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NASA NTRS2020

Direct and Semi-Direct Radiative Forcing of Biomass-Burning Aerosols Over the Southeast Atlantic (SEA) And Its Sensitivity to Absorbing Properties: A Regional Climate Modeling Study

Simulations are performed for the period 2000–2015 by two different regional climate models, ALADIN and RegCM, to quantify the direct and semi-direct radiative effects of biomass-burning aerosols (BBAs) in the southeast Atlantic (SEA) region. Different simulations have been performed using strongly absorbing BBAs in accordance with recent in situ observations over the SEA. For the July–August–September (JAS) season, the single scattering albedo (SSA) and total aerosol optical depth (AOD) simulated by the ALADIN and RegCM models are consistent with the MACv2 climatology and MERRA-2 and CAMS-RA reanalyses near the biomass-burning emission sources. However, the above-cloud AOD is slightly underestimated compared to satellite (MODIS and POLDER) data during the transport over the SEA. The direct radiative effect exerted at the continental and oceanic surfaces by BBAs is significant in both models and the radiative effects at the top of the atmosphere indicate a remarkable regional contrast over SEA (in all-sky conditions), with a cooling (warming) north (south) of 10 ∘S, which is in agreement with the recent MACv2 climatology. In addition, the two models indicate that BBAs are responsible for an important shortwave radiative heating of ∼0.5–1 K per day over SEA during JAS with maxima between 2 and 4 km a.m.s.l. (above mean sea level). At these altitudes, BBAs increase air temperature by ∼0.2–0.5 K, with the highest values being co-located with low stratocumulus clouds. Vertical changes in air temperature limit the subsidence of air mass over SEA, creating a cyclonic anomaly. The opposite effect is simulated over the continent due to the increase in lower troposphere stability. The BBA semi-direct effect on the lower troposphere circulation is found to be consistent between the two models. Changes in the cloud fraction are moderate in response to the presence of smoke, and the models differ over the Gulf of Guinea. Finally, the results indicate an important sensitivity of the direct and semi-direct effects to the absorbing properties of BBAs. Over the stratocumulus (Sc) region, DRE varies from +0.94 W m−2 (scattering BBAs) to +3.93 W m−2 (most absorbing BBAs).

Marc Mallet↗
NASA NTRS2020

Spatial Heterogeneity in CO2, CH4, and Energy Fluxes: Insights from Airborne Eddy Covariance Measurements Over the Mid-Atlantic Region

The exchange of carbon between the Earth’s atmosphere and biosphere influences the atmospheric abundances of carbon dioxide (CO2) and methane (CH4). Airborne eddy covariance can quantify surface-atmosphere exchange from landscape-to-regional scales, offering a unique perspective on carbon cycle dynamics. We use extensive airborne measurements to quantify fluxes of sensible heat, latent heat, CO2, and CH4across multiple ecosystems in the Mid-Atlantic region during September 2016 and May 2017. In conjunction with footprint analysis and land cover information, we use the airborne dataset to explore the effects of landscape heterogeneity on measured fluxes. Our results demonstrate large variability in CO2 uptake over mixed agricultural and forested sites, with fluxes ranging from -3.4 ± 0.7 to -11.5 ± 1.6μmol m-2s-1 for croplands and -9.1 ± 1.5 to -22.7 ± 3.2μmol m-2s-1for forests. We also report substantial CH4emissions of 32.3 ± 17.0to 76.1 ± 29.4nmol m-2s-1 from a brackish herbaceous wetland and 58.4± 12.0 to 181.2 ± 36.8nmol m-2s-1 from a freshwater forested wetland. Comparison of ecosystem-specific aircraft observations with measurements from eddy covariance flux towers along the flight path demonstrate that towers capture ~30–75% of the regional variability in ecosystem fluxes. Diel patterns measured at the tower sites suggest that peak, midday flux measurements from aircraft accurately predicts net daily CO2exchange. We discuss next steps in applying airborne observations to evaluate bottom-up flux models and improve understanding of the biophysical processes that drive carbon exchange from landscape-to-regional scales.

airborne measurements to quantify fluxes of sensib↗
NASA NTRSDate not supplied

Observations of Regional Biomass Burning and Urban Trace Gas Enhancement Ratios in Southeast Asia and their Relationship with Aerosol Composition and Air Quality

Biomass burning in Southeast Asia is a major pollutant source contributing to poor air quality throughout the region. Thus, understanding these emissions is critical for predicting and mitigating their health impacts. While there have been many studies reporting ground-based and satellite measurements, airborne regional scale measurements to tie the two together have not been common. The 2019 Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) field campaign examined Southeast Asian regional sources and their effects on aerosol/cloud interactions using a combination of airborne, shipborne, and ground-based measurements. Flights sampled a variety of airmasses in the Philippine, South China, and Sulu seas during both monsoonal and post-monsoonal conditions. Thus, CAMP2Ex provides a unique opportunity to study how transported and local emissions affect air quality trends and airmass chemical composition. In this analysis, correlated airborne in situ enhancement ratios of CH4 and CO are used to identify source regimes of high urban or biomass burning influence as well as urban regimes with different emission factors. Combined with HYSPLIT backtrajectories, these source regimes are examined for their effects on ozone, NOy, and aerosol chemical composition. For example, while observed O3/CO enhancement ratios remain constant for urban differing source regimes, NOy/CO ratios are observed to vary across these regimes. For biomass burning sources, O3/CO enhancement ratios are observed to be lower than previously reported by measurements in the region.

Joshua Paul Digangi↗
NASA NTRS2021

Cloud Detection over Sunglint Regions with Observations from the Earth Polychromatic Imaging Camera (EPIC)

With the ability to observe the entire sunlit side of the Earth, EPIC data have become an important resource for studying cloud daily variability. Inaccurate cloud masking is a great source of uncertainty. One main region that is prone to error in cloud masking is the sunglint area over ocean surfaces. Cloud detection over these regions is challenging for the EPIC instrument because of its limited spectral channels. Clear sky ocean surface reflectance from visible channels over sunglint is much larger than that over the non-glint areas and can exceed reflectance from thin clouds. This paper presents an improved EPIC ocean cloud masking algorithm (Version 3). Over sunglint regions (glint angle ≤ 25°), the algorithm utilizes EPIC’s oxygen (O2) A-band ratio (764nm to 780 nm) in addition to the 780 nm reflectance observations in masking tests. Outside the sunglint regions, a dynamic reflectance threshold for the Rayleigh corrected 780 nm reflectance is applied. The thresholds are derived as a function of glint angle. When compared with co-located data from the geosynchronous Earth orbit (GEO) and the low Earth orbit (LEO) observations, the consistency of the new ocean cloud mask algorithm has increased by 4~10% and 4~6% in the glint center and granule edges respectively. The false positive rate is reduced by 10~17%. Overall global ocean cloud detection consistency increases by 2%. This algorithm, along with other improvements to the EPIC cloud masks, has been implemented in the EPIC cloud products Version 3. This algorithm will improve the cloud daily variability analysis by removing the artificial peak at local noon time in the glint center latitudes and reducing biases in the early morning and late afternoon cloud fraction over ocean surfaces.

EPIC↗
NASA NTRSDate not supplied

Observations of Regional Biomass Burning and Urban Trace Gas Enhancement Ratios in Southeast Asia and their Relationship with Aerosol Composition and Air Quality

Biomass burning in Southeast Asia is a major pollutant source contributing to poor air quality throughout the region. Thus, understanding these emissions is critical for predicting and mitigating their health impacts. While there have been many studies reporting ground-based and satellite measurements, airborne regional scale measurements to tie the two together have not been common. The 2019 Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) field campaign examined Southeast Asian regional sources and their effects on aerosol/cloud interactions using a combination of airborne, shipboard, and ground-based measurements. Flights sampled a variety of airmasses in the Philippine, South China, and Sulu seas during both the southwest monsoon and monsoon transition periods. Thus, CAMP2Ex provides a unique opportunity to study how transported and local emissions affect air quality trends and airmass chemical composition. In this analysis, correlated airborne in situ enhancement ratios of CH4 to CO are used to identify source regimes of high urban or biomass burning influence as well as urban regimes with different emission factors. Combined with HYSPLIT backtrajectories, these source regimes are examined for their effects on ozone, NOy, and aerosol chemical composition. For example, while observed O3/CO enhancement ratios remain constant for differing urban source regimes, NOy/CO ratios are observed to vary across these regimes. For biomass burning sources, O3/CO enhancement ratios are observed to be lower than previously reported by measurements in the region.

Joshua Paul Digangi↗
NASA NTRSDate not supplied

Observations of Southeast Asian Biomass Burning and Urban Trace Gas Enhancement Ratios: Insights into Regional Air Quality and Aerosol Composition

Southeast Asian biomass burning is a major pollutant source that contributes to poor air quality throughout the region. Thus, understanding these emissions is critical for predicting and mitigating their health impacts. While many studies have reported ground-based and satellite measurements, airborne measurements at a regional scale capable of tying the two together have not been common. The 2019 Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) field project examined Southeast Asian regional sources and their effects on aerosol/cloud interactions using a combination of airborne, shipboard, and ground-based measurements. These flights sampled a variety of airmass sources over the Philippine, South China, and Sulu seas during both the southwest monsoon and monsoon transition periods. Measurements during CAMP2Ex provide a unique opportunity to investigate how these transported and local emissions affected air quality trends and airmass chemical composition. We present correlated airborne in situ enhancement ratios of CH4 to CO, using them to identify source regimes of either high urban or biomass burning influence as well as urban regimes with different emission factors. Combined with backtrajectory analysis using HYSPLIT, source regimes were examined for differences in ozone, reactive nitrogen, and aerosol chemical composition. While observed O3/CO enhancement ratios remain constant for differing urban source regimes, NOy/CO ratios varied across these regimes. For biomass burning sources, O3/CO enhancement ratios are observed to be lower than previously reported by measurements in the region.

Joshua P Digangi↗
NASA NTRSDate not supplied

Regional Variations in the Diurnal Cycle of Tropical Precipitation as Represented by IMERG, ERA5, and Spaceborne Ku Radar

The diurnal cycle of precipitation is highly regional and is typically a product of multiple competing effects that can be highly localized. The diurnal cycle in high precipitation regions such as the Amazon and the Maritime Continent are of particular interest, especially due to the complex coastal effects which take place over the Maritime Continent. The high spatial and temporal resolution provided by the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG) dataset, is used in this study to examine the fine-scale features of the diurnal cycle in these regions. Using an 18-year (2000 – 2018) record of IMERG precipitation observations, diurnal and semidiurnal phase and amplitude are calculated using a fast Fourier transform (FFT) method on precipitation averaged for each half-hour of the day at 0.1°x0.1° spatial resolution. We first introduce an objective method of identifying locations where the diurnal signals are robust and strong. Clear patterns of precipitation phase propagation with distance from shore are shown over both regions, with the diurnal phase and amplitude exhibiting a strong dependence on the distance from the coastline. Semidiurnal cycles are generally weaker than the diurnal cycle except in some isolated locations. Similar analysis is also conducted on the ERA5 reanalysis data in order to evaluate the model’s representation of the precipitation diurnal cycle. The model captures the broad scale patterns of diurnal variability but does not capture all the fine scale patterns nor the exact timing that is observed by IMERG. Comparisons are also made to a long record Ku radar dataset created by combining Tropical Rainfall Measuring Mission (TRMM) and GPM observations, thus providing an additional point of comparison for the timing of the ERA5 precipitation peak, since the timing precipitation can be different, even in between observational datasets.

L J Hayden↗
NASA NTRS2021

Context and future directions for integrating forest carbon into sub-national climate mitigation planning in the RGGI region of the U.S.

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, we collected and reviewed climate mitigation plans for 11 states in the Regional Greenhouse Gas Initiative region of the Eastern U.S. For each state we reviewed the (a) policy framework for climate mitigation, (b) GHG reduction goals, (c) inclusion of forest activities in the state’s climate action plan, (d) existing science used to quantify forest carbon estimates, and (e) stated needs for forest 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.

Rachel L Lamb↗
NASA NTRSDate not supplied

Improving Regional Air Quality Forecasting Through Chemical Data Assimilation and Dynamic Emissions Adjustment

Poor air quality (AQ) is one of the most important human-health and environmental problems facing the United States (US). In addition to the detrimental impacts on human- and environmental-health, poor AQ has an economic cost of ~5% of the US gross domestic product (~$790 billion). AQ managers use AQ analyses and modeling to better understand, anticipate, and avoid poor AQ events. Our research focuses on improving AQ analysis/forecast skill, predictability, and emission estimates through improved and more efficient: (i) modeling and data assimilation strategies; (ii) dynamic emissions adjustment strategies; and (iii) use of satellite remote-sensing Earth observations (e.g., MOPITT, IASI, MODIS, OMI, TROPOMI, TEMPO, etc.). This seminar will review: (i) regional chemical weather forecasting/data assimilation with dynamic emissions adjustment with WRF-Chem/DART; (ii) strategies for efficiently assimilating satellite retrieval profiles with ‘compact phase space retrievals’ (CPSRs); (iii) results from joint assimilation of multiple satellite retrievals at medium (12 km × 12 km) and high (4 km × 4 km) spatial resolutions; and (iv) results from observing system simulation experiments (OSSEs) to investigate whether we can recover COVID-period anthropogenic emissions by assimilating synthetic TEMPO NO2 tropospheric column retrievals with dynamic emissions adjustment. Biographical Sketch: Dr. Mizzi is a Senior Research Fellow working and Dr. Johnson at the NASA Ames Research Center. He holds BA and MS degrees in Environmental Science from the University of Virginia, MS and PhD degrees in Applied Mathematics from the University of Colorado at Boulder (CUB), and a JD degree (with an emphasis in Environmental Law) from the University of Colorado School of Law. He worked at the National Center for Atmospheric Research for nearly 25 years on global atmospheric modeling, dynamic and physical initialization, regional hybrid data assimilation, and most recently on regional chemical data assimilation. He also worked as an environmental attorney and consultant for nearly 15 years. He is an expert in numerical modeling and is recognized internationally as a leading expert in regional, chemical data assimilation with dynamic emissions adjustment. Dr. Mizzi became affiliated with NASA Ames in March 2020 to work on improving AQ analysis/forecast skill, predictability, and ‘top-down’ emissions adjustment though the assimilation of Earth observations. An emphasis of his current work is developing methods for assimilating synthetic TEMPO retrievals to quantify the expected benefits of TEMPO relative to existing AQ observations.

Arthur P. Mizzi↗
NASA NTRSDate not supplied

The electron diffusion region and its relation to the larger-scale environment

The electron diffusion region forms the inner core of the magnetic reconnection machine. In this region, physical processes act to sustain a reconnection electric field, they accelerate to provide current density, and the heat to provide particle pressure in the current layer. The relative roles of these processes depend on whether the overall geometry is anti-parallel or asymmetric, and whether a guide field is present of not, but the overall concept applies in each case. The EDR is embedded in successively larger regions, starting with the ion diffusion region to even larger scales, where the plasma increasingly behaves like an anisotropic MHD plasma. With our new understanding of how the EDR works, it is of great interest to study how EDR processes couple to the larger scale environment. The ultimate efficacy of the magnetic reconnection is shaped by this interesting example of micro-macro coupling. In this presentation, we present some thoughts pertaining to this topic. In particular, we will discuss how information transport between scales is mediated, and how the very small scales can reach balance with the larger, overall system. We will review any pertinent research and present a set of questions inviting future research.

Michael Hesse↗
NASA NTRS2022

Towards Effective Drought Monitoring in the Middle East and North Africa (Mena) Region: Implications From Assimilating Leaf Area Index and Soil Moisture Into the Noah-Mp Land Surface Model for Morocco

The Middle East and North Africa (MENA) region has experienced more frequent and severe drought events in recent decades, leading to increasingly pressing concerns over already strained food and water security. An effective drought monitoring and early warning system is thus critical to support risk mitigation and management by countries in the region. Here we investigate the potential for assimilation of leaf area index (LAI) and soil moisture observations to improve the representation of the overall hydrological and carbon cycles and drought by an advanced land surface model. The results reveal that assimilating soil moisture does not meaningfully improve model representation of the hydrological and biospheric processes for this region, but instead it degrades the simulation of the interannual variation in evapotranspiration (ET) and carbon fluxes, mainly due to model weaknesses in representing prognostic phenology. However, assimilating LAI leads to greater improvement, especially for transpiration and carbon fluxes, by constraining the timing of simulated vegetation growth response to evolving climate conditions. LAI assimilation also helps to correct for the erroneous interaction between the prognostic phenology and irrigation during summertime, effectively reducing a large positive bias in ET and carbon fluxes. Independently assimilating LAI or soil moisture alters the categorization of drought, with the differences being greater for more severe drought categories. We highlight the vegetation representation in response to changing land use and hydroclimate as one of the key processes to be captured for building a successful drought early warning system for the MENA region.

Wanshu Nie↗
NASA NTRSDate not supplied

SERVIR: Cross-Comparison of Carbon Emission Estimates Based on Variable Land Use Land Cover Changes within SERVIR Focus Regions

Deforestation in the tropics contributes approximately one-fifth of the annual global greenhouse gas (GHG) emissions. In an effort to reduce GHG emissions, SERVIR - a joint USAID and NASA initiative - is currently implementing the SERVIR CArbon Pilot (S-CAP) activity. By developing a comprehensive CO2 tracking system in Google Earth Engine based on both remotely sensed data and in situ observations, S-CAP aims to build capacity within the SERVIR regional hubs to improve decision making and local monitoring efforts surrounding GHG emissions. The S-CAP estimations are being completed throughout 11 pilot countries within the SERVIR regions of Asia, Africa, and the Americas to integrate partner contributions and local input into the development process. These emission estimate calculations use country-level REDD+ FREL reported values, global remotely sensed data (e.g. Global Forest Watch) and regional datasets and are completed using the IPCC Guidelines for National Greenhouse Gas Inventories. From the S-CAP activity thus far, country CO2 estimations had varied differences, ranging from 56% to 197% between their lowest and highest estimates. The results from this study show that CO2 emission estimates can differ greatly depending on the land cover data sets used as well as the biomass values, and how a comprehensive database is essential in understanding the full range of impacts that land cover change has in these regions. Using this ensemble approach, SERVIR hubs can better select the most appropriate datasets available for estimating emissions.

Carbon Emission↗
NASA NTRSDate not supplied

Modeling EUV Intensity at the Top of the Transition Region Using SPICE Data on Board Solar Orbiter

The Spectral Imaging of Coronal Environment (SPICE; SPICE Consortium et al. 2020) provides an extraordinary opportunity to study the chromosphere and transition region using EUV wavelengths, e.g., Ne VIII 770 Å, CIII 977 Å, OVI 1032 Å, and Lyman-𝛽 025 Å. We present results modeling Ne VIII 770 Å intensity using images from SPICE and the COronal DEnsity and Temperature (CODET) model. This model is based on relationships between the magnetic field, density, and temperature. It uses a flux transport model, the Potential Field Extrapolation model (PFSS), an emission model based on Chianti atomic database 10.0.2, and an optimization algorithm. In addition, we assume that the emission from the top of the transition region (Ne VIII 770 Å) can be described using the magnetic field in the coronal base at 1.014𝑅⨀ (from PFSS). We found the mean full disc electron density in the transition region obtained from the CODET model for Ne VIII 770 Å (𝑙𝑜𝑔10𝑇[𝐾] = 5.8) corresponds to ∼ 𝑙𝑜𝑔10𝑁 𝑐𝑚−3 >9, and temperature value is 𝑙𝑜𝑔10𝑇[𝐾]~6. This value agrees with the Ne VIII 770 Å temperature formation 𝑙𝑜𝑔10𝑇[𝐾]~5.8. The density above sunspots (𝐵 > 50 𝐺) corresponds to 𝑙𝑜𝑔10𝑁 𝑐𝑚−3 > 9, while in quiet Sun regions (𝐵 < 50 𝐺) the mean density is 𝑙𝑜𝑔10𝑁[𝑐𝑚−3 ] = 8 .

Jenny M Rodriguez Gomez↗
NASA NTRSDate not supplied

The Seasonal Evolution of Atmospheric Vertical Structure of Smoke and Humidity Over the Southeast Atlantic Biomass Burning Region

The atmosphere over the southeast Atlantic Ocean (SEA) sees a consistent springtime biomass burning (BB) smoke from widespread agricultural fires on the African continent. This smoke layer is initially lofted high in a continental mixed layer (~5-6km) and is then transported westward in the free troposphere, where it overlies and ultimately mixes into the SEA stratocumulus-topped oceanic boundary layer. Coincident with this smoke is an elevated humidity signal which is present from the time a given airmass is over the continental source region; this correlation is persistent through the biomass burning season, although varying through the course of the season. ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) was a NASA Earth Venture Suborbital mission with the goal of measuring aerosol, cloud, and atmospheric properties over this region during three deployments in September 2016, August 2017, and October 2018. We use the ORACLES observations to assess this smoke-vapor relationship. First, we discuss the good agreement between the airborne ORACLES dataset and the ECMWF ERA5 and CAMS reanalyses, as well as results from NASA’s MERRA-2, as seen over the three deployment years. We then use the reanalyses to develop a framework in which to understand more broadly the radiative and dynamical interactions between the elevated smoke and water vapor over the SEA through the biomass burning season, beyond the three ORACLES observation periods. Looking at seven years of reanalysis data for the BB season, we find distinct variations between each month/deployment in terms of vertical smoke distribution and correlation to atmospheric specific humidity, due to changing conditions through the BB season. Using k-means clustering of these climatological reanalyses, we identify six canonical atmospheric profile types of varying total atmospheric humidity and vertical structure and describe their changing incidence spatially and throughout the season, and six analogous profile types for carbon monoxide, allowing us to characterize the atmospheric structure of both vapor and BB over time throughout the SEA region. The radiative heating of both aerosol and water vapor has potential to influence the cloud-top entrainment and atmospheric turbulence, thus modifying the underlying stratocumulus cloud properties. We next discuss how these smoke-humidity variations influence both the low-cloud fraction (as observed by MODIS and VIIRS, and output by ERA5) and the boundary layer height in the region. This classification will ultimately allow for a more complete analysis of the broader radiative and dynamical effects of humid aerosols overlying stratocumulus clouds.

Kristina Marie Myers Pistone↗
NASA NTRSDate not supplied

Non-Additivity of the Midlatitude Circulation Response to Regional Arctic Temperature Anomalies: The Role of the Stratosphere

Previous studies have documented the impact of the Arctic sea ice loss and associated warming on the midlatitude weather and climate, especially the influence of sea ice retreat over the Barents-Kara Sea on the North Atlantic and Europe regions. However, less attention has been given to other geographical locations over the Arctic, and to the linear additivity of the circulation response to regional Arctic sea ice loss and temperature anomalies. Using a simplified dry dynamical core model, we demonstrate that responses to regional Arctic temperature anomalies over the Barents-Kara Sea, Baffin Bay-Davis Strait-Labrador Sea, and East Siberia-Chukchi Sea, separately, cause similar equatorward shift of the tropospheric jet, but different stratospheric polar vortex responses. Furthermore, responses to regional Arctic temperature anomalies are not linearly additive, and the residual resembles a positive Northern Annular Mode-like structure. Additional targeted experiments highlight the stratospheric influence in the non-additivity of the midlatitude tropospheric response.

Bithi De↗
NASA NTRS2023

Evaluation of a Regional Crop Model Implementation for Sub-National Yield Assessments in Kenya

CONTEXT: Cropping system models can be used to both assess regional food security and to monitor and predict agricultural drought. Agriculture in Kenya is extremely important to both the economy and food security of the country. OBJECTIVE: This study evaluated a regional implementation of a widely used crop model, the Decision Support System for Agrotechnology Transfer (DSSAT), within a coupled modeling framework, the Regional Hydrologic Extremes Assessment System (RHEAS), over Kenya. The goal of this study was to assess the ability of RHEAS to simulate the annual variability of maize yields at the county level and evaluate the uncertainty inherent in the model and inputs. METHODS: The RHEAS system implements a stochastic ensemble approach to account for field scale variabilities in crop management practices and underlying soil and weather conditions. Satellite-derived datasets were used to evaluate the land surface component of the system and seasonally disaggregated yield for 5 years was used to assess the performance of the cropping system model. RESULTS AND CONCLUSIONS: The median correlation between RHEAS and satellite-derived soil moisture and evapotranspiration estimates were 0.78, and 0.51, respectively, indicating that the model is able to capture the key drivers of the hydrological budget. Overall, RHEAS simulated yearly yield variations with a median correlation of 0.7 with reported yields, with the best performance in the short rains season. However, across both seasons, the RHEAS model was positively biased on the order of ~1.6 MT/ha. The overall median unbiased RMSE was 0.66 MT/ha. The RHEAS system shows skill at simulating extreme departures in anomalies, and a majority of the time (62.5%) the reported yields fall within the interquartile range of the simulations. SIGNIFICANCE: One of the most important areas of improvement for the next generation of agricultural data and models is to better understand and communicate the inherent uncertainties. This is especially critical in data-limited regions. Here we present a modeling system and its implementation that begins to address these concerns. We demonstrate the ability to simulate broad trends in yields at the county level for sub-annual yields with skills that commensurate previous national/annual level studies.

Crop model↗
NASA NTRS2023

Inland Water Greenhouse Gas Budgets for RECCAP2: 2. Regionalization and Homogenization of Estimates

Inland waters are important emitters of the greenhouse gasses (GHGs) carbon dioxide (CO 2 ), methane (CH 4 ), and nitrous oxide (N 2 O) to the atmosphere. In the framework of the 2 nd phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2) initiative, we synthesize existing estimates of GHG emissions from streams, rivers, lakes and reservoirs, and homogenize them with regard to underlying global maps of water surface area distribution and the effects of seasonal ice cover. We then produce regionalized estimates of GHG emissions over 10 extensive land regions. According to our synthesis, inland water GHG emissions have a global warming potential of an equivalent emission of 13.5 (9.9-20.1) and 8.3 (5.7-12.7) Pg CO 2 -eq. yr -1 at a 20 and 100 year horizon (GWP 20 and GWP 100 ), respectively. Contributions of CO 2 dominate GWP100, with rivers being the largest emitter. For GWP 20 , lakes and rivers are equally important emitters, and the warming potential of CH4 is more important than that of CO2. Contributions from N 2 O are about two orders of magnitude lower. Normalized to the area of RECCAP-2 regions, S-America and SE-Asia show the highest emission rates, dominated by riverine CO 2 emissions.

Ronny Lauerwald↗
NASA NTRSDate not supplied

Exploring the Capabilities of a Machine Learning Algorithm to Detect Space Weather-Significant Emerging Active Regions

Active regions are a source of various phenomena responsible for Space Weather disturbances; therefore, developing a technology for early warning about upcoming magnetic activity is crucial to mitigate its impact. However, observational limitations and the high nonlinearity of processes associated with the accumulation of magnetic flux and its interaction with the surrounding plasma during the emergence through the convection zone make early activity detection a challenging problem. To address these challenges, we developed a physics-driven machine learning model that allows us to detect active regions (ARs) before they become visible on the solar surface by analyzing the power spectra of acoustic oscillations observed by the SDO/HMI instrument. This study is based on a time series of Doppler shift maps of 31x31-degree areas tracked with the Carrington rotation rate for four days before and after the emergence. The Doppler shift time series are processed into the oscillation power maps for four frequency ranges and accompanied by line-of-sight magnetograms and the continuum intensity maps from SDO/HMI. The resulting data are converted into a 1D time series representing the mean temporal variations of these quantities. The redacted time series are used as input to predict AR emergence using the Long Short Term Memory (LSTM) method. The training of the LSTM model is based on 40 ARs, which includes an independent analysis for each sub region that exhibits AR emergence or remains quiet. The emergence of magnetic flux (defined as a decrease of the continuum intensity) was detected with the developed LSTM algorithm from 5 to 48 hours before the reported time by NOAA. The developed model is capable of pointing to the time and location of active region formation. In this presentation, we discuss reasons that impact how early in advance the model can identify the upcoming activity and the possibility of improving the current predictive skills and steps to transition to the operational forecast.

Heliophysics↗