Zhenhua Di

Assessment of CMIP5 climate models and projected temperature changes over Northern Eurasia

Assessing the performance of climate models in surface air temperature (SAT) simulation andprojection have received increasing attention during the recent decades. This paper assesses theperformance of the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulatingintra-annual, annual and decadal temperature over Northern Eurasia from 1901 to 2005. Weevaluate the skill of different multi-model ensemble techniques and use the best technique toproject the future SAT changes under different emission scenarios. The results show that most ofthe general circulation models (GCMs) overestimate the annual mean SAT in Northern Eurasiaand the difference between the observation and the simulations primarily comes from the winterseason. Most of the GCMs can approximately capture the decadal SAT trend; however, theaccuracy of annual SAT simulation is relatively low. The correlation coefficient R between eachGCM simulation and the annual observation is in the range of 0.20 to 0.56. The Taylor diagramshows that the ensemble results generated by the simple model averaging (SMA), reliabilityensemble averaging (REA) and Bayesian model averaging (BMA) methods are superior to anysingle GCM output; and the decadal SAT change generated by SMA, REA and BMA are almostidentical during 1901–2005. Heuristically, the uncertainty of BMA simulation is the smallestamong the three multi-model ensemble simulations. The future SAT projection generated by theBMA shows that the SAT in Northern Eurasia will increase in the 21st century by around1.03°C/100yr, 3.11°C/100yr and 7.14°C/100yr under the RCP 2.6, RCP 4.5 and RCP 8.5scenarios, respectively; and the warming accelerates with the increasing latitude. In addition, thespring season contributes most to the decadal warming occurring under the RCP 2.6 and RCP4.5 scenarios, while the winter season contributes most to the decadal warming occurring underthe RCP 8.5 scenario. Generally, the uncertainty of the SAT projections increases with time inthe 21st century.S Online supplementary data available from stacks.iop.org/ERL/9/055007/mmediaKeywords: CMIP5, multi-model ensembles, Northern Eurasia, temperature

Would the ‘real’ observed dataset stand up? A critical examination of eight observed gridded climate datasets for China

This research compared and evaluated the spatio-temporal similarities and differences of eight widely used gridded datasets. The datasets include daily precipitation over East Asia (EA), the Climate Research Unit (CRU) product, the Global Precipitation Climatology Centre (GPCC) product, the University of Delaware (UDEL) product, Precipitation Reconstruction over Land (PREC/L), the Asian Precipitation Highly Resolved Observational (APHRO) product, the Institute of Atmospheric Physics (IAP) dataset from the Chinese Academy of Sciences, and the National Meteorological Information Center dataset from the China Meteorological Administration (CN05). The meteorological variables focus on surface air temperature (SAT) or precipitation (PR) in China. All datasets presented general agreement on the whole spatio-temporal scale, but some differences appeared for specific periods and regions. On a temporal scale, EA shows the highest amount of PR, while APHRO shows the lowest. CRU and UDEL show higher SAT than IAP or CN05. On a spatial scale, the most significant differences occur in western China for PR and SAT. For PR, the difference between EA and CRU is the largest. When compared with CN05, CRU shows higher SAT in the central and southern Northwest river drainage basin, UDEL exhibits higher SAT over the Southwest river drainage system, and IAP has lower SAT in the Tibetan Plateau. The differences in annual mean PR and SAT primarily come from summer and winter, respectively. Finally, potential factors impacting agreement among gridded climate datasets are discussed, including raw data sources, quality control (QC) schemes, orographic correction, and interpolation techniques. The implications and challenges of these results for climate research are also briefly addressed.

Assessing WRF model parameter sensitivity: A case study with 5 day summer precipitation forecasting in the Greater Beijing Area

A global sensitivity analysis method was used to identify the parameters of the Weather Research and Forecasting (WRF) model that exert the most influence on precipitation forecasting. Twenty-three adjustable parameters were selected from seven physical components of the WRF model. The sensitivity was evaluated based on skill scores calculated over nine 5 day precipitation forecasts during the summer seasons from 2008 to 2010 in the Greater Beijing Area in China. We found that eight parameters are more sensitive than others. Storm type seems to have no impact on the list of sensitive parameters but does influence the degree of sensitivity. We also examined the physical interpretation of parameter sensitivity. This analysis is useful for further optimization of the WRF model parameters to improve precipitation forecasting.

A GUI platform for uncertainty quantification of complex dynamical models

Uncertainty quantification (UQ) refers to quantitative characterization and reduction of uncertainties present in computer model simulations. It is widely used in engineering and geophysics fields to assess and predict the likelihood of various outcomes. This paper describes a UQ platform called UQ-PyL (Uncertainty Quantification Python Laboratory), a flexible software platform designed to quantify uncertainty of complex dynamical models. UQ-PyL integrates different kinds of UQ methods, including experimental design, statistical analysis, sensitivity analysis, surrogate modeling and parameter optimization. It is written in Python language and runs on all common operating systems. UQ-PyL has a graphical user interface that allows users to enter commands via pull-down menus. It is equipped with a model driver generator that allows any computer model to be linked with the software. We illustrate the different functions of UQ-PyL by applying it to the uncertainty analysis of the Sacramento Soil Moisture Accounting Model. We will also demonstrate that UQ-PyL can be applied to a wide range of applications. Uncertainty Quantification Python Laboratory (UQ-PyL) software platform is presented.The major steps in UQ analyses of complex dynamical models are explained.Various UQ-PyL functions are illustrated through a hydrological modeling application.

Evaluating Skill of Seasonal Precipitation and Temperature Predictions of NCEP CFSv2 Forecasts over 17 Hydroclimatic Regions in China

AbstractSeasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July–September (JAS)] to late autumn [October–December (OND)] and from winter [December–February (DJF)] to spring [March–May (MAM)]. The skill of CFSv2 precipitation forecasts is lo...

Multiobjective adaptive surrogate modeling‐based optimization for parameter estimation of large, complex geophysical models

Parameter specification is an important source of uncertainty in large, complex geophysical models. These models generally have multiple model outputs that require multiobjective optimization algorithms. Although such algorithms have long been available, they usually require a large number of model runs and are therefore computationally expensive for large, complex dynamic models. In this paper, a multiobjective adaptive surrogate modeling-based optimization (MO-ASMO) algorithm is introduced that aims to reduce computational cost while maintaining optimization effectiveness. Geophysical dynamic models usually have a prior parameterization scheme derived from the physical processes involved, and our goal is to improve all of the objectives by parameter calibration. In this study, we developed a method for directing the search processes toward the region that can improve all of the objectives simultaneously. We tested the MO-ASMO algorithm against NSGA-II and SUMO with 13 test functions and a land surface model - the Common Land Model (CoLM). The results demonstrated the effectiveness and efficiency of MO-ASMO.

Parametric sensitivity analysis of precipitation and temperature based on multi-uncertainty quantification methods in the Weather Research and Forecasting model

Sensitivity analysis (SA) has been widely used to screen out a small number of sensitive parameters for model outputs from all adjustable parameters in weather and climate models, helping to improve model predictions by tuning the parameters. However, most parametric SA studies have focused on a single SA method and a single model output evaluation function, which makes the screened sensitive parameters less comprehensive. In addition, qualitative SA methods are often used because simulations using complex weather and climate models are time-consuming. Unlike previous SA studies, this research has systematically evaluated the sensitivity of parameters that affect precipitation and temperature simulations in the Weather Research and Forecasting (WRF) model using both qualitative and quantitative global SA methods. In the SA studies, multiple model output evaluation functions were used to conduct various SA experiments for precipitation and temperature. The results showed that five parameters (P3, P5, P7, P10, and P16) had the greatest effect on precipitation simulation results and that two parameters (P7 and P10) had the greatest effect for temperature. Using quantitative SA, the two-way interactive effect between P7 and P10 was also found to be important, especially for precipitation. The microphysics scheme had more sensitive parameters for precipitation, and P10 (the multiplier for saturated soil water content) was the most sensitive parameter for both precipitation and temperature. From the ensemble simulations, preliminary results indicated that the precipitation and temperature simulation accuracies could be improved by tuning the respective sensitive parameter values, especially for simulations of moderate and heavy rain.