[1] Using observed data sets mainly for the period 1979–2005, we find that anomalous warming events different from conventional El Nino events occur in the central equatorial Pacific. This unique warming in the central equatorial Pacific associated with a horseshoe pattern is flanked by a colder sea surface temperature anomaly (SSTA) on both sides along the equator. empirical orthogonal function (EOF) analysis of monthly tropical Pacific SSTA shows that these events are represented by the second mode that explains 12% of the variance. Since a majority of such events are not part of El Nino evolution, the phenomenon is named as El Nino Modoki (pseudo-El Nino) (“Modoki” is a classical Japanese word, which means “a similar but different thing”). The El Nino Modoki involves ocean-atmosphere coupled processes which include a unique tripolar sea level pressure pattern during the evolution, analogous to the Southern Oscillation in the case of El Nino. Hence the total entity is named as El Nino–Southern Oscillation (ENSO) Modoki. The ENSO Modoki events significantly influence the temperature and precipitation over many parts of the globe. Depending on the season, the impacts over regions such as the Far East including Japan, New Zealand, western coast of United States, etc., are opposite to those of the conventional ENSO. The difference maps between the two periods of 1979–2004 and 1958–1978 for various oceanic/atmospheric variables suggest that the recent weakening of equatorial easterlies related to weakened zonal sea surface temperature gradient led to more flattening of the thermocline. This appears to be a cause of more frequent and persistent occurrence of the ENSO Modoki event during recent decades.

El Niño Modoki and its possible teleconnection

[1] The Madden-Julian Oscillation (MJO) is the dominant component of the intraseasonal (30–90 days) variability in the tropical atmosphere. It consists of large-scale coupled patterns in atmospheric circulation and deep convection, with coherent signals in many other variables, all propagating eastward slowly (∼5 m s−1) through the portion of the Indian and Pacific oceans where the sea surface is warm. It constantly interacts with the underlying ocean and influences many weather and climate systems. The past decade has witnessed an expeditious progress in the study of the MJO: Its large-scale and multiscale structures are better described, its scale interaction is recognized, its broad influences on tropical and extratropical weather and climate are increasingly appreciated, and its mechanisms for disturbing the ocean are further comprehended. Yet we are facing great difficulties in accurately simulating and predicting the MJO using sophisticated global weather forecast and climate models, and we are unable to explain such difficulties based on existing theories of the MJO. It is fair to say that the MJO remains an unmet challenge to our understanding of the tropical atmosphere and to our ability to simulate and predict its variability. This review, motivated by both the acceleration and gaps in our knowledge of the MJO, intends to synthesize what we currently know and what we do not know on selected topics: its observed basic characteristics, mechanisms, numerical modeling, air-sea interaction, and influences on the El Nino and Southern Oscillation.

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Madden‐Julian Oscillation

Power of the Mann–Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series

(2005). Atmospheric Modeling, Data Assimilation, and Predictability. Technometrics: Vol. 47, No. 4, pp. 521-521.

Atmospheric Modeling, Data Assimilation, and Predictability

Climate change is causing drylands to expand and this work shows that they will cover half of the land surface by 2100 under a moderate emissions scenario. Drylands are home to more than 38% of the total global population and are one of the most sensitive areas to climate change and human activities1,2. Projecting the areal change in drylands is essential for taking early action to prevent the aggravation of global desertification3,4. However, dryland expansion has been underestimated in the Fifth Coupled Model Intercomparison Project (CMIP5) simulations5 considering the past 58 years (1948–2005). Here, using historical data to bias-correct CMIP5 projections, we show an increase in dryland expansion rate resulting in the drylands covering half of the global land surface by the end of this century. Dryland area, projected under representative concentration pathways (RCPs) RCP8.5 and RCP4.5, will increase by 23% and 11%, respectively, relative to 1961–1990 baseline, equalling 56% and 50%, respectively, of total land surface. Such an expansion of drylands would lead to reduced carbon sequestration and enhanced regional warming6,7, resulting in warming trends over the present drylands that are double those over humid regions. The increasing aridity, enhanced warming and rapidly growing human population will exacerbate the risk of land degradation and desertification in the near future in the drylands of developing countries, where 78% of dryland expansion and 50% of the population growth will occur under RCP8.5.

Accelerated dryland expansion under climate change

Various problems in climate research, which require the use of advanced statistical techniques, are considered in this book. The examples emphasize the notion that the knowledge of statistical techniques alone is not sufficient. Instead, good physical understanding of the specific problems in climate research, such as the enormous size of the phase space, the correlation of processes on different time and space scales and the availability of essentially one observational record, is needed to guide the researcher in choosing the right approach to obtain meaningful answers. The second edition of this book, originally based on contributions given during a school sponsored by the European Union on the Italian island of Elba, continues to be based on the general principles that made the first edition a popular choice. The general outline has been kept the same, covering aspects such as the examination of the observational record, stochastic climate models, analytical techniques, e.g. EOF, teleconnections and so on, but the chapters have been revised and updated, in some cases extensively, to cover the advances in the field in the years since the first edition.

Analysis of climate variability : applications of statistical techniques

Analysis of Climate Variability

Swadhin Behera1, Jingjia Luo1, Sebastien Masson1, Toshio Yamagata1,2,Pascale Delecluse3,4, Silvio Gualdi5 and Antonio Navarra5 1Frontier Research System for Global Change, Yokohama, Kanagawa, Japan 2Department of Earth and Planetary Science, The University of Tokyo, Tokyo, Japan 3Laboratoire d’Oceanographie Dynamique et de Climatologie (LODYC), Paris, France 4Laboratoire des Sciences du Climat et de l’Environnement (LSCE ), Orme, France 5Istituto Nazionale di Geofisica e Vulcanologia (INGV), Bologna, Italy corresponding e-mail: yamagata@eps.s.u-tokyo.ac.jp

Impact of the Indian Ocean Dipole on the East African Short Rains: A CGCM Study *

Singular value decomposition analysis (SVDA) is used to analyze an ensemble of three 34-yr general circulation model (GCM) simulations forced with observed sea surface temperature. It is demonstrated how statistical postprocessing based on the leading SVDA modes of simulated and observed fields, primarily precipitation, can be applied to improve the skill of the simulation. For a given limited prediction region, the GCM has the potential to nonlinearly transform the SST information from around the globe and produce a dynamic solution over the region that can be statistically corrected to account for such features as systematic shifts in the location of anomaly dipoles. This paper does not address the separate question of whether the current generation of GCMs contain information above that which could be extracted using linear statistical relationships with SST. For precipitation, examples are drawn from skillful tropical regions, as well as the moderate-to-low skill Pacific–North American and No...

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Reduction of Model Systematic Error by Statistical Correction for Dynamical Seasonal Predictions

In this article, the authors describe an innovative multimodel system developed within the Climate Change and Impact Research: The Mediterranean Environment (CIRCE) European Union (EU) Sixth Framework Programme (FP6) project and used to produce simulations of the Mediterranean Sea regional climate. The models include high-resolution Mediterranean Sea components, which allow assessment of the role of the basin and in particular of the air–sea feedbacks in the climate of the region. The models have been integrated from 1951 to 2050, using observed radiative forcings during the first half of the simulation period and the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B scenario during the second half. The projections show a substantial warming (about 1.5°–2°C) and a significant decrease of precipitation (about 5%) in the region for the scenario period. However, locally the changes might be even larger. In the same period, the projected surface net heat loss de...

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Antonio Navarra

Papers

Analysis of climate variability : applications of statistical techniques

Analysis of Climate Variability

Impact of the Indian Ocean Dipole on the East African Short Rains: A CGCM Study *

Reduction of Model Systematic Error by Statistical Correction for Dynamical Seasonal Predictions

The CIRCE simulations: Regional Climate Change Projections with Realistic Representation of the Mediterranean Sea