Drivers of summer Arctic sea-ice extent in CMIP6 large ensembles revealed by information flow DOI Creative Commons
David Docquier, François Massonnet, Francesco Ragone

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июнь 4, 2024

Abstract Arctic sea-ice extent has strongly decreased since the beginning of satellite observations in late 1970s. While several drivers are known to be implicated, their respective contribution is not fully understood. Here, we apply Liang-Kleeman information flow method five different large ensembles from Coupled Model Intercomparison Project Phase 6 (CMIP6) over 1970-2060 period investigate which fluctuations winter volume, air temperature and ocean heat transport drive changes subsequent summer extent. This allows us go beyond classical correlation analyses. Results show that most important controlling factor at interannual timescale, volume Atlantic Ocean play a secondary role. If replace by net shortwave downward longwave radiations, find sum influences both radiations almost similar influence, with radiation being dominant driving Finally, influence more prominent during periods reduction this overall increased 1970.

Язык: Английский

Noise-shaped hysteresis cycles of the AMOC under increasing CO2 forcing DOI Creative Commons
Matteo Cini, Giuseppe Zappa, Francesco Ragone

и другие.

Chaos An Interdisciplinary Journal of Nonlinear Science, Год журнала: 2025, Номер 35(2)

Опубликована: Фев. 1, 2025

The Atlantic Meridional Overturning Circulation (AMOC) stability landscape is commonly investigated with single-realization hysteresis diagrams driven by freshwater input in the North Ocean. However, effect of CO2 forcing on one side and role internal climate variability timing tipping AMOC other remain less explored. Here, we address this gap running three independent simulations, consisting a slow ramp-up plus ramp-down concentration (0.2 ppm/year) within PlaSim-Large-Scale Geostrophic (LSG) intermediate complexity model. We show that realizations CO2-driven cycle, particularly, recovery, are remarkably affected variability. In even observe reversed where recovers at higher level than collapse point. While statistical Early Warning Signals (EWSs) some success detecting points, also find EWS considerably reduces predictability leads to false positives an approaching tipping. suggest presence may have characteristics deviate substantially from behavior seen simple models caution needed when interpreting results single-experiment realization. Our findings highlight need for probabilistic approach defining “safe operating space” stability, since it might not be possible define single critical threshold prevent collapse.

Язык: Английский

Процитировано

0

Widespread Multi‐Year Droughts in Italy: Identification and Causes of Development DOI Creative Commons
Salvatore Pascale, Francesco Ragone

International Journal of Climatology, Год журнала: 2025, Номер unknown

Опубликована: Март 23, 2025

ABSTRACT Multi‐year droughts pose a significant threat to the security of water resources, putting stress on resilience hydrological, ecological and socioeconomic systems. Motivated by recent multi‐year drought that affected Southwestern Europe Italy from 2021 2023, here we utilise two indices—the Standardised Precipitation Evapotranspiration Index (SPEI) (SPI)—to quantify temporal evolution percentage Italian territory experiencing conditions in period 1901–2023 identify Widespread Multi‐Year Drought (WMYD) events, defined as affecting at least 30% Italy. Seven WMYD events are identified using different precipitation datasets: 1921–1922, 1942–1944, 1945–1946, 2006–2008, 2011–2013, 2017–2018 2021–2023. Correlation analysis between time series areas atmospheric circulation indicates onset spread related specific phases winter North Atlantic Oscillation (NAO), Scandinavian Pattern (SCAND), East Atlantic/Western Russia (EAWR) pattern summer (EA) patterns. Event‐based these episodes reveals variety patterns combinations four teleconnection modes contribute persistently dry during both summer. This study offers new insights into identification understanding meteorological drivers serves first step toward better impacts anthropogenic climate change them.

Язык: Английский

Процитировано

0

Ensemble design for seasonal climate predictions: studying extreme Arctic sea ice lows with a rare event algorithm DOI Creative Commons
Jerome Sauer, François Massonnet, Giuseppe Zappa

и другие.

Earth System Dynamics, Год журнала: 2025, Номер 16(3), С. 683 - 702

Опубликована: Май 6, 2025

Abstract. Initialized ensemble simulations can help identify the physical drivers and assess probabilities of weather climate extremes based on a given initial state. However, significant computational burden complex models makes it challenging to quantitatively investigate extreme events with below few percent. A possible solution overcome this problem is use rare event algorithms, i.e. techniques originally developed in statistical physics that increase sampling efficiency numerical simulations. Here, we apply algorithm intermediate-complexity coupled model PlaSim-LSG study pan-Arctic sea ice area reduction under pre-industrial greenhouse gas conditions. We construct four pairs control simulations, each starting from different winter states. The produce lows 2 orders magnitude smaller than feasible ensembles drastically number compared direct sampling. find for probability level, amplitude negative late-summer anomalies strongly depends baseline thickness but hardly area. Finally, processes two trajectories leading conditional less 0.001 %. In both cases, are preceded by spring anomalies. These related enhanced surface downward longwave radiative sensible heat fluxes an anomalously moist, cloudy warm atmosphere. During summer, favoured open-water-formation efficiency, strong solar radiation ice–albedo feedback. This work highlights most summer conditions result combined effects preconditioning variability, emphasizing need thoughtful design when turning real applications.

Язык: Английский

Процитировано

0

Drivers of summer Arctic sea-ice extent at interannual time scale in CMIP6 large ensembles revealed by information flow DOI Creative Commons
David Docquier, François Massonnet, Francesco Ragone

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Окт. 16, 2024

Язык: Английский

Процитировано

1

Drivers of summer Arctic sea-ice extent in CMIP6 large ensembles revealed by information flow DOI Creative Commons
David Docquier, François Massonnet, Francesco Ragone

и другие.

Research Square (Research Square), Год журнала: 2024, Номер unknown

Опубликована: Июнь 4, 2024

Abstract Arctic sea-ice extent has strongly decreased since the beginning of satellite observations in late 1970s. While several drivers are known to be implicated, their respective contribution is not fully understood. Here, we apply Liang-Kleeman information flow method five different large ensembles from Coupled Model Intercomparison Project Phase 6 (CMIP6) over 1970-2060 period investigate which fluctuations winter volume, air temperature and ocean heat transport drive changes subsequent summer extent. This allows us go beyond classical correlation analyses. Results show that most important controlling factor at interannual timescale, volume Atlantic Ocean play a secondary role. If replace by net shortwave downward longwave radiations, find sum influences both radiations almost similar influence, with radiation being dominant driving Finally, influence more prominent during periods reduction this overall increased 1970.

Язык: Английский

Процитировано

0