scholarly journals Aerosol lifetime and climate change

2012 ◽  
Vol 12 (7) ◽  
pp. 16493-16514 ◽  
Author(s):  
G.-J. Roelofs
Keyword(s):  
Climate Change ◽  
Water Vapor ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Lapse Rate ◽  
Fundamental Relation ◽  
Wide Range

Abstract. The dominant removal mechanism for atmospheric aerosol is activation of particles to cloud droplets and subsequent wet deposition in precipitation. The atmospheric lifetime of aerosol is thus closely coupled to the atmospheric cycling time of water vapor. Changes of hydrological cycle characteristics resulting from climate change therefore directly affect aerosol lifetime, and thus the radiative forcing exerted by aerosol. This study expresses the coupling between water vapor and aerosol lifetimes and their temperature sensitivities in fundamental equations and in terms of the efficiency of processing of air by precipitating clouds. Based on climate model simulations these temperature sensitivities are estimated to be on the order of +5.3% K−1, but this may be an overestimation. Generally, shifting spatial and temporal patterns of aerosol (precursor) emissions and precipitation, and changes in aerosol activation efficiency probably influence aerosol lifetimes more than climate change itself, resulting in a wide range of simulated aerosol lifetime sensitivities between aerosol-climate models. It is possible that the climate sensitivity of models plays a role. It can be argued that climate sensitivity is intrinsically coupled with the simulated (temperature sensitivity of the) aerosol lifetime through the distribution of water vapor and aerosol between the lower and upper troposphere. This implies a fundamental relation between various feedback forcings (water vapor, lapse rate, cloud) and the aerosol forcing, illustrating the key role of the hydrological cycle in different aspects of the climate system.

scholarly journals Evaluating Climate Model Simulations of the Radiative Forcing and Radiative Response at Earth’s Surface

2019 ◽  
Vol 32 (13) ◽  
pp. 4089-4102 ◽  
Author(s):  
Ryan J. Kramer ◽  
Brian J. Soden ◽  
Angeline G. Pendergrass
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Lapse Rate ◽  
Radiation Budget ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract We analyze the radiative forcing and radiative response at Earth’s surface, where perturbations in the radiation budget regulate the atmospheric hydrological cycle. By applying a radiative kernel-regression technique to CMIP5 climate model simulations where CO2 is instantaneously quadrupled, we evaluate the intermodel spread in surface instantaneous radiative forcing, radiative adjustments to this forcing, and radiative responses to surface warming. The cloud radiative adjustment to CO2 forcing and the temperature-mediated cloud radiative response exhibit significant intermodel spread. In contrast to its counterpart at the top of the atmosphere, the temperature-mediated cloud radiative response at the surface is found to be positive in some models and negative in others. Also, the compensation between the temperature-mediated lapse rate and water vapor radiative responses found in top-of-atmosphere calculations is not present for surface radiative flux changes. Instantaneous radiative forcing at the surface is rarely reported for model simulations; as a result, intermodel differences have not previously been evaluated in global climate models. We demonstrate that the instantaneous radiative forcing is the largest contributor to intermodel spread in effective radiative forcing at the surface. We also find evidence of differences in radiative parameterizations in current models and argue that this is a significant, but largely overlooked, source of bias in climate change simulations.

scholarly journals Incorrect Asian aerosols affecting the attribution and projection of regional climate change in CMIP6 models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zhili Wang ◽  
Lei Lin ◽  
Yangyang Xu ◽  
Huizheng Che ◽  
Xiaoye Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Radiative Forcing ◽  
Impact Analysis ◽  
Climate Model ◽  
Regional Climate ◽  
Eastern China ◽  
Coupled Model ◽  
Regional Climate Change ◽  
Anthropogenic Aerosol ◽  
Wide Range

AbstractAnthropogenic aerosol (AA) forcing has been shown as a critical driver of climate change over Asia since the mid-20th century. Here we show that almost all Coupled Model Intercomparison Project Phase 6 (CMIP6) models fail to capture the observed dipole pattern of aerosol optical depth (AOD) trends over Asia during 2006–2014, last decade of CMIP6 historical simulation, due to an opposite trend over eastern China compared with observations. The incorrect AOD trend over China is attributed to problematic AA emissions adopted by CMIP6. There are obvious differences in simulated regional aerosol radiative forcing and temperature responses over Asia when using two different emissions inventories (one adopted by CMIP6; the other from Peking university, a more trustworthy inventory) to driving a global aerosol-climate model separately. We further show that some widely adopted CMIP6 pathways (after 2015) also significantly underestimate the more recent decline in AA emissions over China. These flaws may bring about errors to the CMIP6-based regional climate attribution over Asia for the last two decades and projection for the next few decades, previously anticipated to inform a wide range of impact analysis.

scholarly journals Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6

2019 ◽  
Vol 12 (7) ◽  
pp. 2727-2765 ◽  
Author(s):  
Hiroaki Tatebe ◽  
Tomoo Ogura ◽  
Tomoko Nitta ◽  
Yoshiki Komuro ◽  
Koji Ogochi ◽  
...  
Keyword(s):  
Climate Variability ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Model ◽  
Coupled Model ◽  
Climate Feedback ◽  
Climate Projections ◽  
Internal Climate Variability ◽  
Wide Range ◽  
Mean Climate

Abstract. The sixth version of the Model for Interdisciplinary Research on Climate (MIROC), called MIROC6, was cooperatively developed by a Japanese modeling community. In the present paper, simulated mean climate, internal climate variability, and climate sensitivity in MIROC6 are evaluated and briefly summarized in comparison with the previous version of our climate model (MIROC5) and observations. The results show that the overall reproducibility of mean climate and internal climate variability in MIROC6 is better than that in MIROC5. The tropical climate systems (e.g., summertime precipitation in the western Pacific and the eastward-propagating Madden–Julian oscillation) and the midlatitude atmospheric circulation (e.g., the westerlies, the polar night jet, and troposphere–stratosphere interactions) are significantly improved in MIROC6. These improvements can be attributed to the newly implemented parameterization for shallow convective processes and to the inclusion of the stratosphere. While there are significant differences in climates and variabilities between the two models, the effective climate sensitivity of 2.6 K remains the same because the differences in radiative forcing and climate feedback tend to offset each other. With an aim towards contributing to the sixth phase of the Coupled Model Intercomparison Project, designated simulations tackling a wide range of climate science issues, as well as seasonal to decadal climate predictions and future climate projections, are currently ongoing using MIROC6.

scholarly journals Do Convection-Permitting Regional Climate Models Improve Projections of Future Precipitation Change?

2017 ◽  
Vol 98 (1) ◽  
pp. 79-93 ◽  
Author(s):  
Elizabeth J. Kendon ◽  
Nikolina Ban ◽  
Nigel M. Roberts ◽  
Hayley J. Fowler ◽  
Malcolm J. Roberts ◽  
...  
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Climate Projections ◽  
Convective Storms ◽  
Wide Range

Abstract Regional climate projections are used in a wide range of impact studies, from assessing future flood risk to climate change impacts on food and energy production. These model projections are typically at 12–50-km resolution, providing valuable regional detail but with inherent limitations, in part because of the need to parameterize convection. The first climate change experiments at convection-permitting resolution (kilometer-scale grid spacing) are now available for the United Kingdom; the Alps; Germany; Sydney, Australia; and the western United States. These models give a more realistic representation of convection and are better able to simulate hourly precipitation characteristics that are poorly represented in coarser-resolution climate models. Here we examine these new experiments to determine whether future midlatitude precipitation projections are robust from coarse to higher resolutions, with implications also for the tropics. We find that the explicit representation of the convective storms themselves, only possible in convection-permitting models, is necessary for capturing changes in the intensity and duration of summertime rain on daily and shorter time scales. Other aspects of rainfall change, including changes in seasonal mean precipitation and event occurrence, appear robust across resolutions, and therefore coarse-resolution regional climate models are likely to provide reliable future projections, provided that large-scale changes from the global climate model are reliable. The improved representation of convective storms also has implications for projections of wind, hail, fog, and lightning. We identify a number of impact areas, especially flooding, but also transport and wind energy, for which very high-resolution models may be needed for reliable future assessments.

Incorrect Asian aerosols affecting the attribution and projection of regional climate change in CMIP6 models 

2021 ◽  
Author(s):  
Lei Lin ◽  
Zhili Wang ◽  
Yangyang Xu ◽  
Huizheng Che ◽  
Xiaoye Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
Radiative Forcing ◽  
Impact Analysis ◽  
Climate Model ◽  
Regional Climate ◽  
Eastern China ◽  
Coupled Model ◽  
Regional Climate Change ◽  
Anthropogenic Aerosol ◽  
Wide Range

<p><span>Anthropogenic aerosol (AA) forcing has been shown as a critical driver of climate change over Asia since the mid-20th century. Here we show that almost all Coupled Model Intercomparison Project Phase 6 (CMIP6) models fail to capture the observed dipole pattern of aerosol optical depth (AOD) trends over Asia during 2006–2014, last decade of CMIP6 historical simulation, due to an opposite trend over eastern China compared with observations. The incorrect AOD trend over China is attributed to problematic AA emissions adopted by CMIP6. There are obvious differences in simulated regional aerosol radiative forcing and temperature responses over Asia when using two different emissions inventories (one adopted by CMIP6; the other from Peking university, a more trustworthy inventory) to driving a global aerosol-climate model separately. We further show that some widely adopted CMIP6 pathways (after 2015) also significantly underestimate the more recent decline in AA emissions over China. These flaws may bring about errors to the CMIP6-based regional climate attribution over Asia for the last two decades and projection for the next few decades, previously anticipated to inform a wide range of impact analysis.</span></p>

scholarly journals FAIR v1.3: a simple emissions-based impulse response and carbon cycle model

2018 ◽  
Vol 11 (6) ◽  
pp. 2273-2297 ◽  
Author(s):  
Christopher J. Smith ◽  
Piers M. Forster ◽  
Myles Allen ◽  
Nicholas Leach ◽  
Richard J. Millar ◽  
...  
Keyword(s):  
Climate Change ◽  
Greenhouse Gases ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Future Climate Change ◽  
Climate Response ◽  
Transient Climate Response ◽  
Carbon Cycle Model ◽  
Future Projections

Abstract. Simple climate models can be valuable if they are able to replicate aspects of complex fully coupled earth system models. Larger ensembles can be produced, enabling a probabilistic view of future climate change. A simple emissions-based climate model, FAIR, is presented, which calculates atmospheric concentrations of greenhouse gases and effective radiative forcing (ERF) from greenhouse gases, aerosols, ozone and other agents. Model runs are constrained to observed temperature change from 1880 to 2016 and produce a range of future projections under the Representative Concentration Pathway (RCP) scenarios. The constrained estimates of equilibrium climate sensitivity (ECS), transient climate response (TCR) and transient climate response to cumulative CO2 emissions (TCRE) are 2.86 (2.01 to 4.22) K, 1.53 (1.05 to 2.41) K and 1.40 (0.96 to 2.23) K (1000 GtC)−1 (median and 5–95 % credible intervals). These are in good agreement with the likely Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) range, noting that AR5 estimates were derived from a combination of climate models, observations and expert judgement. The ranges of future projections of temperature and ranges of estimates of ECS, TCR and TCRE are somewhat sensitive to the prior distributions of ECS∕TCR parameters but less sensitive to the ERF from a doubling of CO2 or the observational temperature dataset used to constrain the ensemble. Taking these sensitivities into account, there is no evidence to suggest that the median and credible range of observationally constrained TCR or ECS differ from climate model-derived estimates. The range of temperature projections under RCP8.5 for 2081–2100 in the constrained FAIR model ensemble is lower than the emissions-based estimate reported in AR5 by half a degree, owing to differences in forcing assumptions and ECS∕TCR distributions.

scholarly journals Projected Response of Tropical Cyclone Intensity and Intensification in a Global Climate Model

2018 ◽  
Vol 31 (20) ◽  
pp. 8281-8303 ◽  
Author(s):  
Kieran Bhatia ◽  
Gabriel Vecchi ◽  
Hiroyuki Murakami ◽  
Seth Underwood ◽  
James Kossin
Keyword(s):  
Climate Change ◽  
Radiative Forcing ◽  
Climate Models ◽  
Control Experiment ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
First Century ◽  
Geophysical Fluid Dynamics Laboratory ◽  
Twenty First Century

As one of the first global coupled climate models to simulate and predict category 4 and 5 (Saffir–Simpson scale) tropical cyclones (TCs) and their interannual variations, the High-Resolution Forecast-Oriented Low Ocean Resolution (HiFLOR) model at the Geophysical Fluid Dynamics Laboratory (GFDL) represents a novel source of insight on how the entire TC intensification distribution could be transformed because of climate change. In this study, three 70-yr HiFLOR experiments are performed to identify the effects of climate change on TC intensity and intensification. For each of the experiments, sea surface temperature (SST) is nudged to different climatological targets and atmospheric radiative forcing is specified, allowing us to explore the sensitivity of TCs to these conditions. First, a control experiment, which uses prescribed climatological ocean and radiative forcing based on observations during the years 1986–2005, is compared to two observational records and evaluated for its ability to capture the mean TC behavior during these years. The simulated intensification distributions as well as the percentage of TCs that become major hurricanes show similarities with observations. The control experiment is then compared to two twenty-first-century experiments, in which the climatological SSTs from the control experiment are perturbed by multimodel projected SST anomalies and atmospheric radiative forcing from either 2016–35 or 2081–2100 (RCP4.5 scenario). The frequency, intensity, and intensification distribution of TCs all shift to higher values as the twenty-first century progresses. HiFLOR’s unique response to climate change and fidelity in simulating the present climate lays the groundwork for future studies involving models of this type.

scholarly journals Including the efficacy of land ice changes in deriving climate sensitivity from paleodata

2018 ◽  
Author(s):  
Lennert B. Stap ◽  
Peter Köhler ◽  
Gerrit Lohmann
Keyword(s):  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Temperature Anomaly ◽  
Global Temperature ◽  
Radiative Forcings ◽  
Intercomparison Project ◽  
The Impact

Abstract. The influence of long-term processes in the climate system, such as land ice changes, has to be compensated for when comparing climate sensitivity derived from paleodata with equilibrium climate sensitivity (ECS) calculated by climate models, which is only generated by a CO2 change. Several recent studies found that the impact these long-term processes have on global temperature cannot be quantified directly through the global radiative forcing they induce. This renders the approach of deconvoluting paleotemperatures through a partitioning based on radiative forcings inaccurate. Here, we therefore implement an efficacy factor ε[LI], that relates the impact of land ice changes on global temperature to that of CO2 changes, in our calculation of climate sensitivity from paleodata. We apply our new approach to a proxy-inferred paleoclimate dataset, and find an equivalent ECS of 5.6 ± 1.3 K per CO2 doubling. The substantial uncertainty herein is generated by the range in ε[LI] we use, which is based on a multi-model assemblage of simulated relative influences of land ice changes on the Last Glacial Maximum (LGM) temperature anomaly (46 ± 14 %). The low end of our ECS estimate, which concurs with estimates from other approaches, tallies with a large influence for land ice changes. To separately assess this influence, we analyse output of the PMIP3 climate model intercomparison project. From this data, we infer a functional intermodel relation between global and high-latitude temperature changes at the LGM with respect to the pre-industrial climate, and the temperature anomaly caused by a CO2 change. Applying this relation to our dataset, we find a considerable 64 % influence for land ice changes on the LGM temperature anomaly. This is even higher than the range used before, and leads to an equivalent ECS of 3.8 K per CO2 doubling. Together, our results suggest that land ice changes play a key role in the variability of Late Pleistocene temperatures.

scholarly journals Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6

2018 ◽  
Author(s):  
Hiroaki Tatebe ◽  
Tomoo Ogura ◽  
Tomoko Nitta ◽  
Yoshiki Komuro ◽  
Koji Ogochi ◽  
...  
Keyword(s):  
Climate Variability ◽  
Climate Sensitivity ◽  
Radiative Forcing ◽  
Climate Model ◽  
Coupled Model ◽  
Climate Feedback ◽  
Climate Projections ◽  
Internal Climate Variability ◽  
Wide Range ◽  
Mean Climate

Abstract. The sixth version of the Model for Interdisciplinary Research on Climate (MIROC), called MIROC6, was cooperatively developed by a Japanese modeling community. In the present manuscript, simulated mean climate, internal climate variability, and climate sensitivity in MIROC6 are evaluated and briefly summarized in comparison with the previous version of our climate model (MIROC5) and observations. The results show that overall reproducibility of mean climate and internal climate variability in MIROC6 is better than that in MIROC5. The tropical climate systems (e.g., summertime precipitation in the western Pacific and the eastward propagating Madden-Julian Oscillation) and the mid-latitude atmospheric circulations (e.g., the westerlies, the polar night jet, and troposphere-stratosphere interactions) are significantly improved in MIROC6. These improvements can be attributed to the newly implemented parameterization for shallow convective processes and to the directly resolved stratosphere. While there are significant differences in climates and variabilities between the two models, the effective climate sensitivity of 2.5 K remains the same because the differences in radiative forcing and climate feedback tend to offset each other. With an aim towards contributing to the sixth phase of the Coupled Model Intercomparison Project, designated simulations tackling a wide range of climate science issues, as well as seasonal-to-decadal climate predictions and future climate projections, are currently ongoing using MIROC6.

scholarly journals Changing the Climate Sensitivity of an Atmospheric General Circulation Model through Cloud Radiative Adjustment

2012 ◽  
Vol 25 (19) ◽  
pp. 6567-6584 ◽  
Author(s):  
Andrei P. Sokolov ◽  
Erwan Monier
Keyword(s):  
Climate Change ◽  
Climate Sensitivity ◽  
General Circulation ◽  
Climate Model ◽  
Circulation Model ◽  
Climate Projections ◽  
Regional Patterns ◽  
Adjustment Method ◽  
Wide Range ◽  
Perturbed Physics

Abstract Conducting probabilistic climate projections with a particular climate model requires the ability to vary the model’s characteristics, such as its climate sensitivity. In this study, the authors implement and validate a method to change the climate sensitivity of the National Center for Atmospheric Research (NCAR) Community Atmosphere Model, version 3 (CAM3), through cloud radiative adjustment. Results show that the cloud radiative adjustment method does not lead to physically unrealistic changes in the model’s response to an external forcing, such as doubling CO2 concentrations or increasing sulfate aerosol concentrations. Furthermore, this method has some advantages compared to the traditional perturbed physics approach. In particular, the cloud radiative adjustment method can produce any value of climate sensitivity within the wide range of uncertainty based on the observed twentieth century climate change. As a consequence, this method allows Monte Carlo–type probabilistic climate forecasts to be conducted where values of uncertain parameters not only cover the whole uncertainty range, but cover it homogeneously. Unlike the perturbed physics approach that can produce several versions of a model with the same climate sensitivity but with very different regional patterns of change, the cloud radiative adjustment method can only produce one version of the model with a specific climate sensitivity. As such, a limitation of this method is that it cannot cover the full uncertainty in regional patterns of climate change.

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