Jinhong Zhu

The fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)-based regional climate model (CMM5) capability in simulating the interannual variations of U.S. precipitation and surface air temperature during 1982–2002 is evaluated with a continuous baseline integration driven by the NCEP–Department of Energy (DOE) Second Atmospheric Model Intercomparison Project Reanalysis (R-2). It is demonstrated that the CMM5 has a pronounced downscaling skill for precipitation and temperature interannual variations. The EOF and correlation analyses illustrate that, for both quantities, the CMM5 captures the spatial pattern, temporal evolution, and circulation teleconnections much better than the R-2. In particular, the CMM5 more realistically simulates the precipitation pattern centered in the Northwest, where the representation of the orographic enhancement by the forced uplifting during winter (rainy season) is greatly improved over the R-2. The downscaling skill, however, is sensitive to the cumulus parameterization. This sensitivity is studied by comparing the baseline with a branch summer integration replacing the Grell with the Kain–Fritsch cumulus scheme in the CMM5. The dominant EOF mode of the U.S. summer precipitation interannual variation, identified with the out-of-phase relationship between the Midwest and Southeast in observations, is reproduced more accurately by the Grell than the Kain–Fritsch scheme, which largely underestimates the variation in the Midwest. This pattern is associated with east–west movement of the Great Plains low-level jet (LLJ): a more western position corresponds to a stronger southerly flow bringing more moisture and heavier rainfall in the Midwest and less in the Southeast. The second EOF pattern, which describes the consistent variation over the southern part of the Midwest and the South in observations, is captured better by the Kain–Fritsch scheme than the Grell, whose pattern systematically shifts southward.

The fifth‐generation PSU‐NCAR Mesoscale Model (MM5)‐based regional climate model (CMM5) capability in simulating the U.S. soil temperature and soil moisture annual cycle and interannual variability is evaluated by comparing the 1982–2002 continuous integration driven by the NCEP‐DOE AMIP II reanalysis (R‐2) with observations, the R‐2 derivatives and North American Land Data Assimilation System (NLDAS) products. For the annual cycle, the CMM5 produces more realistic regional details and overall smaller biases than the driving R‐2 and NLDAS outputs. The CMM5 also faithfully simulates interannual variations of soil temperature over the central United States and soil moisture in Illinois and Iowa, where observational data are available. The existing CMM5 differences from observations in soil temperature (moisture) cannot be fully explained by model biases in surface air temperature (precipitation). Inconsistencies between measurements taken under short grass versus model representations beneath other land cover types may play an important role. In particular, such measurements overestimate soil temperature in summer and fall while generating a 1‐month phase lead in the soil moisture annual cycle with respect to croplands in the model. The result emphasizes the need for more comprehensive study on model evaluation and bias understanding of soil temperature and soil moisture.