Search NASA⌕ Search

SEARCH · Search NASA

Results for “CLIMATE MODELS”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,603 records · Page 89
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Rates of Sea‐Level Rise Are Highly Sensitive to Ice Viscosity Parameters in Model Benchmarks

Glacier flow plays a major role in current and future rates of globally averaged sea-level rise. The viscosity of glacial ice, controlling the rate of flow, decreases as stress increases and is highly sensitive to the value of the stress exponent, $n$, in the constitutive equation for viscous flow. Glaciologists and climate modelers almost exclusively assume $n=3$ when modeling ice flow and projecting sea-level rise through forward modeling. However, recent work suggests that $n\approx 4$ better fits observations, prompting the question: How sensitive are projections of sea-level rise to the value of $n$? We use an established community ice flow model and standard benchmark experiments designed as an idealized representation of Pine Island Glacier, West Antarctica. While initializing an $n=3$ model to match observations of an $n=4$ ice sheet is possible, we find that incorrectly assuming $n=3$ when in fact $n=4$ dramatically underestimates rates of sea-level rise. The scale of this error grows nonlinearly with the magnitude of the climate forcing, acting to increase projection uncertainties. Additionally, we find that models often account for this stress-dependent rheology mismatch during model initialization in a way that masks this rheological effect in the short term while leaving model outputs vulnerable to larger biases in longer-term projections. Initializations to observations of Pine Island Glacier display similar rheology-mismatch fingerprints to our idealized example.

climate sensitivity↗
DOE OSTI2022

Understanding the Drivers of Atlantic Multidecadal Variability using a Stochastic Model Hierarchy

The relative importance of ocean and atmospheric dynamics in generating Atlantic Multidecadal Variability (AMV) remains an open question. Comparisons between climate models with SLAB and fully-dynamic (FULL) ocean components are often used to explore this question, but cannot reveal how individual ocean processes generate these differences. We build a hierarchy of physically interpretable stochastic models to investigate the contribution of two upper-ocean processes to AMV: the role of seasonal variation and mixed-layer entrainment. This interpretability arises from the stochastic model’s simplified representation of sea surface temperature (SST), considering only the local upper ocean response to white-noise atmospheric forcing and its impact on surface heat exchange. We focus on understanding differences between SLAB and FULL non-eddy resolving pre-industrial control simulations of the Community Earth System Model 1 (CESM), and estimate the stochastic model parameters from each respective simulation. Despite its simplicity, the stochastic model reproduces temporal characteristics of SST variability in the SPG, including reemergence, seasonal-to-interannual persistence and power spectra. Furthermore, unrealistically persistent SST of the CESM-SLAB ocean simulation is reproduced in the equivalent stochastic model configuration where the mixed-layer depth (MLD) is constant. The stochastic model also reveals that vertical entrainment primarily damps SST variability, thus explaining why SLAB exhibits larger SST variance than FULL. Here, the stochastic model driven by temporally stochastic, spatially coherent forcing patterns reproduces the canonical AMV pattern. However, the amplitude of low-frequency variability remains underestimated, suggesting a role for ocean dynamics beyond entrainment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Pathways of sulfate enhancement by natural and anthropogenic mineral aerosols in China

China, the world’s largest consumer of coal, emits approximately 30 million tons of sulfur dioxide (SO₂) per year. SO₂ is subsequently oxidized to sulfate in the atmosphere. However, large gaps exist between model-predicted and measured sulfate levels in China. Long-term field observations and numerical simulations were integrated to investigate the effect of mineral aerosols on sulfate formation. We found that mineral aerosols contributed a nationwide average of approximately 22% to sulfate production in 2006. The increased sulfate concentration was approximately 2 μg m⁻³ in the entire China. In East China and the Sichuan Basin, the increments reached 6.3 μg m⁻³ and 7.3 μg m⁻³, respectively. Mineral aerosols led to faster SO₂ oxidation through three pathways. First, more SO₂ was dissolved as cloud water alkalinity increased due to water-soluble mineral cations. Sulfate production was then enhanced through the aqueous-phase oxidation of S(IV) (dissolved sulfur in oxidation state +4). The contribution to the national sulfate production was 5%. Second, sulfate was enhanced through S(IV) catalyzed oxidation by transition metals. The contribution to the annual sulfate production was 8%, with 19% during the winter that decreased to 2% during the summer. Third, SO₂ reacts on the surface of mineral aerosols to produce sulfate. The contribution to the national average sulfate concentration was 9% with 16% during the winter and a negligible effect during the summer. The inclusion of mineral aerosols does resolve model discrepancies with sulfate observations in China, especially during the winter. These three pathways, which are not fully considered in most current chemistry-climate models, will significantly impact assessments regarding the effects of aerosol on climate change in China.

Huang, Xin↗
DOE OSTI2015

Climate change impacts on US agriculture and forestry: benefits of global climate stabilization

Increasing atmospheric carbon dioxide levels, higher temperatures, altered precipitation patterns, and other climate change impacts have already begun to affect US agriculture and forestry, with impacts expected to become more substantial in the future. There have been numerous studies of climate change impacts on agriculture or forestry, but relatively little research examining the long-term net impacts of a stabilization scenario relative to a case with unabated climate change. We provide an analysis of the potential benefits of global climate change mitigation for US agriculture and forestry through 2100, accounting for landowner decisions regarding land use, crop mix, and management practices. The analytic approach involves a combination of climate models, a crop process model (EPIC), a dynamic vegetation model used for forests (MC1), and an economic model of the US forestry and agricultural sector (FASOM-GHG). We find substantial impacts on productivity, commodity markets, and consumer and producer welfare for the stabilization scenario relative to unabated climate change, though the magnitude and direction of impacts vary across regions and commodities. Although there is variability in welfare impacts across climate simulations, we find positive net benefits from stabilization in all cases, with cumulative impacts ranging from $32.7 billion to $54.5 billion over the period 2015-2100. Our estimates contribute to the literature on potential benefits of GHG mitigation and can help inform policy decisions weighing alternative mitigation and adaptation actions.

agricultural modeling↗
DOE OSTI2024

Provincial-scale assessment of direct air capture to meet China’s climate neutrality goal under limited bioenergy supply

China has large, estimated potential for direct air carbon capture and storage (DACCS) but its deployment locations and impacts at the subnational scale remain unclear. This is largely because higher spatial resolution studies on carbon dioxide removal (CDR) in China have focused mainly on bioenergy with carbon capture and storage. This study uses a spatially detailed integrated energy-economy-climate model to evaluate DACCS for 31 provinces in China as the country pursues its goal of climate neutrality by 2060. We find that DACCS could expand China’s negative emissions capacity, particularly under sustainability-minded limits on bioenergy supply that are informed by bottom-up studies. But providing low-carbon electricity for multiple GtCO 2 yr -1 DACCS may require over 600 GW of additional wind and solar capacity nationwide and comprise up to 30% of electricity demand in China’s northern provinces. Investment requirements for DACCS range from 330 to 530 billion dollars by 2060 but could be repaid manyfold in the form of avoided mitigation costs, which DACCS deployment could reduce by up to $6 trillion over the same period. Enhanced efforts to lower residual CO 2 emissions that must be offset with CDR under a net-zero paradigm reduce but do not eliminate the use of DACCS for mitigation. For decision-makers and the energy-economy models guiding them, our results highlight the value of expanding beyond the current reliance on biomass for negative emissions in China.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Low-Cloud Feedbacks from Cloud-Controlling Factors: A Review

Abstract The response to warming of tropical low-level clouds including both marine stratocumulus and trade cumulus is a major source of uncertainty in projections of future climate. Climate model simulations of the response vary widely, reflecting the difficulty the models have in simulating these clouds. These inadequacies have led to alternative approaches to predict low-cloud feedbacks. Here, we review an observational approach that relies on the assumption that observed relationships between low clouds and the “cloud-controlling factors” of the large-scale environment are invariant across time-scales. With this assumption, and given predictions of how the cloud-controlling factors change with climate warming, one can predict low-cloud feedbacks without using any model simulation of low clouds. We discuss both fundamental and implementation issues with this approach and suggest steps that could reduce uncertainty in the predicted low-cloud feedback. Recent studies using this approach predict that the tropical low-cloud feedback is positive mainly due to the observation that reflection of solar radiation by low clouds decreases as temperature increases, holding all other cloud-controlling factors fixed. The positive feedback from temperature is partially offset by a negative feedback from the tendency for the inversion strength to increase in a warming world, with other cloud-controlling factors playing a smaller role. A consensus estimate from these studies for the contribution of tropical low clouds to the global mean cloud feedback is 0.25 ± 0.18 W m −2 K −1 (90% confidence interval), suggesting it is very unlikely that tropical low clouds reduce total global cloud feedback. Because the prediction of positive tropical low-cloud feedback with this approach is consistent with independent evidence from low-cloud feedback studies using high-resolution cloud models, progress is being made in reducing this key climate uncertainty.

58 GEOSCIENCES↗
DOE OSTI2014

Recent seasonal asymmetric changes in the NAO (a marked summer decline and increased winter variability) and associated changes in the AO and Greenland Blocking Index

Recent changes are found in the means and variability of the North Atlantic Oscillation (NAO) index. There has been a sustained significant recent decrease in the summer NAO since the 1990s and, at the same time, a striking increase in variability of the winter – especially December – NAO that resulted in three of five (two of five) record high (record low) NAO Decembers occurring during 2004–2013 in the 115-year record. These NAO changes are related to an increasing trend in the Greenland Blocking Index (GBI, high pressure over Greenland) in summer and a more variable GBI in December. The enhanced early winter NAO variability originates mainly at the southern node of the NAO but is also related to the more variable GBI in December. Transition seasons (spring and autumn) have remained relatively unchanged over the last 30 years. These results are corroborated using several NAO indices. The Arctic Oscillation (AO) index, although strongly correlated with the NAO, does not show the recent sustained significant summer decrease, but it does show enhanced early winter variability. These recent observed changes are not present in the current generation of global climate models, although the latest process studies do offer insight into their causes. We invoke several plausible climate forcings and feedbacks to explain the recent NAO changes.

Meteorology & Atmospheric Sciences↗
DOE OSTI2014

Climate Science: Tropical Expansion by Ocean Swing

The tropical belt has become wider over the past decades, but climate models fall short of capturing the full rate of the expansion. The latest analysis of the climate simulations suggests that a long-term swing of the Pacific Decadal Oscillation is the main missing cause.

climate zone↗
DOE OSTI2017

A Hindcast Experiment Using the GCAM 3.0 Agriculture and Land-Use Module

We report results of a “hindcast” experiment focusing on the agricultural and land-use component of the Global Change Assessment Model (GCAM). We initialize GCAM to reproduce observed agriculture and land use in 1990 and forecast agriculture and land use patterns on 1-year time steps to 2010. We report overall model performance for 9 crops in 14 regions. We report areas where the hindcast is in relatively good agreement with observations and areas where the correspondence is poorer. We find that when given observed crop yields as input data, producers in GCAM implicitly have perfect foresight for yields leading to over compensation for year-to-year yield variation. We explore a simple model in which planting decisions are based on expectations but production depends on actual yields and find that this addresses the implicit perfect foresight problem. Second, while existing policies are implicitly calibrated into IAMs, changes in those policies over the period of analysis can have a dramatic effect on the fidelity model output. Third, we demonstrate that IAMs can employ techniques similar to those used by the climate modeling community to evaluate model skill. We find that hindcasting has the potential to yield substantial benefits to the IAM community.

hindcast, gc, agriculture and landuse modeling, va↗
DOE OSTI2020

Soil moisture and hydrology projections of the permafrost region – a model intercomparison

Abstract. This study investigates and compares soil moisture and hydrology projections of broadly used land models with permafrost processes and highlights the causes and impacts of permafrost zone soil moisture projections. Climate models project warmer temperatures and increases in precipitation (P) which will intensify evapotranspiration (ET) and runoff in land models. However, this study shows that most models project a long-term drying of the surface soil (0–20 cm) for the permafrost region despite increases in the net air–surface water flux (P-ET). Drying is generally explained by infiltration of moisture to deeper soil layers as the active layer deepens or permafrost thaws completely. Although most models agree on drying, the projections vary strongly in magnitude and spatial pattern. Land models tend to agree with decadal runoff trends but underestimate runoff volume when compared to gauge data across the major Arctic river basins, potentially indicating model structural limitations. Coordinated efforts to address the ongoing challenges presented in this study will help reduce uncertainty in our capability to predict the future Arctic hydrological state and associated land–atmosphere biogeochemical processes across spatial and temporal scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The effect of large-scale model time step and multiscale coupling frequency on cloud climatology, vertical structure, and rainfall extremes in a superparameterized GCM

The effect of global climate model (GCM) time step—which also controls how frequently global and embedded cloud resolving scales are coupled—is examined in the Superparameterized Community Atmosphere Model ver 3.0. Systematic bias reductions of time-mean shortwave cloud forcing (~10 W/m 2 ) and longwave cloud forcing (~5 W/m 2 ) occur as scale coupling frequency increases, but with systematically increasing rainfall variance and extremes throughout the tropics. An overarching change in the vertical structure of deep tropical convection, favoring more bottom-heavy deep convection as a global model time step is reduced may help orchestrate these responses. The weak temperature gradient approximation is more faithfully satisfied when a high scale coupling frequency (a short global model time step) is used. These findings are distinct from the global model time step sensitivities of conventionally parameterized GCMs and have implications for understanding emergent behaviors of multiscale deep convective organization in superparameterized GCMs. Lastly, the results may also be useful for helping to tune them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Boundary-Layer-Coupled and Decoupled Clouds in Global Storm-Resolving Models: Comparisons With the ARM Observations

The accurate representation of interactions between clouds and planetary boundary layer (PBL) is a persistent challenge in climate models, critical for simulating surface energy budget. The emergence of kilometer-grid-scale global storm resolving models (GSRMs) offers the potential for enhanced details of PBL processes in these complex interactions. This study evaluates the representation of PBL-coupled and decoupled clouds in nine GSRM simulations against extensive ground-based observations by the Department of Energy Atmospheric Radiation Measurement (ARM) program, across six sites encompassing diverse regimes such as marine and continental environments in tropics and midlatitude. By differentiating coupling based on the relative positions between cloud bases and PBL tops, our analysis focuses on the simulation of PBL height, cloud frequency, position and vertical extent. The GSRMs generally exhibit commendable agreements with observed cloud structures and PBL diurnal cycles across different ARM sites. In contrast to the relatively consistent representation of decoupled clouds, discrepancies exist between the simulated and the observed coupled clouds, particularly in areas of intense convection, for example, over tropical rainforests and mountainous regions. These biases are probably associated with the models' tendency to underestimate the boundary layer humidity and the frequency of coupled clouds within different ranges of PBL heights. This study underscores the importance for continuous improvements in the representation of boundary layer and convection within these global kilometer-grid-scale models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Significant and Inevitable End-of-Twenty-First-Century Advances in Surface Runoff Timing in California’s Sierra Nevada

Abstract Using hybrid dynamical–statistical downscaling, 3-km-resolution end-of-twenty-first-century runoff timing changes over California’s Sierra Nevada for all available global climate models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are projected. All four representative concentration pathways (RCPs) adopted by the Intergovernmental Panel on Climate Change’s Fifth Assessment Report are examined. These multimodel, multiscenario projections allow for quantification of ensemble-mean runoff timing changes and an associated range of possible outcomes due to both intermodel variability and choice of forcing scenario. Under a “business as usual” forcing scenario (RCP8.5), warming leads to a shift toward much earlier snowmelt-driven surface runoff in 2091–2100 compared to 1991–2000, with advances of as much as 80 days projected in the 35-model ensemble mean. For a realistic “mitigation” scenario (RCP4.5), the ensemble-mean change is smaller but still large (up to 30 days). For all plausible forcing scenarios and all GCMs, the simulated changes are statistically significant, so that a detectable change in runoff timing is inevitable. Even for the mitigation scenario, the ensemble-mean change is approximately equivalent to one standard deviation of the natural variability at most elevations. Thus, even when greenhouse gas emissions are curtailed, the runoff change is climatically significant. For the business-as-usual scenario, the ensemble-mean change is approximately two standard deviations of the natural variability at most elevations, portending a truly dramatic change in surface hydrology by the century’s end if greenhouse gas emissions continue unabated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Threat by marine heatwaves to adaptive large marine ecosystems in an eddy-resolving model

Marine heatwaves (MHWs), episodic periods of abnormally high sea surface temperature, severely affect marine ecosystems. Large marine ecosystems (LMEs) cover ~22% of the global ocean but account for 95% of global fisheries catches. Yet how climate change affects MHWs over LMEs remains unknown because such LMEs are confined to the coast where low-resolution climate models are known to have biases. Here, using a high-resolution Earth system model and applying a ‘future threshold’ that considers MHWs as anomalous warming above the long-term mean warming of sea surface temperatures, we find that future intensity and annual days of MHWs over the majority of the LMEs remain higher than in the present-day climate. Better resolution of ocean mesoscale eddies enables simulation of more realistic MHWs than low-resolution models. These increases in MHWs under global warming pose a serious threat to LMEs, even if resident organisms could adapt fully to the long-term mean warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The Dependence of Mean Climate State on Shortwave Absorption by Water Vapor

Abstract State-of-the-art climate models exhibit significant spread in the climatological value of atmospheric shortwave absorption (SWA). This study investigates both the possible causes and climatic impacts of this SWA intermodel spread. The intermodel spread of global-mean SWA largely originates from the intermodel difference in water vapor shortwave absorptivity. Hence, we alter the water vapor shortwave absorptivity in the Community Earth System Model, version 1, with the Community Atmosphere Model, version 4 (CESM1-CAM4). Increasing the water vapor shortwave absorptivity leads to a reduction in global-mean precipitation and a La Niña–like cooling over the tropical Pacific. The global-mean atmospheric energy budget suggests that the precipitation is suppressed as a way to compensate for the increased SWA. The precipitation reduction is driven by the weakened surface winds, stabilized planetary boundary layer, and surface cooling. The La Niña–like cooling over the tropical Pacific is attributed to the zonal asymmetry of climatological evaporative damping efficiency and the low cloud enhancement over the eastern basin. Complementary fixed SSTs simulations suggest that the latter is more fundamental and that it primarily arises from atmospheric processes. Consistent with our experiments, the CMIP5/6 models with a higher global-mean SWA tend to produce tropical Pacific toward a more La Niña–like mean state, highlighting the possible role of water vapor shortwave absorptivity for shaping the mean-state climate patterns.

Kim, Hanjun↗
DOE OSTI2025

Simulating Atmospheric Processes in Earth System Models and Quantifying Uncertainties With Deep Learning Multi‐Member and Stochastic Parameterizations

Abstract Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superparameterization embedded in an Earth System Model (ESM). We explore three methods to construct stochastic parameterizations: (a) a single Deep Neural Network (DNN) with Monte Carlo Dropout; (b) a multi‐member parameterization; and (c) a Variational Encoder Decoder with latent space perturbation. We show that the multi‐member parameterization improves the representation of convective processes, especially in the planetary boundary layer, compared to individual DNNs. The respective uncertainty quantification illustrates that methods (b) and (c) are advantageous compared to a dropout‐based DNN parameterization regarding the spread of convective processes. Hybrid simulations with our best‐performing multi‐member parameterizations remained challenging and crash within the first days. Therefore, we develop a pragmatic partial coupling strategy relying on the superparameterization for condensate emulation. Partial coupling reduces the computational efficiency of hybrid Earth‐like simulations but enables model stability over 5 months with our multi‐member parameterizations. However, our hybrid simulations exhibit biases in thermodynamic fields and differences in precipitation patterns. Despite this, the multi‐member parameterizations enable improvements in reproducing tropical extreme precipitation compared to a traditional convection parameterization. Despite these challenges, our results indicate the potential of a new generation of multi‐member machine learning parameterizations leveraging uncertainty quantification to improve the representation of stochasticity of subgrid effects.

Behrens, Gunnar [Deutsches Zentrum für Luft‐ und R↗
DOE OSTI2024

Applying double cropping and interactive irrigation in the North China Plain using WRF4.5

Abstract. Irrigated cultivation exerts a significant influence on the local climate and the hydrological cycle. The North China Plain (NCP) is known for its intricate agricultural system, marked by expansive cropland, high productivity, compact rotation, a semi-arid climate, and intensive irrigation practices. As a result, there has been considerable attention on the potential impact of this intensive irrigated agriculture on the local climate. However, studying the irrigation impact in this region has been challenging due to the lack of an accurate simulation of crop phenology and irrigation practices within the climate model. By incorporating double cropping with interactive irrigation, our study extends the capabilities of the Weather Research Forecast (WRF) model, which has previously demonstrated commendable performance in simulating single-cropping scenarios. This allows for two-way feedback between irrigated crops and climate, further enabling the inclusion of irrigation feedback from both ground and vegetation perspectives. The improved crop modeling system shows significant enhancement in capturing vegetation and irrigation patterns, which is evidenced by its ability to identify crop stages, estimate field biomass, predict crop yield, and project monthly leaf area index. The improved simulation of large-scale irrigated crops in the NCP can further enhance our understanding of the intricate relationship between agricultural development and climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Conducting more inclusive solar geoengineering research: A feminist science framework

Solar geoengineering, or deliberate climate modification, has been receiving increased attention in recent years. Given the far-reaching consequences of any potential solar geoengineering deployments, it is prudent to identify inherent biases, blind spots, and other potential issues at all stages of the research process. Here we articulate a feminist science-based framework to concretely describe how solar geoengineering researchers can be more inclusive of different perspectives and potentially contradictory conclusions, in the process illuminating potential implicit bias and enhancing the conclusions that can be gained from their studies. Importantly, this framework is an adoptable method of practice that can be refined, with the aim of conducting better research in solar geoengineering. As an illustration, we retrospectively apply this framework to a well-read solar geoengineering study (also led by the first author of this study), improving transparency by revealing its implicit values, conclusions made from its evidence base, and the methodologies that study pursues. We conclude here with a set of recommendations for the geoengineering research community whereby more inclusive research can become a regular part of practice. Throughout this process, we illustrate how feminist science scholars can use this approach to study climate modeling.

54 ENVIRONMENTAL SCIENCES↗