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9 results for “rare event data”

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zenodo40/100

Data and scripts for "Simulating AMOC tipping driven by internal climate variability with a rare event algorithm."

This dataset contains supplementary material for the paper Simulating AMOC tipping driven by internal climate variability with a rare event algorithm." (M.Cini, G. Zappa, F. Ragone, S. Corti, 2023) submitted to npj Climate and Atmospheric Science.  Preprint is available at https://www.researchsquare.com/article/rs-3215995/latest. Here we uploaded most relevant data and scripts concerning this study. Feel free to contact us for other resources. This datasets contains:1) The output of one 125-years ensemble simulation performed with the Algorithm.2) Time series of the AMOC index for all the simulations performed. 3) All data-analysis related scripts. These scripts have been used to plot all the figures in the paper.The script for the rare event algorithm is already available in the Supplementary material for "Rare event algorithm study of extreme warm summers and heatwaves over Europe" zenodo repository, available at https://zenodo.org/records/4763283. Simulation Architecture and model setupAll the simulations have been performed with an intermediate complexity coupled climate model, composed by the Planet Simulator (PlaSim) and the Large Scale Geostrophic Ocean (LSG). All simulations are performed at stationary greenhouse gases forcing. More information about model setup and scope of the simulations can be found in the paper. We performed 10 100-member ensemble simulations. First 125 years of the simulation are performed with the algorithm on (k=3). Then, simulations have been restarted at year 120 with the algorithm off (k=0) up to year 400. This last 380 years of simulation have been performed with only 20 members. y2480_k3_ntraj100y2480_k3_ntraj100.tar.gz contains the output of one ensemble simulation (y2480, i.e. the one that starts at year 2480 of the control run simulation) performed with the algorithm, i.e. contains data of 125 years simulation of 100 members. Data is organized in blocks for each years. Every block contains the 4 NetCDF light file for each member and 4 files with full-size output that represent the mean state as the average of the 100 members. The 4 different file name accounts for the 4 different module output of the model: "data" for the atmosphere, "ice" for sea ice, "ocean" for the slab ocean layer in PlaSIM, lsg for LSG dynamical ocean. Time SeriesTime series of the AMOC index are contained in 10 files representing the 10 different simulations performed. In each file are present 125 .txt files, one for each year of the simulation with the algorithm on, with the annual average AMOC indices of the 100 members, and 380 .txt files, one for each year of the simulation with the algorithm off, with the annual average AMOC indices of the 20 members. Response Analysis REAResponse Analysis REA contains the script that generates Fig.2, Fig. S3, Fig. S5, Fig. S6 and Fig. S7 of the paper. In general it provides tools for data analysis of the climate response to an AMOC slowdown.  Be aware that data of the lsg module needs different processing.  AMOC Evolution REAAMOC Evolution REA contains the script that generates Fig.1, Fig. 4, Fig. 5, Fig. 6 and Fig. S2 of the paper. In general it provides tools for analysis of time series and scatter plots of the AMOC evolution.   Causes REACauses REA  contains the script that generates Fig.3, Fig. S4 of the paper. . In general it provides tools for analysis of driving elements of the AMOC decline. More information about these methods can be found in the "Triggering mechanisms" section of the paper.  Be aware that data of the lsg module needs different processing.

opencc-by-4.0Dec 2023View details →

dryad36/100

Data from: Corralling a black swan: natural range of variation in a forest landscape driven by rare, extreme events

The natural range of variation (NRV) is an important reference for ecosystem management, but has been scarcely quantified for forest landscapes driven by infrequent, severe disturbances. Extreme events such as large, stand-replacing wildfires at multi-century intervals are typical for these regimes; however, data on their characteristics are inherently scarce, and, for land management, these events are commonly considered too large and unpredictable to integrate into planning efforts (the proverbial 'Black Swan'). Here, we estimate the NRV of late-seral (mature/old-growth) and early-seral (post-disturbance, pre-canopy-closure) conditions in a forest landscape driven by episodic, large stand-replacing wildfires: the Western Cascade Range of Washington, USA (2.7 million ha). These two seral stages are focal points for conservation and restoration objectives in many regions. Using a state-and-transition simulation approach incorporating uncertainty, we assess the degree to which NRV estimates differ under a broad range of literature-derived inputs regarding: a) overall fire rotations, and b) how fire area is distributed through time – as relatively frequent smaller events (less episodic), or fewer but larger events (more episodic). All combinations of literature-derived fire rotations and temporal distributions (i.e. 'scenarios') indicate that the largest wildfire events (or episodes) burned up to 10^5-10^6 hectares. Under most scenarios, wildfire dynamics produced 5th-95th percentile ranges for late-seral forests of ~47-90% of the region (median 70%), with structurally complex early-seral conditions composing ~1-30% (median 6%). Fire rotation was the main determinant of NRV, but temporal distribution was also important, with more episodic (temporally clustered) fire yielding wider NRV. In smaller landscapes (20,000 ha; typical of conservation reserves and management districts), ranges were 0-100% because fires commonly exceeded the landscape size. Current conditions are outside the estimated NRV, with the majority of the region instead covered by dense mid-seral forests (i.e. a regional landscape with no historical analog). Broad consistency in NRV estimates among widely varied fire regime parameters suggests these ranges are likely relevant even under changing climatic conditions, both historical and future. These results indicate management-relevant NRV estimates can be derived for seral stages of interest in extreme-event landscapes, even when incorporating inherent uncertainties in disturbance regimes.

opencc-zeroSep 2019View details →

dryad36/100

Data from: Improving performance of hurdle models using rare-event weighted logistic regression: An application to maternal mortality data

In this paper, the performance of hurdle models in rare events data is improved by modifying their binary component. The rare-event weighted logistic regression model is adopted in place of logistic regression to deal with class imbalance due to rare events. Poisson Hurdle Rare Event Weighted Logistic Regression (REWLR) and Negative Binomial Hurdle (NBH) REWLR are developed as two-part models which use the REWLR model to estimate the probability of a positive count and a Poisson or NB zero-truncated count model to estimate non-zero counts. The obtained results are numerically validated and then discussed from both the mathematical and the maternal mortality perspective. Numerical simulations are also presented to give a more complete representation of the model dynamics. Results obtained suggest that NB Hurdle REWLR is the best-performing model for zero-inflated count data due to rare events.

opencc-zeroOct 2022View details →

dryad36/100