Search Results

You are looking at 1 - 10 of 13 items for

  • Author or Editor: Dongmin Lee x
  • Refine by Access: All Content x
Clear All Modify Search
Dongmin Kim, Myong-In Lee, and Eunkyo Seo

Abstract

The Q 10 value represents the soil respiration sensitivity to temperature often used for the parameterization of the soil decomposition process has been assumed to be a constant in conventional numerical models, whereas it exhibits significant spatial and temporal variation in the observations. This study develops a new parameterization method for determining Q 10 by considering the soil respiration dependence on soil temperature and moisture obtained by multiple regression for each vegetation type. This study further investigates the impacts of the new parameterization on the global terrestrial carbon flux. Our results show that a nonuniform spatial distribution of Q 10 tends to better represent the dependence of the soil respiration process on heterogeneous surface vegetation type compared with the control simulation using a uniform Q 10. Moreover, it tends to improve the simulation of the relationship between soil respiration and soil temperature and moisture, particularly over cold and dry regions. The modification has an impact on the soil respiration and carbon decomposition process, which changes gross primary production (GPP) through controlling nutrient assimilation from soil to vegetation. It leads to a realistic spatial distribution of GPP, particularly over high latitudes where the original model has a significant underestimation bias. Improvement in the spatial distribution of GPP leads to a substantial reduction of global mean GPP bias compared with the in situ observation-based reference data. The results highlight that the enhanced sensitivity of soil respiration to the subsurface soil temperature and moisture introduced by the nonuniform spatial distribution of Q 10 has contributed to improving the simulation of the terrestrial carbon fluxes and the global carbon cycle.

Full access
Dongmin Kim, Sang-Ki Lee, and Hosmay Lopez

Abstract

This study investigates the impact of the Madden–Julian oscillation (MJO) on U.S. tornadogenesis using atmospheric reanalysis and model experiments. Our analysis shows that the impact of MJO on U.S. tornadogenesis is most significant in May–July and during MJO phases 3–4 and 5–6 (P3456). These MJO phases are characterized by anomalous ascending motion over the Maritime Continent (MC) and anomalous subsidence over the northeast Pacific (EP), generating anomalous diabatic heating and cooling, respectively. These in turn generate large-scale atmospheric conditions conducive to tornadogenesis in the United States, enhancing the North American low-level jet (NALLJ) and thus increasing the influx of warm and moist air from the Gulf of Mexico to the United States and increasing the low-level wind shear and convective available potential energy along its path. Conversely, during MJO phases 1–2 and 7–8, the opposite patterns of atmospheric anomalies appear over the United States producing unfavorable environments for U.S. tornadogenesis. We further investigate the underlying mechanism for MJO-induced atmospheric circulations conducive to U.S. tornadogenesis using a linear baroclinic model (LBM). The LBM is forced by diabatic heating over the MC and cooling over the EP, which characterizes the P3456 MJO phase. The model experiment reproduces an anomalous ridge over the southern United States and associated anomalous low-level anticyclone that enhances the NALLJ and increases tornadic environmental parameters. Additional sensitivity experiments prescribing the diabatic heating over the MC and diabatic cooling over the EP independently demonstrate that diabatic cooling over the EP is the main driver for producing regional atmospheric conditions favorable for U.S. tornadogenesis.

Free access
Dongmin Kim, Sang-Ki Lee, Hosmay Lopez, and Marlos Goes

Abstract

We investigate the potential impacts of the interdecadal Pacific oscillation (IPO) and Atlantic multidecadal oscillation (AMO) on El Niño and the associated atmosphere and ocean dynamics by using the Community Earth System Model–Large Ensemble Simulation (CESM-LENS). The individual effects of IPO and AMO on El Niño frequency and the underlying atmosphere–ocean processes are well reproduced in CESM-LENS and agree with previous studies. However, the sensitivity of El Niño frequency to the AMO is robust mainly during the negative IPO phase and very weak during the positive IPO phase. Further analysis suggests that the atmospheric mean state in the Pacific is much amplified during the negative IPO phase, facilitating the AMO-induced interocean atmospheric teleconnections. More specifically, during the negative IPO phase of the amplified mean state, the positive AMO enhances ascending motion from the northeastern Pacific, which in turn increases subsidence into the southeast Pacific through local anomalous Hadley circulation. The associated low-level easterly wind anomalies in the central equatorial Pacific are also reinforced by amplified upper-level divergence over the Maritime Continent to enhance the negative IPO, which is unfavorable for El Niño occurrence. Conversely, the negative AMO nearly cancels out the suppressing effect of the negative IPO on El Niño occurrence. During the positive IPO phase of the weakened atmospheric mean state, however, the AMO-induced interocean atmospheric teleconnections are much weaker; thus, neither the positive nor the negative AMO has any significant impact on El Niño occurrence.

Free access
Daeho Jin, Lazaros Oreopoulos, Dongmin Lee, Jackson Tan, and Nayeong Cho

Abstract

To better understand cloud–precipitation relationships, we extend the concept of cloud regimes developed from two-dimensional joint histograms of cloud optical thickness and cloud-top pressure from MODIS to include precipitation information. Taking advantage of the high-resolution IMERG precipitation dataset, we derive cloud–precipitation “hybrid” regimes by implementing a k-means clustering algorithm with advanced initialization and objective measures to determine the optimal number of clusters. By expressing the variability of precipitation rates within 1° grid cells as histograms and varying the relative weight of cloud and precipitation information in the clustering algorithm, we obtain several editions of hybrid cloud–precipitation regimes (CPRs) and examine their characteristics. In the deep tropics, when precipitation is weighted weakly, the cloud part centroids of the hybrid regimes resemble their counterparts of cloud-only regimes, but combined clustering tightens the cloud–precipitation relationship by decreasing each regime’s precipitation variability. As precipitation weight progressively increases, the shape of the cloud part centroids becomes blunter, while the precipitation part sharpens. When cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct enough features in the precipitation parts of the centroids to allow us to project them onto the 30-min IMERG domain. Such a projection overcomes the temporal sparseness of MODIS cloud observations associated with substantial rainfall, suggesting great application potential for convection-focused studies for which characterization of the diurnal cycle is essential.

Full access
Lazaros Oreopoulos, Nayeong Cho, Dongmin Lee, Matthew Lebsock, and Zhibo Zhang

Abstract

We evaluate two stochastic subcolumn generators used in GCMs to emulate subgrid cloud variability enabling comparisons with satellite observations and simulations of certain physical processes. Our evaluation necessitated the creation of a reference observational dataset that resolves horizontal and vertical cloud variability. The dataset combines two CloudSat cloud products that resolve two-dimensional cloud optical depth variability of liquid, ice, and mixed phase clouds when blended at ~200 m vertical and ~ 2 km horizontal scales. Upon segmenting the dataset to individual “scenes”, mean profiles of the cloud fields are passed as input to generators that produce scene-level cloud subgrid variability. The assessment of generator performance at the scale of individual scenes and in a mean sense is largely based on inferred joint histograms that partition cloud fraction within predetermined combinations of cloud top pressure – cloud optical thickness ranges. Our main finding is that both generators tend to underestimate optically thin clouds, while one of them also tends to overestimate some cloud types of moderate and high optical thickness. Associated radiative flux errors are also calculated by applying a simple transformation to the cloud fraction histogram errors, and are found to approach values almost as high as 3 W m−2 for the cloud radiative effect in the shortwave part of the spectrum.

Restricted access
Jeongsoon Lee, Gahae Kim, Haeyoung Lee, Dongmin Moon, Jin-bok Lee, and Jeong Sik Lim

Abstract

This study presents a high-precision method, using a preconcentrator–gas chromatograph with microelectron capture detector (GC-μECD), to measure SF6 at ambient levels. Carboxen 1000 was used as an adsorbent for the preconcentrator and exhibited a high adsorption efficiency for N2O and SF6 and low adsorption efficiency for O2. This enabled the selective removal of atmospheric O2 from analytes and improved repeatability of the SF6 peak that followed the O2 peak, in a separation column of activated alumina F1. In addition, the increased sensitivity resulting from preconcentrated SF6 improved the signal-to-noise ratio. This led to better analytical precision in comparison with other measurement methods including the conventional and forecut–backflush (FCBF) methods. The precision-to-drift ratios of the conventional, FCBF, and preconcentration methods were 0.11, 0.10, and 0.03, respectively. Analytical precision of the preconcentration method was 0.08% for 10 consecutive injections; this was the best among the three methods. The long-term drift of the SF6 response was inversely proportional to the laboratory pressure. Based on this finding, room pressure can be used to correct for ECD signal drift, with an uncertainty of 0.14% over a 48-h period, using the preconcentration method. Another advantage of the preconcentration method was the excellent linearity of the SF6 response to a wide range of concentrations, including its ambient concentration.

Open access
Sang-Ki Lee, Hosmay Lopez, Dongmin Kim, Andrew T. Wittenberg, and Arun Kumar

Abstract

This study presents an experimental model for Seasonal Probabilistic Outlook for Tornadoes (SPOTter) in the contiguous United States for March, April, and May and evaluates its forecast skill. This forecast model uses the leading empirical orthogonal function modes of regional variability in tornadic environmental parameters (i.e., low-level vertical wind shear and convective available potential energy), derived from the NCEP Coupled Forecast System, version 2, as the primary predictors. A multiple linear regression is applied to the predicted modes of tornadic environmental parameters to estimate U.S. tornado activity, which is presented as the probability for above-, near-, and below-normal categories. The initial forecast is carried out in late February for March–April U.S. tornado activity and then is updated in late March for April–May activity. A series of reforecast skill tests, including the jackknife cross-validation test, shows that the probabilistic reforecast is overall skillful for predicting the above- and below-normal area-averaged activity in the contiguous United States for the target months of both March–April and April–May. The forecast model also successfully reforecasts the 2011 super-tornado-outbreak season and the other three most active U.S. tornado seasons in 1982, 1991, and 2008, and thus it may be suitable for an operational use for predicting future active and inactive U.S. tornado seasons. However, additional tests show that the regional reforecast is skillful for March–April activity only in the Ohio Valley and South and for April–May activity only in the Southeast and Upper Midwest. These and other limitations of the current model, along with the future advances needed to improve the U.S. regional-scale tornado forecast, are discussed.

Full access
Hye-Mi Kim, Myong-In Lee, Peter J. Webster, Dongmin Kim, and Jin Ho Yoo

Abstract

The relationship between El Niño–Southern Oscillation (ENSO) and tropical storm (TS) activity over the western North Pacific Ocean is examined for the period from 1981 to 2010. In El Niño years, TS genesis locations are generally shifted to the southeast relative to normal years and the passages of TSs tend to recurve to the northeast. TSs of greater duration and more intensity during an El Niño summer induce an increase of the accumulated tropical cyclone kinetic energy (ACE). Based on the strong relationship between the TS properties and ENSO, a probabilistic prediction for seasonal ACE is investigated using a hybrid dynamical–statistical model. A statistical relationship is developed between the observed ACE and large-scale variables taken from the ECMWF seasonal forecast system 4 hindcasts. The ACE correlates positively with the SST anomaly over the central to eastern Pacific and negatively with the vertical wind shear near the date line. The vertical wind shear anomalies over the central and western Pacific are selected as predictors based on sensitivity tests of ACE predictive skill. The hybrid model performs quite well in forecasting seasonal ACE with a correlation coefficient between the observed and predicted ACE at 0.80 over the 30-yr period. A relative operating characteristic analysis also indicates that the ensembles have significant probabilistic skill for both the above-normal and below-normal categories. By comparing the ACE prediction over the period from 2003 to 2011, the hybrid model appears more skillful than the forecast from the Tropical Storm Risk consortium.

Full access
Dongmin Lee, Lazaros Oreopoulos, George J. Huffman, William B. Rossow, and In-Sik Kang

Abstract

The authors examine the daytime precipitation characteristics of the International Satellite Cloud Climatology Project (ISCCP) weather states in the extended tropics (35°S–35°N) for a 10-yr period. The main precipitation dataset used is the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis operational product 3B42 dataset, but Global Precipitation Climatology Project daily data are also used for comparison. It is found that the most convectively active ISCCP weather state (WS1), despite an occurrence frequency below 10%, is the most dominant state with regard to surface precipitation, producing both the largest mean precipitation rates when present and the largest percent contribution to the total precipitation of the tropics; yet, even this weather state appears to not precipitate about half the time, although this may be to some extent an artifact of detection and spatiotemporal matching limitations of the precipitation dataset. WS1 exhibits a modest annual cycle of the domain-average precipitation rate, but notable seasonal shifts in its geographic distribution. The precipitation rates of the other weather states appear to be stronger when occurring before or after WS1. The precipitation rates of the various weather states are different between ocean and land, with WS1 producing higher daytime rates on average over ocean than land, likely because of the larger size and more persistent nature of oceanic WS1s. The results of this study, in addition to advancing the understanding of tropical hydrology, can serve as higher-order diagnostics for evaluating the realism of tropical precipitation distributions in large-scale models.

Full access
Myung-Sook Park, Myong-In Lee, Dongmin Kim, Michael M. Bell, Dong-Hyun Cha, and Russell L. Elsberry

Abstract

The effects of land-based convection on the formation of Tropical Storm Mekkhala (2008) off the west coast of the Philippines are investigated using the Weather Research and Forecasting Model with 4-km horizontal grid spacing. Five simulations with Thompson microphysics are utilized to select the control-land experiment that reasonably replicates the observed sea level pressure evolution. To demonstrate the contribution of the land-based convection, sensitivity experiments are performed by changing the land of the northern Philippines to water, and all five of these no-land experiments fail to develop Mekkhala.

The Mekkhala tropical depression develops when an intense, well-organized land-based mesoscale convective system moves offshore from Luzon and interacts with an oceanic mesoscale system embedded in a strong monsoon westerly flow. Because of this interaction, a midtropospheric mesoscale convective vortex (MCV) organizes offshore from Luzon, where monsoon convection continues to contribute to low-level vorticity enhancement below the midlevel vortex center. In the no-land experiments, widespread oceanic convection induces a weaker midlevel vortex farther south in a strong vertical wind shear zone and subsequently farther east in a weaker monsoon vortex region. Thus, the monsoon convection–induced low-level vorticity remained separate from the midtropospheric MCV, which finally resulted in a failure of the low-level spinup. This study suggests that land-based convection can play an advantageous role in TC formation by influencing the intensity and the placement of the incipient midtropospheric MCV to be more favorable for TC low-level circulation development.

Full access