Ocean circulation and Tropical Variability in the Coupled Model ECHAM5/MPI-OM

This paper describes the mean ocean circulation and the tropical variability simulated by the Max Planck Institute for Meteorology (MPI-M) coupled atmosphere–ocean general circulation model (AOGCM). Results are presented from a version of the coupled model that served as a prototype for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) simulations. The model does not require flux adjustment to maintain a stable climate. A control simulation with present-day greenhouse gases is analyzed, and the simulation of key oceanic features, such as sea surface temperatures (SSTs), large-scale circulation, meridional heat and freshwater transports, and sea ice are compared with observations. A parameterization that accounts for the effect of ocean currents on surface wind stress is implemented in the model. The largest impact of this parameterization is in the tropical Pacific, where the mean state is significantly improved: the strength of the trade winds and the associated equatorial upwelling weaken, and there is a reduction of the model’s equatorial cold SST bias by more than 1 K. Equatorial SST variability also becomes more realistic. The strength of the variability is reduced by about 30% in the eastern equatorial Pacific and the extension of SST variability into the warm pool is significantly reduced. The dominant El Niño–Southern Oscillation (ENSO) period shifts from 3 to 4 yr. Without the parameterization an unrealistically strong westward propagation of SST anomalies is simulated. The reasons for the changes in variability are linked to changes in both the mean state and to a reduction in atmospheric sensitivity to SST changes and oceanic sensitivity to wind anomalies

ENSO feedbacks and their relationships with the mean state in a flux adjusted ensemble

The El Niño Southern Oscillation (ENSO) is governed by a combination of amplifying and damping ocean–atmosphere feedbacks in the equatorial Pacific. Here we quantify these feedbacks in a flux adjusted HadCM3 perturbed physics ensemble under present day conditions and a future emissions scenario using the Bjerknes Stability Index (BJ index). Relationships between feedbacks and both the present day biases and responses under climate change of the mean equatorial Pacific climate are investigated. Despite minimised mean sea surface temperature biases through flux adjustment, the important dominant ENSO feedbacks still show biases with respect to observed feedbacks and inter-ensemble diversity. The dominant positive thermocline and zonal advective feedbacks are found to be weaker in ensemble members with stronger mean zonal advection. This is due to a weaker sensitivity of the thermocline slope and zonal surface ocean currents in the east Pacific to surface wind stress anomalies. A drier west Pacific is also found to be linked to weakened shortwave and latent heat flux damping, suggesting a link between ENSO characteristics and the hydrological cycle. In contrast to previous studies using the BJ index that find positive relationships between the index and ENSO amplitude, here they are weakly or negatively correlated, both for present day conditions and for projected differences. This is caused by strong thermodynamic damping which dominates over positive feedbacks, which alone approximate ENSO amplitude well. While the BJ index proves useful for individual linear feedback analysis, we urge caution in using the total linear BJ index alone to assess the reasons for ENSO amplitude biases and its future change in models

Identifying causes of Western Pacific ITCZ drift in ECMWF System 4 hindcasts

The development of systematic biases in climate models used in operational seasonal forecasting adversely affects the quality of forecasts they produce. In this study, we examine the initial evolution of systematic biases in the ECMWF System 4 forecast model, and isolate aspects of the model simulations that lead to the development of these biases. We focus on the tendency of the simulated intertropical convergence zone in the western equatorial Pacific to drift northwards by between 0.5° and 3° of latitude depending on season. Comparing observations with both fully coupled atmosphere–ocean hindcasts and atmosphere-only hindcasts (driven by observed sea-surface temperatures), we show that the northward drift is caused by a cooling of the sea-surface temperature on the Equator. The cooling is associated with anomalous easterly wind stress and excessive evaporation during the first twenty days of hindcast, both of which occur whether air-sea interactions are permitted or not. The easterly wind bias develops immediately after initialisation throughout the lower troposphere; a westerly bias develops in the upper troposphere after about ten days of hindcast. At this point, the baroclinic structure of the wind bias suggests coupling with errors in convective heating, although the initial wind bias is barotropic in structure and appears to have an alternative origin

Reconstructing extreme AMOC events through nudging of the ocean surface: a perfect model approach

While the Atlantic Meridional Overturning Circulation (AMOC) is thought to be a crucial component of the North Atlantic climate, past changes in its strength are challenging to quantify, and only limited information is available. In this study, we use a perfect model approach with the IPSL-CM5A-LR model to assess the performance of several surface nudging techniques in reconstructing the variability of the AMOC. Special attention is given to the reproducibility of an extreme positive AMOC peak from a preindustrial control simulation. Nudging includes standard relaxation techniques towards the sea surface temperature and salinity anomalies of this target control simulation, and/or the prescription of the wind-stress fields. Surface nudging approaches using standard fixed restoring terms succeed in reproducing most of the target AMOC variability, including the timing of the extreme event, but systematically underestimate its amplitude. A detailed analysis of the AMOC variability mechanisms reveals that the underestimation of the extreme AMOC maximum comes from a deficit in the formation of the dense water masses in the main convection region, located south of Iceland in the model. This issue is largely corrected after introducing a novel surface nudging approach, which uses a varying restoring coefficient that is proportional to the simulated mixed layer depth, which, in essence, keeps the restoring time scale constant. This new technique substantially improves water mass transformation in the regions of convection, and in particular, the formation of the densest waters, which are key for the representation of the AMOC extreme. It is therefore a promising strategy that may help to better constrain the AMOC variability and other ocean features in the models. As this restoring technique only uses surface data, for which better and longer observations are available, it opens up opportunities for improved reconstructions of the AMOC over the last few decades

The impact of ENSO on Southern African rainfall in CMIP5 ocean atmosphere coupled climate models

We study the ability of 24 ocean atmosphere global coupled models from the Coupled Model Intercomparison Project 5 (CMIP5) to reproduce the teleconnections between El Niño Southern Oscillation (ENSO) and Southern African rainfall in austral summer using historical forced simulations, with a focus on the atmospheric dynamic associated with El Niño. Overestimations of summer rainfall occur over Southern Africa in all CMIP5 models. Abnormal westward extensions of ENSO patterns are a common feature of all CMIP5 models, while the warming of the Indian Ocean that happens during El Niño is not correctly reproduced. This could impact the teleconnection between ENSO and Southern African rainfall which is represented with mixed success in CMIP5 models. Large-scale anomalies of suppressed deep-convection over the tropical maritime continent and enhanced convection from the central to eastern Pacific are correctly simulated. However, regional biases occur above Africa and the Indian Ocean, particularly in the position of the deep convection anomalies associated with El Niño, which can lead to the wrong sign in rainfall anomalies in the northwest part of South Africa. From the near-surface to mid-troposphere, CMIP5 models underestimate the observed anomalous pattern of pressure occurring over Southern Africa that leads to dry conditions during El Niño years

Analysis of rainfall seasonality from observations and climate models

Two new indicators of rainfall seasonality based on information entropy, the relative entropy (RE) and the dimensionless seasonality index (DSI), together with the mean annual rainfall, are evaluated on a global scale for recently updated precipitation gridded datasets and for historical simulations from coupled atmosphere--ocean general circulation models. The RE provides a measure of the number of wet months and, for precipitation regimes featuring a distinct wet and dry season, it is directly related to the duration of the wet season. The DSI combines the rainfall intensity with its degree of seasonality and it is an indicator of the extent of the global monsoon region. We show that the RE and the DSI are fairly independent of the time resolution of the precipitation data, thereby allowing objective metrics for model intercomparison and ranking. Regions with different precipitation regimes are classified and characterized in terms of RE and DSI. Comparison of different land observational datasets reveals substantial difference in their local representation of seasonality. It is shown that two-dimensional maps of RE provide an easy way to compare rainfall seasonality from various datasets and to determine areas of interest. Models participating to the Coupled Model Intercomparison Project platform, Phase 5, consistently overestimate the RE over tropical Latin America and underestimate it in West Africa, western Mexico and East Asia. It is demonstrated that positive RE biases in a general circulation model are associated with excessively peaked monthly precipitation fractions, too large during the wet months and too small in the months preceding and following the wet season; negative biases are instead due, in most cases, to an excess of rainfall during the premonsoonal months

A coupled method for initializing El Niño Southern Oscillation forecasts using sea surface temperature

A simple method for initializing coupled general circulation models (CGCMs) using only sea surface temperature (SST) data is comprehensively tested in an extended set of ensemble hindcasts with the Max-Planck-Institute (MPI) climate model, MPI-OM/ECHAM5. In the scheme, initial conditions for both atmosphere and ocean are generated by running the coupled model with SST nudged strongly to observations. Air–sea interaction provides the mechanism through which SST influences the subsurface. Comparison with observations indicates that the scheme is performing well in the tropical Pacific. Results from a 500-yr control run show that the model's El Niño Southern Oscillation (ENSO) variability is quite realistic, in terms of strength, structure and period. The hindcasts performed were six months long, initiated four times per year, consisted of nine ensemble members, and covered the period 1969–2001. The ensemble was generated by only varying atmospheric initial conditions, which were sampled from the initialization run to capture intraseasonal variability. At six-month lead, the model is able to capture all the major ENSO extremes of the period. However, because of poor sampling of ocean initial conditions and model deficiencies, the ensemble-mean anomaly correlation skill for Niño3 SST is only 0.6 at six-month lead. None the less, the results presented here demonstrate the potential of such a simple scheme, and provide a simple method by which SST information may be better used in more complex initialization schemes

Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5

We present the global general circulation model IPSL-CM5 developed to study the long-term response of the climate system to natural and anthropogenic forcings as part of the 5th Phase of the Coupled Model Intercomparison Project (CMIP5). This model includes an interactive carbon cycle, a representation of tropospheric and stratospheric chemistry, and a comprehensive representation of aerosols. As it represents the principal dynamical, physical, and bio-geochemical processes relevant to the climate system, it may be referred to as an Earth System Model. However, the IPSL-CM5 model may be used in a multitude of configurations associated with different boundary conditions and with a range of complexities in terms of processes and interactions. This paper presents an overview of the different model components and explains how they were coupled and used to simulate historical climate changes over the past 150 years and different scenarios of future climate change. A single version of the IPSL-CM5 model (IPSL-CM5A-LR) was used to provide climate projections associated with different socio-economic scenarios, including the different Representative Concentration Pathways considered by CMIP5 and several scenarios from the Special Report on Emission Scenarios considered by CMIP3. Results suggest that the magnitude of global warming projections primarily depends on the socio-economic scenario considered, that there is potential for an aggressive mitigation policy to limit global warming to about two degrees, and that the behavior of some components of the climate system such as the Arctic sea ice and the Atlantic Meridional Overturning Circulation may change drastically by the end of the twenty-first century in the case of a no climate policy scenario. Although the magnitude of regional temperature and precipitation changes depends fairly linearly on the magnitude of the projected global warming (and thus on the scenario considered), the geographical pattern of these changes is strikingly similar for the different scenarios. The representation of atmospheric physical processes in the model is shown to strongly influence the simulated climate variability and both the magnitude and pattern of the projected climate changes