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The parameterization of physical processes is one of the critical elements to properly simulate the regional climate over eastern China. It is essential to conduct detailed analyses on the effect of physical parameterization schemes on regional climate simulation, to provide more reliable regional climate change information. In this paper, we evaluate the 25-year (1983-2007) summer monsoon climate characteristics of precipitation and surface air temperature by using the regional spectral model (RSM) with different physical schemes. The ensemble results using the reliability ensemble averaging (REA) method are also assessed. The result shows that the RSM model has the capacity to reproduce the spatial patterns, the variations, and the temporal tendency of surface air temperature and precipitation over eastern China. And it tends to predict better climatology characteristics over the Yangtze River basin and the South China. The impact of different physical schemes on RSM simulations is also investigated. Generally, the CLD3 cloud water prediction scheme tends to produce larger precipitation because of its overestimation of the low-level moisture. The systematic biases derived from the KF2 cumulus scheme are larger than those from the RAS scheme. The scale-selective bias correction (SSBC) method improves the simulation of the temporal and spatial characteristics of surface air temperature and precipitation and advances the circulation simulation capacity. The REA ensemble results show significant improvement in simulating temperature and precipitation distribution, which have much higher correlation coefficient and lower root mean square error. The REA result of selected experiments is better than that of nonselected experiments, indicating the necessity of choosing better ensemble samples for ensemble. [ABSTRACT FROM AUTHOR]

Abstract The paper summarizes an end-to-end activity connecting the global climate modeling enterprise with users of climate information in Alaska. The effort included retrieval of the requisite observational datasets and model output, a model evaluation and selection procedure, the actual downscaling by the delta method with its inherent bias-adjustment, and the provision of products to a range of users through visualization software that empowers users to explore the downscaled output and its sensitivities. An additional software tool enables users to examine skill metrics and relative rankings of 21 global models for Alaska and six other domains in the Northern Hemisphere. The downscaled temperatures and precipitation are made available as calendar-month decadal means under three different greenhouse forcing scenarios through 2100 for more than 4000 communities in Alaska and western Canada. The visualization package displays the uncertainties inherent in the multi-model ensemble projections. These uncertainties are often larger than the projected changes. Highlights • Downscaled climate data for more than 4000 communities are viewable by users. • A software tool allows users to evaluate skill 22 climate models for eight regions. • Across-model uncertainties often exceed changes of temperature and precipitation. • Projected warming and wetting are largest in winter and summer, respectively. • Emission scenario is a key determinant of future warming of Arctic communities. [ABSTRACT FROM AUTHOR]

The article discusses research which proposes an approach in using the Reanalysis data, applying statistical downscaling and estimating air temperature, relative humidity and precipitation in the southwest of Buenos Aires province in Argentina.

Air temperature and precipitation data from three high mountainous Bulgarian stations were used as well as outputs from nine regional climate models (RCMs) for air temperatures and eight RCMs for precipitation. Data from 40-year experiments driven by the ERA-40 reanalysis (temporal coverage from 1961 to 2000) of the ECMWF were employed for calibration of statistical downscaling models. Statistical methods were used in this research-Spearman and Pearson correlation, Mann-Whitney test, multiple linear regression, generalized linear models, etc. Projections, based on SRES A1B scenario and RCMs driven by four different GCMs, were made for the following future 30-years periods: 2005-2034, 2035-2064, and 2065-2094. RCMs ETHZ-CLM, DMI-ARPEGE-HIRHAM, HadRM3Q0, and HadRM3Q16 show the best correlation with observed air temperatures in mountain stations. RCMs ETHZ-CLM, HadRM3Q16, and RACMO have the best relationship with precipitation. Constructed monthly multiple linear regression models describe well enough air temperatures throughout the entire year. Monthly GLMs describe better precipitation in January, March, August, and September, as well as peak Musala and Cherni vrah precipitation. Projections for future 30-year periods indicate that air temperatures are expected to rise by 2065-2094 at all of the three investigated stations with 2.8 to 3.2 °C. This increase is mainly due to the summer months. Annual precipitation amounts are expected to decrease by the period 2065-2094 at all the three stations with about 7 to 17 %. Some increase of annual precipitation amounts in the beginning of twenty-first century against the general negative trend could happen at Musala station, which is probably due to the increase in frequency of liquid precipitation. [ABSTRACT FROM AUTHOR]

High-resolution data are needed in order to assess potential impacts of extreme events on infrastructure in the mid-latitudes. Dynamical downscaling offers one way to obtain this information. However, prior to implementation in any impacts assessment scheme, model output must be validated and determined fitfor- purpose. This study presents the results from two 8-km resolution perfect boundary experiments over Scandinavia. Two different regional climate models were initialised and driven with ERA interim reanalysis from 1990 to 2010. Reference data come from both gridded products and point-based station observations. In addition to the canonical variables of daily precipitation and temperature, winds were also investigated. The models exhibit systematic cold and wet biases on seasonal time scales ( 1K and -50-100%, respectively). However, frequency-based skill scores for daily precipitation and temperature are high, indicating that the distributions of these variables are generally well captured. Wind speeds over the North and Norwegian Seas were simulated more realistically in the models than in the ERA interim reanalysis. However, most importantly, for impacts assessments, the models should be capable of capturing the timing, intensity and location of short-duration extreme events, in particular precipitation. In this respect, both models outperform the reanalysis over the city of Copenhagen, where recent pluvial floods led to costly damages to infrastructure. [ABSTRACT FROM AUTHOR]

Precipitation extremes and small-scale variability are essential drivers in many climate change impact studies. However, the spatial resolution currently achieved by global climate models (GCMs) and regional climate models (RCMs) is still insufficient to correctly identify the fine structure of precipitation intensity fields. In the absence of a proper physically based representation, this scale gap can be at least temporarily bridged by adopting a stochastic rainfall downscaling technique. In this work, a precipitation downscaling chain is introduced where the global 40-yr ECMWF Re-Analysis (ERA-40) (at about 120-km resolution) is dynamically downscaled using the Protheus RCM at 30-km resolution. The RCM precipitation is then further downscaled using a stochastic downscaling technique, the Rainfall Filtered Autoregressive Model (RainFARM), which has been extended for application to long climate simulations. The application of the stochastic downscaling technique directly to the larger-scale reanalysis field at about 120-km resolution is also discussed. To assess the ability of this approach in reproducing the main statistical properties of precipitation, the downscaled model results are compared with the precipitation data provided by a dense network of 122 rain gauges in northwestern Italy, in the time period from 1958 to 2001. The high-resolution precipitation fields obtained by stochastically downscaling the RCM outputs reproduce well the seasonality and amplitude distribution of the observed precipitation during most of the year, including extreme events and variance. In addition, the RainFARM outputs compare more favorably to observations when the procedure is applied to the RCM output rather than to the global reanalyses, highlighting the added value of reaching high enough resolution with a dynamical model. [ABSTRACT FROM AUTHOR]

This study describes a two-step analogue statistical downscaling method for daily temperature and precipitation. The first step is an analogue approach: the ' n' days most similar to the day to be downscaled are selected. In the second step, a multiple regression analysis using the ' n' most analogous days is performed for temperature, whereas for precipitation, the probability distribution of the ' n' analogous days is used to define the amount of precipitation. Verification of this method has been carried out for the Spanish Iberian Peninsula and the Balearic Islands. Results show good performance for temperature (BIAS close to 0.1 °C and mean absolute errors around 1.9 °C) and an acceptable skill for precipitation (reasonably low BIAS except in autumn with a mean of −18 %, mean absolute error lower than for a reference simulation, i.e. persistence and a well-simulated probability distribution according to two non-parametric tests of similarity). [ABSTRACT FROM AUTHOR]

In this study, we assessed the performance of 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) general climate models (GCMs) for simulating the observed temperature over the Lower Mekong Basin (LMB) in 1961–2004. An improved score-based method was used to rank the performance of the GCMs over the LMB. Two methods of multi-model ensemble (MME), sub-ensemble from the top 25% ranked GCMs and full ensemble from the entire GCMs, were calculated using arithmetic mean (AM) method and downscaled using the Delta method to project future temperature change during two future time periods, the near future (2006–2049) and the far future (2050–2093), under representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5 scenarios) over the LMB. The improved score-based method combining multiple criteria showed a robust assessment of the GCMs performance over the LMB, which can provide good information for projecting future temperature change. The results showed a significant increase in temperature over the LMB under the two ensembles. However, there were differences in the magnitudes of the future temperature increase between the two ensemble methods, with a higher mean annual temperature increase from full ensemble and sub-ensemble at 1.26 °C (1.09 °C), 1.90 °C (1.70 °C), and 2.97 °C (2.78 °C) during 2050–2093 under the RCP2.6, RCP4.5, and RCP8.5 scenarios compared to the values at 0.93 °C (0.87 °C), 0.99 °C (0.95 °C), and 1.09 °C (1.06 °C) during 2006–2049, respectively, relative to the reference time period of 1961–2004. In the future (2006–2093), the temperature is likely to increase at 0.06 °C, 0.18 °C, and 0.39 °C decade−1 under the RCP2.6, RCP4.5, and RCP8.5 scenarios by the sub-ensemble, while a higher temperature increase at 0.08 °C, 0.20 °C, and 0.42 °C was found by the full ensemble over the LMB, relative to the reference time period of 1961–2004. On the whole, the higher warming mainly occurred in the northern and central areas over the LMB, while the lower warming mainly occurred in the southeast and the southwest, especially under the RCP4.5 and RCP8.5 scenarios, with the warming increased with increasing RCP for both ensembles. Moreover, in order to reduce the uncertainty of temperature projection in further studies in the LMB, multiple methods of GCMs ensemble should be considered and compared. [ABSTRACT FROM AUTHOR]