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The sources of predictability for the February 2021 cold air outbreak (CAO) over the central United States, which led to power grid failures and water delivery shortages in Texas, are diagnosed using a machine learning–based prediction model called a linear inverse model (LIM). The flexibility and low computational cost of the LIM allows its forecasts to be used for identifying and assessing the predictability of key physical processes. The LIM may also be run as a climate model for sensitivity and risk analysis for the same reasons. The February 2021 CAO was a subseasonal forecast of opportunity, as the LIM confidently predicted the CAO’s onset and duration four weeks in advance, up to two weeks earlier than other initialized numerical forecast models. The LIM shows that the February 2021 CAO was principally caused by unpredictable internal atmospheric variability and predictable La Niña teleconnections, with nominally predictable contributions from the previous month’s sudden stratospheric warming and the Madden–Julian oscillation. When run as a climate model, the LIM estimates that the February 2021 CAO was in the top 1% of CAO severity and suggests that similarly extreme CAOs could be expected to occur approximately every 20–30 years.

On average, 2-m temperature forecasts over North America for lead times greater than two weeks have generally low skill in operational dynamical models, largely because of the chaotic, unpredictable nature of daily weather. However, for a small subset of forecasts, more slowly evolving climate processes yield some predictable signal that may be anticipated in advance, occasioning “forecasts of opportunity.” Forecasts of opportunity evolve seasonally, since they are a function of the seasonally varying jet stream and various remote forcings such as tropical heating. Prior research has demonstrated that for boreal winter, an empirical dynamical modeling technique called a linear inverse model (LIM), whose forecast skill is typically comparable to operational forecast models, can successfully identify forecasts of opportunity both for itself and for other dynamical models. In this study, we use a set of LIMs to examine how subseasonal North American 2-m temperature potential predictability and forecasts of opportunity vary from boreal winter through summer. We show how LIM skill evolves during the three phases of the spring transition of the North Pacific jet—late winter, spring, and early summer—revealing clear differences in each phase and a distinct skill minimum in spring. We identify a subset of forecasts with markedly higher skill in all three phases, despite LIM temperature skill that is somewhat low on average. However, skill improvements are only statistically significant during winter and summer, again reflecting the spring subseasonal skill minimum. The spring skill minimum is consistent with the skill predicted from theory and arises due to a minimum in LIM forecast signal-to-noise ratio.

A cyclostationary linear inverse model (CSLIM) is used to investigate the seasonal growth of tropical Pacific Ocean El Niño–Southern Oscillation (ENSO) events with canonical, central Pacific (CP), or eastern Pacific (EP) sea surface temperature (SST) characteristics. Analysis shows that all types of ENSO events experience maximum growth toward final states occurring in November and December. ENSO events with EP characteristics also experience growth into May and June, but CP events do not. A single dominant “ENSO mode,” growing from an equatorial heat content anomaly into a characteristic ENSO-type SST pattern in about 9 months (consistent with the delayed/recharge oscillator model of ENSO), is essential for the predictable development of all ENSO events. Notably, its seasonality is responsible for the late-calendar-year maximum in ENSO amplification. However, this ENSO mode alone does not capture the observed growth and evolution of diverse ENSO events, which additionally involve the seasonal evolution of other nonorthogonal Floquet modes. EP event growth occurs when the ENSO mode is initially “covered up” in combination with other Floquet modes. The ENSO mode’s slow seasonal evolution allows it to emerge while the other modes rapidly evolve and/or decay, leading to strongly amplifying and more predictable EP events. CP events develop when the initial state has a substantial contribution from Floquet modes with meridional mode–like SST structures. Thus, while nearly all ENSO events involve the seasonally varying ENSO-mode dynamics, the diversity and predictability of ENSO events cannot be understood without identifying contributions from the remaining Floquet modes. Significance Statement The purpose of this study is to identify structures that lead to seasonal growth of diverse types of El Niño–Southern Oscillation (ENSO) events. An important contribution from this study is that it uses an observationally constrained, empirically derived seasonal model. We find that processes affecting the evolution of diverse ENSO events are strongly seasonally dependent. ENSO events with eastern equatorial Pacific sea surface temperature (SST) characteristics are closely related to a single “ENSO mode” that resembles theoretical models of ENSO variability. ENSO events that have central equatorial Pacific SST characteristics include contributions from additional “meridional mode” structures that evolve via different physical processes. These findings are an important step in evaluating the seasonal predictability of ENSO diversity.

Diagnosis of rapidly developing springtime droughts in the central United States has mostly been made via numerous individual case studies rather than in an aggregate sense. This study investigates common aspects of subseasonal “meteorological drought” evolution, here defined as persistent precipitation minus evapotranspiration (P − ET) deficits, revealed in early (1 April–15 May) and late (16 May–30 June) spring composites of 5-day running mean JRA-55 reanalysis data for three different central U.S. regions during 1958–2018. On average, these droughts are initiated by a quasi-stationary Rossby wave packet (RWP), propagating from the western North Pacific, which arises about a week prior to drought onset. The RWP is related to a persistent ridge west of the incipient drought region and strong subsidence over it. This subsidence is associated with low-level divergent flow that dries the atmosphere and suppresses precipitation for roughly 1–2 weeks, and generally has a greater impact on the local moisture budget than does reduced poleward moisture transport. The resulting “dynamically driven” evaporative demand corresponds to a rapid drying of the root-zone soil moisture, which decreases around 40 percentiles within about 10 days. Anomalous near-surface warmth develops only after the P − ET deficit onset, as does anomalously low soil moisture that then lingers a month or more, especially in late spring. The horizontal scale of the RWPs, and of the related drought anomalies, decreases from early to late spring, consistent with the climatological change in the Pacific Rossby waveguide. Finally, while this composite analysis is based upon strong, persistent P − ET deficits, it still appears to capture much of the springtime development of “flash droughts” as well.

Diagnosing El Niño-Southern Oscillation (ENSO) predictability within operational forecast models is hindered by computational expense, resulting in hindcasts limited in their period of record, initialization frequency, and/or forecast leads. Here, we examine the multi-year predictability of ENSO since the late 1800s based on the subsequent evolution from anomalous states that most closely match observed sea surface temperature and height anomalies in 25 pre-industrial control simulations from the Coupled Model Intercomparison Project Phase 6. We found our ENSO forecast skill is comparable to twice-yearly 20th century hindcasts generated by a European operational forecasting system. However, our monthly initialization indicates that the so-called spring predictability barrier, presents throughout the century, does not impede second-year (~9-18 month leads) ENSO skill, which was notably high both in recent decades and towards the end of the 19th century. Overall, ENSO has exhibited a roughly U-shaped evolution in both amplitude and skill since the late 1800s.

During the worldwide pandemic, youth were isolated from each other. In response, New Jersey 4-H, a part of Rutgers Cooperative Extension, created the Junior Explorers, a virtual exchange program bringing together 4-H members with youth from other countries. This program provides youth ages 10-13 with access to international exchange opportunities that would otherwise be sparse for this age group. This program has demonstrated that Extension professionals can successfully adapt exchanges to a virtual platform. With youth the world over learning virtually, Extension professionals have a unique opportunity to learn from and collaborate with international partners to enhance existing programs.

When in-person programs were suspended during the pandemic, Extension professionals quickly embraced virtual learning platforms to conduct programs. One of the more popular platforms was Zoom. In addition to the standard features that come equipped with Zoom, there are several other free or low-cost online resources that can be used to enhance virtual programs and create a greater sense of community virtually.

Teleconnections from the Tropics energize variations of the North Pacific climate, but detailed diagnosis of this relationship has proven difficult. Simple univariate methods, such as regression on El Niño-Southern Oscillation (ENSO) indices, may be inadequate since the key dynamical processes involved -- including ENSO diversity in the Tropics, re-emergence of mixed layer thermal anomalies, and oceanic Rossby wave propagation in the North Pacific -- have a variety of overlapping spatial and temporal scales. Here we use a multivariate Linear Inverse Model to quantify tropical and extra-tropical multi-scale dynamical contributions to North Pacific variability, in both observations and CMIP6 models. In observations, we find that the Tropics are responsible for almost half of the seasonal variance, and almost three quarters of the decadal variance, along the North American coast and within the subtropical front region northwest of Hawaii. SST anomalies that are generated by local dynamics within the Northeast Pacific have much shorter time scales, consistent with transient weather forcing by Aleutian low anomalies. Variability within the Kuroshio-Oyashio Extension (KOE) region is considerably less impacted by the Tropics, on all time scales. Consequently, without tropical forcing the dominant pattern of North Pacific variability would be a KOE pattern, rather than the Pacific Decadal Oscillation (PDO). In contrast to observations, most CMIP6 historical simulations produce North Pacific variability that maximizes in the KOE region, with amplitude significantly higher than observed. Correspondingly, the simulated North Pacific in all CMIP6 models is shown to be relatively insensitive to the Tropics, with a dominant spatial pattern generally resembling the KOE pattern, not the PDO.

The Indian Ocean has received increasing attention for its large impacts on regional and global climate. However, sea surface temperature (SST) variability arising from Indian Ocean internal processes has not been well understood particularly on decadal and longer time scales, and the external influence from the tropical Pacific has not been quantified. This paper analyzes the interannual-to-decadal SST variability in the tropical Indian Ocean in observations and explores the external influence from the Pacific versus internal processes within the Indian Ocean using a linear inverse model (LIM). Coupling between Indian Ocean and tropical Pacific SST anomalies (SSTAs) is assessed both within the LIM dynamical operator and the unpredictable stochastic noise that forces the system. Results show that the observed Indian Ocean basin (IOB)-wide SSTA pattern is largely a response to the Pacific ENSO forcing, although it in turn has a damping effect on ENSO especially on annual and decadal time scales. On the other hand, the Indian Ocean dipole (IOD) is an Indian Ocean internal mode that can actively affect ENSO; ENSO also has a returning effect on the IOD, which is rather weak on decadal time scale. The third mode is partly associated with the subtropical Indian Ocean dipole (SIOD), and it is primarily generated by Indian Ocean internal processes, although a small component of it is coupled with ENSO. Overall, the amplitude of Indian Ocean internally generated SST variability is comparable to that forced by ENSO, and the Indian Ocean tends to actively influence the tropical Pacific. These results suggest that the Indian–Pacific Ocean interaction is a two-way process.

Low-order linear inverse models (LIMs) have been shown to be competitive with comprehensive coupled atmosphere–ocean models at reproducing many aspects of tropical oceanic variability and predictability. This paper presents an extended cyclostationary linear inverse model (CS-LIM) that includes the annual cycles of the background state and stochastic forcing of tropical sea surface temperature (SST) and sea surface height (SSH) anomalies. Compared to a traditional stationary LIM that ignores such annual cycles, the CS-LIM is better at representing the seasonal modulation of ENSO-related SST anomalies and their phase locking to the annual cycle. Its deterministic as well as probabilistic hindcast skill is comparable to the skill of the North American Multimodel Ensemble (NMME) of comprehensive global coupled models. The explicit inclusion of annual-cycle effects in the CS-LIM improves the forecast skill of both SST and SSH anomalies through SST–SSH coupling. The impact on the SSH skill is particularly marked at longer forecast lead times over the western Pacific and in the vicinity of the Pacific North Equatorial Countercurrent (NECC), consistent with westward propagating oceanic Rossby waves that reflect off the western boundaries as eastward propagating Kelvin waves and influence El Niño development in the region. The higher CS-LIM skill is thus associated with the improved representation of both ENSO phase-locking and Pacific NECC variations. These improvements result from explicitly accounting for not only the annual cycle of the background state, but also that of the stochastic forcing.

The impact of land–atmosphere anomaly coupling on land variability is investigated using a new two-stage climate model experimental design called the “GLACE-Hydrology” experiment. First, as in the GLACE-CMIP5 experiment, twin sets of coupled land–atmosphere climate model (CAM5-CLM4.5) ensembles are performed, with each simulation using the same prescribed observed sea surface temperatures and radiative forcing for the years 1971–2014. In one set, land–atmosphere anomaly coupling is removed by prescribing soil moisture to follow the control model’s seasonally evolving soil moisture climatology (“land–atmosphere uncoupled”), enabling a contrast with the original control set (“land–atmosphere coupled”). Then, the atmospheric outputs from both sets of simulations are used to force land-only ensemble simulations, allowing investigation of the resulting soil moisture variability and memory under both the coupled and uncoupled scenarios. This study finds that in midlatitudes during boreal summer, land–atmosphere anomaly coupling significantly strengthens the relationship between soil moisture and evapotranspiration anomalies, both in amplitude and phase. This allows for decreased moisture exchange between the land surface and atmosphere, increasing soil moisture memory and often its variability as well. Additionally, land–atmosphere anomaly coupling impacts runoff variability, especially in wet and transition regions, and precipitation variability, although the latter has surprisingly localized impacts on soil moisture variability. As a result of these changes, there is an increase in the signal-to-noise ratio, and thereby the potential seasonal predictability, of SST-forced hydroclimate anomalies in many areas of the globe, especially in the midlatitudes. This predictability increase is greater for soil moisture than precipitation and has important implications for the prediction of drought.

The Pacific–North American (PNA) teleconnection pattern has been linked both to tropical phenomena, including the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO), and to internal extratropical processes, including interactions with the zonally varying basic state and synoptic eddies. Many questions remain, however, concerning how these various relationships act, both separately and together, to yield observed PNA variability. Using linear inverse modeling (LIM), this study finds that the development and amplification of PNA anomalies largely results from the interference of modes strongly coupled to sea surface temperatures (SST), such as ENSO, and modes internal to the atmosphere, including the MJO. These SST-coupled and “internal atmospheric” modes form subspaces that are not orthogonal, and PNA growth is shown to occur via non-normal interactions. An internal atmospheric space LIM is developed to examine growth beyond this interference by removing the SST-coupled modes, effectively removing ENSO and retaining MJO variability. Optimal PNA growth in the internal atmospheric space LIM is driven by MJO heating, particularly over the Indian Ocean, and a retrograding northeast Pacific streamfunction anomaly. Additionally, the individual contributions of tropical heating and the extratropical circulation on PNA growth are investigated. The non-normal PNA growth is an important result, demonstrating the difficulty in partitioning PNA variance into contributions from different phenomena. This cautionary result is likely applicable to many geophysical phenomena and should be considered in attribution studies.

We report the synthesis and antimicrobial studies of a new series of naphthyl-substituted pyrazole-derived hydrazones. Many of these novel compounds are potent growth inhibitors of several strains of drug-resistant bacteria. These potent compounds have inclined growth inhibitory properties for planktonic Staphylococcus aureus and Acinetobacter baumannii, and its drug-resistant variants with minimum inhibitory concentration (MIC) as low as 0.78 and 1.56 µg ml-1, respectively. These compounds also show potent activity against S. aureus and A. baumannii biofilm formation and eradication properties. Time Kill Assay shows that these compounds are bactericidal for S. aureus and bacteriostatic for A. baumannii. The probable mode of action is the disruption of the bacterial cell membrane. Furthermore, potent compounds are nontoxic to human cell lines at several fold higher concentrations than the MICs.

There is high demand and a growing expectation for predictions of environmental conditions that go beyond 0–14-day weather forecasts with outlooks extending to one or more seasons and beyond. This is driven by the needs of the energy, water management, and agriculture sectors, to name a few. There is an increasing realization that, unlike weather forecasts, prediction skill on longer time scales can leverage specific climate phenomena or conditions for a predictable signal above the weather noise. Currently, it is understood that these conditions are intermittent in time and have spatially heterogeneous impacts on skill, hence providing strategic windows of opportunity for skillful forecasts. Research points to such windows of opportunity, including El Niño or La Niña events, active periods of the Madden–Julian oscillation, disruptions of the stratospheric polar vortex, when certain large-scale atmospheric regimes are in place, or when persistent anomalies occur in the ocean or land surface. Gains could be obtained by increasingly developing prediction tools and metrics that strategically target these specific windows of opportunity. Across the globe, reevaluating forecasts in this manner could find value in forecasts previously discarded as not skillful. Users’ expectations for prediction skill could be more adequately met, as they are better aware of when and where to expect skill and if the prediction is actionable. Given that there is still untapped potential, in terms of process understanding and prediction methodologies, it is safe to expect that in the future forecast opportunities will expand. Process research and the development of innovative methodologies will aid such progress.

Seasonal to interannual forecasts made by coupled general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor. Here we explore initializing directly on a model’s own attractor, using an analog approach in which model states close to the observed initial state are drawn from a “library” obtained from prior uninitialized CGCM simulations. The subsequent evolution of those “model-analogs” yields an ensemble forecast, without additional model integration. This technique is applied to CGCMs either used operationally by NCEP or as part of the CMIP6 dataset. By selecting from these long control runs those model states whose monthly SST and SSH anomalies best resemble the observations at initialization time, hindcasts are then made for leads of 1-36 months during 1958-2019. Deterministic and probabilistic skill measures of these model-analog hindcasts are comparable to, and in some regions better than, traditionally assimilation-initialized CGCM hindcasts after 1982, for both the individual models and the multi-model ensemble.On average, ENSO skill of AC>0.5 exists for forecast leads of 18 months for forecasts initialized in summer. More important, we find that not only were some notable ENSO events predictable two years (or more) ahead of time, but that we can both identify forecast “hits” and avoid “false alarms” -- at the time of forecast -- by using a simple forecast signal-to-noise metric (SNR; root-mean-squared ensemble mean divided by ensemble spread), determined from the large (O(100) member) model-analog ensemble. That is, our analog ensemble approach can be used to make actionable ocean predictions, where forecasts of opportunity can be identified well in advance.Since these long-lead hindcasts do not require full-field initialization, they have also been extended back prior to 1900. We find that while there has been considerable multi-decadal variation in seasonal ENSO skill, there has been no long-term trend for leads up to about 6-9 months. However, while multi-year ENSO skill appears to have also occurred in the past for a few large ENSO events, in the past thirty years it has occurred with considerably greater frequency, raising the possibility that it is a more recent phenomenon.

International audience; Climate variability in the tropical Pacific affects global climate on a wide range of time scales. On interannual time scales, the tropical Pacific is home to the El Niño–Southern Oscillation (ENSO). Decadal variations and changes in the tropical Pacific, referred to here collectively as tropical Pacific decadal variability (TPDV), also profoundly affect the climate system. Here, we use TPDV to refer to any form of decadal climate variability or change that occurs in the atmosphere, the ocean, and over land within the tropical Pacific. “Decadal,” which we use in a broad sense to encompass multiyear through multidecadal time scales, includes variability about the mean state on decadal time scales, externally forced mean-state changes that unfold on decadal time scales, and decadal variations in the behavior of higher-frequency modes like ENSO.

This paper discusses key changes in New Zealand pastoral agriculture over the last three decades at the national scale, and how these have influenced the performance of grasslands, animal productivity, and the resilience of pastoral livestock systems. It assesses the positive and negative impacts of land-use change, changes in pasture production and supplementary feeding and dry matter (DM) consumed, and the key management practices implemented by farmers to enhance farm system resilience. It also notes environmental and other policy changes and examines how sectors and Government have started to respond. The largest estimated increase in DM consumed by livestock from 1990 to 2018 was from increased supplementary feed in the dairy sector. The largest estimated decline in DM production was due to pasture-land conversion to planted forests, followed by weed and pest impacts. In 2018, the dairy sector consumed the most DM at an estimated 25.0M t/yr, followed by sheep at 16.6M t/yr and beef at 10.1M t/yr. The total consumed DM in 2018 was 51.9 M t/yr which corresponds well with the independently estimated national pasture DM production of 64M t/yr. The environmental impacts of managements to enhance resilience in DM availability are becoming evident and future regulations may limit the extent some of these practices can expand.

Atmospheric blocking is associated with sensible weather impacts such as anomalous precipitation and flooding, cold air outbreaks, and heat waves. Given the asymmetry in the persistence characteristics of anticyclones and cyclones, many studies have emphasized the role of nonlinearities in blocking onset and maintenance. However, previous studies have demonstrated that both linear and nonlinear dynamics can amplify blocks. In this paper the structure and evolution of North Pacific blocking on weekly time scales is investigated using two methods: statistical analysis not requiring linearity, and a linear inverse model (LIM) composed of tropical outgoing longwave radiation and extratropical streamfunction, which relies on purely linear (and linearly parameterized) dynamics. Both approaches produce a similar evolution of North Pacific blocking. Using the LIM, the optimal precursors to blocking are determined, which at a 14-day lead time include an upper-level east Pacific anticyclone and suppressed convection over the central tropical Pacific. The tropics and extratropics both contribute to the deterministic evolution of blocking, with the tropics acting on longer time scales but imposing a weaker response than that contributed by the extratropics. The tropical contribution was driven by La Niña–like conditions that produce a hemispheric anticyclonic anomaly, while the extratropical initial conditions produce an equivalent barotropic, wavelike pattern. The LIM’s ability to reproduce the observed blocking evolution suggests the predictable evolution of blocking on weekly time scales can be modeled in a linear framework, and that subseasonal forecasting of North Pacific blocking needs to consider both tropical and extratropical conditions.