Verification of Carbon Sink Assessment: Can We Exclude Natural Sinks?

Any human-induced terrestrial sink is susceptible to the effects of elevated atmospheric CO₂ concentration, nitrogen deposition, climate variability and other natural or indirect human-induced factors. It has been suggested in climate negotiations that the effects of these factors should be excluded from estimates of carbon sequestration used to meet the emission reduction commitments under the Kyoto Protocol. This paper focuses on the methodologies for factoring out the effects of atmospheric and climate variability/change. We estimate the relative magnitude of the non-human induced effects by using two biosphere models and discuss possibilities for narrowing estimate uncertainty.     

库木库里背斜活动性初步研究

库木库里盆地位于青藏高原北缘,与柴达木盆地一山之隔,是二者的过渡地带,也是高原主体部分向NE扩展的前缘地区;现今构造表现为被3条大型活动构造带(走滑的阿尔金断裂带、东昆仑断裂带和逆冲的祁漫塔格褶皱逆冲系)所夹持。因此,该盆地对于研究青藏高原北缘的构造活动性、活动历史,探讨高原的扩展模式具有十分重要的意义。虽然库木库里盆地南、北两侧均发育活动性很强的大型走滑断裂,但是在盆地中央发育1条大型背斜,走向NWW-SEE,与祁漫塔格褶皱逆冲系和柴达木盆地内的褶皱构造走向一致,说明盆地目前遭受NNE向的挤压。通过对盆地地形横、纵剖面和阶地展布形态的分析,得出背斜有自西向东扩展变形的特征;野外调查和测年结果显示,背斜东段冰川融水形成了大型冰水扇,形成年龄为(87.09±2.31)~(102.4±3.7)ka,进而获得背斜东段自晚更新世以来平均隆升速率的最大值为(2.78±0.28)~(3.28±0.28)mm/a。库木库里盆地整体的活动性很强,在构造上与其北边的柴达木盆地类似,都受控于阿尔金断裂南侧的NNE向的区域挤压作用。     

Tropical synoptic-scale wave disturbances over the western Pacific simulated by a global cloud-system resolving model

Lower-tropospheric tropical synoptic-scale disturbances (TSDs) are associated with severe weather systems in the Asian Monsoon region. Therefore, exact prediction of the development and behavior of TSDs using atmospheric general circulation models is expected to improve weather forecasting for this region. Recent state-of-the art global cloud-system resolving modeling approaches using a Nonhydrostatic Icosahedral Atmospheric Model (NICAM) may improve representation of TSDs. This study evaluates TSDs over the western Pacific in output from an Atmospheric Model Intercomparison Project (AMIP)-like control experiment using NICAM. Data analysis compared the simulated and observed fields. NICAM successfully simulates the average activity, three-dimensional structures, and characteristics of the TSDs during the Northern summer. The variance statistics and spectral analysis showed that the average activity of the simulated TSDs over the western Pacific during Northern summer broadly captures that of observations. The composite analysis revealed that the structures of the simulated TSDs resemble the observed TSDs to a large degree. The simulated TSDs exhibited a typical southeast- to northwest-oriented wave-train pattern that propagates northwestward from near the equator around 150 ^° E toward the southern coast of China. However, the location of the simulated wave train and wave activity center was displaced northward by approximately a few degrees of latitude from that in the observation. This displacement can be attributed to the structure and strength of the background basic flow in the simulated fields. Better representation of the background basic states is required for more successful simulation of TSDs.     

Annual ENSO simulated in a coupled ocean–atmosphere model

Using an output from 200-year integration of the Scale Interaction Experiment of EU project-F1 model (SINTEX-F1), the annual ENSO reproduced in the coupled general circulation model is investigated, suggesting the importance of reproducing an annual cycle in realistically simulating ENSO events. Although many features of the annual ENSO are reproduced, the northward expansion of sea surface temperature anomaly (SSTA) in the eastern tropical Pacific stays south of the equator. It is suggested that this model bias is due to the excitation of the too strong Rossby waves in the southeastern tropical Pacific, which reflect at the western boundary and intrude into the eastern equatorial Pacific. The zonal wind stress anomaly along the equator also plays an important role in generating the equatorial Kelvin waves. The amplitude of SSTA for the annual ENSO mode is reproduced, but its variance is only 20% of the observation; this is again due to the lack of northward migration of seasonal SSTA in the equatorial region and weaker coastal Kelvin waves along South America. Remedies for the model bias are discussed.     

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.     

Impacts of Indian Ocean SST biases on the Indian Monsoon: as simulated in a global coupled model

In this study, the impact of the ocean–atmosphere coupling on the atmospheric mean state over the Indian Ocean and the Indian Summer Monsoon (ISM) is examined in the framework of the SINTEX-F2 coupled model through forced and coupled control simulations and several sensitivity coupled experiments. During boreal winter and spring, most of the Indian Ocean biases are common in forced and coupled simulations, suggesting that the errors originate from the atmospheric model, especially a dry islands bias in the Maritime Continent. During boreal summer, the air-sea coupling decreases the ISM rainfall over South India and the monsoon strength to realistic amplitude, but at the expense of important degradations of the rainfall and Sea Surface Temperature (SST) mean states in the Indian Ocean. Strong SST biases of opposite sign are observed over the western (WIO) and eastern (EIO) tropical Indian Ocean. Rainfall amounts over the ocean (land) are systematically higher (lower) in the northern hemisphere and the south equatorial Indian Ocean rainfall band is missing in the control coupled simulation. During boreal fall, positive dipole-like errors emerge in the mean state of the coupled model, with warm and wet (cold and dry) biases in the WIO (EIO), suggesting again a significant impact of the SST errors. The exact contributions and the distinct roles of these SST errors in the seasonal mean atmospheric state of the coupled model have been further assessed with two sensitivity coupled experiments, in which the SST biases are replaced by observed climatology either in the WIO (warm bias) or EIO (cold bias). The correction of the WIO warm bias leads to a global decrease of rainfall in the monsoon region, which confirms that the WIO is an important source of moisture for the ISM. On the other hand, the correction of the EIO cold bias leads to a global improvement of precipitation and circulation mean state during summer and fall. Nevertheless, all these improvements due to SST corrections seem drastically limited by the atmosphere intrinsic biases, including prominently the unimodal oceanic position of the ITCZ (Inter Tropical Convergence Zone) during summer and the enhanced westward wind stress along the equator during fall.