Homogenised Monthly and Daily Temperature and Precipitation Time Series in China and Greece since 1960

In this paper,we describe and analyze two datasets entitled“Homogenised monthly and daily temperature and precipitation time series in China during 1960–2021”and“Homogenised monthly and daily temperature and precipitation time series in Greece during 1960–2010”.These datasets provide the homogenised monthly and daily mean(TG),minimum(TN),and maximum(TX)temperature and precipitation(RR)records since 1960 at 366 stations in China and 56stations in Greece.The datasets are available at the Science Data Bank repository and can be downloaded from https://doi.org/10.57760/sciencedb.01731 and https://doi.org/10.57760/sciencedb.01720.For China,the regional mean annual TG,TX,TN,and RR series during 1960–2021 showed significant warming or increasing trends of 0.27℃(10 yr)^(-1),0.22℃(10 yr)^(-1),0.35℃(10 yr)^(-1),and 6.81 mm(10 yr)-1,respectively.Most of the seasonal series revealed trends significant at the 0.05level,except for the spring,summer,and autumn RR series.For Greece,there were increasing trends of 0.09℃(10 yr)-1,0.08℃(10 yr)^(-1),and 0.11℃(10 yr)^(-1)for the annual TG,TX,and TN series,respectively,while a decreasing trend of–23.35 mm(10 yr)^(-1)was present for RR.The seasonal trends showed a significant warming rate for summer,but no significant changes were noted for spring(except for TN),autumn,and winter.For RR,only the winter time series displayed a statistically significant and robust trend[–15.82 mm(10 yr)^(-1)].The final homogenised temperature and precipitation time series for both China and Greece provide a better representation of the large-scale pattern of climate change over the past decades and provide a quality information source for climatological analyses.

Homogenized Daily Relative Humidity Series in China during 1960?2017

Surface relative humidity(RH)is a key element for weather and climate monitoring and research.However,RH is not as commonly applied in studying climate change,partly because the observation series of RH are prone to inhomogeneous biases due to non-climate changes in the observation system.A homogenized dataset of daily RH series from 746 stations in Chinese mainland for the period 1960–2017,ChinaRHv1.0,has been developed.Most(685 or 91.82%of the total)station time series were inhomogeneous with one or more break points.The major breakpoints occurred in the early 2000s for many stations,especially in the humid and semi-humid zones,due to the implementation of automated observation across the country.The inhomogeneous biases in the early manual records before this change are positive relative to the recent automatic records,for most of the biased station series.There are more break points detected by using the MASH(Multiple Analysis of Series for Homogenization)method,with biases mainly around?0.5%and 0.5%.These inhomogeneous biases are adjusted with reference to the most recent observations for each station.Based on the adjusted observations,the regional mean RH series of China shows little long-term trend during 1960–2017[0.006%(10 yr)^?1],contrasting with a false decreasing trend[?0.414%(10 yr)?1]in the raw data.It is notable that ERA5 reanalysis data match closely with the interannual variations of the raw RH series in China,including the jump in the early 2000s,raising a caveat for its application in studying climate change in the region.

Machine Learning−based Weather Support for the 2022 Winter Olympics

1.A key support for the 2022 Winter Olympics The XXIV Olympic Winter Games are scheduled to take place from 4 to 22 February 2022,followed by the Paralympic Games from 4 to 13 March,in Beijing and towns in the neighboring Hebei Province,China.Weather plays an extremely important role in the outcome of the games(Chen et al.,2018).It can not only cause a difference between a medal or not,but affect the safety of athletes.Success of the Winter Olympics will greatly depend on weather conditions at the outdoor competition venues,dealing with many weather elements including the snow surface temperature,apparent temperature,gust wind speed,snow,visibility,etc.To ensure that the scheduled games go smoothly,it is imperative to have hourly or even every 10-minutely forecasts as well as updated weather-related risk assessments at the venues for the next 240 hours.So far,the Beijing/Hebei Meteorological Observatory has already started intelligent weather forecasting at 3-km resolution based on the results of numerical weather prediction(NWP)models.However,these experiments have suggested that the current forecasting techniques are incapable of capturing the complex mountain weather variations around some venues.The forecasting capability of NWP is constrained partly by limited knowledge of the local weather mechanisms.

Changing Spring Phenology Dates in the Three-Rivers Headwater Region of the Tibetan Plateau during 1960–2013

The variation of the vegetation growing season in the Three-Rivers Headwater Region of the Tibetan Plateau has recently become a controversial topic. One issue is that the estimated local trend in the start of the vegetation growing season(SOS)based on remote sensing data is easily affected by outliers because this data series is short. In this study, we determine that the spring minimum temperature is the most influential factor for SOS. The significant negative linear relationship between the two variables in the region is evaluated using Moderate Resolution Imaging Spectroradiometer–Normalized Difference Vegetation Index data for 2000–13. We then reconstruct the SOS time series based on the temperature data for 1960–2013.The regional mean SOS shows an advancing trend of 1.42 d(10 yr)during 1960–2013, with the SOS occurring on the 160th and 151st days in 1960 and 2013, respectively. The advancing trend enhances to 6.04 d(10 yr)during the past 14 years. The spatiotemporal variations of the reconstructed SOS data are similar to those deduced from remote sensing data during the past 14 years. The latter exhibit an even larger regional mean trend of SOS [7.98 d(10 yr)] during 2000–13. The Arctic Oscillation is found to have significantly influenced the changing SOS, especially for the eastern part of the region,during 2000–13.

Further-Adjusted Long-Term Temperature Series in China Based on MASH

A set of homogenized monthly mean surface air temperature （SAT） series at 32 stations in China back to the 19th century had previously been developed based on the RHtest method by Cao et al., but some inhomogeneities remained in the dataset. The present study produces a further-adjusted and updated dataset based on the Multiple Analysis of Series for Homogenization （MASH） method. The MASH procedure detects 33 monthly temperature records as erroneous outliers and 152 meaningful break points in the monthly SAT series since 1924 at 28 stations. The inhomogeneous parts are then adjusted relative to the latest homogeneous part of the series. The new data show significant warming trends during 1924-2016 at all the stations, ranging from 0.48 to 3.57℃ （100 yr）^-1, with a regional mean trend of 1.65℃ （100 yr）^-1 ; whereas, the previous results ranged from a slight cooling at two stations to considerable warming, up to 4.5℃ （100 yr）^-1. It is suggested that the further-adjusted data are a better representation of the large-scale pattern of climate change in the region for the past century. The new data axe available online at http：//www.dx.doi.org/10.11922/sciencedb.516.

Impact of Anthropogenic Aerosols on Summer Precipitation in the Beijing–Tianjin–Hebei Urban Agglomeration in China:Regional Climate Modeling Using WRF-Chem

The WRF model with chemistry （WRF-Chem） was employed to simulate the impacts of anthropogenic aerosols on summer precipitation over the Beijing-Tianjin-Hebei urban agglomeration in China. With the aid of a high-resolution gridded inventory of anthropogenic emissions of trace gases and aerosols, we conducted relatively long-term regional simulations, considering direct, semi-direct and indirect effects of the aerosols. Comparing the results of sensitivity experiments with and without emissions, it was found that anthropogenic aerosols tended to enhance summer precipitation over the metropolitan areas. Domain-averaged rainfall was increased throughout the day, except for the time around noon. Aerosols shifted the precipitation probability distribution from light or moderate to extreme rain. Further analysis showed that the anthropogenic aerosol radiative forcing had a cooling effect at the land surface, but a warming effect in the atmosphere. However, enhanced convective strength and updrafts accompanied by water vapor increases and cyclone-like wind shear anomalies were found in the urban areas. These responses may originate from cloud microphysical effects of aerosols on convection, which were identified as the primary cause for the summer rainfall enhancement.

High-resolution Projection Dataset of Agroclimatic Indicators over Central Asia

To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia(CA),six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of three different global climate models from Phase 5 of the Coupled Model Intercomparison Project(CMIP5),and their changes in the near-term future(2031-50)are assessed relative to the reference period(1986-2005).The quantile mapping(QM)method is applied to correct the model data before calculating the indicators.Results show the QM method largely reduces the biases in all the indicators.Growing season length(GSL,day),summer days(SU,day),warm spell duration index(WSDI,day),and tropical nights(TR,day)are projected to significantly increase over CA,and frost days(FD,day)are projected to decrease.However,changes in biologically effective degree days(BEDD,°C)are spatially heterogeneous.The high-resolution projection dataset of agroclimatic indicators over CA can serve as a scientific basis for assessing the future risks to local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security in this region.

Meshless Surface Wind Speed Field Reconstruction Based on Machine Learning

We propose a novel machine learning approach to reconstruct meshless surface wind speed fields,i.e.,to reconstruct the surface wind speed at any location,based on meteorological background fields and geographical information.The random forest method is selected to develop the machine learning data reconstruction model(MLDRM-RF)for wind speeds over Beijing from 2015-19.We use temporal,geospatial attribute and meteorological background field features as inputs.The wind speed field can be reconstructed at any station in the region not used in the training process to cross-validate model performance.The evaluation considers the spatial distribution of and seasonal variations in the root mean squared error(RMSE)of the reconstructed wind speed field across Beijing.The average RMSE is 1.09 m s^(−1),considerably smaller than the result(1.29 m s^(−1))obtained with inverse distance weighting(IDW)interpolation.Finally,we extract the important feature permutations by the method of mean decrease in impurity(MDI)and discuss the reasonableness of the model prediction results.MLDRM-RF is a reasonable approach with excellent potential for the improved reconstruction of historical surface wind speed fields with arbitrary grid resolutions.Such a model is needed in many wind applications,such as wind energy and aviation safety assessments.

The East Asian summer monsoon circulation anomaly index and its interannual variations

Based on the concept of East Asia-Pacific (EAP) teleconnection which influences East Asian summer monsoon, an index for East Asian summer monsoon circulation anomaly was defined and it was pointed out that this index can describle the interannual variation character of summer climate in East Asia, especially in the Yangtze River and Huaihe River Valley.

Re-Assessing Climatic Warming in China since 1900

The regional mean surface air temperature(SAT)in China has risen with a rate of 1.3–1.7℃(100 yr)^-1 since 1900,based on the recently developed homogenized observations.This estimate is larger than those[0.5–0.8℃(100 yr)^-1]adopted in the early National Reports of Climate Change in China.The present paper reviews the studies of the longterm SAT series of China,highlighting the homogenization of station observations as the key progress.The SAT series of China in early studies showed a prominent warm peak in the 1940s,mainly due to inhomogeneous records associated with site-moves of a number of stations from urban to outskirts in the early 1950s,thus leading to underestimates of the centennial warming trend.Parts of China were relatively warm around the 1940s but with differentphase interdecadal variations,while some parts were even relatively cool.This fact is supported by proxy data and could partly be explained by interdecadal changes in large-scale circulation.The effect of urbanization should have a minor contribution to the observed warming in China,although the estimates of such contributions for individual urban stations remain controversial.Further studies relevant to the present topic are discussed.

On the role of anthropogenic warming and wetting in the July 2021 Henan record-shattering rainfall

Unprecedentedly heavy rains hit China's Henan Province during 19-21 July 2021,triggering fatal and costly floods punctuated by over 120 bllion Yuan of economic losses and nearly 400 fatalities.A broad swath of the province witnessed nearly a whole year's worth of rainfall pouring within the three days.The provincial cap-ital city Zhengzhou even registered 201.9 mm in an hour which smashed the records both locally and nationally.Such concen-trated,intense bursts of precipitation instantly overwhelmed the urban drainage system and sent torrents through streets and sub-way tunnels.Though rapid analyses sorted out favorable dynamic and thermodynamic setups,including exceptionally strong updrafts shaped by large-scale circulation anomalies,unique topography,and anomalously abundant water vapor advected jointly by the Northwestern Pacific subtropical high and double typhoons-In-Fa and Cempaka[1],the linkage between climate change and this record-shattering event also gained broad atten-tions yet remains elusive.

Near-Term Projections of Global and Regional Land Mean Temperature Changes Considering Both the Secular Trend and Multidecadal Variability

Near-term climate projections are needed by policymakers; however, these projections are difficult because internally generated climate variations need to be considered. In this study, temperature change scenarios in the near-term period 2017-35 are projected at global and regional scales based on a refined multi-model ensemble approach that considers both the secular trend(ST) and multidecadal variability(MDV) in the Coupled Model Intercomparison Project Phase 5(CMIP5) simulations. The ST and MDV components are adaptively extracted from each model simulation by using the ensemble empirical mode decomposition(EEMD) filter, reconstructed via the Bayesian model averaging(BMA) method for the historical period 1901-2005, and validated for 2006-16. In the simulations of the "medium" representative concentration pathways scenario during 2017-35, the MDV-modulated temperature change projected via the refined approach displays an increase of 0.44℃(90% uncertainty range from 0.30 to 0.58℃) for global land, 0.48℃(90% uncertainty range from 0.29 to 0.67℃) for the Northern Hemispheric land(NL), and 0.29℃(90% uncertainty range from 0.23 to 0.35℃) for the Southern Hemispheric land(SL). These increases are smaller than those projected by the conventional arithmetic mean approach. The MDV enhances the ST in 13 of 21 regions across the world. The largest MDV-modulated warming effect(46%) exists in central America. In contrast,the MDV counteracts the ST in NL, SL, and eight other regions, with the largest cooling effect(220%) in Alaska.

Datasets of meteorological drought events and risks for the developing countries in Eurasia

For the area of Eurasia concentrated with developing countries(referred to here by the abbreviation DPEA),mainly located in Asia and Eastern Europe,this work presents datasets of gridded meteorological drought events and country-based drought risk by combining multiple drought indices and socio-economic data.A basic gridded dataset of the drought events during 1950-2015 is extracted from three drought indices:the self-calibrating Palmer Drought Severity Index,the Standardized Precipitation Index,and the Standardized Precipitation Evapotranspiration Index.The three drought indices generally show consistent characteristics of drought events in DPEA.A second-level dataset(a drought risk dataset)is then produced as the product of drought hazard,exposure and vulnerability during 2000-2015.For drought exposure,the indicators of population and livestock density,agricultural land and water stress are chosen,while drought vulnerability composites multiple social,economic and infrastructural factors.Drought hazard tends to concentrate at the southern rim of Eurasia.Relatively large differences in drought exposure exist between different countries,but for drought vulnerability the differences are small.After considering the socio-economic components in risk assessment,most countries in West,South-Central and South Asia have the highest drought risk in DPEA.The datasets of drought events and risks are available at http://www.dx.doi.org/10.11922/sciencedb.898.

Conceptual study on incorporating user information into forecasting systems

The purpose of improving weather forecast isto enhance the accuracy in weather prediction.An idealforecasting system would incorporate user-end information.In recent years,the meteorological community hasbegun to realize that while general improvements to thephysical characteristics of weather forecasting systems arebecoming asymptotically limited,the improvement fromthe user end still has potential.The weather forecastingsystem should include user interaction because user needsmay change with different weather.A study was conductedon the conceptual forecasting system that included adynamic,user-oriented interactive component.Thisresearch took advantage of the recently implementedTIGGE(THORPEX interactive grand global ensemble)project in China,a case study that was conducted to test thenew forecasting system with reservoir managers in LinyiCity,Shandong Province,a region rich in rivers andreservoirs in eastern China.A self-improving forecastsystem was developed involving user feedback throughouta flood season,changing thresholds for flood-inducingrainfall that were responsive to previous weather andhydrological conditions,and dynamic user-oriented assessmentsof the skill and uncertainty inherent in weatherprediction.This paper discusses ideas for developinginteractive,user-oriented forecast systems.