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

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturall...

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Ensemble empirical mode decomposition: a noise-assisted data analysis method

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.

Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

Historical records show increased aridity over many land areas since 1950. This study looks at observations and model projections from 1923 to 2010, to test the ability of models to predict future drought conditions. Models are able to capture the greenhouse-gas forcing and El Nino–Southern Oscillation mode for historical periods, which inspires confidence in their projections of drought.

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Increasing drought under global warming in observations and models

[1] The Hypothesis that low-frequency variation of ENSO predictability may be caused by stochastic processes is tested using an Intermediate Coupled Model (ICM). Three “perfect model scenario” prediction experiments were carried out, each of which consists of large ensemble predictions. In the first case (reference case), the model is solely forced by stochastic forcing without any explicit decadal-varying background. In the other two experiments, decadal-varying backgrounds with different amplitudes were introduced into the ICM in addition to the stochastic forcing. By comparing the low-frequency variations of ENSO predictability in these experiments, it is found that only when the amplitude of the decadal-varying background is unrealistically strong, the effect of decadal-varying background on ENSO predictability is comparable to or larger than the stochastically induced low-frequency ENSO variation. Therefore, to the extent that the simple coupled system can represent reality, it seems that one can not simply reject the null hypothesis that the decadal variation of ENSO predictability may be stochastically induced.

https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2003GL017505

Testing the stochastic mechanism for low-frequency variations in ENSO predictability

The Atlantic meridional overturning circulation (AMOC) is of great importance in the Earth’s climate system, and reconstructing its structure and variability by combining observations with ...

Impact of Coherent Ocean Stratification on AMOC Reconstruction by Coupled Data Assimilation with a Biased Model

Using eddy-resolving Community Earth System Model (CESM) simulations, this study investigates mesoscale energetics and air–sea interaction at two different time-scale windows in the Kuroshio Extension (KE) region. Based on an energy budget analysis, it is found that both baroclinic and barotropic pathways contribute to eddy energy generation within the low-frequency window (longer than 3 weeks) in this region, while both air–sea heat fluxes and wind stresses act as prominent eddy killers that remove energy from the ocean. In contrast, within the high-frequency window oceanic variability is mainly fed by baroclinic instability and regulated by turbulent thermal wind (TTW) processes, while the positive wind work is derived primarily from ageostrophic flow, i.e., Ekman drift, and along with air–sea heat fluxes has little influence on geostrophic mesoscale eddies.

Mesoscale Energy Balance and Air–Sea Interaction in the Kuroshio Extension: Low-Frequency versus High-Frequency Variability

tropical Atlantic sector using an AGCM coupled to a slab ocean. By construction, the model has systematic biases in its deterministic dynamics because the slab ocean has no ocean dynamics. We show that the linear correction procedure successfully corrects the model biases in the regions where ocean dynamics are expected to be important. [4] In a recent study, Chang et al. [2003] (hereafter CSJ) show that SST anomalies in the tropical north Atlantic (TNA) can be predicted two seasons in advance using an AGCM coupled to a slab ocean, owing to the combined effect of the remote ENSO influence and local thermodynamic air-sea feedbacks. But in the tropical south and equatorial Atlantic, the simple coupled model revealed poor skills and under-performed the persistence forecast. Here we use CSJ’s results as a reference with the exception that the prediction and verification period is expanded from 1959 to 2000. We focus on February–May, the peak months of the gradient mode of variability in the tropical Atlantic [Chiang et al., 2002], allowing us to investigate the importance of ocean dynamics in its evolution.

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A linear tendency correction technique for improving seasonal prediction of SST

The effect of oceanic advection on the predictability of sea surface temperature (SST) is investigated in the framework of a linear stochastic model. An analytical solution of a one-dimensional model shows that even though advection can give rise to a pair of low-frequency normal modes, no enhancement in the predictability is found in terms of domain-averaged error variance. However, a predictable component analysis shows that advection can play a role in redistributing the predictable variance. When forced with a spatially coherent stochastic forcing, advection enables certain regions to be more predictable than others. This analytical result is further examined in a more realistic two-dimensional North Atlantic model with observed mean currents. It is shown that the predictability of SST averaged over the whole North Atlantic basin is determined by the thermal damping time scale (∼3 months), not the advective time scale (∼6 years). However, the most predictable pattern reveals that the predicta...

Ping Chang

Papers

Testing the stochastic mechanism for low-frequency variations in ENSO predictability

Impact of Coherent Ocean Stratification on AMOC Reconstruction by Coupled Data Assimilation with a Biased Model

Mesoscale Energy Balance and Air–Sea Interaction in the Kuroshio Extension: Low-Frequency versus High-Frequency Variability

A linear tendency correction technique for improving seasonal prediction of SST

Effect of Oceanic Advection on the Potential Predictability of Sea Surface Temperature.