Modeling the Tropical Pacific Ocean Using a Regional Coupled Climate Model

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

亚印太交汇区的海洋再分析系统

An ocean reanalysis system for the joining area of Asia and Indian-Pacific Ocean (AIPO) has been developed and is currently delivering reanalysis data sets for study on the air-sea interaction over AIPO and its climate variation over China in the inter-annual time scale.This system consists of a nested ocean model forced by atmospheric reanalysis,an ensemble-based multivariate ocean data assimilation system and various ocean observations.The following report describes the main components of the data assimilation system in detail.The system adopts an ensemble optimal interpolation scheme that uses a seasonal update from a free running model to estimate the background error covariance matrix.In view of the systematic biases in some observation systems,some treatments were performed on the observations before the assimilation.A coarse resolution reanalysis dataset from the system is preliminarily evaluated to demonstrate the performance of the system for the period 1992 to 2006 by comparing this dataset with other observations or reanalysis data.     

Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction

A conceptual coupled ocean-atmosphere model was used to study coupled ensemble data assimilation schemes with a focus on the role of ocean-atmosphere interaction in the assimilation. The optimal scheme was the fully coupled data assimilation scheme that employs the coupled covariance matrix and assimilates observations in both the atmosphere and ocean. The assimilation of synoptic atmospheric variability that captures the temporal fluctuation of the weather noise was found to be critical for the estimation of not only the atmospheric, but also oceanic states. The synoptic atmosphere observation was especially important in the mid-latitude system, where oceanic variability is driven by weather noise. The assimilation of synoptic atmospheric variability in the coupled model improved the atmospheric variability in the analysis and the subsequent forecasts, reducing error in the surface forcing and, in turn, in the ocean state. Atmospheric observation was able to further improve the oceanic state estimation directly through the coupled covariance between the atmosphere and ocean states. Relative to the mid-latitude system, the tropical system was influenced more by ocean-atmosphere interaction and, thus, the assimilation of oceanic observation becomes more important for the estimation of the ocean and atmosphere.     

Three-dimensional variational data assimilation of WindSat ocean surface winds for the genesis and forecasting of tropical storm Henri

With available high-resolution ocean surface wind vectors retrieved from the U.S. Naval Research Laboratorys WindSat on Coriolis, the impact of these data on genesis and forecasting of tropical storm Henri is examined using the non-hydrostatic, fifth-generation mesoscale model (MM5) of Pennsylvania State University-National Center for Atmospheric Research plus its newly released three-dimensional variational data assimilation (3DVAR) system. It is shown that the assimilation of the WindSat-retrieved ocean surface wind vectors in the 3DVAR system improves the model initialization fields by introducing a stronger vortex in the lower troposphere. As a result, the model reproduces the storm formation and track reasonably close to the observations. Compared to the experiment without the WindSat surface winds, the WindSat assimilation reduced an error between the model simulated track and observations of more than 80 km and also improved the storm intensity by nearly 2 hPa. It suggests that these data could provide early detection and prediction of tropical storms or hurricanes.     

Comparative Experiments on the Effect of Radar Data Assimilation and Increasing Horizontal Resolution on Short-term Numerical Weather Prediction

To examine the effect of radar data assimilation and increasing horizontal resolution on the short-term numerical weather prediction, comparative numerical experiments are conducted for a Huabei (North China) torrential rainfall event by using the Advanced Regional Prediction System (ARPS) and ARPS Data Analysis System (ADAS). The experiments use five different horizontal grid spacings, i.e., 18, 15, 9, 6, and 3 km,respectively, under the two different types of analyses: one with radar data, the other without. Results show that, when radar data are not used in the analysis (i.e., only using the conventional observation data), increasing horizontal resolution can improve the short-term prediction of 6 h with better representation of the frontal structure and higher scores of the rainfall prediction, particularly for heavy rain situations. When radar data are assimilated, it significantly improves the rainfall prediction for the first 6 h, especially the locality and intensity of precipitation. Moreover, using radar data in the analysis is more effective in improving the short-term prediction than increasing horizontal resolution of the model alone, which is demonstrated by the fact that by using radar data in the analysis and a coarser resolution of the 18-km grid spacing, the predicted results are as good as that by using a higher resolution of the 3-km grid spacing without radar data. Further study of the results under the radar data assimilation with grid spacing of 18-3 km reveals that the rainfall prediction is more sensitive to the grid spacing in heavy rain situations (more than 40 mm) than in ordinary rain situations (less than 40 mm). When the horizontal grid spacing reduces from 6 to 3 km, there is no obvious improvement to the prediction results. This suggests that there is a limit to how far increasing horizontal resolution can do for the improvement of the prediction. Therefore, an effective approach to improve the short-term numerical prediction is to combine the radar data assimilation with an optimal horizontal resolution.     

IMPROVEMENT OF OCEAN DATA ASSIMILATION SYSTEM AND CLIMATE PREDICTION BY ASSIMILATING ARGO DATA

The Argo(Array for Real-time Geostrophic Oceanography) data from 1998 to 2003 were used in the Beijing Climate Center-Global Ocean Data Assimilation System(BCC-GODAS). The results show that the utilization of Argo global ocean data in BCC-GODAS brings about remarkable improvements in assimilation effects. The assimilated sea surface temperature(SST) of BCC-GODAS can well represent the climatological states of observational data. Comparison experiments based on a global coupled atmosphere-ocean general circulation model(AOCGM) were conducted for exploring the roles of ocean data assimilation system with or without Argo data in improving the climate predictability of rainfall in boreal summer. Firstly, the global ocean data assimilation system BCC-GODAS was used to obtain ocean assimilation data under the conditions with or without Argo data. Then, the global coupled atmosphere-ocean general circulation model(AOCGM) was utilized to do hindcast experiments with the two sets of the assimilation data as initial oceanic fields. The simulated results demonstrate that the seasonal predictability of rainfall in boreal summer, particularly in China, increases greatly when initial oceanic conditions with Argo data are utilized. The distribution of summer rainfall in China hindcast by the AOGCM under the condition when Argo data are used is more in accordance with observation than that when no Agro data are used. The area of positive correlation between hindcast and observation enlarges and the hindcast skill of rainfall over China in summer improves significantly when Argo data are used.     

On the Importance of High-Resolution in Large-Scale Ocean Models

Eddying global ocean models are now routinely used for ocean prediction, and the value-added of a better representation of the observed ocean variability and western boundary currents at that resolution is currently being evaluated in climate models. This overview article begins with a brief summary of the impact on ocean model biases of resolving eddies in several global ocean–sea ice numerical simulations. Then, a series of North and Equatorial Atlantic configurations are used to show that an increase of the horizontal resolution from eddy-resolving to submesoscale-enabled together with the inclusion of high-resolution bathymetry and tides significantly improve the models' abilities to represent the observed ocean variability and western boundary currents. However, the computational cost of these simulations is extremely large, and for these simulations to become routine, close collaborations with computer scientists are essential to ensure that numerical codes can take full advantage of the latest computing architecture.     

An ocean data assimilation system in the Indian Ocean and west Pacific Ocean

The development and application of a regional ocean data assimilation system are among the aims of the Global Ocean Data Assimilation Experiment. The ocean data assimilation system in the regions including the Indian and West Pacific oceans is an endeavor motivated by this goal. In this study, we describe the system in detail. Moreover, the reanalysis in the joint area of Asia, the Indian Ocean, and the western Pacific Ocean(hereafter AIPOcean) constructed using multi-year model integration with data assimilation is used to test the performance of this system. The ocean model is an eddy-resolving,hybrid coordinate ocean model. Various types of observations including in-situ temperature and salinity profiles(mechanical bathythermograph, expendable bathythermograph, Array for Real-time Geostrophic Oceanography, Tropical Atmosphere Ocean Array, conductivity–temperature–depth, station data), remotely-sensed sea surface temperature, and altimetry sea level anomalies, are assimilated into the reanalysis via the ensemble optimal interpolation method. An ensemble of model states sampled from a long-term integration is allowed to change with season, rather than remaining stationary. The estimated background error covariance matrix may reasonably reflect the seasonality and anisotropy. We evaluate the performance of AIPOcean during the period 1993–2006 by comparisons with independent observations, and some reanalysis products. We show that AIPOcean reduces the errors of subsurface temperature and salinity, and reproduces mesoscale eddies. In contrast to ECCO and SODA products, AIPOcean captures the interannual variability and linear trend of sea level anomalies very well. AIPOcean also shows a good consistency with tide gauges.     

Improved ENSO Forecasts by Assimilating Sea Surface Temperature Observations into an Intermediate Coupled Model

A simple method for initializing intermediate coupled models (ICMs) using only sea surface temperature (SST) anomaly data is comprehensively tested in two sets of hindcasts with a new ICM. In the initialization scheme, both the magnitude of the nudging parameter and the duration of the assimilation are considered, and initial conditions for both atmosphere and ocean are generated by running the coupled model with SST anomalies nudged to the observations. A comparison with the observations indicates that the scheme can generate realistic thermal fields and surface dynamic fields in the equatorial Pacific through hindcast experiments. An ideal experiment is performed to get the optimal nudging parameters which include the nudging intensity and nudging time length. Twelve-month-long hindcast experiments are performed with the model over the period 1984–2003 and the period 1997–2003. Compared with the original prediction results, the model prediction skills are significantly improved by the nudging method especially beyond a 6-month lead time during the two different periods. Potential problems and further improvements are discussed regarding the new coupled assimilation system.     

Impact of ocean model resolution on CCSM climate simulations

The current literature provides compelling evidence suggesting that an eddy-resolving (as opposed to eddy-permitting or eddy-parameterized) ocean component model will significantly impact the simulation of the large-scale climate, although this has not been fully tested to date in multi-decadal global coupled climate simulations. The purpose of this paper is to examine how resolved ocean fronts and eddies impact the simulation of large-scale climate. The model used for this study is the NCAR Community Climate System Model version 3.5 (CCSM3.5)—the forerunner to CCSM4. Two experiments are reported here. The control experiment is a 155-year present-day climate simulation using a 0.5° atmosphere component (zonal resolution 0.625 meridional resolution 0.5°; land surface component at the same resolution) coupled to ocean and sea-ice components with zonal resolution of 1.2° and meridional resolution varying from 0.27° at the equator to 0.54° in the mid-latitudes. The second simulation uses the same atmospheric and land-surface models coupled to eddy-resolving 0.1° ocean and sea-ice component models. The simulations are compared in terms of how the representation of smaller scale features in the time mean ocean circulation and ocean eddies impact the mean and variable climate. In terms of the global mean surface temperature, the enhanced ocean resolution leads to a ubiquitous surface warming with a global mean surface temperature increase of about 0.2?°C relative to the control. The warming is largest in the Arctic and regions of strong ocean fronts and ocean eddy activity (i.e., Southern Ocean, western boundary currents). The Arctic warming is associated with significant losses of sea-ice in the high-resolution simulation. The sea surface temperature gradients in the North Atlantic, in particular, are better resolved in the high-resolution model leading to significantly sharper temperature gradients and associated large-scale shifts in the rainfall. In the extra-tropics, the interannual temperature variability is increased with the resolved eddies, and a notable increases in the amplitude of the El Ni?o and the Southern Oscillation is also detected. Changes in global temperature anomaly teleconnections and local air-sea feedbacks are also documented and show large changes in ocean–atmosphere coupling. In particular, local air-sea feedbacks are significantly modified by the increased ocean resolution. In the high-resolution simulation in the extra-tropics there is compelling evidence of stronger forcing of the atmosphere by SST variability arising from ocean dynamics. This coupling is very weak or absent in the low-resolution model.     

High-resolution climate simulation of the last glacial maximum

The climate of the last glacial maximum (LGM) is simulated with a high-resolution atmospheric general circulation model, the NCAR CCM3 at spectral truncation of T170, corresponding to a grid cell size of roughly 75 km. The purpose of the study is to assess whether there are significant benefits from the higher resolution simulation compared to the lower resolution simulation associated with the role of topography. The LGM simulations were forced with modified CLIMAP sea ice distribution and sea surface temperatures (SST) reduced by 1°C, ice sheet topography, reduced CO₂, and 21,000 BP orbital parameters. The high-resolution model captures modern climate reasonably well, in particular the distribution of heavy precipitation in the tropical Pacific. For the ice age case, surface temperature simulated by the high-resolution model agrees better with those of proxy estimates than does the low-resolution model. Despite the fact that tropical SSTs were only 2.1°C less than the control run, there are many lowland tropical land areas 4–6°C colder than present. Comparison of T170 model results with the best constrained proxy temperature estimates (noble gas concentrations in groundwater) now yield no significant differences between model and observations. There are also significant upland temperature changes in the best resolved tropical mountain belt (the Andes). We provisionally attribute this result in part as resulting from decreased lateral mixing between ocean and land in a model with more model grid cells. A longstanding model-data discrepancy therefore appears to be resolved without invoking any unusual model physics. The response of the Asian summer monsoon can also be more clearly linked to local geography in the high-resolution model than in the low-resolution model; this distinction should enable more confident validation of climate proxy data with the high-resolution model. Elsewhere, an inferred salinity increase in the subtropical North Atlantic may have significant implications for ocean circulation changes during the LGM. A large part of the Amazon and Congo Basins are simulated to be substantially drier in the ice age—consistent with many (but not all) paleo data. These results suggest that there are considerable benefits derived from high-resolution model regarding regional climate responses, and that observationalists can now compare their results with models that resolve geography at a resolution comparable to that which the proxy data represent.     

Data assimilation in the ocean circulation model of high spatial resolution using the methods of parallel programming

The parallel implementation of the method of multivariate optimum interpolation (MVOI) for the INMIO ocean circulation model with the horizontal resolution of 1/10° and 49 vertical levels is proposed to correct the model computations with the measurement data. The data assimilation in the high-resolution model with the high degree of scalability is tested. The results of numerical experiments on assimilation of data from ARGO drifters located in the North Atlantic are presented. The model output data were also compared with independent data on sea surface temperature obtained from Aqua (NASA) satellite observations. The skill of the model solution was qualitatively evaluated. It is demonstrated experimentally that data assimilation substantially (to 30%) improves the model output data and reduces the error in the operational 24-hour forecast.     

不同分辨率CCSM4对东亚和中国气候模拟能力分析

本文利用通用气候系统模式CCSM4在三种水平分辨率下的工业化革命前期气候模拟试验,结合观测和再分析资料,比较了各分辨率下模式对中国温度和降水、东亚海平面气压和850 hPa风场的模拟能力,综合评价了模式分辨率对东亚和中国气候模拟的影响.结果表明,三种分辨率对中国温度均具有很好的模拟能力,除春季外,低分辨率(T31,约3.75°×3.75°)对全年温度的模拟能力均要稍好于中(f19,约1.9°×2.5°)、高(f09,约0.9°×1.25°)分辨率;各分辨率对中国降水的模拟能力远不如温度,除冬季外全年都出现的中部地区虚假降水并未因为模式分辨率提高而得到本质改善;对于东亚海平面气压场,低分辨率在冬季模拟能力相对最好,中等分辨率在夏季相对较好,而高分辨率的模拟能力均表现最差;低分辨率对850 hPa东亚冬季风和夏季风的模拟能力均要好于中、高分辨率,而两种较高分辨率的模拟能力则比较接近.总的来说,低分辨率CCSM4在东亚和中国气候模拟中表现出了较大优势,加之其计算代价小,适合进行需要较长时间积分的气候模拟研究.     

GTS的温盐资料在BCC_GODAS中的同化结果分析

分析了从GTS(全球无线通讯系统)获得的2002-2007年海洋温盐观测资料在国家气候中心第2代全球海洋资料同化系统(BCC_GODAS 2.0)中的同化结果。与SODA(简易海洋同化数据)资料的比较表明:GTS中的海洋温盐资料同化对模式温盐场的改进之处主要表现在混合层暖区的范围和中心强度、温跃层中温度槽脊的深度、温跃层附近的温度梯度以及盐度高、低值区的范围和中心强度等方面,同化后全球温盐场的均方根误差得到一定程度的降低。挑选位于不同海区的单点温盐廓线与ARGO(地转海洋学实时观测阵)观测作了进一步比较,结果表明:大多数情况下,同化后温盐廓线的均方根误差得到明显降低,模拟的温盐垂向分布特征也更为准确。与TAO(热带大气海洋观测网)资料的比较也同样表明:同化后的温盐场特征会得到一定程度改善。     

Precipitation,temperature and wind in Norway: dynamical downscaling of ERA40

A novel downscaling approach of the ERA40 (ECMWF 40-years reanalysis) data set has been taken and results for comparison with observations in Norway are shown. The method applies a nudging technique in a stretched global model, focused in the Norwegian Sea (67°N, 5°W). The effective resolution is three times the one of the ERA40, equivalent to about 30 km grid spacing in the area of focus. Longer waves (<T42) in the downscaled solution are nudged towards the ERA40 solution, and thus the large-scale circulation is similar in the two data sets. The shorter waves are free to evolve, and produce high intensities of winds and precipitation. The comparison to observations incorporate numerous station data points of (1) precipitation (#357), (2) temperature (#98) and (3) wind (#10), and for the period 1961–1990, the downscaled data set shows large improvements over ERA40. The daily precipitation shows considerable reduction in bias (from 50 to 11%), and twofold reduction at the 99.9 percentile (from −59 to −29%). The daily temperature showed a bias reduction of about a degree in most areas, and relative large RMSE reduction (from 7.5 to 5.0°C except winter). The wind comparison showed a slight improvement in bias, and significant improvements in RMSE.     

Assessing characteristics of Mediterranean explosive cyclones for different data resolution

A comparison of two objective climatologies of explosive cyclones in the Mediterranean region is performed. The results are derived from two different mean sea-level pressure reanalysis data resolutions, but from the same assimilation model, in order to quantify the pure impact of higher resolution on the identification and characteristics of explosive cyclones, when the assimilation model is the same. The explosive cyclones were identified with the aid of the Melbourne University automatic cyclone finding and tracking scheme over a 40-year period, using the 6-hourly analyses of ERA-40 mean sea-level pressure (MSLP) on: (a) 2.5?×?2.5 and (b) 1?×?1 latitude–longitude grid. The comparison of the two datasets revealed the significant role of the increase in spatial resolution of MSLP data on the identification and tracking process, and the number of the explosive cyclones in the high-resolution dataset is almost four times greater than the respective one in the lower resolution dataset. However, the comparison of explosive cyclone characteristics, including spatial and temporal variations of explosive deepening, revealed differences in the geographical distribution of the location of the maximum explosive deepening and average explosive cyclone Laplacian of the central pressure. These differences are due to the identification in the higher resolution set of smaller scale and secondary explosives along the strongly baroclinic northern Mediterranean boundaries, south of the Alps and the Pyrenees. Explosive deepening appears a bias to the daytime period from 12 to 18 Coordinated Universal Time (UTC) for both datasets, which is more prominent in the LR dataset. Statistically significant difference of pressure tendency between the two datasets appear for the daytime period from 06 to 12 UTC, accounting for better representation of orographic forcing in the HR dataset.     

Effects of tropical cyclones on large-scale circulation and ocean heat transport in the South China Sea

In this study, we investigate the influence of tropical cyclones (TCs) on large-scale circulation and ocean heat transport in the South China Sea (SCS) by using an ocean general circulation model at a 1/8° resolution during 2000–2008. The model uses a data assimilation system to assimilate observations in order to improve the representation of SCS circulation. The results reveal an unexpected deep SCS circulation anomaly induced by TCs, which suggests that effects of TC can penetrate deeper into the ocean. This deep effect may result from the near inertial oscillations excited by TCs. The inertial oscillations can propagate downward to the oceanic interior. The analyses confirm that TCs have two effects on ocean heat transport of the SCS. Firstly, the wind stress curl induced by TCs affects the structure of SCS circulation, and then changes heat transport. Secondly, TCs pump surface heat downward to the thermocline, increasing the heat injection from the atmosphere to the ocean. Two effects together amplify the outflow of the surface heat southward away the SCS through the Mindoro and Karimata Straits. The TC-induced heat transports through the Mindoro, Balabac and Karimata Straits account for 20 % of the total heat transport through three straits. An implication of this study is that ocean models need to simulate the TC effect on heat transport in order to correctly evaluate the role of the SCS through flow in regulating upper ocean circulation and climate in the Indonesian maritime continent and its adjacent regions.     

High-resolution simulations of the last glacial maximum climate over Europe: a solution to discrepancies with continental palaeoclimatic reconstructions?

The analyses of low-resolution models simulations of the last glacial maximum (LGM, 21 kyr BP) climate have revealed a large discrepancy between all the models and pollen-based palaeoclimatic reconstructions. In general, the models are too warm relative to the observations, especially in winter, where the difference is of the order of 10°C over western Europe. One of the causes of this discrepancy may be related to the low spatial resolution of these models. To assess the impact of using high-resolution models on simulated climate sensitivity, we use three approaches to obtain high-resolution climate simulations over Europe: first an atmospheric general circulation model (AGCM) with a stretched grid over Europe, second a homogeneous T106 AGCM (high resolution everywhere on the globe) and last a limited area model (LAM) nested in a low-resolution AGCM. With all three methods, we have performed simulations of the European climate for present and LGM conditions, according to the experimental design recommended by the Palaeoclimate Modeling Intercomparison Project (PMIP). Model results have been compared with updated pollen-based palaeoclimatic indicators for temperature and precipitation that were initially developed in PMIP. For each model, a low-resolution global run was also performed. As expected, the low-resolution simulations underestimate the large cooling indicated by pollen data, especially in winter, despite revised slightly warmer reconstructions of the temperatures of the coldest month, and show results in the range of those obtained in PMIP with similar models. The two high-resolution AGCMs do not improve the temperature field and cannot account for the discrepancy between model results and data, especially in winter. However, they are able to reproduce trends in precipitation more closely than their low-resolution counterparts do, but the simulated climates are still not as arid as depicted by the data. Conversely, the LAM temperature results compare well with climate reconstructions in winter but the simulated hydrological cycle is not consistent with the data. Finally, these results are discussed in regard of other possible causes for discrepancies between models and palaeoclimatic reconstructions for the LGM European climate.