Reducing Uncertainty Surrounding Climate Change Using Emergent Constraints

GSTDTAP

项目编号	1543268
	Reducing Uncertainty Surrounding Climate Change Using Emergent Constraints
	Alexander Hall
主持机构	University of California-Los Angeles
项目开始年	2016
	2016-03-01
项目结束日期	2021-02-28
资助机构	US-NSF
项目类别	Standard Grant
项目经费	999473(USD)
国家	美国
语种	英语
英文摘要	Global climate models (GCMs) are powerful tools for understanding how the climate responds to forcing from greenhouse gas increases and other forcing agents, and they are widely used to provide guidance to policy makers and stakeholders concerned about future climate change. These models capture the basic physics of climate change, but their utility is limited by uncertainties in the representation of important feedback effects. For example in the snow albedo feedback (SAF) warmer conditions lead to reductions in snow cover, replacing a highly reflective snow surface with darker underlying ground cover which absorbs more sunlight, thereby enhancing the initial warming (the opposite holds in a cooling climate). In this project the PIs seek to improve the representation of feedbacks in GCMs by relating observations of present-day climate to simulations of climate change. To do this the PIs use the method of "emergent constraints", which in this context refers to relationships between present-day and future climate change simulations which are robust across an ensemble of GCMs. Previous work by the PIs has shown that the strength of the SAF calculated from future climate simulations is strongly correlated with the SAF derived from the seasonal cycle in present-day simulations from the same models. The SAF derived from the present-day seasonal cycle can be compared with real-world observations, thus providing a means to use available observations to determine which GCMs are most likely to correctly represent the SAF contribution to climate change. The constraint is emergent in the sense that it emerges from a physically motivated examination of the robust collective behavior of an ensemble of GCMs, rather than from a first-principles derivation. The PIs' previous work (see AGS-0135136) established the usefulness of emergent constraints as a way to validate feedback strength in climate change simulations, and the present work seeks to go beyond validation and use such constraints as a means to improve the representation of feedbacks in models. Model development is a more challenging application of emergent constraints than model validation, as the constraints must be specifically related to particular physical parameterizations in the model. In particular, the PIs seek to identify aspects of the land surface component model of the GCM which are key determinants of SAF strength, and determine how they can be adjusted to reduce biases in SAF strength. Possible candidates include the apportionment of snow between the ground and the vegetation canopy. The scope of the work may be expanded to consider the sea ice albedo feedback, which operates similarly to SAF as described above and includes a role for snow on sea ice in determining feedback strength. Further research will consider emergent constraints that determine the extent to which the global hydrological cycle responds to global warming induced by greenhouse gas increases. The global hydrological cycle is expected to accelerate in a warming climate, with increases in global evaporation and precipitation at a rate of a few percent per degree of warming. The reasons for expecting acceleration are well understood, but there is considerable uncertainty in the magnitude of the acceleration, as represented by the large model-to-model spread in climate change simulations. Work here attempts to derive emergent constraints for the hydrological cycle by examining the changes in the atmospheric energy budget that accompany changes in precipitation and evaporation. The emergent constraints connect precipitation-related changes in atmospheric energy budgets from climate change simulations to aspects of atmospheric energetics in present-day simulations, which can then be compared to satellite-based observations. Preliminary work shows, for example, that the clear sky shortwave absorption in climate change simulations is highly correlated with the sensitivity of clear sky shortwave absorption to total precipitable water (TPW) in control simulations. This constraint is of interest because sensitivity to TPW can be constrained by satellite observations, and it is also closely related to the parameterization of shortwave radiative transfer in GCMs. The reduction of uncertainty in model projections of future climate change is of societal as well as scientific interest, as reductions in uncertainty would increase the value of climate change projections used by stakeholders and policy makers confronting the possible impacts of climate change. The PIs will work directly with modeling centers in their model development work, convening special sessions at annual scientific meetings in which results of the project will be shared and key members of the modeling community will offer guidance. Results of these sessions will be synthesized in research publications, in an effort to catalyze a broad effort to improve model parameterizations. In addition, an outreach activity is planned to explain climate models and their uncertainties to the general public. Despite the clear need for accurate, up-to-date, and easily comprehensible information about GCMs, their sources of uncertainty, and the work being done to improve them, such information is not easily available at present. To address this need the project will develop and maintain a website to serve as an educational resource on climate modeling. The website will contain a GCM primer, discussion of sources of uncertainty, and an assessment of the strengths and limitations of GCMs. Further outreach will be conducted through public lectures and demonstrations held at the Discovery Cube, a local science museum. This outreach will include the development of videos to be presented on a Science on a Sphere (SoS) display, and these will be available for use on other SoS displays worldwide. Finally, the project provides for the education and training of a graduate student, thereby developing the future workforce in this scientific area.
来源学科分类	Geosciences - Atmospheric and Geospace Sciences
文献类型	项目
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/69206
专题	环境与发展全球科技态势
推荐引用方式 GB/T 7714	Alexander Hall.Reducing Uncertainty Surrounding Climate Change Using Emergent Constraints.2016.