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Abstract
The South Asian high (SAH) location and intensity are linked with the latent heating of the Tibetan Plateau (TP) and Yangtze River basin. The relationship between SAH variability and its impact on South Asian monsoon (SAM) onset is rarely linked with TP soil moisture. This study uses remotely sensed soil moisture and reanalysis products to quantify the relationship between the TP spring (April–June) soil moisture with SAH and SAM onset during 1988–2008. The results show that the TP spring soil moisture and monsoon onset indices are negatively correlated (R < −0.65), whereas the SAH exhibits a significant positive correlation (R ≥ 0.70) with TP soil moisture. The monsoon onset shows a difference of 20–25 days between the early- and late-onset composites. Significant positive (negative) soil moisture anomalies persist over the TP during the early (late) onset followed by positive (negative) LH (SH) anomalies during early (late)-onset composites. The TP thermal forcing exhibited positive anomalies during the early (late)-onset composites implying significant soil moisture control over the diabatic heating, which favors vertical ascent over the eastern plateau. Such a pattern leads to an earlier formation and movement of the SAH toward the Bay of Bengal (BOB) and southwestward of the TP. Before the early and late monsoon onset composites, the SAH pentad evolution drives the lower-tropospheric westerlies/easterlies toward continental SA. In the Indian Ocean the wind shear and transition from prevailing easterlies into westerlies during the pre-onset, onset, and post-onset pentad results in strong/weak ascent affecting the onset timing over the Arabian Sea and continental SA with less influence over the BOB monsoon onset.
Significance Statement
The Tibetan Plateau heating is one of the key drivers of the Asian monsoon precipitation in the surrounding regions, which has been previously studied in detail. This study explored the Tibetan Plateau spring soil moisture’s effect on South Asian monsoon onset timing. The monsoon onset timing is calculated using changes in wind direction, atmospheric temperature, and relative precipitation magnitude. Results found that the spring soil moisture substantially affects the TP thermal heating and the SA monsoon onset timing and highlights the physical processes leading to changes in the monsoon onset timing. The inclusion of soil moisture in estimating the monsoon onset timing can provide a tangible way of improving our understanding of the monsoon and associated water resources management practices.
Abstract
The South Asian high (SAH) location and intensity are linked with the latent heating of the Tibetan Plateau (TP) and Yangtze River basin. The relationship between SAH variability and its impact on South Asian monsoon (SAM) onset is rarely linked with TP soil moisture. This study uses remotely sensed soil moisture and reanalysis products to quantify the relationship between the TP spring (April–June) soil moisture with SAH and SAM onset during 1988–2008. The results show that the TP spring soil moisture and monsoon onset indices are negatively correlated (R < −0.65), whereas the SAH exhibits a significant positive correlation (R ≥ 0.70) with TP soil moisture. The monsoon onset shows a difference of 20–25 days between the early- and late-onset composites. Significant positive (negative) soil moisture anomalies persist over the TP during the early (late) onset followed by positive (negative) LH (SH) anomalies during early (late)-onset composites. The TP thermal forcing exhibited positive anomalies during the early (late)-onset composites implying significant soil moisture control over the diabatic heating, which favors vertical ascent over the eastern plateau. Such a pattern leads to an earlier formation and movement of the SAH toward the Bay of Bengal (BOB) and southwestward of the TP. Before the early and late monsoon onset composites, the SAH pentad evolution drives the lower-tropospheric westerlies/easterlies toward continental SA. In the Indian Ocean the wind shear and transition from prevailing easterlies into westerlies during the pre-onset, onset, and post-onset pentad results in strong/weak ascent affecting the onset timing over the Arabian Sea and continental SA with less influence over the BOB monsoon onset.
Significance Statement
The Tibetan Plateau heating is one of the key drivers of the Asian monsoon precipitation in the surrounding regions, which has been previously studied in detail. This study explored the Tibetan Plateau spring soil moisture’s effect on South Asian monsoon onset timing. The monsoon onset timing is calculated using changes in wind direction, atmospheric temperature, and relative precipitation magnitude. Results found that the spring soil moisture substantially affects the TP thermal heating and the SA monsoon onset timing and highlights the physical processes leading to changes in the monsoon onset timing. The inclusion of soil moisture in estimating the monsoon onset timing can provide a tangible way of improving our understanding of the monsoon and associated water resources management practices.
Abstract
Surface boundaries in supercells have been suspected of being important in the arrangement and concentration of vorticity for the development and intensification of tornadoes, but there has been little attention given to the effects of the underlying surface roughness on their behavior. This study investigates the impact of surface drag on the structure and evolution of these boundaries, their associated distribution of near-surface vorticity, and tornadogenesis and maintenance. Comparisons between idealized simulations without and with drag introduced in the mature stage of the storm prior to tornadogenesis reveal that the inclusion of surface drag substantially alters the low-level structure, particularly with respect to the number, location, and intensity of surface convergence boundaries. Substantial drag-generated horizontal vorticity induces rotor structures near the surface associated with the convergence boundaries in both the forward and rear flanks of the storm. Stretching of horizontal vorticity and subsequent tilting into the vertical along the convergence boundaries lead to elongated positive vertical vorticity sheets on the ascending branch of the rotors and the opposite on the descending branch. The larger near-surface pressure deficit associated with the faster development of the near-surface cyclone when drag is active creates a downward dynamic vertical pressure gradient force that suppresses vertical growth, leading to a weaker and wider tornado detached from the surrounding convergence boundaries. A conceptual model of the low-level structure of the tornadic supercell is presented that focuses on the contribution of surface drag, with the aim of adding more insight and complexity to previous conceptual models.
Significance Statement
Tornado development is sensitive to near-surface processes, including those associated with front-like boundaries between regions of airflow within the parent storm. However, observations and theory are insufficient to understand these phenomena, and numerical simulation remains vital. In our simulations, we find that a change in a parameter that controls how much the near-surface winds are reduced by friction (or drag) can substantially alter the storm behavior and tornado potential. We investigate how surface drag affects the low-level storm structure, the distribution of regions of near-surface rotation, and the development of tornadoes within the simulation. Our results provide insight into the role of surface drag and lead to an improved conceptual model of the near-surface structure of a tornadic storm.
Abstract
Surface boundaries in supercells have been suspected of being important in the arrangement and concentration of vorticity for the development and intensification of tornadoes, but there has been little attention given to the effects of the underlying surface roughness on their behavior. This study investigates the impact of surface drag on the structure and evolution of these boundaries, their associated distribution of near-surface vorticity, and tornadogenesis and maintenance. Comparisons between idealized simulations without and with drag introduced in the mature stage of the storm prior to tornadogenesis reveal that the inclusion of surface drag substantially alters the low-level structure, particularly with respect to the number, location, and intensity of surface convergence boundaries. Substantial drag-generated horizontal vorticity induces rotor structures near the surface associated with the convergence boundaries in both the forward and rear flanks of the storm. Stretching of horizontal vorticity and subsequent tilting into the vertical along the convergence boundaries lead to elongated positive vertical vorticity sheets on the ascending branch of the rotors and the opposite on the descending branch. The larger near-surface pressure deficit associated with the faster development of the near-surface cyclone when drag is active creates a downward dynamic vertical pressure gradient force that suppresses vertical growth, leading to a weaker and wider tornado detached from the surrounding convergence boundaries. A conceptual model of the low-level structure of the tornadic supercell is presented that focuses on the contribution of surface drag, with the aim of adding more insight and complexity to previous conceptual models.
Significance Statement
Tornado development is sensitive to near-surface processes, including those associated with front-like boundaries between regions of airflow within the parent storm. However, observations and theory are insufficient to understand these phenomena, and numerical simulation remains vital. In our simulations, we find that a change in a parameter that controls how much the near-surface winds are reduced by friction (or drag) can substantially alter the storm behavior and tornado potential. We investigate how surface drag affects the low-level storm structure, the distribution of regions of near-surface rotation, and the development of tornadoes within the simulation. Our results provide insight into the role of surface drag and lead to an improved conceptual model of the near-surface structure of a tornadic storm.
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
Ocean surface currents introduce variations into the surface wind stress that can change the component of the stress aligned with the thermal wind shear at fronts. This modifies the Ekman buoyancy flux, such that the current feedback on the stress tends to generate an effective flux of buoyancy and potential vorticity to the mixed layer. Scaling arguments and idealized simulations resolving both mesoscale and submesoscale turbulence suggest that this pathway for air–sea interaction can be important both locally at individual submesoscale fronts with strong surface currents—where it can introduce equivalent advective heat fluxes exceeding several hundred watts per square meter—and in the spatial mean where it reduces the integrated Ekman buoyancy flux by approximately 50%. The accompanying source of surface potential vorticity injection suggests that at some fronts the current feedback modification of the Ekman buoyancy flux may be significant in terms of both submesoscale dynamics and boundary layer energetics, with an implied modification of symmetric instability growth rates and dissipation that scales similarly to the energy lost through the negative wind work generated by the current feedback. This provides an example of how the shift of dynamical regimes into the submesoscale may promote the importance of air–sea interaction mechanisms that differ from those most active at larger scale.
Abstract
Ocean surface currents introduce variations into the surface wind stress that can change the component of the stress aligned with the thermal wind shear at fronts. This modifies the Ekman buoyancy flux, such that the current feedback on the stress tends to generate an effective flux of buoyancy and potential vorticity to the mixed layer. Scaling arguments and idealized simulations resolving both mesoscale and submesoscale turbulence suggest that this pathway for air–sea interaction can be important both locally at individual submesoscale fronts with strong surface currents—where it can introduce equivalent advective heat fluxes exceeding several hundred watts per square meter—and in the spatial mean where it reduces the integrated Ekman buoyancy flux by approximately 50%. The accompanying source of surface potential vorticity injection suggests that at some fronts the current feedback modification of the Ekman buoyancy flux may be significant in terms of both submesoscale dynamics and boundary layer energetics, with an implied modification of symmetric instability growth rates and dissipation that scales similarly to the energy lost through the negative wind work generated by the current feedback. This provides an example of how the shift of dynamical regimes into the submesoscale may promote the importance of air–sea interaction mechanisms that differ from those most active at larger scale.
Abstract
Based on the conditional nonlinear optimal perturbation for boundary condition method and Regional Ocean Modeling System (ROMS), this study investigates the influence of wind stress uncertainty on predicting the short-term state transitions of the Kuroshio Extension (KE). The optimal time-dependent wind stress errors that lead to maximum prediction errors are obtained for two KE stable-to-unstable and two reverse transitions, which exhibit local multieddies structures with decreasing magnitude as the end time of prediction approaches. The optimal boundary errors initially induce small oceanic errors through Ekman pumping. Subsequently, these errors grow in magnitude as oceanic internal processes take effect, which exerts significant influences on the short-term prediction of the KE state transition process. Specifically, during stable-to-unstable (unstable-to-stable) transitions, the growing error induces an overestimation (underestimation) of the meridional sea surface height gradient across the KE axis, leading to the predicted KE state being more (less) stable. Furthermore, the dynamics mechanism analysis indicates that barotropic instability is crucial for the error growth in the prediction of both the stable-to-unstable and the reverse transition processes due to the horizontal shear of flow field. But work generated by wind stress error plays a more important role in the prediction of the unstable-to-stable transitions because of the synergistic effect of strong wind stress error and strong oceanic error. Eventually, the sensitive areas have been identified based on the optimal boundary errors. Reducing wind stress errors in sensitive areas can significantly improve prediction skills, offering theoretical guidance for devising observational strategies.
Abstract
Based on the conditional nonlinear optimal perturbation for boundary condition method and Regional Ocean Modeling System (ROMS), this study investigates the influence of wind stress uncertainty on predicting the short-term state transitions of the Kuroshio Extension (KE). The optimal time-dependent wind stress errors that lead to maximum prediction errors are obtained for two KE stable-to-unstable and two reverse transitions, which exhibit local multieddies structures with decreasing magnitude as the end time of prediction approaches. The optimal boundary errors initially induce small oceanic errors through Ekman pumping. Subsequently, these errors grow in magnitude as oceanic internal processes take effect, which exerts significant influences on the short-term prediction of the KE state transition process. Specifically, during stable-to-unstable (unstable-to-stable) transitions, the growing error induces an overestimation (underestimation) of the meridional sea surface height gradient across the KE axis, leading to the predicted KE state being more (less) stable. Furthermore, the dynamics mechanism analysis indicates that barotropic instability is crucial for the error growth in the prediction of both the stable-to-unstable and the reverse transition processes due to the horizontal shear of flow field. But work generated by wind stress error plays a more important role in the prediction of the unstable-to-stable transitions because of the synergistic effect of strong wind stress error and strong oceanic error. Eventually, the sensitive areas have been identified based on the optimal boundary errors. Reducing wind stress errors in sensitive areas can significantly improve prediction skills, offering theoretical guidance for devising observational strategies.
Abstract
High-resolution oceanic precipitation estimates are needed to increase our understanding of and ability to monitor ocean–atmosphere coupled processes. Satellite multisensor precipitation products such as IMERG provide global precipitation estimates at relatively high resolution (0.1°, 30 min), but the resolution at which IMERG precipitation estimates are considered reliable is coarser than the nominal resolution of the product itself. In this study, we examine the ability of the Rainfall Autoregressive Model (RainFARM) statistical downscaling technique to produce ensembles of precipitation fields at relatively high spatial and temporal resolution when applied to spatially and temporally coarsened precipitation fields from IMERG. The downscaled precipitation ensembles are evaluated against in situ oceanic rain-rate observations collected by passive aquatic listeners (PALs) in 11 different ocean domains. We also evaluate IMERG coarsened to the same resolution as the downscaled fields to determine whether the process of coarsening then downscaling improves precipitation estimates more than averaging IMERG to coarser resolution only. Evaluations were performed on individual months, seasons, by ENSO phase, and based on precipitation characteristics. Results were inconsistent, with downscaling improving precipitation estimates in some domains and time periods and producing worse performance in others. While the results imply that the performance of the downscaled precipitation estimates is related to precipitation characteristics, it is still unclear what characteristics or combinations thereof lead to the most improvement or consistent improvement when applying RainFARM to IMERG.
Abstract
High-resolution oceanic precipitation estimates are needed to increase our understanding of and ability to monitor ocean–atmosphere coupled processes. Satellite multisensor precipitation products such as IMERG provide global precipitation estimates at relatively high resolution (0.1°, 30 min), but the resolution at which IMERG precipitation estimates are considered reliable is coarser than the nominal resolution of the product itself. In this study, we examine the ability of the Rainfall Autoregressive Model (RainFARM) statistical downscaling technique to produce ensembles of precipitation fields at relatively high spatial and temporal resolution when applied to spatially and temporally coarsened precipitation fields from IMERG. The downscaled precipitation ensembles are evaluated against in situ oceanic rain-rate observations collected by passive aquatic listeners (PALs) in 11 different ocean domains. We also evaluate IMERG coarsened to the same resolution as the downscaled fields to determine whether the process of coarsening then downscaling improves precipitation estimates more than averaging IMERG to coarser resolution only. Evaluations were performed on individual months, seasons, by ENSO phase, and based on precipitation characteristics. Results were inconsistent, with downscaling improving precipitation estimates in some domains and time periods and producing worse performance in others. While the results imply that the performance of the downscaled precipitation estimates is related to precipitation characteristics, it is still unclear what characteristics or combinations thereof lead to the most improvement or consistent improvement when applying RainFARM to IMERG.
Abstract
The expansion of the boreal forest poleward is a potentially important driver of feedbacks between the land surface and Arctic climate. A growing body of work has highlighted the importance of differences in evaporative resistance between different possible future Arctic land covers, which in turn alters humidity and cloudiness in the boundary layer, for these feedbacks. While thus far this problem has been studied primarily with complex Earth system models, we turn to a locally focused, idealized model capable of diagnosing and testing the sensitivity of first-order processes connecting vegetation, the atmospheric boundary layer, and low clouds in this critical region. This allows us to benchmark the mechanisms and results at the center of predictions from larger-scale simulations. A surface dominated by broadleaf trees, characterized by higher albedo and lower surface evaporative resistance, drives cooling and moistening of the boundary layer relative to a surface of needleleaf trees, characterized by lower albedo and higher surface evaporative resistance. Differences in evaporative resistance between these hypothetical Arctic vegetation covers are of equal importance to changes in albedo for the initial response of the boundary layer to boreal expansion, even with our idealized approach. However, compensation between the elevation of the lifting condensation level (LCL) and more rapid growth of the mixed layer over higher evaporative resistance surfaces can minimize changes in the favorability of shallow clouds over different land cover types under some conditions. We then perform two tests on the sensitivity of this compensating effect, to changes in water availability, represented first by a reduction in boundary layer humidity and then by both a reduction in humidity and soil moisture available to our vegetation surface. Finally, given the importance of this potential LCL–mixed-layer height compensation in our idealized modeling results, we look to determine its relevance in observational data from a field campaign in boreal Finland. These observations do confirm that such a coupling plays an important role in cumulus-topped boundary layers over a needleleaf forest surface. While our results confirm some underlying mechanisms at the center of prior work with Earth system models, they also provide motivation for future work to constrain the impact of boreal forest expansion. This will include both large eddy simulations to examine the impact of processes and feedbacks not resolved by a mixed-layer model, as well as a more systematic evaluation and comparison of relevant observations at the site in Finland and sites from prior boreal field campaigns.
Significance Statement
Clouds and vegetation are both important components of the climate system that interact across a range of scales. These interactions are central to understanding how changes at the land surface feedback on climate. For example, if a forest expands or recedes, diagnosing how that will impact clouds will determine whether you predict warming or cooling temperatures from that shift in the forest area. These predictions are often made with complex Earth system models, but we look to a more idealized representation of the land–atmosphere system to diagnose how shallow clouds should respond to changes in surface properties with different scenarios of boreal forest expansion at a more foundational level. This both grounds our understanding of previous analysis and provides helpful direction for future studies of this relevant and impactful land cover change.
Abstract
The expansion of the boreal forest poleward is a potentially important driver of feedbacks between the land surface and Arctic climate. A growing body of work has highlighted the importance of differences in evaporative resistance between different possible future Arctic land covers, which in turn alters humidity and cloudiness in the boundary layer, for these feedbacks. While thus far this problem has been studied primarily with complex Earth system models, we turn to a locally focused, idealized model capable of diagnosing and testing the sensitivity of first-order processes connecting vegetation, the atmospheric boundary layer, and low clouds in this critical region. This allows us to benchmark the mechanisms and results at the center of predictions from larger-scale simulations. A surface dominated by broadleaf trees, characterized by higher albedo and lower surface evaporative resistance, drives cooling and moistening of the boundary layer relative to a surface of needleleaf trees, characterized by lower albedo and higher surface evaporative resistance. Differences in evaporative resistance between these hypothetical Arctic vegetation covers are of equal importance to changes in albedo for the initial response of the boundary layer to boreal expansion, even with our idealized approach. However, compensation between the elevation of the lifting condensation level (LCL) and more rapid growth of the mixed layer over higher evaporative resistance surfaces can minimize changes in the favorability of shallow clouds over different land cover types under some conditions. We then perform two tests on the sensitivity of this compensating effect, to changes in water availability, represented first by a reduction in boundary layer humidity and then by both a reduction in humidity and soil moisture available to our vegetation surface. Finally, given the importance of this potential LCL–mixed-layer height compensation in our idealized modeling results, we look to determine its relevance in observational data from a field campaign in boreal Finland. These observations do confirm that such a coupling plays an important role in cumulus-topped boundary layers over a needleleaf forest surface. While our results confirm some underlying mechanisms at the center of prior work with Earth system models, they also provide motivation for future work to constrain the impact of boreal forest expansion. This will include both large eddy simulations to examine the impact of processes and feedbacks not resolved by a mixed-layer model, as well as a more systematic evaluation and comparison of relevant observations at the site in Finland and sites from prior boreal field campaigns.
Significance Statement
Clouds and vegetation are both important components of the climate system that interact across a range of scales. These interactions are central to understanding how changes at the land surface feedback on climate. For example, if a forest expands or recedes, diagnosing how that will impact clouds will determine whether you predict warming or cooling temperatures from that shift in the forest area. These predictions are often made with complex Earth system models, but we look to a more idealized representation of the land–atmosphere system to diagnose how shallow clouds should respond to changes in surface properties with different scenarios of boreal forest expansion at a more foundational level. This both grounds our understanding of previous analysis and provides helpful direction for future studies of this relevant and impactful land cover change.
Abstract
The forecasting of El Niño amplitude remains uncertain, with false alarms or underestimations often occurring. It has been suggested that westerly wind bursts (WWBs) are crucial atmospheric stochastic forcing that affects the development, amplitude, and predictability of El Niño. Effectively capturing the influence of WWBs in El Niño forecasting systems to mitigate El Niño forecast uncertainties continues to be a widely discussed topic. In this work, two El Niño ensemble forecast scenarios incorporating WWBs were devised utilizing an intermediate coupled model capable of simulating the rational features of El Niño–Southern Oscillation and a refined WWB parameterization scheme. To start with, both the seasonal variations and the dependence on the oceanic background state were considered to improve the parameterization of WWBs. Furthermore, considering the short intrinsic predictability limit of WWBs with respect to the interannual scale of El Niño evolution, a WWB ensemble forecast strategy was designed to obtain their occurrence probability and the statistical features of their magnitude and central location. With this in mind, an ensemble forecast of El Niño events occurring during 1979–2021 based on a WWB ensemble forecast was established (termed WWB-based). For comparison, a conventional El Niño ensemble forecast based on initial condition (IC) perturbations was also conducted (termed IC-based). Results indicated that the WWB-based approach shows better performance in forecasting the El Niño amplitude than the IC-based one. The present approach suggests that an ensemble forecast with proper consideration of the scale interaction between the fast WWBs and interannual variations is more physically consistent.
Significance Statement
Westerly wind bursts (WWBs) are believed to influence the growth and amplitude of El Niño events. Unfortunately, the short intrinsic predictability limit of WWBs brings considerable uncertainty to the forecasting of El Niño amplitude. In this study, a refined parameterization scheme for WWBs, along with an ensemble forecast strategy designed to obtain the occurrence probability of WWBs and the statistical features of their magnitude and central location, was employed in an intermediate coupled model to produce an ensemble forecast for the El Niño events occurring during 1979–2021. The results showed that an ensemble forecast of El Niño based on WWB ensemble forecast can fully consider WWBs’ potential impact on the El Niño system and improve El Niño forecasts.
Abstract
The forecasting of El Niño amplitude remains uncertain, with false alarms or underestimations often occurring. It has been suggested that westerly wind bursts (WWBs) are crucial atmospheric stochastic forcing that affects the development, amplitude, and predictability of El Niño. Effectively capturing the influence of WWBs in El Niño forecasting systems to mitigate El Niño forecast uncertainties continues to be a widely discussed topic. In this work, two El Niño ensemble forecast scenarios incorporating WWBs were devised utilizing an intermediate coupled model capable of simulating the rational features of El Niño–Southern Oscillation and a refined WWB parameterization scheme. To start with, both the seasonal variations and the dependence on the oceanic background state were considered to improve the parameterization of WWBs. Furthermore, considering the short intrinsic predictability limit of WWBs with respect to the interannual scale of El Niño evolution, a WWB ensemble forecast strategy was designed to obtain their occurrence probability and the statistical features of their magnitude and central location. With this in mind, an ensemble forecast of El Niño events occurring during 1979–2021 based on a WWB ensemble forecast was established (termed WWB-based). For comparison, a conventional El Niño ensemble forecast based on initial condition (IC) perturbations was also conducted (termed IC-based). Results indicated that the WWB-based approach shows better performance in forecasting the El Niño amplitude than the IC-based one. The present approach suggests that an ensemble forecast with proper consideration of the scale interaction between the fast WWBs and interannual variations is more physically consistent.
Significance Statement
Westerly wind bursts (WWBs) are believed to influence the growth and amplitude of El Niño events. Unfortunately, the short intrinsic predictability limit of WWBs brings considerable uncertainty to the forecasting of El Niño amplitude. In this study, a refined parameterization scheme for WWBs, along with an ensemble forecast strategy designed to obtain the occurrence probability of WWBs and the statistical features of their magnitude and central location, was employed in an intermediate coupled model to produce an ensemble forecast for the El Niño events occurring during 1979–2021. The results showed that an ensemble forecast of El Niño based on WWB ensemble forecast can fully consider WWBs’ potential impact on the El Niño system and improve El Niño forecasts.
