The role of natural variability in projections of climate change impacts on U.S. ozone pollution

Climate change can impact air quality by altering the atmospheric conditions that determine pollutant concentrations. Over large regions of the U.S., projected changes in climate are expected to favor formation of ground-level ozone and aggravate associated health effects. However, modeling studies exploring air quality-climate interactions have often overlooked the role of natural variability, a major source of uncertainty in projections. Here we use the largest ensemble simulation of climate-induced changes in air quality generated to date to assess its influence on estimates of climate change impacts on U.S. ozone. We find that natural variability can significantly alter the robustness of projections of the future climate's effect on ozone pollution. In this study, a 15 year simulation length minimum is required to identify a distinct anthropogenic-forced signal. Therefore, we suggest that studies assessing air quality impacts use multidecadal simulations or initial condition ensembles. With natural variability, impacts attributable to climate may be difficult to discern before midcentury or under stabilization scenarios

Underestimating Internal Variability Leads to Narrow Estimates of Climate System Properties

Probabilistic estimates of climate system properties often rely on the comparison of model simulations to observed temperature records and an estimate of the internal climate variability. In this study, we investigate the sensitivity of probability distributions for climate system properties in the Massachusetts Institute of Technology Earth System Model to the internal variability estimate. In particular, we derive probability distributions using the internal variability extracted from 25 different Coupled Model Intercomparison Project Phase 5 models. We further test the sensitivity by pooling variability estimates from models with similar characteristics. We find the distributions to be highly sensitive when estimating the internal variability from a single model. When merging the variability estimates across multiple models, the distributions tend to converge to a wider distribution for all properties. This suggests that using a single model to approximate the internal climate variability produces distributions that are too narrow and do not fully represent the uncertainty in the climate system property estimates

Natural Variability in Projections of Climate Change Impacts on Fine Particulate Matter Pollution

Variations in meteorology associated with climate change can impact fine particulate matter (PM2.5) pollution by affecting natural emissions, atmospheric chemistry, and pollutant transport. However, substantial discrepancies exist among model-based projections of PM2.5 impacts driven by anthropogenic climate change. Natural variability can significantly contribute to the uncertainty in these estimates. Using a large ensemble of climate and atmospheric chemistry simulations, we evaluate the influence of natural variability on projections of climate change impacts on PM2.5 pollution in the United States. We find that natural variability in simulated PM2.5 can be comparable or larger than reported estimates of anthropogenic-induced climate impacts. Relative to mean concentrations, the variability in projected PM2.5 climate impacts can also exceed that of ozone impacts. Based on our projections, we recommend that analyses aiming to isolate the effect climate change on PM2.5 use 10 years or more of modeling to capture the internal variability in air quality and increase confidence that the anthropogenic-forced effect is differentiated from the noise introduced by natural variability. Projections at a regional scale or under greenhouse gas mitigation scenarios can require additional modeling to attribute impacts to climate change. Adequately considering natural variability can be an important step toward explaining the inconsistencies in estimates of climate-induced impacts on PM2.5. Improved treatment of natural variability through extended modeling lengths or initial condition ensembles can reduce uncertainty in air quality projections and improve assessments of climate policy risks and benefits

A framework for modeling uncertainty in regional climate change

In this study, we present a new modeling framework and a large ensemble of climate projections to investigate the uncertainty in regional climate change over the United States (US) associated with four dimensions of uncertainty. The sources of uncertainty considered in this framework are the emissions projections, global climate system parameters, natural variability and model structural uncertainty. The modeling framework revolves around the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model with an Earth System Model of Intermediate Complexity (EMIC) (with a two-dimensional zonal-mean atmosphere). Regional climate change over the US is obtained through a two-pronged approach. First, we use the IGSM-CAM framework, which links the IGSM to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Second, we use a pattern-scaling method that extends the IGSM zonal mean based on climate change patterns from various climate models. Results show that the range of annual mean temperature changes are mainly driven by policy choices and the range of climate sensitivity considered. Meanwhile, the four sources of uncertainty contribute more equally to end-of-century precipitation changes, with natural variability dominating until 2050. For the set of scenarios used in this study, the choice of policy is the largest driver of uncertainty, defined as the range of warming and changes in precipitation, in future projections of climate change over the US

The Quasar-frame Velocity Distribution of Narrow CIV Absorbers

We report on a survey for narrow (FWHM < 600 km/s) CIV absorption lines in a sample of bright quasars at redshifts $1.8 \le z < 2.25$ in the Sloan Digital Sky Survey. Our main goal is to understand the relationship of narrow CIV absorbers to quasar outflows and, more generally, to quasar environments. We determine velocity zero-points using the broad MgII emission line, and then measure the absorbers' quasar-frame velocity distribution. We examine the distribution of lines arising in quasar outflows by subtracting model fits to the contributions from cosmologically intervening absorbers and absorption due to the quasar host galaxy or cluster environment. We find a substantial number ($\ge 43\pm6$ per cent) of absorbers with REW $> 0.3$ \AA in the velocity range +750 km/s \la v \la +12000 km/s are intrinsic to the AGN outflow. This `outflow fraction' peaks near $v=+2000$ km/s with a value of $f_{outflow} \simeq 0.81 \pm 0.13$. At velocities below $v \approx +2000$ km/s the incidence of outflowing systems drops, possibly due to geometric effects or to the over-ionization of gas that is nearer the accretion disk. Furthermore, we find that outflow-absorbers are on average broader and stronger than cosmologically-intervening systems. Finally, we find that $\sim 14$ per cent of the quasars in our sample exhibit narrow, outflowing CIV absorption with REW $> 0.3$\AA, slightly larger than that for broad absorption line systems.Comment: 11 pages, 9 figures, accepted for publication in MNRA