Although now over 100 years old, the classification of climate originally formulated by Wladimir Koppen and modified by his collaborators and successors, is still in widespread use. It is widely used in teaching school and undergraduate courses on climate. It is also still in regular use by researchers across a range of disciplines as a basis for climatic regionalisation of variables and for assessing the output of global climate models. Here we have produced a new global map of climate using the Koppen-Geiger system based on a large global data set of long-term monthly precipitation and temperature station time series. Climatic variables used in the Koppen-Geiger system were calculated at each station and interpolated between stations using a two-dimensional (latitude and longitude) thin-plate spline with tension onto a 0.1°×0.1° grid for each continent. We discuss some problems in dealing with sites that are not uniquely classified into one climate type by the Koppen-Geiger system and assess the outcomes on a continent by continent basis. Globally the most common climate type by land area is BWh (14.2%, Hot desert) followed by Aw (11.5%, Tropical savannah). The updated world Koppen-Geiger climate map is freely available electronically in the Supplementary Material Section.

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Updated world map of the Köppen-Geiger climate classification

Drafting Authors: Neil Adger, Pramod Aggarwal, Shardul Agrawala, Joseph Alcamo, Abdelkader Allali, Oleg Anisimov, Nigel Arnell, Michel Boko, Osvaldo Canziani, Timothy Carter, Gino Casassa, Ulisses Confalonieri, Rex Victor Cruz, Edmundo de Alba Alcaraz, William Easterling, Christopher Field, Andreas Fischlin, Blair Fitzharris, Carlos Gay García, Clair Hanson, Hideo Harasawa, Kevin Hennessy, Saleemul Huq, Roger Jones, Lucka Kajfež Bogataj, David Karoly, Richard Klein, Zbigniew Kundzewicz, Murari Lal, Rodel Lasco, Geoff Love, Xianfu Lu, Graciela Magrín, Luis José Mata, Roger McLean, Bettina Menne, Guy Midgley, Nobuo Mimura, Monirul Qader Mirza, José Moreno, Linda Mortsch, Isabelle Niang-Diop, Robert Nicholls, Béla Nováky, Leonard Nurse, Anthony Nyong, Michael Oppenheimer, Jean Palutikof, Martin Parry, Anand Patwardhan, Patricia Romero Lankao, Cynthia Rosenzweig, Stephen Schneider, Serguei Semenov, Joel Smith, John Stone, Jean-Pascal van Ypersele, David Vaughan, Coleen Vogel, Thomas Wilbanks, Poh Poh Wong, Shaohong Wu, Gary Yohe

Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change

We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

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Climate Change 2007: The Physical Science Basis.

Social Network Analysis

Mathematical Notes: Review of Linear Algebra. Eigenvalues and Eigenvectors. Multivariate Statistics. Theory and Methods: Foundations of SSA. Details. Noise. Applications: Signal Detection. Filtering. Prediction. Phase Space Reconstruction. Index.

Singular Spectrum Analysis: A New Tool in Time Series Analysis

Introduction. Mathematical Notes. Physics Notes. On Fractals. Attractors. Bifurcations and Routes to Chaos. Chaos Elsewhere. Reconstruction of Dynamics from Observables. Evidence of Chaos in Controlled and Uncontrolled Experiments. Nonlinear Time Series Forecasting. Other Developments and Trends in the Application of Chaos. Index.

Chaos: From Theory to Applications

We consider climate as a network of many dynamical systems and apply ideas from graph theory to a global data set to study its collective behavior. We find that the network has properties of ‘small-world’ networks (Nature 393 (1999) 440). A detailed investigation of the coupling architecture of this network reveals that the overall dynamics emerge from the interaction of two interweaved subnetworks. One subnetwork operates in the tropics and the other at higher latitudes with the equatorial one acting as an agent that establishes links between the two hemispheres. Both subsystems are ‘small-world’ networks, but there are distinct differences between the two subsystems. The tropical one is an almost fully connected network, whereas the mid-latitude one is more like a scale-free network characterized by dominant super nodes, and multifractal properties. This unique architecture may lead to new insights not only about the dynamics of the climate system but of other spatially extended complex systems with a large number of degrees of freedom.

The architecture of the climate network

The study of networks has recently exploded into a major research tool in many areas of science. The discovery of “small world” and scale-free networks has led to many new insights about the collective behavior of a large number of interacting agents and complex systems. Here we introduce the basic ideas behind networks, as well as some initial applications of networks to the climate system. Our results suggest that the climate system exhibits aspects of small-world networks as well as scale-free networks, with supernodes corresponding to major teleconnection patterns. This result suggests that the organization of teleconnections may play a role in the stability of the climate system. In addition, preliminary work suggests that temporal changes in the network's architecture may be used to identify signatures of global change. These and other applications suggest that networks provide a new tool for investigating and reconstructing climate dynamics from both models and observations.

What Do Networks Have to Do with Climate

NONLINEAR forecasting has recently been shown to distinguish between deterministic chaos and uncorrelated (white) noise added to periodic signals1, and can be used to estimate the degree of chaos in the underlying dynamical system2. Distinguishing the more general class of coloured (autocorrelated) noise has proven more difficult because, unlike additive noise, the correlation between predicted and actual values measured may decrease with time—a property synonymous with chaos. Here, we show that by determining the scaling properties of the prediction error as a function of time, we can use nonlinear prediction to distinguish between chaos and random fractal sequences. Random fractal sequences are a particular class of coloured noise which represent stochastic (infinite-dimensional) systems with power-law spectra. Such sequences have been known to fool other procedures for identifying chaotic behaviour in natural time series9, particularly when the data sets are small. The recognition of this type of noise is of practical importance, as measurements from a variety of dynamical systems (such as three-dimensional turbulence, two-dimensional and geostrophic turbulence, internal ocean waves, sandpile models, drifter trajectories in large-scale flows, the motion of a classical electron in a crystal and other low-dimensional systems) may over some range of frequencies exhibit power-law spectra.

Anastasios A. Tsonis

Papers

Singular Spectrum Analysis: A New Tool in Time Series Analysis

Chaos: From Theory to Applications

The architecture of the climate network

What Do Networks Have to Do with Climate

Nonlinear prediction as a way of distinguishing chaos from random fractal sequences