The influence of synoptic airflow on UK daily precipitation extremes. Part II: regional climate model and E-OBS data validation

We investigate how well the variability of extreme daily precipitation events across the United Kingdom is represented in a set of regional climate models and the E-OBS gridded data set. Instead of simply evaluating the climatologies of extreme precipitation measures, we develop an approach to validate the representation of physical mechanisms controlling extreme precipitation variability. In part I of this study we applied a statistical model to investigate the influence of the synoptic scale atmospheric circulation on extreme precipitation using observational rain gauge data. More specifically, airflow strength, direction and vorticity are used as predictors for the parameters of the generalised extreme value (GEV) distribution of local precipitation extremes. Here we employ this statistical model for our validation study. In a first step, the statistical model is calibrated against a gridded precipitation data set provided by the UK Met Office. In a second step, the same statistical model is calibrated against 14 ERA40 driven 25?km resolution RCMs from the ENSEMBLES project and the E-OBS gridded data set. Validation indices describing relevant physical mechanisms are derived from the statistical models for observations and RCMs and are compared using pattern standard deviation, pattern correlation and centered pattern root mean squared error as validation measures. The results for the different RCMs and E-OBS are visualised using Taylor diagrams. We show that the RCMs adequately simulate moderately extreme precipitation and the influence of airflow strength and vorticity on precipitation extremes, but show deficits in representing the influence of airflow direction. Also very rare extremes are misrepresented, but this result is afflicted with a high uncertainty. E-OBS shows considerable biases, in particular in regions of sparse data. The proposed approach might be used to validate other physical relationships in regional as well as global climate models.     

Coupling statistical and dynamical methods for spatial downscaling of precipitation

The resolution of General Circulation Models (GCMs) is too coarse for climate change impact studies at the catchment or site-specific scales. To overcome this problem, both dynamical and statistical downscaling methods have been developed. Each downscaling method has its advantages and drawbacks, which have been described in great detail in the literature. This paper evaluates the improvement in statistical downscaling (SD) predictive power when using predictors from a Regional Climate Model (RCM) over a GCM for downscaling site-specific precipitation. Our approach uses mixed downscaling, combining both dynamic and statistical methods. Precipitation, a critical element of hydrology studies that is also much more difficult to downscale than temperature, is the only variable evaluated in this study. The SD method selected here uses a stepwise linear regression approach for precipitation quantity and occurrence (similar to the well-known Statistical Downscaling Model (SDSM) and called SDSM-like herein). In addition, a discriminant analysis (DA) was tested to generate precipitation occurrence, and a weather typing approach was used to derive statistical relationships based on weather types, and not only on a seasonal basis as is usually done. The existing data record was separated into a calibration and validation periods. To compare the relative efficiency of the SD approaches, relationships were derived at the same sites using the same predictors at a 300km scale (the National Center for Environmental Prediction (NCEP) reanalysis) and at a 45km scale with data from the limited-area Canadian Regional Climate Model (CRCM) driven by NCEP data at its boundaries. Predictably, using CRCM variables as predictors rather than NCEP data resulted in a much-improved explained variance for precipitation, although it was always less than 50?% overall. For precipitation occurrence, the SDSM-like model slightly overestimated the frequencies of wet and dry periods, while these were well-replicated by the DA-based model. Both the SDSM-like and DA-based models reproduced the percentage of wet days, but the wet and dry statuses for each day were poorly downscaled by both approaches. Overall, precipitation occurrence downscaled by the DA-based model was much better than that predicted by the SDSM-like model. Despite the added complexity, the weather typing approach was not better at downscaling precipitation than approaches without classification. Overall, despite significant improvements in precipitation occurrence prediction by the DA scheme, and even going to finer scales predictors, the SD approach tested here still explained less than 50?% of the total precipitation variance. While going to even smaller scale predictors (10–15?km) might improve results even more, such smaller scales would basically transform the direct outputs of climate models into impact models, thus negating the need for statistical downscaling approaches.     

On the detection of clouds

Abstract

Short‐ and long‐wave sky radiances measured with an all‐sky automatic recording radiometer are used to infer the sky cloud‐cover conditions at 10‐min intervals (night) and 20‐min intervals (day). During the day a simple net short‐wave detector provides supplementary data and can be employed as a clear‐sky indicator. This system of cloud detection is controlled by a PET microcomputer and provides a basis for the automatic computer programmed estimation of cloud cover.     

Covariations in oceanic dimethyl sulfide,its oxidation products and rain acidity at Amsterdam Island in the Southern Indian Ocean

Simultaneous measurements of rain acidity and dimethyl sulfide (DMS) at the ocean surface and in the atmosphere were performed at Amsterdam Island over a 4 year period. During the last 2 years, measurements of sulfur dioxide (SO₂) in the atmosphere and of methane sulfonic acid (MSA) and non-sea-salt-sulfate (nss-SO₄ ^2-) in rainwater were also performed. Covariations are observed between the oceanic and atmospheric DMS concentrations, atmospheric SO₂ concentrations, wet deposition of MSA, nss-SO₄ ^2-, and rain acidity. A comparable summer to winter ratio of DMS and SO₂ in the atmosphere and MSA in precipitation were also observed. From the chemical composition of precipitation we estimate that DMS oxidation products contribute approximately 40% of the rain acidity. If we consider the acidity in excess, then DMS oxidation products contribute about 55%.     

Estimating Sea-Level Allowances for Atlantic Canada using the Fifth Assessment Report of the IPCC

Abstract

Sea-level allowances at 22 tide-gauge sites along the east coast of Canada are determined based on projections of regional sea-level rise for the Representative Concentration Pathway 8.5 (RCP8.5) from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) and on the statistics of historical tides and storm surges (storm tides). The allowances, which may be used for coastal infrastructure planning, increase with time during the twenty-first century through a combination of mean sea-level rise and the increased uncertainty of future projections with time. The allowances show significant spatial variation, mainly a consequence of strong regionally varying relative sea-level change as a result of glacial isostatic adjustment (GIA). A methodology is described for replacement of the GIA component of the AR5 projection with global positioning system (GPS) measurements of vertical crustal motion; this significantly decreases allowances in regions where the uncertainty of the GIA models is large. For RCP8.5 with GPS data incorporated and for the 1995–2100 period, the sea-level allowances range from about 0.5?m along the north shore of the Gulf of St. Lawrence to more than 1?m along the coast of Nova Scotia and southern Newfoundland.     

Terrain-influenced local wind forecasting for the Sasebo typhoon haven: Improved empirical techniques

Although the Sasebo, Japan harbor is usually a “typhoon haven” from tropical cyclone (TC) winds due to terrain-blocking effects, in rare cases damaging winds occur that may be attributed to terrain channeling. An empirical parametric model technique is developed and tested that includes consideration of the TC wind structure, land frictional effects, and terrain influences affecting the maximum wind speeds in the harbor when TCs pass within 200 nautical miles of Sasebo. The terrain influence is represented by two sets of wind direction-dependent acceleration factors. The first set, which is directly from the ratio of the local wind to the adjusted parametric wind for TCs passages during 2003–2010, provides mean values that represent the terrain blocking and channeling effects, but the variability with wind direction may be suspect. The second set derived from a large sample of reanalysis winds not limited to TCs has better variability properties, but is not easily related to just the TC passages. A new nomogram modified to include TC wind structure has higher estimates of Sasebo sustained winds for some TC tracks that may be related to terrain influences, but is limited due to the number of TC structure estimates in the developmental sample. These empirical models have the advantage of ease and low cost for future use in also estimating the combined uncertainty in the local winds in Sasebo harbor due to TCs.     

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec,Canada

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.     

Source identification of PM2.5 particles measured in Gwangju, Korea

The UNMIX and Chemical Mass Balance (CMB) receptor models were used to investigate sources of PM_2.5 aerosols measured between March 2001 and February 2002 in Gwangju, Korea. Measurements of PM_2.5 particles were used for the analysis of carbonaceous species (organic (OC) and elemental carbon (EC)) using the thermal manganese dioxide oxidation (TMO) method, the investigation of seven ionic species using ion chromatography (IC), and the analysis of twenty-four metal species using Inductively Coupled Plasma (ICP)-Atomic Emission Spectrometry (AES)/ICP-Mass Spectrometry (MS). According to annual average PM_2.5 source apportionment results obtained from CMB calculations, diesel vehicle exhaust was the major contributor, accounting for 33.4% of the measured PM_2.5 mass (21.5 μg m^− 3), followed by secondary sulfate (14.6%), meat cooking (11.7%), secondary organic carbon (8.9%), secondary nitrate (7.6%), urban dust (5.5%), Asian dust (4.4%), biomass burning (2.8%), sea salt (2.7%), residual oil combustion (2.6%), gasoline vehicle exhaust (1.9%), automobile lead (0.5%), and components of unknown sources (3.4%). Seven PM_2.5 sources including diesel vehicles (29.6%), secondary sulfate (17.4%), biomass burning (14.7%), secondary nitrate (12.6%), gasoline vehicles (12.4%), secondary organic carbon (5.8%) and Asian dust (1.9%) were identified from the UNMIX analysis. The annual average source apportionment results from the two models are compared and the reasons for differences are qualitatively discussed for better understanding of PM_2.5 sources.Additionally, the impact of air mass pathways on the PM_2.5 mass was evaluated using air mass trajectories calculated with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) backward trajectory model. Source contributions to PM_2.5 collected during the four air mass patterns and two event periods were calculated with the CMB model and analyzed. Results of source apportionment revealed that the contribution of diesel traffic exhaust (47.0%) in stagnant conditions (S) was much higher than the average contribution of diesel vehicle exhaust (33.4%) during the sampling period. During Asian dust (AD) periods when the air mass passed over the Korean peninsula, Asian dust and secondary organic carbon accounted for 25.2 and 23.0% of the PM_2.5 mass, respectively, whereas Asian dust contributed only 10.8% to the PM_2.5 mass during the AD event when the air mass passed over the Yellow Sea. The contribution of biomass burning to the PM_2.5 mass during the biomass burning (BB) event equaled 63.8%.     

高分辨率WRF三维变分同化在北京地区降水预报中的应用

为迎接2008年北京奥运会,改进北京地区的天气预报,建立了一个基于三重嵌套区域(27/9/3 km)的WRF三维变分同化(WRFVar)和WRF模式的高分辨率快速更新循环同化预报(Rapid-Up-date Cycle)系统,并针对2006年8月1日发生在北京地区的强对流天气进行了一系列数值试验,结果表明:高分辨率的快速更新循环系统很好地预报出了此次强降水过程;在WRF三维变分同化里调节背景场误差和观测误差,提高了降水预报的效果;插值得出的3 km背景场误差可以作为一个合理的近似在3 km分辨率的WRFVar中使用,用户可以不必积累高分辨率的预报场去计算背景场误差,从而节省大量计算资源。3 h频次的RUC已经能满足预报要求,更高频次(1 h)的RUC并没有导致预报的进一步提高。     

A STUDY ON THE ORTHOCLASE CRYSTALS AND TWINS OF CHIAOCHENG,SHANSI

The specimens studied were collected by Mr.C.T.Wang (王景尊)from Yuehfangkou (月房溝),a locality at about 1500 m.SW of Shangpei-chuan (上白泉) village,the latter at about 45 km west of Chiaocheng city.