibby, Przemysław Juda, Simone Balmelli, Tobias Hanselmann, Pieter du Preez, and Dirk Furrer
Atmos. Meas. Tech., 17, 4891–4914, https://doi.org/10.5194/amt-17-4891-2024, 2024
ampycloud is a new algorithm developed at MeteoSwiss to characterize the height and sky coverage fraction of cloud layers above aerodromes via ceilometer data. This algorithm was devised as part of a larger effort to fully automate the creation of meteorological aerodrome reports (METARs) at Swiss civil airports. The ampycloud algorithm is implemented as a Python package that is made publicly available to the community under the 3-Clause BSD license.
High-resolution wind speed measurements with quadcopter uncrewed aerial systems: calibration and verification in a wind tunnel with an active grid
Johannes Kistner, Lars Neuhaus, and Norman Wildmann
Atmos. Meas. Tech., 17, 4941–4955, https://doi.org/10.5194/amt-17-4941-2024, 2024
We use a fleet of multicopter drones to measure wind. To improve the accuracy of this wind measurement and to evaluate this improvement, we conducted experiments with the drones in a wind tunnel under various conditions. This wind tunnel can generate different kinds and intensities of wind. Here we measured with the drones and with other sensors as a reference and compared the results. We were able to improve our wind measurement and show how accurately it works in different situations.
Retrieving cloud base height and geometric thickness using the oxygen A-band channel of GCOM-C/SGLI
Takashi M. Nagao, Kentaroh Suzuki, and Makoto Kuji
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-141,2024
Preprint under review for AMT (discussion: open, 0 comments)
In satellite remote sensing, estimating cloud base height (CBH) is more challenging than estimating cloud top height because the cloud base is obscured by the cloud itself. We developed an algorithm using the specific channel (known as the oxygen A-band channel) of the SGLI instrument on JAXA’s GCOM-C satellite to estimate CBH together with other cloud properties. This algorithm can provide global distributions of CBH across various cloud types, including liquid, ice, and mixed-phase clouds.
Deep-learning-driven simulations of boundary layer clouds over the Southern Great Plains
Tianning Su and Yunyan Zhang
Geosci. Model Dev., 17, 6319–6336, https://doi.org/10.5194/gmd-17-6319-2024, 2024
Using 2 decades of field observations over the Southern Great Plains, this study developed a deep-learning model to simulate the complex dynamics of boundary layer clouds. The deep-learning model can serve as the cloud parameterization within reanalysis frameworks, offering insights into improving the simulation of low clouds. By quantifying biases due to various meteorological factors and parameterizations, this deep-learning-driven approach helps bridge the observation–modeling divide.
Mixed-precision computing in the GRIST dynamical core for weather and climate modelling
Siyuan Chen, Yi Zhang, Yiming Wang, Zhuang Liu, Xiaohan Li, and Wei Xue
Geosci. Model Dev., 17, 6301–6318, https://doi.org/10.5194/gmd-17-6301-2024, 2024
This study explores strategies and techniques for implementing mixed-precision code optimization within an atmosphere model dynamical core. The coded equation terms in the governing equations that are sensitive (or insensitive) to the precision level have been identified. The performance of mixed-precision computing in weather and climate simulations was analyzed.
