The ARPEGE/IFS atmosphere model: a contribution to the French community climate modelling

A new atmospheric model has been developed jointly by Météo-France, and the European Centre for Medium-range Weather Forecasts (ECMWF) under the acronyms ARPEGE (action de recherche petite echelle grande echelle, which means research project on small and large scales) and IFS (integrated forecast system). This model includes, inter alia, an atmospheric general circulation model (GCM) which is intended by the French climate modelling community to be used for studying the anthropogenic climate impact. A preliminary version of this model has been available since 1992. This paper describes its main characteristics. Three 10-year integrations of this model having spectral horizontal resolutions of T21, T42, and T79 have been performed using prescribed monthly mean sea surface temperatures (SST) observed from 1979 until 1988. The results of these integrations are presented and compared with the observed climatology. The comparison is made for the winter (DJF) and summer (JJA) periods. It is shown that the model is capable of reproducing the observed climatology in a generally successful manner.     

Mechanisms of tropical Pacific interannual-to-decadal variability in the ARPEGE/ORCA global coupled model

Spatial and temporal structures of interannual-to-decadal variability in the tropical Pacific Ocean are investigated using results from a global atmosphere–ocean coupled general circulation model. The model produces quite realistic mean state characteristics, despite a sea surface temperature cold bias and a thermocline that is shallower than observations in the western Pacific. The periodicity and spatial patterns of the modelled El Niño Southern Oscillations (ENSO) compare well with those observed over the last 100 years, although the quasi-biennial timescale is dominant. Lag-regression analysis between the mean zonal wind stress and the 20°C isotherm depth suggests that the recently proposed recharge-oscillator paradigm is operating in the model. Decadal thermocline variability is characterized by enhanced variance over the western tropical South Pacific (~7°S). The associated subsurface temperature variability is primarily due to adiabatic displacements of the thermocline as a whole, arising from Ekman pumping anomalies located in the central Pacific, south of the equator. Related wind anomalies appear to be caused by SST anomalies in the eastern equatorial Pacific. This quasi-decadal variability has a timescale between 8 years and 20 years. The relationship between this decadal tropical mode and the low-frequency modulation of ENSO variance is also discussed. Results question the commonly accepted hypothesis that the low-frequency modulation of ENSO is due to decadal changes of the mean state characteristics.     

Hierarchical evaluation of IPCC AR4 coupled climate models with systematic consideration of model uncertainties

The capability of reproducing observed surface air temperature (SAT) changes for the twentieth century is assessed using 22 multi-models which contribute to the Intergovernmental Panel on Climate Change Fourth Assessment Report. A Bayesian method is utilized for model evaluation by which model uncertainties are considered systematically. We provide a hierarchical analysis for global to sub-continental regions with two settings. First, regions of different size are evaluated separately at global, hemispheric, continental, and sub-continental scales. Second, the global SAT trend patterns are evaluated with gradual refinement of horizontal scales (higher dimensional analysis). Results show that models with natural plus anthropogenic forcing (MME_ALL) generally exhibit better skill than models with anthropogenic only forcing (MME_ANTH) at all spatial scales for different trend periods (entire twentieth century and its first and second halves). This confirms previous studies that suggest the important role of natural forcing. For the second half of the century, we found that MME_ANTH performs well compared to MME_ALL except for a few models with overestimated warming. This indicates not only major contributions of anthropogenic forcing over that period but also the applicability of both MMEs to observationally-constrained future predictions of climate changes. In addition, the skill-weighted averages with the Bayes factors [Bayesian model averaging (BMA)] show a general superiority over other error-based weighted averaging methods, suggesting a potential advantage of BMA for climate change predictions.     

Review of simulations of climate variability and change with the GFDL R30 coupled climate model

A review is presented of the development and simulation characteristics of the most recent version of a global coupled model for climate variability and change studies at the Geophysical Fluid Dynamics Laboratory, as well as a review of the climate change experiments performed with the model. The atmospheric portion of the coupled model uses a spectral technique with rhomboidal 30 truncation, which corresponds to a transform grid with a resolution of approximately 3.75° longitude by 2.25° latitude. The ocean component has a resolution of approximately 1.875° longitude by 2.25° latitude. Relatively simple formulations of river routing, sea ice, and land surface processes are included. Two primary versions of the coupled model are described, differing in their initialization techniques and in the specification of sub-grid scale oceanic mixing of heat and salt. For each model a stable control integration of near millennial scale duration has been conducted, and the characteristics of both the time-mean and variability are described and compared to observations. A review is presented of a suite of climate change experiments conducted with these models using both idealized and realistic estimates of time-varying radiative forcing. Some experiments include estimates of forcing from past changes in volcanic aerosols and solar irradiance. The experiments performed are described, and some of the central findings are highlighted. In particular, the observed increase in global mean surface temperature is largely contained within the spread of simulated global mean temperatures from an ensemble of experiments using observationally-derived estimates of the changes in radiative forcing from increasing greenhouse gases and sulfate aerosols.     

CMIP1 evaluation and intercomparison of coupled climate models

 The climates simulated by 15 coupled atmosphere/ocean climate models participating in the first phase of the Coupled Model Intercomparison Project (CMIP1) are intercompared and evaluated. Results for global means, zonal averages, and geographical distributions of basic climate variables are assembled and compared with observations. The current generation of climate models reproduce the major features of the observed distribution of the basic climate parameters, but there is, nevertheless, a considerable scatter among model results and between simulated and observed values. This is particularly true for oceanic variables. Flux adjusted models generally produce simulated climates which are in better accord with observations than do non-flux adjusted models; however, some non-flux adjusted model results are closer to observations than some flux adjusted model results. Other model differences, such as resolution, do not appear to provide a clear distinction among model results in this generation of models. Many of the systematic differences (those differences common to most models), evident in previous intercomparison studies are exhibited also by the CMIP1 group of models although often with reduced magnitudes. As is characteristic of intercomparison results, different climate variables are simulated with different levels of success by different models and no one model is “best” for all variables. There is some evidence that the “mean model” result, obtained by averaging over the ensemble of models, provides an overall best comparison to observations for climatological mean fields. The model deficiencies identified here do not suggest immediate remedies and the overall success of the models in simulating the behaviour of the complex non-linear climate system apparently depends on the slow improvement in the balance of approximations that characterize a coupled climate model. Of course, the results of this and similar studies provide only an indication, at a particular time, of the current state and the moderate but steady evolution and improvement of coupled climate models. Received: 26 January 2000 / Accepted: 9 June 2000     

Polar amplification in a coupled climate model with locked albedo

In recent years, a substantial reduction of the sea ice in the Arctic has been observed. At the same time, the near-surface air in this region is warming at a rate almost twice as large as the global average—this phenomenon is known as the Arctic amplification. The role of the ice-albedo feedback for the Arctic amplification is still a matter of debate. Here the effect of the surface-albedo feedback (SAF) was studied using a coupled climate model CCSM3 from the National Center for Atmospheric Research. Experiments, where the SAF was suppressed by locking the surface albedo in the entire coupled model system, were conducted. The results reveal polar temperature amplification when this model, with suppressed albedo, is forced by a doubling of the atmospheric CO₂ content. Comparisons with variable albedo experiments show that SAF amplifies the surface-temperature response in the Arctic area by about 33%, whereas the corresponding value for the global-mean surface temperature is about 15%. Even though SAF is an important process underlying excessive warming at high latitudes, the Arctic amplification is only 15% larger in the variable than in the locked-albedo experiments. It is found that an increase of water vapour and total cloud cover lead to a greenhouse effect, which is larger in the Arctic than at lower latitudes. This is expected to explain a part of the Arctic surface–air-temperature amplification.     

Decadal-timescale changes of the Atlantic overturning circulation and climate in a coupled climate model with a hybrid-coordinate ocean component

A wide range of statistical tools is used to investigate the decadal variability of the Atlantic Meridional Overturning Circulation (AMOC) and associated key variables in a climate model (CHIME, Coupled Hadley-Isopycnic Model Experiment), which features a novel ocean component. CHIME is as similar as possible to the 3rd Hadley Centre Coupled Model (HadCM3) with the important exception that its ocean component is based on a hybrid vertical coordinate. Power spectral analysis reveals enhanced AMOC variability for periods in the range 15–30 years. Strong AMOC conditions are associated with: (1) a Sea Surface Temperature (SST) anomaly pattern reminiscent of the Atlantic Multi-decadal Oscillation (AMO) response, but associated with variations in a northern tropical-subtropical gradient; (2) a Surface Air Temperature anomaly pattern closely linked to SST; (3) a positive North Atlantic Oscillation (NAO)-like pattern; (4) a northward shift of the Intertropical Convergence Zone. The primary mode of AMOC variability is associated with decadal changes in the Labrador Sea and the Greenland Iceland Norwegian (GIN) Seas, in both cases linked to the tropical activity about 15 years earlier. These decadal changes are controlled by the low-frequency NAO that may be associated with a rapid atmospheric teleconnection from the tropics to the extratropics. Poleward advection of salinity anomalies in the mixed layer also leads to AMOC changes that are linked to processes in the Labrador Sea. A secondary mode of AMOC variability is associated with interannual changes in the Labrador and GIN Seas, through the impact of the NAO on local surface density.     

A coupled simple climate model and its global analysis

Summary An atmosphere-land coupled simple climate model is constructed and its climatic properties are analyzed by introducing a global analysis method, cell mapping. The simple model is a nonlinear six order simplified climate model featured with chaotic dynamics, dissipation, and forcing source, which are the main features of the real climate system. The cell mapping method is applied with this coupled system. Numerical experiments are carried out for investigating the interactions between the fast-changing atmospheric variables and slow-changing underlying surface variables. The predictability of the system is also investigated via the global analysis, with which the evolution of the system is translated to the evolution of probability transition on a Markov Chain. An effective scheme is proposed for computing the probability transition matrix for the coupled system. Predictions can be made based on the combination of dynamics and statistics. The importance of constructing the coupled model is shown by globally analyzing the predictability of the coupled system. The coupling mechanism prolongs the memorization of initial information, and then the predictability as well.     

Monte Carlo climate change forecasts with a global coupled ocean-atmosphere model

Four time-dependent greenhouse warming experiments were performed with the same global coupled atmosphere-ocean model, but with each simulation using initial conditions from different snapshots of the control run climate. The radiative forcing — the increase in equivalent CO₂ concentrations from 1985–2035 specified in the Intergovernmental Panel on Climate Change (IPCC) scenario A — was identical in all four 50-year integrations. This approach to climate change experiments is called the Monte Carlo technique and is analogous to a similar experimental set-up used in the field of extended range weather forecasting. Despite the limitation of a very small sample size, this approach enables the estimation of both a mean response and the between-experiment variability, information which is not available from a single integration. The use of multiple realizations provides insights into the stability of the response, both spatially, seasonally and in terms of different climate variables. The results indicate that the time evolution of the global mean warming signal is strongly dependent on the initial state of the climate system. While the individual members of the ensemble show considerable variation in the pattern and amplitude of near-surface temperature change after 50 years, the ensemble mean climate change pattern closely resembles that obtained in a 100-year integration performed with the same model. In global mean terms, the climate change signals for near surface temperature, the hydrological cycle and sea level significantly exceed the variability among the members of the ensemble. Due to the high internal variability of the modelled climate system, the estimated detection time of the global mean temperature change signal is uncertain by at least one decade. While the ensemble mean surface temperature and sea level fields show regionally significant responses to greenhouse-gas forcing, it is not possible to identify a significant response in the precipitation and soil moisture fields, variables which are spatially noisy and characterized by large variability between the individual integrations.     

Modeling the tropical Pacific Ocean using a regional coupled climate model

A high-resolution tropical Pacific general circulation model (GCM) coupled to a global atmospheric GCM is described in this paper. The atmosphere component is the 5°×4°global general circulation model of the Institute of Atmospheric Physics (IAP) with 9 levels in the vertical direction. The ocean component with a horizontal resolution of 0.5°, is based on a low-resolution model (2°×1°in longitude-latitude).Simulations of the ocean component are first compared with its previous version. Results show that the enhanced ocean horizontal resolution allows an improved ocean state to be simulated; this involves (1) an apparent decrease in errors in the tropical Pacific cold tongue region, which exists in many ocean models,(2) more realistic large-scale flows, and (3) an improved ability to simulate the interannual variability and a reduced root mean square error (RMSE) in a long time integration. In coupling these component models, a monthly "linear-regression" method is employed to correct the model's exchanged flux between the sea and the atmosphere. A 100-year integration conducted with the coupled GCM (CGCM) shows the effectiveness of such a method in reducing climate drift. Results from years 70 to 100 are described.The model produces a reasonably realistic annual cycle of equatorial SST. The large SSTA is confined to the eastern equatorial Pacific with little propagation. Irregular warm and cold events alternate with a broad spectrum of periods between 24 and 50 months, which is very realistic. But the simulated variability is weaker than the observed and is also asymmetric in the sense of the amplitude of the warm and cold events.     

A coupled ocean-atmosphere climate model: temperature versus salinity effects on the thermohaline circulation

A simple nonlinear three-box ocean model of the North Atlantic Ocean including the rudiments of eddy mixing, vertical stratification and thermohaline circulation is first presented. It is subject to uniform latitudinal differential heating, q, and net evaporation m _e , and includes a linear equation of state. Two quite different limiting steady-state solutions exist. The first has a warm saline surface water and a cold, low-salinity deep ocean; deep water is primarily formed in higher latitudes by the prevalence of differential heating. A second limiting solution consists of a warm saline deep ocean underlying a cool, low-salinity surface ocean; deep water is formed primarily in lower latitudes as a consequence of large differential evaporation. A coupled ocean-atmosphere model, in which the oceanic surface heat fluxes are determined internally but with differential evaporation at the ocean surface m _e remaining an external parameter, is next presented. The atmosphere component is a simple energy balance model that emphasizes the vertical fluxes of radiative, sensible and latent heat fluxes but does not include temperature-albedo feedback. Model response depends on the external parameters m _e and , controlling the magnitude of the thermohaline-driven circulation, and on the magnitudes of the eddy mixing coefficients and the solar constant. For small m _e , a steady-state solution corresponding to a cold fresh deep ocean is found, qualitatively similar to the modern ocean. For large m _e , a steady-state solution with a warm saline deep ocean occurs; this solution resembles conceptual models that have been proposed for the warm saline Cretaceous ocean. There exists an intermediate region of values of m _e for which the solutions are more complex. On the lower end of this region, both the cold fresh deep-ocean and warm saline deep-ocean circulations coexist as stable equilibria. On the upper end, the cold-deep ocean becomes unstable, manifesting oscillations with growing amplitude, and ultimately reaches the warm saline deep-ocean solution. In the neighborhood of a cusp on the , m _e plane, that is, for relatively small , more complex behaviour occurs, which has not yet been fully analyzed. The model response in the region of complexity is not sensitive to changes in the solar constant but is sensitive to the eddy mixing coefficients.     

Tropical–extratropical climate interaction as revealed in idealized coupled climate model experiments

Tropical–extratropical climate interactions are studied by idealized experiments with a prescribed 2°C SST anomaly at different latitude bands in a coupled climate model. Instead of focusing on intrinsic climate variability, this work investigates the mean climate adjustment to remote external forcing. The extratropical impact on tropical climate can be as strong as the tropical impact on extratropical climate, with the remote sea surface temperature (SST) response being about half the magnitude of the imposed SST change in the forcing region. The equatorward impact of extratropical climate is accomplished by both the atmospheric bridge and the oceanic tunnel. About two-thirds of the tropical SST change comes from the atmospheric bridge, while the remaining one-third comes from the oceanic tunnel. The equatorial SST increase is first driven by the reduced latent heat flux and the weakened poleward surface Ekman transport, and then enhanced by the decrease in subtropical cells’ strength and the equatorward subduction of warm anomalies. In contrast, the poleward impact of tropical climate is accomplished mainly by the atmospheric bridge, which is responsible for extratropical temperature changes in both the surface and subsurface. Sensitivity experiments also show the dominant role of the Southern Hemisphere oceans in the tropical climate change. CCR contribution number 829; DAS-PKU contribution number 002.     

The North Atlantic Oscillation in coupled climate models: a CMIP1 evaluation

This study investigates the North Atlantic Oscillation (NAO) simulated by 17 global coupled ocean-atmosphere models participating in the Coupled Model Intercomparison Project (CMIP). Robust NAO indices are defined by calculating the leading principal components of winter time mean surface temperatures (land and sea) in the North Atlantic region (120°W-60°E, 20-80°N). Encouragingly, 13 out of 17 of the models capture the NAO surface temperature quadrupole pattern with centres of action over Northwest Europe, the northwest Atlantic, the southeastern USA, and the Middle East. The northern dipole is better captured than the southern dipole which is often simulated too far eastwards over the Atlantic Ocean. Out of the 17 models, ten models produce NAO indices that vary similar to the observations as stationary "weakly red noise" with only small correlations between successive winters (r < 0.3). Another five models drift monotonically towards warmer conditions, and two models exhibit long-term stochastic trends. Several of the models significantly overestimate the teleconnection between NAO and the tropical ENSO phenomenon.     

Effects of Amazonian deforestation on climate: a numerical experiment with a coupled biosphere-atmosphere model with soil hydrology

Summary A coupled biosphere-atmosphere statistical-dynamical model (SDM) is used to study the climatic effects of Amazonian deforestation. A soil moisture model based on BATS has been incorporated into the SDM in order to study the biogeophysical feedback of change in surface characteristics to regional climate due to the deforestation. In the control experiment, the mean annual and mean seasonal climate is well simulated by the model when compared with NCEP/NCAR reanalysis data. In the deforestation experiment, the evergreen broadleaf trees in the Amazonian region are substituted by short grass. The effects of Amazonian deforestation on regional climate are analysed taking into account the model simulations for the land portion of the latitude belts comprising the tropical region. Amazonian deforestation results in regional climate changes such as a decrease in evaporation, precipitation, available surface net radiation and soil moisture content, and an increase in temperatures and sensible heat flux. The reduction in transpiration was responsible for the most part of the decrease in total evapotranspiration. The reduction in precipitation was larger than the decrease in evapotranspiration so that runoff was reduced. The simulation of the diurnal cycle of the surface temperature shows an increase in temperature during the day and a decrease at night, which is in agreement with observations, whereas earlier GCM experiments showed an increase both during the day and night. In general, the changes in temperature and energy fluxes are in good agreement with GCM experiments, showing that the SDM is able to simulate the characteristics of the tropical climate that are associated with the substitution of forest by pasture areas.     

The CNRM-CM5.1 global climate model: description and basic evaluation

A new version of the general circulation model CNRM-CM has been developed jointly by CNRM-GAME (Centre National de Recherches Météorologiques—Groupe d’études de l’Atmosphère Météorologique) and Cerfacs (Centre Européen de Recherche et de Formation Avancée) in order to contribute to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The purpose of the study is to describe its main features and to provide a preliminary assessment of its mean climatology. CNRM-CM5.1 includes the atmospheric model ARPEGE-Climat (v5.2), the ocean model NEMO (v3.2), the land surface scheme ISBA and the sea ice model GELATO (v5) coupled through the OASIS (v3) system. The main improvements since CMIP3 are the following. Horizontal resolution has been increased both in the atmosphere (from 2.8° to 1.4°) and in the ocean (from 2° to 1°). The dynamical core of the atmospheric component has been revised. A new radiation scheme has been introduced and the treatments of tropospheric and stratospheric aerosols have been improved. Particular care has been devoted to ensure mass/water conservation in the atmospheric component. The land surface scheme ISBA has been externalised from the atmospheric model through the SURFEX platform and includes new developments such as a parameterization of sub-grid hydrology, a new freezing scheme and a new bulk parameterisation for ocean surface fluxes. The ocean model is based on the state-of-the-art version of NEMO, which has greatly progressed since the OPA8.0 version used in the CMIP3 version of CNRM-CM. Finally, the coupling between the different components through OASIS has also received a particular attention to avoid energy loss and spurious drifts. These developments generally lead to a more realistic representation of the mean recent climate and to a reduction of drifts in a preindustrial integration. The large-scale dynamics is generally improved both in the atmosphere and in the ocean, and the bias in mean surface temperature is clearly reduced. However, some flaws remain such as significant precipitation and radiative biases in many regions, or a pronounced drift in three dimensional salinity.     

The Interannual Variability of Climate in a Coupled Ocean-Atmosphere Model

In this paper, the interannual variability simulated by the coupled ocean-atmosphere general circulation model of the Institute of Atmospheric Physics (IAP CGCM) in 40 year integrations is analyzed, and compared with that by the corresponding IAP AGCM which uses the climatic sea surface temperature as the boundary condition in 25 year integrations.The mean climatic states of January and July simulated by IAP CGCM are in good agreement with that by IAP AGCM, i.e., no serious ‘climate drift’ occurs in the CGCM simulation. A comparison of the results from AGCM and CGCM indicates that the standard deviation of the monthly averaged sea level pressure simulated by IAP CGCM is much greater than that by IAP AGCM in tropical region. In addition, both Southern Oscillation (SO) and North Atlantic Oscillation (NAO) can be found in the CGCM simulation for January, but these two oscillations do not exist in the AGCM simulation.The interannual variability of climate may be classified into two types: one is the variation of the annual mean, another is the variation of the annual amplitude. The ocean-atmosphere interaction mainly increases the first type of variability. By means of the rotated EOF, the most important patterns corresponding to the two types of interannual variability are found to have different spatial and temporal characteristics.     

Sensitivity of a coupled climate model to canopy interception capacity

The canopy interception capacity is a small but key part of the surface hydrology, which affects the amount of water intercepted by vegetation and therefore the partitioning of evaporation and transpiration. However, little research with climate models has been done to understand the effects of a range of possible canopy interception capacity parameter values. This is in part due to the assumption that it does not significantly affect climate. Near global evapotranspiration products now make evaluation of canopy interception capacity parameterisations possible. We use a range of canopy water interception capacity values from the literature to investigate the effect on climate within the climate model HadCM3. We find that the global mean temperature is affected by up to ?0.64 K globally and ?1.9 K regionally. These temperature impacts are predominantly due to changes in the evaporative fraction and top of atmosphere albedo. In the tropics, the variations in evapotranspiration affect precipitation, significantly enhancing rainfall. Comparing the model output to measurements, we find that the default canopy interception capacity parameterisation overestimates canopy interception loss (i.e. canopy evaporation) and underestimates transpiration. Overall, decreasing canopy interception capacity improves the evapotranspiration partitioning in HadCM3, though the measurement literature more strongly supports an increase. The high sensitivity of climate to the parameterisation of canopy interception capacity is partially due to the high number of light rain-days in the climate model that means that interception is overestimated. This work highlights the hitherto underestimated importance of canopy interception capacity in climate model hydroclimatology and the need to acknowledge the role of precipitation representation limitations in determining parameterisations.