Search Results
Sensitivity of polar stratospheric ozone loss to uncertainties in chemical reaction kinetics
The impact and significance of uncertainties in model calculations of stratospheric ozone loss resulting from known uncertainty in chemical kinetics parameters is evaluated in trajectory chemistry simulations for the Antarctic and Arctic polar vortices. The uncertainty in modeled ozone loss is derived from Monte Carlo scenario simulations varying the kinetic (reaction and photolysis rate) parameters within their estimated uncertainty bounds. Simulations of a typical winter/spring Antarctic vortex scenario and Match scenarios in the Arctic produce large uncertainty in ozone loss rates and integrated seasonal loss. The simulations clearly indicate that the dominant source of model uncertainty in polar ozone loss is uncertainty in the Cl2O 2 photolysis reaction, which arises from uncertainty in laboratory-measured molecular cross sections at atmospherically important wavelengths. This estimated uncertainty in JCl 2O2 from laboratory measurements seriously hinders our ability to model polar ozone loss within useful quantitative error limits. Atmospheric observations, however, suggest that the Cl2O2 photolysis uncertainty may be less than that derived from the lab data. Comparisons to Match, South Pole ozonesonde, and Aura Microwave Limb Sounder (MLS) data all show that the nominal recommended rate simulations agree with data within uncertainties when the Cl2O2 photolysis error is reduced by a factor of two, in line with previous in situ ClOx measurements. Comparisons to simulations using recent cross sections from Pope et al. (2007) are outside the constrained error bounds in each case. Other reactions producing significant sensitivity in polar ozone loss include BrO + ClO and its branching ratios. These uncertainties challenge our confidence in modeling polar ozone depletion and projecting future changes in response to changing halogen emissions and climate. Further laboratory, theoretical, and possibly atmospheric studies are needed.
Quantifying Water Scarcity in Northern China Within the Context of Climatic and Societal Changes and South-to-North Water Diversion
With the increasing pressure from population growth and economic development, northern China (NC) faces a grand challenge of water scarcity, which can be further exacerbated by climatic and societal changes. The South-to-North Water Diversion (SNWD) project is designed to mitigate the water scarcity in NC. However, few studies have quantified the impact of the SNWD on water scarcity within the context of climatic and societal changes and its potential effects on economic and agricultural food in the region. We used water supply stress index (WaSSI) to quantify water scarcity within the context of environmental change in NC and developed a method to estimate the economic and agricultural impacts of the SNWD. Focuses were put on alleviating the water supply shortage and economic and agricultural benefits for the water-receiving NC. We find that societal changes, especially economic growth, are the major contributors to water scarcity in NC during 2009–2099. To completely mitigate the water scarcity of NC, at least an additional water supply of 13 billion m3/year (comparable to the annual diversion water by SNWD Central Route) will be necessary. Although SNWD alone cannot provide the full solution to NC's water shortage in next few decades, it can significantly alleviate the water supply stress in NC (particularly Beijing), considerably increasing the agricultural production (more than 115 Tcal/year) and bringing economic benefits (more than 51 billion RMB/year) through supplying industrial and domestic water use. Additionally, the transfer project could have impacts on the ecological environment in the exporting regions. ©2020. The Authors.
Characterizing the evolution of climate networks
Complex network theory has been successfully applied to understand the structural and functional topology of many dynamical systems from nature, society and technology. Many properties of these systems change over time, and, consequently, networks reconstructed from them will, too. However, although static and temporally changing networks have been studied extensively, methods to quantify their robustness as they evolve in time are lacking. In this paper we develop a theory to investigate how networks are changing within time based on the quantitative analysis of dissimilarities in the network structure. Our main result is the common component evolution function (CCEF) which characterizes network development over time. To test our approach we apply it to several model systems, ErdA's-Rényi networks, analytically derived flow-based networks, and transient simulations from the START model for which we control the change of single parameters over time. Then we construct annual climate networks from NCEP/NCAR reanalysis data for the Asian monsoon domain for the time period of 1970-2011 CE and use the CCEF to characterize the temporal evolution in this region. While this real-world CCEF displays a high degree of network persistence over large time lags, there are distinct time periods when common links break down. This phasing of these events coincides with years of strong El Niño/Southern Oscillation phenomena, confirming previous studies. The proposed method can be applied for any type of evolving network where the link but not the node set is changing, and may be particularly useful to characterize nonstationary evolving systems using complex networks.
Impacts of temperature extremes on European vegetation during the growing season
Temperature is a key factor controlling plant growth and vitality in the temperate climates of the mid-latitudes like in vast parts of the European continent. Beyond the effect of average conditions, the timings and magnitudes of temperature extremes play a particularly crucial role, which needs to be better understood in the context of projected future rises in the frequency and/or intensity of such events. In this work, we employ event coincidence analysis (ECA) to quantify the likelihood of simultaneous occurrences of extremes in daytime land surface temperature anomalies (LSTAD) and the normalized difference vegetation index (NDVI). We perform this analysis for entire Europe based upon remote sensing data, differentiating between three periods corresponding to different stages of plant development during the growing season. In addition, we analyze the typical elevation and land cover type of the regions showing significantly large event coincidences rates to identify the most severely affected vegetation types. Our results reveal distinct spatio-temporal impact patterns in terms of extraordinarily large co-occurrence rates between several combinations of temperature and NDVI extremes. Croplands are among the most frequently affected land cover types, while elevation is found to have only a minor effect on the spatial distribution of corresponding extreme weather impacts. These findings provide important insights into the vulnerability of European terrestrial ecosystems to extreme temperature events and demonstrate how event-based statistics like ECA can provide a valuable perspective on environmental nexuses.
SPITFIRE within the MPI Earth system model: Model development and evaluation
Quantification of the role of fire within the Earth system requires an adequate representation of fire as a climate-controlled process within an Earth system model. To be able to address questions on the interaction between fire and the Earth system, we implemented the mechanistic fire model SPITFIRE, in JSBACH, the land surface model of the MPI Earth system model. Here, we document the model implementation as well as model modifications. We evaluate our model results by comparing the simulation to the GFED version 3 satellite-based data set. In addition, we assess the sensitivity of the model to the meteorological forcing and to the spatial variability of a number of fire relevant model parameters. A first comparison of model results with burned area observations showed a strong correlation of the residuals with wind speed. Further analysis revealed that the response of the fire spread to wind speed was too strong for the application on global scale. Therefore, we developed an improved parametrization to account for this effect. The evaluation of the improved model shows that the model is able to capture the global gradients and the seasonality of burned area. Some areas of model-data mismatch can be explained by differences in vegetation cover compared to observations. We achieve benchmarking scores comparable to other state-of-the-art fire models. The global total burned area is sensitive to the meteorological forcing. Adjustment of parameters leads to similar model results for both forcing data sets with respect to spatial and seasonal patterns. Key Points The SPITFIRE fire model was evaluated within the JSBACH land surface model A modified wind speed response improved the spatial pattern of burned area Regional gradients in burned area are driven by vegetation and fuel properties.
Quantifying Southern Annular Mode paleo-reconstruction skill in a model framework
Past attempts to reconstruct the Southern Annular Mode (SAM) using paleo-archives have resulted in records which can differ significantly from one another prior to the window over which the proxies are calibrated. This study attempts to quantify not only the skill with which we may expect to reconstruct the SAM but also to assess the contribution of regional bias in proxy selection and the impact of non-stationary proxy–SAM teleconnections on a resulting reconstruction. This is achieved using a pseudoproxy framework with output from the GFDL CM2.1 global climate model. Reconstructions derived from precipitation fields perform better, with 89 % of the reconstructions calibrated over a 61 year window able to reproduce at least 50 % of the inter-annual variance in the SAM, as opposed to just 25 % for surface air temperature (SAT)-derived reconstructions. Non-stationarity of proxy–SAM teleconnections, as defined here, plays a small role in reconstructions, but the range in reconstruction skill is not negligible. Reconstructions are most likely to be skilful when proxies are sourced from a geographically broad region with a network size of at least 70 proxies.