• Corpus ID: 1113183

Title Modeling and assessing climatic trends

@inproceedings{Craigmile2017TitleMA,
  title={Title Modeling and assessing climatic trends},
  author={Peter F. Craigmile},
  year={2017}
}
Climate studies often fit linear trends to data. In many cases simplifying assumptions such as independent errors and constant variance are used. We review a variety of approaches to estimating linear trends, and illustrate with US temperature data how oversimplified assumptions may lead to false significance. We outline a variety of methods to fit nonlinear trend models. Using the Berkeley Earth global data set we show that a bent cable fit is better than a linear fit for this series. We also… 

Figures from this paper

References

SHOWING 1-10 OF 105 REFERENCES
Space‐time modelling of trends in temperature series
Classical assessments of temperature trends are based on the analysis of a small number of time series. Considering trend to be only smooth changes of the mean value of a stochastic process through
Statistical Modeling of Spatial Extremes
The areal modeling of the extremes of a natural process such as rainfall or temperature is important in environmental statistics; for example, understanding extreme areal rainfall is crucial in flood
Reconstructing past temperatures from natural proxies and estimated climate forcings using short- and long-memory models
rst linking the latent temperature series to three main external forcings (solar irradiance, greenhouse gas concentration, and volcanism), and the second linking the observed temperature proxy data
A Spatio-Temporal Model for Mean, Anomaly, and Trend Fields of North Atlantic Sea Surface Temperature
We consider the problem of fitting a statistical model to 30 years of sea surface temperature records collected over a large portion of the Northern Atlantic. The observations were collected sparsely
Statistical modeling of extreme value behavior in North American tree-ring density series
Many analyses of the paleoclimate record include conclusions about extremes, with a focus on the unprecedented nature of recent climate events. While the use of extreme value theory is becoming
Climate change, Hurst phenomenon, and hydrologic statistics
The intensive research of the recent years on climate change has led to the strong conclusion that climate has ever, through the planet history, changed irregularly on all time scales. Climate
Nonparametric spatial models for extremes: application to extreme temperature data
TLDR
A Dirichlet-based copula model is presented that is a flexible alternative to parametric copula models such as the normal and t-copula and fitted using a Bayesian framework that allow us to take into account different sources of uncertainty in the data and models.
CAN A REGIONAL CLIMATE MODEL REPRODUCE OBSERVED EXTREME TEMPERATURES
Using output from a regional Swedish climate model and observations from the Swedish synoptic observational network, we compare seasonal minimum temperatures from model output and observations using
Long-Lead Prediction of Pacific SSTs via Bayesian Dynamic Modeling
TLDR
This article presents a new procedure for long-lead forecasting of tropical Pacific SST fields that expresses qualitative aspects of scientific paradigms for SST dynamics in a statistical manner and acquires considerable predictive skill.
A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling
We analyze output from six regional climate models (RCMs) via a spatial Bayesian hierarchical model. The primary advantage of this approach is that the statistical model naturally borrows strength
...
1
2
3
4
5
...