Julian X. L. Wang

Regional Climate Model Simulation of U.S. Precipitation during 1982-2002. Part I: Annual Cycle

The fifth-generation PSU‐NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) capability in simulating the U.S. precipitation annual cycle is evaluated with a 1982‐2002 continuous baseline integration driven by the NCEP‐DOE second Atmospheric Model Intercomparison Project (AMIP II) reanalysis. The causes for major model biases (differences from observations) are studied through supplementary seasonal sensitivity experiments with various driving lateral boundary conditions (LBCs) and physics representations. It is demonstrated that the CMM5 has a pronounced rainfall downscaling skill, producing more realistic regional details and overall smaller biases than the driving global reanalysis. The precipitation simulation is most skillful in the Northwest, where orographic forcing dominates throughout the year; in the Midwest, where mesoscale convective complexes prevail in summer; and in the central Great Plains, where nocturnal low-level jet and rainfall peaks occur in summer. The actual model skill, however, is masked by existing large LBC uncertainties over datapoor areas, especially over oceans. For example, winter dry biases in the Gulf States likely result from LBC errors in the south and east buffer zones. On the other hand, several important regional biases are identified with model physics deficiencies. In particular, summer dry biases in the North American monsoon region and along the east coast of the United States can be largely rectified by replacing the Grell with the Kain‐Fritsch cumulus scheme. The latter scheme, however, yields excessive rainfall in the Atlantic Ocean but large deficits over the Midwest. The fall dry biases over the lower Mississippi River basin, common to all existing global and regional models, remain unexplained and the search for their responsible physical mechanisms will be challenging. In addition, the representation of cloud‐radiation interaction is essential in determining the precipitation distribution and regional water recycling, for which the new scheme implemented in the CMM5 yields significant improvement.

Regional Climate–Weather Research and Forecasting Model

The CWRF is developed as a climate extension of the Weather Research and Forecasting model (WRF) by incorporating numerous improvements in the representation of physical processes and integration of external (top, surface, lateral) forcings that are crucial to climate scales, including interactions between land, atmosphere, and ocean; convection and microphysics; and cloud, aerosol, and radiation; and system consistency throughout all process modules. This extension inherits all WRF functionalities for numerical weather prediction while enhancing the capability for climate modeling. As such, CWRF can be applied seamlessly to weather forecast and climate prediction. The CWRF is built with a comprehensive ensemble of alternative parameterization schemes for each of the key physical processes, including surface (land, ocean), planetary boundary layer, cumulus (deep, shallow), microphysics, cloud, aerosol, and radiation, and their interactions. This facilitates the use of an optimized physics ensemble approac...

Surface Boundary Conditions for Mesoscale Regional Climate Models

Abstract This paper utilizes the best available quality data from multiple sources to develop consistent surface boundary conditions (SBCs) for mesoscale regional climate model (RCM) applications. The primary SBCs include 1) fields of soil characteristic (bedrock depth, and sand and clay fraction profiles), which for the first time have been consistently introduced to define 3D soil properties; 2) fields of vegetation characteristic fields (land-cover category, and static fractional vegetation cover and varying leaf-plus-stem-area indices) to represent spatial and temporal variations of vegetation with improved data coherence and physical realism; and 3) daily sea surface temperature variations based on the most appropriate data currently available or other value-added alternatives. For each field, multiple data sources are compared to quantify uncertainties for selecting the best one or merged to create a consistent and complete spatial and temporal coverage. The SBCs so developed can be readily incorpor...

Do CGCMs Simulate the North American Monsoon Precipitation Seasonal–Interannual Variability?

Abstract This study uses the most recent simulations from all available fully coupled atmosphere–ocean general circulation models (CGCMs) to investigate whether the North American monsoon (NAM) precipitation seasonal–interannual variations are simulated and, if so, whether the key underlying physical mechanisms are correctly represented. This is facilitated by first identifying key centers where observed large-scale circulation fields and sea surface temperatures (SSTs) are significantly correlated with the NAM precipitation averages over the core region (central–northwest Mexico) and then examining if the modeled and observed patterns agree. Two new findings result from the analysis of observed NAM interannual variations. First, precipitation exhibits significantly high positive (negative) correlations with 200-hPa meridional wind centered to the northwest (southeast) of the core region in June and September (July and August). As such, wet conditions are associated with strong anomalous southerly upper-l...

Impacts of extreme 2013–2014 winter conditions on Lake Michigan's fall heat content, surface temperature, and evaporation

Since the late 1990s, the Laurentian Great Lakes have experienced persistent low water levels and above average over-lake evaporation rates. During the winter of 2013–2014, the lakes endured the most persistent, lowest temperatures and highest ice cover in recent history, fostering speculation that over-lake evaporation rates might decrease and that water levels might rise. To address this speculation, we examined interseasonal relationships in Lake Michigans thermal regime. We find pronounced relationships between winter conditions and subsequent fall heat content, modest relationships with fall surface temperature, but essentially no correlation with fall evaporation rates. Our findings suggest that the extreme winter conditions of 2013–2014 may have induced a shift in Lake Michigans thermal regime and that this shift coincides with a recent (and ongoing) rise in Great Lakes water levels. If the shift persists, it could (assuming precipitation rates remain relatively constant) represent a return to thermal and hydrologic conditions not observed on Lake Michigan in over 15 years.

A multilevel ocean mixed layer model resolving the diurnal cycle: Development and validation

The representation of transient air-sea interactions is critical to the prediction of the sea surface temperature diurnal cycle and daily variability. This study develops a multi-level upper ocean model to more realistically resolve these interactions. The model is based on the one-dimensional turbulence kinetic energy closure developed by Noh et al. [2011], and incorporates new numerical techniques and improved schemes for model physics. The primary improvements include: (1) a surface momentum flux penetration scheme to better depict velocity shear in the diurnal mixed layer; (2) a solar penetration scheme to improve the penetration of visible and near-infrared bands of solar radiation into the mixed layer ocean; (3) a scheme to resolve the cool-skin and warm-layer effects on sea skin temperature; (4) a vertical grid stretch scheme to achieve higher near-surface resolution with fewer vertical levels; (5) a trapezoidal time integration scheme for flexible time steps; (6) a relaxation term of the previous daily mean difference between observed and modeled the sea surface temperature. According to the numerical experiments based on the TOGA-COARE IMET mooring buoy data and the validation by observations from the National Data Buoy Center, NOAA, the results indicate that the new upper ocean mixed layer model improves the simulation of the diurnal cycle of SST and sea skin temperature, especially in amplitude. This article is protected by copyright. All rights reserved.

Improvement of Cloud Radiative Forcing and Its Impact on Weather Forecasts

The global numerical weather prediction model GRAPES at the National Meteorological Center of the China Meteorological Administration is subject to substantial systematic discrepancies from satellite-retrieved cloud cover, cloud water contents, and radiative fluxes. In particular, GRAPES produces insufficient total cloud cover and liquid water amounts and, consequently, greatly underestimates cloud radiative forcings and causes substantial radiation budget errors. Along with updates of several physics components, new parameterization schemes are incorporated in this study to more realistically represent cloud-radiation interactions. These schemes include predictions for cloud cover, liquid water, and effective radius as well as radiative effects of partial clouds and in-cloud inhomogeneity. As a result, radiation fluxes and cloud radiative forcings at both the surface and top of the atmosphere agree much better with the best available satellite data. The global mean model biases in most radiation fluxes using the new physics are approximately three times smaller than using the original physics. These improvements enhance the model weather forecast skills for key surface variables, including precipitation and 2 m temperature, and for height and temperature in the lower troposphere. Although non- trivial biases still exist, this study nonetheless represents the first essential step toward correcting the radiation imbalance before tackling other formulation deficiencies so that significantly enhanced GRAPES weather forecast skills can eventually be achieved.

Intensified dust storm activity and Valley fever infection in the southwestern United States: Dust and Valley Fever Intensification

Abstract Climate models have consistently projected a drying trend in the southwestern United States, aiding speculation of increasing dust storms in this region. Long‐term climatology is essential to documenting the dust trend and its response to climate variability. We have reconstructed long‐term dust climatology in the western United States, based on a comprehensive dust identification method and continuous aerosol observations from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network. We report here direct evidence of rapid intensification of dust storm activity over American deserts in the past decades (1988–2011), in contrast to reported decreasing trends in Asia and Africa. The frequency of windblown dust storms has increased 240% from 1990s to 2000s. This dust trend is associated with large‐scale variations of sea surface temperature in the Pacific Ocean, with the strongest correlation with the Pacific Decadal Oscillation. We further investigate the relationship between dust and Valley fever, a fast‐rising infectious disease caused by inhaling soil‐dwelling fungus (Coccidioides immitis and C. posadasii) in the southwestern United States. The frequency of dust storms is found to be correlated with Valley fever incidences, with a coefficient (r) comparable to or stronger than that with other factors believed to control the disease in two endemic centers (Maricopa and Pima County, Arizona).