Simulation and verification.

<p>(A) The risk of terrorist attacks predicted by the RF-based model. (B) Verification of the prediction accuracy of the RF model using terrorist attacks data in 2015. Terrorism event locations that have no history of terrorist attacks in the past and lie in the low risk region of the prediction map belong to A. Terrorism event locations that have no history of terrorist attacks in the past and lie in the high risk region of the prediction map belong to B. Terrorism event locations that have a history of terrorist attacks and lie in the high risk region of the prediction map belong to C. Terrorism event locations that have a history of terrorist attacks and lie in the low risk region of the prediction map belong to D.</p

Measuring the discriminatory power of each factors.

<p>Measuring the discriminatory power of each factors.</p

Selecting the optimal models using five repetitions of a 10-fold cross-validation.

<p>(A) Tuning the parameters of NNET. (B) Tuning the parameter of SVM. (C) Tuning the parameter of RF. (D) Comparing the performance of multiple models based on the same versions of the training data during the cross-validation process. (E) Receiver operating characteristic (ROC) curves of NNET, SVM and RF applied to training samples. (F) ROC curves of NNET, SVM and RF applied to validation samples.</p

Spatial distribution of samples.

<p>(A) Training samples with 11,978 assessment units. Among the units, 5,989 have experienced terrorist attacks that resulted in casualties (= high risk), whereas 5,989 have not (= low risk). (B) Validation samples with 3,992 assessment units. Among the units, 1,996 have experienced terrorist attacks that resulted in casualties (= high risk), whereas 1,996 have not (= low risk).</p

Workflow of the simulation the risk of terrorist attacks in all regions worldwide.

<p>Workflow of the simulation the risk of terrorist attacks in all regions worldwide.</p

Global and regional climate responses to national-committed emission reductions under the Paris agreement

<p>To stabilize global mean temperature change within the range of 1.5–2.0°C in accordance with the Paris Agreement, countries worldwide submitted their Intended Nationally Determined Contributions with their proposed emission reductions. However, it remains unclear what the resulting climate change in terms of temperature and precipitation would be in response to the Intended Nationally Determined Contribution emission efforts. This study quantifies the global and regional temperature and precipitation changes in response to the updated Intended Nationally Determined Contribution scenarios, using simulations of 14 Fifth Coupled Climate Model Intercomparison Project models. Our results show that Intended Nationally Determined Contribution emissions would lead to a global mean warming of 1.4°C (1.3–1.7°C) in 2030 and 3.2°C (2.6–4.3°C) in 2100, above the preindustrial level (the 1850–1900 average). Spatially, the Arctic is projected to have the largest warming, 2.5 and 3 times the global average for 2030 and 2100, respectively, with strongest positive trends at 70–85°N over Asia, Europe and North America (6.5–9.0°C). The excessive warming under Intended Nationally Determined Contribution scenarios is substantially above the 1.5°C or 2.0°C long-term stabilization level. Global mean precipitation is projected to be similar to preindustrial levels in 2030, and an increase of 6% (4–9%) by 2100 compared with the preindustrial level. Regional precipitation changes will be heterogeneous, with significant increases over the equatorial Pacific (about +120%) and strong decreases over the Mediterranean, North Africa and Central America (−15% – −30%). These results clearly show that it is necessary to adjust and strengthen national mitigation efforts on current Intended Nationally Determined Contributions to meet the long-term temperature target.</p

PAGES2k_v2.0.0-ts.pklz

Python-readable data structur

Quality-control dashboards, South America

Quality-control dashboards for South American record

Loading instructions

in GitHub Markdown format<div><br></div><div>Change note [5 April 2019]: Original instructions in LoadData.md are now obsolete, following updates to the LiPD utilities. The instructions now point to recently published Jupyter notebooks that illustrate how to use the PAGES 2k LiPD files to reproduce some figures from the paper, or carry out other analyses.<br></div

Quality-control dashboards, Arctic

Quality-control dashboards for Arctic record