AGW Observer

Observations of anthropogenic global warming

New research – composition of atmosphere (August 15, 2016)

Posted by Ari Jokimäki on August 15, 2016

Some of the latest papers on composition of atmosphere are shown below. First a few highlighted papers with abstracts and then a list of some other papers. If this subject interests you, be sure to check also the other papers – they are by no means less interesting than the highlighted ones.

Highlights

Role of OH variability in the stalling of the global atmospheric CH4 growth rate from 1999 to 2006 (McNorton et al. 2016) http://www.atmos-chem-phys.net/16/7943/2016/

Abstract: The growth in atmospheric methane (CH4) concentrations over the past 2 decades has shown large variability on a timescale of several years. Prior to 1999 the globally averaged CH4 concentration was increasing at a rate of 6.0 ppb yr−1, but during a stagnation period from 1999 to 2006 this growth rate slowed to 0.6 ppb yr−1. From 2007 to 2009 the growth rate again increased to 4.9 ppb yr−1. These changes in growth rate are usually ascribed to variations in CH4 emissions. We have used a 3-D global chemical transport model, driven by meteorological reanalyses and variations in global mean hydroxyl (OH) concentrations derived from CH3CCl3 observations from two independent networks, to investigate these CH4 growth variations. The model shows that between 1999 and 2006 changes in the CH4 atmospheric loss contributed significantly to the suppression in global CH4 concentrations relative to the pre-1999 trend. The largest factor in this is relatively small variations in global mean OH on a timescale of a few years, with minor contributions of atmospheric transport of CH4 to its sink region and of atmospheric temperature. Although changes in emissions may be important during the stagnation period, these results imply a smaller variation is required to explain the observed CH4 trends. The contribution of OH variations to the renewed CH4 growth after 2007 cannot be determined with data currently available.

Diverse policy implications for future ozone and surface UV in a changing climate (Butler et al. 2016) http://iopscience.iop.org/article/10.1088/1748-9326/11/6/064017/meta

Abstract: Due to the success of the Montreal Protocol in limiting emissions of ozone-depleting substances, concentrations of atmospheric carbon dioxide, nitrous oxide, and methane will control the evolution of total column and stratospheric ozone by the latter half of the 21st century. As the world proceeds down the path of reducing climate forcing set forth by the 2015 Conference of the Parties to the United Nations Framework Convention on Climate Change (COP 21), a broad range of ozone changes are possible depending on future policies enacted. While decreases in tropical stratospheric ozone will likely persist regardless of the future emissions scenario, extratropical ozone could either remain weakly depleted or even increase well above historical levels, with diverse implication for ultraviolet (UV) radiation. The ozone layer’s dependence on future emissions of these gases creates a complex policy decision space for protecting humans and ecosystems, which includes unexpected options such as accepting nitrous oxide emissions in order to maintain historical column ozone and surface UV levels.

Changes in surface aerosol extinction trends over China during 1980–2013 inferred from quality-controlled visibility data (Li et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016GL070201/abstract

Abstract: Pollution in China has been attracting extensive attention both globally and regionally, especially due to the perceptually worsening “smog” condition in recent years. We use routine visibility measurements from 1980 to 2013 at 272 WMO stations in China to assess the temporal changes in the magnitude and the sign of pollution trends. A strict and comprehensive quality control procedure is enforced by considering several issues not typically addressed in previous studies. Two methods are used to independently estimate the trend and its significance level. Results show that in general, a strong increase in Aerosol Extinction Coefficient (AEC) over the majority of China is observed in the 1980s, followed by a moderate decrease in the 1990s, another increase in the 2000s, and a shift to decrease since around 2006 for some regions. Seasonally, winter and fall trends appear to be the strongest, while summer has the lowest trend.

The millennium water vapour drop in chemistry–climate model simulations (Brinkop et al. 2016) http://www.atmos-chem-phys.net/16/8125/2016/

Abstract: This study investigates the abrupt and severe water vapour decline in the stratosphere beginning in the year 2000 (the “millennium water vapour drop”) and other similarly strong stratospheric water vapour reductions by means of various simulations with the state-of-the-art Chemistry-Climate Model (CCM) EMAC (ECHAM/MESSy Atmospheric Chemistry Model). The model simulations differ with respect to the prescribed sea surface temperatures (SSTs) and whether nudging is applied or not. The CCM EMAC is able to most closely reproduce the signature and pattern of the water vapour drop in agreement with those derived from satellite observations if the model is nudged. Model results confirm that this extraordinary water vapour decline is particularly obvious in the tropical lower stratosphere and is related to a large decrease in cold point temperature. The drop signal propagates under dilution to the higher stratosphere and to the poles via the Brewer–Dobson circulation (BDC). We found that the driving forces for this significant decline in water vapour mixing ratios are tropical sea surface temperature (SST) changes due to a coincidence with a preceding strong El Niño–Southern Oscillation event (1997/1998) followed by a strong La Niña event (1999/2000) and supported by the change of the westerly to the easterly phase of the equatorial stratospheric quasi-biennial oscillation (QBO) in 2000. Correct (observed) SSTs are important for triggering the strong decline in water vapour. There are indications that, at least partly, SSTs contribute to the long period of low water vapour values from 2001 to 2006. For this period, the specific dynamical state of the atmosphere (overall atmospheric large-scale wind and temperature distribution) is important as well, as it causes the observed persistent low cold point temperatures. These are induced by a period of increased upwelling, which, however, has no corresponding pronounced signature in SSTs anomalies in the tropics. Our free-running simulations do not capture the drop as observed, because a) the cold point temperature has a low bias and thus the water vapour variability is reduced and b) because they do not simulate the appropriate dynamical state. Large negative water vapour declines are also found in other years and seem to be a feature which can be found after strong combined El Niño/La Niña events if the QBO west phase during La Niña changes to the east phase.

Evaluation of 4 years of continuous δ13C(CO2) data using a moving Keeling plot method (Vardag, Hammer & Levin, 2016)
http://www.biogeosciences.net/13/4237/2016/

Abstract: Different carbon dioxide (CO2) emitters can be distinguished by their carbon isotope ratios. Therefore measurements of atmospheric δ13C(CO2) and CO2 concentration contain information on the CO2 source mix in the catchment area of an atmospheric measurement site. This information may be illustratively presented as the mean isotopic source signature. Recently an increasing number of continuous measurements of δ13C(CO2) and CO2 have become available, opening the door to the quantification of CO2 shares from different sources at high temporal resolution. Here, we present a method to compute the CO2 source signature (δS) continuously and evaluate our result using model data from the Stochastic Time-Inverted Lagrangian Transport model. Only when we restrict the analysis to situations which fulfill the basic assumptions of the Keeling plot method does our approach provide correct results with minimal biases in δS. On average, this bias is 0.2 ‰ with an interquartile range of about 1.2 ‰ for hourly model data. As a consequence of applying the required strict filter criteria, 85 % of the data points – mainly daytime values – need to be discarded. Applying the method to a 4-year dataset of CO2 and δ13C(CO2) measured in Heidelberg, Germany, yields a distinct seasonal cycle of δS. Disentangling this seasonal source signature into shares of source components is, however, only possible if the isotopic end members of these sources – i.e., the biosphere, δbio, and the fuel mix, δF – are known. From the mean source signature record in 2012, δbio could be reliably estimated only for summer to (−25.0 ± 1.0) ‰ and δF only for winter to (−32.5 ± 2.5) ‰. As the isotopic end members δbio and δF were shown to change over the season, no year-round estimation of the fossil fuel or biosphere share is possible from the measured mean source signature record without additional information from emission inventories or other tracer measurements.

Other papers

Intercomparison of in situ NDIR and column FTIR measurements of CO2 at Jungfraujoch (Schibig et al. 2016) http://www.atmos-chem-phys.net/16/9935/2016/

Evaluation of 4 years of continuous δ13C(CO2) data using a moving Keeling plot method (Vardag, Hammer & Levin, 2016) http://www.biogeosciences.net/13/4237/2016/

Intra-seasonal variability of atmospheric CO2 concentrations over India during summer monsoons (Kumar et al. 2016) http://www.sciencedirect.com/science/article/pii/S1352231016305428

Impact of ENSO on variability of AIRS retrieved CO2 over India (Kumar et al. 2016) http://www.sciencedirect.com/science/article/pii/S1352231016305209

Large XCH4 anomaly in summer 2013 over northeast Asia observed by GOSAT (Ishizawa et al. 2016) http://www.atmos-chem-phys.net/16/9149/2016/

Can we detect regional methane anomalies? A comparison between three observing systems (Cressot et al. 2016) http://www.atmos-chem-phys.net/16/9089/2016/

Non-homogeneous vertical distribution of methane over Indian region using surface, aircraft and satellite based data (Kavitha & Nair, 2016) http://www.sciencedirect.com/science/article/pii/S1352231016305015

A probabilistic study of the return of stratospheric ozone to 1960 levels (Södergren et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016GL069700/abstract

The representation of solar cycle signals in stratospheric ozone – Part 1: A comparison of recently updated satellite observations (Maycock et al. 2016) http://www.atmos-chem-phys.net/16/10021/2016/

Summer ozone concentrations in the vicinity of the Great Salt Lake (Horel et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/asl.680/abstract

Impact of emissions and +2 °C climate change upon future ozone and nitrogen dioxide over Europe (Watson et al. 2016) http://www.sciencedirect.com/science/article/pii/S1352231016305714

Natural and Anthropogenic Aerosol Trends from Satellite and Surface Observations and Model Simulations over the North Atlantic Ocean from 2002 to 2012 (Jongeward et al. 2016) http://journals.ametsoc.org/doi/abs/10.1175/JAS-D-15-0308.1

Aerosol Lidar Observations of Atmospheric Mixing in Los Angeles: Climatology and Implications for Greenhouse Gas Observations (Ware et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016JD024953/abstract

Future aerosol emissions: a multi-model comparison (Smith et al. 2016) http://rd.springer.com/article/10.1007%2Fs10584-016-1733-y

Multi-Year Study of the Dependence of Sea Salt Aerosol on Wind Speed and Sea Ice Conditions in the Coastal Arctic (May et al. 2016) http://onlinelibrary.wiley.com/doi/10.1002/2016JD025273/abstract

Effects of climate changes on dust aerosol over East Asia from RegCM3 (Zhang et al. 2016) http://www.sciencedirect.com/science/article/pii/S1674927816300053

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

 
%d bloggers like this: