A large ensemble of multidecadal atmospheric general circulation model (AGCM) simulations is examined to determine a quantity central to the model's potential predictability the fraction of the simulated monthly air temperature (T2M) variability that is tied to the imposed sea surface temperature (SST) boundary conditions as opposed to the background atmospheric noise. Combining this information with ensemble simulation data from other AGCMs in turn allows an intermodel comparison of two separate quantities: model potential T2M predictability and the underlying (in the absence of noise) teleconnections between SSTs and continental T2M. To a large extent, the models tend to agree with each other regarding both they all show, for example, the expected highest predictability in the tropics as well as low potential predictability in central Asia, and, particularly for DJF and MAM, they show very similar ocean-land teleconnections throughout the Americas. However, the models do show some differences in teleconnections, indicating room for model improvement that could, in principle, lead to benefits for long-term prediction. Importantly, by combining the model results with observational temperature data, we provide a new estimation of real-world predictability, a property of Nature that is not directly observable. The latter results suggest, with caveats, that for monthly T2M in the tropics, AGCMs tend to overestimate the ratio of predictable variance to noise-derived variance.

The 1993 U.S. Midwest summer flood occurred in a year marked by a number of apparently disparate climate extremes including an unusually cold spring Pacific warm pool, a record deep spring Aleutian low, and record wet conditions that spanned the Northern Hemisphere midlatitude land areas during June and July. Here, we provide a dynamical framework that links these extremes and accounts for the uniqueness of the Midwest flooding event. In particular, we show that the deep springtime Aleutian low was part of a wave response forced by unusually strong precipitation/heating anomalies in the equatorial Pacific just west of the date line—heating that was linked to the unusually cold Pacific warm pool juxtaposed to the east with positive SST anomalies tied to a weak but unusually timed El Niño event. The deep springtime Aleutian low in turn produced unusually cold summer North Pacific SSTs and set the stage for the summer’s eddy-driven enhancement of the midlatitude jet and unusually strong hemisphericwide transient (baroclinic) wave activity. The resulting transient vorticity forcing produced two pronounced stationary waves—one in June anchored over northern Eurasia, and another in July anchored over the Pacific/North American region, resulting in record precipitation anomalies over northwestern Eurasia and the northern Great Plains, respectively.

In late December of 2022 and the fi rst half of January 2023, an unprecedented series of atmospheric rivers (ARs) produced near -record heavy rains and fl ooding over much of California. Here, we employ the NASA GEOS AGCM run in a "replay" mode, together with more idealized simulations with a stationary wave model, to identify the remote forcing regions, mechanisms, and underlying predictability of this fl ooding event. In particular, the study addresses the underlying causes of a persistent positive Paci fi c-North American (PNA)-like circulation pattern that facilitated the development of the ARs. We show that the pattern developed in late December as a result of vorticity forcing in the North Paci fi c jet exit region. We further provide evidence that this vorticity forcing was the result of a chain of events initiated in mid -December with the development of a Rossby wave (as a result of forcing linked to the MJO) that propagated from the northern Indian Ocean into the North Paci fi c. As such, both the initiation of the event and the eventual development of the PNA depended critically on internally generated Rossby wave forcings, with the North Paci fi c jet playing a key role. This, combined with contemporaneous SST (La Ni & ntilde;a) forcing that produced a circulation response in the AGCM that was essentially opposite to the positive PNA, underscores the fundamental lack of predictability of the event at seasonal time scales. Forecasts produced with the GEOS-coupled model suggest that useful skill in predicting the PNA and extreme precipitation over California was in fact limited to lead times shorter than about 3 weeks.

Much of Siberia experienced exceptional warmth during the spring of 2020, which followed an unusually warm winter over the same region. Here, we investigate the drivers of the spring warmth from the perspective of atmo-spheric dynamics and remote influences, focusing on monthly-time-scale features of the event. We find that the warm anomalies were associated with separate quasi-stationary Rossby wave trains emanating from the North Atlantic in April and May. The wave trains are shown to be extreme manifestations of the dominant modes of spring subseasonal meridional wind variability over the Northern Hemisphere. Using a large ensemble of simulations from NASA's GEOS atmospheric model, in which the model is constrained to remain close to observations over selected regions, we further elucidate the remote drivers of the unusual spring temperatures in Siberia. In both April and May, the wave trains were likely forced from an upstream region including eastern North America and the western North Atlantic. Analysis with a stationary wave model shows that transient vorticity flux forcing over and downwind of the North Atlantic, which is strongly related to storm activity caused by internal variability, is key to generating the wave trains, suggesting limited subseasonal predict-ability of the Rossby waves and hence the exceptional Siberian warmth. Our observational and model analyses also suggest that anomalous tropical atmospheric heating contributed to the unusual warmth in Siberia through a teleconnection involv-ing upper-troposphere dynamics and the mean meridional circulation. This tropical-extratropical teleconnection offers a possible physical mechanism by which anthropogenic climate change influenced the extreme Siberian warmth.

This paper summarizes the current challenges in climate and weather research and provides suggestions for future research directions in global observing systems, in modelling and prediction, and in academic environment and education systems.

Past work has shown that a land surface model's (LSM) implicit (not explicitly coded) relationships between soil moisture and both evapotranspiration (ET) and runoff largely determine the LSM's hydrological behavior. Here we estimate the relationships that appear to be operating in the real world and compare them to those of the LSM component of a state-of-the-art Earth system model (ESM). The two sets of relationships are determined by calibrating them within a simple water balance model (WBM): once using stream gauge observations from small, unregulated rivers over the eastern half of the United States, and once using the runoffs generated by the LSM as part of a state-of-the-art atmospheric reanalysis. Hydrological simulations and subseasonal hydrological forecasts performed with the two calibrated versions of the WBM provide two key results. First, the version calibrated to the LSM-generated runoffs does successfully reproduce, to first order, the hydrological behavior of the full LSM within its ESM environment. Second, of the two WBM versions, the one calibrated to the observations reproduces more accurately a broad collection of fully independent streamflow observations as well as a similarly broad collection of in situ soil moisture measurements. Taken together, the two results suggest that the observations-calibrated ET and runoff efficiency functions do successfully represent, at least to some degree, soil moisture controls over hydrological variability in nature and can serve as potentially useful targets for further LSM development. Significance StatementFor all their complexity, and for all the work that underlies their development, the land surface model components of Earth system models may be suboptimal in fundamental yet unstudied ways. Here we estimate how the joint control of soil moisture over evapotranspiration and runoff processes in nature differs from that built implicitly into a state-of-the-art land model. Validation exercises demonstrate how this difference appears to lead to reduced accuracy in the land model's simulation and forecasting of such hydrological variables as streamflow and soil moisture. Our results indicate that the relationships estimated for nature could serve as a potentially valuable target for further land model development.

Drought is the deadliest natural disaster on Earth due to its long duration, wide spatial coverage and direct connection with the food supply for human beings. Drought develops slowly and thus is called a silent killer. This paper reviews the history of research on droughts and mega-droughts. Interannual droughts are in many places driven by the El Nino-Southern Oscillation, multi-decadal mega-droughts are often driven by the Atlantic Multi-Decadal Oscillation, while centennial mega-droughts are often driven by the Global Inter-Centennial Oscillation. More generally, droughts are affected by multiple factors including global sea surface temperature anomalies, local land-atmosphere feedbacks, internal atmospheric variability and external forcings from outside the climate system. Possible future research directions are also suggested.

Much of northern Eurasia experienced record high temperatures during the first three months of 2020, and the eastern United States experienced a significant heat wave during March. In this study, we show that the above episodes of extraordinary warmth reflect to a large extent the unusual persistence and large amplitude of three well-known modes of atmospheric variability: the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), and the Pacific-North American (PNA) pattern. We employ a "replay" approach in which simulations with the NASA GEOS AGCM are constrained to remain close to MERRA-2 over specified regions of the globe in order to identify the underlying forcings and regions that acted to maintain these modes well beyond their typical submonthly time scales. We show that an extreme positive AO played a major role in the surface warming over Eurasia, with forcing from the tropical Pacific and Indian Ocean regions acting to maintain its positive phase. Forcing from the tropical Indian Ocean and Atlantic regions produced positive NAO-like responses, contributing to the warming over eastern North America and Europe. The strong heat wave that developed over eastern North America during March was primarily associated with an extreme negative PNA that developed as an instability of the North Pacific jet, with tropical forcing providing support for a prolonged negative phase. A diagnosis of the zonally symmetric circulation shows that the above extratropical surface warming occurred underneath a deep layer of tropospheric warming, driven by stationary eddy-induced changes in the mean meridional circulation.

For similar to 100 years, the continental patterns of avian migration in North America have been described in the context of three or four primary flyways. This spatial compartmentalization often fails to adequately reflect a critical characterization of migration-phenology. This shortcoming has been partly due to the lack of reliable continental-scale data, a gap filled by our current study. Here, we leveraged unique radar-based data quantifying migration phenology and used an objective regionalization approach to introduce a new spatial framework that reflects interannual variability. Therefore, the resulting spatial classification is intrinsically different from the "flyway concept." We identified two regions with distinct interannual variability of spring migration across the contiguous United States. This data-driven framework enabled us to explore the climatic cues affecting the interannual variability of migration phenology, "specific to each region" across North America. For example, our "two-region" approach allowed us to identify an east-west dipole pattern in migratory behavior linked to atmospheric Rossby waves. Also, we revealed that migration movements over the western United States were inversely related to interannual and low-frequency variability of regional temperature. A similar link, but weaker and only for interannual variability, was evident for the eastern region. However, this region was more strongly tied to climate teleconnections, particularly to the east Pacific-North Pacific (EP-NP) pattern. The results suggest that oceanic forcing in the tropical Pacific-through a chain of processes including Rossby wave trains-controls the climatic conditions, associated with bird migration over the eastern United States. Our spatial platform would facilitate better understanding of the mechanisms responsible for broadscale migration phenology and its potential future changes.

Record-breaking heatwaves and wildfires immersed Siberia during the boreal spring of 2020 following an anomalously warm winter. Springtime heatwaves are becoming more common in the region, with statistically significant trends in the frequency, magnitude, and duration of heatwave events over the past four decades. Mechanisms by which the heatwaves occur and contributing factors differ by season. Winter heatwave frequency is correlated with the atmospheric circulation, particularly the Arctic Oscillation, while the frequency of heatwaves during the spring months is highly correlated with aspects of the land surface including snow cover, albedo, and latent heat flux. Idealized AMIP-style experiments are used to quantify the contribution of suppressed Arctic sea ice and snow cover over Siberia on the atmospheric circulation, surface energy budget, and surface air temperature in Siberia during the winter and spring of 2020. Sea ice concentration contributed to the strength of the stratospheric polar vortex and Arctic Oscillation during the winter months, thereby influencing the tropospheric circulation and surface air temperature over Siberia. Warm temperatures across the region resulted in an earlier-than-usual recession of the winter snowpack. The exposed land surface contributed to up to 20% of the temperature anomaly during the spring through the albedo feedback and changes in the ratio of the latent and sensible heat fluxes. This, in combination with favorable atmospheric circulation patterns, resulted in record-breaking heatwaves in Siberia in the spring of 2020.

Much of the southeast United States experienced record dry conditions during September of 2019, with the area in abnormally dry to exceptional drought conditions growing from 25% at the beginning of the month to 80% by the end of the month. The drought ended just as abruptly due to above-normal rain that fell during the second half of October. In this study we employed MERRA-2 and the GEOS-5 AGCM to diagnose the underlying causes of the drought’s onset, maintenance, and demise. The basic approach involves performing a series of AGCM simulations in which the model is constrained to remain close to MERRA-2 over prespecified areas that are external to the drought region. The start of the drought appears to have been forced by anomalous heating in the central/western tropical Pacific that resulted in low-level anticyclonic flow and a tendency for descending motion over much of the Southeast. An anomalous ridge associated with a Rossby wave train (emanating from the Indian Ocean region) is found to be the main source of the most intense temperature and precipitation anomalies that develop over the Southeast during the last week of September. A second Rossby wave train (emanating from the same region) is responsible for the substantial rain that fell during the second half of October to end the drought. The links to the Indian Ocean dipole (with record positive values) as well as a waning El Niño allow some speculation as to the likelihood of similar events occurring in the future.