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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Aerosol Properties and Radiative Forcing over Kanpur during Severe Aerosol Loading Conditions

Atmospheric aerosols over India exhibit large spatio-temporal fluctuation driven by the local monsoon system, emission rates and seasonally-changed air masses. The northern part of India is well-known for its high aerosol loading throughout the year due to anthropogenic emissions, dust influence and biomass burning. On certain circumstances and, under favorable weather conditions, the aerosol load can be severe, causing significant health concerns and climate implications. The present work analyzes the aerosol episode (AE) days and examines the modification in aerosol properties and radiative forcing during the period 2001-2010 based on Kanpur-AERONET sun photometer data. As AEs are considered the days having daily-mean aerosol optical depth (AOD) above the decadal mean + 1 STD (standard deviation); the threshold value is defined at 0.928. The results identify 277 out of 2095 days (13.2%) of AEs over Kanpur, which are most frequently observed during post-monsoon (78 cases, 18.6%) and monsoon (76, 14.7%) seasons due to biomass-burning episodes and dust influence, respectively. On the other hand, the AEs in winter and pre-monsoon are lower in both absolute and percentage values (65, 12.5% and 58, 9.1%, respectively). The modification in aerosol properties on the AE days is strongly related to season. Thus, in post-monsoon and winter the AEs are associated with enhanced presence of fine-mode aerosols and Black Carbon from anthropogenic pollution and any kind of burning, while in pre-monsoon and monsoon seasons they are mostly associated with transported dust. Aerosol radiative forcing (ARF) calculated using SBDART shows much more surface (~-69 to -97 Wm-2) and Top of Atmosphere cooling (-20 to -30 Wm-2) as well as atmospheric heating (~43 to 71 Wm-2) during the AE days compared to seasonal means. These forcing values are mainly controlled by the higher AODs and the modified aerosol characteristics (Angstrom α, SSA) during the AE days in each season and may cause severe climate implications over Ganges Basin with further consequences on atmospheric heating, cloud microphysics, monsoon rainfall and melting of Himalayan glaciers.

India↗
DOE OSTI2019

Modeling the contributions of Northern Hemisphere dust sources to dust outflow from East Asia

Mineral dust aerosols can substantially influence the Earth's climate by altering the radiation budget and modifying cloud microphysical and radiative properties. Through long-range transport, dust aerosols could have a global impact. Here, using a fully coupled meteorology-chemistry model (WRF-Chem) and the tracer-tagging technique, we conduct quasi-global simulations to investigate the characteristics of dust intercontinental transport in the Northern Hemisphere and the source contributions to dust outflow from East Asia. Model results show that total dust emission from the main deserts (i.e., North Africa desert (NFD), Middle East desert (MED) and East Asia desert (EAD)) is about 4531 Tg yr-1, in which 66% is from NFD, 24% is from MED and 10% is from EAD. During long-range transport, the NFD and MED dust plumes are separated into two branches by the Tibetan Plateau, with higher dust concentration of 4 µgm-3 in the northern branch (37° N-60° N). The imported dust mass from NFD and MED to East Asia is 16.0 Tg yr-1 and 33.8 Tg yr-1, respectively; the local dust mass emitted from EAD can circumnavigate and import about 0.41 Tg yr-1 of dust into East Asia. As part of the “Asian outflow”, the MED and NFD dust is transported above 600 hPa with concentration of 2 µgm-3, while the EAD dust is mainly transported below 600 hPa with concentration of 3 µgm-3. The total outflow dust mass from East Asia is 12.0, 9.7 and 7.7 Tg yr-1 from EAD, NFD and MED, respectively. Dust particles dominate the aerosol mass concentration with a fraction of 56.5% in southern East Asia and 75.4% in northern East Asia. Moreover, dust number from EAD dominates the total aerosol number north of 37oN below 600 hPa, with a size range of 0.156–0.625 µm, but the NFD dust number is mainly above 600 hPa. MED dust number dominates the total column aerosol number south of 37 oN, with a size range of 0.312–0.625 µm. Quantifying dust source contributions and the associated dust number loading and size distribution over East Asia is important for understanding the role of East Asian dust outflow in climate and hydrological cycle.

dust aerosol, mass flux, source contribution, dust↗
DOE OSTI2021

Quantifying physical parameterization uncertainties associated with land-atmosphere interactions in the WRF model over Amazon

The Weather Research and Forecasting (WRF) model can be used to diagnose regional land-atmosphere (L-A) coupling strength in the absence of sufficient observations but subjected to uncertainties associated with model physical parameterizations. In this study, we propose a framework to quantify and reduce model physical parameterization uncertainties associated with surface fluxes and L-A coupling. An ensemble of WRF simulations with different physical schemes is used to simulate surface fluxes and land-atmosphere coupling strength over the Amazon region. The physical parameterizations investigated include cloud microphysics (MP), land surface processes (LSM), planetary boundary layer (PBL), surface layer (SL), and cumulus (CU). We perform 120 ensemble simulations using the WRF model and different combinations of six MPs, three LSMs, six PBLs and SLs and three CUs. The measurements from the GoAMAZON field campaign and satellite data are used to evaluate model performance. A Multi-way analysis of variance (ANOVA) approach is applied to quantify the relative importance of different physics processes on L-A coupling. The Tukey’s test is used to sort schemes that have no significant differences into one group. The suite of physics that result in the best simulations of the corresponding variables are selected based on the Taylor skill score. Results show that the relative importance of processes and their interaction vary with the variables of interest. For example, CU was the most important process in modulating soil moisture, 2 m-humidity, latent heat, and net radiation. LSM showed dominant effects on 2 m-temperature and also has the largest impact on sensible heat and the lifting condensation level. The best physical parameterization ensembles show much narrower ranges of the variables of interest than the priori ensemble. Results of this study show the roles of different physical processes in modulating L-A interactions, quantify model uncertainties from physical processes, and provide insights for improving the model physics parameterizations.

Wang, Chen↗
DOE OSTI2020

The Community Earth System Model version 2 (CESM2)

An overview of the Community Earth System Model version 2 (CESM2) is provided, including a discussion of the challenges encountered during its development and how they were addressed. In addition, an evaluation of a pair of CESM2 long pre-industrial control and historical ensemble simulations is presented. These simulations were performed using the nominal 1° horizontal resolution configuration of the coupled model with both the “low-top” (40 km, with limited chemistry) and “high-top” (140 km, with comprehensive chemistry) versions of the atmospheric component. CESM2 contains many substantial science and infrastructure improvements and new capabilities since its previous major release, CESM1, resulting in improved historical simulations in comparison to CESM1 and available observations. These include major reductions in low latitude precipitation and short-wave cloud forcing biases; better representation of the Madden-Julian Oscillation; better El Niño – Southern Oscillation-related teleconnections; and a global land carbon accumulation trend that agrees well with observationally-based estimates. Most tropospheric and surface features of the low- and high-top simulations are very similar to each other, so these improvements are present in both configurations. CESM2 has an equilibrium climate sensitivity of 5.1-5.3°C, larger than in CESM1, primarily due to a combination of relatively small changes to cloud microphysics and boundary layer parameters. In contrast, CESM2’s transient climate response of 1.9-2.0°C is comparable to that of CESM1. The model outputs from these and many other simulations are available to the research community, and theyrepresent CESM2’s contributions to the Coupled Model Intercomparison Project phase 6 (CMIP6).

Danabasoglu, Gokhan↗
DOE OSTI2022

The Global Atmosphere‐aerosol Model ICON‐A‐HAM2.3–Initial Model Evaluation and Effects of Radiation Balance Tuning on Aerosol Optical Thickness

Abstract The Hamburg Aerosol Module version 2.3 (HAM2.3) from the ECHAM6.3‐HAM2.3 global atmosphere‐aerosol model is coupled to the recently developed icosahedral nonhydrostatic ICON‐A (icon‐aes‐1.3.00) global atmosphere model to yield the new ICON‐A‐HAM2.3 atmosphere‐aerosol model. The ICON‐A and ECHAM6.3 host models use different dynamical cores, parameterizations of vertical mixing due to sub‐grid scale turbulence, and parameter settings for radiation balance tuning. Here, we study the role of the different host models for simulated aerosol optical thickness (AOT) and evaluate impacts of using HAM2.3 and the ECHAM6‐HAM2.3 two‐moment cloud microphysics scheme on several meteorological variables. Sensitivity runs show that a positive AOT bias over the subtropical oceans is remedied in ICON‐A‐HAM2.3 because of a different default setting of a parameter in the moist convection parameterization of the host models. The global mean AOT is biased low compared to MODIS satellite instrument retrievals in ICON‐A‐HAM2.3 and ECHAM6.3‐HAM2.3, but the bias is larger in ICON‐A‐HAM2.3 because negative AOT biases over the Amazon, the African rain forest, and the northern Indian Ocean are no longer compensated by high biases over the sub‐tropical oceans. ICON‐A‐HAM2.3 shows a moderate improvement with respect to AOT observations at AERONET sites. A multivariable bias score combining biases of several meteorological variables into a single number is larger in ICON‐A‐HAM2.3 compared to standard ICON‐A and standard ECHAM6.3. In the tropics, this multivariable bias is of similar magnitude in ICON‐A‐HAM2.3 and in ECHAM6.3‐HAM2.3. In the extra‐tropics, a smaller multivariable bias is found for ICON‐A‐HAM2.3 than for ECHAM6.3‐HAM2.3.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Hurricane‐Like Vortices in Conditionally Unstable Moist Convection

Abstract This study investigates the emergence of hurricane‐like vortices in idealized simulations of rotating moist convection. A Boussinesq atmosphere with simplified thermodynamics for phase transitions is forced by prescribing the temperature and humidity at the upper and lower boundaries. The governing equations are solved numerically using a variable‐density incompressible Navier‐Stokes solver with adaptive mesh refinement to explore the behavior of moist convection under a broad range of conditions. In the absence of rotation, convection aggregates into active patches separated by large unsaturated regions. Rotation modulates this statistical equilibrium state so that the self‐aggregated convection organizes hurricane‐like vortices. The warm and saturated air converges to the center of the vortices, and the latent heat released through the upwelling, forms the warm core structure. These hurricane‐like vortices share characteristics similar to tropical cyclones in the earth's atmosphere. The hurricane‐like vortices occur under conditionally unstable conditions where the potential energy given at the boundaries is large enough, corresponding to a moderate rate of rotation. This regime shares many similar characteristics to the tropical atmosphere indicating that the formation of intense meso‐scale vortices is a general characteristic of rotating moist convection. The model used here does not include any interactions with radiation, wind‐evaporation feedback, or cloud microphysics, indicating that, while these processes may be relevant for tropical cyclogenesis in the Earth atmosphere, they are not its primary cause. Instead, our results confirm that the formation and maintenance of hurricane‐like vortices involve a combination of atmospheric dynamics under the presence of rotation and of phase transitions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Wintertime synoptic patterns of midlatitude boundary layer clouds over the western North Atlantic: Climatology and insights from in-situ ACTIVATE observations

The winter synoptic evolution of the western North Atlantic and its influence on the atmospheric boundary layer is described by means of a regime classification based on Self Organizing Maps applied to 12 year of data (2009-2020). The regimes are classified into categories according to daily 600-hPa geopotential height: dominant ridge, trough to ridge eastward transition (trough-ridge), dominant trough, and ridge to trough eastward transition (ridge-trough). A fifth synoptic regime resembles the winter climatological mean. Coherent changes in sea-level pressure and large-scale winds are in concert with the synoptic regimes: 1) the ridge regime is associated with a well-developed anticyclone; 2) the trough-ridge gives rise to a low pressure center over the ocean, ascents, and northerly winds over the coastal zone; 3) trough is associated with the eastward displacement of a cyclone, coastal subsidence, and northerly winds, all representative characteristics of cold-air outbreaks; 4) the ridge-trough regime features the development of an anticyclone and weak coastal winds. Low clouds are characteristic of the trough regime, with both trough and trough-ridge featuring synoptic maxima in cloud droplet number concentration (N d ). The N d increase is primarily observed near the coast, concomitant with strong surface heat fluxes exceeding by more than 400 W m -2 compared to fluxes further east. Five consecutive days of aircraft observations collected during the ACTIVATE campaign corroborates the climatological characterization, confirming the occurrence of high N d for days identified as trough. This study emphasizes the role of boundary-layer dynamics and aerosol activation and their roles in modulating cloud microphysics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Aerosol Decline Accelerates the Increasing Extreme Precipitation in China

Extreme precipitation is becoming more intense and frequent. The increasing trends in extreme precipitation in China in warm season related to changes in aerosols and greenhouse gases (GHGs) are investigated using observations, reanalysis data and model simulations. A significant accelerating increase in extreme precipitation occurred around 2010, with the trend in accumulated extreme rainfall amount (R95pTOT) increasing from 2.88 mm per decade during 2000–2010 to 22.88 mm per decade during 2010–2023. The sudden acceleration of the increasing extreme precipitation is largely attributed to the reverse in aerosol trends associated with China’s clean air actions, which affects extreme precipitation through perturbing cloud microphysics and atmospheric dynamics, accounting for half of the change in R95pTOT trends. Future aerosol reduction to achieve carbon neutrality is shown to continue to intensify the extreme precipitation, which overweighs the effect induced by GHGs, highlighting the importance of aerosol changes in modulating future climate and weather extremes.

58 GEOSCIENCES↗
DOE OSTI2025

Evaluation of Marine Boundary Layer Clouds Over the Northeast Pacific During the CSET Campaign in E3SM Version 2

It is still challenging to reproduce marine boundary layer (MBL) clouds well in large-scale models despite their importance to the Earth's radiation budget and hydrological cycle. This study evaluates representation of the MBL and clouds in the Energy Exascale Earth System Model (E3SM) version 2. This study compares the E3SM simulation results with remote sensing and reanalysis data during the Cloud System Evolution in the Trades (CSET) field campaign to better understand the stratocumulus-to-cumulus cloud transition (SCT) over the northeast Pacific. E3SM results are extracted along the CSET Lagrangian trajectories. The comparison shows that the E3SM simulation applying horizontal wind nudging performs well in reproducing thermodynamic variables of the MBL and evolution trends of cloud variables along the trajectories. However, substantial overestimations of aerosol and cloud drop number (N d ) are observed, which is explained as an issue with version 2 of the model. Cloud fraction (CF) does decrease from the Californian coast to Hawaii in the E3SM simulation, but most CF values indicate either an overcast or almost clear sky, which differs from satellite and reanalysis data. The effect of N d overestimation on CF evolution is assessed via prescribed-N d simulations. Those simulations with N d modifications show negligible CF changes. A comparison of estimated inversion strength (EIS) also shows that the simulated EIS values are similar to those of reanalysis data. Our study suggests that cloud macrophysics and boundary layer processes are more important in improving the simulation to capture the SCT than refining the model's thermodynamics or cloud microphysics.

Choi, Kyoung Ock [Univ. of Washington, Seattle, WA↗
DOE OSTI2026

Understanding Biases in Simulated Cloud Radiative Effects in E3SMv3

This study systematically investigates biases in cloud radiative effects (CREs) within the recently released Energy Exascale Earth System Model version 3 (E3SMv3). Compared to its previous version (E3SMv2), E3SMv3 shows excessively strong shortwave CRE over tropical and subtropical oceans, the Southern Ocean, and the Northern Hemisphere storm tracks, which is primarily caused by an overestimation of optically intermediate low clouds. The model also displays excessive longwave CRE over the Maritime Continent and other tropical deep convection regions, resulting from an overestimation of optically thick high clouds. Implementing the Predicted Particle Properties (P3) scheme for stratiform clouds played the most significant role in these cloud changes. In addition, using a double-moment scheme for convective clouds contributed to the increase in intermediate low clouds and optically thick clouds in tropical deep convection regions. Error metrics for total cloud amount (E TCA ), cloud properties (E ctp-τ ), and cloud properties weighted by their SW and LW radiative impacts (E SW and E LW ) indicate that E3SMv3's ability to reproduce observed cloud radiative effect of low, middle, and high clouds has not been improved compared to E3SMv2. Nevertheless, the performance of E3SMv3 remains well within the spread of CMIP6 models, with E SW and E LW values smaller than those in most CMIP6 models. This study underscores the importance of integrating diverse satellite observations for robust cloud evaluation and using cloud-radiation relationships as consistency check for model errors. It suggests that further model developments focus on improving cloud microphysics and their interactions with radiation.

Environmental sciences↗
DOE OSTI2026

Overview of the Nephele Perturbed Parameter Ensemble for Aerosol‐Cloud Interactions in E3SMv3

Aerosol-cloud interactions (aci) are the leading source of uncertainty in inferring climate sensitivity from the historical record. Earth system models (ESMs) struggle to represent aci because the processes responsible for these phenomena occur at much finer time and space scales than can be resolved by any ESM. Observational constraints provide key benchmarks to test ESMs, but cannot be used alone to fully understand aci processes except in very specific cases where causality is controlled; some degree of modeling is required to infer aci and estimate radiative forcing. Here, we generate and characterize a perturbed parameter ensemble (PPE) in version 3 of the Energy Exascale ESM (E3SMv3). We perturb 25 parameters that govern aci processes over 250 members and integrate the model over present-day and preindustrial aerosol emissions. We find that the process representation in E3SMv3 is flexible and can generate global-mean effective radiative forcings due to aci (ERFaci) ranging from −3.0 to +0.9 W m −2 . The positive ERFaci values simulated by a portion of the PPE are implausible and result from parameter combinations that produce unrealistic top-of-atmosphere energy fluxes. While global-mean cloud droplet number concentration always increases in response to anthropogenic aerosol, cloud liquid water path can both increase and decrease, suggesting that precipitation suppression is not the only aerosol-cloud adjustment represented by E3SMv3. Analysis of which processes control liquid cloud adjustment in the PPE points toward stratiform precipitation processes and aerosol activation, which is consistent with many previous ESMs, as well as the new two-moment convective cloud microphysics in E3SMv3.

Nugent, Jacqueline M. [Univ. of Wyoming, Laramie, ↗
DOE OSTI2026

Crowdsourcing the Frontier: Advancing Hybrid Physics‐ML Climate Simulation via a $\$$50,000 Kaggle Competition

Subgrid machine-learning (machine learning [ML]) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher-resolution physics without incurring the prohibitive computational cost associated with more explicit physics-based simulations. However, important issues, ranging from online instability to inconsistent online performance, have limited their operational use for long-term climate projections. To more rapidly drive progress in solving these issues, domain scientists and ML researchers opened up the offline aspect of this problem to the broader ML and data science community with the release of ClimSim, a NeurIPS Data sets and Benchmarks publication, and an associated Kaggle competition. This paper reports on the downstream results of the Kaggle competition by coupling emulators inspired by the winning teams' architectures to an interactive climate model (including full cloud microphysics, a regime historically prone to online instability) and systematically evaluating their online performance. Our results demonstrate that online stability in the low-resolution real-geography setting is reproducible across multiple diverse architectures, which we consider a key milestone. All tested architectures exhibit strikingly similar offline and online biases, though their responses to architecture-agnostic design choices (e.g., expanding the list of input variables) can differ significantly. Multiple Kaggle-inspired architectures achieve state-of-the-art results on certain metrics such as zonal mean bias patterns and global Root Mean Squared Error, indicating that crowdsourcing the essence of the offline problem is one path to improving online performance in hybrid physics-AI climate simulation.

Environmental sciences↗
DOE OSTI2025

Interpretable ensemble learning unveils main aerosol optical properties in predicting cloud condensation nuclei number concentration

Variations in cloud condensation nuclei number concentration (N CCN ) significantly influence cloud microphysics, yet direct N CCN measurements remain challenging. Here, we present an N CCN ensemble learning (NEL) model utilizing ensemble learning and interpretability analysis on aerosol optical parameters. Validated at two land sites, two ocean sites and one polar site within the Atmospheric Radiation Measurement program, the mean absolute percentage error range of the NEL model across different environments is from 12% to 36%, demonstrating high accuracy. Key findings reveal that aerosol optical parameters can serve as predictors for N CCN . Aerosol scattering and backscattering coefficients, absorption coefficient, backscatter fraction (BSF), and Ångström exponent (AE) are positively correlated with N CCN , while single scattering albedo shows negative correlations. N CCN prediction at land sites is highly sensitive to BSF, largely driven by the backscattering coefficient, as fine particles dominate in these sites. At ocean sites, N CCN prediction is more sensitive to AE, primarily influenced by the scattering coefficient, due to the higher proportion of larger particles. At the polar site, N CCN prediction shows sensitivity to both BSF and AE, mainly driven by the scattering coefficient, as polar sites are cleaner and contain larger particles. These differences reflect the variation in particle size and number concentration across different environments.

Atmospheric science↗
DOE OSTI2017

Establishing a Numerical Modeling Framework for Hydrologic Engineering Analyses of Extreme Storm Events

In this study a numerical modeling framework for simulating extreme storm events was established using the Weather Research and Forecasting (WRF) model. Such a framework is necessary for the derivation of engineering parameters such as probable maximum precipitation that are the cornerstone of large water management infrastructure design. Here this framework was built based on a heavy storm that occurred in Nashville (USA) in 2010, and verified using two other extreme storms. To achieve the optimal setup, several combinations of model resolutions, initial/boundary conditions (IC/BC), cloud microphysics and cumulus parameterization schemes were evaluated using multiple metrics of precipitation characteristics. The evaluation suggests that WRF is most sensitive to IC/BC option. Simulation generally benefits from finer resolutions up to 5 km. At the 15km level, NCEP2 IC/BC produces better results, while NAM IC/BC performs best at the 5km level. Recommended model configuration from this study is: NAM or NCEP2 IC/BC (depending on data availability), 15km or 15km-5km nested grids, Morrison microphysics and Kain-Fritsch cumulus schemes. Validation of the optimal framework suggests that these options are good starting choices for modeling extreme events similar to the test cases. This optimal framework is proposed in response to emerging engineering demands of extreme storm events forecasting and analyses for design, operations and risk assessment of large water infrastructures.

atmospheric model↗
DOE OSTI2020

Cyclone Fani: the tug-of-war between regional warming and anthropogenic aerosol effects

Before Cyclone Amphan took place in 2020, Cyclone Fani (May 2019) is the strongest pre-monsoon cyclone in the Bay of Bengal (BOB) since 1991, killing 90 people in eastern India and Bangladesh while causing US$1.81 billion of damages. Fani developed during a period of high concentration of anthropogenic aerosols in the BOB with abnormally high sea surface temperature (SST), thereby presenting an opportunity to understand the compound effects of atmospheric aerosols and regional climate warming on a tropical cyclone. A quantitative attribution analysis was conducted using the Weather Research and Forecasting model with chemistry (WRF-Chem) run at the convection-permitting (4 km) grid spacing, accompanied by an ensemble of coarser-resolution simulations to quantify the uncertainty. The removal of post-1990 trends in the tropospheric variables and SST from WRF-Chem's initial conditions (IC) and boundary conditions (BC, including the lateral and lower boundary conditions) resulted in a reduction of cyclone precipitation by about 51% during the 5 d of April 28-May 2. The removal of tropospheric warming shows approximately twice as strong an effect on Fani (39% reduction in precipitation) as that of SST warming (22% reduction). When aerosol's direct and indirect effects were removed from the simulations, i.e., no aerosol influence on radiation and cloud microphysics, Fani initially strengthened but later weakened, as measured by geopotential height and precipitation amounts. These results suggest that aerosol and its interaction with the atmosphere acted to mitigate the strengthening effect of anthropogenic warming on Fani, but was not strong enough to entirely counteract it. Although the ensemble of coarser simulations appears to overestimate Cyclone Fani in terms of precipitation, the direction of the effects is in agreement with that obtained from the 4 km simulations. Given the increasing anthropogenic aerosols in the BOB, future attribution studies using more sophisticated dynamical aerosol models on BOB tropical cyclones are urged.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Progress Towards Accelerating the Unified Model on Hybrid Multi-Core Systems

The cloud microphysics scheme, CASIM, and the radiation scheme, SOCRATES, are two computationally intensive parts within the Met Office's Unified Model (UM). This study enables CASIM and SOCRATES to use accelerated multi-core systems for optimal computational performance of the UM. Using profiling to guide our efforts, we refactored the code for optimal threading and kernel arrangement and implemented OpenACC directives manually or through the CLAW source-to-source translator. Initial porting results achieved 10.02x and 9.25x speedup in CASIM and SOCRATES respectively on 1 GPU compared with 1 CPU core. A granular performance analysis of the strategy and bottlenecks are discussed. These improvements will enable UM to run on heterogeneous computers and a path forward for further improvements is provided.

Zhang, Wei↗
DOE OSTI2023

Reducing Southern Ocean Shortwave Radiation Errors in the ERA5 Reanalysis with Machine Learning and 25 Years of Surface Observations

Earth system models struggle to simulate clouds and their radiative effects over the Southern Ocean, partly due to a lack of measurements and targeted cloud microphysics knowledge. We have evaluated biases of downwelling shortwave radiation in the ERA5 climate reanalysis using 25 years (1995–2019) of summertime surface measurements, collected on the Research and Supply Vessel (RSV) Aurora Australis, the Research Vessel (R/V) Investigator, and at Macquarie Island. During October–March daylight hours, the ERA5 simulation of SW down exhibited large errors (mean bias = 54 W m -2 , mean absolute error = 82 W m -2 , root-mean-square error = 132 W m -2 , and R 2 = 0.71). To determine whether we could improve these statistics, we bypassed ERA5’s radiative transfer model for SW down with machine learning–based models using a number of ERA5’s gridscale meteorological variables as predictors. These models were trained and tested with the surface measurements of SW down using a 10-fold shuffle split. An extreme gradient boosting (XGBoost) and a random forest–based model setup had the best performance relative to ERA5, both with a near complete reduction of the mean bias error, a decrease in the mean absolute error and root-mean-square error by 25% ± 3%, and an increase in the R 2 value of 5% ± 1% over the 10 splits. Large improvements occurred at higher latitudes and cyclone cold sectors, where ERA5 performed most poorly. We further interpret our methods using Shapley additive explanations. Our results indicate that data-driven techniques could have an important role in simulating surface radiation fluxes and in improving reanalysis products.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

CalWater Field Studies Designed to Quantify the Roles of Atmospheric Rivers and Aerosols in Modulating U.S. West Coast Precipitation in a Changing Climate

The variability of precipitation and water supply along the U.S. West Coast creates major challenges to the region’s economy and environment, as evidenced by the recent California drought. This variability is strongly influenced by atmospheric rivers (AR), which deliver much of the precipitation along the U.S. West Coast and can cause flooding, and by aerosols (from local sources and transported from remote continents and oceans) that modulate clouds and precipitation. A better understanding of these processes is needed to reduce uncertainties in weather predictions and climate projections of droughts and floods, both now and under changing climate conditions.To address these gaps a group of meteorologists, hydrologists, climate scientists, atmospheric chemists, and oceanographers have created an interdisciplinary research effort, with support from multiple agencies. From 2009-2011 a series of field campaigns (CalWater 1) collected atmospheric chemistry, cloud microphysics and meteorological measurements in California and associated modeling and diagnostic studies were carried out. Based on remaining gaps, a vision was developed to extend these studies offshore over the Eastern North Pacific and to enhance land based measurements from 2014-2018 (CalWater 2). The data set and selected results from CalWater 1 are summarized here. The goals of CalWater-2, and measurements to date, are then described. Through this work, CalWater is producing new findings and exploring new technologies to evaluate and improve global climate models and their regional performance and to develop tools supporting water and hydropower management. These advances also have potential to enhance hazard mitigation by improving near-term weather prediction and subseasonal and seasonal outlooks.

54 ENVIRONMENTAL SCIENCES↗