Climatic Series Research Articles

Enhanced time series predictability with well-defined structures

For any given time series, how to optimize its forecast strategies and what prediction model is adopted are of great importance. In order to reach this goal, insight from analyzing predictability of series with known structure information is necessary. Time series generated by theoretical models with four kinds of known predictive structures, i.e., short-term correlation, long-term correlation, and multifractal and chaotic patterns, are applied to demonstrate that there is a well-defined relation between series’ intrinsic predictability and prediction accuracy of any specific prediction model. And results show that both intrinsic predictability and prediction accuracy are enhanced by these well-defined structures. There are different regimes in the relation between intrinsic predictability and prediction accuracy for series with different known deterministic or stochastic predictive structures. These regimes in the relation between intrinsic predictability and prediction accuracy can guide us to preselect a suitable prediction model and forecast strategies for any underlying series by only analyzing the permutation entropy of a given series. Results from three pieces of climate series further confirm that insights from theoretical series with known structure information indeed work well.

Synergistic effects of climate and land-use change influence broad-scale avian population declines.

Climate and land-use changes are expected to be the primary drivers of future global biodiversity loss. Although theory suggests that these factors impact species synergistically, past studies have either focused on only one in isolation or have substituted space for time, which often results in confounding between drivers. Tests of synergistic effects require congruent time series on animal populations, climate change and land-use change replicated across landscapes that span the gradient of correlations between the drivers of change. Using a unique time series of high-resolution climate (measured as temperature and precipitation) and land-use change (measured as forest change) data, we show that these drivers of global change act synergistically to influence forest bird population declines over 29years in the Pacific Northwest of the United States. Nearly half of the species examined had declined over this time. Populations declined most in response to loss of early seral and mature forest, with responses to loss of early seral forest amplified in landscapes that had warmed over time. In addition, birds declined more in response to loss of mature forest in areas that had dried over time. Climate change did not appear to impact populations in landscapes with limited habitat loss, except when those landscapes were initially warmer than the average landscape. Our results provide some of the first empirical evidence of synergistic effects of climate and land-use change on animal population dynamics, suggesting accelerated loss of biodiversity in areas under pressure from multiple global change drivers. Furthermore, our findings suggest strong spatial variability in the impacts of climate change and highlight the need for future studies to evaluate multiple drivers simultaneously to avoid potential misattribution of effects.

Identification of climate changes based on antropogenic transformation of landscapes

Formulation of the problem. Functioning of natural geosystems is aimed at preserving their structure, but economic activity leads to imbalances, decreases in order, loss of internal organization and the inability to maintain the necessary stabilization. Analysis of previous research. Studies of the last decades have shown that the climatic conditions of Ukraine can be unfavorable for the life and economic activity of the society. Problems of further research. To find the solution to the problem of preserving the ability of the biosphere to self-regulation, self-restoration by optimizing anthropogenic load on natural systems is very important. Identification of climate change factors is aimed at finding the most active sources of environmental impact in order to determine the real threat of global climate change. The purpose of the research is to determine the causes, areas of initial formation, further development of negative meteorological processes and phenomena on the territory of Kharkiv region during the last decades and to study parameters and causes of air masses transformation, changes in their properties and various resistance to anthropogenic impact. Research methods are observation with the help of various devices and technical means. An integrated approach using field-based observations and technology of climatic series analysis with the help of statistical methods for climate information processing, characterized by high accuracy, allow us to determine their spatial-temporal features and future projections to the natural environment. Presentation of the main research material. Optimal strategic socio-economic development of the regions requires identification of specific links between natural and socio-economic entities, as well as studying large-scale and profound by the consequences violations of the ecosystem at the planetary level - the biosphere and its components. There is a general tendency towards directed climate change, the so-called "global warming", which threatens natural landscapes, upsetting and destroying them. Practical value. There are causal relationships between climatogenic changes, influence of human activity and the reaction of landscapes to them. This is manifested in creation of a variety of transformed landscapes (agrarian, residential, industrial). The consequence of anthropogenic transformations is the change in the parameters and characteristics of the components of nature. Research results. Indicators are the basis for information blocks concerning decision-making in the areas of socio-economic development. Climate parameters of a certain territory may differ from zonal ones and in combination with anthropogenic transformation affect the general condition of the landscapes, lead to the intensification of adverse hydrometeorological and exogenous geodynamic processes and phenomena (erosion, deluvial processes, landslides, suffusion, deflation, flooding.

Siberian spruce tree ring anatomy: imprint of development processes and their high-temporal environmental regulation

Wood anatomy was offered as spatiotemporal proxy record for tracheid differentiation kinetics due to its advantages in terms of much longer cover period and less demanding measurements. In this study, external and internal regulation of earlywood-to-latewood transition and properties of latewood of Picea obovata Ledeb were considered. The values and interrelations between cell number, tree ring width, maximal and mean radial cell diameter, maximal cell wall thickness and position of the transition to thick-walled tracheids were investigated within site and along the altitudinal gradient. Correlations with moving 21-day climatic series were used to estimate high-resolutional external influences. Relationships between tree ring traits are spatially stable and close within one stage of differentiation and between cells production and expansion. Relationships between sites differ in upper and lower parts of the gradient. Most of traits respond to the primary limiting factors near summer solstice; however, maximal cell wall thickness responds positively to the temperatures at the + 10 °C threshold. Altitudinal anatomical patterns revealed interaction of intrinsic and external factors in the regulation of tracheid differentiation. Timing of climatic response highlighted role of photoperiod as a trigger in the earlywood-to-latewood transition, and crucial role of the growth season ending for latewood development.

On the use of observations in assessment of multi-model climate ensemble

The Bayesian weighted averaging (BWA) method is commonly used to integrate over multi-model ensembles of climate series. This method relies on two criteria to assign weights to individual outputs: model skill in reproducing historical observations, and inter-model agreement in simulating future period. Observations are generally thought to be relevant for correcting biases in model outputs in the BWA framework. However, they concurrently may introduce unpredictable impacts in the context of the downscaling process, in particular, when model output on precipitation is of interest. Specifically, the posterior distribution may excessively depend on few ‘outlier models’ being close to the observation, when all other models fail to capture observation of the historical period—a common situation for precipitation metrics. Another issue emerges for climates with very dry months: the inclusion of observation in BWA may result in a significant spread of the posterior distribution into the negative region. To address these problems, a modified version of the BWA method that removes observations in the initial phase of downscaling (computation of Factors of Change) and adds them in the estimation of posterior distributions is explored in this work. Comparisons of simulation results for the locations of Miami (FL), Fresno (CA), and Flint (MI) between the modified BWA and the traditional BWA demonstrate consistent outcomes with regards to the effect of observation in the Bayesian framework. Further, the modified BWA approach generally reduces uncertainty, as compared to ‘simple averaging’ in the Bayesian context, which assigns equal weights to all model outputs.

Assessment of Water Supply–Demand Using Water Evaluation and Planning (WEAP) Model for Ur River Watershed, Madhya Pradesh, India

Many watersheds experience scarcity of water for agricultural and domestic use for most part of the year. Ur river watershed in Tikamgarh district of Madhya Pradesh, India, falls under a drought prone region of India. Water allocation and management are essential for sustainable agriculture for this region. Water Evaluation and Planning system (WEAP)-based decision support system can prove to be an effective tool for water allocation, supply and demand analysis. In the present study, spatially distributed model by using WEAP-MABIA method has been developed for analysis and simulation of agricultural water demands in the Ur river watershed. WEAP-MABIA method uses dual crop coefficient approach which helps in computing the separate soil evaporation and transpiration under various water availability situations. Year 2012–2013 is used as base year for customizing WEAP model for 8 subwatersheds. The model was calibrated using PEST tool, available in WEAP. The calibrated model was used for estimating future water demands and unmet demands by using future climate series, from 2015 to 2030, of IPCC scenario- RCP 4.5 of GDFL-ESM2M model. As per GFDLESM2M model (RCP4.5) predictions, rainfall is going to greatly vary in the coming years. The years 2020–2021 and 2028–2029 may experience very dry climatic conditions with 500 mm or less annual rainfall, while 2017–2018, 2023–2024 and 2025–2026 may experience heavy showers (1200 mm). Straight effects of this rainfall pattern could be seen in future water availability for agriculture and resultant crop yield. A high unmet demand exists in the case of agriculture since the first priority for water supply is meant to be for domestic purpose. Area under agriculture in Ur river watershed is large, while respective water supply is low. This gap puts an extra pressure on water resources leading to over extraction of groundwater and related problems. Looking at this scenario, water allocation requires great attention to narrow down the gap between existing demands and water supply. Since area under agriculture is large, water-efficient crops should be more emphasized. Also, efficient agricultural practices and rain water harvesting should be promoted in the study area. Since the watershed falls in semiarid condition and river flows are seasonal, different stress/deficit irrigation scenarios can be built using customized WEAP model to get higher yield.

The Construction and Comparison of Regional Drought Severity-Duration-Frequency Curves in Two Colombian River Basins—Study of the Sumapaz and Lebrija Basins

Accurate classification of drought-severity is one of the most challenging issues in designing regional monitoring and control plans, especially in developing countries, where resources are scarce and must be carefully optimized to maximize social benefit. Typically, drought assessment is performed using drought indices which enable the interpretation of complex climatic information series for operational purposes. Frequency analyses are also useful for estimating future occurrence probabilities, even on regional scales. This study generated regional Severity-Duration-Frequency (SDF) curves for two Colombian catchments (Sumapaz and Lebrija River Basins), and 7 index-calculation procedures. First, the relationships between the two catchments were analyzed to obtain differences between drought indices. Second, the consistency among the indices that identified the same drought types for each region was evaluated. Finally, historical regional drought occurrences were selected, characterized, and located in local SDF curves to determine their gravity. It was concluded that (i) curves for the same indices displayed similar behavior, when comparing the two case studies; (ii) a certain degree of consistency existed in regional curves, which identify the same drought types (meteorological and agricultural droughts being the most coherent); (iii) meteorological drought regional events, identified through different drought-indices methodologies, were the most common for both case studies, followed by agricultural droughts and hydrological droughts; (iv) when analyzing occurrences with higher return periods, there is coherence when using different methodologies; and (v) identified historical events, which are located on larger return period zones of SDF curves (around 10, 25, and 50 years), had large impacts on regional socio-economic issues. Hence, it was possible to confirm that regional SDF curves could become potentially useful tools for the prioritization of drought-vulnerable zones.

Comparison of methods for extracting annual cycle with changing amplitude in climate series

Extracting annual cycle properly from climate series is important in the study of annual cycle and anomaly series. However, the extracting approaches are various and may lead to inconsistent results. Since the real annual cycle is unknown in observed records, the reliability and applicability of them are hard to estimate. In this study, five popular decomposition methods used to extract annual cycle in climate series are evaluated through idealized numerical experiments for the first time; i.e., fitting sinusoids, complex demodulation, ensemble empirical mode decomposition (EEMD), nonlinear mode decomposition (NMD) and seasonal trend decomposition procedure based on loess (STL). Their performances are examined by comparing the extracted annual cycles and its amplitude with the preset one. The annual cycles are set with three different changing amplitudes: constant, linear increasing and nonlinearly varying; superposed with fluctuations of different long-term persistence (LTP) strength. Results indicate that (1) NMD performs best in depicting annual cycle and obtaining its amplitude change; (2) fitting sinusoids, complex demodulation and EEMD methods are more sensitive to LTP strength of superimposed fluctuations, which leads to over-fitted annual cycles and noisy amplitude changes, oppositely, the STL are less responsive to the variation of annual cycle; (3) when overall long-time trend of annual cycle change is the main concern, all of these methods performed well. However, over short time scales, the errors on account of noise and LTP are common in the first three methods and STL is too rough to give the details of amplitude change. Those results are also verified by applying them to observed records and the case with both amplitude and phase change.

How Well Does the Mechanistic Water Quality Model CE‐QUAL‐W2 Represent Biogeochemical Responses to Climatic and Hydrologic Forcing?

AbstractMechanistic water quality models are applied globally for water quality management in rivers and lakes. However, it is unclear how well these models represent the response of lakes to changes in climate and hydrologic conditions. To address this question, we conducted a series of climate and hydrologic sensitivity analyses using a parameterized water quality model, CE‐QUAL‐W2, for the Spokane River and Lake Spokane system. Two experimental tests with climate and flow forcing showed that the model predictions were rather insensitive to climatic‐driven hydrologic changes and a wide range of hydrologic conditions (1st to 99th percentile flows) for two different wastewater treatment plant discharge scenarios. The model realistically represented some aspects of Lake Spokane's observed responses to climatic and hydrologic variability, for example, the water residence time and some water quality trends. However, the model overestimated the reservoir total phosphorus (TPR) concentration by ~37% and the chlorophyll a (Chl a) concentration by ~31% and underestimated the minimum volume‐weighted hypolimnetic dissolved oxygen (DOMIN) concentration by ~26% for a wide range of flows compared to the observed data. In contrast to the model's water quality insensitivity, the modeled temperature was more sensitive to flow than was actually observed in Lake Spokane. This study provides guidance for improving the response of one of the most important mechanistic water quality models to climatic and hydrologic forcing and should help modelers and decision‐makers to develop a rigorous assessment of climate and flow sensitivity to improve the margin of safety when setting total maximum daily load targets.

Comparing causal techniques for rainfall variability analysis using causality algorithms in Iran

Causal analysis (CA) is a strong quantitative approach whose mechanisms have climatic predictions. In this study, we studied the patterns of causality (PC) on the effect of rainfall (ER) using climatic series collected from 170 stations for the period 1975–2014 in Iran. Next, we predicted the causal relationships of climatic variables using causal models, including first-generation techniques (FGT), second-generation techniques (SGT), third-generation techniques (TGT), and causal hybrid techniques (CHT). Then, we estimated the causal models using partial squares algorithms (PSA), mechanical equations modeling algorithms (MEMA) such as exploratory and confirmatory methods, and spatial variability methods such as geostatistics and spatial statistical methods. Finally, we evaluated the quality of the methods using the goodness of fit indices, including absolute fit indices (AFI), comparative fit indices (CFI), and parsimonious fit indices (PFI). The results showed that CHT algorithm more suitably predicted the climatic spatiotemporal effect variability (SEV) by extracting direct, indirect, and total effects of climatic variables. Based on the CHT algorithm, the highest and lowest effect values were observed in total effects of winter rainfall (0.98) and summer rainfall variables (0.1), respectively. The SEV ranged from 0.8 to 0.98 for the winter rainfall total effects of CHT in Iran. Using CHT, most of the predicted SEV, particularly the rainfall series, displayed SEV varying from 80% to 98% of the winter rainfall total effects to the annual rainfall in Iran. Similarly, based on the CHT, the highest and lowest SEV values were in western, eastern, and southern regions and in central regions, respectively. In addition, the SEV varied within the range of 0.6–0.74 (varying from 60% to 74% for the autumn rainfall total effects of the annual rainfall in Iran) for the autumn rainfall total effects in Iran. Finally, the SEV of this type of analytical pattern as well as designated subject of CA applications in the atmospheric science and environmental science are discussed.

Time series analysis of quarterly rainfall and temperature (1900–2012) in sub-Saharan African countries

In this paper, we examine the statistical properties of rainfall data and temperature in six sub-Saharan African countries in the western, eastern, and southern regions (Botswana, Ethiopia, Ghana, Nigeria, Uganda, and South Africa) using time series data spanning between 1900 and 2012. By using linear trends, seasonality, and long-range dependence models, in fractional or I(d) frameworks, the results first indicate that time trends are required in most cases to explain the time series properties of the climatic series. Evidence of anti-persistence (d 0) is found for the temperature data. Evidence of structural breaks are only found in the cases of Ethiopia, Ghana, and Uganda for the temperature data. With both series displaying significant evidence of seasonality and by working with the seasonally differenced data, the results show evidence of I(0) behavior or anti-persistence (d 0) for the temperature data. Testing the causality between the two variables, the results indicate evidence of causality in the two directions in all cases except for the case of the temperature on the rainfall in South Africa. The implication of the results obtained here is that erratic or constant rainfall is expected in Africa in the future while temperature is likely to continue to increase, and these subsequently lead to future warming experiences.

Evaluating millennial to contemporary time scales of glacier change in Val Viola, Central Italian Alps

ABSTRACTTo improve current understanding of ongoing deglaciation dynamics in relation to climatic forcing, it is critical to build long-term series of climate and glacier changes. This task is typically hampered by availability and resolution of Quaternary glacier paleo-reconstructions. To explore opportunities and challenges, we present a case study from Val Viola, which integrates area, volume and ELA changes across a 13k-year time window, including four Younger Dryas–Early Holocene glacier stadials and eight post-LIA periods. Results suggest that relevant shifts in climatic forcing associated with the Pleistocene–Holocene transition and post-LIA deglaciation phases are of comparable magnitude, with an atmospheric temperature increase of about 1.5–2°C. Post-LIA decline in glacierized areas (68.9 ± 6%) is comparable with retreat rates recorded in other Italian glaciers, but is greater than elsewhere in the Alps, where glaciers are comparably larger. Glacier stability in the particularly warm 2007–2015 period testifies to the decoupling attained by small glaciers from synoptic atmospheric conditions. We argue that this is caused by enhanced wind drift and avalanche accumulation, occurred in response to morphological changes on ice surfaces following progressive glacier shrinking. This positive feedback not only could delay glacier extinction in certain physiographic settings but also could introduce bias in paleo-glaciological reconstructions of climatic conditions.

Future climate and runoff projections across South Asia from CMIP5 global climate models and hydrological modelling

Study regionThe South Asia. Study focusThis paper presents future climate and runoff projections for the South Asia region under the RCP8.5 scenario with climate change informed by 42 CMIP5 GCMs. Runoff is projected for 0.5° grids using hydrological models with future climate inputs obtained by empirically scaling the historical climate series. New hydrological insightsThe modelling results indicate that future runoff will increase throughout most of the region except in the far north-east and far north-west. The median projection shows increases in mean annual runoff of 20–30% in the Indian sub-continent for 2046–2075 relative to 1976–2005. The change in runoff is driven mainly by the change in precipitation, moderated (in wetter futures) or intensified (in drier futures) by higher temperature and potential evaporation. The paper also investigates the uncertainties of the projection due to scaling methods and selection of GCMs. The difference in runoff projections from different scaling methods is small relative to the large uncertainty in precipitation projections from the GCMs. Sub-selecting only the “better” performing GCMs shows marginal difference in the uncertainty range of projected runoff. For the broad scale projections presented here, it is best to use projections informed by all the GCMs to provide an indication of the full uncertainty range.

The PILOTE-N model for improving water and nitrogen management practices: Application in a Mediterranean context

In this paper we present PILOTE-N, a crop model devoted to the calculation of crop production from the joint water and nitrogen soil status effects. It is the extension of PILOTE, for contexts in which water might not be the sole limiting factor for crop yield, but the same model structure and credo of parsimonious parameterisation have been kept, assuming simplified descriptions of the physical processes at play. One original aspect of PILOTE-N is the calculation of Leaf Area Index (LAI) and cumulative nitrogen plant demand from similar logistic-type functions. LAI is controlled by specific temperature sums, shape parameters and the occurrence of water and nitrogen stresses, while the time average of LAI values over critical phenological periods also affects the predicted harvest index and crop yield. As specific plant parameters are known from PILOTE, calibration was devoted to nitrogen parameters. Which governing the daily-averaged mineralization rate in a first step, then the two shape parameters of the potential nitrogen plant demand (from the dilution curve) in a second step and, at last, which allowing the link between nitrogen uptake and nitrogen of the soil. Model performance was evaluated using multiple initial soil conditions, irrigation and fertilization strategies for corn, durum wheat and sorghum, over a climatic series of 14 years, at the experimental plot of Lavalette (Montpellier, South-East of France), hence in a Mediterranean context characterized by severe, water stresses during summer, typically exceeding nitrogen stresses. Crop yield as well as the dynamics of nitrogen budget were correctly simulated (R2 > 0.94 for grain yield, total dry matter and nitrogen in plant). The robustness of such a simple and easy-to-calibrate tool is expected to facilitate its use and implementation in other agro-pedoclimatic contexts, to decipher the effect of abiotic stresses and improve irrigation and fertilization scenarios when included in dedicated tools.

An Integrated Approach to Evaluate Urban Adaptive Capacity to Climate Change

Climate change and accelerated urbanization have posed severe challenges to urban development, resulting in a growing series of climate and environmental problems that have a significant impact on industrial production and urban life. In a developing country such as China, more than 57% of the population lives in urban areas. It is vital for these cities to adapt to climate-induced risks. A better understanding of how to improve adaptive capacity could enhance the ability to achieve a desirable state when the city experiences stress. This paper used an integrated approach for evaluating the urban adaptive capacity to climate change. It developed the evaluation index system of urban adaptive capacity (UAC) based on the driver–pressure–state–impact–response model (DPSIR), and adopted grey relational analysis (GRA) and the entropy method to analyze the level of UAC in Changsha, the capital city of Hunan Province, from 2006 to 2015. The results revealed that the UAC of Changsha showed a significant increase from 2006 to 2015. Among the five first-grade indicators, the response dimension had the greatest influence on the improvement of UAC. The study may provide suggestions for adaptive capacity building and sustainable development in other urban areas.

Dynamic equilibrium planform of embayed beaches: Part 2. Design procedure and engineering applications

The design of equilibrium beach systems employing beach nourishment techniques in conjunction with coastal support structures has received increased interest from coastal engineers in recent years. One such solution to remedy coastal erosion problems is to design an equilibrium pocket beach in order to stabilize the shoreline. Such coastal landforms may exist in nature in static or dynamic equilibrium states. The concept of the Static Equilibrium Planform (SEP) has received much more attention from coastal engineers than that paid to the Dynamic Equilibrium Planform (DEP). This paper presents a design procedure for embayed beaches in dynamic equilibrium which have a specific net littoral drift rate. The proposed methodology employs the net sediment transport rate passing through the bay, together with the time series of the wave climate impinging on the beach, in order to compute the angle (γd). This angle represents the difference in degrees between the direction of the mean wave energy flux at the diffraction point (θEF) and the orientation of the dynamic equilibrium beach (θDB), to be utilized in a newly derived DEP formula to predict the shoreline shape in the long term. The procedure was applied and validated using prototype bays in dynamic equilibrium with different net sediment transport rates along the Brazilian coast, producing good results. Furthermore, it was employed as a tool to investigate the influence of changes in the net annual littoral drift rate on the (DEP) shape in a case study along the Spanish coast. Taken together, these confirm its utility as a valuable tool for coastal management and engineering practice.

Warm nights in the Argentine Pampas: Modelling its impact on wheat and barley shows yield reductions

Efforts to anticipate how climate change and variability will affect future crop production can benefit from understanding the impacts of current and historic changes. This study aimed to quantify and compare the impact of increased night temperature on potential yield and phenology of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) crops modelled using APSIM with historical climate series (1961–2014) in sites representative of the Argentinean Pampas. For each site, the sowing date was adjusted to avoid frost and heat events at flowering, based on historical probability. The critical period was the more sensitive crop phase (shortened by 0.6d decade−1) for the observed asymmetric warming; regional minimum temperature trend of ca. 0.14 and 0.16°Cdecade−1 in wheat and barley, respectively. Wheat and barley yields declined across the region between ca. 2% and 9% per °C increase in the minimum temperature during the critical period, linked to lower cumulative radiation capture as a result of a shorter crop phase and lower incident radiation due to displacement towards winter. Regional variability in the simulated yield response to the observed night warming was mainly explained by differences the response of incident solar radiation during the critical period to the minimum temperature increase.

Methodological proposal for the analysis of the evolution of glaciers since the Little Ice Age and its application in the Tröllaskagi Peninsula (northern Iceland)

In this paper we propose a methodological sequence for the study of glaciers and climate change, and for the use of glaciers as indicators of climatic evolution. Our proposal includes different techniques focused on: mapping glacier extents at different dates, measuring front variations, calculating areas and volumes, analyzing glacier Equilibrium-Line Altitudes (ELA), statistical treatment of climate series, and the application of glacier-climate models that relate temperature and precipitation and enable paleoclimate reconstruction. This methodology was tested by remote monitoring of three highly sensitive debris-free glaciers in the Tröllaskagi peninsula (northern Iceland) since the end of the Little Ice Age (LIA), and the results show an average retreat of 1.3 km as well as a reduction in area and volume of 25% and 33% as a result of the warming that began at the end of the LIA. The application of the glacier-climate models suggests a climate that was up to 49% less humid at the LIA maximum. The bibliographic review of the methods utilized enables us to validate our methodological proposal and the results obtained, and ensures its application in different areas of study.

Recurrence quantification analysis of extremes of maximum and minimum temperature patterns for different climate scenarios in the Mesochora catchment in Central-Western Greece

A methodological framework based on nonlinear recurrence analysis is proposed to examine the historical data evolution of extremes of maximum and minimum daily mean areal temperature patterns over time under different climate scenarios. The methodology is based on both historical data and atmospheric General Circulation Model (GCM) produced climate scenarios for the periods 1961–2000 and 2061–2100 which correspond to 1 × CO2 and 2 × CO2 scenarios. Historical data were derived from the actual daily observations coupled with atmospheric circulation patterns (CPs). The dynamics of the temperature was reconstructed in the phase-space from the time series of temperatures. The statistically comparing different temperature patterns were based on some discriminating statistics obtained by the Recurrence Quantification Analysis (RQA). Moreover, the bootstrap method of Schinkel et al. (2009) was adopted to calculate the confidence bounds of RQA parameters based on a structural preserving resampling. The overall methodology was implemented to the mountainous Mesochora catchment in Central-Western Greece. The results reveal substantial similarities between the historical maximum and minimum daily mean areal temperature statistical patterns and their confidence bounds, as well as the maximum and minimum temperature patterns in evolution under the 2 × CO2 scenario. A significant variability and non-stationary behaviour characterizes all climate series analyzed. Fundamental differences are produced from the historical and maximum 1 × CO2 scenarios, the maximum 1 × CO2 and minimum 1 × CO2 scenarios, as well as the confidence bounds for the two CO2 scenarios. The 2 × CO2 scenario reflects the strongest shifts in intensity, duration and frequency in temperature patterns. Such transitions can help the scientists and policy makers to understand the effects of extreme temperature changes on water resources, economic development, and health of ecosystems and hence to proceed to effective proactive management of extreme phenomena. The impacts of the findings on the predictability of the extreme daily mean areal temperature patterns are also commented.

Projection of future runoff change using climate elasticity method derived from Budyko framework in major basins across China

Abstract This study established a climate elasticity method based on Budyko hypothesis and enhanced it by selecting the most effective Budyko-type formula to strengthen the runoff change prediction reliability. The spatiotemporal variations in hydrologic variables (i.e., runoff, precipitation and potential evaporation) during historical period were revealed first and the climate elasticities of runoff were investigated. The proposed climate elasticity method was also applied to project the spatiotemporal variations in future runoff and its key influencing factors in 35 watersheds across China. Wherein, the future climate series were retrieved by consulting the historical series, informed by four global climate models (GCMs) under representative concentration pathways from phase five of the Coupled Model Intercomparison Project. Wang-Tang equation was selected as the optimal Budyko-type equation for its best ability in reproducing the runoff change (with a coefficient of determination and mean absolute error of 0.998 and 1.36 mm, respectively). Observed runoff presents significant decreasing trends in the northern and increasing trends in the southern regions of China, and generally its change is identified to be more sensitive to climatic variables in Hai River Basin and lower Yellow River Basin. Compared to the runoff during the reference period, positive change rates in the north and negative change rates in the south of China in the mid-21st century can be practically generalized from the majority of GCMs projections. This maybe resulted from the increasing precipitation, especially in parts of northern basins. Meanwhile, GCMs project a consistently upward trend in potential evaporation although significant decreasing trends occur in the majority of catchments for the historical period. The results indicate that climate change will possibly bring some changes to the water resources over China in the mid-21st century and some countermeasures of water resources planning and management should be taken.