**Meet the editor**

Dr Leonard M. Druyan is a Senior Research Scientist and the director of the Center for Climate Systems Research (CCSR) at Columbia University in New York City. Dr Druyan earned a PhD in Meteorology from New York University in 1971. He was previously a Senior Resident Research Associate sponsored by the US National Research Council, a meteorologist with NASA and the

US National Weather Service and an associate professor of Geography at Bar Ilan University. He has been a principle investigator on grants from NASA, the US National Science Foundation and the US Department of Energy. He has published more than 75 research papers in numerous journals and volumes, among them the Journal of Applied Meteorology, Monthly Weather Review, Journal of Climate, Journal of Atmospheric Science, Journal of Geophysical Research, Geophysical Research Letters, Bulletin of the AMS, Climate Dynamics, International Journal of Climatology, Climatic Change, Atmosphere-Ocean, Climate Research, and Meteorology and Atmospheric Physics. His current research focuses on investigating the variability of the West African climate via regional climate model simulations.

Contents

**Preface IX**

**Part 1 Atmospheric Models 1**

Chapter 1 **Tropical Channel Model 3**

Chapter 2 **Seasonal Climate Prediction and**

June-Yi Lee and Bin Wang

Chapter 3 **Regional Climate Model Applications**

Chapter 4 **Advances in Streamflow Prediction:**

and Yolanda Castro-Díez

Chapter 5 **Impact of Global Climate Change**

**on Regional Water Resources:**

Wang Lu and Zhu Chang-jun

Chapter 6 **Nonparametric Statistical Downscaling**

Paul Nyeko-Ogiramoi, Patrick Willems, Gaddi Ngirane-Katashaya and Victor Ntegeka

Pallav Ray, Chidong Zhang, Jim Dudhia, Tim Li and Mitchell W. Moncrieff

**Predictability of Atmospheric Circulation 19**

Leonard M. Druyan and Matthew Fulakeza

**Part 2 Hydrology and Extreme Climate Events 59**

**A Multimodel Statistical Approach**

**for West Africa and the Tropical Eastern Atlantic 43**

**for Application on Water Resources Management 61**

Sonia R. Gámiz-Fortis, María Jesús Esteban-Parra

**A Case Study in the Huai River Basin 87**  Ju Qin, Hao Zhen-chun, Ou Geng-xin,

**of Precipitation from Global Climate Models 109**

### Contents

### **Preface XI**

**Part 1 Atmospheric Models 1**  Chapter 1 **Tropical Channel Model 3**  Pallav Ray, Chidong Zhang, Jim Dudhia, Tim Li and Mitchell W. Moncrieff Chapter 2 **Seasonal Climate Prediction and Predictability of Atmospheric Circulation 19**  June-Yi Lee and Bin Wang Chapter 3 **Regional Climate Model Applications for West Africa and the Tropical Eastern Atlantic 43**  Leonard M. Druyan and Matthew Fulakeza **Part 2 Hydrology and Extreme Climate Events 59**  Chapter 4 **Advances in Streamflow Prediction: A Multimodel Statistical Approach for Application on Water Resources Management 61**  Sonia R. Gámiz-Fortis, María Jesús Esteban-Parra and Yolanda Castro-Díez Chapter 5 **Impact of Global Climate Change on Regional Water Resources: A Case Study in the Huai River Basin 87**  Ju Qin, Hao Zhen-chun, Ou Geng-xin, Wang Lu and Zhu Chang-jun Chapter 6 **Nonparametric Statistical Downscaling of Precipitation from Global Climate Models 109**  Paul Nyeko-Ogiramoi, Patrick Willems, Gaddi Ngirane-Katashaya and Victor Ntegeka

X Contents

### **Part 4 Aerosols 247**

	- **Part 5 Modeling Climate Impacts 311**

### Preface

'Climate models' is a very broad topic, so a single volume can only offer a small sampling of relevant research activities. This volume of 14 chapters includes descriptions of a variety of modeling studies for a variety of geographic regions by an international roster of authors.

The climate research community generally uses the rubric 'climate models' to refer to organized sets of computer instructions that produce simulations of climate evolution. The code is based on physical relationships that describe the shared variability of meteorological parameters such as temperature, humidity, precipitation rate, circulation, radiation fluxes, etc. Three-dimensional climate models are integrated over time in order to compute the temporal and spatial variations of these parameters. Model domains can be global or regional and the horizontal and vertical resolutions of the computational grid vary from model to model. Considering the entire climate system requires accounting for interactions between solar insolation, atmospheric, oceanic and continental processes, the latter including land hydrology and vegetation. Model simulations may concentrate on one or more of these components, but the most sophisticated models will estimate the mutual interactions of all of these environments. Advances in computer technology have prompted investments in more complex model configurations that consider more phenomena interactions than were possible with yesterday's computers. However, not every attempt to add to the computational layers is rewarded by better model performance. Extensive research is required to test and document any advantages gained by greater sophistication in model formulation. One purpose for publishing climate model research results is to present purported advances for evaluation by the scientific community.

While some versions of environmental simulation models are used for weather prediction, the appellation 'climate models' implies applications related to time-mean conditions.  Moreover, climate models are also profitably employed to evaluate the sensitivity of components of the climate system to environmental influences. For example, how are regional precipitation anomalies related to seasurface temperature anomaly patterns? In a specific case, what impact does El Niño have on southeast US winter rainfall?

#### XII Preface

The wide range of topics covered by *Climate Models* provides an introduction to some of the scientific themes of modern environmental research. Ray et al. introduce the innovative concept of the Tropical Channel Model (TCM), which is a compromise between a regional climate model and a GCM. The TCM domain is global in the zonal direction, but driven by a global data set at its northern and southern boundaries. Authors believe that it offers the advantage of quantifying the extra-tropical influences on the tropics. TCMs based on the MM5, WRF and ECHAM4 are presented. The MM5 TCM successfully initiates a Madden-Julian Oscillation (MJO) event two months after initialization, suggesting that this event is stimulated by extra-tropical influences.

Preface XI

rates with greater spatial detail than possible with the GCM alone. The model output includes evapotranspiration, runoff generation, runoff dividing computations and flow routing. Runoff is divided into surface flow, subsurface flow and groundwater

Global Climate change projections are usually made at a spatial resolution that is too coarse for many applications. Nyeko-Ogiramoi et al. describe a method of downscaling rainfall time series to spatial scales relevant for hydrological climate change impacts at catchment level, including impacts on high and low flow extremes. Their statistical "quantile perturbation" downscaling technique assumes that relative changes in time series obtained from GCM simulations of the contemporary and future climate can be applied to the observed climate to obtain projections of the future climate. The technique is illustrated for outputs from a suite of GCMs targeting

García-Cueto and Santillán-Soto discuss projections of return frequencies of heat waves in Mexicali and floods in Villahermosa, two Mexico cities. Their modeling approach applies the statistics of extremes based on historical climate data. The analysis provides considerable insight into expected extremes of maximum surface temperature and precipitation rates. Since the analysis is based on contemporary and historical records, which reflect contemporary and historical climate forcing, its

Mabuchi reviews the importance of interactions between land surface processes and climate. The chapter describes the structure of the biosphere-atmosphere interaction model (BAIM) and gives examples of its performance. Applications of the coupled models include assessing impacts of deforestation on climate and the responses of the biosphere to climate variability over seven sub-regions of Asia. The author suggests that the studies using the physical climate model coupled with the biological scheme are useful for improving our understanding of the physical and biological mechanisms in regional and global climate systems and for improving estimates for future

Saito shows results of land process modeling of freezing and thawing of subsurface soil layers, which is particularly relevant at high latitudes. Results are presented for offline simulations using several model configurations forced by atmospheric data. Each model version can also be incorporated into fully coupled climate models. Moderate skill is shown for simulations of the areal extent of seasonally frozen ground and permafrost, but the simulated inter-annual variability of active layer depth and river discharge is poor. The chapter offers important lessons on the sensitivity of results to model configuration, and it emphasizes the challenges arising from the

Nieves et al. describe climate simulations based on two simplified models of the climate system. The second is termed a model of "intermediate complexity" and it

the Lake Victoria (Africa) catchments in the upper River Nile basin.

relevance for future greenhouse forcing is subject to interpretation.

flow.

scenarios.

sparsity of validation data.

Lee and Wang present a study of the predictability of tropospheric circulation based on ensemble simulations of eight atmosphere-ocean-land coupled global climate models. The investigation is based on 9-15 member ensemble predictions per model of seasonal means with one month lead time. Seasonal mean geopotential height fields are validated against NCEP/DOE reanalysis data. The chapter discusses the efficacy of multiple model ensembles, approaches for computing signal to noise ratios and the limits of predictability. Much of the estimated predictability is attributable to transitions between ENSO phases, so predictability tends to be higher over the tropics than over higher latitudes.

Druyan and Fulakeza introduce the concept of regional climate models, cousins to the global models, but which are integrated over limited geographical areas. Their usual advantage is a higher computational resolution that has the potential to elicit more spatial detail from simulations, such as in analyses of African easterly waves. The authors summarize some results from a history of regional climate model studies for West Africa and the neighboring Atlantic Ocean. The thorough evaluation of model performance for historical events is preliminary to its ultimate application for projections of decadal climate change in a warming world.

Gámiz-Fortis et al. study streamflow variability and predictability of each of four river systems in the Iberian Peninsula. The approach accounts for the physical characteristics of the specific basins and exploits statistically significant correlations between river streamflow time series in each river basin and large-scale sea surface temperature (SST) variability from preceding seasons (as predictors). Results demonstrate a range of moderate to strong influence of Atlantic SST on streamflow. Authors suggest that the predictability of the streamflows at seasonal, inter-annual and decadal time scales will be useful for the management of the increasingly limited water resources in Portugal and Spain. Although the research is site-specific, its conceptual basis and lessons learned should be transferable to other areas of the world facing similar problems.

Ju et al. describe the results of coupled GCM/hydrology modeling for the Huaihe River Basin in Eastern China. They are especially concerned with long-range projections that will inform about future frequencies of floods. Their dynamic hydrology model, driven by decadal GCM climate projections, simulates river flow rates with greater spatial detail than possible with the GCM alone. The model output includes evapotranspiration, runoff generation, runoff dividing computations and flow routing. Runoff is divided into surface flow, subsurface flow and groundwater flow.

X Preface

than over higher latitudes.

facing similar problems.

The wide range of topics covered by *Climate Models* provides an introduction to some of the scientific themes of modern environmental research. Ray et al. introduce the innovative concept of the Tropical Channel Model (TCM), which is a compromise between a regional climate model and a GCM. The TCM domain is global in the zonal direction, but driven by a global data set at its northern and southern boundaries. Authors believe that it offers the advantage of quantifying the extra-tropical influences on the tropics. TCMs based on the MM5, WRF and ECHAM4 are presented. The MM5 TCM successfully initiates a Madden-Julian Oscillation (MJO) event two months after initialization, suggesting that this event is stimulated by extra-tropical influences.

Lee and Wang present a study of the predictability of tropospheric circulation based on ensemble simulations of eight atmosphere-ocean-land coupled global climate models. The investigation is based on 9-15 member ensemble predictions per model of seasonal means with one month lead time. Seasonal mean geopotential height fields are validated against NCEP/DOE reanalysis data. The chapter discusses the efficacy of multiple model ensembles, approaches for computing signal to noise ratios and the limits of predictability. Much of the estimated predictability is attributable to transitions between ENSO phases, so predictability tends to be higher over the tropics

Druyan and Fulakeza introduce the concept of regional climate models, cousins to the global models, but which are integrated over limited geographical areas. Their usual advantage is a higher computational resolution that has the potential to elicit more spatial detail from simulations, such as in analyses of African easterly waves. The authors summarize some results from a history of regional climate model studies for West Africa and the neighboring Atlantic Ocean. The thorough evaluation of model performance for historical events is preliminary to its ultimate application for

Gámiz-Fortis et al. study streamflow variability and predictability of each of four river systems in the Iberian Peninsula. The approach accounts for the physical characteristics of the specific basins and exploits statistically significant correlations between river streamflow time series in each river basin and large-scale sea surface temperature (SST) variability from preceding seasons (as predictors). Results demonstrate a range of moderate to strong influence of Atlantic SST on streamflow. Authors suggest that the predictability of the streamflows at seasonal, inter-annual and decadal time scales will be useful for the management of the increasingly limited water resources in Portugal and Spain. Although the research is site-specific, its conceptual basis and lessons learned should be transferable to other areas of the world

Ju et al. describe the results of coupled GCM/hydrology modeling for the Huaihe River Basin in Eastern China. They are especially concerned with long-range projections that will inform about future frequencies of floods.

Global Climate change projections are usually made at a spatial resolution that is too coarse for many applications. Nyeko-Ogiramoi et al. describe a method of downscaling rainfall time series to spatial scales relevant for hydrological climate change impacts at catchment level, including impacts on high and low flow extremes. Their statistical "quantile perturbation" downscaling technique assumes that relative changes in time series obtained from GCM simulations of the contemporary and future climate can be applied to the observed climate to obtain projections of the future climate. The technique is illustrated for outputs from a suite of GCMs targeting the Lake Victoria (Africa) catchments in the upper River Nile basin.

García-Cueto and Santillán-Soto discuss projections of return frequencies of heat waves in Mexicali and floods in Villahermosa, two Mexico cities. Their modeling approach applies the statistics of extremes based on historical climate data. The analysis provides considerable insight into expected extremes of maximum surface temperature and precipitation rates. Since the analysis is based on contemporary and historical records, which reflect contemporary and historical climate forcing, its relevance for future greenhouse forcing is subject to interpretation.

Mabuchi reviews the importance of interactions between land surface processes and climate. The chapter describes the structure of the biosphere-atmosphere interaction model (BAIM) and gives examples of its performance. Applications of the coupled models include assessing impacts of deforestation on climate and the responses of the biosphere to climate variability over seven sub-regions of Asia. The author suggests that the studies using the physical climate model coupled with the biological scheme are useful for improving our understanding of the physical and biological mechanisms in regional and global climate systems and for improving estimates for future scenarios.

Saito shows results of land process modeling of freezing and thawing of subsurface soil layers, which is particularly relevant at high latitudes. Results are presented for offline simulations using several model configurations forced by atmospheric data. Each model version can also be incorporated into fully coupled climate models. Moderate skill is shown for simulations of the areal extent of seasonally frozen ground and permafrost, but the simulated inter-annual variability of active layer depth and river discharge is poor. The chapter offers important lessons on the sensitivity of results to model configuration, and it emphasizes the challenges arising from the sparsity of validation data.

Nieves et al. describe climate simulations based on two simplified models of the climate system. The second is termed a model of "intermediate complexity" and it

#### XIV Preface

includes simulations of the ocean thermohaline circulation (THC). Modeling experiments test the sensitivity of the climate to a range of perturbations. For example, the chapter evaluates the climate impacts resulting from stochastic noise in the variability of fresh water discharge into the North Atlantic. Results show that stochastic noise in this forcing, which serves as a proxy for real variability not accounted for in most modeling experiments, can induce major transitions in the climate system. The interpretation of results assumes that the idealized models are legitimate proxies for the real climate system.

Preface XIII

**Leonard M. Druyan**  Columbia University,

USA

2080 in order to predict changes in orangutan habitat. Three of the four climate change scenarios predict decreases of about 5% in orangutan habitat, and the fourth predicts

*Acknowledgments:* Logistics for bringing the contributions together were made possible by the dedicated efforts of Ms. Irena Voric and her InTech team, and I gratefully acknowledge their assistance. Valuable feedback on several chapters was solicited and received from the following volunteer reviewers who lent their scientific expertise to suggest improvements in presentation: Dr Gary Russell, Dr Michael Puma, Dr Pavan Racherla, Dr Susanne Bauer. Ultimately, however, the authors are responsible for their product and I accept responsibility for any remaining errors that were inadvertently

even more severe reductions in range.

left uncorrected.

Minute solid particles (aerosols) are an important component of air pollution. Panicker and Lee present a review of modeling approaches for evaluating aerosol impacts on climate. They summarize the scattering of solar radiation properties of many types of aerosols, scattering that attenuates solar radiation intensity at the Earth's surface. Black soot, on the other hand, increases the absorption of sunlight. The chapter outlines different approaches to account for these effects in climate models. Another section describes the aerosol indirect effect- its influence on cloud microphysics. A final section reviews recent modeling research concerning the influence of aerosol radiative forcing on the South Asian monsoon.

Aerosols suspended in the atmosphere both reflect and absorb sunlight, so their net impact on the atmospheric energy budget is important, but complex. Garcia et al. survey the radiative properties of atmospheric aerosols based on data from AERONET, a network of 180 monitoring stations in different parts of the world. AERONET stations are essential for monitoring the spatial and temporal heterogeneity of aerosol characteristics. The chapter organizes results based on some six different aerosol types and some 14 source regions, documenting the net radiative balance of each aerosol type in their seasonal cycles. These data will help evaluate how well climate model simulations account for aerosol impacts on climate variability.

Since atmospheric aerosols interact with the radiation budget, climate models that include realistic aerosol transport algorithms are advantageous. McGraw introduces two independent methods for testing and correcting moment sequences undergoing advective transport in atmospheric models. Each method is designed to operate on single grid cells, requiring no modification to the transport scheme or storage of information from previous time steps or neighboring grid cells. Either method, incorporated as part of an aerosol or cloud module, should be compatible with climate models' transport schemes.

Although the chapter by Carne et al. does not describe a conventional climate simulation model, it does remind us that there are important applications of climate change projections made by simulation models. The authors describe a time budget model for predicting changes in orangutan biogeography within Sumatra and Borneo. Their model is developed by screening of regression equations relating contemporary climate variables to feeding, moving and resting time and the composition of orangutan diets. The model is then applied to four IPCC climate projections targeting 2080 in order to predict changes in orangutan habitat. Three of the four climate change scenarios predict decreases of about 5% in orangutan habitat, and the fourth predicts even more severe reductions in range.

XII Preface

includes simulations of the ocean thermohaline circulation (THC). Modeling experiments test the sensitivity of the climate to a range of perturbations. For example, the chapter evaluates the climate impacts resulting from stochastic noise in the variability of fresh water discharge into the North Atlantic. Results show that stochastic noise in this forcing, which serves as a proxy for real variability not accounted for in most modeling experiments, can induce major transitions in the climate system. The interpretation of results assumes that the idealized models are

Minute solid particles (aerosols) are an important component of air pollution. Panicker and Lee present a review of modeling approaches for evaluating aerosol impacts on climate. They summarize the scattering of solar radiation properties of many types of aerosols, scattering that attenuates solar radiation intensity at the Earth's surface. Black soot, on the other hand, increases the absorption of sunlight. The chapter outlines different approaches to account for these effects in climate models. Another section describes the aerosol indirect effect- its influence on cloud microphysics. A final section reviews recent modeling research concerning the influence of aerosol radiative

Aerosols suspended in the atmosphere both reflect and absorb sunlight, so their net impact on the atmospheric energy budget is important, but complex. Garcia et al. survey the radiative properties of atmospheric aerosols based on data from AERONET, a network of 180 monitoring stations in different parts of the world. AERONET stations are essential for monitoring the spatial and temporal heterogeneity of aerosol characteristics. The chapter organizes results based on some six different aerosol types and some 14 source regions, documenting the net radiative balance of each aerosol type in their seasonal cycles. These data will help evaluate how well

climate model simulations account for aerosol impacts on climate variability.

Since atmospheric aerosols interact with the radiation budget, climate models that include realistic aerosol transport algorithms are advantageous. McGraw introduces two independent methods for testing and correcting moment sequences undergoing advective transport in atmospheric models. Each method is designed to operate on single grid cells, requiring no modification to the transport scheme or storage of information from previous time steps or neighboring grid cells. Either method, incorporated as part of an aerosol or cloud module, should be compatible with climate

Although the chapter by Carne et al. does not describe a conventional climate simulation model, it does remind us that there are important applications of climate change projections made by simulation models. The authors describe a time budget model for predicting changes in orangutan biogeography within Sumatra and Borneo. Their model is developed by screening of regression equations relating contemporary climate variables to feeding, moving and resting time and the composition of orangutan diets. The model is then applied to four IPCC climate projections targeting

legitimate proxies for the real climate system.

forcing on the South Asian monsoon.

models' transport schemes.

*Acknowledgments:* Logistics for bringing the contributions together were made possible by the dedicated efforts of Ms. Irena Voric and her InTech team, and I gratefully acknowledge their assistance. Valuable feedback on several chapters was solicited and received from the following volunteer reviewers who lent their scientific expertise to suggest improvements in presentation: Dr Gary Russell, Dr Michael Puma, Dr Pavan Racherla, Dr Susanne Bauer. Ultimately, however, the authors are responsible for their product and I accept responsibility for any remaining errors that were inadvertently left uncorrected.

> **Leonard M. Druyan**  Columbia University, USA

**Part 1**

**Atmospheric Models**

**Part 1**

**Atmospheric Models**

**1**

*USA*

**Tropical Channel Model**

Tim Li1 and Mitchell W. Moncrieff3

Pallav Ray1, Chidong Zhang2, Jim Dudhia3,

*3National Center for Atmospheric Research (NCAR),*

*1International Pacific Research Center (IPRC), University of Hawaii,*

*2Rosenstiel School of Marine and Atmospheric Science (RSMAS), University of Miami,*

Numerical models are primary tools used for weather forecast and climate projections. Richardson (1922) made the first effort to predict weather numerically. Subsequently, a number of simple numerical and analytical models were proposed to explain the general circulation of the atmosphere (e.g., Charney, 1948; Eady, 1949; Philips, 1956; Matsuno, 1966; Gill, 1980). The present state-of-the-art models are constantly being evaluated and refined with the help of observations and theory to better understand the earth's weather and climate. Our gradual progress in developing and utilizing complex numerical models have lead to a hierarchy of models: i) atmospheric global climate models (AGCMs, Smagornisky et al., 1965; Benwell and Bretherton, 1968; Phillips and Shukla, 1973; Manabe, 1975; Simmons and Bengtsson, 1984); ii) coupled atmosphere-ocean GCMs (Manabe and Bryan, 1969); iii) hydrostatic and non-hydrostatic regional models (Wang and Halpern, 1970; Dudhia, 1993); (iv) cloud-system resolving models with regional domains (Grabowski et al. 1998; Grabowski and Moncrieff, 2001) and global domains (Miura et al., 2007; Satoh et al., 2008). A comprehensive review of the present day numerical models can be found in Tao and

Each category of model has advantages and disadvantages in terms of area coverage, spatial resolution, computational efficiency, and the representations of the physical processes; i.e., convection, microphysics, radiation, and surface exchange. Typically, regional models have higher resolution albeit a limited computational domain. On the other hand, GCMs with lower resolution cut the advantage of global coverage. A recent development is the introduction of tropical channel models (TCMs) which are defined as models that are global in the zonal direction but bounded in the meridional direction. A TCM has the following considerable advantages over the aforementioned modeling approaches: (1) A standard regional model needs boundary conditions in the zonal and the meridional directions, whereas a TCM is continuous in the east-west direction and thereby isolates the influences that arrive solely from the meridional boundaries (i.e., extratropics). It also allows free circumnavigation of tropical modes in the zonal direction. (2) The use of lateral boundary conditions only in the meridional direction enables a controlled quantification of the effects of extratropical disturbances on the tropics. 3) TCMs with nonhydrostatic dynamical cores

**1. Introduction**

Moncrieff (2009).

### **Tropical Channel Model**

Pallav Ray1, Chidong Zhang2, Jim Dudhia3, Tim Li1 and Mitchell W. Moncrieff3 *1International Pacific Research Center (IPRC), University of Hawaii, 2Rosenstiel School of Marine and Atmospheric Science (RSMAS), University of Miami, 3National Center for Atmospheric Research (NCAR), USA*

### **1. Introduction**

Numerical models are primary tools used for weather forecast and climate projections. Richardson (1922) made the first effort to predict weather numerically. Subsequently, a number of simple numerical and analytical models were proposed to explain the general circulation of the atmosphere (e.g., Charney, 1948; Eady, 1949; Philips, 1956; Matsuno, 1966; Gill, 1980). The present state-of-the-art models are constantly being evaluated and refined with the help of observations and theory to better understand the earth's weather and climate. Our gradual progress in developing and utilizing complex numerical models have lead to a hierarchy of models: i) atmospheric global climate models (AGCMs, Smagornisky et al., 1965; Benwell and Bretherton, 1968; Phillips and Shukla, 1973; Manabe, 1975; Simmons and Bengtsson, 1984); ii) coupled atmosphere-ocean GCMs (Manabe and Bryan, 1969); iii) hydrostatic and non-hydrostatic regional models (Wang and Halpern, 1970; Dudhia, 1993); (iv) cloud-system resolving models with regional domains (Grabowski et al. 1998; Grabowski and Moncrieff, 2001) and global domains (Miura et al., 2007; Satoh et al., 2008). A comprehensive review of the present day numerical models can be found in Tao and Moncrieff (2009).

Each category of model has advantages and disadvantages in terms of area coverage, spatial resolution, computational efficiency, and the representations of the physical processes; i.e., convection, microphysics, radiation, and surface exchange. Typically, regional models have higher resolution albeit a limited computational domain. On the other hand, GCMs with lower resolution cut the advantage of global coverage. A recent development is the introduction of tropical channel models (TCMs) which are defined as models that are global in the zonal direction but bounded in the meridional direction. A TCM has the following considerable advantages over the aforementioned modeling approaches: (1) A standard regional model needs boundary conditions in the zonal and the meridional directions, whereas a TCM is continuous in the east-west direction and thereby isolates the influences that arrive solely from the meridional boundaries (i.e., extratropics). It also allows free circumnavigation of tropical modes in the zonal direction. (2) The use of lateral boundary conditions only in the meridional direction enables a controlled quantification of the effects of extratropical disturbances on the tropics. 3) TCMs with nonhydrostatic dynamical cores

Tropical Channel Model 5

The model domains and the simulations are shown in Fig.1 and Table 1, respectively. The simulation 1DOM, with a model domain of 21°S-21°N, has a horizontal resolution of 111 km (D1 in Fig. 1). This is an ideal set up to study the intraseasonal varibility like the MJO which is of global scale in the zonal direction (Madden and Julian, 1971, 1972; Li and Zhou, 2009), and most of its variance is confined within the 20° latitude zones (Zhang and Dong, 2004). The purpose of the option of a two-way nested inner domain of 37 km over the Indian and western Pacific Oceans (D2 in Fig. 1) is to assess the effect of increasing horizontal resolution. This is our simulation 2DOM. Both simulations were integrated for four months from 1 March to 30 June, 2002, with sea surface temperature (SST) prescribed from

Fig. 1. Outer domain for the TMM5 (D1, 0-360°, 21°S-21°N) and the nested domain (D2, 37-183°E, 11°S-11°N). Domains D1 and D2 have resolutions of 111 km and 37 km,

Table 1. The description of the simulations using the tropical channel MM5.

the model

Another tropical channel model is based on the Weather Research and Forecasting (WRF) model developed at the NCAR. We refer this as tropical WRF or TWRF (http://www.nrcm.ucar.edu). Conceptually, the configuration of TWRF is similar to TMM5. The horizontal grid-spacing of the TWRF is 36 km, and the meridional boundaries are placed at 30°S and 45°N. The model top is at 50 hPa, and 35 vertical levels are used. Output is archived every 3 hours. The configuration of the TWRF is shown in Fig. 2. The inner domains have grid-spacings of 12 km (domain D2 in Fig. 2) and 4 km (D3 in Fig. 2) respectively, and they are located over the warm pool region of the Indian and west Pacific oceans. Two-way interactions occur between the domains in the nested simulations. No

cumulus parameterization is applied in the 4 km (cloud-system resolving) domain.

Preliminary simulations over the maritime continent evaluated the skill of the TWRF simulations. Based on these tests, the suite of parameterizations selected for this present study are: Kain-Fritsch cumulus parameterization (KF, Kain, 2004), WSM6 cloud microphysics (Hong et al., 2004), CAM 3.0 radiation scheme (Collins et al., 2006), YSU boundary layer

Single domain simulation to test and validate

Nested domain (111 km / 37 km)

Experiment Integration Time Remarks

1DOM 1 March- 30 June, 2002

2DOM 1 March- 30 June, 2002

**2.2 TCM based on WRF**

observations (see section 2.4).

respectively.

can have higher resolution and more sophisticated physics compared to GCMs. This is essential to capture the multi-scale organized convection in the tropics and its influence on the general circulation (Arakawa and Schubert, 1974; Chen et al., 1996; Houze, 2004; Moncrieff, 2010).

The objective of this chapter is to describe several TCMs that have been developed recently with an emphasis on their applications for the simulation and understanding of the tropical mean state and variabilty including the Madden-Julian oscillation (MJO), tropical cyclones (TCs) and double intertropical convergence zone (ITCZ).

Section 2 describes the models, data, and the design of the numerical simulations. Section 3 includes diagnoses of the simulations from three different TCMs. Section 4 summarizes the results along with the implications and limitations of the tropical channel model approach.

### **2. Model**

We describe three different TCMs; two of them are constructed based on two different nonhydrostatic mesoscale models, and the third one is based on a hydrostatic GCM.

### **2.1 TCM based on MM5**

A TCM was developed based on the fifth-generation Pennsylvania State University-National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5; Dudhia, 1993; Grell et al., 1995). This atmosphere-only channel model employs a Mercator projection centered at the equator with open boundaries in the North-South direction. The model domain covers the entire tropics, with overlapping east-west boundaries. Tests ensured that simulated perturbations propagate zonally through the overlapping grids without distortion. Especially, two test runs are made in which the overlapping zone is located over the western Pacific and Atlantic Oceans, respectively. Results from these two simulations are the same over these regions with or without the overlapping zone. Hereafter, we refer to this tropical channel model as tropical MM5 (TMM5).

The dynamics and physics packages of TMM5 are same as those of the regular regional MM5, based on equations for a fully compressible, non-hydrostatic atmosphere. This TCM retains the two-way nesting capabilities. The spatial differencing is centered and of second order. There are 28 unevenly spaced full-sigma levels, with the maximum resolution in the boundary layer and the model top at 50 hPa. All nested domains are activated at the initial time of the simulation. The output is taken every 3 hours.

Several simulations are performed spanning between one to several weeks and are used to evaluate the skill of simulations by TMM5 against observations and reanalyses. Based on these tests and the work of Gustafson and Weare (2004a,b), the selected parameterizations are: (i) Betts-Miller convective scheme (Betts and Miller, 1986); (ii) explicit moisture calculations using a simple ice scheme (Dudhia, 1989); (iii) planetary boundary layer (PBL) scheme of the NCEP Eta model (Janjic, 1994), and (iv) Rapid Radiative Transfer Model longwave (RRTM, Mlïawer et al., 1997) and Dudhia (1989) shortwave radiation. The success of the Betts-Miller scheme, designed for the coarse resolution climate models, is perhaps because the simulations are in the hydrostatic regime at the resolutions employed (grid spacing of 111 and 37 km). Over the land, a 5-layer soil model option of the MM5 is used.

can have higher resolution and more sophisticated physics compared to GCMs. This is essential to capture the multi-scale organized convection in the tropics and its influence on the general circulation (Arakawa and Schubert, 1974; Chen et al., 1996; Houze, 2004;

The objective of this chapter is to describe several TCMs that have been developed recently with an emphasis on their applications for the simulation and understanding of the tropical mean state and variabilty including the Madden-Julian oscillation (MJO), tropical cyclones

Section 2 describes the models, data, and the design of the numerical simulations. Section 3 includes diagnoses of the simulations from three different TCMs. Section 4 summarizes the results along with the implications and limitations of the tropical channel model approach.

We describe three different TCMs; two of them are constructed based on two different non-

A TCM was developed based on the fifth-generation Pennsylvania State University-National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5; Dudhia, 1993; Grell et al., 1995). This atmosphere-only channel model employs a Mercator projection centered at the equator with open boundaries in the North-South direction. The model domain covers the entire tropics, with overlapping east-west boundaries. Tests ensured that simulated perturbations propagate zonally through the overlapping grids without distortion. Especially, two test runs are made in which the overlapping zone is located over the western Pacific and Atlantic Oceans, respectively. Results from these two simulations are the same over these regions with or without the overlapping zone. Hereafter, we refer to

The dynamics and physics packages of TMM5 are same as those of the regular regional MM5, based on equations for a fully compressible, non-hydrostatic atmosphere. This TCM retains the two-way nesting capabilities. The spatial differencing is centered and of second order. There are 28 unevenly spaced full-sigma levels, with the maximum resolution in the boundary layer and the model top at 50 hPa. All nested domains are activated at the initial

Several simulations are performed spanning between one to several weeks and are used to evaluate the skill of simulations by TMM5 against observations and reanalyses. Based on these tests and the work of Gustafson and Weare (2004a,b), the selected parameterizations are: (i) Betts-Miller convective scheme (Betts and Miller, 1986); (ii) explicit moisture calculations using a simple ice scheme (Dudhia, 1989); (iii) planetary boundary layer (PBL) scheme of the NCEP Eta model (Janjic, 1994), and (iv) Rapid Radiative Transfer Model longwave (RRTM, Mlïawer et al., 1997) and Dudhia (1989) shortwave radiation. The success of the Betts-Miller scheme, designed for the coarse resolution climate models, is perhaps because the simulations are in the hydrostatic regime at the resolutions employed (grid spacing of 111 and 37 km). Over the land, a 5-layer soil model option of the MM5 is used.

hydrostatic mesoscale models, and the third one is based on a hydrostatic GCM.

(TCs) and double intertropical convergence zone (ITCZ).

this tropical channel model as tropical MM5 (TMM5).

time of the simulation. The output is taken every 3 hours.

Moncrieff, 2010).

**2. Model**

**2.1 TCM based on MM5**

The model domains and the simulations are shown in Fig.1 and Table 1, respectively. The simulation 1DOM, with a model domain of 21°S-21°N, has a horizontal resolution of 111 km (D1 in Fig. 1). This is an ideal set up to study the intraseasonal varibility like the MJO which is of global scale in the zonal direction (Madden and Julian, 1971, 1972; Li and Zhou, 2009), and most of its variance is confined within the 20° latitude zones (Zhang and Dong, 2004). The purpose of the option of a two-way nested inner domain of 37 km over the Indian and western Pacific Oceans (D2 in Fig. 1) is to assess the effect of increasing horizontal resolution. This is our simulation 2DOM. Both simulations were integrated for four months from 1 March to 30 June, 2002, with sea surface temperature (SST) prescribed from observations (see section 2.4).

Fig. 1. Outer domain for the TMM5 (D1, 0-360°, 21°S-21°N) and the nested domain (D2, 37-183°E, 11°S-11°N). Domains D1 and D2 have resolutions of 111 km and 37 km, respectively.

Table 1. The description of the simulations using the tropical channel MM5.

### **2.2 TCM based on WRF**

Another tropical channel model is based on the Weather Research and Forecasting (WRF) model developed at the NCAR. We refer this as tropical WRF or TWRF (http://www.nrcm.ucar.edu). Conceptually, the configuration of TWRF is similar to TMM5. The horizontal grid-spacing of the TWRF is 36 km, and the meridional boundaries are placed at 30°S and 45°N. The model top is at 50 hPa, and 35 vertical levels are used. Output is archived every 3 hours. The configuration of the TWRF is shown in Fig. 2. The inner domains have grid-spacings of 12 km (domain D2 in Fig. 2) and 4 km (D3 in Fig. 2) respectively, and they are located over the warm pool region of the Indian and west Pacific oceans. Two-way interactions occur between the domains in the nested simulations. No cumulus parameterization is applied in the 4 km (cloud-system resolving) domain.

Preliminary simulations over the maritime continent evaluated the skill of the TWRF simulations. Based on these tests, the suite of parameterizations selected for this present study are: Kain-Fritsch cumulus parameterization (KF, Kain, 2004), WSM6 cloud microphysics (Hong et al., 2004), CAM 3.0 radiation scheme (Collins et al., 2006), YSU boundary layer

Tropical Channel Model 7

the model. To overcome this problem, a GCM-based framework, in which the boundary conditions come from the parallel simulation ('control') of the same model can be used. Such a GCM-based framework also presents a global view of the atmospheric variabilty and can

The GCM used is the atmosphere-only ECHAM4, which captures the tropical variability reasonably well (Sperber et al., 2005; Lin et al., 2006; Zhang et al., 2006). The model is integrated for 20 years using the prescribed monthly SST. This is our control simulation ('control' in Table 3). In the other experiment ('NS' in Table 3), the model prognostic variables are nudged toward the 'controlled' annual cycle state over the 20°-30° latitudinal zone (red in Fig. 3) to remove the extratropical influences without interfering with the influences from the zonal direction. By comparing the simulations in the control and the NS

Fig. 3. Schematic diagram of the numerical experiments in which the prognostic variables in 20°-30° latitude zones (red) are relaxed toward the controlled climatological annual cycle.

Model validation uses a number of observations and reanalyses data. They include: the NCEP-NCAR Re-analysis (Kalnay et al., 1996) winds; The European Centre for Medium-Range Weather Forecasts (ECMWF) 40-years Re-analysis (ERA40) winds (Uppala et al., 2005); Surface winds from the European Remote Sensing (ERS) satellites (Bentamy et al., 1999) and the NCEP-DOE Reanalysis (NCEP2, Kanamitsu et al., 2002); And two precipitation datasets including the merged analysis of precipitation (CMAP; Xie and Arkin, 1997), and the global precipitation climatological project (GPCP, Huffman et al.,

conditions for other simulations

extratropical influence on the tropics

To evaluate the role of the

Control ECHAM4 atmosphere only To provide lateral boundary

Table 3. The description of the simulations using ECHAM4. (NS: North South).

experiments, the influences of the extratropics on the tropics can be estimated.

be used for forecasting.

See text and Table 3 for further details.

1997) combined precipitation dataset.

**2.4 Data**

NS Relaxed to the annual cycle

Experiment Description Purpose

derived from the control simulation over 20°-30° latitudes

scheme (Hong et al., 2006), and Noah land surface model (Chen and Dudhia, 2001). Note that the Betts-Miller scheme (BM, Betts and Miller, 1986) available within the MM5 differs from the Betts-Miller scheme (BMJ, Janjic, 1994) available within the WRF.

Fig. 2. Model domains for the tropical channel WRF (D1, 0°-360°, 30°S-45°N) and the nested domains (D2, 79°-183°E, 21°S-16°N and D3, 90°-157°E, 6°S-10°N). Domains D1, D2, and D3 have resolutions of 36 km, 12 km and 4 km, respectively. The simulation from 1996 to 2000 includes only the outer domain. The southern boundary was further moved to 45°S for the simulation from 1 December 1999 to 1 January 2006. The domain for the TMM5 is also marked here by the dashed lines (0°-360°, 21°S-21°N). See the text and Table 2 for further details.

The simulations using the TWRF are listed in Table 2. The simulation 1DOM in Table 2 is used to document the mean state. A similar multi-year simulation with a larger domain (1DOM\_2 in Table 2) is also considered to evaluate its skill in capturing double ITCZ and tropical cyclones. Two other experiments (2DOM and 3DOM in Table 2) document the effect of increased horizontal resolution over the Indo-Pacific warm pool region.

Table 2. The description of TWRF simulations. The horizontal resolution is 36 km for all the simulations except the nested runs (2DOM and 3DOM). See Fig. 2 for domain definitions and text for further details. [DOM : Domain].

### **2.3 TCM based on ECHAM4**

TCMs based on regional models like MM5 and WRF, require boundary conditions from different datasets (other models or reanalysis). One disadvantage is that the variabilty in the prescribed boundary conditions may be different from the intrinsic variabilty produced by

scheme (Hong et al., 2006), and Noah land surface model (Chen and Dudhia, 2001). Note that the Betts-Miller scheme (BM, Betts and Miller, 1986) available within the MM5 differs from the

Fig. 2. Model domains for the tropical channel WRF (D1, 0°-360°, 30°S-45°N) and the nested domains (D2, 79°-183°E, 21°S-16°N and D3, 90°-157°E, 6°S-10°N). Domains D1, D2, and D3 have resolutions of 36 km, 12 km and 4 km, respectively. The simulation from 1996 to 2000 includes only the outer domain. The southern boundary was further moved to 45°S for the simulation from 1 December 1999 to 1 January 2006. The domain for the TMM5 is also marked here by the dashed lines (0°-360°, 21°S-21°N). See the text and Table 2 for further details.

The simulations using the TWRF are listed in Table 2. The simulation 1DOM in Table 2 is used to document the mean state. A similar multi-year simulation with a larger domain (1DOM\_2 in Table 2) is also considered to evaluate its skill in capturing double ITCZ and tropical cyclones. Two other experiments (2DOM and 3DOM in Table 2) document the effect

Table 2. The description of TWRF simulations. The horizontal resolution is 36 km for all the simulations except the nested runs (2DOM and 3DOM). See Fig. 2 for domain definitions

TCMs based on regional models like MM5 and WRF, require boundary conditions from different datasets (other models or reanalysis). One disadvantage is that the variabilty in the prescribed boundary conditions may be different from the intrinsic variabilty produced by

1-way nested from NCEP-NCAR reanalysis with lateral boundaries at 30°S and 45°N

Same as 1DOM, but with lateral boundaries at 45°S and 45°N

2-way nested domains (36 km / 12 km)

2-way nested domains (36 km / 12 km /4 km), 4 km domain is cloud resolving.

of increased horizontal resolution over the Indo-Pacific warm pool region.

Experiment Integration Time Description

1DOM 1 January 1996- 1

1DOM\_2 1 December 1999-1

2DOM 1 January 1996-12

3DOM 1 January 1997-1

**2.3 TCM based on ECHAM4**

and text for further details. [DOM : Domain].

January 2001

January 2006

February 1998

July 1997

Betts-Miller scheme (BMJ, Janjic, 1994) available within the WRF.

the model. To overcome this problem, a GCM-based framework, in which the boundary conditions come from the parallel simulation ('control') of the same model can be used. Such a GCM-based framework also presents a global view of the atmospheric variabilty and can be used for forecasting.

The GCM used is the atmosphere-only ECHAM4, which captures the tropical variability reasonably well (Sperber et al., 2005; Lin et al., 2006; Zhang et al., 2006). The model is integrated for 20 years using the prescribed monthly SST. This is our control simulation ('control' in Table 3). In the other experiment ('NS' in Table 3), the model prognostic variables are nudged toward the 'controlled' annual cycle state over the 20°-30° latitudinal zone (red in Fig. 3) to remove the extratropical influences without interfering with the influences from the zonal direction. By comparing the simulations in the control and the NS experiments, the influences of the extratropics on the tropics can be estimated.

Fig. 3. Schematic diagram of the numerical experiments in which the prognostic variables in 20°-30° latitude zones (red) are relaxed toward the controlled climatological annual cycle. See text and Table 3 for further details.

Table 3. The description of the simulations using ECHAM4. (NS: North South).

### **2.4 Data**

Model validation uses a number of observations and reanalyses data. They include: the NCEP-NCAR Re-analysis (Kalnay et al., 1996) winds; The European Centre for Medium-Range Weather Forecasts (ECMWF) 40-years Re-analysis (ERA40) winds (Uppala et al., 2005); Surface winds from the European Remote Sensing (ERS) satellites (Bentamy et al., 1999) and the NCEP-DOE Reanalysis (NCEP2, Kanamitsu et al., 2002); And two precipitation datasets including the merged analysis of precipitation (CMAP; Xie and Arkin, 1997), and the global precipitation climatological project (GPCP, Huffman et al., 1997) combined precipitation dataset.

Tropical Channel Model 9

demonstrate that the simulated MJO initiation is critically dependent on the time-varying lateral boundary conditions from the reanalysis (Ray et al., 2009). When such lateral boundary conditions are replaced by time-independent conditions, the model fails to reproduce the MJO initiation. In particular, the diagnoses of the zonal momentum budget for the MJO initiation region reveals that the advection by meridional winds is important

Fig. 4. Time-longitude diagrams of daily U850 anomalies (m s-1) averaged over 10°S-10°N from the (a) NCEP-NCAR reanalysis (NNR), (b) TMM5 single domain simulation (1DOM in Table 1), and (c) TMM5 nested domain simulation (2DOM in Table 1). A 3-day running

We describe the performance of the TWRF in terms of its ability to capture the mean

Fig. 5 shows the mean precipitation from two different datasets and TWRF simulation (1DOM in Table 2). The model lacks precipitation over the equatorial Indian Ocean and the west African monsoonal region. However the model overestimates precipitation in the west Pacific, particularly in the region north of maritime continent and in the south Pacific convergence zone (SPCZ). The error in the mean state is found to be a primary reason for the poor simulation of the MJO in this model (Ray et al., 2011), even when higher resolution nested domains are included (2DOM and 3DOM in Table 2). However, such error does not seem to affect the simulation of convectively coupled Kelvin waves in the model (Tulich et al., 2011). Overall, precipitation is overestimated in the southern hemishere, and underestimated close to the equator (5°S-5°N, Murthi et al., 2011). The large bias in the precipitation over the southern Indian Ocean was thought to be due to the interactions between tropical cyclones and the southern boundaries. To rectify this problem, southern boundaries are further moved to 45°S in another experiment (1DOM\_2 in Table 2). However, this does not improve the result significantly, indicating potential

prior to the initiation of this MJO (Ray and Zhang, 2010).

mean is applied.

**3.2.1 Mean state**

**3.2 Tropical WRF (TWRF)**

precipitation, double ITCZ, and hurricane statistics.

The NCEP global tropospheric analyses (final or 'FNL' data, 1°x1°, 6 hourly) provide initial and lateral boundary conditions for the TMM5. The SSTs for TMM5 are also from this reanalysis, which contain intraseasonal fluctuations. The initial and boundary conditions of the TWRF are from the NCEP-NCAR reanalysis. The SSTs for TWRF are from the Atmospheric Model Intercomparison Project (AMIP; 1°x1°, 6-hourly; Taylor et al., 2000). For brevity, both reanalysis and CMAP/GPCP precipitation will be referred to as "observations".

### **3. Results**

### **3.1 Tropical MM5 (TMM5)**

The TMM5 simulations show considerable ability to capture an MJO event in comparison to most GCMs (Slingo et al., 1996; Lin et al., 2006; Zhang et al. 2006; Kim et al., 2009). The MJO event appeared in April-May 2002 (Fig. 4a), with the eastward propagating zonal wind anomalies switching from easterlies to westerlies on intraseasonal timescales. This event occured during a season in which the MJO is closest to the equator but, on average, is weaker than in other seasons (Zhang and Dong, 2004). After initiation, however, it propagated eastward at a speed slightly faster than the average phase speed of the MJO (5 m s-1, marked by a straight line in Fig. 4a).

The start time (1 March) of the TMM5 simulations is about two months before the initiation of the MJO phase with active deep convection and low-level westerlies in May over the Indian Ocean. This choice of start time assesses the model's capability to reproduce the initiation of the MJO event, namely, the intraseasonal transition from low-level easterlies to westerlies (or from convectively inactive to active periods). In numerical models, forecast of future development of the MJO tends to have greater predictability when the MJO is already present at the initial time (Jones et al., 2000). Nevertheless, numerical experiments suggested predictability limit is about 10-15 days for rainfall, and about 25-30 days for upper-level winds (Waliser et al., 2003).

The results from the simulations 1DOM and 2DOM (Table 1) are shown in Fig. 4b and Fig. 4c, respectively. Simulated zonal winds at 850 hPa (hereafter U850) in D1 (middle panel) exhibits the same intraseasonal switch between easterly and westerly anomalies and eastward propagation over the Indian Ocean. Over the western Pacific, however, it moves faster than in reanalysis. This problem appears to be partially remedied by including the higher resolution nested domain D2 (Fig. 4c). In both simulations, the amplitudes of the anomalies are larger than that in reanalysis. Notice that the westward propagating synopticscale westerly anomalies embedded in the MJO envelope over the Indian Ocean are captured by the simulation with the nested domain (2DOM in Table 1). These detailed structures in simulated anomalies are perhaps due to the higher resolution of the model.

The most interesting result from this simulation is that the initiation of the MJO event over the Indian Ocean is reproduced by the model at about the same time as shown by reanalysis two months after the model initial time. The MJO is thought to be unpredictable beyond two to three weeks (e.g., Waliser et al., 2003). If this is correct, then the reproduction of the U850 anomalies by TMM5 cannot be attributed to the initial conditions.

The above results lead to a hypothesis that this MJO event is generated by the influences from the lateral boundaries. This hypothesis is supported by a series of sensitivity tests that

The NCEP global tropospheric analyses (final or 'FNL' data, 1°x1°, 6 hourly) provide initial and lateral boundary conditions for the TMM5. The SSTs for TMM5 are also from this reanalysis, which contain intraseasonal fluctuations. The initial and boundary conditions of the TWRF are from the NCEP-NCAR reanalysis. The SSTs for TWRF are from the Atmospheric Model Intercomparison Project (AMIP; 1°x1°, 6-hourly; Taylor et al., 2000). For brevity, both

The TMM5 simulations show considerable ability to capture an MJO event in comparison to most GCMs (Slingo et al., 1996; Lin et al., 2006; Zhang et al. 2006; Kim et al., 2009). The MJO event appeared in April-May 2002 (Fig. 4a), with the eastward propagating zonal wind anomalies switching from easterlies to westerlies on intraseasonal timescales. This event occured during a season in which the MJO is closest to the equator but, on average, is weaker than in other seasons (Zhang and Dong, 2004). After initiation, however, it propagated eastward at a speed slightly faster than the average phase speed of the MJO (5

The start time (1 March) of the TMM5 simulations is about two months before the initiation of the MJO phase with active deep convection and low-level westerlies in May over the Indian Ocean. This choice of start time assesses the model's capability to reproduce the initiation of the MJO event, namely, the intraseasonal transition from low-level easterlies to westerlies (or from convectively inactive to active periods). In numerical models, forecast of future development of the MJO tends to have greater predictability when the MJO is already present at the initial time (Jones et al., 2000). Nevertheless, numerical experiments suggested predictability limit is about 10-15 days for rainfall, and about 25-30 days for upper-level

The results from the simulations 1DOM and 2DOM (Table 1) are shown in Fig. 4b and Fig. 4c, respectively. Simulated zonal winds at 850 hPa (hereafter U850) in D1 (middle panel) exhibits the same intraseasonal switch between easterly and westerly anomalies and eastward propagation over the Indian Ocean. Over the western Pacific, however, it moves faster than in reanalysis. This problem appears to be partially remedied by including the higher resolution nested domain D2 (Fig. 4c). In both simulations, the amplitudes of the anomalies are larger than that in reanalysis. Notice that the westward propagating synopticscale westerly anomalies embedded in the MJO envelope over the Indian Ocean are captured by the simulation with the nested domain (2DOM in Table 1). These detailed structures in simulated anomalies are perhaps due to the higher resolution of the model.

The most interesting result from this simulation is that the initiation of the MJO event over the Indian Ocean is reproduced by the model at about the same time as shown by reanalysis two months after the model initial time. The MJO is thought to be unpredictable beyond two to three weeks (e.g., Waliser et al., 2003). If this is correct, then the reproduction of the U850

The above results lead to a hypothesis that this MJO event is generated by the influences from the lateral boundaries. This hypothesis is supported by a series of sensitivity tests that

anomalies by TMM5 cannot be attributed to the initial conditions.

reanalysis and CMAP/GPCP precipitation will be referred to as "observations".

**3. Results**

**3.1 Tropical MM5 (TMM5)**

winds (Waliser et al., 2003).

m s-1, marked by a straight line in Fig. 4a).

demonstrate that the simulated MJO initiation is critically dependent on the time-varying lateral boundary conditions from the reanalysis (Ray et al., 2009). When such lateral boundary conditions are replaced by time-independent conditions, the model fails to reproduce the MJO initiation. In particular, the diagnoses of the zonal momentum budget for the MJO initiation region reveals that the advection by meridional winds is important prior to the initiation of this MJO (Ray and Zhang, 2010).

Fig. 4. Time-longitude diagrams of daily U850 anomalies (m s-1) averaged over 10°S-10°N from the (a) NCEP-NCAR reanalysis (NNR), (b) TMM5 single domain simulation (1DOM in Table 1), and (c) TMM5 nested domain simulation (2DOM in Table 1). A 3-day running mean is applied.

### **3.2 Tropical WRF (TWRF)**

We describe the performance of the TWRF in terms of its ability to capture the mean precipitation, double ITCZ, and hurricane statistics.

### **3.2.1 Mean state**

Fig. 5 shows the mean precipitation from two different datasets and TWRF simulation (1DOM in Table 2). The model lacks precipitation over the equatorial Indian Ocean and the west African monsoonal region. However the model overestimates precipitation in the west Pacific, particularly in the region north of maritime continent and in the south Pacific convergence zone (SPCZ). The error in the mean state is found to be a primary reason for the poor simulation of the MJO in this model (Ray et al., 2011), even when higher resolution nested domains are included (2DOM and 3DOM in Table 2). However, such error does not seem to affect the simulation of convectively coupled Kelvin waves in the model (Tulich et al., 2011). Overall, precipitation is overestimated in the southern hemishere, and underestimated close to the equator (5°S-5°N, Murthi et al., 2011). The large bias in the precipitation over the southern Indian Ocean was thought to be due to the interactions between tropical cyclones and the southern boundaries. To rectify this problem, southern boundaries are further moved to 45°S in another experiment (1DOM\_2 in Table 2). However, this does not improve the result significantly, indicating potential

Tropical Channel Model 11

Figure 6 shows the seasonal cycles in SST (shaded) and surface wind divergence (contoured) over the equatorial eastern Pacific (100°W-140°W). Note that our simulation period includes the strong ENSO event of 1997-98 (McPhaden, 1999). During March-April, the convergence south and north of the equator is captured well by the model. Over the equator, the observations show weak convergence, but the simulation indicates divergence. During boreal summer, the model shows much stronger convergence south of the equator compared to the observations. Overall, the TWRF has a distinct double ITCZ and no

Fig. 6. Seasonal cycles of SST (shaded, °C) and surface wind divergence (contours, 106 s-1) from the (a) ERS, (b) ERA40, (c) NCEP2, and (d) TWRF. All are averaged over the eastern Pacific (100°W-140°W) during the period 1996-2000. Solid (dashed) contours represent

Using an objective tracking algoritm similar to Walsh et al. (2004), Tulich et al. (2011) estimated the genesis locations and tracks of tropical cyclones from the model simulation 1DOM\_2 (Table 2). Fig. 7 shows the latitudinal distribution of TC genesis events over three

convergence (divergence).

**3.2.3 Tropical cyclones**

significant improvement occurred when nested domains were employed.

problems with the model physics (Tulich et al., 2011; Murthi et al., 2011). Nevertheless, the model reasonably captures the initiation of certain MJO events that are influenced by the extratropics (Ray et al., 2011), and genesis of tropical cyclones from easterly waves (see section 3.2.3).

Fig. 5. Annual mean rainfall (mm day-1) during 1996-2000 from the (a) CMAP (b) GPCP and (c) TWRF simulation 1DOM in Table 2.

### **3.2.2 Double ITCZ**

It is known that the presence of a too-strong double ITCZ in the Pacific is a common bias in AGCMs (e.g., Meehl and Arblaster, 1998), as well as in the coupled GCMs (Mechoso et al., 1995). We examine the double ITCZ with respect to surface wind convergence and precipitation over the eastern Pacific, since the double ITCZ is most prominent over the eastern Pacific during March and April (Zhang, 2001).

problems with the model physics (Tulich et al., 2011; Murthi et al., 2011). Nevertheless, the model reasonably captures the initiation of certain MJO events that are influenced by the extratropics (Ray et al., 2011), and genesis of tropical cyclones from easterly waves

Fig. 5. Annual mean rainfall (mm day-1) during 1996-2000 from the (a) CMAP (b) GPCP and

It is known that the presence of a too-strong double ITCZ in the Pacific is a common bias in AGCMs (e.g., Meehl and Arblaster, 1998), as well as in the coupled GCMs (Mechoso et al., 1995). We examine the double ITCZ with respect to surface wind convergence and precipitation over the eastern Pacific, since the double ITCZ is most prominent over the

(c) TWRF simulation 1DOM in Table 2.

eastern Pacific during March and April (Zhang, 2001).

**3.2.2 Double ITCZ**

(see section 3.2.3).

Figure 6 shows the seasonal cycles in SST (shaded) and surface wind divergence (contoured) over the equatorial eastern Pacific (100°W-140°W). Note that our simulation period includes the strong ENSO event of 1997-98 (McPhaden, 1999). During March-April, the convergence south and north of the equator is captured well by the model. Over the equator, the observations show weak convergence, but the simulation indicates divergence. During boreal summer, the model shows much stronger convergence south of the equator compared to the observations. Overall, the TWRF has a distinct double ITCZ and no significant improvement occurred when nested domains were employed.

Fig. 6. Seasonal cycles of SST (shaded, °C) and surface wind divergence (contours, 106 s-1) from the (a) ERS, (b) ERA40, (c) NCEP2, and (d) TWRF. All are averaged over the eastern Pacific (100°W-140°W) during the period 1996-2000. Solid (dashed) contours represent convergence (divergence).

### **3.2.3 Tropical cyclones**

Using an objective tracking algoritm similar to Walsh et al. (2004), Tulich et al. (2011) estimated the genesis locations and tracks of tropical cyclones from the model simulation 1DOM\_2 (Table 2). Fig. 7 shows the latitudinal distribution of TC genesis events over three

Tropical Channel Model 13

Fig. 9 compares the simulated mean state from the observations and the model over the Indo-Pacific region. Lower tropospheric winds are westerly over the Indian Ocean (Fig. 9a, shaded) but easterlies prevail in the upper troposphere (Fig. 9a, contoured). This is captured well by the control simulation (Fig. 9b). However, in the experiment NS (Table 3), U850 is weaker over the equatorial western Indian Ocean, and U200 is of opposite sign over much of the Indian Ocean (Fig. 9c), and is underestimated over the west Pacific. The precipitation and OLR in the control (Fig. 9e) are similar to those in the observations (Fig. 9d), although precipitation (OLR) is everestimated (underestimated). Note that unlike TWRF, the control simulation does not lack precipitation over the equatorial Indian Ocean (Fig. 5c). However, in the NS, precipitation is significantly reduced over the equatorial Indian Ocean, and is increased over the SPCZ region (Fig. 9f). The ITCZ is shifted further from the equator in NS, with very little precipitation north of the equatorial Pacific. The MJO variance in the NS is also reduced substantially compared to the control indicating possible influences from the extratropics. To what extent the mean state is responsible for the lack of variability remains

Fig. 9. (left) Mean zonal winds at 850 hPa (U850, m s-1, shaded) and at 200 hPa (U200, m s-1, contoured). (right) Mean precipitation (mm day-1, shaded) and OLR (W m-2, contoured). All are averaged over 20 years. Contour intervals are 4 m s-1 for U200, and 10 W m-2 for OLR.

For U200, solid (dashed) lines represent westerlies (easterlies).

**3.3 Tropical ECHAM4**

to be investigated more thoroughly (Ray and Li, 2011).

different ocean basins. The error in the distribution is presumably due to the error in the model's easterly wave climatology that is intimately linked to the genesis of TCs (Landsea, 1993; Frank and Roundy, 2006). The model greatly overestimates the number of storms over the western Pacific (Fig. 7a), but underestimates over the north Atlantic (Fig. 7c). The model also overestimates over the eastern Pacific (Fig. 7b). Overall, while the TWRF simulation captures the global and seasonal distribution of tropical cyclones, their numbers are overestimated.

However, the simulation with a nested domain of 12 km over the Atlantic during the 2005 hurricane season produces resonable cyclone statistics (Fig. 8, Done et al., 2011). Compared to 27 observed tropical storms, the model produces 29 storms, indicating great improvement with the increase in the horizontal resolution.

Fig. 7. Histogram of TC genesis events versus latitude for the TWRF (sold) and observations (dashed) over the (a) NW Pacific, (b) NE Pacific, and (c) N Atlantic. The total number of genesis events are indicated next to the corresponding histogram. (From Tulich et al., 2011).

Fig. 8. Initial locations (black circles) and tracks (blue lines) of tropical cyclones in the Atlantic Basin during 2005 in (top) the IBTrACS dataset and (bottom) TWRF simulation. (From Done et al., 2011).

different ocean basins. The error in the distribution is presumably due to the error in the model's easterly wave climatology that is intimately linked to the genesis of TCs (Landsea, 1993; Frank and Roundy, 2006). The model greatly overestimates the number of storms over the western Pacific (Fig. 7a), but underestimates over the north Atlantic (Fig. 7c). The model also overestimates over the eastern Pacific (Fig. 7b). Overall, while the TWRF simulation captures the global and seasonal distribution of tropical cyclones, their numbers are

However, the simulation with a nested domain of 12 km over the Atlantic during the 2005 hurricane season produces resonable cyclone statistics (Fig. 8, Done et al., 2011). Compared to 27 observed tropical storms, the model produces 29 storms, indicating great improvement

Fig. 7. Histogram of TC genesis events versus latitude for the TWRF (sold) and observations (dashed) over the (a) NW Pacific, (b) NE Pacific, and (c) N Atlantic. The total number of genesis events are indicated next to the corresponding histogram. (From Tulich et al., 2011).

Fig. 8. Initial locations (black circles) and tracks (blue lines) of tropical cyclones in the Atlantic Basin during 2005 in (top) the IBTrACS dataset and (bottom) TWRF simulation.

overestimated.

(From Done et al., 2011).

with the increase in the horizontal resolution.

### **3.3 Tropical ECHAM4**

Fig. 9 compares the simulated mean state from the observations and the model over the Indo-Pacific region. Lower tropospheric winds are westerly over the Indian Ocean (Fig. 9a, shaded) but easterlies prevail in the upper troposphere (Fig. 9a, contoured). This is captured well by the control simulation (Fig. 9b). However, in the experiment NS (Table 3), U850 is weaker over the equatorial western Indian Ocean, and U200 is of opposite sign over much of the Indian Ocean (Fig. 9c), and is underestimated over the west Pacific. The precipitation and OLR in the control (Fig. 9e) are similar to those in the observations (Fig. 9d), although precipitation (OLR) is everestimated (underestimated). Note that unlike TWRF, the control simulation does not lack precipitation over the equatorial Indian Ocean (Fig. 5c). However, in the NS, precipitation is significantly reduced over the equatorial Indian Ocean, and is increased over the SPCZ region (Fig. 9f). The ITCZ is shifted further from the equator in NS, with very little precipitation north of the equatorial Pacific. The MJO variance in the NS is also reduced substantially compared to the control indicating possible influences from the extratropics. To what extent the mean state is responsible for the lack of variability remains to be investigated more thoroughly (Ray and Li, 2011).

Fig. 9. (left) Mean zonal winds at 850 hPa (U850, m s-1, shaded) and at 200 hPa (U200, m s-1, contoured). (right) Mean precipitation (mm day-1, shaded) and OLR (W m-2, contoured). All are averaged over 20 years. Contour intervals are 4 m s-1 for U200, and 10 W m-2 for OLR. For U200, solid (dashed) lines represent westerlies (easterlies).

Tropical Channel Model 15

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### **4. Conclusion**

Tropical channel models (TCMs) are defined as models that are global in the zonal direction but bounded in the meridional direction. Such a model can be constructed using existing regional models or even the GCMs. Two TCMs based on regional models MM5 and WRF, and the third one based on the ECHAM4 GCM, were described. Although all three TCMs are atmosphere only, they could be coupled to an ocean model. Therefore the TCM is a unique tool to study the tropics and its interactions with the extratropics.

The simulations using TCMs are far from perfect. There are biases in the mean state, ITCZ, MJO, and other tropical modes. The gross fetures of an MJO event are reproduced after two months from the start of simulation using the TCM based on MM5 (TMM5). This is well beyond the usual MJO predictability limit of 15-20 days associated with global models (Waliser et al., 2003). The TCM simulations are forced by presecribed boundary conditions, and are not initial value problems. Longer integration of another TCM based on WRF (TWRF) does not lead to better tropical variabilty compared to that in GCMs. This is unexpected since, compared to a GCM, a TCM simulation has the added constraint of specified meridional boundary conditions. The error in the mean state is a possible reason for the poor representation of tropical variabilty in TWRF. It is not known to what extent the error in the mean state inhibits tropical variabilty, although it is likely to be model dependent.

These studies suggest a new practice in tropical prediction: a high-resolution domain of the tropics nested within a relatively coarse-resolution global model. The latter is known to suffer less from deficiencies in cumulus parameterizations in the extratropics because of the strong dynamic large-scale control of convection there. This approach of global nested domains, currently being explored by some modeling groups, permits two-way tropicalextratropical interactions with a more precise treatment of tropical convection and much more computational efficiency than a global high-resolution model. The results call for further attention to the untapped potential of high-resolution nonhydrostatic models (Moncrieff et al., 2007) in the simulation and forecasting of tropical atmospheric convection, such as that undertaken within the WCRP-WWRP/THORPEX coordinated project, the Year of Tropical Convection (YOTC, Waliser and Moncrieff, 2008; see www.ucar.edu/yotc).

### **5. Acknowledgment**

Acknowledgment is made to the NCAR, which is sponsored by the National Science Foundation, for making the TWRF model output available. The authors thank Shuyi Chen and Joe Tenerelli, who helped build the TMM5. The NCEP-NCAR reanalysis data were taken from NOAA/CDC. The NCEP 'FNL' data were taken from the NCAR's mass storage. This work was supported by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), NASA (NNX07AG53G), and NOAA (NA09OAR4320075), which sponsor research at the International Pacific Research Center (IPRC). This is IPRC contribution 823.

### **6. References**

Arakawa, A., & Schubert, W.H. (1974). Interaction of a cumulus cloud ensemble with the large-scale circulation. Part I, *J. Atmos. Sci*., Vol.31, pp. 674-701

Tropical channel models (TCMs) are defined as models that are global in the zonal direction but bounded in the meridional direction. Such a model can be constructed using existing regional models or even the GCMs. Two TCMs based on regional models MM5 and WRF, and the third one based on the ECHAM4 GCM, were described. Although all three TCMs are atmosphere only, they could be coupled to an ocean model. Therefore the TCM is a

The simulations using TCMs are far from perfect. There are biases in the mean state, ITCZ, MJO, and other tropical modes. The gross fetures of an MJO event are reproduced after two months from the start of simulation using the TCM based on MM5 (TMM5). This is well beyond the usual MJO predictability limit of 15-20 days associated with global models (Waliser et al., 2003). The TCM simulations are forced by presecribed boundary conditions, and are not initial value problems. Longer integration of another TCM based on WRF (TWRF) does not lead to better tropical variabilty compared to that in GCMs. This is unexpected since, compared to a GCM, a TCM simulation has the added constraint of specified meridional boundary conditions. The error in the mean state is a possible reason for the poor representation of tropical variabilty in TWRF. It is not known to what extent the error in the mean state inhibits tropical variabilty, although it is likely to be model

These studies suggest a new practice in tropical prediction: a high-resolution domain of the tropics nested within a relatively coarse-resolution global model. The latter is known to suffer less from deficiencies in cumulus parameterizations in the extratropics because of the strong dynamic large-scale control of convection there. This approach of global nested domains, currently being explored by some modeling groups, permits two-way tropicalextratropical interactions with a more precise treatment of tropical convection and much more computational efficiency than a global high-resolution model. The results call for further attention to the untapped potential of high-resolution nonhydrostatic models (Moncrieff et al., 2007) in the simulation and forecasting of tropical atmospheric convection, such as that undertaken within the WCRP-WWRP/THORPEX coordinated project, the Year of Tropical Convection (YOTC, Waliser and Moncrieff, 2008; see www.ucar.edu/yotc).

Acknowledgment is made to the NCAR, which is sponsored by the National Science Foundation, for making the TWRF model output available. The authors thank Shuyi Chen and Joe Tenerelli, who helped build the TMM5. The NCEP-NCAR reanalysis data were taken from NOAA/CDC. The NCEP 'FNL' data were taken from the NCAR's mass storage. This work was supported by the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), NASA (NNX07AG53G), and NOAA (NA09OAR4320075), which sponsor research at the International Pacific Research Center (IPRC). This is IPRC contribution 823.

Arakawa, A., & Schubert, W.H. (1974). Interaction of a cumulus cloud ensemble with the

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**4. Conclusion**

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**2**

*USA*

**Seasonal Climate Prediction and**

June-Yi Lee and Bin Wang

*University of Hawaii, Honolulu, HI,*

**Predictability of Atmospheric Circulation**

While the detailed evolution of weather events may not be predictable beyond the span of a few days to two weeks due to the chaotic internal dynamics of atmospheric motion (Lorenz 1965), the statistical behavior of weather, that is the time or space averages, may be predictable over timescales of a season or longer due to the interaction between the atmosphere and the slowly varying lower boundary including ocean and land surface properties (Shukla 1998; Wang et al. 2009). In the past few decades, climate scientists have made tremendous advances in understanding and modeling the variability and predictability of the climate system. As a result, prediction of seasonal-to-interannual climate variations and the associated uncertainties using multiple dynamical models has become operational (Palmer et al. 2004; NOAA Climate Test Bed 2006; Lee et al. 2009). This chapter reviews and discusses the current status of seasonal climate prediction for the upper-tropospheric atmospheric circulation over the Northern Hemisphere (NH), in particular, using eight coupled, state-of-the art models that have participated in the Asia-Pacific Economic Cooperation Climate Center/Climate Prediction and its Application to Society (APCC/CliPAS) (Wang et al. 2009; Lee et al. 2010) and ENSEMBLE-based predictions of climate changes and their impactS (ENSEMBLES) project (Weisheimer et al. 2009; Alessandri et al. 2011). This is an extension of work from Lee et al. (2011), which addressed prediction and predictability of the NH summer upper-tropospheric

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**1. Introduction**

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Reyes et al. 2005).

*Department of Meteorology and International Pacific Research Center,*

### **Seasonal Climate Prediction and Predictability of Atmospheric Circulation**

June-Yi Lee and Bin Wang

*Department of Meteorology and International Pacific Research Center, University of Hawaii, Honolulu, HI, USA*

### **1. Introduction**

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While the detailed evolution of weather events may not be predictable beyond the span of a few days to two weeks due to the chaotic internal dynamics of atmospheric motion (Lorenz 1965), the statistical behavior of weather, that is the time or space averages, may be predictable over timescales of a season or longer due to the interaction between the atmosphere and the slowly varying lower boundary including ocean and land surface properties (Shukla 1998; Wang et al. 2009). In the past few decades, climate scientists have made tremendous advances in understanding and modeling the variability and predictability of the climate system. As a result, prediction of seasonal-to-interannual climate variations and the associated uncertainties using multiple dynamical models has become operational (Palmer et al. 2004; NOAA Climate Test Bed 2006; Lee et al. 2009). This chapter reviews and discusses the current status of seasonal climate prediction for the upper-tropospheric atmospheric circulation over the Northern Hemisphere (NH), in particular, using eight coupled, state-of-the art models that have participated in the Asia-Pacific Economic Cooperation Climate Center/Climate Prediction and its Application to Society (APCC/CliPAS) (Wang et al. 2009; Lee et al. 2010) and ENSEMBLE-based predictions of climate changes and their impactS (ENSEMBLES) project (Weisheimer et al. 2009; Alessandri et al. 2011). This is an extension of work from Lee et al. (2011), which addressed prediction and predictability of the NH summer upper-tropospheric circulation.

The multi-model ensemble (MME) approach was designed to quantifying forecast uncertainties due to model formulation near the turn of this century (Krishnamurti et al. 1999, 2000; Doblas-Reyes et al. 2000; Shukla et al. 2000; Palmer et al. 2000). The idea behind the MME is that if the model parameterization schemes are independent of each other, the model errors associated with the model parameterization schemes may be random in nature; thus, an averaging may cancel out the model errors contained in individual models. In general, the MME prediction is superior to the predictions made by any single-model component for both two-tier systems (Krishnamurti et al. 1999, 2000; Palmer et al. 2000; Shukla et al. 2000; Barnston et al. 2003) and one-tier systems (Hagedorn et al. 2005, Doblas-Reyes et al. 2005).

Seasonal Climate Prediction and Predictability of Atmospheric Circulation 21

mentioned in the text. The eight coupled models are from CAWCR, NCEP, and GFDL in the CliPAS project (Lee et al. 2011) and CMCC-INGV, ECMWF, IFM-GEOMAR, MF, and UKMO in the ENSEMBLES project (Weisheimer et al. 2009). None of the coupled models has flux adjustments. A brief summary of the coupled models and their retrospective forecasts is presented in Table 2. For more models' descriptions, refer to Lee et al. (2011) for the three CliPAS models and Weisheimer et al. (2009) for the five ENSEMBLES models.

The common retrospective forecast period of the models covers the 25 years of 1981-2005. All models were integrated from around February 1 to at least May 31 for the boreal spring season (hereafter MAM), from around May 1 to at least August 31 for the boreal summer season (hereafter JJA), from around August 1 to at least November 30 for boreal fall season (hereafter SON), and from around November 1 to at least February 28 for boreal winter season (hereafter DJF). Each model has a different ensemble size (Table 2). The one-month lead MME prediction was made by simply averaging the eight coupled models' ensemble

The observed data for validating models' performance are as follows. Data for 200-hPa zonal wind and 200-hPa GPH are from the NCEP/department of Energy (DOE) reanalysis II data (Kanamitsu et al. 2002). SST data is from the improved Extended Reconstructed Sea

AMIP Atmospheric general circulation model intercomparison project

DEMETER Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction ECMWF European Centre for Medium-Range Weather Forecast

IFM-GEOMAR Leibniz Institute of Marine Sciences at Kiel University

NCEP National Center for Environmental Prediction POAMA Predictive Ocean Atmosphere Model for Australia

Table 1. Acronym names of institutions and models used in the text.

ENSEMBLES ENSEMBLE-based predictions of climate changes and their impactS

Surface Temperature Version 2 (ERSST V2) data (Smith and Reynolds 2004).

APCC Asia-Pacific Economic Cooperation Climate Center CAWCR Centre for Australia Weather and Climate Research

CliPAS Climate Prediction and its Application to Society CMCC-INGV Euro-Mediterranean Centre for Climate Change

means.

**2.2 Validation data**

Acronym Full names

CFS Climate Forecast System

GFDL Geophysical Fluid Dynamic Lab

MF Météo France

UKMO UK Met Office

Climate forecast skill and predictability depend on the spatial location and season. Using 14 climate prediction models, Wang et al. (2009) showed that the one-month lead seasonal MME prediction is more (less) skillful in predicting wintertime atmospheric variability in the Pacific North America (East Asia) region compared to its summer counterpart. Jia et al. (2011) demonstrated that the current climate models have a season-dependent forecast skill regarding the dominant atmospheric circulation pattern in the NH extratropics. While the climate models have a significant skill for the leading atmospheric pattern and the associated time variation in MAM, JJA, and DJF, they have a difficulty in capturing the leading mode in SON.

The source of predictability of seasonal atmospheric anomalies over the NH extratropics is mainly attributable to teleconnection patterns, often linked to tropical boundary forcing. In boreal winter, the Pacific-North American (PNA) pattern and North Atlantic Oscillation (NAO) tend to significantly influence surface climate conditions and they explain a significant part of the interannual variance of the atmospheric anomalies over the NH extratropics (Wallace and Guztler 1981; Barnston and Livezey 1987; Trenberth et al. 1998; Hoerling et al. 2001). In boreal summer, the circumglobal teleconection (CGT) pattern is dominant over the NH extratropics (Ding and Wang 2005), acting as a significant source of climate variability and predictability over the region (Lee et al. 2011; Ding et al. 2011).

How to determine the signal variance (the predictable part of total variance) and predictability of atmospheric variability on seasonal time scale is still an open issue. According to the conventional signal-to-noise ratio approach determined by ensemble simulations of a stand-alone atmospheric model (Charney and Shukla 1981; Shukla 1998; Rowell 1998; Kang and Shukla 2006), the summertime atmospheric variability in the NH extratropics is less predictable than its winter counterpart and far less than that in the tropics. However, predictability obtained from the AGCM-alone approach is highly modeldependent. To better estimate predictability of seasonal-to-interannual climate variations, Wang et al. (2007) and Lee et al. (2011) suggested a "predictable mode analysis (PMA)" approach, which relies on identification of the predictable leading modes of the interannual variations in observations and retrospective MME forecast. The predictability is estimated by the fractional variance accounted for by the predictable leading modes. This chapter uses the PMA approach to estimate predictability of upper-level atmospheric circulation in each season in comparison with Mean Square Error Method suggested by Kumar et al. (2007).

Section 2 introduces the observational and prediction data and analysis methods used in this study. In Section 3, the current status of dynamical prediction of the seasonal atmospheric circulation is investigated using eight coupled models' hindcast data. Section 4 is devoted to estimate potential predictability for seasonal-mean atmospheric circulation anomalies. The summary is given in section 5.

### **2. Data and analysis method**

### **2.1 Retrospective forecast data**

This study uses one-month lead seasonal hindcast products of eight fully coupled oceanland atmosphere models. Table 1 lists the acronyms of the institutions and models mentioned in the text. The eight coupled models are from CAWCR, NCEP, and GFDL in the CliPAS project (Lee et al. 2011) and CMCC-INGV, ECMWF, IFM-GEOMAR, MF, and UKMO in the ENSEMBLES project (Weisheimer et al. 2009). None of the coupled models has flux adjustments. A brief summary of the coupled models and their retrospective forecasts is presented in Table 2. For more models' descriptions, refer to Lee et al. (2011) for the three CliPAS models and Weisheimer et al. (2009) for the five ENSEMBLES models.

The common retrospective forecast period of the models covers the 25 years of 1981-2005. All models were integrated from around February 1 to at least May 31 for the boreal spring season (hereafter MAM), from around May 1 to at least August 31 for the boreal summer season (hereafter JJA), from around August 1 to at least November 30 for boreal fall season (hereafter SON), and from around November 1 to at least February 28 for boreal winter season (hereafter DJF). Each model has a different ensemble size (Table 2). The one-month lead MME prediction was made by simply averaging the eight coupled models' ensemble means.

### **2.2 Validation data**

20 Climate Models

Climate forecast skill and predictability depend on the spatial location and season. Using 14 climate prediction models, Wang et al. (2009) showed that the one-month lead seasonal MME prediction is more (less) skillful in predicting wintertime atmospheric variability in the Pacific North America (East Asia) region compared to its summer counterpart. Jia et al. (2011) demonstrated that the current climate models have a season-dependent forecast skill regarding the dominant atmospheric circulation pattern in the NH extratropics. While the climate models have a significant skill for the leading atmospheric pattern and the associated time variation in MAM, JJA, and DJF, they have a difficulty in capturing the

The source of predictability of seasonal atmospheric anomalies over the NH extratropics is mainly attributable to teleconnection patterns, often linked to tropical boundary forcing. In boreal winter, the Pacific-North American (PNA) pattern and North Atlantic Oscillation (NAO) tend to significantly influence surface climate conditions and they explain a significant part of the interannual variance of the atmospheric anomalies over the NH extratropics (Wallace and Guztler 1981; Barnston and Livezey 1987; Trenberth et al. 1998; Hoerling et al. 2001). In boreal summer, the circumglobal teleconection (CGT) pattern is dominant over the NH extratropics (Ding and Wang 2005), acting as a significant source of climate variability and predictability over the region (Lee et al. 2011;

How to determine the signal variance (the predictable part of total variance) and predictability of atmospheric variability on seasonal time scale is still an open issue. According to the conventional signal-to-noise ratio approach determined by ensemble simulations of a stand-alone atmospheric model (Charney and Shukla 1981; Shukla 1998; Rowell 1998; Kang and Shukla 2006), the summertime atmospheric variability in the NH extratropics is less predictable than its winter counterpart and far less than that in the tropics. However, predictability obtained from the AGCM-alone approach is highly modeldependent. To better estimate predictability of seasonal-to-interannual climate variations, Wang et al. (2007) and Lee et al. (2011) suggested a "predictable mode analysis (PMA)" approach, which relies on identification of the predictable leading modes of the interannual variations in observations and retrospective MME forecast. The predictability is estimated by the fractional variance accounted for by the predictable leading modes. This chapter uses the PMA approach to estimate predictability of upper-level atmospheric circulation in each season in comparison with Mean Square Error Method suggested by Kumar et al. (2007).

Section 2 introduces the observational and prediction data and analysis methods used in this study. In Section 3, the current status of dynamical prediction of the seasonal atmospheric circulation is investigated using eight coupled models' hindcast data. Section 4 is devoted to estimate potential predictability for seasonal-mean atmospheric circulation anomalies. The

This study uses one-month lead seasonal hindcast products of eight fully coupled oceanland atmosphere models. Table 1 lists the acronyms of the institutions and models

leading mode in SON.

Ding et al. 2011).

summary is given in section 5.

**2. Data and analysis method 2.1 Retrospective forecast data**

The observed data for validating models' performance are as follows. Data for 200-hPa zonal wind and 200-hPa GPH are from the NCEP/department of Energy (DOE) reanalysis II data (Kanamitsu et al. 2002). SST data is from the improved Extended Reconstructed Sea Surface Temperature Version 2 (ERSST V2) data (Smith and Reynolds 2004).

Table 1. Acronym names of institutions and models used in the text.

Seasonal Climate Prediction and Predictability of Atmospheric Circulation 23

The skill score ranges from 0 (no skill at all) to 1 (for perfect forecast). It should be mentioned that we reordered the EOF modes of the MME prediction according to the skill score because the order of the predicted EOF mode is not necessarily the same as its observed counterpart. In order to reorder the predicted EOF modes, the skill score for the first observed mode was calculated against all of the predicted modes and then the predicted mode that had the best skill score with the first observed mode was taken as the first predicted mode. Repeating the above process, other predicted modes were similarly determined. In the case of 200-hPa geopotential height (Z200) in all season, there was no

The upper tropospheric circulation is represented by geopotential height at 200-hPa (Z200 hereafter). Figure 1 compares the observed and hindcast interannual variance of the upperlevel circulation along with the climatological jet stream in each season. Individual ensemble hindcasts of each model are used to estimate standard deviation of the Z200 anomalies for individual models in each season. The composite of individual models' total standard deviation is used for the MME. Note that the MME is able to capture the location of jet streams in both hemispheres and the atmospheric variability centers over the Tropics in all seasons. However, it has difficulty in capturing the observed variability centers particularly over the NH extratropics, except over the PNA region for most seasons. Models tend to

**3. Dynamical prediction of seasonal-mean atmospheric circulation**

overestimate the tropical variability and underestimate the high latitude variability.

Season-dependent MME forecast skills are investigated for the period 1981-2005. Figure 2 shows the TCC skill of one-month lead MME predictions for Z200 in each season. The skillful forecast for Z200 is primarily confined in the tropics (Fig. 2a) in all seasons that are consistent with earlier results (Peng et al. 2000). Nonetheless, the MME has significant skills over some specific geographical locations in the NH extratropics depending on season, suggesting that predictable patterns (or modes) of the seasonal upper-level circulation in the region may exist. For example, atmospheric variability over the PNA region is more predictable during MAM and DJF while that over the Asian region is more predictable

In the tropics, the area-averaged TCC skill is the lowest in JJA and SON. On the other hand, in the entire NH extratropics (0-360oE, 30-80oN) TCC is the highest in JJA, although over the PNA region it is higher in DJF than in JJA. The TCC skill over the Southern Hemisphere (SH) shows a season-dependent pattern as well. The skill tends to increase in southern hemisphere subtropics and midlatitude during DJF and SON. The South Pacific convergence region tends to have higher skill than other southern hemisphere regions.

We also investigate the TCC skill for 500-hPa geopotential height (Z500) which is often used for representing mid-tropospheric atmospheric circulation. Comparison between Fig. 2 and 3 indicates that Z200 is more predictable than Z500 although the spatial pattern of the TCC

change of mode order until the 3rd predicted mode.

**3.1 Variance**

**3.2 Season-dependence**

during JJA and SON.

for Z200 is very similar to that for Z500.

Table 2. Description of the coupled models and their retrospective forecast used in this study.

### **2.3 Forecast quality measures**

The measure of prediction skill includes the temporal correlation coefficient (TCC) skill, evaluating the interannual variability, and anomaly pattern correlation coefficient (PCC) skill for spatial similarity. We also calculated the area-averaged TCC skill over the NH tropics (Eq-30°N) and extratropics (30°-80°N), respectively, taking latitudinal weight into account.

We define a skill score as representing the coupled models' capability in predicting empirical orthogonal function (EOF) modes in terms of the PCC score for eigenvector and TCC score for the principal component (PC) time series for each mode. The skill score for each mode (*i*) is calculated by

$$\text{Skill Score(i)} = \sqrt{\text{PCC(i)} \times \text{TCC(i)} \dots}$$

The skill score ranges from 0 (no skill at all) to 1 (for perfect forecast). It should be mentioned that we reordered the EOF modes of the MME prediction according to the skill score because the order of the predicted EOF mode is not necessarily the same as its observed counterpart. In order to reorder the predicted EOF modes, the skill score for the first observed mode was calculated against all of the predicted modes and then the predicted mode that had the best skill score with the first observed mode was taken as the first predicted mode. Repeating the above process, other predicted modes were similarly determined. In the case of 200-hPa geopotential height (Z200) in all season, there was no change of mode order until the 3rd predicted mode.

### **3. Dynamical prediction of seasonal-mean atmospheric circulation**

### **3.1 Variance**

22 Climate Models

ACOM3 0.5-1.5o lat x 2.0o lon L31

MOM4

MOM3 1/3olat x 5/8olon L27

OPA 8.2

HOPE-E 1.4o x 0.3o-1.4o L29 <sup>9</sup>

OPA 8.2 2.0o lat x 2.0o lon L31

OPA 8.0

MPI-OM1 2.5o lat x 0.5o-2.5o lon L23

2.0o x2.0o L31 <sup>9</sup>

member Reference

1/3olat x 1olon L50 <sup>10</sup>Delworth et al

9

182GPx152GP L31 <sup>9</sup>Daget et al. (2009)

10 Zhong et al (2005)

15 Saha et al (2006)

(2006)

Alessandri et al (2011) Pietro and Masina (2009)

Stockdale et al. (2011) Balmaseda et al. (2008)

Keenlyside et al. (2005) Jungclaus et al. (2006)

Salas Melia (2002)

(2008)

<sup>9</sup>Collins et al.

Name AGCM OGCM Ensemble

T47 L17

AM2.1 2olat x 2.5olon L24

T62 L64

T63 L19

T159 L62

T42 L19

T95 L40

T42 L19

Table 2. Description of the coupled models and their retrospective forecast used in this

The measure of prediction skill includes the temporal correlation coefficient (TCC) skill, evaluating the interannual variability, and anomaly pattern correlation coefficient (PCC) skill for spatial similarity. We also calculated the area-averaged TCC skill over the NH tropics (Eq-30°N) and extratropics (30°-80°N), respectively, taking latitudinal weight into

We define a skill score as representing the coupled models' capability in predicting empirical orthogonal function (EOF) modes in terms of the PCC score for eigenvector and TCC score for the principal component (PC) time series for each mode. The skill score for

*Skill Score i PCC i TCC i* () () () = × .

Institute Model

GFDL CM2.1

CMCC-

IFM-

study.

account.

CAWCR POAMA1.5 BAM 3.0d

NCEP CFS GFS

INGV CMCC ECHAM5

ECMWF ECMWF IFS CY31R1

GEOMAR IFM ECHAM4

MF MF IFS

UKMO UKMO ECHAM5

**2.3 Forecast quality measures**

each mode (*i*) is calculated by

The upper tropospheric circulation is represented by geopotential height at 200-hPa (Z200 hereafter). Figure 1 compares the observed and hindcast interannual variance of the upperlevel circulation along with the climatological jet stream in each season. Individual ensemble hindcasts of each model are used to estimate standard deviation of the Z200 anomalies for individual models in each season. The composite of individual models' total standard deviation is used for the MME. Note that the MME is able to capture the location of jet streams in both hemispheres and the atmospheric variability centers over the Tropics in all seasons. However, it has difficulty in capturing the observed variability centers particularly over the NH extratropics, except over the PNA region for most seasons. Models tend to overestimate the tropical variability and underestimate the high latitude variability.

### **3.2 Season-dependence**

Season-dependent MME forecast skills are investigated for the period 1981-2005. Figure 2 shows the TCC skill of one-month lead MME predictions for Z200 in each season. The skillful forecast for Z200 is primarily confined in the tropics (Fig. 2a) in all seasons that are consistent with earlier results (Peng et al. 2000). Nonetheless, the MME has significant skills over some specific geographical locations in the NH extratropics depending on season, suggesting that predictable patterns (or modes) of the seasonal upper-level circulation in the region may exist. For example, atmospheric variability over the PNA region is more predictable during MAM and DJF while that over the Asian region is more predictable during JJA and SON.

In the tropics, the area-averaged TCC skill is the lowest in JJA and SON. On the other hand, in the entire NH extratropics (0-360oE, 30-80oN) TCC is the highest in JJA, although over the PNA region it is higher in DJF than in JJA. The TCC skill over the Southern Hemisphere (SH) shows a season-dependent pattern as well. The skill tends to increase in southern hemisphere subtropics and midlatitude during DJF and SON. The South Pacific convergence region tends to have higher skill than other southern hemisphere regions.

We also investigate the TCC skill for 500-hPa geopotential height (Z500) which is often used for representing mid-tropospheric atmospheric circulation. Comparison between Fig. 2 and 3 indicates that Z200 is more predictable than Z500 although the spatial pattern of the TCC for Z200 is very similar to that for Z500.

Seasonal Climate Prediction and Predictability of Atmospheric Circulation 25

Fig. 2. The temporal correlation coefficient (TCC) skill for the one-month lead prediction of 200-hPa geopotential height (Z200) in (a) MAM, (b) JJA, (c) SON, and (d) DJF obtained from the eight coupled models' multi-model ensemble (MME) for the period of 1981-2005 in the Northern Hemisphere (NH). Solid (dashed) line represents statistical significance of the correlation coefficients at 95% (99%) confidence level. The numbers in the left upper corners indicate averaged correlation skill over the tropics (T: 0-360oE, Eq-30oN) and extratropics

(E: 0-360oE, 30-80oN) in the NH.

Fig. 1. Standard deviation of geopotential height (shading) anomalies and climatological mean of zonal wind (contour) at 200 hPa in each season obtained from (a) observations and (b) the one-month lead MME seasonal prediction. The units are *m* for geopotential height and *m s-1* for zonal wind. 20 and 30 *m s-1* zonal wind are contoured.

Fig. 1. Standard deviation of geopotential height (shading) anomalies and climatological mean of zonal wind (contour) at 200 hPa in each season obtained from (a) observations and (b) the one-month lead MME seasonal prediction. The units are *m* for geopotential height

and *m s-1* for zonal wind. 20 and 30 *m s-1* zonal wind are contoured.

Fig. 2. The temporal correlation coefficient (TCC) skill for the one-month lead prediction of 200-hPa geopotential height (Z200) in (a) MAM, (b) JJA, (c) SON, and (d) DJF obtained from the eight coupled models' multi-model ensemble (MME) for the period of 1981-2005 in the Northern Hemisphere (NH). Solid (dashed) line represents statistical significance of the correlation coefficients at 95% (99%) confidence level. The numbers in the left upper corners indicate averaged correlation skill over the tropics (T: 0-360oE, Eq-30oN) and extratropics (E: 0-360oE, 30-80oN) in the NH.

Seasonal Climate Prediction and Predictability of Atmospheric Circulation 27

Figures 4-7 show the first and second EOF modes of the NH Z200 variability for observations and the one-month lead MME prediction in each season. Similar to the major modes of JJA Z200 discussed in Lee et al. (2011), the first two EOF modes of the NH Z200 variability in other seasons are also understood in terms of ENSO teleconnection dynamics. For all seasonal cases, a strong positive (negative) phase in the first mode tends to occur simultaneously or after the mature phase of La Niña (El Niño), and is thus driven by prolonged impacts of the ENSO from the preceding SON to following JJA. The second mode, on the other hand, is regulated by the developing phase of the ENSO starting from the preceding DJF to following SON on the interannual time scale and is correlated with the SST anomalies over the North Pacific, North Atlantic, and tropical Western Pacific on the

Fig. 4. Spatial patterns of the first (left panels) and second (right panels) eigenvectors of 200 hPa GPH anomalies in MAM obtained from observation (a, d) and 8-coupled modes' MME prediction (b, e), respectively. The numbers in the right lower corners in (d) and (e) indicate pattern correlation coefficient (PCC) between observation and the corresponding prediction. (c) and (f) principal components (PCs) of the first and the second EOF modes obtained from

observation (black solid line) and MME (red dashed line). The numbers within the parenthesis in the figure legend in lower panels indicate the temporal correlation

coefficients (TCC) between the observed and MME PC time series.

interdecadal time scale.

Fig. 3. Same as Fig. 2 except for 500-hPa geopotential height (Z500).

### **3.3 Predictable modes**

It is important to understand the source of the prediction skill in the current coupled models and identify predictable modes of climate variability. To this end, we evaluate how well the MME hindcast captures the first two dominant modes of the Z200 variability in each season. To identify the major modes of upper-tropospheric atmospheric circulation, we first applied EOF analysis, using the correlation matrix, to the seasonal Z200 over the entire NH (0-360°E, Eq-80°) for observations and for the MME prediction. All data were interpolated to the same geographic grid to avoid the latitudinal weighting effect and data were normalized by their own standard deviation.

Fig. 3. Same as Fig. 2 except for 500-hPa geopotential height (Z500).

It is important to understand the source of the prediction skill in the current coupled models and identify predictable modes of climate variability. To this end, we evaluate how well the MME hindcast captures the first two dominant modes of the Z200 variability in each season. To identify the major modes of upper-tropospheric atmospheric circulation, we first applied EOF analysis, using the correlation matrix, to the seasonal Z200 over the entire NH (0-360°E, Eq-80°) for observations and for the MME prediction. All data were interpolated to the same geographic grid to avoid the latitudinal weighting effect and data were normalized by their

**3.3 Predictable modes**

own standard deviation.

Figures 4-7 show the first and second EOF modes of the NH Z200 variability for observations and the one-month lead MME prediction in each season. Similar to the major modes of JJA Z200 discussed in Lee et al. (2011), the first two EOF modes of the NH Z200 variability in other seasons are also understood in terms of ENSO teleconnection dynamics. For all seasonal cases, a strong positive (negative) phase in the first mode tends to occur simultaneously or after the mature phase of La Niña (El Niño), and is thus driven by prolonged impacts of the ENSO from the preceding SON to following JJA. The second mode, on the other hand, is regulated by the developing phase of the ENSO starting from the preceding DJF to following SON on the interannual time scale and is correlated with the SST anomalies over the North Pacific, North Atlantic, and tropical Western Pacific on the interdecadal time scale.

Fig. 4. Spatial patterns of the first (left panels) and second (right panels) eigenvectors of 200 hPa GPH anomalies in MAM obtained from observation (a, d) and 8-coupled modes' MME prediction (b, e), respectively. The numbers in the right lower corners in (d) and (e) indicate pattern correlation coefficient (PCC) between observation and the corresponding prediction. (c) and (f) principal components (PCs) of the first and the second EOF modes obtained from observation (black solid line) and MME (red dashed line). The numbers within the parenthesis in the figure legend in lower panels indicate the temporal correlation coefficients (TCC) between the observed and MME PC time series.

Seasonal Climate Prediction and Predictability of Atmospheric Circulation 29

Fig. 6. Same as Fig. 4 except for SON.

Fig. 5. Same as Fig. 4 except for JJA.

The predictability source of the first two modes can be more clearly depicted by the lead-lag correlation coefficients for seasonal SST against the first and second PC of Z200 shown Fig. 8. We just show the DJF case, since the other seasons have similar results. Figure 8 indicates that the first mode is associated with a typical or conventional eastern Pacific El Nino with complimentary same-sign SST anomalies occurring in Indian Ocean and Atlantic. On the other hand, the second mode seems to be associated with a central Pacific El Nino with pronounced opposite-sign SST anomalies in the western Pacific and Atlantic but little SST anomalies in the Indian Ocean. This suggest that the tropical precipitation anomalies associated with the locations of the tropical SST anomalies are critical important for determining the extratropical response.

The MME is capable of predicting the spatial and temporal structures of the two leading EOF modes one-month ahead with high fidelity in each season, except for the second EOF mode of DJF Z200. The MME's forecast skill for the EOF modes will be more discussed in section 4.2.

The predictability source of the first two modes can be more clearly depicted by the lead-lag correlation coefficients for seasonal SST against the first and second PC of Z200 shown Fig. 8. We just show the DJF case, since the other seasons have similar results. Figure 8 indicates that the first mode is associated with a typical or conventional eastern Pacific El Nino with complimentary same-sign SST anomalies occurring in Indian Ocean and Atlantic. On the other hand, the second mode seems to be associated with a central Pacific El Nino with pronounced opposite-sign SST anomalies in the western Pacific and Atlantic but little SST anomalies in the Indian Ocean. This suggest that the tropical precipitation anomalies associated with the locations of the tropical SST anomalies are critical important for

The MME is capable of predicting the spatial and temporal structures of the two leading EOF modes one-month ahead with high fidelity in each season, except for the second EOF mode of DJF Z200. The MME's forecast skill for the EOF modes will be more discussed in

Fig. 5. Same as Fig. 4 except for JJA.

determining the extratropical response.

section 4.2.

Fig. 6. Same as Fig. 4 except for SON.

Seasonal Climate Prediction and Predictability of Atmospheric Circulation 31

Fig. 8. Spatial patterns of the lead-lag correlation coefficients for seasonal mean SST against (a) the first PC and (b) the second PC, respectively, of DJF Z200 obtained from observations.

Correlation coefficients which are statistically significant at 99% confidence level are

contoured.

Fig. 7. Same as Fig. 4 except for DJF.