Abstract
The Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) product combines CERES and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites to create a record of earth radiation budget (ERB) and the associated cloud properties. As the Terra and Aqua orbits are no longer maintained at a fixed mean local time, EBAF recently transitioned to the CERES and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on NOAA-20 to avoid introducing a time-dependent bias in the record. To ensure smooth transitions between the Terra, combined Terra and Aqua (Terra+Aqua), and NOAA-20 portions of the record, regional climatological adjustments derived from the overlap period between missions are applied to anchor the entire record to Terra+Aqua. We estimate the random error in global monthly anomalies following the transitions to be <0.15 W m−2 for top-of-atmosphere (TOA) flux and <0.1% for cloud fraction, much smaller than the standard deviation in the corresponding anomalies. As the number of ERB instruments will decrease from four to one in just 10 years, there is a high probability that a data gap in the EBAF record will occur, making it challenging to maintain continuity. We estimate that there is a 33% probability of a data gap in 2028 and a 60% probability in 2035. Bridging a data gap using computed TOA fluxes from one satellite product and one atmospheric reanalysis results in errors that are a factor of 4 larger than those obtained when there is overlap between successive missions.
Abstract
The Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) product combines CERES and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on the Terra and Aqua satellites to create a record of earth radiation budget (ERB) and the associated cloud properties. As the Terra and Aqua orbits are no longer maintained at a fixed mean local time, EBAF recently transitioned to the CERES and Visible Infrared Imaging Radiometer Suite (VIIRS) instruments on NOAA-20 to avoid introducing a time-dependent bias in the record. To ensure smooth transitions between the Terra, combined Terra and Aqua (Terra+Aqua), and NOAA-20 portions of the record, regional climatological adjustments derived from the overlap period between missions are applied to anchor the entire record to Terra+Aqua. We estimate the random error in global monthly anomalies following the transitions to be <0.15 W m−2 for top-of-atmosphere (TOA) flux and <0.1% for cloud fraction, much smaller than the standard deviation in the corresponding anomalies. As the number of ERB instruments will decrease from four to one in just 10 years, there is a high probability that a data gap in the EBAF record will occur, making it challenging to maintain continuity. We estimate that there is a 33% probability of a data gap in 2028 and a 60% probability in 2035. Bridging a data gap using computed TOA fluxes from one satellite product and one atmospheric reanalysis results in errors that are a factor of 4 larger than those obtained when there is overlap between successive missions.
Abstract
Although links between the atmospheric convergence zone and the local ocean dipole in the South Atlantic are well established, relationships between the South Pacific convergence zone (SPCZ) and the South Pacific quadrupole (SPQ) remain largely unexplored. Based on maximum covariance analysis applied to a 110-yr monthly coupled atmosphere–ocean reanalysis, we describe a coupled quadrupole mode (CQM) that connects the SPCZ and SPQ during austral summer [December–February (DJF)]. The CQM is linked to the “enhanced SPCZ” mode in the atmosphere and the SPQ in the ocean, with the atmospheric signal leading the ocean signal by about 1 month. This coupled mode essentially represents the atmospheric and oceanic responses to a stationary Rossby wave train that propagates from low- to high latitudes before reflecting back toward lower latitudes around 150°E. Coupled atmosphere–ocean feedbacks help to maintain anomalous convective activity in the SPCZ and related circulation anomalies. The stationary waves that organize the CQM are often rooted in anomalous convection over the Maritime Continent and have close connections with the atmospheric wavenumber-4 mode in the midlatitude Southern Hemisphere.
Significance Statement
In this study, we investigate the relationships between coherent large-scale patterns in the South Pacific Ocean and the overlying atmosphere. These patterns, which we refer to as a coupled quadrupole for their four centers of action, impact both local communities and the global climate by shaping rainfall and temperature anomalies across the “four corners” of the South Pacific: east–west and north–south. We show that this coupled quadrupole arises as the joint atmospheric and oceanic response to a large-scale wave that arcs across the entire South Pacific basin more than 10 km above the surface and that feedbacks from the ocean to the atmosphere help it to last longer.
Abstract
Although links between the atmospheric convergence zone and the local ocean dipole in the South Atlantic are well established, relationships between the South Pacific convergence zone (SPCZ) and the South Pacific quadrupole (SPQ) remain largely unexplored. Based on maximum covariance analysis applied to a 110-yr monthly coupled atmosphere–ocean reanalysis, we describe a coupled quadrupole mode (CQM) that connects the SPCZ and SPQ during austral summer [December–February (DJF)]. The CQM is linked to the “enhanced SPCZ” mode in the atmosphere and the SPQ in the ocean, with the atmospheric signal leading the ocean signal by about 1 month. This coupled mode essentially represents the atmospheric and oceanic responses to a stationary Rossby wave train that propagates from low- to high latitudes before reflecting back toward lower latitudes around 150°E. Coupled atmosphere–ocean feedbacks help to maintain anomalous convective activity in the SPCZ and related circulation anomalies. The stationary waves that organize the CQM are often rooted in anomalous convection over the Maritime Continent and have close connections with the atmospheric wavenumber-4 mode in the midlatitude Southern Hemisphere.
Significance Statement
In this study, we investigate the relationships between coherent large-scale patterns in the South Pacific Ocean and the overlying atmosphere. These patterns, which we refer to as a coupled quadrupole for their four centers of action, impact both local communities and the global climate by shaping rainfall and temperature anomalies across the “four corners” of the South Pacific: east–west and north–south. We show that this coupled quadrupole arises as the joint atmospheric and oceanic response to a large-scale wave that arcs across the entire South Pacific basin more than 10 km above the surface and that feedbacks from the ocean to the atmosphere help it to last longer.
Abstract
The tropical Pacific convergence zone plays a crucial role in the global climate system. Previous research studies emphasized the cross-seasonal influence of the South Pacific quadrupole (SPQ) mode on the tropical Pacific climate. This study assesses the relationship between austral summer SPQ and austral winter tropical precipitation in phase 6 of the Coupled Model Intercomparison Project (CMIP6) models. The analysis emphasizes the historical experiments conducted within this time frame, spanning from 1979 to 2014. Our findings reveal that the SPQ is accurately represented in all CMIP6 models, but the connection between SPQ and precipitation is inadequately simulated in most models. To investigate the reasons behind these intermodel differences in reproducing SPQ-related processes, we categorize models into two groups. The comparisons demonstrate that the fidelity of model simulations in replicating the SPQ–tropical precipitation relationship hinges significantly on their capacity to reproduce the positive wind–evaporation–sea surface temperature (WES; SST) feedback over both the southwestern Pacific (25°–10°S; 150°E–160°W) and the southeastern Pacific (30°–10°S; 140°–80°W). This positive WES feedback propagates the SPQ signal into the tropics, intensifying the meridional gradient of SST anomaly in the tropical western-central Pacific, which consequently amplifies convection and rainfall in that area. In the group of models that failed to simulate this relationship accurately, the weakened WES feedback can be traced back to biases in wind speed and its variation. Furthermore, this WES feedback establishes a connection between SPQ and El Niño–Southern Oscillation (ENSO). A better rendition of the SPQ–tropical rainfall connection tends to result in a better simulation of the onset of SPQ-related ENSO events. As a result, this study advances our comprehension of extratropical impacts on the tropics, with the potential to enhance the accuracy of tropical climate simulation and prediction.
Significance Statement
Tropical rainfall plays an important role in the global climate system. Beyond the well-known influence of El Niño–Southern Oscillation (ENSO) on the tropical rainfall, the sea surface temperature (SST) anomaly in the South Pacific has a cross-seasonal impact on the precipitation over the tropical Pacific via air–sea coupled processes. Such SST anomaly pattern shows a quadrupole structure in the extratropical South Pacific, known as the South Pacific quadrupole (SPQ) mode. However, the relationship between SPQ and tropical precipitation remains poorly simulated in most state-of-the-art climate models. One primary reason for this gap between observed and simulated relationships is the underestimation of wind speed and its variation over the south tropical Pacific in these models. This limitation undermines their ability to accurately represent the air–sea interactions that drive tropical precipitation patterns, leading to inaccuracies in simulations. Our study aims to bridge this knowledge gap by enhancing our understanding of the extratropical effects on the tropical Pacific. By exploring the mechanisms underlying the SPQ–precipitation connection, we expect to improve the simulation and prediction capabilities of tropical climate models, thereby enhancing our ability to forecast and adapt to future climatic changes.
Abstract
The tropical Pacific convergence zone plays a crucial role in the global climate system. Previous research studies emphasized the cross-seasonal influence of the South Pacific quadrupole (SPQ) mode on the tropical Pacific climate. This study assesses the relationship between austral summer SPQ and austral winter tropical precipitation in phase 6 of the Coupled Model Intercomparison Project (CMIP6) models. The analysis emphasizes the historical experiments conducted within this time frame, spanning from 1979 to 2014. Our findings reveal that the SPQ is accurately represented in all CMIP6 models, but the connection between SPQ and precipitation is inadequately simulated in most models. To investigate the reasons behind these intermodel differences in reproducing SPQ-related processes, we categorize models into two groups. The comparisons demonstrate that the fidelity of model simulations in replicating the SPQ–tropical precipitation relationship hinges significantly on their capacity to reproduce the positive wind–evaporation–sea surface temperature (WES; SST) feedback over both the southwestern Pacific (25°–10°S; 150°E–160°W) and the southeastern Pacific (30°–10°S; 140°–80°W). This positive WES feedback propagates the SPQ signal into the tropics, intensifying the meridional gradient of SST anomaly in the tropical western-central Pacific, which consequently amplifies convection and rainfall in that area. In the group of models that failed to simulate this relationship accurately, the weakened WES feedback can be traced back to biases in wind speed and its variation. Furthermore, this WES feedback establishes a connection between SPQ and El Niño–Southern Oscillation (ENSO). A better rendition of the SPQ–tropical rainfall connection tends to result in a better simulation of the onset of SPQ-related ENSO events. As a result, this study advances our comprehension of extratropical impacts on the tropics, with the potential to enhance the accuracy of tropical climate simulation and prediction.
Significance Statement
Tropical rainfall plays an important role in the global climate system. Beyond the well-known influence of El Niño–Southern Oscillation (ENSO) on the tropical rainfall, the sea surface temperature (SST) anomaly in the South Pacific has a cross-seasonal impact on the precipitation over the tropical Pacific via air–sea coupled processes. Such SST anomaly pattern shows a quadrupole structure in the extratropical South Pacific, known as the South Pacific quadrupole (SPQ) mode. However, the relationship between SPQ and tropical precipitation remains poorly simulated in most state-of-the-art climate models. One primary reason for this gap between observed and simulated relationships is the underestimation of wind speed and its variation over the south tropical Pacific in these models. This limitation undermines their ability to accurately represent the air–sea interactions that drive tropical precipitation patterns, leading to inaccuracies in simulations. Our study aims to bridge this knowledge gap by enhancing our understanding of the extratropical effects on the tropical Pacific. By exploring the mechanisms underlying the SPQ–precipitation connection, we expect to improve the simulation and prediction capabilities of tropical climate models, thereby enhancing our ability to forecast and adapt to future climatic changes.
Abstract
An important source of errors in predicting landfalling atmospheric rivers (ARs), and their associated extreme precipitation and streamflow, are inaccuracies in model initial conditions in ARs offshore. These inaccuracies are particularly evident in the marine boundary layer (MBL) where the tendency of ARs to transport warm air poleward over progressively cooler SSTs generates a stable MBL (SMBL).
The SMBL’s vertical structure and key modulating processes are documented using >1000 dropsondes along 99 transects of ARs from the AR Reconnaissance and CalWater field campaigns. The SMBL depth, modulated by sensible heat loss to the ocean, is typically 300–800 m in the AR core, with vertical wind shears ranging from 5–50 m s−1 km−1, representing a highly variable decoupling of the AR aloft from conditions at the ocean surface.
Simulated backward air parcel trajectories originating from dropsonde locations within the AR core are used to calculate the 24-h change in SST experienced by an air parcel (DSST24) beneath each AR. The DSST24 varies from −13°C to +2°C and is directly related to the strength of the AR and its orientation relative to the SST gradient. The DSST24, therefore, distinguishes weak and strong decoupling regimes (WDR, SDR).
In SDR cases, relative to WDR cases, the SMBL is characterized by greater sensible heat loss to the ocean, as well as stronger static stability, vertical wind shear, low-level jet, and horizontal water vapor transport. In SDR cases, the SMBL is deeper in the core than in adjacent warm and cold sectors.
Abstract
An important source of errors in predicting landfalling atmospheric rivers (ARs), and their associated extreme precipitation and streamflow, are inaccuracies in model initial conditions in ARs offshore. These inaccuracies are particularly evident in the marine boundary layer (MBL) where the tendency of ARs to transport warm air poleward over progressively cooler SSTs generates a stable MBL (SMBL).
The SMBL’s vertical structure and key modulating processes are documented using >1000 dropsondes along 99 transects of ARs from the AR Reconnaissance and CalWater field campaigns. The SMBL depth, modulated by sensible heat loss to the ocean, is typically 300–800 m in the AR core, with vertical wind shears ranging from 5–50 m s−1 km−1, representing a highly variable decoupling of the AR aloft from conditions at the ocean surface.
Simulated backward air parcel trajectories originating from dropsonde locations within the AR core are used to calculate the 24-h change in SST experienced by an air parcel (DSST24) beneath each AR. The DSST24 varies from −13°C to +2°C and is directly related to the strength of the AR and its orientation relative to the SST gradient. The DSST24, therefore, distinguishes weak and strong decoupling regimes (WDR, SDR).
In SDR cases, relative to WDR cases, the SMBL is characterized by greater sensible heat loss to the ocean, as well as stronger static stability, vertical wind shear, low-level jet, and horizontal water vapor transport. In SDR cases, the SMBL is deeper in the core than in adjacent warm and cold sectors.
Abstract
Ensemble prediction systems are commonly used to demonstrate the potential uncertainty of weather forecasts. These systems also help provide weather predictions to prepare for disasters. Specifically, a type of prediction called a quantitative precipitation forecast (QPF) is calculated from the average of multiple forecasts. The probability-matched mean (PMM) method is used to improve these QPF predictions when they underestimate rainfall. However, the PMM method can exhibit limitations when dealing with certain types of rain patterns. To address this issue, this study focuses on improving short-term heavy rainfall predictions using an artificial intelligence (AI) algorithm that combines deep neural networks (DNNs), including convolutional neural networks (CNNs), with a space-based attention mechanism (convolutional block attention module [CBAM]).
Four experiments were conducted to evaluate the new method, including those performed to optimize the timing of a 24-hour QPF model, adjust the training dataset, and refine the training algorithm. Two specific weather events were assessed: rainfall influenced by southwest winds after typhoons and afternoon thunderstorm rainfall. A comparison of the results obtained via the root mean square error (RMSE) and structural similarity index measure (SSIM) reveals that the accuracy and distribution of the QPF predictions significantly improved over those of the PMM method. Compared with the PMM method, our approach can reduce the RMSE from 77.51 to 19.52 in the Mei-yu case, an improvement of approximately 74.82%. The SSIM also increased from 0.33 to 0.56, indicating a 70.9% enhancement. Overall, the new approach successfully enhances rainfall prediction accuracy via AI techniques and has the potential to be applied in disaster preparedness operations.
Abstract
Ensemble prediction systems are commonly used to demonstrate the potential uncertainty of weather forecasts. These systems also help provide weather predictions to prepare for disasters. Specifically, a type of prediction called a quantitative precipitation forecast (QPF) is calculated from the average of multiple forecasts. The probability-matched mean (PMM) method is used to improve these QPF predictions when they underestimate rainfall. However, the PMM method can exhibit limitations when dealing with certain types of rain patterns. To address this issue, this study focuses on improving short-term heavy rainfall predictions using an artificial intelligence (AI) algorithm that combines deep neural networks (DNNs), including convolutional neural networks (CNNs), with a space-based attention mechanism (convolutional block attention module [CBAM]).
Four experiments were conducted to evaluate the new method, including those performed to optimize the timing of a 24-hour QPF model, adjust the training dataset, and refine the training algorithm. Two specific weather events were assessed: rainfall influenced by southwest winds after typhoons and afternoon thunderstorm rainfall. A comparison of the results obtained via the root mean square error (RMSE) and structural similarity index measure (SSIM) reveals that the accuracy and distribution of the QPF predictions significantly improved over those of the PMM method. Compared with the PMM method, our approach can reduce the RMSE from 77.51 to 19.52 in the Mei-yu case, an improvement of approximately 74.82%. The SSIM also increased from 0.33 to 0.56, indicating a 70.9% enhancement. Overall, the new approach successfully enhances rainfall prediction accuracy via AI techniques and has the potential to be applied in disaster preparedness operations.
Abstract
El Niño–Southern Oscillation (ENSO) exhibits a considerable asymmetry in sea surface temperature anomalies (SSTa), as El Niño events tend to be stronger and centered further east than La Niña events. Here, we analyze ENSO asymmetry in observations and preindustrial control integrations of 32 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Observations indicate a significant link between strong eastern Pacific (EP) El Niño events and the ENSO amplitude and asymmetry. The large CMIP6 database confirms this strong link. Most CMIP6 models suffer from an equatorial Pacific cold SST bias. This cold tongue bias hinders the southward migration of the ITCZ toward the equator over the eastern equatorial Pacific, which is characteristic of strong EP El Niño events in observations. Therefore, many models underestimate the eastern equatorial Pacific rainfall anomalies and simulate a wind stress feedback over the western Pacific that is too weak and too far west. As a result, the cold tongue bias exerts a strong control on the climate models’ ability to generate strong EP El Niño events and therefore on the ENSO overall amplitude and asymmetry. We discuss the relevance of these results in view of a potential increase of strong EP El Niño events under global warming.
Significance Statement
El Niño and La Niña are asymmetric, as El Niño events tend to be stronger and further east than La Niña events. Due to an equatorial Pacific cold tongue bias with too cold SSTs, the simulation of ENSO asymmetry is degraded in many climate models participating in CMIP6. Here, we show how the cold tongue bias influences ENSO asymmetry. The cold bias hampers the simulation of strong eastern Pacific El Niños by making it more difficult for SST to exceed the threshold for deep atmospheric convection over the eastern equatorial Pacific. Recent research indicates that climate models with a realistic ENSO asymmetry agree on the projected ENSO under global warming. The results of this study suggest that reducing the cold tongue bias has the potential to enhancing the reliability of future ENSO projections.
Abstract
El Niño–Southern Oscillation (ENSO) exhibits a considerable asymmetry in sea surface temperature anomalies (SSTa), as El Niño events tend to be stronger and centered further east than La Niña events. Here, we analyze ENSO asymmetry in observations and preindustrial control integrations of 32 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Observations indicate a significant link between strong eastern Pacific (EP) El Niño events and the ENSO amplitude and asymmetry. The large CMIP6 database confirms this strong link. Most CMIP6 models suffer from an equatorial Pacific cold SST bias. This cold tongue bias hinders the southward migration of the ITCZ toward the equator over the eastern equatorial Pacific, which is characteristic of strong EP El Niño events in observations. Therefore, many models underestimate the eastern equatorial Pacific rainfall anomalies and simulate a wind stress feedback over the western Pacific that is too weak and too far west. As a result, the cold tongue bias exerts a strong control on the climate models’ ability to generate strong EP El Niño events and therefore on the ENSO overall amplitude and asymmetry. We discuss the relevance of these results in view of a potential increase of strong EP El Niño events under global warming.
Significance Statement
El Niño and La Niña are asymmetric, as El Niño events tend to be stronger and further east than La Niña events. Due to an equatorial Pacific cold tongue bias with too cold SSTs, the simulation of ENSO asymmetry is degraded in many climate models participating in CMIP6. Here, we show how the cold tongue bias influences ENSO asymmetry. The cold bias hampers the simulation of strong eastern Pacific El Niños by making it more difficult for SST to exceed the threshold for deep atmospheric convection over the eastern equatorial Pacific. Recent research indicates that climate models with a realistic ENSO asymmetry agree on the projected ENSO under global warming. The results of this study suggest that reducing the cold tongue bias has the potential to enhancing the reliability of future ENSO projections.
Abstract
This work continued the investigation of the relationship between phase transition rates and mass flux in trade wind cumulus clouds. The latter were simulated by an LES model initialized with soundings from the RICO field project. In Part I (Kogan, 2022) we demonstrated that a very high correlation exists between integral phase transition rates and upward mass flux. In this study we focused on the vertically dependent variables and showed that a similar high correlation exists between the condensation rate 𝒞 (z) and the upward mass flux ℳ (z).
Based on condensation theory, we showed that under quasi-steady approximation condensation rates can be calculated by a linear function of ℳ with the slope coefficient dependent only on temperature and pressure. The model data showed that the error of such approximation is less than a few tenths of a percent.
The parameterization of the evaporation process is more complex, mostly because of the slow evaporation of raindrops as they fall through the cloud’s unsaturated areas. Nevertheless, it was possible to define the fraction of evaporation to condensation rate as a function of vertical coordinate z and cloud thickness H. This function can be quite accurately approximated by the 3rd order polynomials of z and H. It is suggested that proposed formulation of evaporation together with the quasi-steady formulation of condensation can serve as a parameterization of water phase transition rates in shallow cumulus clouds.
Abstract
This work continued the investigation of the relationship between phase transition rates and mass flux in trade wind cumulus clouds. The latter were simulated by an LES model initialized with soundings from the RICO field project. In Part I (Kogan, 2022) we demonstrated that a very high correlation exists between integral phase transition rates and upward mass flux. In this study we focused on the vertically dependent variables and showed that a similar high correlation exists between the condensation rate 𝒞 (z) and the upward mass flux ℳ (z).
Based on condensation theory, we showed that under quasi-steady approximation condensation rates can be calculated by a linear function of ℳ with the slope coefficient dependent only on temperature and pressure. The model data showed that the error of such approximation is less than a few tenths of a percent.
The parameterization of the evaporation process is more complex, mostly because of the slow evaporation of raindrops as they fall through the cloud’s unsaturated areas. Nevertheless, it was possible to define the fraction of evaporation to condensation rate as a function of vertical coordinate z and cloud thickness H. This function can be quite accurately approximated by the 3rd order polynomials of z and H. It is suggested that proposed formulation of evaporation together with the quasi-steady formulation of condensation can serve as a parameterization of water phase transition rates in shallow cumulus clouds.
Abstract
The Madden–Julian oscillation (MJO) is believed to play a significant role in triggering El Niño–Southern Oscillation (ENSO) events and affect the dynamics of ENSO. In this study, the dynamic forcing effects of MJO on the equatorial oceanic dynamic fields and the onsets of different types of ENSO events are investigated through sensitive experiments using spatiotemporally filtered forcing based on an anomalous shallow water model. The comparisons between observations and model responses provide meaningful insights into the extent of MJO’s impacts on sea surface dynamics relative to other atmospheric variabilities. The following conclusions are made. First, the MJO-forced perturbations on zonal currents are stronger and more significant than those on sea surface heights. Second, MJO is essential for improving zonal current simulation in the western-central Pacific and generating activity centers of zonal currents in the eastern Pacific in the model. Third, MJO tends to contribute to the onset of El Niño events rather than La Niña events. Strong intraseasonal oceanic Kelvin waves forced by MJO are confirmed in simulations during the onset stages of the 1997/98 and 2004/05 events. The 120-day running standard deviations of zonal current and sea surface height anomaly series forced by MJO exhibit positive skewness similar to those of the 20–100-day band-passed observational series. Yet, not all the onsets of historical ENSO events are in company with strong MJO-related perturbations. Additionally, the wind stress formula can amplify the responses of zonal current and sea surface height anomalies to synoptic forcings with periods shorter than 20 days through entraining lower-frequency variabilities.
Significance Statement
The Madden–Julian oscillation (MJO) is believed to be able to trigger El Niño–Southern Oscillation (ENSO) events and influence our understanding of the fundamental nature of ENSO. In this study, spatiotemporally filtered forcing experiments are implemented on an anomalous shallow water model. The results show that MJO is more important for improving the simulation of surface zonal currents rather than the sea surface heights and tends to contribute to the onset of El Niño events rather than La Niña events through triggering strong intraseasonal oceanic Kelvin waves.
Abstract
The Madden–Julian oscillation (MJO) is believed to play a significant role in triggering El Niño–Southern Oscillation (ENSO) events and affect the dynamics of ENSO. In this study, the dynamic forcing effects of MJO on the equatorial oceanic dynamic fields and the onsets of different types of ENSO events are investigated through sensitive experiments using spatiotemporally filtered forcing based on an anomalous shallow water model. The comparisons between observations and model responses provide meaningful insights into the extent of MJO’s impacts on sea surface dynamics relative to other atmospheric variabilities. The following conclusions are made. First, the MJO-forced perturbations on zonal currents are stronger and more significant than those on sea surface heights. Second, MJO is essential for improving zonal current simulation in the western-central Pacific and generating activity centers of zonal currents in the eastern Pacific in the model. Third, MJO tends to contribute to the onset of El Niño events rather than La Niña events. Strong intraseasonal oceanic Kelvin waves forced by MJO are confirmed in simulations during the onset stages of the 1997/98 and 2004/05 events. The 120-day running standard deviations of zonal current and sea surface height anomaly series forced by MJO exhibit positive skewness similar to those of the 20–100-day band-passed observational series. Yet, not all the onsets of historical ENSO events are in company with strong MJO-related perturbations. Additionally, the wind stress formula can amplify the responses of zonal current and sea surface height anomalies to synoptic forcings with periods shorter than 20 days through entraining lower-frequency variabilities.
Significance Statement
The Madden–Julian oscillation (MJO) is believed to be able to trigger El Niño–Southern Oscillation (ENSO) events and influence our understanding of the fundamental nature of ENSO. In this study, spatiotemporally filtered forcing experiments are implemented on an anomalous shallow water model. The results show that MJO is more important for improving the simulation of surface zonal currents rather than the sea surface heights and tends to contribute to the onset of El Niño events rather than La Niña events through triggering strong intraseasonal oceanic Kelvin waves.
Abstract
This paper explored temporal changes in magnitude and seasonality of low, median, and high inflows of 51 dams across the West and Southwest United States over the 1993-2022 period. Changes in precipitation, air temperature (an indicator of snowpack and evaporation), soil moisture, and vegetation were also examined to identify potential reasons for the temporal trends in dam inflows. Using monotonic and non-monotonic tests, we found a general downward trend in dam inflows, particularly across the Upper Colorado and California regions. More than 30% of the dams showed a downward trend in their annual median inflows, high inflows during spring, and median inflows during fall. The downward trend of dam inflows was associated with decreasing precipitation and soil moisture, and rising temperatures. While vegetation exhibited positive associations with inflows, it did not seem to be a primary factor for explaining the inflow trends. We also observed shifts in the seasonality of low and high inflows; there was an increase in the proportion of inflows occurring during summer and fall, and a decrease in winter proportions for low inflows. Similarly, high inflows exhibited an increase in spring proportions and a decrease in fall proportions. Our changepoint analyses detected non-monotonic trends between 2002 and 2012 in ~13% of the dams; the majority were located in the Upper Colorado and California regions. More than half of these changepoints were in 2011, likely due to widespread droughts then. Our study has implications for reservoir managers to identify changes that dams experience over time and assist them in proposing actions that maintain the dams’ functionality.
Abstract
This paper explored temporal changes in magnitude and seasonality of low, median, and high inflows of 51 dams across the West and Southwest United States over the 1993-2022 period. Changes in precipitation, air temperature (an indicator of snowpack and evaporation), soil moisture, and vegetation were also examined to identify potential reasons for the temporal trends in dam inflows. Using monotonic and non-monotonic tests, we found a general downward trend in dam inflows, particularly across the Upper Colorado and California regions. More than 30% of the dams showed a downward trend in their annual median inflows, high inflows during spring, and median inflows during fall. The downward trend of dam inflows was associated with decreasing precipitation and soil moisture, and rising temperatures. While vegetation exhibited positive associations with inflows, it did not seem to be a primary factor for explaining the inflow trends. We also observed shifts in the seasonality of low and high inflows; there was an increase in the proportion of inflows occurring during summer and fall, and a decrease in winter proportions for low inflows. Similarly, high inflows exhibited an increase in spring proportions and a decrease in fall proportions. Our changepoint analyses detected non-monotonic trends between 2002 and 2012 in ~13% of the dams; the majority were located in the Upper Colorado and California regions. More than half of these changepoints were in 2011, likely due to widespread droughts then. Our study has implications for reservoir managers to identify changes that dams experience over time and assist them in proposing actions that maintain the dams’ functionality.
Abstract
Numerous tools and indices exist for wildland fire managers to anticipate and track changes in wildfire risk driven by variability in weather and climate conditions at hourly to seasonal scales. However, in working closely with southwest U.S. managers, we learned of a simple meteorological metric being informally used, but not widely accessible in existing tools or information products, to gauge short-term changes in wildfire risk. This metric, termed ‘Burn Period’ (BP), is the local count of hours per day with relative humidity values equal to or less than 20%. Our collaboration led to the development of an experimental tool called the ‘Burn Period Tracker’ to ease access and promote use of BP values for planning and response. This study is a climatological analysis of BP values at 124 fire weather stations across Arizona and New Mexico for the period 2000-2022 to aid in interpretation and understanding of this use-inspired metric. BP values reflect the strong seasonality in temperature and moisture deficit-driven wildfire risk across the southwest U.S., with risk climbing through the arid spring season, peaking in June, and then falling rapidly with the onset of the summer monsoon in July. Regression analyses show that short-term variability in BP values are driven by variability in low level atmospheric moisture in all months with strongest relationships during the summer after the onset of the monsoon. This study highlights the utility of BP as a short-term wildfire planning tool as well as an example of collaborative weather and climate services development.
Abstract
Numerous tools and indices exist for wildland fire managers to anticipate and track changes in wildfire risk driven by variability in weather and climate conditions at hourly to seasonal scales. However, in working closely with southwest U.S. managers, we learned of a simple meteorological metric being informally used, but not widely accessible in existing tools or information products, to gauge short-term changes in wildfire risk. This metric, termed ‘Burn Period’ (BP), is the local count of hours per day with relative humidity values equal to or less than 20%. Our collaboration led to the development of an experimental tool called the ‘Burn Period Tracker’ to ease access and promote use of BP values for planning and response. This study is a climatological analysis of BP values at 124 fire weather stations across Arizona and New Mexico for the period 2000-2022 to aid in interpretation and understanding of this use-inspired metric. BP values reflect the strong seasonality in temperature and moisture deficit-driven wildfire risk across the southwest U.S., with risk climbing through the arid spring season, peaking in June, and then falling rapidly with the onset of the summer monsoon in July. Regression analyses show that short-term variability in BP values are driven by variability in low level atmospheric moisture in all months with strongest relationships during the summer after the onset of the monsoon. This study highlights the utility of BP as a short-term wildfire planning tool as well as an example of collaborative weather and climate services development.