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

Solar variability and climate

Recent precise observations of solar global parameters are used to calibrate an upgraded solar model which takes into account magnetic fields in the solar interior. Historical data about sunspot numbers (from 1500 to the present) and solar radius changes (between 1715 and 1979) are used to compute solar variability on years to centuries timescales. The results show that although the 11 year variability of the total irradiance is of the order of 0.1%, additional, longer lived changes of the order of 0.1% may have occurred in the past centuries. These could, for example, account for the occurrence of climate excursions such as little ice ages.Comment: LaTeX, JGR preprint with AGU++ v16.b and AGUTeX 5.0, use packages graphicx; 6 pages, 4 figures, submitted to JGR-Space physic

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

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

Solar Irradiance Variability and Climate

The brightness of the Sun varies on all time scales on which it has been observed, and there is increasing evidence that it has an influence on climate. The amplitudes of such variations depend on the wavelength and possibly on the time scale. Although many aspects of this variability are well established, the exact magnitude of secular variations (going beyond a solar cycle) and the spectral dependence of variations are under discussion. The main drivers of solar variability are thought to be magnetic features at the solar surface. The climate reponse can be, on a global scale, largely accounted for by simple energetic considerations, but understanding the regional climate effects is more difficult. Promising mechanisms for such a driving have been identified, including through the influence of UV irradiance on the stratosphere and dynamical coupling to the surface. Here we provide an overview of the current state of our knowledge, as well as of the main open questions

How Predictable are Temperature-series Undergoing Noise-controlled Dynamics in the Mediterranean

Mediterranean is thought to be sensitive to global climate change, but its future interdecadal variability is uncertain for many climate models. A study was made of the variability of the winter temperature over the Mediterranean Sub-regional Area (MSA), employing a reconstructed temperature series covering the period 1698 to 2010. This paper describes the transformed winter temperature data performed via Empirical Mode Decomposition for the purposes of noise reduction and statistical modeling. This emerging approach is discussed to account for the internal dependence structure of natural climate variability

Comparing the Model-simulated Global Warming Signal to Observations Using Empirical Estimates of Unforced Noise

The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20th century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario’s forced signal, but is likely inconsistent with the steepest emission scenario’s forced signal

Radiation variability and correlation studies

The determination of variability of the emitted and reflected components of outgoing radiation from the earth-atmosphere system is discussed. The effects of variability on climate and weather are considered, and meteorological and climate variables to be correlated with radiation budget measurements are determined