Sea ice in the Barents Sea: seasonal to interannual variability and climate feedbacks in a global coupled model

Sea ice variability in the Barents Sea and its impact on climate are analyzed using a 465-year control integration of a global coupled atmosphere–ocean–sea ice model. Sensitivity simulations are performed to investigate the response to an isolated sea ice anomaly in the Barents Sea. The interannual variability of sea ice volume in the Barents Sea is mainly determined by variations in sea ice import into Barents Sea from the Central Arctic. This import is primarily driven by the local wind field. Horizontal oceanic heat transport into the Barents Sea is of minor importance for interannual sea ice variations but is important on longer time scales. Events with strong positive sea ice anomalies in the Barents Sea are due to accumulation of sea ice by enhanced sea ice imports and related NAO-like pressure conditions in the years before the event. Sea ice volume and concentration stay above normal in the Barents Sea for about 2 years after an event. This strongly increases the albedo and reduces the ocean heat release to the atmosphere. Consequently, air temperature is much colder than usual in the Barents Sea and surrounding areas. Precipitation is decreased and sea level pressure in the Barents Sea is anomalously high. The large-scale atmospheric response is limited with the main impact being a reduced pressure over Scandinavia in the year after a large ice volume occurs in the Barents Sea. Furthermore, high sea ice volume in the Barents Sea leads to increased sea ice melting and hence reduced surface salinity. Generally, the climate response is smallest in summer and largest in winter and spring.     

The Interannual Variability and Predictability in a Global Climate Model

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Decadal climate variability simulated in a coupled general circulation model

A 1000 year integration of the CSIRO coupled ocean-atmosphere general circulation model is used to study low frequency (decadal to centennial) climate variability in precipitation and temperature. The model is shown to exhibit sizeable decadal variability for these fields, generally accounting for approximately 20 to 40% of the variability (greater than one year) in precipitation and up to 80% for temperature. An empirical orthogonal function (EOF) analysis is applied to the model output to show some of the major statistical modes of low frequency variability. The first EOF spatial pattern looks very much like that of the interannual ENSO pattern. It bears considerable resemblance to observational estimates and is centred in the Pacific extending into both hemispheres. It modulates both precipitation and temperature globally. The EOF has a time evolution that appears to be more than just red noise. Finally, the link between SST in the Pacific with Australian rainfall variability seen in observations is also evident in the model. Received: 29 August 1998 / Accepted: 31 July 1999     

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.     

Climate sensitivity to changes in solar insolation in a simple coupled climate model

A simple coupled ocean, atmosphere and sea-ice model is presented. The idealised model consists of a zonally averaged land and ocean strip of constant angular width extending from pole to pole. The meridional energy transport in the ocean is modelled by contributions from the large scale thermohaline overturning cells and from horizontal diffusive fluxes. The atmospheric meridional energy transports are parametrised as diffusive fluxes in addition to advective transports in the Hadley domain. This parametrisation resolves the equatorward moisture transport as well as the poleward transport of potential energy in the upper branch of the Hadley circulation. The model reproduces the annual averaged meridional energy transports in the climate system with a small number of free model parameters. The basic feedbacks between the three climatic components are studied by investigating the model's sensitivity towards reductions in the solar insolation. It is found that the meridional energy transport in the ocean does not amplify the ice albedo feedback. This has important implications for modelling the climate sensitivity in atmosphere-only models, as these would exaggerate the sensitivity to changes in the solar insolation if their parametrisations of the meridional energy transport are constrained by surface temperatures. The role of the dependence of the atmospheric transports on the meridional temperature gradient is shown to have a significant influence on the sensitivity on the coupled model, and the inclusion of seasonal cycles greatly increase the models sensitivity. The Hadley circulation does significantly alter the strength of the ice-albedo feedback in the coupled model. The idealised configuration of the model makes it a useful tool for studying the feedbacks in the ocean-atmosphere-sea ice system in the context of the "Snowball Earth" hypothesis.     

Glacial termination: sensitivity to orbital and CO2 forcing in a coupled climate system model

 To study glacial termination and related feedback mechanisms, a continental ice dynamics model is globally and asynchronously coupled to a physical climate (atmosphere-ocean-sea ice) model. The model performs well under present-day, 11 kaBP (thousand years before present) and 21 kaBP perpetual forcing. To address the ice-sheet response under the effects of both perpetual orbital and CO₂ forcing, sensitivity experiments are conducted with two different orbital configurations (11 kaBP and 21 kaBP) and two different atmospheric CO₂ concentrations (200 ppmv and 280 ppmv). This study reveals that, although both orbital and CO₂ forcing have an impact on ice-sheet maintenance and deglacial processes, and although neither acting alone is sufficient to lead to complete deglaciation, orbital forcing seems to be more important. The CO₂ forcing has a large impact on climate, not uniformly or zonally over the globe, but concentrated over the continents adjacent to the North Atlantic. The effect of increased CO₂ (from 200 ppmv to 280 ppmv) on surface air temperature has its peak there in winter associated with a reduction in sea-ice extent in the northern North Atlantic. These changes are accompanied by an enhancement in the intensity of the meridional overturning and poleward ocean heat transport in the North Atlantic. On the other hand, the effect of orbital forcing (from 21 kaBP to 11 kaBP) has its peak in summer. Since the summer temperature, rather than winter temperature, is found to be dominant for the ice-sheet mass balance, orbital forcing has a larger effect than CO₂ forcing in deglaciation. Warm winter sea surface temperature arising from increased CO₂ during the deglaciation contributes to ice-sheet nourishment (negative feedback for ice-sheet retreat) through slightly enhanced precipitation. However, the precipitation effect is totally overwhelmed by the temperature effect. Our results suggest that the last deglaciation was initiated through increasing summer insolation with CO₂ providing a powerful feedback. Received: 22 February 2000 / Accepted: 17 September 2000     

Conceptual model for millennial climate variability: a possible combined solar-thermohaline circulation origin for the ~1,500-year cycle

Dansgaard-Oeschger and Heinrich events are the most pronounced climatic changes over the last 120,000 years. Although many of their properties were derived from climate reconstructions, the associated physical mechanisms are not yet fully understood. These events are paced by a ~1,500-year periodicity whose origin remains unclear. In a conceptual model approach, we show that this millennial variability can originate from rectification of an external (solar) forcing, and suggest that the thermohaline circulation, through a threshold response, could be the rectifier. We argue that internal threshold response of the thermohaline circulation (THC) to solar forcing is more likely to produce the observed DO cycles than amplification of weak direct ~1,500-year forcing of unknown origin, by THC. One consequence of our concept is that the millennial variability is viewed as a derived mode without physical processes on its characteristic time scale. Rather, the mode results from the linear representation in the Fourier space of nonlinearly transformed fundamental modes.     

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.     

Seasonal to interannual climate predictability in mid and high northern latitudes in a global coupled model

The upper limit of climate predictability in mid and high northern latitudes on seasonal to interannual time scales is investigated by performing two perfect ensemble experiments with the global coupled atmosphere–ocean–sea ice model ECHAM5/MPI-OM. The ensembles consist of six members and are initialized in January and July from different years of the model’s 300-year control integration. The potential prognostic predictability is analyzed for a set of oceanic and atmospheric climate parameters. The predictability of the atmospheric circulation is small except for southeastern Europe, parts of North America and the North Pacific, where significant predictability occurs with a lead time of up to half a year. The predictability of 2 m air temperature shows a large land–sea contrast with highest predictabilities over the sub polar North Atlantic and North Pacific. A combination of relatively high persistence and advection of sea surface temperature anomalies into these areas leads to large predictability. Air temperature over Europe, parts of North America and Asia shows significant predictability of up to half a year in advance. Over the ice-covered Arctic, air temperature is not predictable at time scales exceeding 2 months. Sea ice thickness is highly predictable in the central Arctic mainly due to persistence and in the Labrador Sea due to dynamics. Surface salinity is highly predictable in the Arctic Ocean, northern North Atlantic and North Pacific for several years in advance. We compare the results to the predictability due to persistence and show the importance of dynamical processes for the predictability.     

Empirical Estimates of Global Climate Sensitivity： An Assessment of Strategies Using a Coupled GCM

A control integration with the normal solar constant and one with it increased by 2.5% in the National Center for Atmospheric Research （NCAR） coupled atmosphere-ocean Climate System Model were conducted to see how well the actual realized global warming could be predicted just by analysis of the control results. This is a test, within a model context, of proposals that have been advanced to use knowledge of the present day climate to make ＂empirical＂ estimates of global climate sensitivity. The scaling of the top-of-the-atmosphere infrared flux and the planetary albedo as functions of surface temperature was inferred by examining four different temporal and geographical variations of the control simulations. Each of these inferences greatly overestimates the climate sensitivity of the model, largely because of the behavior of the cloud albedo. In each inference the control results suggest that cloudiness and albedo decrease with increasing surface temperature. However, the experiment with the increased solar constant actually has higher albedo and more cloudiness at most latitudes. The increased albedo is a strong negative feedback, and this helps account for the rather weak sensitivity of the climate in the NCAR model. To the extent that these model results apply to the real world, they suggest empirical evaluation of the scaling of global-mean radiative properties with surface temperature in the present day climate provides little useful guidance for estimates of the actual climate sensitivity to global changes.     

Simulation of Indian summer monsoon intraseasonal oscillations in a superparameterized coupled climate model: need to improve the embedded cloud resolving model

The characteristic features of Indian summer monsoon (ISM) and monsoon intraseasonal oscillations (MISO) are analyzed in the 25 year simulation by the superparameterized Community Climate System Model (SP-CCSM). The observations indicate the low frequency oscillation with a period of 30–60 day to have the highest power with a dominant northward propagation, while the faster mode of MISO with a period of 10–20 day shows a stationary pattern with no northward propagation. SP-CCSM simulates two dominant quasi-periodic oscillations with periods 15–30 day and 40–70 day indicating a systematic low frequency bias in simulating the observed modes. Further, contrary to the observation, the SP-CCSM 15–30 day mode has a significant northward propagation; while the 40–70 day mode does not show prominent northward propagation. The inability of the SP-CCSM to reproduce the observed modes correctly is shown to be linked with inability of the cloud resolving model (CRM) to reproduce the characteristic heating associated with the barotropic and baroclinic vertical structures of the high-frequency and the low-frequency modes. It appears that the superparameterization in the General Circulation Model (GCM) certainly improves seasonal mean model bias significantly. There is a need to improve the CRM through which the barotropic and baroclinic modes are simulated with proper space and time distribution.     

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.     

Storm tracks in a warmer climate: sensitivity studies with a simplified global circulation model

 A simplified global circulation model is used to analyse a greenhouse warming experiment simulated by a comprehensive general circulation model. The given GCM scenario and control climates are assimilated by the simplified model using a dynamical relaxation technique. Two sets of sensitivity experiments investigate the influence of upper and lower tropospheric changes in baroclinicity on the Northern Hemisphere winter storm tracks. The results show that the three-dimensional structure of both the background flow and the changes in baroclinicity are important for the behaviour of mid-latitude eddy activity in relation to modifications of the baroclinicity. In general, the mid-latitude eddy activity is more sensitive to lower than to upper level changes in baroclinicity. The results further suggest that the simulated storm track changes in the GCM scenario are dominated by local modes of baroclinic instability. Received: 17 December 1996 / Accepted: 14 May 1998     

Sea‐ice dynamics and CO2 sensitivity in a global climate model

Abstract

Present‐day results and CO₂ sensitivity are described for two versions of a global climate model (genesis) with and without sea‐ice dynamics. Sea‐ice dynamics is modelled using the cavitating‐fluid method of Flato and Hibler (1990, 1992). The atmospheric general circulation model originated from the NCAR Community Climate Model version 1, but is heavily modified to include new treatments of clouds, penetrative convection, planetary boundary‐layer mixing, solar radiation, the diurnal cycle and the semi‐Lagrangian transport of water vapour. The surface models include an explicit model of vegetation (similar to BATS and SiB), multilayer models of soil, snow and sea ice, and a slab ocean mixed layer.

When sea‐ice dynamics is turned off, the CO₂‐induced warming increases drastically around ～60–80°S in winter and spring. This is due to the much greater (and unrealistic) compactness of the Antarctic ice cover without dynamics, which is reduced considerably when CO₂ is doubled and exposes more open ocean to the atmosphere. With dynamics, the winter ice is already quite dispersed for 1 × CO₂ so that its compactness does not decrease as much when CO₂ is doubled.     

Potential decadal predictability and its sensitivity to sea ice albedo parameterization in a global coupled model

Decadal prediction is one focus of the upcoming 5th IPCC Assessment report. To be able to interpret the results and to further improve the decadal predictions it is important to investigate the potential predictability in the participating climate models. This study analyzes the upper limit of climate predictability on decadal time scales and its dependency on sea ice albedo parameterization by performing two perfect ensemble experiments with the global coupled climate model EC-Earth. In the first experiment, the standard albedo formulation of EC-Earth is used, in the second experiment sea ice albedo is reduced. The potential prognostic predictability is analyzed for a set of oceanic and atmospheric parameters. The decadal predictability of the atmospheric circulation is small. The highest potential predictability was found in air temperature at 2?m height over the northern North Atlantic and the southern South Atlantic. Over land, only a few areas are significantly predictable. The predictability for continental size averages of air temperature is relatively good in all northern hemisphere regions. Sea ice thickness is highly predictable along the ice edges in the North Atlantic Arctic Sector. The meridional overturning circulation is highly predictable in both experiments and governs most of the decadal climate predictability in the northern hemisphere. The experiments using reduced sea ice albedo show some important differences like a generally higher predictability of atmospheric variables in the Arctic or higher predictability of air temperature in Europe. Furthermore, decadal variations are substantially smaller in the simulations with reduced ice albedo, which can be explained by reduced sea ice thickness in these simulations.     

Sea-ice effects on climate model sensitivity and low frequency variability

A change in a sea-ice parameter in a global coupled climate model results in a reduction in amplitude (of about 60%) and a shortening of the predominant period of decadal low frequency variability in the time series of globally averaged surface air temperature. These changes are global in extent and also are reflected in time series of area-averaged SSTs in the equatorial eastern Pacific Ocean, the principal components of the first EOFs of global surface air temperature and sea level pressure, Asian monsoon precipitations and other quantities. Coupled ocean-atmosphere-sea ice processes acting on a global scale are modified to produce these changes. Global climate sensitivity is reduced when ice albedo feedback is weakened due to the change in sea ice that makes it more difficult to melt. The changes in the amplitude and time scale of the low frequency variability in the model are traced to changes in the base state of the climate simulations as affected by modifications associated with the changes in sea ice. Making sea ice more difficult to melt results in increased sea-ice area, colder high latitudes, increased meridional surface temperature gradients, and, to a first order, stronger surface winds in most regions which strengthen near-surface currents, particularly in the Northern Hemisphere, and decreases the advection time scale in the upper ocean gyres. Additionally, in the North Atlantic there is enhanced meridional overturning due to increased density mainly in the Greenland Sea region. This also contributes to an intensified North Atlantic gyre. The changes in base state due to the sea ice change result in a more predominant decadal time scale of near 14 years and significantly reduced contributions from lower frequencies in the range of 15–40 year periods. Received: 11 December 1998 / Accepted: 4 October 1999     

Mean climate state simulated by a coupled ocean-atmosphere general circulation model

Summary The result of a 100-year integration of a coupled ocean-atmosphere general circulation model (CGCM) is analyzed, and compared with that of a 25-year integration of the corresponding uncoupled atmospheric general circulation model (AGCM) and observed data. The large-scale circulation patterns of mean climate state simulated by the CGCM are in good agreement with the observed ones, although differences exit in the positions and intensities between the simulated and the observed patterns. Having compared the standard deviations of monthly mean sea level pressure simulated by the CGCM to those by the AGCM, we found that the interaction between ocean and atmosphere mainly increases the interannual variability in the tropics especially in summer. The CGCM can also produce El Niño and Southern Oscillation (ENSO) events, whereas the AGCM cannot reproduce the main features of the Southern Oscillation. This implies that the air-sea interaction may be a principal mechanism for the occurrence of ENSO phenomena. The fundamental features of simulated regional climates are also analyzed. The CGCM can reproduce principal characteristics of surface air temperature and precipitation at five selected typical regions (desert region, plain region, monsoon region etc.). The distributions of annual mean surface ait temperature and precipitation in East Asia can also be reasonably simulated.With 9 Figures