Spatial Analysis in Climatology

Authored by: Douglas Nychka , Christopher K. Wikle

Handbook of Environmental and Ecological Statistics

Print publication date:  September  2017
Online publication date:  January  2019

Print ISBN: 9781498752022
eBook ISBN: 9781315152509
Adobe ISBN:


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An emerging view of the Earth’s physical environment is as a system that encompasses the interactions and dynamics among many different components, including the atmosphere, the ocean, the cryopsphere, the land surface, and the external drivers from human activities. Climatology is concerned with multivariate interactions in the Earth system over a wide range of spatial and temporal scales. Here the focus is on the distribution of phenomena as opposed to specific events. To assist in understanding the components of such a system, researchers collect observations, explore relationships among variables, perform inference, and predict behavior across and between these scales of variability. Thus, by its very nature, climatology is a spatial science and closely dependent on maps (their creation, interaction and evolution). Indeed, one of the most important climate classification schemes, the so-called Köppen climate classification, was developed by Wladimir Köppen in 1884 to display world climate zones visually on a map. As climatology gradually developed from a qualitative geographical science to a more physically and statistically based science, the use of rigorous statistical methods to account for spatial information became more commonplace. Besides interpreting observations of the Earth system, spatial analysis is also important for interpreting the output of numerical simulations (e.g. climate models) and for transforming irregular data sets into more convenient data products. Thus, throughout this chapter we take a broad view of climate “data” that can include the results of deterministic models, derived parameters, and previous analyses. Another running theme throughout this chapter is that, although spatial statistical methods have the advantage of formal uncertainty quantification owing to their probabilistic foundation, there are instances in the climate sciences where such advanced methods are not used, either due to lack of demonstrated benefit, a focus on other issues (e.g. systematic biases), or ease of explanation to data users. It is also the case that many of the exploratory and descriptive methods that have traditionally been used in climate science and in meteorology and oceanography (see Chapter 33) have motivated the development of new formal statistical methods for spatial data.

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