Characterizing the Relative Importance Assigned to Physical Variables by Climate Scientists when Assessing Atmospheric Climate Model Fidelity

Evaluating a climate model＇s fidelity（ability to simulate observed climate） is a critical step in establishing confidence in the model＇s suitability for future climate projections, and in tuning climate model parameters. Model developers use their judgement in determining which trade-offs between different aspects of model fidelity are acceptable. However, little is known about the degree of consensus in these evaluations, and whether experts use the same criteria when different scientific objectives are defined. Here, we report on results from a broad community survey studying expert assessments of the relative importance of different output variables when evaluating a global atmospheric model＇s mean climate. We find that experts adjust their ratings of variable importance in response to the scientific objective, for instance, scientists rate surface wind stress as significantly more important for Southern Ocean climate than for the water cycle in the Asian watershed. There is greater consensus on the importance of certain variables（e.g., shortwave cloud forcing） than others（e.g., aerosol optical depth）. We find few differences in expert consensus between respondents with greater or less climate modeling experience,and no statistically significant differences between the responses of climate model developers and users. The concise variable lists and community ratings reported here provide baseline descriptive data on current expert understanding of certain aspects of model evaluation, and can serve as a starting point for further investigation, as well as developing more sophisticated evaluation and scoring criteria with respect to specific scientific objectives.

Restimate:Recovery Estimation Tool for Resilience Planning

The U.S.National Institute of Standards and Technology(NIST)published the Community Resilience Planning Guide in 2016.The NIST Guide advocates for a participatory process for developing a performance measurement framework for the jurisdiction’s resilience against a scenario hazard.The framework centers around tables of expected and desired recovery times for selected community assets,such as electricity,water,and natural gas infrastructures.The NIST Guide does not provide a method for estimating the expected recovery times.However,building high-fidelity computer models for such estimations requires substantial resources that even larger ju-risdictions cannot cost-justify.The most promising approach to recovery time estimation is to systematically use data elicited from people to tap into the wisdom of the(knowledgeable)crowd.This paper describes a novel research-through-design project to enable the computer-supported elicitation of recovery time series data.This work is the first in the literature to examine people’s ability to estimate recovery curves and how design in-fluences such estimations.Its main contribution to resilience planning is three-fold:development of a new elicitation tool called Restimate,understanding its potential user base,and providing insights into how it can facilitate resilience planning.Restimate is the first tool to enable evidence-based expert elicitation in any community with limited resources for resilience planning.Beyond resilience planning,those who facilitate high-stakes planning activities under large uncertainties(e.g.,mission-critical system design and planning)will benefit from a similar research-through-design process.

A model to determine the lake nutrient standards for drinking water sources in Yunnan-Guizhou Plateau Ecoregion, China

Lake eutrophication （LE） has become an increasingly severe environmental problem recently. However, there has been no nutrient standard established for LE control in many developing countries such as China. This study proposes a structural equation model to assist in the establishment of a lake nutrient standard for drinking water sources in Yunnan-Guizhou Plateau Ecoregion （Yungui Ecoregion）, China. The modeling results indicate that the most predictive indicator for designated use-attainment is total phosphorus （TP） （total effect =-0.43）, and chlorophyll a （Chl-a） is recommended as the second important indicator （total effect =-0.41）. The model is further used for estimating the probability of use-attainment associated with lake water as a drinking water source and various levels of candidate criteria （based on the reference conditions and the current environmental quality standards for surface water）. It is found that these candidate criteria cannot satisfy the designated 100% use-attainment. To achieve the short-term target （85% attainment of the designated use）, TP and Chl-a values ought to be less than 0.02 mg/L and 1.4 μg/L, respectively. When used as a long-term target （90% or greater attainment of the designated use）, the TP and Chl-a values are suggested to be less than 0.018 mg/L and 1 μg/L, respectively.