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- Author or Editor: Jinbo Wang x
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On the Long-Wavelength Validation of the SWOT KaRIn MeasurementAbstract
The Surface Water and Ocean Topography (SWOT) mission will measure the sea surface height (SSH) using a Ka-band radar interferometer (KaRIn) over a swath off the nadir of the satellite tracks. The mission requires calibration and validation (CalVal) of the SSH wavenumber spectrum at wavelengths between 15 and 1000 km. The CalVal in the short-wavelength range (15–150 km) requires in situ observations. In the long-wavelength range (150–1000 km), the CalVal will use the onboard Jason-class nadir altimeter. Using a high-resolution global ocean simulation, this study identifies the spatial scales beyond which the nadir and off-nadir observations can be considered comparable. Our results suggest that the ocean signals at nadir can represent off-nadir ocean signals at wavelengths longer than 120 and 70 km along the midswath and the inner edge of the KaRIn grid, respectively, indicating that the nadir altimeter is able to fulfill its goal to validate the long-wavelength KaRIn measurement. The wavelength along the inner edge is limited around 70 km because the onboard nadir altimeter cannot resolve spatial scales longer than ~70 km. These wavelengths provide a reference point for the required spatial coverage of the SWOT SSH in situ CalVal.
Abstract
The Surface Water and Ocean Topography (SWOT) mission will measure the sea surface height (SSH) using a Ka-band radar interferometer (KaRIn) over a swath off the nadir of the satellite tracks. The mission requires calibration and validation (CalVal) of the SSH wavenumber spectrum at wavelengths between 15 and 1000 km. The CalVal in the short-wavelength range (15–150 km) requires in situ observations. In the long-wavelength range (150–1000 km), the CalVal will use the onboard Jason-class nadir altimeter. Using a high-resolution global ocean simulation, this study identifies the spatial scales beyond which the nadir and off-nadir observations can be considered comparable. Our results suggest that the ocean signals at nadir can represent off-nadir ocean signals at wavelengths longer than 120 and 70 km along the midswath and the inner edge of the KaRIn grid, respectively, indicating that the nadir altimeter is able to fulfill its goal to validate the long-wavelength KaRIn measurement. The wavelength along the inner edge is limited around 70 km because the onboard nadir altimeter cannot resolve spatial scales longer than ~70 km. These wavelengths provide a reference point for the required spatial coverage of the SWOT SSH in situ CalVal.
Abstract
Linear and nonlinear radiating instabilities of an eastern boundary current are studied using a barotropic quasigeostrophic model in an idealized meridional channel. The eastern boundary current is meridionally uniform and produces unstable modes in which long waves are most able to radiate. These long radiating modes are easily suppressed by friction because of their small growth rates. However, the long radiating modes can overcome friction by nonlinear energy input transferred from the more unstable trapped mode and play an important role in the energy budget of the boundary current system. The nonlinearly powered long radiating modes take away part of the perturbation energy from the instability origin to the ocean interior. The radiated instabilities can generate zonal striations in the ocean interior that are comparable to features observed in the ocean. Subharmonic instability is identified to be responsible for the nonlinear resonance between the radiating and trapped modes, but more general nonlinear triad interactions are expected to apply in a highly nonlinear environment.
Abstract
Linear and nonlinear radiating instabilities of an eastern boundary current are studied using a barotropic quasigeostrophic model in an idealized meridional channel. The eastern boundary current is meridionally uniform and produces unstable modes in which long waves are most able to radiate. These long radiating modes are easily suppressed by friction because of their small growth rates. However, the long radiating modes can overcome friction by nonlinear energy input transferred from the more unstable trapped mode and play an important role in the energy budget of the boundary current system. The nonlinearly powered long radiating modes take away part of the perturbation energy from the instability origin to the ocean interior. The radiated instabilities can generate zonal striations in the ocean interior that are comparable to features observed in the ocean. Subharmonic instability is identified to be responsible for the nonlinear resonance between the radiating and trapped modes, but more general nonlinear triad interactions are expected to apply in a highly nonlinear environment.
Abstract
The Kerguelen Plateau is a major topographic feature in the Southern Ocean. Located in the Indian sector and spanning nearly 2000 km in the meridional direction from the polar to the subantarctic region, it deflects the eastward-flowing Antarctic Circumpolar Current and influences the physical circulation and biogeochemistry of the Southern Ocean. The Kerguelen Plateau is known to govern the local dynamics, but its impact on the large-scale ocean circulation has not been explored. By comparing global ocean numerical simulations with and without the Kerguelen Plateau, this study identifies two major Kerguelen Plateau effects: 1) The plateau supports a local pressure field that pushes the Antarctic Circumpolar Current northward. This process reduces the warm-water transport from the Indian to the Atlantic Ocean. 2) The plateau-generated pressure field shields the Weddell Gyre from the influence of the warmer subantarctic and subtropical waters. The first effect influences the strength of the Antarctic Circumpolar Current and the Agulhas leakage, both of which are important elements in the global thermohaline circulation. The second effect results in a zonally asymmetric response of the subpolar gyres to Southern Hemisphere wind forcing.
Abstract
The Kerguelen Plateau is a major topographic feature in the Southern Ocean. Located in the Indian sector and spanning nearly 2000 km in the meridional direction from the polar to the subantarctic region, it deflects the eastward-flowing Antarctic Circumpolar Current and influences the physical circulation and biogeochemistry of the Southern Ocean. The Kerguelen Plateau is known to govern the local dynamics, but its impact on the large-scale ocean circulation has not been explored. By comparing global ocean numerical simulations with and without the Kerguelen Plateau, this study identifies two major Kerguelen Plateau effects: 1) The plateau supports a local pressure field that pushes the Antarctic Circumpolar Current northward. This process reduces the warm-water transport from the Indian to the Atlantic Ocean. 2) The plateau-generated pressure field shields the Weddell Gyre from the influence of the warmer subantarctic and subtropical waters. The first effect influences the strength of the Antarctic Circumpolar Current and the Agulhas leakage, both of which are important elements in the global thermohaline circulation. The second effect results in a zonally asymmetric response of the subpolar gyres to Southern Hemisphere wind forcing.
On the Spatial Scales to be Resolved by the Surface Water and Ocean Topography Ka-Band Radar InterferometerAbstract
The Surface Water and Ocean Topography (SWOT) mission aims to measure the sea surface height (SSH) at a high spatial resolution using a Ka-band radar interferometer (KaRIn). The primary oceanographic objective is to characterize the ocean eddies at a spatial resolution of 15 km for 68% of the ocean surface. This resolution is derived from the ratio between the wavenumber spectrum of the conventional altimeter (projected to submesoscale) and the SWOT SSH errors. While the 15-km threshold is useful as a global approximation of the spatial scales resolved by SWOT (SWOT scale), it can be misleading for regional studies. Here we revisit the problem using a high-resolution (~2-km horizontal grid spacing) tide-resolving global ocean simulation and map the SWOT scale as a function of location and season. The results show that the SWOT scale increases, in general, from about 15 km at low latitudes to ~30–45 km at mid- and high latitudes but with a large geographical dependence. A SWOT scale smaller than 30 km is expected in the high-latitude energetic regions. The SWOT scale varies seasonally as a result of the seasonality in both the noise and ocean signals. The seasonality also has a geographical dependence. Both eddies and internal gravity waves/tides contribute significantly to the SWOT scale variation. Our analysis provides model predictions for interpreting the anticipated observations from SWOT and guidance for the development of analysis methodologies.
Abstract
The Surface Water and Ocean Topography (SWOT) mission aims to measure the sea surface height (SSH) at a high spatial resolution using a Ka-band radar interferometer (KaRIn). The primary oceanographic objective is to characterize the ocean eddies at a spatial resolution of 15 km for 68% of the ocean surface. This resolution is derived from the ratio between the wavenumber spectrum of the conventional altimeter (projected to submesoscale) and the SWOT SSH errors. While the 15-km threshold is useful as a global approximation of the spatial scales resolved by SWOT (SWOT scale), it can be misleading for regional studies. Here we revisit the problem using a high-resolution (~2-km horizontal grid spacing) tide-resolving global ocean simulation and map the SWOT scale as a function of location and season. The results show that the SWOT scale increases, in general, from about 15 km at low latitudes to ~30–45 km at mid- and high latitudes but with a large geographical dependence. A SWOT scale smaller than 30 km is expected in the high-latitude energetic regions. The SWOT scale varies seasonally as a result of the seasonality in both the noise and ocean signals. The seasonality also has a geographical dependence. Both eddies and internal gravity waves/tides contribute significantly to the SWOT scale variation. Our analysis provides model predictions for interpreting the anticipated observations from SWOT and guidance for the development of analysis methodologies.
Decomposition of the Multimodal Multidirectional M2 Internal Tide FieldAbstract
The M2 internal tide field contains waves of various baroclinic modes and various horizontal propagation directions. This paper presents a technique for decomposing the sea surface height (SSH) field of the multimodal multidirectional internal tide. The technique consists of two steps: first, different baroclinic modes are decomposed by two-dimensional (2D) spatial filtering, utilizing their different horizontal wavelengths; second, multidirectional waves in each mode are decomposed by 2D plane wave analysis. The decomposition technique is demonstrated using the M2 internal tide field simulated by the MITgcm. This paper focuses on a region lying off the U.S. West Coast ranging 20°–50°N, 220°–245°E. The lowest three baroclinic modes are separately resolved from the internal tide field; each mode is further decomposed into five waves of arbitrary propagation directions in the horizontal. The decomposed fields yield unprecedented details on the internal tide’s generation and propagation, which cannot be observed in the harmonically fitted field. The results reveal that the mode-1 M2 internal tide in the study region is dominantly from the Hawaiian Ridge to the west but also generated locally at the Mendocino Ridge and continental slope. The mode-2 and mode-3 M2 internal tides are generated at isolated seamounts, as well as at the Mendocino Ridge and continental slope. The Mendocino Ridge radiates both southbound and northbound M2 internal tides for all three modes. Their propagation distances decrease with increasing mode number: mode-1 waves can travel over 2000 km, while mode-3 waves can only be tracked for 300 km. The decomposition technique may be extended to other tidal constituents and to the global ocean.
Abstract
The M2 internal tide field contains waves of various baroclinic modes and various horizontal propagation directions. This paper presents a technique for decomposing the sea surface height (SSH) field of the multimodal multidirectional internal tide. The technique consists of two steps: first, different baroclinic modes are decomposed by two-dimensional (2D) spatial filtering, utilizing their different horizontal wavelengths; second, multidirectional waves in each mode are decomposed by 2D plane wave analysis. The decomposition technique is demonstrated using the M2 internal tide field simulated by the MITgcm. This paper focuses on a region lying off the U.S. West Coast ranging 20°–50°N, 220°–245°E. The lowest three baroclinic modes are separately resolved from the internal tide field; each mode is further decomposed into five waves of arbitrary propagation directions in the horizontal. The decomposed fields yield unprecedented details on the internal tide’s generation and propagation, which cannot be observed in the harmonically fitted field. The results reveal that the mode-1 M2 internal tide in the study region is dominantly from the Hawaiian Ridge to the west but also generated locally at the Mendocino Ridge and continental slope. The mode-2 and mode-3 M2 internal tides are generated at isolated seamounts, as well as at the Mendocino Ridge and continental slope. The Mendocino Ridge radiates both southbound and northbound M2 internal tides for all three modes. Their propagation distances decrease with increasing mode number: mode-1 waves can travel over 2000 km, while mode-3 waves can only be tracked for 300 km. The decomposition technique may be extended to other tidal constituents and to the global ocean.
Abstract
A major challenge for food security worldwide is the large interannual variability of crop yield, and climate change is expected to further exacerbate this volatility. Accurate prediction of the crop response to climate variability and change is critical for short-term management and long-term planning in multiple sectors. In this study, using maize in the U.S. Corn Belt as an example, we train and validate multiple machine learning (ML) models predicting crop yield based on meteorological variables and soil properties using the leaving-one-year-out approach, and compare their performance with that of a widely used process-based crop model (PBM). Our proposed long short-term memory model with attention (LSTMatt) outperforms other ML models (including other variations of LSTM developed in this study) and explains 73% of the spatiotemporal variance of the observed maize yield, in contrast to 16% explained by the regionally calibrated PBM; the magnitude of yield prediction errors in LSTMatt is about one-third of that in the PBM. When applied to the extreme drought year 2012 that has no counterpart in the training data, the LSTMatt performance drops but still shows advantage over the PBM. Findings from this study suggest a great potential for out-of-sample application of the LSTMatt model to predict crop yield under a changing climate.
Significance Statement
Changing climate is expected to exacerbate extreme weather events, thus affecting global food security. Accurate estimation and prediction of crop productivity under extremes are crucial for long-term agricultural decision-making and climate adaptation planning. Here we seek to improve crop yield prediction from meteorological features and soil properties using machine learning approaches. Our long short-term memory (LSTM) model with attention and shortcut connection explains 73% of the spatiotemporal variance of the observed maize yield in the U.S. Corn Belt and outperforms a widely used process-based crop model even in an extreme drought year when meteorological conditions are significantly different from the training data. Our findings suggest great potential for out-of-sample application of the LSTM model to predict crop yield under a changing climate.
Abstract
A major challenge for food security worldwide is the large interannual variability of crop yield, and climate change is expected to further exacerbate this volatility. Accurate prediction of the crop response to climate variability and change is critical for short-term management and long-term planning in multiple sectors. In this study, using maize in the U.S. Corn Belt as an example, we train and validate multiple machine learning (ML) models predicting crop yield based on meteorological variables and soil properties using the leaving-one-year-out approach, and compare their performance with that of a widely used process-based crop model (PBM). Our proposed long short-term memory model with attention (LSTMatt) outperforms other ML models (including other variations of LSTM developed in this study) and explains 73% of the spatiotemporal variance of the observed maize yield, in contrast to 16% explained by the regionally calibrated PBM; the magnitude of yield prediction errors in LSTMatt is about one-third of that in the PBM. When applied to the extreme drought year 2012 that has no counterpart in the training data, the LSTMatt performance drops but still shows advantage over the PBM. Findings from this study suggest a great potential for out-of-sample application of the LSTMatt model to predict crop yield under a changing climate.
Significance Statement
Changing climate is expected to exacerbate extreme weather events, thus affecting global food security. Accurate estimation and prediction of crop productivity under extremes are crucial for long-term agricultural decision-making and climate adaptation planning. Here we seek to improve crop yield prediction from meteorological features and soil properties using machine learning approaches. Our long short-term memory (LSTM) model with attention and shortcut connection explains 73% of the spatiotemporal variance of the observed maize yield in the U.S. Corn Belt and outperforms a widely used process-based crop model even in an extreme drought year when meteorological conditions are significantly different from the training data. Our findings suggest great potential for out-of-sample application of the LSTM model to predict crop yield under a changing climate.
Abstract
A new method is proposed for extrapolating subsurface velocity and density fields from sea surface density and sea surface height (SSH). In this, the surface density is linked to the subsurface fields via the surface quasigeostrophic (SQG) formalism, as proposed in several recent papers. The subsurface field is augmented by the addition of the barotropic and first baroclinic modes, whose amplitudes are determined by matching to the sea surface height (pressure), after subtracting the SQG contribution. An additional constraint is that the bottom pressure anomaly vanishes. The method is tested for three regions in the North Atlantic using data from a high-resolution numerical simulation. The decomposition yields strikingly realistic subsurface fields. It is particularly successful in energetic regions like the Gulf Stream extension and at high latitudes where the mixed layer is deep, but it also works in less energetic eastern subtropics. The demonstration highlights the possibility of reconstructing three-dimensional oceanic flows using a combination of satellite fields, for example, sea surface temperature (SST) and SSH, and sparse (or climatological) estimates of the regional depth-resolved density. The method could be further elaborated to integrate additional subsurface information, such as mooring measurements.
Abstract
A new method is proposed for extrapolating subsurface velocity and density fields from sea surface density and sea surface height (SSH). In this, the surface density is linked to the subsurface fields via the surface quasigeostrophic (SQG) formalism, as proposed in several recent papers. The subsurface field is augmented by the addition of the barotropic and first baroclinic modes, whose amplitudes are determined by matching to the sea surface height (pressure), after subtracting the SQG contribution. An additional constraint is that the bottom pressure anomaly vanishes. The method is tested for three regions in the North Atlantic using data from a high-resolution numerical simulation. The decomposition yields strikingly realistic subsurface fields. It is particularly successful in energetic regions like the Gulf Stream extension and at high latitudes where the mixed layer is deep, but it also works in less energetic eastern subtropics. The demonstration highlights the possibility of reconstructing three-dimensional oceanic flows using a combination of satellite fields, for example, sea surface temperature (SST) and SSH, and sparse (or climatological) estimates of the regional depth-resolved density. The method could be further elaborated to integrate additional subsurface information, such as mooring measurements.
Abstract
The transition scale L t from balanced geostrophic motions to unbalanced wave motions, including near-inertial flows, internal tides, and inertia–gravity wave continuum, is explored using the output from a global 1/48° horizontal resolution Massachusetts Institute of Technology general circulation model (MITgcm) simulation. Defined as the wavelength with equal balanced and unbalanced motion kinetic energy (KE) spectral density, L t is detected to be geographically highly inhomogeneous: it falls below 40 km in the western boundary current and Antarctic Circumpolar Current regions, increases to 40–100 km in the interior subtropical and subpolar gyres, and exceeds, in general, 200 km in the tropical oceans. With the exception of the Pacific and Indian sectors of the Southern Ocean, the seasonal KE fluctuations of the surface balanced and unbalanced motions are out of phase because of the occurrence of mixed layer instability in winter and trapping of unbalanced motion KE in shallow mixed layer in summer. The combined effect of these seasonal changes renders L t to be 20 km during winter in 80% of the Northern Hemisphere oceans between 25° and 45°N and all of the Southern Hemisphere oceans south of 25°S. The transition scale’s geographical and seasonal changes are highly relevant to the forthcoming Surface Water and Ocean Topography (SWOT) mission. To improve the detection of balanced submesoscale signals from SWOT, especially in the tropical oceans, efforts to remove stationary internal tidal signals are called for.
Abstract
The transition scale L t from balanced geostrophic motions to unbalanced wave motions, including near-inertial flows, internal tides, and inertia–gravity wave continuum, is explored using the output from a global 1/48° horizontal resolution Massachusetts Institute of Technology general circulation model (MITgcm) simulation. Defined as the wavelength with equal balanced and unbalanced motion kinetic energy (KE) spectral density, L t is detected to be geographically highly inhomogeneous: it falls below 40 km in the western boundary current and Antarctic Circumpolar Current regions, increases to 40–100 km in the interior subtropical and subpolar gyres, and exceeds, in general, 200 km in the tropical oceans. With the exception of the Pacific and Indian sectors of the Southern Ocean, the seasonal KE fluctuations of the surface balanced and unbalanced motions are out of phase because of the occurrence of mixed layer instability in winter and trapping of unbalanced motion KE in shallow mixed layer in summer. The combined effect of these seasonal changes renders L t to be 20 km during winter in 80% of the Northern Hemisphere oceans between 25° and 45°N and all of the Southern Hemisphere oceans south of 25°S. The transition scale’s geographical and seasonal changes are highly relevant to the forthcoming Surface Water and Ocean Topography (SWOT) mission. To improve the detection of balanced submesoscale signals from SWOT, especially in the tropical oceans, efforts to remove stationary internal tidal signals are called for.
Abstract
Reconstructability of upper-ocean vertical velocity w and vorticity ζ fields from high-resolution sea surface height (SSH) data is explored using the global 1/48° horizontal-resolution MITgcm output in the context of the forthcoming Surface Water and Ocean Topography (SWOT) mission. By decomposing w with an omega equation of the primitive equation system and by taking into account the measurement design of the SWOT mission, this study seeks to reconstruct the subinertial, balanced w and ζ signals. By adopting the effective surface quasigeostrophic (eSQG) framework and applying to the Kuroshio Extension region of the North Pacific, we find that the target and reconstructed fields have a spatial correlation of ~0.7 below the mixed layer for w and 0.7–0.9 throughout the 1000-m upper ocean for ζ in the error-free scenario. By taking the SWOT sampling and measurement errors into account, the spatial correlation is found to decrease to 0.4–0.6 below the mixed layer for w and 0.6–0.7 for ζ, respectively. For both w and ζ reconstruction, the degradation due to the SWOT errors is more significant in the surface layer and for smaller-scale signals. The impact of errors lessens with the increasing depth and lengthening horizontal scales.
Abstract
Reconstructability of upper-ocean vertical velocity w and vorticity ζ fields from high-resolution sea surface height (SSH) data is explored using the global 1/48° horizontal-resolution MITgcm output in the context of the forthcoming Surface Water and Ocean Topography (SWOT) mission. By decomposing w with an omega equation of the primitive equation system and by taking into account the measurement design of the SWOT mission, this study seeks to reconstruct the subinertial, balanced w and ζ signals. By adopting the effective surface quasigeostrophic (eSQG) framework and applying to the Kuroshio Extension region of the North Pacific, we find that the target and reconstructed fields have a spatial correlation of ~0.7 below the mixed layer for w and 0.7–0.9 throughout the 1000-m upper ocean for ζ in the error-free scenario. By taking the SWOT sampling and measurement errors into account, the spatial correlation is found to decrease to 0.4–0.6 below the mixed layer for w and 0.6–0.7 for ζ, respectively. For both w and ζ reconstruction, the degradation due to the SWOT errors is more significant in the surface layer and for smaller-scale signals. The impact of errors lessens with the increasing depth and lengthening horizontal scales.
Abstract
A combination of in situ and remotely sensed observations are used to constrain the imprint of submesoscale turbulence in the sea surface height (SSH) field. The distribution of SSH variance across frequencies and wavenumbers is estimated by comparing an empirical model spectrum to two sets of observations. First, submesoscale SSH variance is constrained using a pair of GPS buoys spaced at about 10 km. From these data, one can estimate frequency spectra not only of SSH variance but also of the variance in the SSH difference between the buoys. The ratio between these two spectral estimates is sensitive to how much SSH variance is present in the submesoscale range and thus constrains the spectral roll-off of SSH variance in wavenumber space. Second, a combination of moored current meters and nadir altimetry is used to obtain an independent constraint. This constraint is enabled by geostrophy and the nonseparability of the wavenumber–frequency spectrum of SSH variance revealed by the GPS data. The frequency spectra of kinetic energy and SSH variance follow different power laws, and the difference constrains the spectral content in wavenumber space, allowing for a constraint without the need to actually resolve the submesoscales in space. In all four locations studied, spanning the midlatitude and subtropical ocean, these constraints indicate that the wavenumber spectral roll-off of submesoscale SSH variance is between about k− 4 and k− 5, where k is the wavenumber. These estimates are consistent with previous observations, model results, and theoretical predictions. They provide for a strong prior for the interpretation of upcoming high-resolution satellite data.
Abstract
A combination of in situ and remotely sensed observations are used to constrain the imprint of submesoscale turbulence in the sea surface height (SSH) field. The distribution of SSH variance across frequencies and wavenumbers is estimated by comparing an empirical model spectrum to two sets of observations. First, submesoscale SSH variance is constrained using a pair of GPS buoys spaced at about 10 km. From these data, one can estimate frequency spectra not only of SSH variance but also of the variance in the SSH difference between the buoys. The ratio between these two spectral estimates is sensitive to how much SSH variance is present in the submesoscale range and thus constrains the spectral roll-off of SSH variance in wavenumber space. Second, a combination of moored current meters and nadir altimetry is used to obtain an independent constraint. This constraint is enabled by geostrophy and the nonseparability of the wavenumber–frequency spectrum of SSH variance revealed by the GPS data. The frequency spectra of kinetic energy and SSH variance follow different power laws, and the difference constrains the spectral content in wavenumber space, allowing for a constraint without the need to actually resolve the submesoscales in space. In all four locations studied, spanning the midlatitude and subtropical ocean, these constraints indicate that the wavenumber spectral roll-off of submesoscale SSH variance is between about k− 4 and k− 5, where k is the wavenumber. These estimates are consistent with previous observations, model results, and theoretical predictions. They provide for a strong prior for the interpretation of upcoming high-resolution satellite data.
