In the ocean, temperature extremes have adverse effects on precipitation patterns, sea level change, and migration/damage of ecosystems. It has been found that most species are more sensitive to extreme events like marine heatwaves (MHWs), implying the severe impacts of MHWs on ecology. These events are driven by various atmospheric and oceanic processes. In recent years, these extreme events are more frequent and intense globally and their increasing trend is expected to continue in the upcoming decades. They have the potential to devastate marine habitats, and ecosystems together with ensuing socioeconomic consequences. It recently attracted public interest and scientific researchers, which motivates us to analyze the recent MHW events in the Bay of Bengal region. we have isolated 107 MHW events (above the 90th percentile threshold) in this region of the Indian Ocean and investigated the variation in duration, intensity, and frequency of MHW events during our test period (1982-2021). Our study reveals that the average of three MHW events per year in the study region with an increasing linear trend of 1.11 MHW events per decade. In the analysis, we found the most intense event has a maximum intensity was 5.29°C (above the climatology mean), while the mean intensity was 2.03°C. In addition, we observed net heat flux accompanied by anticyclonic eddies to be the primary cause of these events. Also, an effort has been made to understand the relationship between climate modes, sea surface height, and the difference between evaporation and precipitation with the occurrence of MHW events.
The West African Monsoon (WAM) strongly drives precipitation variability and seasonality across continental West Africa and the tropical Eastern Atlantic. However, the evolution of the WAM in the late Cenozoic, in response to changes in vegetation, atmospheric CO 2 , orbital forcings, paleogeography, and orography as well as its teleconnections such as the mean location of the African Easterly Jet (AEJ), Tropical Easterly Jet (TEJ), SubTropical Jet (STJ), Inter-Tropical Discontinuity (ITD) and low-level westerly flow is not well constrained. We contribute to understanding past WAM dynamics by performing high-resolution, time-specific paleoclimate simulation using General Circulation Model ECHAM5. We focus our analysis on the migration and intensification of the WAM and its associated atmospheric thermodynamic structure which influence the rainfall seasonality and patterns across the Sahel, Guinea Coast, and Sahara regions.
The Government of India announced its commitment to reach net-zero greenhouse gas emissions by 2070 at the recent COP 26 summit. Modeling projections suggest that meeting this target would likely require substantial amounts of CO2 capture and storage (CCS) from large-point sources (LPS). Our analysis first reveals the key co-benefits for India in the adoption of CCS, viz. energy security, lower aggregate costs of carbon mitigation, higher resilience and lower stranded assets. For instance, we estimate that stranding of >100 GW and >70 GW of coal- and gas-fired power capacity could be avoided with the presence of CCS in the power sector mix.This analysis is further supplemented by our recent estimates on CO2 storage potential estimates in Indian geologic formations. Our results indicate that the storage capacity via enhanced oil recovery (EOR) is 1.2 GtCO2 after incorporating engineering and geologic constraints. Similarly, the storage capacity in unminable coal fields is estimated to be 3.5-6.3 GtCO2. Even though the combined storage potential in these formations is constrained, they should be actively considered within policy-making as they predominantly lie within areas of dense areas of LPS, thus creating possibilities of CCS hubs and clusters. In addition, 291 GtCO2 could be sequestered in saline aquifers and 97-316 GtCO2 in basalts; though, these values are subject to higher uncertainties. A number of saline aquifers may be characterized as having storage potential equivalent to several years of LPS emissions (>10 GtCO2) along with high storage feasibility.Our ongoing analysis attempts a more evolved approach towards source-sink mapping in India by combining the storage potential estimates with geospatial layers of LPS. Large power plants, which emit >20 MtCO2 annually, and high-purity CO2 sources such as refineries, are of particular interest. Preliminary source-sink mapping results show substantial clustering opportunities in eastern India, which has active coalbed methane extraction undertaken by five companies, and western India, with large industrial sources interspersed with EOR sites. The results of this analysis will also inform decision-makers on future LPS siting opportunities if a policy thrust on CCS is undertaken for meeting net-zero targets over the next two decades.
A NASA sponsored study conducted at John Hopkins University Applied Physics Lab culminated in a community-inspired heliospheric mission concept called the Interstellar Probe (ISP). The ISP’s science goals include understanding our habitable astrosphere by investigating its interactions with the interstellar medium, and determining the structure, composition, and variability of its constituents. A suite of instruments were proposed to achieve these and other science objectives. The instruments include a Lyman-a spectrograph for velocity-resolved measurements of neutral H atoms. The capability to address key components of the ISP’s science objectives by utilizing high spectral resolution Lyman-a measurements are described in this presentation. These findings have been submitted as a community White Paper to the recent Heliophysics decadal survey.
Accurate estimates of the soil water balance components are critical for optimizing irrigation water use in agricultural fields. Estimates are normally obtained using simple water balance models and for representative areas, not taking into consideration the within variability of soil properties. In this study, we used the MOHID-Land distributed process-based model to compute the variability of the soil water balance components in a 23ha almond field located in southern Portugal, at a resolution of 5m. The main objective was the possible assessment of management zones for improving water productivity in that water-scarce region. An electromagnetic induction survey was carried out first to obtain electromagnetic conductivity images which provided the spatial distribution of the real soil electrical conductivity (ff) with depth. The spatial distribution of ff was then correlated to soil particle size distribution using an in-situ calibration. Afterward, pedotransfer functions were applied to define the soil hydraulic parameters necessary to run the distributed model and map the within soil variability at the field scale. Irrigation data was monitored on-site, at two locations, while weather data was extracted from a local meteorological station. The distributed modeling approach included the definition of potential evapotranspiration fluxes computed from the product of the reference evapotranspiration obtained according to the FAO56 Penman-Monteith equation and a crop coefficient for each stage of almond’s growing season, the variable-saturated flow using the Richards equation, and root zone water stress following a macroscopic approach. Modeling results were used to present the maps of the variability of the seasonal actual crop transpiration and soil evaporation, the mean soil moisture, seasonal runoff, and seasonal percolation. Then, management zones for improving irrigation water use in the studied almond field were proposed.
Plant roots are responsible for essential functions like nutrient uptake, anchorage, and storage. Study of root uptake mechanisms for macro nutrients like nitrogen, phosphorus, potassium, and sulphur is vital to our understanding of their role in plant growth and development. Small signaling peptides (SSPs), are hormones which regulate diverse plant developmental processes including root growth. However, their involvement in regulation of nutrient uptake by roots is poorly understood. We recently developed a hydroponics- based plant growth system which combines ion chromatography with synthetic peptide application, to analyze the depletion rates of nutrients by Medicago truncatula roots. Application of the synthetic SSP MtCEP1 and AtCEP1 led to enhanced uptake of nitrates, sulphates, and phosphates. To further elucidate the molecular mechanism of nutrient uptake mediated by these peptides, we conducted an RNAseq of M. truncatula roots treated with the peptides. A differential gene expression analysis revealed thousands of peptide responsive genes. Several known nitrate transporters and a sulphate transporter AtSULTR3:5-like gene showed enhanced expression under both, MtCEP1 and AtCEP1 peptide application. Multiple, as of yet uncharacterized, CEP peptide responsive pathway regulatory genes such as kinases and transcription factors were also identified through this transcriptomic analysis. This study highlights the potential of phenomics enabled biology to uncover target genes for improving agriculturally important traits such as nutrient uptake.
Vegetation acts as a critical link between the geosphere, biosphere, and atmosphere, regulating the flux of water to the atmosphere via transpiration (E) and the input of carbon from the atmosphere to plants and soil via photosynthetic carbon assimilation (A). The rate of A is known to be seasonally dynamic, however, few studies have investigated how the ratio between E and A, known as the water use efficiency (WUE), changes with phenology. WUE directly impacts regional to global carbon and water cycles and lack of knowledge regarding the dynamics of WUE remains among the largest uncertainties in current earth system model (ESM) projections of carbon and water exchange in temperate forests. Here we attempt to reduce this knowledge gap by studying these dynamics across a range of eight deciduous tree species common to temperate forests of North America. Using gas exchange and spectroscopic measurements, we investigated seasonal patterns in leaf level physiological, biochemical, and anatomical properties, including the seasonal progress of WUE and foliar capacity for carbon assimilation, which corollate with seasonal leaf phenology. We incorporate these findings into a modeling framework that contains the same representation of A, E, and canopy scaling found in ESMs to explore the impact of parameterization, which tracks phenological status, on model forecasts. Our results indicate that both photosynthetic capacity and WUE are seasonally dynamic processes which are not synchronized. WUE increased from a minimum at leaf out toward a more conservative behavior at the mid-summer growth peak. This pattern was explained by a decreased stomatal aperture and a decrease in cuticular leakage with leaf aging. We also observed a seasonal increase in maximum carboxylation capacity, with maximum rates of A and modeled tree net primary productivity (NPP) occurring later toward the end of the summer. This change was primarily driven by an increase in foliar nitrogen content, and a shift in the ratio of Vcmax to Jmax between expanding and mature leaves. By applying our revised parameterization, which captures seasonal dynamics of gas exchange, into our model framework we aim to improve the process representation of leaf function in a temperate forest, and more faithfully represent dynamics of NPP and E in the early and late growth season.
We present evidence of damping of equatorial noise due to Finite-Larmor-radius (FLR) effect in the inner Van Allen belt. Detail observation of the FLR phenomenon in the inner belt region has not been reported until now. Waves primarily damped by the FLR mechanism can influence the energy dependent proton density structure. We analyze a typical case recorded by the Van Allen probe that involves FLR damping of equatorial noise, which was propagating radially towards the Earth, at L-shell ~1.7. As a result of this damping, protons in the energy range of ~18 – 21 MeV at L-shell ~1.7 – 2 get energized. This kind of wave-particle interaction should be included in the future models of the inner Van Allen belt. This phenomenon may also account for the unknown proton loss mechanism reported in Selesnick and Albert (2019).
A cloud event in the altitude range of 53-65 km was observed with lidars over Yanqing (40.5°N, 116°E) and Pingquan (41°N, 118.7°E) on 30 October 2018. Clouds with a multilayer structure first occurred within the line view of lidar at dawn (03:40-06:00LT). They were faint and tenuous, and the maximum volume backscatter coefficient (VBSC) was 1.4×10-10m-1sr-1. At twilight, clouds with multilayer structures were reobserved via lidars, but they became much thicker, with a maximum VBSC of 11.2×10-10m-1sr-1. The structure of the cloud layers varied with time, and they faded completely at approximately ~00:30 LT (+1 day). Measurements from SABER/TIMED were utilized for analysis, and it was found that before the onset of cloud event, a temperature anomaly occurred in the mesosphere over Beijing, and water vapor was also very abundant. The frost point temperature profile of water vapor was estimated, and lidar measurements showed that the atmospheric temperature was close to the frost point of water vapor in the vicinity of the stratopause when the mesosphere was undergoing a low-temperature phase. It was a rare mesospheric cloud event observed with lidars at rather low latitudes, and the clouds probably resulted from the nucleation of saturated water vapor due to the occurrence of a temperature anomaly in the mesosphere.
[This presentation is published at https://doi.org/10.1111/1440-1703.12317] Dead organic matter (DOM), which consists of leaf litter, fine woody debris (FWD; < 3 cm diameter), downed coarse woody debris (CWDlog), and standing or suspended coarse woody debris (CWDsnag), plays a crucial role in forest carbon cycling. However, the contributions of each DOM type on stand-scale carbon storage (necromass) and stand-scale CO2 efflux (Rstand) estimates are not well understood. In addition, there is little knowledge of the effect of each DOM type on the accuracy of stand-scale estimates of total necromass and Rstand. This study investigated characteristics of necromass and Rstand from DOM in a subtropical forest in Okinawa island, Japan, to quantify the effect of each DOM type on total necromass, total Rstand, and estimate error of total necromass and Rstand. The CWDsnag accounted for the highest proportion (54%) of total necromass (1499.7 g C m–2), followed by CWDlog (24%), FWD (11%), and leaf litter (11%). Leaf litter accounted for the highest proportion (37%) of total Rstand (340.6 g C m–2 yr–1), followed by CWDsnag (25%), CWDlog (20%), and FWD (17%). The CWDsnag was distributed locally with 173% of the coefficient of variation for necromass, which was approximately two times higher than those of leaf litter and FWD (72–73%). Our spatial analysis revealed, for accurate estimates of CWDsnag and CWDlog necromass, sampling areas of ≥ 28750 m2 and ≥ 2058‒42875 m2 were required, respectively, under the condition of 95% confidence level and 0.1 of accepted error. In summary, CWD considerably contributed to stand-scale carbon storage and efflux in this subtropical forest, resulting in a major source of errors in the stand-scale estimates. In forests where frequent tree death is likely to occur, necromass and Rstand of CWD are not negligible in considering the carbon cycling as in this study, and therefore need to be estimated accurately.
Fronts are ubiquitous in the climate system. In the Southern Ocean, fronts delineate water masses, which correspond to upwelling and downwelling branches of the overturning circulation. A robust understanding of Southern Ocean fronts is key to projecting future changes in overturning and the associated air-sea partitioning of heat and carbon. Classically, oceanographers define Southern Ocean fronts as a small number of continuous linear features that encircle Antarctica. However, modern observational and theoretical developments are challenging this traditional framework to accommodate more localized views of fronts [Chapman et al. 2020]. In this work, we present two related methods for calculating fronts from oceanographic data. The first method uses unsupervised classification (specifically, Gaussian Mixture Modeling or GMM) and an interclass metric to define fronts. This approach produces a discontinuous, probabilistic view of front location, emphasising the fact that the boundaries between water masses are not uniformly sharp across the entire Southern Ocean. The second method uses Sobel edge detection to highlight rapid changes [Hjelmervik & Hjelmervik, 2019]. This approach produces a more local view of fronts, with the advantage that it can highlight the movement of individual eddy-like features (such as the Agulhas rings). The fronts detected using the Sobel method are moderately correlated with the magnitude of the velocity field, which is consistent with the theoretically expected spatial coincidence of fronts and jets. We will present our python GitHub repository, which will allow researchers to easily apply these methods to their own datasets. Figure caption Two methods for interpretable front detection. Solid lines represent classical fronts. (a) The “inter-class” metric, which indicates the probability that a grid cell is a boundary between two classes. The classes are defined by GMM of principal component values (PCs) derived from both temperature and salinity. The different colors indicate different class boundaries. (b) Sobel edge detection: approximately the magnitude of the spatial gradient of the PCs divided by each field’s standard deviation, which highlights locations of rapid change.
It is important that we prepare tomorrow’s scientists, decision makers, and communities to address the societal impacts of a changing climate. In order to respond to, manage, and adapt to those changes, citizens of all ages need accurate, up-to-date information, knowledge of the sciences, and analytical skills to make responsible decisions and long-term resiliency plans regarding these challenging topics. The Climate Literacy and Energy Awareness Network (CLEAN, http://cleanet.org) is 1) providing teaching resources for educators through the CLEAN Collection and pedagogical support for teaching climate and energy science; and 2) facilitating a professionally diverse community of climate and energy literacy stakeholders, called the CLEAN Network, to share and leverage efforts to extend the reach and effectiveness of climate and energy education. This presentation will provide an overview of the CLEAN web portal and techniques we have used to market it. We will showcase the CLEAN Collection, which is comprised of 700+ resources (curricula, activities, videos, visualizations, and demonstrations/experiments) that have been reviewed for scientific accuracy, pedagogical effectiveness, and technical quality. Recent activities of the CLEAN Network will be highlighted. We will present findings from our web analytics work, which monitors visitor use of the CLEAN web portal. Through analytics data, we will show evidence of successful CLEAN marketing efforts. The results of our recent pop-up survey, which has been completed by CLEAN visitors from six continents, will also be discussed. Survey results will provide detailed information about how our audiences use the web portal. We anticipate that our insights from the CLEAN network can aid other climate and energy education programs in effectively increasing the visibility of their vital work.
Spectral-based vegetation indices (VI) have been shown to be good proxies of grapevine stem water potential (Ψstem), potentially assisting in irrigation-decision making of commercial vineyards. However, VI-Ψstem correlations are mostly reported at the leaf or canopy scales using sensors attached to leaves or very-high-spatial resolution images derived from sensors mounted on small airplanes or drones. Here, for the first time, we take advantage of the high spatial resolution (3-m), near-daily images acquired from Planet’s nano-satellites constellation to derive VI-Ψstem correlations at the vineyard scale. Weekly Ψstem were measured along the growing season of 2017 in six vines in 81 commercial vineyards and in 60 pairs of vines in a 2.4 ha experimental vineyard in Israel. The clip application programming interface (API), provided by Planet, and Google Earth Engine platform were used to derive spatially continuous time series of four VIs: GNDVI, NDVI, EVI, and SAVI in the 82 vineyards. Results show that per-week multivariable linear models using variables extracted from VI time series successfully tracked spatial variations in Ψstem across the experimental vineyard (Pearson’s-r = 0.45–0.84: N=60). A simple linear regression model enabled monitoring seasonal changes in Ψstem along the growing season in the vineyard (r = 0.80–0.82). Planet VIs and seasonal Ψstem data from the 82 vineyards were used to derive a ‘global’ model for in-season monitoring of Ψstem at the vineyard-level (r = 0.81: RMSE = 17.5%: N=970). The ‘global’ model, which requires only a few VI variables extracted from Planet images, may be used for real-time weekly assessment of Ψstem in Mediterranean vineyards, substantially reducing expenses of conventional monitoring efforts.
Onsite wastewater treatment systems (OWTSs), or septic tank systems, are commonly used throughout the United States and are generally effective at remediating wastewater. However, malfunctioning OWTSs can introduce excess nutrients (i.e., nitrogen and phosphorous) and pathogens (i.e., E. coli) into the environment. There is increasing evidence that OWTSs can be a significant, and potentially underestimated, nonpoint source (NPS) of pollution. Thus, the objectives of this research were to (1) develop a model to assess the pollution potential from OWTSs using GIS-based multi-criteria decision analyses (MCDA) and (2) evaluate the relationship between the pollution potential from OWTSs and water pollutants. This study was completed in the Choccolocco Creek watershed, Alabama. The main tributary in this watershed, the Choccolocco Creek, is an impaired waterbody due to elevated E. coli concentrations. An MCDA was developed to model the pollution potential from OWTSs using environmental and OWTS variables. Similarly, an OWTS site unsuitability analysis, that only included environmental variables, was used to predict where OWTS may poorly perform, if OWTS data are not accessible in other areas. Water samples were taken along Choccolocco Creek to measure nitrogen, phosphorous, and E. coli concentrations. Pollutant concentrations were correlated to modeled pollution potential from OWTSs and OWTS site unsuitability, to compare how the exclusion of OWTS data changes the results. Additionally, land cover distribution was correlated to pollutant concentrations to account for other potential NPSs of water pollution. All water pollutants were significantly, positively correlated to OWTS count. Additionally, E. coli and nitrogen concentrations were significantly, positively correlated to pollution potential from OWTSs. This suggests that OWTSs may contribute to water pollution within the watershed. Furthermore, the location of areas most probable to have OWTS pollution varied between models, highlighting the importance of accounting for OWTSs as a NPS of water pollution. The methods presented could be adapted for other watersheds and used to guide best watershed management practices.
The current contribution presents wintertime climatology from 2012 to 2020 of mixed-phase clouds and their radiative effects when coupled to the sea ice states. Measurements from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) at the North Slope Alaska (NSA) site in Utqiagvik, Alaska are being analyzed.Classification of cloud hydrometeors in the liquid, ice or mixed-phase states was primary determined by the Cloudnet algorithm, developed by the Finish Meteorological Institute, and applied to a set of ground-based remote sensing instruments from NSA . To evaluate the influence by sea ice, which plays an important role on the Arctic surface-atmosphere interaction, the statistics are separated into cases when clouds are coupled or decoupled to specific sea ice conditions, like presence of leads or polynyas in the vicinity of NSA .We found that clouds coupled to sea ice with presence of leads have shown distinguished features like the increase of total liquid content, lower cloud base heights and less ice content when compared to decoupled cases. Nevertheless, these results rely on Cloudnet accurately detecting cloud droplets within mixed-phase clouds. Arctic cloud radiative effects (CRE) have already been studied from short expeditions like the SHEBA campaign (Shupe et al., 2004) and middle-term ground observations in Barrow (Shupe et al., 2015) and Ny-Ålesund, Svalbard (Ebell et al., 2020). We extend similar CRE studies for 8 years during wintertime, where longwave up- and down-welling flux measurements from NSA are used to estimate surface net fuxes and other cloud radiative features for cases when clouds are coupled or decoupled to sea ice conditions and their sensitivity to different gradients of air-surface temperature when leads or polynyas are present.