Methane has exhibited significant interannual variability with a slowdown in its growth rate beginning in the 1980s. We use a 3‐D chemical transport model accounting for interannually varying emissions, transport, and sinks to analyze trends in CH4 from 1988 to 1997. Variations in CH4 sources were based on meteorological and country‐level socioeconomic data. An inverse method was used to optimize the strengths of sources and sinks for a base year, 1994. We present a best‐guess budget along with sensitivity tests. The analysis suggests that the sum of emissions from animals, fossil fuels, landfills, and wastewater estimated using Intergovernmental Panel on Climate Change default methodology is too high. Recent bottom‐up estimates of the source from rice paddies appear to be too low. Previous top‐down estimates of emissions from wetlands may be a factor of 2 higher than bottom‐up estimates because of possible overestimates of OH. The model captures the general decrease in the CH4 growth rate observed from 1988 to 1997 and the anomalously low growth rates during 1992–1993. The slowdown in the growth rate is attributed to a combination of slower growth of sources and increases in OH. The economic downturn in the former Soviet Union and Eastern Europe made a significant contribution to the decrease in the growth rate of emissions. The 1992–1993 anomaly can be explained by fluctuations in wetland emissions and OH after the eruption of Mount Pinatubo. The results suggest that the recent slowdown of CH4 may be temporary.

A much-cited bar chart provided by the Intergovernmental Panel on Climate Change displays the climate impact, as expressed by radiative forcing in watts per meter squared, of individual chemical species. The organization of the chart reflects the history of atmospheric chemistry, in which investigators typically focused on a single species of interest. However, changes in pollutant emissions and concentrations are a symptom, not a cause, of the primary driver of anthropogenic climate change: human activity. In this paper, we suggest organizing the bar chart according to drivers of change-that is, by economic sector. Climate impacts of tropospheric ozone, fine aerosols, aerosol-cloud interactions, methane, and long-lived greenhouse gases are considered. We quantify the future evolution of the total radiative forcing due to perpetual constant year 2000 emissions by sector, most relevant for the development of climate policy now, and focus on two specific time points, near-term at 2020 and long-term at 2100. Because sector profiles differ greatly, this approach fosters the development of smart climate policy and is useful to identify effective opportunities for rapid mitigation of anthropogenic radiative forcing.global warming | mitigation | air pollution | ozone | aerosols C arbon dioxide (CO 2 ) is the most important single contributor to global climate change and therefore mitigation policies and actions must focus on this species even though impacts may take decades to be realized. The coemitted air pollutants tropospheric ozone (O 3 ) and fine aerosol particles also significantly affect global climate but in complex ways involving both warming and cooling (1). These air pollutants, hereafter referred to as shortlived species (SLS), have short atmospheric lifetimes of days to weeks such that changes in their precursor emissions will have a swift change in radiative forcing. Their combined climate forcing effect since preindustrial times may outweigh that of CO 2 (2). Concerns about the rapid rate at which climate is changing at present place urgent emphasis on exploiting the potential benefit of SLS (especially O 3 and black carbon) reductions in global climate change. The ability to evaluate these benefits is somewhat confounded by the coemitted aerosols that cool the climate, complex interactions between gas and aerosol pollutants, and the lack of useful metrics for air pollutants with uneven spatial distributions.O 3 is a greenhouse gas that warms the atmosphere. Most fine aerosol particles, including sulfate, nitrate, and organic carbon, scatter solar radiation back to space and lead to cooling, except for black carbon, which absorbs solar radiation and warms the atmosphere. Aerosols also affect climate by modifying the properties of clouds. Hygroscopic aerosols that serve as efficient cloud condensation nuclei can increase cloud droplet number concentrations (CDNC) and reduce cloud droplet effective sizes (R eff ) if cloud liquid water content remains unchanged (3)-the first indirect effect. A consequence ...

Abstract. The tropical tropopause layer (TTL) is the transition region between the well-mixed convective troposphere and the radiatively controlled stratosphere with air masses showing chemical and dynamical properties of both regions. The representation of the TTL in meteorological reanalysis data sets is important for studying the complex interactions of circulation, convection, trace gases, clouds, and radiation. In this paper, we present the evaluation of climatological and long-term TTL temperature and tropopause characteristics in the reanalysis data sets ERA-Interim, ERA5, JRA-25, JRA-55, MERRA, MERRA-2, NCEP-NCAR (R1), and CFSR. The evaluation has been performed as part of the SPARC (Stratosphere–troposphere Processes and their Role in Climate) Reanalysis Intercomparison Project (S-RIP). The most recent atmospheric reanalysis data sets (ERA-Interim, ERA5, JRA-55, MERRA-2, and CFSR) all provide realistic representations of the major characteristics of the temperature structure within the TTL. There is good agreement between reanalysis estimates of tropical mean temperatures and radio occultation data, with relatively small cold biases for most data sets. Temperatures at the cold point and lapse rate tropopause levels, on the other hand, show warm biases in reanalyses when compared to observations. This tropopause-level warm bias is related to the vertical resolution of the reanalysis data, with the smallest bias found for data sets with the highest vertical resolution around the tropopause. Differences in the cold point temperature maximize over equatorial Africa, related to Kelvin wave activity and associated disturbances in TTL temperatures. Interannual variability in reanalysis temperatures is best constrained in the upper TTL, with larger differences at levels below the cold point. The reanalyses reproduce the temperature responses to major dynamical and radiative signals such as volcanic eruptions and the quasi-biennial oscillation (QBO). Long-term reanalysis trends in temperature in the upper TTL show good agreement with trends derived from adjusted radiosonde data sets indicating significant stratospheric cooling of around −0.5 to −1 K per decade. At 100 hPa and the cold point, most of the reanalyses suggest small but significant cooling trends of −0.3 to −0.6 K per decade that are statistically consistent with trends based on the adjusted radiosonde data sets. Advances of the reanalysis and observational systems over the last decades have led to a clear improvement in the TTL reanalysis products over time. Biases of the temperature profiles and differences in interannual variability clearly decreased in 2006, when densely sampled radio occultation data started being assimilated by the reanalyses. While there is an overall good agreement, different reanalyses offer different advantages in the TTL such as realistic profile and cold point temperature, continuous time series, or a realistic representation of signals of interannual variability. Their use in model simulations and in comparisons with climate model output should be tailored to their specific strengths and weaknesses.