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The term “weather whiplash” was recently coined to describe abrupt swings in weather conditions from one extreme to another, such as from a prolonged, frigid cold spell to anomalous warmth or from drought to heavy precipitation. These events are often highly disruptive to agriculture, ecosystems, and daily activities. In this study, we propose and demonstrate a novel metric to identify weather whiplash events (WWEs) and track their frequency over time. We define a WWE as a transition from one persistent continental‐scale circulation regime to another distinctly different pattern, as determined using an objective pattern clustering analysis called self‐organizing maps. We focus on the domain spanning North America and the eastern N. Pacific Ocean. A matrix of representative atmospheric patterns in 500‐hPa geopotential height anomalies is created from 72 years of daily fields. We analyze the occurrence of WWEs originating with long‐duration events (LDEs) (defined as lasting four or more days) in each pattern, as well as the associated extremes in temperature and precipitation. A WWE is detected when the pattern 2 days following a LDE is substantially different, measured using internal matrix distances and thresholds. Changes in WWE frequency are assessed objectively based on reanalysis and historical climate model simulations, andmore »for the future using climate model projections. Temporal changes in the future under representative concentration pathway 8.5 forcing are more robust than those in recent decades. We find consistent increases in WWEs originating in patterns with an anomalously warm Arctic and decreases in cold‐Arctic patterns.

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Northeastern US heat waves have usually been considered in terms of a single circulation pattern, the high-pressure circulation typical of most heat waves occurring in other parts of the world. However, k-means clustering analysis from 1980–2018 shows there are four distinct patterns of Northeast heat wave daily circulation, each of which has its own seasonality, heat-producing mechanisms (associated moisture, subsidence, and temperature advection), and impact on electricity demand. Monthly analysis shows statistically-significant positive trends occur in late summer for two of the patterns and early summer for a third pattern, while the fourth pattern shows a statistically significant negative trend in early summer. These results demonstrate that heat waves in a particular geographic area can be initiated and maintained by a variety of mechanisms, resulting in heat wave types with distinct impacts and potential links to climate change, and that pattern analysis is an effective tool to distinguish these differences.

The Arctic has experienced a warming rate higher than the global mean in the past decades, but previous studies show that there are large uncertainties associated with future Arctic temperature projections. In this study, near- surface mean temperatures in the Arctic are analyzed from 22 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Compared with the ERA5 reanalysis, most CMIP6 models underestimate the observed mean temperature in the Arctic during 1979–2014. The largest cold biases are found over the Greenland Sea the Barents Sea, and the Kara Sea. Under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the multimodel ensemble mean of 22 CMIP6 models exhibits significant Arctic warming in the future and the warming rate is more than twice that of the global/Northern Hemisphere mean. Model spread is the largest contributor to the overall uncertainty in projections, which accounts for 55.4% of the total uncertainty at the start of projections in 2015 and remains at 32.9% at the end of projections in 2095. Internal variability uncertainty accounts for 39.3% of the total uncertainty at the start of projections but decreases to 6.5% at the end of the twenty-first century, while scenario uncertainty rapidly increases from 5.3% tomore »60.7% over the period from 2015 to 2095. It is found that the largest model uncertainties are consistent cold bias in the oceanic regions in the models, which is connected with excessive sea ice area caused by the weak Atlantic poleward heat transport. These results suggest that large intermodel spread and uncertainties exist in the CMIP6 models’ simulation and projection of the Arctic near- surface temperature and that there are different responses over the ocean and land in the Arctic to greenhouse gas forcing. Future research needs to pay more attention to the different characteristics and mechanisms of Arctic Ocean and land warming to reduce the spread.« less

Although sudden stratospheric warmings (SSWs) can improve subseasonal-to-seasonal forecasts, it is unclear whether the two types of SSW - displacements and splits - have different near- surface effects. To examine the longer-term (i.e., multi-week lead) tropospheric response to displacements and splits, we utilize an intermediate-complexity model and impose wave-1 and wave-2 stratospheric heating perturbations spun-off from a control run. At longer lags, the tropospheric response is found to be insensitive to both the wavenumber and location of the imposed heating, in agreement with freely evolving displacements and splits identified in the control run. At shorter lags, however, large differences are found between displacements and splits in both the control run and the different wavenumber- forced events. In particular, in the control run, the free-running splits have an immediate barotropic response throughout the stratosphere and troposphere whereas displacements take 1–2 weeks before a near-surface response becomes evident. Interestingly, this barotropic response found during CTRL splits is not captured by the barotropically forced wave-2 events, indicating that the zonal-mean tropospheric circulation is somehow coupled with the generation of the wave-2 splits. It is also found that in the control run, displacements yield stronger Polar-Cap temperature anomalies than splits, yet both still yieldmore »similar magnitude tropospheric responses. Hence, the strength of the stratospheric warming is not the only governing factor in the surface response. Overall, SSW classification based on vortex morphology may be useful for subseasonal but not seasonal tropospheric prediction.« less

Pronounced changes in the Arctic environment add a new potential driver of anomalous weather patterns in midlatitudes that affect billions of people. Recent studies of these Arctic/midlatitude weather linkages, however, state inconsistent conclusions. A source of uncertainty arises from the chaotic nature of the atmosphere. Thermodynamic forcing by a rapidly warming Arctic contributes to weather events through changing surface heat fluxes and large-scale temperature and pressure gradients. But internal shifts in atmospheric dynamics—the variability of the location, strength, and character of the jet stream, blocking, and stratospheric polar vortex (SPV)—obscure the direct causes and effects. It is important to understand these associated processes to differentiate Arctic-forced variability from natural variability. For example in early winter, reduced Barents/Kara Seas sea-ice coverage may reinforce existing atmospheric teleconnections between the North Atlantic/Arctic and central Asia, and affect downstream weather in East Asia. Reduced sea ice in the Chukchi Sea can amplify atmospheric ridging of high pressure near Alaska, influencing downstream weather across North America. In late winter southward displacement of the SPV, coupled to the troposphere, leads to weather extremes in Eurasia and North America. Combined tropical and sea ice conditions can modulate the variability of the SPV. Observational evidence for Arctic/midlatitude weathermore »linkages continues to accumulate, along with understanding of connections with pre-existing climate states. Relative to natural atmospheric variability, sea-ice loss alone has played a secondary role in Arctic/midlatitude weather linkages; the full influence of Arctic amplification remains uncertain.« less