Analysis of polarimetric satellite measurements suggests stronger cooling due to aerosol-cloud interactions

Anthropogenic aerosol emissions lead to an increase in the amount of Cloud Condensation Nuclei and consequently an increase in cloud droplet number concentration and cloud albedo. The cor-responding negative radiative forcing due to aerosol cloud interactions (RFaci) is one of the most uncertain radiative forcing terms as reported in the 5th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). We show that previous observation-based studies underestimate aerosol-cloud interactions because they used measurements of aerosol optical properties that are not directly related to cloud formation and are hampered by measurement uncertainties. We have overcome this problem by the use of new polarimetric satellite retrievals of the relevant aerosol properties (aerosol number, size, shape). The resulting estimate of RFaci= -1.14 Wm−2(range be-tween -0.84 and -1.72 Wm−2) is more than a factor 2 stronger than the IPCC estimate that include

Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observations during 2003-2016

The growth rate of atmospheric carbon dioxide (CO2) reflects the net effect of emissions and uptake resulting from anthropogenic and natural carbon sources and sinks. Annual mean CO2 growth rates have been determined from satellite retrievals of column-averaged dry-air mole fractions of CO2, i.e. XCO2, for the years 2003 to 2016. The XCO2 growth rates agree with National Oceanic and Atmospheric Administration (NOAA) growth rates from CO2 surface observations within the uncertainty of the satellite-derived growth rates (mean difference ± standard deviation: 0.0±0.3 ppm year−1; R: 0.82). This new and independent data set confirms record-large growth rates of around 3 ppm year−1 in 2015 and 2016, which are attributed to the 2015–2016 El Niño. Based on a comparison of the satellite-derived growth rates with human CO2 emissions from fossil fuel combustion and with El Niño Southern Oscillation (ENSO) indices, we estimate by how much the impact of ENSO dominates the impact of fossil-fuel-burning-related emissions in explaining the variance of the atmospheric CO2 growth rate. Our analysis shows that the ENSO impact on CO2 growth rate variations dominates that of human emissions throughout the period 2003–2016 but in particular during the period 2010–2016 due to strong La Niña and El Niño events. Using the derived growth rates and their uncertainties, we estimate the probability that the impact of ENSO on the variability is larger than the impact of human emissions to be 63 % for the time period 2003–2016. If the time period is restricted to 2010–2016, this probability increases to 94 %

Evaluation of GOME ozone profiles from nine different algorithms

An evaluation is made of ozone profiles retrieved from measurements of the nadir-viewing Global Ozone Monitoring Experiment (GOME) instrument. Currently four different approaches are used to retrieve ozone profile information from GOME measurements, which differ in the use of external information and a priori constraints. In total nine different algorithms will be evaluated exploiting the Optimal Estimation (Royal Netherlands Meteorological Institute, Rutherford Appleton Laboratory, University of Bremen, National Oceanic and Atmospheric Administration, Smithsonian Astrophysical Observatory), Phillips-Tikhonov Regularization (Space Research Organization Netherlands), Neural Network (Center for Solar Energy and Hydrogen Research, Tor Vergata University), and Data Assimilation (German Aerospace Center) approaches. Analysis tools are used to interpret data sets that provide averaging kernels. In the interpretation of these data, the focus is on the vertical resolution, the indicative altitude of the retrieved value, and the fraction of a priori information. The evaluation is completed with a comparison of the results to lidar data from the NDSC (Network for Detection of Stratospheric Change) stations in Andoya (Norway), Observatoire Haute Provence (France), Mauna Loa (USA), Lauder (New Zealand) and Dumont d’Urville (Antarctic) for the years 1997–1999. In total the comparison involves nearly 1000 ozone profiles, and allows the analysis of GOME data measured in different global regions and hence observational circumstances. The main conclusion of this paper is that unambiguous information on the ozone profile can at best be retrieved in the altitude range 15–48 km with a vertical resolution of 10 to 15 km, precision of 5–10%, and a bias up to 5% or 20% depending on the success of recalibration of the input spectra. The sensitivity of retrievals to ozone at lower altitudes varies from scheme to scheme and includes significant influence from a priori assumptions