Accounting for CO<Subscript>2</Subscript> variability over East Asia with a regional joint inversion system and its preliminary evaluation

A regional surface carbon dioxide (CO₂) flux inversion system, the Tan-Tracker-Region, was developed by incorporating an assimilation scheme into the Community Multiscale Air Quality (CMAQ) regional chemical transport model to resolve fine-scale CO₂ variability over East Asia. The proper orthogonal decomposition-based ensemble four-dimensional variational data assimilation approach (POD-4DVar) is the core algorithm for the joint assimilation framework, and simultaneous assimilations of CO₂ concentrations and surface CO₂ fluxes are applied to help reduce the uncertainty in initial CO₂ concentrations. A persistence dynamical model was developed to describe the evolution of the surface CO₂ fluxes and help avoid the “signal-to-noise” problem; thus, CO₂ fluxes could be estimated as a whole at the model grid scale, with better use of observation information. The performance of the regional inversion system was evaluated through a group of single-observation-based observing system simulation experiments (OSSEs). The results of the experiments suggest that a reliable performance of Tan-Tracker-Region is dependent on certain assimilation parameter choices, for example, an optimized window length of approximately 3 h, an ensemble size of approximately 100, and a covariance localization radius of approximately 320 km. This is probably due to the strong diurnal variation and spatial heterogeneity in the fine-scale CMAQ simulation, which could affect the performance of the regional inversion system. In addition, because all observations can be artificially obtained in OSSEs, the performance of Tan-Tracker-Region was further evaluated through different densities of the artificial observation network in different CO₂ flux situations. The results indicate that more observation sites would be useful to systematically improve the estimation of CO₂ concentration and flux in large areas over the model domain. The work presented here forms a foundation for future research in which a thorough estimation of CO₂ flux variability over East Asia could be performed with the regional inversion system.     

Simulation of Non-Homogeneous CO_2 and Its Impact on Regional Temperature in East Asia

Carbon dioxide(CO_2) is an important greenhouse gas that influences regional climate through disturbing the earth's energy balance. The CO_2 concentrations are usually prescribed homogenously in most climate models and the spatiotemporal variations of CO_2 are neglected. To address this issue,a regional climate model(RegCM4) is modified to investigate the non-homogeneous distribution of CO_2 and its effects on regional longwave radiation flux and temperature in East Asia. One-year simulation is performed with prescribed surface CO_2 fluxes that include fossil fuel emission, biomass burning, air-sea exchange, and terrestrial biosphere flux. Two numerical experiments(one using constant prescribed CO_2 concentrations in the radiation scheme and the other using the simulated CO_2 concentrations that are spatially non-homogeneous) are conducted to assess the impact of non-homogeneous CO_2 on the regional longwave radiation flux and temperature. Comparison of CO_2 concentrations from the model with the observations from the GLOBALVIEW-CO_2 network suggests that the model can well capture the spatiotemporal patterns of CO_2 concentrations. Generally, high CO_2 mixing ratios appear in the heavily industrialized eastern China in cold seasons, which probably relates to intensive human activities. The accommodation of non-homogeneous CO_2 concentrations in the radiative transfer scheme leads to an annual mean change of -0.12 W m~(-2) in total sky surface upward longwave flux in East Asia. The experiment with non-homogeneous CO_2 tends to yield a warmer lower troposphere.Surface temperature exhibits a maximum difference in summertime, ranging from -4.18 K to 3.88 K, when compared to its homogeneous counterpart. Our results indicate that the spatial and temporal distributions of CO_2 have a considerable impact on regional longwave radiation flux and temperature, and should be taken into account in future climate modeling.     

Effect of Data Assimilation Parameters on The Optimized Surface CO<Subscript>2</Subscript> Flux in Asia

In this study, CarbonTracker, an inverse modeling system based on the ensemble Kalman filter, was used to evaluate the effects of data assimilation parameters (assimilation window length and ensemble size) on the estimation of surface CO₂ fluxes in Asia. Several experiments with different parameters were conducted, and the results were verified using CO₂ concentration observations. The assimilation window lengths tested were 3, 5, 7, and 10 weeks, and the ensemble sizes were 100, 150, and 300. Therefore, a total of 12 experiments using combinations of these parameters were conducted. The experimental period was from January 2006 to December 2009. Differences between the optimized surface CO₂ fluxes of the experiments were largest in the Eurasian Boreal (EB) area, followed by Eurasian Temperate (ET) and Tropical Asia (TA), and were larger in boreal summer than in boreal winter. The effect of ensemble size on the optimized biosphere flux is larger than the effect of the assimilation window length in Asia, but the importance of them varies in specific regions in Asia. The optimized biosphere flux was more sensitive to the assimilation window length in EB, whereas it was sensitive to the ensemble size as well as the assimilation window length in ET. The larger the ensemble size and the shorter the assimilation window length, the larger the uncertainty (i.e., spread of ensemble) of optimized surface CO₂ fluxes. The 10-week assimilation window and 300 ensemble size were the optimal configuration for CarbonTracker in the Asian region based on several verifications using CO₂ concentration measurements.     

Assessment of the biospheric contribution to surface atmospheric CO2 concentrations over East Asia with a regional chemical transport model

A regional chemical transport model, RAMS-CMAQ, was employed to assess the impacts of biosphere–atmosphere CO2 exchange on seasonal variations in atmospheric CO2 concentrations over East Asia. Simulated CO2 concentrations were compared with observations at 12 surface stations and the comparison showed they were generally in good agreement. Both observations and simulations suggested that surface CO2 over East Asia features a summertime trough due to biospheric absorption, while in some urban areas surface CO2 has a distinct summer peak, which could be attributed to the strong impact from anthropogenic emissions. Analysis of the model results indicated that biospheric fluxes and fossil-fuel emissions are comparably important in shaping spatial distributions of CO2 near the surface over East Asia. Biospheric flux plays an important role in the prevailing spatial pattern of CO2 enhancement and reduction on the synoptic scale due to the strong seasonality of biospheric CO2 flux. The elevation of CO2 levels by the biosphere during winter was found to be larger than 5ppm in North China and Southeast China, and during summertime a significant depletion( 7 ppm) occurred in most areas,except for the Indo-China Peninsula where positive bioflux values were found.     

RegCM4对中国东部区域气候模拟的辐射收支分析

利用卫星和再分析数据,评估了区域气候模式Reg CM4对中国东部地区辐射收支的基本模拟能力,重点关注地表净短波(SNS)、地表净长波(SNL)、大气顶净短波(TNS)、大气顶净长波(TNL)4个辐射分量。结果表明:1)短波辐射的误差值在夏季较大,而长波辐射的误差值在冬季较大。但各辐射分量模拟误差的空间分布在冬、夏季都有较好的一致性。2)对于地表辐射通量,SNS表现为正偏差(向下净短波偏多),在各分量中误差最大,区域平均误差值近50 W/m2;SNL表现为负偏差(向上净长波偏多);对于大气顶辐射通量,TNS和TNL分别表现为"北负南正"的误差分布和整体正偏差。3)利用空间相关和散点线性回归方法对4个辐射分量的模拟误差进行归因分析,发现在云量、地表反照率、地表温度三个直接影响因子中,云量模拟误差的贡献最大,中国东部地区云量模拟显著偏少。     

Dust storm ensemble forecast experiments in East Asia

The ensemble Kalman filter (EnKF), as a unified approach to both data assimilation and ensemble forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting a dust episode in the East Asia during 23–30 May 2007. The errors in the input wind field, dust emission intensity, and dry deposition velocity are among important model uncertainties and are considered in the model error perturbations. These model errors are not assumed to have zero-means. The model error me...     

Probabilistic multimodel ensemble prediction of decadal variability of East Asian surface air temperature based on IPCC-AR5 near-term climate simulations

Based on near-term climate simulations for IPCC-AR5 (The Fifth Assessment Report), probabilistic multimodel ensemble prediction (PMME) of decadal variability of surface air temperature in East Asia (20°-50 °N, 100°-145°E) was conducted using the multivariate Gaussian ensemble kernel dressing (GED) methodology. The ensemble system exhibited high performance in hindcasting the decadal (1981-2010) mean and trend of temperature anomalies with respect to 1961-90, with a RPS of 0.94 and 0.88 respectively. The interpretation of PMME for future decades (2006-35) over East Asia was made on the basis of the bivariate probability density of the mean and trend. The results showed that, under the RCP4.5 (Representative Concentration Pathway 4.5 W m-2 ) scenario, the annual mean temperature increases on average by about 1.1-1.2 K and the temperature trend reaches 0.6-0.7 K (30 yr)-1 . The pattern for both quantities was found to be that the temperature increase will be less intense in the south. While the temperature increase in terms of the 30-yr mean was found to be virtually certain, the results for the 30-yr trend showed an almost 25% chance of a negative value. This indicated that, using a multimodel ensemble system, even if a longer-term warming exists for 2006-35 over East Asia, the trend for temperature may produce a negative value. Temperature was found to be more affected by seasonal variability, with the increase in temperature over East Asia more intense in autumn (mainly), faster in summer to the west of 115°E, and faster still in autumn to the east of 115°E.     

Numerical Simulation of CO2 Concentrations in East Asia with RAMS-CMAQ

The regional air quality modeling system RAMS-CMAQ(Regional Atmospheric Modeling System and Models-3 Community Multi-scale Air Quality) was developed by incorporating a vegetation photosynthesis and respiration module(VPRM) and used to simulate temporal-spatial variations in atmospheric CO2 concentrations in East Asia,with prescribed surface CO2 fluxes(i.e.,fossil fuel emission,biomass burning,sea-air CO2 exchange,and terrestrial biosphere CO2 flux).Comparison of modeled CO2 mixing ratios with eight ground-based in-situ measurements demonstrated that the model was able to capture most observed CO2 temporal-spatial features.Simulated CO2 concentrations were generally in good agreement with observed concentrations.Results indicated that the accumulated impacts of anthropogenic emissions contributed more to increased CO2 concentrations in urban regions relative to remote locations.Moreover,RAMS-CMAQ analysis demonstrates that surface CO2 concentrations in East Asia are strongly influenced by terrestrial ecosystems.     

The First Global Carbon Dioxide Flux Map Derived from TanSat Measurements

Space-borne measurements of atmospheric greenhouse gas concentrations provide global observation constraints for top-down estimates of surface carbon flux.Here,the first estimates of the global distribution of carbon surface fluxes inferred from dry-air CO_2 column (XCO_2) measurements by the Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite (Tan Sat) are presented.An ensemble transform Kalman filter (ETKF) data assimilation system coupled with the GEOS-Chem global chemistry transport model is used to optimally fit model simulations with the Tan Sat XCO_2 observations,which were retrieved using the Institute of Atmospheric Physics Carbon dioxide retrieval Algorithm for Satellite remote sensing (IAPCAS).High posterior error reduction (30%–50%) compared with a priori fluxes indicates that assimilating satellite XCO_2 measurements provides highly effective constraints on global carbon flux estimation.Their impacts are also highlighted by significant spatiotemporal shifts in flux patterns over regions critical to the global carbon budget,such as tropical South America and China.An integrated global land carbon net flux of 6.71±0.76 Gt C yr~(-1) over12 months (May 2017–April 2018) is estimated from the Tan Sat XCO_2 data,which is generally consistent with other inversions based on satellite data,such as the JAXA GOSAT and NASA OCO-2 XCO_2 retrievals.However,discrepancies were found in some regional flux estimates,particularly over the Southern Hemisphere,where there may still be uncorrected bias between satellite measurements due to the lack of independent reference observations.The results of this study provide the groundwork for further studies using current or future Tan Sat XCO_2 data together with other surfacebased and space-borne measurements to quantify biosphere–atmosphere carbon exchange.     

SIMULATION OF PRESENT CLIMATE OVER EAST ASIA BY A REGIONAL CLIMATE MODEL

A 15-year simulation of climate over East Asia is conducted with the latest version of a regional climate model RegCM3 nested in one-way mode to the ERA40 Re-analysis data. The performance of themodel in simulating present climate over East Asia and China is investigated. Results show that RegCM3 can reproduce well the atmospheric circulation over East Asia. The simulation of the main distribution patterns of surface air temperature and precipitation over China and their seasonal cycle/evolution, are basically agree with that of the observation. Meanwhile a general cold bias is found in the simulation. AS for the precipitation, the model tends to overestimate the precipitation in northern China while underestimate it in southern China, particularly in winter. In general, the model has better performance in simulating temperature than precipitation.     

The effect of optimization and the nesting domain on carbon flux analyses in Asia using a carbon tracking system based on the ensemble Kalman filter

To estimate the surface carbon flux in Asia and investigate the effect of the nesting domain on carbon flux analyses in Asia, two experiments with different nesting domains were conducted using the CarbonTracker developed by the National Oceanic and Atmospheric Administration. CarbonTracker is an inverse modeling system that uses an ensemble Kalman filter (EnKF) to estimate surface carbon fluxes from surface CO₂ observations. One experiment was conducted with a nesting domain centered in Asia and the other with a nesting domain centered in North America. Both experiments analyzed the surface carbon fluxes in Asia from 2001 to 2006. The results showed that prior surface carbon fluxes were underestimated in Asia compared with the optimized fluxes. The optimized biosphere fluxes of the two experiments exhibited roughly similar spatial patterns but different magnitudes. Weekly cumulative optimized fluxes showed more diverse patterns than the prior fluxes, indicating that more detailed flux analyses were conducted during the optimization. The nesting domain in Asia produced a detailed estimate of the surface carbon fluxes in Asia and exhibited better agreement with the CO₂ observations. Finally, the simulated background atmospheric CO₂ concentrations in the experiment with the nesting domain in Asia were more consistent with the observed CO₂ concentrations than those in the experiment with the nesting domain in North America. The results of this study suggest that surface carbon fluxes in Asia can be estimated more accurately using an EnKF when the nesting domain is centered in Asian regions.     

Evaluation of East Asian climatology as simulated by seven coupled models

Using observation and reanaiysis data throughout 1961-1990, the East Asian surface air temperature, precipitation and sea level pressure climatology as simulated by seven fully coupled atmosphere-ocean models, namely CCSR/NIES, CGCM2, CSIRO-Mk2, ECHAM4/OPYC3, GFDL-R30, HadCM3, and NCARPCM, axe systematically evaluated in this study. It is indicated that the above models can successfully reproduce the annual and seasonal surface air temperature and precipitation climatology in East Asia, with relatively good performance for boreal autumn and annual mean. The models‘ ability to simulate surface air temperature is more reliable than precipitation. In addition, the models can dependably capture the geographical distribution pattern of annual, boreal winter, spring and autumn sea level pressure in East Asia. In contrast, relatively large simulation errors axe displayed when simulated boreal summer sea level pressure is compaxed with reanalysis data in East Asia. It is revealed that the simulation errors for surface air temperature, precipitation and sea level pressure axe generally large over and around the Tibetan Plateau. No individual model is best in every aspect. As a whole, the ECHAM4/OPYC3 and HadCM3 performances axe much better, whereas the CGCM2 is relatively poorer in East Asia. Additionally, the seven-model ensemble mean usually shows a relatively high reliability.     

A Local Implementation of the POD-Based Ensemble 4DVar with R-Localization

The purpose of this paper is to provide a robust and flexible implementation of a proper orthogonal decomposition-based ensemble four-dimensional variational assimilation method(PODEn4DVar) through Rlocalization.With R-localization,the implementation of the local PODEn4DVar analysis can be coded for parallelization with enhanced assimilation precision.The feasibility and effectiveness of the PODEn4DVar local implementation with R-localization are demonstrated in a two-dimensional shallow-water equation model with simulated observations(OSSEs) in comparison with the original version of the PODEn4DVar with B-localization and that without localization.The performance of the PODEn4DVar with localization shows a significant improvement over the scheme with no localization,particularly under the imperfect model scenario.Moreover,the R-localization scheme is capable of outperforming the Blocalization case to a certain extent.Further,the assimilation experiments also demonstrate that PODEn4DVar with R-localization is most efficient due to its easy parallel implementation.     

An ocean data assimilation system in the Indian Ocean and west Pacific Ocean

The development and application of a regional ocean data assimilation system are among the aims of the Global Ocean Data Assimilation Experiment. The ocean data assimilation system in the regions including the Indian and West Pacific oceans is an endeavor motivated by this goal. In this study, we describe the system in detail. Moreover, the reanalysis in the joint area of Asia, the Indian Ocean, and the western Pacific Ocean(hereafter AIPOcean) constructed using multi-year model integration with data assimilation is used to test the performance of this system. The ocean model is an eddy-resolving,hybrid coordinate ocean model. Various types of observations including in-situ temperature and salinity profiles(mechanical bathythermograph, expendable bathythermograph, Array for Real-time Geostrophic Oceanography, Tropical Atmosphere Ocean Array, conductivity–temperature–depth, station data), remotely-sensed sea surface temperature, and altimetry sea level anomalies, are assimilated into the reanalysis via the ensemble optimal interpolation method. An ensemble of model states sampled from a long-term integration is allowed to change with season, rather than remaining stationary. The estimated background error covariance matrix may reasonably reflect the seasonality and anisotropy. We evaluate the performance of AIPOcean during the period 1993–2006 by comparisons with independent observations, and some reanalysis products. We show that AIPOcean reduces the errors of subsurface temperature and salinity, and reproduces mesoscale eddies. In contrast to ECCO and SODA products, AIPOcean captures the interannual variability and linear trend of sea level anomalies very well. AIPOcean also shows a good consistency with tide gauges.     

A land data assimilation system using the MODIS-derived land data and its application to numerical weather prediction in East Asia

Land Data Assimilation Systems have been developed to generate the surface initial conditions such as soil moisture and temperature for better prediction of weather and climate. We have constructed Korea Land Data Assimilation System (KLDAS) based on an uncoupled land surface modeling framework that integrates high-resolution in-situ observation, satellite data, land surface information from the WRF Preprocessing System (WPS) and the MODIS land products over the East Asia. To present better surface conditions, the KLDAS is driven by atmospheric forcing data from the in-situ rainfall gauges and satellite. In this study, we 1) briefly introduce the KLDAS, 2) evaluate the meteorological states near the surface and the surface fluxes reproduced by the KLDAS against the in-situ observation, and then 3) examine the performance of the mesoscale model initialized by the KLDAS. We have generated a 5-year, 10 km, hourly atmospheric forcing dataset for use in KLDAS operating across East Asia. The KLDAS has effectively reproduced the observed patterns of soil moisture, soil temperature, and surface fluxes. Further scrutiny reveals that the numerical simulations incorporating the KLDAS outputs show better agreement in both the simulated near-surface conditions and rainfall distribution over the Korean Peninsula, compared to those without the KLDAS.     

土壤湿度对东亚夏季气候潜在可预报性影响的数值模拟

利用全球大气环流模式NCARCAM3进行了在给定的观测海温条件下的22a（1979—2000年）5—8月的2组集合试验。运用方差分析方法,分析了在气候态和年际变化的表层土壤湿度情况下,CAM3模式模拟的东亚夏季气候潜在可预报性及其差异。结果表明：在给定的观测海温条件下,采用气候态的土壤湿度时,CAM3模式模拟的东亚夏季气候的潜在可预报性偏低;而采用年际变化的土壤湿度时,模拟的夏季气候潜在可预报性有所提高,尤其是在中国西北地区;后者模拟的中国西北地区夏季降水和气温的潜在可预报性比前者的模拟结果提高0．1以上。其原因可能是：采用年际变化的土壤湿度时,模式可以更好地模拟出中国西北地区的地表蒸发量和湍流热通量的年际变化,进而使得模式对该地区夏季气候的预报技巧得到提高。     

通用陆面模式CLM在东亚不同典型下垫面的验证试验

利用野外观测资料,考察了通用陆面过程模式(CLM)对东亚地区3种典型下垫面(高原稀疏植被下垫面、森林、水田)的模拟能力.验证结果表明,在高原稀疏植被下垫面,CLM模拟的地表气温跟实测较为接近,同时CLM还可以较好地模拟出土壤温度随时间和深度的变化特征,但模式模拟的地面温度的幅值跟观测相比显著偏小;对于能量通量而言,除感热通量外,CLM所模拟出的其它能量通量的变化均与观测实况比较一致.对于淮河流域的森林下垫面,CLM所模拟出陆气间的各能量通量均与实测较为接近,尤以夏季(8月份)的模拟性能最好.对于水田下垫面,CLM模式较好地模拟出了各能量通量的主要变化特征及其季节差异,如水田的净辐射以及潜热通量夏季最大,而感热通量则是秋季最大等.     

基于GRAPES四维变分的静止轨道微波观测系统模拟试验研究

地球静止轨道微波观测同时具有高频次、大视场和穿透云雨的全天候观测能力，因此中国在规划第二代静止轨道气象卫星“风云四号”系列时已经明确提出对静止轨道微波观测的需求。基于中国候选的50、118、183、380和425 GHz五频段静止轨道微波辐射计载荷方案，开展了地球静止轨道微波观测系统模拟试验，理解其观测时间分辨率、频段选取和观测误差对台风预报的影响。试验基于GRAPES全球四维变分同化系统，同化静止轨道微波模拟观测资料，以2018年台风“玛利亚”和“山竹”为例分析了静止轨道微波资料同化对台风预报的影响。试验结果表明，对于静止轨道微波资料同化应用，提高观测资料的时间分辨率、增加通道数量和降低噪声水平能够有效提升台风数值预报性能。      

Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction

A conceptual coupled ocean-atmosphere model was used to study coupled ensemble data assimilation schemes with a focus on the role of ocean-atmosphere interaction in the assimilation. The optimal scheme was the fully coupled data assimilation scheme that employs the coupled covariance matrix and assimilates observations in both the atmosphere and ocean. The assimilation of synoptic atmospheric variability that captures the temporal fluctuation of the weather noise was found to be critical for the estimation of not only the atmospheric, but also oceanic states. The synoptic atmosphere observation was especially important in the mid-latitude system, where oceanic variability is driven by weather noise. The assimilation of synoptic atmospheric variability in the coupled model improved the atmospheric variability in the analysis and the subsequent forecasts, reducing error in the surface forcing and, in turn, in the ocean state. Atmospheric observation was able to further improve the oceanic state estimation directly through the coupled covariance between the atmosphere and ocean states. Relative to the mid-latitude system, the tropical system was influenced more by ocean-atmosphere interaction and, thus, the assimilation of oceanic observation becomes more important for the estimation of the ocean and atmosphere.     

亚印太交汇区的海洋再分析系统

An ocean reanalysis system for the joining area of Asia and Indian-Pacific Ocean (AIPO) has been developed and is currently delivering reanalysis data sets for study on the air-sea interaction over AIPO and its climate variation over China in the inter-annual time scale.This system consists of a nested ocean model forced by atmospheric reanalysis,an ensemble-based multivariate ocean data assimilation system and various ocean observations.The following report describes the main components of the data assimilation system in detail.The system adopts an ensemble optimal interpolation scheme that uses a seasonal update from a free running model to estimate the background error covariance matrix.In view of the systematic biases in some observation systems,some treatments were performed on the observations before the assimilation.A coarse resolution reanalysis dataset from the system is preliminarily evaluated to demonstrate the performance of the system for the period 1992 to 2006 by comparing this dataset with other observations or reanalysis data.