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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2018

Remote Sensing of Evaporation

Evaporation of water from the Earth's surface into the atmosphere is central to the terrestrial energy, water and carbon cycle. Remote sensing approaches to measure evaporation (ET) combine observable inputs to the energy and water balance within statistical or process-based methodologies. These ET products draw on satellite observations from across the electro-magnetic spectrum. The ET retrievals are grouped in two main categories. One category includes approaches that combine meteorological descriptions of potential evaporation with a range of strategies to estimate evaporative stress. A second category includes surface energy balance approaches that retrieve latent heat from the thermal signatures. For each category a practical implementation example is described, including an outlook on progress towards multi-model assessment of global evaporation.

Transpiration↗
NASA NTRS2019

MERRA-2 Ocean: The NASA Global Modeling and Assimilation Office's Weakly Coupled Atmosphere-Ocean Reanalysis Using GEOS-S2S Version 3

The NASA Modern Era Reanalysis for Research and Applications (MERRA2) has been a respected and widely used reanalysis that has so far been restricted to the atmosphere. Now a newly released version of the atmosphere/ocean coupled data assimilation system (AODAS) has been developed by the NASA/Goddard Global Modeling and Assimilation Office to perform a retrospective ocean reanalysis from 1982 to present. In addition to assimilating all available in situ data (e.g. Argo, mooring, XBT and CTD data) and altimetry information into the ocean, the new version (GEOS-S2S Version 3) model includes a higher resolution, eddy-permitting ocean model than previous versions, a more realistic implementation of the atmosphere-ocean interface layer, and an improved coupling between glacier and ocean (among other improvements). In addition, this ocean data assimilation was expanded to include the assimilation of satellite sea surface salinity. The MERRA-2 AODAS will be described, and preliminary results will be shown from the assimilation reanalysis and from retrospective forecasts issued using a new ensemble strategy. Following the Global Ocean Data Assimilation Experiment (GODAE) protocols, we will present Class 1 through Class 4 validation results from the ocean reanalysis. Results indicate an improved ocean mixed layer depth, improved salinity near Greenland, an improved diurnal cycle of the sea surface skin temperature, an improved estimate of ocean evaporation, and better representation of western boundary currents (e.g. Gulf Stream) from our new ocean reanalysis. One of the motivations of this project is to provide optimal initial states for ENSO forecasting. Therefore, we will also present some preliminary results of retrospective ENSO forecasts. After thorough testing, it is expected that the GEOS-S2S Version 3 will replace our contributions to North American Multi-Model Ensemble (NMME), WCRP Subseasonal to Seasonal (S2S), and IRI seasonal prediction forecast projects.

Molod, Andrea↗
NASA NTRS2020

An Introduction to the NASA GMAO Coupled Atmosphere-Ocean System - GEOS-S2S Version 3

Recently NASA's Global Modeling and Assimilation Office (GMAO) has developed a new Subseasonal to Seasonal Prediction system Version 3 (GEOS-S2S-3). This upgrade replaces the GEOS-S2S-2 which is NASA's current contribution to the North American Multi-Model Experiment seasonal prediction project (Kirtman et al., 2014). The main improvements for our S2S-3 system include 1) a higher resolution MOM5 (Griffies et al., 2005) ocean model (now 0.25o x 0.25o x 50 layers), 2) an improved atmospheric/ocean interface layer (Akella and Suarez, 2018), and 3) assimilation of a long-track satellite salinity into the ocean model (Hackert et al, 2019). Atmospheric forcing is provided by the NASA MERRA-2 reanalysis (Gelaro et al., 2017). Initialization for the ocean relies on the GMAO ocean reanalysis system which assimilates all available in situ temperature and salinity, satellite sea surface salinity, and sea level using the Local Ensemble Transform Kalman Filter (LETKF) implementation of (Penny et al., 2013) on a 5 day assimilation cycle with 20 fixed ensemble members.In this presentation, we will authenticate our new S2S-3 ocean reanalysis using standard GODAE validation metrics. For example, we will compare gridded fields of mean and standard deviation of the ocean reanalysis versus observed fields. We will show correlation/RMS of model versus observations and temperature and salinity mean profiles for the various basins and latitude bands. Basin-scale volume transports, such as the Atlantic Meridional Overturning Circulation and the Indonesian Throughflow will be validated. Equatorial ocean waves will be compared by decomposing sea level into Kelvin and Rossby components. For each of these metrics, we plan to validate the results and then compare our new S2S-3 against the current production version, S2S-2. Finally, we will compare 9-month seasonal forecasts initialized from these two systems for the tropical Pacific NINO3.4 region over the period 1981-present.

Molod, Andrea↗
NASA NTRS2020

Observational Constraints on the Effective Climate Sensitivity from the Historical Period

The observed warming in the atmosphere and ocean can be used to estimate the climate sensitivity linked to present-day feedbacks, which is referred to as the effective climate sensitivity (Shist). However, such an estimate is affected by uncertainty in the radiative forcing, particularly aerosols, over the historical period. Here, we make use of detection and attribution techniques to derive the surface air temperature and ocean warming that can be attributed directly to greenhouse gas increases. These serve as inputs to a simple energy budget to infer the likelihood of Shist in response to observed greenhouse gases increases over two time periods (1862–2012 and 1955–2012). The benefit of using greenhouse gas attributable quantities is that they are not subject to uncertainties in the aerosol forcing (other than uncertainty in the attribution to greenhouse gas versus aerosol forcing not captured by the multi-model aerosol response pattern). The resulting effective climate sensitivity estimate, Shist, ranges from 1.3 °Cto 3.1 °C(5%–95% range) over the full instrumental period (1862–2012) for our best estimate, and gets slightly wider when considering further uncertainties. This estimate increases to 1.7 °C–4.6 °C if using the shorter period (1955–2012).We also evaluate the climate model simulated surface air temperature and ocean heat content increase in response to greenhouse gas forcing over the same periods, and compare them with the observationally-constrained values.We find that that the ocean warming simulated in greenhouse gas only simulations in models considered here is consistent with that attributed to greenhouse gas increases from observations, while one model simulates more greenhouse gas-induced surface air warming than observed. However, other models with sensitivity outside our range show greenhouse gas warming that is consistent with that attributed in observations, emphasising that feedbacks during the historical period may differ from the feedbacks at CO2 doubling and from those at true equilibrium.

Tokarska, Katarzyna B↗
NASA NTRS2020

An Overview of CMIP5 and CMIP6 Simulated Cloud Ice, Radiation Fields, Surface Wind Stress, Sea Surface Temperatures and Precipitation over Tropical and Subtropical Oceans

The potential links between ice water path (IWP), radiation, circulation, sea surface temperature (SST) and precipitation over the Pacific and Atlantic Oceans resulting from the falling ice radiative effects (FIREs) are examined from present day model outputs of CMIP5 and CMIP6. The latter is divided into two subsets with (SON6) and without FIREs (NOS6) as more models with FIREs are included in CMIP6 than in CMIP5. Improvement in floating cloud ice (~20 g m-2) is noticeable over convective regions in CMIP6 relative to CMIP5. The inclusion of FIREs in SON6 subset may contribute to reduce biases of overestimated outgoing longwave radiation and downward surface shortwave and overestimated reflected shortwave at the top of the atmosphere (TOA) by magnitudes of 4?8 W m-2 over convective regions against CERES, compared to NOS6 subset. The reduced biases in radiative fluxes in convective regions stabilize the atmosphere and lead to circulation, SST, cloud and precipitation changes over the trade-wind regions, as seen from improved radiative fluxes (4?15 W m-2), surface wind stress biases, SST (0.2?0.8 K) and precipitation (1 mm day-1) biases. The significant improvement from NOS6 to SON6 leads to improved multi-model means for CMIP6 relative to CMIP5 for radiation fields over the trade wind regions but the degradation over convective zones is attributed to NOS6 subset. The results suggest that other sources of uncertainty and deficiencies in climate models may play significant roles for reducing discrepancies although FIREs, via radiation-circulation coupling, may be one of the factors that help to reduce regional biases.

Jui-Lin F Li↗
NASA NTRSDate not supplied

A Framework for Evaluating Climate Model Performance Metrics

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is often used to add value to model projections: consensus projections have been shown to consistently outperform individual models. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, certain models reproduce climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables?e.g., outgoing longwave radiation and surface temperature. Metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument and surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing weighted and unweighted model ensembles. For example, one tested metric weights the ensemble by how well models reproduce the time-series probability distribution of the cloud forcing component of reflected shortwave radiation. The weighted ensemble for this metric indicates lower simulated precipitation (up to .7 mm/day) in tropical regions than the unweighted ensemble: since CMIP5 models have been shown to overproduce precipitation, this result could indicate that the metric is effective in identifying models which simulate more realistic precipitation. Ultimately, the goal of the framework is to identify performance metrics for advising better methods for ensemble averaging models and create better climate predictions.

Noel C Baker↗
NASA NTRS2020

Use of Climate Change Projections for Resilience Planning in Rio de Janeiro, Brazil

In this study we use three different methodologies to document and compare temperature and precipitation projections for the city of Rio de Janeiro (RJ) over the 21st century. It aims to explore in what way the differences and similarities of those methodologies and their outcomes support the incorporation of climate risks in urban planning and improve effective urban climate change governance. We compared the projections for RJ from the Eta Regional Climate Model from the Brazilian National Institute for Space Research nested in two Hadley Center Global Climate Models (GCMs) (Eta-HadCM3 method and Eta-HadGEM2-ES method) and 33 GCMs from the Coupled Model Intercomparison Project Phase 5 multi-model dataset (Urban Climate Change Research Network - UCCRN method). The three methods showed increasing temperatures for RJ at the end of the century. Precipitation projections span a 13% decrease to a 12% increase when using the UCCRN method or are reduced between 0.4 and 0.5%, when using the Eta-HadGEM2-ES method. However, the middle range of the projections from UCCRN and Eta-HadGEM2-ES is similar. The three methods project an increase of warm days and nights and a decrease of cold days and nights. Nevertheless, although the directions of change are the same applying the three methods, the magnitude differs when considering warm and cold nights. Hence, city stakeholders are better informed when we apply different projection methods as it gives them the opportunity to consider the level of risk they are willing to bear in the future. We observed that defining climate change projections on the city scale based on clear communication and an interactive process between scientists and stakeholders can be used to inform citywide adaptation strategies and sector-specific uses, as well as promote urban climate risk governance.

urban governance and management↗
NASA NTRS2020

Algorithm Performance Dataset from NASA Open-Source Software

NASA Langley Research Center has recently developed and released the open-source software Multi Model Monte Carlo with Python (MXMCPy- LAR-19756-1) as a general capability for computing the statistics of outputs from an expensive, high-fidelity model by leveraging faster, low-fidelity models for speedup. Given a fixed computational budget and a collection of models with varying cost/accuracy, multi model Monte Carlo (MC) seeks a sample allocation strategy across the models that results in an estimator with optimal variance reduction. MXMCPy is a versatile tool that enables convenient access to many existing multi-model MC approaches (over a dozen algorithms available) within one modular and extensible package [1]. With MXMCPy, users can easily compare existing methods to determine the best choice for their particular problem,while developers have a basis for implementing and sharing new variance reduction approaches. However,there is currently very little understanding about which algorithm will perform best for a given problem (defined by the correlation between and relative cost of the available models) without a brute force search.

Geoffrey F Bomarito↗
NASA NTRSDate not supplied

OCO-2 Satellite-imposed Constraints on Terrestrial Biospheric CO2 Flux over South Asia

The spatiotemporal variability of terrestrial biospheric carbon dioxide (CO2) flux over South Asia is poorly understood. The inverse model “top-down” CO2 flux estimates which rely on atmospheric CO2 observational data are impeded by sparse in situ measurements over this region. The Orbiting Carbon Observatory 2 (OCO-2) provides much needed global retrievals of column-average CO2 dry-air mole fraction (XCO2) with the finest spatial and temporal resolution and highest sensitivity to surface fluxes available to-date. This study conducted global inverse model simulations as part of the second OCO-2 Multi-model Intercomparison Project assimilating version 9 of the OCO-2 XCO2 retrievals in land nadir (LN) and land glint (LG) observing modes and global in situ (IS) measurements. The four-dimensional variational assimilation system with the GEOS-Chem global chemical transport model was used to estimate CO2 fluxes from 2015 to 2018. We then assessed the spatiotemporal variability of optimized CO2 Net ecosystem Exchange (NEE) fluxes aggregated over the South Asia region. The most robust result found by assimilating OCO-2 observations was the constraints imposed on the seasonal cycle of terrestrial biospheric fluxes over South Asia. The seasonality of South Asian NEE estimated by assimilating OCO-2 or global IS data showed a larger seasonal cycle compared to the current understanding of NEE in this region (represented by the prior NEE used in the model). The satellite-in situ joint inversion (IS + LN + LG) led to land flux seasonal amplitude (absolute magnitude of the difference between peak and trough of monthly mean values over a year) of 4.0 PgC yr-1, compared to the prior model (1.5 PgC yr-1). Moreover, OCO-2 data imposed a phase shift in the seasonal cycle, resulting in a large CO2 source in April and a large uptake in September. Most of the features in the seasonal cycle imposed by OCO-2 data are in agreement with previous “top-down” studies assimilating regional aircraft observations. Nevertheless, we recommend a closer examination of the novel features highlighted by the OCO-2 column data over South Asia in future studies by conducting regional inverse modelling with denser in situ and vertically-resolved regional observations along with satellite data.

OCO-2↗
NASA NTRS2020

The Pliocene Model Intercomparison Project Phase 2: Large-scale Climate Features and Climate Sensitivity

The Pliocene epoch has great potential to improve our understanding of the long-term climatic and environmental consequences of an atmospheric CO2 concentration near ∼400 parts per million by volume. Here we present the large-scale features of Pliocene climate as simulated by a new ensemble of climate models of varying complexity and spatial resolution based on new reconstructions of boundary conditions (the Pliocene Model Intercomparison Project Phase 2; PlioMIP2). As a global annual average, modelled surface air temperatures increase by between 1.7 and 5.2 ∘C relative to the pre-industrial era with a multi-model mean value of 3.2 ∘C. Annual mean total precipitation rates increase by 7 % (range: 2 %–13 %). On average, surface air temperature (SAT) increases by 4.3 ∘C over land and 2.8 ∘C over the oceans. There is a clear pattern of polar amplification with warming polewards of 60∘ N and 60∘ S exceeding the global mean warming by a factor of 2.3. In the Atlantic and Pacific oceans, meridional temperature gradients are reduced, while tropical zonal gradients remain largely unchanged. There is a statistically significant relationship between a model's climate response associated with a doubling in CO2 (equilibrium climate sensitivity; ECS) and its simulated Pliocene surface temperature response. The mean ensemble Earth system response to a doubling of CO2 (including ice sheet feedbacks) is 67 % greater than ECS; this is larger than the increase of 47 % obtained from the PlioMIP1 ensemble. Proxy-derived estimates of Pliocene sea surface temperatures are used to assess model estimates of ECS and give an ECS range of 2.6–4.8 ∘C. This result is in general accord with the ECS range presented by previous Intergovernmental Panel on Climate Change (IPCC) Assessment Reports.

Pliocene Model Intercomparison Project↗
NASA NTRS2020

Evaluation of Arctic Warming in Mid-Pliocene Climate Simulations

Palaeoclimate simulations improve our understanding of the climate, inform us about the performance of climate models in a different climate scenario, and help to identify robust features of the climate system. Here, we analyse Arctic warming in an ensemble of 16 simulations of the mid-Pliocene Warm Period (mPWP), derived from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2). The PlioMIP2 ensemble simulates Arctic (60–90∘ N) annual mean surface air temperature (SAT) increases of 3.7 to 11.6 ∘C compared to the pre-industrial period, with a multi-model mean (MMM) increase of 7.2 ∘C. The Arctic warming amplification ratio relative to global SAT anomalies in the ensemble ranges from 1.8 to 3.1 (MMM is 2.3). Sea ice extent anomalies range from −3.0 to −10.4×106 km2, with a MMM anomaly of −5.6×106 km2, which constitutes a decrease of 53 % compared to the pre-industrial period. The majority (11 out of 16) of models simulate summer sea-ice-free conditions (≤1×106 km2) in their mPWP simulation. The ensemble tends to underestimate SAT in the Arctic when compared to available reconstructions, although the degree of underestimation varies strongly between the simulations. The simulations with the highest Arctic SAT anomalies tend to match the proxy dataset in its current form better. The ensemble shows some agreement with reconstructions of sea ice, particularly with regard to seasonal sea ice. Large uncertainties limit the confidence that can be placed in the findings and the compatibility of the different proxy datasets. We show that while reducing uncertainties in the reconstructions could decrease the SAT data–model discord substantially, further improvements are likely to be found in enhanced boundary conditions or model physics. Lastly, we compare the Arctic warming in the mPWP to projections of future Arctic warming and find that the PlioMIP2 ensemble simulates greater Arctic amplification than CMIP5 future climate simulations and an increase instead of a decrease in Atlantic Meridional Overturning Circulation (AMOC) strength compared to pre-industrial period. The results highlight the importance of slow feedbacks in equilibrium climate simulations, and that caution must be taken when using simulations of the mPWP as an analogue for future climate change.

Palaeoclimate simulations↗
NASA NTRS2019

Prior biosphere model impact on global terrestrial CO2 fluxes estimated from OCO-2 retrievals

This study assesses the impact of different state of the art global biospheric CO2 flux models, when applied as prior information, on inverse model “top-down” estimates of terrestrial CO2 fluxes obtained when assimilating Orbiting Carbon Observatory 2 (OCO-2) observations. This is done with a series of observing system simulation experiments (OSSEs) using synthetic CO2 column-average dry air mole fraction (XCO2) retrievals sampled at the OCO-2 satellite spatiotemporal frequency. The OSSEs utilized a 4-D variational (4D-Var) assimilation system with the GEOS-Chem global chemical transport model (CTM) to estimate CO2 net ecosystem exchange (NEE) fluxes using synthetic OCO-2 observations. The impact of biosphere models in inverse model estimates of NEE is quantified by conducting OSSEs using the NASA-CASA, CASA-GFED, SiB-4, and LPJ models as prior estimates and using NEE from the multi-model ensemble mean of the Multiscale Synthesis and Terrestrial Model Intercomparison Project as the “truth”. Results show that the assimilation of simulated XCO2 retrievals at OCO-2 observing modes over land results in posterior NEE estimates which generally reproduce “true” NEE globally and over terrestrial TransCom-3 regions that are well-sampled. However, we find larger spread among posterior NEE estimates, when using different prior NEE fluxes, in regions and seasons that have limited OCO-2 observational coverage and a large range in “bottom-up” NEE fluxes. Seasonally averaged posterior NEE estimates had standard deviations (SD) of ∼10 % to ∼50 % of the multi-model-mean NEE for different TransCom-3 land regions with significant NEE fluxes (regions/seasons with a NEE flux ≥0.5 PgC/yr). On a global average, the seasonally averaged residual impact of the prior model NEE assumption on the posterior NEE spread is ∼10 %–20 % of the posterior NEE mean. Additional OCO-2 OSSE simulations demonstrate that posterior NEE estimates are also sensitive to the assumed prior NEE flux uncertainty statistics, with spread in posterior NEE estimates similar to those when using variable prior model NEE fluxes. In fact, the sensitivity of posterior NEE estimates to prior error statistics was larger than prior flux values in some regions/times in the tropics and Southern Hemisphere where sufficient OCO-2 data were available and large differences between the prior and truth were evident. Overall, even with the availability of spatiotemporally dense OCO-2 data, noticeable residual differences (up to ∼20 %–30 % globally and 50 % regionally) in posterior NEE flux estimates remain that were caused by the choice of prior model flux values and the specification of prior flux uncertainties.

Sajeev Philip↗
NASA NTRS2020

Attribution of Chemistry-Climate Model Initiative (CCMI) Ozone Radiative Flux Bias from Satellites

The top-of-atmosphere (TOA) outgoing longwave flux over the 9.6-μm ozone band is a fundamental quantity for understanding chemistry-climate coupling. However, observed TOA fluxes are hard to estimate as they exhibit considerable variability in space and time that depend on the distributions of clouds, ozone (O3), water vapor (H2O), air temperature (Ta), and surface temperature (Ts). Benchmarking present day fluxes and quantifying the relative influence of their drivers is the first step for estimating climate feedbacks from ozone radiative forcing and predicting radiative forcing evolution. To that end, we constructed observational instantaneous radiative kernels (IRKs) under clear-sky conditions, representing the sensitivities of the TOA flux in the 9.6-μm ozone band to the vertical distribution of geophysical variables, including O3, H2O, Ta, and Ts based upon the Aura Tropospheric Emission Spectrometer (TES) measurements. Applying these kernels to present-day simulations from the Chemistry-Climate Model Initiative (CCMI) project as compared to a 2006 reanalysis assimilating satellite observations, we show that the models have large differences in TOA flux, attributable to different geophysical variables. In particular, model simulations continue to diverge from observations in the tropics, as reported in previous studies of the Atmospheric Chemistry Climate Model Inter-comparison Project (ACCMIP) simulations. The principal culprits are tropical mid and upper tropospheric ozone followed by tropical lower tropospheric H2O. Five models out of the eight studied here have TOA flux biases exceeding 100 mWm-2 attributable to tropospheric ozone bias. Another set of five models have flux biases over 50 mWm-2 due to H2O. On the other hand, Ta radiative bias is negligible in all models (no more than 30 mWm-2). We found that AM3 and CMAM have the lowest TOA flux biases globally but are a result of cancellation of opposite biases due to difference processes. Overall, the multi-model ensemble mean bias is –133±98 mWm-2, indicating that they are too atmospherically opaque due to trapping too much radiation in the atmosphere by overestimated tropical tropospheric O3 and H2O. Having too much O3 and H2O in the troposphere would have different impacts on the sensitivity of TOA flux to O3 and these competing effects add more uncertainties on the ozone radiative forcing. We find that the inter-model TOA outgoing longwave radiation (OLR) difference is well anti-correlated with their ozone band flux bias. This suggests that there is significant radiative compensation in the calculation of model outgoing longwave radiation.

Aura Tropospheric Emission Spectrometer (TES) meas↗
NASA NTRS2021

Future Climate Change Significantly Alters Interannual Wheat Yield Variability Over Half of Harvested Areas

Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation (CV)) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). About 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies.

Climate change↗
NASA NTRSDate not supplied

The Response of the Quasi-Biennial Oscillation to Increased CO and its Modulation by Composition Feedbacks

The Quasi-Biennial Oscillation (QBO) impacts the large-scale circulation by altering lower stratospheric meridional temperature gradients which alter the propagation of upward propagating planetary waves. Proposed teleconnections of the QBO include impacts on the stratospheric polar vortices, extratropical surface winter climate, and the Madden-Julian Oscillation. However, long-term projections of the QBO remain highly uncertain. While recent multi-model investigations (Richter et al. 2019, Butchart et al. 2020) show that the amplitude of the QBO weakens robustly among models, changes in QBO period and stability remain highly uncertain. Here we examine the QBO response to increased greenhouse gases using the NASA Goddard Institute for Space Studies Middle Atmosphere Model E2.2 (Rind et al., 2020; Orbe et al. 2020). Compared to lower vertical resolution versions of ModelE, E2.2 also has a higher model top (0.002 hPa) and employs additional interactive non-orographic gravity wave drag sources from convection and shear, which produce a sufficiently realistic QBO, thus rendering it suitable for use in climate change studies. Overall, we find that both the QBO period and amplitude decrease in response to increased CO , the former related to increased lower stratospheric momentum fluxes (associated with convection) and the latter associated partly with a stronger residual mean circulation. Experiments integrated using fully interactive chemistry also reveal that ozone feedbacks significantly impact the magnitude of the QBO amplitude response. Finally, integrations using fixed (pre-industrial) sea surface temperatures (SST) show that the QBO amplitude responds differently to rapid adjustments versus SST feedbacks, as compared to the QBO period.

Quasi-Biennial Oscillation↗
DOE OSTI2025

Evaluation of the 2022 West Nile virus forecasting challenge, USA

Abstract Background West Nile virus (WNV) is the most common cause of mosquito-borne disease in the continental USA, with an average of ~1200 severe, neuroinvasive cases reported annually from 2005 to 2021 (range 386–2873). Despite this burden, efforts to forecast WNV disease to inform public health measures to reduce disease incidence have had limited success. Here, we analyze forecasts submitted to the 2022 WNV Forecasting Challenge, a follow-up to the 2020 WNV Forecasting Challenge. Methods Forecasting teams submitted probabilistic forecasts of annual West Nile virus neuroinvasive disease (WNND) cases for each county in the continental USA for the 2022 WNV season. We assessed the skill of team-specific forecasts, baseline forecasts, and an ensemble created from team-specific forecasts. We then characterized the impact of model characteristics and county-specific contextual factors (e.g., population) on forecast skill. Results Ensemble forecasts for 2022 anticipated a season at or below median long-term WNND incidence for nearly all (> 99%) counties. More counties reported higher case numbers than anticipated by the ensemble forecast median, but national caseload (826) was well below the 10-year median (1386). Forecast skill was highest for the ensemble forecast, though the historical negative binomial baseline model and several team-submitted forecasts had similar forecast skill. Forecasts utilizing regression-based frameworks tended to have more skill than those that did not and models using climate, mosquito surveillance, demographic, or avian data had less skill than those that did not, potentially due to overfitting. County-contextual analysis showed strong relationships with the number of years that WNND had been reported and permutation entropy (historical variability). Evaluations based on weighted interval score and logarithmic scoring metrics produced similar results. Conclusions The relative success of the ensemble forecast, the best forecast for 2022, suggests potential gains in community ability to forecast WNV, an improvement from the 2020 Challenge. Similar to the previous challenge, however, our results indicate that skill was still limited with general underprediction despite a relative low incidence year. Potential opportunities for improvement include refining mechanistic approaches, integrating additional data sources, and considering different approaches for areas with and without previous cases. Graphical Abstract

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Data from: Coupled machine learning-ecosystem ensemble models substantially improve predictions of nitrous oxide (N 2 O) fluxes from US croplands

Nitrous oxide (N₂O) is a potent and persistent greenhouse gas, with rising atmospheric concentrations driven in part by inefficient use of synthetic nitrogen (N) fertilizers in agriculture. Predicting soil N₂O emissions is challenging due to high spatial and temporal variability arising from complex soil biogeochemical processes. Process-based ecosystem models and standalone machine learning (ML) approaches without extensive site-specific calibration often miss high emission episodes. Here, we show how an Ensemble Modeling System (EMS) based on outputs from an ensemble of ecosystem models coupled to an ensemble of ML models can improve predictions and understanding of N2O fluxes from US cropland. Trained and validated on approximately 12,000 N2O chamber measurements at 17 U.S. Midwest sites (six crops, 35 management practices), the EMS accurately predicted daily fluxes of N2O at both training (R² = 0.84, RMSE = 16.4 g N ha⁻¹ d⁻¹) and held-out testing sites (R² = 0.84, RMSE = 6.2 g N ha⁻¹ d⁻¹). Analyses identified six dominant N₂O drivers: soil organic carbon (SOC), NH₄⁺, NO₃⁻, water-filled pore space (WFPS), soil temperature, and biomass production. Wet, warm soils produced large N₂O peaks only with sufficient SOC and mineral N; in low-SOC soils, fluxes remained low. Incorporating these drivers into process-based models might significantly improve their predictive capacity. The EMS demonstrates a strong potential to predict N₂O fluxes at unseen sites, enabling more reliable regional inventories, improved gap-filling where measurements are sparse, and enhanced understanding of mechanisms to advance targeted mitigation strategies in food, feed, and bioenergy crops.

agricultural sciences↗
NASA NTRS2016

Uncertainty of Wheat Water Use: Simulated Patterns and Sensitivity to Temperature and CO2

Projected global warming and population growth will reduce future water availability for agriculture. Thus, it is essential to increase the efficiency in using water to ensure crop productivity. Quantifying crop water use (WU; i.e. actual evapotranspiration) is a critical step towards this goal. Here, sixteen wheat simulation models were used to quantify sources of model uncertainty and to estimate the relative changes and variability between models for simulated WU, water use efficiency (WUE, WU per unit of grain dry mass produced), transpiration efficiency (Teff, transpiration per kg of unit of grain yield dry mass produced), grain yield, crop transpiration and soil evaporation at increased temperatures and elevated atmospheric carbon dioxide concentrations ([CO2]). The greatest uncertainty in simulating water use, potential evapotranspiration, crop transpiration and soil evaporation was due to differences in how crop transpiration was modelled and accounted for 50 of the total variability among models. The simulation results for the sensitivity to temperature indicated that crop WU will decline with increasing temperature due to reduced growing seasons. The uncertainties in simulated crop WU, and in particularly due to uncertainties in simulating crop transpiration, were greater under conditions of increased temperatures and with high temperatures in combination with elevated atmospheric [CO2] concentrations. Hence the simulation of crop WU, and in particularly crop transpiration under higher temperature, needs to be improved and evaluated with field measurements before models can be used to simulate climate change impacts on future crop water demand.

Sensitivity↗