A TRMM-Calibrated infrared technique for rainfall estimation: application on rain events over eastern Mediterranean

The aim is to evaluate the use of a satellite infrared (IR) technique for estimating rainfall over the eastern Mediterranean. The Convective-Stratiform Technique (CST), calibrated by coincident, physically retrieved rain rates from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR), is applied over the Eastern Mediterranean for four rain events during the six month period of October 2004 to March 2005. Estimates from this technique are verified over a rain gauge network for different time scales. Results show that PR observations can be applied to improve IR-based techniques significantly in the conditions of a regional scale area by selecting adequate calibration areas and periods. They reveal, however, the limitations of infrared remote sensing techniques, originally developed for tropical areas, when applied to precipitation retrievals in mid-latitudes

Mutagenicity of Acridines in a Reversion Assay Based on Tetracycline Resistance in Plasmid pBR322 in Escherichia Coli

The mutagenicity of a series of acridine compounds was studied in an assay based on the reversion of mutations in the tetracycline-resistance gene (tet) of plasmid pBR322 in Escherichia coli. Mutations that restore the tetracycline-resistant phenotype were detected in tetracycline-sensitive strains carrying mutant plasmids. Mutations that revert by +2, +1, −1, and −2 frameshift mutations and by base-pair substitutions were used to analyze the mutagenicity of two simple acridines, two acridine mustards, and a nitroacridine. The simple acridines (9-aminoacridine and quinacrine) effectively induced −1 frameshifts and weakly induced +1 frameshifts. The acridine mustards (quinacrine mustard and ICR-191) were more potent inducers of −1 and +1 frameshifts than the simple acridines. Reactive acridines, including both the mustards and the nitroacridine Entozon, were effective inducers of −2 frameshifts but the simple acridines were not. The two classes of reactive acridines differed from one another, in that the mustards were better inducers of +1 frameshifts than Entozon, whereas Entozon was a particularly potent inducer of −2 frameshifts. None of the compounds induced +2 frameshifts, and the induction of base-pair substitutions was negligible. These results confirm and extend studies showing that adduct-forming acridines are stronger frameshift mutagens than simple intercalating acridines and that the acridines differ from one another not only in overall mutagenic potency but also in the prevalence of different classes of frameshift mutations

U.S. Accession to the Association of Southeast Asian Nations' Treaty of Amity and Cooperation (TAC)

This report analyzes the legal and diplomatic issues involved with accession to the Treaty of Amity and Cooperation (TAC)

Towards a Model Climatology of Relative Humidity in the Upper Troposphere for Estimation of Contrail and Contrail-Induced Cirrus

The formation of contrails and contrail cirrus is very sensitive to the relative humidity of the upper troposphere. To reduce uncertainty in an estimate of the radiative impact of aviation-induced cirrus, a model must therefore be able to reproduce the observed background moisture fields with reasonable and quantifiable fidelity. Here we present an upper tropospheric moisture climatology from a 26-year ensemble of simulations using the GEOS CCM. We compare this free-running model's moisture fields to those obtained from the MLS and AIRS satellite instruments, our most comprehensive observational databases for upper tropospheric water vapor. Published comparisons have shown a substantial wet bias in GEOS-5 assimilated fields with respect to MLS water vapor and ice water content. This tendency is clear as well in the GEOS CCM simulations. The GEOS-5 moist physics in the GEOS CCM uses a saturation adjustment that prevents supersaturation, which is unrealistic when compared to in situ moisture observations from MOZAIC aircraft and balloon sondes as we will show. Further, the large-scale satellite datasets also consistently underestimate super-saturation when compared to the in-situ observations. We place these results in the context of estimates of contrail and contrail cirrus frequency

Ozone sensitivity to varying greenhouse gases and ozone-depleting substances in CCMI-1 simulations

Ozone fields simulated for the first phase of the Chemistry-Climate Model Initiative (CCMI-1) will be used as forcing data in the 6th Coupled Model Intercomparison Project. Here we assess, using reference and sensitivity simulations produced for CCMI-1, the suitability of CCMI-1 model results for this process, investigating the degree of consistency amongst models regarding their responses to variations in individual forcings. We consider the influences of methane, nitrous oxide, a combination of chlorinated or brominated ozone-depleting substances, and a combination of carbon dioxide and other greenhouse gases. We find varying degrees of consistency in the models' responses in ozone to these individual forcings, including some considerable disagreement. In particular, the response of total-column ozone to these forcings is less consistent across the multi-model ensemble than profile comparisons. We analyse how stratospheric age of air, a commonly used diagnostic of stratospheric transport, responds to the forcings. For this diagnostic we find some salient differences in model behaviour, which may explain some of the findings for ozone. The findings imply that the ozone fields derived from CCMI-1 are subject to considerable uncertainties regarding the impacts of these anthropogenic forcings. We offer some thoughts on how to best approach the problem of generating a consensus ozone database from a multi-model ensemble such as CCMI-1

Chemical Mechanisms and their Applications in the Goddard Earth Observing System (GEOS) Earth System Model

NASA's Goddard Earth Observing System (GEOS) Earth System Model (ESM) is a modular, general circulation model (GCM) and data assimilation system (DAS) that is used to simulate and study the coupled dynamics, physics, chemistry, and biology of our planet. GEOS is developed by the Global Modeling and Assimilation Office (GMAO) at NASA Goddard Space Flight Center. It generates near-real-time analyzed data products, reanalyses, and weather and seasonal forecasts to support research targeted to understanding interactions among Earth-System processes. For chemistry, our efforts are focused on ozone and its influence on the state of the atmosphere and oceans, and on trace-gas data assimilation and global forecasting at mesoscale discretization. Several chemistry and aerosol modules are coupled to the GCM, which enables GEOS to address topics pertinent to NASA's Earth Science Mission. This manuscript describes the atmospheric chemistry components of GEOS and provides an overview of its Earth System Modeling Framework (ESMF)-based software infrastructure, which promotes a rich spectrum of feedbacks that influence circulation and climate, and impact human and ecosystem health. We detail how GEOS allows model users to select chemical mechanisms and emission scenarios at run time, establish the extent to which the aerosol and chemical components communicate, and decide whether either or both influence the radiative transfer calculations. A variety of resolutions facilitates research on spatial and temporal scales relevant to problems ranging from hourly changes in air quality to trace gas trends in a changing climate. Samples of recent GEOS chemistry applications are provided

Enhancing long-term trend simulation of the global tropospheric hydroxyl (TOH) and its drivers from 2005 to 2019: a synergistic integration of model simulations and satellite observations

The tropospheric hydroxyl (TOH) radical is a key player in regulating oxidation of various compounds in Earth's atmosphere. Despite its pivotal role, the spatiotemporal distributions of OH are poorly constrained. Past modeling studies suggest that the main drivers of OH, including NO2, tropospheric ozone (TO3), and H2O(v), have increased TOH globally. However, these findings often offer a global average and may not include more recent changes in diverse compounds emitted on various spatiotemporal scales. Here, we aim to deepen our understanding of global TOH trends for more recent years (2005–2019) at 1×1°. To achieve this, we use satellite observations of HCHO and NO2 to constrain simulated TOH using a technique based on a Bayesian data fusion method, alongside a machine learning module named the Efficient CH4-CO-OH (ECCOH) configuration, which is integrated into NASA's Goddard Earth Observing System (GEOS) global model. This innovative module helps efficiently predict the convoluted response of TOH to its drivers and proxies in a statistical way. Aura Ozone Monitoring Instrument (OMI) NO2 observations suggest that the simulation has high biases for biomass burning activities in Africa and eastern Europe, resulting in a regional overestimation of up to 20 % in TOH. OMI HCHO primarily impacts the oceans, where TOH linearly correlates with this proxy. Five key parameters, i.e., TO3, H2O(v), NO2, HCHO, and stratospheric ozone, can collectively explain 65 % of the variance in TOH trends. The overall trend of TOH influenced by NO2 remains positive, but it varies greatly because of the differences in the signs of anthropogenic emissions. Over the oceans, TOH trends are primarily positive in the Northern Hemisphere, resulting from the upward trends in HCHO, TO3, and H2O(v). Using the present framework, we can tap the power of satellites to quickly gain a deeper understanding of simulated TOH trends and biases.</p

Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)

We present an overview of state-of-The-Art chemistry-climate and chemistry transport models that are used within phase 1 of the Chemistry-Climate Model Initiative (CCMI-1). The CCMI aims to conduct a detailed evaluation of participating models using process-oriented diagnostics derived from observations in order to gain confidence in the models' projections of the stratospheric ozone layer, tropospheric composition, air quality, where applicable global climate change, and the interactions between them. Interpretation of these diagnostics requires detailed knowledge of the radiative, chemical, dynamical, and physical processes incorporated in the models. Also an understanding of the degree to which CCMI-1 recommendations for simulations have been followed is necessary to understand model responses to anthropogenic and natural forcing and also to explain intermodel differences. This becomes even more important given the ongoing development and the ever-growing complexity of these models. This paper also provides an overview of the available CCMI-1 simulations with the aim of informing CCMI data users.This work has been supported by NIWA as part of its government-funded, core research. Olaf Morgenstern acknowledges support from the Royal Society Marsden Fund, grant 12-NIW-006, and under the Deep South National Science Challenge. The authors wish to acknowledge the contribution of NeSI high-performance computing facilities to the results of this research. New Zealand’s national facilities are provided by the New Zealand eScience Infrastructure (NeSI) and funded jointly by NeSI’s collaborator institutions and through the Ministry of Business, Innovation & Employment’s Research Infrastructure programme (https://www.nesi.org.nz). The SOCOL team acknowledges support from the Swiss National Science Foundation under grant agreement CRSII2_147659 (FUPSOL II). CCSRNIES’s research was supported by the Environment Research and Technology Development Fund (2-1303) of the Ministry of the Environment, Japan, and computations were performed on NEC-SX9/A(ECO) computers at the CGER, NIES. Wuhu Feng (NCAS) provided support for the TOMCAT simulations. Neal Butchart, Steven C. Hardiman, and Fiona M. O’Connor and the development of HadGEM3-ES were supported by the Joint UK DECC/Defra Met Office Hadley Centre Climate Programme (GA01101). Neal Butchart and Steven C. Hardiman also acknowledge additional support from the European Project 603557-STRATOCLIM under the FP7-ENV.2013.6.1-2 programme. Fiona M. O’Connor acknowledges additional support from the Horizon 2020 European Union’s Framework Programme for Research and Innovation CRESCENDO project under grant agreement no. 641816. Slimane Bekki acknowledges support from the European Project 603557-STRATOCLIM under the FP7-ENV.2013.6.1-2 programme and from the Centre National d’Etudes Spatiales (CNES, France) within the SOLSPEC project. Kane Stone and Robyn Schofield acknowledge funding from the Australian Government’s Australian Antarctic science grant program (FoRCES 4012), the Australian Research Council’s Centre of Excellence for Climate System Science (CE110001028), the Commonwealth Department of the Environment (grant 2011/16853), and computational support from National computational infrastructure INCMAS project q90. The CNRM-CM chemistry–climate people acknowledge the support from Météo-France, CNRS, and CERFACS, and in particular the work of the entire team in charge of the CNRM/CERFACS climate model

Review of the global models used within the Chemistry-Climate Model Initiative (CCMI)

We present an overview of state-of-the-art chemistry-climate and -transport models that are used within the Chemistry Climate Model Initiative (CCMI). CCMI aims to conduct a detailed evaluation of participating models using process-oriented diagnostics derived from observations in order to gain confidence in the models’ projections of the stratospheric ozone layer, air quality, where applicable global climate change, and the interactions between them. Interpretation of these diagnostics requires detailed knowledge of the radiative, chemical, dynamical, and physical processes incorporated in the models. Also an understanding of the degree to which CCMI recommendations for simulations have been followed is necessary to understand model response to anthropogenic and natural forcing and also to explain inter-model differences. This becomes even more important given the ongoing development and the ever-growing complexity of these models. This paper also provides an overview of the available CCMI simulations with the aim to inform CCMI data users