The energy balance experiment EBEX-2000. Part II: Intercomparison of eddy-covariance sensors and post-field data processing methods

The eddy-covariance method is the primary way of measuring turbulent fluxes directly. Many investigators have found that these flux measurements often do not satisfy a fundamental criterion—closure of the surface energy balance. This study investigates to what extent the eddy-covariance measurement technology can be made responsible for this deficiency, in particular the effects of instrumentation or of the post-field data processing. Therefore, current eddy-covariance sensors and several post-field data processing methods were compared. The differences in methodology resulted in deviations of 10% for the sensible heat flux and of 15% for the latent heat flux for an averaging time of 30 min. These disparities were mostly due to different sensor separation corrections and a linear detrending of the data. The impact of different instrumentation on the resulting heat flux estimates was significantly higher. Large deviations from the reference system of up to 50% were found for some sensor combinations. However, very good measurement quality was found for a CSAT3 sonic together with a KH20 krypton hygrometer and also for a UW sonic together with a KH20. If these systems are well calibrated and maintained, an accuracy of better than 5% can be achieved for 30-min values of sensible and latent heat flux measurements. The results from the sonic anemometers Gill Solent-HS, ATI-K, Metek USA-1, and R.M. Young 81000 showed more or less larger deviations from the reference system. The LI-COR LI-7500 open-path H₂O/CO₂ gas analyser in the test was one of the first serial numbers of this sensor type and had technical problems regarding direct solar radiation sensitivity and signal delay. These problems are known by the manufacturer and improvements of the sensor have since been made. The National Center for Atmospheric Research is supported by the National Science Foundation.     

Introduction to the Regional Coupled Model WRF4-LICOM: Performance and Model Intercomparison over the Western North Pacific

Regional coupled modeling is one of the frontiers of regional climate modeling, but intercomparison has not been well coordinated. In this study, a community regional climate model, WRF4, with a resolution of 15 km, was coupled with a high-resolution(0.1°) North Pacific Ocean model(LICOM_np). The performance of the regional coupled model,WRF4_LICOM, was compared to that of another regional coupled model, RegCM4_LICOM, which was a coupling of version 4 of the Regiona...     

Current status of ENSO prediction skill in coupled ocean–atmosphere models

The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO.     

Role of land-surface temperature feedback on model performance for the stable boundary layer

At present a variety of boundary-layer schemes is in use in numerical models and often a large variation of model results is found. This is clear from model intercomparisons, such as organized within the GEWEX Atmospheric Boundary Layer Study (GABLS). In this paper we analyze how the specification of the land-surface temperature affects the results of a boundary-layer scheme, in particular for stable conditions. As such we use a well established column model of the boundary layer and we vary relevant parameters in the turbulence scheme for stable conditions. By doing so, we can reproduce the outcome for a variety of boundary-layer models. This is illustrated with the original set-up of the second GABLS intercomparison study using prescribed geostrophic winds and land-surface temperatures as inspired by (but not identical to) observations of CASES-99 for a period of more than two diurnal cycles. The model runs are repeated using a surface temperature that is calculated with a simple land-surface scheme. In the latter case, it is found that the range of model results in stable conditions is reduced for the sensible heat fluxes, and the profiles of potential temperature and wind speed. However, in the latter case the modelled surface temperatures are rather different than with the original set-up, which also impacts on near-surface air temperature and wind speed. As such it appears that the model results in stable conditions are strongly influenced by non-linear feedbacks in which the magnitude of the geostrophic wind speed and the related land-surface temperature play an important role.     

A new temperature based method to separate rain and snow

This paper presents the development and testing of a new method to estimate daily snowfall from precipitation and associated temperature records. The new method requires two variables; the threshold mean daily air temperature at which 50% of precipitation is considered snow, and the temperature range within which mixed precipitation can occur. Sensitivity analyses using 15 climate stations across south‐western Alberta, Canada, and ranging from prairie to alpine regions investigates the sensitivity of those two variables on mean annual snowfall (MAS), the coefficient of determination, and the MAS‐weighted coefficient of determination. Existing methods, including the static threshold method, one linear transition method used by Quick and Pipes, and the Leavesley method employed in the PRMS hydrological modelling system are compared with the new method, using a total of 963 years of daily data from the 15 climate stations used for the sensitivity analyses. Four different approaches to using the two input variables (threshold temperature and range) were tested and statistically compared: mean annual variables based on the 15 stations, mean annual variables for each station, mean monthly variables for each station, and a sine curve representing seasonal variation of the variables. In almost all cases the proposed new method resulted in higher MAS‐weighted coefficients of determination, and, on average, they were significantly different from those of other methods. The paper concludes with a decision tree to help decide which method and approach to apply under a variety of data availabilities. Copyright © 2008 John Wiley & Sons, Ltd.     

Intercomparison of the JULES and CABLE land surface models through assimilation of remotely sensed soil moisture in southeast Australia

Numerous land surface models exist for predicting water and energy fluxes in the terrestrial environment. These land surface models have different conceptualizations (i.e., process or physics based), together with structural differences in representing spatial variability, alternate empirical methods, mathematical formulations and computational approach. These inherent differences in modeling approach, and associated variations in outputs make it difficult to compare and contrast land surface models in a straight-forward manner. While model intercomparison studies have been undertaken in the past, leading to significant progress on the improvement of land surface models, additional framework towards identification of model weakness is needed. Given that land surface models are increasingly being integrated with satellite based estimates to improve their prediction skill, it is practical to undertake model intercomparison on the basis of soil moisture data assimilation. Consequently, this study compares two land surface models: the Joint UK Land Environment Simulator (JULES) and the Community Atmosphere Biosphere Land Exchange (CABLE) for soil moisture estimation and associated assessment of model uncertainty. A retrieved soil moisture data set from the Soil Moisture and Ocean Salinity (SMOS) mission was assimilated into both models, with their updated estimates validated against in-situ soil moisture in the Yanco area, Australia. The findings show that the updated estimates from both models generally provided a more accurate estimate of soil moisture than the open loop estimate based on calibration alone. Moreover, the JULES output was found to provide a slightly better estimate of soil moisture than the CABLE output at both near-surface and deeper soil layers. An assessment of the updated membership in decision space also showed that the JULES model had a relatively stable, less sensitive, and more highly convergent internal dynamics than the CABLE model.     

气候变化情景下北江飞来峡水库极端入库洪水预估

以北江飞来峡水库上游为研究对象,构建了网格分辨率为0.25°×0.25°的VIC(Variable Infiltration Capacity)水文模型,应用CMIP5多模式输出的降尺度结果与VIC模型耦合,对RCP2.6、RCP4.5和RCP8.5情景下未来时期(2020-2050年)飞来峡水库的入库洪水进行预估,并根据IPCC第5次评估报告处理和表达不确定性的方法来描述预估结论的可信度。结果表明,2020-2050年飞来峡水库年最大洪峰流量和年最大7日、15日洪量在RCP2.6情景下"大约可能"呈增加趋势,在RCP4.5和RCP8.5情景下"较为可能"呈增加趋势,水库防洪安全风险增大。与历史时期(1970-2000年)相比,未来水库极端入库洪水增加的可能性从大到小依次为RCP4.5、RCP2.6和RCP8.5情景,其中设计洪水100年、50年和20年一遇的洪峰流量在3种排放情景下均呈上升趋势,100年、50年和20年一遇的最大7日、15日洪量在RCP4.5情景下以上升为主,而在RCP2.6和RCP8.5情景下则主要呈减少态势。     

Spatial and temporal intercomparison of four global burned area products

We characterize the agreement and disagreement of four publically available burned products (Fire CCI, Copernicus Burnt Area, MODIS MCD45A1, and MODIS MCD64A1) at a finer spatial and temporal scale than previous assessments using a grid of three-dimensional cells defined both in space and in time. Our analysis, conducted using seven years of data (2005–2011), shows that estimates of burned area vary greatly between products in terms of total area burned, the location of burning, and the timing of the burning. We use regional and monthly units for analysis to provide insight into the variation between products that can be lost when considering products yearly and/or globally. Comparison with independent, contemporaneous MODIS active fire observations provides one indication of which products most reasonably capture the burning regime. Our results have implications for the use of global burned area products in fire ecology, management and emissions applications.     

西风带和南亚季风对三江源雨季水汽输送及降水的影响

为了进一步明确西风带和南亚季风对三江源流域的水汽输送作用及对流域降水的影响,利用ERA-20C再分析数据集对三江源流域1948-2010年雨季水汽通量场进行经验正交函数（Empirical Orthogonal Function,EOF）分解,得到可表征西风带和南亚季风水汽输送在三江源流域强弱关系的经验正交函数模态及指标,揭示当西风带和南亚季风分别控制流域时的水汽输送和降水分布规律。结果表明:西风带控制流域水汽输送时,流域内部水汽输送方向为由西北向东南,西风带和南亚季风在流域南边界附近汇集,流域东部及南部（澜沧江源区大部,黄河源区东部）降水显著增加;南亚季风控制流域水汽输送时,流域内部水汽输送方向为由南向北,两支水汽输送路径在流域以北汇集,流域北部（长江源区大部,黄河源区偏北部）降水显著增加。西风带和南亚季风的水汽输送均对三江源流域具有重要作用,两支水汽输送路径分别控制流域时可引起流域内部不同区域降水的显著增加。