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Jie Cao, Ping Yao, Lin Wang, and Kui Liu

Abstract

Based on reanalysis and observational datasets, this study proposes a reasonable mechanism for summer rainfall variations over the low-latitude highlands (LLH) of China, in which a subtropical Indian Ocean dipole (SIOD)-like pattern is the key external thermal forcing. In summers with a positive SIOD-like pattern, sea surface temperature (SST) anomalies may lead to lower-tropospheric divergence over the tropical Indian Ocean and convergence over the subtropical southwestern Indian Ocean and Arabian Sea. The convergence over the Arabian Sea can induce easterly anomalies of the divergent wind component off the eastern coast of the Bay of Bengal (BOB), while the divergence over the tropical Indian Ocean can change the interhemispheric vertical circulation and produce a descending motion over the same area and cyclonic anomalies in the rotational wind component over the Indian peninsula. The combined effect of the divergent and rotational wind anomalies and enhanced interhemispheric vertical circulation facilitates easterly anomalies and weakens climatological water vapor flux to the northern BOB. Therefore, anomalous water vapor divergence and less precipitation are observed over the LLH. In summers with a negative SIOD-like pattern, the situation is approximately the same but with opposite polarity and a weaker role of the divergent wind component. Further analyses indicate that the summertime SIOD-like pattern can be traced to preceding seasons, especially in positive SIOD-like years. The SST–wind–evaporation feedback mechanism could account for maintenance of the SIOD-like pattern. These results provide efficient prediction potential for summer rainfall variations over the LLH.

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Donghai Wu, Shilong Piao, Yongwen Liu, Philippe Ciais, and Yitong Yao

Abstract

Earth system models (ESMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were diagnosed as having large discrepancies in their land carbon turnover times, which partly explains the differences in the future projections of terrestrial carbon storage from the models. Carvalhais et al. focused on evaluation of model-based ecosystem carbon turnover times τ eco in relation with climate factors. In this study, τ eco from models was analyzed separately for biomass and soil carbon pools, and its spatial dependency upon temperature and precipitation was evaluated using observational datasets. The results showed that 8 of 14 models slightly underestimated global biomass carbon turnover times τ veg (modeled median of 8 yr vs observed 11 yr), and 11 models grossly underestimated the soil carbon turnover time τ soil (modeled median of 16 yr vs observed 26 yr). The underestimation of global carbon turnover times in ESMs was mainly due to values for τ veg and τ soil being too low in the high northern latitudes and arid and semiarid regions. In addition, the models did not capture the observed spatial climate sensitivity of carbon turnover time in these regions. Modeled τ veg and τ soil values were generally weakly correlated with climate variables, implying that differences between carbon cycle models primarily originated from structural differences rather than from differences in atmospheric climate models (i.e., related to temperature and precipitation). This study indicates that most models do not reproduce the underlying processes driving regional τ veg and τ soil, highlighting the need for improving the model parameterization and adding key processes such as biotic disturbances and permafrost–carbon climate responses.

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Lei Wang, Tandong Yao, Chenhao Chai, Lan Cuo, Fengge Su, Fan Zhang, Zhijun Yao, Yinsheng Zhang, Xiuping Li, Jia Qi, Zhidan Hu, Jingshi Liu, and Yuanwei Wang

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The TP-River project is constructing a monitoring network for 13 major rivers at the Third Pole to quantify the total river runoff and its response to monsoon and westerly winds.

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Lei Wang, Tandong Yao, Chenhao Chai, Lan Cuo, Fengge Su, Fan Zhang, Zhijun Yao, Yinsheng Zhang, Xiuping Li, Jia Qi, Zhidan Hu, Jingshi Liu, and Yuanwei Wang

Abstract

Monitoring changes in river runoff at the Third Pole (TP) is important because rivers in this region support millions of inhabitants in Asia and are very sensitive to climate change. Under the influence of climate change and intensified cryospheric melt, river runoff has changed markedly at the TP, with significant effects on the spatial and temporal water resource distribution that threaten water supply and food security for people living downstream. Despite some in situ observations and discharge estimates from state-of-the-art remote sensing technology, the total river runoff (TRR) for the TP has never been reliably quantified, and its response to climate change remains unclear. As part of the Chinese Academy of Sciences’ “Pan-Third Pole Environment Study for a Green Silk Road,” the TP-River project aims to construct a comprehensive runoff observation network at mountain outlets (where rivers leave the mountains and enter the plains) for 13 major rivers in the TP region, thereby enabling TRR to be accurately quantified. The project also integrates discharge estimates from remote sensing and cryosphere–hydrology modeling to investigate long-term changes in TRR and the relationship between the TRR variations and westerly/monsoon. Based on recent efforts, the project provides the first estimate (656 ± 23 billion m3) of annual TRR for the 13 TP rivers in 2018. The annual river runoff at the mountain outlets varies widely between the different TP rivers, ranging from 2 to 176 billion m3, with higher values mainly corresponding to rivers in the Indian monsoon domain, rather than in the westerly domain.

Open access
Dong-Peng Guo, Peng Zhao, Ran Wan, Ren-Tai Yao, and Ji-Min Hu

Abstract

This paper applied a commercial computational fluid dynamics code, STAR-CD, with the renormalization group k–ε turbulence model to simulate the flow and dispersion of contaminants released from a source on the windward side of a hill under different thermal stratifications. In the wake region, the influence of atmospheric stratification on the flow field is inconspicuous under neutral and unstable conditions because of the effect of mechanical disturbance. However, this influence becomes slightly conspicuous under stable conditions. When atmospheric stratification is stable, in the range of z/H < 1.0 (where z is height above the surface and H is height of the hill), the velocity deficits are smaller than those under neutral and unstable conditions. The maximum turbulence kinetic energy (TKE) appears in the wake regions, and the variation in TKE is significantly lower than that under neutral and unstable conditions. When atmospheric stratification is unstable, the vertical and horizontal spread of the plume is slightly greater than that under neutral and stable conditions and the maximum concentration is less than that under neutral conditions. When the Froude number is large (~11; Brunt–Väisälä frequency = 0.52), atmospheric stratification is slightly stable, the structure of flow around the hill is generally similar to that under neutral conditions, and the high-concentration regions are large on the windward side of the hill. Smaller high-concentration regions just appear on the windward side of the hill under unstable conditions. The pollutant concentrations in the wake region of the hill increase as a result of the effect of thermal stability, and the vertical spreading range of the plume along the downwind axis (x axis) is larger than that under neutral and stable conditions.

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Anthony D. Del Genio, William Kovari, Mao-Sung Yao, and Jeffrey Jonas

Abstract

Precipitation processes in convective storms are potentially a major regulator of cloud feedback. An unresolved issue is how the partitioning of convective condensate between precipitation-size particles that fall out of updrafts and smaller particles that are detrained to form anvil clouds will change as the climate warms. Tropical Rainfall Measuring Mission (TRMM) observations of tropical oceanic convective storms indicate higher precipitation efficiency at warmer sea surface temperature (SST) but also suggest that cumulus anvil sizes, albedos, and ice water paths become insensitive to warming at high temperatures. International Satellite Cloud Climatology Project (ISCCP) data show that instantaneous cirrus and deep convective cloud fractions are positively correlated and increase with SST except at the highest temperatures, but are sensitive to variations in large-scale vertical velocity. A simple conceptual model based on a Marshall–Palmer drop size distribution, empirical terminal velocity–particle size relationships, and assumed cumulus updraft speeds reproduces the observed tendency for detrained condensate to approach a limiting value at high SST. These results suggest that the climatic behavior of observed tropical convective clouds is intermediate between the extremes required to support the thermostat and adaptive iris hypotheses.

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Shizuo Liu, Qigang Wu, Lin Wang, Steven R. Schroeder, Yang Zhang, Yonghong Yao, and Haibo Hu

Abstract

Northern Hemisphere (NH) snow cover extent (SCE) has diminished in spring and early summer since the 1960s. Historical simulations from phase 5 of the Coupled Model Intercomparison Project (CMIP5) estimated about half as much NH SCE reduction as observed, and thus underestimated the associated climate responses. This study investigates atmospheric responses to realistic decreasing snow anomalies using multiple ensemble transient integrations of climate models forced by observed light and heavy NH snow cover years, specifically satellite-based observations of NH SCE and snow water equivalent from March to August in 1990 (light snow) and 1985 (heavy snow), as a proxy for the trend. The primary atmospheric responses to March–August NH snow reduction are decreased soil moisture, increased surface air temperature, general tropospheric warming in the extratropics and the Arctic, increased geopotential heights, and weakening of the midlatitude jet stream and eddy kinetic energy. The localized response is maintained by persistent increased diabatic heating due to reduced snow anomalies and resulting soil moisture drying, and the remote atmospheric response results partly from horizontal propagation of stationary Rossby wave energy and also from a transient eddy feedback mechanism. In summer, atmospheric responses are significant in both the Arctic and the tropics and are mostly induced by contemporaneous snow forcing, but also by the summer soil moisture dry anomaly associated with early snow melting.

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Ning Lu, Kevin E. Trenberth, Jun Qin, Kun Yang, and Ling Yao

Abstract

Long-term trends in precipitable water (PW) are an important component of climate change assessments for the Tibetan Plateau (TP). PW products from Moderate Resolution Imaging Spectroradiometer (MODIS) are able to provide good spatial coverage of PW over the TP but limited in time coverage, while the meteorological stations in the TP can estimate long-term PW but unevenly distributed. To detect the decadal trend in PW over the TP, Bayesian inference theory is used to construct long-term and spatially continuous PW data for the TP based on the station and MODIS observations. The prior information on the monthly-mean PW from MODIS and the 63 stations over the TP for 2000–06 is used to get the posterior probability knowledge that is utilized to build a Bayesian estimation model. This model is then operated to estimate continuous monthly-mean PW for 1970–2011 and its performance is evaluated using the monthly MODIS PW anomalies (2007–11) and annual GPS PW anomalies (1995–2011), with RMSEs below 0.65 mm, to demonstrate that the model estimation can reproduce the PW variability over the TP in both space and time. Annual PW series show a significant increasing trend of 0.19 mm decade−1 for the TP during the 42 years. The most significant PW increase of 0.47 mm decade−1 occurs for 1986–99 and an insignificant decrease occurs for 2000–11. From the comparison of the PW data from JRA-55, ERA-40, ERA-Interim, MERRA, NCEP-2, and ISCCP, it is found that none of them are able to show the actual long-term trends and variability in PW for the TP as the Bayesian estimation.

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Chao Liu, Bin Yao, Vijay Natraj, Fuzhong Weng, Tianhao Le, Run-Lie Shia, and Yuk L. Yung

Abstract

With the increasing use of satellite and ground-based high-spectral-resolution (HSR) measurements for weather and climate applications, accurate and efficient radiative transfer (RT) models have become essential for accurate atmospheric retrievals, for instrument calibration, and to provide benchmark RT solutions. This study develops a spectral data compression (SDCOMP) RT model to simulate HSR radiances in both solar and infrared spectral regions. The SDCOMP approach “compresses” the spectral data in the optical property and radiance domains, utilizing principal component analysis (PCA) twice to alleviate the computational burden. First, an optical-property-based PCA is performed for a given atmospheric scenario (atmospheric, trace gas, and aerosol profiles) to simulate relatively low-spectral-resolution radiances at a small number of representative wavelengths. Second, by using precalculated principal components from an accurate radiance dataset computed for a large number of atmospheric scenarios, a radiance-based PCA is carried out to extend the low-spectral-resolution results to desired HSR results at all wavelengths. This procedure ensures both that individual monochromatic RT calculations are efficiently performed and that the number of such computations is optimized. SDCOMP is approximately three orders of magnitude faster than numerically exact RT calculations. The resulting monochromatic radiance has relative errors less than 0.2% in the solar region and brightness temperature differences less than 0.1 K for over 95% of the cases in the infrared region. The efficiency and accuracy of SDCOMP not only make it useful for analysis of HSR measurements, but also hint at the potential for utilizing this model to perform RT simulations in mesoscale numerical weather and general circulation models.

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Chundi Hu, Qigang Wu, Song Yang, Yonghong Yao, Duo Chan, Zhenning Li, and Kaiqiang Deng

Abstract

In this study, the authors apply a lagged maximum covariance analysis (MCA) to capture the cross-seasonal coupled patterns between the Southern Ocean sea surface temperature (SOSST) and extratropical 500-hPa geopotential height anomalies in the Southern Hemisphere, from which Niño-3.4 signals and their linear trends are removed to a certain extent. Statistically significant results show that the dominant feature of ocean–atmosphere interaction is likely the effect of atmosphere on SOSST anomalies, with a peak occurring when the atmosphere leads the SOSST by 1 month.

However, the most eye-capturing phenomenon is that the austral autumn atmospheric signal, characterized by a negatively polarized Antarctic Oscillation (AAO), is significantly related to the gradual evolution of preceding SOSST anomalies, suggesting that the SOSST anomalies tend to exert an effect on the Southern Hemisphere atmospheric circulation. A regression analysis based on SOSST anomaly centers confirms these features. It is also demonstrated that the gradual evolution of changes in SOSST is mainly driven by internal atmospheric variability via surface turbulent heat flux associated with cold or warm advection and that the atmospheric circulation experiences a change from a typical positive AAO to a negative phase in this process. These findings indicate that such a long lead cross-seasonal covariance could contribute to a successful prediction of AAO-related atmospheric circulation in austral autumn from the perspective of SOSST anomalies, with lead times up to 6–7 months.

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