The Predictive Skill of Eurasian Snow Cover and Arctic Sea Ice on Mid-High Latitude Winter Weather

第6回極域科学シンポジウム分野横断セッション：[IA] 急変する北極気候システム及びその全球的な影響の総合的解明―GRENE北極気候変動研究事業研究成果報告2015―11月19日（木）　国立極地研究所 2階 大会議

The role of the Siberian high in northern hemisphere climate variability

The dominant mode of sea level pressure (SLP) variability during the winter months in the Northern Hemisphere (NH) is characterized by a dipole with one anomaly center covering the Arctic with the opposite sign anomaly stretched across the mid-latitudes. Associated with the SLP anomaly, is a surface temperature anomaly induced by the anomalous circulation. We will show that this anomaly pattern originates in the early fall, on a much more regional scale, in Siberia. As the season progresses this anomaly pattern propagates and amplifies to dominate much of the extratropical NH, making the Siberian high a dominant force in NH climate variability in winter. Also since the SLP and surface temperature anomalies originate in a region of maximum fall snow cover variability, we argue that snow cover partially forces the phase of winter variability and can potentially be used for the skillful prediction of winter climate.National Science Foundation (U.S.) (Grant ATM-9902433

The NAO, the AO, and global warming: How closely related?

ABSTRACT The North Atlantic Oscillation (NAO) and the closely related Arctic Oscillation (AO) strongly affect Northern Hemisphere (NH) surface temperatures with patterns reported similar to the global warming trend. The NAO and AO were in a positive trend for much of the 1970s and 1980s with historic highs in the early 1990s, and it has been suggested that they contributed significantly to the global warming signal. The trends in standard indices of the AO, NAO, and NH average surface temperature for December-February, 1950, and the associated patterns in surface temperature anomalies are examined. Also analyzed are factors previously identified as relating to the NAO, AO, and their positive trend: North Atlantic sea surface temperatures (SSTs), Indo-Pacific warm pool SSTs, stratospheric circulation, and Eurasian snow cover. Recently, the NAO and AO indices have been decreasing; when these data are included, the overall trends for the past 30 years are weak to nonexistent and are strongly dependent on the choice of start and end date. In clear distinction, the wintertime hemispheric warming trend has been vigorous and consistent throughout the entire period. When considered for the whole hemisphere, the NAO/AO patterns can also be distinguished from the trend pattern. Thus the December-February warming trend may be distinguished from the AO and NAO in terms of the strength, consistency, and pattern of the trend. These results are insensitive to choice of index or dataset. While the NAO and AO may contribute to hemispheric and regional warming for multiyear periods, these differences suggest that the large-scale features of the global warming trend over the last 30 years are unrelated to the AO and NAO. The related factors may also be clearly distinguished, with warm pool SSTs linked to the warming trend, while the others are linked to the NAO and AO

More-persistent weak stratospheric polar vortex states linked to cold extremes

The extratropical stratosphere in boreal winter is characterized by a strong circumpolar westerly jet, confining the coldest temperatures at high latitudes. The jet, referred to as the stratospheric polar vortex, is predominantly zonal and centered around the pole; however, it does exhibit large variability in wind speed and location. Previous studies showed that a weak stratospheric polar vortex can lead to cold-air outbreaks in the midlatitudes, but the exact relationships and mechanisms are unclear. Particularly, it is unclear whether stratospheric variability has contributed to the observed anomalous cooling trends in midlatitude Eurasia. Using hierarchical clustering, we show that over the last 37 years, the frequency of weak vortex states in mid- to late winter (January and February) has increased, which was accompanied by subsequent cold extremes in midlatitude Eurasia. For this region, 60% of the observed cooling in the era of Arctic amplification, that is, since 1990, can be explained by the increased frequency of weak stratospheric polar vortex states, a number that increases to almost 80% when El Niño–Southern Oscillation (ENSO) variability is included as well

Seasonal predictability of wintertime precipitation in Europe using the snow advance index

This study tests the applicability of Eurasian snow cover increase in October, as described by the recently published snow advance index (SAI), for forecasting December–February precipitation totals in Europe. On the basis of a classical correlation analysis, global significance was obtained and locally significant correlation coefficients of up to 0.89 and 20.78 were found for the Iberian Peninsula and southern Norway, respectively. For a more robust assessment of these results, a linear regression approach is followed to hindcast the precipitation sums in a 1-yr-out cross-validation framework, using the SAI as the only predictor variable. With this simple empirical approach, local-scale precipitation could be reproduced with a correlation of up to 0.84 and 0.71 for the Iberian Peninsula and southern Norway, respectively, while catchment aggregations on the Iberian Peninsula could be hindcast with a correlation of up to 0.73. These findings are confirmed when repeating the hindcast approach to a degraded but much longer version of the SAI. With the recommendation to monitor the robustness of these results as the sample size of the SAI increases, the authors encourage its use for the purpose of seasonal forecasting in southern Norway and the Iberian Peninsula, where general circulation models are known to perform poorly for the variable in question.SB, RM, and JMG acknowledge funding from the CICYT Project CGL2010-21869 and from QWeCI (EU Grant 243964) and the CSIC JAE-PREDOC program. JC is supported by the National Science Foundation Grants ARC-0909459 and ARC-0909457, and NOAA Grant NA10OAR4310163. The authors are thankful for the helpful comments of the three anonymous reviewers and acknowledge the E-OBS dataset from the ENSEMBLES project (http://ensembles-eu.metoffice.com/) as well the ECA&D(http:// eca.knmi.nl/) and AEMET station datasets

S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts

The discipline of seasonal climate prediction began as an exercise in simple statistical techniques. However, today the large government forecast centers almost exclusively rely on complex fully coupled dynamical forecast systems for their subseasonal to seasonal (S2S) predictions while statistical techniques are mostly neglected and those techniques still in use have not been updated in decades. In this Opinion Article, we argue that new statistical techniques mostly developed outside the field of climate science, collectively referred to as machine learning, can be adopted by climate forecasters to increase the accuracy of S2S predictions. We present an example of where unsupervised learning demonstrates higher accuracy in a seasonal prediction than the state-of-the-art dynamical systems. We also summarize some relevant machine learning methods that are most applicable to climate prediction. Finally, we show by comparing real-time dynamical model forecasts with observations from winter 2017/2018 that dynamical model forecasts are almost entirely insensitive to polar vortex (PV) variability and the impact on sensible weather. Instead, statistical forecasts more accurately predicted the resultant sensible weather from a mid-winter PV disruption than the dynamical forecasts. The important implication from the poor dynamical forecasts is that if Arctic change influences mid-latitude weather through PV variability, then the ability of dynamical models to demonstrate the existence of such a pathway is compromised. We conclude by suggesting that S2S prediction will be most beneficial to the public by incorporating mixed or a hybrid of dynamical forecasts and updated statistical techniques such as machine learning

Adaptive Bias Correction for Improved Subseasonal Forecasting

Subseasonal forecasting \unicode{x2013} predicting temperature and precipitation 2 to 6 weeks \unicode{x2013} ahead is critical for effective water allocation, wildfire management, and drought and flood mitigation. Recent international research efforts have advanced the subseasonal capabilities of operational dynamical models, yet temperature and precipitation prediction skills remains poor, partly due to stubborn errors in representing atmospheric dynamics and physics inside dynamical models. To counter these errors, we introduce an adaptive bias correction (ABC) method that combines state-of-the-art dynamical forecasts with observations using machine learning. When applied to the leading subseasonal model from the European Centre for Medium-Range Weather Forecasts (ECMWF), ABC improves temperature forecasting skill by 60-90% and precipitation forecasting skill by 40-69% in the contiguous U.S. We couple these performance improvements with a practical workflow, based on Cohort Shapley, for explaining ABC skill gains and identifying higher-skill windows of opportunity based on specific climate conditions.Comment: 16 pages of main paper and 2 pages of appendix tex

Online Learning with Optimism and Delay

Inspired by the demands of real-time climate and weather forecasting, we develop optimistic online learning algorithms that require no parameter tuning and have optimal regret guarantees under delayed feedback. Our algorithms -- DORM, DORM+, and AdaHedgeD -- arise from a novel reduction of delayed online learning to optimistic online learning that reveals how optimistic hints can mitigate the regret penalty caused by delay. We pair this delay-as-optimism perspective with a new analysis of optimistic learning that exposes its robustness to hinting errors and a new meta-algorithm for learning effective hinting strategies in the presence of delay. We conclude by benchmarking our algorithms on four subseasonal climate forecasting tasks, demonstrating low regret relative to state-of-the-art forecasting models.Comment: ICML 2021. 9 pages of main paper and 26 pages of appendix tex

Arctic warming amplifies climate change and its impacts

This ScienceBrief Review examines the evidence linking Arctic warming to the amplification of climate change impacts in Arctic, boreal and mid-latitude regions. It synthesises findings from more than 190 peer-reviewed scientific articles gathered using ScienceBrief. The evidence shows that the Arctic region has warmed at least twice as much as the global average, leading to a number of environmental consequences. The extent and thickness of sea-ice have decreased and rates of permafrost thaw have increased in recent decades. The impacts of rising mean annual temperatures have been exacerbated by an increase in heatwaves this century. These changes amplify climate change and its impacts. Permafrost thaw and wildfires are releasing greenhouse gases and amplifying climate change, while the loss of sea ice is reducing the amount of solar energy reflected by the Earth’s surface. There is ongoing debate about how changes in the Arctic energy balance influence patterns of extreme weather in the mid-latitudes