Abstract
Radiative feedbacks over interannual time scales can be potentially useful for global warming estimation. However, the diversity of the lead–lag relationships in global mean surface temperature (GMST) and net radiation flux at the top of the atmosphere (GMTOA) create uncertainty during the estimation of radiative feedbacks. In this study, key physical processes controlling lead–lag relationships were elucidated by categorizing preindustrial control simulations of CMIP6 into three groups based on cross correlation values of GMTOA against GMST at lag 0 and lag +1 year. The diversity in the lead–lag relationships was primarily caused by the climatological state difference of the atmosphere over the equatorial Pacific, which modulated the strength of convective activity and sensitivity of low-level clouds. Diminished atmospheric stability caused enhanced convective activity, more efficient energy release, and smaller lags. In addition, enhanced stability in the lower atmosphere rendered the low-level clouds more sensitive to sea surface temperature changes and considerably delayed the radiative response. The climatological state difference of the atmosphere resulted from model-inherent atmospheric conditions. These findings suggest that the diversity of lead–lag relationships of GMST and GMTOA over interannual time scales could represent the characteristics of general atmospheric circulation models and possible solutions of the actual atmosphere, which could also affect long-term feedback features.
Abstract
Radiative feedbacks over interannual time scales can be potentially useful for global warming estimation. However, the diversity of the lead–lag relationships in global mean surface temperature (GMST) and net radiation flux at the top of the atmosphere (GMTOA) create uncertainty during the estimation of radiative feedbacks. In this study, key physical processes controlling lead–lag relationships were elucidated by categorizing preindustrial control simulations of CMIP6 into three groups based on cross correlation values of GMTOA against GMST at lag 0 and lag +1 year. The diversity in the lead–lag relationships was primarily caused by the climatological state difference of the atmosphere over the equatorial Pacific, which modulated the strength of convective activity and sensitivity of low-level clouds. Diminished atmospheric stability caused enhanced convective activity, more efficient energy release, and smaller lags. In addition, enhanced stability in the lower atmosphere rendered the low-level clouds more sensitive to sea surface temperature changes and considerably delayed the radiative response. The climatological state difference of the atmosphere resulted from model-inherent atmospheric conditions. These findings suggest that the diversity of lead–lag relationships of GMST and GMTOA over interannual time scales could represent the characteristics of general atmospheric circulation models and possible solutions of the actual atmosphere, which could also affect long-term feedback features.
Abstract
Two global atmospheric circulation datasets (ERA5 and NCEP-FNL) with horizontal resolutions of 0.25°×0.25° are investigated in terms of kinetic energy (KE) spectra at 200 hPa (roughly between 11 and 12 km). The horizontal KE (HKE) in NCEP-FNL is larger and flatter than that in ERA5 at subsynoptic scales and mesoscales. Restoring the energy of this wavenumber range to the physical space shows that the HKE in NCEP-FNL is larger than that in ERA5 over most areas but smaller mainly in the Indo-Pacific warm pool. The spectral budgets show that at these scales, the positive contribution from net vertical flux in ERA5 is stronger than that in NCEP-FNL, while the negative contribution from available potential energy (APE) conversion is smaller; assuming that the atmosphere is in a quasi-stationary state, more dissipation is found in ERA5 than in NCEP-FNL, which should be responsible for the HKE spectrum in ERA5 to be steeper and weaker than that in NCEP-FNL. Our formulation shows that the APE conversion and net vertical flux are related to the pressure vertical velocity (PVV). The APE conversion and net vertical flux differences between the two datasets, like the PVV difference, are mainly from the tropical region. At large scales, the vertical motion in ERA5 is larger than that in NCEP-FNL. The amplitude differences of the PVV spectra between two datasets are consistent with those of the large-scale precipitation spectra associated with microphysics parameterizations. These results support that vertical motion is a key dynamical factor explaining energy discrepancies at mesoscales.
Abstract
Two global atmospheric circulation datasets (ERA5 and NCEP-FNL) with horizontal resolutions of 0.25°×0.25° are investigated in terms of kinetic energy (KE) spectra at 200 hPa (roughly between 11 and 12 km). The horizontal KE (HKE) in NCEP-FNL is larger and flatter than that in ERA5 at subsynoptic scales and mesoscales. Restoring the energy of this wavenumber range to the physical space shows that the HKE in NCEP-FNL is larger than that in ERA5 over most areas but smaller mainly in the Indo-Pacific warm pool. The spectral budgets show that at these scales, the positive contribution from net vertical flux in ERA5 is stronger than that in NCEP-FNL, while the negative contribution from available potential energy (APE) conversion is smaller; assuming that the atmosphere is in a quasi-stationary state, more dissipation is found in ERA5 than in NCEP-FNL, which should be responsible for the HKE spectrum in ERA5 to be steeper and weaker than that in NCEP-FNL. Our formulation shows that the APE conversion and net vertical flux are related to the pressure vertical velocity (PVV). The APE conversion and net vertical flux differences between the two datasets, like the PVV difference, are mainly from the tropical region. At large scales, the vertical motion in ERA5 is larger than that in NCEP-FNL. The amplitude differences of the PVV spectra between two datasets are consistent with those of the large-scale precipitation spectra associated with microphysics parameterizations. These results support that vertical motion is a key dynamical factor explaining energy discrepancies at mesoscales.
Abstract
Large-amplitude internal solitary waves were recently observed in a coastal plain estuary and were hypothesized to evolve from an internal lee wave generated at the channel-shoal interface. To test this mechanism, a 3D nonhydrostatic model with nested domains and adaptive grids was used to investigate the generation of the internal solitary waves and their subsequent nonlinear evolution. A complex sequence of wave propagation and transformation was documented and interpreted using the nonlinear wave theory based on the Korteweg-de Vries equation. During the ebb tide a mode-2 internal lee wave is generated by the interaction between lateral flows and channel-shoal topography. This mode-2 lee wave subsequently propagates onto the shallow shoal and transforms into a mode-1 wave of elevation as strong mixing on the flood tide erases stratification in the bottom boundary layer and the lower branch of the mode-2 wave. The mode-1 wave of elevation evolves into an internal solitary wave due to nonlinear steepening and spatial changes in the wave phase speed. As the solitary wave of elevation continues to propagate over the shoaling bottom, the leading edge moves ahead as a rarefaction wave while the trailing edge steepens and disintegrates into a train of rank-ordered internal solitary waves, due to the combined effects of shoaling and dispersion. Strong turbulence in the bottom boundary layer dissipates wave energy and causes the eventual destruction of the solitary waves. In the meantime, the internal solitary waves can generate elevated shear and dissipation rate in local regions.
Abstract
Large-amplitude internal solitary waves were recently observed in a coastal plain estuary and were hypothesized to evolve from an internal lee wave generated at the channel-shoal interface. To test this mechanism, a 3D nonhydrostatic model with nested domains and adaptive grids was used to investigate the generation of the internal solitary waves and their subsequent nonlinear evolution. A complex sequence of wave propagation and transformation was documented and interpreted using the nonlinear wave theory based on the Korteweg-de Vries equation. During the ebb tide a mode-2 internal lee wave is generated by the interaction between lateral flows and channel-shoal topography. This mode-2 lee wave subsequently propagates onto the shallow shoal and transforms into a mode-1 wave of elevation as strong mixing on the flood tide erases stratification in the bottom boundary layer and the lower branch of the mode-2 wave. The mode-1 wave of elevation evolves into an internal solitary wave due to nonlinear steepening and spatial changes in the wave phase speed. As the solitary wave of elevation continues to propagate over the shoaling bottom, the leading edge moves ahead as a rarefaction wave while the trailing edge steepens and disintegrates into a train of rank-ordered internal solitary waves, due to the combined effects of shoaling and dispersion. Strong turbulence in the bottom boundary layer dissipates wave energy and causes the eventual destruction of the solitary waves. In the meantime, the internal solitary waves can generate elevated shear and dissipation rate in local regions.
Abstract
Statistical post-processing of global ensemble weather forecasts is revisited by leveraging recent developments in machine learning. Verification of past forecasts is exploited to learn systematic deficiencies of numerical weather predictions in order to boost post-processed forecast performance. Here, we introduce PoET, a post-processing approach based on hierarchical transformers. PoET has 2 major characteristics: 1) the post-processing is applied directly to the ensemble members rather than to a predictive distribution or a functional of it, and 2) the method is ensemble-size agnostic in the sense that the number of ensemble members in training and inference mode can differ. The PoET output is a set of calibrated members that has the same size as the original ensemble but with improved reliability. Performance assessments show that PoET can bring up to 20% improvement in skill globally for 2m temperature and 2% for precipitation forecasts and outperforms the simpler statistical member-by-member method, used here as a competitive benchmark. PoET is also applied to the ENS10 benchmark dataset for ensemble post-processing and provides better results when compared to other deep learning solutions that are evaluated for most parameters. Furthermore, because each ensemble member is calibrated separately, downstream applications should directly benefit from the improvement made on the ensemble forecast with post-processing.
Abstract
Statistical post-processing of global ensemble weather forecasts is revisited by leveraging recent developments in machine learning. Verification of past forecasts is exploited to learn systematic deficiencies of numerical weather predictions in order to boost post-processed forecast performance. Here, we introduce PoET, a post-processing approach based on hierarchical transformers. PoET has 2 major characteristics: 1) the post-processing is applied directly to the ensemble members rather than to a predictive distribution or a functional of it, and 2) the method is ensemble-size agnostic in the sense that the number of ensemble members in training and inference mode can differ. The PoET output is a set of calibrated members that has the same size as the original ensemble but with improved reliability. Performance assessments show that PoET can bring up to 20% improvement in skill globally for 2m temperature and 2% for precipitation forecasts and outperforms the simpler statistical member-by-member method, used here as a competitive benchmark. PoET is also applied to the ENS10 benchmark dataset for ensemble post-processing and provides better results when compared to other deep learning solutions that are evaluated for most parameters. Furthermore, because each ensemble member is calibrated separately, downstream applications should directly benefit from the improvement made on the ensemble forecast with post-processing.
Abstract
The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.
Abstract
The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.
Abstract
The Weddell Gyre is one of the dominant features of the Southern Ocean circulation and its dynamics have been linked to processes of climatic relevance. Variability in the strength of the gyre’s horizontal transport has been linked to heat transport towards the Antarctic margins and changes in the properties and rates of export of bottom waters from the Weddell Sea region to the abyssal global ocean. However, the precise physical mechanisms that force variability in the Weddell’s lateral circulation across different timescales remain unknown. In this study, we use a barotropic vorticity budget from a mesoscale eddy active model simulation to attribute changes in gyre strength to variability in possible driving processes. We find that the Weddell Gyre’s circulation is sensitive to bottom friction associated with the overflowing dense waters at its western boundary. In particular, an increase in the production of dense waters at the southwestern continental shelf strengthens the bottom flow at the gyre’s western boundary, yet this drives a weakening of the depth-integrated barotropic circulation via increased bottom friction. Strengthening surface winds initially accelerates the gyre, but within a few years the response reverses once dense water production and export increases. These results reveal that the gyre can weaken in response to stronger surface winds, putting into question the traditional assumption of a direct relationship between surface stress and gyre strength in regions where overflowing dense water forms part of the depth-integrated flow.
Abstract
The Weddell Gyre is one of the dominant features of the Southern Ocean circulation and its dynamics have been linked to processes of climatic relevance. Variability in the strength of the gyre’s horizontal transport has been linked to heat transport towards the Antarctic margins and changes in the properties and rates of export of bottom waters from the Weddell Sea region to the abyssal global ocean. However, the precise physical mechanisms that force variability in the Weddell’s lateral circulation across different timescales remain unknown. In this study, we use a barotropic vorticity budget from a mesoscale eddy active model simulation to attribute changes in gyre strength to variability in possible driving processes. We find that the Weddell Gyre’s circulation is sensitive to bottom friction associated with the overflowing dense waters at its western boundary. In particular, an increase in the production of dense waters at the southwestern continental shelf strengthens the bottom flow at the gyre’s western boundary, yet this drives a weakening of the depth-integrated barotropic circulation via increased bottom friction. Strengthening surface winds initially accelerates the gyre, but within a few years the response reverses once dense water production and export increases. These results reveal that the gyre can weaken in response to stronger surface winds, putting into question the traditional assumption of a direct relationship between surface stress and gyre strength in regions where overflowing dense water forms part of the depth-integrated flow.
Abstract
The presence of an aerosol layer in the upper troposphere/lower stratosphere (UT/LS) in South America was identified with the Modern-Era Retrospective analysis for Research and Application Aerosol Reanalysis Version 2 (MERRA-2). This layer, which we shall refer to as the South American Tropopause Aerosol Layer (SATAL) was identified over the Amazon Basin at altitudes between 11-14 km. It exhibits a seasonal behavior similar to the Asian Tropopause Aerosol Layer (ATAL) and the North American Tropopause Aerosol Layer (NATAL). The SATAL is observed from October to March, coinciding with the presence of the South American monsoon. It forms first in the eastern Amazon Basin in October, then moves to the Southern Amazon, where it weakens in December-January and finally dissipates in February-March. We hypothesize that two main factors influence the SATAL formation in the UT/LS: 1) the source of aerosols from Africa; 2) the updraft mass flux from deep convective systems during the active phase of the South American Monsoon System that transports aerosols to the UT/LS. Further satellite observations of aerosols and field campaigns are needed to provide useful information to find the origin and composition of the aerosols in the UT/LS during the South American Monsoon.
Abstract
The presence of an aerosol layer in the upper troposphere/lower stratosphere (UT/LS) in South America was identified with the Modern-Era Retrospective analysis for Research and Application Aerosol Reanalysis Version 2 (MERRA-2). This layer, which we shall refer to as the South American Tropopause Aerosol Layer (SATAL) was identified over the Amazon Basin at altitudes between 11-14 km. It exhibits a seasonal behavior similar to the Asian Tropopause Aerosol Layer (ATAL) and the North American Tropopause Aerosol Layer (NATAL). The SATAL is observed from October to March, coinciding with the presence of the South American monsoon. It forms first in the eastern Amazon Basin in October, then moves to the Southern Amazon, where it weakens in December-January and finally dissipates in February-March. We hypothesize that two main factors influence the SATAL formation in the UT/LS: 1) the source of aerosols from Africa; 2) the updraft mass flux from deep convective systems during the active phase of the South American Monsoon System that transports aerosols to the UT/LS. Further satellite observations of aerosols and field campaigns are needed to provide useful information to find the origin and composition of the aerosols in the UT/LS during the South American Monsoon.
Abstract
Previous studies have indicated that the extratropics can influence ENSO via specific processes. However, it is still unclear to what extent ENSO is influenced by the extratropics in observation. Now we assess this issue by applying the regional data assimilation (RDA) approach in an advanced model, the GFDL CM2.1. Our study confirms a strong extratropical impact on observed ENSO. Quantitatively, the extratropical atmospheric variability poleward of 20° explains 56% of the observed variance of ENSO and greatly influences ∼67% of observed El Niño events during 1969–2008. This extratropical impact is still significant even as far as poleward of 30°. Furthermore, the impact from the southern extratropics is slightly stronger than that from the northern extratropics, partly caused by the Pacific ITCZ location north of the equator and different mixed-layer depth along the Northern Pacific Meridional Mode (NPMM) and the Southern Pacific Meridional Model (SPMM). Our study further shows that all of three super El Niño events, 1972/73, 1982/83 and, 1997/98, are influenced greatly by both hemispheric extratropics, with NPMM and SPMM interfering constructively, while most weak and moderate El Niño events are triggered by only one hemispheric extratropics, with NPMM and SPMM interfering destructively. Besides the extratropical Pacific influence on ENSO via NPMM/SPMM, the extratropics also has a potential impact on ENSO by influencing other tropical oceans and then by inter-basin interactions.
Abstract
Previous studies have indicated that the extratropics can influence ENSO via specific processes. However, it is still unclear to what extent ENSO is influenced by the extratropics in observation. Now we assess this issue by applying the regional data assimilation (RDA) approach in an advanced model, the GFDL CM2.1. Our study confirms a strong extratropical impact on observed ENSO. Quantitatively, the extratropical atmospheric variability poleward of 20° explains 56% of the observed variance of ENSO and greatly influences ∼67% of observed El Niño events during 1969–2008. This extratropical impact is still significant even as far as poleward of 30°. Furthermore, the impact from the southern extratropics is slightly stronger than that from the northern extratropics, partly caused by the Pacific ITCZ location north of the equator and different mixed-layer depth along the Northern Pacific Meridional Mode (NPMM) and the Southern Pacific Meridional Model (SPMM). Our study further shows that all of three super El Niño events, 1972/73, 1982/83 and, 1997/98, are influenced greatly by both hemispheric extratropics, with NPMM and SPMM interfering constructively, while most weak and moderate El Niño events are triggered by only one hemispheric extratropics, with NPMM and SPMM interfering destructively. Besides the extratropical Pacific influence on ENSO via NPMM/SPMM, the extratropics also has a potential impact on ENSO by influencing other tropical oceans and then by inter-basin interactions.
Abstract
Water scarcity threatens agriculture in California. During the last two decades, historically severe droughts have led to severe water shortages. Under projected changes in climate, droughts of greater severity and duration will exacerbate this situation. California produces 80% of the world’s almonds, which require consistent water supplies for irrigation. Almonds are the most commonly grown crop in California, covering nearly 1.4 million acres over about 8,000 farms. In response to these challenges, almond growers are considering a myriad of management strategies to save water and mitigate climate change. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to identify water and orchard management opportunities to maximize water use efficiency and carbon sequestration in almonds and other woody perennial tree crops. The project combines satellite, uncrewed aerial vehicles, and proximal sensing technologies to retrieve key variables used to model surface fluxes and biophysical properties. We aim to advance our understanding of water management and cultural practices on water-carbon relationships in tree-perennial agroecosystems. Through new methods, such as Evapotranspiration-based irrigation scheduling, even a modest 10% decrease in almond orchard irrigation across the state equates to about a third of the water in Lake Oroville, California’s second-largest reservoir, at average levels. From a carbon perspective, almond orchards could sequester 8% of the state’s current greenhouse gas emissions by transitioning toward climate-smart practices. As such, the almond industry is uniquely positioned to curb water-use and contribute to climate change mitigation while maintaining economic viability of almond production. An overview of initial results related to evapotranspiration observational and modeling uncertainty, and carbon sequestration potential are presented in this article.
Abstract
Water scarcity threatens agriculture in California. During the last two decades, historically severe droughts have led to severe water shortages. Under projected changes in climate, droughts of greater severity and duration will exacerbate this situation. California produces 80% of the world’s almonds, which require consistent water supplies for irrigation. Almonds are the most commonly grown crop in California, covering nearly 1.4 million acres over about 8,000 farms. In response to these challenges, almond growers are considering a myriad of management strategies to save water and mitigate climate change. The Tree-crop Remote sensing of Evapotranspiration eXperiment (T-REX) aims to identify water and orchard management opportunities to maximize water use efficiency and carbon sequestration in almonds and other woody perennial tree crops. The project combines satellite, uncrewed aerial vehicles, and proximal sensing technologies to retrieve key variables used to model surface fluxes and biophysical properties. We aim to advance our understanding of water management and cultural practices on water-carbon relationships in tree-perennial agroecosystems. Through new methods, such as Evapotranspiration-based irrigation scheduling, even a modest 10% decrease in almond orchard irrigation across the state equates to about a third of the water in Lake Oroville, California’s second-largest reservoir, at average levels. From a carbon perspective, almond orchards could sequester 8% of the state’s current greenhouse gas emissions by transitioning toward climate-smart practices. As such, the almond industry is uniquely positioned to curb water-use and contribute to climate change mitigation while maintaining economic viability of almond production. An overview of initial results related to evapotranspiration observational and modeling uncertainty, and carbon sequestration potential are presented in this article.
