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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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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Do responses to different anthropogenic forcings add linearly in climate models?

Many detection and attribution and pattern scaling studies assume that the global climate response to multiple forcings is additive: that the response over the historical period is statistically indistinguishable from the sum of the responses to individual forcings. Here, we use the NASA Goddard Institute for Space Studies (GISS) and National Center for Atmospheric Research Community Climate System Model (CCSM4) simulations from the CMIP5 archive to test this assumption for multi-year trends in global-average, annual-average temperature and precipitation at multiple timescales. We find that responses in models forced by pre-computed aerosol and ozone concentrations are generally additive across forcings. However, we demonstrate that there are significant nonlinearities in precipitation responses to different forcings in a configuration of the GISS model that interactively computes these concentrations from precursor emissions. We attribute these to differences in ozone forcing arising from interactions between forcing agents. Lastly, our results suggest that attribution to specific forcings may be complicated in a model with fully interactive chemistry and may provide motivation for other modeling groups to conduct further single-forcing experiments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Global Temperature Responses to Large Tropical Volcanic Eruptions in Paleo Data Assimilation Products and Climate Model Simulations Over the Last Millennium

Abstract Large volcanic eruptions are one of the dominant perturbations to global and regional atmospheric temperatures on timescales of years to decades. Discrepancies remain, however, in the estimated magnitude and persistence of the surface temperature cooling caused by volcanic eruptions, as characterized by paleoclimatic proxies and climate models. We investigate these discrepancies in the context of large tropical eruptions over the Last Millennium using two state‐of‐the‐art data assimilation products, the Paleo Hydrodynamics Data Assimilation product (PHYDA) and the Last Millennium Reanalysis (LMR), and simulations from the National Center for Atmospheric Research Community Earth System Model‐Last Millennium Ensemble (NCAR CESM‐LME). We find that PHYDA and LMR estimate mean global and hemispheric cooling that is similar in magnitude and persistence once effects from eruptions occurring in short succession are removed. The estimates also compare well to Northern‐Hemisphere reconstructions based solely or partially on tree‐ring density, which have been proposed as the most accurate proxy estimates of surface cooling due to volcanism. All proxy‐based estimates also agree well with the magnitude of the mean cooling simulated by the CESM‐LME. Differences remain, however, in the spatial patterns of the temperature responses in the PHYDA, LMR, and the CESM‐LME. The duration of cooling anomalies also persists for several years longer in the PHYDA and LMR relative to the CESM‐LME. Our results demonstrate progress in resolving discrepancies between proxy‐ and model‐based estimates of temperature responses to volcanism, but also indicate these estimates must be further reconciled to better characterize the risks of future volcanic eruptions.

Tejedor, E.↗
NASA NTRS2024

Leveraging Scientific Community Knowledge for Air Quality Model Chemistry Parameterizations

Air pollution contributes to adverse health outcomes. Approximately 121 million people in the United States—one third of the population—live where National Ambient Air Quality Standards (NAAQS) are violated. In most cases, the criteria pollutants exceeding standards are ozone (O 3 ) and fine particles (PM 2.5 ). In addition, 188 substances known or suspected to cause cancer or other serious health effects are designated as hazardous air pollutants (HAPs). Essentially, all O 3 and significant portions of PM 2.5 and HAPs are produced in the atmosphere through chemical and physical processes. In the case of PM 2.5 , subcomponents formed primarily from precursor gases—sulfate, nitrate, ammonium, and secondary organic aerosol (SOA)—account for 60% of the U.S. county-level annual mean concentration. In addition, 47% of the cancer risk and 25% of the noncancer risk from HAPs have been attributed to atmospheric chemistry rather than direct emissions. In this article, we introduce the role of chemical mechanisms in air quality models, a new atmospheric science community effort, and needs for further mechanism development.

Havala O.T. Pye↗
DOE OSTI2020

Integrating Light Curve and Atmospheric Modeling of Transiting Exoplanets

Spectral retrieval techniques are currently our best tool to interpret the observed exoplanet atmospheric data. Said techniques retrieve the optimal atmospheric components and parameters by identifying the best fit to an observed transmission/emission spectrum. Over the past decade, our understanding of remote worlds in our galaxy has flourished thanks to the use of increasingly sophisticated spectral retrieval techniques and the collective effort of the community working on exoplanet atmospheric models. A new generation of instruments in space and from the ground is expected to deliver higher quality data in the next decade; it is therefore paramount to upgrade current models and improve their reliability, their completeness, and the numerical speed with which they can be run. In this paper, we address the issue of reliability of the results provided by retrieval models in the presence of systematics of unknown origin. More specifically, we demonstrate that if we fit directly individual light curves at different wavelengths (L-retrieval), instead of fitting transit or eclipse depths, as it is currently done (S-retrieval), the said methodology is more sensitive against astrophysical and instrumental noise. This new approach is tested, in particular, when discrepant simulated observations from Hubble Space Telescope/Wide Field Camera 3 and Spitzer/IRAC are combined. We find that while S-retrievals converge to an incorrect solution without any warning, L-retrievals are able to flag potential discrepancies between the data sets.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2006

Modeling the Optical Properties of Biomass Burning Aerosols: Young Smoke Aerosols From Savanna Fires and Comparisons to Observations from SAFARI 2000

Annually, farmers in southern Africa manage their land resources and prepare their fields for cultivation by burning crop residual debris, with a peak in the burning season occurring during August and September. The emissions from these fires in southern Africa are among the greatest from fires worldwide, and the gases and aerosol particles produced adversely affect air quality large distances from their source regions, and can even be tracked in satellite imagery as they cross the Atlantic and Pacific Ocean basins. During August and September 2000 an international group of researchers participating in the Southern African Regional Science Initiate field experiment (SAFARI 2000) made extensive ground-based, airborne, and satellite measurements of these gases and aerosols in order to quantify their amounts and effects on Earth's atmosphere. In this study we interpreted the measurements of smoke aerosol particles made during SAFARI 2000 in order to better represent these particles in a numerical model simulating their transport and fate. Typically, smoke aerosols emitted from fires are concentrated by mass in particles about 0.3 micrometers in diameter (1,000,000 micrometers = 1 meter, about 3 feet); for comparison, the thickness of a human hair is about 50 micrometers, almost 200 times as great. Because of the size of these particles, at the surface they can be easily inhaled into the lungs, and in high concentrations have deleterious health effects on humans. Additionally, these particles reflect and absorb sunlight, impacting both visibility and the balance of sunlight reaching -Earth's surface, and ultimately play a role in modulating Earth's climate. Because of these important effects, it is important that numerical models used to estimate Earth's climate response to changes in atmospheric composition accurately represent the quantity and evolution of smoke particles. In our model, called the Community Aerosol and Radiation Model for Atmospheres (CARMA) we used estimates of smoke emissions based on field studies and observations made with the NASA Terra and TRMM satellites. The meteorology used to calculate the transport was based on an assimilation of observed meteorological conditions provided by the National Center for Atmospheric Research.

Matichuk, R. I.↗
NASA NTRS1994

Examination of tracer transport in the NCAR CCM2 by comparison of CFCl3 simulations with ALE/GAGE observations

The latest version of the National Center for Atmospheric Research (NCAR) community climate model (CCM2) contains a semi-Lagrangian tracer transport scheme for the purpose of advecting water vapor and for including chemistry in the climate model. One way to diagnose the CCM2 transport is to simulate CFCl3 in the CCM2 since it has a well-known industry-based source distribution and a photochemical sink and to compare the model results to Atmospheric Lifetime Experiment/Global Atmospheric Gases Experiment ALE/GAGE observations around the globe. In this paper we focus on this comparison and discuss the synoptic scale issues of tracer transport where appropriate. We compare the model and observations on both 12-hour and monthly timescales. The higher-frequency events allow us to diagnose the synoptic scale transport in the CCM2 associated with the observational sites and to determine uncertainties in our high-resolution source distribution. We find that the CCM2 does simulate many of the key features such as pollution events and some seasonal transports, but there are still some dynamical features of tracer transport such as the storm track dynamics and cross-equatorial flow that merit further study in both the model and the real atmosphere.

Hartley, Dana E.↗
DOE OSTI2014

The impacts of deglacial meltwater forcing on the South Atlantic Ocean deep circulation since the Last Glacial Maximum

A NCAR-CCSM3 (National Center for Atmospheric Research – Community Climate System Model version 3) state-of-the-art transient paleoclimate simulation with prescribed freshwater inflows is used to investigate the changes and evolution of the South Atlantic water mass structure from the Last Glacial Maximum (LGM) to the present day. Model results show that 21 000 yr ago the water column was substantially stratified due to the presence of a saltier-than-today Antarctic Bottom Water (AABW), forming a salinity barrier that prevented dense waters from the Northern Hemisphere from sinking. This salinity barrier started to erode after the termination of the Heinrich event 1, when its associated meltwater was transported southward, freshening the AABW. The removal of the barrier after 14 ka triggered the production of the North Atlantic Deep Water (NADW), which spread into the deeper layers of the South Atlantic at the onset of the Holocene. At this point, the NADW acquired its modern-day structure, establishing a deeper Atlantic meridional overturning circulation (AMOC).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Impact of Radiatively Interactive Dust Aerosols in the NASA GEOS-5 Climate Model: Sensitivity to Dust Particle Shape and Refractive Index

We investigate the radiative effects of dust aerosols in the NASA GEOS-5 atmospheric general circulation model. GEOS-5 is improved with the inclusion of a sectional aerosol and cloud microphysics module, the Community Aerosol and Radiation Model for Atmospheres (CARMA). Into CARMA we introduce treatment of the dust and sea salt aerosol lifecycle, including sources, transport evolution, and sinks. The aerosols are radiatively coupled to GEOS-5, and we perform a series of multi-decade AMIP-style simulations in which dust optical properties (spectral refractive index and particle shape distribution) are varied. Optical properties assuming spherical dust particles are from Mie theory, while those for non-spherical shape distributions are drawn from a recently available database for tri-axial ellipsoids. The climatologies of the various simulations generally compare well to data from the MODIS, MISR, and CALIOP space-based sensors, the ground-based AERONET, and surface measurements of dust deposition and concentration. Focusing on the summertime Saharan dust cycle we show significant variability in our simulations resulting from different choices of dust optical properties. Atmospheric heating due to dust enhances surface winds over important Saharan dust sources, and we find a positive feedback where increased dust absorption leads to increased dust emissions. We further find that increased dust absorption leads to a strengthening of the summertime Hadley cell circulation, increasing dust lofting to higher altitudes and strengthening the African Easterly Jet. This leads to a longer atmospheric residence time, higher altitude, and generally more northward transport of dust in simulations with the most absorbing dust optical properties. We find that particle shape, although important for radiance simulations, is a minor effect compared to choices of refractive index, although total atmospheric forcing is enhanced by greater than 10 percent for simulations incorporating a spheroidal shape distribution versus ellipsoidal or spherical shapes.

radiative forcing↗
NASA NTRS2013

Influence of Leaf Area Index Prescriptions on Simulations of Heat, Moisture, and Carbon Fluxes

Leaf-area index (LAI), the total one-sided surface area of leaf per ground surface area, is a key component of land surface models. We investigate the influence of differing, plausible LAI prescriptions on heat, moisture, and carbon fluxes simulated by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model over the Australian continent. A 15-member ensemble monthly LAI data-set is generated using the MODIS LAI product and gridded observations of temperature and precipitation. Offline simulations lasting 29 years (1980-2008) are carried out at 25 km resolution with the composite monthly means from the MODIS LAI product (control simulation) and compared with simulations using each of the 15-member ensemble monthly-varying LAI data-sets generated. The imposed changes in LAI did not strongly influence the sensible and latent fluxes but the carbon fluxes were more strongly affected. Croplands showed the largest sensitivity in gross primary production with differences ranging from -90 to 60 %. PFTs with high absolute LAI and low inter-annual variability, such as evergreen broadleaf trees, showed the least response to the different LAI prescriptions, whilst those with lower absolute LAI and higher inter-annual variability, such as croplands, were more sensitive. We show that reliance on a single LAI prescription may not accurately reflect the uncertainty in the simulation of the terrestrial carbon fluxes, especially for PFTs with high inter-annual variability. Our study highlights that the accurate representation of LAI in land surface models is key to the simulation of the terrestrial carbon cycle. Hence this will become critical in quantifying the uncertainty in future changes in primary production.

land-surface modeling↗
DOE OSTI2014

Comparison of Subantarctic Mode Water and Antarctic Intermediate Water formation rates in the South Pacific between NCAR-CCSM4 and observations

Average formation rates for Subantarctic Mode (SAMW) and Antarctic Intermediate Waters (AAIW) in the South Pacific are calculated from the National Center for Atmospheric Research Community Climate System Model version 4 (NCAR-CCSM4), using chlorofluorocarbon inventories. CFC-12 inventories and formation rates are compared to ocean observations. CCSM4 accurately simulates the southeast Pacific as the main formation region for SAMW and AAIW. CCSM4 formation rates for SAMW are 3.4 Sv, about half of the observational rate. Shallow mixed layers and a thinner SAMW in CCSM4 are responsible for lower formation rates. A formation rate of 8.1 Sv for AAIW in CCSM4 is higher than observations. Higher inventories in CCSM4 in the southwest and central Pacific, and higher surface concentrations are the main reasons for higher formation rates of AAIW. This comparison of model and observations is useful for understanding the uptake and transport of other gases, e.g., CO2 by the model.

antarctic↗
DOE OSTI2021

Distinct roles of land cover in regulating spatial variabilities of temperature responses to radiative effects of aerosols and clouds

Surface temperature responses to radiative perturbations due to aerosols and clouds are complicated by the land surface properties. To disentangle these complexities, this study, from a terrestrial surface energy budget perspective, isolates the underlying biophysical processes from the instantaneous radiative effects of aerosols and clouds on surface temperature using the National Center for Atmospheric Research Community Earth System Model version 1.2.1. It is found that in comparison with the global heterogeneous distributions of instantaneous radiative perturbations at the surface induced by aerosols and clouds, the spatial variations of the corresponding surface temperature responses to aerosol direct radiative effects (DRE) during the daytime and cloud radiative effects (CRE) during the nighttime are amplified. It is because of the consistent global distribution of the local surface climate sensitivity (a function of land cover properties such as surface roughness and Bowen ratio) with those of daytime DRE and nighttime CRE. By applying identical anthropogenic aerosol and precursor emissions over eight major past, present and projected future anthropogenic aerosol emitting regions (i.e. Brazil, China, East Africa, India, Indonesia, South Africa, United States and Western Europe), surface temperature responses to aerosol radiative cooling in the daytime and cloud radiative warming in the nighttime over these regions positively regulated by local surface climate sensitivities are prominent.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1994

Simulation of the modern arctic climate by the NCAR CCM1

The National Center of Atmospheric Research (NCAR) Community Climate Model Version 1 (CCM1's) simulation of the modern arctic climate is evaluated by comparing a five-year seasonal cycle simulation with the European Center for Medium-Range Weather Forecasts (ECMWF) global analyses. The sea level pressure (SLP), storm tracks, vertical cross section of height, 500-hPa height, total energy budget, and moisture budget are analyzed to investigate the biases in the simulated arctic climate. The results show that the model simulates anomalously low SLP, too much storm activity, and anomalously strong baroclinicity to the west of Greenland and vice versa to the east of Greenland. This bias is mainly attributed to the model's topographic representation of Greenland. First, the broadened Greenland topography in the model distorts the path of cyclone waves over the North Atlantic Ocean. Second, the model oversimulates the ridge over Greenland, which intensifies its blocking effect and steers the cyclone waves clockwise around it and hence produces an artificial circum-Greenland trough. These biases are significantly alleviated when the horizontal resolution increases to T42. Over the Arctic basin, the model simulates large amounts of low-level (stratus) clouds in winter and almost no stratus in summer, which is opposite to the observations. This bias is mainly due to the location of the simulated SLP features and the negative anomaly of storm activity, which prevent the transport of moisture into this region during summer but favor this transport in winter. The moisture budget analysis shows that the model's net annual precipitation (P-E) between 70 deg N and the North Pole is 6.6 times larger than the observations and the model transports six times more moisture into this region. The bias in the advection term is attributed to the positive moisture fixer scheme and the distorted flow pattern. However, the excessive moisture transport into the Arctic basin does not solely result from the advection term. The contribution by the moisture fixer is as large as from advection. By contrast, the semi-Lagrangian transport scheme used in the CCM2 significantly improves the moisture simulation for this region; however, globally the error is as serious as for the positive moisture fixer scheme. Finally, because the model has such serious problems in simulating the present arctic climate, its simulations of past and future climate change for this region are questionable.

Bromwich, David H.↗
NASA NTRS1994

Sensitivity of a GCM climate simulation to differences in continental versus maritime cloud drop size

Extensive observations indicate a distinct difference between maritime and continental effective drop size, r(sub e), for warm clouds. The latest version of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM2) is used to explore the sensitivity of differentiating between continental and maritime r(sub e) on the simulated climate. The results of this study indicate that the smaller drop size over continents leads to a reduction of surface-absorbed solar radiation from 20 to 60 W/sq m. This reduction in surface solar flux leads to a cooling of the continents by up to -3.5 K. The reduction in surface solar flux and temperature also leads to a reduction in latent heat flux and precipitation over land. In the January simulation, there is a significant shift in tropical precipitation associated with the Australian monsoon. This shift leads to a response in the extra tropical geopotential height field over the western United States. All of these changes reduce biases in the current version of CCM2.

Kiehl, J. T.↗
DOE OSTI2019

Evaluation of Machine Learning Approaches to Estimate Aerosol Mixing State Metrics in Atmospheric Models

Aerosol mixing state describes how aerosol compositions are distributed among atmospheric aerosol particles in a population. Oversimplified assumptions of aerosol mixing state in atmospheric modeling can introduce errors in estimations of weather and climate-relevant aerosol microphysical properties. A more comprehensive representation of the aerosol mixing state can be achieved in principle with a Particle-resolved Monte Carlo (PartMC) model but at added computational cost that may be prohibitive for direct invocation in operational numerical weather prediction or multi-year climate simulations. The aim of our research is to explore the machine learning (ML) methodologies for estimating aerosol mixing state metrics, which we define here in three different ways: with respect to (a) hygroscopicity; (b) optical properties; and (c) chemical species abundance. We adopted a data-driven approach, leveraging deep learning and statistical learning techniques, to take advantage of massive PartMC model simulations. First, we performed particle-resolved simulations by PartMC to create a series of scenarios considering a range of global environmental conditions. Each scenario consists of aerosol populations and corresponding mixing state metrics. The gas concentration, aerosol mass concentration, environmental variables and mixing state metrics of each population constitute the datasets for machine learning implementations. We have adopted and evaluated various configurations of machine learning methodologies in this investigation, embracing deep learning, Extreme Gradient Boosting (XGBoost) algorithm, and the ensemble approaches. After a rigorous model selection process, we identified an appropriate model to derive estimates of aerosol mixing state metrics. We used the computational and data resources of the Oak Ridge Leadership Computing Facility (OLCF) and the ORNL Compute and Data Environment for Science (CADES). The NVIDIA DGX-1 hardware was used for the prototyping of the ML models. Our approach has allowed us to gain a new understanding of how machine learning methodologies can be applied to improve the representation of aerosol mixing state in atmospheric models and benefit the atmospheric research community. Next, we plan to extend our research and methodology to quantify some of the aerosol-related uncertainties in the E3SM Atmospheric Model (EAM) Version 1.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

On the representation of aerosol activation and its influence on model-derived estimates of the aerosol indirect effect

Abstract. Interactions between aerosol particles and clouds contribute a great deal of uncertainty to the scientific community's understanding of anthropogenic climate forcing. Aerosol particles serve as the nucleation sites for cloud droplets, establishing a direct linkage between anthropogenic particulate emissions and clouds in the climate system. To resolve this linkage, the community has developed parameterizations of aerosol activation which can be used in global climate models to interactively predict cloud droplet number concentrations (CDNCs). However, different activation schemes can exhibit different sensitivities to aerosol perturbations in different meteorological or pollution regimes. To assess the impact these different sensitivities have on climate forcing, we have coupled three different core activation schemes and variants with the CESM-MARC (two-Moment, Multi-Modal, Mixing-state-resolving Aerosol model for Research of Climate (MARC) coupled with the National Center for Atmospheric Research's (NCAR) Community Earth System Model (CESM; version 1.2)). Although the model produces a reasonable present-day CDNC climatology when compared with observations regardless of the scheme used, ΔCDNCs between the present and preindustrial era regionally increase by over 100 % in zonal mean when using the most sensitive parameterization. These differences in activation sensitivity may lead to a different evolution of the model meteorology, and ultimately to a spread of over 0.8 W m −2 in global average shortwave indirect effect (AIE) diagnosed from the model, a range which is as large as the inter-model spread from the AeroCom intercomparison. Model-derived AIE strongly scales with the simulated preindustrial CDNC burden, and those models with the greatest preindustrial CDNC tend to have the smallest AIE, regardless of their ΔCDNC. This suggests that present-day evaluations of aerosol-climate models may not provide useful constraints on the magnitude of the AIE, which will arise from differences in model estimates of the preindustrial aerosol and cloud climatology.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Generalized Aerosol/Chemistry Interface (GIANT): A Community Effort to Advance Collaborative Science across Weather and Climate Models

Atmospheric aerosol and chemistry modules are key elements in Earth system models (ESMs), as they predict air pollutant concentrations and properties that can impact human health, weather, and climate. The current uncertainty in climate projections is partly due to the inaccurate representation of aerosol direct and indirect forcing. Aerosol/chemistry parameterizations used within ESMs and other atmospheric models span large structural and parameter uncertainties that are difficult to assess independently of their host models. Moreover, there is a strong need for a standardized interface between aerosol/chemistry modules and the host model to facilitate portability of aerosol/chemistry parameterizations from one model to another, allowing not only a comparison between different parameterizations within the same modeling framework, but also quantifying the impact of different model frameworks on aerosol/chemistry predictions. To address this need, we have initiated a new community effort to coordinate the construction of a Generalized Aerosol/Chemistry Interface (GIANT) for use across weather and climate models. We aim to organize a series of community workshops and hackathons to design and build GIANT, which will serve as the interface between a range of aerosol/chemistry modules and the physics and dynamics components of atmospheric host models. GIANT will leverage ongoing efforts at the U.S. modeling centers focused on building next-generation ESMs and the international AeroCom initiative to implement this common aerosol/chemistry interface. GIANT will create transformative opportunities for scientists and students to conduct innovative research to better characterize structural and parametric uncertainties in aerosol/chemistry modules, and to develop a common set of aerosol/chemistry parameterizations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Modeling the Mechanisms of Linear and Nonlinear ENSO Responses to the Pacific Meridional Mode

Interactions between the Pacific meridional mode (PMM) and El Niño–Southern Oscillation (ENSO) are investigated using the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM) and an intermediate coupled model (ICM). The two models are configured so that the CESM simulates the PMM but not ENSO, and the ICM simulates ENSO but not the PMM, allowing for a clean separation between the PMM evolution and the subsequent ENSO response. An ensemble of CESM simulations is run with an imposed surface heat flux associated with the North Pacific Oscillation (NPO) generating a sea surface temperature (SST) and wind response representative of the PMM. The PMM wind is then applied as a forcing to the ICM to simulate the ENSO response. The positive (negative) ensemble-mean PMM wind forcing results in a warm (cold) ENSO event although the responses are not symmetric (warm ENSO events are larger in amplitude than cold ENSO events), and large variability between ensemble members suggests that any individual ENSO event is strongly influenced by natural variability contained within the CESM simulations. Sensitivity experiments show that 1) direct forcing of Kelvin waves by PMM winds dominates the ENSO response, 2) seasonality of PMM forcing and ENSO growth rates influences the resulting ENSO amplitude, 3) ocean dynamics within the ICM dominate the ENSO asymmetry, and 4) the nonlinear relationship between PMM wind anomalies and surface wind stress may enhance the La Niña response to negative PMM variations. Implications for ENSO variability are discussed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Parameterizing Subgrid Variations of Land Surface Heat Fluxes to the Atmosphere Improves Boreal Summer Land Precipitation Simulation With the NCAR CESM1.2

Subgrid horizontal variations of land surface heat fluxes to the atmosphere resulting from subgrid land cover heterogeneity are important in land-atmosphere interaction in global climate models (GCMs). To incorporate it, a parameterization using stochastic sampling based on truncated normal distributions diagnosed from the land model and internal ensemble mean of multiple calls to the planetary boundary layer and deep convection schemes is developed. After its implementation in the National Center for Atmospheric Research (NCAR) Community Earth System Model version 1.2 (CESM1.2), large changes of simulated land precipitation are found to occur in regions where large subgrid variations of surface heat fluxes exist. The simulated precipitation is improved in boreal summer, especially over eastern China and the coastal areas of the Bay of Bengal. The improved precipitation is mainly a result of improved large-scale moisture convergence and advection by altered vertical diffusion and convection.

58 GEOSCIENCES↗