A flexible climate model for use in integrated assessments

 Because of significant uncertainty in the behavior of the climate system, evaluations of the possible impact of an increase in greenhouse gas concentrations in the atmosphere require a large number of long-term climate simulations. Studies of this kind are impossible to carry out with coupled atmosphere ocean general circulation models (AOGCMs) because of their tremendous computer resource requirements. Here we describe a two dimensional (zonally averaged) atmospheric model coupled with a diffusive ocean model developed for use in the integrated framework of the Massachusetts Institute of Technology (MIT) Joint Program on the Science and Policy of Global Change. The 2-D model has been developed from the Goddard Institute for Space Studies (GISS) GCM and includes parametrizations of all the main physical processes. This allows it to reproduce many of the nonlinear interactions occurring in simulations with GCMs. Comparisons of the results of present-day climate simulations with observations show that the model reasonably reproduces the main features of the zonally averaged atmospheric structure and circulation. The model’s sensitivity can be varied by changing the magnitude of an inserted additional cloud feedback. Equilibrium responses of different versions of the 2-D model to an instantaneous doubling of atmospheric CO₂ are compared with results of similar simulations with different AGCMs. It is shown that the additional cloud feedback does not lead to any physically inconsistent results. On the contrary, changes in climate variables such as precipitation and evaporation, and their dependencies on surface warming produced by different versions of the MIT 2-D model are similar to those shown by GCMs. By choosing appropriate values of the deep ocean diffusion coefficients, the transient behavior of different AOGCMs can be matched in simulations with the 2-D model, with a unique choice of diffusion coefficients allowing one to match the performance of a given AOGCM for a variety of transient forcing scenarios. Both surface warming and sea level rise due to thermal expansion of the deep ocean in response to a gradually increasing forcing are reasonably reproduced on time scales of 100–150 y. However a wide range of diffusion coefficients is needed to match the behavior of different AOGCMs. We use results of simulations with the 2-D model to show that the impact on climate change of the implied uncertainty in the rate of heat penetration into the deep ocean is comparable with that of other significant uncertainties. Received: 10 March 1997 / Accepted: 20 October 1997     

Influence of SST biases on future climate change projections

We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977?C1999 in the historical period and 2077?C2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean?Catmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection.     

The role of ocean dynamics in producing decadal climate variability in the North Pacific

 Decadal time scale climate variability in the North Pacific has implications for climate both locally and over North America. A crucial question is the degree to which this variability arises from coupled ocean/atmosphere interactions over the North Pacific that involve ocean dynamics, as opposed to either purely thermodynamic effects of the oceanic mixed layer integrating in situ the stochastic atmospheric forcing, or the teleconnected response to tropical variability. The part of the variability that is coming from local coupled ocean/atmosphere interactions involving ocean dynamics is potentially predictable by an ocean/atmosphere general circulation model (O/A GCM), and such predictions could (depending on the achievable lead time) have distinct societal benefits. This question is examined using the results of fully coupled O/A GCMs, as well as targeted numerical experiments with stand-alone ocean and atmosphere models individually. It is found that coupled ocean/atmosphere interactions that involve ocean dynamics are important to determining the strength and frequency of a decadal-time scale peak in the spectra of several oceanic variables in the Kuroshio extension region off Japan. Local stochastic atmospheric heat flux forcing, integrated by the oceanic mixed layer into a red spectrum, provides a noise background from which the signal must be extracted. Although teleconnected ENSO responses influence the North Pacific in the 2–7 years/cycle frequency band, it is shown that some decadal-time scale processes in the North Pacific proceed without ENSO. Likewise, although the effects of stochastic atmospheric forcing on ocean dynamics are discernible, a feedback path from the ocean to the atmosphere is suggested by the results. Received: 23 January 2000 / Accepted: 10 January 2001     

Predictability of SST forced climate signals in two atmospheric general circulation models

 The predictability of atmospheric responses to global sea surface temperature (SST) anomalies is evaluated using ensemble simulations of two general circulation models (GCMs): the GENESIS version 1.5 (GEN) and the ECMWF cycle 36 (ECM). The integrations incorporate observed SST variations but start from different initial land and atmospheric states. Five GEN 1980–1992 and six ECM 1980–1988 realizations are compared with observations to distinguish predictable SST forced climate signals from internal variability. To facilitate the study, correlation analysis and significance evaluation techniques are developed on the basis of time series permutations. It is found that the annual mean global area with realistic signals is variable dependent and ranges from 3 to 20% in GEN and 6 to 28% in ECM. More than 95% of these signal areas occur between 35 °S–35 °N. Due to the existence of model biases, robust responses, which are independent of initial condition, are identified over broader areas. Both GCMs demonstrate that the sensitivity to initial conditions decreases and the predictability of SST forced responses increases, in order, from 850 hPa zonal wind, outgoing longwave radiation, 200 hPa zonal wind, sea-level pressure to 500 hPa height. The predictable signals are concentrated in the tropical and subtropical Pacific Ocean and are identified with typical El Ni?o/ Southern Oscillation phenomena that occur in response to SST and diabatic heating anomalies over the equatorial central Pacific. ECM is less sensitive to initial conditions and better predicts SST forced climate changes. This results from (1) a more realistic basic climatology, especially of the upper-level wind circulation, that produces more realistic interactions between the mean flow, stationary waves and tropical forcing; (2) a more vigorous hydrologic cycle that amplifies the tropical forcing signals, which can exceed internal variability and be more efficiently transported from the forcing region. Differences between the models and observations are identified. For GEN during El Ni?o, the convection does not carry energy to a sufficiently high altitude, while the spread of the tropospheric warming along the equator is slower and the anomaly magnitude smaller than observed. This impacts model ability to simulate realistic responses over Eurasia and the Indian Ocean. Similar biases exist in the ECM responses. In addition, the relationships between upper and lower tropospheric wind responses to SST forcing are not well reproduced by either model. The identification of these model biases leads to the conclusion that improvements in convective heat and momentum transport parametrizations and basic climate simulations could substantially increase predictive skill. Received: 25 April 1996 / Accepted: 9 December 1996     

A transient climate change simulation with greenhouse gas and aerosol forcing: experimental design and comparison with the instrumental record for the twentieth century

 The Canadian Centre for Climate Modelling and Analysis (CCCma) global coupled model is used to investigate the potential climate effects of increasing greenhouse gas (GHG) concentrations and changes in sulfate aerosol loadings. The forcing scenario adopted closely resembles that of Mitchell et al. for both the greenhouse gas and aerosol components. Its implementation in the model and the resulting changes in forcing are described. Five simulations of 200 years in length, nominally for the years 1900 to 2100, are available for analysis. They consist of a control simulation without change in forcing, three independent simulations with the same greenhouse gas and aerosol changes, and a single simulation with greenhouse gas only forcing. Simulations of the evolution of temperature and precipitation from 1900 to the present are compared with available observations. Temperature and precipitation are primary climate variables with reasonable temporal and spatial coverage in the observational record for the period. The simulation of potential climate change from the present to the end of the twenty-first century, based on projected GHG and aerosol forcing changes, is discussed in a companion paper. For the historical period dealt with here, the GHG and aerosol forcing has changed relatively little compared to the forcing changes projected to the end of the twenty-first century. Nevertheless, the forced climate signal for temperature in the model is reasonably consistent with the observed global mean temperature from the instrumental record. This is true also for the trend in zonally averaged temperature as a function of latitude and for some aspects of the geographical and regional distributions of temperature. Despite the modest change in overall forcing, the difference between GHG+aerosol and GHG-only forcing is discernible in the temperature response for this period. Changes in precipitation, on the other hand, are much less evident in both the instrumental and simulated record. There is an apparent increasing trend in average precipitation in both the observations and the model results over that part of the land for which observations are available. Regional and geographical changes and trends (which are less affected by sampling considerations), if they exist, are masked by the large natural variability of precipitation in both model and observations. Received: 24 September 1998 / Accepted: 8 October 1999     

Constraining climate model properties using optimal fingerprint detection methods

 We present a method for constraining key properties of the climate system that are important for climate prediction (climate sensitivity and rate of heat penetration into the deep ocean) by comparing a model's response to known forcings over the twentieth century against climate observations for that period. We use the MIT 2D climate model in conjunction with results from the Hadley Centre's coupled atmosphere–ocean general circulation model (AOGCM) to determine these constraints. The MIT 2D model, which is a zonally averaged version of a 3D GCM, can accurately reproduce the global-mean transient response of coupled AOGCMs through appropriate choices of the climate sensitivity and the effective rate of diffusion of heat anomalies into the deep ocean. Vertical patterns of zonal mean temperature change through the troposphere and lower stratosphere also compare favorably with those generated by 3-D GCMs. We compare the height–latitude pattern of temperature changes as simulated by the MIT 2D model with observed changes, using optimal fingerprint detection statistics. Using a linear regression model as in Allen and Tett this approach yields an objective measure of model-observation goodness-of-fit (via the residual sum of squares weighted by differences expected due to internal variability). The MIT model permits one to systematically vary the model's climate sensitivity (by varying the strength of the cloud feedback) and rate of mixing of heat into the deep ocean and determine how the goodness-of-fit with observations depends on these factors. This provides an efficient framework for interpreting detection and attribution results in physical terms. With aerosol forcing set in the middle of the IPCC range, two sets of model parameters are rejected as being implausible when the model response is compared with observations. The first set corresponds to high climate sensitivity and slow heat uptake by the deep ocean. The second set corresponds to low sensitivities for all magnitudes of heat uptake. These results demonstrate that fingerprint patterns must be carefully chosen, if their detection is to reduce the uncertainty of physically important model parameters which affect projections of climate change. Received: 19 April 2000 / Accepted: 13 April 2001     

A nonlinear impulse response model of the coupled carbon cycle-climate system (NICCS)

 Impulse-response-function (IRF) models are designed for applications requiring a large number of climate change simulations, such as multi-scenario climate impact studies or cost-benefit integrated-assessment studies. The models apply linear response theory to reproduce the characteristics of the climate response to external forcing computed with sophisticated state-of-the-art climate models like general circulation models of the physical ocean-atmosphere system and three-dimensional oceanic-plus-terrestrial carbon cycle models. Although highly computer efficient, IRF models are nonetheless capable of reproducing the full set of climate-change information generated by the complex models against which they are calibrated. While limited in principle to the linear response regime (less than about 3 ^∘C global-mean temperature change), the applicability of the IRF model presented has been extended into the nonlinear domain through explicit treatment of the climate system's dominant nonlinearities: CO₂ chemistry in ocean water, CO₂ fertilization of land biota, and sublinear radiative forcing. The resultant nonlinear impulse-response model of the coupled carbon cycle-climate system (NICCS) computes the temporal evolution of spatial patterns of climate change for four climate variables of particular relevance for climate impact studies: near-surface temperature, cloud cover, precipitation, and sea level. The space-time response characteristics of the model are derived from an EOF analysis of a transient 850-year greenhouse warming simulation with the Hamburg atmosphere-ocean general circulation model ECHAM3-LSG and a similar response experiment with the Hamburg carbon cycle model HAMOCC. The model is applied to two long-term CO₂ emission scenarios, demonstrating that the use of all currently estimated fossil fuel resources would carry the Earth's climate far beyond the range of climate change for which reliable quantitative predictions are possible today, and that even a freezing of emissions to present-day levels would cause a major global warming in the long term. Received: 28 January 2000 / Accepted: 9 March 2001     

High-resolution simulations of West African climate using regional climate model (RegCM3) with different lateral boundary conditions

To downscale climate change scenarios, long-term regional climatologies employing global model forcing are needed for West Africa. As a first step, this work examines present-day integrations (1981–2000) with a regional climate model (RCM) over West Africa nested in both reanalysis data and output from a coupled atmospheric–ocean general circulation model (AOGCM). Precipitation and temperature from both simulations are compared to the Climate Research Unit observations. Their spatial distributions are shown to be realistic. Annual cycles are considerably correlated. Simulations are also evaluated with respect to the driving large-scale fields. RCM offers some improvements compared to the AOGCM driving field. Evaluation of seasonal precipitation biases reveals that RCM dry biases are highest on June–August around mountains. They are associated to cold biases in temperature which, in turn, are connected to wet biases in precipitation outside orographic zones. Biases brought through AOGCM forcing are relatively low. Despite these errors, the simulations produce encouraging results and show the ability of the AOGCM to drive the RCM for future projections.     

CO2 climate sensitivity and snow-sea-ice albedo parameterization in an atmospheric GCM coupled to a mixed-layer ocean model

The snow-sea-ice albedo parameterization in an atmospheric general circulation model (GCM), coupled to a simple mixed-layer ocean and run with an annual cycle of solar forcing, is altered from a version of the same model described by Washington and Meehl (1984). The model with the revised formulation is run to equilibrium for 1 × CO₂ and 2 × CO₂ experiments. The 1 ×CO₂ (control) simulation produces a global mean climate about 1° warmer than the original version, and sea-ice extent is reduced. The model with the altered parameterization displays heightened sensitivity in the global means, but the geographical patterns of climate change due to increased carbon dioxide (CO₂) are qualitatively similar. The magnitude of the climate change is affected, not only in areas directly influenced by snow and ice changes but also in other regions of the globe, including the tropics where sea-surface temperature, evaporation, and precipitation over the oceans are greater. With the less-sensitive formulation, the global mean surface air temperature increase is 3.5 °C, and the increase of global mean precipitation is 7.12%. The revised formulation produces a globally averaged surface air temperature increase of 4.04 °C and a precipitation increase of 7.25%, as well as greater warming of the upper tropical troposphere. Sensitivity of surface hydrology is qualitatively similar between the two cases with the larger-magnitude changes in the revised snow and ice-albedo scheme experiment. Variability of surface air temperature in the model is comparable to observations in most areas except at high latitudes during winter. In those regions, temporal variation of the sea-ice margin and fluctuations of snow cover dependent on the snow-ice-albedo formulation contribute to larger-than-observed temperature variability. This study highlights an uncertainty associated with results from current climate GCMs that use highly parameterized snow-sea-ice albedo schemes with simple mixed-layer ocean models.The National Center for Atmospheric Research is sponsored by the National Science Foundation.     

Global high resolution versus Limited Area Model climate change projections over Europe: quantifying confidence level from PRUDENCE results

Four high resolution atmospheric general circulation models (GCMs) have been integrated with the standard forcings of the PRUDENCE experiment: IPCC-SRES A2 radiative forcing and Hadley Centre sea surface temperature and sea-ice extent. The response over Europe, calculated as the difference between the 2071–2100 and the 1961–1990 means is compared with the same diagnostic obtained with nine Regional Climate Models (RCM) all driven by the Hadley Centre atmospheric GCM. The seasonal mean response for 2m temperature and precipitation is investigated. For temperature, GCMs and RCMs behave similarly, except that GCMs exhibit a larger spread. However, during summer, the spread of the RCMs—in particular in terms of precipitation—is larger than that of the GCMs. This indicates that the European summer climate is strongly controlled by parameterized physics and/or high-resolution processes. The temperature response is larger than the systematic error. The situation is different for precipitation. The model bias is twice as large as the climate response. The confidence in PRUDENCE results comes from the fact that the models have a similar response to the IPCC-SRES A2 forcing, whereas their systematic errors are more spread. In addition, GCM precipitation response is slightly but significantly different from that of the RCMs.     

Development of global coupled ocean-atmosphere general circulation models

It has long been believed that a climate model capable of realistically simulating many features of global climate, variability, and climate change must interactively represent the major components of the dynamically coupled climate system, particularly the atmosphere, ocean, and cryosphere. This effort traditionally has been constrained by computing power, our understanding of the observed system, and climate modeling capability. With the advent of supercomputers, improved understanding of global climate processes, and computationally efficient general circulation climate models, we have witnessed a rapid increase in the simulation of global climate by coupling together various representations of atmosphere, ocean, and sea ice. Beginning in the late 1960s and continuing through the early 1980s, general circulation models (GCMs) of the atmosphere, ocean, and sea ice were coupled and run asynchronously to produce credible simulations of the global climate. Systematic errors in these component models later led some modeling groups to use flux correction or flux adjustment, whereby either one or several of the variables at the air-sea interface are adjusted to bring the simulations in closer agreement with observations. Further advances in computing power and climate modeling techniques in the past few years have allowed global coupled ocean-atmosphere GCMs to be run synchronously (i.e., atmosphere and ocean communicate at least once each model day). Computing constraints, combined with the need for multidecadal climate integrations, still only allow relatively coarse-grid ocean GCMs to be coupled to correspondingly coarse-grid atmospheric models (on the order of 500 km × 500 km). However, results from this current generation of global, coupled GCMs have revealed interesting characteristics associated with ocean dynamics and global climate in experiments with gradual increases of carbon dioxide. Another somewhat surprising aspect of the global-coupled GCM simulations is the appearance of some features associated with the El Niño-Southern Oscillation. Along with concurrent efforts with other types of limited-domain, dynamical coupled models, this has led to the realization that inherent unstable coupled modes exist in the climate system that are the unique product of the interactive coupling of the atmosphere and the ocean. All of these efforts are leading to the next generation of coupled ocean-atmosphere GCMs. These models will run on even faster and larger-memory computers and will have higher-resolution atmosphere and ocean components, more accurate sea-ice formulations, improved cloud-radiation schemes, and increasingly realistic land-surface processes.This paper was presented at the International Conference on Modelling of Global Climate Change and Variability, held in Hamburg 11–15 September 1989 under the auspices of the Meteorological Institute of the University of Hamburg and the Max Planck Institute for Meteorology. Guest Editor for these papers is Dr. L. DümenilThe National Center for Atmospheric Research is sponsored by the National Science Foundation     

The Canadian Centre for Climate Modelling and Analysis global coupled model and its climate

 A global, three-dimensional climate model, developed by coupling the CCCma second-generation atmospheric general circulation model (GCM2) to a version of the GFDL modular ocean model (MOM1), forms the basis for extended simulations of past, current and projected future climate. The spin-up and coupling procedures are described, as is the resulting climate based on a 200 year model simulation with constant atmospheric composition and external forcing. The simulated climate is systematically compared to available observations in terms of mean climate quantities and their spatial patterns, temporal variability, and regional behavior. Such comparison demonstrates a generally successful reproduction of the broad features of mean climate quantities, albeit with local discrepancies. Variability is generally well-simulated over land, but somewhat underestimated in the tropical ocean and the extratropical storm-track regions. The modelled climate state shows only small trends, indicating a reasonable level of balance at the surface, which is achieved in part by the use of heat and freshwater flux adjustments. The control simulation provides a basis against which to compare simulated climate change due to historical and projected greenhouse gas and aerosol forcing as described in companion publications. Received: 24 September 1998 / Accepted: 8 October 1999     

Optimal detection and attribution of climate change: sensitivity of results to climate model differences

Fingerprint techniques for the detection of anthropogenic climate change aim to distinguish the climate response to anthropogenic forcing from responses to other external influences and from internal climate variability. All these responses and the characteristics of internal variability are typically estimated from climate model data. We evaluate the sensitivity of detection and attribution results to the use of response and variability estimates from two different coupled ocean atmosphere general circulation models (HadCM2, developed at the Hadley Centre, and ECHAM3/LSG from the MPI für Meteorologie and Deutsches Klimarechenzentrum). The models differ in their response to greenhouse gas and direct sulfate aerosol forcing and also in the structure of their internal variability. This leads to differences in the estimated amplitude and the significance level of anthropogenic signals in observed 50-year summer (June, July, August) surface temperature trends. While the detection of anthropogenic influence on climate is robust to intermodel differences, our ability to discriminate between the greenhouse gas and the sulfate aerosol signals is not. An analysis of the recent warming, and the warming that occurred in the first half of the twentieth century, suggests that simulations forced with combined changes in natural (solar and volcanic) and anthropogenic (greenhouse gas and sulfate aerosol) forcings agree best with the observations.     

Origin of regional climate differences: role of boundary conditions and model formulation in two GCMs

Model differences in projections of extratropical regional climate change due to increasing greenhouse gases are investigated using two atmospheric general circulation models (AGCMs): ECHAM4 (Max Planck Institute, version 4) and CCM3 (National Center for Atmospheric Research Community Climate Model version 3). Sea-surface temperature (SST) fields calculated from observations and coupled versions of the two models are used to force each AGCM in experiments based on time-slice methodology. Results from the forced AGCMs are then compared to coupled model results from the Coupled Model Intercomparison Project 2 (CMIP2) database. The time-slice methodology is verified by showing that the response of each model to doubled CO₂ and SST forcing from the CMIP2 experiments is consistent with the results of the coupled GCMs. The differences in the responses of the models are attributed to (1) the different tropical SST warmings in the coupled simulations and (2) the different atmospheric model responses to the same tropical SST warmings. Both are found to have important contributions to differences in implied Northern Hemisphere (NH) winter extratropical regional 500 mb height and tropical precipitation climate changes. Forced teleconnection patterns from tropical SST differences are primarily responsible for sensitivity differences in the extratropical North Pacific, but have relatively little impact on the North Atlantic. There are also significant differences in the extratropical response of the models to the same tropical SST anomalies due to differences in numerical and physical parameterizations. Differences due to parameterizations dominate in the North Atlantic. Differences in the control climates of the two coupled models from the current climate, in particular for the coupled model containing CCM3, are also demonstrated to be important in leading to differences in extratropical regional sensitivity.     

An investigation of the sensitivity of a land surface model to climate change using a reduced form model

 In an illustration of a model evaluation methodology, a multivariate reduced form model is developed to evaluate the sensitivity of a land surface model to changes in atmospheric forcing. The reduced form model is constructed in terms of a set of ten integrative response metrics, including the timing of spring snow melt, sensible and latent heat fluxes in summer, and soil temperature. The responses are evaluated as a function of a selected set of six atmospheric forcing perturbations which are varied simultaneously, and hence each may be thought of as a six-dimensional response surface. The sensitivities of the land surface model are interdependent and in some cases illustrate a physically plausible feedback process. The important predictors of land surface response in a changing climate are the atmospheric temperature and downwelling longwave radiation. Scenarios characterized by warming and drying produce a large relative response compared to warm, moist scenarios. The insensitivity of the model to increases in precipitation and atmospheric humidity is expected to change in applications to coupled models, since these parameters are also strongly implicated, through the representation of clouds, in the simulation of both longwave and shortwave radiation. Received: 27 March 2000 / Accepted: 11 September 2000     

Uncertainties linked to land-surface processes in climate change simulations

 The impact of climate change on the hydrology of continental surfaces is critical for human activities but the response of the surface to this perturbation may also affect the sensitivity of the climate. This complex feedback is simulated in general circulation models (GCMs) used for climate change predictions by their land-surface schemes. The present study attempts to quantify the uncertainty associated with these schemes and what impact it has on our confidence in the simulated climate anomalies. Four GCMs, each coupled to two different land-surface schemes, are used to explore the spectrum of uncertainties. It is shown that, in this sample, surface processes have a significant contribution to our ability to predict surface temperature changes and perturbations of the hydrological cycle in an environment with doubled greenhouse gas concentration. The results reveal that the uncertainty introduced by land-surface processes in the simulated climate is different from its impact on the sensitivity of GCMs to climate change, indeed an alteration of the surface parametrization with little impact on model climate can affect sensitivity significantly. This result leads us to believe that the validation of land-surface schemes should not be limited to the current climate but should also cover their sensitivity to variations in climatic forcing. Received: 24 June 1999 / Accepted: 20 April 2000     

Overview of the CMIP6 Historical Experiment Datasets with the Climate System Model CAS FGOALS-f3-L

The three-member historical simulations by the Chinese Academy of Sciences Flexible Global Ocean–Atmosphere–Land System model, version f3-L(CAS FGOALS-f3-L), which is contributing to phase 6 of the Coupled Model Intercomparison Project(CMIP6), are described in this study. The details of the CAS FGOALS-f3-L model, experiment settings and output datasets are briefly introduced. The datasets include monthly and daily outputs from the atmospheric, oceanic, land and sea-ice component models of CAS FGOALS-f3-L, and all these data have been published online in the Earth System Grid Federation(ESGF, https://esgf-node.llnl.gov/projects/cmip6/). The three ensembles are initialized from the 600th, 650th and 700th model year of the preindustrial experiment(piControl) and forced by the same historical forcing provided by CMIP6 from 1850 to 2014. The performance of the coupled model is validated in comparison with some recent observed atmospheric and oceanic datasets. It is shown that CAS FGOALS-f3-L is able to reproduce the main features of the modern climate, including the climatology of air surface temperature and precipitation,the long-term changes in global mean surface air temperature, ocean heat content and sea surface steric height, and the horizontal and vertical distribution of temperature in the ocean and atmosphere. Meanwhile, like other state-of-the-art coupled GCMs, there are still some obvious biases in the historical simulations, which are also illustrated. This paper can help users to better understand the advantages and biases of the model and the datasets.     

Climate variability in millennium integrations with coupled atmosphere-ocean GCMs: a spectral view

 Climate variations in four millennium integrations obtained with coupled GCMs are studied from a spectral point of view. It is shown that the bulk of these variations can be described by two distinctly different types of spectra. The type-I spectra, characterized by a high-frequency ω⁻² slope (with ω being frequency) and a low-frequency plateau, indicate the dominance of short-term fluctuations in generating climate variations. They are obtained for many atmospheric variables and variables representing predominantly the upper ocean and the high-latitude part of the deep ocean. The time scale, at which the spectra level off, varies from a few days for grid-point time series of atmospheric variables, to a few months for time series of large-scale atmospheric patterns, several years for SST anomalies in the tropical Pacific, and a few decades for variables describing oceanic baroclinic waves. The type-II spectra are obtained in the ocean interior, which is shielded from the fluctuating forcing at the surface. Since the ocean model does not produce oceanic eddies, the disappearance of type-I spectra in the deep ocean indicates that the fluctuating surface forcing does not fully penetrate into the deep ocean. While type-I spectra are supported by observations, type-II spectra might describe a model specific phenomenon and the realism of these spectra is still a open question. Received: 12 January 2000 / Accepted: 14 June 2000     

Modelling the sensitivity of Mediterranean Outflow to anthropogenically forced climate change

 The possible future impact of anthropogenic forcing upon the circulation of the Mediterranean, and the exchange through the Strait of Gibraltar is investigated using a Cox-type model of the Mediterranean at 0.25° × 0.25° resolution, forced by “control” and “greenhouse” scenarios provided by the HadCM2 coupled climate model. The current structure of the Mediterranean forced by the “control” climate is compared with observations: certain aspects of the present circulation are reproduced, but others are absent or incorrectly represented. Deficiencies are most probably due to weaknesses in the forcing climatology generated by the climate model, so some caution must be exercised in interpreting the enhanced greenhouse simulation. Comparison of the control and greenhouse scenarios suggests that deep-water production in the Mediterranean may be reduced or cease in the relatively near future. The results also suggest that the Mediterranean outflow, may become warmer and more saline, but less dense, and hence shallower. The volume of the exchange at the Strait of Gibraltar seems to be relatively insensitive to future climate change, however. Our results indicate that a parameterisation of Gibraltar exchange and Mediterranean Outflow Water (MOW) production may be able to provide adequate representation of the changes we observe for the purposes of the current generation of climate models. Received: 10 August 1998 / Accepted: 11 October 1999     

On the origin of equatorial Atlantic biases in coupled general circulation models

Many coupled ocean–atmosphere general circulation models (GCMs) suffer serious biases in the tropical Atlantic including a southward shift of the intertropical convergence zone (ITCZ) in the annual mean, a westerly bias in equatorial surface winds, and a failure to reproduce the eastern equatorial cold tongue in boreal summer. The present study examines an ensemble of coupled GCMs and their uncoupled atmospheric component to identify common sources of error. It is found that the westerly wind bias also exists in the atmospheric GCMs forced with observed sea surface temperature, but only in boreal spring. During this time sea-level pressure is anomalously high (low) in the western (eastern) equatorial Atlantic, which appears to be related to deficient (excessive) precipitation over tropical South America (Africa). In coupled simulations, this westerly bias leads to a deepening of the thermocline in the east, which prevents the equatorial cold tongue from developing in boreal summer. Thus reducing atmospheric model errors during boreal spring may lead to improved coupled simulations of tropical Atlantic climate.