Search

While political debates on climate change become increasingly heated, our houses and city infrastructure continue to suffer from an increasing trend of damages due to adverse atmospheric events, from heavier-than-usual rainfalls to heat waves, droughts, and floods. Adapting our homes and critical infrastructure to sustain the effects of climate dynamics requires novel data-driven interdisciplinary approaches for efficient risk mitigation. We develop a new systematic framework based on the machinery of statistical and machine learning tools to evaluate water-related home insurance risks and quantify uncertainty due to varying climate model projections. Furthermore, we introduce the concept of data depth to the analysis of weather and climate ensembles, which remains a novel territory for statistical depth methodology as well as the field of environmental risk and ensemble forecasting in general. We illustrate the new data-driven methodology for risk analysis in application to rainfall-related home insurance in the Canadian Prairies over 2002–2011. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

The Canadian Cordillera in the province of British Columbia witnesses numerous rock falls every year. Studies on the recorded rock fall data in this area show that rock fall hazard follows weather conditions, especially precipitation and freeze-thaw cycles. This relationship indicates that a weather-based approach can be implemented to estimate possible changes in the rock fall hazard due to climate change. In this paper, we used a statistical approach to quantify the relationship between monthly weather averages and rock fall frequencies for a section of a transportation corridor along the Fraser River in British Columbia, Canada. In this regard, von Mises distributions are used to find the best-fitted models to the monthly precipitation and freeze-thaw cycles, and proper relative weights are applied to the models in order to calibrate them to the rock fall monthly frequency. The calibrated model is used with input data from climatic predictions for 2041–2070 and 2071–2100 to see how rock fall distribution will be affected due to climate change in the future decades. Results show that between 9 and 19%, more rock fall is anticipated in future winters. Rock falls are expected to decrease in other months, especially in October, November, March, and April. This paper presents a method to predict changes in rock fall hazard seasonality due to climate change and illustrates the method with a case study along a section of the Canadian Cordillera. [ABSTRACT FROM AUTHOR]

Concurrent compound dry and hot events (CDHEs) amplified more damange on the ecosystems and human society than individual extremes. Under climate change, compound dry and hot events become more frequent on a global scale. This paper proposes a mathematical method to quantitatively attribute changes of CDHEs to changes of precipitation, change in temperature and change in the dependence between precipitation and temperature. The attribution is achieved by formulating the total differential equation of the return period of CDHEs among Meta-gaussian model. A case study of China is devised based on monthly precipitation and temperature data during the period from 1921 to 2020 for 80 major river basins. It is found that temperature is the main driving factor of increases in CDHEs for 49 major river basins in China, except for the upper and middle reaches of the Yangtze River. In West China, precipitation changes drove the increase in CDHEs in 18 river basins (23%), particularly in parts of North Xinjiang, Qinghai and Gansu. On the other hand, dependence between precipitation and temperature dominated changes of CDHEs in 13 river basins (16%) of China with other factors, including parts of South China, East China and Northwestern China. Furthermore, changes in both the mean and spread of precipitation and temperature can also contribute to changes in CDHEs. [ABSTRACT FROM AUTHOR]

Changes in patterns of meteorological parameters, like precipitations, temperature, wind, etc., are causing significant increases in various extreme events. And these extreme events, i.e., floods, heatwaves, hurricanes, droughts, etc., lead to a shortage of water resources, crop failures, wildfires, and economic losses. However, Global Circulation Models (GCMs) are considered the most important tools for quantifying climate change. Therefore, we selected 20 different GCMs of precipitation in our research, as the frequency of extreme events, like drought and flood, is highly related to changes in precipitation patterns. However, this research introduced a new weighting scheme — MCFSAWS-Ensemble: Monte Carlo Feature Selection Adaptive Weighting Scheme to Ensemble multiple GCMs, whereas, Monte Carlo Feature Selection (MCFS) is one of the most popular algorithms for discovering important variables. However, the proposed weighting scheme (MCFSAWS-Ensemble) is mainly based on two sources. Initially, it evaluates the prior performance of each GCM model to define their relative importance using MCFS. Then, it computes value by value difference between the observed and simulated model. In addition, the application of this paper is based on the monthly time series data of precipitation in the Tibet Plateau region of China. In addition, we used twenty GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to analyze the implications of the MCFSAWS-Ensemble. Further, we compared the performance of the MCFSAWS-Ensemble scheme with Simple Model Averaging (SMA) through Mean Average Error (MAE) and correlation statistics. The results of this research indicate that the proposed weighting scheme (MCFSAWS-Ensemble) is more accurate than the SMA approach. Consequently, we recommend the use of advanced machine learning algorithms such as MCFS for making accurate multi-model ensembles. [ABSTRACT FROM AUTHOR]

The spatial and temporal variability of the main climatic indicators on the territory of the Volga Federal District in the 20th–21st centuries is considered. A general increasing trend in air temperature is revealed, and a heterogeneous pattern of precipitation changes is shown. The temperature variations until the end of the 21st century are analyzed on the example of Kazan using 40 CMIP6 climate models for four anthropogenic scenarios. In the paper, there is an assessment of the dynamics of agroclimatic resources on the territory of the Volga Federal District: the duration of the growing season, the sum of positive temperatures, total precipitation, and photosynthetic radiation. A correlation has been found between the temperature fluctuations in the region and the atmospheric circulation indices (NAO, AO, SCAND, and EAWR). For the territory of Tatarstan, a degree of aridity and waterlogging is evaluated using the agroclimatic indices: the Budyko's dryness index, the Selyaninov's hydrothermal coefficient, the Sapozhnikova's moisture index. It is shown that there is a tendency toward an increase in the duration of the growing season, its heat supply and aridity in the region in summer. A statistical estimation of the dependence of spring wheat yield on the agroclimatic indices on the territory of the Republic of Tatarstan is given. [ABSTRACT FROM AUTHOR]

In the paper, the frequency and spatial distribution of atmospheric and hydrological droughts over the territory of Belarus in 1945–2020 is assessed. Aridity is evaluated using the standardized precipitation and streamflow indices (SPI and SSFI). Trends in monthly total precipitation are determined, the features of the streamflow formation during the open-channel period are revealed. The frequency of anticyclones that determines hydrometeorological conditions on the territory of Belarus is calculated. The statistically significant increase in the number of the days with anticyclonic weather over the country during the warm season is found. The increasing frequency of dry conditions in summer months against the background of a rise in the precipitation intensity is highlighted. A simultaneous increase in the frequency of very-low-flow periods on rivers and an increase in the height of warm rainfall floods in some river basins are revealed. [ABSTRACT FROM AUTHOR]

The overview of papers dealing with the analysis of current and future climate change on the territory of Russia and their impact on the crop productivity is presented. Using the reanalysis data for 1950–2020, trends in air temperature and precipitation are estimated for different regions of Russia. A correlation was found between changes in temperature and atmospheric circulation indices. [ABSTRACT FROM AUTHOR]

This paper presents results from high-resolution climate change simulations that permit convection and resolve mesoscale orography at 3-km grid spacing over Fenno-Scandinavia using the HARMONIE-Climate (HCLIM) model. Two global climate models (GCMs) have been dynamically down-scaled for the RCP4.5 and RCP8.5 emission scenarios and for both near and far future periods in the 21st century. The warmer and moister climate conditions simulated in the GCMs lead to changes in precipitation characteristics. Higher precipitation amounts are simulated in fall, winter and spring, while in summer, precipitation increases in northern Fenno-Scandinavia and decreases in the southern parts of the domain. Both daily and sub-daily intense precipitation over Fenno-Scandinavia become more frequent at the expense of low-intensity events, with most pronounced shifts in summer. In the Scandinavian mountains, pronounced changes occur in the snow climate with a shift in precipitation falling as snow to rain, reduced snow cover and less days with a significant snow depth. HCLIM at 3-km grid spacing exhibits systematically different change responses in several aspects, e.g. a smaller shift from snow to rain in the western part of the Scandinavian mountains and a more consistent decrease in the urban heat island effect by the end of the 21st century. Most importantly, the high-resolution HCLIM shows a significantly stronger increase in summer hourly precipitation extremes compared to HCLIM at the intermediate 12-km grid spacing. In addition, an analysis of the statistical significance of precipitation changes indicates that simulated time periods of at least a couple of decades is recommended to achieve statistically robust results, a matter of important concern when running such high-resolution climate model experiments. The results presented here emphasizes the importance of using "convection-permitting" models to produce reliable climate change information over the Fenno-Scandinavian region. [ABSTRACT FROM AUTHOR]

Research into weather circulation changes over the UK for future climate has mainly focused on changes in the Summer and Winter seasons, with less analysis on seasonality and the transition seasons. Using the 30 Met Office weather patterns we examine the influence of climate change on seasonality through atmospheric circulation using a number of climate models. Changes in seasonality are important as they can have large impacts on many sectors including agriculture, energy and tourism. This paper finds a noticeable increase in Autumn over the UK in the frequency of drier summer-type regimes and a decrease in stormy winter types that emerge as early as the 2020s. The change in circulation signal once isolated from the overall signal is responsible for a 4–12% decrease in Autumn mean rainfall on average for England by the end of this century (where the values in the range are dependent on the emissions scenario). This change is projected over English regions that are already experiencing water stress, and with predictions of drier summers over the UK in future, this could further increase drought risk. The change in circulation in Autumn also moderates the large increase in the number of large-scale extreme daily rainfall events over the same regions predicted due to climate change. While this future circulation change is replicated across all the climate models used, large differences remain in the strength of the signal between models. The climate models used replicate the frequency of the 30 weather patterns well for all seasons. [ABSTRACT FROM AUTHOR]

Global warming and climate change impacts intensify hydrological cycle and consequently unprecedented drought and flood appear in different parts of the world. Meteorological drought assessments are widely evaluated by the concept of standardized precipitation index (SPI), which provides drought classification. Its application is based on the probabilistic standardization procedure, but in the literature, there is a confusion with the statistical standardization procedure. This paper provides distinctive differences between the two approaches and provides the application of a better method. As a novel approach, SPI classification is coupled with fuzzy clustering procedure, which provides drought evaluation procedure based on two variables jointly, precipitation and temperature, which is referred to as the standard precipitation-temperature index (SPTI). The final product is in the form of fuzzy c-means clustering in five clusters with exposition of annual drought membership degrees (MDs) for each cluster and resulting objective function. The application of the proposed fuzzy methodology is presented for the long-term annual precipitation and temperature records from New Jersey Statewide records. [ABSTRACT FROM AUTHOR]

Long instrumental records are fundamental to study climate changes, but their reliability and quality have to be checked before any statistical research. Moreover, when metadata are used to solve some problems, data interpretation may change and require further work to refine the series. A thorough revision of the three-century precipitation series in Padua (1713–2018) shows that the results of previous analyses were affected by serious biases. This paper clarifies key features concerning early instruments, exposure, relocation and observational protocols. The daily analysis pointed out a number of problems, bias, irregular sampling and underestimations that have passed unobserved so far. A comparison with the parallel series of Bologna and Venice made it possible to distinguish bias from the climate signal or to reconstruct missing data. The instrumental threshold was recognized to be fundamental to determine the frequency of precipitation, but less important with respect to its amount. This paper provides a methodological example to test the goodness of long instrumental series, in particular to identify problems related to metadata and observations, which is the preliminary step to perform a sound correction and obtain a reliable series. It also includes the set of original raw data, transformed into modern units. [ABSTRACT FROM AUTHOR]

Xinjiang is located in the core China's 'Belt and Road' development, and northern Xinjiang is an important region for economic development. In recent years, due to the strong influence of global climate change and human disturbance, regional climate instability and ecological-economic-social system sensitivity have grown. In this paper, seasonal, interannual, interdecadal, spatial, abrupt, and periodic variations of temperature and precipitation in northern Xinjiang were analyzed using daily surface air temperature and precipitation data from 49 meteorological stations during 1961–2017. At the same time, the driving factors of climate change are discussed. Methods included linear regression, cumulative anomaly, the Mann-Kendall test, and Morlet wavelet analysis. The results indicated that during the study period, annual mean temperature and annual precipitation increased significantly at rates of 0.35°C/10 yr and 13.25 mm/10 yr, respectively, with abrupt changes occurring in 1994 and 1986. Annual mean temperature and annual precipitation in all four seasons showed increasing trends, with the maximum increases in winter of 0.42°C/10 yr and 3.95 mm/10 yr, respectively. The general climate in northern Xinjiang showed a trend towards increasingly warm and humid. In terms of spatial distribution, the temperature and precipitation in high mountainous areas increased the most, while basins areas increased only slightly. Periodic change analysis showed that annual mean temperature and annual precipitation experienced two climatic shifts from cold to warm and dry to wet, respectively. Population change, economic development and land use change are important factors affecting climate change, and more research should be done in this field. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to elucidate a potential use of the large samples of seasonal means that hindcasts provide for investigating different aspects of climate variability. This use of hindcasts complements their traditional uses in bias correction, real-time forecast calibration, and prediction skill assessment. For seasonal hindcast data from NCEP CFSv2 we show that a sample size 5208 for each target season is achievable. To demonstrate the utility of the proposed concept, we use this large sample dataset to illustrate how it could be used in documenting spatial variability in various moments of seasonal mean precipitation PDF over the US, and further, quantify nuances in the variations in precipitation PDF at different geographical locations with the amplitude of ENSO SSTs. It is our hope that analysis presented in this paper will accelerate utilization of seasonal hindcast datasets in furthering our understanding of different aspects of climate variability. With the advantage of the large sample size, we demonstrated that the precipitation PDF at the each grid of the CONUS can be represented by gamma distribution for a more concise and effective way to summarize precipitation variability. The availability of the large sample dataset also allowed us to analyze the statistical characteristic of the precipitation responses to the different amplitudes of ENSO SSTs. The results show that for strong warm events, enhancement in precipitation has larger amplitude than decrease in precipitation for cold events in the regions of Southern California and southeastern US. The variation of the precipitation signal over the other sub-regions including the southwestern US, mid-northwest, and mid-east shows more linear relationship with the ENSO SSTs. In response to anomalous ENSO SSTs, although the PDF of December-January-February seasonal mean precipitation anomaly is shifted from its climatological PDF, there is still a large overlap between precipitation PDFs for ENSO and its climatological counterpart. This uncertainty in seasonal mean outcomes of precipitation, therefore, limits the seasonal prediction skill. [ABSTRACT FROM AUTHOR]

Translating changes in land surface conditions and climate into changes in precipitation, runoff and flood frequencies over a range of catchment scales is a pressing challenge for hydrologic design and flood risk management today. In this paper, we describe a novel approach for modeling areal runoff frequency from heavy-to-extreme precipitation in large urban areas using a simple but general stochastic model for runoff at point scale and bi- and univariate parametric probability distributions for positive point precipitation and areal runoff, respectively. We apply the approach to the Dallas–Fort Worth area using a 22-year historical multisensor precipitation dataset from the National Weather Service to characterize how the different factors that specify the second-order statistics of precipitation, imperviousness and soil water holding capacity at point scale may shape areal runoff frequency, and to assess how changes in precipitation climatology and land surface conditions may change areal runoff frequency as a function of catchment scale and magnitude of precipitation. The results indicate that areal runoff frequency is impacted most significantly by changes in climatological mean and coefficient of variation of positive point precipitation, water holding capacity of soil, imperviousness, and spatial correlation scale of positive point precipitation given the probability of occurrence of heavy-to-extreme precipitation, and that a very small number of low-order statistics of point precipitation may describe areal runoff frequency given the conditional probability distribution models for point precipitation and areal runoff. The approach presented hence offers a parsimonious physically-based alternative to purely numerical approaches based on integrated modeling, or empirical approaches based solely on statistical modeling toward predictive modeling of areal precipitation and runoff frequencies under changing hydroclimatological conditions. [ABSTRACT FROM AUTHOR]

The availability of reliable meteorological records is crucial for the development of a number of environmental studies. Unfortunately, these records are not always complete, usually show errors and/or have an insufficient length. This paper presents a gap filling and data record extension methodology for minimum temperature, maximum temperature, and precipitation. It uses climatic information from the NCEP-NCAR Reanalysis project, identifying pixels (grid cells) within a Reanalysis domain that have the highest Pearson's correlation coefficient with the variable of interest. Nine stations in the Maipo River basin (Santiago, Chile) were selected for a reconstruction experiment (from 1950 to 1970) and a subsequent gap filling experiment (from 1970 to 2012). A generalized linear mixed model with a bidirectional stepwise fit procedure was used to model temperature, whereas precipitation occurrence was represented using a generalized linear mixed model with binomial distribution, and precipitation amount used an exponential generalized linear model. The performance of the algorithm was compared with inverse distance weighting and spline interpolation methods and further evaluated using the Standardized Precipitation Evapotranspiration Index, contrasting real versus modeled data. Values of the coefficient of determination averaged 0.76 (0.74–0.84) minimum temperature, 0.73 (0.73–0.81) for maximum temperature, and 0.68 (0.51–0.78) for precipitation. Root-mean-squared error was around 1.5 °C and 5 mm for temperature and precipitation, respectively. The model explains local variation of climatic variables and indicators and can be replicated anywhere, as the Reanalysis data are easily accessible and have a worldwide coverage. [ABSTRACT FROM AUTHOR]

Downscaling techniques are the required tools to link the global climate model outputs provided at a coarse grid resolution to finer scale surface variables appropriate for climate change impact studies. Besides the at-site temporal persistence, the downscaled variables have to satisfy the spatial dependence naturally observed between the climate variables at different locations. Furthermore, the precipitation spatial intermittency should be fulfilled. Because of the complexity in describing these properties, they are often ignored, which can affect the effectiveness of the hydrologic process modeling. This study is a continuation of the work by Khalili and Nguyen (Clim Dyn 49(7-8):2261-2278. 10.1007/s00382-016-3443-6, 2017) regarding the multi-site statistical downscaling of daily precipitation series. Different approach of multi-site statistical downscaling based on the concept of the spatial autocorrelation is presented in this paper. This approach has proven to give effective results for multi-site multivariate statistical downscaling of daily extreme temperature time series (Khalili et al. in Int J Climatol 33:15-32. 10.1002/joc.3402, 2013). However, more challenges are presented by the precipitation variable because of the high spatio-temporal variability and intermittency. The proposed approach consists of logistic and multiple regression models, linking the global climate predictors to the precipitation occurrences and amounts respectively, and using the spatial autocorrelation concept to reproduce the spatial dependence observed between the precipitation series at different sites. An empirical technique has also been involved in this approach in order to fulfill the precipitation intermittency property. The proposed approach was performed using observed daily precipitation data from ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset. The results have proven the ability of the proposed approach to adequately reproduce the observed precipitation occurrence and amount characteristics, temporal and spatial dependence, spatial intermittency and temporal variability. [ABSTRACT FROM AUTHOR]

This paper examines an asymmetric spatiotemporal connection and climatic impact between the winter atmospheric blocking activity in the Euro-Atlantic sector and the life cycle of the North Atlantic Oscillation (NAO) during the period 1950-2012. Results show that, for positive NAO (NAO+) events, the instantaneous blocking (IB) frequency exhibits an enhancement along the southwest-northeast (SW-NE) direction from the eastern Atlantic to northeastern Europe (SW-NE pattern, hereafter), which is particularly evident during the NAO+ decaying stage. By contrast, for negative NAO (NAO−) events, the IB frequency exhibits a spatially asymmetric southeast-northwest (SE-NW) distribution from central Europe to the North Atlantic and Greenland (SE-NW pattern, hereafter). Moreover, for NAO− (NAO+) events, the most marked decrease (increase) in the surface air temperature (SAT) in winter over northern Europe is in the decaying stage. For NAO+ events, the dominant positive temperature and precipitation anomalies exhibit the SW-NE-oriented distribution from western to northeastern Europe, which is parallel to the NAO+-related blocking frequency distribution. For NAO- events, the dominant negative temperature anomaly is in northern and central Europe, whereas the dominant positive precipitation anomaly is distributed over southern Europe along the SW-NE direction. In addition, the downward infrared radiation controlled by the NAO’s circulation plays a crucial role in the SAT anomaly distribution. It is further shown that the NAO’s phase can act as an asymmetric impact on the European climate through producing this asymmetric spatiotemporal connection with the Euro-Atlantic IB frequency. [ABSTRACT FROM AUTHOR]

Global Climate Models (GCMs) have been extensively used in many climate change impact studies. However, the coarser resolution of these GCM outputs is not adequate to assess the potential effects of climate change on local scale. Downscaling techniques have thus been proposed to resolve this problem either by dynamical or statistical approaches. The statistical downscaling (SD) methods are widely privileged because of their simplicity of implementation and use. However, many of them ignore the observed spatial dependence between different locations, which significantly affects the impact study results. An improved multi-site SD approach is thus presented in this paper to downscaling of daily precipitation at many sites concurrently. This approach is based on a combination of multiple regression models for rainfall occurrences and amounts and the Singular Value Decomposition technique, which models the stochastic components of these regression models to preserve accurately the space-time statistical properties of the daily precipitation. Furthermore, this method was able to describe adequately the intermittency property of the precipitation processes. The proposed approach has been assessed using 10 rain gauges located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the National Centers for Environmental Prediction/National Centre for Atmospheric Research re-analysis data set. The results have indicated the ability of the proposed approach to reproduce accurately multiple observed statistical properties of the precipitation occurrences and amounts, the at-site temporal persistence, the spatial dependence between sites and the temporal variability and spatial intermittency of the precipitation processes. [ABSTRACT FROM AUTHOR]

How soil moisture affects precipitation is an important question-with far reaching consequences, from weather prediction to centennial climate change-, albeit a poorly understood one. In this paper, an analysis of soil moisture-precipitation interactions over France based on observations is presented. A first objective of this paper is to investigate how large scale circulation modulates soil moisture-precipitation interactions, thanks to a weather regime approach. A second objective is to study the influence of soil moisture not only on precipitation but also on the difference between precipitation and evapotranspiration. Indeed, to have a total positive soil moisture-precipitation feedback, the potential decrease in precipitation associated with drier soils should be larger than the decrease in evapotranspiration that drier soils may also cause. A potential limited impact of soil moisture on precipitation is found for some weather regimes, but its sign depends on large scale circulation. Indeed, antecedent dry soil conditions tend to lead to smaller precipitation for the negative phase of the North Atlantic Oscillation (NAO) regime but to larger precipitation for the Atlantic Low regime. This differential response of precipitation to soil moisture anomalies depending on large scale circulation is traced back to different responses of atmospheric stability. For all circulation regimes, dry soils tend to increase the lifted condensation level, which is unfavorable to precipitation. But for the negative phase of the NAO, low soil moisture tends to lead to an increase of atmospheric stability while it tends to lead to a decrease of stability for Atlantic Low. Even if the impact of soil moisture anomalies varies depending on large scale circulation (it is larger for Atlantic low and the positive phase of the NAO), dry soils always lead to a decrease in evapotranspiration. As the absolute effect of antecedent soil moisture on evapotranspiration is always much larger than its effects on precipitation, for all circulation regimes dry soil anomalies subsequently lead to positive precipitation minus evapotranspiration anomalies i.e. the total soil moisture feedback is found to be negative. This negative feedback is stronger for the Atlantic Low and the positive phase of the NAO regimes. [ABSTRACT FROM AUTHOR]

The projected climate change signals of a five-member high resolution ensemble, based on two global climate models (GCMs: ECHAM5 and CCCma3) and two regional climate models (RCMs: CLM and WRF) are analysed in this paper (Part II of a two part paper). In Part I the performance of the models for the control period are presented. The RCMs use a two nest procedure over Europe and Germany with a final spatial resolution of 7 km to downscale the GCM simulations for the present (1971-2000) and future A1B scenario (2021-2050) time periods. The ensemble was extended by earlier simulations with the RCM REMO (driven by ECHAM5, two realisations) at a slightly coarser resolution. The climate change signals are evaluated and tested for significance for mean values and the seasonal cycles of temperature and precipitation, as well as for the intensity distribution of precipitation and the numbers of dry days and dry periods. All GCMs project a significant warming over Europe on seasonal and annual scales and the projected warming of the GCMs is retained in both nests of the RCMs, however, with added small variations. The mean warming over Germany of all ensemble members for the fine nest is in the range of 0.8 and 1.3 K with an average of 1.1 K. For mean annual precipitation the climate change signal varies in the range of −2 to 9 % over Germany within the ensemble. Changes in the number of wet days are projected in the range of ±4 % on the annual scale for the future time period. For the probability distribution of precipitation intensity, a decrease of lower intensities and an increase of moderate and higher intensities is projected by most ensemble members. For the mean values, the results indicate that the projected temperature change signal is caused mainly by the GCM and its initial condition (realisation), with little impact from the RCM. For precipitation, in addition, the RCM affects the climate change signal significantly. [ABSTRACT FROM AUTHOR]

A five-member ensemble of regional climate model (RCM) simulations for Europe, with a high resolution nest over Germany, is analysed in a two-part paper: Part I (the current paper) presents the performance of the models for the control period, and Part II presents results for near future climate changes. Two different RCMs, CLM and WRF, were used to dynamically downscale simulations with the ECHAM5 and CCCma3 global climate models (GCMs), as well as the ERA40-reanalysis for validation purposes. Three realisations of ECHAM5 and one with CCCma3 were downscaled with CLM, and additionally one realisation of ECHAM5 with WRF. An approach of double nesting was used, first to an approximately 50 km resolution for entire Europe and then to a domain of approximately 7 km covering Germany and its near surroundings. Comparisons of the fine nest simulations are made to earlier high resolution simulations for the region with the RCM REMO for two ECHAM5 realisations. Biases from the GCMs are generally carried over to the RCMs, which can then reduce or worsen the biases. The bias of the coarse nest is carried over to the fine nest but does not change in amplitude, i.e. the fine nest does not add additional mean bias to the simulations. The spatial pattern of the wet bias over central Europe is similar for all CLM simulations, and leads to a stronger bias in the fine nest simulations compared to that of WRF and REMO. The wet bias in the CLM model is found to be due to a too frequent drizzle, but for higher intensities the distributions are well simulated with both CLM and WRF at the 50 and 7 km resolutions. Also the spatial distributions are close to high resolution gridded observations. The REMO model has low biases in the domain averages over Germany and no drizzle problem, but has a shift in the mean precipitation patterns and a strong overestimation of higher intensities. The GCMs perform well in simulating the intensity distribution of precipitation at their own resolution, but the RCMs add value to the distributions when compared to observations at the fine nest resolution. [ABSTRACT FROM AUTHOR]

The Arctic cell as a reversed and closed loop next to the Polar cell has been recently revealed in the Northern Hemisphere (NH). In this paper, we study the interannual variability of the Arctic and Polar cell strengths during 1979-2012, and their influence on surface air temperature (SAT), precipitation, and sea-ice concentration (SIC) at mid- and high-latitudes of the NH. We show that there is a significant negative correlation between the Arctic and Polar cell strengths. Both the Arctic and Polar cell strengths can well indicate the recurring climatic anomalies of SAT, precipitation, and SIC in their extreme winters. The surface large-scale cold-warm and dry-wet anomalous patterns in these extreme winters are directly linked with the vertical structure of height and temperature anomalies in the troposphere. Results suggest that the past climatic anomalies are better indicated by the strength anomalies of the Polar and Arctic cells than the traditional indices of mid-high latitude pattern such as the Arctic Oscillation and North Atlantic Oscillation. This study illustrates a three-dimensional picture of atmospheric variable anomalies in the troposphere that result in surface climatic anomalies. [ABSTRACT FROM AUTHOR]

Statistical downscaling techniques are required to refine atmosphere-ocean global climate data and provide reliable meteorological information such as a realistic temporal variability and relationships between sites and variables in a changing climate. To this end, the present paper introduces a modular structure combining two statistical tools of increasing interest during the last years: (1) Gaussian copula and (2) quantile regression. The quantile regression tool is employed to specify the entire conditional distribution of downscaled variables and to address the limitations of traditional regression-based approaches whereas the Gaussian copula is performed to describe and preserve the dependence between both variables and sites. A case study based on precipitation and maximum and minimum temperatures from the province of Quebec, Canada, is used to evaluate the performance of the proposed model. Obtained results suggest that this approach is capable of generating series with realistic correlation structures and temporal variability. Furthermore, the proposed model performed better than a classical multisite multivariate statistical downscaling model for most evaluation criteria. [ABSTRACT FROM AUTHOR]

The Eastern Mediterranean region has been exposed to drought episodes, which have been occurring more frequently during the last decades. The objective of the present paper is to study the precipitation regime of the Damascus (Mazzeh) meteoric station by analysing drought characteristics using the Standardized Precipitation Index (SPI) and comparing this with the drought in Cyprus. The cumulative drought conceptis proposed to characterize long-term hydrologic drought, which affects the shallow groundwater productivity in terms of quantity and quality. Gamma probability distribution was fitted to the long-term annual precipitation in Damascus from 1918-1919 to 2007-2008 ( n = 90 years). Generally, a decreasing trend of 17% to the mean annual rainfall of Damascus and 13% to the mean annual rainfall of Cyprus was estimated between 1970 and 2000. The SPI identifies three major extended drought periods: (1) 9 years of severe drought (1954-1963) with an average 20% precipitation deficit per year compared to the mean. (2) 8 years of severe drought (1983-1991) with a 27% deficit per year on average. (3) 9 years of extreme drought (1993-2002) with a 31% deficit per year on average. The cumulative standardized precipitation index (SPI 30) demonstrates positive values for the first period and is indicative of having no effect on the global water balance. SPI 30 exhibits sensitive equilibrium with near zero values / a near zero value (±1.5) for the second period. For the third period, however, the SPI 30 decreases below −10 indicating an extreme hydrological drought that has negative consequences on the recent groundwater recharge. It is required to develop and implement a sustainable groundwater management strategy to reduce long-terms drought risks. Generally, the SPI 30 in Cyprus is parallel to that in Damascus with a 3-5 year delay. Thus, the central zone of the Eastern Mediterranean region is facing big challenges and has been suffering from three decades of moderate to severe hydrological drought (SPI 30=−5 to −10) causing a severe decrease in springs discharges of the region. Therefore, in order to reduce the climate change effects on water resources, it is necessary to adopt a sustainable proactive management plan during the frequent severe droughts. [ABSTRACT FROM AUTHOR]

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)-two contrasting SD methods-in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector. [ABSTRACT FROM AUTHOR]

The Andes Cordillera plays a role in driving moisture and heat from tropical onto subtropical South America. It forces the development of a lee-side trough that covers most of western Argentina and a low-level jet that maximizes over Paraguay, eastern Bolivia and northern Argentina and is tightly linked to precipitation variability over much of central and southeastern South America. Its steep slopes and the large zonal gradients in topography between the Equator and 40°S are misrepresented in climate simulations using Global Climate Models (GCM) with resolutions coarser than about 100 km, since they naturally have a poor representation of the Andes and related circulation features. This paper analyses the impact of varying artificially the altitude of the Andes Cordillera in a GCM as well as increasing the horizontal resolution to study how these variations determine moisture fluxes and precipitation over selected regions of South America. Results show that the height of the Andes is crucial in shaping moisture fluxes pathways onto subtropical South America all year long. In particular, the low-level jet is only simulated when the Andes heights are doubled. At the same time, the relationship between the Andes shape and the location of the Bolivian High in summer is also discussed. In terms of precipitation, the lowest bias in the simulations is achieved when the horizontal resolution is increased, while in particular near the Andes foothills the simulated annual rainfall is largely determined by the Mountains shape. [ABSTRACT FROM AUTHOR]

Since climatic condition is the important foundation for human subsistence and development and the key factor in sustainable development of economy and society, climate change has been a global issue attracting great attentions of politicians, scientists, governments, and the public alike throughout the world. Existing climate regionalization in China aims to characterize the regional differences in climate based on years of the mean value of different climate indexes. However, with the accelerating climate change nowadays, existing climate regionalization cannot represent the regional difference of climate change, nor can it reflect the disasters and environmental risks incurred from climate changes. This paper utilizes the tendency value and fluctuation value of temperature and precipitation from 1961 to 2010 to identify the climate change quantitatively, and completes the climate change regionalization in China (1961-2010) with county administrative regionalization as the unit in combination with China's terrain feature. Level-I regionalization divides China's climate change (1961-2010) into five tendency zones based on the tendency of temperature and precipitation, which are respectively Northeast China-North China warm-dry trend zone, East China-Central China wet-warm trend zone, Southwest China-South China dry-warm trend zone, Southeast Tibet-Southwest China wet-warm trend zone, and Northwest China-Qinghai-Tibet Plateau warm-wet trend zone; level-II regionalization refers to fourteen fluctuation regions based on level-I regionalization according to the fluctuation of temperature and precipitation. [ABSTRACT FROM AUTHOR]

On a hemispheric scale, it is now well established that stratospheric ozone depletion has been the principal driver of externally forced atmospheric circulation changes south of the Equator in the last decades of the 20th Century. The impact of ozone depletion has been felt over the entire hemisphere, as reflected in the poleward drift of the midlatitude jet, the southward expansion of the summertime Hadley cell and accompanying precipitation trends deep into the subtropics. On a regional scale, however, surface impacts directly attributable to ozone depletion have yet to be identified. In this paper we focus on South Eastern South America (SESA), a region that has exhibited one of the largest wetting trends during the 20th Century. We study the impact of ozone depletion on SESA precipitation using output from 6 different climate models, spanning a wide range of complexity. In all cases we contrast pairs of model integrations with and without ozone depletion, but with all other forcings identically specified. This allows for unambiguous attribution of the computed precipitation trends. All 6 climate models consistently reveal that stratospheric ozone depletion results in a significant wetting of SESA over the period 1960-1999. Taken as a whole, these model results strongly suggest that the impact of ozone depletion on SESA precipitation has been as large as, and quite possibly larger than, the one caused by increasing greenhouse gases over the same period. [ABSTRACT FROM AUTHOR]

This paper presents results of trend analysis and change point detection of annual and seasonal precipitation, and mean temperature (TM), maximum temperature (TMAX) and minimum temperature (TMIN) time series of the period 1950-2007. Investigations were carried out for 50 precipitation stations and 39 temperature stations located in southwest Iran. Three statistical tests including Pettitt's test, Sequential Mann-Kendall test (SQ-MK test) and Mann-Kendall rank test (MK-test) were used for the analysis. The results obtained for precipitation series indicated that most stations showed insignificant trends in annual and seasonal series. Out of the stations which showed significant trends, highest numbers were observed during winter season while no significant trends were detected in summer precipitation. Moreover, no decreasing significant trends were detected by statistical tests in annual and seasonal precipitation series. The analysis of temperature trends revealed a significant increase during summer and spring seasons. TMAX was more stable than TMIN and TM, and winter was stable compared to summer, spring and autumn seasons. The results of change point detection indicated that most of the positive significant mutation points in TM, TMAX and TMIN began in the 1990s. [ABSTRACT FROM AUTHOR]

Precipitation over the contiguous United States exhibits large multi-decadal oscillations since the early twentieth century, and they often lead to dry (e.g., 1946-1976 and 1999-present) and wet (e.g., 1977-1998) periods and apparent precipitation trends (e.g., from the 1950s to 1990s) over most of the western and central US. The exact cause of these inter-decadal variations is not fully understood. Using observational and reanalysis data and model simulations, this paper examines the influence of the Inter-decadal Pacific Oscillation (IPO) on US precipitation. The IPO is a leading mode of sea surface temperatures (SSTs) seen mostly in the Pacific Ocean. It is found that decadal precipitation variations over much of the West and Central US, especially the Southwest, closely follow the evolution of the IPO ( r = 0.85 during 1923-2010 for the Southwest US), and the dry and wet periods are associated, respectively, with the cold and warm phases of the IPO. In particular, the apparent upward trend from the 1950s-1990s and the dry decade thereafter in precipitation over much of the West and Central US are largely caused by the IPO cycles, which switched to a warm phase around 1977 and back to a cold phase around 1999. An atmospheric model forced with observed SSTs reproduces much of this association of US precipitation with the IPO ( r = 0.95 between smoothed observed and simulated Southwest US precipitation during 1950-2009 and r = 0.88 between the simulated Southwest US precipitation and the IPO). Atmospheric reanalysis and model data both show a strong high (low) pressure center and anti-cyclonic (cyclonic) anomaly circulation over the North Pacific in the lower troposphere during cold (warm) phases of the IPO, which lead to dry and cold northwesterly and northerly winds and below-normal precipitation over much of the West US during IPO cold periods. The IPO induced changes are most pronounced during the boreal cold season. The results reinforce the notion that tropical Pacific SSTs (and the accompanying SST anomalies in the North Pacific) have large impacts on US precipitation and highlight the need to understand and simulate the IPO for decadal prediction of US precipitation. [ABSTRACT FROM AUTHOR]

Runoffs in the Yellow River and Yangtze River basins, China, have been changing constantly during the last half century. In this paper, data from eight river gauging stations and 529 meteorological stations, inside and adjacent to the study basins, were analyzed and compared to quantify the hydrological processes involved, and to evaluate the role of human activities in changing river discharges. The Inverse Distance Weighted (IDW) interpolation method was used to obtain climatic data coverage from station observations. According to the runoff coefficient equation, the effect of human activities and climate can be expressed by changes in runoff coefficients and changes in precipitation, respectively. Annual runoff coefficients were calculated for the period 1950-2008, according to the correlation between respective hydrological series and regional precipitation. Annual precipitation showed no obvious trend in the upper reaches of the Yellow River but a marked downward trend in the middle and downstream reaches, with declines of 8.8 and 9.8 mm/10 a, respectively. All annual runoff series for the Yellow River basin showed a significant downward trend. Runoff declined by about 7.8 mm/10 a at Sanmenxia and 10.8 mm/10 a at Lijin. The series results indicated that an abrupt change occurred in the late 1980s to early 1990s. The trend of correlations between annual runoff and precipitation decreased significantly at the Yellow River stations, with rates ranging from 0.013/10 a to 0.019/10 a. For the hydrologic series, all precipitation series showed a downward trend in the Yangtze River basin with declines ranging from about 24.7 mm/10 a at Cuntan to 18.2 mm/10 a at Datong. Annual runoff series for the upper reaches of the Yangtze River decreased significantly, at rates ranging from 9.9 to 7.2 mm/10 a. In the middle and lower reaches, the runoff series showed no significant trend, with rates of change ranging from 2.1 to 2.9 mm/10 a. Human activities had the greatest influence on changes in the hydrological series of runoff, regardless of whether the effect was negative or positive. During 1970-2008, human activities contributed to 83% of the reduction in runoff in the Yellow River basin, and to 71% of the increase in runoff in the Yangtze River basin. Moreover, the impacts of human activities across the entire basin increased over time. In the 2000s, the impact of human activities exceeded that of climate change and was responsible for 84% of the decrease and 73% of the increase in runoff in the Yellow River and Yangtze River basins, respectively. The average annual runoff from 1980 to 2008 fell by about 97%, 83%, 83%, and 91%, compared with 1951-1969, at the Yellow River stations Lanzhou, Sanmenxia, Huayuankou and Lijin, respectively. Most of the reduction in runoff was caused by human activities. Changes in precipitation also caused reductions in runoff of about 3%, 17%, 17%, and 9% at these four stations, respectively. Falling precipitation rates were the main explanation for runoff changes at the Yangtze River stations Cuntan, Yichang, Hankou, and Datong, causing reductions in runoff of 89%, 74%, 43%, and 35%, respectively. Underlying surface changes caused decreases in runoff in the Yellow River basin and increases in runoff in the Yangtze River basin. Runoff decreased in arid areas as a result of increased water usage, but increased in humid and sub-humid areas as a result of land reclamation and mass urbanization leading to decreases in evaporation and infiltration. [ABSTRACT FROM AUTHOR]

The accurate representation of rainfall in models of global climate has been a challenging task for climate modelers owing to its small space and time scales. Quantifying this variability is important for comparing simulations of atmospheric behavior with real time observations. In this regard, this paper compares both the statistical and dynamically forced aspects of precipitation variability simulated by the high-resolution (36 km) Nested Regional Climate Model (NRCM), with satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset and simulations from the Community Atmosphere Model (CAM) at T85 spatial resolution. Six years of rainfall rate data (2000-2005) from within the Tropics (30°S-30°N) have been used in the analysis and results are presented in terms of long-term mean rain rates, amplitude and phase of the annual cycle and seasonal mean maps of precipitation. Our primary focus is on characterizing the annual cycle of rainfall over four land regions of the Tropics namely, the Indian Monsoon, the Amazon, Tropical Africa and the North American monsoon. The lower tropospheric circulation patterns are analyzed in both the observations and the models to identify possible causes for biases in the simulated precipitation. The 6-year mean precipitation simulated by both models show substantial biases throughout the global Tropics with NRCM/CAM systematically underestimating/overestimating rainfall almost everywhere. The seasonal march of rainfall across the equator, following the motion of the sun, is clearly seen in the harmonic vector maps. The timing of peak rainfall (phase) produced by NRCM is in closer agreement with the observations compared to CAM. However like the long-time mean, the magnitude of seasonal mean rainfall is greatly underestimated by NRCM throughout the Tropical land mass. Some of these regional biases can be attributed to erroneous circulation and moisture surpluses/deficits in the lower troposphere in both models. Overall, the results seem to indicate that employing a higher spatial resolution (36 km) does not significantly improve simulation of precipitation. We speculate that a combination of several physics parameterizations and lack of model tuning gives rise to the observed differences between NRCM and the observations. [ABSTRACT FROM AUTHOR]

Interannual variability is an important modulator of synoptic and intraseasonal variability in South America. This paper seeks to characterize the main modes of interannual variability of seasonal precipitation and some associated mechanisms. The impact of this variability on the frequency of extreme rainfall events and the possible effect of anthropogenic climate change on this variability are reviewed. The interannual oscillations of the annual total precipitation are mainly due to the variability in austral autumn and summer. While autumn is the dominant rainy season in the northern part of the continent, where the variability is highest (especially in the northeastern part), summer is the rainy season over most of the continent, thanks to a summer monsoon regime. In the monsoon season, the strongest variability occurs near the South Atlantic Convergence Zone (SACZ), which is one of the most important features of the South American monsoon system. In all seasons but summer, the most important source of variability is ENSO (El Niño Southern Oscillation), although ENSO shows a great contribution also in summer. The ENSO impact on the frequency of extreme precipitation events is also important in all seasons, being generally even more significant than the influence on seasonal rainfall totals. Climate change associated with increasing emission of greenhouse gases shows potential to impact seasonal amounts of precipitation in South America, but there is still great uncertainty associated with the projected changes, since there is not much agreement among the models' outputs for most regions in the continent, with the exception of southeastern South America and southern Andes. Climate change can also impact the natural variability modes of seasonal precipitation associated with ENSO. [ABSTRACT FROM AUTHOR]

How the Hadley circulation changes in response to global climate change and how its change impacts upon regional and global climates has generated a lot of interest in the literature in the past few years. In this paper, consistent and statistically significant poleward expansion of the Hadley circulation in the past few decades is demonstrated, using independent observational datasets as proxy measures of the Hadley circulation. Both observational outgoing longwave radiation and precipitation datasets show an annual average total poleward expansion of the Hadley cells of about 3.6° latitude. Sea level pressure from observational and reanalysis datasets show smaller magnitudes of poleward expansion, of about 1.2° latitude. Ensemble general circulation model simulations forced by observed time-varying sea surface temperatures were found to generate a total poleward expansion of about 1.23° latitude. Possible mechanisms behind the changes in the horizontal extent of the Hadley circulation are discussed. [ABSTRACT FROM AUTHOR]

Feeding the world’s growing population is a serious challenge. Food insecurity is concentrated in developing nations, where drought and low soil fertility are primary constraints to food production. Many crops in developing countries are supported by weathered soils in which nutrient deficiencies and ion toxicities are common. Many systems have declining soil fertility due to inadequate use of fertility inputs, ongoing soil degradation, and increasingly intense resource use by burgeoning populations. Climate models predict that warmer temperatures and increases in the frequency and duration of drought during the 21st century will have net negative effects on agricultural productivity. The potential effects of climate change on soil fertility and the ability of crops to acquire and utilize soil nutrients is poorly understood, but is essential for understanding the future of global agriculture. This paper explores how rising temperature, drought and more intense precipitation events projected in climate change scenarios for the 21st century might affect soil fertility and the mineral nutrition of crops in developing countries. The effects of climate change on erosion rates, soil organic carbon losses, soil moisture, root growth and function, root-microbe associations and plant phenology as they relate to mineral nutrition are discussed. Our analysis suggests that the negative impacts of climate change on soil fertility and mineral nutrition of crops will far exceed beneficial effects, which would intensify food insecurity, particularly in developing countries. [ABSTRACT FROM AUTHOR]

This paper analyzes the spatial dependence of annual diurnal temperature range (DTR) trends from 1950–2004 on the annual climatology of three variables: precipitation, cloud cover, and leaf area index (LAI), by classifying the global land into various climatic regions based on the climatological annual precipitation. The regional average trends for annual minimum temperature ( T min) and DTR exhibit significant spatial correlations with the climatological values of these three variables, while such correlation for annual maximum temperature ( T max) is very weak. In general, the magnitude of the downward trend of DTR and the warming trend of T min decreases with increasing precipitation amount, cloud cover, and LAI, i.e., with stronger DTR decreasing trends over drier regions. Such spatial dependence of T min and DTR trends on the climatological precipitation possibly reflects large-scale effects of increased global greenhouse gases and aerosols (and associated changes in cloudiness, soil moisture, and water vapor) during the later half of the twentieth century. [ABSTRACT FROM AUTHOR]