COSMO-CLM2: a new version of the COSMO-CLM model coupled to the Community Land Model

This study presents an evaluation of a new biosphere-atmosphere Regional Climate Model. COSMO-CLM² results from the coupling between the non-hydrostatic atmospheric model COSMO-CLM version 4.0 and the Community Land Model version 3.5 (CLM3.5). In this coupling, CLM3.5 replaces a simpler land surface parameterization (TERRA_ML) used in the standard COSMO-CLM. Compared to TERRA_ML, CLM3.5 comprises a more complete representation of land surface processes including hydrology, biogeophysics, biogeochemistry and vegetation dynamics. Historical climate simulations over Europe with COSMO-CLM and with the new COSMO-CLM² are evaluated against various data products. The simulated climate is found to be substantially affected by the coupling with CLM3.5, particularly in summer. Radiation fluxes as well as turbulent fluxes at the surface are found to be more realistically represented in COSMO-CLM². This subsequently leads to improvements of several aspects of the simulated climate (cloud cover, surface temperature and precipitation). We show that a better partitioning of turbulent fluxes is the central factor allowing for the better performances of COSMO-CLM² over COSMO-CLM. Despite these improvements, some model deficiencies still remain, most notably a substantial underestimation of surface net shortwave radiation. Overall, these results highlight the importance of land surface processes in shaping the European climate and the benefit of using an advanced land surface model for regional climate simulations.     

Physical and economic bias in climate change research: a scientometric study of IPCC Third Assessment Report

This study demonstrates that IPCC Third Assessment Report is strongly dominated by Natural sciences, especially the Earth sciences. The Social sciences are dominated by Economics. The IPCC assessment also results in the separation of the Earth, Biological and Social sciences. The integration that occurs is mainly between closely related scientific fields. The research community consequently imposes a physical and economic bias and a separation of scientific fields that the IPCC reproduces in the policy sphere. It is argued that this physical and economic bias distorts a comprehensive understanding of climate change and that the weak integration of scientific fields hinders climate change from being fully addressed as an integral environmental and social problem. If climate change is to be understood, evaluated and responded to in its fullness, the IPCC must broaden its knowledge base and challenge the anthropocentric worldview that places humans outside of nature.     

Evaluation of methods of spatial interpolation for monthly rainfall data over the state of Rio de Janeiro,Brazil

Five deterministic methods of spatial interpolation of monthly rainfall were compared over the state of Rio de Janeiro, southeast Brazil. The methods were the inverse distance weight (IDW), nearest neighbor (NRN), triangulation with linear interpolation (TLI), natural neighbor (NN), and spline tension (SPT). A set of 110 weather stations was used to test the methods. The selection of stations had two criteria: time series longer than 20 years and period of data from 1960 to 2009. The methods were evaluated using cross-validation, linear regression between values observed and interpolated, root mean square error (RMSE), coefficient of determination (r ²), coefficient of variation (CV, %), and the Willmott index of agreement (d). The results from different methods are influenced by the meteorological systems and their seasonality, as well as by the interaction with the topography. The methods presented higher precision (r ²) and accuracy (d, RMSE) during the summer and transition to autumn, in comparison with the winter or spring months. The SPT had the highest precision and accuracy in relation to other methods, in addition to having a good representation of the spatial patterns expected for rainfall over the complex terrain of the state and its high spatial variability.     

Evolution of aerosol vertical distribution during particulate pollution events in Shanghai

A set of micro pulse lidar(MPL)systems operating at 532 nm was used for ground-based observation of aerosols in Shanghai in 2011.Three typical particulate pollution events(e.g.,haze)were examined to determine the evolution of aerosol vertical distribution and the planetary boundary layer(PBL)during these pollution episodes.The aerosol vertical extinction coefficient(VEC)at any given measured altitude was prominently larger during haze periods than that before or after the associated event.Aerosols originating from various source regions exerted forcing to some extent on aerosol loading and vertical layering,leading to different aerosol vertical distribution structures.Aerosol VECs were always maximized near the surface owing to the potential influence of local pollutant emissions.Several peaks in aerosol VECs were found at altitudes above 1 km during the dust-and bioburning-influenced haze events.Aerosol VECs decreased with increasing altitude during the local-polluted haze event,with a single maximum in the surface atmosphere.PM2.5 increased slowly while PBL and visibility decreased gradually in the early stages of haze events;subsequently,PM2.5 accumulated and was exacerbated until serious pollution bursts occurred in the middle and later stages.The results reveal that aerosols from different sources impact aerosol vertical distributions in the atmosphere and that the relationship between PBL and pollutant loadings may play an important role in the formation of pollution.     

Impact of single-point GPS integrated water vapor estimates on short-range WRF model forecasts over southern India

Specifying physically consistent and accurate initial conditions is one of the major challenges of numerical weather prediction (NWP) models. In this study, ground-based global positioning system (GPS) integrated water vapor (IWV) measurements available from the International Global Navigation Satellite Systems (GNSS) Service (IGS) station in Bangalore, India, are used to assess the impact of GPS data on NWP model forecasts over southern India. Two experiments are performed with and without assimilation of GPS-retrieved IWV observations during the Indian winter monsoon period (November–December, 2012) using a four-dimensional variational (4D-Var) data assimilation method. Assimilation of GPS data improved the model IWV analysis as well as the subsequent forecasts. There is a positive impact of ～10 % over Bangalore and nearby regions. The Weather Research and Forecasting (WRF) model-predicted 24-h surface temperature forecasts have also improved when compared with observations. Small but significant improvements were found in the rainfall forecasts compared to control experiments.     

Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960–2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001–2010) and the predicting period (2011–2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.     

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.     

Summer temperature variability across four urban neighborhoods in Knoxville,Tennessee, USA

The urban heat island (UHI) is a well-documented effect of urbanization on local climate, identified by higher temperatures compared to surrounding areas, especially at night and during the warm season. The details of a UHI are city-specific, and microclimates may even exist within a given city. Thus, investigating the spatiotemporal variability of a city’s UHI is an ongoing and critical research need. We deploy ten weather stations across Knoxville, Tennessee, to analyze the city’s UHI and its differential impacts across urban neighborhoods: two each in four neighborhoods, one in more dense tree cover and one in less dense tree cover, and one each in downtown Knoxville and Ijams Nature Center that serve as control locations. Three months of temperature data (beginning 2 July 2014) are analyzed using paired-sample t tests and a three-way analysis of variance. Major findings include the following: (1) Within a given neighborhood, tree cover helps negate daytime heat (resulting in up to 1.19 ^°C lower maximum temperature), but does not have as large of an influence on minimum temperature; (2) largest temperature differences between neighborhoods occur during the day (0.38–1.16 ^°C difference), but larger differences between neighborhoods and the downtown control occur at night (1.04–1.88 ^°C difference); (3) presiding weather (i.e., air mass type) has a significant, consistent impact on the temperature in a given city, and lacks the differential impacts found at a larger-scale in previous studies; (4) distance from city center does not impact temperature as much as land use factors. This is a preliminary step towards informing local planning with a scientific understanding of how mitigation strategies may help minimize the UHI and reduce the effects of extreme weather on public health and well-being.     

Climate Change Impacts for the Conterminous USA: An Integrated Assessment Summary

This special issue of Climatic Change describes an effort to improve methodology for integrated assessment of impacts and consequences of climatic change. Highlights of the seven foregoing Parts (papers) that constitute this special issue are summarized here. The methodology developed involves construction of scenarios of climate change that are used to drive individual sectoral models for simulating impacts on crop production, irrigation demand, water supply and change in productivity and geography of unmanaged ecosystems. Economic impacts of the changes predicted by integrating the results of the several sectoral simulation models are calculated through an agricultural land-use model. While these analyses were conducted for the conterminous United States alone, their global implications are also considered in this summary as is the need for further improvements in integrated assessment methodology.     

红壤丘陵区农业开发中的生态环境效应及其调控

文章以江西余江县为例。通过实验研究，历史资料对比分析及邻近参照等方法。研究红壤区农业开发中的生态环境效应。农业开发引起的微观环境效应在于改变土壤水分及其运动状况，加速土壤物质迁移及降低土壤肥力质量；区域性效应是植被减少，农业面源污染，水土流失扩大及气候异常；全球性影响除区域集聚效应外，则是农业活动中产生的温室气体对全球气候的影响等，采取的调控对策主要是：保持合理开发，适当减轻土地压力，优化农业开发模式，更新农业耕作技术。强化环境整治与生态建设。