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Jinwon Kim and L. Mahrt

Abstract

The momentum flux by orographic gravity waves and the turbulent heat flux in wave-breaking regions are estimated from aircraft data from ALPEX. The fluxes on 6 March 1982 are controlled by low-level directional shear of the mean flow and associated critical level with wave stress decreasing toward the critical level. On 25 March 1982 a critical level does not occur and wave stress is approximately constant with height within the observational domain. The calculation of these fluxes appears to be the first direct comparison between simple models of gravity-wave momentum flux and observed atmospheric fluxes.

To develop a simple formulation of gravity wave drag for large-scale models, the gravity-wave stress super-saturation theory by Lindzen is generalized for the application to vertically varying mean flows. The wave momentum flux estimated from the generalized model agrees well with the observations for the two ALPEX days. For the 6 March case, the vertical divergence of wave momentum flux below the critical level is comparable to the Coriolis term in the momentum equation. The effective height of the surface topography required for the formulation of the wave momentum flux at the ground surface is estimated from the data and found to agree with the formulation of Stern and Pierrehumbert.

Wave breaking below the critical level leads to a convectively unstable region 10–20 km wide where well-organized turbulent-scale convection occurs. The magnitude of the observed upward turbulent heat flux can be approximated by using the flux gradient relationship in which the mixing length and modified shear are derived from the generalized wave-stress supersaturation condition. However, the net turbulent heat flux across the entire width of the mountain waves appears to be small due to cancellation between the upward heat flux in the convectively unstable region and the downward heat flux at the back of the wave. The spatially averaged wave-scale heat flux is also small for the data analyzed here.

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Norman L. Miller and Jinwon Kim

Precipitation and river flow during a January 1995 flood event over the Russian River watershed in the northern Coastal Range of California were simulated using the University of California Lawrence Livermore National Laboratory's Coupled Atmosphere–River Flow Simulation (CARS) System. The CARS System unidirectionally links a primitive equation atmospheric mesoscale model to a physically based, fully distributed hydrologic model by employing an automated land analysis system. Using twice-daily National Meteorological Center eta model initial data to provide the large-scale forcing to the mesoscale model, the CARS System has closely simulated the observed river flow during the flooding stage, where the simulated river flow was within 10% of the observed river flow at the Hopland gauge station on the Russian River.

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Jinwon Kim and Jung-Eun Lee

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In preparation for studying the effects of increased CO2 on the hydrologic cycle in the western United States, an 8-yr hindcast was performed using a regional climate model (RCM) driven by the large-scale forcing from the NCEP–NCAR reanalysis. The simulated precipitation characteristics agree well with observations, especially in the winter. The simulated precipitation compares with rain gauge data at similar accuracy as the NCEP reanalysis, but the RCM-generated precipitation is more accurate than the reanalysis data at the scales of individual basins. Important characteristics of the hydrologic cycle of the region, such as seasonal snowfall, frequency of heavy and extreme daily precipitation events, and interannual variations of precipitation associated with the North American monsoon are also well represented in the hindcast. Compared to the Climate Research Unit, University of East Anglia (CRU), analysis, the simulated low-level air temperatures show cold biases except in summer. The temperature biases are difficult to quantify, however, due to suspected warm biases in the CRU data. The RCM overestimates surface insolation and outgoing longwave radiation at the top of the atmosphere (OLR-TOA). The errors in the simulated radiation are smaller over the land than the ocean. Both simulated and observed OLR-TOA suggest strong influence of low-level temperatures on the seasonal variations of OLR-TOA in the region. The results suggest that the RCM employed in this study possesses reasonable skill for studying regional climate change signals in the western United States.

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Jinwon Kim and Hyun-Suk Kang

Abstract

To understand the influence of the Sierra Nevada on the water cycle in California the authors have analyzed low-level winds and water vapor fluxes upstream of the mountain range in regional climate model simulations. In a low Froude number (Fr) regime, the upstream low-level wind disturbances are characterized by the rapid weakening of the crosswinds and the appearance of a stagnation point over the southwestern foothills. The weakening of the low-level inflow is accompanied by the development of along-ridge winds that take the form of a barrier jet over the western slope of the mountain range. Such upstream wind disturbances are either weak or nonexistent in a high-Fr case. A critical Fr (Frc) of 0.35 inferred in this study is within the range of those suggested in previous observational and numerical studies. The depth of the blocked layer estimated from the along-ridge wind profile upstream of the northern Sierra Nevada corresponds to Frc between 0.3 and 0.45 as well. Associated with these low-level wind disturbances are significant low-level southerly moisture fluxes over the western slope and foothills of the Sierra Nevada in the low-Fr case, which result in significant exports of moisture from the southern Sierra Nevada to the northern region. This along-ridge low-level water vapor transport by blocking-induced barrier jets in a low-Fr condition may result in a strong north–south precipitation gradient over the Sierra Nevada.

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Jinwon Kim, Yu Gu, and K. N. Liou

Abstract

To understand the regional impact of the atmospheric aerosols on the surface energy and water cycle in the southern Sierra Nevada characterized by extreme variations in terrain elevation, the authors examine the aerosol radiative forcing on surface insolation and snowmelt for the spring of 1998 in a regional climate model experiment. With a prescribed aerosol optical thickness of 0.2, it is found that direct aerosol radiative forcing influences spring snowmelt primarily by reducing surface insolation and that these forcings on surface insolation and snowmelt vary strongly following terrain elevation. The direct aerosol radiative forcing on surface insolation is negative in all elevations. It is nearly uniform in the regions below 2000 m and decreases with increasing elevation in the region above 2000 m. This elevation dependency in the direct aerosol radiative forcing on surface insolation is related to the fact that the amount of cloud water and the frequency of cloud formation are nearly uniform in the lower elevation region, but increase with increasing elevation in the higher elevation region. This also suggests that clouds can effectively mask the direct aerosol radiative forcing on surface insolation. The direct aerosol radiative forcing on snowmelt is notable only in the regions above 2000 m and is primarily via the reduction in the surface insolation by aerosols. The effect of this forcing on low-level air temperature is as large as −0.3°C, but its impact on snowmelt is small because the sensible heat flux change is much smaller than the insolation change. The direct aerosol radiative forcing on snowmelt is significant only when low-level temperature is near the freezing point, between −3° and 5°C. When low-level temperature is outside this range, the direct aerosol radiative forcing on surface insolation has only a weak influence on snowmelt. The elevation dependency of the direct aerosol radiative forcing on snowmelt is related with this low-level temperature effect as the occurrence of the favored temperature range is most frequent in high elevation regions.

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Jinwon Kim, Jongyoun Kim, John D. Farrara, and John O. Roads

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The impacts of the sea surface temperatures (SSTs) in the northern Gulf of California (GC) on warm-season rainfall in the Arizona–New Mexico (AZNM) and the northwestern Mexico (NWM) regions associated with the North American monsoon (NAM) are examined from two sets of seasonal simulations in which different SSTs were prescribed in the GC. The simulations reproduced important features in the low-level mesoscale circulations and upper air fields around the time of monsoon rainfall onset in AZNM such as sea-breeze-like diurnal variations in the low-level winds between the GC and the land, development of south-southeasterly winds over the GC and the western slope of the Sierra Madre Occidental after the onset of rainfall, and the strengthening of the 500-hPa high over AZNM around the onset of monsoon rainfall in AZNM.

The simulated temporal variations in the upper air fields and daily rainfall, as well as the mesoscale circulation around the GC, suggest that the GC SSTs affect the water cycle around the GC mainly by altering mesoscale circulation and water vapor fluxes, but they have minimal impacts on the onset timing of monsoon rainfall in NWM and AZNM. With higher SSTs in the NGC, rainfall in NWM and AZNM increases in response to enhanced water vapor fluxes from the GC into the land. The enhanced onshore component of the low-level water vapor fluxes from the GC with higher GC SSTs results from two opposing effects: weakened sea-breeze-like circulation between the GC and the surrounding lands that tends to reduce the water vapor fluxes from the GC, and increased evaporation from the GC that tends to increase the water vapor fluxes. The simulations also suggest that the development of south-southeasterly low-level winds over the GC after monsoon rainfall onset plays an important role in enhancing rainfall as longer fetches over the GC can provide more water vapor into the low atmosphere.

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Boksoon Myoung, Seung Hee Kim, Jinwon Kim, and Menas C. Kafatos

Abstract

It is reported herein that the North Atlantic Oscillation (NAO), which has been known to directly affect winter weather conditions in western Europe and the eastern United States, is also linked to surface air temperature over the broad southwestern U.S. (SWUS) region, encompassing California, Nevada, Arizona, New Mexico, Utah, and Colorado, in the early warm season. The authors have performed monthly time-scale correlations and composite analyses using three different multidecadal temperature datasets. Results from these analyses reveal that NAO-related upstream circulation positively affects not only the means, but also the extremes, of the daily maximum and minimum temperatures in the SWUS. This NAO effect is primarily linked with the positioning of upper-tropospheric anticyclones over the western United States that are associated with development of the positive NAO phase through changes in lower-tropospheric wind directions as well as suppression of precipitation and enhanced shortwave radiation at the surface. The effect is observed in the SWUS only during the March–June period because the monthly migration of anticyclones over the western United States follows the migration of the NAO center over the subtropical Atlantic Ocean. The link between the SWUS temperatures and NAO has been strengthened in the last 30-yr period (1980–2009), compared to the previous 30-yr period (1950–79). In contrast to the NAO–SWUS temperature relationship, El Niño–Southern Oscillation (ENSO) and the Pacific decadal oscillation (PDO) show only marginal correlation strengths in several limited regions for the same 60-yr period.

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Boksoon Myoung, Seung Hee Kim, Jinwon Kim, and Menas C. Kafatos

Abstract

This study examines the relationship between the North Atlantic Oscillation (NAO) and snowmelt in spring in the upper southwestern states of the United States (UP_SW) including California, Nevada, Utah, and Colorado, using SNOTEL datasets for 34 yr (1980–2014). Statistically significant negative correlations are found between NAO averages in the snowmelt period and timings of snowmelt (i.e., positive NAO phases in spring enhance snowmelt, and vice versa). It is also found that correlations between El Niño–Southern Oscillation and snowmelt are negligible in the region. The NAO–snowmelt relationship is most pronounced below the 2800-m level; above this level, the relationship becomes weaker. The underlying mechanism for this link is that a positioning of upper-tropospheric anticyclonic (cyclonic) circulations over the western United States that are associated with development of the positive (negative) NAO phases tends to bring warmer and drier (colder and wetter) spring weather conditions to the region. The temperature variations related with the NAO phases also strongly modulate the snowfall–rainfall partitioning. The relationship between the NAO and spring snowmelt can serve as key information for the warm season water resources management in the UP_SW.

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Phaedon C. Kyriakidis, Norman L. Miller, and Jinwon Kim

Abstract

A Monte Carlo framework is adopted for propagating uncertainty in dynamically downscaled seasonal forecasts of area-averaged daily precipitation to associated streamflow response calculations. Daily precipitation is modeled as a mixture of two stochastic processes: a binary occurrence process and a continuous intensity process, both exhibiting serial correlation. The parameters of these processes (e.g., the proportion of wet days and the average wet-day precipitation intensity in a month) are derived from the forecast record. Parameter uncertainty is characterized via an empirical Bayesian model, whereby such parameters are modeled as random with a specific joint probability distribution. The hyperparameters specifying this probability distribution are derived from historical precipitation records at the study basin. Simulated parameter values are then generated using the Bayesian model, leading to alternative synthetic daily precipitation records simulated via the stochastic precipitation model. The set of such synthetic precipitation records is finally input to a physically based deterministic hydrologic model for propagating uncertainty in forecasted precipitation to hydrologic impact assessment studies.

The stochastic simulation approach is applied for generating an ensemble (set) of synthetic area-averaged daily precipitation records at the Hopland basin in the northern California Coast Range for the winter months (December through February: DJF) of 1997/98. The parameters of the stochastic precipitation model are derived from a seasonal precipitation forecast based on the Regional Climate System Model (RCSM), available at a 36-km2 grid spacing. The large-scale forcing input to RCSM for dynamical downscaling was a seasonal prediction of the University of California, Los Angeles, Atmospheric General Circulation Model. A semidistributed deterministic hydrologic model (“TOPMODEL”) is then used for calculating the streamflow response for each member of the area-averaged precipitation ensemble set. Uncertainty in the parameters of the stochastic precipitation model is finally propagated to associated streamflow response, by considering parameter values derived from historical (DJF 1958–92) area-averaged precipitation records at Hopland.

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Phaedon C. Kyriakidis, Jinwon Kim, and Norman L. Miller

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A geostatistical framework for integrating lower-atmosphere state variables and terrain characteristics into the spatial interpolation of rainfall is presented. Lower-atmosphere state variables considered are specific humidity and wind, derived from an assimilated data product from the National Centers for Environmental Prediction and the National Center for Atmospheric Research (NCEP–NCAR reanalysis). These variables, along with terrain elevation and its gradient from a 1-km-resolution digital elevation model, are used for constructing additional rainfall predictors, such as the amount of moisture subject to orographic lifting; these latter predictors quantify the interaction of lower-atmosphere characteristics with local terrain. A “first-guess” field of precipitation estimates is constructed via a multiple regression model using collocated rain gauge observations and rainfall predictors. The final map of rainfall estimates is derived by adding to this initial field a field of spatially interpolated residuals, which accounts for local deviations from the regression-based first-guess field. Several forms of spatial interpolation (kriging), which differ in the degree of complexity of the first-guess field, are considered for mapping the seasonal average of daily precipitation for the period from 1 November 1981 to 31 January 1982 over a region in northern California at 1-km resolution. The different interpolation schemes are compared in terms of cross-validation statistics and the spatial characteristics of cross-validation errors. The results indicate that integration of low-atmosphere and terrain information in a geostatistical framework could lead to more accurate representations of the spatial distribution of rainfall than those found in traditional analyses based only on rain gauge data. The magnitude of this latter improvement, however, would depend on the density of the rain gauge stations, on the spatial variability of the precipitation field, and on the degree of correlation between rainfall and its predictors.

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