729 research outputs found
Three arguments for increasing weather persistence in boreal summer –and why we should care
No full textClimate Coffee organised by ECRA and Blue-Action on 10 February 2022
Dim Coumou, Vrije Universiteit Amsterdam, Institute for Environmental Studies (homepage)
Is global warming making summer circulation more persistent
Stratospheric winds trigger cold spells:Changes in Arctic sea ice and snow cover can alter wind patterns at high altitudes
No full textThe role of the Pacific Decadal Oscillation and ocean-atmosphere interactions in driving US temperature variability
No full textHeatwaves can have devastating impact on society and reliable early warnings at several weeks lead time are needed. Previous studies showed that north-Pacific sea surface temperatures (SST) can provide long-lead predictability for eastern US temperature, mediated by an atmospheric Rossby wave. The exact mechanisms, however, are not well understood. Here we analyze two different Rossby waves associated with temperature variability in western and eastern US, respectively. Causal discovery analyses reveal that both waves are characterized by positive ocean-atmosphere feedbacks at daily timescales. Only for the eastern US, a long-lead causal link from SSTs to the Rossby wave exists, which generates summer temperature predictability. We show that this SST forcing mechanism originates from the evolution of the winter-to-spring Pacific Decadal Oscillation (PDO). During pronounced winter-to-spring PDO phases (either positive or negative) eastern US summer temperature forecast skill more than doubles, providing a temporary window of enhanced long-lead predictability.</p
The dynamical core of the Aeolus 1.0 statistical-dynamical atmosphere model: Validation and parameter optimization
Full text linkWe present and validate a set of equations for representing the atmosphere's large-scale general circulation in an Earth system model of intermediate complexity (EMIC). These dynamical equations have been implemented in Aeolus 1.0, which is a statistical-dynamical atmosphere model (SDAM) and includes radiative transfer and cloud modules (Coumou et al., 2011; Eliseev et al., 2013). The statistical dynamical approach is computationally efficient and thus enables us to perform climate simulations at multimillennia timescales, which is a prime aim of our model development. Further, this computational efficiency enables us to scan large and high-dimensional parameter space to tune the model parameters, e.g., for sensitivity studies. Here, we present novel equations for the large-scale zonal-mean wind as well as those for planetary waves. Together with synoptic parameterization (as presented by Coumou et al., 2011), these form the mathematical description of the dynamical core of Aeolus 1.0. We optimize the dynamical core parameter values by tuning all relevant dynamical fields to ERA-Interim reanalysis data (1983-2009) forcing the dynamical core with prescribed surface temperature, surface humidity and cumulus cloud fraction. We test the model's performance in reproducing the seasonal cycle and the influence of the El Niño-Southern Oscillation (ENSO). We use a simulated annealing optimization algorithm, which approximates the global minimum of a high-dimensional function. With non-tuned parameter values, the model performs reasonably in terms of its representation of zonal-mean circulation, planetary waves and storm tracks. The simulated annealing optimization improves in particular the model's representation of the Northern Hemisphere jet stream and storm tracks as well as the Hadley circulation. The regions of high azonal wind velocities (planetary waves) are accurately captured for all validation experiments. The zonal-mean zonal wind and the integrated lower troposphere mass flux show good results in particular in the Northern Hemisphere. In the Southern Hemisphere, the model tends to produce too-weak zonal-mean zonal winds and a too-narrow Hadley circulation. We discuss possible reasons for these model biases as well as planned future model improvements and applications
Hidden amongst Chaos: Dynamics and predictability of weather on subseasonal-to-seasonal timescales
Full text linkWeather shapes societies in various ways, from daily routines to infrastructure, but the rapid climate change caused by human activities is exposing vulnerabilities to extreme weather events. While medium-range weather forecasting has improved, long-term forecasts spanning weeks to months, known as subseasonal-to-seasonal (S2S) timescales, remain challenging. The thesis focuses on using data-driven methods to improve the skill and understanding of subseasonal to seasonal (S2S) weather forecasts, with a particular emphasis on North America. The author explores four main areas: (1) predicting temperature extremes in the eastern United States (US), (2) studying the ocean-atmosphere interaction driving predictability in the eastern US, (3) predicting harvest failure in the eastern US, and (4) identifying challenges, opportunities, and a vision for exploring S2S dynamics and predictability using data-driven methods. (1) For temperature extremes, an algorithm is developed to extract a reliable preceding sea surface temperature (SST) pattern from the North Pacific, improving forecast skill for heatwave events. The trade-off between extremity and spatial aggregation is explored, indicating compromises needed for reliable forecasts at longer lead-times. Predicting event probabilities within wider time windows enhances forecast skill for moderate hot events up to 60 days ahead. (2) To address the issue of trustworthiness in purely statistical machine learning models, the author emphasizes the importance of incorporating physical understanding into forecast models. They employ causal discovery methods to learn physical relationships from data. By applying a causal discovery algorithm, they study the interaction between the atmospheric Rossby wave and the underlying ocean, revealing that summer temperature predictability in the eastern US originates from low-frequency variability in the north Pacific. The study demonstrates that the low-frequency Pacific variability, driven by atmosphere-to-ocean forcing and two-way feedbacks in winter and spring, leads to an upward forcing from the ocean to the atmosphere in summer. The presence of a strong horseshoe-shaped SST pattern in spring enhances predictability by causing more frequent and persistent atmospheric waves, which result in a high-pressure system, higher temperatures, and reduced rainfall in the eastern US. (3) The winter-to-spring horseshoe sea surface temperature (SST) pattern holds significant importance as it suggests the potential predictability of hot and dry weather in the mid-to-eastern United States (US) at long lead-times. This predictability opens up opportunities for the agricultural sector, enabling informed decisions to be made prior to the planting season. The author employs a response-guided dimensionality reduction method and a causal inference-based selection step to extract reliable input features from observational SST and soil moisture datasets. Using this approach, the forecast model successfully predicts poor soybean harvest years as early as February 1st, several months before sowing. This provides farmers with valuable information for decision-making, such as adjusting sowing density, avoiding drought-prone areas, or selecting drought-resistant seeds. (4) The thesis suggests that S2S forecasting potential and value may have been underestimated. However, challenges remain, such as establishing best practices for data-driven forecasting. The author advocates for dedicated open-source software and a collaborative community. Furthermore, operationalizing forecasts and supporting the required infrastructure are essential for societal benefits
The influence of mid-latitude storm tracks on hot, cold, dry and wet extremes
Full text linkChanges in mid-latitude circulation can strongly affect the number and intensity of extreme weather events. In particular, high-amplitude quasi-stationary planetary waves have been linked to prolonged weather extremes at the surface. In contrast, analyses of fast-traveling synoptic-scale waves and their direct influence on heat and cold extremes are scarce though changes in such waves have been detected and are projected for the 21st century. Here we apply regression analyses of synoptic activity with surface temperature and precipitation in monthly gridded observational data. We show that over large parts of mid-latitude continental regions, summer heat extremes are associated with low storm track activity. In winter, the occurrence of cold spells is related to low storm track activity over parts of eastern North America, Europe, and central- to eastern Asia. Storm tracks thus have a moderating effect on continental temperatures. Pronounced storm track activity favors monthly rainfall extremes throughout the year, whereas dry spells are associated with a lack thereof. Trend analyses reveal significant regional changes in recent decades favoring the occurrence of cold spells in the eastern US, droughts in California and heat extremes over Eurasia
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Early prediction of extreme stratospheric polar vortex states based on causal precursors
Full text linkVariability in the stratospheric polar vortex (SPV) can influence the tropospheric circulation and thereby winter weather. Early predictions of extreme SPV states are thus important to improve forecasts of winter weather including cold spells. However, dynamical models are usually restricted in lead time because they poorly capture low-frequency processes. Empirical models often suffer from overfitting problems as the relevant physical processes and time lags are often not well understood. Here we introduce a novel empirical prediction method by uniting a response-guided community detection scheme with a causal discovery algorithm. This way, we objectively identify causal precursors of the SPV at subseasonal lead times and find them to be in good agreement with known physical drivers. A linear regression prediction model based on the causal precursors can explain most SPV variability (r2 = 0.58), and our scheme correctly predicts 58% (46%) of extremely weak SPV states for lead times of 1–15 (16–30) days with false-alarm rates of only approximately 5%. Our method can be applied to any variable relevant for (sub)seasonal weather forecasts and could thus help improving long-lead predictions
Erratum to: increased record-breaking precipitation events under global warming (Climatic Change, (2015), 10.1007/s10584-015-1434-y)
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