Understanding and simulating the link between African easterly waves and Atlantic tropical cyclones using a regional climate model: the role of domain size and lateral boundary conditions

Using a suite of lateral boundary conditions, we investigate the impact of domain size and boundary conditions on the Atlantic tropical cyclone and african easterly Wave activity simulated by a regional climate model. Irrespective of boundary conditions, simulations closest to observed climatology are obtained using a domain covering both the entire tropical Atlantic and northern African region. There is a clear degradation when the high-resolution model domain is diminished to cover only part of the African continent or only the tropical Atlantic. This is found to be the result of biases in the boundary data, which for the smaller domains, have a large impact on TC activity. In this series of simulations, the large-scale Atlantic atmospheric environment appears to be the primary control on simulated TC activity. Weaker wave activity is usually accompanied by a shift in cyclogenesis location, from the MDR to the subtropics. All ERA40-driven integrations manage to capture the observed interannual variability and to reproduce most of the upward trend in tropical cyclone activity observed during that period. When driven by low-resolution global climate model (GCM) integrations, the regional climate model captures interannual variability (albeit with lower correlation coefficients) only if tropical cyclones form in sufficient numbers in the main development region. However, all GCM-driven integrations fail to capture the upward trend in Atlantic tropical cyclone activity. In most integrations, variations in Atlantic tropical cyclone activity appear uncorrelated with variations in African easterly wave activity.     

The ACPI Project, Element 1: Initializing a Coupled Climate Model from Observed Conditions

A problem for climate change studies with coupled ocean-atmosphere models has been how to incorporate observed initial conditions into the ocean, which holds most of the `memory' of anthropogenic forcing effects. The first difficulty is the lack of comprehensive three-dimensional observations of the current ocean temperature (T) and salinity (S) fields to initialize to. The second problem is that directly imposing observed T and S fields into the model results in rapid drift back to the model climatology, with the corresponding loss of the observed information. Anthropogenic forcing scenarios therefore typically initialize future runs by starting with pre-industrial conditions. However, if the future climate depends on the details of the present climate, then initializing the model to observations may provide more accurate forecasts. Also, this ～ 130 yr spin up imposes substantial overhead if only a few decades of predictions are desired. A new technique to address these problems is presented. In lieu of observed T and S, assimilated ocean data were used. To reduce model drift, an anomaly coupling scheme was devised. This consists of letting the model's climatological (pre-industrial) oceanic and atmospheric heat contents and transports balance each other, while adding on the (much smaller) changes in heat content since the pre-industrial era as anomalies. The result is model drift of no more than 0.2 K over 50 years, significantly smaller than the forced response of 1.0 K. An ensemble of runs with these assimilated initial conditions is then compared to a set spun up from pre-industrial conditions. No systematic differences were found, i.e., the model simulation of the ocean temperature structure in the late 1990s is statistically indistinguishable from the assimilated observations. However, a model with a worse representation of the late 20th century climate might show significant differences if initialized in this way.     

Influence of the temperate and boreal forests on the Northern Hemisphere climate in the Météo-France climate model

A temperate and boreal deforestation experiment has been performed at Météo-France using the ARPEGE climate model. A first simulation was performed as a control with a present-day vegetation map, and another one with all forests north of 45 °N replaced by meadows. Prescribed monthly mean climatological SSTs were used in both integrations. The ARPEGE climate model includes a physically based land surface scheme, which has been tested both on snowfree and snow-covered sites, and has a relatively high horizontal resolution. Results of the 4-year integrations suggest that forests exert a strong influence on the surface climate of the temperate and boreal regions. Deforestation induces a significant cooling which modifies the atmospheric circulation simulated in the high latitudes, and also in the tropics. The most important impact is observed during the melting season which is delayed by the forest removal. This result is consistent with preliminary stand-alone experiments showing that the atmospheric boundary layer can be heated by the forest, even if the ground is covered by snow. The study confirms that vegetation feedbacks should be included when performing future climate studies such as doubled CO₂ experiments, eventhough many uncertainties still remain with regard to other physical aspects of the climate models. Received: 5 September 1995 / Accepted: 12 August 1996     

Dependence of the agcm climatology on the method of prescribing surface boundary conditions and its climatological implication

By using IAP 9L AGCM, two sets of long-term climatological integration have been per-formed with the two different interpolation procedures for generating the daily surface boundary conditions. One interpolation procedure is the so-called “traditional” scheme, for which the daily surface boundary conditions are obtained by linearly interpolating between the observed monthly mean values, however the observed monthly means cannot be preserved after interpolation. The other one is the “new” scheme, for which the daily surface boundary conditions are obtained by linearly interpolating between the "artificial" monthly mean values which are based on, but are dif-ferent from the observed ones, after interpolating with this new scheme, not only the observed monthly mean values are preserved, the time series of the new generated daily values is also more consistent with the observation. Comparison of the model results shows that the differences of the globally or zonally averaged fields between these two integrations are quite small, and this is due to the compensating effect between the different regions. However, the differences of the two patterns (the global or regional geographical distributions), are quite significant, for example, the magni-tude of the difference in the JJA mean rainfall between these two integrations can exceed 2 mm/day over Asian monsoon regions, and the difference in DJF mean surface air temperature can also exceed 2oC over this region. The fact that the model climatology depends quite strongly on the method of prescribing the daily surface boundary conditions suggests that in order to validate the climate model or to predict the short-term climate anomalies, either the " new* interpolation scheme or the high frequency surface boundary conditions (e.g., daily or weekly data instead of the monthly data) should be introduced. Meanwhile, as for the coupled model, the daily coupling scheme between the different component cli?mate models (e.g., atmospheric and oceanic general circulation models) is preferred in order to partly eliminate the “climate drift” problem which may appear during the course of direct coupling.     

Relevance of soil moisture for seasonal climate predictions: a preliminary study

In the framework of the Global Soil Wetness Project (GSWP), the ISBA land-surface scheme of the ARPEGE atmospheric general circulation model has been forced with meteorological observations and analyses in order to produce a two-year (1987–1988) soil moisture climatology at a 1^°×1^° horizontal resolution. This climatology is model dependent, but it is the climatology that the ARPEGE model would produce if its precipitation and radiative fluxes were perfectly simulated. In the present study, ensembles of seasonal simulations (March to September) have been performed for 1987 and 1988, in which the total soil water content simulated by ARPEGE is relaxed towards the GSWP climatology. The results indicate that the relaxation has a positive impact on both the model's climatology and the simulated interannual variability, thereby confirming the utility of the GSWP soil moisture data for prescribing initial or boundary conditions in comprehensive climate and numerical weather prediction models. They also demonstrate the relevance of soil moisture for achieving realistic simulations of the Northern Hemisphere summer climate. In order to get closer to the framework of seasonal predictions, additional experiments have been performed in which GSWP is only used for initialising soil moisture at the beginning of the summer season (the relaxation towards GSWP is removed on 1st June). The results show a limited improvement of the interannual variability, compared to the simulations initialised from the ARPEGE climatology. However, some regional patterns of the precipitation differences between 1987 and 1988 are better captured, suggesting that seasonal predictions can benefit from a better initialisation of soil moisture.     

An efficient climate forecasting method using an intermediate complexity Earth System Model and the ensemble Kalman filter

We present the implementation and results of a model tuning and ensemble forecasting experiment using an ensemble Kalman filter for the simultaneous estimation of 12 parameters in a low resolution coupled atmosphere-ocean Earth System Model by tuning it to realistic data sets consisting of Levitus ocean temperature/salinity climatology, and NCEP/NCAR atmospheric temperature/humidity reanalysis data. The resulting ensemble of tuned model states is validated by comparing various diagnostics, such as mass and heat transports, to observational estimates and other model results. We show that this ensemble has a very reasonable climatology, with the 3-D ocean in particular having comparable realism to much more expensive coupled numerical models, at least in respect of these averaged indicators. A simple global warming experiment is performed to investigate the response and predictability of the climate to a change in radiative forcing, due to 100 years of 1% per annum atmospheric CO₂ increase. The equilibrium surface air temperature rise for this CO₂ increase is 4.2±0.1°C, which is approached on a time scale of 1,000 years. The simple atmosphere in this version of the model is missing several factors which, if included, would substantially increase the uncertainty of this estimate. However, even within this ensemble, there is substantial regional variability due to the possibility of collapse of the North Atlantic thermohaline circulation (THC), which switches off in more than one third of the ensemble members. For these cases, the regional temperature is not only 3–5°C colder than in the warmed worlds where the THC remains switched on, but is also 1–2°C colder than the current climate. Our results, which illustrate how objective probabilistic projections of future climate change can be efficiently generated, indicate a substantial uncertainty in the long-term future of the THC, and therefore the regional climate of western Europe. However, this uncertainty is only apparent in long-term integrations, with the initial transient response being similar across the entire ensemble. Application of this ensemble Kalman filtering technique to more complete climate models would improve the objectivity of probabilistic forecasts and hence should lead to significantly increased understanding of the uncertainty of our future climate.     

An examination of climate sensitivity for idealised climate change experiments in an intermediate general circulation model

Radiative forcing and climate sensitivity have been widely used as concepts to understand climate change. This work performs climate change experiments with an intermediate general circulation model (IGCM) to examine the robustness of the radiative forcing concept for carbon dioxide and solar constant changes. This IGCM has been specifically developed as a computationally fast model, but one that allows an interaction between physical processes and large-scale dynamics; the model allows many long integrations to be performed relatively quickly. It employs a fast and accurate radiative transfer scheme, as well as simple convection and surface schemes, and a slab ocean, to model the effects of climate change mechanisms on the atmospheric temperatures and dynamics with a reasonable degree of complexity. The climatology of the IGCM run at T-21 resolution with 22 levels is compared to European Centre for Medium Range Weather Forecasting Reanalysis data. The response of the model to changes in carbon dioxide and solar output are examined when these changes are applied globally and when constrained geographically (e.g. over land only). The CO₂ experiments have a roughly 17% higher climate sensitivity than the solar experiments. It is also found that a forcing at high latitudes causes a 40% higher climate sensitivity than a forcing only applied at low latitudes. It is found that, despite differences in the model feedbacks, climate sensitivity is roughly constant over a range of distributions of CO₂ and solar forcings. Hence, in the IGCM at least, the radiative forcing concept is capable of predicting global surface temperature changes to within 30%, for the perturbations described here. It is concluded that radiative forcing remains a useful tool for assessing the natural and anthropogenic impact of climate change mechanisms on surface temperature.     

Systematic optimisation and climate simulation of FAMOUS, a fast version of HadCM3

FAMOUS is an unfluxadjusted coupled atmosphere-ocean general circulation model (AOGCM) based on the Met Office Hadley Centre AOGCM HadCM3. Its parametrisations of physical and dynamical processes are almost identical to those of HadCM3, but by virtue of reduced horizontal and vertical resolution and increased timestep it runs about ten times faster. The speed of FAMOUS means that parameter sensitivities can be investigated more thoroughly than in slower higher-resolution models, with the result that it can be tuned closer to its target climatology. We demonstrate a simple method for systematic tuning of parameters, resulting in a configuration of FAMOUS whose climatology is significantly more realistic than would be expected for a model of its resolution and speed. FAMOUS has been tuned to reproduce the behaviour of HadCM3 as nearly as possible, in order that experiments with each model are of maximum relevance to the physical interpretation of the other. Analysis of the control climate and climate change simulation of FAMOUS show that it possesses sufficient skill for its intended purposes in Earth system science as a tool for long-timescale integrations and for large ensembles of integrations, when HadCM3 cannot be afforded. Thus, it can help to bridge the gap between models of intermediate complexity and the higher-resolution AOGCMs used for policy-relevant climate prediction.     

A Gcm Study of the Impact of Greenhouse Gas Increase on the Frequency of Occurrence of Tropical Cyclones

In order to make inferences on the possible future changes of tropical cyclogenesis frequency, we apply the diagnostic computation of the Yearly Genesis Parameter (YGP) proposed by Gray (1975) to the large-scale fields simulated by a GCM. The YGP is an empirical diagnostic of the frequency of Tropical Cyclones (TCs) based on six physical parameters computed from seasonal means of atmospheric and oceanic variables. In this paper, we apply the YGP diagnostic to the results of three climate simulations performed with the atmospheric General Circulation Model (GCM) of Météo-France: ARPEGE-Climat. In a control simulation of the current climate, it is shown that the model has a realistic tropical climatology and that the computed YGP reproduces the geographical distribution of the tropical cyclogenesis frequency. The YGP is then applied to two simulations corresponding to two scenarios of doubled carbon dioxide concentration. The two experiments differ by the sea surface temperatures (SSTs) used as a lower boundary condition. In both simulations the YGP gives a large increase of total cyclogenesis frequency, but without extension of the area of possible cyclone genesis. The increase in YGP is due essentially to the contribution of the ocean thermal energy factor in the thermodynamical potential. The dynamical parameters, on the contrary, limit the cyclogenesis increase and are a major explanation of the difference between the two experiments. This is in agreement with the results of the previous similar study of Ryan et al. (1992) concerning the importance of large-scale atmospheric circulation modifications on tropical cyclone climatology. After discussing the observed relationships between ocean surface temperature and large-scale convection, and questioning the use of a fixed temperature threshold in the diagnosis of tropical cyclone frequency, we propose a modification to the YGP consisting in replacing the thermodynamical potential by a term proportional to the convective precipitation computed by the GCM. For the simulation of the present climate this modification affects only marginally the geographical distribution of tropical cyclone genesis, but for the doubled CO₂ case, the modified YGP diagnoses a more limited increase in TC genesis in the Northern Hemisphere and a small reduction in the Southern Hemisphere, which seems in better agreement with other recent modelling studies with high resolution climate models (Bengtsson et al., 1996). We conclude that the modified YGP based on convective precipitation could serve as a useful diagnostic of tropical cyclone genesis, and should be tested in simulations with other GCMs.     

Evaluation of ocean data assimilation in CAS-ESM-C: Constraining the SST field

A weakly coupled assimilation system, in which SST observations are assimilated into a coupled climate model(CASESM-C) through an ensemble optimal interpolation scheme, was established. This system is a useful tool for historical climate simulation, showing substantial advantages, including maintaining the atmospheric feedback, and keeping the oceanic fields from drifting far away from the observation, among others. During the coupled model integration, the bias of both surface and subsurface oceanic fields in the analysis can be reduced compared to unassimilated fields. Based on 30 model years of output from the system, the climatology and interannual variability of the climate system were evaluated. The results showed that the system can reasonably reproduce the climatological global precipitation and SLP, but it still suffers from the double ITCZ problem. Besides, the ENSO footprint, which is revealed by ENSO-related surface air temperature, geopotential height and precipitation during El Ni ?no evolution, is basically reproduced by the system. The system can also simulate the observed SST–rainfall relationships well on both interannual and intraseasonal timescales in the western North Pacific region, in which atmospheric feedback is crucial for climate simulation.     

Seasonal climate and variability of the ECMWF ERA-40 model

The climate and variability of seasonal ensemble integrations, made with a recent version of ECMWF model (used for ERA-40 production) at relatively high horizontal resolution (T_L159), have been studied for the 10-year period, 1980–1989. The model systematic error over the Atlantic-European region has been substantially improved when compared with the earlier model versions (e.g. from the PROVOST and AMIP-2 projects). However, it has worsened over the Pacific-North American region. This systematic error reduces the amplitude of planetary waves and has a negative impact on intraseasonal variability and predictability of the PNA mode. The signal-to-noise analysis yields results similar to earlier model versions: only during relatively strong ENSO events do some parts of the extratropics exhibit potential predictability. For precipitation, there is more disagreement between observed and model climatologies over sea than over land, but interannual variations over many parts of the tropical ocean are reasonably well represented. The south Asian monsoon in the model is severely weakened when compared to observations; this is seen in both poor climatology and interannual variability. Overall, comparing the ERA-40 model with earlier versions, there seems to be a balance between model improvements and deteriorations due to systematic errors. For the seasonal time-scale predictability, it is not clear that this model cycle constitutes an advantage over the earlier versions. Therefore, since it is not always possible to achieve distinct improvements in model climate and variability, a careful and detailed strategy ought to be considered when introducing a new model version for operational seasonal forecasting.     

Limitations of time-slice experiments for predicting regional climate change over South Asia

While time-slice simulations with atmospheric general circulation models (GCMs) have been used for many years to regionalize climate projections and/or assess their uncertainties, there is still no consensus about the method used to prescribe sea surface temperature (SST) in such experiments. In the present study, the response of the Indian summer monsoon to increasing amounts of greenhouse gases and sulfate aerosols is compared between a reference climate scenario and three sets of time-slice experiments, consisting of parallel integrations for present-day and future climates. Different monthly mean SST boundary conditions have been tested in the present-day integrations: raw climatological SST derived from the reference scenario, observed climatological SST, and observed SST with interannual variability. For future climate, the SST forcing has been obtained by superimposing climatological monthly mean SST anomalies derived from the reference scenario onto the present-day SST boundary conditions. None of these sets of time-slice experiments is able to capture accurately the response of the Indian summer monsoon simulated in the transient scenario. This finding suggests that the ocean–atmosphere coupling is a fundamental feature of the climate system. Neglecting the SST feedback and variability at the intraseasonal to interannual time scales has a significant impact on the projected monsoon response to global warming. Adding interannual variability in the prescribed SST boundary conditions does not mitigate the problem, but can on the contrary reinforce the discrepancies between the forced and coupled experiments. The monsoon response is also shown to depend on the simulated control climate, and can therefore be sensitive to the use of observed rather than model-derived SSTs to drive the present-day atmospheric simulation, as well as to any approximation in the prescribed radiative forcing. While such results do not challenge the use of time-slice experiments for assessing uncertainties and understanding mechanisms in transient scenarios, they emphasize the need for high-resolution coupled atmosphere-ocean GCMs for dynamical downscaling, or at least for high-resolution atmospheric GCMs coupled with a slab or a regional ocean model.     

A regional climate model for the western United States

A numerical approach to modeling climate on a regional scale is developed whereby large-scale weather systems are simulated with a global climate model (GCM) and the GCM output is used to provide the boundary conditions needed for high-resolution mesoscale model simulations over the region of interest. In our example, we use the National Center for Atmospheric Research (NCAR) community climate model (CCM1) and the Pennsylvania State University (PSU)/NCAR Mesoscale Model version 4 (MM4) to apply this approach over the western United States (U.S.). The topography, as resolved by the 500-km mesh of the CCM1, is necessarily highly distorted, but with the 60-km mesh of the MM4 the major mountain ranges are distinguished. To obtain adequate and consistent representations of surface climate, we use the same radiation and land surface treatments in both models, the latter being the recently developed Biosphere-Atmosphere Transfer Scheme (BATS). Our analysis emphasizes the simulation at four CCM1 points surrounding Yucca Mountain, NV, because of the need to determine its climatology prior to certification as a high-level nuclear waste repository.We simulate global climate for three years with CCM1/BATS and describe the resulting January surface climatology over the western U.S. The details of the precipitation patterns are unrealistic because of the smooth topography. Selecting five January CCM1 storms that occur over the western U.S. with a total duration of 20 days for simulation with the MM4, we demonstrate that the mesoscale model provides much improved wintertime precipitation patterns. The storms in MM4 are individually much more realistic than those in CCM1. A simple averaging procedure that infers a mean January rainfall climatology calculated from the 20 days of MM4 simulation is much closer to the observed than is the CCM1 climatology. The soil moisture and subsurface drainage simulated over 3–5 day integration periods of MM4, however, remain strongly dependent on the initial CCM1 soil moisture and thus are less realistic than the rainfall. Adequate simulation of surface soil water may require integrations of the mesoscale model over time periods.The National Center for Atmospheric Research is sponsored by the National Science Foundation. of up to several months or longer.     

Boreal forest and tundra ecosystems as components of the climate system

The effects of terrestrial ecosystems on the climate system have received most attention in the tropics, where extensive deforestation and burning has altered atmospheric chemistry and land surface climatology. In this paper we examine the biophysical and biogeochemical effects of boreal forest and tundra ecosystems on atmospheric processes. Boreal forests and tundra have an important role in the global budgets of atmospheric CO₂ and CH₄. However, these biogeochemical interactions are climatically important only at long temporal scales, when terrestrial vegetation undergoes large geographic redistribution in response to climate change. In contrast, by masking the high albedo of snow and through the partitioning of net radiation into sensible and latent heat, boreal forests have a significant impact on the seasonal and annual climatology of much of the Northern Hemisphere. Experiments with the LSX land surface model and the GENESIS climate model show that the boreal forest decreases land surface albedo in the winter, warms surface air temperatures at all times of the year, and increases latent heat flux and atmospheric moisture at all times of the year compared to simulations in which the boreal forest is replaced with bare ground or tundra. These effects are greatest in arctic and sub-arctic regions, but extend to the tropics. This paper shows that land-atmosphere interactions are especially important in arctic and sub-arctic regions, resulting in a coupled system in which the geographic distribution of vegetation affects climate and vice versa. This coupling is most important over long time periods, when changes in the abundance and distribution of boreal forest and tundra ecosystems in response to climatic change influence climate through their carbon storage, albedo, and hydrologic feedbacks.     

Sensitivity of ENSO characteristics to a new interactive flux correction scheme in a coupled GCM

A fast coupled global climate model (CGCM) is used to study the sensitivity of El Ni?o Southern Oscillation (ENSO) characteristics to a new interactive flux correction scheme. With no flux correction applied our CGCM reveals typical bias in the background state: for instance, the cold tongue in the tropical east Pacific becomes too cold, thus degrading atmospheric sensitivity to variations of sea surface temperature (SST). Sufficient atmospheric sensitivity is essential to ENSO. Our adjustment scheme aims to sustain atmospheric sensitivity by counteracting the SST drift in the model. With reduced bias in the forcing of the atmosphere, the CGCM displays ENSO-type variability that otherwise is absent. The adjustment approach employs a one-way anomaly coupling from the ocean to the atmosphere: heat fluxes seen by the ocean are based on full SST, while heat fluxes seen by the atmosphere are based on anomalies of SST. The latter requires knowledge of the model??s climatological SST field, which is accumulated interactively in the spin-up phase (??training??). Applying the flux correction already during the training period (by utilizing the evolving SST climatology) is necessary for efficiently reducing the bias. The combination of corrected fluxes seen by the atmosphere and uncorrected fluxes seen by the ocean implies a restoring mechanism that counteracts the bias and allows for long stable integrations in our CGCM. A suite of sensitivity runs with varying training periods is utilized to study the effect of different levels of bias in the background state on important ENSO properties. Increased duration of training amplifies the coupled sensitivity in our model and leads to stronger amplitudes and longer periods of the Nino3.4 index, increased emphasis of warm events that is reflected in enhanced skewness, and more pronounced teleconnections in the Pacific. Furthermore, with longer training durations we observe a mode switch of ENSO in our model that closely resembles the observed mode switch related to the mid-1970s ??climate shift??.     

Performance of the OPA/ARPEGE-T21 global ocean-atmosphere coupled model

 The climatology of the OPA/ARPEGE-T21 coupled general circulation model (GCM) is presented. The atmosphere GCM has a T21 spectral truncation and the ocean GCM has a 2^°×1.5^° average resolution. A 50-year climatic simulation is performed using the OASIS coupler, without flux correction techniques. The mean state and seasonal cycle for the last 10 years of the experiment are described and compared to the corresponding uncoupled experiments and to climatology when available. The model reasonably simulates most of the basic features of the observed climate. Energy budgets and transports in the coupled system, of importance for climate studies, are assessed and prove to be within available estimates. After an adjustment phase of a few years, the model stabilizes around a mean state where the tropics are warm and resemble a permanent ENSO, the Southern Ocean warms and almost no sea-ice is left in the Southern Hemisphere. The atmospheric circulation becomes more zonal and symmetric with respect to the equator. Once those systematic errors are established, the model shows little secular drift, the small remaining trends being mainly associated to horizontal physics in the ocean GCM. The stability of the model is shown to be related to qualities already present in the uncoupled GCMs used, namely a balanced radiation budget at the top-of-the-atmosphere and a tight ocean thermocline. Received: 1 February 1996 / Accepted: 1 August 1996     

利用大气环流模式模拟北大西洋海温异常强迫响应

北大西洋地区的海温异常能够在多大程度上对大气产生影响,一直是一个有争议的问题。作者利用伴随北大西洋涛动出现的海温异常对大气环流模式CAM2.0.1进行强迫,考察了模式在冬季（12月、1月和2月）对三核型海温异常的响应。通过与欧洲中期天气预报中心提供的再分析资料的对比,发现该模式可以通过海温强迫在一定程度上再现具有北大西洋涛动特征的温度场和环流场。在北大西洋及其沿岸地区,模式模拟出了三核型的准正压响应,与经典的北大西洋涛动型大气异常是一致的。模式结果与北大西洋地区大气内部主导模态的差别主要体现在两个方面:一是异常中心位置多偏向于大洋上空,在陆地上的异常响应强度很弱;二是高纬地区对海温异常的响应不显著,没有强迫出与实际的大气模态相对应的异常中心,表明该地区海洋的反馈作用较弱。     

A climate version of the regional atmospheric modeling system

Summary  The Regional Atmospheric Modeling System (RAMS) has been widely used to simulate relatively short-term atmospheric processes. To perform full-year to multi-year model integrations, a climate version of RAMS (ClimRAMS) has been developed, and is used to simulate diurnal, seasonal, and annual cycles of atmospheric and hydrologic variables and interactions within the central United States during 1989. The model simulation uses a 200-km grid covering the conterminous United States, and a nested, 50-km grid covering the Great Plains and Rocky Mountain states of Kansas, Nebraska, South Dakota, Wyoming, and Colorado. The model’s lateral boundary conditions are forced by six-hourly NCEP reanalysis products. ClimRAMS includes simplified precipitation and radiation sub-models, and representations that describe the seasonal evolution of vegetation-related parameters. In addition, ClimRAMS can use all of the general RAMS capabilities, like its more complex radiation sub-models, and explicit cloud and precipitation microphysics schemes. Thus, together with its nonhydrostatic and fully-interactive telescoping-grid capabilities, ClimRAMS can be applied to a wide variety of problems. Because of non-linear interactions between the land surface and atmosphere, simulating the observed climate requires simulating the observed diurnal, synoptic, and seasonal cycles. While previous regional climate modeling studies have demonstrated their ability to simulate the seasonal cycles through comparison with observed monthly-mean temperature and precipitation data sets, this study demonstrates that a regional climate model can also capture observed diurnal and synoptic variability. Observed values of daily precipitation and maximum and minimum screen-height air temperature are used to demonstrate this ability. Received September 27, 1999 Revised December 11, 1999     

A Climate Version of the Regional Atmospheric Modeling System

Summary The Regional Atmospheric Modeling System (RAMS) has been widely used to simulate relatively short-term atmospheric processes. To perform full-year to multi-year model integrations, a climate version of RAMS (ClimRAMS) has been developed, and is used to simulate diurnal, seasonal, and annual cycles of atmospheric and hydrologic variables and interactions within the central United States during 1989. The model simulation uses a 200-km grid covering the conterminous United States, and a nested, 50-km grid covering the Great Plains and Rocky Mountain states of Kansas, Nebraska, South Dakota, Wyoming, and Colorado. The model’s lateral boundary conditions are forced by six-hourly NCEP reanalysis products. ClimRAMS includes simplified precipitation and radiation sub-models, and representations that describe the seasonal evolution of vegetation-related parameters. In addition, ClimRAMS can use all of the general RAMS capabilities, like its more complex radiation sub-models, and explicit cloud and precipitation microphysics schemes. Thus, together with its nonhydrostatic and fully-interactive telescoping-grid capabilities, ClimRAMS can be applied to a wide variety of problems. Because of non-linear interactions between the land surface and atmosphere, simulating the observed climate requires simulating the observed diurnal, synoptic, and seasonal cycles. While previous regional climate modeling studies have demonstrated their ability to simulate the seasonal cycles through comparison with observed monthly-mean temperature and precipitation data sets, this study demonstrates that a regional climate model can also capture observed diurnal and synoptic variability. Observed values of daily precipitation and maximum and minimum screen-height air temperature are used to demonstrate this ability. Received September 27, 1999 Revised December 11, 1999     

A new general circulation model: formulation and preliminary results in a single- and multi-processor environment

This article describes a new general circulation model (GCM) developed jointly by The University of New South Wales (UNSW) and the University of Hamburg. The model is versatile in that it can be run as a medium-range (1 to 15 days) global numerical weather prediction (NWP) model; as an extended range (15 to 30 days) NWP model; and as a GCM for periods extending from seasons, through annual and decadal periods, and beyond. The model can be coupled with ocean models that vary in complexity from simple "swamp" oceans to complex ocean GCMs. The atmospheric GCM also has a number of novel features, particularly in the numerical integration scheme which is a high-order, mass-conserving, semi-implicit semi-Lagrangian scheme, thereby removing the stability restriction on the time-step and allowing efficient long-term integrations. The emphasis here will be on demonstrating that the new model performs effectively on the usual measures of skill (statistics such as mean errors, root-mean-square errors and anomaly correlations) in several standard applications upon which new models usually are assessed. These applications include medium range weather forecasts out to 10 days on a daily basis over a one year period; a limited 10-year simulation climatology, prediction of atmospheric anomalies using SST anomalies in an El Nino year; and an alternative two-way approach to regional modelling (the "down-scaling problem") made possible because the unconditional stability of the semi-implicit, semi-Lagrangian formulation permits large variations in grid spacing without changing the time step size. Finally, the model is run on a variety of parallel computing platforms and it is shown that near-linear speed-up can be attained. This is significant for both medium range NWP and very long-term GCM integrations. Received: 28 February 1996 / Accepted: 30 July 1996