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The Northern Annular Mode (NAM) dominates variability of the Northern Hemisphere (NH) wintertime extratropical circulation in both the troposphere and stratosphere. Changes in the tropospheric NAM (i.e., changes in the position and strength of the polar jet stream) directly alter NH mid-latitude temperature and precipitation patterns, making forecasting these changes a significant priority for subseasonal-to-seasonal (S2S) forecasts during boreal winter. This study examines fundamental characteristics of the wintertime tropospheric circulation pattern in the hindcast simulations of the North American Multi-Model Ensemble (NMME) Phase-2 model suite through examining how the models capture sudden stratospheric warming (SSW) events, known to precede large changes in the tropospheric NAM by 2–6 weeks. Findings indicate that the NMME Phase-2 models have an overall mixed performance in capturing the characteristics of the NAM and its teleconnections. Biases are apparent in the dominant nodes of the tropospheric NAM pattern, storm tracks and associated wave fluxes in the Atlantic, and a systematic underestimation of intraseasonal variability of the NH stratospheric polar vortex in the models (i.e., the stratospheric NAM). We then investigate the ability of the models to simulate the life cycle of model-identified SSW events, including pre- and post-SSW circulation patterns and sensible weather conditions. Specific model biases include inconsistent geopotential height precursor fields, weaker-than-observed vertical wave propagation prior to SSW events, and incorrect surface temperature regimes following the events. Together, the results suggest potential pathways forward for improving subseasonal winter weather forecasts associated with the NAM using the NMME Phase-2 models.

There is a growing demand for understanding sources of predictability on subseasonal to seasonal (S2S) time scales. Predictability at subseasonal time scales is believed to come from processes varying slower than the atmosphere such as soil moisture, snowpack, sea ice, and ocean heat content. The stratosphere as well as tropospheric modes of variability can also provide predictability at subseasonal time scales. However, the contributions of the above sources to S2S predictability are not well quantified. Here we evaluate the subseasonal prediction skill of the Community Earth System Model, version 1 (CESM1), in the default version of the model as well as a version with the improved representation of stratospheric variability to assess the role of an improved stratosphere on prediction skill. We demonstrate that the subseasonal skill of CESM1 for surface temperature and precipitation is comparable to that of operational models. We find that a better-resolved stratosphere improves stratospheric but not surface prediction skill for weeks 3–4.

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.

Recent research demonstrates that dynamical models sometimes fail to represent observed teleconnection patterns associated with predictable modes of climate variability. As a result, model forecast skill may be reduced. We address this gap in skill through the application of a Bayesian postprocessing technique—the calibration, bridging, and merging (CBaM) method—which previously has been shown to improve probabilistic seasonal forecast skill over Australia. Calibration models developed from dynamical model reforecasts and observations are employed to statistically correct dynamical model forecasts. Bridging models use dynamical model forecasts of relevant climate modes (e.g., ENSO) as predictors of remote temperature and precipitation. Bridging and calibration models are first developed separately using Bayesian joint probability modeling and then merged using Bayesian model averaging to yield an optimal forecast. We apply CBaM to seasonal forecasts of North American 2-m temperature and precipitation from the North American Multimodel Ensemble (NMME) hindcast. Bridging is done using the model-predicted Niño-3.4 index. Overall, the fully merged CBaM forecasts achieve higher Brier skill scores and better reliability compared to raw NMME forecasts. Bridging enhances forecast skill for individual NMME member model forecasts of temperature, but does not result in significant improvements in precipitation forecast skill, possibly because the models of the NMME better represent the ENSO–precipitation teleconnection pattern compared to the ENSO–temperature pattern. These results demonstrate the potential utility of the CBaM method to improve seasonal forecast skill over North America.

CPC requires the reforecast-calibrated Global Ensemble Forecast System (GEFS) to support the production of their official 6–10- and 8–14-day temperature and precipitation forecasts. While a large sample size of forecast–observation pairs is desirable to generate the necessary model climatology and variances, and covariances to observations, sampling by reforecasts could be done to use available computing resources most efficiently. A series of experiments was done to assess the impact on calibrated forecast skill of using a smaller sample size than the current available reforecast dataset. This study focuses on the skill of week-2 probabilistic forecasts of the 7-day-mean 2-m temperature and accumulated precipitation. The tercile forecasts are expressed as being below-, near-, and above-normal temperature/median precipitation over the continental United States (CONUS). Calibration statistics were calculated using an ensemble regression technique from 25 yr of daily, 11-member GEFS reforecasts for 1986–2010, which were then used to postprocess the GEFS model forecasts for 2011–13. In assessing the skill of calibrated model output using a reforecast dataset with fewer years and ensemble members, and an ensemble run less frequently than daily, it was determined that reductions in the number of ensemble members to six or fewer and reductions in the frequency of reforecast runs from daily to once a week were achievable with minimal loss of skill. However, reducing the number of years of reforecasts to less than 25 resulted in a greater skill degradation. The loss of skill was statistically significant using only 18 yr of reforecasts from 1993 to 2010 to generate model statistics.

Soil moisture and phenology are seasonally varying modes of the land system. Due to their seasonal persistence, they have the ability to predictably influence seasonal weather. Hence, their use in seasonal forecasts can potentially improve the skill of the forecasts. However a complete measure of their influence in geographical locations and in different seasons is not known. As a result, modern seasonal forecasting techniques have not been able to fully exploit their persistence in improving skill of seasonal forecasts. By measuring similarity between model ensemble members that are forced by soil moisture and phenology respectively, in this study, we identify global hot spots where soil moisture and phenology impact key atmospheric variables in spring and summer seasons. Results indicate that over South East Asia (SEA) and the Sahel the phenology and soil moisture impact precipitation to an equal extent. Results show that 5–7 % of the variance in Indian summer monsoon precipitation is caused by soil moisture and phenology anomalies. Prior to the monsoon they influence predictors of the SEA monsoon. Hence, their persistence can be used to improve skill of seasonal forecasts, particularly of mesoscale systems like the SEA monsoon.

Previous work has shown that the combined influence of El Niño–Southern Oscillation (ENSO) and the Madden–Julian oscillation (MJO) significantly impacts the wintertime circulation over North America for lead times up to at least 4 weeks. These findings suggest that both the MJO and ENSO may prove beneficial for generating a seamless prediction link between short-range deterministic forecasts and longer-range seasonal forecasts. To test the feasibility of this link, wintertime (December–March) probabilistic 2-m temperature (T2m) forecasts over North America are generated solely on the basis of the linear trend and statistical relationships with the initial state of the MJO and ENSO. Overall, such forecasts exhibit substantial skill for some regions and some initial states of the MJO and ENSO out to a lead time of approximately 4 weeks. In addition, the primary ENSO T2m regions of influence are nearly orthogonal to those of the MJO, which suggests that the MJO and ENSO generally excite different patterns within the continuum of large-scale atmospheric teleconnections. The strong forecast skill scores for some regions and initial states confirm the promise that information from the MJO and ENSO may offer forecasts of opportunity in weeks 3 and 4, which extend beyond the current 2-week extended-range outlooks of the National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Center (CPC), and an intraseasonal link to longer-range probabilistic forecasts.

Recent studies have shown that the Madden–Julian Oscillation (MJO) impacts the leading modes of intraseasonal variability in the northern hemisphere extratropics, providing a possible source of predictive skill over North America at intraseasonal timescales. We find that a k-means cluster analysis of mid-level geopotential height anomalies over the North American region identifies several wintertime cluster patterns whose probabilities are strongly modulated during and after MJO events, particularly during certain phases of the El Nino-Southern Oscillation (ENSO). We use a simple new optimization method for determining the number of clusters, k, and show that it results in a set of clusters which are robust to changes in the domain or time period examined. Several of the resulting cluster patterns resemble linear combinations of the Arctic Oscillation (AO) and the Pacific/North American (PNA) teleconnection pattern, but show even stronger responses to the MJO and ENSO than clusters based on the AO and PNA alone. A cluster resembling the positive (negative) PNA has elevated probabilities approximately 8–14 days following phase 6 (phase 3) of the MJO, while a negative AO-like cluster has elevated probabilities 10–20 days following phase 7 of the MJO. The observed relationships are relatively well reproduced in the 11-year daily reforecast dataset from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). This study statistically links MJO activity in the tropics to common intraseasonal circulation anomalies over the North American sector, establishing a framework that may be useful for improving extended range forecasts over this region.

A principal component decomposition of monthly sea surface temperature (SST) variability in the tropical Pacific Ocean demonstrates that nearly all of the linear trends during 1950–2010 are found in two leading patterns. The first SST pattern is strongly related to the canonical El Nino-Southern Oscillation (ENSO) pattern. The second pattern shares characteristics with the first pattern and its existence solely depends on the presence of linear trends across the tropical Pacific Ocean. The decomposition also uncovers a third pattern, often referred to as ENSO Modoki, but the linear trend is small and dataset dependent over the full 61-year record and is insignificant within each season. ENSO Modoki is also reflected in the equatorial zonal SST gradient between the Nino-4 region, located in the west-central Pacific, and the Nino-3 region in the eastern Pacific. It is only in this zonal SST gradient that a marginally significant trend arises early in the Northern Hemisphere spring (March–May) during El Nino and La Nina and also in the late summer (July–September) during El Nino. Yet these SST trends in the zonal gradient do not unequivocally represent an ENSO Modoki-like dipole because they are exclusively associated with significant positive SST trends in either the eastern or western Pacific, with no corresponding significant negative trends. Insignificant trends in the zonal SST gradient are evident during the boreal wintertime months when ENSO events typically mature. Given the presence of positive SST trends across much of the equatorial Pacific Ocean, using fixed SST anomaly thresholds to define ENSO events likely needs to be reconsidered.

There is strong evidence that Indian Ocean sea surface temperatures (SSTs) influence the climate variability of Southern Asia and Africa; hence, accurate prediction of these SSTs is a high priority. In this study, we use canonical correlation analysis (CCA) to design empirical models to assess the predictability of tropical Indian Ocean SST from sea level pressure (SLP) and SST themselves with lead-times up to one year. One model uses the first twelve empirical orthogonal functions (EOFs) of SLP over the Indian Ocean using different lead-times to predict SST. A CCA model with EOFs of SST as the predictor at the same lead-times is compared to SLP as a predictor and shows the auto-correlation of the system. A CCA using the first five extended empirical orthogonal functions (EEOFs) of sea level pressure over the Indian Ocean basin for an interval of two years combined with SST EOFs as predictors is found to produce the greatest correlation between forecast and observed SSTs. This model obtains higher skill by explicitly considering the development in time of SLP anomalies in the region. The skill of this model, assessed from retroactive forecasts of an 18 year period, shows improvement relative to other empirical forecasts particularly for the central and eastern Indian Ocean and boreal autumn months preceding the Southern Hemisphere summer rainfall season. This is likely due to the limited domain of this model identifying modes of variability that are more pronounced in these areas during this season. Finally, a nonlinear canonical correlation analysis (NLCCA) derived from a neural network is used to analyze the leading nonlinear modes. These nonlinear modes differ from the linear CCA modes with distinct cold and warm SST phases suggesting a nonlinear relationship between SST and SLP over the tropical Indian Ocean.

The relationship between the Northern Hemisphere and Southern Hemisphere deep water circulation systems is explored in experiments with gradual and impulsive freshwater input through the St. Lawrence. With sufficient freshwater volume input (50 Sv years), North Atlantic Deep Water (NADW) cessation occurs, as does substantial cooling in the Northern Hemisphere. The colder temperatures are accompanied by increased mass and sea level pressure in the Northern Hemisphere, with corresponding lower pressure in the Southern Hemisphere. The low-pressure response occurs at high southern latitudes, consistent with the Antarctic Annular Mode, the leading mode of variability in the current climate. Stronger winds, associated with this increased cyclonicity, intensify the Antarctic Circumpolar Current (ACC) with heat flux divergences in the South Atlantic and convergences and warming in the Indian Ocean. Weddell Sea Bottom Water production increases in response to the South Atlantic high-latitude cooling and sea ice growth, hence acting as a “seesaw” with the decreasing NADW, and even global Antarctic Bottom Water increases, although not as strongly. The initial “seesaw” response occurs within a few years, although it takes some 100 years to maximize due to the response time of the ACC. The South Atlantic cooling occurs approximately in phase with the North Atlantic, so the “seesaw” is not in temperature within that ocean basin; however, warming in portions of the southern Indian Ocean occurs out of phase with the Northern Hemisphere cooling. NADW does not resume of its own accord once complete cessation occurs even when freshwater input is stopped, but when increased evaporation is used to force NADW formation temporarily, Weddell Sea Bottom Water decreases accordingly.

Land-surface parameterizations based on a statistical-dynamical have been suggested recently to improve the representation of the surface forcing from heterogeneous land in atmospheric models. With this approach, land-surface characteristics are prescribed by probability density functions (PDFs) rather than single 'representative' values as in 'big-leaf' parameterizations. Yet the use of many PDFs results in an increased computational burden and requires the complex problem of representing covariances between PDFs to be addressed. In this study, a sensitivity analysis of a land-surface parameterization for atmospheric modeling was performed to evaluate the surface parameters most important to the variability of surface heat fluxes. The Fourier amplitude sensitivity test (FAST) used for this analysis determines the relative contribution of individual input parameters to the variance of energy fluxes resulting from a heterogeneous surface. By simultaneously varying all parameters according to their individual probability density functions, the number of computations needed is very much reduced by this technique. This analysis demonstrates that most of the variability of surface heat fluxes may be described by the distributions of relative stomatal conductance and surface roughness. Thus, the statistical-dynamical approach may be simplified by the use of only these two probability density functions.

Varying magnitudes of freshwater input through the St. Lawrence are added to different versions of the GISS coupled atmosphere-ocean model. The results show a generally linear response in percentage North Atlantic Deep Water (NADW) reduction to the volume of water added without any obvious threshold effects. When the estimated best guess freshwater input for the Allerod-Younger Dryas interval is added, only small reductions in NADW production occur in less than a century, with complete cessation requiring more than 300 years. If the ocean circulation was weaker in the Allerod preceding the Younger Dryas, somewhat larger percentage reductions occur, and it would take 150–200 years for a complete shutdown. The freshening is aided by a positive feedback associated with the reduction in evaporation relative to precipitation in the North Atlantic, amounting to some 20–30% of the added freshwater runoff. When the freshwater input is stopped after complete NADW shutdown, NADW production does not resume. With complete NADW cessation, cooling eventually occurs throughout the Northern Hemisphere, including regions where it is not thought to have arisen during the Younger Dryas, and the globe cools by 1°C due primarily to the sea ice albedo feedback; while in many regions the observed cooling is reproduced, the cooling in Greenland is much less than is estimated. With only 50% NADW cessation, cooling is primarily around the North Atlantic. No rapid warming is seen in response to freshwater cessation. The sensitivity displayed here for NADW reduction due to freshwater input from the St. Lawrence is roughly similar to that associated with increased precipitation accompanying global warming in the next century, so a similar sensitivity may arise with Younger Dryas freshwater inputs from other suspected locations (Greenland, Europe).