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Comparison of ADCP and ECCOv4r4 Currents in the Pacific Equatorial UndercurrentAbstract
The Pacific Equatorial Undercurrent (EUC) flows eastward across the Pacific at the equator in the thermocline. Its variability is related to El Niño. Moored acoustic Doppler current profiler (ADCP) measurements recorded at four widely separated sites along the equator in the EUC were compared to currents generated by version 4 release 4 of the Estimating the Circulation and Climate of the Ocean (ECCOv4r4) global model–data synthesis product. We are interested to learn how well ECCOv4r4 currents could complement sparse in situ current measurements. ADCP measurements were not assimilated in ECCOv4r4. Comparisons occurred at 5-m depth intervals at 165°E, 170°W, 140°W, and 110°W over time intervals of 10–14 years from 1995 to 2010. Hourly values of ECCOv4r4 and ADCP EUC core speeds were strongly correlated, similar for the EUC transport per unit width (TPUW). Correlations were substantially weaker at 110°W. Although we expected means and standard deviations of ECCOv4r4 currents to be smaller than ADCP values because of ECCOv4r4’s grid representation error, the large differences were unforeseen. The appearance of ECCOv4r4 diurnal-period current oscillations was surprising. As the EUC moved eastward from 170° to 140°W, the ECCOv4r4 TPUW exhibited a much smaller increase compared to the ADCP TPUW. A consequence of smaller ECCOv4r4 EUC core speeds was significantly fewer instances of gradient Richardson number (Ri) less than 1/4 above and below the depth of the core speed compared to Ri computed with ADCP observations. We present linear regression analyses to use monthly-mean ECCOv4r4 EUC core speeds and TPUWs as proxies for ADCP measurements.
Significance Statement
Hundreds of scientific papers have used ECCO data products generated with a continually evolving state-of-the-art ocean-model–data synthesis system. We ask, How representative is the latest version of ECCO equatorial ocean currents? We use independent in situ current measurements as the reference dataset to establish the accuracy of ECCO currents in the tropical Pacific. Attention is focused on the Pacific Equatorial Undercurrent (EUC) because it contributes to the formation of El Niño and La Niña events. ECCO EUC core speeds were smaller in magnitude and less variable in time compared to observations. As a consequence, ECCO currents generated smaller vertical mixing in the EUC compared to that inferred from current measurements. We developed a linear regression model to improve representation of monthly-mean ECCO currents.
Abstract
The Pacific Equatorial Undercurrent (EUC) flows eastward across the Pacific at the equator in the thermocline. Its variability is related to El Niño. Moored acoustic Doppler current profiler (ADCP) measurements recorded at four widely separated sites along the equator in the EUC were compared to currents generated by version 4 release 4 of the Estimating the Circulation and Climate of the Ocean (ECCOv4r4) global model–data synthesis product. We are interested to learn how well ECCOv4r4 currents could complement sparse in situ current measurements. ADCP measurements were not assimilated in ECCOv4r4. Comparisons occurred at 5-m depth intervals at 165°E, 170°W, 140°W, and 110°W over time intervals of 10–14 years from 1995 to 2010. Hourly values of ECCOv4r4 and ADCP EUC core speeds were strongly correlated, similar for the EUC transport per unit width (TPUW). Correlations were substantially weaker at 110°W. Although we expected means and standard deviations of ECCOv4r4 currents to be smaller than ADCP values because of ECCOv4r4’s grid representation error, the large differences were unforeseen. The appearance of ECCOv4r4 diurnal-period current oscillations was surprising. As the EUC moved eastward from 170° to 140°W, the ECCOv4r4 TPUW exhibited a much smaller increase compared to the ADCP TPUW. A consequence of smaller ECCOv4r4 EUC core speeds was significantly fewer instances of gradient Richardson number (Ri) less than 1/4 above and below the depth of the core speed compared to Ri computed with ADCP observations. We present linear regression analyses to use monthly-mean ECCOv4r4 EUC core speeds and TPUWs as proxies for ADCP measurements.
Significance Statement
Hundreds of scientific papers have used ECCO data products generated with a continually evolving state-of-the-art ocean-model–data synthesis system. We ask, How representative is the latest version of ECCO equatorial ocean currents? We use independent in situ current measurements as the reference dataset to establish the accuracy of ECCO currents in the tropical Pacific. Attention is focused on the Pacific Equatorial Undercurrent (EUC) because it contributes to the formation of El Niño and La Niña events. ECCO EUC core speeds were smaller in magnitude and less variable in time compared to observations. As a consequence, ECCO currents generated smaller vertical mixing in the EUC compared to that inferred from current measurements. We developed a linear regression model to improve representation of monthly-mean ECCO currents.
Abstract
Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s−1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model “improvement,” such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement.
Significance Statement
Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.
Abstract
Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA’s 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s−1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model “improvement,” such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1’s nest producing the smallest errors overall. HRRR’s ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement.
Significance Statement
Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors.
Abstract
Air–sea coupling system in the southwestern Indian Ocean (SWIO; 35°–55°S, 40°–75°E) exhibits predominant multidecadal variability that is the strongest during austral summer. It is characterized by an equivalent barotropic atmospheric high (low) pressure over warm (cold) sea surface temperature (SST) anomalies and a poleward (equatorward) shift of the westerlies during the positive (negative) phase. In this study, physical processes of this multidecadal variability are investigated by using observations/reanalysis and CMIP6 model simulations. Results suggest that the multidecadal fluctuation can be explained by the modulation of the Atlantic meridional overturning circulation (AMOC) and the local air–sea positive feedback in the SWIO. In both observations/reanalysis and CMIP6 model simulations, the AMOC fluctuation presents a significantly negative correlation with the multidecadal SST variation in the SWIO when the AMOC is leading by about a decade. The mechanisms are that the preceding AMOC variation can cause an interhemispheric dipolar pattern of SST anomalies in the Atlantic Ocean. Subsequently, the SST anomalies in the midlatitudes of the South Atlantic can propagate to the SWIO by the oceanic Rossby wave under the influence of the Antarctic Circumpolar Current (ACC). Once the SST anomalies reach the SWIO, these SST anomalies in the oceanic front can affect the baroclinicity in the lower troposphere to influence the synoptic transient eddy and then cause the atmospheric circulation anomaly via the eddy–mean flow interaction. Subsequently, the anomalous atmospheric circulation over the SWIO can significantly strengthen the SST anomalies through modifying the oceanic meridional temperature advection and latent and sensible heat flux.
Abstract
Air–sea coupling system in the southwestern Indian Ocean (SWIO; 35°–55°S, 40°–75°E) exhibits predominant multidecadal variability that is the strongest during austral summer. It is characterized by an equivalent barotropic atmospheric high (low) pressure over warm (cold) sea surface temperature (SST) anomalies and a poleward (equatorward) shift of the westerlies during the positive (negative) phase. In this study, physical processes of this multidecadal variability are investigated by using observations/reanalysis and CMIP6 model simulations. Results suggest that the multidecadal fluctuation can be explained by the modulation of the Atlantic meridional overturning circulation (AMOC) and the local air–sea positive feedback in the SWIO. In both observations/reanalysis and CMIP6 model simulations, the AMOC fluctuation presents a significantly negative correlation with the multidecadal SST variation in the SWIO when the AMOC is leading by about a decade. The mechanisms are that the preceding AMOC variation can cause an interhemispheric dipolar pattern of SST anomalies in the Atlantic Ocean. Subsequently, the SST anomalies in the midlatitudes of the South Atlantic can propagate to the SWIO by the oceanic Rossby wave under the influence of the Antarctic Circumpolar Current (ACC). Once the SST anomalies reach the SWIO, these SST anomalies in the oceanic front can affect the baroclinicity in the lower troposphere to influence the synoptic transient eddy and then cause the atmospheric circulation anomaly via the eddy–mean flow interaction. Subsequently, the anomalous atmospheric circulation over the SWIO can significantly strengthen the SST anomalies through modifying the oceanic meridional temperature advection and latent and sensible heat flux.
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.
Airborne Radar Doppler Spectrum Width as a Scale-Dependent Turbulence MetricAbstract
High-resolution airborne cloud Doppler radars such as the W-band Wyoming Cloud Radar (WCR) have, since the 1990s, investigated cloud microphysical, kinematic, and precipitation structures down to 30-m resolution. These measurements revolutionized our understanding of fine-scale cloud structure and the scales at which cloud processes occur. Airborne cloud Doppler radars may also resolve cloud turbulent eddy structure directly at 10-m scales. To date, cloud turbulence has been examined as variances and dissipation rates at coarser resolution than individual pulse volumes. The present work advances the potential of near-vertical pulse-pair Doppler spectrum width as a metric for turbulent air motion. Doppler spectrum width has long been used to investigate turbulent motions from ground-based remote sensors. However, complexities of airborne Doppler radar and spectral broadening resulting from platform and hydrometeor motions have limited airborne radar spectrum width measurements to qualitative interpretation only. Here we present the first quantitative validation of spectrum width from an airborne cloud radar. Echoes with signal-to-noise ratio greater than 10 dB yield spectrum width values that strongly correlate with retrieved mean Doppler variance for a range of nonconvective cloud conditions. Further, Doppler spectrum width within turbulent regions of cloud also shows good agreement with in situ eddy dissipation rate (EDR) and gust probe variance. However, the use of pulse-pair estimated spectrum width as a metric for turbulent air motion intensity is only suitable for turbulent air motions more energetic than the magnitude of spectral broadening, estimated to be <0.4 m s−1 for the WCR in these cases.
Significance Statement
Doppler spectrum width is a widely available airborne radar measurement previously considered too uncertain to attribute to atmospheric turbulence. We validate, for the first time, the response of spectrum width to turbulence at and away from research aircraft flight level and demonstrate that under certain conditions, spectrum width can be used to diagnose atmospheric turbulence down to scales of tens of meters. These high-resolution turbulent air motion intensity measurements may better connect to cloud hydrometeor process and growth response seen in coincident radar reflectivity structures proximate to turbulent eddies.
Abstract
High-resolution airborne cloud Doppler radars such as the W-band Wyoming Cloud Radar (WCR) have, since the 1990s, investigated cloud microphysical, kinematic, and precipitation structures down to 30-m resolution. These measurements revolutionized our understanding of fine-scale cloud structure and the scales at which cloud processes occur. Airborne cloud Doppler radars may also resolve cloud turbulent eddy structure directly at 10-m scales. To date, cloud turbulence has been examined as variances and dissipation rates at coarser resolution than individual pulse volumes. The present work advances the potential of near-vertical pulse-pair Doppler spectrum width as a metric for turbulent air motion. Doppler spectrum width has long been used to investigate turbulent motions from ground-based remote sensors. However, complexities of airborne Doppler radar and spectral broadening resulting from platform and hydrometeor motions have limited airborne radar spectrum width measurements to qualitative interpretation only. Here we present the first quantitative validation of spectrum width from an airborne cloud radar. Echoes with signal-to-noise ratio greater than 10 dB yield spectrum width values that strongly correlate with retrieved mean Doppler variance for a range of nonconvective cloud conditions. Further, Doppler spectrum width within turbulent regions of cloud also shows good agreement with in situ eddy dissipation rate (EDR) and gust probe variance. However, the use of pulse-pair estimated spectrum width as a metric for turbulent air motion intensity is only suitable for turbulent air motions more energetic than the magnitude of spectral broadening, estimated to be <0.4 m s−1 for the WCR in these cases.
Significance Statement
Doppler spectrum width is a widely available airborne radar measurement previously considered too uncertain to attribute to atmospheric turbulence. We validate, for the first time, the response of spectrum width to turbulence at and away from research aircraft flight level and demonstrate that under certain conditions, spectrum width can be used to diagnose atmospheric turbulence down to scales of tens of meters. These high-resolution turbulent air motion intensity measurements may better connect to cloud hydrometeor process and growth response seen in coincident radar reflectivity structures proximate to turbulent eddies.
Abstract
The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-h precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL-AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distributions of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step toward better simulation of precipitation intermittence in future model development. Last, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at subdaily time scales, as model evaluation heavily relies on high-quality observations.
Significance Statement
High-frequency precipitation data, such as 3-hourly or finer resolution, provide detailed and precise information about the intensity, timing, and location of individual precipitation events. This information is essential for evaluating physically based numerical weather and climate models, which are important tools for understanding and predicting precipitation changes. We compared several global high-resolution observation datasets with nine CMIP6 GCMs and a series of GFDL-AM4.0 model simulations to evaluate the precipitation diurnal cycle and variance, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these metrics. Despite the impact of these factors on the simulated precipitation diurnal cycle and variance being evident, our results also show that they are not consistently aligned with observed features. This highlights the need for further investigation into model deficiencies at the process level. Therefore, conducting routine examinations of these metrics could be a crucial first step toward improving the simulation of precipitation intermittency in future model development. Additionally, given the large uncertainties, there is an urgent need for a detailed evaluation of observational precipitation products, particularly at subdaily time scales.
Abstract
The diurnal cycle of precipitation and precipitation variances at different time scales are analyzed in this study based on multiple high-resolution 3-h precipitation datasets. The results are used to evaluate nine CMIP6 models and a series of GFDL-AM4.0 model simulations, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these two precipitation features. It is found that although diurnal amplitudes are reasonably simulated, models generally generate too early diurnal peaks over land, with a diurnal phase peaking around noon instead of the observed late afternoon (or early evening) peak. As for precipitation variances, irregular subdaily fluctuations dominate the total variance, followed by variance of daily mean precipitation and variance associated with the mean diurnal cycle. While the spatial and zonal distributions of precipitation variances are generally captured by the models, significant biases are present in tropical regions, where large mean precipitation biases are observed. The comparisons based on AM4.0 model simulations demonstrate that the inclusion of ocean coupling, adoption of a new microphysics scheme, and increasing of horizontal resolution have limited impacts on these two simulated features, emphasizing the need for future investigation into these model deficiencies at the process level. Conducting routine examinations of these metrics would be a crucial first step toward better simulation of precipitation intermittence in future model development. Last, distinct differences in these two features are found among observational datasets, highlighting the urgent need for a detailed evaluation of precipitation observations, especially at subdaily time scales, as model evaluation heavily relies on high-quality observations.
Significance Statement
High-frequency precipitation data, such as 3-hourly or finer resolution, provide detailed and precise information about the intensity, timing, and location of individual precipitation events. This information is essential for evaluating physically based numerical weather and climate models, which are important tools for understanding and predicting precipitation changes. We compared several global high-resolution observation datasets with nine CMIP6 GCMs and a series of GFDL-AM4.0 model simulations to evaluate the precipitation diurnal cycle and variance, with the goal of examining the impact of SST diurnal cycle, varying horizontal resolutions, and different microphysics schemes on these metrics. Despite the impact of these factors on the simulated precipitation diurnal cycle and variance being evident, our results also show that they are not consistently aligned with observed features. This highlights the need for further investigation into model deficiencies at the process level. Therefore, conducting routine examinations of these metrics could be a crucial first step toward improving the simulation of precipitation intermittency in future model development. Additionally, given the large uncertainties, there is an urgent need for a detailed evaluation of observational precipitation products, particularly at subdaily time scales.
Abstract
The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard regime assignment, where each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a sequential probabilistic regime assignment using Bayes’s theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes’s theorem tells us that the probability of being in a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time t can be used to inform the prior probabilities at time t + 1, which are then used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Niño years.
Significance Statement
Atmospheric circulation regimes are recurrent and persistent patterns that characterize the atmospheric circulation on time scales of 1–3 weeks. They are relevant for predictability on these time scales as mediators of weather. In this study we propose a novel approach to assigning atmospheric states to six predefined wintertime circulation regimes over the North Atlantic and Europe, which can be applied in real time. This approach introduces a probabilistic, instead of deterministic, regime assignment and uses prior knowledge on the regime dynamics. It allows us to better identify the regime persistence and indicates when a state does not clearly belong to one regime. Making use of an ensemble of model simulations, we can identify more pronounced interannual variability by using the full ensemble to inform prior knowledge on the regimes.
Abstract
The standard approach when studying atmospheric circulation regimes and their dynamics is to use a hard regime assignment, where each atmospheric state is assigned to the regime it is closest to in distance. However, this may not always be the most appropriate approach as the regime assignment may be affected by small deviations in the distance to the regimes due to noise. To mitigate this we develop a sequential probabilistic regime assignment using Bayes’s theorem, which can be applied to previously defined regimes and implemented in real time as new data become available. Bayes’s theorem tells us that the probability of being in a regime given the data can be determined by combining climatological likelihood with prior information. The regime probabilities at time t can be used to inform the prior probabilities at time t + 1, which are then used to sequentially update the regime probabilities. We apply this approach to both reanalysis data and a seasonal hindcast ensemble incorporating knowledge of the transition probabilities between regimes. Furthermore, making use of the signal present within the ensemble to better inform the prior probabilities allows for identifying more pronounced interannual variability. The signal within the interannual variability of wintertime North Atlantic circulation regimes is assessed using both a categorical and regression approach, with the strongest signals found during very strong El Niño years.
Significance Statement
Atmospheric circulation regimes are recurrent and persistent patterns that characterize the atmospheric circulation on time scales of 1–3 weeks. They are relevant for predictability on these time scales as mediators of weather. In this study we propose a novel approach to assigning atmospheric states to six predefined wintertime circulation regimes over the North Atlantic and Europe, which can be applied in real time. This approach introduces a probabilistic, instead of deterministic, regime assignment and uses prior knowledge on the regime dynamics. It allows us to better identify the regime persistence and indicates when a state does not clearly belong to one regime. Making use of an ensemble of model simulations, we can identify more pronounced interannual variability by using the full ensemble to inform prior knowledge on the regimes.
Abstract
Stratocumulus occur in closed- or open-cell states, which tend to be associated with high or low cloud cover and the absence or presence of precipitation, respectively. Thus, the transition between these states has substantial implications for the role of this cloud type in Earth’s radiation budget. In this study, we analyze transitions between these states using an ensemble of 127 large-eddy simulations, covering a wide range of conditions. Our analysis is focused on the behavior of these clouds in a cloud fraction (fc ) scene albedo (A) phase space, which has been shown in previous studies to be a useful framework for interpreting system behavior. For the transition from closed to open cells, we find that precipitation creates narrower clouds and scavenges cloud droplets for all fc . However, precipitation decreases the cloud depth for fc > 0.8 only, causing a rapid decrease in A. For fc < 0.8, the cloud depth actually increases due to mesoscale organization of the cloud field. As the cloud deepening balances the effects of cloud droplet scavenging in terms of influence on A, changes in A are determined by the decreasing fc only, causing a linear decrease in A for fc < 0.8. For the transition from open to closed cells, we find that longwave radiative cooling drives the cloud development, with cloud widening dominating for fc < 0.5. For fc > 0.5, clouds begin to deepen gradually due to the decreasing efficiency of lateral expansion. The smooth switch between cloud widening and deepening leads to a more gentle change in A compared to the transitions under precipitating conditions.
Significance Statement
By reflecting a substantial fraction of solar shortwave radiation back to space, shallow clouds constitute a major cooling agent in Earth’s radiation budget. To constrain this effect, a profound understanding of cloud cover and cloud albedo is necessary. In this study, we analyze the processes that drive the variability in these cloud properties in stratocumulus clouds, a very common cloud type covering approximately 20% of the globe. For these clouds, we show that changes from low to high or high to low cloud cover are different due to the underlying cloud micro- and macrophysics, elucidating this crucial aspect of aerosol–cloud–climate interactions.
Abstract
Stratocumulus occur in closed- or open-cell states, which tend to be associated with high or low cloud cover and the absence or presence of precipitation, respectively. Thus, the transition between these states has substantial implications for the role of this cloud type in Earth’s radiation budget. In this study, we analyze transitions between these states using an ensemble of 127 large-eddy simulations, covering a wide range of conditions. Our analysis is focused on the behavior of these clouds in a cloud fraction (fc ) scene albedo (A) phase space, which has been shown in previous studies to be a useful framework for interpreting system behavior. For the transition from closed to open cells, we find that precipitation creates narrower clouds and scavenges cloud droplets for all fc . However, precipitation decreases the cloud depth for fc > 0.8 only, causing a rapid decrease in A. For fc < 0.8, the cloud depth actually increases due to mesoscale organization of the cloud field. As the cloud deepening balances the effects of cloud droplet scavenging in terms of influence on A, changes in A are determined by the decreasing fc only, causing a linear decrease in A for fc < 0.8. For the transition from open to closed cells, we find that longwave radiative cooling drives the cloud development, with cloud widening dominating for fc < 0.5. For fc > 0.5, clouds begin to deepen gradually due to the decreasing efficiency of lateral expansion. The smooth switch between cloud widening and deepening leads to a more gentle change in A compared to the transitions under precipitating conditions.
Significance Statement
By reflecting a substantial fraction of solar shortwave radiation back to space, shallow clouds constitute a major cooling agent in Earth’s radiation budget. To constrain this effect, a profound understanding of cloud cover and cloud albedo is necessary. In this study, we analyze the processes that drive the variability in these cloud properties in stratocumulus clouds, a very common cloud type covering approximately 20% of the globe. For these clouds, we show that changes from low to high or high to low cloud cover are different due to the underlying cloud micro- and macrophysics, elucidating this crucial aspect of aerosol–cloud–climate interactions.
Abstract
Annual spring and summer runoff from western Colorado is relied upon by 40 million people, six states, and two countries. Cool season precipitation and snowpack have historically been robust predictors of seasonal runoff in western Colorado. Forecasts made with this information allow water managers to plan for the season ahead. Antecedent hydrological conditions, such as root zone soil moisture and groundwater storage, and weather conditions following peak snowpack also impact seasonal runoff. The roles of such factors were scrutinized in 2020 and 2021: seasonal runoff was much lower than expectations based on snowpack values alone. We investigate the relative importance of meteorological and hydrological conditions occurring before and after the snowpack season in predicting seasonal runoff in western Colorado. This question is critical because the most effective investment strategy for improving forecasts depends on if errors arise before or after the snowpack season. This study is conducted using observations from the Snow Telemetry Network, root zone soil moisture and groundwater data from the Western Land Data Assimilation Systems, and a random forest–based statistical forecasting framework. We find that on average, antecedent root zone soil moisture and groundwater storage values do not add significant skill to seasonal water supply forecasts in western Colorado. In contrast, using precipitation and temperature data after the time of peak snowpack improves water supply forecasts significantly. The 2020 and 2021 runoffs were hampered by dry conditions both before and after the snowpack season. Both antecedent soil moisture and spring/summer precipitation data improved water supply forecast accuracy in these years.
Significance Statement
Seasonal water supply forecasts in western Colorado are highly valuable because spring and summer runoff from this region helps support the water supply of 40 million people. Accurate forecasts improve the management of the region’s water. Heavy investments have been made in improving our ability to monitor antecedent hydrological conditions in western Colorado, such as root zone soil moisture and groundwater. However, results from this study indicate that the largest source of uncertainty in western Colorado runoff forecasts is future weather. Therefore, improved subseasonal-to-seasonal weather forecasts for western Colorado are what is most needed to improve regional water supply forecasts, and the ability to properly manage western Colorado water.
Abstract
Annual spring and summer runoff from western Colorado is relied upon by 40 million people, six states, and two countries. Cool season precipitation and snowpack have historically been robust predictors of seasonal runoff in western Colorado. Forecasts made with this information allow water managers to plan for the season ahead. Antecedent hydrological conditions, such as root zone soil moisture and groundwater storage, and weather conditions following peak snowpack also impact seasonal runoff. The roles of such factors were scrutinized in 2020 and 2021: seasonal runoff was much lower than expectations based on snowpack values alone. We investigate the relative importance of meteorological and hydrological conditions occurring before and after the snowpack season in predicting seasonal runoff in western Colorado. This question is critical because the most effective investment strategy for improving forecasts depends on if errors arise before or after the snowpack season. This study is conducted using observations from the Snow Telemetry Network, root zone soil moisture and groundwater data from the Western Land Data Assimilation Systems, and a random forest–based statistical forecasting framework. We find that on average, antecedent root zone soil moisture and groundwater storage values do not add significant skill to seasonal water supply forecasts in western Colorado. In contrast, using precipitation and temperature data after the time of peak snowpack improves water supply forecasts significantly. The 2020 and 2021 runoffs were hampered by dry conditions both before and after the snowpack season. Both antecedent soil moisture and spring/summer precipitation data improved water supply forecast accuracy in these years.
Significance Statement
Seasonal water supply forecasts in western Colorado are highly valuable because spring and summer runoff from this region helps support the water supply of 40 million people. Accurate forecasts improve the management of the region’s water. Heavy investments have been made in improving our ability to monitor antecedent hydrological conditions in western Colorado, such as root zone soil moisture and groundwater. However, results from this study indicate that the largest source of uncertainty in western Colorado runoff forecasts is future weather. Therefore, improved subseasonal-to-seasonal weather forecasts for western Colorado are what is most needed to improve regional water supply forecasts, and the ability to properly manage western Colorado water.
Abstract
Six profiling floats measured water-mass properties (T, S), horizontal velocities (u, υ), and microstructure thermal-variance dissipation rates χT in the upper ∼1 km of the Iceland and Irminger Basins in the eastern subpolar North Atlantic from June 2019 to April 2021. The floats drifted into slope boundary currents to travel counterclockwise around the basins. Pairs of velocity profiles half an inertial period apart were collected every 7–14 days. These half-inertial-period pairs are separated into subinertial eddy (sum) and inertial/semidiurnal (difference) motions. Eddy flow speeds are ∼O(0.1) m s−1 in the upper 400 m, diminishing to ∼O(0.01) m s−1 by ∼800-m depth. In late summer through early spring, near-inertial motions are energized in the surface layer and permanent pycnocline to at least 800-m depth almost simultaneously (within the 14-day temporal resolution), suggesting rapid transformation of large-horizontal-scale surface-layer inertial oscillations into near-inertial internal waves with high vertical group velocities through interactions with eddy vorticity gradients (effective β). During the same period, internal-wave vertical shear variance was 2–5 times canonical midlatitude magnitudes and dominantly clockwise-with-depth (downward energy propagation). In late spring and early summer, shear levels are comparable to canonical midlatitude values and dominantly counterclockwise-with-depth (upward energy propagation), particularly over major topographic ridges. Turbulent diapycnal diffusivities K ∼ O(10−4) m2 s−1 are an order of magnitude larger than canonical midlatitude values. Depth-averaged (10–1000 m) diffusivities exhibit factor-of-3 month-by-month variability with minima in early August.
Abstract
Six profiling floats measured water-mass properties (T, S), horizontal velocities (u, υ), and microstructure thermal-variance dissipation rates χT in the upper ∼1 km of the Iceland and Irminger Basins in the eastern subpolar North Atlantic from June 2019 to April 2021. The floats drifted into slope boundary currents to travel counterclockwise around the basins. Pairs of velocity profiles half an inertial period apart were collected every 7–14 days. These half-inertial-period pairs are separated into subinertial eddy (sum) and inertial/semidiurnal (difference) motions. Eddy flow speeds are ∼O(0.1) m s−1 in the upper 400 m, diminishing to ∼O(0.01) m s−1 by ∼800-m depth. In late summer through early spring, near-inertial motions are energized in the surface layer and permanent pycnocline to at least 800-m depth almost simultaneously (within the 14-day temporal resolution), suggesting rapid transformation of large-horizontal-scale surface-layer inertial oscillations into near-inertial internal waves with high vertical group velocities through interactions with eddy vorticity gradients (effective β). During the same period, internal-wave vertical shear variance was 2–5 times canonical midlatitude magnitudes and dominantly clockwise-with-depth (downward energy propagation). In late spring and early summer, shear levels are comparable to canonical midlatitude values and dominantly counterclockwise-with-depth (upward energy propagation), particularly over major topographic ridges. Turbulent diapycnal diffusivities K ∼ O(10−4) m2 s−1 are an order of magnitude larger than canonical midlatitude values. Depth-averaged (10–1000 m) diffusivities exhibit factor-of-3 month-by-month variability with minima in early August.
