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Changhai Liu
,
Mitchell W. Moncrieff
, and
Wojciech W. Grabowski

Abstract

Convection and cloud processes are examined in a hierarchy of two-dimensional numerical realizations of cloud systems observed during the 19–26 December 1992 period of the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment. The hierarchy consists of cloud-resolving simulations at a 2-km resolution, and two sets of 15-km resolution simulations; one attempts to treat convection explicitly and the other parameterizes convection using the Kain–Fritsch scheme.

The Kain–Fritsch parameterization shows reasonable results but shortcomings are found in comparison with the cloud-resolving model. (i) The entraining plumes in the parameterization excessively overshoot the tropopause, which produces a cold bias mostly through adiabatic cooling. The attendant moisture detrainment overproduces cirrus cloud. (ii) Because parameterized downdrafts detrain at the lowest level they generate a surface cold bias. (iii) The scheme fails to represent the trimodal convection (cumulonimbus reaching the tropopause, cumulus congestus around the melting level, and shallow convection regimes) realized by the cloud-resolving simulation and also seen in observations. The lack of shallow convection and cumulus congestus leads to an overprediction of the low-level moisture. (iv) The simulations are sensitive to the magnitude of moisture feedback from the convective parameterization to the grid scale but less sensitive to whether the moisture is in vapor or condensed phase.

These deficiencies are mostly a consequence of the single-plume model that represents updrafts and downdrafts in the parameterization scheme, along with the lack of a shallow convection scheme. A more realistic model of entrainment and detrainment that reduces overshoot and represents the cumulus congestus is required. Realistic downdraft detrainment and relative humidity are needed to improve the downdraft parameterization and alleviate the surface temperature bias.

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Jun-Ichi Yano
,
Changhai Liu
, and
Mitchell W. Moncrieff

Abstract

Atmospheric convection has a tendency to organize on a hierarchy of scales ranging from the mesoscale to the planetary scales, with the latter especially manifested by the Madden–Julian oscillation. The present paper examines two major competing mechanisms of self-organization in a cloud-resolving model (CRM) simulation from a phenomenological thermodynamic point of view.

The first mechanism is self-organized criticality. A saturation tendency of precipitation rate with increasing column-integrated water, reminiscent of critical phenomena, indicates self-organized criticality. The second is a self-regulation mechanism that is known as homeostasis in biology. A thermodynamic argument suggests that such self-regulation maintains the column-integrated water below a threshold by increasing the precipitation rate. Previous analyses of both observational data as well as CRM experiments give mixed results.

In this study, a CRM experiment over a large-scale domain with a constant sea surface temperature is analyzed. This analysis shows that the relation between the column-integrated total water and precipitation suggests self-organized criticality, whereas the one between the column-integrated water vapor and precipitation suggests homeostasis. The concurrent presence of these two mechanisms is further elaborated by detailed statistical and budget analyses. These statistics are scale invariant, reflecting a spatial scaling of precipitation processes.

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Justin R. Minder
,
Theodore W. Letcher
, and
Changhai Liu

Abstract

The character and causes of elevation-dependent warming (EDW) of surface temperatures are examined in a suite of high-resolution ( km) regional climate model (RCM) simulations of climate change over the Rocky Mountains using the Weather Research and Forecasting Model. A clear EDW signal is found over the region, with warming enhanced in certain elevation bands by as much as 2°C. During some months warming maximizes at middle elevations, whereas during others it increases monotonically with elevation or is nearly independent of elevation. Simulated EDW is primarily caused by the snow albedo feedback (SAF). Warming maximizes in regions of maximum snow loss and albedo reduction. The role of the SAF is confirmed by sensitivity experiments wherein the SAF is artificially suppressed. The elevation dependence of free-tropospheric warming appears to play a secondary role in shaping EDW. No evidence is found for a contribution from elevation-dependent water vapor feedbacks. Sensitivity experiments show that EDW depends strongly on certain aspects of RCM configuration. Simulations using 4- and 12-km horizontal grid spacings show similar EDW signals, but substantial differences are found when using a grid spacing of 36 km due to the influence of terrain resolution on snow cover and the SAF. Simulations using the Noah and Noah-MP land surface models (LSMs) exhibit large differences in EDW. These are caused by differences between LSMs in their representations of midelevation snow extent and in their parameterization of subpixel fractional snow cover. These lead to albedo differences that act to modulate the simulated SAF and its effect on EDW.

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Changhai Liu
,
Kyoko Ikeda
,
Gregory Thompson
,
Roy Rasmussen
, and
Jimy Dudhia

Abstract

An investigation was conducted on the effects of various physics parameterizations on wintertime precipitation predictions using a high-resolution regional climate model. The objective was to evaluate the sensitivity of cold-season mountainous snowfall to cloud microphysics schemes, planetary boundary layer (PBL) schemes, land surface schemes, and radiative transfer schemes at a 4-km grid spacing applicable to the next generation of regional climate models.

The results indicated that orographically enhanced precipitation was highly sensitive to cloud microphysics parameterizations. Of the tested 7 parameterizations, 2 schemes clearly outperformed the others that overpredicted the snowfall amount by as much as ~30%–60% on the basis of snow telemetry observations. Significant differences among these schemes were apparent in domain averages, spatial distributions of hydrometeors, latent heating profiles, and cloud fields. In comparison, model results showed relatively weak dependency on the land surface, PBL, and radiation schemes, roughly in the order of decreasing level of sensitivity.

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Hsiao-ming Hsu
,
Mitchell W. Moncrieff
,
Wen-wen Tung
, and
Changhai Liu

Abstract

Directionally averaged time series of precipitation rates for eight warm seasons (1996–2003) over the continental United States derived from Next Generation Weather Radar (NEXRAD) measurements are analyzed using spectral decomposition methods. For the latitudinally averaged data, in addition to previously identified diurnal and semidiurnal cycles, the temporal spectra show cross-scale self-similarity and periodicity. This property is revealed by a power-law scaling with an exponent of −4/3 for the frequency band higher than semidiurnal and −3/4 for the 1–3-day band. For the longitudinally averaged series the scaling exponent for the frequency band higher than semidiurnal changes from −4/3 to −5/3 revealing anisotropic properties.

The dominant periods and propagation speeds display temporal variability on about 1/2, 1, 4, 11, and 25 days. Composite patterns describing periods of <5 days display the eastward propagation characteristic of classical mesoscale convective organization. The lower-frequency (>5 days) patterns propagate westward suggesting the influence of large-scale waves, and both dominant periods and propagation speeds show marked interannual variability. The implied dependence between propagation and mean-flow for <5 days is consistent with the macrophysics of warm-season convective organization, and extends known dynamical mechanisms to a statistical framework.

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Jason M. Keeler
,
Robert M. Rauber
,
Brian F. Jewett
,
Greg M. McFarquhar
,
Roy M. Rasmussen
,
Lulin Xue
,
Changhai Liu
, and
Gregory Thompson

Abstract

Cloud-top generating cells (GCs) are a common feature atop stratiform clouds within the comma head of winter cyclones. The dynamics of cloud-top GCs are investigated using very high-resolution idealized WRF Model simulations to examine the role of shear in modulating the structure and intensity of GCs. Simulations were run for the same combinations of radiative forcing and instability as in Part II of this series, but with six different shear profiles ranging from 0 to 10 m s−1 km−1 within the layer encompassing the GCs.

The primary role of shear was to modulate the organization of GCs, which organized as closed convective cells in simulations with radiative forcing and no shear. In simulations with shear and radiative forcing, GCs organized in linear streets parallel to the wind. No GCs developed in the initially stable simulations with no radiative forcing. In the initially unstable and neutral simulations with no radiative forcing or shear, GCs were exceptionally weak, with no clear organization. In moderate-shear (Δuz = 2, 4 m s−1 km−1) simulations with no radiative forcing, linear organization of the weak cells was apparent, but this organization was less coherent in simulations with high shear (Δuz = 6, 8, 10 m s−1 km−1). The intensity of the updrafts was primarily related to the mode of radiative forcing but was modulated by shear. The more intense GCs in nighttime simulations were either associated with no shear (closed convective cells) or strong shear (linear streets). Updrafts within GCs under conditions with radiative forcing were typically ~1–2 m s−1 with maximum values < 4 m s−1.

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Xiaoqin Jing
,
Lulin Xue
,
Yan Yin
,
Jing Yang
,
Daniel F. Steinhoff
,
Andrew Monaghan
,
David Yates
,
Changhai Liu
,
Roy Rasmussen
,
Sourav Taraphdar
, and
Olivier Pauluis

Abstract

The regional climate of the Arabian Gulf region is modeled using a set of simulations based on the Weather Research and Forecasting (WRF) Model, including a 30-yr benchmark simulation driven by reanalysis data, and two bias-corrected Community Earth System Model (CESM)-driven (BCD) WRF simulations for retrospective and future periods that both include 10-yr convection-permitting nested simulations. The modeled precipitation is cross-validated using Tropical Rainfall Measuring Mission data, rain gauge data, and the baseline dataset from the benchmark simulation. The changes in near-surface temperature, precipitation, and ambient conditions are investigated using the BCD WRF simulations. The results show that the BCD WRF simulation well captures the precipitation distribution, the precipitation variability, and the thermodynamic properties. In a warmer climate under the RCP8.5 scenario around the year 2070, the near-surface temperature warms by ~3°C. Precipitation increases over the Arabian Gulf, and decreases over most of the continental area, particularly over the Zagros Mountains. The wet index decreases while the maximum dry spell increases in most areas of the model domain. The future changes in precipitation are determined by both the thermodynamics and dynamics. The thermodynamic impact, which is controlled by the warming and moistening, results in more precipitation over the ocean but not over the land. The dynamic impact, which is controlled by changes in the large-scale circulation, results in decrease in precipitation over mountains. The simulations presented in this study provide a unique dataset to study the regional climate in the Arabian Gulf region for both retrospective and future climates.

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Ethan D. Gutmann
,
Roy M. Rasmussen
,
Changhai Liu
,
Kyoko Ikeda
,
David J. Gochis
,
Martyn P. Clark
,
Jimy Dudhia
, and
Gregory Thompson

Abstract

Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation from a 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.

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Ethan D. Gutmann
,
Roy M. Rasmussen
,
Changhai Liu
,
Kyoko Ikeda
,
Cindy L. Bruyere
,
James M. Done
,
Luca Garrè
,
Peter Friis-Hansen
, and
Vidyunmala Veldore

Abstract

Tropical cyclones have enormous costs to society through both loss of life and damage to infrastructure. There is good reason to believe that such storms will change in the future as a result of changes in the global climate system and that such changes may have important socioeconomic implications. Here a high-resolution regional climate modeling experiment is presented using the Weather Research and Forecasting (WRF) Model to investigate possible changes in tropical cyclones. These simulations were performed for the period 2001–13 using the ERA-Interim product for the boundary conditions, thus enabling a direct comparison between modeled and observed cyclone characteristics. The WRF simulation reproduced 30 of the 32 named storms that entered the model domain during this period. The model simulates the tropical cyclone tracks, storm radii, and translation speeds well, but the maximum wind speeds simulated were less than observed and the minimum central pressures were too large. This experiment is then repeated after imposing a future climate signal by adding changes in temperature, humidity, pressure, and wind speeds derived from phase 5 of the Coupled Model Intercomparison Project (CMIP5). In the current climate, 22 tracks were well simulated with little changes in future track locations. These simulations produced tropical cyclones with faster maximum winds, slower storm translation speeds, lower central pressures, and higher precipitation rates. Importantly, while these signals were statistically significant averaged across all 22 storms studied, changes varied substantially between individual storms. This illustrates the importance of using a large ensemble of storms to understand mean changes.

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Andreas F. Prein
,
Gregory J. Holland
,
Roy M. Rasmussen
,
James Done
,
Kyoko Ikeda
,
Martyn P. Clark
, and
Changhai H. Liu

Abstract

Summer and winter daily heavy precipitation events (events above the 97.5th percentile) are analyzed in regional climate simulations with 36-, 12-, and 4-km horizontal grid spacing over the headwaters of the Colorado River. Multiscale evaluations are useful to understand differences across horizontal scales and to evaluate the effects of upscaling finescale processes to coarser-scale features associated with precipitating systems.

Only the 4-km model is able to correctly simulate precipitation totals of heavy summertime events. For winter events, results from the 4- and 12-km grid models are similar and outperform the 36-km simulation. The main advantages of the 4-km simulation are the improved spatial mesoscale patterns of heavy precipitation (below ~100 km). However, the 4-km simulation also slightly improves larger-scale patterns of heavy precipitation.

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