Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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

Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems

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.

/pdf/climate-change-2007-the-physical-science-basis-1x93ttz9jt.pdf

Climate Change 2007: The Physical Science Basis.

Impatti, adattamento e vulnerabilita Le cause e le responsabilita dei cambiamenti climatici sono state trattate sul numero di ottobre della rivista Cda. Approfondiamo l’argomento presentando il documento: “Cambiamenti climatici 2007: impatti, adattamento e vulnerabilita” votato ad aprile 2007 dal secondo gruppo di lavoro del Comitato Intergovernativo sui Cambiamenti Climatici (Intergovernmental Panel on Climate Change). Si tratta del secondo di tre documenti che compongono il quarto rapporto sui cambiamenti climatici.

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Climate Change 2007: Impacts, Adaptation and Vulnerability.

It is common knowledge that increasing CO2 concentration plays a major role in enhancement of the greenhouse effect and contributes to global warming. The purpose of this study is to complement the conventional and established theory that increased CO2 concentration due to human emissions causes an increase of temperature, by considering the reverse causality. Since increased temperature causes an increase in CO2 concentration, the relationship of atmospheric CO2 and temperature may qualify as belonging to the category of “hen-or-egg” problems, where it is not always clear which of two interrelated events is the cause and which the effect. We examine the relationship of global temperature and atmospheric carbon dioxide concentration at the monthly time step, covering the time interval 1980–2019, in which reliable instrumental measurements are available. While both causality directions exist, the results of our study support the hypothesis that the dominant direction is T → CO2. Changes in CO2 follow changes in T by about six months on a monthly scale, or about one year on an annual scale. We attempt to interpret this mechanism by involving biochemical reactions, as at higher temperatures soil respiration, and hence CO2 emission, are increasing.

https://www.mdpi.com/2413-4155/2/4/83/pdf

atmospheric temperature and co2: hen-or-egg causality?

Causality is a central concept in science, in philosophy and in life. However, reviewing various approaches to it over the entire knowledge tree, from philosophy to science and to scientific and technological applications, we locate several problems, which prevent these approaches from defining sufficient conditions for the existence of causal links. We thus choose to determine necessary conditions that are operationally useful in identifying or falsifying causality claims. Our proposed approach is based on stochastics, in which events are replaced by processes. Starting from the idea of stochastic causal systems, we extend it to the more general concept of hen-or-egg causality, which includes as special cases the classic causal, and the potentially causal and anti-causal systems. Theoretical considerations allow the development of an effective algorithm, applicable to large-scale open systems, which are neither controllable nor repeatable. The derivation and details of the algorithm are described in this paper, while in a companion paper we illustrate and showcase the proposed framework with a number of case studies, some of which are controlled synthetic examples and others real-world ones arising from interesting scientific problems.

Revisiting causality using stochastics: 1. Theory

There have been many events connected with damaging abundance of water in Polish towns in recent decades. Some of them have been caused by flood waves on rivers passing through towns. Others have been caused by intense precipitation overwhelming the capacity of storm sewer systems. A brief review of the topical area of urban floods in Poland in last decades is provided. Mechanisms responsible for increase of flood risk are discussed in a systematic manner. In result of multiple mechanisms, the frequency of inundations has increased and is likely to increase further. Examples of sustainable management issues related to floods are reviewed. Flood protection and flood preparedness are examined in the sustainability context.

Urban Flooding and Sustainable Land Management – Polish Perspective

In this study, we apply a previously calibrated SWAT model of the Vistula and Odra basins in order to assess hydrological impacts of climate change in the Upper Vistula Basin. Raw projections from an ensemble of nine EUR-11 CORDEX climate model runs (precipitation and minimum and maximum temperatures) assuming an intermediate greenhouse gas emission scenario of 4.5 W/m2 were adjusted using a quantile-mapping correction approach. We analysed changes between two future horizons 2024–2050 (near future) and 2074–2100 (far future) and a reference period (1974–2000). We found that, for the near future, all climate models agree well about ubiquitous warming on both seasonal and annual scales, while eight models agree about an increase in projected mean annual precipitation and total runoff. For the far future, an increase in temperature, in mean annual precipitation, as well as in the total runoff, is projected using all climate models. Results also highlighted a higher temperature increase in winter than in other seasons and a higher increase in minimum temperature than in maximum temperature. The highest runoff increase is projected in winter, consistently, by all climate models. In addition, we assessed projected changes in high streamflow indicator based on the 90th monthly flow percentile (Q90). Based on the median of climate model simulations, we found that the mean basin-wide increase in monthly Q90 is 6.4 and 15 % for the near future and the far future, respectively. Nevertheless, the range of projected changes in precipitation, runoff and high flows calculated across the whole ensemble remains relatively high and spatial patterns are not fully consistent across different climate models.

Hydroclimatic Projections for the Upper Vistula Basin

The aim of the present contribution, opening a session on climate change and hydrology at the 2002 Nordic Hydrological Conference in Roros, Norway, is to discuss essential water-related findings of the Third Assessment Report (TAR) of the Intergovernmental Panel on Climate Change (IPCC), with particular reference to region-specific issues of the Nordic region. Discussion of impacts of climate variability and change embraces both already observed effects and projections for the future. After review of changes in hydrological processes, climate-related impacts on extreme hydrological events – floods and droughts – are outlined. Finally, adaptation and vulnerability are dealt with, including presentation of key water-related regional concerns in various parts of the World.

Zbigniew W. Kundzewicz

Papers

atmospheric temperature and co2: hen-or-egg causality?

Revisiting causality using stochastics: 1. Theory

Urban Flooding and Sustainable Land Management – Polish Perspective

Hydroclimatic Projections for the Upper Vistula Basin

Water and climate: The IPCC TAR perspective