ALBERT

Description: [1]  An aircraft field study [POST; Physics Of Stratocumulus Top] was conducted off the central California Coast in July and August of 2008 to deal with the known difficulty of measuring entrainment rates in the radiatively important stratocumulus [Sc] prevalent in that area. The CIRPAS Twin Otter research aircraft flew 15 quasi-Lagrangian flights in unbroken Sc and carried a full complement of probes including three high data-rate probes UFT-M [Ultra-Fast Temperature probe], PVM [Particulate Volume Monitor] probe, and gust probe. The probes’ co-location near the nose of the Twin Otter permitted estimation of entrainment fluxes and rates with an incloud resolution of 1 m. [2]  Results include the following: Application of the conditional-sampling variation of classical mixed layer theory for calculating the entrainment rate into cloud top for POST flights is shown to be inadequate for most of the Sc. Estimated rates resemble previous results after theory is modified to take into account both entrainment and evaporation at cloud top given the strong wind shear and mixing at cloud top. Entrainment rates show a tendency to decrease for large shear values, and the largest rates are for the smallest temperature jumps across the inversion. Measurements indirectly suggest that entrained parcels are primarily cooled by infrared flux divergence rather than cooling from droplet evaporation, while detrainment at cloud top causes droplet evaporation and cooling in the EIL [Entrainment Interface Layer] above cloud top.

Description: The preconditioning of major sudden stratospheric warmings (SSWs) is investigated with two long time series using reanalysis (ERA-40) and model (MAECHAM5/MPI-OM) data. Applying planetary wave analysis, we distinguish between wavenumber-1 and wavenumber-2 major SSWs based on the wave activity of zonal wavenumbers 1 and 2 during the prewarming phase. For this analysis an objective criterion to identify and classify the preconditioning of major SSWs is developed. Major SSWs are found to occur with a frequency of six and seven events per decade in the reanalysis and in the model, respectively, thus highlighting the ability of MAECHAM5/MPI-OM to simulate the frequency of major SSWs realistically. However, from these events only one quarter are wavenumber-2 major warmings, representing a low (∼0.25) wavenumber-2 to wavenumber-1 major SSW ratio. Composite analyses for both data sets reveal that the two warming types have different dynamics; while wavenumber-1 major warmings are preceded only by an enhanced activity of the zonal wavenumber-1, wavenumber-2 events are either characterized by only the amplification of zonal wavenumber-2 or by both zonal wavenumber-1 and zonal wavenumber-2, albeit at different time intervals. The role of tropospheric blocking events influencing these two categories of major SSWs is evaluated in the next step. Here, the composite analyses of both reanalysis and model data reveal that blocking events in the Euro-Atlantic sector mostly lead to the development of wavenumber-1 major warmings. The blocking–wavenumber-2 major warming connection can only be statistical reliable analyzed with the model time series, demonstrating that blocking events in the Pacific region mostly precede wavenumber-2 major SSWs.

Description: Endocannabinoid signaling critically regulates emotional and motivational states via activation of cannabinoid receptor 1 (CB1) in the brain. The nucleus accumbens (NAc) functions to gate emotional and motivational responses. Although expression of CB1 in the NAc is low, manipulation of CB1 signaling within the NAc triggers robust emotional/motivational alterations related...

Description: The stratospheric climate and variability from simulations of sixteen chemistry-climate models is evaluated. On average the polar night jet is well reproduced though its variability is less well reproduced with a large spread between models. Polar temperature biases are less than 5 K except in the Southern Hemisphere (SH) lower stratosphere in spring. The accumulated area of low temperatures responsible for polar stratospheric cloud formation is accurately reproduced for the Antarctic but underestimated for the Arctic. The shape and position of the polar vortex is well simulated, as is the tropical upwelling in the lower stratosphere. There is a wide model spread in the frequency of major sudden stratospheric warnings (SSWs), late biases in the breakup of the SH vortex, and a weak annual cycle in the zonal wind in the tropical upper stratosphere. Quantitatively, “metrics” indicate a wide spread in model performance for most diagnostics with systematic biases in many, and poorer performance in the SH than in the Northern Hemisphere (NH). Correlations were found in the SH between errors in the final warming, polar temperatures, the leading mode of variability, and jet strength, and in the NH between errors in polar temperatures, frequency of major SSWs, and jet strength. Models with a stronger QBO have stronger tropical upwelling and a colder NH vortex. Both the qualitative and quantitative analysis indicate a number of common and long-standing model problems, particularly related to the simulation of the SH and stratospheric variability.

Description: [1]  Monthly zonal mean climatologies of atmospheric measurements from satellite instruments can have biases due to the non-uniform sampling of the atmosphere by the instruments. We characterize potential sampling biases in stratospheric trace gas climatologies of the Stratospheric Processes and their Role in Climate (SPARC) Data Initiative using chemical fields from a chemistry climate model simulation and sampling patterns from 16 satellite-borne instruments. The exercise is performed for the long-lived stratospheric trace gases O 3 and H 2 O. Monthly sample biases for O 3 exceed 10% for many instruments in the high latitude stratosphere and in the upper troposphere/lower stratosphere, while annual mean sampling biases reach values of up to 20% in the same regions for some instruments. Sampling biases for H 2 O are generally smaller than for O 3 , although still notable in the upper troposphere/lower stratosphere and Southern Hemisphere high latitudes. The most important mechanism leading to monthly sampling bias is the non-uniform temporal sampling of many instruments, i.e., the fact that for many instruments, monthly means are produced from measurements which span less than the full month in question. Similarly, annual mean sampling biases are well explained by non-uniformity in the month-to-month sampling by different instruments. Non-uniform sampling in latitude and longitude are shown to also lead to non-negligible sampling biases, which are most relevant for climatologies which are otherwise free of sampling biases due to non-uniform temporal sampling.

Description: [1]  A comprehensive quality assessment of the ozone products from 18 limb-viewing satellite instruments is provided by means of a detailed inter-comparison. The ozone climatologies in the form of monthly zonal mean time series covering the upper troposphere to lower mesosphere are obtained from LIMS, SAGE I, SAGE II, UARS-MLS, HALOE, POAM II, POAM III, SMR, OSIRIS, SAGE III, MIPAS, GOMOS, SCIAMACHY, ACE-FTS, ACE-MAESTRO, Aura-MLS, HIRDLS, and SMILES within 1978-2010. The inter-comparisons focus on mean biases based on monthly and annual zonal mean fields, on inter-annual variability and on seasonal cycles. Additionally, the physical consistency of the data sets is tested through diagnostics of the quasi-biennial oscillation and the Antarctic ozone hole. The comprehensive evaluations reveal that the uncertainty in our knowledge of the atmospheric ozone mean state is smallest in the tropical middle stratosphere and in the midlatitude lower/middle stratosphere, where we find a 1σ multi-instrument spread of less than ±5%. While the overall agreement among the climatological data sets is very good for large parts of the stratosphere, individual discrepancies have been identified including unrealistic month-to-month fluctuations, large biases in particular atmospheric regions, or inconsistencies in the seasonal cycle. Notable differences between the data sets exist in the tropical lower stratosphere and at high latitudes, with a multi-instrument spread of ±30% at the tropical tropopause and ±15% at polar latitudes. In particular, large relative differences are identified in the Antarctic polar cap during the time of the ozone hole, with a spread between the monthly zonal mean fields of ±50%. Differences between the climatological data sets are suggested to be partially related to inter-instrumental differences in vertical resolution and geographical sampling. The evaluations as a whole provide guidance on what data sets are the most reliable for applications such as studies of ozone variability, model-measurement comparisons and detection of long-term trends. A detailed comparison versus SAGE II data is presented, which can help identify suitable candidates for long-term data merging studies.

Description: A long-standing problem in cloud physics is the broadening of the cloud droplet spectrum in warm cumulus clouds. To isolate the changes of the droplet size distribution (DSD) due to entrainment and turbulent mixing, we used the Explicit Mixing Parcel Model (EMPM). The EMPM explicitly represents spatial variability due to entrainment and turbulent mixing down to the smallest turbulence scales in a one-dimensional domain. Several thousand individual droplets evolve by condensation or evaporation according to their local environments. We used EMPM results to characterize the evolution of the DSD due to entrainment and isobaric mixing for a wide range of conditions in a 20-m domain, including variations in entrained environmental air fraction, the turbulence dissipation rate, the size of the entrained blobs, and the relative humidity of the entrained air. We found that the broadening of the DSD due to entrainment and isobaric mixing for a specific value of the entrained air relative humidity depends only on the eddy mixing time scale and the LWC after mixing. Broadening increases substantially as the evaporation time scale decreases due to decreasing relative humidity of the entrained air. Our results also show that it is possible to parameterize the effects of entrainment and mixing on the droplet number concentration. The comprehensive results obtained for one set of values of entrained air relative humidity, droplet size, and droplet concentration should be extended to other values.

Description: Nuclear collisions recreate conditions in the universe microseconds after the Big Bang. Only a very small fraction of the emitted fragments are light nuclei, but these states are of fundamental interest. We report the observation of antihypertritons--comprising an antiproton, an antineutron, and an antilambda hyperon--produced by colliding gold nuclei at high energy. Our analysis yields 70 +/- 17 antihypertritons ((Lambda)(3)-H) and 157 +/- 30 hypertritons (Lambda3H). The measured yields of Lambda3H ((Lambda)(3)-H) and 3He (3He) are similar, suggesting an equilibrium in coordinate and momentum space populations of up, down, and strange quarks and antiquarks, unlike the pattern observed at lower collision energies. The production and properties of antinuclei, and of nuclei containing strange quarks, have implications spanning nuclear and particle physics, astrophysics, and cosmology.〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉STAR Collaboration -- Abelev, B I -- Aggarwal, M M -- Ahammed, Z -- Alakhverdyants, A V -- Alekseev, I -- Anderson, B D -- Arkhipkin, D -- Averichev, G S -- Balewski, J -- Barnby, L S -- Baumgart, S -- Beavis, D R -- Bellwied, R -- Betancourt, M J -- Betts, R R -- Bhasin, A -- Bhati, A K -- Bichsel, H -- Bielcik, J -- Bielcikova, J -- Biritz, B -- Bland, L C -- Bonner, B E -- Bouchet, J -- Braidot, E -- Brandin, A V -- Bridgeman, A -- Bruna, E -- Bueltmann, S -- Bunzarov, I -- Burton, T P -- Cai, X Z -- Caines, H -- Calderon, M -- Catu, O -- Cebra, D -- Cendejas, R -- Cervantes, M C -- Chajecki, Z -- Chaloupka, P -- Chattopadhyay, S -- Chen, H F -- Chen, J H -- Chen, J Y -- Cheng, J -- Cherney, M -- Chikanian, A -- Choi, K E -- Christie, W -- Chung, P -- Clarke, R F -- Codrington, M J M -- Corliss, R -- Cramer, J G -- Crawford, H J -- Das, D -- Dash, S -- Davila Leyva, A -- De Silva, L C -- Debbe, R R -- Dedovich, T G -- DePhillips, M -- Derevschikov, A A -- Derradi de Souza, R -- Didenko, L -- Djawotho, P -- Dogra, S M -- Dong, X -- Drachenberg, J L -- Draper, J E -- Dunlop, J C -- Dutta Mazumdar, M R -- Efimov, L G -- Elhalhuli, E -- Elnimr, M -- Engelage, J -- Eppley, G -- Erazmus, B -- Estienne, M -- Eun, L -- Evdokimov, O -- Fachini, P -- Fatemi, R -- Fedorisin, J -- Fersch, R G -- Filip, P -- Finch, E -- Fine, V -- Fisyak, Y -- Gagliardi, C A -- Gangadharan, D R -- Ganti, M S -- Garcia-Solis, E J -- Geromitsos, A -- Geurts, F -- Ghazikhanian, V -- Ghosh, P -- Gorbunov, Y N -- Gordon, A -- Grebenyuk, O -- Grosnick, D -- Grube, B -- Guertin, S M -- Gupta, A -- Gupta, N -- Guryn, W -- Haag, B -- Hamed, A -- Han, L-X -- Harris, J W -- Hays-Wehle, J P -- Heinz, M -- Heppelmann, S -- Hirsch, A -- Hjort, E -- Hoffman, A M -- Hoffmann, G W -- Hofman, D J -- Hollis, R S -- Huang, B -- Huang, H Z -- Humanic, T J -- Huo, L -- Igo, G -- Iordanova, A -- Jacobs, P -- Jacobs, W W -- Jakl, P -- Jena, C -- Jin, F -- Jones, C L -- Jones, P G -- Joseph, J -- Judd, E G -- Kabana, S -- Kajimoto, K -- Kang, K -- Kapitan, J -- Kauder, K -- Keane, D -- Kechechyan, A -- Kettler, D -- Kikola, D P -- Kiryluk, J -- Kisiel, A -- Klein, S R -- Knospe, A G -- Kocoloski, A -- Koetke, D D -- Kollegger, T -- Konzer, J -- Kopytine, M -- Koralt, I -- Koroleva, L -- Korsch, W -- Kotchenda, L -- Kouchpil, V -- Kravtsov, P -- Krueger, K -- Krus, M -- Kumar, L -- Kurnadi, P -- Lamont, M A C -- Landgraf, J M -- LaPointe, S -- Lauret, J -- Lebedev, A -- Lednicky, R -- Lee, C-H -- Lee, J H -- Leight, W -- Levine, M J -- Li, C -- Li, L -- Li, N -- Li, W -- Li, X -- Li, Y -- Li, Z -- Lin, G -- Lindenbaum, S J -- Lisa, M A -- Liu, F -- Liu, H -- Liu, J -- Ljubicic, T -- Llope, W J -- Longacre, R S -- Love, W A -- Lu, Y -- Luo, X -- Ma, G L -- Ma, Y G -- Mahapatra, D P -- Majka, R -- Mal, O I -- Mangotra, L K -- Manweiler, R -- Margetis, S -- Markert, C -- Masui, H -- Matis, H S -- Matulenko, Yu A -- McDonald, D -- McShane, T S -- Meschanin, A -- Milner, R -- Minaev, N G -- Mioduszewski, S -- Mischke, A -- Mitrovski, M K -- Mohanty, B -- Mondal, M M -- Morozov, B -- Morozov, D A -- Munhoz, M G -- Nandi, B K -- Nattrass, C -- Nayak, T K -- Nelson, J M -- Netrakanti, P K -- Ng, M J -- Nogach, L V -- Nurushev, S B -- Odyniec, G -- Ogawa, A -- Okada, H -- Okorokov, V -- Olson, D -- Pachr, M -- Page, B S -- Pal, S K -- Pandit, Y -- Panebratsev, Y -- Pawlak, T -- Peitzmann, T -- Perevoztchikov, V -- Perkins, C -- Peryt, W -- Phatak, S C -- Pile, P -- Planinic, M -- Ploskon, M A -- Pluta, J -- Plyku, D -- Poljak, N -- Poskanzer, A M -- Potukuchi, B V K S -- Powell, C B -- Prindle, D -- Pruneau, C -- Pruthi, N K -- Pujahari, P R -- Putschke, J -- Qiu, H -- Raniwala, R -- Raniwala, S -- Ray, R L -- Redwine, R -- Reed, R -- Ritter, H G -- Roberts, J B -- Rogachevskiy, O V -- Romero, J L -- Rose, A -- Roy, C -- Ruan, L -- Sahoo, R -- Sakai, S -- Sakrejda, I -- Sakuma, T -- Salur, S -- Sandweiss, J -- Sangaline, E -- Schambach, J -- Scharenberg, R P -- Schmitz, N -- Schuster, T R -- Seele, J -- Seger, J -- Selyuzhenkov, I -- Seyboth, P -- Shahaliev, E -- Shao, M -- Sharma, M -- Shi, S S -- Sichtermann, E P -- Simon, F -- Singaraju, R N -- Skoby, M J -- Smirnov, N -- Sorensen, P -- Sowinski, J -- Spinka, H M -- Srivastava, B -- Stanislaus, T D S -- Staszak, D -- Stevens, J R -- Stock, R -- Strikhanov, M -- Stringfellow, B -- Suaide, A A P -- Suarez, M C -- Subba, N L -- Sumbera, M -- Sun, X M -- Sun, Y -- Sun, Z -- Surrow, B -- Svirida, D N -- Symons, T J M -- Szanto de Toledo, A -- Takahashi, J -- Tang, A H -- Tang, Z -- Tarini, L H -- Tarnowsky, T -- Thein, D -- Thomas, J H -- Tian, J -- Timmins, A R -- Timoshenko, S -- Tlusty, D -- Tokarev, M -- Trainor, T A -- Tram, V N -- Trentalange, S -- Tribble, R E -- Tsai, O D -- Ulery, J -- Ullrich, T -- Underwood, D G -- Van Buren, G -- van Leeuwen, M -- van Nieuwenhuizen, G -- Vanfossen, J A Jr -- Varma, R -- Vasconcelos, G M S -- Vasiliev, A N -- Videbaek, F -- Viyogi, Y P -- Vokal, S -- Voloshin, S A -- Wada, M -- Walker, M -- Wang, F -- Wang, G -- Wang, H -- Wang, J S -- Wang, Q -- Wang, X L -- Wang, Y -- Webb, G -- Webb, J C -- Westfall, G D -- Whitten, C Jr -- Wieman, H -- Wingfield, E -- Wissink, S W -- Witt, R -- Wu, Y -- Xie, W -- Xu, H -- Xu, N -- Xu, Q H -- Xu, W -- Xu, Y -- Xu, Z -- Xue, L -- Yang, Y -- Yepes, P -- Yip, K -- Yoo, I-K -- Yue, Q -- Zawisza, M -- Zbroszczyk, H -- Zhan, W -- Zhang, J -- Zhang, S -- Zhang, W M -- Zhang, X P -- Zhang, Y -- Zhang, Z P -- Zhao, J -- Zhong, C -- Zhou, J -- Zhou, W -- Zhu, X -- Zhu, Y H -- Zoulkarneev, R -- Zoulkarneeva, Y -- New York, N.Y. -- Science. 2010 Apr 2;328(5974):58-62. doi: 10.1126/science.1183980. Epub 2010 Mar 4.〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/20203011" target="_blank"〉PubMed〈/a〉

Description: The ultraviolet (UV) Ozone Monitoring Instrument (OMI), launched on NASA’s Aura satellite in July 2004, was the first space-based sensor to provide operational sulphur dioxide (SO 2 ) measurements (OMSO2) for use by the scientific community. Herein, we discuss the application of OMSO2 data for the monitoring of global volcanic SO 2 emissions, with an emphasis on lower tropospheric volcanic plumes. We review the algorithms used to produce OMSO2 data and highlight some key measurement sensitivity issues. The data processing scheme used to generate web-based OMSO2 data subsets for volcanic regions and estimate SO 2 burdens in volcanic plumes is outlined. We describe three techniques to derive SO 2 emission rates from the OMSO2 measurements, and employ one method (using single OMI pixels to estimate SO 2 fluxes) to elucidate SO 2 flux detection thresholds on a global scale. Applications of OMSO2 data to volcanic degassing studies are demonstrated using four case studies. These examples show how OMSO2 measurements correlate with changes in eruptive activity at Kilauea volcano (Hawaii), constrain small, potentially significant SO 2 releases from reawakening, historically inactive volcanoes, track long-term changes in SO 2 degassing from Nyiragongo volcano (D.R. Congo), and detect SO 2 emissions from the remote Lastarria Volcano (Chile), in the actively deforming Lazufre region.