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Jeffrey D. Duda and David D. Turner

Abstract

The Method of Object-based Diagnostic Evaluation (MODE) is used to perform an object-based verification of approximately 1400 forecasts of composite reflectivity from the operational HRRR during April–September 2019. In this study, MODE is configured to prioritize deep, moist convective storm cells typical of those that produce severe weather across the central and eastern United States during the warm season. In particular, attributes related to distance and size are given the greatest attribute weights for computing interest in MODE. HRRR tends to overforecast all objects, but substantially overforecasts both small objects at low-reflectivity thresholds and large objects at high-reflectivity thresholds. HRRR tends to either underforecast objects in the southern and central plains or has a correct frequency bias there, whereas it overforecasts objects across the southern and eastern United States. Attribute comparisons reveal the inability of the HRRR to fully resolve convective-scale features and the impact of data assimilation and loss of skill during the initial hours of the forecasts. Scalar metrics are defined and computed based on MODE output, chiefly relying on the interest value. The object-based threat score (OTS), in particular, reveals similar performance of HRRR forecasts as does the Heidke skill score, but with differing magnitudes, suggesting value in adopting an object-based approach to forecast verification. The typical distance between centroids of objects is also analyzed and shows gradual degradation with increasing forecast length.

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Kai-Chih Tseng, Nathaniel C. Johnson, Eric D. Maloney, Elizabeth A. Barnes, and Sarah B. Kapnick

Abstract

The excitation of the Pacific–North American (PNA) teleconnection pattern by the Madden–Julian oscillation (MJO) has been considered one of the most important predictability sources on subseasonal time scales over the extratropical Pacific and North America. However, until recently, the interactions between tropical heating and other extratropical modes and their relationships to subseasonal prediction have received comparatively little attention. In this study, a linear inverse model (LIM) is applied to examine the tropical–extratropical interactions. The LIM provides a means of calculating the response of a dynamical system to a small forcing by constructing a linear operator from the observed covariability statistics of the system. Given the linear assumptions, it is shown that the PNA is one of a few leading modes over the extratropical Pacific that can be strongly driven by tropical convection while other extratropical modes present at most a weak interaction with tropical convection. In the second part of this study, a two-step linear regression is introduced that leverages a LIM and large-scale climate variability to the prediction of hydrological extremes (e.g., atmospheric rivers) on subseasonal time scales. Consistent with the findings of the first part, most of the predictable signals on subseasonal time scales are determined by the dynamics of the MJO–PNA teleconnection while other extratropical modes are important only at the shortest forecast leads.

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Jiayu Zhang, Ping Huang, Fei Liu, and Shijie Zhou

Abstract

This study investigates what forms the spatial pattern of the amplitude changes in tropical intraseasonal and interannual variability—represented by the two most important variables, precipitation (ΔP′) and circulation (Δω′)—under global warming, based on 24 models from the phase 5 of the Coupled Model Intercomparison Project (CMIP5). Diagnostic analyses reveal that the moisture budget and thermodynamic energy equations related to the ΔP′ and Δω′ proposed separately in previous studies are simultaneously tenable. As a result, we investigate the mechanism for the spatial pattern of Δω′ from the perspective of the moist static energy (MSE) balance mainly considering the positive contribution from vertical advection. Therefore, based on the simplified MSE balance, the spatial pattern of Δω′ can be approximately projected based on three factors: background circulation variability ω′, the vertical gradient of mean-state MSE M¯, and its future change ΔM¯. Under global warming, the middle-level vertical gradient of MSE increases slightly over the Indian Ocean and the Maritime Continent and decreases over the equatorial Pacific where the increase in sea surface temperature (SST) exceeds the tropical mean. The vertical gradient of mean-state MSE is modified by the increase in vertical gradients of moisture and dry static energy (DSE) simultaneously. In short, the change in the vertical gradient of mean-state MSE under global warming can influence the moisture budget and thermodynamic energy balances, resulting in the spatial pattern of ΔP′ and Δω′ at intraseasonal and interannual time scales consequently, mainly determined by the lower boundary moisture condition in the response of the SST change pattern.

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Dylan Oldenburg, Robert C. J. Wills, Kyle C. Armour, LuAnne Thompson, and Laura C. Jackson

Abstract

Ocean heat transport (OHT) plays a key role in climate and its variability. Here, we identify modes of low-frequency North Atlantic OHT variability by applying a low-frequency component analysis (LFCA) to output from three global climate models. The first low-frequency component (LFC), computed using this method, is an index of OHT variability that maximizes the ratio of low-frequency variance (occurring at decadal and longer time scales) to total variance. Lead–lag regressions of atmospheric and ocean variables onto the LFC time series illuminate the dominant mechanisms controlling low-frequency OHT variability. Anomalous northwesterly winds from eastern North America over the North Atlantic act to increase upper ocean density in the Labrador Sea region, enhancing deep convection, which later increases OHT via changes in the strength of the Atlantic meridional overturning circulation (AMOC). The strengthened AMOC carries warm, salty water into the subpolar gyre, reducing deep convection and weakening AMOC and OHT. This mechanism, where changes in AMOC and OHT are driven primarily by changes in Labrador Sea deep convection, holds not only in models where the climatological (i.e., time-mean) deep convection is concentrated in the Labrador Sea, but also in models where the climatological deep convection is concentrated in the Greenland–Iceland–Norwegian (GIN) Seas or the Irminger and Iceland Basins. These results suggest that despite recent observational evidence suggesting that the Labrador Sea plays a minor role in driving the climatological AMOC, the Labrador Sea may still play an important role in driving low-frequency variability in AMOC and OHT.

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Zane Martin, Clara Orbe, Shuguang Wang, and Adam Sobel

Abstract

Observational studies show a strong connection between the intraseasonal Madden–Julian oscillation (MJO) and the stratospheric quasi-biennial oscillation (QBO): the boreal winter MJO is stronger, more predictable, and has different teleconnections when the QBO in the lower stratosphere is easterly versus westerly. Despite the strength of the observed connection, global climate models do not produce an MJO–QBO link. Here the authors use a current-generation ocean–atmosphere coupled NASA Goddard Institute for Space Studies global climate model (Model E2.1) to examine the MJO–QBO link. To represent the QBO with minimal bias, the model zonal-mean stratospheric zonal and meridional winds are relaxed to reanalysis fields from 1980 to 2017. The model troposphere, including the MJO, is allowed to freely evolve. The model with stratospheric nudging captures QBO signals well, including QBO temperature anomalies. However, an ensemble of nudged simulations still lacks an MJO–QBO connection.

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Brett Chrisler and Justin P. Stachnik

Abstract

Recent studies have examined moist entropy (ME) as a proxy for moist static energy (MSE) and the relative role of the underlying processes responsible for changes in ME that potentially affect MJO propagation. This study presents an analysis of the intraseasonally varying (ISV) ME anomalies throughout the lifetime of observed MJO events. A climatology of continuing and terminating MJO events is created from an event identification algorithm using common tracking indices including the OLR-based MJO index (OMI), filtered OMI (FMO), real-time multivariate MJO (RMM), and velocity potential MJO (VPM) index. ME composites for all indices show a statistically significant break in the wavenumber-1 oscillation at day 0 for terminating events in nearly all domains except RMM phase 6 and phase 7. The ME tendency is decomposed into horizontal and vertical advection, sensible and latent heat fluxes, and shortwave and longwave radiative fluxes using ERA-Interim data. The relative role of each processes toward the eastward propagation is discussed as well as their effects on MJO stabilization. Statistically significant differences occur for all terms by day −10. A domain sensitivity test is performed where eastward propagation is favored for vertical advection given a larger, asymmetric domain for continuing events. A reduced eastward propagation from vertical advection is evident 2–3 days before similar differences in horizontal advection for terminating events. The importance of horizontal advection for the eastward propagation of the MJO is discussed in addition to the relative destabilization from vertical advection in the convectively suppressed region downstream of future terminating MJOs.

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Xu Zhang, Yuhua Yang, Baode Chen, and Wei Huang

Abstract

The quantitative precipitation forecast in the 9-km operational modeling system (without the use of a convection parameterization scheme) at the Shanghai Meteorological Service (SMS) usually suffers from excessive precipitation at the grid scale and less-structured precipitation patterns. Two scale-aware convection parameterizations were tested in the operational system to mitigate these deficiencies. Their impacts on the warm-season precipitation forecast over China were analyzed in case studies and two-month retrospective forecasts. The results from case studies show that the importance of convection parameterization depends on geographical regions and weather regimes. Considering a proper magnitude of parameterized convection can produce more realistic precipitation distribution and reduce excessive gridscale precipitation in southern China. In northeast and southwest China, however, the convection parameterization plays an insignificant role in precipitation forecast because of strong synoptic-scale forcing. A statistical evaluation of the two-month retrospective forecasts indicates that the forecast skill for precipitation in the 9-km operational system is improved by choosing proper convection parameterization. This study suggests that improvement in contemporary convection parameterizations is needed for their usage for various meteorological conditions and reasonable partitioning between parameterized and resolved convection.

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Evan A. Kalina, Isidora Jankov, Trevor Alcott, Joseph Olson, Jeffrey Beck, Judith Berner, David Dowell, and Curtis Alexander

Abstract

The High-Resolution Rapid Refresh Ensemble (HRRRE) is a 36-member ensemble analysis system with 9 forecast members that utilizes the Advanced Research version of the Weather Research and Forecasting (ARW-WRF) dynamic core and the physics suite from the operational Rapid Refresh/High-Resolution Rapid Refresh deterministic modeling system. A goal of HRRRE development is a system with sufficient spread among members, comparable in magnitude to the random error in the ensemble mean, to represent the range of possible future atmospheric states. HRRRE member diversity has traditionally been obtained by perturbing the initial and lateral boundary conditions of each member, but recent development has focused on implementing stochastic approaches in HRRRE to generate additional spread. These techniques were tested in retrospective experiments and in the May 2019 Hazardous Weather Testbed Spring Experiment (HWT-SE). Results show a 6%–25% increase in the ensemble spread in 2-m temperature, 2-m mixing ratio, and 10-m wind speed when stochastic parameter perturbations are used in HRRRE (HRRRE-SPP). Case studies from HWT-SE demonstrate that HRRRE-SPP performed similar to or better than the operational High-Resolution Ensemble Forecast system, version 2 (HREFv2), and the nonstochastic HRRRE. However, subjective evaluations provided by HWT-SE forecasters indicated that overall, HRRRE-SPP predicted lower probabilities of severe weather (using updraft helicity as a proxy) compared to HREFv2. A statistical analysis of the performance of HRRRE-SPP and HREFv2 from the 2019 summer convective season supports this claim, but also demonstrates that the two systems have similar reliability for prediction of severe weather using updraft helicity.

Open access
Yoshi N. Sasaki and Chisato Umeda

Abstract

It has been reported that the sea surface temperature (SST) trend of the East China Sea during the twentieth century was a couple of times larger than the global mean SST trend. However, the detailed spatial structure of the SST trend in the East China Sea and its mechanism have not been understood. The present study examines the SST trend in the East China Sea from 1901 to 2010 using observational data and a Regional Ocean Modeling System (ROMS) with an eddy-resolving horizontal resolution. A comparison among two observational datasets and the model output reveals that enhanced SST warming occurred along the Kuroshio and along the coast of China over the continental shelf. In both regions, the SST trends were the largest in winter. The heat budget analysis using the model output indicates that the upper-layer temperature rises in both regions were induced by the trend of ocean advection, which was balanced in relation to the increase of surface net heat release. In addition, the rapid SST warming along the Kuroshio was induced by the acceleration of the Kuroshio. Sensitivity experiments revealed that this acceleration was likely caused by the negative wind stress curl anomalies over the North Pacific. In contrast, the enhanced SST warming along the China coast resulted from the ocean circulation change over the continental shelf by local atmospheric forcing.

Open access
Valerio Capecchi

Abstract

We investigate the potential added value of running three limited-area ensemble systems (with the WRF, Meso-NH, and MOLOCH models and a grid spacing of approximately 2.5 km) for two heavy-precipitation events in Italy. Such high-resolution ensembles include an explicit treatment of convective processes and dynamically downscale the ECMWF global ensemble predictions, which have a grid spacing of approximately 18 km. The predictions are verified against rain gauge data, and their accuracy is evaluated over that of the driving coarser-resolution ensemble system. Furthermore, we compare the simulation speed (defined as the ratio of simulation length to wall-clock time) of the three limited-area models to estimate the computational effort for operational convection-permitting ensemble forecasting. We also study how the simulation wall-clock time scales with increasing numbers of computing elements (from 36 to 1152 cores). Objective verification methods generally show that convection-permitting forecasts outperform global forecasts for both events, although precipitation peaks remain largely underestimated for one of the two events. Comparing simulation speeds, the MOLOCH model is the fastest and the Meso-NH is the slowest one. The WRF Model attains efficient scalability, whereas it is limited for the Meso-NH and MOLOCH models when using more than 288 cores. We finally demonstrate how the model simulation speed has the largest impact on a joint evaluation with the model performance because the accuracy of the three limited-area ensembles, amplifying the forecasting capability of the global predictions, does not differ substantially.

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