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

Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.

https://www.cell.com/trends/ecology-evolution/pdf/S0169-5347(06)00303-X.pdf

Ensemble forecasting of species distributions

This chapter assesses long-term projections of climate change for the end of the 21st century and beyond, where the forced signal depends on the scenario and is typically larger than the internal variability of the climate system. Changes are expressed with respect to a baseline period of 1986–2005, unless otherwise stated.

Long-term Climate Change: Projections, Commitments and Irreversibility

There is now a large published literature on the strengths and weaknesses of downscaling methods for different climatic variables, in different regions and seasons. However, little attention is given to the choice of downscaling method when examining the impacts of climate change on hydrological systems. This review paper assesses the current downscaling literature, examining new developments in the downscaling field specifically for hydrological impacts. Sections focus on the downscaling concept; new methods; comparative methodological studies; the modelling of extremes; and the application to hydrological impacts.

Consideration is then given to new developments in climate scenario construction which may offer the most potential for advancement within the ‘downscaling for hydrological impacts’ community, such as probabilistic modelling, pattern scaling and downscaling of multiple variables and suggests ways that they can be merged with downscaling techniques in a probabilistic climate change scenario framework to assess the uncertainties associated with future projections. Within hydrological impact studies there is still little consideration given to applied research; how the results can be best used to enable stakeholders and managers to make informed, robust decisions on adaptation and mitigation strategies in the face of many uncertainties about the future. It is suggested that there is a need for a move away from comparison studies into the provision of decision-making tools for planning and management that are robust to future uncertainties; with examination and understanding of uncertainties within the modelling system. Copyright © 2007 Royal Meteorological Society

/pdf/linking-climate-change-modelling-to-impacts-studies-recent-k16pcnifay.pdf

Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling

This chapter assesses long-term projections of climate change for the end of the 21st century and beyond, where the forced signal depends on the scenario and is typically larger than the internal variability of the climate system. Changes are expressed with respect to a baseline period of 1986-2005, unless otherwise stated.

Chapter 12 - Long-term climate change: Projections, commitments and irreversibility

Exploring The Linearity of The Climate Response To External Forcing

Figure 1. Relationship between climate sensitivity (colours), effective ocean heat capacity and 20th century warmingattributable to changes in greenhouse gases. Diamonds show simulation results based on (a) uniform sampling of climatesensitivity, S, and (b) uniform sampling of feedback strength, or l. Black contours enclose the region consistent withobservations at the 5% level. Panel (c) shows distributions of climate sensitivity based on these observations, assuming auniform initial distribution in sensitivity (red), in feedback strength (blue) and in attributable warming and heat capacity(black), as well as in forecast warming under the A1FI (black dotted) and B1 scenarios (black dashed) and in TCR (greencurve). Panel (d) shows a reconstruction of the original data (thick grey curve) using assuming a neutral prior in sensitivity(red), feedback strength (blue) attributable warming (black solid line), A1FI (black dashed) and B1 scenarios (black dotted)and TCR (green). Panels (e) and (f) show 1990–2100 global mean temperature change under the A1FI and B1 scenariosrespectively, starting from a uniform distribution for S (red), l (blue) and forecast warming (black).

2 Constraining climate forecasts: The role of prior assumptions

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Exploring The Linearity of The Climate Response To External Forcing

2 Constraining climate forecasts: The role of prior assumptions