Impact of Land Use Changes on Surface Warming in China

Land use changes such as urbanization, agriculture, pasturing, deforestation, desertification and irrigation can change the land surface heat flux directly, and also change the atmospheric circulation indirectly, and therefore affect the local temperature. But it is difficult to separate their effects from climate trends such as greenhouse-gas effects. Comparing the decadal trends of the observation station data with those of the NCEP/NCAR Reanalysis (NNR) data provides a good method to separate the effects because the NNR is insensitive to land surface changes. The effects of urbanization and other land use changes over China are estimated by using the difference between the station and the NNR surface temperature trends. Our results show that urbanization and other land use changes may contribute to the observed 0.12℃ (10yr)-1 increase for daily mean surface temperature, and the0.20℃ (10yr)-1 and 0.03℃ (10 yr)-1 increases for the daily minimum and maximum surface temperatures, respectively. The urban heat island effect and the effects of other land-use changes may also play an important role in the diurnal temperature range change. The spatial pattern of the differences in trends shows a marked heterogeneity. The land surface degradation such as deforestation and desertification due to human activities over northern China, and rapidly-developed urbanization over southern China, may have mostly contributed to the increases at stations north of about 38°N and in Southeast China, respectively. Furthermore, the vegetation cover increase due to irrigation and fertilization may have contributed to the decreasing trend of surface temperature over the lower Yellow River Basin. The study illustrates the possible impacts of land use changes on surface temperature over China.

Impact of Spectral Nudging on the Downscaling of Tropical Cyclones in Regional Climate Simulations

This study investigated the simulations of three months of seasonal tropical cyclone （TC） activity over the western North Pacific using the Advanced Research WRF Model. In the control experiment （CTL）, the TC frequency was considerably overestimated. Additionally, the tracks of some TCs tended to have larger radii of curvature and were shifted eastward. The large-scale environments of westerly monsoon flows and subtropical Pacific highs were unreasonably simulated. The overestimated frequency of TC formation was attributed to a strengthened westerly wind field in the southern quadrants of the TC center. In comparison with the experiment with the spectral nudging method, the strengthened wind speed was mainly modulated by large-scale flow that was greater than approximately 1000 km in the model domain. The spurious formation and undesirable tracks of TCs in the CTL were considerably improved by reproducing realistic large-scale atmospheric monsoon circulation with substantial adjustment between large-scale flow in the model domain and large-scale boundary forcing modified by the spectral nudging method. The realistic monsoon circulation took a vital role in simulating realistic TCs. It revealed that, in the downscaling from large-scale fields for regional climate simulations, scale interaction between model-generated regional features and forced large-scale fields should be considered, and spectral nudging is a desirable method in the downscaling method.

Effect of Length Scale Tuning of Background Error in WRF-3DVAR System on Assimilation of High-Resolution Surface Data for Heavy Rainfall Simulation

We investigated the impact of tuning the length scale of the background error covariance in the Weather Research and Forecasting （WRF） three-dimensional variational assimilation （3DVAR） system. In particular, we studied the effect of this parameter on the assimilation of high-resolution surface data for heavy rainfall forecasts associated with mesoscale convective systems over the Korean Peninsula. In the assimilation of high-resolution surface data, the National Meteorological Center method tended to exaggerate the length scale that determined the shape and extent to which observed information spreads out. In this study, we used the difference between observation and background data to tune the length scale in the assimilation of high-resolution surface data. The resulting assimilation clearly showed that the analysis with the tuned length scale was able to reproduce the small-scale features of the ideal field effectively. We also investigated the effect of a double-iteration method with two different length scales, representing large and small-length scales in the WRF-3DVAR. This method reflected the large and small-scale features of observed information in the model fields. The quantitative accuracy of the precipitation forecast using this double iteration with two different length scales for heavy rainfall was high; results were in good agreement with observations in terms of the maximum rainfall amount and equitable threat scores. The improved forecast in the experiment resulted from the development of well-identified mesoscale convective systems by intensified low-level winds and their consequent convergence near the rainfall area.

Observation and Numerical Simulations with Radar and Surface Data Assimilation for Heavy Rainfall over Central Korea

This study investigated the impact of multiple-Doppler radar data and surface data assimilation on forecasts of heavy rainfall over the central Korean Peninsula;the Weather Research and Forecasting(WRF) model and its three-dimensional variational data assimilation system(3DVAR) were used for this purpose. During data assimilation,the WRF 3DVAR cycling mode with incremental analysis updates(IAU) was used. A maximum rainfall of 335.0 mm occurred during a 12-h period from 2100 UTC 11 July 2006 to 0900 UTC 12 July 2006.Doppler radar data showed that the heavy rainfall was due to the back-building formation of mesoscale convective systems(MCSs).New convective cells were continuously formed in the upstream region,which was characterized by a strong southwesterly low-level jet(LLJ).The LLJ also facilitated strong convergence due to horizontal wind shear,which resulted in maintenance of the storms.The assimilation of both multiple-Doppler radar and surface data improved the accuracy of precipitation forecasts and had a more positive impact on quantitative forecasting(QPF) than the assimilation of either radar data or surface data only.The back-building characteristic was successfully forecasted when the multiple-Doppler radar data and surface data were assimilated.In data assimilation experiments,the radar data helped forecast the development of convective storms responsible for heavy rainfall,and the surface data contributed to the occurrence of intensified low-level winds.The surface data played a significant role in enhancing the thermal gradient and modulating the planetary boundary layer of the model,which resulted in favorable conditions for convection.

Response of North Pacific and North Atlantic decadal variability to weak global warming

The Pacific Decadal Oscillation （PDO） and the Atlantic Multidecadal Variability （AMV） are the two dominant low-frequency modes in the climate system. This research focused on the response of these two modes under weak global warming. Observational data were derived from the Hadley Center Sea Ice and Sea Surface Temperature dataset （HadISST） and coupled model outputs from the Coupled Model Intercomparison Project Phase 5 （CMIP5）. Changes in PDO and AMV were examined using four models （bcc-csml-1, CCSM4, IPSL-CM5A-LR, and MPI- ESM-LR） with long weak global warming scenarios （RCP2.6）. These models captured the two low-frequency modes in both pre-industrial run and RCP2.6 run. Under weak global warming, the time scales of PDO and AMV significantly decreased while the amplitude only slightly decreased. Interestingly, the standard deviation of the North Pacific sea surface temperature anomaly （SSTA） decreased only in decadal time scale, and that of the North Atlantic SSTA decreased both in interannual and decadal time scales. The coupled system consists of a slow ocean component, which has a decadal time scale, and a fast atmospheric component, which is calculated by subtracting the decadal from the total. Results suggest that under global warming, PDO change is dominated by ocean dynamics, and AMV change is dominated by ocean dynamics and stochastic atmosphere forcing.

Evaluation of Heavy Rainfall Model Forecasts over the Korean Peninsula Using Different Physical Parameterization Schemes and Horizontal Resolution

In this study, the accuracy of a Pennsylvania State University–National Center for Atmospheric Research mesoscale model （PSU/NCAR MM5） for predicting heavy summer precipitation over the Korean Peninsula was investigated. A total of 1800 simulations were performed using this model for 30 heavy rainfall events employing four cumulus parameterization schemes （CPS）, two grid-scale resolvable precipitation schemes （GRS）, and two planetary boundary layer （PBL） schemes in three model resolutions （90 km, 30 km, and 10 km）. The heavy rainfall events were mesoscale convective systems developed under the influence of mid-latitude baroclinic systems with low-level moisture transport from the ocean. The predictive accuracy for maximum rainfall was approximately 80% for 10-km resolution and was 60% for 30-km resolution. The predictive accuracy for rainfall position extended to ～150 km from the observed position for both resolutions. Simulated rainfall was most sensitive to CPS, then to PBL schemes, and then to GRS. In general, the Grell （GR） scheme and the Anthes and Kuo （AK） scheme showed a better prediction capability for heavy rainfall than did the Betts-Miller （BM） scheme and the Kain-Fritsch （KF） scheme. The GR scheme also performed well in the 24-h and 12-h precipitation predictions： the parameterized convective rainfall in GR is directly related to synoptic-scale forcing. The models without CPS performed better for rainfall amounts but worse for rainfall position than those with CPS. The MM5 model demonstrated substantial predictive capacity using synoptic-scale initial conditions and lateral boundary data because heavy summer rainfall in Korea occurs in a strong synoptic-scale environment.

Interdecadal Variability of East Asian Summer Monsoon Precipitation over 220 Years (1777-1997)

In this study, long-term （1777–1997） precipitation data for Seoul, Korea, wetness indices from eastern China, and modern observations are used to identify the interdecadal variability in East Asian summer monsoon precipitation over the last 220 years. In the East Asian monsoon region, two long-term timescales of dry–wet transitions for the interdecadal variability and quasi-40-and quasi-60-year timescales are dominant in the 220-year precipitation data of Seoul, as well as in the wetness indices over China. The wet and dry spells between Seoul （southern China） and northern China are out-of-phase （out-of-phase） at the quasi-60-year timescale, and in-phase （out-of-phase by approximately 90 ？ before 1900 and in-phase after 1900） at the quasi-40-year timescale. In particular, during the last century, the dominant long-term timescales over East Asia tend to decrease from the quasi-60-year to the quasi-40-year with increasing time. The dominant quasi-40-year and quasi-60-year timescales of the Seoul precipitation in Korea are strongly correlated with these timescales of the northern Pacific Ocean.

Large training dataset is crucial for analogue-based precipitation reconstruction during the early Holocene

A long-standing question regarding the Holocene evolution of the East Asian Summer Monsoon(EASM)is whether it peaked during the early Holocene(EH)as inferred from rainfall-related proxy records[1,2],or during the mid-Holocene as inferred from ecoenvironmental records from northern China[3,4].Cheng et al.