Search NASA⌕ Search

SEARCH · Search NASA

Results for “GCMs”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Future projections in the climatology of global low-level jets from CORDEX-CORE simulations

The potential changes in the strength and location of five low-level jets (LLJs) located within four Coordinated Regional Climate Downscaling Experiment (CORDEX) domains are examined for present and future climate conditions using an ensemble of simulations conducted with the RegCM4 regional model at a 25 km horizontal grid spacing. Lateral and lower boundary forcing fields are from three General Circulation Models (GCMs), and we analyse a historical period (1995–2014) along with two future periods (2041–2060 and 2080–2099) under the Representative Concentration Pathways 2.6 and 8.5. The RegCM4, as driven by the GCMs, is capable of capturing most of the observed climatological features of the LLJs, both in terms of spatial location and seasonal evolution. Analysis of the influence of global warming on the LLJs shows a consistent strengthening of the jets and a shift in their location under both warming scenarios. The Monsoon and West African westerly LLJs exhibit a northward shift, while the Caribbean and South American LLJs present a westward expansion. The use of an ensemble of high-resolution simulations is found to provide a key element for a robust assessment of changes in LLJs associated with future global warming scenarios.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Future projections in tropical cyclone activity over multiple CORDEX domains from RegCM4 CORDEX-CORE simulations

The characteristics of tropical cyclone (TC) activity over five TC basins lying within four Coordinated Regional Downscaling Experiment (CORDEX) domains are examined for present and future climate conditions using a new ensemble of simulations completed as part of the CORDEX-CORE initiative with the regional climate model RegCM4. The simulations are conducted at a 25 km horizontal grid spacing and are driven by three CMIP5 general circulation models (GCMs) under two Representative Concentration Pathways (RCP2.6 and RCP8.5). The RegCM4 captures most features of the observed TC climatology, except for the TC intensity, which is thus statistically adjusted using a bias correction procedure to account for the effect of the coarse model resolution. The RegCM4 exhibits an improved simulation of several TC statistics compared to the driving GCMs, over most basins analyzed. In future climate conditions we find significant increases in TC frequency over the North Indian Ocean, the Northwest Pacific and Eastern Pacific regions, which are consistent with an increase in mid-tropospheric relative humidity. The North Atlantic and Australasia regions show a decrease in TC frequency, mostly associated with an increase in wind shear. We also find a consistent increase in future storm rainfall rates associated with TCs and in the frequency of the most intense TCs over most domains. Finally, our study shows robust responses often, but not always, in line with previous studies, still implying the presence of significant uncertainties in the projection of TC characteristics, which need to be addressed using large ensembles of simulations with high-resolution models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assessment of CMIP6 Performance and Projected Temperature and Precipitation Changes Over South America

We evaluate the performance of a large ensemble of Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over South America for a recent past reference period and examine their projections of twenty-first century precipitation and temperature changes. The future changes are computed for two time slices (2040–2059 and 2080–2099) relative to the reference period (1995–2014) under four Shared Socioeconomic Pathways (SSPs, SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5). The CMIP6 GCMs successfully capture the main climate characteristics across South America. However, they exhibit varying skill in the spatiotemporal distribution of precipitation and temperature at the sub-regional scale, particularly over high latitudes and altitudes. Future precipitation exhibits a decrease over the east of the northern Andes in tropical South America and the southern Andes in Chile and Amazonia, and an increase over southeastern South America and the northern Andes—a result generally consistent with earlier CMIP (3 and 5) projections. However, most of these changes remain within the range of variability of the reference period. In contrast, temperature increases are robust in terms of magnitude even under the SSP1–2.6. Future changes mostly progress monotonically from the weakest to the strongest forcing scenario, and from the mid-century to late-century projection period. There is an increase in the seasonality of the intra-annual precipitation distribution, as the wetter part of the year contributes relatively more to the annual total. Furthermore, an increasingly heavy-tailed precipitation distribution and a rightward shifted temperature distribution provide strong indications of a more intense hydrological cycle as greenhouse gas emissions increase. The relative distance of an individual GCM from the ensemble mean does not substantially vary across different scenarios. We found no clear systematic linkage between model spread about the mean in the reference period and the magnitude of simulated sub-regional climate change in the future period. Overall, these results could be useful for regional climate change impact assessments across South America.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Application-specific optimal model weighting of global climate models: A red tide example

Global climate models (GCMs) and Earth system models (ESMs) provide many climate services with environmental relevance. The High Resolution Model Inter-comparison Project (HighResMIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) provides model runs of GCMs and ESMs to address regional phenomena. Developing a parsimonious ensemble of CMIP6 requires multiple ensemble methods such as independent-model subset selection, prescreening-based subset selection, and model weighting. The work presented here focuses on application-specific optimal model weighting, with prescreening-based subset selection. As such, independent ensemble members are categorized, selected, and weighted based on their ability to reproduce physically-interpretable features of interest that are problem-specific. We discuss the strengths and caveats of optimal model weighting using a case study of red tide prediction in the Gulf of Mexico along the West Florida Shelf. Red tide is a common name of specific harmful algal blooms that occur worldwide, causing adverse socioeconomic and environmental impacts. Our results indicate the importance of prescreening-based subset selection as optimal model weighting can underplay robust ensemble members by optimizing error cancellation. Prescreening-based subset selection also provides insights about the validity of the model weights. By illustrating the caveats of using non-representative models when optimal model weighting is used, the findings and discussion of this study are pertinent to many other climate services.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Statistically Steady State Large-Eddy Simulations Forced by an Idealized GCM: 1. Forcing Framework and Simulation Characteristics

Using large-eddy simulations (LES) systematically has the potential to inform parameterizations of subgrid-scale (SGS) processes in general circulation models (GCMs), such as turbulence, convection, and clouds. Here we show how LES can be run to emulate grid columns of GCMs to generate a library of LES across a cross-section of dynamical regimes. The LES setup replicates the thermodynamic and water budgets in GCM grid columns. Resolved horizontal and vertical transports of heat and water and large-scale pressure gradients from the GCM are prescribed as forcing in the LES. The LES satisfies the same (slab-ocean) surface boundary conditions as the GCM, leaving the LES temperatures free to adjust. Radiative transfer is treated in a unied but highly idealized manner (a semi-gray atmosphere without cloud radiative effects) in both the GCM and LES. We show that the LES with these forcing and boundary conditions reaches statistically steady states without nudging to reference profiles. These steady states provide a training dataset for developing GCM parameterizations. The same LES setup also provides a good basis for studying the cloud response to global warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A new method to construct a horizontal resolution-dependent wind speed adjustment factor for tropical cyclones in global climate model simulations

A new method to construct a horizontal resolution-dependent wind speed adjustment factor for evaluating tropical cyclones (TCs) in global climate models (GCMs) is presented here. In contrast to the previous studies that used idealized axisymmetric wind fields, this study analyzes 48 hours of 10-second surface wind fields from 1-km TC simulations. The adjustment factor is derived from filtering the simulated TC wind fields onto various horizontal grid spacings typical of those used in GCMs. The new adjustment factor leads to TCs with greater intensity than the existing adjustment factors for horizontal grid spacings smaller than 30 km. This difference is attributed to more realistic wind fields in the TC simulations that contain highly asymmetric, localized patches of higher wind speeds instead of axisymmetric wind fields. Applying the new adjustment factor to select GCM simulations suggests the common interpretation of low-intensity bias in GCM-simulated TCs might be slightly exaggerated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Implementation of Framework for Improvement by Vertical Enhancement Into Energy Exascale Earth System Model

Abstract The low cloud bias in global climate models (GCMs) remains an unsolved problem. Coarse vertical resolution in GCMs has been suggested to be a significant cause of low cloud bias because planetary boundary layer parameterizations cannot resolve sharp temperature and moisture gradients often found at the top of subtropical stratocumulus layers. This work aims to ameliorate the low cloud problem by implementing a new computational method, the Framework for Improvement by Vertical Enhancement (FIVE), into the Energy Exascale Earth System Model (E3SM). Three physics schemes representing microphysics, radiation, and turbulence as well as vertical advection are interfaced to vertically enhanced physics (VEP), which allows for these processes to be computed on a higher vertical resolution grid compared to the rest of the E3SM model. We demonstrate the better representation of subtropical boundary layer clouds with FIVE while limiting additional computational cost from the increased number of levels. When the vertical resolution approaches the large eddy simulation‐like vertical resolution in VEP, the climatological low cloud amount shows a significant increase of more than 30% in the southeastern Pacific Ocean. Using FIVE to improve the representation of low‐level clouds does not come with any negative side effects associated with the simulation of mid‐ and high‐level cloud and precipitation, that can occur when running the full model at higher vertical resolution.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning

Abstract Deficiencies in convection trigger functions, used in deep convection parameterizations in General Circulation Models (GCMs), have critical impacts on climate simulations. A novel convection trigger function is developed using the machine learning (ML) classification model XGBoost. The large‐scale environmental information associated with convective events is obtained from the long‐term constrained variational analysis forcing data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites representing, respectively, continental mid‐latitude and tropical convection. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The performance of the ML trigger is compared with four convective trigger functions commonly used in GCMs: dilute convective available potential energy (CAPE), undilute CAPE, dilute dynamic CAPE (dCAPE), and undilute dCAPE. The ML trigger substantially outperforms the four CAPE‐based triggers in terms of the F 1 score metric, widely used to estimate the performance of ML methods. The site‐specific ML trigger functions can achieve, respectively, 91% and 93% F 1 scores at SGP and MAO. The unified trigger also has a 91% F 1 score, with virtually no degradation from the site‐specific training, suggesting the potential of a global ML trigger function. The ML trigger alleviates a GCM deficiency regarding the overprediction of convection occurrence, offering a promising improvement to the simulation of the diurnal cycle of precipitation. Furthermore, to overcome the black box issue of the ML methods, insights derived from the ML model are discussed, which may be leveraged to improve traditional CAPE‐based triggers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The Influence of Ocean Coupling on Simulated and Projected Tropical Cyclone Precipitation in the HighResMIP–PRIMAVERA Simulations

This study aims to quantify the impacts of atmosphere–ocean coupling on simulated and projected tropical cyclone (TC) precipitation globally. We used global climate model (GCM) simulations over 1950–2050 from the High Resolution Model Intercomparison Project (HighResMIP) and compared its fully coupled atmosphere–ocean GCMs (AOGCMs) with atmosphere-only GCMs (AGCMs). We find that ocean coupling generally leads to decreased TC precipitation over ocean and land. Large-scale sea surface temperature (SST) biases are critical drivers of the precipitation difference, with secondary contributions from local TC–ocean feedbacks via SST cold wakes. The two driving factors, attributed to ocean coupling in the AOGCMs, influence TC precipitation in association with decreased TC intensity and specific humidity. The AOGCMs and AGCMs consistently project TC precipitation increases in 2015–2050 relative to 1950–2014 over ocean for all basins, and for landfalling TCs in the North Atlantic and western North Pacific.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Biased Estimates of Equilibrium Climate Sensitivity and Transient Climate Response Derived From Historical CMIP6 Simulations

Abstract This study assesses the effective climate sensitivity (EffCS) and transient climate response (TCR) derived from global energy budget constraints within historical simulations of eight CMIP6 global climate models (GCMs). These calculations are enabled by use of the Radiative Forcing Model Intercomparison Project (RFMIP) simulations, which permit accurate quantification of the radiative forcing. Long‐term historical energy budget constraints generally underestimate EffCS from CO 2 quadrupling and TCR from CO 2 ramping, owing to changes in radiative feedbacks and changes in ocean heat uptake efficiency. Atmospheric GCMs forced by observed warming patterns produce lower values of EffCS that are more in line with those inferred from observed historical energy budget changes. The differences in the EffCS estimates from historical energy budget constraints of models and observations are traced to discrepancies between modeled and observed historical surface warming patterns.

Dong, Yue↗
DOE OSTI2022

Effects of Improved Simulation of Precipitation on Evapotranspiration and Its Partitioning Over Land

Evapotranspiration (ET) is a key component of the global hydrological cycle, which is strongly modulated by the occurrence of different rainfall intensities. Global climate models (GCMs) commonly suffer from “too much light rain” and a negative bias in the ratio of transpiration (T) to ET (T/ET). It is unclear whether these biases are related. Here, in this work, we show that with the improved simulation of probability density functions of rainfall intensity by suppressing light-rain occurrence using a stochastic convection parameterization in the NCAR CESM1.2, the canopy T increases in tropical forests while evaporation from canopy interception and bare soil decreases. The simulated T/ET is increased by 2.5% globally and up to 8% regionally, primarily attributable to reduced fraction of wet leaves due to less frequent light rain despite its weak intensity. These results imply that excessive light rain is an important cause of the negative T/ET bias in GCMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Evolving CO 2 rather than SST leads to a factor of ten decrease in GCM convergence time

The high computational cost of Global Climate Models (GCMs) is a problem that limits their use in many areas. Recently an inverse climate modeling (InvCM) method, which fixes the global mean sea surface temperature (SST) and evolves the CO 2 mixing ratio to equilibrate climate, has been implemented in a cloud resolving model. In this paper, we apply InvCM to ExoCAM GCM aquaplanet simulations, allowing the SST pattern to evolve while maintaining a fixed global-mean SST. We find that InvCM produces the same climate as normal slab-ocean simulations but converges an order of magnitude faster. We then use InvCM to calculate the equilibrium CO 2 for SSTs ranging from 290 K to 340 K at 1 K intervals and reproduce the large increase in climate sensitivity at an SST of about 315 K at much higher temperature resolution. The speedup provided by InvCM could be used to equilibrate GCMs at higher spatial resolution or to perform broader parameter space exploration in order to gain new insight into the climate system. Additionally, InvCM could be used to find unstable and hidden climate states, and to find climate states close to bifurcations such as the runaway greenhouse transition.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections?

Abstract We present an intercomparison of a suite of high‐resolution downscaled climate projections based on a six‐member General Circulation Model (GCM) ensemble from Coupled Models Intercomparison Project (CMIP6). The CMIP6 GCMs have been downscaled using dynamical and statistical downscaling techniques based on two meteorological reference observations over the conterminous United States. We use the regional climate model, RegCM4, for dynamical downscaling, double bias correction constructed analogs method for statistical downscaling, and Daymet and Livneh datasets as the reference observations for statistical training and bias‐correction. We evaluate the performances of downscaled data in both historical and future periods under the SSP585 scenario. While dynamical downscaling improves the simulation of some performance evaluation indices, it adds an extra bias in others, highlighting the need for statistical correction before its use in impact assessments. Downscaled datasets after bias‐correction compare exceptionally well with observations. However, the choice of downscaling techniques and the underlying reference observations influence the hydroclimate characteristics of downscaled data. For instance, the statistical downscaling generally preserves the GCMs climate change signal but overestimates the frequency of hot extremes. Similarly, simulated future changes are sensitive to the choice of reference observations, particularly for precipitation extremes that exhibit a higher projected increase in the ensembles trained and/or corrected by Daymet than Livneh. Overall, these results demonstrate that multiple factors, including downscaling techniques and reference observations, can substantially influence the outcome of downscaled climate projections and stress the need for a comprehensive understanding of such method‐based uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Description and Evaluation of a New Deep Convective Cloud Model Considering In-cloud Inhomogeneity

Convections still need to be parameterized in general circulation models (GCMs) in the coming decades. Performances of GCMs are significantly influenced by the convection schemes used. In contrast to most conventional cloud models that ignore in-cloud inhomogeneities, a new convective cloud model explicitly considering in-cloud inhomogeneities is developed. The new model adopts a single bulk plume approach, but divides the plume into a series of interacting sub-plumes in order to mimic the transition from the plume core to its edges. We implemented the new cloud model in NCAR Community Atmosphere Model version 5.0 (CAM5) and evaluated its performance. Single-column tests show a stronger mass flux profile and low-level detrainment with the new model. In global simulations, the long-lasting wet bias in the tropical free troposphere of CAM5 is alleviated. Spatial pattern and intensity distribution of precipitation is improved, but the global hydrological cycle is slightly over-enhanced. The diurnal cycle of tropical precipitation is also better captured though the precipitation peak still occurs earlier than the observations and the amplitude of the cycle tends to weaken. MJO simulation is slightly improved with the new scheme. In addition, the new cloud model generated more tropical cyclones, in better agreement with observations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6)

Abstract Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC ( R BC , the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use R BC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust R BC to make the modeled BC mixing state index ( χ mode ) match the one predicted by the ML model ( χ ML ). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χ mode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χ ML (66%). The R BC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

Future Climate Projections for South Florida: Improving the Accuracy of Air Temperature and Precipitation Extremes With a Hybrid Statistical Bias Correction Technique

Projecting future climate variables is essential for comprehending the potential impacts on hydroclimatic hazards like floods and droughts. Evaluating these impacts is challenging due to the coarse spatial resolution of global climate models (GCMs); therefore, bias correction is widely used. Here, we applied two statistical methods—standard empirical quantile mapping (EQM) and a hybrid approach, EQM with linear correction (EQM-LIN)—to bias correct precipitation and air temperature simulated by nine GCMs. We used historical observations from 20 weather stations across South Florida to project future climate under three shared socioeconomic pathways (SSPs). Compared to the EQM, the hybrid EQM-LIN method improved R 2 of daily quantiles by up to 30% over the historical period and improved MAE up to 70% in months that contain most extreme values. Projected extreme precipitation at the weather stations showed that, compared to the EQM-LIN, the EQM method underestimates the high quantiles by up to 26% in SSP585. The projected changes in annual maximum precipitation from historical period (1985–2014) to near future (2040–2069) and far future (2070–2100) were between 2% and 16% across the study area. Projected future precipitation suggested a slight decrease during summer but an increase in fall. This, along with rising summer temperatures, suggested that South Florida can experience rapid oscillations from warmer summers and increased flooding in fall under future climate. Additionally, our comparative analyses with globally and nationally downscaled studies showed that such coarse scale studies do not represent the climatic extremes well, particularly for high quantile precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Complementing Dynamical Downscaling With Super‐Resolution Convolutional Neural Networks

Despite advancements in Artificial Intelligence (AI) methods for climate downscaling, significant challenges remain for their practicality in climate research. Current AI-methods exhibit notable limitations, such as limited application in downscaling Global Climate Models (GCMs), and accurately representing extremes. To address these challenges, we implement an AI-based methodology using super-resolution convolutional neural networks (SRCNN), trained and evaluated on 40 years of daily precipitation data from a reanalysis and a high-resolution dynamically downscaled counterpart. The dynamical downscaled simulations, constrained using spectral nudging, enable the replication of historical events at a higher resolution. This allows the SRCNN to emulate dynamical downscaling effectively. Modifications, such as incorporating elevation data and data pre-processing enhances overall model performance, while using exponential and quantile loss functions improve the simulation of extremes. Our findings show SRCNN models efficiently and skillfully downscale precipitation from GCMs. Future work will expand this methodology to downscale additional variables for future climate projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Stable Simulation of the Community Atmosphere Model Using Machine‐Learning Physical Parameterization Trained With Experience Replay

In recent years, machine learning (ML) models have been used to improve physical parameterizations of general circulation models (GCMs). A significant challenge of integrating ML models into GCMs is the online instability when they are coupled for long‐term simulation. We present a new strategy that demonstrates robust online stability when the physical parameterization package of an atmospheric GCM is replaced by a deep ML model. The method uses experience replay with a multistep training scheme of the ML model in which the model's own output at the previous time step is used in the training. Predicted physics tendencies in the replay buffer with the most recent errors in the training iterations are reused, making the ML model learn from its own errors. The training method reduces the gap between the offline and online environments of the ML model. The method is used to train the ML model as the physical parameterization of the Community Atmosphere Model (CAM5) with training data from the Multi‐scale Modeling Framework high resolution simulations. Three 6‐year online simulations of the CAM5 are carried out by using the ML physics package. The simulated spatial distributions of precipitation, surface temperature and zonally averaged atmospheric fields demonstrate overall better accuracy than that of the standard CAM5 and benchmark model even without the use of additional physical constraints or tuning. This work is the first to demonstrate a solution to address the online instability problem in climate modeling with ML physics by using experience replay.

54 ENVIRONMENTAL SCIENCES↗