Mapping the mean monthly precipitation of a small island using kriging with external drifts

This study focuses on the spatial distribution of mean annual and monthly precipitation in a small island (1128 km²) named Martinique, located in the Lesser Antilles. Only 35 meteorological stations are available on the territory, which has a complex topography. With a digital elevation model (DEM), 17 covariates that are likely to explain precipitation were built. Several interpolation methods, such as regression-kriging (????????, ????????, and ????????) and external drift kriging (??????) were tested using a cross-validation procedure. For the regression methods, predictors were chosen by established techniques whereas a new approach is proposed to select external drifts in a kriging which is based on a stepwise model selection by the Akaike Information Criterion (AIC). The prediction accuracy was assessed at validation sites with three different skill scores. Results show that using methods with no predictors such as inverse distance weighting (??????) or universal kriging (????) is inappropriate in such a territory. ?????? appears to outperform regression methods for any criteria, and selecting predictors by our approach improves the prediction of mean annual precipitation compared to kriging with only elevation as drift. Finally, the predicting performance was also studied by varying the size of the training set leading to less conclusive results for ?????? and its performance. Nevertheless, the proposed method seems to be a good way to improve the mapping of climatic variables in a small island.     

Spatial and temporal variabilities of rainfall data using functional data analysis

The paper analyzes equivalent data for a low density meteorological station network (spatially discontinuous data) and poor temporal homogeneity of thunderstorm observational data. Due to that, a Regional Climate Model (RegCM) dataset was tested. The Most Unstable Convective Available Potential Energy index value (MUCAPE) above the 200 J kg^?1 threshold was selected as a predictor describing favorable conditions for the occurrence of thunderstorms. The quality of the dataset was examined through a comparison between model results and soundings from several aerological stations in Central Europe. Good, statistically significant (0.05 significance level) results were obtained through correlation analysis; the value of Pearson’s correlation coefficient was above 0.8 in every single case. Then, using methods associated with gridded climatology, data series for 44 weather stations were derived and an analysis of correlation between RegCM modeled data and in situ thunderstorm observations was conducted with coefficients in the range of 0.75–0.90. The possibility of employing the dataset in thunderstorm climatology analysis was checked via a few examples by mapping monthly, seasonal, and annual means. Moreover, long-term variability and trend analysis along with modeled MUCAPE data were tested. As a result, the RegCM modeled MUCAPE gridded dataset was proposed as an easily available, suitable, and valuable predictor for thunderstorm climatology analysis and mapping. Finally, some limitations are discussed and recommendations for further improvements are given.     

Analysis of extreme precipitation characteristics in low mountain areas based on three-dimensional copulas—taking Kuandian County as an example

Rainfall in New South Wales (NSW), located in the southeast of the Australian continent, is known to be influenced by four major climate drivers: the El Niño/Southern Oscillation (ENSO), the Interdecadal Pacific Oscillation (IPO), the Southern Annular Mode (SAM) and the Indian Ocean Dipole (IOD). Many studies have shown the influences of ENSO, IPO modulation, SAM and IOD on rainfall in Australia and on southeast Australia in particular. However, only limited work has been undertaken using a multiple regression framework to examine the extent of the combined effect of these climate drivers on rainfall. This paper analysed the role of these combined climate drivers and their interaction on the rainfall in NSW using Bayesian Model Averaging (BMA) to account for model uncertainty by considering each of the linear models across the whole model space which is equal to the set of all possible combinations of predictors to find the model posterior probabilities and their expected predictor coefficients. Using BMA for linear regression models, we are able to corroborate and confirm the results from many previous studies. In addition, the method gives the ranking order of importance and the probability of the association of each of the climate drivers and their interaction on the rainfall at a site. The ability to quantify the relative contribution of the climate drivers offers the key to understand the complex interaction of drivers on rainfall, or lack of rainfall in a region, such as the three big droughts in southeastern Australia which have been the subject of discussion and debate recently on their causes.     

Validating the dynamic downscaling ability of WRF for East Asian summer climate

This work aims, as a first step, to analyze rainfall variability in Northern Algeria, in particular extreme events, during the period from 1940 to 2010. Analysis of annual rainfall shows that stations in the northwest record a significant decrease in rainfall since the 1970s. Frequencies of rainy days for each percentile (5th, 10th, 25th, 50th, 75th, 90th, 95th, and 99th) and each rainfall interval class (1–5, 5–10, 10–20, 20–50, and ≥50 mm) do not show a significant change in the evolution of daily rainfall. The Tenes station is the only one to show a significant decrease in the frequency of rainy days up to the 75th percentile and for the 10–20-mm interval class. There is no significant change in the temporal evolution of extreme events in the 90th, 95th, and 99th percentiles. The relationships between rainfall variability and general atmospheric circulation indices for interannual and extreme event variability are moderately influenced by the El Niño-Southern Oscillation and Mediterranean Oscillation. Significant correlations are observed between the Southern Oscillation Index and annual rainfall in the northwestern part of the study area, which is likely linked with the decrease in rainfall in this region. Seasonal rainfall in Northern Algeria is affected by the Mediterranean Oscillation and North Atlantic Oscillation in the west. The ENSEMBLES regional climate models (RCMs) are assessed using the bias method to test their ability to reproduce rainfall variability at different time scales. The Centre National de Recherches Météorologiques (CNRM), Czech Hydrometeorological Institute (CHMI), Eidgenössische Technische Hochschule Zürich (ETHZ), and Forschungszentrum Geesthacht (GKSS) models yield the least biased results.     

Photochemical activation of chlorine by iron-oxide aerosol

The photochemical activation of chlorine by dissolved iron in artificial sea-salt aerosol droplets and by highly dispersed iron oxide (Fe₂O₃) aerosol particles (mainly hematite, specific surface ~150 m² g^?1) exposed to gaseous HCl, was investigated in humidified air in a Teflon simulation chamber. Employing the radical-clock technique, we quantified the production of gaseous atomic chlorine (Cl) from the irradiated aerosol. When the salt aerosol contained Fe₂O₃ at pH 6, no significant Cl production was observed, even if the dissolution of iron was forced by “weathering” (repeatedly freezing and thawing for five times). Adjusting the pH in the stock suspension to 2.6, 2.2, and 1.9 and equilibrating for one week resulted in a quantifiable amount of dissolved iron (0.03, 0.2, and 0.6 mmol L^?1, respectively) and in gaseous Cl production rates of ~1.6, 6, and 8?×?10²¹ atoms cm^?2 h^?1, respectively. In a further series of experiments, the pure Fe₂O₃ aerosol was exposed to various levels of gaseous hydrogen chloride (HCl). The resulting Cl production rates ranged from 8?×?10²⁰ Cl atoms cm^?2 h^?1 (at ~4 ppb HCl) to 5?×?10²² Cl atoms cm^?2 h^?1 (at ~350 ppb HCl) and confirmed the uptake and conversion of HCl to atomic Cl (at HCl to Cl conversion yields of 2–5 %, depending on the relative humidity). The Fe₂O₃ experiments indicate that iron-induced Cl formation may be important for highly soluble combustion-aerosol particles in marine environments in the presence of gaseous HCl.     

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.     

Momentum- and Heat-Flux Parametrization at Dome C,Antarctica: A Sensitivity Study

An extensive meteorological observational dataset at Dome C, East Antarctic Plateau, enabled estimation of the sensitivity of surface momentum and sensible heat fluxes to aerodynamic roughness length and atmospheric stability in this region. Our study reveals that (1) because of the preferential orientation of snow micro-reliefs (sastrugi), the aerodynamic roughness length \(z_{0}\) varies by more than two orders of magnitude depending on the wind direction; consequently, estimating the turbulent fluxes with a realistic but constant \(z_{0}\) of 1 mm leads to a mean friction velocity bias of \(24\,\%\) in near-neutral conditions; (2) the dependence of the ratio of the roughness length for heat \(z_{0t}\) to \(z_{0}\) on the roughness Reynolds number is shown to be in reasonable agreement with previous models; (3) the wide range of atmospheric stability at Dome C makes the flux very sensitive to the choice of the stability functions; stability function models presumed to be suitable for stable conditions were evaluated and shown to generally underestimate the dimensionless vertical temperature gradient; as these models differ increasingly with increases in the stability parameter z / L, heat flux and friction velocity relative differences reached \(100\,\%\) when \(z/L > 1\); (4) the shallowness of the stable boundary layer is responsible for significant sensitivity to the height of the observed temperature and wind data used to estimate the fluxes. Consistent flux results were obtained with atmospheric measurements at heights up to 2 m. Our sensitivity study revealed the need to include a dynamical parametrization of roughness length over Antarctica in climate models and to develop new parametrizations of the surface fluxes in very stable conditions, accounting, for instance, for the divergence in both radiative and turbulent fluxes in the first few metres of the boundary layer.     

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.     

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.