Interpretation of interbasin exchange in an isopycnal ocean model

 This work concerns an analysis of inter-basin and inter-layer exchanges in the component ocean part of the coupled ECHAM4/OPYC3 general circulation model, aimed at documenting the simulation of North Atlantic Deep Water (NADW) and related thermohaline circulations in the Indian and Pacific Oceans. The modeled NADW is formed mainly in the Greenland– Iceland–Norwegian Seas through a composite effect of deep convection and downward cross-isopycnal transport. The modeled deep-layer outflow of NADW can reach 16 Sv near 30 °S in the South Atlantic, with the corresponding upper-layer return flow mainly coming from the “cold water path” through Drake Passage. Less than 4 Sv of the Agulhas “leakage” water contributes to the replacement of NADW along the “warm water path”. In the South Atlantic Ocean, the model shows that some intermediate isopycnal layers with potential densities ranging between 27.0 and 27.5 are the major water source that compensate the NADW return flow and enhance the Circumpolar Deep Water (CDW) flowing from the Atlantic into Indian Ocean. The modeled thermohaline circulations in the Indian and Pacific Oceans indicate that the Indian Ocean may play the major role in converting deep water into intermediate water. About 16 Sv of the CDW-originating deep water enters the Indian Ocean northward of 31 °S, of which more than 13 Sv “upwell” mainly near the continental boundaries of Africa, South Asia and Australia through inter-layer exchanges and return to the Antarctic Circumpolar Current (ACC) as intermediate-layer water. As a contrast, only 4 Sv of Pacific intermediate water is connected to “upwelling” flow southward across 31 °S while the magnitude of northward deep flow across 31 °S in the Pacific Ocean is significantly greater than that in the Indian Ocean. The model suggests that a large portion of the deep waters entering the Pacific Ocean (about 14 Sv) “upwells” continually into some upper layers through the thermocline, and becomes the source of the Indonesian throughflow. Uncertainties in these results may be related to the incomplete adjustment of the model’s isopycnal layers and the sensitivity of the Indonesian throughflow to the model’s geography and topography. Received: 12 August 1997/Accepted: 12 March 1998     

Simulated Climate Change Effects on Year-Round Water Temperatures in Temperate Zone Lakes

A deterministic, one-dimensional model is presented to simulate daily water temperature profiles and associated ice and snow covers for dimictic and polymictic lakes of the temperate zone. The lake parameters required as model input are surface area (A_s), maximum depth (H_MAX), and Secchi depth (z_s), the latter, used as a measure of light attenuation and trophic state. The model is driven by daily weather data and operates year-round over multiple years. The model has been tested with extensive data (over 5,000 temperature points). Standard error between simulated and measured water temperatures is 1.4°C in the open water season and 0.5°C in the ice cover season. The model is applied to simulate the sensitivity of Minnesota lake water temperature characteristics to climate change. The projected climate changes due to a doubling of atmospheric CO₂ are obtained from the output of the Canadian Climate Center General Circulation Model (CCC GCM) and the Goddard Institute of Space Studies General Circulation Model (GISS GCM). Simulated lake temperature characteristics have been plotted in a coordinate system with a lake geometry ratio (A _s ^0.25 /H_MAX) on one axis and Secchi depth on the other. The lake geometry ratio expresses a lake's susceptibility to stratification. By interpolation, the sensitivity of lake temperature characteristics to changes of water depth and Secchi depth under the projected climate scenarios can therefore be obtained. Selected lake temperature characteristics simulated with past climate conditions (1961–1979) and with a projected 2 × CO₂ climate scenario as input are presented herein in graphical form. The simulation results show that under the 2 × CO₂ climate scenario ice formation is delayed and ice cover period is shortened. These changes cause water temperature modifications throughout the year.     

Numerical simulation of tropical cyclones by an axisymmetric nonhydrostatic model

Summary As an approach to study the mesoscale processes within a typhoon, an axisymmetric nonhydrostatic numerical model is developed without the use of convective parameterization. Many simulated characteristics are consistent with radar and aircraft observations, such as the maximum of vertical and tangential wind, the inflow concentrated near the surface, the outward slope of the eyewall updraft, etc. The model duplicates not only the outward propagation of mesoscale convective systems, but also the inward movement of convective rings, the rate of which coincides with the observation. Besides, the model gives good simulations of the life cycle of convective rings, and indicates that the convective rings far from the eyewall play important roles in the fluctuation of typhoon intensity. Numerical results also exhibit the existence of coupling between outer and inner core structure.Analyses of the simulations show that convective momentum transport generates local maximum absolute angular momentum in the middle and upper troposphere. The momentum anomaly results in symmetric instability, which provides the environment to form convective rings. While the momentum anomaly moves outward with the outflow in the middle and upper troposphere, it initiates a series of convective rings with aid of other direct factors, which explains the outward propagation of convective systems.The simulations exhibit the life cycle of a typical convective ring in terms of three stages, or the developing, mature and dissipating stage. Analysis shows that the symmetric instability and the convective instability promote each other, and their cooperation makes the life of convective rings longer.With 8 Figures     

Urban heat island effect on annual mean temperature during the last 50 years in China

Summary Based on Chinas fifth population survey (2000) data and homogenized annual mean surface air temperature data, the urban heat island (UHI) effect on the warming during the last 50 years in China was analyzed in this study. In most cities with population over 10⁴, where there are national reference stations and principal stations, most of the temperature series are inevitably affected by the UHI effect. To detect the UHI effect, the annual mean surface air temperature (SAT) time series were firstly classified into 5 subregions by using Rotated Principal Components Analysis (RPCA) according to its high and low frequency climatic change features. Then the average UHI effect on each subregions regional annual mean STA was studied. Results indicate that the UHI effect on the annual mean temperatures includes three aspects: increase of the average values, decrease of variances and change of the climatic trends. The effect on the climatic trends is different from region to region. In the Yangtze River Valley and South China, the UHI effect enhances the warming trends by about 0.011°C/decade. In the other areas, such as Northeast, North-China, and Northwest, UHI has little impact on the warming trends of the regional annual temperature; while in the Southwest of China, introducing UHI stations slows down the warming trend by –0.006°C/decade. But no matter what subregion it is, the total warming/cooling of these effects is much smaller than the background change in regional temperature. The average UHI effect for the entire country, during the last 50 years is less than 0.06°C, which agrees well with the IPCC (2001). This suggests that we cannot conclude that urbanization during the last 50 years has had much obvious effect on the observed warming in China.     

Performance of a multi-RCM ensemble for South Eastern South America

The ability of four regional climate models to reproduce the present-day South American climate is examined with emphasis on La Plata Basin. Models were integrated for the period 1991–2000 with initial and lateral boundary conditions from ERA-40 Reanalysis. The ensemble sea level pressure, maximum and minimum temperatures and precipitation are evaluated in terms of seasonal means and extreme indices based on a percentile approach. Dispersion among the individual models and uncertainties when comparing the ensemble mean with different climatologies are also discussed. The ensemble mean is warmer than the observations in South Eastern South America (SESA), especially for minimum winter temperatures with errors increasing in magnitude towards the tails of the distributions. The ensemble mean reproduces the broad spatial pattern of precipitation, but overestimates the convective precipitation in the tropics and the orographic precipitation along the Andes and over the Brazilian Highlands, and underestimates the precipitation near the monsoon core region. The models overestimate the number of wet days and underestimate the daily intensity of rainfall for both seasons suggesting a premature triggering of convection. The skill of models to simulate the intensity of convective precipitation in summer in SESA and the variability associated with heavy precipitation events (the upper quartile daily precipitation) is far from satisfactory. Owing to the sparseness of the observing network, ensemble and observations uncertainties in seasonal means are comparable for some regions and seasons.     

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.     

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

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