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Small-scale thermal upwellings under the northern East African Rift from S travel time tomography
(2016)
There is a long-standing debate over how many and what types of plumes underlie the East African Rift and whether they do or do not drive its extension and consequent magmatism and seismicity. Here we present a new tomographic study of relative teleseismic S and SKS residuals that expands the resolution from previous regional studies below the northern East African Rift to image structure from the surface to the base of the transition zone. The images reveal two low-velocity clusters, below Afar and west of the Main Ethiopian Rift, that extend throughout the upper mantle and comprise several smaller-scale (about 100 km diameter), low-velocity features. These structures support those of our recent P tomographic study below the region. The relative magnitude of S to P residuals is around 3.5, which is consistent with a predominantly thermal nature of the anomalies. The S and P velocity anomalies in the low-velocity clusters can be explained by similar excess temperatures in the range of 100–200°C, consistent with temperatures inferred from other seismic, geochemical, and petrological studies. Somewhat stronger VS anomalies below Afar than west of the Main Ethiopian Rift may include an expression of volatiles and/or melt in this region. These results, together with a comparison with previous larger-scale tomographic models, indicate that these structures are likely small-scale upwellings with mild excess temperatures, rising from a regional thermal boundary layer at the base of the upper mantle.
The Fire Modeling Intercomparison Project (FireMIP), phase 1: experimental and analytical protocols
(2016)
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over two decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. Here we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. The works published in this journal are distributed under the Creative Commons Attribution 3.0 License. This license does not affect the Crown copy-right work, which is re-usable under the Open Government Licence (OGL). The Creative Commons Attribution 3.0 License and the OGL are interoperable and do not conflict with, reduce, or limit each other.
The important role of fire in regulating vegetation community composition and contributions to emissions of greenhouse gases and aerosols make it a critical component of dynamic global vegetation models and Earth system models. Over 2 decades of development, a wide variety of model structures and mechanisms have been designed and incorporated into global fire models, which have been linked to different vegetation models. However, there has not yet been a systematic examination of how these different strategies contribute to model performance. Here we describe the structure of the first phase of the Fire Model Intercomparison Project (FireMIP), which for the first time seeks to systematically compare a number of models. By combining a standardized set of input data and model experiments with a rigorous comparison of model outputs to each other and to observations, we will improve the understanding of what drives vegetation fire, how it can best be simulated, and what new or improved observational data could allow better constraints on model behavior. In this paper, we introduce the fire models used in the first phase of FireMIP, the simulation protocols applied, and the benchmarking system used to evaluate the models. We have also created supplementary tables that describe, in thorough mathematical detail, the structure of each model.
In this study, we aim to reconstruct a relevant and new database of monthly zonal mean distribution of carbon dioxide (CO2) at global scale extending from the upper-troposphere (UT) to stratosphere (S). This product can be used for model and satellite validation in the UT/S, as a prior for inversion modelling and mainly to analyse a plausible feature of the stratospherictropospheric exchange as well as the stratospheric circulation and its variability. To do so, we investigate the ability of a Lagrangian trajectory model guided by ERA-Interim reanalysis to construct the CO2 abundance in the UT/S. From 10 year backward trajectories and tropospheric observations of CO2, we reconstruct upper-tropospheric and stratospheric CO2 over the period 2000–2010. The inter-comparisons of the reconstructed CO2 with mid-latitude vertical profiles measured by balloon samples as well as quasi-horizontal air samples from ER-2 aircraft during SOLVE and CONTRAIL campaigns exhibit a remarkable agreement. That demonstrates the potential of Lagrangian model to reconstruct CO2 in the UT/S. The zonal mean distribution exhibits relatively large CO2 in the tropical stratosphere due to the seasonal variation of the tropical upwelling of Brewer-Dobson circulation. During winter and spring, the tropical pipe is relatively isolated but is less confined during summer and autumn so that high CO2 values are more readily transported out of the tropics to the mid- and high latitude stratosphere. The shape of the vertical profiles suggests that relatively high CO2 above 20 km altitude mainly enter the stratosphere through tropical upwelling. CO2 mixing ratio is relatively low in the polar and tropical regions above 25 km. On average the CO2 mixing ratio decreases with altitude by 6-8 ppmv from the UT to stratosphere (e.g. up to 35 km) and is nearly constant with altitude.
In this study, we construct a new monthly zonal mean carbon dioxide (CO2) distribution from the upper troposphere to the stratosphere over the 2000–2010 time period. This reconstructed CO2 product is based on a Lagrangian backward trajectory model driven by ERA-Interim reanalysis meteorology and tropospheric CO2 measurements. Comparisons of our CO2 product to extratropical in situ measurements from aircraft transects and balloon profiles show remarkably good agreement. The main features of the CO2 distribution include (1) relatively large mixing ratios in the tropical stratosphere; (2) seasonal variability in the extratropics, with relatively high mixing ratios in the summer and autumn hemisphere in the 15–20 km altitude layer; and (3) decreasing mixing ratios with increasing altitude from the upper troposphere to the middle stratosphere ( ∼ 35 km). These features are consistent with expected variability due to the transport of long-lived trace gases by the stratospheric Brewer–Dobson circulation. The method used here to construct this CO2 product is unique from other modelling efforts and should be useful for model and satellite validation in the upper troposphere and stratosphere as a prior for inversion modelling and to analyse features of stratosphere–troposphere exchange as well as the stratospheric circulation and its variability.
The fractional release factor (FRF) gives information on the amount of a halocarbon that is released at some point in the stratosphere from its source form to the inorganic form, which can harm the ozone layer through catalytic reactions. The quantity is of major importance because it directly affects the calculation of the Ozone Depletion Potential (ODP). To apply FRF in this context, steady-state values are needed, thus representing a molecular property for a given atmospheric situation. In particular, these values should be independent of the tropospheric trends of the respective halogenated trace gases.
We analyzed the temporal evolution of FRF from ECHAM/MESSy Atmospheric Chemistry (EMAC) model simulations for several halocarbons and nitrous oxide between 1965–2011 on different mean age levels and found that the current formulation of FRF yields highly time-dependent values. We show that this is caused by the way that the tropospheric trend is handled in the current calculation method of FRF.
Taking into account chemical loss in the calculation of stratospheric mixing ratios reduces the time-dependence in correlations of different tracers. Therefore we implemented a loss term in the formulation of FRF and applied the parameterization of a "mean arrival time" to our data set.
We find that the time-dependence in FRF can almost be compensated by applying a new trend correction in the calculation of FRF. We suggest that this new method should be used to calculate time-independent FRF, which can then be used e.g. for the calculation of ODP
The fractional release factor (FRF) gives information on the amount of a halocarbon that is released at some point into the stratosphere from its source form to the inorganic form, which can harm the ozone layer through catalytic reactions. The quantity is of major importance because it directly affects the calculation of the ozone depletion potential (ODP). In this context time-independent values are needed which, in particular, should be independent of the trends in the tropospheric mixing ratios (tropospheric trends) of the respective halogenated trace gases. For a given atmospheric situation, such FRF values would represent a molecular property.
We analysed the temporal evolution of FRF from ECHAM/MESSy Atmospheric Chemistry (EMAC) model simulations for several halocarbons and nitrous oxide between 1965 and 2011 on different mean age levels and found that the widely used formulation of FRF yields highly time-dependent values. We show that this is caused by the way that the tropospheric trend is handled in the widely used calculation method of FRF.
Taking into account chemical loss in the calculation of stratospheric mixing ratios reduces the time dependence in FRFs. Therefore we implemented a loss term in the formulation of the FRF and applied the parameterization of a mean arrival time to our data set.
We find that the time dependence in the FRF can almost be compensated for by applying a new trend correction in the calculation of the FRF. We suggest that this new method should be used to calculate time-independent FRFs, which can then be used e.g. for the calculation of ODP.
Medium range hydrological forecasts in mesoscale catchments are only possible with the use of hydrological models driven by meteorological forecasts, which in particular contribute quantitative precipitation forecasts (QPF). QPFs are accompanied by large uncertainties, especially for longer lead times, which are propagated within the hydrometeorological model system. To deal with this limitation of predictability, a probabilistic forecasting system is tested, which is based on a hydrological-meteorological ensemble prediction system. The meteorological component of the system is the operational limited-area ensemble prediction system COSMO-LEPS that downscales the global ECMWF ensemble to a horizontal resolution of 10 km, while the hydrological component is based on the semi-distributed hydrological model PREVAH with a spatial resolution of 500 m.
Earlier studies have mostly addressed the potential benefits of hydrometeorological ensemble systems in short case studies. Here we present an analysis of hydrological ensemble hindcasts for two years (2005 and 2006). It is shown that the ensemble covers the uncertainty during different weather situations with an appropriate spread-skill relationship. The ensemble also shows advantages over a corresponding deterministic forecast, even under consideration of an artificial spread.
Volatile organic compounds (VOCs) were analyzed in air and snow samples at the Jungfraujoch high alpine research station in Switzerland as part of CLACE 5 (CLoud and Aerosol Characterization Experiment) during February/March 2006. The fluxes of individual compounds in ambient air were calculated from gas phase concentrations and wind speed. The highest flux values were observed for the aromatic hydrocarbons benzene (14.3 µg m−2s−1), 1,3,5-trimethylbenzene (5.27 µg m−2s−1), toluene (4.40 µg m−2s−1), and the aliphatic hydrocarbons i-butane (7.87 µg m−2s−1), i-pentane (3.61 µg m−2s−1) and n-butane (3.23 µg m−2s−1). The fluxes were used to calculate the efficiency of removal of VOCs by snow, and the effect of temperature on removal efficiency. The removal efficiency was calculated at – 24◦C (−13.7◦C) and ranged from 37% (35%) for o-xylene to 93% (63%) for i-pentane. The distribution coefficients of VOCs between the air and snow phases were derived from published poly-parameter linear free energy relationship (pp-LFER) data, and compared with distribution coef- ficients obtained from the simultaneous measurements of VOC concentrations in air and snow at Jungfraujoch. The coefficients calculated from pp-LFER exceeded those values measured in the present study, which indicates more efficient snow scavenging of the VOCs investigated than suggested by theoretical predictions.
Quantification of spatially and temporally resolved water flows and water storage variations for all land areas of the globe is required to assess water resources, water scarcity and flood hazards, and to understand the Earth system. This quantification is done with the help of global hydrological models (GHMs). What are the challenges and prospects in the development and application of GHMs? Seven important challenges are presented. (1) Data scarcity makes quantification of human water use difficult even though significant progress has been achieved in the last decade. (2) Uncertainty of meteorological input data strongly affects model outputs. (3) The reaction of vegetation to changing climate and CO2 concentrations is uncertain and not taken into account in most GHMs that serve to estimate climate change impacts. (4) Reasons for discrepant responses of GHMs to changing climate have yet to be identified. (5) More accurate estimates of monthly time series of water availability and use are needed to provide good indicators of water scarcity. (6) Integration of gradient-based groundwater modelling into GHMs is necessary for a better simulation of groundwater–surface water interactions and capillary rise. (7) Detection and attribution of human interference with freshwater systems by using GHMs are constrained by data of insufficient quality but also GHM uncertainty itself. Regarding prospects for progress, we propose to decrease the uncertainty of GHM output by making better use of in situ and remotely sensed observations of output variables such as river discharge or total water storage variations by multi-criteria validation, calibration or data assimilation. Finally, we present an initiative that works towards the vision of hyperresolution global hydrological modelling where GHM outputs would be provided at a 1-km resolution with reasonable accuracy.