Quantifying the Uncertainty in Climate Predictions
نویسندگان
چکیده
Uncertainties in projections of future climate change caused by an increase in greenhouse gas concentrations have been a subject of intensive study in recent years. However, in most cases, uncertainties in parameters and characteristics of models used to obtain those projections, such as climate sensitivity or radiative forcing, are described only by ranges of possible values. The resulting uncertainties in variables describing climate change, such as surface warming or sea level rise, are therefore also given just by ranges of possible values. However, for assessing the possible impact of climate change, it would be more useful to have probability distributions for these variables. There are two significant difficulties in obtaining such distributions. First, it is necessary to know probability distributions for the above mentioned uncertain parameters and model characteristics. Second, existing climate and economics models are computationally too expensive for traditional methods of uncertainty propagation such as Monte Carlo simulation. We demonstrate a method for calculating probability distributions for surface air temperature change and sea level rise that result from uncertainties in climate sensitivity and the rate of heat uptake by the deep ocean. These distributions are obtained by applying the Deterministic Equivalent Modeling Method to the MIT climate model. This method provides an effective way of deriving an approximation for the model and allows the propagation of uncertainty. The range and probability distribution of climate sensitivity are based on expert assessments of parameters, while those for the rate of heat uptake are based on the results of simulations with coupled atmosphere-ocean GCMs. As an example of propagating correlated uncertainties, we also show the results of calculations in which the uncertainty in projected increases in forcing is also taken into account. The probability distribution for forcing, associated with an increase in atmospheric CO2 concentrations is calculated based on the distributions for anthropogenic CO2 emissions and the rate of oceanic carbon uptake. The probability distribution for emissions has been calculated in an independent study, while the rate of ocean carbon uptake is assumed to be related to that of heat.
منابع مشابه
Quantifying Uncertainty in Climate Change Analyses
Uncertainty is a feature of any planning study, whether climate change is explicitly included or not. Accounting for and disclosing uncertainty is an established component of good planning practices. In water resources planning this has traditionally included uncertainties associated with natural climate and hydrologic variability, future population and economic conditions, and future technolog...
متن کاملQuantifying Uncertainties in Climate System Properties using Recent Climate Observations
We apply the optimal fingerprint detection algorithm to three independent diagnostics of the recent climate record and derive joint probability density distributions for three uncertain properties of the climate system. The three properties are climate sensitivity, the rate of heat uptake by the deep ocean, and the strength of the net aerosol forcing. Knowing the probability distribution for th...
متن کاملIncorporating uncertainty in predictive species distribution modelling.
Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often u...
متن کاملInfluences of increasing temperature on Indian wheat: quantifying limits to predictability
As climate changes, temperatures will play an increasing role in determining crop yield. Both climate model error and lack of constrained physiological thresholds limit the predictability of yield. We used a perturbed-parameter climate model ensemble with two methods of bias-correction as input to a regional-scale wheat simulation model over India to examine future yields. This model configurat...
متن کاملQuantifying Uncertainty for Non-Gaussian Ensembles in Complex Systems
Many situations in complex systems require quantitative estimates of the lack of information in one probability distribution relative to another. In short term climate and weather prediction, examples of these issues might involve the lack of information in the historical climate record compared with an ensemble prediction, or the lack of information in a particular Gaussian ensemble prediction...
متن کاملBioclimate envelope model predictions for natural resource management: dealing with uncertainty
1. Bioclimate envelope models are widely used to predict the potential distribution of species under climate change, but they are conceptually also suitable tomatch policies and practices to anticipated or observed climate change, for example through species choice in reforestation. Projections of bioclimate envelope models, however, come with large uncertainties due to different climate change...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1998