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X. S. Qin and Y. Lu

Abstract

Climate change is expected to lead to more frequent and intensive flooding problems for watersheds in the south part of China. This study presented a coupled Long Ashton Research Station Weather Generator (LARS-WG) and Semidistributed Land Use–Based Runoff Processes (SLURP) approach for flood frequency analysis and applied it to the Heshui watershed, China. LARS-WG, as a weather generator, was used to offer 46 sets of climate data from seven general circulation models (GCMs) under various emission scenarios (i.e., A1B, B1, and A2) over near-term and future periods (i.e., T 1, 2011–30; T 2, 2046–65; and T 3, 2080–99). SLURP is a continuous, spatially distributed hydrological model that uses parameters from physiographic data to simulate the hydrological cycle from precipitation to runoff. Flood frequency analysis based on Pearson type III distributions was followed to analyze statistics of annual peaks. The final results (from ensembles of multimodels and multiscenarios) indicated that the magnitudes of a 200-yr return flood for T 1, T 2, and T 3 would increase by 5.23%, 4.08%, and 12.92%, respectively, in comparison to the baseline level; those under the most extreme condition (i.e., worst scenario) would be 25.18%, 31.00%, and 44.46%, respectively. Various GCMs and emission scenarios suggested different results. But the ECHAM5/Max Planck Institute Ocean Model was found to give a more worrying intensification of flood risks and the Commonwealth Scientific and Industrial Research Organisation Mark, version 3.0, and the Community Climate System Model, version 3, were relatively conservative. The study results were useful in helping gain insight into the flood risks and its uncertainty under future climate change conditions for the Heshui watershed, and the proposed methodology is also applicable to many other watersheds in Southeast Asia with similar climatic conditions.

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The UNDP Climate Change Country Profiles

Improving the Accessibility of Observed and Projected Climate Information for Studies of Climate Change in Developing Countries

C. McSweeney, M. New, G. Lizcano, and X. Lu
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S. Lu, H. X. Lin, A. W. Heemink, G. Fu, and A. J. Segers

Abstract

Volcanic ash forecasting is a crucial tool in hazard assessment and operational volcano monitoring. Emission parameters such as plume height, total emission mass, and vertical distribution of the emission plume rate are essential and important in the implementation of volcanic ash models. Therefore, estimation of emission parameters using available observations through data assimilation could help to increase the accuracy of forecasts and provide reliable advisory information. This paper focuses on the use of satellite total-ash-column data in 4D-Var based assimilations. Experiments show that it is very difficult to estimate the vertical distribution of effective volcanic ash injection rates from satellite-observed ash columns using a standard 4D-Var assimilation approach. This paper addresses the ill-posed nature of the assimilation problem from the perspective of a spurious relationship. To reduce the influence of a spurious relationship created by a radiate observation operator, an adjoint-free trajectory-based 4D-Var assimilation method is proposed, which is more accurate to estimate the vertical profile of volcanic ash from volcanic eruptions. The method seeks the optimal vertical distribution of emission rates of a reformulated cost function that computes the total difference between simulated and observed ash columns. A 3D simplified aerosol transport model and synthetic satellite observations are used to compare the results of both the standard method and the new method.

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Y. Liu, Z. Liu, S. Zhang, R. Jacob, F. Lu, X. Rong, and S. Wu

Abstract

Parameter estimation provides a potentially powerful approach to reduce model bias for complex climate models. Here, in a twin experiment framework, the authors perform the first parameter estimation in a fully coupled ocean–atmosphere general circulation model using an ensemble coupled data assimilation system facilitated with parameter estimation. The authors first perform single-parameter estimation and then multiple-parameter estimation. In the case of the single-parameter estimation, the error of the parameter [solar penetration depth (SPD)] is reduced by over 90% after ~40 years of assimilation of the conventional observations of monthly sea surface temperature (SST) and salinity (SSS). The results of multiple-parameter estimation are less reliable than those of single-parameter estimation when only the monthly SST and SSS are assimilated. Assimilating additional observations of atmospheric data of temperature and wind improves the reliability of multiple-parameter estimation. The errors of the parameters are reduced by 90% in ~8 years of assimilation. Finally, the improved parameters also improve the model climatology. With the optimized parameters, the bias of the climatology of SST is reduced by ~90%. Overall, this study suggests the feasibility of ensemble-based parameter estimation in a fully coupled general circulation model.

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Y. Liu, Z. Liu, S. Zhang, X. Rong, R. Jacob, S. Wu, and F. Lu

Abstract

Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. For a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. Here, an adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.

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K.-M. Lau, V. Ramanathan, G.-X. Wu, Z. Li, S. C. Tsay, C. Hsu, R. Sikka, B. Holben, D. Lu, G. Tartari, M. Chin, P. Koudelova, H. Chen, Y. Ma, J. Huang, K. Taniguchi, and R. Zhang

Aerosol- and moonsoon-related droughts and floods are two of the most serious environmental hazards confronting more than 60% of the population of the world living in the Asian monsoon countries. In recent years, thanks to improved satellite and in situ observations, and better models, great strides have been made in aerosol and monsoon research, respectively. There is now a growing body of evidence suggesting that interaction of aerosol forcing with monsoon dynamics may alter the redistribution of energy in the atmosphere and at the Earth s surface, thereby influencing monsoon water cycle and climate. In this article, the authors describe the scientific rationale and challenges for an integrated approach to study the interactions between aerosol and monsoon water cycle dynamics. A Joint Aerosol-Monsoon Experiment (JAMEX) is proposed for 2007–11, with enhanced observations of the physical and chemical properties, sources and sinks, and long-range transport of aerosols, in conjunction with meteorological and oceanographic observations in the Indo-Pacific continental and oceanic regions. JAMEX will leverage on coordination among many ongoing and planned national research programs on aerosols and monsoons in China, India, Japan, Nepal, Italy, and the United States, as well as international research programs of the World Climate Research Program (WCRP) and the World Meteorological Organization (WMO).

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D. A. Knopf, K. R. Barry, T. A. Brubaker, L. G. Jahl, K. A. Jankowski, J. Li, Y. Lu, L. W. Monroe, K. A. Moore, F. A. Rivera-Adorno, K. A. Sauceda, Y. Shi, J. M. Tomlin, H. S. K. Vepuri, P. Wang, N. N. Lata, E. J. T. Levin, J. M. Creamean, T. C. J. Hill, S. China, P. A. Alpert, R. C. Moffet, N. Hiranuma, R. C. Sullivan, A. M. Fridlind, M. West, N. Riemer, A. Laskin, P. J. DeMott, and X. Liu

Abstract

Prediction of ice formation in clouds presents one of the grand challenges in the atmospheric sciences. Immersion freezing initiated by ice-nucleating particles (INPs) is the dominant pathway of primary ice crystal formation in mixed-phase clouds, where supercooled water droplets and ice crystals coexist, with important implications for the hydrological cycle and climate. However, derivation of INP number concentrations from an ambient aerosol population in cloud-resolving and climate models remains highly uncertain. We conducted an aerosol–ice formation closure pilot study using a field-observational approach to evaluate the predictive capability of immersion freezing INPs. The closure study relies on collocated measurements of the ambient size-resolved and single-particle composition and INP number concentrations. The acquired particle data serve as input in several immersion freezing parameterizations, which are employed in cloud-resolving and climate models, for prediction of INP number concentrations. We discuss in detail one closure case study in which a front passed through the measurement site, resulting in a change of ambient particle and INP populations. We achieved closure in some circumstances within uncertainties, but we emphasize the need for freezing parameterization of potentially missing INP types and evaluation of the choice of parameterization to be employed. Overall, this closure pilot study aims to assess the level of parameter details and measurement strategies needed to achieve aerosol–ice formation closure. The closure approach is designed to accurately guide immersion freezing schemes in models, and ultimately identify the leading causes for climate model bias in INP predictions.

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