Despite the recently reported beginning of a recovery in global stratospheric ozone (O 3 ), an unexpected O 3 decline in the tropical mid-stratosphere (around 30-35 km altitude) was observed in satellite measurements during the first decade of the 21st century. We use SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) measurements for the period 2004-2012 to confirm the significant O 3 decline. The SCIAMACHY observations show that the decrease in O 3 is accompanied by an increase in NO 2 .To reveal the causes of these observed O 3 and NO 2 changes, we performed simulations with the TOMCAT 3-D chemistry-transport model (CTM) using different chemical and dynamical forcings. For the 2004-2012 time period, the TOMCAT simulations reproduce the SCIAMACHYobserved O 3 decrease and NO 2 increase in the tropical mid-stratosphere. The simulations suggest that the positive changes in NO 2 (around 7 % decade −1 ) are due to similar positive changes in reactive odd nitrogen (NO y ), which are a result of a longer residence time of the source gas N 2 O and increased production via N 2 O + O( 1 D). The model simulations show a negative change of 10 % decade −1 in N 2 O that is most likely due to variations in the deep branch of the Brewer-Dobson Circulation (BDC). Interestingly, modelled annual mean "age of air" (AoA) does not show any sig-nificant changes in transport in the tropical mid-stratosphere during 2004-2012.However, further analysis of model results demonstrates significant seasonal variations. During the autumn months (September-October) there are positive AoA changes that imply transport slowdown and a longer residence time of N 2 O allowing for more conversion to NO y , which enhances O 3 loss. During winter months (January-February) there are negative AoA changes, indicating faster N 2 O transport and less NO y production. Although the variations in AoA over a year result in a statistically insignificant linear change, nonlinearities in the chemistry-transport interactions lead to a statistically significant negative N 2 O change.
Abstract. In this paper we assessed the influence of biomass burning during forest fires throughout summer (1 June-31 August) 2010 on aerosol abundance, dynamics, and its properties over Ukraine. We also considered influences and effects over neighboring countries: European Russia, Estonia, Belarus, Poland, Moldova, and Romania.We used MODIS satellite instrument data to study fire distribution. We also used ground-based remote measurements from the international sun photometer network AERONET plus MODIS and CALIOP satellite instrument data to determine the aerosol content and optical properties in the atmosphere over Eastern Europe. We applied the HYSPLIT model to investigate atmospheric dynamics and model pathways of particle transport.As with previous studies, we found that the highest aerosol content was observed over Moscow in the first half of August 2010 due to the proximity of the most active fires. Large temporal variability of the aerosol content with pronounced pollution peaks during 7-17 August was observed at the Ukrainian (Kyiv and Sevastopol), Belarusian (Minsk), Estonian (Toravere), and Romanian (Bucharest) AERONET sites.We analyzed aerosol spatiotemporal distribution over Ukraine using MODIS AOD 550 nm and further compared with the Kyiv AERONET site sun photometer measurements; we also compared CALIOP AOD 532 nm with MODIS AOD data. We analyzed vertical distribution of aerosol extinction at 532 nm, retrieved from CALIOP measurements, for the territory of Ukraine at locations where high AOD values were observed during intense fires. We estimated the influence of fires on the spectral single scattering albedo, size distribution, and complex refractive indices using Kyiv AERONET measurements performed during summer 2010.In this study we showed that the maximum AOD in the atmosphere over Ukraine recorded in summer 2010 was caused by particle transport from the forest fires in Russia. Those fires caused the highest AOD 500 nm over the Kyiv site, which in August 2010 exceeded multiannual monthly mean for the entire observational period (2008-2016, excluding 2010) by a factor of 2.2. Also, the influence of fires resulted in a change of the particle microphysics in the polluted regions.
Abstract. The climate system and its spatio-temporal changes are strongly affected by modes of long-term internal variability, like the Pacific Decadal Varibility (PDV) and the Atlantic Multidecadal Variability (AMV). As they alternate between warm and cold phases, the interplay between PDV and AMV varies over decadal to multidecadal timescales. Here, we use a causal discovery method to derive fingerprints in the Atlantic-Pacific interactions and investigate their phase-dependent changes. Dependent on the phases of PDV and AMV, different regimes with characteristic causal fingerprints are identified in reanalyses in a first step. In a second step, a regime-oriented causal model evaluation is performed to evaluate the ability of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing the observed changing interactions between PDV, AMV and their extra-tropical teleconnections. The causal graphs obtained from reanalyses detect a direct opposite-sign response from AMV on PDV when analysing the complete 1900–2014 period, and during several defined regimes within that period, for example, when AMV is going through its negative (cold) phase. Reanalyses also demonstrate a same-sign response from PDV on AMV during the cold phase of PDV. Historical CMIP6 simulations exhibit varying skill in simulating the observed causal patterns. Generally, Large Ensemble (LE) simulations showed better network similarity when PDV and AMV are out of phase compared to other regimes. Also, the two largest ensembles (in terms of number of members) were found to contain realizations with similar causal fingerprints to observations. For most regimes, these same models showed higher network similarity when compared to each other. This work shows how causal discovery on LEs complements the available diagnostics and statistics metrics of climate variability to provide a powerful tool for climate model evaluation.
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