Analysis of photosynthetically active radiation and applied parameterization model for estimating of PAR in the North China Plain

As photosynthetically active radiation (PAR) variability and PAR estimating methods play an important role in climate change and ecological process research, PAR variation trends and broadband global solar radiation (R _s ) ratios (PAR/R _s ) in the North China Plain (NCP) are examined using in situ PAR and R _s observed data for 2005 to 2011. The annual average PAR value found in the NCP is 22.9 mol m^?2 d^?1. The highest and lowest values were recorded at Changwu and Luancheng sites, respectively. The highest PAR/R _s value was found in Jiaozhouwan due to large water vapor volumes present in this area. PAR/R _s levels have increased in the NCP due to a decrease in fine aerosols and increase in water vapor concentration. From these analysis results, a parameterization model that can be applied to all sky conditions was checked. Empirical estimation model comparisons for obtaining PAR values indicate that model was least accurate when R _s was used independently. When the model included R _s, the clearness index (K _s) and the solar zenith angle, the model estimated PAR values with acceptable accuracy. A parameterization model was constructed by considering K _s and attenuation factors of PAR under clear weather conditions (ρ _clear). The improved parameterization model more accurately predicts values for local sites and for various observation sites.     

大气复合污染是灰霾内因

使用北京区域环境气象数值预报系统2015年11、12月和2016年1、5、6、7月PM_2.5浓度的模式模拟结果与同期有限观测资料进行相关分析,探索在站点稀少情况下如何确定最优测站分布策略的方法。通过模拟结果与有限观测资料的结合,使用观测资料筛选比较好的模拟结果,可以得到有较高空间分辨率和一定时间序列长度的资料, 进而可以进行站点代表性分析确定最优站点分布。基于该方法分析了北京地区气溶胶观测的站点分布策略。北京地区PM_2.5浓度呈南高北低的分布, 另一个显著的特点是有沿地形分布的特点。冬季与夏季明显的区别是,夏季PM_2.5浓度变化比较大的区域在西南部,而冬季该大值区沿地形向东北扩展比较明显。总体上,观测站点分布宜在北京西南部采用较小的站距,向北向东站点距离逐步增大。针对冬季观测站点分布的显著特征是西部沿山一带需要较高的站点密度。要获得相同质量的空间分布特征,冬季站点总体密度比夏季高。

     

Iap general circulation models: a first step towards developing a local area model for weather prediction in nigeria

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1961-2009年大同市太阳辐射变化特征及与气象要素的关系

在全球气候变暖背景下,地面接收太阳辐射总量发生改变,探讨自然和人为因素对其影响成为热点。利用线性趋势分析、M-K突变检验和小波分析等方法,分析了1961-2009年大同市地面接收太阳辐射的变化特征,验证地面接收太阳辐射的突变年份和周期变化及未来变化趋势,探讨太阳辐射与相关气象要素的关系。结果表明：近49 a大同市地面接收太阳辐射年平均值为5617.28 MJ/m²,整体呈现明显下降趋势,但2000年后又缓慢上升;大同市地面接收太阳总辐射在1975年发生突变;太阳总辐射的振荡周期为9a,现正处于太阳辐射偏高年份;日照时数逐年递减,地面接收太阳辐射减少,二者呈现显著正相关;降水量在波动中缓慢减少,而地面接收太阳辐射总量减少,二者呈现一定正相关;云量变化逐年减少,但与地面接收太阳辐射相关性不明显,气候的自然因素变化与地面接收太阳辐射的相关性较小。     

A comparison among olive flowering trends in different Mediterranean areas (south-central Italy) in relation to meteorological variations

Phenological observations of the anthesic phases of olive flowering in a central Mediterranean area were recorded over a 9-year period. The aim of this research was to compare the flowering dates in relationship to the meteorological changes. Pollen emission from anthers was monitored by remote instrumentation placed directly in olive groves and phenological data regarding daily pollen concentrations (pollen/m³) were recorded using a pollen monitoring methodology. The rhythm of the phenological phases emerged as dependent on the meteorological trend of the spring forcing temperature. Generally, the phenomenon of pollen emission occurred progressively earlier prior to 2001, while in the following 5 years, the trend seemed to be inverted, showing a progressive delay of flowering. The spring quarterly mean temperature trends registered by GISS data in Europe confirmed the presence of diverse meteorological behavior during the study period, probably causing the biological divergences that were monitored. The principal result of the present contribution is to evidence the relativity of empirical investigations and observations considering different time intervals. This is due to the partial, brief series (9 years) of flowering dates which have to be considered as part of a longer series (26 years) in order to have a complete vision of the true phenomenon.     

Quantifying how climatic factors affect variation in plant disease severity: A general method using a new way to analyze meteorological data

A general method is presented for analyzing how climatic conditions affect plant disease severity. An example of its application is given for the analysis of stripe rust (caused by Puccinia striiformis) data on winter wheat cultivar Gaines and climatic data collected at Pullman, WA. for 1968–1986. A computer program WINDOW was written to identify the climatic factors most highly correlated with disease. This program is designed to utilize meteorological data for an entire growing season of a crop as well as to include climatic conditions preceding planting. This program uses an iterative process to examine variable-length segments of meteorological data in a more exhaustive analysis than previously possible. Climatic factors considered include: mean maximum, minimum, and average temperature; total and frequency of precipitation; consecutive days with and without precipitation; accumulation of negative and positive degree days; and number of days with extreme temperature events. Variables that were highly correlated with disease were the basis for regression models that were developed to predict disease severity index for each of the three cultivars. Two- and three-variable models explained, respectively, 75 and 76% of the variation in disease from year to year. Predictions (which could be made early enough in the growing season to allow application of chemical control) were evaluated on the basis of whether years with severe disease were accurately predicted. Models were validated using Allen's PRESS statistic and by application to new data. The method is potentially applicable to studies of how climatic conditions affect the populations or productivity of other types of organisms.This research was supported by a National Science Foundation Grant (ATM 85-03115), Climate Dynamics Program, Division of Atmospheric Sciences.     

Productivity response of climax temperate forests to elevated temperature and carbon dioxide: a north american comparison between two global models

We assess the appropriateness of using regression- and process-based approaches for predicting biogeochemical responses of ecosystems to global change. We applied a regression-based model, the Osnabruck Model (OBM), and a process-based model, the Terrestrial Ecosystem Model (TEM), to the historical range of temperate forests in North America in a factorial experiment with three levels of temperature (+0 °C, +2 °C, and +5 °C) and two levels of CO₂ (350 ppmv and 700 ppmv) at a spatial resolution of 0.5° latitude by 0.5° longitude. For contemporary climate (+0 °C, 350 ppmv), OBM and TEM estimate the total net primary productivity (NPP) for temperate forests in North America to be 2.250 and 2.602 × 10¹⁵ g C ? yr^?1, respectively. Although the continental predictions for contemporary climate are similar, the responses of NPP to altered climates qualitatively differ; at +0 °C and 700 ppmv CO₂, OBM and TEM predict median increases in NPP of 12.5% and 2.5%, respectively. The response of NPP to elevated temperature agrees most between the models in northern areas of moist temperate forest, but disagrees in southern areas and in regions of dry temperate forest. In all regions, the response to CO₂ is qualitatively different between the models. These differences occur, in part, because TEM includes known feedbacks between temperature and ecosystem processes that affect N availability, photosynthesis, respiration, and soil moisture. Also, it may not be appropriate to extrapolate regression-based models for climatic conditions that are not now experienced by ecosystems. The results of this study suggest that the process-based approach is able to progress beyond the limitations of the regression-based approach for predicting biogeochemical responses to global change.     

Observational estimate of climate sensitivity from changes in the rate of ocean heat uptake and comparison to CMIP5 models

Climate sensitivity is estimated based on 0–2,000 m ocean heat content and surface temperature observations from the second half of the 20th century and first decade of the 21st century, using a simple energy balance model and the change in the rate of ocean heat uptake to determine the radiative restoration strength over this time period. The relationship between this 30–50 year radiative restoration strength and longer term effective sensitivity is investigated using an ensemble of 32 model configurations from the Coupled Model Intercomparison Project phase 5 (CMIP5), suggesting a strong correlation between the two. The mean radiative restoration strength over this period for the CMIP5 members examined is 1.16 Wm^?2K^?1, compared to 2.05 Wm^?2K^?1 from the observations. This suggests that temperature in these CMIP5 models may be too sensitive to perturbations in radiative forcing, although this depends on the actual magnitude of the anthropogenic aerosol forcing in the modern period. The potential change in the radiative restoration strength over longer timescales is also considered, resulting in a likely (67 %) range of 1.5–2.9 K for equilibrium climate sensitivity, and a 90 % confidence interval of 1.2–5.1 K.     

Modeling land surface phenology in a mixed temperate forest using MODIS measurements of leaf area index and land surface temperature

The development of satellite-derived vegetation indices and metrics has enabled researchers to monitor land surface phenology (LSP). While the use of satellite data to monitor LSP is prevalent, there has been minimal effort to model LSP in temperate climates using satellite observations of the land surface. Satellite-derived LSP models are beneficial for studying past and future changes in phenology and related ecosystem processes (e.g., water, energy, and carbon fluxes). The purpose of this study was to model LSP during the spring in a mixed temperate forest using satellite-derived measurements of leaf area index (LAI) and land surface temperature (LST). As part of the model validation process, the use of LST as a proxy for air temperature to model LSP was also investigated. The results indicate that LST derived from the MODIS Terra sensor at 10:30?a.m. (local solar time) can be used to develop a LSP model that predicts the full profile of LAI from winter dormancy to maturity and the date when LAI reaches half of the annual maximum (LAI_50%) with relatively low error. In addition, the modeled LAI values closely tracked in situ observations of the phenological development of the dominant deciduous tree species located in the study area where the model was developed. A comparison of LST and daily maximum air temperature at two levels above the ground surface revealed distinct differences and nonlinearities in the relationship between these two variables. However, accumulated growing degree-days calculated from each of these variables were similar because the largest differences between LST and daily maximum air temperature occurred prior to the beginning of heat accumulation. Consequently, the model predictions of LAI_50% derived from the use of LST and daily maximum air temperature were similar. When the developed model was applied in two other mixed forests, the errors were larger due to substantial interannual variability in the relationship between LAI and heat accumulation and systematic differences in this relationship between sites. Although the model cannot be successfully applied in these other mixed forests, the ability of the model to capture a consistent relationship between satellite estimates of LAI and LST in the study area where it was developed demonstrates that satellite observations of the land surface can be used in certain locations to create LSP phenology models. When validated, the models can be used to examine past and future changes in phenology and related ecosystem processes.     

Similarities and differences of two evapotranspiration models with routinely measured meteorological variables: application to a cropland and grassland in northeast China

Local actual evapotranspiration can be estimated with routinely measured meteorological variables using the Penman–Monteith model with surface resistance parameterized via the Katerji and Perrier approach (Agronomie 3(6):513–521, 1983; PM–KP model), or the nonlinear complementary relationship (CR) model proposed by Han et al. (Hydrol Process 26:3973–3981, 2012). A comparative study was carried out to evaluate the consistencies and differences of two models, as well as the performances of them for a cropland and grassland in northeast China. The departure of the actual evapotranspiration from the potential evaporation is described as a function of the ratio of the surface resistance to the aerodynamic resistance in the Penman–Monteith model, but the ratio of the aerodynamic term to the radiation term in the CR models. The two ratios are connected using a semi-empirical linear function by the Katerji and Perrier approach. The nonlinear CR model can be regarded as replacing the linear function by a power function after mathematical processing. On the other hand, the PM–KP model can be also considered as a CR-type model. On the daily basis at a maize cropland and degraded grassland in semiarid Northeast China, the nonlinear CR model with locally calibrated parameters performed better with data occupying all the growth stages, but the performances of the two models are similar during the early-, mid-, and late-season stages, respectively. On the half-hourly basis, the PM–KP model and the nonlinear CR model both performed well. It is deduced that on the daily basis the nonlinear CR model is more suitable for the cropland and grassland, but further comparisons are needed on the hourly basis.     

A new general circulation model: formulation and preliminary results in a single- and multi-processor environment

This article describes a new general circulation model (GCM) developed jointly by The University of New South Wales (UNSW) and the University of Hamburg. The model is versatile in that it can be run as a medium-range (1 to 15 days) global numerical weather prediction (NWP) model; as an extended range (15 to 30 days) NWP model; and as a GCM for periods extending from seasons, through annual and decadal periods, and beyond. The model can be coupled with ocean models that vary in complexity from simple "swamp" oceans to complex ocean GCMs. The atmospheric GCM also has a number of novel features, particularly in the numerical integration scheme which is a high-order, mass-conserving, semi-implicit semi-Lagrangian scheme, thereby removing the stability restriction on the time-step and allowing efficient long-term integrations. The emphasis here will be on demonstrating that the new model performs effectively on the usual measures of skill (statistics such as mean errors, root-mean-square errors and anomaly correlations) in several standard applications upon which new models usually are assessed. These applications include medium range weather forecasts out to 10 days on a daily basis over a one year period; a limited 10-year simulation climatology, prediction of atmospheric anomalies using SST anomalies in an El Nino year; and an alternative two-way approach to regional modelling (the "down-scaling problem") made possible because the unconditional stability of the semi-implicit, semi-Lagrangian formulation permits large variations in grid spacing without changing the time step size. Finally, the model is run on a variety of parallel computing platforms and it is shown that near-linear speed-up can be attained. This is significant for both medium range NWP and very long-term GCM integrations. Received: 28 February 1996 / Accepted: 30 July 1996     

A comparison of a GCM response to historical anthropogenic land cover change and model sensitivity to uncertainty in present-day land cover representations

This study assesses the sensitivity of the fully coupled NCAR-DOE PCM to three different representations of present-day land cover, based on IPCC SRES land cover information. We conclude that there is significant model sensitivity to current land cover characterization, with an observed average global temperature range of 0.21 K between the simulations. Much larger contrasts (up to 5 K) are found on the regional scale; however, these changes are largely offsetting on the global scale. These results show that significant biases can be introduced when outside data sources are used to conduct anthropogenic land cover change experiments in GCMs that have been calibrated to their own representation of present-day land cover. We conclude that hybrid systems that combine the natural vegetation from the native GCM datasets combined with human land cover information from other sources are best for simulating such impacts. We also performed a prehuman simulation, which had a 0.39 K ~higher average global temperature and, perhaps of greater importance, temperature changes regionally of about 2 K. In this study, the larger regional changes coincide with large-scale agricultural areas. The initial cooling from energy balance changes appear to create feedbacks that intensify mid-latitude circulation features and weaken the summer monsoon circulation over Asia, leading to further cooling. From these results, we conclude that land cover change plays a significant role in anthropogenically forced climate change. Because these changes coincide with regions of the highest human population this climate impact could have a disproportionate impact on human systems. Therefore, it is important that land cover change be included in past and future climate change simulations.     

A Methodological Study on Using Weather Research and Forecasting(WRF) Model Outputs to Drive a One-Dimensional Cloud Model简

A new method for driving a One-Dimensional Stratiform Cold （1DSC） cloud model with Weather Research and Fore casting （WRF） model outputs was developed by conducting numerical experiments for a typical large-scale stratiform rainfall event that took place on 4-5 July 2004 in Changchun, China. Sensitivity test results suggested that, with hydrometeor pro files extracted from the WRF outputs as the initial input, and with continuous updating of soundings and vertical velocities （including downdraft） derived from the WRF model, the new WRF-driven 1DSC modeling system （WRF-1DSC） was able to successfully reproduce both the generation and dissipation processes of the precipitation event. The simulated rainfall intensity showed a time-lag behind that observed, which could have been caused by simulation errors of soundings, vertical velocities and hydrometeor profiles in the WRF output. Taking into consideration the simulated and observed movement path of the precipitation system, a nearby grid point was found to possess more accurate environmental fields in terms of their similarity to those observed in Changchun Station. Using profiles from this nearby grid point, WRF-1DSC was able to repro duce a realistic precipitation pattern. This study demonstrates that 1D cloud-seeding models do indeed have the potential to predict realistic precipitation patterns when properly driven by accurate atmospheric profiles derived from a regional short range forecasting system, This opens a novel and important approach to developing an ensemble-based rain enhancement prediction and operation system under a probabilistic framework concept.     

The change of North China climate in transient simulations using the IPCC SRES A2 and B2 scenarios with a coupled atmosphere-ocean general circulation model

This paper applies the newest emission scenarios of the sulfur and greenhouse gases, namely IPCC SRES A2 and B2 scenarios, to investigate the change of the North China climate with an atmosphere-ocean coupled general circulation model. In the last three decades of the 21st century, the global warming enlarges the land-sea thermal contrast, and hence, causes the East Asian summer (winter) monsoon circulation to be strengthened (weakened). The rainfall seasonality strengthens and the summer precipitation increases significantly in North China. It is suggested that the East Asian rainy area would expand northward to North China in the last three decades of the 21st century. In addition, the North China precipitation would increase significantly in September. In July, August, and September, the interannual variability of the precipitation enlarges evidentlv over North China. implying a risk of flooding in the future.     

Sensitivity of modern climate to the presence,strength and salinity of Mediterranean-Atlantic exchange in a global general circulation model

Mediterranean Outflow Water (MOW) is thought to be a key contributor to the strength and stability of Atlantic Meridional Overturning Circulation (AMOC), but the future of Mediterranean-Atlantic water exchange is uncertain. It is chiefly dependent on the difference between Mediterranean and Atlantic temperature and salinity characteristics, and as a semi-enclosed basin, the Mediterranean is particularly vulnerable to future changes in climate and water usage. Certainly, there is strong geologic evidence that the Mediterranean underwent dramatic salinity and sea-level fluctuations in the past. Here, we use a fully coupled atmosphere–ocean General Circulation Model to examine the impact of changes in Mediterranean-Atlantic exchange on global ocean circulation and climate. Our results suggest that MOW strengthens and possibly stabilises the AMOC not through any contribution towards NADW formation, but by delivering relatively warm, saline water to southbound Atlantic currents below 800 m. However, we find almost no climate signal associated with changes in Mediterranean-Atlantic flow strength. Mediterranean salinity, on the other hand, controls MOW buoyancy in the Atlantic and therefore affects its interaction with the shallow-intermediate circulation patterns that govern surface climate. Changing Mediterranean salinity by a factor of two reorganises shallow North Atlantic circulation, resulting in regional climate anomalies in the North Atlantic, Labrador and Greenland-Iceland-Norwegian Seas of ±4 °C or more. Although such major variations in salinity are believed to have occurred in the past, they are unlikely to occur in the near future. However, our work does suggest that changes in the Mediterranean’s hydrological balance can impact global-scale climate.