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L. Ruby Leung and Yun Qian

Abstract

This paper examines the sensitivity of regional climate simulations to increasing spatial resolution via nesting by means of a 20-yr simulation of the western United States at 40-km resolution and a 5-yr simulation at 13-km resolution for the Pacific Northwest and California. The regional simulation at 40-km resolution shows a lack of precipitation along coastal hills, good agreement with observations on the windward slopes of the Cascades and Sierra Nevada, but overprediction on the leeside and the basins beyond. Snowpack is grossly underpredicted throughout the western United States when compared against snowpack telemetry (snotel) observations. During winter, higher spatial resolution mainly improves the precipitation simulation in the coastal hills and basins. Along the Cascades and the Sierra Nevada range, precipitation is strongly amplified at the higher spatial resolution. Higher resolution generally improves the spatial distribution of precipitation to yield a higher spatial correlation between simulations and observations. During summer, higher resolution improves not only the spatial distribution but also the regional mean precipitation.

In the Olympic Mountains and along the Coastal Range, increased precipitation at higher resolution reflects mainly a shift from light to heavy precipitation events. In the Cascades and Sierra Nevada, increased precipitation is mainly associated with more frequent heavy precipitation at higher resolution. Changes in precipitation from 40- to 13-km resolution depend on synoptic conditions such as wind direction and moisture transport. The use of higher spatial resolution improves snowpack more than precipitation. However, results presented in this paper suggest that accuracy in the snow simulation is also limited by factors such as deficiencies in the land surface model or biases in other model variables.

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L. Ruby Leung, Yun Qian, and Xindi Bian

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The regional climate of the western United States shows clear footprints of interaction between atmospheric circulation and orography. The unique features of this diverse climate regime challenges climate modeling. This paper provides detailed analyses of observations and regional climate simulations to improve our understanding and modeling of the climate of this region. The primary data used in this study are the 1/8° gridded temperature and precipitation based on station observations and the NCEP–NCAR global reanalyses. These data were used to evaluate a 20-yr regional climate simulation performed using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (Penn State–NCAR) Mesoscale Model (MM5) driven by large-scale conditions of the NCEP–NCAR reanalyses. Regional climate features examined include seasonal mean and extreme precipitation; distribution of precipitation rates; and precipitation intensity, frequency, and seasonality. The relationships between precipitation and surface temperature are also analyzed as a means to evaluate how well regional climate simulations can be used to simulate surface hydrology, and relationships between precipitation and elevation are analyzed as diagnostics of the impacts of surface topography and spatial resolution. The latter was performed at five east–west transects that cut across various topographic features in the western United States.

These analyses suggest that the regional simulation realistically captures many regional climate features. The simulated seasonal mean and extreme precipitation are comparable to observations. The regional simulation produces precipitation over a wide range of precipitation rates comparable to observations. Obvious biases in the simulation include the oversimulation of precipitation in the basins and intermountain West during the cold season, and the undersimulation in the Southwest in the warm season. There is a tendency of reduced precipitation frequency rather than intensity in the simulation during the summer in the Northwest and Southwest, which leads to the insufficient summer mean precipitation in those areas. Because of the general warm biases in the simulation, there is also a tendency for more precipitation events to be associated with warmer temperatures, which can affect the simulation of snowpack and runoff.

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Renping Lin, Tianjun Zhou, and Yun Qian

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With the motivation to identify whether a reasonably simulated atmospheric circulation would necessarily lead to a successful reproduction of monsoon precipitation, the performances of five sets of reanalysis data [NCEP–U.S. Department of Energy (DOE) Atmospheric Model Intercomparison Project II (AMIP-II) reanalysis (NCEP-2), 40-yr ECMWF Re-Analysis (ERA-40), Japanese 25-yr Reanalysis Project (JRA-25), Interim ECMWF Re-Analysis (ERA-Interim), and Modern-Era Retrospective Analysis for Research and Applications (MERRA)] in reproducing the climatology, interannual variation, and long-term trend of global monsoon (GM) precipitation are comprehensively evaluated. To better understand the variability and long-term trend of GM precipitation, the authors also examined the major components of water budget, including evaporation, water vapor convergence, and the change in local column water vapor, based on the five reanalysis datasets. Results show that all five reanalysis datasets reasonably reproduce the climatology of GM precipitation. ERA-Interim (NCEP-2) shows the highest (lowest) skill among the five datasets. The observed GM precipitation shows an increasing tendency during 1979–2011 along with a strong interannual variability, which is reasonably reproduced by five reanalysis datasets. The observed increasing trend of GM precipitation is dominated by contributions from the Asian, North American, Southern African, and Australian monsoons. All five datasets fail in reproducing the increasing tendency of the North African monsoon precipitation. The wind convergence term in the water budget equation dominates the GM precipitation variation, indicating a consistency between the GM precipitation and the seasonal change of prevailing wind.

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L. Ruby Leung, Yun Qian, Xindi Bian, and Allen Hunt

Abstract

The hydroclimate of the western United States is influenced by strong interannual variability of atmospheric circulation, much of which is associated with the El Niño–Southern Oscillation (ENSO). Precipitation anomalies during ENSO often show opposite and spatially coherent dry and wet patterns in the Northwest and California or vice versa. The role of orography in establishing mesoscale ENSO anomalies in the western United States is examined based on observed precipitation and temperature data at 1/8° spatial resolution and a regional climate simulation at 40-km spatial resolution. Results show that during El Niño or La Niña winters, strong precipitation anomalies are found in northern California, along the southern California coast, and in the northwest mountains such as the Olympic Mountains, the Cascades, and the northern Rockies. These spatial features, which are strongly affected by topography, are surprisingly well reproduced by the regional climate simulation.

A spatial feature investigated further is the positive–negative–positive precipitation anomaly found during El Niño years in the Olympic Mountains, and on the west side and east side of the Cascades in both observations and regional simulation. Observed streamflows of river basins located in those areas are found to be consistent with the precipitation anomalies. The spatial distribution of the precipitation anomalies is investigated by relating flow direction and moisture to the orientation of mountains and orographic precipitation. On the west side of the north–south-oriented Cascade Range, the increase in atmospheric moisture is not enough to compensate for the loss of orographic precipitation associated with a change in flow direction toward the southwest during El Niño years. In California, both the increase in atmospheric moisture and shift in wind direction toward the southwest enhance precipitation along the Sierra, which is oriented northwest to southeast. The spatial signature of the interactions between large-scale circulation and topography may provide useful information for seasonal predictions or climate change detection.

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Yun Qian, Maoyi Huang, Ben Yang, and Larry K. Berg

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In this study, the authors incorporate an operational-like irrigation scheme into the Noah land surface model as part of the Weather Research and Forecasting Model (WRF). A series of simulations, with and without irrigation, is conducted over the Southern Great Plains (SGP) for an extremely dry (2006) and wet (2007) year. The results show that including irrigation reduces model bias in soil moisture and surface latent heat (LH) and sensible heat (SH) fluxes, especially during a dry year. Irrigation adds additional water to the surface, leading to changes in the planetary boundary layer. The increase in soil moisture leads to increases in the surface evapotranspiration and near-surface specific humidity but decreases in the SH and surface temperature. Those changes are local and occur during daytime. There is an irrigation-induced decrease in both the lifting condensation level (Z LCL) and mixed-layer depth. The decrease in Z LCL is larger than the decrease in mixed-layer depth, suggesting an increasing probability of shallow clouds. The simulated changes in precipitation induced by irrigation are highly variable in space, and the average precipitation over the SGP region only slightly increases. A high correlation is found among soil moisture, SH, and Z LCL. Larger values of soil moisture in the irrigated simulation due to irrigation in late spring and summer persist into the early fall, suggesting that irrigation-induced soil memory could last a few weeks to months. The results demonstrate the importance of irrigation parameterization for climate studies and improve the process-level understanding on the role of human activity in modulating land–air–cloud interactions.

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Liwei Zou, Yun Qian, Tianjun Zhou, and Ben Yang

Abstract

In this study, the authors calibrated the performance of the Regional Climate Model, version 3 (RegCM3), with the Massachusetts Institute of Technology (MIT)–Emanuel cumulus parameterization scheme over the Coordinated Regional Climate Downscaling Experiment (CORDEX) East Asia domain by tuning seven selected parameters based on the multiple very fast simulated annealing (MVFSA) approach. The seven parameters were selected based on previous studies using RegCM3 with the MIT–Emanuel convection scheme. The results show the simulated spatial pattern of rainfall, and the probability density function distribution of daily rainfall rates is significantly improved in the optimal simulation. Sensitivity analysis suggests that the parameter relative humidity criteria (RHC) has the largest effect on the model results. Followed by an increase of RHC, an increase of total rainfall is found over the northern equatorial western Pacific, mainly contributed by the increase of explicit rainfall. The increases of the convergence of low-level water vapor transport and the associated increases in cloud water favor the increase of explicit rainfall. The identified optimal parameters constrained by total rainfall have positive effects on the low-level circulation and surface air temperature. Furthermore, the optimized parameters based on the chosen extreme case are transferable to a normal case and the model’s new version with a mixed convection scheme.

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Weihong Qian, Yun Chen, Man Jiang, and Qi Hu

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Abnormally low temperature (LT) events in spring and autumn can cause severe damage to spring and autumn rice production in the mid- to lower Yangtze River valley in China. Advanced predictions of such events can help mitigate their damage. However, the current methods have limited success in describing and predicting those weather events. In this study, a new method is proposed to decompose any one of the meteorological variables into its climatic component and an anomaly, and the anomaly is used in identifying signals of the LT events. The method is used in 20 strong spring LT events and 44 autumn events during 1960–2008. The results show the advanced ability of this method to clearly describe the LT events as compared with the vague indications of such events that are produced by conventional methods currently in practice in China. In addition, the composite profile of vertical anomalies shows that a negative center of geopotential height anomalies at around 300 hPa, coexisting with a strong cold center of temperature anomalies at 850 hPa, is a signature for LT events. For the 44 autumn LT events and 20 spring LT events during 1960–2008, their early disturbances were identified up to 10.2 days and 6.9 days, respectively, before the occurrence of the LT events in the valley. This result suggests that identifying the early disturbances and extracting anomalous signals from the products of current medium-range weather forecast models may be a potential way to improve the prediction skill for LT events in the valley.

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L. Ruby Leung, Yun Qian, Jongil Han, and John O. Roads

Abstract

Estimating water budgets of river basins in the western United States is a challenge because of the effects of complex terrain and lack of comprehensive observational datasets. This study aims at comparing different estimates of cold season water budgets of the Columbia River (CRB) and Sacramento–San Joaquin River (SSJ) basins. An intercomparison was performed based on the NCEP–NCAR reanalysis I (NRA1), NCEP–Department of Energy (DOE) reanalysis II (NRA2), ECMWF reanalyses (ERA), regional climate simulations produced by the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) and NCEP Regional Spectral Model (RSM) driven by the reanalyses, and two precipitation datasets gridded at 2.5° and ⅛° for 7 yr between 1986 and 1993. The purpose of the intercomparison was to understand the effects of spatial resolution, model configuration and associated parameterizations, and large-scale conditions on basin-scale water budgets.

Overall, the regional simulations were superior to the global reanalyses in terms of the spatial distribution of mean precipitation and precipitation anomalies. However, cold season precipitation was generally amplified in the regional models. Basin mean precipitation was typically higher than observed in the regional models and less than observed in the reanalyses. The amplification was the largest in the RSM simulation driven by NRA2, which had the biggest difference between the reanalyzed and regional simulation of basin mean precipitation. ERA and the MM5 simulations driven by ERA provided the best basin mean precipitation estimates when compared to the ⅛° observational dataset.

Large differences remain in estimating the water budgets of western river basins, such as CRB and SSJ. In terms of atmospheric moisture flux, there was a 15%–20% difference between the global reanalyses. In terms of basin mean precipitation, differences among the reanalyses, regional simulations, and observations were as large as 100% of the overall mean. There were large differences in spatial distribution of precipitation between the RSM and MM5 simulations because of terrain representations and other factors. Runoff and snowpack showed the most sensitivity to model differences in spatial resolution, physics parameterizations, and model representations. Better simulations of basin mean precipitation did not necessarily imply superior simulations of runoff or snowpack.

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Larry K. Berg, Laura D. Riihimaki, Yun Qian, Huiping Yan, and Maoyi Huang

Abstract

This study utilizes six commonly used reanalysis products, including the NCEP–Department of Energy Reanalysis 2 (NCEP2), NCEP Climate Forecast System Reanalysis (CFSR), ECMWF interim reanalysis (ERA-Interim), Japanese 25-year Reanalysis Project (JRA-25), Modern-Era Retrospective Analysis for Research and Applications (MERRA), and North American Regional Reanalysis (NARR), to evaluate features of the southern Great Plains low-level jet (LLJ) above the U.S. Department of Energy’s Atmospheric Radiation Measurement Program (ARM) Climate Research Facility (ACRF) Southern Great Plains site. Two sets of radiosonde data are utilized: the six-week Midlatitude Continental Convective Clouds Experiment (MC3E) and a 10-yr period spanning 2001 through 2010. All six reanalyses are compared to MC3E data, while only the NARR, MERRA, and CFSR are compared to the 10-yr data. The reanalyses are able to represent most aspects of the composite LLJ profile, although there is a tendency for each reanalysis to overestimate the wind speed between the nose of the LLJ (at approximately 900 mb) and a pressure level of 700 mb. There are large discrepancies in the number of LLJs observed and derived from the reanalysis, particularly for strong LLJs, leading to an underestimate of the moisture transport associated with LLJs. When the 10-yr period is considered, the NARR and CFSR overestimate and MERRA underestimates the total moisture transport, but all three underestimate the transport associated with strong LLJs by factors of 1.4, 2.0, and 2.7 for CFSR, NARR, and MERRA, respectively. During MC3E there were differences in the patterns of moisture convergence and divergence, but the patterns are more consistent during the 10-yr period.

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Jie Jiang, Tianjun Zhou, Hailong Wang, Yun Qian, David Noone, and Wenmin Man

Abstract

Central Asia is a semiarid to arid region that is sensitive to hydrological changes. We use the Community Atmosphere Model, version 5 (CAM5), equipped with a water-tagging capability, to investigate the major moisture sources for climatological precipitation and its long-term trends over central Asia. Europe, the North Atlantic Ocean, and local evaporation, which explain 33.2% ± 1.5%, 23.0% ± 2.5%, and 19.4% ± 2.2% of the precipitation, respectively, are identified as the most dominant moisture sources for northern central Asia (NCA). For precipitation over southern central Asia (SCA), Europe, the North Atlantic, and local evaporation contribute 25.4% ± 2.7%, 18.0% ± 1.7%, and 14.7% ± 1.9%, respectively. In addition, the contributions of South Asia (8.6% ± 1.7%) and the Indian Ocean (9.5% ± 2.0%) are also substantial for SCA. Modulated by the seasonal meridional shift in the subtropical westerly jet, moisture originating from the low and midlatitudes is important in winter, spring, and autumn, whereas northern Europe contributes more to summer precipitation. We also explain the observed drying trends over southeastern central Asia in spring and over NCA in summer during 1956–2005. The drying trend over southeastern central Asia in spring is mainly due to the decrease in local evaporation and weakened moisture fluxes from the Arabian Peninsula and Arabian Sea associated with the warming of the western Pacific Ocean. The drying trend over NCA in summer can be attributed to a decrease in local evaporation and reduced moisture from northern Europe that is due to the southward shift of the subtropical westerly jet.

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