scholarly journals Supplementary material to "Stratospheric Modulation of Arctic Oscillation Extremes as Represented by Extended-Range Ensemble Forecasts"

2021 ◽  
Author(s):  
Jonas Spaeth ◽  
Thomas Birner
Keyword(s):  
Arctic Oscillation ◽  
Ensemble Forecasts ◽  
Extended Range ◽  
Supplementary Material

scholarly journals Stratospheric Modulation of Arctic Oscillation Extremes as Represented by Extended-Range Ensemble Forecasts

2021 ◽  
Author(s):  
Jonas Spaeth ◽  
Thomas Birner
Keyword(s):  
Sample Size ◽  
Arctic Oscillation ◽  
The Arctic ◽  
Large Set ◽  
Ensemble Forecasts ◽  
Extended Range ◽  
Tropospheric Circulation ◽  
Statistical Sense ◽  
Real Atmosphere ◽  
The Arctic Oscillation

Abstract. The Arctic Oscillation (AO) describes a seesaw pattern of variations in atmospheric mass over the polar cap. It is by now well established that the AO pattern is in part determined by the state of the stratosphere. In particular, sudden stratospheric warmings (SSWs) are known to nudge the tropospheric circulation toward a more negative phase of the AO, which is associated with a more equatorward shifted jet and enhanced likelihood for blocking and cold air outbreaks in mid-latitudes. SSWs are also thought to contribute to the occurrence of extreme AO events. However, statistically robust results about such extremes are difficult to obtain from observations or meteorological (re-)analyses due to the limited sample size of SSW events in the observational record (roughly 6 SSWs per decade). Here we exploit a large set of extended-range ensemble forecasts within the subseasonal-to-seasonal (S2S) framework to obtain an improved characterization of the modulation of AO extremes due to stratosphere-troposphere coupling. Specifically, we greatly boost the sample size of stratospheric events by using potential SSWs (p-SSWs), i.e., SSWs that are predicted to occur in individual forecast ensemble members regardless of whether they actually occurred in the real atmosphere. For example, for the ECMWF S2S ensemble this gives us a total of 6101 p-SSW events for the period 1997–2021. A standard lag-composite analysis around these p-SSWs validates our approach, i.e., the associated composite evolution of stratosphere-troposphere coupling matches the known evolution based on reanalyses data around real SSW events. Our statistical analyses further reveal that following p-SSWs, relative to climatology: 1) persistently negative AO states (> 1 week duration) are 16 % more likely, 2) the likelihood for extremely negative AO states (< −3σ) is enhanced by at least 35 %, while that for extremely positive AO states (> +3σ) is reduced to almost zero, 3) a p-SSW preceding an extremely negative AO state within 4 weeks is causal for this AO extreme (in a statistical sense) up to a degree of 27 %. A corresponding analysis relative to strong stratospheric vortex events reveals similar insights into the stratospheric modulation of positive AO extremes.

Exploring winter predictability in Europe using the ECMWF hindcasts

2021 ◽  
Author(s):  
Daniele Mastrangelo ◽  
Ignazio Giuntoli ◽  
Piero Malguzzi
Keyword(s):  
Lead Time ◽  
High Impact ◽  
Temperature Fields ◽  
Lead Times ◽  
Economic Sectors ◽  
Ensemble Forecasts ◽  
Modes Of Variability ◽  
Predictive Skill ◽  
Extended Range ◽  
Impact Events

&lt;p&gt;The accuracy and reliability in predicting winter anomalies, particularly high-impact events, is crucial for economic sectors like energy production and trade. Sub-seasonal predictions can provide a useful tool for early detection of these events. In this context, this study aims to target atmospheric patterns leading to skillful winter predictions at S2S lead times.&lt;/p&gt;&lt;p&gt;With a focus on Europe, we explore extended range predictability (up to 35 days) in the ECMWF hindcast dataset (1999-2018). This dataset provides a sizable sample for assessing the winter months predictive skill of the model and can be considered as a preparatory step to the use of the more comprehensive real-time ensemble forecasts.&lt;/p&gt;&lt;p&gt;The verification is performed on geopotential and temperature fields against the ERA5 reanalysis. We first identify the most skillful predictions both in terms of lead-time and period of initialization. Later we assess whether these skillful predictions correspond to high-impact events, especially cold spells. Finally, in the attempt to identify the potential drivers of improved predictability, we track back to the dominant Euro-Atlantic modes of variability present in the initial atmospheric states of the well predicted events.&lt;/p&gt;

scholarly journals Hindcasting the January 2009 Arctic Sudden Stratospheric Warming and Its Influence on the Arctic Oscillation with Unified Parameterization of Orographic Drag in NOGAPS. Part I: Extended-Range Stand-Alone Forecast

2010 ◽  
Vol 25 (6) ◽  
pp. 1628-1644 ◽  
Author(s):  
Young-Joon Kim ◽  
Maria Flatau
Keyword(s):  
Arctic Oscillation ◽  
The Arctic ◽  
Stratospheric Warming ◽  
Sudden Stratospheric Warming ◽  
Stratospheric Temperature ◽  
Extended Range ◽  
Tropospheric Circulation ◽  
The Arctic Oscillation ◽  
The Impact ◽  
Orographic Drag

Abstract A very strong Arctic major sudden stratospheric warming (SSW) event occurred in late January 2009. The stratospheric temperature climbed abruptly and the zonal winds reversed direction, completely splitting the polar stratospheric vortex. A hindcast of this event is attempted by using the Navy Operational Global Atmospheric Prediction System (NOGAPS), which includes the full stratosphere with its top at around 65 km. As Part I of this study, extended-range (3 week) forecast experiments are performed using NOGAPS without the aid of data assimilation. A unified parameterization of orographic drag is designed by combining two parameterization schemes; one by Webster et al., and the other by Kim and Arakawa and Kim and Doyle. With the new unified orographic drag scheme implemented, NOGAPS is able to reproduce the salient features of this Arctic SSW event owing to enhanced planetary wave activity induced by more comprehensive subgrid-scale orographic drag processes. The impact of the SSW on the tropospheric circulation is also investigated in view of the Arctic Oscillation (AO) index, which calculated using 1000-hPa geopotential height. The NOGAPS with upgraded orographic drag physics better simulates the trend of the AO index as verified by the Met Office analysis, demonstrating its improved stratosphere–troposphere coupling. It is argued that the new model is more suitable for forecasting SSW events in the future and can serve as a tool for studying various stratospheric phenomena.

scholarly journals Cloud-Resolving Typhoon Rainfall Ensemble Forecasts for Taiwan with Large Domain and Extended Range through Time-Lagged Approach

2016 ◽  
Vol 31 (1) ◽  
pp. 151-172 ◽  
Author(s):  
Chung-Chieh Wang ◽  
Shin-Yi Huang ◽  
Shin-Hau Chen ◽  
Chih-Sheng Chang ◽  
Kazuhisa Tsuboki
Keyword(s):  
Lead Time ◽  
Precipitation Forecast ◽  
Lead Times ◽  
Ensemble Forecasts ◽  
Worst Case ◽  
Large Domain ◽  
Wide Range ◽  
Extended Range ◽  
Time Lagged ◽  
The Government

Abstract In this study, the performance of a new ensemble quantitative precipitation forecast (QPF) system for Taiwan, with a cloud-resolving grid spacing of 2.5 km, a large domain of 1860 km × 1360 km, and an extended range of 8 days, is evaluated for six typhoons during 2012–13. Obtaining the probability (ensemble) information through a time-lagged approach, this system combines the strengths of high resolution (for QPF) and longer lead time (for hazard preparation) in an innovative way. For the six typhoons, in addition to short ranges (≤3 days), the system produced a decent QPF at a longest range up to days 8, 4, 6, 3, 6, and 7, providing greatly extended lead times, especially for slow-moving storms that pose higher threats. Moreover, since forecast uncertainty (reflected in the spread) is reduced with lead time, this system can provide a wide range of rainfall scenarios across Taiwan with longer lead times, each highly realistic for the associated track, allowing for advanced preparation for worst-case scenarios. Then, as the typhoon approaches and the predicted tracks converge, the government agencies can make adjustments toward the scenario of increasing likelihood. This strategy fits well with the conventional wisdom of “hoping for the best, but preparing for the worst” when facing natural hazards. Overall, the system presented herein compares favorably in usefulness to a typical 24-member ensemble (5-km grid size, 750 km × 900 km, 3-day forecasts) currently in operation using similar computational resources. Requiring about 1500 cores to execute four 8-day runs per day, it is not only powerful but also affordable and feasible.

scholarly journals Enhanced extended-range predictability of the 2018 late-winter Eurasian cold spell due to the stratosphere

2020 ◽  
Author(s):  
Lisa-Ann Kautz ◽  
Inna Polichtchouk ◽  
Thomas Birner ◽  
Hella Garny ◽  
Joaquim Pinto
Keyword(s):  
North Atlantic ◽  
Large Scale ◽  
Causal Link ◽  
Late Winter ◽  
Cold Spell ◽  
Invariant State ◽  
Ensemble Forecasts ◽  
Probability Of Occurrence ◽  
Extended Range ◽  
Time Invariant

&lt;p&gt;A severe cold spell with surface temperatures reaching 10 K below climatology hit Eurasia during late February/early March 2018. This cold spell was associated with a Scandinavian blocking pattern followed by an extreme negative North Atlantic Oscillation (NAO) phase. Here we explore the predictability of this cold spell/NAO event using ensemble forecasts from the Subseasonal-to-Seasonal (S2S) archive. We find that this event was predicted with the observed strength roughly 10 days in advance. However, the probability of occurrence of the cold spell was doubled up to 25 days in advance, when a sudden stratospheric warming (SSW) occurred. Our results indicate that the amplitude of the cold spell was increased by the regime shift to the negative NAO phase at the end of February, which was likely favored by the SSW. We quantify the contribution of the SSW to the enhanced extended-range forecast skill for this particular event by running forecast ensembles in which the evolution of the stratosphere is nudged to a) the observed evolution, and b) a time invariant state. In the experiment with the observed stratospheric evolution nudged, the probability of occurrence of a strong cold spell is enhanced to 45%, while it is at its climatological value of 5% when the stratosphere is nudged to a time invariant state. These results showing enhanced predictability of surface extremes following SSWs extend previous observational evidence, which is mostly based on composite analyses, to a single event. Our results support that it is the subsequent evolution throughout the lower stratosphere following the SSW, rather than the occurrence of the SSW itself, that is crucial in coupling to large-scale tropospheric flow patterns. However, we caution that probabilistic gain in predictability alone is insufficient to conclude about a causal link between the SSW and the cold spell event.&lt;br&gt;&lt;br&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Predictability of Sudden Stratospheric Warmings in the ECMWF Extended-Range Forecast System

2018 ◽  
Vol 146 (4) ◽  
pp. 1063-1075 ◽  
Author(s):  
Alexey Yu. Karpechko
Keyword(s):  
Lead Time ◽  
The Arctic ◽  
Lead Times ◽  
Forecast System ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Extended Range ◽  
Medium Range ◽  
Tropospheric Circulation ◽  
Mean Square Errors

The skill of the Arctic stratospheric retrospective ensemble forecasts (hindcasts) of the European Centre for Medium-Range Weather Forecasts extended-range system is analyzed with a focus on the predictability of the major sudden stratospheric warmings (SSWs) during the period 1993–2016. Thirteen SSWs took place during this period. It is found that forecasts initialized 10–15 days before the SSWs show worse skill in the stratosphere than forecasts initialized during normal conditions in terms of root-mean-square errors but not in terms of anomaly correlation. Using the spread of ensemble members to estimate forecasted SSW probability, it is shown that some SSWs can be predicted with high (>0.9) probability at lead times of 12–13 days if a difference of 3 days between actual and forecasted SSW is allowed. Focusing on SSWs with significant impacts on the tropospheric circulation, on average, the forecasted SSW probability is found to increase from nearly 0 at 1-month lead time to 0.3 at day 13 before SSW, and then rapidly increases to nearly 1 at day 7. The period between days 8 and 12 is when most of the SSWs are predicted, with a probability of 0.5–0.9, which is considerably larger than the observed SSW occurrence frequency. Therefore, this period can be thought of as an estimate of the SSW predictability limit in this system. Indications that the predictability limit for some SSWs may be longer than 2 weeks are also found; however, this result is inconclusive and more studies are needed to understand when and why such long predictability is possible.

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