Abstract
We introduce a new multivariate data set that utilizes multiple spacecraft collecting in-situ and remote sensing heliospheric measurements shown to be linked to physical processes responsible for generating solar energetic particles (SEPs). Using the Geostationary Operational Environmental Satellites (GOES) flare event list from Solar Cycle (SC) 23 and part of SC 24 (1998–2013), we identify 252 solar events (>C-class flares) that produce SEPs and 17,542 events that do not. For each identified event, we acquire the local plasma properties at 1 au, such as energetic proton and electron data, upstream solar wind conditions, and the interplanetary magnetic field vector quantities using various instruments onboard GOES and the Advanced Composition Explorer spacecraft. We also collect remote sensing data from instruments onboard the Solar Dynamic Observatory, Solar and Heliospheric Observatory, and the Wind solar radio instrument WAVES. The data set is designed to allow for variations of the inputs and feature sets for machine learning (ML) in heliophysics and has a specific purpose for forecasting the occurrence of SEP events and their subsequent properties. This paper describes a data set created from multiple publicly available observation sources that is validated, cleaned, and carefully curated for our ML pipeline. The data set has been used to drive the newly-developed Multivariate Ensemble of Models for Probabilistic Forecast of SEPs (MEMPSEP; see MEMPSEP-I (Chatterjee et al., 2024, https://doi.org/10.1029/2023SW003568) and MEMPSEP-II (Dayeh et al., 2024, https://doi.org/10.1029/2023SW003697) for accompanying papers).
Abstract
The Sun continuously affects the interplanetary environment through a host of interconnected and dynamic physical processes. Solar flares, Coronal Mass Ejections (CMEs), and Solar Energetic Particles (SEPs) are among the key drivers of space weather in the near-Earth environment and beyond. While some CMEs and flares are associated with intense SEPs, some show little to no SEP association. To date, robust long-term (hours-days) forecasting of SEP occurrence and associated properties (e.g., onset, peak intensities) does not effectively exist and the search for such development continues. Through an Operations-2-Research support, we developed a self-contained model that utilizes a comprehensive data set and provides a probabilistic forecast for SEP event occurrence and its properties. The model is named Multivariate Ensemble of Models for Probabilistic Forecast of Solar Energetic Particles (MEMPSEP). MEMPSEP workhorse is an ensemble of Convolutional Neural Networks that ingests a comprehensive data set (MEMPSEP-III by Moreland et al. (2024, https://doi.org/10.1029/2023SW003765)) of full-disc magnetogram-sequences and in situ data from different sources to forecast the occurrence (MEMPSEP-I—this work) and properties (MEMPSEP-II by Dayeh et al. (2024, https://doi.org/10.1029/2023SW003697)) of a SEP event. This work focuses on estimating true SEP occurrence probabilities achieving a 2.5% improvement in reliability and a Brier score of 0.14. The outcome provides flexibility for the end-users to determine their own acceptable level of risk, rather than imposing a detection threshold that optimizes an arbitrary binary classification metric. Furthermore, the model-ensemble, trained to utilize the large class-imbalance between events and non-events, provides a clear measure of uncertainty in our forecast.
Abstract
Solar climate intervention refers to a group of methods for reducing climate risks associated with anthropogenic warming by reflecting sunlight. Marine cloud brightening (MCB), one such approach, proposes to inject sea-salt aerosol into one or more regional marine boundary layer to increase marine cloud reflectivity. Here, we assess the potential influence of various MCB experiments on Africa's climate using simulations from the Community Earth System Model (CESM2) with the Community Atmosphere Model (CAM6) as its atmospheric component. We analyzed four idealized MCB experiments under a medium-range background forcing scenario (SSP2-4.5), which brighten clouds over three subtropical ocean regions: (a) Northeast Pacific (MCBNEP); (b) Southeast Pacific (MCBSEP); (c) Southeast Atlantic (MCBSEA); and (d) these three regions simultaneously (MCBALL). Our results suggest that the climate impacts of MCB in Africa are highly sensitive to the deployment region. MCBSEP would produce the strongest global cooling effect and thus could be the most effective in decreasing temperatures, increasing precipitation, and reducing the intensity and frequency of temperature and precipitation extremes across most parts of Africa, especially West Africa, in the future (2035–2054) compared to the historical climate (1995–2014). MCB in other regions produces less cooling and wetting despite similar radiative forcings. While the projected changes under MCBALL are similar to those of MCBSEP, MCBNEP and MCBSEA could see more residual warming and induce a warmer future than under SSP2-4.5 in some regions across Africa. All MCB experiments are more effective in cooling maximum temperature and related extremes than minimum temperature and related extremes.
Closing the gap in the tropics: the added value of radio-occultation data for wind field monitoring across the Equator
Julia Danzer, Magdalena Pieler, and Gottfried Kirchengast
Atmos. Meas. Tech., 17, 4979–4995, https://doi.org/10.5194/amt-17-4979-2024, 2024
We investigated the potential of radio occultation (RO) data for climate-oriented wind field monitoring, focusing on the equatorial band within ±5° latitude. In this region, the geostrophic balance breaks down, and the equatorial balance approximation takes over. The study encourages the use of RO wind fields for mesoscale climate monitoring for the equatorial region, showing a small improvement in the troposphere when including the meridional wind in the zonal-mean total wind speed.
Shortwave Array Spectroradiometer-Hemispheric (SAS-He): design and evaluation
Evgueni Kassianov, Connor J. Flynn, James C. Barnard, Brian D. Ermold, and Jennifer M. Comstock
Atmos. Meas. Tech., 17, 4997–5013, https://doi.org/10.5194/amt-17-4997-2024, 2024
Conventional ground-based radiometers commonly measure solar radiation at a few wavelengths within a narrow spectral range. These limitations prevent improved retrievals of aerosol, cloud, and surface characteristics. To address these limitations, an advanced ground-based radiometer with expanded spectral coverage and hyperspectral capability is introduced. Its good performance is demonstrated using reference data collected over three coastal regions with diverse types of aerosols and clouds.
Analysis of model error in forecast errors of extended atmospheric Lorenz 05 systems and the ECMWF system
Hynek Bednář and Holger Kantz
Geosci. Model Dev., 17, 6489–6511, https://doi.org/10.5194/gmd-17-6489-2024, 2024
The forecast error growth of atmospheric phenomena is caused by initial and model errors. When studying the initial error growth, it may turn out that small-scale phenomena, which contribute little to the forecast product, significantly affect the ability to predict this product. With a negative result, we investigate in the extended Lorenz (2005) system whether omitting these phenomena will improve predictability. A theory explaining and describing this behavior is developed.
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, 2024
In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Development of a plant carbon–nitrogen interface coupling framework in a coupled biophysical-ecosystem–biogeochemical model (SSiB5/TRIFFID/DayCent-SOM v1.0)
Zheng Xiang, Yongkang Xue, Weidong Guo, Melannie D. Hartman, Ye Liu, and William J. Parton
Geosci. Model Dev., 17, 6437–6464, https://doi.org/10.5194/gmd-17-6437-2024, 2024
A process-based plant carbon (C)–nitrogen (N) interface coupling framework has been developed which mainly focuses on plant resistance and N-limitation effects on photosynthesis, plant respiration, and plant phenology. A dynamic C / N ratio is introduced to represent plant resistance and self-adjustment. The framework has been implemented in a coupled biophysical-ecosystem–biogeochemical model, and testing results show a general improvement in simulating plant properties with this framework.
LIGHT-bgcArgo-1.0: using synthetic float capabilities in E3SMv2 to assess spatiotemporal variability in ocean physics and biogeochemistry
Cara Nissen, Nicole S. Lovenduski, Mathew Maltrud, Alison R. Gray, Yohei Takano, Kristen Falcinelli, Jade Sauvé, and Katherine Smith
Geosci. Model Dev., 17, 6415–6435, https://doi.org/10.5194/gmd-17-6415-2024, 2024
Autonomous profiling floats have provided unprecedented observational coverage of the global ocean, but uncertainties remain about whether their sampling frequency and density capture the true spatiotemporal variability of physical, biogeochemical, and biological properties. Here, we present the novel synthetic biogeochemical float capabilities of the Energy Exascale Earth System Model version 2 and demonstrate their utility as a test bed to address these uncertainties.
Description and validation of Vehicular Emissions from Road Traffic (VERT) 1.0, an R-based framework for estimating road transport emissions from traffic flows
Giorgio Veratti, Alessandro Bigi, Sergio Teggi, and Grazia Ghermandi
Geosci. Model Dev., 17, 6465–6487, https://doi.org/10.5194/gmd-17-6465-2024, 2024
In this study, we present VERT (Vehicular Emissions from Road Traffic), an R package designed to estimate transport emissions using traffic estimates and vehicle fleet composition data. Compared to other tools available in the literature, VERT stands out for its user-friendly configuration and flexibility of user input. Case studies demonstrate its accuracy in both urban and regional contexts, making it a valuable tool for air quality management and transport scenario planning.
The Community Fire Behavior Model for coupled fire-atmosphere modeling: Implementation in the Unified Forecast System
Pedro Angel Jimenez y Munoz, Maria Frediani, Masih Eghdami, Daniel Rosen, Michael Kavulich, and Timothy W. Juliano
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-124,2024
Preprint under review for GMD (discussion: open, 0 comments)
We present the Community Fire Behavior model (CFBM) a fire behavior model designed to facilitate coupling to atmospheric models. We describe its implementation in the Unified Forecast System (UFS). Simulations of the Cameron Peak ire allowed us to verify our implementation. Our vision is to foster collaborative development in fire behavior modeling with the ultimate goal of increasing our fundamental understanding of fire science and minimizing the adverse impacts of wildland fires.
An improved hydro-biogeochemical model (CNMM-DNDC V6.0) for simulating dynamical forest-atmosphere exchanges of carbon and evapotranspiration at typical sites subject to subtropical and temperate monsoon climates in eastern Asia
Wei Zhang, Xunhua Zheng, Siqi Li, Shenghui Han, Chunyan Liu, Zhisheng Yao, Rui Wang, Kai Wang, Xiao Chen, Guirui Yu, Zhi Chen, Jiabing Wu, Huimin Wang, Junhua Yan, and Yong Li
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-141,2024
Preprint under review for GMD (discussion: open, 0 comments)
Process-oriented biogeochemical models are promising tools for estimating the carbon fluxes of forest ecosystems. In this study, the hydro-biogeochemical model of CNMM-DNDC was improved by incorporating a new forest growth module derived from the Biome-BGC. The updated model was validated using the multiple-year observed carbon fluxes and showed better performance in capturing the daily dynamics and annual variations. The sensitive eco-physiological parameters were also identified.
ML4Fire-XGBv1.0: Improving North American wildfire prediction by integrating a machine-learning fire model in a land surface model
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https//doi.org/10.5194/gmd-2024-151,2024
Preprint under review for GMD (discussion: open, 0 comments)
This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Exploring the use of seasonal forecasts to adapt flood insurance premiums
Viet Dung Nguyen, Jeroen Aerts, Max Tesselaar, Wouter Botzen, Heidi Kreibich, Lorenzo Alfieri, and Bruno Merz
Nat. Hazards Earth Syst. Sci., 24, 2923–2937, https://doi.org/10.5194/nhess-24-2923-2024, 2024
Our study explored how seasonal flood forecasts could enhance insurance premium accuracy. Insurers traditionally rely on historical data, yet climate fluctuations influence flood risk. We employed a method that predicts seasonal floods to adjust premiums accordingly. Our findings showed significant year-to-year variations in flood risk and premiums, underscoring the importance of adaptability. Despite limitations, this research aids insurers in preparing for evolving risks.
Abstract
Horizontal and vertical moisture advection in the lower troposphere of the Arctic under progressing global warming is examined using a large-scale ensemble model data set. Advection is decomposed into terms related to the basic state of the atmosphere and transient eddies and compared against a non-warming experiment. During summer, horizontal moisture advection increases mainly by transient eddies advecting moisture from the lower latitudes. During winter, enhanced evaporation due to reduced sea ice becomes a source of moisture diminishing the role of transient eddies moistening the atmosphere. This effect intensifies under extreme global warming, turning the change in total horizontal advection in the lower troposphere negative. Diminished horizontal advection during winter is counteracted by vertical advection accompanied with enhanced evaporation and upper-level horizontal advection maintaining the increase in column moisture. These results improve our understanding of how the water cycle in the Arctic responds via atmospheric processes under global warming.
Abstract
The lack of high vertical resolution atmospheric thermodynamic structure observations inside or near major weather events impedes our understanding of physical processes and their predictability in numerical weather prediction (NWP) models. Airborne Global Navigation Satellite System (GNSS) radio occultation (airborne radio occultation [ARO]) has proven to be a viable remote sensing option to offer dense soundings near flight tracks. The global fleet of commercial aircraft already equipped with GNSS receivers could be leveraged to produce an unprecedented number of ARO soundings along global flight paths. Eleven cases of atmospheric bending angle and refractivity profiles were successfully retrieved and compared with the colocated European Center for Medium-Range Weather Forecasting global reanalysis data. Good quality measurements are obtained with median refractivity differences less than 1% in the middle and upper troposphere, between 5.5 and 11.5 km. Given the use of aircraft data (e.g., Aircraft Meteorological DAta Relay) for data assimilation, incorporating ARO profiles would be a valuable addition, further enhancing the accuracy of aviation and weather forecasts.
Abstract
The time it takes for an ecosystem to recover from a disturbance is a key to environmental management. Conventionally, recovery is defined as a return to the pre-disturbance state, assuming ecosystem stationarity. However, this view does not account for the impact of external forces like climate change, imposing non-stationarity and trends. Alternatively, the counterfactual approach views recovery as the state the ecosystem would have achieved if the disturbance had not occurred, accounting for external forces. Here, we present a simple method to estimate the counterfactual recovery time. By implementing our method to the greening of the Arctic region, we showed that counterfactual greening recovery is twice as long as conventional recovery over the region. We argue that the well-documented greening of the region acts as an external force, leading to such a large difference. We advocate for embracing the counterfactual definition of recovery, as it aligns with realistic decision-making processes.
Abstract
Effects of eccentricity and horizontal electric field (E
H) on the binary-collision outcomes of water drops are examined using numerically calculated collision characteristics from previous studies and results of simulation experiment conducted by the authors. For a fixed collision kinetic energy (CKE), filament breakups can occur at all values of eccentricity but events of coalescence decrease, and that of sheet breakup increase with increasing eccentricity in absence of E
H. However, as E
H increases to ∼300 kVm−1 it opposes the variability of the coalescence and sheet breakup events with eccentricity. When E
H exceeds ∼300 kVm−1 the collision outcomes might be determined only by the CKE and E
H. The calculated value of coalescence efficiency and total number of fragments after a binary collision decreases with an increase in E
H. It is argued that an electric field can significantly modify drop size distribution in thunderclouds and needs to be considered for development of precipitation.
Abstract
Following the Hunga Tonga–Hunga Ha'apai (HTHH) eruption in January 2022, significant reductions in stratospheric hydrochloric acid (HCl) were observed in the Southern Hemisphere mid-latitudes during the latter half of 2022, suggesting potential chlorine activation. The objective of this study is to comprehensively understand the loss of HCl in the aftermath of HTHH. Satellite measurements and a global chemistry-climate model are employed for the analysis. We find strong agreement of 2022 anomalies between the modeled and the measured data. The observed tracer-tracer relations between nitrous oxide (N2O) and HCl indicate a significant role of chemical processing in the observed HCl reduction, especially during the austral winter of 2022. Further examining the roles of chlorine gas-phase and heterogeneous chemistry, we find that heterogeneous chemistry emerges as the primary driver for the chemical loss of HCl, and the reaction between hypobromous acid (HOBr) and HCl on sulfate aerosols is the dominant loss process.
Abstract
While shallow creep along the Haiyuan fault is a key element in estimating earthquake potential, both the creep rate and spatial distribution inferred from InSAR and repeating earthquakes are still controversial. In this study, we resolve two potentially separated creeping patches along the Laohushan fault (LHSF) based on dense near-field GPS measurements of 39 stations. The largest creeping patch, which extends ∼20 km along-strike and ∼9 km down-dip with a slip rate of 4.2 mm/yr, spatially correlates with seismicity, especially repeating earthquakes. The locked segment is capable of producing an earthquake of Mw 7.3 ± 0.1, with moment rate of (1.08 ± 0.39) × 1017 N⋅m/yr, possibly following the cycle since the 1092 M8 event. The lack of GPS measurements in the near-field makes it unclear whether the 8 km section between these two patches is slowly creeping below detection threshold or has relocked due to change in environmental condition.
