Search NASA⌕ Search

Engineering topics

Lee, Jiwoo

Publications and source records attributed to Lee, Jiwoo.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Estimating Uncertainty in Simulated ENSO Statistics

Abstract Large ensembles of model simulations are frequently used to reduce the impact of internal variability when evaluating climate models and assessing climate change induced trends. However, the optimal number of ensemble members required to distinguish model biases and climate change signals from internal variability varies across models and metrics. Here we analyze the mean, variance and skewness of precipitation and sea surface temperature in the eastern equatorial Pacific region often used to describe the El Niño–Southern Oscillation (ENSO), obtained from large ensembles of Coupled model intercomparison project phase 6 climate simulations. Leveraging established statistical theory, we develop and assess equations to estimate, a priori, the ensemble size or simulation length required to limit sampling‐based uncertainties in ENSO statistics to within a desired tolerance. Our results confirm that the uncertainty of these statistics decreases with the square root of the time series length and/or ensemble size. Moreover, we demonstrate that uncertainties of these statistics are generally comparable when computed using either pre‐industrial control or historical runs. This suggests that pre‐industrial runs can sometimes be used to estimate the expected uncertainty of statistics computed from an existing historical member or ensemble, and the number of simulation years (run duration and/or ensemble size) required to adequately characterize the statistic. This advance allows us to use existing simulations (e.g., control runs that are performed during model development) to design ensembles that can sufficiently limit diagnostic uncertainties arising from simulated internal variability. These results may well be applicable to variables and regions beyond ENSO.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

xCDAT: A Python Package for Simple and Robust Analysis of Climate Data

xCDAT (Xarray Climate Data Analysis Tools) is an open-source Python package that extends Xarray (Hoyer & Hamman, 2017) for climate data analysis on structured grids. xCDAT streamlines analysis of climate data by exposing common climate analysis operations through a set of straightforward APIs. Some of xCDAT’s key features include spatial averaging, temporal averaging, and regridding. These features are inspired by the Community Data Analysis Tools (CDAT) library (Dean N. Williams et al., 2009) (D. N. Williams, 2014) (Doutriaux et al., 2019) and leverage powerful packages in the Xarray ecosystem including xESMF (Zhuang et al., 2023), xgcm (Abernathey et al., 2022), and CF xarray (Cherian et al., 2023). To ensure general compatibility across various climate models, xCDAT operates on datasets that are compliant with the Climate and Forecast (CF) metadata conventions (Hassell et al., 2017).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Systematic and objective evaluation of Earth system models: PCMDI Metrics Package (PMP) version 3

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Modes of Variability in E3SM and CESM Large Ensembles

An adequate characterization of internal modes of climate variability (MoV) is a prerequisite for both accurate seasonal predictions and climate change detection and attribution. Assessing the fidelity of climate models in simulating MoV is therefore essential; however, doing so is complicated by the large intrinsic variations in MoV and the limited span of the observational record. Large ensembles (LEs) provide a unique opportunity to assess model fidelity in simulating MoV and quantify intermodel contrasts. Here, in this work, these goals are pursued in four recently produced LEs: the Energy Exascale Earth System Model (E3SM) versions 1 and 2 LEs, and the Community Earth System Model (CESM) versions 1 and 2 LEs. In general, the representation of global coupled modes is found to improve across successive E3SM and CESM versions in conjunction with the fidelity of the base state climate while the patterns of extratropical modes are well simulated across the ensembles. Various persistent shortcomings for all MoV are however identified and discussed. The results both demonstrate the successes of these recent model versions and suggest the potential for continued improvement in the representation of MoV with advances in model physics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

PCMDI PMP Interactive Visualization Web Page, Atmospheric River Metrics

We present an open-source Python-based framework that can be used for objective evaluation of Atmospheric Rivers (ARs) in climate models. With a comprehensive pool of pre-defined metrics, this package is useful for systematic AR evaluation across a range of model simulations, such as the MIPs in CMIP5/6 models and large ensemble experiments.

Dong, Bo↗
DOE OSTI2023

Xarray Climate Data Analysis Tools

xCDAT is an extension of xarray for climate data analysis on structured grids. It serves as a modern successor to the Community Data Analysis Tools (CDAT) library. Xarray is an "open source project and Python package that introduces labels in the form of dimensions, coordinates, and attributes on top of raw NumPy-like arrays, which allows for more intuitive, more concise, and less error-prone user experience. Xarray includes a large and growing library of domain-agnostic functions for advanced analytics and visualization with these data structures" (source: https://xarray.dev/). The goal of xCDAT is to provide generalizable features and utilities for simple and robust analysis of climate data. xCDAT's design philosophy is focused on reducing the overhead required to accomplish certain tasks in xarray. Some key xCDAT features are inspired by or ported from the core CDAT library, while others leverage powerful libraries in the xarray ecosystem (e.g., xESMF and cf_xarray) to deliver robust APIs.

Vo, Tom↗
DOE OSTI2022

Diurnal cycle of precipitation over global monsoon systems in CMIP6 simulations

Accurate simulation of the diurnal cycle of precipitation over different climate regimes, including global monsoon systems, is an ongoing challenge for current state-of-the-art climate models. Here, in this study, the diurnal cycles of summertime precipitation from 21 CMIP6 historical simulations are evaluated with the NASA IMERG data via the diurnal harmonic amplitude and phase from the Fourier analysis in seven regional monsoon systems: Northern and Southern Africa monsoons, South Asia monsoon, East Asia monsoon, Australia monsoon, and North and South America monsoons. Particularly, we focus on four subregions including ocean, coastal ocean, land, and coastal land considering the different underlying physical processes. It is found that the CMIP6 historical simulations perform fairly well on reproducing the diurnal cycle of precipitation over ocean and coastal ocean, while the simulated diurnal harmonic phase and amplitude over land and coastal land show larger biases and model spread compared to biases over ocean and coastal ocean. The simulation of precipitation diurnal cycle is substantially improved in the multi-model mean of both CMIP6 historical and CMIP6 AMIP simulations compared with that of CMIP5 historical simulations. But the model bias of precipitation diurnal cycle is in general similar between CMIP6 AMIP and CMIP6 historical simulations, which suggests that the impact of the interactive ocean on the simulated diurnal precipitation averaged over a large domain is generally minor. The influence of model resolution on the diurnal cycle is shown in mixed directions across various subregions, and significant improvement from the low- to high-resolution models is only notable over coastal ocean.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Benchmarking Simulated Precipitation Variability Amplitude across Time Scales

Here, objective performance metrics that measure precipitation variability across time scales from subdaily to interannual are presented and applied to Historical simulations of Coupled Model Intercomparison Project phase 5 and 6 (CMIP5 and CMIP6) models. Three satellite-based precipitation estimates (IMERG, TRMM, and CMORPH) are used as reference data. We apply two independent methods to estimate temporal variability of precipitation and compare the consistency in their results. The first method is derived from power spectra analysis of 3-hourly precipitation, measuring forced variability by solar insolation (diurnal and annual cycles) and internal variability at different time scales (subdaily, synoptic, subseasonal, seasonal, and interannual). The second method is based on time averaging and facilitates estimating the seasonality of subdaily variability. Supporting the robustness of our metric, we find a near equivalence between the results obtained from the two methods when examining simulated-to-observed ratios over large domains (global, tropics, extratropics, land, or ocean). Additionally, we demonstrate that our model evaluation is not very sensitive to the discrepancies between observations. Our results reveal that CMIP5 and CMIP6 models in general overestimate the forced variability while they underestimate the internal variability, especially in the tropical ocean and higher-frequency variability. The underestimation of subdaily variability is consistent across different seasons. The internal variability is overall improved in CMIP6, but remains underestimated, and there is little evidence of improvement in forced variability. Increased horizontal resolution results in some improvement of internal variability at subdaily and synoptic time scales, but not at longer time scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Numerical Errors in Ice Microphysics Parameterizations and their Effects on Simulated Regional Climate

The major characteristics of ice microphysics in Weather Research and Forecasting (WRF) Double-Moment 6-class (WDM6) bulk-type cloud microphysics originate from the diagnosed ice number concentration, which is a function of the cloud-ice mixing ratio. In this study, we correct numerical errors in ice microphysics processes of the WDM6, in which the cloud-ice shape is assumed as single bullets and examine the impact on regional climate simulations. Here, by rederiving the relationships between cloud microphysics characteristics, including the one linking the cloud-ice mixing ratio and number concentration, we remove numerical errors intrinsic to the description of cloud-ice characteristics in the original WDM6 microphysics scheme. The revised WDM6 is tested using a WRF framework for regional climate simulations over the East Asian region. We find that our correction to the WDM6 improves the model’s performance in capturing the observed distribution of the monsoon rain band. A reduction in cloud ice is significant in the revised WDM6, which strengthens the Western North Pacific High. By conducting the additional sensitivity experiment in which the characteristics of cloud-ice shape are revised as the one for the column type, our study also finds out that the impacts of the existing numerical errors on the simulated monsoon is as large as the ones of the changes in cloud-ice shape.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Superior Daily and Sub-Daily Precipitation Statistics for Intense and Long-Lived Storms in Global Storm-Resolving Models

Here, daily and sub-daily precipitation statistics are investigated from three global model ensembles: (a) global storm-resolving models (GSRMs) with typical horizontal resolutions of ~4 km, (b) “high”-resolution global models with typical resolutions of ~50 km and (c) “standard”-resolution global models with typical resolutions of ~100 km. Compared to two satellite rainfall datasets, GSRMs convincingly exhibit superior performance for statistics of heavier rain rate events including their diurnal cycle, spatial propagation and the amount contributed by intense precipitation, but not for statistics of weaker or shorter duration precipitation. Both high- and standard-resolution models fail to simulate the correct phase and amplitude of diurnal cycle of precipitation and the propagating convection in the Central US, but high-resolution models show relative improvement in the distribution of precipitation frequency and amount, especially for intense precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Improving Seasonal Forecast Using Probabilistic Deep Learning

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Role of ripples in altering the electronic and chemical properties of graphene

Ripples of graphene are known to manipulate electronic and hydrogenation properties of graphitic materials. More detailed work is needed to elucidate the structure–property relationship of these systems. In this work, the density functional theory is used to compute the energy and electronic structure of the graphene models with respect to variable curvatures and hydrogen adsorption sites. The magnitude of finite bandgap opening depends on the orientation of ripples, and the hydrogen adsorption energy depends on the local curvature of graphene. An adsorbed hydrogen alters the local curvature, resulting in relatively weakened adsorption on the neighboring three sites, which gives a rationale to experimentally observed dynamic equilibrium stoichiometry (H:C = 1:4) of hydrogenated graphene. The surface diffusion transition state energy of adsorbed hydrogen is computed, which indicates that the Eley–Rideal surface recombination mechanism may be important to establish the dynamic equilibrium, instead of the commonly assumed Langmuir–Hinshelwood mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Learning to Correct Climate Projection Biases

The fidelity of climate projections is often undermined by biases in climate models due to their simplification or misrepresentation of unresolved climate processes. While various bias correction methods have been developed to post-process model outputs to match observations, existing approaches usually focus on limited, low-order statistics, or break either the spatiotemporal consistency of the target variable, or its dependency upon model resolved dynamics. We develop a Regularized Adversarial Domain Adaptation (RADA) methodology to overcome these deficiencies, and enhance efficient identification and correction of climate model biases. Instead of pre-assuming the spatiotemporal characteristics of model biases, we apply discriminative neural networks to distinguish historical climate simulation samples and observation samples. The evidences based on which the discriminative neural networks make distinctions are applied to train the domain adaptation neural networks to bias correct climate simulations. We regularize the domain adaptation neural networks using cycle-consistent statistical and dynamical constraints. An application to daily precipitation projection over the contiguous United States shows that our methodology can correct all the considered moments of daily precipitation at approximately $1^\circ$ resolution, ensures spatiotemporal consistency and inter-field correlations, and can discriminate between different dynamical conditions. Our methodology offers a powerful tool for disentangling model parameterization biases from their interactions with the chaotic evolution of climate dynamics, opening a novel avenue toward big-data enhanced climate predictions.

58 GEOSCIENCES↗
DOE OSTI2021

Evaluation and projection of long period return values of extreme daily precipitation in the CMIP5 and CMIP6 models

Using a non-stationary Generalized Extreme Value statistical method, we calculate selected extreme daily precipitation indices and their 20 year return values from the CMIP5 and CMIP6 climate models over the historical and future periods. We evaluate model performance of these indices and their return values in replicating similar quantities calculated from multiple gridded observational products. Difficulties in interpreting model quality in the context of observational uncertainties are discussed. Projections are framed in terms of specified global warming target temperatures rather than at specific times and under specific emissions scenarios. The change in framing shifts projection uncertainty due to differences in model climate sensitivity from the values of the projections to the timing of the global warming target. At their standard resolutions, we find there are no meaningful differences between the two generations of models in their quality or projections of simulated extreme daily precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Robust evaluation of ENSO in climate models: How many ensemble members are needed?

Large ensembles of model simulations require considerable resources, and thus defining an appropriate ensemble size for a particular application is an important experimental design criterion. We estimate the ensemble size (N) needed to assess a model's ability to capture observed El Niño-Southern Oscillation (ENSO) behavior by utilizing the recently developed International CLIVAR ENSO Metrics Package (Planton et al., 2021). Using the larger ensembles available from CMIP6 and the US CLIVAR Large Ensemble Working Group, we find that larger ensembles are needed to robustly capture baseline ENSO characteristics (N > 50) and physical processes (N > 50) than the background climatology (N ≥ 12) and remote ENSO teleconnections (N ≥ 6). While these results vary somewhat across metrics and models, our study quantifies how larger ensembles are required to robustly evaluate simulated ENSO behavior, thereby providing some guidance for the design of model ensembles.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

RADA

The code for regularized adversarial domain adaption is provided here. The RADA methodology is developed for correcting climate model biases using an adversarial learning strategy. The code here covers the data processing, the algorithm, the baseline models, and the evaluation.

Anderson, GemmaJ.↗