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2021 Vol. 38, No. 9

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News & Views
The First Global Carbon Dioxide Flux Map Derived from TanSat Measurements
Dongxu YANG, Yi LIU, Liang FENG, Jing WANG, Lu YAO, Zhaonan CAI, Sihong ZHU, Naimeng LU, Daren LYU
2021, 38(9): 1433-1443. doi: 10.1007/s00376-021-1179-7
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 CO2 column (XCO2) measurements by the Chinese Global Carbon Dioxide Monitoring Scientific Experimental Satellite (TanSat) 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 TanSat XCO2 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 XCO2 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 over 12 months (May 2017–April 2018) is estimated from the TanSat XCO2 data, which is generally consistent with other inversions based on satellite data, such as the JAXA GOSAT and NASA OCO-2 XCO2 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 TanSat XCO2 data together with other surface-based and space-borne measurements to quantify biosphere–atmosphere carbon exchange.
Original Paper
A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts
Lei HAN, Mingxuan CHEN, Kangkai CHEN, Haonan CHEN, Yanbiao ZHANG, Bing LU, Linye SONG, Rui QIN
2021, 38(9): 1444-1459. doi: 10.1007/s00376-021-0215-y
Correcting the forecast bias of numerical weather prediction models is important for severe weather warnings. The refined grid forecast requires direct correction on gridded forecast products, as opposed to correcting forecast data only at individual weather stations. In this study, a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model (ECMWF-IFS): 2-m temperature, 2-m relative humidity, 10-m wind speed, and 10-m wind direction, with a forecast lead time of 24 h to 240 h in North China. First, the forecast correction problem is transformed into an image-to-image translation problem in deep learning under the CU-net architecture, which is based on convolutional neural networks. Second, the ECMWF-IFS forecasts and ECMWF reanalysis data (ERA5) from 2005 to 2018 are used as training, validation, and testing datasets. The predictors and labels (ground truth) of the model are created using the ECMWF-IFS and ERA5, respectively. Finally, the correction performance of CU-net is compared with a conventional method, anomaly numerical correction with observations (ANO). Results show that forecasts from CU-net have lower root mean square error, bias, mean absolute error, and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h. CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics, whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity. For the correction of the 10-m wind direction forecast, which is often difficult to achieve, CU-net also improves the correction performance.
A Positivity-preserving Conservative Semi-Lagrangian Multi-moment Global Transport Model on the Cubed Sphere
Jie TANG, Chungang CHEN, Xueshun SHEN, Feng XIAO, Xingliang LI
2021, 38(9): 1460-1473. doi: 10.1007/s00376-021-0393-7
A positivity-preserving conservative semi-Lagrangian transport model by multi-moment finite volume method has been developed on the cubed-sphere grid. Two kinds of moments (i.e., point values (PV moment) at cell interfaces and volume integrated average (VIA moment) value) are defined within a single cell. The PV moment is updated by a conventional semi-Lagrangian method, while the VIA moment is cast by the flux form formulation to assure the exact numerical conservation. Different from the spatial approximation used in the CSL2 (conservative semi-Lagrangian scheme with second order polynomial function) scheme, a monotonic rational function which can effectively remove non-physical oscillations is reconstructed within a single cell by the PV moments and VIA moment. To achieve exactly positive-definite preserving, two kinds of corrections are made on the original conservative semi-Lagrangian with rational function (CSLR) scheme. The resulting scheme is inherently conservative, non-negative, and allows a Courant number larger than one. Moreover, the spatial reconstruction can be performed within a single cell, which is very efficient and economical for practical implementation. In addition, a dimension-splitting approach coupled with multi-moment finite volume scheme is adopted on cubed-sphere geometry, which benefitsthe implementation of the 1D CSLR solver with large Courant number. The proposed model is evaluated by several widely used benchmark tests on cubed-sphere geometry. Numerical results show that the proposed transport model can effectively remove nonphysical oscillations and preserve the numerical non-negativity, and it has the potential to transport the tracers accurately in a real atmospheric model.
Thinner Sea Ice Contribution to the Remarkable Polynya Formation North of Greenland in August 2018
Xiaoyi SHEN, Chang-Qing KE, Bin CHENG, Wentao XIA, Mengmeng LI, Xuening YU, Haili LI
2021, 38(9): 1474-1485. doi: 10.1007/s00376-021-0136-9
In August 2018, a remarkable polynya was observed off the north coast of Greenland, a perennial ice zone where thick sea ice cover persists. In order to investigate the formation process of this polynya, satellite observations, a coupled ice-ocean model, ocean profiling data, and atmosphere reanalysis data were applied. We found that the thinnest sea ice cover in August since 1978 (mean value of 1.1 m, compared to the average value of 2.8 m during 1978−2017) and the modest southerly wind caused by a positive North Atlantic Oscillation (mean value of 0.82, compared to the climatological value of −0.02) were responsible for the formation and maintenance of this polynya. The opening mechanism of this polynya differs from the one formed in February 2018 in the same area caused by persistent anomalously high wind. Sea ice drift patterns have become more responsive to the atmospheric forcing due to thinning of sea ice cover in this region.
Application of Backward Nonlinear Local Lyapunov Exponent Method to Assessing the Relative Impacts of Initial Condition and Model Errors on Local Backward Predictability
Xuan LI, Jie FENG, Ruiqiang DING, Jianping LI
2021, 38(9): 1486-1496. doi: 10.1007/s00376-021-0434-2
Initial condition and model errors both contribute to the loss of atmospheric predictability. However, it remains debatable which type of error has the larger impact on the prediction lead time of specific states. In this study, we perform a theoretical study to investigate the relative effects of initial condition and model errors on local prediction lead time of given states in the Lorenz model. Using the backward nonlinear local Lyapunov exponent method, the prediction lead time, also called local backward predictability limit (LBPL), of given states induced by the two types of errors can be quantitatively estimated. Results show that the structure of the Lorenz attractor leads to a layered distribution of LBPLs of states. On an individual circular orbit, the LBPLs are roughly the same, whereas they are different on different orbits. The spatial distributions of LBPLs show that the relative effects of initial condition and model errors on local backward predictability depend on the locations of given states on the dynamical trajectory and the error magnitudes. When the error magnitude is fixed, the differences between the LBPLs vary with the locations of given states. The larger differences are mainly located on the inner trajectories of regimes. When the error magnitudes are different, the dissimilarities in LBPLs are diverse for the same given state.
Estimation and Long-term Trend Analysis of Surface Solar Radiation in Antarctica: A Case Study of Zhongshan Station
Zhaoliang ZENG, Zemin WANG, Minghu DING, Xiangdong ZHENG, Xiaoyu SUN, Wei ZHU, Kongju ZHU, Jiachun AN, Lin ZANG, Jianping GUO, Baojun ZHANG
2021, 38(9): 1497-1509. doi: 10.1007/s00376-021-0386-6
Long-term, ground-based daily global solar radiation (DGSR) at Zhongshan Station in Antarctica can quantitatively reveal the basic characteristics of Earth’s surface radiation balance and validate satellite data for the Antarctic region. The fixed station was established in 1989, and conventional radiation observations started much later in 2008. In this study, a random forest (RF) model for estimating DGSR is developed using ground meteorological observation data, and a high-precision, long-term DGSR dataset is constructed. Then, the trend of DGSR from 1990 to 2019 at Zhongshan Station, Antarctica is analyzed. The RF model, which performs better than other models, shows a desirable performance of DGSR hindcast estimation with an R2 of 0.984, root-mean-square error of 1.377 MJ m−2, and mean absolute error of 0.828 MJ m−2. The trend of DGSR annual anomalies increases during 1990–2004 and then begins to decrease after 2004. Note that the maximum value of annual anomalies occurs during approximately 2004/05 and is mainly related to the days with precipitation (especially those related to good weather during the polar day period) at this station. In addition to clouds and water vapor, bad weather conditions (such as snowfall, which can result in low visibility and then decreased sunshine duration and solar radiation) are the other major factors affecting solar radiation at this station. The high-precision, long-term estimated DGSR dataset enables further study and understanding of the role of Antarctica in global climate change and the interactions between snow, ice, and atmosphere.
Variational Quality Control of Non-Gaussian Innovations in the GRAPES m3DVAR System: Mass Field Evaluation of Assimilation Experiments
Jie HE, Xulin MA, Xuyang GE, Juanjuan LIU, Wei CHENG, Man-Yau CHAN, Ziniu XIAO
2021, 38(9): 1510-1524. doi: 10.1007/s00376-021-0336-3
The existence of outliers can seriously influence the analysis of variational data assimilation. Quality control allows us to effectively eliminate or absorb these outliers to produce better analysis fields. In particular, variational quality control (VarQC) can process gray zone outliers and is thus broadly used in variational data assimilation systems. In this study, governing equations are derived for two VarQC algorithms that utilize different contaminated Gaussian distributions (CGDs): Gaussian plus flat distribution and Huber norm distribution. As such, these VarQC algorithms can handle outliers that have non-Gaussian innovations. Then, these VarQC algorithms are implemented in the Global/Regional Assimilation and PrEdiction System (GRAPES) model-level three-dimensional variational data assimilation (m3DVAR) system. Tests using artificial observations indicate that the VarQC method using the Huber distribution has stronger robustness for including outliers to improve posterior analysis than the VarQC method using the Gaussian plus flat distribution. Furthermore, real observation experiments show that the distribution of observation analysis weights conform well with theory, indicating that the application of VarQC is effective in the GRAPES m3DVAR system. Subsequent case study and long-period data assimilation experiments show that the spatial distribution and amplitude of the observation analysis weights are related to the analysis increments of the mass field (geopotential height and temperature). Compared to the control experiment, VarQC experiments have noticeably better posterior mass fields. Finally, the VarQC method using the Huber distribution is superior to the VarQC method using the Gaussian plus flat distribution, especially at the middle and lower levels.
Melt Pond Scheme Parameter Estimation Using an Adjoint Model
Yang LU, Xiaochun WANG, Jihai DONG
2021, 38(9): 1525-1536. doi: 10.1007/s00376-021-0305-x
Melt ponds significantly affect Arctic sea ice thermodynamic processes. The melt pond parameterization scheme in the Los Alamos sea ice model (CICE6.0) can predict the volume, area fraction (the ratio between melt pond area to sea ice area in a model grid), and depth of melt ponds. However, this scheme has some uncertain parameters that affect melt pond simulations. These parameters could be determined through a conventional parameter estimation method, which requires a large number of sensitivity simulations. The adjoint model can calculate the parameter sensitivity efficiently. In the present research, an adjoint model was developed for the CESM (Community Earth System Model) melt pond scheme. A melt pond parameter estimation algorithm was then developed based on the CICE6.0 sea ice model, melt pond adjoint model, and L-BFGS (Limited-memory Broyden-Fletcher-Goldfard-Shanno) minimization algorithm. The parameter estimation algorithm was verified under idealized conditions. By using MODIS (Moderate Resolution Imaging Spectroradiometer) melt pond fraction observation as a constraint and the developed parameter estimation algorithm, the melt pond aspect ratio parameter in CESM scheme, which is defined as the ratio between pond depth and pond area fraction, was estimated every eight days during summertime for two different regions in the Arctic. One region was covered by multi-year ice (MYI) and the other by first-year ice (FYI). The estimated parameter was then used in simulations and the results show that: (1) the estimated parameter varies over time and is quite different for MYI and FYI; (2) the estimated parameter improved the simulation of the melt pond fraction.
Seasonal Variations of CH4 Emissions in the Yangtze River Delta Region of China Are Driven by Agricultural Activities
Wenjing HUANG, Timothy J. GRIFFIS, Cheng HU, Wei XIAO, Xuhui LEE
2021, 38(9): 1537-1551. doi: 10.1007/s00376-021-0383-9
Developed regions of the world represent a major atmospheric methane (CH4) source, but these regional emissions remain poorly constrained. The Yangtze River Delta (YRD) region of China is densely populated (about 16% of China's total population) and consists of large anthropogenic and natural CH4 sources. Here, atmospheric CH4 concentrations measured at a 70-m tall tower in the YRD are combined with a scale factor Bayesian inverse (SFBI) modeling approach to constrain seasonal variations in CH4 emissions. Results indicate that in 2018 agricultural soils (AGS, rice production) were the main driver of seasonal variability in atmospheric CH4 concentration. There was an underestimation of emissions from AGS in the a priori inventories (EDGAR—Emissions Database for Global Atmospheric Research v432 or v50), especially during the growing seasons. Posteriori CH4 emissions from AGS accounted for 39% (4.58 Tg, EDGAR v432) to 47% (5.21 Tg, EDGAR v50) of the total CH4 emissions. The posteriori natural emissions (including wetlands and water bodies) were 1.21 Tg and 1.06 Tg, accounting for 10.1% (EDGAR v432) and 9.5% (EDGAR v50) of total emissions in the YRD in 2018. Results show that the dominant factor for seasonal variations in atmospheric concentration in the YRD was AGS, followed by natural sources. In summer, AGS contributed 42% (EDGAR v432) to 64% (EDGAR v50) of the CH4 concentration enhancement while natural sources only contributed about 10% (EDGAR v50) to 15% (EDGAR v432). In addition, the newer version of the EDGAR product (EDGAR v50) provided more reasonable seasonal distribution of CH4 emissions from rice cultivation than the old version (EDGAR v432).
The Significant Role of Radiosonde-measured Cloud-base Height in the Estimation of Cloud Radiative Forcing
Hui XU, Jianping GUO, Jian LI, Lin LIU, Tianmeng CHEN, Xiaoran GUO, Yanmin LYU, Ding WANG, Yi HAN, Qi CHEN, Yong ZHANG
2021, 38(9): 1552-1565. doi: 10.1007/s00376-021-0431-5
The satellite-based quantification of cloud radiative forcing remains poorly understood, due largely to the limitation or uncertainties in characterizing cloud-base height (CBH). Here, we use the CBH data from radiosonde measurements over China in combination with the collocated cloud-top height (CTH) and cloud properties from MODIS/Aqua to quantify the impact of CBH on shortwave cloud radiative forcing (SWCRF). The climatological mean SWCRF at the surface (SWCRFSUR), at the top of the atmosphere (SWCRFTOA), and in the atmosphere (SWCRFATM) are estimated to be −97.14, −84.35, and 12.79 W m−2, respectively for the summers spanning 2010 to 2018 over China. To illustrate the role of the cloud base, we assume four scenarios according to vertical profile patterns of cloud optical depth (COD). Using the CTH and cloud properties from MODIS alone results in large uncertainties for the estimation of SWCRFATM, compared with those under scenarios that consider the CBH. Furthermore, the biases of the CERES estimation of SWCRFATM tend to increase in the presence of thick clouds with low CBH. Additionally, the discrepancy of SWCRFATM relative to that calculated without consideration of CBH varies according to the vertical profile of COD. When a uniform COD vertical profile is assumed, the largest SWCRF discrepancies occur during the early morning or late afternoon. By comparison, the two-point COD vertical distribution assumption has the largest uncertainties occurring at noon when the solar irradiation peaks. These findings justify the urgent need to consider the cloud vertical structures when calculating the SWCRF which is otherwise neglected.
The Initial Errors in the Tropical Indian Ocean that Can Induce a Significant “Spring Predictability Barrier” for La Niña Events and Their Implication for Targeted Observations
Qian ZHOU, Wansuo DUAN, Xu WANG, Xiang LI, Ziqing ZU
2021, 38(9): 1566-1579. doi: 10.1007/s00376-021-0427-1
Initial errors in the tropical Indian Ocean (IO-related initial errors) that are most likely to yield the Spring Prediction Barrier (SPB) for La Niña forecasts are explored by using the CESM model. These initial errors can be classified into two types. Type-1 initial error consists of positive sea temperature errors in the western Indian Ocean and negative sea temperature errors in the eastern Indian Ocean, while the spatial structure of Type-2 initial error is nearly opposite. Both kinds of IO-related initial errors induce positive prediction errors of sea temperature in the Pacific Ocean, leading to under-prediction of La Niña events. Type-1 initial error in the tropical Indian Ocean mainly influences the SSTA in the tropical Pacific Ocean via atmospheric bridge, leading to the development of localized sea temperature errors in the eastern Pacific Ocean. However, for Type-2 initial error, its positive sea temperature errors in the eastern Indian Ocean can induce downwelling error and influence La Niña predictions through an oceanic channel called Indonesian Throughflow. Based on the location of largest SPB-related initial errors, the sensitive area in the tropical Indian Ocean for La Niña predictions is identified. Furthermore, sensitivity experiments show that applying targeted observations in this sensitive area is very useful in decreasing prediction errors of La Niña. Therefore, adopting a targeted observation strategy in the tropical Indian Ocean is a promising approach toward increasing ENSO prediction skill.
Response of Growing Season Gross Primary Production to El Niño in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging
Yueyue LI, Li DAN, Jing PENG, Junbang WANG, Fuqiang YANG, Dongdong GAO, Xiujing YANG, Qiang YU
2021, 38(9): 1580-1595. doi: 10.1007/s00376-021-0265-1
Gross primary production (GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems. A set of validated monthly GPP data from 1957 to 2010 in 0.5° × 0.5° grids of China was weighted from the Multi-scale Terrestrial Model Intercomparison Project using Bayesian model averaging (BMA). The spatial anomalies of detrended BMA GPP during the growing seasons of typical El Niño years indicated that GPP response to El Niño varies with Pacific Decadal Oscillation (PDO) phases: when the PDO was in the cool phase, it was likely that GPP was greater in northern China (32°–38°N, 111°–122°E) and less in the Yangtze River valley (28°–32°N, 111°–122°E); in contrast, when PDO was in the warm phase, the GPP anomalies were usually reversed in these two regions. The consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this phenomenon. The previously published findings on how El Niño during different phases of PDO affecting rainfall in eastern China make the statistical relationship between GPP and El Niño in this study theoretically credible. This paper not only introduces an effective way to use BMA in grids that have mixed plant function types, but also makes it possible to evaluate the carbon cycle in eastern China based on the prediction of El Niño and PDO.
Re-examining Tropical Cyclone Fullness Using Aircraft Reconnaissance Data
Zhehan CHEN, Qingqing LI
2021, 38(9): 1596-1607. doi: 10.1007/s00376-021-0282-0
We use FLIGHT+ aircraft reconnaissance data for tropical cyclones (TCs) in the North Atlantic and Eastern Pacific from 1997 to 2015 to re-examine TC fullness (TCF) characteristics at the flight level. The results show a strong positive correlation between the flight-level TCF and the intensity of TCs, with the flight-level TCF increasing much more rapidly than the near-surface TCF with increasing intensity of the TCs. The tangential wind in small-TCF hurricanes is statistically significantly stronger near the eye center than that in large-TCF hurricanes. Large-TCF hurricanes have a ring-like vorticity structure. No significant correlation is observed between the flight-level TCF and the comparative extent of the vorticity-skirt region occupied in the outer core skirt. The proportion of the rapid filamentation zone in the outer core skirt increases with increasing flight-level TCF. The differences in entropy between the radius of the maximum wind and the outer boundary of the outer core skirt also increase with increasing flight-level TCF.
Research Highlight
A New Semi-Lagrangian Finite Volume Advection Scheme Combines the Best of Both Worlds
2021, 38(9): 1608-1609. doi: 10.1007/s00376-021-1181-0