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- ItemConsecutive extreme flooding and heat wave in Japan: Are they becoming a norm?(Hoboken, NJ : Wiley, 2019) Wang, Simon S.-Y.; Kim, Hyungjun; Coumou, Dim; Yoon, Jin-Ho; Zhao, Lin; Gillies, Robert R.[No abstract available]
- ItemTropical and mid-latitude teleconnections interacting with the Indian summer monsoon rainfall: a theory-guided causal effect network approach(Gƶttingen : Copernicus Publ., 2020) Di Capua, Giorgia; Kretschmer, Marlene; Donner, Reik V.; van den Hurk, Bart; Vellore, Ramesh; Krishnan, Raghavan; Coumou, DimThe alternation of active and break phases in Indian summer monsoon (ISM) rainfall at intraseasonal timescales characterizes each ISM season. Both tropical and mid-latitude drivers influence this intraseasonal ISM variability. The circumglobal teleconnection observed in boreal summer drives intraseasonal variability across the mid-latitudes, and a two-way interaction between the ISM and the circumglobal teleconnection pattern has been hypothesized. We use causal discovery algorithms to test the ISM circumglobal teleconnection hypothesis in a causal framework. A robust causal link from the circumglobal teleconnection pattern and the North Atlantic region to ISM rainfall is identified, and we estimate the normalized causal effect (CE) of this link to be about 0.2 (a 1 standard deviation shift in the circumglobal teleconnection causes a 0.2 standard deviation shift in the ISM rainfall 1 week later). The ISM rainfall feeds back on the circumglobal teleconnection pattern, however weakly. Moreover, we identify a negative feedback between strong updraft located over India and the Bay of Bengal and the ISM rainfall acting at a biweekly timescale, with enhanced ISM rainfall following strong updraft by 1 week. This mechanism is possibly related to the boreal summer intraseasonal oscillation. The updraft has the strongest CE of 0.5, while the MaddenāJulian oscillation variability has a CE of 0.2ā0.3. Our results show that most of the ISM variability on weekly timescales comes from these tropical drivers, though the mid-latitude teleconnection also exerts a substantial influence. Identifying these local and remote drivers paves the way for improved subseasonal forecasts.
- ItemMeridionally Extending Anomalous Wave Train over Asia During Breaks in the Indian Summer Monsoon([Cham] : Springer International Publishing, 2019) Umakanth, Uppara; Vellore, Ramesh K.; Krishnan, R.; Choudhury, Ayantika Dey; Bisht, Jagat S.H.; Di Capua, Giorgia; Coumou, Dim; Donner, Reik V.Anomalous interactions between the Indian summer monsoon (ISM) circulation and subtropical westerlies are known to trigger breaks in the ISM on subseasonal time-scales, characterised by a pattern of suppressed rainfall over central-north India, and enhanced rainfall over the foothills of the centralāeastern Himalayas (CEH). An intriguing feature during ISM breaks is the formation of a mid-tropospheric cyclonic circulation anomaly extending over the subtropical and mid-latitude areas of the Asian continent. This study investigates the mechanism of the aforesaid Asian continental mid-tropospheric cyclonic circulation (ACMCC) anomaly using observations and simplified model experiments. The results of our study indicate that the ACMCC during ISM breaks is part of a larger meridional wave train comprising of alternating anticyclonic and cyclonic anomalies that extend poleward from the monsoon region to the Arctic. A leadālag analysis of mid-tropospheric circulation anomalies suggests that the meridional wave-train generation is linked to latent heating (LH) anomalies over the CEH foothills, Indo-China, and the Indian landmass during ISM breaks. By conducting sensitivity experiments using a simplified global atmospheric general circulation model forced with satellite-derived three-dimensional LH, it is demonstrated that the combined effects of the enhanced LH over the CEH foothills and Indo-China and decreased LH over the Indian landmass during ISM breaks are pivotal for generating the poleward extending meridional wave train and the ACMCC anomaly. At the same time, the spatial extent of the mid-latitude cyclonic anomaly over Far-East Asia is also influenced by the anomalous LH over centralāeastern China. While the present findings provide interesting insights into the role of LH anomalies during ISM breaks on the poleward extending meridional wave train, the ACMCC anomaly is found to have important ramifications on the daily rainfall extremes over the Indo-China region. It is revealed from the present analysis that the frequency of extreme rainfall occurrences over Indo-China shows a twofold increase during ISM break periods as compared to active ISM conditions. Ā© 2019, The Author(s).
- ItemAssessing changes in risk of amplified planetary waves in a warming world(Hoboken, NJ : Wiley, 2019) Huntingford, Chris; Mitchell, Dann; Kornhuber, Kai; Coumou, Dim; Osprey, Scott; Allen, MylesSummer weather extremes are often associated with high-amplitude atmospheric planetary waves (Petoukhov et al., 2013). Such conditions lead to stationary weather patterns, triggering heat waves and sometimes prolonged intense rainfall. These wave events, referred to as periods of Quasi-Resonant Amplification (QRA), are relatively rare though and hence provide only a few data points in the meteorological record to analyse. Here, we use atmospheric models coupled to boundary conditions that have evolved slowly (i.e., climate), to supplement measurements. Specifically we assess altered probabilities of resonant episodes by employing a unique massive ensemble of atmosphere-only climate simulations to populate statistical distributions of event occurrence. We focus on amplified waves during the two most extreme European heat waves on record, in years 2003 and 2015 (Russo et al., 2015). These years are compared with other modelled recent years (1987ā2011), and critically against a modelled world without climate change. We find that there are differences in the statistical characteristics of wave event likelihood between years, suggesting a strong dependence on the known and prescribed Sea Surface Temperature (SST) patterns. The differences are larger than those projected to have occurred under climate change since the pre-industrial period. However, this feature of small differences since pre-industrial is based on single large ensembles, with members consisting of a range of estimates of SST adjustment from pre-industrial to present. Such SST changes are from projections by a set of coupled atmosphereāocean (AOGCM) climate models. When instead an ensemble for pre-industrial estimates is subdivided into simulations according to which AOGCM the SST changes are based on, we find differences in QRA occurrence. These differences suggest that to reliably estimate changes to extremes associated with altered amplification of planetary waves, and under future raised greenhouse gas concentrations, likely requires reductions in any spread of future modelled SST patterns. Ā© 2019 The Authors. Atmospheric Science Letters published by John Wiley & Sons Ltd on behalf of the Royal Meteorological Society.
- ItemS2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts(Malden, MA : Wiley-Blackwell, 2018) Cohen, Judah; Coumou, Dim; Hwang, Jessica; Mackey, Lester; Orenstein, Paulo; Totz, Sonja; Tziperman, EliThe 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.
- ItemThe influence of aggregation and statistical postāprocessing on the subseasonal predictability of European temperatures(Weinheim [u.a.] : Wiley, 2020) Straaten, Chiem; Whan, Kirien; Coumou, Dim; Hurk, Bart; Schmeits, MauriceThe succession of European surface weather patterns has limited predictability because disturbances quickly transfer to the large-scale flow. Some aggregated statistics, however, such as the average temperature exceeding a threshold, can have extended predictability when adequate spatial scales, temporal scales and thresholds are chosen. This study benchmarks how the forecast skill horizon of probabilistic 2-m temperature forecasts from the subseasonal forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF) evolves with varying scales and thresholds. We apply temporal aggregation by rolling-window averaging and spatial aggregation by hierarchical clustering. We verify 20āyears of re-forecasts against the E-OBS dataset and find that European predictability extends at maximum into the fourth week. Simple aggregation and standard statistical post-processing extend the forecast skill horizon with two and three skilful days on average, respectively. The intuitive notion that higher levels of aggregation capture large-scale and low-frequency variability and can therefore tap into extended predictability holds in many cases. However, we show that the effect can be saturated and that there exist regional optimums beyond which extra aggregation reduces the forecast skill horizon. We expect such windows of predictability to result from specific physical mechanisms that only modulate and extend predictability locally. To optimize subseasonal forecasts for Europe, aggregation should thus be limited in certain cases.
- ItemAlberta wildfire 2016: Apt contribution from anomalous planetary wave dynamics([London] : Macmillan Publishers Limited, part of Springer Nature, 2018) Petoukhov, Vladimir; Petri, Stefan; Kornhuber, Kai; Thonicke, Kirsten; Coumou, Dim; Schellnhuber, Hans JoachimIn May-June 2016 the Canadian Province of Alberta suffered one of the most devastating wildfires in its history. Here we show that in mid-April to early May 2016 the large-scale circulation in the mid- and high troposphere of the middle and sub-polar latitudes of the northern hemisphere featured a persistent high-amplitude planetary wave structure dominated by the non-dimensional zonal wave number 4. The strongest anticyclonic wing of this structure was located over western Canada. In combination with a very strong El NiƱo event in winter 2015/2016 this favored highly anomalous, tinder-dry and high-temperature conditions at the surface in that area, entailing an increased fire hazard there. This critically contributed to the ignition of the Alberta Wildfire in May 2016, appearing to be the costliest disaster in Canadian history thus far.