Zaitao Pan

Studies have analyzed U.S. tornado variability and correlated F1–F5 tornado occurrence with various natural climate oscillations and anthropogenic factors. Using a relatively new empirical mode decommission (EMD) method that extracts time-frequency modes adaptively without priori assumptions like traditional time-series analysis methods, this study decomposes U.S. tornado variability during 1954–2022 into intrinsic modes on specific temporal scales. Correlating the intrinsic mode functions (IMFs) of EMD with climate indices found that 1. the U.S. overall tornado count is negatively (positively) correlated with the Atlantic Multidecadal Oscillation (AMO) index (the Southern Oscillation Index (SOI)); 2. the negative (positive) correlation tends to be more prevalent in the western (eastern) U.S.; 3. the increase in weak (F1–F2) and decrease in strong (F3–F5) tornadoes after around 2000, when both the AMO and the Pacific Decadal Oscillation (PDO) shifted phases, are likely related to their secular trends and low-frequency IMFs; and 4. the emerging Dixie Tornado Alley coincides with an amplifying intrinsic mode of the SOI that correlates positively with the eastern U.S. and Dixie Alley tornadoes. The long-term persistence of these climate indices can offer potential guidance for future planning for tornado hazards.

Historical changes and possible future projections of temperature extremes in China, in terms of return values of annual extreme temperatures, are examined based on daily maximum and minimum temperatures from station observations and multiple models of the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP). The observations suggest that increases in temperature extremes are largely attributable to the changing mean climate, while the varying natural variability also has an important impact, which depends on the index of the variability. The models simulate warm extremes reasonably well but underestimate the spatial heterogeneity and temporal trend of cold extremes in China. In comparison, Coupled Model Intercomparison Project Phase 6 (CMIP6) models have higher skill in simulating temperature extremes in China, showing smaller biases and intermodel variability. MRI‐ESM2‐0 and NorESM2‐LM from CMIP6 and GFDL‐ESM2M and NorESM1‐M from CMIP5 are selected as reference models based on the better performance in reproducing observed temperature extremes in China. In the future, projections from CMIP6 multimodel ensemble (MME, represented as the multimodel median) and reference models all show a continued uptick in temperature extremes, with statistically significant increases in warm extremes mainly in the north and increases in cold extremes prominent in most parts of China. Different individual models, which have similar historical simulations, yield divergent future trends of temperature extremes, which may be associated with different climate sensitivities of models. In addition, MME usage should be treated with caution since its smoothing on spatial heterogeneity and possible information from poor models.

Despite the accelerated global warming, the central‐eastern U.S. experienced summer daytime cooling during the 20^th century. A number of studies have attributed various factors responsible for this abnormal regional cooling, termed the “warming hole (WH)”. This study demonstrates that another WH also existed in the south‐central China that shares similar temporal trend and geographical location to the U.S. WH, including the cooling reversal around 1995. Generalizing the two WHs unravels that they both occurred (a) to the east of major mountain ranges, (b) in heavily cultivated agricultural regions, and (c) near the terminus of the low‐level jets. This study further finds that the WH patterns are the leading mode of surface maximum temperature variability in summer of their respective regions. The WH cooling was consistent with reduction in cloud‐forced solar radiation supported by moisture convergence through weakening of low‐level southerly flow downstream of China WH and strengthening of the southerly flow upstream of U.S. WH. The long‐term trends of WH temperature are highly correlated to the Atlantic Multidecadal Oscillation (AMO) and WH cooling turned into warming around 1995 when the AMO shifted to the positive phase, which combined with overall regional warming, may explain why the summer WH phenomenon has been in diminution for past two decades or so. Given the AMO's association with global climate trend, the WH phenomenon could also be viewed as a local thermal response to the global warming via AMO modulation of regional dynamic and moisture circulation.

The diurnal variation of the planetary boundary layer height (PBLH) over the contiguous United States (CONUS) is examined using Aircraft Meteorological Data Reports (AMDAR) at 54 major airports and the ERA5 reanalysis product. The bulk Richardson number method is used to derive the PBLH at hourly scales from the AMDAR profiles. It is found that the estimated PBLH is sensitive to the value of the critical bulk Richardson number and to the consideration of the surface friction effect, especially under stable and near‐neutral conditions. Parameters constrained by field campaign observations are finally selected to estimate the PBLH. The results reveal that the diurnal climatology of PBLH exhibits seasonal and spatial variations, with greater diurnal variations in spring and summer and in the western regions. It is demonstrated that the traditional twice‐daily radiosonde data may roughly capture the diurnal amplitude of PBLH in the western but not the eastern CONUS. Compared to the AMDAR data, the ERA5 reanalysis product overestimates the daytime PBLH throughout the CONUS by 18–41% and shows an earlier peak time by 1–2 hr in the western regions and a later peak time by 1 hr in the eastern regions.

Regional climate simulations driven by three sets of initial and lateral boundary conditions—analyzed observations, GCM control climate, and GCM enhanced greenhouse‐gas scenario climate—are used to assess model accuracy in predicting soil moisture and to examine changes in soil moisture in the scenario climate. Simulated soil moisture does not show noticeable drift during the 10‐year simulations. Observed and simulated soil moisture for Illinois and Iowa correspond reasonably well for the top 10 cm soil layer but a consistent low bias is present in the top 1 m. Growing season depletion of soil water is simulated well but recharge after growing season is slower than observed, at least in part due to underprediction of precipitation in autumn. This suggests that improvements in simulating soil moisture depend greatly on improvements in simulating precipitation. The climate change scenario produces drier soil in the top 10 cm during winter but wetter top soil in warm seasons because of greater precipitation, while top l m soil is wetter in all seasons.

Under strong warm advection, sensible and latent heat fluxes may provide larger energy for surface snowmelt than does net radiation flux. With these thermally stable conditions, the height of the first model level may be well above the surface-layer depth and thus outside the range of applicability of the surface-layer similarity theory on which the models' surface thermal flux computation is based. This situation can strongly affect the magnitude of simulated surface thermal fluxes and snowmelt. To explore this issue, the impact of selected heights of the first model level on the simulated surface fluxes and snowmelt under stable surface stratification conditions was investigated. Simulations using a mesoscale atmospheric model considering two extreme contrasts in surface roughness were performed. Setting the first model level to 3 or 10 m, which typically was within the stable surface layer, yielded nearly the same contribution of simulated surface turbulent thermal fluxes to snowmelt. When the... Abstract Under strong warm advection, sensible and latent heat fluxes may provide larger energy for surface snowmelt than does net radiation flux. With these thermally stable conditions, the height of the first model level may be well above the surface-layer depth and thus outside the range of applicability of the surface-layer similarity theory on which the models' surface thermal flux computation is based. This situation can strongly affect the magnitude of simulated surface thermal fluxes and snowmelt. To explore this issue, the impact of selected heights of the first model level on the simulated surface fluxes and snowmelt under stable surface stratification conditions was investigated. Simulations using a mesoscale atmospheric model considering two extreme contrasts in surface roughness were performed. Setting the first model level to 3 or 10 m, which typically was within the stable surface layer, yielded nearly the same contribution of simulated surface turbulent thermal fluxes to snowmelt. When the...