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The link between weather regimes (WRs) and polar low (PL) activity is examined over the North Atlantic and North Pacific basins. Compared to earlier studies based on limited, regional PL datasets, our study conducts a more complete evaluation of regional WR–PL relationships using an expanded PL climatology. Our findings show that PL activity is increased over the Norwegian and Barents Seas during the Atlantic ridge regime and decreased over the former region during the Scandinavian blocking regime, with negative impacts also stretching to the Irminger Sea. Over the Labrador and Irminger Seas, PL activity is modulated strongest by the North Atlantic Oscillation (NAO), with positive impacts during the positive phase and vice versa. Over the North Pacific, the Arctic low contributes to increased PL activity over most regions, while the opposite is true for the Pacific wave train regime. The variability of PL activity associated with WRs is strongly related to changes in key environmental conditions. In general, regions of enhanced (reduced) PL activity are coincident with anomalous low-level northerly (southerly) flow and reduced (increased) static stability. Further analysis shows that certain persistent WRs can strongly modulate PL activity over some regions, either due to the amplification or propagation of favorable or unfavorable conditions, which cautions the limitation of regarding WRs as stationary patterns. A previously developed PL genesis potential index is shown to represent the observed impacts well, which serves to confirm the robustness of our findings and suggests the potential applicability of WRs to the subseasonal prediction of PLs. Significance Statement: Polar lows are intense small-scale (∼300 km) cyclones that form over high-latitude oceanic regions. The hazardous impacts they pose to coastal communities and maritime and air operations in the Arctic motivate their skillful prediction, which remains a major challenge at lead times beyond a few days. In this study, we relate PLs and the key environmental conditions that favor their development to weather regimes, which are recurrent large-scale circulation patterns that can persist for weeks at a time. We find that weather regimes have strong impacts on polar low activity through the modulation of key environmental conditions. These relationships can potentially be utilized in the extended-range prediction of polar lows. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) in the Bay of Bengal (BoB) are among the most devastating events in nature, significantly influencing human life. They affect the region between April and December, being separated into two distinct phases: the premonsoon (April–June, AMJ) and postmonsoon (October–December, OND), with little activity during the monsoon peak. In this article, the influence of the tropical easterly jet (TEJ) on the observed BoB TC activity, and the expected changes under global warming conditions, are investigated using reanalyses and Coupled Model Intercomparison Project phase 6 (CMIP6) models. The general assumption is that TC activity should increase in response to warmer sea surface temperature (SST). However, several dynamical processes are needed to create organized convection, which is a prerequisite for TC development. The results show that the frequency and accumulated energy should decrease in the future (2020–49), compared to the historical climate (1950–79), during both phases. However, the northern BoB TCs' intensity increases during OND. The change of the mean TEJ and intermittent shear relaxations in the future climate are found to play a major role in the change of TC activity rather than the SST. During AMJ, a southward shift of the mean TEJ and its intermittent shear relaxations are associated with reduced TC activity. During OND, the decrease of the frequency of TEJ and subtropical jet (STJ) intermittent shear relaxations are associated with the reduction of TC activity. This study clarifies the importance of both the mean and intermittency of the TEJ in determining the future of TC activity in the BoB. [ABSTRACT FROM AUTHOR]

The double–intertropical convergence zone (ITCZ) bias has been an outstanding problem among climate models for two decades. However, it remains unclear how much of this tropical bias is attributed to the extratropics and tropics itself, respectively. Applying the regional data assimilation (RDA) method, we follow up a previous study with a more advanced model of GFDL CM2.1 to quantify the influence of extratropical atmosphere on the double-ITCZ bias. Our study reveals that this tropical bias is influenced to a large extent by the extratropics between 20° and 30°, with little impact from the extratropics poleward of 30°. This vital role of subtropics in the double-ITCZ bias is likely determined by the meridional extent of Hadley circulation from zonal-mean perspective. Besides, the vital role of subtropics is also supported by wind–evaporation–SST feedback in the subtropical southeastern Pacific from a regional perspective. [ABSTRACT FROM AUTHOR]

The tropical rain belt varies between unimodal and bimodal meridional precipitation distributions, both regionally and on seasonal to geological time scales. Here we show that this variation is largely driven by equatorial precipitation inhibition, and quantify it using an equatorial modality index (EMI) that varies continuously between 1 and 2 for purely unimodal and bimodal distributions. We show that tropical modality is a fundamental characteristic of tropical climate, which we define as annual-mean EMI. We examine large-scale aspects of tropical modality across 73 climate models from phases 5 and 6 of the Coupled Model Intercomparison Project, 45 paleo simulations (;300 million years ago to present), and observations. We find increased tropical modality to be strongly related to increased width of the tropical rain belt, wider and weaker meridional overturning circulation, colder equatorial cold tongues, and more severe double intertropical convergence zone bias in modern climate models. Tropical sectors (or global zonal means) with low tropical modality are characterized by monsoonal seasonal variations (i.e., seasonal migrations of rainbands following the sun). In sectors with high tropical modality we identify three important seasonal modes: (i) migration of the precipitation distribution toward the warmer hemisphere, (ii) variation in the latitudinal separation between hemispheric rainbands, and (iii) seesaw variation in the intensity of the hemispheric rainbands. In high tropical modality sectors, due to contrasting shifts of the migration and separation modes, counter to general wisdom, seasonal migrations of tropical rainbands cannot be generally assumed to follow the sun. [ABSTRACT FROM AUTHOR]

The Las Vegas metropolitan area in Nevada has experienced extensive urban growth since 1950 coincident with regional and local climate change. This study explores the nonstationary flood history of the Las Vegas Wash (LVW) watershed by deconstructing it into its constituent physical drivers. Observations and reanalysis products are used to examine the hydroclimatology, hydrometeorology, and hydrology of flash flooding in the watershed. Annual peak flows have increased nonlinearly over the past seven decades, with an abrupt changepoint detected in the mid-1990s, which is attributed to the implementation of flood conveyance systems rather than changes in land use. The LVW watershed exhibits two pronounced flood seasons, associated with distinct synoptic atmospheric circulations: winter floods linked to inland-penetrating atmospheric rivers and summer floods linked to the North American monsoon. El Niño–Southern Oscillation also plays a role in modulating extreme rainfall and the resultant floods because annual maximum daily rainfall totals positively correlate with El Niño, with Spearman's correlation coefficient of 0.36 (p value < 0.05). Winter maximum daily rainfall totals have increased since 1950, whereas summer daily rainfall maxima have decreased. The trends in hydrometeorological drivers interact with urbanization to shift flood seasonality toward more frequent winter floods in the LVW watershed. A process-based understanding of the flood hydrology of the watershed also provides insights into flood frequency analysis and flood forecasting. [ABSTRACT FROM AUTHOR]

Linear inverse modeling or principal oscillation pattern (POP) analysis is a widely applied tool in climate science for extracting from data dominant spatial patterns together with their dynamics as approximated by a linear Markov model. The system is projected onto a principal linear subspace and the system matrix is estimated from data. The eigenmodes of the system matrix are the POPs, with the eigenvalues providing their decay time scales and oscillation frequencies. Usually, the subspace is spanned by the leading principal components (PCs) and empirical orthogonal functions (EOFs). Outside of climate science, this procedure is now more commonly referred to as dynamic mode decomposition (DMD). Here, we use optimal mode decomposition (OMD) to address the full linear inverse modeling problem of simultaneous optimization of the principal subspace and the linear operator. The method is illustrated on two pedagogical examples and then applied to a three-level quasigeostrophic atmospheric model with realistic mean state and variability. The OMD models significantly outperform the EOF/DMD models in predicting the time evolution of the large-scale flow modes. The advantage of the OMD models stems from finding more persistent modes as well as from better capturing the nonnormality of the linear operator and the associated nonmodal growth. The dynamics of the large-scale flow modes turn out to be markedly non-Markovian and the OMD modes are superior to the EOF/DMD modes also in a modeling setting with a higher-order vector autoregressive process. The OMD modes could also be used as basis functions for a nonlinear dynamical model although they are not optimized for that purpose. Potential applications of the OMD method in weather and climate science include ENSO or MJO prediction, reduced-rank data assimilation, and generation of initial perturbations for ensemble prediction. [ABSTRACT FROM AUTHOR]

Precipitation estimates are highly uncertain in complex regions such as High Mountain Asia (HMA), where ground measurements are very difficult to obtain and atmospheric dynamics poorly understood. Though gridded products derived from satellite-based observations and/or reanalysis can provide temporally and spatially distributed estimates of precipitation, there are significant inconsistencies in these products. As such, to date, there is little agreement in the community on the best and most accurate gridded precipitation product in HMA, which is likely area dependent because of HMA's strong heterogeneities and complex orography. Targeting these gaps, this article presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets [the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), and the ECMWF reanalysis ERA5] with a localized probability matched mean (LPM) approach. We evaluate the performance of the LPM estimate along with a simple ensemble mean (EM) estimate to overcome the differences and disparities of the three selected constituent products on long-term averages and trends in HMA. Our analysis demonstrates that LPM reduces the high biases embedded in the ensemble members and provides more realistic spatial patterns compared to EM. LPM is also a good alternative for merging data products with different spatiotemporal resolutions. By filtering disparities among the individual ensemble members, LPM overcomes the problem of a certain product performing well only in a particular area and provides a consensus estimate with plausible temporal trends. [ABSTRACT FROM AUTHOR]

Anomalous sea levels along the mid-Atlantic and South Atlantic coasts of the United States are often linked to atmosphere–ocean dynamics, remote- and local-scale forcing, and other factors linked to cyclone passage, winds, waves, and storm surge. Herein, we examine sea level variability along the U.S. Atlantic coast through satellite altimeter and coastal tide gauge data within the context of synoptic-scale weather pattern forcing. Altimetry data, derived from sea level anomaly (SLA) data between 1993 and 2019, were compared with self-organizing map (SOM)-based atmospheric circulation and surface wind field categorizations to reveal spatiotemporal patterns and their interrelationships with high-water-level conditions at tide gauges. Regional elevated sea level patterns and variability were strongly associated with synergistic patterns of atmospheric circulation and wind. Recurring atmospheric patterns associated with high-tide flooding events and flood risk were identified, as were specific regional oceanographic variability patterns of SLA response. The incorporation of combined metrics of wind and circulation patterns further isolate atmospheric drivers of high-tide flood events and may have particular significance for predicting future flood events over multiple spatial and temporal scales. Significance Statement: Mean sea level and minor to moderate coastal flood events, also called blue-sky or high-tide floods, are increasing along many U.S. coastlines. While the drivers of such events are numerous, here we identified key contributing weather patterns and environmental factors linked to increased risk of regional and local high-water conditions along the Atlantic coast. Our results indicate that the predictability of elevated sea levels and high-tide floods is highly dependent upon atmospheric drivers including wind and circulation patterns and, if applied in a tested modeling framework, may prove useful for predicting future floods at various time scales. [ABSTRACT FROM AUTHOR]

To better understand the dynamics and impacts of blocking events, their 3D structure needs to be further investigated. We present a comprehensive composite analysis of the 3D structure of blocks and its response to future climate change over the North Pacific, the North Atlantic, and Russia in summers and winters using reanalysis and two large-ensemble datasets from CESM1 and GFDL-CM3. In reanalysis, over both ocean and land, the anomalous winds are equivalent-barotropic in the troposphere and stratosphere, and temperature anomalies are positive throughout the troposphere and negative in the lower stratosphere. The main seasonal and regional differences are that blocks are larger and/or stronger in winters; over oceans, the temperature anomaly is shifted westward due to latent heating. Analyzing the temperature tendency equation shows that in all three sectors, adiabatic warming due to subsidence is the main driver of the positive temperature anomaly; however, depending on season and region, meridional thermal advection and latent heating might have leading-order contributions too. Both GCMs are found to reproduce the climatological 3D structure remarkably well, but sometimes disagree on future changes. Overall, the future summertime response is weakening of all fields (except for specific humidity), although the impact on near-surface temperature is not necessarily weakened; for example, the blocking-driven near-surface warming over Russia intensifies. The wintertime response is strengthening of all fields, except for temperature in some cases. Responses of geopotential height and temperature are shifted westward in winters, most likely due to latent heating. Results highlight the importance of process-level analyses of blocks' 3D structure for improved understanding of the resulting temperature extremes and their future changes. [ABSTRACT FROM AUTHOR]

Teleconnections are sources of predictability for regional weather and climate, but the relative contributions of different teleconnections to regional anomalies are usually not understood. While physical knowledge about the involved mechanisms is often available, how to quantify a particular causal pathway from data are usually unclear. Here, we argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. We illustrate some of the key concepts of this theory with concrete examples of well-known atmospheric teleconnections. We further discuss the particular challenges and advantages these imply for climate science and argue that a systematic causal approach to statistical inference should become standard practice in the study of teleconnections. [ABSTRACT FROM AUTHOR]