Huikyo Lee

CMIP5 Climate Model Analyses: Climate Extremes in the United States

This is the fourth in a series of four articles on historical and projected climate extremes in the United States. Here, we examine the results of historical and future climate model experiments from the phase 5 of the Coupled Model Intercomparison Project (CMIP5) based on work presented at the World Climate Research Programme (WCRP) Workshop on CMIP5 Climate Model Analyses held in March 2012. Our analyses assess the ability of CMIP5 models to capture observed trends, and we also evaluate the projected future changes in extreme events over the contiguous Unites States. Consistent with the previous articles, here we focus on model-simulated historical trends and projections for temperature extremes, heavy precipitation, large-scale drivers of precipitation variability and drought, and extratropical storms. Comparing new CMIP5 model results with earlier CMIP3 simulations shows that in general CMIP5 simulations give similar patterns and magnitudes of future temperature and precipitation extremes in the Unite...

Evaluation of the Surface Climatology over the Conterminous United States in the North American Regional Climate Change Assessment Program Hindcast Experiment Using a Regional Climate Model Evaluation System

AbstractSurface air temperature, precipitation, and insolation over the conterminous United States region from the North American Regional Climate Change Assessment Program (NARCCAP) regional climate model (RCM) hindcast study are evaluated using the Jet Propulsion Laboratory (JPL) Regional Climate Model Evaluation System (RCMES). All RCMs reasonably simulate the observed climatology of these variables. RCM skill varies more widely for the magnitude of spatial variability than the pattern. The multimodel ensemble is among the best performers for all these variables. Systematic biases occur across these RCMs for the annual means, with warm biases over the Great Plains (GP) and cold biases in the Atlantic and the Gulf of Mexico (GM) coastal regions. Wet biases in the Pacific Northwest and dry biases in the GM/southern Great Plains also occur in most RCMs. All RCMs suffer problems in simulating summer rainfall in the Arizona–New Mexico region. RCMs generally overestimate surface insolation, especially in the...

PDO and ENSO modulations intensified decadal sea level variability in the tropical Pacific

According to long-term sea level reconstruction and steric sea level data, regional sea levels in the tropical Pacific have oscillated between east and west on a decadal time scale over the past 60 years, but the oscillation has been intensified significantly in the last three decades. Using conditional composite analysis, we show that the recent intensification in sea level variability is caused by modulation between the Pacific Decadal Oscillation (PDO) and El Nino-Southern Oscillation (ENSO), i.e., an El Nino in a positive PDO or a La Nina in a negative PDO phase. Our analysis of meteorological fields indicates that atmospheric circulation associated with the changes in ENSO-PDO phase relationship plays a positive role in enhancing the decadal sea level oscillation. The intensified sea level oscillation, when superimposed on the global trend of sea level rise, will have profound implications for coastal communities, therefore, the combined effect of PDO and ENSO should be taken into account in the decadal sea level prediction in the tropical Pacific.

Surface Temperature Probability Distributions in the NARCCAP Hindcast Experiment: Evaluation Methodology, Metrics, and Results

AbstractMethodology is developed and applied to evaluate the characteristics of daily surface temperature distributions in a six-member regional climate model (RCM) hindcast experiment conducted as part of the North American Regional Climate Change Assessment Program (NARCCAP). A surface temperature dataset combining gridded station observations and reanalysis is employed as the primary reference. Temperature biases are documented across the distribution, focusing on the median and tails. Temperature variance is generally higher in the RCMs than reference, while skewness is reasonably simulated in winter over the entire domain and over the western United States and Canada in summer. Substantial differences in skewness exist over the southern and eastern portions of the domain in summer. Four examples with observed long-tailed probability distribution functions (PDFs) are selected for model comparison. Long cold tails in the winter are simulated with high fidelity for Seattle, Washington, and Chicago, Illi...

Exploring a graph theory based algorithm for automated identification and characterization of large mesoscale convective systems in satellite datasets

Mesoscale convective systems are high impact convectively driven weather systems that contribute large amounts to the precipitation daily and monthly totals at various locations globally. As such, an understanding of the lifecycle, characteristics, frequency and seasonality of these convective features is important for several sectors and studies in climate studies, agricultural and hydrological studies, and disaster management. This study explores the applicability of graph theory to creating a fully automated algorithm for identifying mesoscale convective systems and determining their precipitation characteristics from satellite datasets. Our results show that applying graph theory to this problem allows for the identification of features from infrared satellite data and the seamlessly identification in a precipitation rate satellite-based dataset, while innately handling the inherent complexity and non-linearity of mesoscale convective systems.

Using joint probability distribution functions to evaluate simulations of precipitation, cloud fraction and insolation in the North America Regional Climate Change Assessment Program (NARCCAP)

This study evaluates model fidelity in simulating relationships between seasonally averaged precipitation, cloud fraction and surface insolation from the North American Regional Climate Change Assessment Project (NARCCAP) hindcast using observational data from ground stations and satellites. Model fidelity is measured in terms of the temporal correlation coefficients between these three variables and the similarity between the observed and simulated joint probability distribution functions (JPDFs) in 14 subregions over the conterminous United States. Observations exhibit strong negative correlations between precipitation/cloud fraction and surface insolation for all seasons, whereas the relationship between precipitation and cloud fraction varies according to regions and seasons. The skill in capturing these observed relationships varies widely among the NARCCAP regional climate models, especially in the Midwest and Southeast coast regions where observations show weak (or even negative) correlations between precipitation and cloud fraction in winter due to frequent non-precipitating stratiform clouds. Quantitative comparison of univariate and JPDFs indicates that model performance varies markedly between regions as well as seasons. This study also shows that comparison of JPDFs is useful for summarizing the performance of and highlighting problems with some models in simulating cloud fraction and surface insolation. Our quantitative metric may be useful in improving climate models by highlighting shortcomings in the formulations related with the physical processes involved in precipitation, clouds and radiation or other multivariate processes in the climate system.

The Influence of Recurrent Modes of Climate Variability on the Occurrence of Monthly Temperature Extremes over South America

The associations between extreme temperature months and four prominent modes of recurrent climate variability are examined over South America. Associations are computed as the percent of extreme temperature months concurrent with the upper and lower quartiles of the El Nino–Southern Oscillation (ENSO), the Atlantic Nino, the Pacific Decadal Oscillation (PDO), and the Southern Annular Mode (SAM) index distributions, stratified by season. The relationship is strongest for ENSO, with nearly every extreme temperature month concurrent with the upper or lower quartiles of its distribution in portions of northwestern South America during some seasons. The likelihood of extreme warm temperatures is enhanced over parts of northern South America when the Atlantic Nino index is in the upper quartile, while cold extremes are often association with the lowest quartile. Concurrent precipitation anomalies may contribute to these relations. The PDO shows weak associations during December, January, and February, while in June, July, and August its relationship with extreme warm temperatures closely matches that of ENSO. This may be due to the positive relationship between the PDO and ENSO, rather than the PDO acting as an independent physical mechanism. Over Patagonia, the SAM is highly influential during spring and fall, with warm and cold extremes being associated with positive and negative phases of the SAM, respectively. Composites of sea level pressure anomalies for extreme temperature months over Patagonia suggest an important role of local synoptic scale weather variability in addition to a favorable SAM for the occurrence of these extremes.

Sensitivity of CONUS Summer Rainfall to the Selection of Cumulus Parameterization Schemes in NU-WRF Seasonal Simulations

AbstractThis study investigates the sensitivity of daily rainfall rates in regional seasonal simulations over the contiguous United States (CONUS) to different cumulus parameterization schemes. Daily rainfall fields were simulated at 24-km resolution using the NASA-Unified Weather Research and Forecasting (NU-WRF) Model for June–August 2000. Four cumulus parameterization schemes and two options for shallow cumulus components in a specific scheme were tested. The spread in the domain-mean rainfall rates across the parameterization schemes was generally consistent between the entire CONUS and most subregions. The selection of the shallow cumulus component in a specific scheme had more impact than that of the four cumulus parameterization schemes. Regional variability in the performance of each scheme was assessed by calculating optimally weighted ensembles that minimize full root-mean-square errors against reference datasets. The spatial pattern of the seasonally averaged rainfall was insensitive to the sel...

Evaluating Hourly Rainfall Characteristics over the U.S. Great Plains in Dynamically Downscaled Climate Model Simulations using NASA-Unified WRF

Accurate simulation of extreme precipitation events remains a challenge in climate models. This study utilizes hourly precipitation data from ground stations and satellite instruments to evaluate rainfall characteristics simulated by the NASA-Unified Weather Research and Forecasting (NU-WRF) regional climate model at horizontal resolutions of 4, 12, and 24 km over the Great Plains of the United States. We also examined the sensitivity of the simulated precipitation to different spectral nudging approaches and the cumulus parameterizations. The rainfall characteristics in the observations and simulations were defined as a hourly diurnal cycle of precipitation and a joint probability distribution function (JPDF) between duration and peak intensity of precipitation events over the Great Plains in summer. We calculated a JPDF for each dataset and the overlapping area between observed and simulated JPDFs to measure the similarity between the two JPDFs. Comparison of the diurnal precipitation cycles between observations and simulations does not reveal the added value of high-resolution simulations. However, the performance of NU-WRF simulations measured by the JPDF metric strongly depends on horizontal resolution. The simulation with the highest resolution of 4 km shows the best agreement with the observations in simulating duration and intensity of wet spells. Spectral nudging does not affect the JPDF significantly. The effect of cumulus parameterizations on the JPDFs is considerable but smaller than that of horizontal resolution. The simulations with lower resolutions of 12 and 24 km show reasonable agreement but only with the high-resolution observational data that are aggregated into coarse resolution and spatially averaged.

Development of a Model Performance Metric and Its Application to Assess Summer Precipitation over the U.S. Great Plains in Downscaled Climate Simulations

AbstractSeveral dynamically downscaled climate simulations with various spatial resolutions (24, 12, and 4 km) and spectral nudging strengths (0, 600, and 2000 km) have been run over the contiguous United States from 2000 to 2009 using the high-resolution NASA Unified Weather and Research Forecasting (NU-WRF) regional model initialized and constrained by the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). This paper summarizes the authors’ efforts on the development of a model performance metric and its application to assess summer precipitation over the U.S. Great Plains (USGP) in these downscaled climate simulations. A new model performance metric T was first developed that uses both the linear correlation coefficient and mean square error and is consistent with other commonly used metrics, but gives a bigger separation between good and bad simulations. This metric T was then applied to the summer mean precipitation spatial pattern, diurnal Hovmoller diagram, and di...