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Xinyuan Feng
,
Changhai Liu
,
Guangzhou Fan
,
Xiaodong Liu
, and
Caiyun Feng

Abstract

The southwest vortex (SWV) is a meso-α-scale cyclonic low pressure system originating in southwest China. It is a high-impact precipitation-generating weather system in this region and a large downstream area. A climatology of the SWV over 1979–2010 is presented here. Results indicate that the SWV is a common regional weather system with about 73 annual occurrences. Two primary genesis regions are identified, one in the Sichuan basin and another one in the southeast flank of the Tibetan Plateau. SWV genesis displays seasonality with a spring–summer preference and diurnal variations. The average life cycle, horizontal dimension, and translation speed are 15.1 h, 435 km, and 8.6 m s−1, respectively. SWVs are classified into four types that show regional and seasonal contrasts in structure. In type I, the winter–spring elevated dry vortex in the basin is vertically confined to a shallow layer between 850 and 600 hPa and tilts northeastward. The low-level vortex has a cold center, and the middle to upper levels feature baroclinicity. In type II, the nighttime warm-season precipitating vortex system in the basin has a deep structure with the cyclonic vorticity extending from the surface into the upper troposphere. The nonsevere precipitating vortex (type IIa) is weakly baroclinic and tilts northward with height, whereas the severe precipitating vortex (type IIb) is vertically aligned. For type III, in the southern mountains, the shallow surface-based vortex develops in a well-mixed boundary layer and vertically tilts in the upslope direction and has a warm and low-humidity core. For type IV, the heavy precipitating vortex in the mountainous region is large, deep, and nearly upright with a fairly barotropic environment.

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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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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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Roy Rasmussen
,
Changhai Liu
,
Kyoko Ikeda
,
David Gochis
,
David Yates
,
Fei Chen
,
Mukul Tewari
,
Michael Barlage
,
Jimy Dudhia
,
Wei Yu
,
Kathleen Miller
,
Kristi Arsenault
,
Vanda Grubišić
,
Greg Thompson
, and
Ethan Gutmann

Abstract

Climate change is expected to accelerate the hydrologic cycle, increase the fraction of precipitation that is rain, and enhance snowpack melting. The enhanced hydrological cycle is also expected to increase snowfall amounts due to increased moisture availability. These processes are examined in this paper in the Colorado Headwaters region through the use of a coupled high-resolution climate–runoff model. Four high-resolution simulations of annual snowfall over Colorado are conducted. The simulations are verified using Snowpack Telemetry (SNOTEL) data. Results are then presented regarding the grid spacing needed for appropriate simulation of snowfall. Finally, climate sensitivity is explored using a pseudo–global warming approach. The results show that the proper spatial and temporal depiction of snowfall adequate for water resource and climate change purposes can be achieved with the appropriate choice of model grid spacing and parameterizations. The pseudo–global warming simulations indicate enhanced snowfall on the order of 10%–25% over the Colorado Headwaters region, with the enhancement being less in the core headwaters region due to the topographic reduction of precipitation upstream of the region (rain-shadow effect). The main climate change impacts are in the enhanced melting at the lower-elevation bound of the snowpack and the increased snowfall at higher elevations. The changes in peak snow mass are generally near zero due to these two compensating effects, and simulated wintertime total runoff is above current levels. The 1 April snow water equivalent (SWE) is reduced by 25% in the warmer climate, and the date of maximum SWE occurs 2–17 days prior to current climate results, consistent with previous studies.

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