Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks

This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.

Evaluating the Capabilities of Soil Enthalpy, Soil Moisture and Soil Temperature in Predicting Seasonal Precipitation

Soil enthalpy （H） contains the combined effects of both soil moisture （w） and soil temperature （T） in the land surface hydrothermal process. In this study, the sensitivities of H to w and T are investigated using the multi-linear regression method. Results indicate that T generally makes positive contributions to H, while w exhibits different （positive or negative） impacts due to soil ice effects. For example, w negatively contributes to H if soil contains more ice; however, after soil ice melts, w exerts positive contributions. In particular, due to lower w interannual variabilities in the deep soil layer （i.e., the fifth layer）, H is more sensitive to T than to w. Moreover, to compare the potential capabilities of H, w and T in precipitation （P） prediction, the Huanghe-Huaihe Basin （HHB） and Southeast China （SEC）, with similar sensitivities of H to w and T, are selected. Analyses show that, despite similar spatial distributions of H-P and T-P correlation coefficients, the former values are always higher than the latter ones. Furthermore, H provides the most effective signals for P prediction over HHB and SEC, i.e., a significant leading correlation between May H and early summer （June） P. In summary, H, which integrates the effects of T and w as an independent variable, has greater capabilities in monitoring land surface heating and improving seasonal P prediction relative to individual land surface factors （e.g., T and w）.

Seasonal Forecasts of Precipitation during the First Rainy Season in South China Based on NUIST-CFS1.0

Current dynamical models experience great difficulties providing reliable seasonal forecasts of regional/local rainfall in South China.This study evaluates seasonal forecast skill for precipitation in the first rainy season(FRS,i.e.,April–June)over South China from 1982 to 2020 based on the global real-time Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0,previously known as SINTEX-F).The potential predictability and the practical forecast skill of NUIST-CFS1.0 for FRS precipitation remain low in general.But NUIST-CFS1.0 still performs better than the average of nine international models in terms of correlation coefficient skill in predicting the interannual precipitation anomaly and its related circulation index.NUIST-CFS1.0 captures the anomalous Philippines anticyclone,which transports moisture and heat northward to South China,favoring more precipitation in South China during the FRS.By examining the correlations between sea surface temperature(SST)and FRS precipitation and the Philippines anticyclone,we find that the model reasonably captures SST-associated precipitation and circulation anomalies,which partly explains the predictability of FRS precipitation.A dynamical downscaling model with 30-km resolution forced by the large-scale circulations of the NUIST-CFS1.0 predictions could improve forecasts of the climatological states and extreme precipitation events.Our results also reveal interesting interdecadal changes in the predictive skill for FRS precipitation in South China based on the NUIST-CFS1.0 hindcasts.These results help improve the understanding and forecasts for FRS precipitation in South China.

Seasonal Prediction of Summer Precipitation over East Africa Using NUIST-CFS1.0

East Africa is particularly vulnerable to precipitation variability, as the livelihood of much of the population depends on rainfed agriculture. Seasonal forecasts of the precipitation anomalies, when skillful, can therefore improve implementation of coping mechanisms with respect to food security and water management. This study assesses the performance of Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUISTCFS1.0) on forecasting June–September(JJAS) seasonal precipitation anomalies over East Africa. The skill in predicting the JJAS mean precipitation initiated from 1 May for the period of 1982–2019 is evaluated using both deterministic and probabilistic verification metrics on grid cell and over six distinct clusters. The results show that NUIST-CFS1.0 captures the spatial pattern of observed seasonal precipitation climatology, albeit with dry and wet biases in a few parts of the region. The model has positive skill across a majority of Ethiopia, Kenya, Uganda, and Tanzania, whereas it doesn’t exceed the skill of climatological forecasts in parts of Sudan and southeastern Ethiopia. Positive forecast skill is found over regions where the model shows better performance in reproducing teleconnections related to oceanic SST. The prediction performance of NUIST-CFS1.0 is found to be on a level that is potentially useful over a majority of East Africa.

Deep Learning for Seasonal Precipitation Prediction over China

Despite significant progress having been made in recent years,the forecast skill for seasonal precipitation over China remains limited.In this study,a deep-learning-based statistical prediction model for seasonal precipitation over China was developed.The model was trained to learn the distribution of the seasonal precipitation using simultaneous general circulation data.First,it was pre-trained with the hindcasts of several general circulation models(GCMs),and evaluation of the test set suggested that the pre-trained model could basically reproduce the GCM-predicted precipitation,with the anomaly pattern correlation coefficients(PCCs)greater than 0.80.Then,transfer learning was applied by using ECMWF Reanalysis v5(ERA5)data and gridded precipitation observational data over China,to further correct the systemic errors in the model.As a result,using general circulation fields from reanalysis as the input,this hybrid model performed reasonably well in simulating the seasonal precipitation over China,with the PCC reaching 0.71.In addition,the results using the circulation fields predicted by GCMs as the input were also assessed.In general,the proposed model improves the PCC over China by 0.10-0.13,as compared to the raw GCM outputs,for lead times of 1-4 months.This deep learning model has been used at the National Climate Center of China Meteorological Administration for the past two years to provide guidance for summer precipitation prediction over China and has performed extremely well.

Quantifying the Spatial Characteristics of the Moisture Transport Affecting Precipitation Seasonality and Recycling Variability in Central Asia

Moisture contribution and transport pathways for Central Asia(CA)are quantitatively examined using the Lagrangian water cycle model based on reanalysis and observational data to explain the precipitation seasonality and the moisture transport variation during 1979-2015.Westerly-related(northwesterly and westerly)transport explains 42%of CA precipitation and dominates in southwest CA,where precipitation is greatest in the cold season.Southeast CA,including part of Northwest China,experiences its maximum precipitation in the warm season and is solely dominated by southerly transport,which explains about 48%of CA precipitation.The remaining 10%of CA precipitation is explained by northerly transport,which steadily impacts north CA and causes a maximum in precipitation in the warm season.Most CA areas are exposed to seasonally varying moisture transport,except for southeast and north CA,which are impacted by southerly and northerly transport year-round.In general,the midlatitude westerlies-driven transport and the Indian monsoon-driven southerly-related transport explain most of the spatial differences in precipitation seasonality over CA.Moreover,the contribution ratio of local evaporation in CA to precipitation exhibits significant interdecadal variability and a meridionally oriented tripole of moisture transport anomalies.Since the early 2000s,CA has experienced a decade of anomalously low local moisture contribution,which seems jointly determined by the weakened moisture contribution from midlatitudes(the Atlantic,Europe,and CA itself)and the enhanced contribution from high latitudes(West Siberia and the Arctic)and tropical areas(South Asia and the Indian Ocean).

Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia

Global warming and climate change is one of the most extensively researched and discussed topical issues affecting the environment.Although there are enough historical evidence to support the theory that climate change is a natural phenomenon,many research scientists are widely in agreement that the increase in temperature in the 20 th century is anthropologically related.The associated effects are the variability of rainfall and cyclonic patterns that are being observed globally.In Southeast Asia the link between global warming and the seasonal atmospheric flow during the monsoon seasons shows varying degree of fuzziness.This study investigates the impact of climate change on the seasonality of monsoon Asia and its effect on the variability of monsoon rainfall in Southeast Asia.The comparison of decadal variation of precipitation and temperature anomalies before the 1970 s found general increases which were mostly varying.But beyond the 1970 s,global precipitation anomalous showed increases that almost corresponded with increases in global temperature anomalies for the same period.There are frequent changes and a shift westward of the Indian summer monsoon.Although precipitation is observed to be 70%below normal levels,in some areas the topography affects the intensity of rainfall.These shifting phenomenon of other monsoon season in the region are impacting on the variability of rainfall and the onset of monsoons in Southeast Asia and is predicted to delay for 15 days the onset of the monsoon in the future.The variability of monsoon rainfall in the SEA region is observed to be decadal and the frequency and intensity of intermittent flooding of some areas during the monsoon season have serious consequences on the human,financial,infrastructure and food security of the region.

Ground-Based Radar Reflectivity Mosaic of Mei-yu Precipitation Systems over the Yangtze River–Huaihe River Basins

The 3D radar reflectivity produced by a mosaic software system, with measurements from 29 operational weather radars in the Yangtze River–Huaihe River Basins（YRHRB） during the mei-yu season of 2007, is compared to coincident TRMM PR observations in order to evaluate the value of the ground-based radar reflectivity mosaic in characterizing the 3D structures of mei-yu precipitation. Results show reasonable agreement in the composite radar reflectivity between the two datasets,with a correlation coefficient of 0.8 and a mean bias of -1 dB. The radar mosaic data at constant altitudes are reasonably consistent with the TRMM PR observations in the height range of 2–5 km, revealing essentially the same spatial distribution of radar echo and nearly identical histograms of reflectivity. However, at altitudes above 5 km, the mosaic data overestimate reflectivity and have slower decreasing rates with height compared to the TRMM PR observations. The areas of convective and stratiform precipitation, based on the mosaic reflectivity distribution at 3-km altitude, are highly correlated with the corresponding regions in the TRMM products, with correlation coefficients of 0.92 and 0.97 and mean relative differences of -7.9% and -2.5%, respectively. Finally, the usefulness of the mosaic reflectivity at 3-km altitude at 6-min intervals is illustrated using a mesoscale convective system that occurred over the YRHRB.

Dynamic Downscaling of Summer Precipitation Prediction over China in 1998 Using WRF and CCSM4

To study the prediction of the anomalous precipitation and general circulation for the summer（June–July–August） of1998, the Community Climate System Model Version 4.0（CCSM4.0） integrations were used to drive version 3.2 of the Weather Research and Forecasting（WRF3.2） regional climate model to produce hindcasts at 60 km resolution. The results showed that the WRF model produced improved summer precipitation simulations. The systematic errors in the east of the Tibetan Plateau were removed, while in North China and Northeast China the systematic errors still existed. The improvements in summer precipitation interannual increment prediction also had regional characteristics. There was a marked improvement over the south of the Yangtze River basin and South China, but no obvious improvement over North China and Northeast China. Further analysis showed that the improvement was present not only for the seasonal mean precipitation, but also on a sub-seasonal timescale. The two occurrences of the Mei-yu rainfall agreed better with the observations in the WRF model,but were not resolved in CCSM. These improvements resulted from both the higher resolution and better topography of the WRF model.

PREDICTION OF FLOOD SEASON PRECIPITATION IN SOUTHWEST CHINA BASED ON IMPROVED PSO-PLS

In order to achieve the best predictive effect of the Partial Least Squares(PLS) regression model, Particle Swarm Optimization(PSO) algorithm is applied to automatically filter the optimal subset of a set of candidate factors of PLS regression model in this study. An improved version of the Particle Swarm Optimization-Partial Least Squares(PSO-PLS) regression model is applied to the station data of precipitation in Southwest China during flood season.Using the PSO-PLS regression method, the prediction of flood season precipitation in Southwest China has been studied. By introducing the precipitation period series of the mean generating function(MGF) extension as an alternative factor, the MGF improved PSO-PLS regression model was also built up to improve the prediction results.Randomly selected 10%, 20%, 30% of the modeling samples were used as a test trial; random cross validation was conducted on the MGF improved PSO-PLS regression model. The results show that the accuracy of PSO-PLS regression model and the MGF improved PSO-PLS regression model are better than that of the traditional PLS regression model.The training results of the three prediction models with regard to the regional and single station precipitation are considerable, whereas the forecast results indicate that the PSO-PLS regression method and the MGF improved PSO-PLS regression method are much better than the traditional PLS regression method. The MGF improved PSO-PLS regression model has the best forecast performance on precipitation anomaly during the flood season in the southwest of China among three models. The average precipitation(PS score) of 36 stations is 74.7. With the increase of the number of modeling samples, the PS score remained stable. This shows that the PSO algorithm is objective and stable. The MGF improved PSO-PLS regression prediction model is also showed to have good prediction stability and ability.

The Operational Forecasting of Total Precipitation in Flood Seasons (April to September) of 5 Years (1983-1987)

Ⅰ.INTRODUCTION We have discovered that there exists a good corresponding relationship between theanomalous axes of soil temperature at a depth of 1.6m in winter (December to February) andprecipitations in following flood season (Tang et al., 1982a). We have also designed a simplethermodynamical model and applied it to the forecasting of precipitations in the flood season(Tang et al., 1982 b,c). The practical forecast started from 1975. Before 1980, however, therewere only 40-50 stations in China for measuring the soil temperature at a 1.6m depth. Since1980, the stations have been increased to a total of about 180, but no available mean valueshad been obtained from newly added stations before 1982. Therefore the analysis and map-ping of anomalies of soil temperature was not performed until 1983, and from then on theprecision of analysis has been greatly improved. The following is the actual situation of forecast in five years from 1983 to 1987.

IMPACT OF SSTA OF SOUTHERN HEMISPHERE ON FLOOD SEASON PRECIPITATION ANOMALIES IN YUNNAN

Based on the reanalysis data of monthly mean global SST and wind from the NCEP/NCAR and the observation data of rain seasons in 124 stations of Yunnan province from 1961 to 2006, we applied the analytical methods of correlation analysis and composite analysis and a significance testing method to two sets of samples of average differences. The goal is to investigate into the influence of the Southern Hemispheric(SH) SST on the summer precipitation in Yunnan from January to May so as to identify the key time and marine regions. Physical mechanisms are obtained by analyzing the influence of sea level wind and the key marine regions on the precipitation during Yunnan's rain season.Results show that there is indeed significant relationship between the SST in SH and summer precipitation in Yunnan.The key areas for influencing the summer precipitation are mainly distributed in a region called "West Wind Drift" in the SH, including the Southeast Indian, southern Australia, west coast of eastern Pacific off Chile, Peru and the southwest Atlantic Magellan. Besides, the most significant marine region is the west coast of Chile and Peru(cold-current areas of the eastern Pacific). Diagnostic analysis results also showed that monsoons in the Bay of Bengal, a cross-equatorial flow in the Indian Ocean near the equator and southwest monsoon in India weaken during the warm phase of the Peruvian cold current in the eastern Pacific. Otherwise, they strengthen.

Analysis on the Precipitation Characteristics in the Rainy Season in Liupanshui City in Recent 50 Years

[Objective] The research aimed to study the variation rule of precipitation in the rainy season in Liupanshui City in recent 50 years. [Method] Based on the monthly precipitation data from three observatories (Liuzhi, Panxian and Shuicheng) of Liupanshui City from May to September during 1960-2009, the interannual, interdecadal variation and mutation characteristics of precipitation in the rainy season in Liupanshui City in recent 50 years were analyzed by using the linear tendency estimation, sliding T-test and Morlet wavelet analysis method. [Result] The rainfall in the rainy season in Liupanshui City in recent 50 years presented the decline trend, and the linear tendency rate was -15.4 mm/10 a. The precipitation in the rainy season in Liupanshui City had the obvious interannual and interdecadal variation characteristics. It was the obvious rainless period in the metaphase of 1960s, and the precipitation was comparatively more in late 1960s. It was the relatively rainless period in the whole 1970s. From late 1970s to late 1980s, the precipitation in the rainy season entered into the pluvial period, and it was the period when the precipitation was the most in recent 50 years. The precipitation was relatively less from late 1980s to metaphase of 1990s. It was the pluvial period in the middle and late periods of 1990s, and it was the rainless period when entered into the 21st century. The sliding T-test showed that the precipitation mutation point in the rainy season in Liupanshui City in recent 50 years was in 2002. The wavelet analysis showed that the precipitation in the rainy season in Liupanshui City had the significant multiple time scale characteristic. In the interdecadal scale, the precipitation had the significant 16-year periodic oscillation which stably existed in 50 years. In the interannual scale, the precipitation had the quasi-8-year periodic oscillation. [Conclusion] The research provided the scientific basis for the accurate forecast of drought and flood disasters, disaster prevention and reduction in the city.

Spatiotemporal variation of rainy season span and precipitation recorded by lacustrine laminated pollen in the Tibetan Plateau during the past two millennia

The quantitative reconstruction of the length of the rainy season and precipitation on the Tibetan Plateau(TP) is crucial for revealing the spatiotemporal evolution of the Westerlies and Asian summer monsoon, as well as its ecological and environmental effects. Accurately determining the start and end times of the rainy season on the Plateau remains challenging.This study determined the start and end times of the rainy season in different locations on the Plateau by identifying precipitation inflection points. We calculated the duration and precipitation amount of the rainy season and established a transfer function between the modern pollen assemblages and them. Then, we reconstructed the rainy season variations in Kusai Lake(northern TP) and Jiang Co(central TP) during the past two millennia. The results showed that, the rainy season precipitation in Kusai Lake recorded five periods of high precipitation: AD 580–680, 1000–1100, 1200–1450, 1550–1780, and 1920–present, corresponding to the stages with long rainy season. The rainy season precipitation sequence in Jiang Co recorded four periods of high precipitation: AD 80–500, 800–950, 1250–1450, and 1780–present, which is consistent with the long rainy season before AD 1000 but unclear afterward. Spatially, rainy season precipitation on the Plateau exhibited four patterns: “wet in both north and south” may be related to abnormally strong summer monsoons;“dry in both north and south” likely associated with weak Westerly wind and weak summer monsoon;“wet in the south and dry in the north” linked to strong summer monsoon and weak Westerly wind;and “dry in the south and wet in the north” connected to weak summer monsoon and strong Westerly wind. This study revealed the spatiotemporal evolution pattern of the rainy season onset and end, duration, and precipitation amount on the Plateau over the past two millennia. It provides natural background support for further understanding the coupling between Westerly wind and Asian summer monsoon.

Spatiotemporal patterns of burned areas,fire drivers,and fire probability across the equatorial Andes

Field-based fire studies in the equatorial Andes indicate that fires are strongly associated with biophysical and anthropogenic variables.However,fire controls and fire regimes at the regional scale remain undocumented.Therefore,this paper describes spatial and temporal burned-area patterns,identifies biophysical and anthropogenic fire drivers,and quantifies fire probability across 6°of latitude and 3°of longitude in the equatorial Andes.The spatial and temporal burned-area analysis was carried out based on 18 years(2001-2018)of the MCD64 A1 MODIS burned-area product.Climate,topography,vegetation,and anthropogenic variables were integrated in a logistic regression model to identify the significance of explanatory variables and determine fire occurrence probability.A total of 5779 fire events were registered during the 18 years of this study,located primarily along the western cordillera of the Andes and spreading from North to South.Eighty-eight percent of these fires took place within two fire hotspots located in the northwestern and southwestern corners of the study area.Ninety-nine percent occurred during the second part of the year,between June and December.The largest density of fires was primarily located on herbaceous vegetation and shrublands.Results show that mean monthly temperature,precipitation and NDVI during the prefire season,the location of land cover classes such as forest and agriculture,distance to roads and urban areas,slope,and aspect were the most important determinants of spatial and temporal fire distribution.The logistic regression model achieved a good accuracy in predicting fire probability(80%).Probability was higher in the southwestern and northern corners of the study area,and lower towards the north in the western and eastern piedmonts of the Andes.This analysis contributes to the understanding of fires in mountains within the tropics.The results here presented have the potential to contribute to fire management and control in the region.

Effects of PHC on Water Quality of Jiaozhou BayⅢ.Land Transfer Process

Based on investigation data of PHC content in Jiaozhou Bay,China from 1979 to 1983,the seasonal variations of PHC content and monthly changes of precipitation in Jiaozhou Bay were analyzed. The results showed that seen from the spatial and temporal distribution,the seasonal variation of PHC content in the surface water of Jiaozhou Bay was based on the flow of the rivers as well as human activity,so PHC content in the rivers depended on the flow of the rivers and human activity,and the peaks and valleys of PHC content appeared in various seasons. The seasonal variation of PHC content in the surface water of Jiaozhou Bay depended on its land transfer process. The land transfer process was composed of use of PHC by mankind,deposition of PHC in soil and on the earth＇s surface,and transportation of PHC to offshore waters of sea by rivers and surface runoff. PHC content depended on mankind during the process from being used to entering soil and on precipitation during the process of being transported from soil to ocean.

Simulation and Projection of Changes in Rainy Season Precipitation over China Using the WRF Model

The Weather Research and Forecasting （WRF） model is used in a regional climate model configuration to simulate past precipitation climate of China during the rainy season （May-September） of 1981-2000, and to investigate potential future （2041-2060 and 2081-2100） changes in precipitation over China relative to the reference period 1981-2000. WRF is run with initial conditions from a coupled general circulation model, i.e., the high-resolution version of MIROC （Model for Interdisciplinary Research on Climate）. WRF reproduces the observed distribution of rainy season precipitation in 1981-2000 and its interannual variations better than MIROC. MIROC projects increases in rainy season precipitation over most parts of China and decreases of more than 25 mm over parts of Taiwan and central Tibet by the mid-21st century. WRF projects decreases in rainfall over southern Tibetan Plateau, Southwest China, and northwestern part of Northeast China, and increases in rainfall by more than 100 mm along the southeastern margin of the Tibetan Plateau and over the lower reaches of the Yangtze River during 2041-2060. MIROC projects further increases in rainfall over most of China by the end of the 21st century, although simulated rainfall decreases by more than 25 mm over parts of Taiwan, Guangxi, Guizhou, and central Tibet. WRF projects increased rainfall of more than 100 mm along the southeastern margin of the Tibetan Plateau and over the lower reaches of the Yangtze River and decreased rainfall over Southwest China, and southern Tibetan Plateau by the end of the 21st century.

Wildfire associated with a deciduous broadleaved forest from the Neogene Baoshan Basin at the southeastern margin of the Tibetan Plateau

Wildfire bears a close relationship with vegetation as its fuel source.The southeastern margin of the Tibetan Plateau witnesses frequent wildfires among various types of vegetation,whereas such wide in-teractions between wildfire and vegetation remain poorly studied from geological times.In this study,we reported a local fire using sedimentary macroscopic charcoals from the latest Miocene to early Pliocene of the Baoshan Basin in this region,and then inferred the local vegetation at the time of the fire event based chiefly on the coexistent fruit and seed fossil assemblage.Our taxonomic results show that the charcoal assemblage is probably dominated by broadleaved plants and the fruit and seed fossil assemblage is apparently dominated by Salix(Salicaceae)followed by Sambucus(Adoxaceae),suggesting a deciduous broadleaved forest in which the fire likely occurred.Under a seasonally dry climate associated with the Asian monsoon,this type of vegetation might be prone to natural fire,because in the wet rainy season the plants grew well to accumulate biofuel and in the dry season abundant ground litter resulting from leaf decay would be desiccated to become highly flammable.Due to the fire-tolerant habit of Salix as the dominant plant,the forest might be in return adapted to the fire event or even more fires that potentially followed.All these may suggest a close relationship between the fire event and the reconstructed vegetation.Our finding documents a new type of wildfire—vegetation interaction,namely the interaction between wildfire and deciduous broadleaved forest,from the geological past at the southeastern margin of the Tibetan Plateau.It therefore sheds new light on the wildfire history coupling vegetation change in the region.

1901–2013年中亚季节降水时空变化特征及其与ENSO的关系（英文）

The vulnerable ecosystem of the arid and semiarid region in Central Asia is sensi- tive to precipitation variations. Long-term changes of the seasonal precipitation can reveal the evolution rules of the precipitation climate. Therefore, in this study, the changes of the sea- sonal precipitation over Central Asia have been analyzed during the last century （1901-2013） based on the latest global monthly precipitation dataset Global Precipitation Climatology Centre （GPCC） Full Data Reanalysis Version 7, as well as their relations with El Ni~_o- Southern Oscillation （ENSO）. Results show that the precipitation in Central Asia is mainly concentrated in spring and summer seasons, especially in spring. For the whole study period, increasing trends were found in spring and winter, while decreasing trends were detected in summer and fall. Inter-annual signals with 3-7 years multi-periods were derived to explain the dominant components for seasonal precipitation variability. In terms of the dominant spatial pattern, Empirical orthogonal function （EOF） results show that the spatial distribution of EOF-1 mode in summer is different from those of the other seasons during 1901-2013. Moreover, significant ENSO-associated changes in precipitation are evident during the fall, winter, spring, and absent during summer. The lagged associations between ENSO and seasonal precipitation are also obtained in Central Asia. The ENSO-based composite analy- ses show that these water vapor fluxes of spring, fall and winter precipitation are mainly generated in Indian and North Atlantic Oceans during El Nino. The enhanced westerlies strengthen the western water vapor path for Central Asia, thereby causing a rainy winter.

Climate and anthropogenic drivers of changes in abundance of C4 annuals and perennials in grasslands on the Mongolian Plateau

Background:C4 plants have increased substantially during the past several decades in the grasslands of the Mongolian Plateau due to regional warming.Here,we explore how the patterns of abundances of C4 annuals and C4 perennials change over space and time.Methods:A total of 280 sites with C4 plants were surveyed in four types of grasslands in 9 years.The relative biomasses of C4 plants(PC4),C4 annuals(PA4),and C4 perennials(PP4)were calculated.Structural equation modeling was used to analyze the drivers of changes in PA4 and PP4.Results:At the regional scale,PA4 on average was 11%(±19%,SD)and PP4 was 13%(±19%,SD).Spatially,C4 annuals dominated the C4 communities within an east–west belt region along 44°N and tended to spread toward northern latitudes(about 0.5°)and higher altitudes in the east mountainous areas.The abundance of C4 annuals decreased,while that of C4 perennials increased.The patterns of C4 annuals and C4 perennials were mainly controlled by temperature,growing season precipitation,and dynamics between the two life forms.Conclusions:C4 annuals exhibited competitive advantages in normal and wet years,while C4 perennials had competitive advantages in dry years.Grazing as a main human disturbance increased C4 annuals,but had no significant effect on C4 perennials.