The equivalent source method of Spherical Elementary Current Systems (SECS) has contributed valuable results for spatial magnetic interpolation purposes where no observations are available, as well as for modeling equivalent currents both in the ionosphere and in the subsurface, thus providing a separation between external and internal sources. It has been successfully applied to numerous Space Weather (SW) events, whereas some advantages have been reported over other techniques such as Fourier or Spherical (Cap) Harmonic Analysis. Although different modalities of SECS exist (either 1-D, 2-D or 3-D) depending on the number of space dimensions involved, the method provides a sequence of instantaneous pictures of the source current. We present an extension of SECS consisting in the introduction of a temporal dependence in the formulation based on a cubic B-splines expansion. The technique thus adds one dimension, becoming 4-D in general (e.g., 3D + t), and its application is envisaged for, though not restricted to, the analysis of past events including heterogeneous geomagnetic datasets, such as those containing gaps, different sampling rates or diverse data sources. A synthetic model based on the Space Weather Modeling Framework (SWMF) is used to show the efficacy of the extended scheme. We apply this method to characterize the current systems of past and significant SW events producing geomagnetically induced currents (GIC), which we exemplify with an outstanding geomagnetic sudden commencement (SC) occurred on March 24, 1991.

Measurements of dust size usually obtain the optical or the projected area-equivalent diameters, whereas model calculations of dust impacts use the geometric and the aerodynamic diameters. As such, accurate conversions between the four types of diameters are critical. However, most current conversions assume dust is spherical, which is problematic as numerous studies show that dust is highly aspherical. Here, we obtain conversions between different diameter types that account for dust asphericity. Our conversions indicate that optical particle counters using optical diameter to determine dust size underestimate dust geometric diameter at coarse sizes. We further use the diameter conversions to obtain a consistent observational constraint of size distributions of emitted dust in terms of geometric and aerodynamic diameters. The resulting size distributions are coarser than accounted for by parameterizations used in climate models, which which underestimate the mass of emitted dust within 10≤D_geo≤20 μm by a factor of ~2 and do not account for dust emission with D_geo≥20 μm. This finding suggests that current models substantially underestimate coarse dust emission.

Years of the Maritime Continent or YMC was designed to improve our knowledge of the weather-climate systems over the MC and their numerical simulation and prediction skill. Since its first field campaign in November 2017, many intensive observations have been carried out under the coordination with the MC countries. Although currently some observations have been postponed due to COVID-19 pandemic, field campaigns are expected to continue beyond 2021. Up to now, some key information could be obtained, which suggest future approaches. In particular, diurnal cycle of rain near the coast is one of major targets, as they are dominant component of precipitation in this region. Some studies suggest the important role of temperature contrast between land and ocean, and inaccurate initial sea surface temperature conditions might cause a delay of offshore propagation of rainfall region. This fact provides a clue to improve simulation of precipitation behavior over the MC. In addition, during the campaign, several new observation tools have been introduced. For example, autonomous surface vehicle (ASV) had been deployed and measured surface meteorology as well as sea surface condition. Besides, GNSS-derived water vapor measurement was successfully carried out. Those results suggest that such ASVs equipped with new tools can be used to monitor accurate sea surface condition without significant cost and time. However, usually those instruments are not allowed to operate freely in the MC. Thus, based on collaboration with the MC countries, researchers in the MC are highly expected to take this role as regional representative. In this presentation, we will also show other results which provide tips for future direction.

GOSAT and GOSAT-2 have simultaneously observed both reflected SWIR solar light and TIR emissions with a single FTS mechanism with the Thermal And Near-infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) and TANSO-FTS-2 since 2009 and 2018, respectively. Their linear polarization bands provide information on light-path modification and make accurate remote sensing possible, even under aerosol and thin-cloud contaminated conditions. We can retrieve the difference between the partial column-averaged dry-air mole fractions of the two individual layers of lower and upper troposphere (LT and UT) by combining TIR and SWIR spectra data simultaneously, thereby constraining the accurate total column density of XCO2 and XCH4. TANSO-FTS has a two-axis agile pointing system, which allows cross-track and along-track motions It was originally designed for grid scan observations and viewing onboard calibration sources. After the pointing mechanism was switched from primary to secondary on 26 January 2015, We decided to make more frequent target observations, by uploading AT and CT pointing angles and observation timing as commands from the ground every day About 1000 locations are allocated to target observations such as calibration and validation site, megacities, or large emission sources. We define vertical layers of LT and UT not by temperature, but by the retrieved Psurf from individual O2 A band data. The pressure-height ranges of the LT and UT were taken as 0.6–1 Psurf and 0.2–0.6 Psurf, respectively. As the LT includes the entire boundary layer, analysis using XCO2 (LT) and XCH4 (LT) can double the signal of local emissions and remove the effects of CO2 and CH4 variability in the UT, which typically extends over a much wider area. We have targeted intense measurements over mega cities since 2016. We assume the average density of the upper troposphere is a background. We define XCO2 anomalies XCO2(LT)-XCO2(UT average), which show enhancement caused by local anthropogenic emissions. In 2020, we detected lower anomalies than previous years over mega cities such as Tokyo, Beijing, and New York.

Representing climate-crop interactions is critical to earth system modeling. Despite recent progress in modeling dynamic crop growth and irrigation in land surface models (LSMs), transitioning these models from field to regional scales is still challenging. This study applies the Noah-MP LSM with dynamic crop-growth and irrigation schemes to jointly simulate the crop yield and irrigation amount for corn and soybean in the central U.S. The model performance of crop yield and irrigation amount are evaluated at county-level against the USDA reports and USGS water withdrawal data, respectively. The bulk simulation (with uniform planting/harvesting management and no irrigation) produces significant biases in crop yield estimates for all planting regions, with root-mean-square-errors (RMSEs) being 28.1% and 28.4% for corn and soybean, respectively. Without an irrigation scheme, the crop yields in the irrigated regions are reduced due to water stress with RMSEs of 48.7% and 20.5%. Applying a dynamic irrigation scheme effectively improves crop yields in irrigated regions and reduces RMSEs to 22.3% and 16.8%. In rainfed regions, the model overestimates crop yields. Applying spatially-varied planting and harvesting dates at state-level reduces crop yields and irrigation amount for both crops, especially in northern states. A “nitrogen-stressed” simulation is conducted and found that the improvement of irrigation on crop yields are limited when the crops are under nitrogen stress. Several uncertainties in modeling crop growth are identified, including yield-gap, planting date, rubisco capacity, and discrepancies between available datasets, pointing to future efforts to incorporating spatially-varying crop parameters to better constrain crop growing seasons.

This paper presents the results of modeling the lower ionosphere response to solar X-ray flares of C-, M- and X-classes. The model is based on a 5-component scheme of the ionization-recombination cycle of the ionospheric D-region. Input parameters of the plasma-chemical model under different heliogeophysical conditions corresponding to selected X-ray flares were determined by using data received from AURA, SDO and GOES satellites. Verification of the obtained results was carried out with use of ground-based radiophysical measurements taken at the geophysical observatory Mikhnevo. Results of comparing the calculated and experimental the radio wave amplitude variations along six European very low frequency (VLF) paths show that the root mean square error (RMSE) does not exceed 1.5 dB for ~70% of cases including X-class flares during which the amplitude jump on some paths reaches 8 dB. Qualitative and quantitative analysis of the verification results of the VLF signal amplitude has showed the good predictive capability of the built model for describing weak and moderate ionospheric disturbances.

Social scientists have a long history of documenting disasters and natural extreme events’ behavioural response through the collection of perishable post-event data (Gruntfest 1977; Quarantelli and Dynes, 1977; Stalling, 1987; Quarantelli, 1997, 2003; Drabeck, 1999). Such empirical and theoretical foundations constitute a strong background to understand crisis responses and advance our knowledge of the drivers of human behavioural responses to fast evolving weather-related events. Outputs from this field of research show that public warning and behavioural response is a social process that takes several phases before a protective action is put in place (Mileti, 1995; Trainor et al., 2008, Parker et al., 2009, Lindell et al., 2004). These authors identified factors related to the characteristics of the hazard, the warning information characteristics, the situational and personal characteristics of the receiver and the socio-cultural context as strong determinants of the public behavioural response. In fast-moving events like flash-floods, the amount of time available to detect the threat and respond to it is so limited that protective actions often consist in dealing with contingent situations triggered by the irruption of dangerous circumstances in the middle of daily life activities and routines (Ruin et al., 2008, 2009; Terti et al., 2015). Understanding how people actually detect potentially dangerous circumstances and manage to timely adapt their routine to cope with the speed of the hazard evolution remains a challenge. Based on insights from post-event interviews, online surveys were used to quantitatively document behavioural responses associated with 3 catastrophic flash flood events that happened in southern France in 2014 and 2015. The coupled analysis of responses to these surveys with hydrometeorological parameters allows to better understand the link between the event magnitude and self-protective behaviours in the context of short-fuse weather events as flash floods. Knowledge gained from such an integrated approach is necessary for drawing lessons for the development of coupled human-natural system modeling and the prediction of the human vulnerability dynamics in short-fuse weather events.

This project aims to quantify the resiliency of prairie ecosystems in the U.S. Pacific Northwest (PNW) to climate change. Prairies in this region sustain over one million beef cows, and cow­-calf production costs are expected to increase to offset warming-induced plant productivity loss. We investigated the above- and belowground effects of experimental warming in prairie ecosystems by assessing biogeochemical controls on and patterns of asymbiotic nitrogen fixation (ANF), plant species diversity, and legume cover to address a major challenge for sustainable agriculture in the region. We hypothesize that the effect of warming on prairie functional diversity increases soil asymbiotic nitrogen inputs by decreasing legume cover and soil nitrogen availability. We quantified the effects of decadal warming stress (+2.5ºC) on soil biogeochemical properties and plant species and functional diversity during fall and spring seasons in three sites along a 520­km latitudinal gradient—from central Washington to southern Oregon—representing a drought severity gradient. At each site, we collected composite soil samples from five co-­located prairie plots under control (ambient) and warming conditions. We incubated these soils using 15N-labeled dinitrogen (15N2), and quantified total soil carbon­, total and available nitrogen, and available phosphorus and iron pools to better understand the underlying mechanisms governing warming-­induced changes in ANF. We used a point intercept technique to survey plot-level plant community composition and calculate Shannon’s diversity index and percent cover of legumes (members of Fabaceae according to the Integrated Taxonomic Information System). Warming significantly decreased plant species diversity which also decreased along the drought severity gradient. Legume cover significantly increased from 3.1% in the north to 9.2% in the south. ANF response to warming varied by season and site, where rates increased with the drought severity gradient in the fall but decreased during the spring. Total soil inorganic nitrogen availability was the strongest predictor of ANF response to warming in the spring but not in the fall. Our study highlights the importance of using soil-plant-atmosphere interactions to assess prairie ecosystem resilience to climate change in the PNW.

Tropical cyclones (TCs) are one of the most fascinating examples of organized convection. In idealized simulations of rotating radiative-convective equilibrium (RRCE), TCs can self-emerge without the need for a pre-existing disturbance. In a recent study, Ramírez Reyes and Yang (2021) showed that in contrast to prevailing knowledge (e.g., Muller and Romps (2018)), spontaneous TC genesis can occur without radiative and surface-flux feedbacks. Here we explore the hypothesis that under these conditions, the moisture-entrainment-convection (MEC) feedback is responsible for the spontaneous TC genesis. In the MEC feedback, a moister environment favors new deep convective events, and their associated large-scale circulations and detrainment processes further moisten the environment, leading to aggregation of deep convection. We examine the role of the MEC feedback in spontaneous TC genesis using RRCE simulations (Ramírez Reyes and Yang, 2021), in which we weaken the MEC feedback by relaxing the clear-sky specific humidity to its horizontal average throughout the entire column. TCs can still self-emerge when we weaken the MEC feedback, but TC intensity monotonically decreases as we reduce the relaxation time scale. TCs can no longer appear in the 100-day simulations when the relaxation time scale is reduced to 3 hours. We then relax the clear-sky specific humidity to its horizontal mean at individual vertical layers and find that weakening the MEC feedback below 7km significantly decreases TC intensity. However, nudging specific humidity above 7km has a much weaker impact. We will also present further analyses to test the hypothesis by using vorticity, the available potential energy (APE) budget and a vertically resolved moist static energy framework (Yao, Yang, Tan 2021). Ramírez Reyes, A., & Yang, D. (2020). Spontaneous Cyclogenesis without Radiative and Surface-Flux Feedbacks. arXiv preprint arXiv:2004.08662. Muller, C. J., & Romps, D. M. (2018). Acceleration of tropical cyclogenesis by self-aggregation feedbacks. Proceedings of the National Academy of Sciences, 115(12), 2930-2935. Yao, L., Yang, D., & Tan, Z. M. (2020). A Vertically Resolved MSE Framework Highlights the Role of the Boundary Layer in Convective Self-Aggregation. arXiv preprint arXiv:2008.10158

Tropical cyclones dominate the disturbance regime experienced by forest ecosystems in many parts of the world. Interactions between cyclone disturbance regimes and nutrient availability strongly influence forest ecosystem dynamics. However, uncertainty exists over the importance of soil fertility properties (i.e., total soil phosphorus-P concentration) in mediating forest resistance and recovery from cyclone disturbance. We hypothesized that forests on soils with low total P (e.g., developed on limited-P parent material) have a higher resistance to but a slower recovery from cyclone disturbance than forests on high P soils. We investigated cyclone impacts on litterfall, an essential conduit for nutrient recycling in forest ecosystems. We compiled site-level forest litterfall data from 53 studies and datasets associated with 15 naturally-occurring one simulated tropical cyclone in 23 sites within five regions (Taiwan, Australia, Mexico, Hawaii, and the Caribbean)and four cyclone basins. We calculated the effect sizes of cyclone disturbance on the litterfall mass and nutrient (P and nitrogen-N) concentrations and fluxes during the first (< five) years post-disturbance across a total soil P gradient. We also assessed the effect of 20 covariates on the degree of cyclone impact on litterfall. Total litterfall mass flux increased by 4820%following cyclone disturbance. Such an initial increase in litterfall mass reflects the magnitude of cyclone-derived plant material input to the forest floor, which was highest in the Caribbean and lowest in Taiwan. Among 20 covariates, soil P and region were the best predictors of cyclone effects on total litterfall mass, explaining 80% of the variance. The effect sizes increased linearly with soil P and region, from significantly lower in Taiwan (low-P) to largest in the Caribbean (high-P). Total litterfall P and N fluxes increased significantly post-cyclone, whereas the increase in leaf P flux was twice as that in Nflux. Results highlight the importance of understanding the interactions between disturbance and nutrient gradients in forest ecosystems to understand forest responses to altered cyclone regimes expected under climate change.

Background/Question/Methods This project attempts to quantify the resilience of prairie ecosystems to climate change in the Pacific Northwest (PNW). In this region, prairie ecosystems currently sustain ~1.3 million beef cows and calf production costs are expected to increase to offset drought-induced plant productivity loss. Here, we investigate patterns of asymbiotic nitrogen fixation (ANF) and biogeochemical controls, that also influence plant community composition and prairie productivity, under experimental drought to address a major challenge for sustainable agriculture in the region. We hypothesize that the effect of drought on prairie vegetation cover increases soil asymbiotic N inputs by diminishing the dominance of symbiotic root-fungal networks. To test this hypothesis, we quantified the impacts of decadal drought stress on soil ANF using 15N-labeled dinitrogen (15N2) incubations of soils from high- and low-diversity prairies across a 520-km latitudinal gradient (i.e., southern Oregon-SOR, central Oregon-COR, and central Washington-CWA) representing increasingly severe Mediterranean conditions. We also quantified total soil organic carbon-C, total, and available N, and available phosphorus-P and iron-Fe pools to better understand underlying mechanisms governing drought-induced changes in ANF. At each site, composite soil samples (n = 3) were collected from five co-located high- and low-diversity prairie plots under control (ambient) and drought (-40% precipitation) conditions. Results/Conclusions We found that soil ANF response to drought increased with the PNW Mediterranean drought intensity gradient; while ANF rates increased nearly two-fold in the southernmost site (SOR), a significant decrease in ANF was verified in the northernmost site (CWA). ANF response to drought also varied depending on plant diversity, where low-diversity prairies had a more predictable response to drought than high-diversity prairies. For instance, ANF in SOR high-diversity prairies was suppressed but no change was verified in COR high diversity prairies. Soil C and N contents were generally higher in high-diversity prairies whereas treatment had no significant effect across sites. Soil P availability, also affected by drought, and pH were the most important variables explaining ANF variability across vegetation types and sites. Based on our findings, low-diversity prairies in central WA may be those most severely impacted by increased climate change-induced drought stress. Our study highlights the importance of using soil-plant-atmosphere interactions to assess prairie ecosystem resilience to drought in the PNW.

When rain falls over the ocean, it produces a vertical salinity profile that is fresher at the surface. This fresh water will be mixed downward by turbulent diffusion through gravity waves and the wind stress, which dissipates over a few hours until the upper layer (1-5 m depth) becomes well mixed. Therefore, there will be a transient bias between the bulk salinity, measured by in-situ instruments, and the satellite-measured SSS (representative of the first cm of the ocean depth). Based on observations of Aquarius (AQ) SSS under rain conditions, a rain impact model (RIM) was developed to estimate the change in SSS due to the accumulation of precipitation previous to the time of the satellite observation. RIM uses ocean surface salinities, from the HYCOM (Hybrid Coordinate Ocean Model) and the NOAA global rainfall product CMORPH, to model transient changes in the near-surface salinity profile. Also, the RIM analysis has been applied to SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive), with similar results observed. The original version of RIM assumes a constant vertical diffusivity and neglects the effects of wind and wave mixing. However, it has been shown that the persistence of rain-induced salinity gradients depends on wind speed, with freshening due to rain during weak winds (less than 2 m/s) persisting for 8 hours or more. Moreover, the mechanical mixing of the ocean caused by wind and waves rapidly reduces the salinity stratification caused by rain. Also, previous results using RIM, in the presence of moderate/high wind speeds, show that the model overestimates the effect of rain on the SSS, which suggests that for RIM to accurately model the near-surface salinity stratification, the effect of wind needs to be included in the model. To address this issue, this paper will focus on an improved RIM-2 that parameterizes the effects of wind on the vertical diffusivity (Kz). Results will be presented that compare RIM and RIM-2 calculations at different depths for several Kz parametrizations. Also, comparisons, between RIM-2 at depths of several meters with measurements from in-situ salinity instruments, will be presented.

Tikhonov’s regularization method is the standard technique applied to obtain models of the subsurface conductivity dis- tribution from electric or electromagnetic measurements by solving UT (m) = ||F(m) - d||^2 + P(m): The second term correspond to the stabilizing functional, with P(m) = ||m||^2 the usual approach, and the regularization parameter. Due to the roughness penalizer inclusion, the model developed by Tikhonov’s algorithm tends to smear discontinuities, a feature that may be undesirable. An important requirement for the regularizer is to allow the recovery of edges, and smooth the homogeneous parts. As is well known, Total Variation (TV) is now the standard approach to meet this requirement. Recently, Wang et.al. proved convergence for alternating direction method of multipliers in nonconvex, nonsmooth optimization. In this work we present a study of several algorithms for model recovering of Geosounding data based on Inmal Convolution, and also on hybrid, TV and second order TV and nonsmooth, nonconvex regularizers, observing their performance on synthetic and real data. The algorithms are based on Bregman iteration and Split Bregman method, and the geosounding method is the low-induction numbers magnetic dipoles. Non-smooth regularizers are considered using the Legendre-Fenchel transform.

We have used empirical models for electric potentials and the magnetic fields in both space and on the ground to obtain maps of the height-integrated Pedersen and Hall ionospheric conductivities at high latitudes. This calculation required use of both “curl-free” and “divergence-free” components of the ionospheric currents, with the former obtained from magnetic fields that are used in a model of the field-aligned currents. The second component is from the equivalent current, usually associated with Hall currents, derived from the ground-level magnetic field. Conductances were calculated for varying combinations of the Interplanetary magnetic field (IMF) magnitude and orientation angle, as well as the dipole tilt angle. The results show that reversing the sign of the Y component of the IMF produces substantially different conductivity patterns. The Hall conductivities are largest on the dawn side in the upward, Region 2 field-aligned currents. Low electric field strengths in the Harang discontinuity lead to inconclusive results near midnight. Calculations of the Joule heating, obtained from the electric field and both components of the ionospheric current, are compared with the Poynting flux in space. The maps show some differences, while their integrated totals match to within 1%. Some of the Poynting flux that enters the polar cap is dissipated as Joule heating within the auroral ovals, where the conductivity is enhanced, confirming the Poynting Flux theorem proposed by Richmond in 2010, for the first time using realistic electric fields, ionospheric currents, and conductivity.

Ion temperature is a key parameter that influences dynamics in the magnetosphere, such as particle transport and wave-particle interactions. Measurements of ion heating and energization yields information about phenomena such as magnetic reconnection, bursty bulk flows, and ion injections. Taking advantage of the global view provided by energetic neutral atom imaging, a database of ion temperature maps during geomagnetic storms occurring throughout the NASA TWINS mission has been created. These ion temperature maps and relevant metadata are publicly available on CDAWeb to facilitate comparison to in situ measurements and model output, for use as boundary conditions for simulations, and for other relevant studies. A preliminary study of average plasma sheet ion temperatures calculated from these maps has revealed a common occurrence of decreasing ion temperature concurrent with a sharp negative gradient in the IMF B. A preliminary case study for one storm is presented.

Haptophytes and Dinoflagellates are two cosmopolitan algae associated with dimethylsulfoniopropionate (DMSP) synthesis, which regulates the marine biogenic flux of dimethylsulfide (DMS) to the atmosphere and subsequently affects marine aerosols. Attempting to reveal the potential impact of atmospheric deposition on the growth of main DMSP producers, four bioassay experiments were conducted in the western North Pacific (WNP) by adding aerosols, nutrients and trace metals. Our results showed that the percentage of main DMSP producers increased substantially from coastal regions (<1%) to the open ocean (~17%) with the dominance of Dinophyceae and Haptophyceae, respectively. Aerosol additions largely increased the percentage of DMSP species in the open WNP. Specifically, atmospheric DIN and soluble Cu, and Fe promoted Chrysochromulina, and Phaeocystis and E. huxleyi, respectively. It is very likely that atmospheric deposition could lift the relative abundance of main DMSP producers in the vast oligotrophic oceans and contribute to the climate change.

In this study, we evaluated the variations of air quality in Lanzhou, a typical city in Northwestern China impacted by the COVID-19 lockdown. The mass concentration and chemical composition of non-refractory submicron particulate matter (NR-PM) were determined by a high-resolution aerosol mass spectrometry from January to March 2020. The concentration of NR-PM dropped by 40% from pre- to during control period. The five aerosol components (sulfate, nitrate, ammonium, chloride, and organic aerosol (OA)) were all decreased during control period with the largest from secondary inorganic species (70% of the total reduction), whereas the OA sources did not vary synchronously. OA from coal and biomass burning remained stably from pre- to during control period, while traffic and cooking related emissions were reduced by 30% and 50%, respectively. The production rates for secondary inorganic and organic aerosols were also evaluated and represented a decreased trend from pre- to during control periods.

Spectra of small-scale sprite structures, downward and upward propagating streamers, glow, and beads, were recorded with a slit-less spectrograph at 10,000 frames per second (fps) from aircraft missions in 2009 and 2013. The spectra are dominated by emissions from molecular nitrogen, the 1 positive band in the red, and in the blue the 2 positive band plus the 1 negative band of molecular nitrogen ions. The excitation threshold for the blue emissions is higher than for the red emissions so the blue/red ratio can, in principle, be used as a proxy for the electron energy leading to the emissions. We extracted for analysis time series of spectra from 11 sprites: 18 time series from downwards propagating streamers, 6 from upward propagating streamers, 14 from glow and 12 from beads. The total number of spectra in the 50 time series is 953. Blue emissions are almost exclusively associated with streamers indicating the more energetic nature of streamers compared with glow and beads. Both downward and upward propagating streamers start and end with low blue emissions indicating time variations in the associated processes. Because the red and blue nitrogen emissions are significantly affected by quenching, which is altitude dependent, and we do not have sufficiently accurate altitudes, the observed spectral blue/red ratios cannot be directly applied to sprite models.

Over–the–Horizon (OTH) communication is strongly dependent on the state of the ionosphere, which is susceptible to solar flares. Trans–ionospheric high frequency (HF) signals experience a strong attenuation following a solar flare, commonly referred to as Short–Wave Fadeout (SWF). In this study, we examine the role of dispersion relation–collision frequency formulations on the estimation of flare–driven HF absorption seen in Riometer observation using a data assimilation framework. Specifically, the framework first uses modified solar irradiance models (such as EUVAC, FISM), which incorporate high–resolution solar flux data from GOES satellite X-ray sensors, to compute the enhanced ionization produced during the flare events. The framework then uses different dispersion relation–collision frequency formulations to estimate enhanced HF absorption. Finally, the modeled HF absorption is compared against the data to determine which combination of dispersion relation–collision frequency formulation best reproduces the Riometer observations. From the modeling work, we find that the Appleton–Hartree dispersion relation in combination with Schunk–Nagy collision frequency profile produces the best agreement with Riometer data.

The term “ionospheric sluggishness” is used to describe the time delay between maximum radio absorption in the ionosphere following the time of maximum irradiance during a solar flare. Sluggishness is one of the characteristic properties known to be maximized around D-region heights and can be used for studying lower ionospheric (D-region) and mesospheric chemistry. This article is our first attempt to estimate ionospheric sluggishness using high frequency (HF, 3 – 30 MHz) instruments. Specifically, we report on first estimates of sluggishness from riometer and SuperDARN observations following a solar flare and propose two new methods to estimate sluggishness. Sluggishness is shown to be anti-correlated with the peak solar X-ray flux and positively correlated with solar zenith angle and geographic latitude. The choice of instrument, method, and reference solar waveband effects the sluggishness estimation. A simulation study was performed to estimate the effective recombination coefficient, which was found to vary between 4-5 orders of magnitude. We suggest that the effective recombination coefficient is highly sensitive to D-region’s negative and positive ion chemistry.

Phenology and its interannual variability are altered through anthropogenic climate change. Feedbacks of plant phenology to the regional climate system affect fluxes of energy, water, CO2, biogenic volatile organic compounds as well as canopy conductance, surface roughness length, and are influencing the seasonality of albedo. We performed simulations with the regional climate model COSMO-CLM (CCLM) with 3km horizontal resolution over Germany covering the period 1999 to 2015 to study the sensitivity of grass phenology to different environmental conditions by implementing a new phenology module. We provide new evidence that the standard annually-recurring phenology of CCLM is improved by the new calculation of leaf area index (LAI) dependent upon surface temperature, day length, and water availability. Results with the new phenology implemented in the model showed a significantly higher correlation with observations than simulations with the standard phenology. The interannual variability of LAI, the representation of years with extremely warm spring or extremely dry summer, and the start of the growing season also improved with the new phenology module. The number of hot days with maximum temperature exceeding the 90th percentile and heavy precipitation events (> 20mm) with the new phenology are in very good agreement with the observations. We also show that lower LAI values in summer lead to a decrease of latent heat flux in the model due to less evapotranspiration. The CCLM simulation with improved representation of the phenology should be used in future applications with an extension on more plant functional types.

Rovers and landers on Mars have experienced local, regional, and planetary-scale dust storms. However, in situ documentation of active lifting within storms has remained elusive. Over 5-11 January 2022 (LS 153°-156°), a dust storm passed over the Perseverance rover site. Peak visible optical depth was ~2, and visibility across the crater was briefly reduced. Pressure tides and temperatures responded to the storm. Winds up to 20 m s-1 rotated around the site before the wind sensor was damaged. The rover imaged 21 dust-lifting events—gusts and dust devils—in one 25-minute period, and at least three events mobilized sediment near the rover. Rover tracks and drill cuttings were extensively modified, and debris was moved onto the rover deck. Migration of small ripples was seen, but there was no large-scale change in undisturbed areas. This work presents an overview of observations and initial results from the study of the storm.

The authors describe a tropical cyclone risk model for the Philippines, using methods that are open-source and can be straightforwardly generalized to other countries. Wind fields derived from historical observations, as well as those from an environmentally forced tropical cyclone hazard model (using environmental forcing from the recent historical period) are combined with data representing exposed value and vulnerability to determine asset losses. Exposed value is represented by the LitPop dataset, which assumes total asset value is distributed across a country following population density and nightlights data. Vulnerability is assumed to follow a functional form previously proposed by Emanuel, with free parameters chosen by a sensitivity analysis in which simulated and historical reported damages are compared for different parameter values. Use of different vulnerability parameters for the region around Manila yields much better agreement between simulated and actually reported losses than does a single set of parameters for the entire country. Even then, however, the model predicts no losses for a substantial number of historical storms which did in fact produce them, a difference the authors hypothesize is at least in part due to the use of wind speed as the sole metric of TC hazard, omitting explicit representation of flooding due to storm surge and/or rainfall.

Discretized numerical models of the atmosphere are usually intended to faithfully represent an underlying set of continuous equations, but this necessary condition is violated sometimes by subtle pathologies that have crept into the discretized equations. Such pathologies can introduce undesirable artifacts, such as sawtooth noise, into the model solutions. The presence of these pathologies can be detected by numerical convergence testing. This study employs convergence testing to verify the discretization of the Cloud Layers Unified By Binormals (CLUBB) model of clouds and turbulence. That convergence testing identifies two aspects of CLUBB’s equation set that contribute to undesirable noise in the solutions. First, numerical limiters (i.e. clipping) used by CLUBB introduce discontinuities or slope discontinuities in model fields. Second, this noise can be amplified by an advective term in CLUBB’s background diffusion. Smoothing the limiters and removing the advective component of the background diffusion reduces the noise and restores the expected first-order convergence in CLUBB’s solutions. These model reformulations improve the results at coarser, near-operational grid spacing and time step in cumulus cloud and dry turbulence tests. In addition, convergence testing is proved to be a valuable tool for detecting pathologies, including unintended discontinuities and grid dependence, in the model equation set.