Publications


Convective forcing fluctuations in a cloud-resolving model: Relevance to the stochastic parameterization problem

JOURNAL OF CLIMATE 20 (2007) 187-202

GJ Shutts, TN Palmer


Recent Advances in Radiation Transfer Parametrizations. ECMWF Tech Memo.

(2007) 539

J-J Morcrette, P Bechtold, A Beljaars, A Weisheimer


Initialisation strategies for decadal hindcasts for the 1960-2005 period within the ENSEMBLES project. ECMWF Tech Memo.

(2007) 521

A Weisheimer, FJ Doblas-Reyes, P Rogel, N Keenlyside, MA Balmaseda, J Murphy, D Smith, M Collins, B Bhaskaran, TN Palmer


Seasonal Forecast Datasets - A resource for Calibrating Regional Climate Change Projections?

CLIVAR Exchanges 43 (2007) 6-7

TN Palmer, FJ Doblas-Reyes, A Weisheimer, M Rodwell


Preface

Journal of Climate 19 (2006) 4975-4976

A Busalacchi, TN Palmer


Changing frequency of occurrence of extreme seasonal temperatures under global warming (vol 32, art no L20721, 2005)

GEOPHYSICAL RESEARCH LETTERS 33 (2006) ARTN L07712

A Weisheimer, TN Palmer


Developments in dynamical seasonal forecasting relevant to agricultural management

CLIMATE RESEARCH 33 (2006) 19-26

FJ Doblas-Reyes, R Hagedorn, TN Palmer


Impact of increasing greenhouse gas concentrations in seasonal ensemble forecasts

GEOPHYSICAL RESEARCH LETTERS 33 (2006) ARTN L07708

FJ Doblas-Reyes, R Hagedorn, TN Palmer, JJ Morcrette


Malaria early warnings based on seasonal climate forecasts from multi-model ensembles

NATURE 439 (2006) 576-579

MC Thomson, FJ Doblas-Reyes, SJ Mason, R Hagedorn, SJ Connor, T Phindela, AP Morse, TN Palmer


DEMETER and the application of seasonal forecasts

in Predictability of Weather and Climate, 9780521848824 (2006) 674-692

R Hagedorn, FJ Doblas-Reyes, TN Palmer

© Cambridge University Press 2006 and 2010. A multimodel ensemble-based system for seasonal-to-interannual prediction has been developed in a joint European project known as DEMETER (Development of a European Multi-Model Ensemble System for Seasonal to Interannual Prediction). The DEMETER system comprises seven global coupled atmosphere-ocean models, each running from an ensemble of initial conditions. Comprehensive hindcast evaluation demonstrates the enhanced reliability and skill of the multimodel ensemble over a more conventional single-model ensemble approach. In addition, applications of seasonal ensemble forecasts have been incorporated into the DEMETER system. As an example of this innovative end-to-end system strategy, the use of DEMETER data in malaria forecasting processes is discussed. The strategy followed in DEMETER deals with important problems such as communication across disciplines, downscaling of climate simulations, and use of probabilistic forecast information. This illustrates the economic value of seasonal-to-interannual prediction for society as a whole. Introduction Seasonal-timescale climate predictions are now made routinely at a number of operational meteorological centres around theworld, using comprehensive coupled models of the atmosphere, oceans, and land surface (Stockdale et al., 1998; Mason et al., 1999; Alves et al., 2002; Kanamitsu et al., 2002). They are clearly of value to a wide cross-section of society, for personal, commercial and humanitarian reasons (Thomson et al., 2000; Hartmann et al., 2002b). However, the successful transition from research activity to full operational practice has led some potential users of seasonal forecasts to have unrealistic expectations of what is practicable (‘We are getting married in six months time-should we order a marquee for the wedding reception, or will it be dry that day?’).


Predictability of weather and climate: From theory to practice

in Predictability of Weather and Climate, 9780521848824 (2006) 1-29

TN Palmer

© Cambridge University Press 2006 and 2010. Introduction A revolution in weather and climate forecasting is in progress, made possible by theoretical advances in our understanding of the predictability of weather and climate on the one hand, and by the extraordinary developments in supercomputer technology on the other. Specifically, through ensemble prediction, whose historical development has been documented by Lewis (2005), weather and climate forecasting is set to enter a new era, addressing quantitatively the prediction of weather and climate risk in a range of commercial and humanitarian applications. This chapter gives some background to this revolution, with specific examples drawn from a range of timescales. Perspectives on predictability: theoretical and practical Predictions of weather and climate are necessarily uncertain; our observations of weather and climate are uncertain and incomplete, the models into which we assimilate this data and predict the future are uncertain, and external effects such as volcanoes and anthropogenic greenhouse emissions are also uncertain. Fundamentally, therefore, we should think of weather and climate prediction in terms of equations whose basic prognostic variables are probability densities ρ(X, t), where X denotes some climatic variable and t denotes time. In this way, ρ(X, t)dV represents the probability that, at time t, the true value of X lies in some small volume dV of state space. Prognostic equations for ρ, the Liouville and Fokker-Planck equations, are described in Ehrendorfer (this volume). In practice these equations are solved by ensemble techniques, as described in Buizza (this volume).


Predictability of weather and climate

, 2006

T Palmer, R Hagedorn

© Cambridge University Press 2006 and 2010. The topic of predictability of weather and climate has advanced significantly over recent years, both through an increased understanding of the phenomena that affect predictability, and through development of techniques used to forecast state-dependent predictability. This book brings together some of the world’s leading experts on predictability of weather and climate. It addresses predictability from the theoretical to the practical, on timescales from days to decades. Topics such as the predictability of weather phenomena, coupled ocean–atmosphere systems and anthropogenic climate change are among those included. Ensemble systems for forecasting predictability are discussed extensively. Ed Lorenz, father of chaos theory, makes a contribution to theoretical analysis with a previously unpublished paper. This well-balanced volume will be a valuable resource for many years. High-quality chapter authors and extensive subject coverage will make it appeal to people with an interest in weather and climate forecasting and environmental science, from graduate students to researchers.


Developments in dynamical seasonal forecasting relevant to agricultural management

Climate Research 33 (2006) 19-26

FJ Doblas-Reyes, R Hagedorn, TN Palmer

Recent developments in dynamical seasonal forecasting of potential relevance to agricultural management are discussed. These developments emphasize the importance of using a fully probabilistic approach at all stages of the forecasting process, from the dynamical ocean-atmosphere models used to predict climate variability at seasonal and interannual time scales, through the models used to downscale the global output to finer scales, to the end-user forecast models. The final goal is to create an end-to-end multi-scale (both in space and time) integrated prediction system that provides skilful, useful predictions of variables with socio-economic interest. Multi-model ensemble predictions made with the leading European global coupled climate models as part of the DEMETER (Development of a European Multi-model Ensemble system for seasonal to inTERannual prediction) project are used as an example to illustrate the potential of producing useful probabilistic predictions of seasonal climate fluctuations and of applying them to crop yield forecasting. © Inter-Research 2006.


Erratum: "Changing frequency of occurrence of extreme seasonal temperatures under global warming" (Geophysical Research Letters (2005) vol. 32 10.1029/2005GL023365)

Geophysical Research Letters 33 (2006)

A Weisheimer, TN Palmer


Influence of a stochastic parameterization on the frequency of occurrence of North Pacific weather regimes in the ECMWF model

GEOPHYSICAL RESEARCH LETTERS 32 (2005) ARTN L23811

T Jung, TN Palmer, GJ Shutts


The rationale behind the success of multi-model ensembles in seasonal forecasting - I. Basic concept

TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY 57 (2005) 219-233

R Hagedorn, FJ Doblas-Reyes, TN Palmer


The rationale behind the success of multi-model ensembles in seasonal forecasting - II. Calibration and combination

TELLUS SERIES A-DYNAMIC METEOROLOGY AND OCEANOGRAPHY 57 (2005) 234-252

FJ Doblas-Reyes, R Hagedorn, TN Palmer


Recurrent climate winter regimes in reconstructed and modelled 500 hPa geopotential height fields over the North Atlantic/European sector 1659-1990

CLIMATE DYNAMICS 24 (2005) 809-822

C Casty, D Handorf, CC Raible, JF Gonzalez-Rouco, A Weisheimer, E Xoplaki, J Luterbacher, K Dethloff, H Wanner


Probabilistic prediction of climate using multi-model ensembles: from basics to applications.

Philos Trans R Soc Lond B Biol Sci 360 (2005) 1991-1998

TN Palmer, FJ Doblas-Reyes, R Hagedorn, A Weisheimer

The development of multi-model ensembles for reliable predictions of inter-annual climate fluctuations and climate change, and their application to health, agronomy and water management, are discussed.


Changing frequency of occurrence of extreme seasonal temperatures under global warming

Geophysical Research Letters 32 (2005) 1-5

A Weisheimer, TN Palmer

Using a multi-model multi-scenario ensemble of integrations made for the forthcoming fourth assessment report of the Intergovernmental Panel on Climate Change, the frequency of occurrence of extreme seasonal temperatures at the end of the 21st Century is estimated. In this study an extreme temperature is defined as lying above the 95 percentile of the simulated temperature distribution for 20th Century climate. The model probability of extreme warm seasons is heterogeneous over the globe and rises to over 90% in large parts of the tropics. This would correspond to an average return period of such anomalous warm seasons of almost one year. The reliability of these results is assessed using the bounding box technique, previously used to quantify the reliability of seasonal climate forecasts. It is shown that the dramatic increase in extreme warm seasons arises from the combined effect of a shift and a broadening of the temperature distributions. Copyright 2005 by the American Geophysical Union.

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