Quantifying and Understanding Forced Changes to Unforced Modes of Atmospheric Circulation Variability over the North Pacific in a Coupled Model Large Ensemble

Abstract

While much attention has been given to understanding how anthropogenic radiative forcing influences the mean state of the climate system, far less scrutiny has been paid to how it may modulate naturally occurring modes of variability. This study investigates forced changes to unforced modes of wintertime atmospheric circulation variability and associated impacts on precipitation over the North Pacific and adjacent regions based on the 40-member Community Earth System Model version 1 Large Ensemble across the 1920-2100 period. Each simulation is subject to the same radiative forcing protocol but starts from a slightly different initial condition, leading to different sequences of internal variability. Evolving forced changes in the amplitude and spatial character of the leading internal modes of 500 hPa geopotential height variability are determined by applying Empirical Orthogonal Function analysis across the ensemble dimension at each time step. The results show that the leading modes of internal variability intensify and expand their region of influence in response to anthropogenic forcing, with concomitant impacts on precipitation. Linkages between the Pacific and Atlantic sectors, and between the tropics and extratropics, are also enhanced in the future. These projected changes are driven partly by teleconnections from amplified ENSO activity and partly by dynamical processes intrinsic to the extra-tropical atmosphere. The marked influence of anthropogenic forcing on the characteristics of internal extratropical atmospheric circulation variability presents fundamental societal challenges to future water resource planning, flood control, and drought mitigation.

Anthropogenic aerosols mask increases in US rainfall by greenhouse gases

“…While the analyses in this paper focus on precipitation, our results contribute to mounting evidence of anthropogenically-driven increases in flood risk, particularly in the western United States. A recent study by [54] shows that increases in flood hazard have been "masked" over the past fifty years by natural variability, which agrees with the more general conclusion in [55] regarding the amplification of natural variability under climate change. Ultimately, a combination of the increased moisture-holding capacity of the atmosphere forced by WMGHGs, decreased masking by anthropogenic aerosols, and an amplification of natural circulation variability from large-scale warming points toward dramatic increases in flood risk in the near future.…”

A range of outcomes: the combined effects of internal variability and anthropogenic forcing on regional climate trends over Europe

“…Apparent model bias due to sampling uncertainty must be kept in mind when assessing the fidelity of simulated modes of internal variability (e.g., Wittenberg, 2009;Capotondi et al, 2020;Fasullo et al, 2020;McKenna and Maycock, 2021), transient climate sensitivity (Dong et al, 2020;Andrews et al, 2022), and signal-to-noise properties of initial-value predictions and forced responses (e.g., Scaife and Smith, 2018;Smith et al, 2020;Klavans et al, 2021). In particular, even with 100 years of data, sampling uncertainty is a limiting factor for evaluating ENSO properties in climate models, including its global atmospheric teleconnections and associated climate impacts (Deser et al, , 2018Capotondi et al, 2020) and forced changes thereof (Stevenson et al, 2012;Maher et al, 2018;O'Brien and Deser, 2023). This issue is particularly acute for model assessment of modes of decadal variability such as PDV and AMV due to the paucity of samples in the short instrumental record (Deser and Phillips, 2021;Fasullo et al, 2020).…”

“…For example, forced changes in ocean heat content may be readily detected with just a few members (Fasullo and Nerem, 2018), while forced changes in atmospheric circulation (Deser et al, 2012) or precipitation and temperature extremes (Tebaldi et al, 2021) may require 20-30 members. Detecting forced changes in the characteristics of internal variability itself, such as its amplitude, spatial pattern, and remote teleconnections, may necessitate even larger ensembles (Milinski et al, 2020;Bódai et al, 2020Bódai et al, , 2022O'Brien and Deser, 2023). Initial-condition large ensembles (LEs for short) have proven to be enormously useful for separating internal variability and forced climate change on regional scales in models and for providing robust sampling of models' internal variability by pooling together all of the ensemble members (e.g., Deser et al, 2012;Kay et al, 2015;Maher et al, 2019;Deser et al, 2020a;Lehner et al, 2020).…”

The Role of Internal Variability in Regional Climate Change

“…They have also been used to assess externally-forced changes in the characteristics of simulated internal variability, including extreme events for which large sample sizes are crucial (e.g., Tebaldi et al, 2021;O'Brien and Deser, 2022). Additionally, they have served as methodological testbeds for evaluating approaches to detection and attribution of anthropogenic climate change in the (single) observational record (e.g., Deser et al, 2016;Barnes et al, 2019;Sippel et al, 2019 andSanter et al 2019;Bonfils et al, 2019;Wills et al, 2020).…”

“…Apparent model bias due to sampling uncertainty must be kept in mind when assessing fidelity of simulated modes of internal variability (e.g., Wittenberg et al 20xx;Capotondi et al 2020;Fasullo et al 2021;McKenna and Maycock, 2021), transient climate sensitivity (Dong et al 2021;Andrews et al 2022), and "signal-to-noise" properties of initial-value predictions and forced responses (e.g., Scaife and Smith, 2018;Smith et al, 2020;Klavans et al 2021). In particular, even with 100 years of data, sampling uncertainty is a limiting factor for evaluating ENSO properties in climate models, including its global atmospheric teleconnections and associated climate impacts Capotondi et al 2020) and forced changes thereof (Stevenson et al 2012;Maher et al 2018;Maher et al 2022;O'Brien and Deser, 2022). This issue is particularly acute for model assessment of modes of decadal variability such as PDV and AMV due to the paucity of samples in the short instrumental record (Deser and Phillips 2021;Fasullo et al 2021).…”