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A consistent set of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation parameters, including leaf and stem area index (LAI and SAI, respectively), land-cover category (LCC), fractional vegetation cover (FVC), and albedo parameterization are developed, and their impacts on North American regional climate are evaluated based on 10-yr Climate-Weather and Research Forecasting Model (CWRF) simulations. As compared with the previous Advanced Very High Resolution Radiometer (AVHRR) set, MODIS LCC increases grassland and cropland fractions in the central Great Plains and Midwest, respectively. Evergreen needleleaf forest converts to mixed forest in the Southeast, and mixed forest converts to evergreen needleleaf in Canada. FVC decreases by 0.05-0.3 over the central Great Plains but increases by 0.1-0.35 over the northern Rocky Mountains, Canada, and the U.S. Southeast. MODIS LAI is less than AVHRR by 2-6, except in the central Great Plains, eastern Rocky Mountains, and central Mexico. LCC and FVC changes over the central Great Plains reduce CWRF warm biases by 0.71°C and wet biases by 0.36 mm day−1. Large LAI reductions cause latent and sensible heat fluxes to decrease by 0.78-5.81 and 0.91-6.54 W m−2, respectively. They also lessen cold biases over the Gulf States and Southeast and wet biases over the North American monsoon region and Canada during summer. In densely vegetated regions including eastern Canada, the Ohio Valley, and the mid-Atlantic region, spring and summer precipitation decreases and temperature increases result from LAI reductions that cause positive evapotranspiration-precipitation-soil moisture feedbacks. Conversely, precipitation and temperature decreases in sparely vegetated regions, such as the Great Plains, result from FVC reductions that cause negative albedo-evapotranspiration-precipitation-soil moisture feedbacks. [ABSTRACT FROM AUTHOR]

Observations reveal that, in summer, westward extension of the Bermuda high enhances the Great Plains low-level jet (LLJ) that transports more moisture northward, causing precipitation increases in the Midwest and decreases in the Gulf States. Meanwhile, more warm air advection from the Gulf of Mexico to the southern Great Plains and stronger clear-sky radiative heating under high pressures over the Southeast result in warmer surface temperatures across the Gulf states. The enhanced LLJ transport of cleaner marine air from the Gulf reduces surface ozone across the southern Great Plains-Midwest. In contrast, larger transport of more polluted air from the Midwest to New England and more frequent air stagnation under high pressures in the Southeast increase ozone over most of the eastern coastal states. This Bermuda high-induced ozone change reversal between the southern Great Plains-Midwest and eastern coastal states, with a magnitude of 6 and 13.5 ppb, respectively, in summer-mean maximum daily 8-h average, exhibits strong decadal variations that should be considered in the U.S. air quality dynamic management. The observed Bermuda high signatures over the Gulf states can be well captured by regional climate and air quality models. Notable model deficiencies exist over the northern Great Plains-Midwest that are more remote to the Bermuda high and LLJ control. The regional models largely reduce these deficiencies from general circulation models (GCMs). Only 7 out of 51 GCMs can represent all key regional signatures of the Bermuda high, while none can simulate its strong association with planetary sea surface temperature anomalies. The result indicates a great challenge for GCMs to predict Bermuda high variability and change. [ABSTRACT FROM AUTHOR]

Regional climate model (RCM) simulations, driven by low and high climate-sensitivity coupled general circulation models (CGCMs) under various future emissions scenarios, were compared to projected changes in heat wave characteristics. The RCM downscaling reduces the CGCM biases in heat wave threshold temperature by a factor of 2, suggesting a higher credibility in the future projections. All of the RCM simulations suggest that there is a high probability of heat waves of unprecedented severity by the end of the twenty-first century if a high emissions path is followed. In particular, the annual 3-day heat wave temperature increases generally by 3°–8°C; the number of heat wave days increases by 30–60 day yr−1 over much of the western and southern United States with slightly smaller increases elsewhere; the variance spectra for intermediate, 3–7 days (prolonged, 7–14 days), temperature extremes increase (decrease) in the central (western) United States. If a lower emissions path is followed, then the outcomes range from quite small changes to substantial increases. In all cases, the mean temperature climatological shift is the dominant change in heat wave characteristics, suggesting that adaptation and acclimatization could reduce effects. [ABSTRACT FROM AUTHOR]

The observed lack of twentieth-century warming in the central United States (CUS), denoted here as the “warming hole,” was examined in 55 simulations driven by external historical forcings and in 19 preindustrial control (unforced) simulations from 18 coupled general circulation models (CGCMs). Twentieth-century CUS trends were positive for the great majority of simulations, but were negative, as observed, for seven simulations. Only a few simulations exhibited the observed rapid rate of warming (cooling) during 1901–40 (1940–79). Those models with multiple runs (identical forcing but different initial conditions) showed considerable intramodel variability with trends varying by up to 1.8°C century-1, suggesting that internal dynamic variability played a major role at the regional scale. The wide range of trend outcomes, particularly for those models with multiple runs, and the small number of simulations similar to observations in both the forced and unforced experiments suggest that the warming hole is not a robust response of contemporary CGCMs to the estimated external forcings. A more likely explanation based on these models is that the observed warming hole involves external forcings combined with internal dynamic variability that is much larger than typically simulated. The observed CUS temperature variations are positively correlated with North Atlantic (NA) sea surface temperatures (SSTs), and both NA SSTs and CUS temperature are negatively correlated with central equatorial Pacific (CEP) SSTs. Most models simulate rather well the connection between CUS temperature and NA SSTs. However, the teleconnections between NA and CEP SSTS and between CEP SSTs and CUS temperature are poorly simulated and the models produce substantially less NA SST variability than observed, perhaps hampering their ability to reproduce the warming hole. [ABSTRACT FROM AUTHOR]

China's rainfall interannual predictability is generally believed to depend upon the accurate representation of its annual cycle as well as teleconnections with planetary surface anomalies, including tropical east Pacific sea surface temperature and Eurasian snow and soil moisture. A suite of general circulation model (GCM) simulations is used to ascertain the existence of these relationships. First, a comparison of thirty 1980-88 Atmospheric Model Intercomparison Project (AMIP) GCM simulations shows no clear correspondence between model skill to reproduce observed rainfall annual cycle and interannual variability. Thus, accurate representation of either component does not ensure the realistic simulation of the other. Second, diagnosis of the 1903-94 and 195097 National Center for Atmospheric Research (NCAR) Community Climate Model, version 3 (CCM3), ensemble integrations indicates the existence of teleconnections in which spring planetary surface anomalies lead China's summer rainfall variations. These teleconnections, however, are sensitive to initial conditions, which define distinct dynamic regimes during the integration period. In addition, analysis of the NCAR Climate System Model (CSM) 300-yr equilibrium simulation reveals that the teleconnections display decadal variations. These results cast doubt on the traditional physical mechanisms that explain China's rainfall teleconnections and, hence, emphasize the need to incorporate interactions between planetary surface anomalies and specific dynamic regimes. [ABSTRACT FROM AUTHOR]

A regional climate model (RCM) is being developed for U.S. Midwest applications on the basis of the newly released Pennsylvania State University–NCAR Fifth-Generation Mesoscale Model (MM5), version 3.3. This study determines the optimal RCM domain and effective data assimilation technique to accurately integrate lateral boundary conditions (LBCs) across the buffer zones. The LBCs are constructed from both the NCEP–NCAR and ECMWF reanalyses to depict forcing uncertainties. The RCM domain was chosen to correctly represent the governing physical processes while minimizing LBC errors. Sensitivity experiments are conducted for the Midwest 1993 summer flood to investigate buffer zone treatment impacts on RCM performance. The results demonstrate the superiority of the buffer zone treatment that consists of the physically based domain choice and revised assimilation technique. Given this treatment, the RCM realistically simulates both temporal variations and spatial distributions in the major flood area (MFA). This success is identified with the accurate representation of both the midlatitude upper-level jet stream and Great Plains low-level jet (LLJ). The RCM reproduces different climate regimes, where observed rainfall was identified with the periodic (5 day) passage of midlatitude cyclones in June and persistent synoptic circulations in July. The model also correctly simulates the MFA rainfall diurnal cycle (with the peak amount at 0900 UTC), which follows the LLJ cycle by approximately 3 h. On the other hand, RCM performance is substantially reduced when the southern buffer zone extends to the Tropics, where large forcing errors exist. In particular, the RCM generates a weaker LLJ and, as a consequence, a decreased amount and delayed diurnal cycle of the MFA rainfall. In addition, the MM5 default LBC data assimilation technique produces considerable model biases, whereas the revised technique improves overall RCM performance and reduces sensitivi... [ABSTRACT FROM AUTHOR]