186 research outputs found

    Iodine chemistry in the troposphere and its effect on ozone

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    Despite the potential influence of iodine chemistry on the oxidizing capacity of the troposphere, reactive iodine distributions and their impact on tropospheric ozone remain almost unexplored aspects of the global atmosphere. Here we present a comprehensive global modelling experiment aimed at estimating lower and upper limits of the inorganic iodine burden and its impact on tropospheric ozone. Two sets of simulations without and with the photolysis of IxOy oxides (i.e. I2O2, I2O3 and I2O4) were conducted to define the range of inorganic iodine loading, partitioning and impact in the troposphere. Our results show that the most abundant daytime iodine species throughout the middle to upper troposphere is atomic iodine, with an annual average tropical abundance of (0.15-0.55) pptv. We propose the existence of a "tropical ring of atomic iodine" that peaks in the tropical upper troposphere (∼11-14 km) at the equator and extends to the sub-tropics (30°N-30°S). Annual average daytime I = IO ratios larger than 3 are modelled within the tropics, reaching ratios up to ∼20 during vigorous uplift events within strong convective regions. We calculate that the integrated contribution of catalytic iodine reactions to the total rate of tropospheric ozone loss (IOx Loss) is 2-5 times larger than the combined bromine and chlorine cycles. When IxOy photolysis is included, IOx Loss represents an upper limit of approximately 27, 14 and 27% of the tropical annual ozone loss for the marine boundary layer (MBL), free troposphere (FT) and upper troposphere (UT), respectively, while the lower limit throughout the tropical troposphere is ∼9 %. Our results indicate that iodine is the second strongest ozone-depleting family throughout the global marine UT and in the tropical MBL. We suggest that (i) iodine sources and its chemistry need to be included in global tropospheric chemistry models, (ii) experimental programs designed to quantify the iodine budget in the troposphere should include a strategy for the measurement of atomic I, and (iii) laboratory programs are needed to characterize the photochemistry of higher iodine oxides to determine their atmospheric fate since they can potentially dominate halogen-catalysed ozone destruction in the troposphere

    The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6

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    Model experiment description paperProjections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 °C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017-2018 time frame, and output from the climate model projections made available and analyses performed over the 2018-2020 period.CRESCENDO project members (V. Eyring, P. Friedlingstein, E. Kriegler, R. Knutti, J. Lowe, K. Riahi, D. van Vuuren) acknowledge funding received from the Horizon 2020 European Union’s Framework Programme for Research and Innovation under grant agreement no. 641816. C. Tebaldi, G. A. Meehl and B. M. Sanderson acknowledge the support of the Regional and Global Climate Modeling Program (RGCM) of the U.S. Department of Energy’s, Office of Science (BER), Cooperative Agreement DE-FC02-97ER6240

    A framework for interpolating scattered data using space-filling curves

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    The analysis of spatial data occurs in many disciplines and covers a wide variety activities. Available techniques for such analysis include spatial interpolation which is useful for tasks such as visualization and imputation. This paper proposes a novel approach to interpolation using space-filling curves. Two simple interpolation methods are described and their ability to interpolate is compared to several interpolation techniques including natural neighbour interpolation. The proposed approach requires a Monte-Carlo step that requires a large number of iterations. However experiments demonstrate that the number of iterations will not change appreciably with larger datasets

    Weaker land–climate feedbacks from nutrient uptake during photosynthesis-inactive periods

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    Terrestrial carbon–climate feedbacks depend on two large and opposing fluxes—soil organic matter decomposition and photosynthesis—that are tightly regulated by nutrients . Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 represented nutrient dynamics poorly , rendering predictions of twenty-first century carbon–climate feedbacks highly uncertain. Here, we use a new land model to quantify the effects of observed plant nutrient uptake mechanisms missing in most other ESMs. In particular, we estimate the global role of root nutrient competition with microbes and abiotic processes during periods without photosynthesis. Nitrogen and phosphorus uptake during these periods account for 45 and 43%, respectively, of annual uptake, with large latitudinal variation. Globally, night-time nutrient uptake dominates this signal. Simulations show that ignoring this plant uptake, as is done when applying an instantaneous relative demand approach, leads to large positive biases in annual nitrogen leaching (96%) and N O emissions (44%). This N O emission bias has a GWP equivalent of ~2.4 PgCO yr , which is substantial compared to the current terrestrial CO sink. Such large biases will lead to predictions of overly open terrestrial nutrient cycles and lower carbon sequestration capacity. Both factors imply over-prediction of positive terrestrial feedbacks with climate in current ESMs. 1,2 1,3 −1 2 2 2

    Cloud impacts on photochemistry: Building a climatology of photolysis rates from the Atmospheric Tomography mission

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    Abstract. Measurements from actinic flux spectroradiometers on board the NASA DC-8 during the Atmospheric Tomography (ATom) mission provide an extensive set of statistics on how clouds alter photolysis rates (J values) throughout the remote Pacific and Atlantic Ocean basins. J values control tropospheric ozone and methane abundances, and thus clouds have been included for more than three decades in tropospheric chemistry modeling. ATom made four profiling circumnavigations of the troposphere capturing each of the seasons during 2016–2018. This work examines J values from the Pacific Ocean flights of the first deployment, but publishes the complete Atom-1 data set (29 July to 23 August 2016). We compare the observed J values (every 3 s along flight track) with those calculated by nine global chemistry–climate/transport models (globally gridded, hourly, for a mid-August day). To compare these disparate data sets, we build a commensurate statistical picture of the impact of clouds on J values using the ratio of J-cloudy (standard, sometimes cloudy conditions) to J-clear (artificially cleared of clouds). The range of modeled cloud effects is inconsistently large but they fall into two distinct classes: (1) models with large cloud effects showing mostly enhanced J values aloft and or diminished at the surface and (2) models with small effects having nearly clear-sky J values much of the time. The ATom-1 measurements generally favor large cloud effects but are not precise or robust enough to point out the best cloud-modeling approach. The models here have resolutions of 50–200 km and thus reduce the occurrence of clear sky when averaging over grid cells. In situ measurements also average scattered sunlight over a mixed cloud field, but only out to scales of tens of kilometers. A primary uncertainty remains in the role of clouds in chemistry, in particular, how models average over cloud fields, and how such averages can simulate measurements. NERC ACSIS LTSM projec
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