2022 Vol. 39, No. 9
Display Method:
2022, 39(9): 1-1.
Abstract:
2022, 39(9): 1387-1397.
doi: 10.1007/s00376-022-1309-x
Abstract:
Climatologically, among all ocean basins, the western North Pacific (WNP) has the largest annual number of tropical cyclones (TCs) of around 26 while the Atlantic has around 13, yielding a difference of 13. However, the difference is –7 in 2020, with 30 TCs in the Atlantic and 23 in the WNP, which is the most negative difference within the last 46 years. In fact, during the last 26 years, the difference in TC number is below 10 in ten years, with four years being negative. Such a decreasing difference in TC number can be attributed to the natural multidecadal variation of the Atlantic Multidecadal Oscillation and Interdecadal Pacific Oscillation, as well as other external forcings such as anthropogenic aerosol forcing and increased greenhouse gases, with the additional impact from the La Niña condition. This result has significant implications on climate model projections of future TC activity in the two ocean basins.
Climatologically, among all ocean basins, the western North Pacific (WNP) has the largest annual number of tropical cyclones (TCs) of around 26 while the Atlantic has around 13, yielding a difference of 13. However, the difference is –7 in 2020, with 30 TCs in the Atlantic and 23 in the WNP, which is the most negative difference within the last 46 years. In fact, during the last 26 years, the difference in TC number is below 10 in ten years, with four years being negative. Such a decreasing difference in TC number can be attributed to the natural multidecadal variation of the Atlantic Multidecadal Oscillation and Interdecadal Pacific Oscillation, as well as other external forcings such as anthropogenic aerosol forcing and increased greenhouse gases, with the additional impact from the La Niña condition. This result has significant implications on climate model projections of future TC activity in the two ocean basins.
2022, 39(9): 1398-1416.
doi: 10.1007/s00376-022-1316-y
Abstract:
In this study, we perform a stand-alone sensitivity study using the Los Alamos Sea ice model version 6 (CICE6) to investigate the model sensitivity to two Ice-Ocean (IO) boundary condition approaches. One is the two-equation approach that treats the freezing temperature as a function of the ocean mixed layer (ML) salinity, using two equations to parametrize the IO heat exchanges. Another approach uses the salinity of the IO interface to define the actual freezing temperature, so an equation describing the salt flux at the IO interface is added to the two-equation approach, forming the so-called three-equation approach. We focus on the impact of the three-equation boundary condition on the IO heat exchange and associated basal melt/growth of the sea ice in the Arctic Ocean. Compared with the two-equation simulation, our three-equation simulation shows a reduced oceanic turbulent heat flux, weakened basal melt, increased ice thickness, and reduced sea surface temperature (SST) in the Arctic. These impacts occur mainly at the ice edge regions and manifest themselves in summer. Furthermore, in August, we observed a downward turbulent heat flux from the ice to the ocean ML in two of our three-equation sensitivity runs with a constant heat transfer coefficient (0.006), which caused heat divergence and congelation at the ice bottom. Additionally, the influence of different combinations of heat/salt transfer coefficients and thermal conductivity in the three-equation approach on the model simulated results is assessed. The results presented in this study can provide insight into sea ice model sensitivity to the three-equation IO boundary condition for coupling the CICE6 to climate models.
In this study, we perform a stand-alone sensitivity study using the Los Alamos Sea ice model version 6 (CICE6) to investigate the model sensitivity to two Ice-Ocean (IO) boundary condition approaches. One is the two-equation approach that treats the freezing temperature as a function of the ocean mixed layer (ML) salinity, using two equations to parametrize the IO heat exchanges. Another approach uses the salinity of the IO interface to define the actual freezing temperature, so an equation describing the salt flux at the IO interface is added to the two-equation approach, forming the so-called three-equation approach. We focus on the impact of the three-equation boundary condition on the IO heat exchange and associated basal melt/growth of the sea ice in the Arctic Ocean. Compared with the two-equation simulation, our three-equation simulation shows a reduced oceanic turbulent heat flux, weakened basal melt, increased ice thickness, and reduced sea surface temperature (SST) in the Arctic. These impacts occur mainly at the ice edge regions and manifest themselves in summer. Furthermore, in August, we observed a downward turbulent heat flux from the ice to the ocean ML in two of our three-equation sensitivity runs with a constant heat transfer coefficient (0.006), which caused heat divergence and congelation at the ice bottom. Additionally, the influence of different combinations of heat/salt transfer coefficients and thermal conductivity in the three-equation approach on the model simulated results is assessed. The results presented in this study can provide insight into sea ice model sensitivity to the three-equation IO boundary condition for coupling the CICE6 to climate models.
2022, 39(9): 1417-1431.
doi: 10.1007/s00376-021-1280-y
Abstract:
Three types of previously used numerical methods are revisited for computing the streamfunction ψ and velocity potential χ from the horizontal velocity v in limited domains. The first type, called the SOR-based method, uses a classical successive over-relaxation (SOR) scheme to compute ψ (or χ) first with an arbitrary boundary condition (BC) and then χ (or ψ) with the BC derived from v . The second type, called the spectral method, uses spectral formulations to construct the inner part of (ψ, χ)—the inversion of (vorticity, divergence) with a homogeneous BC, and then the remaining harmonic part of (ψ, χ) with BCs from v . The third type, called the integral method, uses integral formulas to compute the internally induced (ψ, χ)—the inversion of domain-internal (vorticity, divergence) using the free-space Greenꞌs function without BCs and then the remaining harmonic ψ (or χ) with BCs from v minus the internally-induced part. Although these methods have previously been successfully applied to flows in large-scale and synoptic-scale domains, their accuracy is compromised when applied to complex flows over mesoscale domains, as shown in this paper. To resolve this problem, two hybrid approaches, the integral-SOR method and the integral-spectral method, are developed by combining the first step of the integral method with the second step adopted from the SOR-based and spectral methods, respectively. Upon testing these methods on real-case complex flows, the integral-SOR method is significantly more accurate than the integral-spectral method, noting that the latter is still generally more accurate than the three previously-used methods. The integral-SOR method is recommended for future applications and diagnostic studies of complex flows.
Three types of previously used numerical methods are revisited for computing the streamfunction ψ and velocity potential χ from the horizontal velocity v in limited domains. The first type, called the SOR-based method, uses a classical successive over-relaxation (SOR) scheme to compute ψ (or χ) first with an arbitrary boundary condition (BC) and then χ (or ψ) with the BC derived from v . The second type, called the spectral method, uses spectral formulations to construct the inner part of (ψ, χ)—the inversion of (vorticity, divergence) with a homogeneous BC, and then the remaining harmonic part of (ψ, χ) with BCs from v . The third type, called the integral method, uses integral formulas to compute the internally induced (ψ, χ)—the inversion of domain-internal (vorticity, divergence) using the free-space Greenꞌs function without BCs and then the remaining harmonic ψ (or χ) with BCs from v minus the internally-induced part. Although these methods have previously been successfully applied to flows in large-scale and synoptic-scale domains, their accuracy is compromised when applied to complex flows over mesoscale domains, as shown in this paper. To resolve this problem, two hybrid approaches, the integral-SOR method and the integral-spectral method, are developed by combining the first step of the integral method with the second step adopted from the SOR-based and spectral methods, respectively. Upon testing these methods on real-case complex flows, the integral-SOR method is significantly more accurate than the integral-spectral method, noting that the latter is still generally more accurate than the three previously-used methods. The integral-SOR method is recommended for future applications and diagnostic studies of complex flows.
2022, 39(9): 1432-1450.
doi: 10.1007/s00376-022-1341-x
Abstract:
How to accurately address model uncertainties with consideration of the rapid nonlinear error growth characteristics in a convection-allowing system is a crucial issue for performing convection-scale ensemble forecasts. In this study, a new nonlinear model perturbation technique for convective-scale ensemble forecasts is developed to consider a nonlinear representation of model errors in the Global and Regional Assimilation and Prediction Enhanced System (GRAPES) Convection-Allowing Ensemble Prediction System (CAEPS). The nonlinear forcing singular vector (NFSV) approach, that is, conditional nonlinear optimal perturbation-forcing (CNOP-F), is applied in this study, to construct a nonlinear model perturbation method for GRAPES-CAEPS. Three experiments are performed: One of them is the CTL experiment, without adding any model perturbation; the other two are NFSV-perturbed experiments, which are perturbed by NFSV with two different groups of constraint radii to test the sensitivity of the perturbation magnitude constraint. Verification results show that the NFSV-perturbed experiments achieve an overall improvement and produce more skillful forecasts compared to the CTL experiment, which indicates that the nonlinear NFSV-perturbed method can be used as an effective model perturbation method for convection-scale ensemble forecasts. Additionally, the NFSV-L experiment with large perturbation constraints generally performs better than the NFSV-S experiment with small perturbation constraints in the verification for upper-air and surface weather variables. But for precipitation verification, the NFSV-S experiment performs better in forecasts for light precipitation, and the NFSV-L experiment performs better in forecasts for heavier precipitation, indicating that for different precipitation events, the perturbation magnitude constraint must be carefully selected. All the findings above lay a foundation for the design of nonlinear model perturbation methods for future CAEPSs.
How to accurately address model uncertainties with consideration of the rapid nonlinear error growth characteristics in a convection-allowing system is a crucial issue for performing convection-scale ensemble forecasts. In this study, a new nonlinear model perturbation technique for convective-scale ensemble forecasts is developed to consider a nonlinear representation of model errors in the Global and Regional Assimilation and Prediction Enhanced System (GRAPES) Convection-Allowing Ensemble Prediction System (CAEPS). The nonlinear forcing singular vector (NFSV) approach, that is, conditional nonlinear optimal perturbation-forcing (CNOP-F), is applied in this study, to construct a nonlinear model perturbation method for GRAPES-CAEPS. Three experiments are performed: One of them is the CTL experiment, without adding any model perturbation; the other two are NFSV-perturbed experiments, which are perturbed by NFSV with two different groups of constraint radii to test the sensitivity of the perturbation magnitude constraint. Verification results show that the NFSV-perturbed experiments achieve an overall improvement and produce more skillful forecasts compared to the CTL experiment, which indicates that the nonlinear NFSV-perturbed method can be used as an effective model perturbation method for convection-scale ensemble forecasts. Additionally, the NFSV-L experiment with large perturbation constraints generally performs better than the NFSV-S experiment with small perturbation constraints in the verification for upper-air and surface weather variables. But for precipitation verification, the NFSV-S experiment performs better in forecasts for light precipitation, and the NFSV-L experiment performs better in forecasts for heavier precipitation, indicating that for different precipitation events, the perturbation magnitude constraint must be carefully selected. All the findings above lay a foundation for the design of nonlinear model perturbation methods for future CAEPSs.
2022, 39(9): 1451-1471.
doi: 10.1007/s00376-022-1402-1
Abstract:
Representation of cloud microphysical processes is one of the key aspects of numerical models. An improved double-moment bulk cloud microphysics scheme (named IMY) was created based on the standard Milbrandt-Yau (MY) scheme in the Weather Research and Forecasting (WRF) model for the East Asian monsoon region (EAMR). In the IMY scheme, the shape parameters of raindrops, snow particles, and cloud droplet size distributions are variables instead of fixed constants. Specifically, the shape parameters of raindrop and snow size distributions are diagnosed from their respective shape-slope relationships. The shape parameter for the cloud droplet size distribution depends on the total cloud droplet number concentration. In addition, a series of minor improvements involving detailed cloud processes have also been incorporated. The improved scheme was coupled into the WRF model and tested on two heavy rainfall cases over the EAMR. The IMY scheme is shown to reproduce the overall spatial distribution of rainfall and its temporal evolution, evidenced by comparing the modeled results with surface gauge observations. The simulations also successfully capture the cloud features by using satellite and ground-based radar observations as a reference. The IMY has yielded simulation results on the case studies that were comparable, and in ways superior to MY, indicating that the improved scheme shows promise. Although the simulations demonstrated a positive performance evaluation for the IMY scheme, continued experiments are required to further validate the scheme with different weather events.
Representation of cloud microphysical processes is one of the key aspects of numerical models. An improved double-moment bulk cloud microphysics scheme (named IMY) was created based on the standard Milbrandt-Yau (MY) scheme in the Weather Research and Forecasting (WRF) model for the East Asian monsoon region (EAMR). In the IMY scheme, the shape parameters of raindrops, snow particles, and cloud droplet size distributions are variables instead of fixed constants. Specifically, the shape parameters of raindrop and snow size distributions are diagnosed from their respective shape-slope relationships. The shape parameter for the cloud droplet size distribution depends on the total cloud droplet number concentration. In addition, a series of minor improvements involving detailed cloud processes have also been incorporated. The improved scheme was coupled into the WRF model and tested on two heavy rainfall cases over the EAMR. The IMY scheme is shown to reproduce the overall spatial distribution of rainfall and its temporal evolution, evidenced by comparing the modeled results with surface gauge observations. The simulations also successfully capture the cloud features by using satellite and ground-based radar observations as a reference. The IMY has yielded simulation results on the case studies that were comparable, and in ways superior to MY, indicating that the improved scheme shows promise. Although the simulations demonstrated a positive performance evaluation for the IMY scheme, continued experiments are required to further validate the scheme with different weather events.
2022, 39(9): 1472-1486.
doi: 10.1007/s00376-021-1207-7
Abstract:
The quantitative precipitation forecast (QPF) performance by numerical weather prediction (NWP) methods depends fundamentally on the adopted physical parameterization schemes (PS). However, due to the complexity of the physical mechanisms of precipitation processes, the uncertainties of PSs result in a lower QPF performance than their prediction of the basic meteorological variables such as air temperature, wind, geopotential height, and humidity. This study proposes a deep learning model named QPFNet, which uses basic meteorological variables in the ERA5 dataset by fitting a non-linear mapping relationship between the basic variables and precipitation. Basic variables forecasted by the highest-resolution model (HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF) were fed into QPFNet to forecast precipitation. Evaluation results show that QPFNet achieved better QPF performance than ECMWF HRES itself. The threat score for 3-h accumulated precipitation with depths of 0.1, 3, 10, and 20 mm increased by 19.7%, 15.2%, 43.2%, and 87.1%, respectively, indicating the proposed performance QPFNet improved with increasing levels of precipitation. The sensitivities of these meteorological variables for QPF in different pressure layers were analyzed based on the output of the QPFNet, and its performance limitations are also discussed. Using DL to extract features from basic meteorological variables can provide an important reference for QPF, and avoid some uncertainties of PSs.
The quantitative precipitation forecast (QPF) performance by numerical weather prediction (NWP) methods depends fundamentally on the adopted physical parameterization schemes (PS). However, due to the complexity of the physical mechanisms of precipitation processes, the uncertainties of PSs result in a lower QPF performance than their prediction of the basic meteorological variables such as air temperature, wind, geopotential height, and humidity. This study proposes a deep learning model named QPFNet, which uses basic meteorological variables in the ERA5 dataset by fitting a non-linear mapping relationship between the basic variables and precipitation. Basic variables forecasted by the highest-resolution model (HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF) were fed into QPFNet to forecast precipitation. Evaluation results show that QPFNet achieved better QPF performance than ECMWF HRES itself. The threat score for 3-h accumulated precipitation with depths of 0.1, 3, 10, and 20 mm increased by 19.7%, 15.2%, 43.2%, and 87.1%, respectively, indicating the proposed performance QPFNet improved with increasing levels of precipitation. The sensitivities of these meteorological variables for QPF in different pressure layers were analyzed based on the output of the QPFNet, and its performance limitations are also discussed. Using DL to extract features from basic meteorological variables can provide an important reference for QPF, and avoid some uncertainties of PSs.
2022, 39(9): 1487-1500.
doi: 10.1007/s00376-021-1390-6
Abstract:
Langmuir turbulence is a complex turbulent process in the ocean upper mixed layer. The Coriolis parameter has an important effect on Langmuir turbulence through the Coriolis–Stokes force and Ekman effect, however, this effect on Langmuir turbulence has not been systematically investigated. Here, the impact of the Coriolis parameter on Langmuir turbulence with a change of latitude (LAT) from 20°N to 80°N is studied using a non-hydrostatic large eddy simulation model under an ideal condition. The results show that the ratio of the upper mixed layer depth to Ekman depth scale (RME) RME = 0.266 (LAT = 50°N) is a key value (latitude) for the modulation effect of the Coriolis parameter on the mean and turbulent statistics of Langmuir turbulence. It is found that the rate of change of the sea surface temperature, upper mixed layer depth, entrainment flux, crosswind velocity, downwind vertical momentum flux, and turbulent kinetic energy budget terms associated with Langmuir turbulence are more evident at RME ≤ 0.266 (LAT ≤ 50°N) than at RME ≥ 0.266 (LAT ≥ 50°N). However, the rate of change of the depth-averaged crosswind vertical momentum flux does not have a clear variation between RME ≤ 0.266 and RME ≥ 0.266. The complex changes of both Langmuir turbulence characteristics and influence of Langmuir turbulence on the upper mixed layer with latitude presented here may provide more information for further improving Langmuir turbulence parameterization.
Langmuir turbulence is a complex turbulent process in the ocean upper mixed layer. The Coriolis parameter has an important effect on Langmuir turbulence through the Coriolis–Stokes force and Ekman effect, however, this effect on Langmuir turbulence has not been systematically investigated. Here, the impact of the Coriolis parameter on Langmuir turbulence with a change of latitude (LAT) from 20°N to 80°N is studied using a non-hydrostatic large eddy simulation model under an ideal condition. The results show that the ratio of the upper mixed layer depth to Ekman depth scale (RME) RME = 0.266 (LAT = 50°N) is a key value (latitude) for the modulation effect of the Coriolis parameter on the mean and turbulent statistics of Langmuir turbulence. It is found that the rate of change of the sea surface temperature, upper mixed layer depth, entrainment flux, crosswind velocity, downwind vertical momentum flux, and turbulent kinetic energy budget terms associated with Langmuir turbulence are more evident at RME ≤ 0.266 (LAT ≤ 50°N) than at RME ≥ 0.266 (LAT ≥ 50°N). However, the rate of change of the depth-averaged crosswind vertical momentum flux does not have a clear variation between RME ≤ 0.266 and RME ≥ 0.266. The complex changes of both Langmuir turbulence characteristics and influence of Langmuir turbulence on the upper mixed layer with latitude presented here may provide more information for further improving Langmuir turbulence parameterization.
2022, 39(9): 1501-1518.
doi: 10.1007/s00376-022-1285-1
Abstract:
The lack of in situ observations and the uncertainties of the drag coefficient at high wind speeds result in limited understanding of heat flux through the air-sea interface and thus inaccurate estimation of typhoon intensity in numerical models. In this study, buoy observations and numerical simulations from an air-sea coupled model are used to assess the surface heat flux changes and impacts of the drag coefficient parameterization schemes on its simulations during the passage of Typhoon Kalmaegi (2014). Three drag coefficient schemes, which make the drag coefficient increase, level off, and decrease, respectively, are considered. The air-sea coupled model captured both trajectory and intensity changes better than the atmosphere-only model, though with relatively weaker sea surface cooling (SSC) compared to that captured by buoy observations, which led to relatively higher heat flux and thus a stronger typhoon. Different from previous studies, for a moderate typhoon, the coupled simulation with the increasing drag coefficient scheme outputted an intensity most consistent with the observation because of the strongest SSC, reasonable ratio of latent and sensible heat exchange coefficients, and an obvious reduction in the overestimated surface heat flux among all experiments. Results from sensitivity experiments showed that surface heat flux was significantly determined by the drag coefficient-induced SSC rather than the resulting wind speed changes. Only when SSC differs indistinctively (<0.4°C) between the coupled simulations, heat flux showed a weak positive correlation with the drag coefficient-impacted 10-m wind speed. The drag coefficient also played an important role in decreasing heat flux even a long time after the passage of Kalmaegi because of the continuous upwelling from deeper ocean layers driven by the impacted momentum flux through the air-sea interface.
The lack of in situ observations and the uncertainties of the drag coefficient at high wind speeds result in limited understanding of heat flux through the air-sea interface and thus inaccurate estimation of typhoon intensity in numerical models. In this study, buoy observations and numerical simulations from an air-sea coupled model are used to assess the surface heat flux changes and impacts of the drag coefficient parameterization schemes on its simulations during the passage of Typhoon Kalmaegi (2014). Three drag coefficient schemes, which make the drag coefficient increase, level off, and decrease, respectively, are considered. The air-sea coupled model captured both trajectory and intensity changes better than the atmosphere-only model, though with relatively weaker sea surface cooling (SSC) compared to that captured by buoy observations, which led to relatively higher heat flux and thus a stronger typhoon. Different from previous studies, for a moderate typhoon, the coupled simulation with the increasing drag coefficient scheme outputted an intensity most consistent with the observation because of the strongest SSC, reasonable ratio of latent and sensible heat exchange coefficients, and an obvious reduction in the overestimated surface heat flux among all experiments. Results from sensitivity experiments showed that surface heat flux was significantly determined by the drag coefficient-induced SSC rather than the resulting wind speed changes. Only when SSC differs indistinctively (<0.4°C) between the coupled simulations, heat flux showed a weak positive correlation with the drag coefficient-impacted 10-m wind speed. The drag coefficient also played an important role in decreasing heat flux even a long time after the passage of Kalmaegi because of the continuous upwelling from deeper ocean layers driven by the impacted momentum flux through the air-sea interface.
2022, 39(9): 1519-1535.
doi: 10.1007/s00376-021-1336-z
Abstract:
Predicting warm-sector torrential rainfall over South China, which is famous for its destructive power, is one of the most challenging issues of the current numerical forecast field. Insufficient understanding of the key mechanisms underlying this type of event is the root cause. Since understanding the energetics is crucial to understanding the evolutions of various types of weather systems, a general methodology for investigating energetics of torrential rainfall is provided in this study. By applying this methodology to a persistent torrential rainfall event which had concurrent frontal and warm-sector precipitation, the first physical image on the energetics of the warm-sector torrential rainfall is established. This clarifies the energy sources for producing the warm-sector rainfall during this event. For the first time, fundamental similarities and differences between the warm-sector and frontal torrential rainfall are shown in terms of energetics. It is found that these two types of rainfall mainly differed from each other in the lower-tropospheric dynamical features, and their key differences lay in energy sources. Scale interactions (mainly through downscale energy cascade and transport) were a dominant factor for the warm-sector torrential rainfall during this event, whereas, for the frontal torrential rainfall, they were only of secondary importance. Three typical signals in the background environment are found to have supplied energy to the warm-sector torrential rainfall, with the quasi-biweekly oscillation having contributed the most.
Predicting warm-sector torrential rainfall over South China, which is famous for its destructive power, is one of the most challenging issues of the current numerical forecast field. Insufficient understanding of the key mechanisms underlying this type of event is the root cause. Since understanding the energetics is crucial to understanding the evolutions of various types of weather systems, a general methodology for investigating energetics of torrential rainfall is provided in this study. By applying this methodology to a persistent torrential rainfall event which had concurrent frontal and warm-sector precipitation, the first physical image on the energetics of the warm-sector torrential rainfall is established. This clarifies the energy sources for producing the warm-sector rainfall during this event. For the first time, fundamental similarities and differences between the warm-sector and frontal torrential rainfall are shown in terms of energetics. It is found that these two types of rainfall mainly differed from each other in the lower-tropospheric dynamical features, and their key differences lay in energy sources. Scale interactions (mainly through downscale energy cascade and transport) were a dominant factor for the warm-sector torrential rainfall during this event, whereas, for the frontal torrential rainfall, they were only of secondary importance. Three typical signals in the background environment are found to have supplied energy to the warm-sector torrential rainfall, with the quasi-biweekly oscillation having contributed the most.
2022, 39(9): 1536-1560.
doi: 10.1007/s00376-022-1314-0
Abstract:
Microwave Land Surface Emissivity (MLSE) over China under both clear and cloudy sky conditions was retrieved using measurements of recalibrated microwave brightness temperatures (Tbs) from Fengyun-3B Microwave Radiation Imager (FY-3B MWRI), combined with cloud properties derived from Himawari-8 Advanced Himawari Imager (AHI) observations. The contributions from cloud particles and atmospheric gases to the upwelling Tbs at the top of atmosphere were calculated and removed in radiative transfer. The MLSEs at horizontal polarizations at 10.65, 18.7, and 36.5 GHz during 7 July 2015 to 30 June 2019 over China showed high values in the southeast vegetated area and low values in the northwest barren, or sparsely vegetated, area. The maximum values were found in the belt area of the Qinling-Taihang Mountains and the eastern edge of the Qinghai-Tibet Plateau, which is highly consistent with MLSEs derived from AMSR-E. It demonstrates that the measurements of FY-3B MWRI Tbs, including its calibration and validation, are reliable, and the retrieval algorithm developed in this study works well. Seasonal variations of MLSE in China are mainly driven by the combined effects of vegetation, rainfall, and snow cover. In tropical and southern forest regions, the seasonal variation of MLSE is small due to the enhancement from vegetation and the suppression from rainfall. In the boreal area, snow causes a significant decrease of MLSE at 36.5 GHz in winter. Meanwhile, the MLSE at lower frequencies experiences less suppression. In the desert region in Xinjiang, increases of MLSEs at all frequencies are observed with increasing snow cover.
Microwave Land Surface Emissivity (MLSE) over China under both clear and cloudy sky conditions was retrieved using measurements of recalibrated microwave brightness temperatures (Tbs) from Fengyun-3B Microwave Radiation Imager (FY-3B MWRI), combined with cloud properties derived from Himawari-8 Advanced Himawari Imager (AHI) observations. The contributions from cloud particles and atmospheric gases to the upwelling Tbs at the top of atmosphere were calculated and removed in radiative transfer. The MLSEs at horizontal polarizations at 10.65, 18.7, and 36.5 GHz during 7 July 2015 to 30 June 2019 over China showed high values in the southeast vegetated area and low values in the northwest barren, or sparsely vegetated, area. The maximum values were found in the belt area of the Qinling-Taihang Mountains and the eastern edge of the Qinghai-Tibet Plateau, which is highly consistent with MLSEs derived from AMSR-E. It demonstrates that the measurements of FY-3B MWRI Tbs, including its calibration and validation, are reliable, and the retrieval algorithm developed in this study works well. Seasonal variations of MLSE in China are mainly driven by the combined effects of vegetation, rainfall, and snow cover. In tropical and southern forest regions, the seasonal variation of MLSE is small due to the enhancement from vegetation and the suppression from rainfall. In the boreal area, snow causes a significant decrease of MLSE at 36.5 GHz in winter. Meanwhile, the MLSE at lower frequencies experiences less suppression. In the desert region in Xinjiang, increases of MLSEs at all frequencies are observed with increasing snow cover.
2022, 39(9): 1561-1578.
doi: 10.1007/s00376-022-1389-7
Abstract:
Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River (MLYR) is of urgent demand for the local economic and societal development. This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology (NUIST-CFS1.0, previously SINTEX-F). The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands, initialized from 1 March. In addition, the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature (SST) anomalies, but the individual members show great discrepancies, indicating large uncertainty in the forecasts. Furthermore, the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982–2020, albeit with underestimated magnitudes. The Weather Forecast and Research (WRF) downscaling hindcast experiments with a finer resolution of 30 km, which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method, display improved predictions of the extreme summer precipitation anomalies to some extent. However, the performance of the downscaling predictions is highly dependent on the global model forecast skill, suggesting that further improvements on both the global and regional climate models are needed.
Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River (MLYR) is of urgent demand for the local economic and societal development. This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology (NUIST-CFS1.0, previously SINTEX-F). The results show that the model can provide moderate skill in predicting the interannual variations of the MLYR rainbands, initialized from 1 March. In addition, the nine-member ensemble mean can realistically reproduce the links between the MLYR precipitation and tropical sea surface temperature (SST) anomalies, but the individual members show great discrepancies, indicating large uncertainty in the forecasts. Furthermore, the NUIST-CFS1.0 can predict five of the seven extreme summer precipitation anomalies over the MLYR during 1982–2020, albeit with underestimated magnitudes. The Weather Forecast and Research (WRF) downscaling hindcast experiments with a finer resolution of 30 km, which are forced by the large-scale information of the NUIST-CFS1.0 predictions with a spectral nudging method, display improved predictions of the extreme summer precipitation anomalies to some extent. However, the performance of the downscaling predictions is highly dependent on the global model forecast skill, suggesting that further improvements on both the global and regional climate models are needed.
2022, 39(9): 1579-1587.
doi: 10.1007/s00376-022-1291-3
Abstract:
Marine heatwaves (MHWs) are prolonged high-temperature extreme events in the ocean that can be devastating to marine life and seriously impact climate systems and economies. This paper describes the accessibility, content, characteristics, and potential applications of an MHW dataset to facilitate its use in scientific research. Daily intensities of global MHWs from 1982 to 2020 were analyzed using gridded SST data sourced from the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation (OI) SST V2 high-resolution (0.25°) dataset. The analysis shows a linear increase in the frequency of MHWs in most ocean regions of the world as well as significant interdecadal changes. This data product can be used as a basic dataset to study the seasonal to decadal changes in extreme ocean events and explore the effects of global warming on the surface layers of oceans during the last 40 years.
Marine heatwaves (MHWs) are prolonged high-temperature extreme events in the ocean that can be devastating to marine life and seriously impact climate systems and economies. This paper describes the accessibility, content, characteristics, and potential applications of an MHW dataset to facilitate its use in scientific research. Daily intensities of global MHWs from 1982 to 2020 were analyzed using gridded SST data sourced from the National Oceanic and Atmospheric Administration (NOAA) Optimum Interpolation (OI) SST V2 high-resolution (0.25°) dataset. The analysis shows a linear increase in the frequency of MHWs in most ocean regions of the world as well as significant interdecadal changes. This data product can be used as a basic dataset to study the seasonal to decadal changes in extreme ocean events and explore the effects of global warming on the surface layers of oceans during the last 40 years.
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