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Rongqian Yang
,
Kenneth Mitchell
,
Jesse Meng
, and
Michael Ek

Abstract

To examine the impact from land model upgrades and different land initializations on the National Centers for Environmental Prediction (NCEP)’s Climate Forecast System (CFS), extensive T126 CFS experiments are carried out for 25 summers with 10 ensemble members using the old Oregon State University (OSU) land surface model (LSM) and the new Noah LSM. The CFS using the Noah LSM, initialized in turn with land states from the NCEP–Department of Energy Global Reanalysis 2 (GR-2), Global Land Data System (GLDAS), and GLDAS climatology, is compared to the CFS control run using the OSU LSM initialized with the GR-2 land states. Using anomaly correlation as a primary measure, the summer-season prediction skill of the CFS using different land models and different initial land states is assessed for SST, precipitation, and 2-m air temperature over the contiguous United States (CONUS) on an ensemble basis.

Results from these CFS experiments indicate that upgrading from the OSU LSM to the Noah LSM improves the overall CONUS June–August (JJA) precipitation prediction, especially during ENSO neutral years. Such an enhancement in CFS performance requires the execution of a GLDAS with the very same Noah LSM as utilized in the land component of the CFS, while improper initializations of the Noah LSM using the GR-2 land states lead to degraded CFS performance. In comparison with precipitation, the land upgrades have a relatively small impact on both of the SST and 2-m air temperature predictions.

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Yongkang Xue
,
Ratko Vasic
,
Zavisa Janjic
,
Fedor Mesinger
, and
Kenneth E. Mitchell

Abstract

This study investigates the capability of the dynamic downscaling method (DDM) in a North American regional climate study using the Eta/Simplified Simple Biosphere (SSiB) Regional Climate Model (RCM). The main objective is to understand whether the Eta/SSiB RCM is capable of simulating North American regional climate features, mainly precipitation, at different scales under imposed boundary conditions. The summer of 1998 was selected for this study and the summers of 1993 and 1995 were used to confirm the 1998 results. The observed precipitation, NCEP–NCAR Global Reanalysis (NNGR), and North American Regional Reanalysis (NARR) were used for evaluation of the model’s simulations and/or as lateral boundary conditions (LBCs). A spectral analysis was applied to quantitatively examine the RCM’s downscaling ability at different scales.

The simulations indicated that choice of domain size, LBCs, and grid spacing were crucial for the DDM. Several tests with different domain sizes indicated that the model in the North American climate simulation was particularly sensitive to its southern boundary position because of the importance of moisture transport by the southerly low-level jet (LLJ) in summer precipitation. Among these tests, only the RCM with 32-km resolution and NNGR LBC or with 80-km resolution and NARR LBC, in conjunction with appropriate domain sizes, was able to properly simulate precipitation and other atmospheric variables—especially humidity over the southeastern United States—during all three summer months—and produce a better spectral power distribution than that associated with the imposed LBC (for the 32-km case) and retain spectral power for large wavelengths (for the 80-km case). The analysis suggests that there might be strong atmospheric components of high-frequency variability over the Gulf of Mexico and the southeastern United States.

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Randal D. Koster
,
Zhichang Guo
,
Rongqian Yang
,
Paul A. Dirmeyer
,
Kenneth Mitchell
, and
Michael J. Puma

Abstract

The soil moisture state simulated by a land surface model is a highly model-dependent quantity, meaning that the direct transfer of one model’s soil moisture into another can lead to a fundamental, and potentially detrimental, inconsistency. This is first illustrated with two recent examples, one from the National Centers for Environmental Prediction (NCEP) involving seasonal precipitation forecasting and another from the realm of ecological modeling. The issue is then further addressed through a quantitative analysis of soil moisture contents produced as part of a global offline simulation experiment in which a number of land surface models were driven with the same atmospheric forcing fields. These latter comparisons clearly demonstrate, on a global scale, the degree to which model-simulated soil moisture variables differ from each other and that these differences extend beyond those associated with model-specific layer thicknesses or soil texture. The offline comparisons also show, however, that once the climatological statistics of each model’s soil moisture variable are accounted for (here, through a simple scaling using the first two moments), the different land models tend to produce very similar information on temporal soil moisture variability in most parts of the world. This common information can perhaps be used as the basis for successful mappings between the soil moisture variables in different land models.

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