2019 Vol. 36, No. 7
Display Method:
2019, (7): 679-696.
doi: 10.1007/s00376-019-8194-y
Abstract:
The terrestrial carbon (C) cycle plays an important role in global climate change, but the vegetation and environmental drivers of C fluxes are poorly understood. We established a global dataset with 1194 available data across site-years including gross primary productivity (GPP), ecosystem respiration (ER), net ecosystem productivity (NEP), and relevant environmental factors to investigate the variability in GPP, ER and NEP, as well as their covariability with climate and vegetation drivers. The results indicated that both GPP and ER increased exponentially with the increase in mean annual temperature (MAT) for all biomes. Besides MAT, annual precipitation (AP) had a strong correlation with GPP (or ER) for non-wetland biomes. Maximum leaf area index (LAI) was an important factor determining C fluxes for all biomes. The variations in both GPP and ER were also associated with variations in vegetation characteristics. The model including MAT, AP and LAI explained 53% of the annual GPP variations and 48% of the annual ER variations across all biomes. The model based on MAT and LAI explained 91% of the annual GPP variations and 92.9% of the annual ER variations for the wetland sites. The effects of LAI on GPP, ER or NEP highlighted that canopy-level measurement is critical for accurately estimating ecosystem-atmosphere exchange of carbon dioxide. The present study suggests a significance of the combined effects of climate and vegetation (e.g., LAI) drivers on C fluxes and shows that climate and LAI might influence C flux components differently in different climate regions.
The terrestrial carbon (C) cycle plays an important role in global climate change, but the vegetation and environmental drivers of C fluxes are poorly understood. We established a global dataset with 1194 available data across site-years including gross primary productivity (GPP), ecosystem respiration (ER), net ecosystem productivity (NEP), and relevant environmental factors to investigate the variability in GPP, ER and NEP, as well as their covariability with climate and vegetation drivers. The results indicated that both GPP and ER increased exponentially with the increase in mean annual temperature (MAT) for all biomes. Besides MAT, annual precipitation (AP) had a strong correlation with GPP (or ER) for non-wetland biomes. Maximum leaf area index (LAI) was an important factor determining C fluxes for all biomes. The variations in both GPP and ER were also associated with variations in vegetation characteristics. The model including MAT, AP and LAI explained 53% of the annual GPP variations and 48% of the annual ER variations across all biomes. The model based on MAT and LAI explained 91% of the annual GPP variations and 92.9% of the annual ER variations for the wetland sites. The effects of LAI on GPP, ER or NEP highlighted that canopy-level measurement is critical for accurately estimating ecosystem-atmosphere exchange of carbon dioxide. The present study suggests a significance of the combined effects of climate and vegetation (e.g., LAI) drivers on C fluxes and shows that climate and LAI might influence C flux components differently in different climate regions.
2019, (7): 697-710.
doi: 10.1007/s00376-019-8196-9
Abstract:
Cloud dominates influence factors of atmospheric radiation, while aerosol-cloud interactions are of vital importance in its spatiotemporal distribution. In this study, a two-moment (mass and number) cloud microphysics scheme, which significantly improved the treatment of the coupled processes of aerosols and clouds, was incorporated into version 1.1 of the IAP/ LASG global Finite-volume Atmospheric Model (FAMIL1.1). For illustrative purposes, the characteristics of the energy balance and cloud radiative forcing (CRF) in an AMIP-type simulation with prescribed aerosols were compared with those in observational/reanalysis data. Even within the constraints of the prescribed aerosol mass, the model simulated global mean energy balance at the top of the atmosphere (TOA) and at the Earth's surface, as well as their seasonal variation, are in good agreement with the observational data. The maximum deviation terms lie in the surface downwelling longwave radiation and surface latent heat flux, which are 3.5 W m-2 (1%) and 3 W m-2 (3.5%), individually. The spatial correlations of the annual TOA net radiation flux and the net CRF between simulation and observation were around 0.97 and 0.90, respectively. A major weakness is that FAMIL1.1 predicts more liquid water content and less ice water content over most oceans. Detailed comparisons are presented for a number of regions, with a focus on the Asian monsoon region (AMR). The results indicate that FAMIL1.1 well reproduces the summer-winter contrast for both the geographical distribution of the longwave CRF and shortwave CRF over the AMR. Finally, the model bias and possible solutions, as well as further works to develop FAMIL1.1 are discussed.
Cloud dominates influence factors of atmospheric radiation, while aerosol-cloud interactions are of vital importance in its spatiotemporal distribution. In this study, a two-moment (mass and number) cloud microphysics scheme, which significantly improved the treatment of the coupled processes of aerosols and clouds, was incorporated into version 1.1 of the IAP/ LASG global Finite-volume Atmospheric Model (FAMIL1.1). For illustrative purposes, the characteristics of the energy balance and cloud radiative forcing (CRF) in an AMIP-type simulation with prescribed aerosols were compared with those in observational/reanalysis data. Even within the constraints of the prescribed aerosol mass, the model simulated global mean energy balance at the top of the atmosphere (TOA) and at the Earth's surface, as well as their seasonal variation, are in good agreement with the observational data. The maximum deviation terms lie in the surface downwelling longwave radiation and surface latent heat flux, which are 3.5 W m-2 (1%) and 3 W m-2 (3.5%), individually. The spatial correlations of the annual TOA net radiation flux and the net CRF between simulation and observation were around 0.97 and 0.90, respectively. A major weakness is that FAMIL1.1 predicts more liquid water content and less ice water content over most oceans. Detailed comparisons are presented for a number of regions, with a focus on the Asian monsoon region (AMR). The results indicate that FAMIL1.1 well reproduces the summer-winter contrast for both the geographical distribution of the longwave CRF and shortwave CRF over the AMR. Finally, the model bias and possible solutions, as well as further works to develop FAMIL1.1 are discussed.
2019, (7): 711-720.
doi: 10.1007/s00376-019-9009-x
Abstract:
The Microwave Temperature Sounder (MWTS)-2 has a total of 13 temperature-sounding channels with the capability of observing radiance emissions from near the surface to the stratosphere. Similar to the Advanced Technology Microwave Sounder (ATMS), striping pattern noise, primarily in the cross-track direction, exists in MWTS-2 radiance observations. In this study, an algorithm based on principal component analysis (PCA) combined with ensemble empirical mode decomposition (EEMD) is described and applied to MWTS-2 brightness temperature observations. It is arguably necessary to smooth the first three principal component (PC) coefficients by removing the first four intrinsic mode functions (IMFs) using the EEMD method (denoted as PC3/IMF4). After the PC3/IMF4 noise mitigation, the striping pattern noise is effectively removed from the brightness temperature observations. The noise level in MWTS-2 observations is significantly higher than that detected in ATMS observations. In May 2014, the scanning profile of MWTS-2 was adjusted from varying-speed scanning to constant-speed scanning. The impact on striping noise levels brought on by this scan profile change is also analyzed here. The striping noise in brightness temperature observations worsened after the profile change. Regardless of the scan profile change, the striping noise mitigation method reported in this study can more or less suppress the noise levels in MWTS-2 observations.
The Microwave Temperature Sounder (MWTS)-2 has a total of 13 temperature-sounding channels with the capability of observing radiance emissions from near the surface to the stratosphere. Similar to the Advanced Technology Microwave Sounder (ATMS), striping pattern noise, primarily in the cross-track direction, exists in MWTS-2 radiance observations. In this study, an algorithm based on principal component analysis (PCA) combined with ensemble empirical mode decomposition (EEMD) is described and applied to MWTS-2 brightness temperature observations. It is arguably necessary to smooth the first three principal component (PC) coefficients by removing the first four intrinsic mode functions (IMFs) using the EEMD method (denoted as PC3/IMF4). After the PC3/IMF4 noise mitigation, the striping pattern noise is effectively removed from the brightness temperature observations. The noise level in MWTS-2 observations is significantly higher than that detected in ATMS observations. In May 2014, the scanning profile of MWTS-2 was adjusted from varying-speed scanning to constant-speed scanning. The impact on striping noise levels brought on by this scan profile change is also analyzed here. The striping noise in brightness temperature observations worsened after the profile change. Regardless of the scan profile change, the striping noise mitigation method reported in this study can more or less suppress the noise levels in MWTS-2 observations.
2019, (7): 721-732.
doi: 10.1007/s00376-019-9001-5
Abstract:
This paper proposes a hybrid method, called CNOP-4DVar, for the identification of sensitive areas in targeted observations, which takes the advantages of both the conditional nonlinear optimal perturbation (CNOP) and four-dimensional variational assimilation (4DVar) methods. The proposed CNOP-4DVar method is capable of capturing the most sensitive initial perturbation (IP), which causes the greatest perturbation growth at the time of verification; it can also identify sensitive areas by evaluating their assimilation effects for eliminating the most sensitive IP. To alleviate the dependence of the CNOP-4DVar method on the adjoint model, which is inherited from the adjoint-based approach, we utilized two adjoint-free methods, NLS-CNOP and NLS-4DVar, to solve the CNOP and 4DVar sub-problems, respectively. A comprehensive performance evaluation for the proposed CNOP-4DVar method and its comparison with the CNOP and CNOP-ensemble transform Kalman filter (ETKF) methods based on 10 000 observing system simulation experiments on the shallow-water equation model are also provided. The experimental results show that the proposed CNOP-4DVar method performs better than the CNOP-ETKF method and substantially better than the CNOP method.
This paper proposes a hybrid method, called CNOP-4DVar, for the identification of sensitive areas in targeted observations, which takes the advantages of both the conditional nonlinear optimal perturbation (CNOP) and four-dimensional variational assimilation (4DVar) methods. The proposed CNOP-4DVar method is capable of capturing the most sensitive initial perturbation (IP), which causes the greatest perturbation growth at the time of verification; it can also identify sensitive areas by evaluating their assimilation effects for eliminating the most sensitive IP. To alleviate the dependence of the CNOP-4DVar method on the adjoint model, which is inherited from the adjoint-based approach, we utilized two adjoint-free methods, NLS-CNOP and NLS-4DVar, to solve the CNOP and 4DVar sub-problems, respectively. A comprehensive performance evaluation for the proposed CNOP-4DVar method and its comparison with the CNOP and CNOP-ensemble transform Kalman filter (ETKF) methods based on 10 000 observing system simulation experiments on the shallow-water equation model are also provided. The experimental results show that the proposed CNOP-4DVar method performs better than the CNOP-ETKF method and substantially better than the CNOP method.
2019, (7): 733-752.
doi: 10.1007/s00376-019-8189-8
Abstract:
The Charney model is reexamined using a new mathematical tool, the multiscale window transform (MWT), and the MWT-based localized multiscale energetics analysis developed by Liang and Robinson to deal with realistic geophysical fluid flow processes. Traditionally, though this model has been taken as a prototype of baroclinic instability, it actually undergoes a mixed one. While baroclinic instability explains the bottom-trapped feature of the perturbation, the second extreme center in the perturbation field can only be explained by a new barotropic instability when the Charney-Green number γ? 1, which takes place throughout the fluid column, and is maximized at a height where its baroclinic counterpart stops functioning. The giving way of the baroclinic instability to a barotropic one at this height corresponds well to the rectification of the tilting found on the maps of perturbation velocity and pressure. Also established in this study is the relative importance of barotropic instability to baroclinic instability in terms of γ. When $\gamma\gg1$, barotropic instability is negligible and hence the system can be viewed as purely baroclinic; when γ?1, however, barotropic and baroclinic instabilities are of the same order; in fact, barotropic instability can be even stronger. The implication of these results has been discussed in linking them to real atmospheric processes.
The Charney model is reexamined using a new mathematical tool, the multiscale window transform (MWT), and the MWT-based localized multiscale energetics analysis developed by Liang and Robinson to deal with realistic geophysical fluid flow processes. Traditionally, though this model has been taken as a prototype of baroclinic instability, it actually undergoes a mixed one. While baroclinic instability explains the bottom-trapped feature of the perturbation, the second extreme center in the perturbation field can only be explained by a new barotropic instability when the Charney-Green number γ? 1, which takes place throughout the fluid column, and is maximized at a height where its baroclinic counterpart stops functioning. The giving way of the baroclinic instability to a barotropic one at this height corresponds well to the rectification of the tilting found on the maps of perturbation velocity and pressure. Also established in this study is the relative importance of barotropic instability to baroclinic instability in terms of γ. When $\gamma\gg1$, barotropic instability is negligible and hence the system can be viewed as purely baroclinic; when γ?1, however, barotropic and baroclinic instabilities are of the same order; in fact, barotropic instability can be even stronger. The implication of these results has been discussed in linking them to real atmospheric processes.
2019, (7): 753-765.
doi: 10.1007/s00376-019-8277-9
Abstract:
By utilizing operational forecast products from TIGGE (The International Grand Global Ensemble) during 2006 to 2015, the forecasting performances of the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), Japan Meteorology Agency (JMA) and China Meteorological Administration (CMA) for the onset of North Atlantic Oscillation (NAO) events are assessed against daily NCEP-NCAR reanalysis data. Twenty-two positive NAO (NAO+) and nine negative NAO (NAO-) events are identified during this time period. For these NAO events, control forecasts, one member of the ensemble that utilizes the currently most proper estimate of the analysis field and the best description of the model physics, are able to predict their onsets three to five days in advance. Moreover, the failure proportion for the prediction of NAO- onset is higher than that for NAO+ onset, which indicates that NAO- onset is harder to forecast. Among these four operational centers, ECMWF has performs best in predicting NAO onset, followed by NCEP, JMA, and then CMA. The forecasting performance of the ensemble mean is also investigated. It is found that, compared with the control forecast, the ensemble mean does not improve the forecasting skill with respect to the onset time of NAO events. Therefore, a confident forecast of NAO onset can only be achieved three to five days in advance.
By utilizing operational forecast products from TIGGE (The International Grand Global Ensemble) during 2006 to 2015, the forecasting performances of the European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), Japan Meteorology Agency (JMA) and China Meteorological Administration (CMA) for the onset of North Atlantic Oscillation (NAO) events are assessed against daily NCEP-NCAR reanalysis data. Twenty-two positive NAO (NAO+) and nine negative NAO (NAO-) events are identified during this time period. For these NAO events, control forecasts, one member of the ensemble that utilizes the currently most proper estimate of the analysis field and the best description of the model physics, are able to predict their onsets three to five days in advance. Moreover, the failure proportion for the prediction of NAO- onset is higher than that for NAO+ onset, which indicates that NAO- onset is harder to forecast. Among these four operational centers, ECMWF has performs best in predicting NAO onset, followed by NCEP, JMA, and then CMA. The forecasting performance of the ensemble mean is also investigated. It is found that, compared with the control forecast, the ensemble mean does not improve the forecasting skill with respect to the onset time of NAO events. Therefore, a confident forecast of NAO onset can only be achieved three to five days in advance.
2019, (7): 766-769.
doi: 10.1007/s00376-019-9022-0
Abstract:
The knowledge garnered in environmental science takes a crucial part in informing decision-making in various fields, including agriculture, transportation, energy, public health and safety, and more. Understanding the basic processes in each of these fields relies greatly on progress being made in conceptual, observational and technological approaches. However, existing instruments for environmental observations are often limited as a result of technical and practical constraints. Current technologies, including remote sensing systems and ground-level measuring means, may suffer from obstacles such as low spatial representativity or a lack of precision when measuring near ground-level. These constraints often limit the ability to carry out extensive meteorological observations and, as a result, the capacity to deepen the existing understanding of atmospheric phenomena and processes. Multi-system informatics and sensing technology have become increasingly distributed as they are embedded into our environment. As they become more widely deployed, these technologies create unprecedented data streams with extraordinary levels of coverage and immediacy, providing a growing opportunity to complement traditional observation techniques using the large volumes of data created. Commercial microwave links that comprise the data transfer infrastructure of cellular communication networks are an example of these types of systems. This viewpoint letter briefly reviews various works on the subject and presents aspects concerning the added value that may be obtained as a result of the integration of these new means, which are becoming available for the first time in this era, for studying and monitoring atmospheric phenomena.
The knowledge garnered in environmental science takes a crucial part in informing decision-making in various fields, including agriculture, transportation, energy, public health and safety, and more. Understanding the basic processes in each of these fields relies greatly on progress being made in conceptual, observational and technological approaches. However, existing instruments for environmental observations are often limited as a result of technical and practical constraints. Current technologies, including remote sensing systems and ground-level measuring means, may suffer from obstacles such as low spatial representativity or a lack of precision when measuring near ground-level. These constraints often limit the ability to carry out extensive meteorological observations and, as a result, the capacity to deepen the existing understanding of atmospheric phenomena and processes. Multi-system informatics and sensing technology have become increasingly distributed as they are embedded into our environment. As they become more widely deployed, these technologies create unprecedented data streams with extraordinary levels of coverage and immediacy, providing a growing opportunity to complement traditional observation techniques using the large volumes of data created. Commercial microwave links that comprise the data transfer infrastructure of cellular communication networks are an example of these types of systems. This viewpoint letter briefly reviews various works on the subject and presents aspects concerning the added value that may be obtained as a result of the integration of these new means, which are becoming available for the first time in this era, for studying and monitoring atmospheric phenomena.
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