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Jingzhuo Wang, Jing Chen, Hanbin Zhang, Hua Tian, and Yining Shi

Abstract

Ensemble forecasting is a method to faithfully describe initial and model uncertainties in a weather forecasting system. Initial uncertainties are much more important than model uncertainties in the short-range numerical prediction. Currently, initial uncertainties are described by the ensemble transform Kalman filter (ETKF) initial perturbation method in Global and Regional Assimilation and Prediction Enhanced System–Regional Ensemble Prediction System (GRAPES-REPS). However, an initial perturbation distribution similar to the analysis error cannot be yielded in the ETKF method of the GRAPES-REPS. To improve the method, we introduce a regional rescaling factor into the ETKF method (we call it ETKF_R). We also compare the results between the ETKF and ETKF_R methods and further demonstrate how rescaling can affect the initial perturbation characteristics as well as the ensemble forecast skills. The characteristics of the initial ensemble perturbation improve after applying the ETKF_R method. For example, the initial perturbation structures become more reasonable, the perturbations are better able to explain the forecast errors at short lead times, and the lower kinetic energy spectrum as well as perturbation energy at the initial forecast times can lead to a higher growth rate of themselves. Additionally, the ensemble forecast verification results suggest that the ETKF_R method has a better spread–skill relationship, a faster ensemble spread growth rate, and a more reasonable rank histogram distribution than ETKF. Furthermore, the rescaling has only a minor impact on the assessment of the sharpness of probabilistic forecasts. The above results all suggest that ETKF_R can be effectively applied to the operational GRAPES-REPS.

Open access
Jun-Chao Yang, Yu Zhang, Ingo Richter, and Xiaopei Lin

Abstract

Moisture transport from the Atlantic to Pacific is important for the basin-scale freshwater budget and the formation of meridional ocean circulation. Although the climatological tropical Atlantic-to-Pacific moisture transport (TAPMORT) has been well investigated, few studies have focused on its variability. Here we investigate the interannual variability of TAPMORT based on the atmospheric reanalysis datasets. The TAPMORT interannual variability is dominated by the variations of transbasin winds across Central America, and peaks in late boreal summer and late boreal winter. 1) In late summer, a developing El Niño and a mature Atlantic Niña set up an interbasin sea surface temperature (SST) gradient that strengthens the low-level jet across Central America and therefore TAPMORT (with weakened TAPMORT for opposite signed events). This process typically occurs from July to September, with a peak in August. 2) In late winter, the strengthened southern North American center of the Pacific–North American (PNA)-like pattern intensifies the TAPMORT variations. Although atmospheric interannual variability dominates these variations, extreme El Niño events are also important for the teleconnections. This process shows a single peak in February, in contrast to the persistent peak in late summer. We further demonstrate that the persistent TAPMORT variability in late summer dominates the moisture divergence over the northwestern tropical Atlantic and modulates freshwater flux there. Thus, our study improves the understanding of how TAPMORT interannual variability and the related interbasin SST gradient regulate the northwestern tropical Atlantic freshwater budget and the related salinity variability.

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Lei Zhang, Weiqing Han, and Zeng-Zhen Hu

Abstract

An unprecedented extreme positive Indian Ocean dipole event (pIOD) occurred in 2019, which has caused widespread disastrous impacts on countries bordering the Indian Ocean, including the East African floods and vast bushfires in Australia. Here we investigate the causes for the 2019 pIOD by analyzing multiple observational datasets and performing numerical model experiments. We find that the 2019 pIOD was triggered in May by easterly wind bursts over the tropical Indian Ocean associated with the dry phase of the boreal summer intraseasonal oscillation, and it was sustained by the local atmosphere–ocean interaction thereafter. During September–November, warm sea surface temperature anomalies (SSTA) in the central-western tropical Pacific Ocean further enhanced the Indian Ocean’s easterly winds, bringing the pIOD to an extreme magnitude. The central-western tropical Pacific warm SSTA was strengthened by two consecutive Madden–Julian oscillation (MJO) events that originated from the tropical Indian Ocean. Our results highlight the important roles of cross-basin and cross-time-scale interactions in generating extreme IOD events. The lack of accurate representation of these interactions may be the root for a short lead time in predicting this extreme pIOD with a state-of-the-art climate forecast model.

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Lisa N. Murphy, Jeremy M. Klavans, Amy C. Clement, and Mark A. Cane

Abstract

This paper attempts to enhance our understanding of the causes of Atlantic multidecadal variability (AMV). Following the literature, we define the AMV as the SST averaged over the North Atlantic basin, linearly detrended and low-pass filtered. There is an ongoing debate about the drivers of the AMV, which include internal variability generated from the ocean or atmosphere (or both) and external radiative forcing. We test the role of these factors in explaining the time history, variance, and spatial pattern of the AMV using a 41-member ensemble from a fully coupled version of CESM and a 10-member ensemble of the CESM atmosphere coupled to a slab ocean. The large ensemble allows us to isolate the role of external forcing versus internal variability, and the model differences allow us to isolate the role of coupled ocean circulation. Both with and without coupled ocean circulation, external forcing explains more than half of the variance of the observed AMV time series, indicating its important role in simulating the twentieth-century AMV phases. In this model the net effect of ocean processes is to reduce the variance of the AMV. Dynamical ocean coupling also reduces the ability of the model to simulate the characteristic spatial pattern of the AMV, but forcing has little impact on the pattern. Historical forcing improves the time history and variance of the AMV simulation, while the more realistic ocean representation reduces the variance below that observed and lowers the correlation with observations.

Open access
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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