One of the central challenges for global food security is the growing pressure from increasingly frequent extreme weather events that results in sharp drops in crop yield and disruptions in the food supply. Such pressure can potentially be alleviated by international crop trade, which plays a crucial role in reallocating food commodities from surplus to deficit regions. However, few studies have examined the influence of extreme weather events and the synchrony of crop yield anomalies on trade linkages among nations. To investigate such influence, we used the international trade network of wheat as an example, developed relevant covariates, and tested specialized statistical and machine learning methods. The results show that countries with higher differences in extreme weather stress tend to have higher import volumes and more trade partners. Trade partnerships are more likely to be established between countries with synchronous yield variations. These findings indicate that increase in heat stress and co-occurring yield loss could lead to future higher dependence on imports, especially for vulnerable import dependent nations, and affect the stability of wheat supply. Hence, the current international trade network needs to be improved by contemplating the patterns of extreme weather and yield synchrony among countries.
Satellite observations of coastal Louisiana indicate an overall land loss over recent decades, which could be attributed to climate- and human-induced factors, including sea level rise (SLR). Climate-induced hydrological change (CHC) has impacted the way flood control structures are used, altering the spatiotemporal water distribution. Based on “what-if” scenarios, we determine relative impacts of SLR and CHC on increased flood risk over southern Louisiana and examine the role of water management via flood control structures in mitigating flood risk over the region. Our findings show that CHC has increased flood risk over the past 28 years. The number of affected people increases as extreme hydrological events become more exceptional. Water management reduces flood risk to urban areas and croplands, especially during exceptional hydrological events. For example, currently (i.e., 2016-2020 period), CHC-induced flooding puts an additional 73km2 of cropland under flood risk at least half of the time (median flood event) and 65km2 once a year (annual flood event), when compared to a past period (1993-1997). A ten- to twenty-fold increase relative to SLR-induced flooding. CHC also increases population vulnerability in southern Louisiana to flooding; additional 9900 residents currently live under flood risk at least half of the time, and that number increases to 27,400 for annual flood events. Residents vulnerable to SLR-induced flooding is lower (6000 and 3300 residents, respectively). Conclusions are that CHC is a major factor that should be accounted for flood resilience and that water management interventions can mitigate risks to human life and activities.
Understanding terrestrial ecosystems and their response to anthropogenic climate change requires quantification of land-atmosphere carbon exchange. However, top-down and bottom-up estimates of large-scale land-atmosphere fluxes, including the northern extratropical growing season net flux (GSNF), show significant discrepancies. We develop a data-driven metric for the GSNF using atmospheric carbon dioxide concentration observations collected during the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations (HIPPO) and Atmospheric Tomography Mission (ATom) flight campaigns. This aircraft-derived metric is bias corrected using three independent atmospheric inversion systems. We estimate the northern extratropical GSNF to be 5.7 ± 0.2 Pg C and use it to evaluate net biosphere productivity from the Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. While the model-to-model spread in the GSNF has decreased in CMIP6 models relative to that of the CMIP5 models, there is still disagreement on the magnitude and timing of seasonal carbon uptake with most models underestimating the GSNF and overestimating the length of the growing season relative to the observations. We also use an emergent constraint approach to estimate annual northern extratropical gross primary productivity to be 56 ± 15 Pg C, heterotrophic respiration to be 25 ± 11 Pg C, and net primary productivity to be 28 ± 10 Pg C. The flux inferred from these aircraft observations provides an additional constraint on large-scale, gross fluxes in prognostic Earth system models that may ultimately improve our ability to accurately predict carbon-climate feedbacks.
The 2021 Pacific Northwest heatwave featured record-smashing high temperatures, raising questions about whether extremes are changing faster than the mean, and challenging our ability to estimate the probability of the event. Here, we identify and draw on the strong relationship between the climatological higher-order statistics of temperature (skewness and kurtosis) and the magnitude of extreme events to quantify the likelihood of comparable events using a large climate model ensemble (CESM2-LE). In general, CESM2 can simulate temperature anomalies as extreme as those observed in 2021, but they are rare: temperature anomalies that exceed 4.5σ occur with an approximate frequency of one in a hundred thousand years. The historical data does not indicate that the upper tail of temperature is warming faster than the mean; however, future projections for locations with similar climatological moments to the Pacific Northwest do show significant positive trends in the probability of the most extreme events.
The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) mission has collected global surface elevation measurements for over three years. ICESat-2 carries the Advanced Topographic Laser Altimeter (ATLAS) instrument, which emits laser light at 532 nm, and ice and snow absorb weakly at this wavelength. Previous modeling studies found that melting snow could induce significant bias to altimetry signals, but there is no formal assessment on ICESat-2 acquisitions during the Northern Hemisphere melting season. In this work, we performed two case studies over the Greenland Ice Sheet to quantify volumetric scattering in ICESat-2 signals over snow. Elevation data from ICESat-2 was compared to Airborne Topographic Mapper (ATM) data to quantify bias. We used snow optical grain sizes derived from ATM and the Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) to attribute altimetry bias to snowpack properties. For the first case study, the mean optical grain sizes were 340±65 µm (AVIRIS-NG) and 670±420 µm (ATM), which corresponded with a mean altimetry bias of 4.81±1.76 cm in ATM. We observed larger grain sizes for the second case study, with a mean grain size of 910±381 µm and biases of 6.42±1.77 cm (ICESat-2) and 9.82±0.97 cm (ATM). Although these altimetry biases are within the accuracy requirements of the ICESat-2 mission, we cannot rule out more significant errors over coarse-grained snow, particularly during the Northern Hemisphere melting season.
The spatial scales of whistler-mode waves, determined by their generation process, propagation, and damping, are important for assessing the scaling and efficiency of wave-particle interactions affecting the dynamics of the radiation belts. We use multi-point wave measurements by two Van Allen Probes in 2013-2019 covering all MLTs at L=2-6 to investigate the spatial extent of active regions of chorus and hiss waves, their wave amplitude distribution in the source/generation region, and the scales of chorus wave packets, employing a time-domain correlation technique to the spacecraft approaches closer than 1000 km, which happened every 70 days in 2012-2018 and every 35 days in 2018-2019. The correlation of chorus wave power dynamics using is found to remain significant up to inter-spacecraft separations of 400 km to 750 km transverse to the background magnetic field direction, consistent with previous estimates of the chorus wave packet extent. Our results further suggest that the chorus source region can be slightly asymmetrical, more elongated in either the azimuthal or radial direction, which could also explain the aforementioned two different scales. An analysis of average chorus and hiss wave amplitudes at separate locations similarly shows the reveals different radial and azimuthal extents of the corresponding wave active regions, complementing previous results based on THEMIS spacecraft statistics mainly at larger L>6. Both the chorus source region scale and the chorus active region size appear smaller inside the outer radiation belt (at L< 6) than at higher L-shells.
Despite strong impacts that aerosols have on climate and air quality, significant gaps remain in our knowledge concerning their long-range transport, especially extreme transport events. With this consideration in mind and by leveraging the “atmospheric river” concept, this work develops an objective global algorithm for detecting aerosol atmospheric rivers (AARs), shows a climatology of AARs, elucidates their contributions to major global aerosol transport pathways, and illustrates how AARs can drive extreme cases of poor air quality conditions. Our methodology separately accounts for dust, carbonaceous (accounting for organic and black carbon separately where appropriate), sea salt and sulfate aerosols. Findings show there are a number of long-range regional transport pathways where AARs account for a sizable fraction (40-80%) of the total transport in relatively few events (20-40 AAR days/year). This study highlights the role of AARs in establishing source-receptor relationships that can drive regional air-quality and extremes.
The usage of the term Knowledge Graph (KG) has gained significant popularity since 2012, when Google introduced its own knowledge graph, and how they used it to enhance their searches and question answering systems. While various definitions and interpretations for knowledge graphs have been presented, what remains consistent is that knowledge graphs are commonly used with reasonsers to make inferences about data, based on assertions and axioms written by human experts. But knowledge graphs, which store complex, multi-dimensional data contain hidden patterns and trends that cannot be explored simply using reasoners. In such a case it becomes necessary to extract parts of the knowledge graph (focusing on the instances related to one property at a time) and analyze them individually in order to conduct a focused but tractable exploration of the domain. In this presentation, we present one way to gain insights from knowledge graphs, using network science. To achieve this goal, we have formalised the partitioning of knowledge graphs to unipartite knowledge networks, and present various ways to explore and analyse such knowledge networks to form scientific hypotheses, gain scientific insights and make discoveries.
The non-negative Polar Cap PCC index built from PCN (North) and PCS (South) indices correlates better with the solar wind merging electric field and is more representative for the total energy input from the solar wind to the magnetosphere and for the development of geomagnetic disturbances represented by the Kp index and ring current indices than either of the hemispheric indices. The present work shows that the ring current index, Dst, to a high degree of accuracy can be derived from a source function built from PCC indices. The integration of the PCC-based source function throughout the interval from 1992 to 2018 without attachment to the real Dst indices based on low latitude magnetic observations has generated equivalent Dst values that correlate very well (R=0.86) with the real Dst index values, which are represented with a mean deviation less than 1 nT and an overall rms deviation less than 13 nT. The precise correlation between the real and equivalent Dst values has been used to correct the PCC indices for saturation effects at high intensity disturbance conditions where the Dst index may take values beyond-100 nT. The relations between PCC and the ring current indices, Dst and ASY-H have been used, in addition, to derive the precise timing between polar cap convection processes reflected in the polar cap indices and the formation of the partial and total ring current systems. Building the ring current is considered to represent the energy input from the solar wind, which also powers auroral disturbance processes such as substorms and upper atmosphere heating. With current available PC indices, detailed and accurate SYM-H or Dst index values could be derived up to nearly one hour ahead of actual time by integration of the PCC-based source function from any previous quiet state. Thus, the PCC indices enabling accurate estimates of the energy input from the solar wind are powerful tools for space weather monitoring and for solar-terrestrial research. 1. Introduction. In the early Space Age, Dungey (1961) formulated the concept of magnetic merging processes taking place at the front of the magnetosphere between the Interplanetary Magnetic Field (IMF), when southward oriented, and the geomagnetic field, followed by the draping of the combined field over the pole and reconnection processes in the tail region, where the solar wind magnetic fields as well as the geomagnetic fields were restored. The model implies a two-cell convection system, where the high-latitude antisunward ionospheric and magnetospheric plasma drift across the polar cap and the return flow in a sunward motion along auroral latitudes generate the two-cell “forward convection” patterns, now termed DP2. Later, Dungey (1963) extended his model to include cases where IMF is northward (NBZ conditions),
In a complex reservoir with such a significant degree of heterogeneity, it’s a demand to characterize the reservoir using different seismic attributes based on the available data within certain time constraints. Pre-stack seismic inversion and Amplitude variation with offset (AVO) are among those techniques which give excellent results in particular for the gas bearing clastic reservoirs delineation because of the remarkable contrast between the latter and the surrounded rocks. However, challenges arise of data shortage in seismic and /or well data may obstacle applying these techniques. Moreover, if the prediction of water saturation (Sw) is needed using the seismic data, it represents a serious challenge because of the independent nonlinear relationship between water saturation and seismic attributes and inversion products. Prediction of water saturation is necessary not only for characterizing pay from non-pay reservoirs but for the economics also. Therefore, Extended Elastic Impedance has been performed to produce a 3D volume of water saturation over the reservoir interval then, 3D Sweetness volume was used in order to grasp the geometry of the sand bodies that have been charged with gas in addition to its internal architecture which could illustrate the different stages in the evolution of the Saffron channel system and the sand bodies distribution both vertically and spatially consequently increase the production and decrease the development risk.
Extratropical cyclones are weather phenomena with significant transfer of energy between the surface (over the ocean or on land) and the atmosphere. Recurrently, reanalysis data are used to understand the behavior of cyclonic tracks and to study extreme events, with constant updates and validations with the observational base in the Northern Hemisphere. However, studies using cyclone tracking in the Southwestern Atlantic, has proven more difficult. This disagreement seems to be in function of the structure and intensity of the forcing factors that influence both cyclogenesis and the displacement to the South Atlantic, when compared to the Northern Hemisphere. In this work, synoptic pressure charts at sea level, manually made and processed by the Brazilian Navy every 12 hours between the years 2010 and 2020, as a product resulting from a consensus among Navy meteorologists, were used to study the cyclonic pathways in the Southwestern Atlantic (METAREA V). Data obtained for all cyclones identified in the charts, based on their position and displacement, formed a database with 10737 cyclones, containing speed, dimensions, and pressure gradient. The cyclones identified have a higher radius frequency between 200/400 km and a faster-moving center shift. In addition, about 60% of cyclones associated with cold fronts have a life cycle ranging from 3 to 4 days. There is also a expressive cyclogenesis between latitudes 23ºS and 43ºS where, in austral autumn winter, increases its frequency over the ocean and close to the southern Brazilian coast. During spring, the greater cyclogenesis frequency occurs over the continent, close to Chaco area in Argentina and Uruguay. The impacts of these statistical figures on the south and southeastern Brazilian coast, mainly the continental insertion point of the cold fronts and cyclonic displacement that influence rough seas and storm surges, are discussed in this work. Keywords: EXTRATROPICAL CYCLONES, CYCLONE TRACK, SYNOPTIC CHARTS, SOUTHWESTERN ATLANTIC
Global Sensitivity Analysis (GSA) has long been recognized as an indispensable tool for model analysis. GSA has been extensively used for model simplification, identifiability analysis, and diagnostic tests, among others. Nevertheless, computationally efficient methodologies are sorely needed for GSA, not only to reduce the computational overhead, but also to improve the quality and robustness of the results. This is especially the case for process-based hydrologic models, as their simulation time is often too high and is typically beyond the availability for a comprehensive GSA. We overcome this computational barrier by developing an efficient variance-based sensitivity analysis using copulas. Our data-driven method, called VISCOUS, approximates the joint probability density function of the given set of input-output pairs using Gaussian mixture copula to provide a given-data estimation of the sensitivity indices. This enables our method to identify dominant hydrologic factors by recycling pre-computed set of model evaluations or existing input-output data, and thus avoids augmenting the computational cost. We used two hydrologic models of increasing complexity (HBV and VIC) to assess the performance of the proposed method. Our results confirm that VISCOUS and the original variance-based method can detect similar important and unimportant factors. However, while being robust, our method can substantially reduce the computational cost. The results here are particularly significant for, though not limited to, process-based models with many uncertain parameters, large domain size, and high spatial and temporal resolution.
Terrestrial biosphere models (TBMs) play a key role in detection and attribution of carbon cycle processes at local to global scales and in projections of the coupled carbon-climate system. TBM evaluation commonly involves direct comparison to eddy-covariance flux measurements. This study uses atmospheric CO2 mole fraction ([CO2]) measured in situ from aircraft and tower, in addition to flux-measurements from summer 2016 to evaluate the CASA TBM. WRF-Chem is used to simulate [CO2] using biogenic CO2 fluxes from a CASA parameter-based ensemble and CarbonTracker version 2017 (CT2017) in addition to transport and CO2 boundary condition ensembles. The resulting “super ensemble” of modeled [CO2] demonstrates that the biosphere introduces the majority of uncertainty to the simulations. Both aircraft and tower [CO2] data show that the CASA ensemble net ecosystem exchange (NEE) of CO2 is biased high (NEE too positive) and identify the maximum light use efficiency Emax a key parameter that drives the spread of the CASA ensemble. These findings are verified with flux-measurements. The direct comparison of the CASA flux ensemble with flux-measurements indicates that modeled [CO2] biases are mainly due to missing sink processes in CASA. Separating the daytime and nighttime flux, we discover that the underestimated net uptake results from missing sink processes that result in overestimation of respiration. NEE biases are smaller in the CT2017 posterior biogenic fluxes, which assimilates observed [CO2]. Flux tower analyses, however, reveal unrealistic overestimation of nighttime respiration in CT2017.
In support of the American College & University Presidents’ Climate Leadership Commitments, the University of Maryland College Park (UMD) has established a goal to become climate neutral by 2050. While much progress has been made to lower the University’s carbon footprint across multiple emissions sectors, tree conservation or restoration has traditionally been excluded due to concerns about the reliability and consistency of the science. For the past several years, faculty and students in UMD’s Department of Geographical Sciences have been working with state governments across the region to inform climate action planning with advanced forest carbon science. However, with student support and leadership, we identified an opportunity to retool this same science to help UMD “walk the walk” and advance our own forest climate goals in parallel with Maryland and other U.S. Climate Alliance states. By partnering with the Office of Sustainability and other land management entities, we have been able to directly inform the campus climate action plan with robust forest carbon estimates as well as influence and support the carbon budgeting process of all universities that have pledged support for the “Carbon Commitment.” Unlike state governments, the university’s approach to sustainability broadly follows that of a corporation, requiring enhanced collaboration to ensure the science is provided in user-relevant formats while remaining consistent with science approaches utilized by state partners. Our experience during the first year of this project underscores the value of building out scientific approaches that meet specific stakeholder needs while remaining poised to adapt these tools in support of new partnerships and collaborations.
International frameworks for climate mitigation that build from national actions have been developed under the United National Framework Convention on Climate Change and advanced most recently through the Paris Climate Agreement. In parallel, sub-national actors have set greenhouse gas (GHG) reduction goals and developed corresponding climate mitigation plans. Within the U.S., multi-state coalitions have formed to facilitate coordination of related science and policy. Here, utilizing the forum of the NASA Carbon Monitoring System’s Multi-State Working Group (MSWG), we collected and reviewed climate mitigation plans for 11 states in the Regional Greenhouse Gas Initiative (RGGI) region of the Eastern U.S. For each state we reviewed the 1) policy framework for climate mitigation, 2) GHG reduction goals, 3) inclusion of forest carbon in the state’s climate action plan, 4) existing science used to estimate forest carbon, and 5) stated needs for carbon monitoring science. Across the region, we found important differences across all categories. While all states have GHG reduction goals and framework documents, nearly three-quarters of all states do not account for forest carbon when planning GHG reductions; those that do account for forest carbon use a variety of scientific methods with various levels of planning detail and guidance. We suggest that a common, efficient, standardized forest carbon monitoring system would provide important benefits to states and the geographic region as a whole. In addition, such a system would allow for more effective transparency and progress tracking to support state, national, and international efforts to increase ambition and implementation of climate goals.
The 2018 eruption of Kīlauea volcano produced the largest and most destructive lava flows in the lower East Rift Zone (LERZ) in the past 200 years. Average effusion rates exceeded 100 m3 s-1 (DRE) for more than two months as lava covered > 30 km2 of land area. The largest and longest-lived lava flow was produced by fissure 8 and had flow advance rates exceeding 100 m hr-1 and a run-out length of 13 km. While residents were able to safely evacuate from this rapidly advancing flow, hundreds of structures were destroyed. We integrate observed eruption parameters from the fissure 8 flow with numerical models for lava flows to investigate how eruption rate, topography, and rheology affect the initial path, advance rate, and extent of a lava flow. Many numerical models have been created to represent the advance and/or extent of lava flows. We apply both 1D and 2D, rules-based and physics-based models to explore the advantages and limitations of these model types. First, we validate the models for fissure 8 flow parameters using existing datasets from field observations and sample analysis. Second, we vary the eruption rate and lava rheology to test the influence of these parameters on the advance rate and flow extent. This analysis demonstrates the level of confidence that can be associated with modeling results when estimating difficult-to-constrain parameters during an eruption. Third, using input digital elevation models (DEM) of different resolutions, we examine the sensitivity of model accuracy to DEM resolution, with a specific focus on the influence on flow advance of smaller-scale topographic features that may not be resolved in coarse-resolution DEMs. Through better understanding of how different parameters control flow emplacement, and how to best apply the models describing that emplacement, we aim to improve the ability to estimate advance rate and flow path during (and prior to) the initial stages of flow emplacement and provide more detailed hazard assessments for future eruptions.
If the university can be thought of as an incubator for ideas and thought leadership, then each department is a learning ecosystem unto itself. The IDEEAS (Inclusion, Diversity, and Equity in Earth and Atmospheric Sciences) Working Group formed organically in Cornell’s Earth and Atmospheric Sciences department as a grassroots group with a desire to improve the department ecosystem. Self-selected from the full cross-section of the department, our members comprise students, staff, researchers, faculty, and emeriti. IDEEAS is a non-hierarchical group within the very hierarchical setting of academia, and our work provides a model for disrupting traditional power structures while leveraging their influence to reimagine how an academic unit could and should function. IDEEAS is not a committee; we are a collective. We believe that, irrespective of rank or role, every member of the department community has the capacity to practice leadership. As such, we lead by action. Each IDEEAS project or initiative is organized around an action team, who collectively carry out a community-informed vision of the culture we would like to co-create with the rest of the department. Our commitment to collective leadership empowers constituencies (e.g., students, non-academic staff, post-docs) who have traditionally lacked a pathway to provide input or participate in department-level decision making. IDEEAS is developing formal channels of communication between the group and department leadership in an effort to develop a sustainable ecosystem that will outlive its founders. IDEEAS events combine community building and intentional learning opportunities to promote critical reflection and foster connections. Events included a well-attended kickoff party with facilitated conversation that drew 56 attendees (~40% of the department), and community conversations about implicit bias and structural racism. IDEEAS organizers have been critically responsive during ongoing COVID19 isolation, providing numerous opportunities for social connection and using the disruption as a catalyst to cultivate connection and build community resilience that will outlast the pandemic. We invite discussion and collaboration with those engaged in similar justice, equity, diversity, and inclusion work in the geosciences.
Sparse observational data in developing regions leads to uncertainty about how hydro-climatic factors influence crop phases and productivity, knowledge of which is essential to mitigating food security threats induced by climate change. In this study, NASA Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Measurement (GPM), and Global Land Data Assimilation System (GLDAS) data products bypass spatiotemporal limitations and drive machine learning algorithms developed to characterize hydro-climate-productivity interactions. Extensive feature engineering processes these products into nearly 4000 metrics, designed to decompose crop growing season hydro-climate conditions. Dimensionality reduction with bidirectional step-wise regression, Multi-Adaptive-Regression-Splines (MARS), and Random Forest algorithms are explored to determine key temporal hydro-climate drivers to agricultural productivity, with each method recognizing unique linear and non-linear predictors. Finally, multi-variate regression, MARS, and Random Forest models are trained on the drivers to predict seasonal crop yield. We apply this hydro-climate-productivity framework to investigate rabi wheat productivity on Pakistan’s Potohar Plateau. Here, we identify six of wheat’s ten phenological phases that display strong hydro-climate responses, with the shooting phase exhibiting sensitivity to precipitation intensity, minimum soil moisture, and sub-zero temperatures. In addition, the plateau’s heterogeneous climate-productivity connections are captured well by the calibrated models, strengthening their application for studying broader climate change impacts. The integration of remote sensing products and machine learning offers an effective framework to bypass in-situ data limitations and decompose climate-crop productivity relationships, thus improving drought onset recognition and food security forecasting.