Hai Lin

The Warm Arctic–Cold Eurasian pattern (WACE) is a pair of opposing surface air temperature anomalies over the polar region and the midlatitude Eurasian continent, which is usually associated with Arctic Sea ice melt and midlatitude extreme weather. The Southern Annular Mode (SAM) is the leading mode of atmospheric variability in the Southern Hemisphere. Here we show evidence of an inter-hemispheric connection between SAM and WACE during boreal winter. A positive SAM leads to a negative WACE (and vice versa) 30–40 days later. This lagged remote influence of SAM is transmitted via a Rossby wave that crosses the equator in the eastern Pacific and tropical Atlantic. The ensuing WACE temperature pattern is then amplified by the greenhouse effect associated with the local humidity response. SAM also leads to a convection anomaly in the equatorial Atlantic which generates a Northern Hemisphere Rossby wave that reinforces WACE. The link between SAM and WACE revealed here indicates a dynamical origin for WACE. This long-distance teleconnection, and the lagged association between SAM and WACE may be relevant for subseasonal-to-seasonal predictions.

In contrast to boreal winter when extratropical seasonal predictions benefit greatly from ENSO-related teleconnections, our understanding of forecast skill and sources of predictability in summer is limited. Based on 40 years of hindcasts of the Canadian Seasonal to Interannual Prediction System, version 3 (CanSIPSv3), this study shows that predictions for the Northern Hemisphere summer surface air temperature are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. These midlatitude regions of statistically significant predictive skill appear to be connected to each other through an upper-tropospheric circumglobal wave train. Although a large part of the forecast skill for the surface air temperature and 500-hPa geopotential height is attributable to the linear trend associated with global warming, there is significant long-lead seasonal forecast skill related to interannual variability. Two additional idealized hindcast experiments are performed to help shed light on sources of the long-lead forecast skill using one of the CanSIPSv3 models and its uncoupled version. It is found that tropical ENSO-related sea surface temperature (SST) anomalies contribute to the forecast skill in the western United States, while land surface conditions in winter, including snow cover and soil moisture, in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill in these regions. This implies that improving land surface initial conditions and model representation of land surface processes is crucial for the further development of a seasonal forecasting system. Significance Statement: Useful seasonal predictions in the boreal summer midlatitude regions are of great value. In this study, we show that predictions for the boreal summer season are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. The forecast skill in these regions is associated with a circumglobal teleconnection atmospheric circulation pattern. Sources of the long-lead forecast skill include the global warming–related trend and anomalies in the ocean and land surface initial conditions. It is found that the wintertime snow cover and soil moisture in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill.

In contrast to boreal winter when extratropical seasonal predictions benefit greatly from ENSO-related teleconnections, our understanding of forecast skill and sources of predictability in summer is limited. Based on 40 years of hindcasts of the Canadian Seasonal to Interannual Prediction System, version 3 (CanSIPSv3), this study shows that predictions for the Northern Hemisphere summer surface air temperature are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. These midlatitude regions of statistically significant predictive skill appear to be connected to each other through an upper-tropospheric circumglobal wave train. Although a large part of the forecast skill for the surface air temperature and 500-hPa geopotential height is attributable to the linear trend associated with global warming, there is significant long-lead seasonal forecast skill related to interannual variability. Two additional idealized hindcast experiments are performed to help shed light on sources of the long-lead forecast skill using one of the CanSIPSv3 models and its uncoupled version. It is found that tropical ENSO-related sea surface temperature (SST) anomalies contribute to the forecast skill in the western United States, while land surface conditions in winter, including snow cover and soil moisture, in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill in these regions. This implies that improving land surface initial conditions and model representation of land surface processes is crucial for the further development of a seasonal forecasting system. Significance Statement Useful seasonal predictions in the boreal summer midlatitude regions are of great value. In this study, we show that predictions for the boreal summer season are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. The forecast skill in these regions is associated with a circumglobal teleconnection atmospheric circulation pattern. Sources of the long-lead forecast skill include the global warming–related trend and anomalies in the ocean and land surface initial conditions. It is found that the wintertime snow cover and soil moisture in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill.

Subseasonal‐to‐seasonal (S2S) forecasts of atmospheric rivers (ARs) are in high demand in the water supply management and flood control communities. This study focuses on a new metric, the accumulated water vapor transport associated with ARs, which is closely related to the winter precipitation over the western U.S., and provides a multi‐model S2S prediction skill assessment. The prediction skill is evaluated at lead time 1–4 weeks in four dynamical model hindcast data sets from National Centers for Environmental Prediction (NCEP), European Center for Medium‐Range Weather Forecasts (ECMWF), Environment and Climate Change Canada (ECCC), and Global Modeling and Assimilation Office (GMAO) at National Aeronautics and Space Administration. Three reanalysis data sets are used to evaluate the uncertainty of prediction skills related to the choice of references. The AR‐related water vapor transport is underestimated in ECMWF and ECCC over most of the investigated region, while its maximum has a southeastward shift in NCEP and GMAO at lead time 3–4 weeks. The root mean square error, anomaly correlation coefficient, and Brier skill score are calculated to quantify the prediction skill in both deterministic and probabilistic sense. At week‐3 lead time, the models have significant skill near the lower latitudes (<40°N) of the eastern North Pacific, extending northeastward to the California coastal area. Models have higher skills in forecasting no and strong AR cases than weak cases. The Madden–Julian Oscillation can modulate the prediction skill at week‐3 lead over central and Southern California, but with large uncertainties across models.

The warm Arctic–cold continent pattern (WACC) of near-surface air temperature variability has often been associated with the connection between Arctic sea ice reduction and cold weather over the midlatitude continents. Whether the existence of this pattern is due to variability of sea ice or is caused by atmospheric internal dynamics is subject to debate. Based on a long integration of a primitive equation atmospheric model (SGCM), this study examines the origin of the warm Arctic–cold North American pattern (WACNA), which is characterized by a pair of opposite surface air temperature anomalies over the high-latitude Chukchi–Bering Sea region and the North American continent, in boreal winter on the intraseasonal time scale. The model atmosphere is maintained by a time-independent forcing, so that atmospheric internal dynamics is the only source of variability. It is found that the SGCM model simulates well the behavior of the observed WACNA pattern. The WACNA pattern develops by interacting with the time-mean flow and synoptic-scale transient eddies. Two pathways of Rossby wave propagation are associated with WACNA. The northern pathway originates from eastern Siberia moving eastward across the Bering Strait to Canada, and the southern pathway is rooted in the subtropical waveguide propagating across the eastern North Pacific. Our simulation of this pattern implies that tropospheric dynamics alone can generate the WACNA, and the predictability associated with this pattern is likely limited by its internal dynamics nature. Significance Statement: The warm Arctic–cold continent pattern of temperature variability has often been associated with the connection between Arctic sea ice reduction and cold weather over the midlatitude continents, which implies possible impacts of polar warming on midlatitude climate. There has been debate on whether the existence of this pattern depends on variability of sea ice or can be caused by processes within the atmosphere. In this study, we use a simple atmospheric model, which has a constant forcing; thus, atmospheric internal dynamics is the only source of variability. We show that atmospheric internal dynamics alone can generate the warm Arctic–cold North American pattern. The result has implications for our understanding of the impact of global warming.

The Global Ensemble Prediction System (GEPS) of Environment and Climate Change Canada was recently upgraded to a coupled atmosphere, ocean, and sea‐ice version from an uncoupled atmosphere‐only system. This has been operational since July 2019, with over a year of forecasts now available to evaluate the system throughout all seasons. Using metrics that score the forecast error in ice‐edge position, the spatial probability score and the integrated ice‐edge error, we investigate the spread–error relationship in probabilistic Arctic sea‐ice forecasts from the system and compare this with the skill of the system relative to persistence and a companion Global Deterministic Prediction System (GDPS). Within this ensemble framework, we explore the advantages of having a probabilistic forecast and probe its usefulness in addressing the errors in the system. Both the ensemble GEPS and the deterministic GDPS systems show enhanced sea‐ice prediction over persistence in all months except May and June, when significant biases exist in the systems in shallow‐sea and shelf regions. We attribute a significant portion of these biases to problems modelling landfast ice, but other sources of bias, including significant uncertainties in initializing and verifying sea‐ice analysis, also contribute. The lowest errors in the systems are found during September and continue at reasonably low levels through much of the boreal winter. The minimum and maximum extent periods, along with the early freeze‐up period, are shown to be periods for which the ensemble system offers enhanced benefits over a single deterministic forecast. For these periods, the errors are low and strongly correlated spatially with the ensemble spread. Nevertheless, we find that the ensemble system would likely still benefit from further improvement of the spread/error relationship in the system, currently hampered due to ensemble perturbations that are produced solely in the atmospheric component.

Major disruptions of the winter season, high-latitude stratospheric polar vortices can result in stratospheric anomalies that persist for months. These sudden stratospheric warming events are recognized as an important potential source of forecast skill for surface climate on subseasonal to seasonal timescales. Realizing this skill in operational subseasonal forecast models remains a challenge, as models must capture both the evolution of the stratospheric polar vortices in addition to their coupling to the troposphere. The processes involved in this coupling remain a topic of open research. We present here the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project. SNAPSI is a new model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortex disturbances for surface predictability in subseasonal to seasonal forecast models. Based on a set of controlled, subseasonal ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. First, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. Second, to attribute specific extreme events to stratospheric variability. Third, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models. Fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. Although not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. The output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of subseasonal forecasts.