Sanderson, Ben | Search NASA

DOE OSTI2018

Changes in a suite of indicators of extreme temperature and precipitation under 1.5 and 2 degrees warming

Following the 2015 Paris agreement, the Intergovernmental Panel on Climate Change was tasked with assessing climate change impacts and mitigation options for a world that limits warming to 1.5 °Cina special report. To aid the scientific assessment process three low-warming ensembles were generated over the 21st century based on the Paris targets using NCAR-DOE community model, CESM1-CAM5. This study used those simulation results and computed ten extreme climate indices, from deinitions created by the Expert Team on Climate Change Detection and Indices, to determine if the three different scenarios cause different intensity and frequency of extreme precipitation or temperature over the 21 st century. After computing the indices, statistical tests were used to determine if significant changes affect their characteristics. It was found that at the grid point level significant changes emerge in all scenarios, for nearly all indices. The temperature indices show widespread significant change, while the behavior of precipitation indices reflects the larger role that internal variability plays, even by the end of the century. Nonetheless differences can be assessed, in substantial measure for many of these indices: changes in nearly all indices have a strong correlation to global mean temperature, so that scenarios and times with greater temperature change experience greater index changes for many regions. This is particularly true of the temperature-related indices, but can be assessed for some regions also for the indices related to precipitation intensity. These results thus show that even for scenarios that are separated by only half of a degree in global average temperature, the statistics of extremes are significantly different.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2016

Evaluating lossy data compression on climate simulation data within a large ensemble

High-resolution Earth system model simulations generate enormous data volumes, and retaining the data from these simulations often strains institutional storage resources. Further, these exceedingly large storage requirements negatively impact science objectives, for example, by forcing reductions in data output frequency, simulation length, or ensemble size. To lessen data volumes from the Community Earth System Model (CESM), we advocate the use of lossy data compression techniques. While lossy data compression does not exactly preserve the original data (as lossless compression does), lossy techniques have an advantage in terms of smaller storage requirements. To preserve the integrity of the scientific simulation data, the effects of lossy data compression on the original data should, at a minimum, not be statistically distinguishable from the natural variability of the climate system, and previous preliminary work with data from CESM has shown this goal to be attainable. However, to ultimately convince climate scientists that it is acceptable to use lossy data compression, we provide climate scientists with access to publicly available climate data that have undergone lossy data compression. In particular, we report on the results of a lossy data compression experiment with output from the CESM Large Ensemble (CESM-LE) Community Project, in which we challenge climate scientists to examine features of the data relevant to their interests, and attempt to identify which of the ensemble members have been compressed and reconstructed. We find that while detecting distinguishing features is certainly possible, the compression effects noticeable in these features are often unimportant or disappear in post-processing analyses. In addition, we perform several analyses that directly compare the original data to the reconstructed data to investigate the preservation, or lack thereof, of specific features critical to climate science. Overall, we conclude that applying lossy data compression to climate simulation data is both advantageous in terms of data reduction and generally acceptable in terms of effects on scientific results.

54 ENVIRONMENTAL SCIENCES↗