Construction and Application of a Regional Kilometer-Scale Carbon Source and Sink Assimilation Inversion System(CCMVS-R)

CO_(2)is one of the most important greenhouse gases(GHGs)in the earth’s atmosphere.Since the industrial era,anthropogenic activities have emitted excessive quantities of GHGs into the atmosphere,resulting in climate warming since the 1950s and leading to an increased frequency of extreme weather and climate events.In 2020,China committed to striving for carbon neutrality by 2060.This commitment and China’s consequent actions will result in significant changes in global and regional anthropogenic carbon emissions and therefore require timely,comprehensive,and objective monitoring and verification support(MVS)systems.The MVS approach relies on the top-down assimilation and inversion of atmospheric CO_(2)concentrations,as recommended by the Intergovernmental Panel on Climate Change(IPCC)Inventory Guidelines in 2019.However,the regional high-resolution assimilation and inversion method is still in its initial stage of development.Here,we have constructed an inverse system for carbon sources and sinks at the kilometer level by coupling proper orthogonal decomposition(POD)with four-dimensional variational(4DVar)data assimilation based on the weather research and forecasting-greenhouse gas(WRF-GHG)model.Our China Carbon Monito ring and Verification Support at the Regional level(CCMVS-R)system can continuously assimilate information on atmospheric CO_(2)and other related information and realize the inversion of regional and local anthropogenic carbon emissions and natural terrestrial ecosystem carbon exchange.Atmospheric CO_(2)data were collected from six ground-based monito ring sites in Shanxi Province,China to verify the inversion effect of regio nal anthropogenic carbon emissions by setting ideal and real experiments using a two-layer nesting method(at 27 and 9 km).The uncertainty of the simulated atmospheric CO_(2)decreased significantly,with a root-mean-square error of CO_(2)concentration values between the ideal value and the simulated after assimilation was close to 0.The total anthropogenic carbon emissions in Shanxi Province in 2019 from the assimilated inversions were approximately 28.6%(17%-38%)higher than the mean of five emission inventories using the bottomup method,showing that the top-down CCMVS-R system can obtain more comprehensive information on anthropogenic carbon emissions.

A New Method for Deriving High-Vertical-Resolution Wind Vector Data from the L-Band Radar Sounding System in China

High-vertical-resolution radiosonde wind data are highly valuable for describing the dynamics of the meso-and microscale atmosphere. However, the current algorithm used in China's L-band radar sounding system for calculating highvertical-resolution wind vectors excessively smooths the data, resulting in significant underestimation of the calculated kinetic energy of gravity waves compared to similar products from other countries, which greatly limits the effective utilization of the data. To address this issue, this study proposes a novel method to calculate high-vertical-resolution wind vectors that utilizes the elevation angle, azimuth angle, and slant range from L-band radar. In order to obtain wind data with a stable quality, a two-step automatic quality control procedure, including the RMSE-F(root-mean-square error F) test and elemental consistency test are first applied to the slant range data, to eliminate continuous erroneous data caused by unstable signals or radar malfunctions. Then, a wind calculation scheme based on a sliding second-order polynomial fitting is utilized to derive the high-vertical-resolution radiosonde wind vectors. The evaluation results demonstrate that the wind data obtained through the proposed method show a high level of consistency with the high-resolution wind data observed using the Vaisala Global Positioning System and the data observed by the new Beidou Navigation Sounding System. The calculation of the kinetic energy of gravity waves in the recalculated wind data also reaches a level comparable to the Vaisala observations.

A quality control method for high-resolution radiosonde temperature and wind data

This study proposes a method to derive the climatological limit thresholds that can be used in an operational/historical quality control procedure for Chinese high vertical resolution(5–10 m)radiosonde temperature and wind speed data.The whole atmosphere is divided into 64 vertical bins,and the profiles are constructed by the percentiles of the values in each vertical bin.Based on the percentile profiles(PPs),some objective criteria are developed to obtain the thresholds.Tibetan Plateau field data are used to validate the effectiveness of the method in the application of experimental data.The results show that the derived thresholds for 120 operational stations and 3 experimental stations are effective in detecting the gross errors,and those PPs can clearly and instantly illustrate the characteristics of a radiosonde variable and reveal the distribution of errors.

CRA-40/Atmosphere—The First-Generation Chinese Atmospheric Reanalysis(1979–2018):System Description and Performance Evaluation

Atmospheric reanalysis reproduces the past atmospheric conditions through assimilation of historical meteorological observations with fixed version of a numerical weather prediction(NWP)model and data assimilation(DA)system.It is widely used in weather,climate,and even business-related research and applications.This paper reports the development of CMA’s first-generation global atmospheric reanalysis(RA)covering 1979–2018(CRA-40;CRA refers to CMA-RA).CRA-40 is produced by using the Global Spectral Model(GSM)/Gridpoint Statistical Interpolation(GSI)at a 6-h time interval and a TL574 spectral(34-km)resolution with the model top at 0.27 hPa.A large number of reprocessed satellite data and widely collected conventional observations were assimilated during the reanalyzing process,including the reprocessed atmospheric motion vector(AMV)products from FY-2C/D/E/G satellites,dense conventional observations(at about 120 radiosonde and 2400 synoptic stations)over China,as well as MWHS-2 and GNSS-RO observations from FY-3C.The reanalysis fitting to observations is improved over time,especially for surface pressure with root-mean-square error reduced from 1.05 hPa in 1979 to 0.8 hPa,and for upper air temperature from 1.65 K in 1979 to 1.35 K,in 2018.The patterns of global analysis increments for temperature,specific humidity,and zonal wind are consistent with the changes in the observing system.Near surface temperature from the model’s 6-h forecast reflects the global warming trend reasonably.The CRA-40 precipitation pattern matches well with those of GPCP and other reanalyses.CRA-40 also successfully captures the QBO and its vertical and temporal development,hemispherical atmospheric circulation change,and moisture transport by the East Asian summer monsoon.CRA is now operationally running in near real time as a climate data assimilation system in CMA.

A 10-Yr Global Land Surface Reanalysis Interim Dataset(CRA-Interim/Land):Implementation and Preliminary Evaluation

A land surface reanalysis dataset covering the most recent decades is able to provide temporally consistent initial conditions for weather and climate models,and thus is crucial to verifying/improving numerical weather/climate forecasts/predictions.In this paper,we report the development of a 10-yr China Meteorological Administration(CMA)global Land surface ReAnalysis Interim dataset(CRA-Interim/Land;2007–2016,6-h intervals,approximately 34-km horizontal resolution).The dataset was produced and evaluated by using the Global Land Data Assimilation System(GLDAS)and NCEP Climate Forecast System Reanalysis(CFSR)global land surface reanalysis datasets,as well as in situ observations in China.The results show that the global spatial patterns and monthly variations of the CRA-Interim/Land,GLDAS,and CFSR climatology are highly consistent,while the soil moisture and temperature values of the CRA-Interim/Land dataset are in between those of the GLDAS and CFSR datasets.Compared with ground observations in China,CRA-Interim/Land soil moisture is comparable to or better than that of GLDAS and CFSR datasets for the 0–10-cm soil layer and has higher correlations and slightly lower root mean square errors(RMSE)for the 10–40-cm soil layer.However,CRA-Interim/Land shows negative biases in 10–40-cm soil moisture in Northeast China and north of central China.For ground temperature and the soil temperature in different layers,CRA-Interim/Land behaves better than the CFSR,especially in East and central China.CRA-Interim/Land has added value over the land components of CRA-Interim due to the introduction of global precipitation observations and improved soil/vegetation parameters.Therefore,this dataset is potentially a critical supplement to the CRA-Interim.Further evaluation of the CRA-Interim/Land,assimilation of near-surface atmospheric forcing variables,and extension of the current dataset to 40 yr(1979–2018)are in progress.

Integration, Quality Assurance, and Usage of Global Aircraft Observations in CRA

This paper presents a detailed description of integration, quality assurance procedure, and usage of global aircraft observations for China's first generation global atmospheric reanalysis(CRA) product(1979–2018). An integration method named "classified integration" is developed. Aircraft observations from nine different sources are integrated into the Integrated Global Meteorological Observation Archive from Aircraft(IGMOAA), a new dataset from the National Meteorological Information Center(NMIC) of the China Meteorological Administration(CMA). IGMOAA consists of global aircraft temperature, wind, and humidity data from the surface to 100 h Pa, extending from 1973 to the present. Compared with observations assimilated in the Climate Forecast System Reanalysis(CFSR) of NCEP,the observation number of IGMOAA increased by 12.9% between 2010 and 2014, mainly as a result of adding more Chinese Aircraft Meteorological Data Relay(AMDAR) data. Complex quality control procedures for aircraft observations of NCEP are applied to detect data errors. Observations are compared with ERA-Interim reanalysis from 1979 to 2018 to investigate data quality of different types and aircraft, and subsequently to develop the blacklists for CRA. IGMOAA data have been assimilated in CRA in 2018 and are real-time updated at the CMA Data-as-a-Service(CMADaa S) platform. For CRA, the fits to observations improve over time. From 1994 to 2018, root-meansquare error(RMSE) of observations relative to CRA background decreases from 1.8 to 1.0℃ for temperature above 300 h Pa, and from 4.5 to 3 m s^(-1) for zonal wind. The RMSE for humidity appears to exhibit an apparent seasonal variation with larger errors in summer and smaller ones in winter.

RELATIONSHIP BETWEEN HORIZONTAL EXTINCTION COEFFICIENT AND PM_(10) CONCENTRATION IN XI'AN,CHINA,DURING 1980-2002

Based on daily visibility data obtained from 1980-2002 and air pollution index data from 2001-2004 in Xi＇an, long-term variations and relationships for daily horizontal extinction coefficient and mass concentration of PM10 have been evaluated. A decreasing trend was found in horizontal extinction coefficient during the past 23 years, with higher values observed in 1980s relative to 1990s, and the highest and lowest values in winter and summer, respectively. Significant correlation and similar seasonal variations existed between horizontal extinction coefficient and PM10 concentration, suggesting the high influence of PM10 to the visibility drop at a site in the Guanzhong Plain of central China during the past two decades.

Understanding the role of SST anomaly in extreme rainfall of 2020 Meiyu season from an interdecadal perspective

Extreme Meiyu rainfall in 2020,starting from early June to the end of July,has occurred over the Yangtze River valley(YRV),with record-breaking accumulated precipitation amount since 1961.The present study aims to examine the possible effect of sea surface temperature(SST)on the YRV rainfall in Meiyu season from the interdecadal perspective.The results indicate that YRV rainfall in June exhibits more significant variability on interdecadal time scale than that in July.The interdecadal-filtered atmospheric circulation in June,compared with the counterpart in July,shows a more predominant and better-organized Western North Pacific Anticyclone(WNPAC)anomaly,which could transport abundant moisture to the YRV by anomalous southwesterly prevailing in northwestern flank of anomalous WNPAC.Both observation and numerical experiment indicate that the interdecadal change of the SST anomaly in tropical western Indian Ocean(TWI)from preceding May to June can significantly affect the anomalous WNPAC,leading to enhanced YRV rainfall in June.The TWI SST anomaly shifts from a cold phase to a warm phase around the early 2000s,with a magnitude of 0.7°C in 2020,which implies that such interdecadal warming might partly contribute to the heavy rainfall in June 2020 by providing a large-scale favorable background flow.

A New Globally Reconstructed Sea Surface Temperature Analysis Dataset since 1900

A new globally reconstructed sea surface temperature(SST)analysis dataset developed by the China Meteorological Administration(CMA-SST),available on 2°×2°and monthly resolutions since 1900,is described and assessed in this study.The dataset has been constructed from a newly developed integrated dataset with denser and wider sampling of in situ SST observations and follows similar analysis techniques to the Extended Reconstructed SST,version 5(ERSST.v5).Assessments show that the larger observation quantity of the input data source is beneficial to making the reconstructed SSTs more realistic than those reconstructed with ICOADS 3.0+GTS(International Comprehensive Ocean-Atmosphere Dataset 3.0 and Global Telecommunication System),especially in China’s offshore sea area.Besides,a specific parameter for bias correction has been upgraded to be self-adaptive to the input data source,and serves as a mediator to improve the accuracy of the reconstructed SSTs.Generally,the reconstructed CMA-SST dataset is comparable to currently congeneric products.Its biases are similar to those of ERSST.v5,the Centennial Observation-Based Estimates of SST version 2(COBE-SST2),the Hadley Centre Sea Ice and SST dataset version 2(Had ISST2),and the Hadley Centre SST dataset version 3(Had SST3);and more specifically,they are closest to ERSST.v5 and lower than Had ISST2 and Had SST3 at high latitudes of the Southern Hemisphere where in situ observations are limited.Moreover,its temporal characteristics,such as the year-to-year variations of globally averaged SST anomalies and time series of the Nino-3.4,Atlantic multidecadal oscillation,and Pacific decadal oscillation indices are also a good match to those of congeneric products.Although the warming rates of CMA-SST are a little higher in many regions over the periods 1900-2019 and 1950-2019,they are found to be acceptable and within the quantified uncertainties of ERSST.v5.However,there are noticeable differences in the strength and stability of spatial standard deviations among the various datasets,as well as low correlations between CMA-SST and the other products around 60°S where in situ sampling is very limited.These aspects necessitate further investigation and improvement of CMA-SST.

Development of an Integrated Global Land Surface Dataset from 1901 to 2018

We developed an integrated global land surface dataset(IGLD)at the National Meteorological Information Center of China Meteorological Administration.The IGLD consists of hourly data for 75 variables from five data sources.It contains not only the most widely used variables(e.g.,pressure,temperature,dew-point temperature,and precipitation),but also visibility,cloud cover,snow depth,and so on.A hierarchy of data sources was created to identify duplicate records.The records located higher in the hierarchy were adopted preferentially in the IGLD.A comprehensive quality control procedure including extreme value test,internal consistency check,and spatiotemporal consistency check,was applied to the IGLD.The IGLD consists of land surface observations at more than 20,000 global sites from 1901 to 2018,of which about 17,000 stations are currently active.The number of global observatories generally increased over time,except for the 1960 s to 1970 s.It increased from about 2300 in 1951 to 17,000 in 2018.The observations over America,Europe,and eastern Asia always showed a high temporal integrity and dense spatial coverage,whereas measurements were sparser in South America,Africa,Russia,and the Mediterranean regions.In general,the standard and intermediate standard times for observation suggested by the World Meteorological Organization(WMO)were followed globally,except in Australia,where there were few data measured on the WMO schedule.The IGLD has been used in the China’s first generation global atmospheric reanalysis product(CRA)and the global daily precipitation dataset.

Bias Adjustment and Analysis of Chinese Daily Historical Radiosonde Temperature Data

The discontinuities in historical Chinese radiosonde datasets are attributed to artificial errors. In order to reflect more realistically basic conditions of the atmosphere over China and provide more reasonable radiosonde data as input to climate change analysis and to atmospheric reanalysis data assimilation systems, this paper proposes a scheme to identify breakpoints and adjust biases in daily radiosonde observations. The ongoing ECMWF Re Analysis-Interim(ERA-Interim) 12-h forecasts are used as reference series in the scheme, complemented by the ECMWF Twentieth Century Reanalysis(ERA-20 C). A series of breakpoint identification schemes are developed and combined with metadata to detect breakpoints. The Quantile-Matching(QM) method is applied to test and adjust daily radiosonde data on 12 mandatory pressure levels collected at 80 sounding stations during 1979–2013. The adjusted temperatures on mandatory levels are interpolated to significant levels for temperature adjustment on these levels. The adjustment scheme not only solves the data discontinuity problem caused by changes in observational instruments and bias correction methods, but also solves the discontinuity problem in the 1200 minus 0000 UTC temperature time series on mandatory levels at individual sounding stations. Before the adjustment, obvious discontinuities can be found in the deviation field between the raw radiosonde data and ERA-Interim reanalysis with relatively large deviations before 2001. The deviation discontinuity is mainly attributed to the nationwide upgrade of the radiosonde system in China around 2001. After the adjustment, the time series of deviations becomes more continuous. In addition, compared with the adjusted temperature data on mandatory levels over 80 radiosonde stations in China contained in the Radiosonde Observation Correction Using Reanalyses(RAOBCORE) 1.5, the dataset adjusted by the method proposed in the present study exhibits higher quality than RAOBCORE 1.5, while discontinuities still exist in the time series of temperature at 0000, 1200, and 1200 minus 0000 UTC in RAOBCORE 1.5.

Bias Analysis and Correction of Ground Surface Temperature Observations across China

Based on the ground surface temperature(GST)and snow surface temperature(SST)measurements during the period of adjustment from manual to automatic observation systems in China,the influence of observation methods on GST and its relationship with snow cover is analyzed.GST is corrected by SST,and the correction effect is evaluated.The results show that,during the parallel observation period,the winter GSTs from automatic observations are generally higher than those from manual observations,with the automatically observed national daily GST 1.18°C higher.The adjustment has a greater impact on GSTs at 0200,0800,and 2000 Beijing Time(BT)than at 1400 BT,and it has the greatest impact in Northeast and Northwest China,with deviations of 5.24 and 2.09°C,respectively.The GST deviation is closely related to the snow depth and annual snow totals.The average daily GST deviation increases at the rate of 0.66°C per 1-cm increase of snow depth when it is<15 cm,while it tends to be stable at around 10°C for snow depth over 15 cm.The GST deviation at a station is affected by its winter snow totals in Northeast and Northwest China,where the largest deviations are found where snow totals are all above 1000 cm.After the correction with SST,the mean deviation between the automatic and manual observations as well as the false trend can be effectively reduced.Following the correction,the mean deviation of daily GST decreases by 5.8°C,and its trend decreases from 1.87 to 0.65°C decade-1.