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Qing Liu and Cornelius J. F. Schuurmans

Abstract

In this paper, the authors show that the effect of a tropical Pacific anomalous forcing can he primarily linear or nonlinear depending on its sign and longitudinal position. Using a nine-level steady-state model both the linear and nonlinear steady-state responses to tropical anomalous diabatic warming or cooling in the midtroposphere were computed. These sources were centered at 130°W and at the date line.

At 130°W the atmospheric response to tropical heating or cooling is primarily linear. The amplitudes are small and of opposite sign for heating and cooling. The response agrees well with the results of corresponding general circulation model (GCM) experiments. For a heating at the date line, the modification of the linear response by the nonlinear terms is substantial. The nonlinear response to the heating is much stronger than the linear response, whereas the nonlinear response to cooling is weaker. The main effect of the nonlinear terms is to modify the amplitudes; the structure of the response is only slightly adjusted. Both the linear and the nonlinear steady-state responses to tropical beating at the dale line result in an anti-Pacific-North American pattern. This is not in agreement with the results of corresponding GCM experiments.

The tropically forced stationary perturbations can have a substantial effect on the stability properties of the planetary-scale time-mean state. This may lead to strong nonlinear transient feedback effects and consequently a strong modification of the direct steady-state response. We have shown that the effect of a persistent heating or cooling at 130°W hardly affects the stability properties of the time-mean state. However, the effect of a heating at the date line is to strongly enhance the low-frequency variability. We hypothesize that this causes large additional transient feedback effects that substantially modify the character of the direct linear or nonlinear steady-state response. This may account for the discrepancy between the steady-state model and general circulation model results for the heating case at the date line.

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Qing Bao, Jing Yang, Yimin Liu, Guoxiong Wu, and Bin Wang

Abstract

Anomalous warming occurred over the Tibetan Plateau (TP) before and during the disastrous freezing rain and heavy snow hitting central and southern China in January 2008. The relationship between the TP warming and this extreme event is investigated with an atmospheric general circulation model. Two perpetual runs were performed. One is forced by the climatological mean sea surface temperatures in January as a control run; and the other has the same model setting as the control run except with an anomalous warming over the TP that mimics the observed temperature anomaly. The numerical results demonstrate that the TP warming induces favorable circulation conditions for the occurrence of this extreme event, which include the deepened lower-level South Asian trough, the enhanced lower-level southwesterly moisture transport in central-southern China, the lower-level cyclonic shear in the southerly flow over southeastern China, and the intensified Middle East jet stream in the middle and upper troposphere. Moreover, the anomalous TP warming results in a remarkable cold anomaly near the surface and a warm anomaly aloft over central China, forming a stable stratified inversion layer that favors the formation of the persistent freezing rain. The possible physical linkages between the TP warming and the relevant resultant circulation anomalies are proposed. The potential reason of the anomalous TP warming during the 2007–08 winter is also discussed.

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Yelin Jiang, Guiling Wang, Weiguang Liu, Amir Erfanian, Qing Peng, and Rong Fu

Abstract

This study investigates the potential effects of historical deforestation in South America using a regional climate model driven with reanalysis data. Two different sources of data were used to quantify deforestation during the 1980s to 2010s, leading to two scenarios of forest loss: smaller but spatially continuous in scenario 1 and larger but spatially scattered in scenario 2. The model simulates a generally warmer and drier local climate following deforestation. Vegetation canopy becomes warmer due to reduced canopy evapotranspiration, and ground becomes warmer due to more radiation reaching the ground. The warming signal for surface air is weaker than for ground and vegetation, likely due to reduced surface roughness suppressing the sensible heat flux. For surface air over deforested areas, the warming signal is stronger for the nighttime minimum temperature and weaker or even becomes a cooling signal for the daytime maximum temperature, due to the strong radiative effects of albedo at midday, which reduces the diurnal amplitude of temperature. The drying signals over deforested areas include lower atmospheric humidity, less precipitation, and drier soil. The model identifies the La Plata basin as a region remotely influenced by deforestation, where a simulated increase of precipitation leads to wetter soil, higher ET, and a strong surface cooling. Over both deforested and remote areas, the deforestation-induced surface climate changes are much stronger in scenario 2 than scenario 1; coarse-resolution data and models (such as in scenario 1) cannot represent the detailed spatial structure of deforestation and underestimate its impact on local and regional climates.

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Randal D. Koster, Qing Liu, Sarith P. P. Mahanama, and Rolf H. Reichle

Abstract

The assimilation of remotely sensed soil moisture information into a land surface model has been shown in past studies to contribute accuracy to the simulated hydrological variables. Remotely sensed data, however, can also be used to improve the model itself through the calibration of the model’s parameters, and this can also increase the accuracy of model products. Here, data provided by the Soil Moisture Active Passive (SMAP) satellite mission are applied to the land surface component of the NASA GEOS Earth system model using both data assimilation and model calibration in order to quantify the relative degrees to which each strategy improves the estimation of near-surface soil moisture and streamflow. The two approaches show significant complementarity in their ability to extract useful information from the SMAP data record. Data assimilation reduces the ubRMSE (the RMSE after removing the long-term bias) of soil moisture estimates and improves the timing of streamflow variations, whereas model calibration reduces the model biases in both soil moisture and streamflow. While both approaches lead to an improved timing of simulated soil moisture, these contributions are largely independent; joint use of both approaches provides the highest soil moisture simulation accuracy.

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Yingbin He, Dongmei Liu, Yanmin Yao, Qing Huang, Jianping Li, Youqi Chen, Shuqin Shi, Li Wan, Shikai Yu, and Deying Wang

Abstract

In this paper, an integrated indicator-based system is established to map the suitability of spring soybean cultivation in northeast China. The indicator system incorporates both biophysical and socioeconomic factors, including the effects of temperature, precipitation, and sunshine on the individual development stages of the spring soybean life cycle. Spatial estimates of crop suitability derived using this indicator system are also compared with spring soybean planting areas to identify locations where there is scope for structural adjustment in soybean farming. Results of this study indicate that northeast China is moderately suited to spring soybean cultivation. Areas classified as suitable, moderately suitable, and unsuitable for soybean cultivation, respectively, occupy approximately 9.09 × 104, 11.45 × 104, and 7.99 × 104 km2, accounting for 11.5%, 10.11%, and 14.49% of the total area of northeast China. The Songnen and Sanjiang Plains are identified as the most and least suitable places, respectively, for spring soybean growth. A comparative analysis indicates that the suitable, moderately suitable, and unsuitable areas account for 24.78%, 46.30%, and 28.92%, respectively, of the total area presently under soybean cultivation. The analysis suggests that soybean cultivation in Heilongjiang Province is generally unfavorable, with equivalent percentages of 15.39%, 51.70%, and 32.91%. Results suggest that agricultural structural adjustment may be required to encourage farmers to grow spring soybeans. It is anticipated that this study will provide a basis for follow-up studies on crop cultivation suitability.

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Rolf H. Reichle, Randal D. Koster, Gabriëlle J. M. De Lannoy, Barton A. Forman, Qing Liu, Sarith P. P. Mahanama, and Ally Touré

Abstract

The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, and runoff for 1979–present. This study introduces a supplemental and improved set of land surface hydrological fields (“MERRA-Land”) generated by rerunning a revised version of the land component of the MERRA system. Specifically, the MERRA-Land estimates benefit from corrections to the precipitation forcing with the Global Precipitation Climatology Project pentad product (version 2.1) and from revised parameter values in the rainfall interception model, changes that effectively correct for known limitations in the MERRA surface meteorological forcings. The skill (defined as the correlation coefficient of the anomaly time series) in land surface hydrological fields from MERRA and MERRA-Land is assessed here against observations and compared to the skill of the state-of-the-art ECMWF Re-Analysis-Interim (ERA-I). MERRA-Land and ERA-I root zone soil moisture skills (against in situ observations at 85 U.S. stations) are comparable and significantly greater than that of MERRA. Throughout the Northern Hemisphere, MERRA and MERRA-Land agree reasonably well with in situ snow depth measurements (from 583 stations) and with snow water equivalent from an independent analysis. Runoff skill (against naturalized stream flow observations from 18 U.S. basins) of MERRA and MERRA-Land is typically higher than that of ERA-I. With a few exceptions, the MERRA-Land data appear more accurate than the original MERRA estimates and are thus recommended for those interested in using MERRA output for land surface hydrological studies.

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Qing Liu, Rolf H. Reichle, Rajat Bindlish, Michael H. Cosh, Wade T. Crow, Richard de Jeu, Gabrielle J. M. De Lannoy, George J. Huffman, and Thomas J. Jackson

Abstract

The contributions of precipitation and soil moisture observations to soil moisture skill in a land data assimilation system are assessed. Relative to baseline estimates from the Modern Era Retrospective-analysis for Research and Applications (MERRA), the study investigates soil moisture skill derived from (i) model forcing corrections based on large-scale, gauge- and satellite-based precipitation observations and (ii) assimilation of surface soil moisture retrievals from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E). Soil moisture skill (defined as the anomaly time series correlation coefficient R) is assessed using in situ observations in the continental United States at 37 single-profile sites within the Soil Climate Analysis Network (SCAN) for which skillful AMSR-E retrievals are available and at 4 USDA Agricultural Research Service (“CalVal”) watersheds with high-quality distributed sensor networks that measure soil moisture at the scale of land model and satellite estimates. The average skill of AMSR-E retrievals is R = 0.42 versus SCAN and R = 0.55 versus CalVal measurements. The skill of MERRA surface and root-zone soil moisture is R = 0.43 and R = 0.47, respectively, versus SCAN measurements. MERRA surface moisture skill is R = 0.56 versus CalVal measurements. Adding information from precipitation observations increases (surface and root zone) soil moisture skills by ΔR ~ 0.06. Assimilating AMSR-E retrievals increases soil moisture skills by ΔR ~ 0.08. Adding information from both sources increases soil moisture skills by ΔR ~ 0.13, which demonstrates that precipitation corrections and assimilation of satellite soil moisture retrievals contribute important and largely independent amounts of information.

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Rolf H. Reichle, Qing Liu, Joseph V. Ardizzone, Wade T. Crow, Gabrielle J. M. De Lannoy, Jianzhi Dong, John S. Kimball, and Randal D. Koster

Abstract

Soil Moisture Active Passive (SMAP) mission L-band brightness temperature (Tb) observations are routinely assimilated into the Catchment land surface model to generate Level-4 soil moisture (L4_SM) estimates of global surface and root-zone soil moisture at 9-km, 3-hourly resolution with ~2.5-day latency. The Catchment model in the L4_SM algorithm is driven with 1/4°, hourly surface meteorological forcing data from the Goddard Earth Observing System (GEOS). Outside of Africa and the high latitudes, GEOS precipitation is corrected using Climate Prediction Center Unified (CPCU) gauge-based, 1/2°, daily precipitation. L4_SM soil moisture was previously shown to improve over land model-only estimates that use CPCU precipitation but no Tb assimilation (CPCU_SIM). Here, we additionally examine the skill of model-only (CTRL) and Tb assimilation-only (SMAP_DA) estimates derived without CPCU precipitation. Soil moisture is assessed versus in situ measurements in well-instrumented regions and globally through the instrumental variable (IV) method using independent soil moisture retrievals from the Advanced Scatterometer. At the in situ locations, SMAP_DA and CPCU_SIM have comparable soil moisture skill improvements relative to CTRL for the unbiased root-mean-square error (surface and root-zone) and correlation metrics (root-zone only). In the global average, SMAP Tb assimilation increases the surface soil moisture anomaly correlation by 0.10–0.11 compared to an increase of 0.02–0.03 from the CPCU-based precipitation corrections. The contrast is particularly strong in central Australia, where CPCU is known to have errors and observation-minus-forecast Tb residuals are larger when CPCU precipitation is used. Validation versus streamflow measurements in the contiguous United States reveals that CPCU precipitation provides most of the skill gained in L4_SM runoff estimates over CTRL.

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Xin Li, Guodong Cheng, Shaomin Liu, Qing Xiao, Mingguo Ma, Rui Jin, Tao Che, Qinhuo Liu, Weizhen Wang, Yuan Qi, Jianguang Wen, Hongyi Li, Gaofeng Zhu, Jianwen Guo, Youhua Ran, Shuoguo Wang, Zhongli Zhu, Jian Zhou, Xiaoli Hu, and Ziwei Xu

A major research plan entitled “Integrated research on the ecohydrological process of the Heihe River Basin” was launched by the National Natural Science Foundation of China in 2010. One of the key aims of this research plan is to establish a research platform that integrates observation, data management, and model simulation to foster twenty-first-century watershed science in China. Based on the diverse needs of interdisciplinary studies within this research plan, a program called the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) was implemented. The overall objective of HiWATER is to improve the observability of hydrological and ecological processes, to build a world-class watershed observing system, and to enhance the applicability of remote sensing in integrated ecohydrological studies and water resource management at the basin scale. This paper introduces the background, scientific objectives, and experimental design of HiWATER. The instrumental setting and airborne mission plans are also outlined. The highlights are the use of a flux observing matrix and an eco-hydrological wireless sensor network to capture multiscale heterogeneities and to address complex problems, such as heterogeneity, scaling, uncertainty, and closing water cycle at the watershed scale. HiWATER was formally initialized in May 2012 and will last four years until 2015. Data will be made available to the scientific community via the Environmental and Ecological Science Data Center for West China. International scientists are welcome to participate in the field campaign and use the data in their analyses.

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Fan Yang, Qing He, Jianping Huang, Mamtimin Ali, Xinghua Yang, Wen Huo, Chenglong Zhou, Xinchun Liu, Wenshou Wei, Caixia Cui, Minzhong Wang, Hongjun Li, Lianmei Yang, Hongsheng Zhang, Yuzhi Liu, Xinqian Zheng, Honglin Pan, Lili Jin, Han Zou, Libo Zhou, Yongqiang Liu, Jiantao Zhang, Lu Meng, Yu Wang, Xiaolin Qin, Yongjun Yao, Houyong Liu, Fumin Xue, and Wei Zheng

CAPSULE

The Desert Environment and Climate Observation Network (DECON) could promote collaborative research on desert dust-storms, boundary-layer and land-atmosphere interactions to better understand the status and role of the Taklimakan desert.

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