Abstract:The spatial and temporal changes in net primary productivity (NPP) during 1901-2005 were studied using six earth system models. The relationship between NPP changes and the climatic factors of air temperature and precipitation was analyzed. The results show that: (1) In the past 100 years, the global NPP has shown an upward trend. The trend coefficient of the ensemble model average is 0.88, which passes the 99.9% confidence test. The trend in the Northern Hemisphere is more pronounced than in the Southern Hemisphere. (2) In the past 100 years, high NPP values of 800 g(C) m^-2 a^-1 or higher are mainly distributed in tropical rainforest areas in the equatorial regions of South America, Equatorial Africa, the Indochina Peninsula, and Indonesia. Low NPP values are mainly distributed in the high latitudes of the Northern Hemisphere, Northern Africa, the arid and semi-arid regions of the Asian continent, and the northwestern Tibetan Plateau. (3) The global NPP had a positive correlation with temperature in most of the regions during this century period. This correlation only becomes negative for South America, Africa, and India near the equator, mainly because radiation in these areas is a limiting factor of NPP. The 100-year changes in the global NPP and precipitation are also primarily positively correlated in most regions, but are negatively correlated in the arid and semi-arid regions of Northern Africa and Western Asia. (4) The six earth system models yield relatively consistent NPP and temperature/precipitation changes in most of the 21 regions of the world. In West Africa, the pattern changes are inconsistent where the uncertainty of the NPP simulation is greater, followed by the Mediterranean region. (5) In particular, the evolution of the NPP in the East Asia region and climate is synchronized and highly correlated, which reflects the strong process of atmospheric interaction with vegetation.

Abstract:The potential vegetation distribution, the net primary production (NPP), net ecosystem production (NEP), burned area, carbon emissions from fires, soil temperature, and soil moisture in Northeast China from 1997 to 2010 was simulated by using a high-resolution climate-driven field and global dynamic vegetation model, i.e., Lund-Potsdam-Jena Wetland Hydrology and Methane (LPJ-WHyMe) model. The LPJ-WHyMe model is characterized by the capability to describe the physical processes of freezing and thawing, as well as the humidity and temperature of multiple layers in the soil. The five main plant functional types in Northeast China are temperate broad-leaved summergreen tree, boreal needle-leaved evergreen tree, boreal needle-leaved summergreen tree, boreal broad-leaved summergreen tree, and C3 perennial grass. During the period under study in Northeast China, the average value of NPP is 376 g(C) m^-2, ranging from 324.15 g(C) m^-2 to 424.86 g(C) m^-2. The introduction of the mechanism of fire further improves the simulation capability of the LPJ-WHyMe model for NEP. The average value of NEP is 42.36 g(C) m^-2. The annual average burned area is 0.84% and the carbon emission from fire is 42.41 g(C) m^-2 in Northeast China. Overall, the model overestimated the burned area and carbon emission from fire. Moreover, the model still has some limitations in the simulation of fire in Northeast China. A positive correlation between soil and air temperatures is observed in Northeast China, and the correlation in each layer decreases with the increase in depth. A positive correlation between soil moisture and precipitation and a negative correlation between soil moisture and air temperature are observed in Northeast China. These results show that the LPJ-WHyMe model is effective in simulating the potential vegetation distribution and carbon cycle in Northeast China.

Abstract:Both observations and numerical simulations with bin microphysics indicated that aerosol concentrations and size distributions play an important role in cloud droplet spectrum evolution and precipitation formation. With limited prognostic variables, current bulk microphysics parameterization cannot simulate the cloud droplet spectrum evolution properly because of the abnormal broadening problem during condensation. No studies of the effect of the size distribution of aerosols on cloud droplet spectra with bulk scheme simulations have been conducted. The newly developed triple-moment bulk scheme includes an additional spectrum shape parameter and overcomes the abnormal broadening problem, which can be better applied to simulate the main characteristics of cloud droplet spectrum evolution than double-moment schemes. To analyze the effect of the size distribution of aerosols on the cloud droplet spectrum evolution using triple-moment microphysical bulk scheme IAP-LACS which has been developed by Key Laboratory of Cloud-Precipitation Physics and Severe Storms (LACS) of Insitute of Atmospheric Physics (IAP). This work focuses on the effects of three parameters of the size distribution of aerosols (i.e., number concentration, geometric radius, and standard deviation) on the cloud droplet spectrum evolution using WRF-LES ideal simulations. The results of the numerical sensitivity tests for the three parameters show that the newly developed triple-moment water vapor growth scheme coupled with the explicit aerosol activation process is a powerful tool to simulate the effect of the size distribution of aerosols on the cloud droplet spectrum evolution. Aerosol number concentration significantly affects the shape of the cloud droplet spectrum. A high aerosol number concentration results in a narrow droplet spectrum with activated droplets and small average size. By contrast, a low aerosol number concentration produces less droplets but with large radius. Enlarging the geometric radius, which means moving the aerosol spectrum toward a large particle size, leads to cloud droplets with a large size. Notably, the standard deviation plays a less important role in the cloud droplet spectrum than the aerosol number concentration and geometric radius.

Abstract:This work aimed to design an observational network of the air-sea boundary layer over the Bohai and the Yellow Sea, China, and further investigate the effects of these observations on a U.S. numerical prediction model (Weather Research and Forecasting model, WRF). Statistical analysis of the regional characteristics of the air-sea elements, model error, and observation system simulation experiments (OSSE) were conducted. Evaluations of the observation network were conducted under different wind and weather conditions and the advantages and disadvantages of each configuration scheme were weighed. The 6-h NCEP/NCAR FNL (NCEP Final Operational Global) reanalysis data, NCEP real-time global daily sea surface temperature (RTG_SST) analysis data, and buoy and oil-platform observational data were used. Results showed that the forecast humidity and wind were greatly affected by the actual direction and velocity of the wind, and humidity was better predicted under easterly and northerly wind conditions. Moreover, in the moderate southerly wind case, the forecast winds were closer to the observations. It was found that the forecast accuracy of temperature could be significantly improved by configuring the station network based on regional characteristics. Based on a comprehensive overview of the model simulations, suggestions for configuring the air-sea observation stations so as to improve numerical forecast accuracy are provided.

Abstract:The tropical Pacific plays an important role in modulating the global climate. However, large discrepancies remain among the various estimates of the long-term trend in the tropical Pacific under global warming. Using multiple sea surface temperature (SST) and subsurface temperature datasets, this study investigates the long-term trend of SST in the tropical Pacific and the long-term trend of subsurface temperature in the equatorial Pacific based on the Theil-Sen trend estimation method. Our results indicate a cooling trend in the Pacific cold tongue region and a warming trend in the rest of the tropical Pacific under global warming. That is, the long-term trend of oceanic temperature has a La Niña-like pattern. Furthermore, this La Niña-like pattern in the tropical Pacific is induced by the cold tongue mode (CTM). A positive CTM is characterized by the cold temperature anomaly in the Pacific cold tongue region and the warm temperature anomaly in the rest of the tropical Pacific. Moreover, the time series of the CTM mainly exhibits a strong long-term trend, which is induced by the ocean dynamical processes in response to global warming. The La Niña-like pattern of the long-term trend of the SST and subsurface temperature in the equatorial Pacific indicates the different aspects of the CTM.

Abstract:Utilizing the ground sand concentration net grid products forecasted by the China Meteorological Administration (CMA) Unified Atmospheric Chemistry Environment (CUACE) numerical model, human forecasts of sand-dust rank products, observed ground sand-dust rank, and the observed particulate pollution (PM10), this paper calculated the Threat Score (TS) from 2012 to 2014, which contained a total of 25 processes of sand-dust weather, to select the best observations and radii of circular interpolation for an objective verification of the sand-dust forecast. For the net grid CUACE numerical model products, different radii were chosen by using the circular interpolation to generate the interpolated station sand-dust forecast. Several conclusions were drawn from the studyresults (1) For the sand-dust rank observations, the radii approximately linearly declined with TS in the float dust-dust rank; the TS rarely changed when the radius changed in the sand-dust rank above the sandstorm, and the best radius selected was the minimum radius, i.e., 0.5° (longitude/latitude). For the PM10, the TS changed markedly when the radius changed in the float dust, but rarely changed when the radius changed in the sand rank above the sandstorm, when the best radius selected averaged 3.5°. If the sand was very weak, the radius could be properly decreased to 1°. (2) For the CUACE numerical model product, the usability of the TS of the observed sand-dust rank was better than the observed PM10, while the two were nearly the same in rank above the sandstorm. Thus, the best observation used for the objective verification of the CUACE numerical model product, generally, was observed ground sand rank. For the human sand rank forecast, the TS of the observed ground sand-dust rank was much higher than the PM10, and the best observation for the verification was the observed ground sand rank.

Abstract:Observations and European Centre for Medium-Range Forecast (ECMWF) reanalysis data were used to compare the ability of ensemble forecast members [with a horizontal resolution of 0.5º(latitude)×0.5º(longitude)] to provide dynamic and thermodynamic factors relating to a cyclone that exploded over the southern Yellow Sea in February 2018. According to validation results for the track and strength of the cyclone and surface wind, two sets of ensemble forecast members were selected as good and bad forecast members, respectively, and the main conclusions were subsequently obtained from a comparative investigation between them as follows: 1) When the cyclone had explosively developed, the trough and vortex at 500 hPa and 850 hPa, respectively, strengthened rapidly, and the southwest jet streams at lower and upper levels simultaneously increased rapidly in association with the rapid strengthening of synoptic systems; this provided a favorable condition for explosive cyclone development. 2) With the rapid strengthening of ascending motion after the cyclone had entered the Yellow Sea, convergence and divergence at lower and upper levels were respectively intensified; this promoted a pressure reduction and the cyclone then explosively developed. Following convergence of water vapor at a mid-low level, latent heat was released under the ascending motion, which promoted divergence and convergence at upper and lower levels, respectively, and then enhanced ascending motion. Therefore, the rapid strengthening of water vapor flux convergence caused the cyclone to explosively develop. The downward transmission of the high potential vorticity (PV) and baroclinicity at a lower level were strengthened, the degree of stability weakened, and cyclonic vorticity was enhanced. These factors were beneficial for explosive cyclone development, and it finally increased to a medium-strength explosive cyclone. 3) Although the forecast fields from the two ensemble sets were both weaker than those of analysis data, the good ensemble members caught the rapid strengthening of the synoptic system at a mid-upper level over the cyclone and the rapid developing processes of these key factors and physical quantities (such as vertical motion, PV, temperature advection, and water vapor). Therefore, the ability of the good members to track the strengthening cyclone was found to be superior to that of bad members.

Abstract:To investigate the variation characteristics of rainstorms in the middle-lower reaches of the Yangtze River from 1958 to 2017, four rainstorm characteristic variables were defined based on daily precipitation data of 426 stations. The tendency analysis and mutation test were conducted using a linear trend analysis, cumulative anomaly test, sliding t-test, and the Pettitt test. Results showed that: 1) There was a gradual decrease of the average annual rainstorm and rainstorm days from central Jiangxi to the surrounding areas, and a gradual increase of the average annual rainstorm intensity and rainstorm variation coefficient from south to north. The four rainstorm characteristics variables showed evident seasonal differences. Most rainstorms occurred in the order: summer > spring > autumn > winter. However, the largest rainstorm variation coefficient can be found in winter, showing strong volatility in winter. 2) An increasing tendency in average annual rainstorms, rainstorm days, and rainstorm intensity was observed at 74% of all the stations. The linear trend rates of the average annual rainstorms and rainstorm days gradually increase from northwest to southeast. In addition, the ratio of stations having significant increasing (p < 0.05) in the annual rainstorms and rainstorm days were 17.8% and 16.7%, respectively. 3) Results of the cumulative anomaly test, sliding t-test, and Pettitt test indicated that a significant change of rainstorms occurred in 1988 in the middle-lower Yangtze River Valley. Meanwhile, the average and trend rates of three rainstorm characteristic variables significantly increased after 1988.

Abstract:Based on monthly precipitation data from CRU TS v4.0 (Climatic Research Unit Timeseries version 4.0), output of the CMIP5 (Coupled Model Intercomparison Project Phase 5) historical experiments and RCP4.5 (Representative Concentration Pathway 4.5) scenario from 24 models, a variety of simple and multiple regression methods were designed to bias-correct projected precipitation for China. These included simple regression (SR), simple regression with log-transformed rainfall (SR-Log), simple regression with year-to-year rainfall increment as predictand (SR-Increment), simple regression with year-to-year log-transformed rainfall increment as predictand (SR-Log-Increment), multiple regression (MR), multiple regression with log-transformed rainfall (MR-Log), multiple regression with year-to-year rainfall increment as predictand (MR-Increment), multiple regression with year-to-year log-transformed rainfall increment as predictand (MR-Log-Increment), and simple removal of climate drift (RCD). Bias-corrected results for projected precipitation over mainland China for 2006-2015 showed that univariate regression correction methods were generally better than multi-variate methods and simple RCD. SR-Log performed best, with rate of precipitation anomaly having the same sign with observation (AR) and precipitation anomaly percentage correlation coefficient (APCC) were the highest, reaching 69% and 0.5, respectively. On the other hand, SR-log-Increment obtained the highest correlation coefficient of precipitation anomaly (ACC) among the different methods. The distributions of precipitation anomaly with the same sign with respect to observation, using different bias-correction methods, showed that the SR-Log performed better in the north than in the south. To the contrary, SR-Increment and SR-Log-Increment performed better in the south than in the north. As a result, the AR, ACC and APCC of the SR-Log or MR-Log were lower than those of the SR-Log-Increment and MR-Log-Increment over southern China (east of 95°E and south of 35°N), while the opposite was true for northern and western China. Therefore, the best regression correction method for model precipitation was regional-dependent, possibly reflecting the differences in statistical properties of precipitation in different regions. Using synthesis of regional regression models, i.e., using SR-Increment in the southern region and SR-Log for the rest of China, the AR of projected precipitation for 2006-2015 improved to 72% while ACC and APCC declined slightly, as the increment regression method increased the variance of the projected precipitation. Projected precipitation for 2016-2045 was bias-corrected by the synthesis of regional regressions method. The results showed that, compared with the average of 1976-2005, the precipitation anomaly pattern for the next 30 years would display a “dry in the north and south, wet in the middle” pattern. Precipitation would decrease by 10%-20% in the middle and lower Yangtze River, middle and west of the regions south of the Yangtze River, the northeastern part of southwestern China, and the coastal regions of southern China and Hainan; precipitation would increase by 10%-40% in the Huai River basin, three rivers source regions, and Taiwan. Minimal changes, or slightly less precipitation was projected over the eastern part of northwestern China, northern China, and most of northeastern China. According to the variance of precipitation anomaly percentage, the spread (uncertainty) of the model group was smaller in the east and larger in the west. It indicated that the projected less precipitation areas were more uncertain such as in the central northwestern, and western Qinghai-Tibet Plateau. In addition, the northern part of the Hetao area, the southern part of northern China, and the eastern part of the south of the Yangtze River corresponded to the “obscured areas,” where the precipitation anomaly in the projections and observations showed opposite signs for the verification period 2006-2015. As such, the projected precipitation over these regions may not be of value. Consequently, alternative methods need to be developed in the future for further improvement.

Abstract:In this study, the relationship between the land surface albedo over the Tibetan Plateau and the plateau summer monsoon are statistically analyzed using a representative plateau monsoon index, i.e., Dynamic Plateau Monsoon Index (DPMI) based on MODIS surface albedo and ECMWF/ERA-Interim reanalysis data from 2000 to 2016. The main results are as follows: 1) There is a close relationship between the land surface albedo over the Tibetan Plateau in November and the onset of plateau summer monsoon. The land surface albedo of the Tibetan Plateau in November is low (high), and the plateau summer monsoon starts early (late) in April of the following year. 2) When the land surface albedo over the Tibetan Plateau in November is low, the sensible heat flux of the main body of the plateau on the atmosphere is strong in the latter period, which significantly strengthens the uplifting movement of the plateau in April. Thus, the plateau becomes conducive to the transfer of heat to the upper air, resulting in enhanced tropospheric heating, increased tropospheric temperature in the plateau, and strengthened plateau monsoon circulation system, eventually leading to the early plateau monsoon seasonal changes, and vice versa.