Depositional ice nucleation on NX illite and mixtures of NX illite with organic acids

Mineral dust particles are known to be efficient ice nuclei in the atmosphere. Previous work has probed heterogeneous ice nucleation on various laboratory dust samples including Arizona Test Dust, kaolinite, montmorillonite, and illite as atmospheric dust surrogates. However, it has recently been suggested that NX illite may be a better representation of atmospheric dust. Hiranuma et al. (2015) performed a laboratory comparison for immersion ice nucleation on NX illite, but here we focus on depositional ice nucleation because of its importance in low temperature cirrus cloud formation. A Raman microscope setup was used to examine the ice-nucleating efficiency of NX illite. Organic coatings on the NX illite particles were also investigated using a mixture of 5 dicarboxylic acids (M5). The ratio of NX illite to M5 was varied from 1:10 to 100:1. It was found that NX illite efficiently nucleates ice with S_ice = 1.07 ± 0.01 at ?47 °C, with S_ice slightly increasing at lower temperatures. In contrast, pure M5 is a poorer ice nucleus with S_ice = 1.30 ± 0.02 at ?40 °C, relatively independent of temperature. Further, it was found that M5 coatings on the order of several monolayers thick hindered the ice nucleating ability of NX illite. Optical images suggest that at colder temperatures (< ?50 °C) 1:1 NX illite:M5 particles and pure M5 particles nucleate ice depositionally, while at warmer temperatures (> ?50 °C) subsaturated immersion ice nucleation dominates. These experiments suggest that mineral dust particles may become less active towards ice nucleation as they age in the atmosphere.     

Multi-criteria evaluation of CMIP5 GCMs for climate change impact analysis

Climate change is expected to have severe impacts on global hydrological cycle along with food-water-energy nexus. Currently, there are many climate models used in predicting important climatic variables. Though there have been advances in the field, there are still many problems to be resolved related to reliability, uncertainty, and computing needs, among many others. In the present work, we have analyzed performance of 20 different global climate models (GCMs) from Climate Model Intercomparison Project Phase 5 (CMIP5) dataset over the Columbia River Basin (CRB) in the Pacific Northwest USA. We demonstrate a statistical multicriteria approach, using univariate and multivariate techniques, for selecting suitable GCMs to be used for climate change impact analysis in the region. Univariate methods includes mean, standard deviation, coefficient of variation, relative change (variability), Mann-Kendall test, and Kolmogorov-Smirnov test (KS-test); whereas multivariate methods used were principal component analysis (PCA), singular value decomposition (SVD), canonical correlation analysis (CCA), and cluster analysis. The analysis is performed on raw GCM data, i.e., before bias correction, for precipitation and temperature climatic variables for all the 20 models to capture the reliability and nature of the particular model at regional scale. The analysis is based on spatially averaged datasets of GCMs and observation for the period of 1970 to 2000. Ranking is provided to each of the GCMs based on the performance evaluated against gridded observational data on various temporal scales (daily, monthly, and seasonal). Results have provided insight into each of the methods and various statistical properties addressed by them employed in ranking GCMs. Further; evaluation was also performed for raw GCM simulations against different sets of gridded observational dataset in the area.     

A mental picture of the greenhouse effect

The popular picture of the greenhouse effect emphasises the radiation transfer but fails to explain the observed climate change. An old conceptual model for the greenhouse effect is revisited and presented as a useful resource in climate change communication. It is validated against state-of-the-art data, and nontraditional diagnostics show a physically consistent picture. The earth’s climate is constrained by well-known and elementary physical principles, such as energy balance, flow, and conservation. Greenhouse gases affect the atmospheric optical depth for infrared radiation, and increased opacity implies higher altitude from which earth’s equivalent bulk heat loss takes place. Such an increase is seen in the reanalyses, and the outgoing long-wave radiation has become more diffuse over time, consistent with an increased influence of greenhouse gases on the vertical energy flow from the surface to the top of the atmosphere. The reanalyses further imply increases in the overturning in the troposphere, consistent with a constant and continuous vertical energy flow. The increased overturning can explain a slowdown in the global warming, and the association between these aspects can be interpreted as an entanglement between the greenhouse effect and the hydrological cycle, where reduced energy transfer associated with increased opacity is compensated by tropospheric overturning activity.     

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.     

Spatiotemporal variability of extreme precipitation in Shaanxi province under climate change

Extreme climate index is one of the useful tools to monitor and detect climate change. The primary objective of this study is to provide a more comprehensively the changes in extreme precipitation between the periods of 1954–1983 and 1984–2013 in Shaanxi province under climate change, which will hopefully provide a scientific understanding of the precipitation-related natural hazards such as flood and drought. Daily precipitation from 34 surface meteorological stations were used to calculated 13 extreme precipitation indices (EPIs) generated by the joint World Meteorological Organization Commission for Climatology (CCI)/World Climate Research Programme (WCRP) project on Climate Variability and Predictability (CLIVAR) expect Team on climate change Detection, Monitoring and Indices (ETCCDMI). Two periods including 1954–1983 and 1984–2013 were selected and five types of precipitation days (R10mm-R100mm) were defined, to provide more evidences of climate change impacts on the extreme precipitation events, and specially, to investigate the changes in different types of precipitation days. The EPIs were generated using RClimRex software, and the trends were analyzed using Mann-Kendall nonparametric test and Sen’s slope estimator. The relationships between the EPIs and the impacts of climate anomalies on typical EPIs were investigated using correlation and composite analysis. The mainly results include: 1) Thirteen EPIs, except consecutive dry day (CDD), were positive trends dominated for the period of 1984–2013, but the trends were not obvious for the period of 1954–1983. Most of the trends were not statistically significant at 5 % significance level. 2) The spatial distributions of stations that exhibited positive and negative trends were scattered. However, the stations that had negative trends mainly distributed in the north of Shaanxi province, and the stations that had positive trends mainly located in the south. 3) The percentage of stations that had positive trends had increased from the period of 1954–1983 to 1984–2013 for all the 13 EPIs except CDD, indicating the possible climate change impacts on extreme precipitation events. 4) The correlations between annual total wet-day precipitation (PRCPTOT) and other 12 EPIs varied for different indices and stations. The composite analysis found that El Niño Southern Oscillation (ENSO) exerted greater impacts on PRCPTOT than other EPIs and greater in the Guanzhong Plain (GZP) than Qinling-Dabashan Mountains (QDM) and Shanbei Plateau (SBP) of Shaanxi province.     

Observed particle sizes and fluxes of Aeolian sediment in the near surface layer during sand-dust storms in the Taklamakan Desert

Monitoring, modeling and predicting the formation and movement of dust storms across the global deserts has drawn great attention in recent decades. Nevertheless, the scarcity of real-time observations of the wind-driven emission, transport and deposition of dusts has severely impeded progress in this area. In this study, we report an observational analysis of sand-dust storm samples collected at seven vertical levels from an 80-m-high flux tower located in the hinterland of the great Taklamakan Desert for ten sand-dust storm events that occurred during 2008–2010. We analyzed the vertical distribution of sandstorm particle grain sizes and horizontal sand-dust sediment fluxes from the near surface up to 80 m high in this extremely harsh but highly representative environment. The results showed that the average sandstorm grain size was in the range of 70 to 85 μm. With the natural presence of sand dunes and valleys, the horizontal dust flux appeared to increase with height within the lower surface layer, but was almost invariant above 32 m. The average flux values varied within the range of 8 to 14 kg m^?2 and the vertical distribution was dominated by the wind speed in the boundary layer. The dominant dust particle size was PM₁₀₀ and below, which on average accounted for 60–80 % of the samples collected, with 0.9–2.5 % for PM_0–2.5, 3.5–7.0 % for PM_0–10, 5.0–14.0 % for PM_0–20 and 20.0–40.0 % for PM_0–50. The observations suggested that on average the sand-dust vertical flux potential is about 0.29 kg m^?2 from the top of the 80 m tower to the upper planetary boundary layer and free atmosphere through the transport of particles smaller than PM₂₀. Some of our results differed from previous measurements from other desert surfaces and laboratory wind-dust experiments, and therefore provide valuable observations to support further improvement of modeling of sandstorms across different natural environmental conditions.     

Evaluation of NCEP TIGGE short-range forecast for Indian summer monsoon intraseasonal oscillation

Potential evapotranspiration (PET) is one of the most critical parameters in the research on agro-ecological systems. The computational methods for the estimation of PET vary in data demands from very simple (empirically based), requiring only information based on air temperatures, to complex ones (more physically based) that require data on radiation, relative humidity, wind speed, etc. The current research is focused on three study areas in Greece that face different climatic conditions due to their location. Twelve PET formulae were used, analyzed and inter-compared in terms of their sensitivity regarding their input coefficients for the Ardas River basin in north-eastern Greece, Sperchios River basin in Central Greece and Geropotamos River basin in South Greece. The aim was to compare all the methods and conclude to which empirical PET method(s) better represent the PET results in each area and thus should be adopted and used each time and which factors influence the results in each case. The results indicated that for the areas that face Mediterranean climatic conditions, the most appropriate method for the estimation of PET was the temperature-based, Hamon’s second version (PET_Ham2). Furthermore, the PET_Ham2 was able to estimate PET almost similarly to the average results of the 12 equations. For the Ardas River basin, the results indicated that both PET_Ham2 and PET_Ham1 can be used to estimate PET satisfactorily. Moreover, the temperature-based equations have proven to produce better results, followed by the radiation-based equations. Finally, PET_ASCE, which is the most commonly used PET equation, can also be applied occasionally in order to provide satisfactory results.     

Warm spells in Northern Europe in relation to atmospheric circulation

This study describes warm spells in Northern Europe and determines the synoptic situations that cause their occurrence. In this article, a relatively warm day was defined as a day when the maximum temperature exceeded the 95th annual percentile, and a warm spell (WS) was considered to be a sequence of at least five relatively warm days. In the analysed multiannual period and within the investigated area, 24 (Kallax) to 53 (Oslo) WSs were observed. The occurrence of WSs was mainly connected with positive anomalies of sea level pressure and a 500-hPa isobaric surface, displaying the presence of high-pressure systems. This occurrence was also accompanied by positive T850 anomalies.     

Long-term trend and variability of precipitation in Chhattisgarh State,India

Spatial and temporal precipitation variability in Chhattisgarh State in India was examined by using monthly precipitation data for 102 years (1901–2002) from 16 stations. The homogeneity of precipitation data was evaluated by the double-mass curve approach and the presence of serial correlation by lag-1 autocorrelation coefficient. Linear regression analysis, the conventional Mann–Kendall (MK) test, and Spearman’s rho were employed to identify trends and Sen’s slope to estimate the slope of trend line. The coefficient of variation (CV) was used to analyze precipitation variability. Spatial interpolation was done by a Kriging process using ArcGIS 9.3. Results of both parametric and non-parametric tests and trend tests showed that at 5 % significance level, annual precipitation exhibited a decreasing trend at all stations except Bilaspur and Dantewada. For both annual and monsoon precipitation, Sen’s test showed a decreasing trend for all stations, except Bilaspur and Dantewada. The highest percentage of variability was observed in winter precipitation (88.75 %) and minimum percentage variability in annual series (14.01 %) over the 102-year periods.     

Variances in the projections,resulting from CLIMEX,Boosted Regression Trees and Random Forests techniques

The aim of this study was to have a comparative investigation and evaluation of the capabilities of correlative and mechanistic modeling processes, applied to the projection of future distributions of date palm in novel environments and to establish a method of minimizing uncertainty in the projections of differing techniques. The location of this study on a global scale is in Middle Eastern Countries. We compared the mechanistic model CLIMEX (CL) with the correlative models MaxEnt (MX), Boosted Regression Trees (BRT), and Random Forests (RF) to project current and future distributions of date palm (Phoenix dactylifera L.). The Global Climate Model (GCM), the CSIRO-Mk3.0 (CS) using the A2 emissions scenario, was selected for making projections. Both indigenous and alien distribution data of the species were utilized in the modeling process. The common areas predicted by MX, BRT, RF, and CL from the CS GCM were extracted and compared to ascertain projection uncertainty levels of each individual technique. The common areas identified by all four modeling techniques were used to produce a map indicating suitable and unsuitable areas for date palm cultivation for Middle Eastern countries, for the present and the year 2100. The four different modeling approaches predict fairly different distributions. Projections from CL were more conservative than from MX. The BRT and RF were the most conservative methods in terms of projections for the current time. The combination of the final CL and MX projections for the present and 2100 provide higher certainty concerning those areas that will become highly suitable for future date palm cultivation. According to the four models, cold, hot, and wet stress, with differences on a regional basis, appears to be the major restrictions on future date palm distribution. The results demonstrate variances in the projections, resulting from different techniques. The assessment and interpretation of model projections requires reservations, especially in correlative models such as MX, BRT, and RF. Intersections between different techniques may decrease uncertainty in future distribution projections. However, readers should not miss the fact that the uncertainties are mostly because the future GHG emission scenarios are unknowable with sufficient precision. Suggestions towards methodology and processing for improving projections are included.