Climate dynamics

Abstract:

Gravity‐wave (GW) parameterizations from 12 general circulation models (GCMs) participating in the Quasi‐Biennial Oscillation initiative (QBOi) are compared with Strateole 2 balloon observations made in the tropical lower stratosphere from November 2019–February 2020 (phase 1) and from October 2021–January 2022 (phase 2). The parameterizations employ the three standard techniques used in GCMs to represent subgrid‐scale non‐orographic GWs, namely the two globally spectral techniques developed by Warner and McIntyre (1999) and Hines (1997), as well as the “multiwaves” approaches following the work of Lindzen (1981). The input meteorological fields necessary to run the parameterizations offline are extracted from the ERA5 reanalysis and correspond to the meteorological conditions found underneath the balloons. In general, there is fair agreement between amplitudes derived from measurements for waves with periods less than 1 $$ 1 $$ h and parameterizations. The correlation between the daily observations and the corresponding results of the parameterization can be around 0.4, which is 99 % $$ 99\% $$ significant, since 1200 days of observations are used. Given that the parameterizations have only been tuned to produce a quasi‐biennial oscillation (QBO) in the models, the 0.4 correlation coefficient of the GW momentum fluxes is surprisingly good. These correlations nevertheless vary between schemes and depend little on their formulation (globally spectral versus multiwaves for instance). We therefore attribute these correlations to dynamical filtering, which all schemes take into account, whereas only a few relate the gravity waves to their sources. Statistically significant correlations are mostly found for eastward‐propagating waves, which may be due to the fact that during both Strateole 2 phases the QBO is easterly at the altitude of the balloon flights. We also found that the probability density functions (pdfs) of the momentum fluxes are represented better in spectral schemes with constant sources than in schemes (“spectral” or “multiwaves”) that relate GWs only to their convective sources.

Abstract:

A novel sub‐sampling method has been used to isolate the dynamic effects of the response of the North Atlantic Oscillation (NAO) and the Siberian High (SH) from the total response to projected Arctic sea‐ice loss under 2°C global warming above preindustrial levels in very large initial‐condition ensemble climate simulations. Thermodynamic effects of Arctic warming are more prominent in Europe while dynamic effects are more prominent in Asia/East Asia. This explains less‐severe cold extremes in Europe but more‐severe cold extremes in Asia/East Asia. For Northern Eurasia, dynamic effects overwhelm the effect of increased moisture from a warming Arctic, leading to an overall decrease in precipitation. We show that the response scales linearly with the dynamic response. However, caution is needed when interpreting inter‐model differences in the response because of internal variability, which can largely explain the inter‐model spread in the NAO and SH response in the Polar Amplification Model Intercomparison Project.

Abstract:

<jats:title>Abstract</jats:title> <jats:p>The importance of extreme event attribution rises as climate change causes severe damage to populations resulting from unprecedented events. In February 2019, a planetary wave shifted along the U.S.-Canadian border, simultaneously leading to troughing with anomalous cold events and ridging over Alaska and northern Canada with abnormal warm events. Also, a dry-stabilized anticyclonic circulation over low latitudes induced warm extreme events over Mexico and U.S. Florida. Most attribution studies compare the climate model simulations under natural or actual forcing conditions and assess probabilistically from a climatological point of view. However, in this study, we use multiple ensembles from an operational forecast model, promising statistical as well as dynamically constrained attribution assessment, often referred to as the storyline approach to extreme event attribution. In the globally averaged results, increasing CO<jats:sub>2</jats:sub> concentrations lead to distinct warming signals at the surface, resulting mainly from diabatic heating. Our study finds that CO<jats:sub>2</jats:sub>-induced warming eventually affects the possibility of extreme events in North America, quantifying the impact of anthropogenic forcing over less than a week’s forecast simulation. Our study assesses the validity of the storyline approach conditional on the forecast lead times, which is hindered by rising noise in CO<jats:sub>2</jats:sub> signals and the declining performance of the forecast model. The forecast-based storyline approach is valid for at least half of the land area within a six-day lead time before the target extreme occurrence. Our attribution results highlight the importance of achieving net-zero emissions ahead of schedule to reduce the occurrence of severe heatwaves.</jats:p>