Impacts of enhanced CCN on the organization of convection and recent reduced counts of monsoon depressions

Monsoon depressions, that form during the Indian summer monsoon season (June to September) are known to be baroclinic disturbances (horizontal scale 2,000–3,000 km) and are driven by deep convection that carries a very large vertical slope towards cold air aloft in the upper troposphere. Deep convection is nearly always organized around the scale of these depressions. In the maintenance of the monsoon depression the generation of eddy kinetic energy on the scale of the monsoon depression is largely governed by the “in scale” covariance of heating and temperature and of vertical velocity and temperature over the region of the monsoon depression. There are normally about 6–8 monsoon depressions during a summer monsoon season. Recent years 2009, 2010 and 2011 saw very few (around 1, 0 and 1 per season respectively). The best numerical models such as those from ECMWF and US (GFS) carried many false alarms in their 3–5 day forecasts, more like 6–8 disturbances. Even in recent years with fewer observed monsoon depressions a much larger number of depressions is noted in ECMWF forecasts. These are fairly comprehensive models that carry vast data sets (surface and satellite based), detailed data assimilation, and are run at very high resolutions. The monsoon depression is well resolved by these respective horizontal resolutions in these models (at 15 and 35 km). These models carry complete and detailed physical parameterizations. The false alarms in their forecasts leads us to suggest that some additional important ingredient may be missing in these current best state of the art models. This paper addresses the effects of pollution for the enhancement of cloud condensation nuclei and the resulting disruption of the organization of convection in monsoon depressions. Our specific studies make use of a high resolution mesoscale model (WRF/CHEM) to explore the impacts of the first and second aerosol indirect effects proposed by Twomey and Albrecht. We have conducted preliminary studies including examination of the evolution of radar reflectivity (computed inversely from the model hydrometeors) for normal and enhanced CCN effects (arising from enhanced monsoon pollution). The time lapse histories show a major disruption in the organization of convection of the monsoon depressions on the time scale of a week to 10 days in these enhanced CCN scenarios.     

Evolving Turbulence Realizations of Atmospheric Flow

A significant difference exists between estimates of contaminant atmospheric transport and dispersion calculated by an ensemble-averaged model and the turbulent details of any particular atmospheric transport and dispersion realization. In some cases, however, it is important to be able to make inferences of these realizations using ensemble-averaged models. It is possible to make such inferences if there are sensors in the field to report contaminant concentration observations. Any information determined about the atmospheric transport and dispersion realization can then be assimilated into a forecast model. This approach can enhance the accuracy of the atmospheric transport and dispersion forecast of a particular event. This work adopts that approach and reports on a genetic algorithm used to optimize the variational problem. Given contaminant sensor measurements and a transport and dispersion model, one can back-calculate unknown source and meteorological parameters. In this case, we demonstrate the dynamic recovery of unknown meteorological variables, including the transport variables that comprise the “outer variability” (wind speed and wind direction) and the dispersion variables that comprise the “inner variability” (contaminant spread). The optimization problem is set up in an Eulerian grid space, where the comparison of the concentration field variable between the predictions and the observations forms the cost function. The transport and dispersion parameters, which are determined from the optimization, are in Lagrangian space. This calculation is applied to continuous and instantaneous releases in a horizontally homogeneous wind field such as that observed during traditional transport and dispersion field experiments. The method proves to be successful at recovering the unknown transport and dispersion parameters for a numerical experiment.     

The challenge to detect and attribute effects of climate change on human and natural systems

Future scenarios of the energy system under greenhouse gas emission constraints depict dramatic growth in a range of energy technologies. Technological growth dynamics observed historically provide a useful comparator for these future trajectories. We find that historical time series data reveal a consistent relationship between how much a technology’s cumulative installed capacity grows, and how long this growth takes. This relationship between extent (how much) and duration (for how long) is consistent across both energy supply and end-use technologies, and both established and emerging technologies. We then develop and test an approach for using this historical relationship to assess technological trajectories in future scenarios. Our approach for “learning from the past” contributes to the assessment and verification of integrated assessment and energy-economic models used to generate quantitative scenarios. Using data on power generation technologies from two such models, we also find a consistent extent - duration relationship across both technologies and scenarios. This relationship describes future low carbon technological growth in the power sector which appears to be conservative relative to what has been evidenced historically. Specifically, future extents of capacity growth are comparatively low given the lengthy time duration of that growth. We treat this finding with caution due to the low number of data points. Yet it remains counter-intuitive given the extremely rapid growth rates of certain low carbon technologies under stringent emission constraints. We explore possible reasons for the apparent scenario conservatism, and find parametric or structural conservatism in the underlying models to be one possible explanation.     

Sensitivity of multi-gas climate policy to emission metrics

The Global Warming Potential (GWP) index is currently used to create CO₂-equivalent emission totals for multi-gas greenhouse targets. While many alternatives have been proposed, it is not possible to uniquely define a metric that captures the different impacts of emissions of substances with widely disparate atmospheric lifetimes, which leads to a wide range of possible index values. We examine the sensitivity of emissions and climate outcomes to the value of the index used to aggregate methane emissions using a technologically detailed integrated assessment model. The methane index is varied between 4 and 70, with a central value of 21, which is the 100-year GWP value currently used in policy contexts. We find that the sensitivity to index value is, at most, 10–18 % in terms of methane emissions but only 2–3 % in terms of the maximum total radiative forcing change, with larger regional emissions differences in some cases. The choice of index also affects estimates of the cost of meeting a given end of century forcing target, with total two-gas mitigation cost increasing by 7–9 % if the index is increased, and increasing in most scenarios from 4 to 23 % if the index is lowered, with a slight (1 %) decrease in total cost in one case. We find that much of the methane abatement occurs as the induced effect of CO₂ abatement rather than explicit abatement, which is one reason why climate outcomes are relatively insensitive to the index value. We also find that the near-term climate benefit of increasing the methane index is small.     

Contributions of individual variation in temperature, solar radiation and precipitation to crop yield in the North China Plain, 1961–2003

An understanding of the relative impacts of the changes in climate variables on crop yield can help develop effective adaptation strategies to cope with climate change. This study was conducted to investigate the effects of the interannual variability and trends in temperature, solar radiation and precipitation during 1961–2003 on wheat and maize yields in a double cropping system at Beijing and Zhengzhou in the North China Plain (NCP), and to examine the relative contributions of each climate variable in isolation. 129 climate scenarios consisting of all the combinations of these climate variables were constructed. Each scenario contained 43 years of observed values of one variable, combined with values of the other two variables from each individual year repeated 43 times. The Agricultural Production Systems Simulator (APSIM) was used to simulate crop yields using the ensemble of generated climate scenarios. The results showed that the warming trend during the study period did not have significant impact on wheat yield potential at both sites, and only had significant negative impact on maize yield potential at Beijing. This is in contrast with previous results on effect of warming. The decreasing trend in solar radiation had a much greater impact on simulated yields of both wheat and maize crops, causing a significant reduction in potential yield of wheat and maize at Beijing. Although decreasing trends in rainfed yield of both simulated wheat and maize were found, the substantial interannual variability of precipitation made the trends less prominent.     

Climate change and infectious diseases: Can we meet the needs for better prediction?

The next generation of climate-driven, disease prediction models will most likely require a mechanistically based, dynamical framework that parameterizes key processes at a variety of locations. Over the next two decades, consensus climate predictions make it possible to produce forecasts for a number of important infectious diseases that are largely independent of the uncertainty of longer-term emissions scenarios. In particular, the role of climate in the modulation of seasonal disease transmission needs to be unravelled from the complex dynamics resulting from the interaction of transmission with herd immunity and intervention measures that depend upon previous burdens of infection. Progress is also needed to solve the mismatch between climate projections and disease projections at the scale of public health interventions. In the time horizon of seasons to years, early warning systems should benefit from current developments on multi-model ensemble climate prediction systems, particularly in areas where high skill levels of climate models coincide with regions where large epidemics take place. A better understanding of the role of climate extremes on infectious diseases is urgently needed.     

Coasts, water levels, and climate change: A Great Lakes perspective

The North American Laurentian Great Lakes hold nearly 20 % of the earth’s unfrozen fresh surface water and have a length of coastline, and a coastal population, comparable to frequently-studied marine coasts. The surface water elevations of the Great Lakes, in particular, are an ideal metric for understanding impacts of climate change on large hydrologic systems, and for assessing adaption measures for absorbing those impacts. In light of the importance of the Great Lakes to the North American and global economies, the Great Lakes and the surrounding region also serve as an important benchmark for hydroclimate research, and offer an example of successful adaptive management under changing climate conditions. Here, we communicate some of the important lessons to be learned from the Great Lakes by examining how the coastline, water level, and water budget dynamics of the Great Lakes relate to other large coastal systems, along with implications for water resource management strategies and climate scenario-derived projections of future conditions. This improved understanding fills a critical gap in freshwater and marine global coastal research.     

A note on climate change adaptation for seaports: a challenge for global ports, a challenge for global society

With 80 % of world trade carried by sea, seaports provide crucial linkages in global supply-chains and are essential for the ability of all countries to access global markets. Seaports are likely to be affected directly and indirectly by climatic changes, with broader implications for international trade and development. Due to their coastal location, seaports are particularly vulnerable to extreme weather events associated with increasing sea levels and tropical storm activity, as illustrated by hurricane “Sandy”. In view of their strategic role as part of the globalized trading system, adapting ports in different parts of the world to the impacts of climate change is of considerable importance. Reflecting the views of a diverse group of stakeholders with expertise in climate science, engineering, economics, policy, and port management, this essay highlights the climate change challenge for ports and suggests a way forward through the adoption of some initial measures. These include both “soft” and “hard” adaptations that may be spearheaded by individual port entities, but will require collaboration and support from a broad range of public and private sector stakeholders and from society at large. In particular, the essay highlights a need to shift to more holistic planning, investment and operation.     

Do people “personally experience” global warming,and if so how,and does it matter?

For most people, the direct and personally observable signals of climate change should be difficult to detect amid the variability of everyday weather. Yet, previous research has shown that some people believe they have personally experienced global warming. Through four related studies, our paper sheds light on what signals of global warming some people believe they are detecting, why, and whether or not it matters. These studies were conducted using population survey and climatic data from a single county in Michigan. Study 1 found that 27% of the county's adult residents felt that they had personally experienced global warming. Study 2 – based on content analysis of people's open-ended responses – found that the most frequently described personal experiences of global warming were changes in seasons (36%), weather (25%), lake levels (24%), animals and plants (20%), and snowfall (19%). Study 3 – based on NOAA climatic data – found that most, but not all, of these detected signals are borne out in the climatic record. Study 4 – using the survey data – found that personal experience of global warming matters in that it predicts perceptions of local risk of global warming, controlling for demographics, political affiliation, and cultural beliefs about national policy outcomes. We conclude that perceived personal experience of global warming appears to heighten people's perception of the risks, likely through some combination of direct experience, vicarious experience (e.g., news media stories), and social construction.