ESSAY: Model interpretation of climate signals: an application to the Asian monsoon climate - The Global Climate System: Patterns, Processes, and Teleconnections - page 290

Figure 9.1 Synergistic
application of observations
and models for climate
diagnostic and prediction
studies.
full, comprehensive description of the Earth's climate system. For such a
description, data assimilation plays a critical role. Data assimilation is the
numerical process by which observations are assimilated into models to
produce a complete set of dynamically consistent data sets for the entire
climate system (Kalnay et al. 1996 ). Climate predictions can be derived either
from observations through statistical techniques or from climate models, or
from a combination, i.e. statistical-dynamical predictions. Data assimilation
can provide models with appropriate initial states to produce more skilful
predictions.
In this chapter, we address the various issues arising from using models to
detect, understand, and predict climate signals. This chapter consists of two
main parts. The first part is devoted to discussions of climate models as a tool
for climate studies, including a brief history of the development of climate
models, model basics, and modeling methodologies used in modeling studies.
The second part is an illustration of the use of climate models to study the
anomalous climate of the Asian monsoon.
9.2 A climate model primer
9.2.1 A brief history
Climate models originate from atmospheric general circulation models (AGCM)
used in numerical weather forecasting. AGCMs for numerical weather fore-
casting were developed during the 1950s and 1960s. (Charney et al. 1950 ;
Smagorinsky et al. 1965 ; Bengtsson and Simmons 1983 ). By the early 1970s
most weather services around the world had adopted numerical weather
prediction models for short-term (days) to medium-range (weeks) weather
forecasting. During that period, climate modelers first began to explore the