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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Collaborative Research: Advancing Arctic Climate Projection Capability at Seasonal to Decadal Scales (Final Technical Report)

The Regional Arctic System Model (RASM) at process resolving configurations has been used to (i) advance understanding of physical processes and feedbacks involved in Arctic amplification and (ii) understand and potentially reduce uncertainty in prediction of arctic climate change at seasonal to decadal scales. RASM consists the atmosphere (Weather and Research Forecasting model, WRF), ocean (Parallel Ocean Program, POP), sea ice (CICE), land hydrology (Variable Infiltration Capacity model, VIC), river routing scheme (RVIC), marine biogeochemistry components and the coupling framework (CPL7). Its domain is pan-Arctic, with the atmosphere and land components configured on a 50-km or 25-km grid and four configurations of the ocean and sea ice components: 1/12°(~9.3km) or 1/48°(~2.4km) and 45 or 60 vertical layers. These RASM configurations have been motivated by the emerging exascale capability for high performance computing to improve model fidelity. The dynamical downscaling of reanalysis allows comparison of RASM results with observations in place and time to: (i) advance system level understanding of physical processes and coupling involved in an event, (ii) optimize model parameter space, (iii) diagnose and reduce model biases and (iv) produce realistic and consistent across all the components initial conditions for predictions and predictability studies, which are all unique capabilities not available in global Earth System Models (ESMs). An evaluation of RASM 1.0 (Cassano et al. 2017) revealed that it had a cold bias over the oceans and a warm bias over land areas due largely to cloud and radiation biases in the model, with too little cloud cover simulated over land and too much cloud cover simulated over sub-polar oceans. This study has motivated an upgrade to WRF version 3.7.1 in RASM and allowed for the inclusion of the radiative impact of convective clouds. A variety of atmospheric physics parameterizations were evaluated against observations (e.g. data from the Arctic Clouds in Summer Experiment (ACSE); Sedlar et al. 2020) to identify an optimal suite of WRF physics options in RASM. The RASM with the optimized WRF physics were used to study the impact of strong mesoscale winds over the ocean around the southern tip of Greenland (DuVivier and Cassano 2016) and their impact on oceanic convection (DuVivier et al. 2017a). Data from the PolarWinds field campaign were used to evaluate WRF boundary layer physics and resolution impacts on the simulation of a Greenland barrier wind event (DuVivier et al. 2017b). The RVIC streamflow routing model has been implemented in RASM to realistically represent high-resolution streamflow processes (Hamman et al. 2017) and to couple the land buoyancy fluxes to the ocean. The RASM-RVIC high-resolution data set of all coastal freshwater fluxes for the Arctic drainage basin and surrounding areas for 1979-2014 was published as a separate product (https://doi.org/10.5281/zenodo.293037). The fidelity of atmospheric momentum transfer to and the response of polar marine Ekman layer in RASM and Community Earth System Model (CESM) was investigated by Roberts et al. (2015). The increased frequency of oceanic flux exchange in CESM, following the RASM guidance, caused a considerable increase in the median inertial ice speed across the Southern Ocean and parts of the Arctic. A comprehensive evaluation of the RASM1.0 atmosphere-ocean-sea ice-land interface was completed by Brunke et al. (2018). RASM was also demonstrated for its capability to simulate extreme events in agreement with observations in space and time (Lee et al. submitted). In particular, the development of three open water events, known as polynyas, have been simulated north of Greenland in February of 2011, 2017 and 2018, in agreement with satellite observations for the past four decades. The optimized RASM sea ice results have been favorably evaluated against satellite observations and a subset of eleven CMIP6 models (Watts et al. submitted). In a complementary project, Jin et al. (2018) have shown that RASM with higher-resolution and new sea-ice processes contributed to lower model errors in sea-ice conditions, concentrations of nutrients and ice algae, in comparison to results from the coarse-resolution (1°) CESM. In two other complementary studies, RASM results were used (i) to explain areas of concentrated use by bowhead whales, the seasonal progression in the use, and the physical environment within those areas (Citta et al. 2015) and (ii) for a synthesis of fall bowhead whales distribution and migration in the Bering-Chukchi-Beaufort (BCB) Sea to investigate whale movements and feeding to the local ocean hydrography and currents (Citta et al. 2018). However, the multi-decadal output from the CESM Large Ensemble yielded unrealistic forcing. Instead, the shorter NCEP CFSv2 9-month forecasts were successfully tested and afforded an increased ensemble size (~30) to demonstrate gains of dynamical downscaling at sub-seasonal to intra-annual time scales. The improved model physics and coupling among RASM model components have yielded more realistic representation of the sea ice cover and consistent across all model components initial conditions. Consequently, RASM demonstrates significant gains compared to simulation of sea ice in the NCEP reanalysis. In addition, RASM 6-month ensemble forecasts yield very realistic sea ice distribution, which demonstrates both significant gains of dynamical downscaling and the continued impact of the initial conditions on forecasts out to 6 months (https://nps.edu/web/rasm/predictions). A follow up study using RASM for dynamical downscaling of the more realistic CESM initialized Decadal Prediction Large Ensemble output is currently ongoing as part of the DOE RGMA HiLAT-RASM project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Benefits of Greenhouse Gas Mitigation on the Supply, Management, and Use of Water Resources in the United States

Climate change impacts on water resources in the U.S. are likely to be far-reaching and substantial, because the water sector spans many parts of the economy, from supply and demand for agriculture, industry, energy production, transportation and municipal use to damages from natural hazards. This paper provides impact and damage estimates from five water resource-related models in the CIRA frame work, addressing drought risk, flooding damages, water supply and demand, and global water scarcity. The four models differ in the water system assessed, their spatial scale, and the units of assessment, but together they provide a quantitative and descriptive richness in characterizing water resource sector effects of climate change that no single model can capture. The results also address the sensitivity of these estimates to greenhouse gas emission scenarios, climate sensitivity alternatives, and global climate model selection. While calculating the net impact of climate change on the water sector as a whole may be impractical, because each of the models applied here uses a consistent set of climate scenarios, broad conclusions can be drawn regarding the patterns of change and the benefits of GHG mitigation policies for the water sector. Two key findings emerge: 1) climate mitigation policy substantially reduces the impact of climate change on the water sector across multiple dimensions; and 2) the more managed the water resources system, the more tempered the climate change impacts and the resulting reduction of impacts from climate mitigation policies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2021

The Brewer–Dobson Circulation in CMIP6

The Brewer–Dobson circulation (BDC) is a key feature of the stratosphere that models need to accurately represent in order to simulate surface climate variability and change adequately. For the first time, the Climate Model Intercomparison Project includes in its phase 6 (CMIP6) a set of diagnostics that allow for careful evaluation of the BDC. Here, the BDC is evaluated against observations and reanalyses using historical simulations. CMIP6 results confirm the well-known inconsistency in the sign of BDC trends between observations and models in the middle and upper stratosphere. Nevertheless, the large uncertainty in the observational trend estimates opens the door to compatibility. In particular, when accounting for the limited sampling of the observations, model and observational trend error bars overlap in 40 % of the simulations with available output. The increasing CO2 simulations feature an acceleration of the BDC but reveal a large spread in the middle-to-upper stratospheric trends, possibly related to the parameterized gravity wave forcing. The very close connection between the shallow branch of the residual circulation and surface temperature is highlighted, which is absent in the deep branch. The trends in mean age of air are shown to be more robust throughout the stratosphere than those in the residual circulation.

Brewer–Dobson circulation↗
DOE OSTI2014

Bioenergy crop productivity and potential climate change mitigation from marginal lands in the United States: An ecosystem modeling perspective

Growing biomass feedstocks from marginal lands is becoming an increasingly attractive choice for producing biofuel as an alternative energy to fossil fuels. Here, we used a biogeochemical model at ecosystem scale to estimate crop productivity and greenhouse gas (GHG) emissions from bioenergy crops grown on marginal lands in the United States. Two broadly tested cellulosic crops, switchgrass, and Miscanthus, were assumed to be grown on the abandoned land and mixed crop–vegetation land with marginal productivity. Production of biomass and biofuel as well as net carbon exchange and nitrous oxide emissions were estimated in a spatially explicit manner. We found that, cellulosic crops, especially Miscanthus could produce a considerable amount of biomass, and the effective ethanol yield is high on these marginal lands. For every hectare of marginal land, switchgrass and Miscanthus could produce 1.0–2.3 kl and 2.9–6.9 kl ethanol, respectively, depending on nitrogen fertilization rate and biofuel conversion efficiency. Nationally, both crop systems act as net GHG sources. Switchgrass has high global warming intensity (100–390 g CO 2 eq l –1 ethanol), in terms of GHG emissions per unit ethanol produced. Miscanthus , however, emits only 21–36 g CO 2 eq to produce every liter of ethanol. To reach the mandated cellulosic ethanol target in the United States, growing Miscanthus on the marginal lands could potentially save land and reduce GHG emissions in comparison to growing switchgrass. Furthermore, the ecosystem modeling is still limited by data availability and model deficiencies, further efforts should be made to classify crop–specific marginal land availability, improve model structure, and better integrate ecosystem modeling into life cycle assessment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Collaborative Research: Towards Advanced Understanding and Predictive Capability of Climate Change in the Arctic Using a High-Resolution Regional Arctic Climate Model

The primary research task completed for this project was the development of the Regional Arctic Climate Model (RACM). This involved coupling existing atmosphere, ocean, sea ice, and land models using the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM) coupler (CPL7). RACM is based on the Weather Research and Forecasting (WRF) atmospheric model, the Parallel Ocean Program (POP) ocean model, the CICE sea ice model, and the Variable Infiltration Capacity (VIC) land model. A secondary research task for this project was testing and evaluation of WRF for climate-scale simulations on the large pan-Arctic model domain used in RACM. This involved identification of a preferred set of model physical parameterizations for use in our coupled RACM simulations and documenting any atmospheric biases present in RACM.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

The Influence of ENSO Diversity on Future Atlantic Tropical Cyclone Activity

El Niño–Southern Oscillation (ENSO) influences seasonal Atlantic tropical cyclone (TC) activity by impacting environmental conditions important for TC genesis. However, the influence of future climate change on the teleconnection between ENSO and Atlantic TCs is uncertain, as climate change is expected to impact both ENSO and the mean climate state. We used the Weather Research and Forecasting Model on a tropical channel domain to simulate 5-member ensembles of Atlantic TC seasons in historical and future climates under different ENSO conditions. Experiments were forced with idealized sea surface temperature configurations based on the Community Earth System Model (CESM) Large Ensemble representing: a monthly varying climatology, eastern Pacific El Niño, central Pacific El Niño, and La Niña. The historical simulations produced fewer Atlantic TCs during eastern Pacific El Niño compared to central Pacific El Niño, consistent with observations and other modeling studies. For each ENSO state, the future simulations produced a similar teleconnection with Atlantic TCs as in the historical simulations. Specifically, La Niña continues to enhance Atlantic TC activity, and El Niño continues to suppress Atlantic TCs, with greater suppression during eastern Pacific El Niño compared to central Pacific El Niño. In addition, we found a decrease in the Atlantic TC frequency in the future relative to historical regardless of ENSO state, which was associated with a future increase in northern tropical Atlantic vertical wind shear and a future decrease in the zonal tropical Pacific sea surface temperature (SST) gradient, corresponding to a more El Niño–like mean climate state. Our results indicate that ENSO will remain useful for seasonal Atlantic TC prediction in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

CMIP5 Scientific Gaps and Recommendations for CMIP6

The Coupled Model Intercomparison Project (CMIP) is an ongoing coordinated international activity of numerical experimentation of unprecedented scope and impact on climate science. Its most recent phase, the fifth phase (CMIP5), has created nearly 2 PB of output from dozens of experiments performed by dozens of comprehensive climate models available to the climate science research community. In so doing, it has greatly advanced climate science. While CMIP5 has given answers to important science questions, with the help of a community survey we identify and motivate three broad topics here that guided the scientific framework of the next phase of CMIP, that is, CMIP6: (1) How does the Earth system respond to changes in forcing? (2) What are the origins and consequences of systematic model biases? (3) How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? CMIP has demonstrated the power of idealized experiments to better understand how the climate system works. We expect that these idealized approaches will continue to contribute to CMIP6. The quantification of radiative forcings and responses was poor, and thus it requires new methods and experiments to address this gap. There are a number of systematic model biases that appear in all phases of CMIP that remain a major climate modeling challenge. In conclusion, these biases need increased attention to better understand their origins and consequences through targeted experiments. Improving understanding of the mechanisms’ underlying internal climate variability for more skillful decadal climate predictions and long-term projections remains another challenge for CMIP6.

58 GEOSCIENCES↗
NASA NTRS2018

Biosignature Anisotropy Modeled on Temperate Tidally Locked M-Dwarf Planets

A planet’s atmospheric constituents (e.g., O2, O3, H2Ov, CO2, CH4, and N2O) can provide clues to its surface habitability, and may offer biosignature targets for remote life detection efforts. The plethora of rocky exoplanets found by recent transit surveys (e.g., the Kepler mission) indicates that potentially habitable systems orbiting K- and M-dwarf stars may have very different orbital and atmospheric characteristics than Earth. To assess the physical distribution and observational prospects of various biosignatures and habitability indicators, it is important to understand how they may change under different astrophysical and geophysical configurations, and to simulate these changes with models that include feedbacks between different subsystems of a planet’s climate. Here we use a three-dimensional (3D) Chemistry–Climate model (CCM) to study the effects of changes in stellar spectral energy distribution (SED), stellar activity, and planetary rotation on Earth analogs and tidally locked planets. Our simulations show that, apart from shifts in stellar SEDs and UV radiation, changes in illumination geometry and rotation-induced circulation can influence the global distribution of atmospheric biosignatures. We find that the stratospheric day-to-nightside mixing ratio differences on tidally locked planets remain low (<20%) across the majority of the canonical biosignatures. Interestingly, however, secondary photosynthetic biosignatures (e.g., C2H6S) show much greater (∼67%) day-to-nightside differences, and point to regimes in which tidal locking could have observationally distinguishable effects on phase curve, transit, and secondary eclipse measurements. Overall, this work highlights the potential and promise for 3D CCMs to study the atmospheric properties and habitability of terrestrial worlds.

Chen, Howard↗
DOE OSTI2015

A comparative analysis of the impacts of climate change and irrigation on land surface and subsurface hydrology in the North China Plain

The Community Land Model 4.0 (CLM4) was used to investigate and compare the effects of climate change and irrigation on terrestrial water cycle. Three climate change scenarios and one irrigation scenario (IRRIG) were simulated in the North China Plain (NCP), which is one of the most vulnerable regions to climate change and human perturbations in China. The climate change scenarios consist of (1) HOT (i.e. temperature increase by 2oC); (2) HOTWET (same with HOT but with an increase of precipitation by 15%); (3) HOTDRY (same with HOT but with a decrease of precipitation by 15%). In the IRRIG scenario, the irrigation scheme was calibrated to simulate irrigation amounts that match the actual irrigation amounts and irrigation was divided between surface water and groundwater withdrawals based on census data. Our results show that the impacts of climate change were more widespread while those of irrigation were concentrated only over the agricultural regions. Specifically, the mean water table depth was simulated to decline persistently by over 1 m annually due to groundwater exploitation during the period of 1980-2000, while much smaller effects were induced by climate change. Although irrigation has comparable effects on surface fluxes and surface soil moisture as climate change, it has much greater effects on water table depth and groundwater storage. Moreover, irrigation has much larger effects on the top layer soil moisture whereas increase in precipitation associated with climate change exerts more influence on lower layer soil moisture. This study emphasizes the need to accurately account for irrigation impacts in adapting to climate change.

climate change↗
DOE OSTI2015

Investigating the value of fusion energy using the Global Change Assessment Model

The availability of fusion energy could prove valuable in meeting carbon mitigation targets over the course of the century. In this work, we use recent cost estimates for future fusion power plants in order to incorporate fusion into the Global Change Assessment Model (GCAM), a long-term energy and environment model used to study the interaction between technology, climate, and public policy. Results show that fusion's growth will depend on: the chosen carbon mitigation target (if any); the availability of competing carbon-neutral options for the provision of baseload electrical power, in particular nuclear fission as well as carbon capture and storage; the chosen discount rate; the initial year of availability; and the assumed costs of fusion electricity. We quantify the present value of the fusion option while varying the assumptions about these other parameters, and we find that it is, in general for our range of assumptions, significantly larger than the estimated cost of a comprehensive R&D plan to develop fusion energy. The results emphasize the wisdom in hedging against uncertainty in future technology availability by pursuing the development of multiple options that could feasibly play a major role in the latter half of the century.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Large Divergence of Projected High Latitude Vegetation Composition and Productivity Due To Functional Trait Uncertainty

Abstract Vegetation distribution and composition are expected to change in northern high latitudes under rapid warming, which regulates ecosystem functions but remains challenging to predict. Vegetation change arises from the interplay of chronic climate trends such as warming and transient demographic processes of recruitment, growth, competition, and mortality. Most predictive models overlooked the role of demographic dynamics controlled by plant traits. Here, we simulate vegetation dynamics at the Kougarok Hillslope site in Alaska under historical and future climates using the E3SM Land Model coupled to the Functionally Assembled Terrestrial Simulator (ELM‐FATES). To evaluate the roles of plant traits, we parameterize the model with 5,265 trait configurations representing diverse physiological and demographic strategies. Results show current modeled biomass, composition, and productivity are most sensitive to traits controlling photosynthetic capacity, carbon allocation, allometry, and phenology. Among all trait configurations, ∼5% reproduce in situ biomass and plant functional type (PFT) composition measured in 2016, that are indistinguishable from these two observed ecosystem states. Notably, these same trait configurations produce diverging biomass, composition, and productivity under future climate, where the uncertainty attributable to traits is twice the change attributable to climate change. The variation of projected productivity arises from emerging PFT composition under novel climate regimes, primarily explained by traits controlling cold‐induced mortality, recruitment, and allometry. Our findings highlight the importance and uncertainty of demographic dynamics and its interaction with climate change in shaping Arctic vegetation change. Improved model predictions will likely benefit from explicit consideration of vegetation demography and better constraints of critical traits.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Early Action on HFCs Mitigates Future Atmospheric Change

As countries take action to mitigate global warming, both by ratifying the UNFCCC Paris Agreement and enacting the Kigali Amendment to the Montreal Protocol to manage hydrofluorocarbons (HFCs), it is important to consider the relative importance of the pertinent greenhouse gases (GHGs), the distinct structure of their atmospheric impacts, and how the timing of potential GHG regulations would affect future changes in atmospheric temperature and ozone. Chemistry-climate model simulations demonstrate that HFCs could contribute substantially to anthropogenic climate change by the mid-21st century, particularly in the upper troposphere and lower stratosphere i.e., global average warming up to 0.19K at 80hPa. Three HFC mitigation scenarios demonstrate the benefits of taking early action in avoiding future atmospheric change: more than 90 of the climate change impacts of HFCs can be avoided if emissions stop by 2030.

Kigali Amendment↗
DOE OSTI2025

Selection of Global Climate Model Data for Downscaling With Generative Machine Learning and Use in the Power Planning for Alignment of Climate and Energy Systems Project

The range of results from climate models and scenarios is important to the understanding of uncertainty in power planning analysis. A U.S. Department of Energy-funded analytic project called Power Planning for Alignment of Climate and Energy Systems is developing data and analytic methods to reflect the effects of climate change on key variables for power system planning, as part of the Grid Modernization Lab Consortium. This project will select and prepare global climate model results for use in power system planning models. A related report (Evaluation of Global Climate Models for Use in Energy Analysis) assesses the performance of various global climate models from the Coupled Model Intercomparison Project Phase 6 data archive for their historical skill with respect to energy system performance and for their future projections under multiple climate change scenarios. Building from that report, we describe the selection of a climate scenario (Shared Socioeconomic Pathway [SSP] 2-4.5) and five climate models: TaiESM1, EC-Earth3-CC, GFDL-CM4, EC-Earth3-Veg, and MPI-ESM1-2-HR. We describe the model selection criteria, which were based on the quality of the match between model results under historical conditions and on the representation of the range of future values for several variables. These results will be downscaled via an open-source generative machine learning method called Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Supply and Demand Drivers of Global Hydrogen Deployment in the Transition toward a Decarbonized Energy System

The role of hydrogen in energy system decarbonization is being actively examined by the research and policy communities. We evaluate the potential ‘hydrogen economy’ in global climate change mitigation scenarios using the Global Change Analysis Model (GCAM). We consider major hydrogen production methods in conjunction with delivery options to understand how hydrogen infrastructure affects its deployment. We also consider a rich set of hydrogen end-use technologies and vary their costs to understand how demand technologies affect deployment. We find that the availability of hydrogen transmission and distribution infrastructure primarily affects the hydrogen production mix, particularly the share produced centrally versus onsite, whereas assumptions about end-use technology primarily affect the scale of hydrogen deployment. In effect, hydrogen can be a distributed energy vector, enabled by onsite renewable electrolysis and, to a lesser extent, by onsite industrial production using natural gas with carbon capture and storage (CCS). While the share of hydrogen in final energy is small relative to the share of other major energy carriers in our scenarios, hydrogen enables decarbonization in difficult-to-electrify end uses such as industrial high-temperature heat. Its contribution to greenhouse gas mitigation increases as the climate objective is tightened.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Impact of Spatial Scales on the Intercomparison of Climate Scenarios

Scenario analysis has been widely applied in climate science to understand the impact of climate change on the future human environment, but intercomparison and similarity analysis of different climate scenarios based on multiple simulation runs remain challenging. Although spatial heterogeneity plays a key role in modeling climate and human systems, little research has been performed to understand the impact of spatial variations and scales on similarity analysis of climate scenarios. To address this issue, the authors developed a geovisual analytics framework that lets users perform similarity analysis of climate scenarios from the Global Change Assessment Model (GCAM) using a hierarchical clustering approach.

Luo, Wei↗
DOE OSTI2022

Dispersal and fire limit Arctic shrub expansion

Abstract Arctic shrub expansion alters carbon budgets, albedo, and warming rates in high latitudes but remains challenging to predict due to unclear underlying controls. Observational studies and models typically use relationships between observed shrub presence and current environmental suitability (bioclimate and topography) to predict shrub expansion, while omitting shrub demographic processes and non-stationary response to changing climate. Here, we use high-resolution satellite imagery across Alaska and western Canada to show that observed shrub expansion has not been controlled by environmental suitability during 1984–2014, but can only be explained by considering seed dispersal and fire. These findings provide the impetus for better observations of recruitment and for incorporating currently underrepresented processes of seed dispersal and fire in land models to project shrub expansion and climate feedbacks. Integrating these dynamic processes with projected fire extent and climate, we estimate shrubs will expand into 25% of the non-shrub tundra by 2100, in contrast to 39% predicted based on increasing environmental suitability alone. Thus, using environmental suitability alone likely overestimates and misrepresents shrub expansion pattern and its associated carbon sink.

54 ENVIRONMENTAL SCIENCES↗