Multicentury integrations from two global coupled ocean–atmosphere–land–ice models [Climate Model versions 2.0 (CM2.0) and 2.1 (CM2.1), developed at the Geophysical Fluid Dynamics Laboratory] are described in terms of their tropical Pacific climate and El Niño–Southern Oscillation (ENSO). The integrations are run without flux adjustments and provide generally realistic simulations of tropical Pacific climate. The observed annual-mean trade winds and precipitation, sea surface temperature, surface heat fluxes, surface currents, Equatorial Undercurrent, and subsurface thermal structure are well captured by the models. Some biases are evident, including a cold SST bias along the equator, a warm bias along the coast of South America, and a westward extension of the trade winds relative to observations. Along the equator, the models exhibit a robust, westward-propagating annual cycle of SST and zonal winds. During boreal spring, excessive rainfall south of the equator is linked to an unrealistic reversal of the simulated meridional winds in the east, and a stronger-than-observed semiannual signal is evident in the zonal winds and Equatorial Undercurrent. Both CM2.0 and CM2.1 have a robust ENSO with multidecadal fluctuations in amplitude, an irregular period between 2 and 5 yr, and a distribution of SST anomalies that is skewed toward warm events as

Strategies to achieve order reduction in four dimensional variational data assimilation (4D-Var) search for an optimal low rank state subspace for the analysis update. A common feature of the reduction methods proposed in atmospheric and oceanographic studies is that the identification of the basis functions relies on the model dynamics only, without properly accounting for the specific details of the data assimilation system (DAS). In this study a general framework of the proper orthogonal decomposition (POD) method is considered and a cost-effective approach is proposed to incorporate DAS information into the order reduction procedure. The sensitivities of the cost functional in 4D-Var data assimilation with respect to the time varying model state are obtained from a backward integration of the adjoint model. This information is further used to define appropriate weights and to implement a dual-weighted proper orthogonal decomposition (DWPOD) method to order reduction. The use of a weighted ensemble data mean and weighted snapshots using the adjoint DAS is a novel element in reduced order 4D-Var data assimilation. Numerical results are presented with a global shallow-water model based on the Lin-Rood flux-form semi-Lagrangian scheme. A simplified 4D-Var DAS is considered

mesoscale vortices

The evolution of global atmospheric model dynamical cores from the first developments in the early 1960s to present day is reviewed. Numerical methods for atmospheric models are not straightforward because of the so-called pole problem. The early approaches include methods based on composite meshes, on quasi-homogeneous grids such as spherical geodesic and cubed sphere, on reduced grids, and on a latitude-longitude grid with short time steps near the pole, none of which were entirely successful. This resulted in the dominance of the spectral transform method after it was introduced. Semi-Lagrangian semi-implicit methods were developed which yielded significant computational savings and became dominant in Numerical Weather Prediction. The need for improved physical properties in climate modeling led to developments in shape preserving and conservative methods. Today the numerical methods development community is extremely active with emphasis placed on methods with desirable physical properties, especially conservation and shape preservation, while retaining the accuracy and efficiency gained in the past. Much of the development is based on quasi-uniform grids. Although the need for better physical properties is emphasized in this paper, another driving force is the need to develop schemes which are capable of running efficiently on computers with thousands of processors and distributed memory. Test cases for dynamical core evaluation are also briefly reviewed. These range from well defined deterministic tests to longer term statistical tests with both idealized forcing and complete parameterization packages but simple geometries. Finally some aspects of coupling dynamical cores to parameterization suites are discussed. 1.

This study examines the sensitivity of a number of important archetypical tracer problems to the numerical method used to solve the equations of tracer transport and atmospheric dynamics. The tracers’ scenarios were constructed to exercise the model for a variety of problems relevant to understanding and modeling the physical, dynamical, and chemical aspects of the climate system. The use of spectral, semi-Lagrangian, and finite volume (FV) numerical methods for the equations is explored. All subgrid-scale physical parameterizations were the same in all model simulations. The model behavior with a few short simulations with passive tracers is explored, and with much longer simulations of radon, SF 6, ozone, a tracer designed to mimic some aspects of a biospheric source/sink of CO 2, and a suite of tracers designed around the conservation laws for thermodynamics and mass in the model. Large differences were seen near the tropopause in the model, where the FV core shows a much reduced level of vertical and meridional mixing. There was also evidence that the subtropical subsidence regions are more isolated from Tropics and midlatitudes in the FV core than seen in the other model simulations. There are also big differences in the stratosphere, particularly for age of air in the stratosphere and ozone. A

The Community Climate System Model (CCSM) is a computer model for simulating the Earth’s climate. The CCSM is built from four individual component models for the atmosphere, ocean, land surface, and sea ice. The notion of a physical/dynamical component of the climate system translates directly to the software component structure. Software design of the CCSM is focused on the goals of modularity, extensibility, and performance portability. These goals are met at both the component level and within the individual component models. Performance portability is the ability of a code to achieve good performance across a variety of computer architectures while maintaining a single source code. As a community model, the CCSM must run on a variety of machine architectures and must perform well on all these architectures for computationally intensive climate simulations.

A 20-yr simulation using a global atmospheric general circulation model with a resolution of 0.5 ° latitude � 0.625 ° longitude is compared with observational findings. The primary goal of this survey is to assess the model performance in reproducing various summertime phenomena related to the continental-scale Asian monsoon in general, and the regional-scale East Asian monsoon in particular. In both model and observed atmospheres, the seasonal march of the precipitation centers associated with the Asian summer monsoon is characterized by onsets occurring earliest over the southeastern Bay of Bengal, followed by rapid northeastward advances over Indochina, the South China Sea–Philippine Sea and the western Pacific, northward evolution in the East Asian sector, as well as northwestward development over the Bay of Bengal, the Indian subcontinent, and the Arabian Sea. This onset sequence is accompanied by southwesterly low-level flows over the rainy regions, as well as northwestward migration of the 200-mb Tibetan anticyclone. Analysis of the heat sources and sinks in various regions illustrates the prominent role of condensational heating in the local energy budget during the mature phases of monsoon development. In accord with observations, the simulated monsoon rains in the East Asian sector are organized about zonally elongated “mei-yu–baiu ” (plum rain) systems. These precipitation features advance to higher latitudes

1 Abstract: Response of the North Pacific Subtropical Countercurrent (STCC) and its variability to global warming is examined in a state-of-the-art coupled model that is forced by increasing greenhouse gas concentrations. Compared with the present climate, the upper ocean is more stratified, and the mixed layer depth (MLD) shoals in warmer climate. The maximum change of winter MLD appears in the Kuroshio-Oyashio-Extension (KOE) region, where the mean MLD is deepest of the North Pacific. This weakens the MLD front and reduces lateral induction. Due to the reduced subduction rate and a decrease in sea surface density in KOE, mode waters form on lighter isopycnals with reduced thickness. Advected southward, the weakened mode waters decelerate the STCC. On decadal time scales, the dominant mode of sea surface height in the central subtropical gyre represents STCC variability. This STCC mode decays as CO2 concentrations double in the 21 st century, due both to weakened mode waters in the mean state and to reduced variability in mode waters. The reduced mode-water variability can be traced upstream to reduced variations in winter MLD front and subduction in the KOE region where mode water forms.

Two coupled atmosphere–ocean general circulation models developed at GFDL show differing stability properties of the Atlantic thermohaline circulation (THC) in the Coupled Model Intercomparison Project/ Paleoclimate Modeling Intercomparison Project (CMIP/PMIP) coordinated “water-hosing ” experiment. In contrast to the R30 model in which the “off ” state of the THC is stable, it is unstable in the CM2.1. This discrepancy has also been found among other climate models. Here a comprehensive analysis is performed to investigate the causes for the differing behaviors of the THC. In agreement with previous work, it is found that the different stability of the THC is closely related to the simulation of a reversed thermohaline circulation (RTHC) and the atmospheric feedback. After the shutdown of the THC, the RTHC is well developed and stable in R30. It transports freshwater into the subtropical North Atlantic, preventing the recovery of the salinity and stabilizing the off mode of the THC. The flux adjustment is a large term in the water budget of the Atlantic Ocean. In contrast, the RTHC is weak and unstable in CM2.1. The atmospheric feedback associated with the southward shift of the Atlantic ITCZ is much more significant. The oceanic freshwater convergence into the subtropical North Atlantic cannot completely compensate for the evaporation, leading to the recovery of the THC in CM2.1. The rapid salinity recovery in the subtropical North

A ‘‘biased twin’ ’ experiment using two coupled general circulation models (CGCMs) that are biased with respect to each other is used to study the impact of deep ocean bias on ensemble ocean data assimilation. The ‘‘observations’ ’ drawn from one CGCM based on the Argo network are assimilated into the other. Traditional ensemble filtering can successfully recover the upper-ocean temperature and salinity of the target model but it usually fails to converge in the deep ocean where the model bias is large compared to the ocean’s intrinsic variability. The inconsistency between the well-constrained upper ocean and poorly constrained deep ocean generates spurious assimilation currents. An adaptively inflated ensemble filter is designed to enhance the consistency of upper- and deep-ocean adjustments, based on ‘‘climatological’ ’ standard deviations being adaptively updated by observations. The new algorithm reduces deep-ocean errors greatly, in particular, reducing current errors up to 70 % and vertical motion errors up to 50%. Specifically, the tropical circulation is greatly improved with a better representation of the undercurrent, upwelling, and Western Boundary Current systems. The structure of the subtropical gyre is also substantially improved. Consequently, the new algorithm leads to better estimates of important global hydrographic features such as global overturning and pycnocline depth. Based on these improved estimates, decadal trends of basin-scale heat content and salinity as well as