DOI: 10.5281/zenodo.7961969
title: Relative impacts of sea ice loss and atmospheric internal variability on winter Arctic to East Asian surface air temperature based on large-ensemble simulations with NorESM2

description: There is no consensus as to whether the cooling trend and the frequent severe mid-latitude winters in the 1990s and 2000s are induced by the Arctic changes. One key factor in this respect is the relative impacts of Arctic sea ice loss (referred to as signal) and the atmospheric internal variability (sometimes behaving as noise). If the signal-to-noise ratio is low, the atmospheric internal variability can easily overwhelm the forced response to Arctic sea ice forcing. <br> In this study, we use reanalysis datasets and three sets of large-ensemble simulations carried out by the Norwegian Earth System Model with a coupled atmosphere-land surface model, forced by seasonal sea ice conditions from preindustrial, present-day, and future periods. The objective is to quantify the relative contributions of Arctic sea ice and unforced atmospheric internal variability to “warm Arctic, cold East Asia” pattern in winters. The datasets used in this study include:<br> (1)The reanalysis data is the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation global atmospheric reanalysis (ERA5), which is publicly available at https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5. This data was used for Figure 2a, Figure 5c, and Figure 6a Hersbach, Hans, et al. "The ERA5 global reanalysis." <em>Quarterly Journal of the Royal Meteorological Society</em> 146.730 (2020): 1999-2049. DOI: https://doi.org/10.1002/qj.3803 <br> (2) Sateliate observed Arctic sea ice extent index which can be downloaded freely from the National Snow and Ice Data Center: https://nsidc.org/data/seaice_index. This data was used for Figure 2a and Figure 5c. Fetterer, F., K. Knowles, W. N. Meier, M. Savoie, and A. K. Windnagel. 2017, updated daily. Sea Ice Index, Version 3. Boulder, Colorado USA. NSIDC: National Snow and Ice Data Center. DOI: https://doi.org/10.7265/N5K072F8 <br> (3) Forcing fields for the PAMIP experiments are available from the input4MIPs data server at https://esgf-node.llnl.gov/search/input4mips/). This data was used for Figure 1. Smith, D. M., and Coauthors, 2019: The Polar Amplification Model Intercomparison Project (PAMIP) contribution to CMIP6: investigating the causes and consequences of polar amplification. <em>Geoscientific Model Development</em>, <strong>12</strong>, 1139-1164. DOI: https://doi.org/10.5194/gmd-12-1139-2019 <br> (4) NorESM2 simulations are publicly available at https://esgf-node.llnl.gov/search/cmip6/. This data was used for all other figures. DOI: https://doi.org/10.5194/gmd-2019-378 This dataset is created to reproduce all the results (e.g., all figures) in this paper. It is created from all the above publicly available dataset. The program codes used to generate this dataset from the publicly available dataset are also provided.
description: This research was supported by the Chinese-Norwegian Collaboration Projects within Climate Systems jointly funded by the National Key Research and Development Program of China (Grant No. 2022YFE0106800) and the Research Council of Norway funded project MAPARC (grant No. 328943). We acknowledge the support from the Research Council of Norway funded project BASIC (grant No. 325440) and the Horizon 2020 project APPLICATE (Grant No. 727862). High-performance computing and storage resources were performed on resources provided by Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway (through projects NN2345K, NS2345K, NS9560K, NS9252K, and NS9034K). Thanks Dr Zachary Labe for sharing his research code: https://github.com/zmlabe/DetectMitigate/tree/main/Scripts