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Assessing the performance of climate models in surface air temperature (SAT) simulation and projection have received increasing attention during the recent decades. This paper assesses the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating intra-annual, annual and decadal temperature over Northern Eurasia from 1901 to 2005. We evaluate the skill of different multi-model ensemble techniques and use the best technique to project the future SAT changes under different emission scenarios. The results show that most of the general circulation models (GCMs) overestimate the annual mean SAT in Northern Eurasia and the difference between the observation and the simulations primarily comes from the winter season. Most of the GCMs can approximately capture the decadal SAT trend; however, the accuracy of annual SAT simulation is relatively low. The correlation coefficient R between each GCM simulation and the annual observation is in the range of 0.20 to 0.56. The Taylor diagram shows that the ensemble results generated by the simple model averaging (SMA), reliability ensemble averaging (REA) and Bayesian model averaging (BMA) methods are superior to any single GCM output; and the decadal SAT change generated by SMA, REA and BMA are almost identical during 1901-2005. Heuristically, the uncertainty of BMA simulation is the smallest among the three multi-model ensemble simulations. The future SAT projection generated by the BMA shows that the SAT in Northern Eurasia will increase in the 21st century by around 1.03°C/100 yr, 3.11°C/100 yr and 7.14°C/100 yr under the RCP 2.6, RCP 4.5 and RCP 8.5 scenarios, respectively; and the warming accelerates with the increasing latitude. In addition, the spring season contributes most to the decadal warming occurring under the RCP 2.6 and RCP 4.5 scenarios, while the winter season contributes most to the decadal warming occurring under the RCP 8.5 scenario. Generally, the uncertainty of the SAT projections increases with time in the 21st century.

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Computer simulation models have been widely used to generate hydrometeorological forecasts. As the raw forecasts contain uncertainties arising from various sources, including model inputs and outputs, model initial and boundary conditions, model structure, and model parameters, it is necessary to apply statistical postprocessing methods to quantify and reduce those uncertainties. Different postprocessing methods have been developed for meteorological forecasts (e.g., precipitation) and for hydrological forecasts (e.g., streamflow) due to their different statistical properties. In this paper, we conduct a comprehensive review of the commonly used statistical postprocessing methods for both meteorological and hydrological forecasts. Moreover, methods to generate ensemble members that maintain the observed spatiotemporal and intervariable dependency are reviewed. Finally, some perspectives on the further development of statistical postprocessing methods for hydrometeorological ensemble forecasting are provided.

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.

The specification of model parameters in numerical weather prediction (NWP) models has great influence on model performance. However, how to specify model parameters properly is not a trivial task because a typical NWP model like the Weather Research and Forecasting (WRF) model contains many model parameters and many model outputs. This article presents the results of an investigation into the sensitivities of different WRF model outputs to the specification of its model parameters. Using a global sensitivity analysis method, the sensitivities are evaluated for surface meteorological variables such as precipitation, surface air temperature, humidity and wind speed, as well as for atmospheric variables such as total precipitable water, cloud cover, boundary‐layer height and outgoing long‐wave radiation at the top of the atmosphere, all simulated by the WRF model using different model parameters. The goal of this study is to identify the parameters that exert most influence on the skill of short‐range meteorological forecasts. The study was performed over the Greater Beijing Region of China. A total of 23 adjustable parameters from seven different physical parametrization schemes were considered. The results indicate that parameter sensitivities vary with different model outputs. However, some of the 23 model parameters considered are shown to be sensitive to all model outputs evaluated, while other parameters may be sensitive to a particular output. The sensitivity results from this research are a basis for further optimizations of the WRF model parameters.

Abstract. Parameter specification usually has significant influence on the performance of land surface models (LSMs). However, estimating the parameters properly is a challenging task due to the following reasons: (1) LSMs usually have too many adjustable parameters (20 to 100 or even more), leading to the curse of dimensionality in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) Regional LSMs are expensive to run, while conventional multi-objective optimization methods need a large number of model runs (typically ∼ 10 5 -10 6 ). It makes parameter optimization computationally prohibitive. An uncertainty quantification framework was developed to meet the aforementioned challenges, which include the following steps: (1) using parameter screening to reduce the number of adjustable parameters, (2) using surrogate models to emulate the responses of dynamic models to the variation of adjustable parameters, (3) using an adaptive strategy to improve the efficiency of surrogate modeling-based optimization; (4) using a weighting function to transfer multi-objective optimization to single-objective optimization. In this study, we demonstrate the uncertainty quantification framework on a single column application of a LSM -the Common Land Model (CoLM), and evaluate the effectiveness and efficiency of the proposed framework. The result indicate that this framework can efficiently achieve optimal parameters in a more effective way. Moreover, this result implies the possibility of calibrating other large complex dynamic models, such as regionalscale LSMs, atmospheric models and climate models.